The polygenic and multifactorial nature of many psychiatric disorders has hampered the personalized medicine approach implementation in clinical practice. However, induced pluripotent stem cell (iPSC) technology has emerged as an innovative tool for patient-specific disease modeling to expand the pathophysiology knowledge and treatment perspectives in the last decade. Current technologies enable adult human somatic cell reprogramming into induced pluripotent stem cells (iPSCs) to generate neural cells and direct neural cell conversion to model organisms that exhibit phenotypes close to human diseases, thereby effectively representing relevant aspects of neuropsychiatric disorders. iPSCs reflect patient pathophysiology and pharmacological responsiveness, particularly when cultured under conditions that recapitulate spatial tissue organization in brain organoids. Recently, the application of iPSCs has been frequently associated with gene editing that targets the disease-causing gene to deepen the illness pathophysiology and conduct drug screening. Moreover, gene editing has provided a unique opportunity to repair the putative causative genetic lesions in patient-derived cells. Here, we review the use of iPSC technology to model and potentially treat neuropsychiatric disorders by illustrating the key studies on a series of mental disorders, including schizophrenia, major depression disorder, bipolar disorder, and autism spectrum disorder. The future perspective will involve the development of organ-on-a-chip platforms that control the microenvironmental conditions to reflect individual pathophysiological by adjusting physiochemical parameters according to personal health data. This strategy could open new ways to build a disease model that considers individual variability and tailors personalized treatments.

Despite many calls to reduce or eliminate the use of mechanical restraint, it is still widely used in many countries. Studies using patient interviews have a very clear message: Patients experience mechanical restraint as the most humiliating intervention. There seems to be a lack of alternatives with violent patients. We developed a method using 30 kg bags, originally designed for fitness purposes, to be attached to a patient's wrist or ankle under 1:1 supervision. The method was tested with 10 experienced nurses and de-escalation trainers. A video was made and presented to six outpatients who had previously experienced mechanical restraint. All participants were interviewed. Transcribed interviews were analysed using qualitative content analysis. All participants approved of the method as a milder and less humiliating alternative to mechanical restraint. Nurses' main concerns were the risk of falls and the use of the bags as a weapon. The latter could be controlled by using an additional bag. Patients were generally positive, especially if there was a history of abuse. The method should be further developed to replace at least part of mechanical restraints. As with all ‘milder means‘, care should be taken to really replace restraint and not to introduce additional coercion.

Mental disorders impact a significant portion of the global population, presenting substantial challenges to healthcare systems due to high hospitalization costs and resource demands. This narrative review explores the economic implications of mental health disorders, focusing on hospitalization costs, the effectiveness of interventions, and the outcomes of recent research. Materials and Methods: A narrative review was conducted, sourcing articles from PubMed and Google Scholar published between 2014 and 2024. The search terms included combinations related to mental illness, costs, hospital care, and mental healthcare interventions. A total of 1,110 articles were initially identified, with 37 studies meeting the inclusion criteria after rigorous screening by two independent researchers. These studies included quantitative and qualitative data covering a range of outcomes such as hospital admission rates, consumer satisfaction, and quality of life. The findings of the present review reveal that mental disorders significantly increase hospitalization costs due to frequent admissions, extended stays, and the need for specialized treatments. Additionally, physical comorbidities in individuals with mental disorders lead to higher healthcare costs and resource use. Integrated care models, early intervention, and preventive strategies show promise in reducing these costs and improving patient outcomes. Conclusions: Our review emphasizes the critical need for integrated healthcare strategies that address both mental and physical health to reduce hospitalization costs and improve outcomes. Effective management of mental health disorders requires comprehensive approaches, including complex outpatient services and preventive care. Future research should focus on standardizing methodologies to provide clearer insights into the economic impact of mental health conditions and guide effective healthcare strategies.

Introduction: The aim is to investigate the possibility of altering the delusional perceptions of psychotic patients by exposing them to a cognitively flexible virtual environment. A flexible vir-tual reality (VR) environment could challenge and enhance the ability to adapt to changing situa-tions and generate novel solutions to core psychotic symptoms. Materials and methods: A systematic review was conducted using different combinations of keywords in different databases. This study was conducted using the PRISMA statement. Results: There is a growing literature on the use of VR to manipulate and normalise perceptual distortions in psychosis. Over the last decade, the number of published studies on VR in mental health has increased 14-fold. Conclusions: A cognitive flexibility virtual environment can alter delusional perception by ex-posing the person to different scenarios that require them to question their assumptions, test their hypotheses, and revise their beliefs. Commonly used biosensors measure heart rate, skin con-ductance and body temperature in relation to different emotional states. Recently, there has been a major shift towards the use of neuroimaging techniques to better understand the pathophysiology of mental disorders. This is where the new wave of biosensor-integrated virtual reality (VR) sys-tems comes in.

Edvard Munch, a founder of Expressionism was pre-occupied with depicting his subjective experience and the most prolific painter of self-portraits since Rembrandt. Based on evidence that faces and self-related images are more salient than other objects it is plausible that self-portraits, in which the subject and object are the same, reflect the artist’s state of mind. Although he suffered from a variety of physical and psychological illness Munch was not diagnosed with any specificity in his lifetime. Posthumous diagnoses include schizophrenia, anxiety, bipolar and other disorders. Recent research has revealed altered visual perception in such patients. The present study empirically analysed three stylistic elements of Munch’s painted self-portraits and portraits: contrast, colour and fractal dimension and his painted productivity, to determine if variations correlate with critical life events and if so whether they indicate states of mind. It found significant increases in contrast and colour brightness and persistent high complexity during critical periods supporting the conclusion that Munch is diagnostically best described as suffering from early onset schizophrenia and comorbid social anxiety disorder.

Many have speculated that events of personal and financial loss in Rembrandt’s life caused depression and that this is revealed by examination of his self-portraits particularly those painted in old age. Some report detecting various physiological diseases associated with aging, including vision impairment, which may have affected his mood and work. Aging and neurodegenerative disease which often accompanies it, are both associated with depression. Recent advances in imaging and computer technology make it possible to empirically examine changes in artistic style which can contribute to understanding artists’ physical and mental health. In the current study changes in contrast, colour and fractal dimension were measured in the entire corpus of Rembrandt’s painted self-portraits and portraits to determine whether changes in style indicate depression or disease. Productivity was also examined as an indirect indicator. Given the high salience attributed to self-images, it also examined whether differences in style could be detected between self-portraits and portraits of related and unrelated others. The results indicate that it is unlikely that Rembrandt suffered from unipolar or bipolar depression, age-related cognitive decline, or neurodegenerative disease. The data are consistent with someone experiencing episodes of low mood consistent with normal grieving and adversity followed by resilient recovery. There is evidence of a gradient in saliency and complexity between self-portraits and related and unrelated portraits, of a less formal style in portraits of related others and of a ‘late’ style identified by leading art historians consistent with macular degeneration. The results are also consistent with an association of aesthetic preference with mid-level fractal values tending towards the Golden Mean long associated with beauty in nature and in art.

Arguably the most important parameter in treating cognitive decline associated with Alzheimer’s disease is the length of time in which improvement, if achieved at all, is sustained. However, monotherapies such as donepezil and memantine are associated with a more rapid decline than no treatment in patients over multi-year follow-ups {Kennedy, 2018 #535}. Furthermore, anti-amyloid antibody treatment, which at best simply slows decline, is associated with accelerated cerebral atrophy [2], resulting in earlier dementia-associated brain volumes for those treated at the MCI stage than untreated patients. In contrast, a precision medicine approach, in which the multiple potential drivers of cognitive decline are identified for each patient and then targeted with a personalized protocol (such as ReCODE), has led to documented improvement in patients with cognitive decline [3]; [4]; [5], but long-term follow-up (>5 years) has not been reported previously. Therefore, we report here sustained cognitive improvement, in some cases for over a decade, in patients treated with a precision medicine protocol—something that has not been reported in patients treated with anti-cholinesterase, glutamate receptor inhibitory, anti-amyloid, or other therapeutic methods. These case studies warrant long-term cohort studies to determine how frequently such sustained cognitive improvements occur in patients treated with precision medicine protocols.

Introduction: Hospitalization in a COVID-19 unit represents a significant source of stress and psychological distress for patients. Our study aims to determine the factors of psychological distress in the therapeutic and psychosocial management of patients hospitalized with a COVID-19 infection. Methods: This cross-sectional study was carried out over 8 months at the COVID-19 zone of the CHU HASSAN II of FES. 99 patients were evaluated within days of diagnosis by psychometric scales of anxiety, depression, and post-traumatic stress. In the course of this follow-up, we found the presence of psychological impacts in these patients, and we tried to find correlating factors between the occurrence of psychiatric disorders and the set of factors related to sociodemographic data, hospitalization course and treatments taken, as well as family support. Results: 35% of patients had scores in favor of post-traumatic stress disorder, 37% of the patients had definite depression and 50% had anxiety. Post-traumatic stress, depression, and anxiety were correlated with sociodemographic factors, hospitalization, treatments received, and length of stay. Conclusion: The prevalence of anxiety-depressive disorders and post-traumatic stress disorder was high in patients with COVID-19, hence the interest in providing psychological and psychosocial care in the emergency department.

Background: We assessed the pertinence of yearly updating the International Working Group on the Diabetic Foot (IWGDF) risk classification in people with diabetes by quantifying the changes in the risk group and its accuracy in identifying those developing an ulcer (DFU) in a primary care setting. Methods: In our retrospective cohort study, we included all people with diabetes with a foot as-sessment registry between January 2016 and December 2018 in the Baixo Alentejo Local Health Unit. Foot-related data was collected at baseline after one and two years. DFU and/or death until December 2019 were registered. The proportion of people changing their risk status each year was calculated. Accuracy measures of the IWGDF classification to predict DFU occurrence at one, two, and three years were calculated. Results: A total of 2097 people were followed for three years, during which 0.1% died, and 12.4% developed a DFU. After two years, 3.6% of the participants had progressed to a higher-risk group. The IWGDF classification presented specificity values superior to 90% and negative predictive values superior to 99%. Conclusion: Foot risk status can be safely updated every two years instead of yearly. The IWGDF classification can accurately identify those not at risk of DFU.

Depression, anxiety, and pain are part of a common phenomenon that requires to be studied together when evaluating patients with chronic pain. The aim of the study was to examine the correlations between depression, anxiety and pain intensity in patients with chronic pain and how they change when a depressive episode was added. A sample of 120 patients with chronic pain (age: 51.90±11.94; 81.7% female; 18.3% male) was studied in two stages with a three-month period between them. Patients with a depressive episode (n=61) were treated with antidepressants and analgesics, while a patient without depression (n=59) was treated only with analgesics. The scales used to achieve the objectives were: HAM-D-17, Spielberger‘s STAI and VAS. Insignificant correlations were found between depression, state and trait anxiety, and pain intensity (p<0,05) in the group without depression, except for a significant one between depression and state anxiety (p<0,01) in the second stage. Significant correlations were identified between all indicators (p<0,01) in the group with depression at the two stages. Those in the second stage were stronger. The interrelations between sensory and affective aspects of pain were stronger when a depressive episode occurs, the severity of which determines their dynamics.

Obsessive–compulsive disorder (OCD) is believed to follow a waxing and waning course, often according to environmental stressors. During the COVID-19 pandemic, pre-existing OCD symptoms were reported to increase and to change from checking to washing behaviors, while new-onset symptoms were predominantly of the hoarding type. In the present study, we followed the evolution of OCD symptoms, anxiety, depression, and insight of illness in forty-six OCD patients throughout the pandemic. Clinical measures were collected at four different time points before and during the COVID-19 pandemic in Italy. Within-subject comparisons were used to compare clinical scale scores across time, and correlations were examined between patients' baseline characteristics and changes in clinical scores. We found that all clinical measures increased during the first Italian lockdown, with respect to the pre-pandemic values. Anxiety decreased during the temporary elimination of restriction provisions, whereas severity of OCD symptoms and insight returned to pre-pandemic values during the second mandatory lockdown. These results were observed only in two sub-groups of patients: those taking benzodiazepines and those with shorter illness duration. Our findings suggest the need of additional clinical attention to these specific sub-groups of OCD patients in case of particularly distressing circumstances, while pointing to a possible adaptive role of their OCD symptoms when the environment requires a higher care of hygiene and extraordinary supply of essential resources.

In the brain, astrocytes regulate shape and functions of the synaptic and vascular compartments through a variety of released factors and membrane-bound proteins. An imbalanced astrocyte activity can therefore have drastic negative impacts on brain development, leading to the onset of severe pathologies. Clinical and pre-clinical studies show alterations in astrocyte cell number, morphology, molecular makeup and astrocyte-dependent processes in different affected brain regions in neurodevelopmental (ND) and neuropsychiatric (NP) disorders. Astrocytes proliferate, differentiate and mature during the critical period of early postnatal brain development, a time window of elevated glia-dependent regulation of a proper balance between synapse formation/elimination, which is pivotal in refining synaptic connectivity. Therefore, any intrinsic and/or extrinsic factors altering these processes during the critical period may result in an aberrant synaptic remodelling and onset of mental disorders. The peculiar bridging position of astrocytes between synaptic and vascular compartments further allows them to “compute” the brain state and consequently secrete factors in the bloodstream, which may serve as diagnostic biomarkers of distinct healthy or disease conditions.Here, we collect recent advancements regarding astrogenesis and astrocyte-mediated regulation of neuronal network remodelling during early postnatal critical periods of brain development, focusing on synapse elimination. We then propose alternative hypotheses for an involvement of aberrancies in these processes in the onset of ND and NP disorders. In light of the well-known differential prevalence of certain brain disorders between males and females, we also discuss putative sex-dependent influences on these neurodevelopmental events.From a translational perspective, understanding age- and sex-dependent astrocyte-specific molecular and functional changes may help to identify biomarkers of distinct cellular (dys)functions in health and disease, favouring the development of diagnostic tools or the selection of tailored treatment options for male/female patients.

Objective: The influence of gender on the number and pattern of co-morbid psychiatric illness was archivally compared between patients with and without an Alcohol Use Disorder (AUD) among psychiatrically treated individuals drawn from a large Danish birth cohort. Method: By age 45, fifteen percent of the Copenhagen Perinatal Birth Cohort (N=8,109) had been treated in a Danish psychiatric facility. Of those 1,247 patients, 368 were assigned an AUD diagnosis (29.5%). All psychiatric diagnoses assigned to these patients, over all admissions, were classified into one of 14 broad ICD-10 F categories for comparative purposes. Results: More of the patients with an AUD diagnosis were male. Patients with an AUD diagnosis were significantly more likely to receive two or more non-AUD psychiatric diagnoses than patients without an AUD diagnosis. The number of psychiatric diagnoses was greater for female than male AUD patients, a difference not found among male and female patients without an AUD diagnosis. For patients with no AUD diagnosis, the influence of gender on the pattern of psychiatric disorders was similar to that commonly reported. In contrast, the influence of gender on the number and pattern of psychiatric disorders for patients with an AUD diagnosis indicated fewer gender differences. Several diagnostic categories that discriminated male from female patients without an AUD-related diagnosis, no longer discriminated male from female patients with an AUD-related diagnosis. Conclusions: Our findings suggest that gender differences associated with psychiatric comorbidity that have been reported in large heterogenous study, should not be generalized to specific diagnostic groups without further study.

The editorial explores the realm of emerging translational research in neurology and psychiatry, providing insights into various subjects, including neurodegenerative disorders and innovative therapeutic methods. The main objective is to study neurological and psychiatric disorders, such as Alzheimer's disease, with a particular emphasis on techniques for neural regeneration and repair. The editorial highlights the importance of taking a comprehensive approach to studying and treating complex neurological and psychiatric conditions. This involves thoroughly reviewing research papers on the causes, development, biomarkers, and treatment options for these conditions. The editorial emphasizes the necessity of using multidisciplinary and integrative methods to gain a deeper understanding and improve the treatment of these conditions. The development of an animal model for recurrent primary dysmenorrhea showcases the innovative techniques employed in the investigation of gynecological disorders, illustrating the capacity of translational research to connect fundamental scientific knowledge with practical medical applications. The cooperative endeavors of researchers and clinicians emphasized in this editorial lay the foundation for revolutionary advancements that hold the potential to enhance outcomes for individuals afflicted with neurological and psychiatric disorders globally.

Every year around 800 000 people commit suicide, this represents one death every 40 seconds. In the search for possible biological biomarkers associated with suicide and/or psychiatric disorders, serum cholesterol levels have been extensively explored. Several studies have indicated that cholesterol and associated proteins, especially apolipoproteins (Apos), may play an important role in the diagnosis, prognosis, and susceptibility of suicide. Here, we describe the current knowledge and findings in the relationship between apolipoproteins and suicide.

Depression is the commonest mental disorder worldwide. Both antidepressants and psychotherapy are effective in treating depression, but response to these treatments is often incomplete. Yoga-based interventions (YBIs) have emerged as a promising adjunctive treatment for depression. Recent research has attempted to identify the biological mechanisms associated with the antidepressant actions of YBIs. In this scoping review, conducted according to the PRISMA-ScR guidelines, the PubMed and Scopus databases were searched to retrieve research on biomarkers of response to YBIs in patients with depression. Eleven studies were included in the review. Based on these studies, YBIs may improve depression through beneficial effects on systemic inflammation, stress axis regulation, cardiac autonomic functioning, and possibly reductions in oxidative stress-related cellular damage. Increases in brain-derived neurotrophic factor (BDNF) may represent a final common pathway upon which these processes converge. Though these results are theoretically plausible, they should be interpreted with caution due to certain methodological limitations in the existing research. Replication of these results and improvements in study methodology, and the evaluation of other biomarkers relevant to the pathophysiology of depression, could further elucidate the mechanism of action of YBIs in this disorder, and identify which patients would benefit most from this treatment approach.

The art of observing and describing behaviors has driven diagnosis and informed basic science in Psychiatry. In recent times, studies of mental illness are focused on understanding the brain’s neurobiology but there is a paucity of information on the potential contributions from peripheral activity to mental health. In Precision Medicine, this common practice leaves a gap between bodily behaviors and genomics that we here propose to address with a new layer of inquiry that includes genes’ expression on tissues inclusive of brain, heart, muscle-skeletal and organs for vital bodily functions. We interrogate genes’ expression on human tissue as a function of disease-associated genes. By removing genes linked to disease from the typical human set, and recomputing the genes’ expressions on the tissues, we can compare the outcomes across mental illnesses, well-known neurological conditions, and non-neurological ones. We find that major neuropsychiatric conditions that are behaviorally defined today (e.g. Autism, Schizophrenia, Depression) through DSM-observation criteria, have strong convergence with well-known neurological ones (e.g. Ataxias, Parkinson), but less overlap with non-neurological ones. Surprisingly, tissues majorly involved in the central control, coordination, adaptation and learning of movements, emotion and memory are maximally affected in psychiatric diagnoses along with peripheral heart and muscle-skeletal tissues. Our results underscore the importance of considering both the brain-body connection and the contributions of the peripheral nervous systems to mental health.

Introduction: Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition in children in Saudi Arabia based on the results of the national mental health survey. ADHD significantly impacts the child and their family dynamics, particularly causing increased stress and diminished life quality for parents. In Saudi Arabia, there is a research gap regarding the quality of life and coping mechanisms of parents managing children with ADHD. This study assesses levels of depression and anxiety, quality of life, and coping strategies used by parents of children diagnosed with ADHD. Methods: We conducted a cross-sectional online survey with 151 parents of ADHD-diagnosed children, utilizing the WHOQOL-BREF for life quality, the brief-COPE for coping strategies, and the Patient Health Questionnaire (PHQ): depression (PHQ9-9 items) and generalized anxiety (GAD7-7 items) modules. Results: Among the parents surveyed, 36% reported moderate to severe depression, while 39.1% experienced moderate to high anxiety levels. Quality of life positively correlated significantly with higher household monthly income (HHI), employment status, sibling count, and effective coping strategies. Conversely, a parent's age, educational level, and particularly maternal status were inversely related to anxiety levels, with fathers displaying higher maladaptive coping scores. Conclusion: The study sheds light on the considerable anxiety and depression experienced by parents of children with ADHD, significantly affecting their quality of life. Lower parents' quality of life is associated with high levels of depression, anxiety, and ineffective coping strategies. These insights highlight the critical need for interventions to aid parental mental health, thereby improving their overall quality of life amidst ADHD challenges.

While the majority of previous studies assessing pain-related variables in psychopaths used electric shocks, little is known about the effectiveness of alternative pain-inducing methods to increase emotional responses such as fear and anxiety. A small sample of healthy undergraduate men (N = 15) was recruited to assess the effectiveness of a heat stimulus to induce pain in an immediate versus delayed punishment paradigm. Although pain catastrophizing, anxiety, and threat of pain did not increase throughout the experiment, participants experienced a significant increase of fear of pain and pain intensity, indicating that the heat stimulus was effective in inducing pain. Furthermore, subjects were slower in initiating the pain stimulus during the first five trials, but no time difference was found during the 15 remaining trials. No correlation was found between psychopathic traits and pain-related variables, with the exception of inconsistent results within the Fearless Dominance factor. Findings are discussed in terms of improvement for a larger scale study involving psychopathic individuals.

The purposes of this study were to identify clinician’s characteristics associated with higher prescription rates of long-acting injectable (LAI) antipsychotics, as well as the information sources influencing medical decision-making about treatment of schizophrenia. We surveyed 202 psychiatrists during 6 regional French conferences (Bordeaux, Lyon, Marseille, Nice, Paris, Strasbourg). Data on the characteristics of practice, prescription rates of antipsychotic and information sources about their clinical decisions were collected. Most of psychiatrists used second-generation antipsychotic (SGA), and preferentially an oral formulation, in the treatment of schizophrenia. SGA LAI was prescribed to 30.4% of schizophrenic patients. The duration and the type of practice did not influence the class or formulation of antipsychotics used. The clinicians following the higher percentage of schizophrenic patients were associated with the higher use of LAI antipsychotics and the lower use of oral SGA. Personal experience, government regulatory approval and guidelines for the treatment of schizophrenia were the 3 main contributing factors guiding the clinical decision-making of clinicians about treatment of schizophrenia. The more clinicians follow schizophrenic patients, the more they use LAI antipsychotic. The development of specialised programmes with top specialists should lead to better use of LAI in the treatment of schizophrenia.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

In recent years, the scientific and technological developments led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, key frame selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines.

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic and theranostics assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical as-sessment inventories, but is considered to be a medical discipline and, as such, uses medical in-tervention methods (e.g., pharmacological, ECT; rTMS; tDCS), and therefore is supposed to oper-ate with the language and methods of nomothetic networks. The idiographic assessments are pro-visionally “quantified” into “structured clinical scales” to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other, biological tests (molecular, neuroimaging) are per-formed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI session in pa-tients suffering from the two main spectra of mental disorders: schizophrenia and major depres-sion. The current status of this research program and future implications to promote the devel-opment of psychiatry as a medical discipline are discussed.

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit (IMU) sensors installed as wearable device. The objective is to learn classification of multiple classes of human activities in real-time. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based, and distance-based, are employed to achieve the desired classification performance. A comparative study of these strategies has been presented in terms of classifiability and classification accuracies. The proposed system is shown to be computationally efficient, which is relevant in the context of limited computing resources on typical wearable devices. The trade-off between classification accuracy and computation complexity is analyzed for different algorithmic hyper-parameters and other system parameters. Extensive experimentation and simulation study have been conducted on a multi-user human activity data from the public do-main in order to validate the proposed learning paradigm.

Previous studies suggested that trauma-focused cognitive behavior therapy (TF-CBT) is effective in reducing symptoms of post-traumatic stress disorder (PTSD) and depression in children and adolescents. However, the effects of app-assisted TF-CBT-based interventions in psychiatric clinics compared to treatment-as-usual (TAU) are under-explored. The purpose of this study was to reveal the added value of the app-assisted TF-CBT-based intervention as an add-on to TAU. The digitalized measures of the Child and Adolescent Trauma Screen (CATS), The Adolescent Dissociative Experience Scale (A-DES), Short Moods and Feelings Questionnaire (SMFQ), PTSD Checklist (PCL-5), and Children's Post-Traumatic Cognitions Inventory (CPTCI) were applied at baseline and post-treatment. The app-assisted TF-CBT-based group participants started with higher levels of negative alterations in cognition and mood compared to TAU participants but at post-treatment, no significant differences were observed between the app-assisted TF-CBT and TAU groups. Within the app-assisted TF-CBT group, post-treatment scores were significantly lower in post-traumatic stress symptoms, intrusion symptoms, alterations in arousal and reactivity, overall post-traumatic cognitions, and belief in being "a fragile person in a scary world”. Within the TAU group, post-treatment scores were notably lower just in dissociation and intrusion symptoms. The study suggests that app-assisted TF-CBT-based intervention can be effective in addressing specific trauma-related symptoms. The findings imply the necessity for screening for traumatic experiences, incorporating trauma-informed care, implementation of TF-CBT into treatment protocols, utilization of app-assisted interventions, tailoring interventions to individual needs, and comprehensive assessment and monitoring of treatment progress.

Despite the clinical significance of partial posttraumatic stress disorder (PTSD), research on its associated cognitive function profiles is notably lacking. This study aimed to identify distinct cognitive function profiles in patients with partial PTSD. Medical records of Vietnamese War Korean Veterans undergoing psychiatric assessments, including the Clinician-Administered PTSD Scale (CAPS) and neurocognitive tests on the same day, were retrospectively reviewed. The participants were divided into partial PTSD (N = 10) and non-PTSD (N = 36) groups based on the CAPS interview. Neurocognitive test score profiles of the partial PTSD and non-PTSD groups were compared. Covariance analysis adjusted for potential confounders in cognitive function. In cognitive profiles, executive function, mental flexibility (measured by Trail Making Test B), and concentration (measured by the Mini-Mental Status Exam) were significantly poorer in the partial PTSD group. When adjusted for age, depressive symptoms, and education level, no significant differences were observed in the cognitive profiles between the partial PTSD and non-PTSD groups. In the current study, executive function and concentration showed deficits in patients with partial PTSD compared with those without PTSD. Partial PTSD cognitive profiles displayed no significant differences from non-PTSD profiles when adjusted for possible confounders such as depressive symptoms.

This systematic review provides a comprehensive neuropsychological analysis of psychotic spectrum disorders, including schizophrenia, bipolar disorder, and depression. It explores cognitive impairments in memory, attention, and executive function, and the relation between brain abnormalities and these deficits. The paper emphasizes the use of neuropsychological tests in diagnosis and early psychosis management, while also discussing interventions, the impact of dementia on psychosis, and the neurobiological patterns associated with these conditions. Additionally, it underlines the importance of childhood trauma in the development of psychosis and examines the effects of substance abuse and brain injury. Neuropsychological evaluations and targeted therapies are highlighted as crucial for the understanding, diagnosis, and treatment of psychotic disorders. The research further includes evidence of neurocognitive dysfunction in individuals with a high risk of psychosis and emphasizes the advanced brain aging in schizophrenia and bipolar disorder. The necessity of comprehensive clinical evaluations and targeted therapies for cognitive impairments is underscored throughout the research. The potential effects of dementia on individuals with psychosis, neurobiological patterns, the role of childhood trauma, the impact of psychosis in dementia patients, and proposed treatments are also explored in depth. Overall, the paper underscores the integral role neuropsychological knowledge plays in understanding, diagnosing, managing, and treating psychotic spectrum disorders.

Anxiety disorders significantly reduce patients' quality of life. Current pharmacological treatments, primarily benzodiazepines and antidepressants, are associated with numerous side effects. Consequently, there is a continual search for alternative methods to traditional therapies that are less burdensome for patients and broaden their therapeutic options. In this review, we examined recent evidence on alternative treatments for anxiety disorders, including physical activity, mindfulness, VR technology, biofeedback, herbal remedies, and transcranial magnetic stimulation (TMS), cryotherapy, hyperbaric therapy, vagus nerve stimulation, MDMA, electroconvulsive therapy (ECT), and eye movement desensitization and reprocessing (EMDR) therapy. Our objective was to determine the role of these selected alternative methods in the treatment of anxiety disorders. Alternative treatments significantly expand the options available to patients and clinicians, with many serving as adjuncts to traditional therapies. Among the methods presented, mindfulness has the most significant therapeutic potential.

The genetic causes of the differentiated, highly treatable and mostly non-fatal papillary thyroid cancer (PTC) are not yet fully known. The widely accepted PTC etiology blames altered sequence or/and expression level of certain biomarker genes. However, our previous studies found that the PTC biomarkers played significantly lower roles than the personalized PTC Gene Master Regulators. Our publicly accessible gene expression data from the cancer nodule and the surrounding normal tissue of surgically removed PTC tumor were re-analyzed to determine the transcriptomic effects on the genomic fabrics responsible for the DNA replication, repair and transcription. Tumor results were compared to the gene expression profiles of the papillary (BCPAP) and the anaplastic (8505C) human thyroid cancer cell lines. The analyses were carried out from the Genomic Fabric Paradigm (GFP) perspective that provides the most theoretically possible comprehensive characterization of the transcriptome and its alterations in disease. The study indicated that, in addition to regulating numerous genes, changes in the homeostatic control of transcript abundances and remodeling of the gene networks had major contributions to the PTC occurrence, resilience and proliferation. Therefore, the molecular mechanisms responsible for gene expression control and inter-coordination should also be considered when designing personalized anti-cancer gene therapies.

Studies and opinion articles present unequal conclusions concerning empathy and morality, mainly because a somewhat heterogeneous and foggy conceptual approach prevails. This scoping review aims to analyse the relationship between empathy and morality and to present a novel conceptual model of empathy applied to moral decisions.A search in PsycINFO, Scopus and PubMed identified articles addressing the relationship between empathy and morality.Thirty-two articles were included. We find an absence of a consensual definition of empathy. It is commonly defined as a predominant emotional process and a factor of bias and prejudice in moral decisions.According to our conceptual model, empathy is a distinct, complex human neuropsychological capacity that mobilises and is dependent on the proper functioning of several brain areas and balances the scales on which are Emotion and Reason, enabling the most thoughtful moral decisions possible in diverse circumstances and contexts.

Dental agenesis is one of the most common developmental anomaly in humans and it is frequently as-sociated with several other oral abnormalities. The present case describes nonfamilial agenesis of per-manent teeth in a twenty-one-year-old boy, with no apparent systemic abnormalities. The treatment in-cluded a personalized and interdisciplinary approach by the means of endodontics, orthodontics, im-plants supported restorations and prosthetic treatments. The treatment plan was thoroughly elaborated using photographic analysis, study models, orthopantomogram, CBCT and cephalograms. Virtual smile design, diagnostic waxing, and mock-up previsualized the treatment objectives. The edentulous spaces were reconstructed by inserting dental implants, and monolithic Zirconia implant supported restorations. The final results showed a highly aesthetic and functional rehabilitation. Periodic checkups have shown that the stability of the result is well maintained and that the implant supported restorations are an op-timal solution for patients with multiple anodontia.

Lung cancer (LC) continues to be an important public health problem being the most common form of cancer and a major cause of cancer deaths worldwide. Despite the great bulk of research to identify genetic susceptibility genes by genome-wide association studies, only few loci associated to nicotine dependence have been consistently replicated. Our previously published study in few phenotypically discordant sib-pairs identified a combination of germline truncating mutations in known cancer susceptibility genes in never smoker early-onset LC patient does not present in their healthy sib. These results firstly demonstrated the presence of an oligogenic combination of disrupted cancer predisposing genes in non-smokers patients, giving experimental support to a model of a “private genetic epidemiology”. Here, we used a combination of whole-exome and RNA sequencing coupled with a discordant sib’s model in a novel cohort of pairs of never-smokers early-onset LC patients and in their healthy sibs used as controls. We selected rare germline variants predicted as deleterious by CADD and SVM bio-informatics tools and absent in the healthy sib. Overall, we identified an average of 200 variants per patient, about 10 of which in cancer predisposing genes. In most of them, RNA sequencing data reinforced the pathogenic role of the identified variants showing: i) downregulation in LC tissue (indicating “second hit” in tumor suppressor genes); ii) upregulation in cancer tissue (likely oncogene); iii) downregulation in both normal and cancer tissue (indicating transcript instability). The combination of the two techniques demonstrates that each patient has an average of six (with a range from four to eight) private mutations with functional effect in tumor predisposing genes. The presence of a unique combination of disrupting events in the affected subjects may explain the absence of familial clustering of non-small cell lung cancer. In conclusion, these findings indicate that each patient has his/her own “predisposing signature” to cancer development and suggest the use of personalized therapeutic strategies in lung cancer.

In the current era of emerging technologies and cutting-edge inventions, it is equally important to consider the ethical aspects of research and innovations to have an ethical space for the sustainable progress and safety of humanity. The field of nanotechnology research and innovation often faces criticism due to its potential adverse impacts on health, the environment, limited accessibility, high costs, and concerns about data privacy. Therefore, it is crucial to address and resolve these ethical issues systematically. In this regard, we present our findings and methods for exploring the application of the ethics technology assessment method by considering its relevance at different stages of innovation development. Specifically, the present work provides a conceptual framework for ethics technology assessment focused on evaluating a significant innovation in nanotechnology within the domain of food safety with specific reference to mycotoxin remediation. By leveraging the value-sensitive design approach, we described various strategies for analyzing the ethical implications of nanotechnology-based innovations in mycotoxin remediation. Through comprehensive ethics technology assessment, nanotechnology-based innovations can be guided toward becoming more value-oriented, thereby making substantial contributions to social progress.

Defensive behavior is a function of specific survival circuits, the “aversive brain system”, that are thought to be conserved across vertebrates, and involve threat detection and the organization of defensive responses to reduce or eliminate threat. In mammals, these circuits involve amygdalar and hypothalamic subnuclei and midbrain circuits. The increased interest in teleost fishes as model organisms in neuroscience created a demand to understand which brain circuits are involved in defensive behavior. Telencephalic and habenular circuits represent a “forebrain circuit” for threat processing and organization of responses, being important to mounting appropriate coping responses. Specific hypothalamic circuits organize neuroendocrine and neurovegetative outputs, but are the less well-studied in fish. A “midbrain circuit” is represented by projections to interneurons in the optic tectum which mediate fast escape responses via projections to the central gray and/or the brainstem escape network. Threatening stimuli (especially visual stimuli) can bypass the “high road” and directly activate this system, initiating escape responses. Increased attention to these circuits in an evolutionary framework is still needed.

The present study explored whether exposure to complex childhood trauma (CCT) is reflected in the resting-state electroencephalography (EEG) temporal alpha power of complex trauma-exposed adolescents, given the association of temporal alpha with fear circuitry (learning and conditioning) in a sample of 25 adolescents and similar controls, ages 12-17 years. Both trauma and psychopathology were screened or assessed, and resting-state EEG was recorded, following a preregistered protocol for data collection. Temporal alpha power, corresponding to T5 and T6 electrode location (10-20 international system), was extracted from resting-state EEG in both eyes-open and eyes-closed conditions. We found that in the eyes-open condition, right, but not left, temporal alpha was significantly higher in adolescents exposed to CCT, relative to healthy controls, suggesting that childhood trauma exposure may have a measurable impact on alpha oscillatory patterns, specifically in the right temporal region. Our study highlights the importance of considering neural markers, such as temporal alpha power, in understanding the long-term consequences of CCT exposure in developmental samples, with possible important clinical implications in guiding neuroregulation interventions.

This study aimed to evaluate the safety and tolerability of STP1, a combination of ibudilast and bumetanide, tailored for the treatment of a specific subgroup of patients with Autism Spectrum Disorder (ASD), namely ASD Phenotype 1 (ASD-Phen1). We conducted a randomized, double-blind, placebo-controlled, parallel-group phase 1b study with two 14-day treatment phases (registered at clinicaltrials.gov as NCT04644003). Nine ASD-Phen1 patients were administered STP1, while three received a placebo. We assessed safety and tolerability, along with electrophysiological markers, such as EEG, Auditory Habituation, and Auditory Chirp Synchronization, to better understand STP1's mechanism of action. Additionally, we used several clinical scales to measure treatment outcomes. The results showed that STP1 was safe and well-tolerated, with electrophysiological markers indicating a significant and dose-related reduction of gamma power in the whole brain and in brain areas associated with executive function and memory. Treatment with STP1 also increased alpha 2 power in frontal and occipital regions and improved habituation and neural synchronization to auditory chirps. Although numerical improvements were observed in several clinical scales, they did not reach statistical significance. Overall, this study suggests that STP1 is safe and well-tolerated in ASD-Phen1 patients and shows indirect target engagement in ASD brain regions of interest.

The shift towards Personalized Cancer Medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

Background: Autism spectrum disorder (ASD) is a persistent neurodevelopment disorder frequently co-occurring with Attention-Deficit/Hyperactivity Disorder (ADHD) and behavior-related disorders. While behavioral therapy is the first-line option to manage ASD core symptoms, pharmacological therapy is sometimes needed to treat acute problems, such as agitation and aggressive behaviors. Recent guidelines recommend neuroleptics to reduce psychomotor agitation in people with ASD. However, since children with ASD are often drug-resistant, alternative treatments are often justified. Reports from literature showed that intra-venous valproate (IV-VPA) can be effective in reducing agitation in psychiatric patients, with lower frequency of adverse events compared to convention-al treatments. However, since findings are occasionally inconsistent, IV-VPA is not an approved option in clinical psychiatry. We aim at improving the knowledge on the IV-VPA treatment option in emergency psychiatry in the pediatric age. Methods: We report the case of a 11-year-old boy suffering from a complex neurodevelopmental condition, who experienced a psychotic episode with severe aggressive and disruptive behaviors, successfully treated with IV-VPA. Besides, we conducted an up-dated literature review on this topic. Conclusion: In our case, first-line therapies proved to be ineffective. On the contrary, IV-VPA led to a safe and prompt clinical success, in line with other reports. Based on our literature review, IV-VPA can be highly effective and can reduce the risk of adverse events that frequently occur with the use of high-dose standard medications in emergency psychiatry.

Background: Assessing the impact of bone health clinical management in breast cancer (BC) patients receiving adjuvant endocrine therapy and designing a personalized clinical pathway to reduce bone loss, in an Italian research hospital. Methods: The primary endpoint was to assess (through the process improvement organizational method) the clinical pathway that post-surgical BC patients prescribed with endocrine therapy undergo to prevent bone loss. The secondary endpoint was to design a personalized clinical pathway for a prompt implementation of guidelines, to assess and possibly prescribe antiresorptive therapy. Results: During the first year of the new diagnostic therapeutic assistance pathway execution, an 60% increase in Dual-Energy X-ray Absorptiometry evaluations within 30 days and a 39.5% increase in antiresorptive therapy prescription within 90 days (since prescription of endocrine therapy) were shown, thus increasing patients’ compliance. Conclusion: Case managers and bone health specialists in this context can improve patients’ adherence to therapies and bone health, helping physicians to expand their collaboration.

Fragile X syndrome (FXS) is caused by the full mutation in the FMR1 gene on the Xq27.3 chromosome region. It is the most common monogenic cause of Autism Spectrum Disorder (ASD) and inherited intellectual disability (ID). Besides ASD, ID and other issues, individuals with FXS may exhibit sleep problems and impairment of circadian rhythm (CR). The Drosophila melanogaster models of FXS, such as dFMR1B55, present excellent model for research in the FXS field. During this study, sleep pattern and CR in dFMR1B55 mutants were analyzed, using a new platform based on continuous high-resolution videography in integration with a highly-customized version of software. This methodology provides more sensitive results, which could be crucial for all further research in this model of fruit flies. The study revealed that dFMR1 B55 male mutants can be considered weak rhythmic flies rather than totally arrhythmic, and present a good alternative animal model of genetic disorder, which includes impairment of CR and sleep behavior. The combination of videography and software used in the current study should be recommended as a gold-standard methodology for such further research.

This study addresses the challenge of treating skin cancer effectively, especially in visible areas like the face, where traditional methods may be undesirable. Current options, including surgery and radiation therapy, have limitations such as complexity, expense, and imprecise dose delivery. The proposed solution involves additively manufacturing personalized patches using diagnostic imaging data, offering a targeted and controlled dose of beta-emitting isotopes like Y-90. The patches, tailored to the tumor's shape and depth, aim to minimize damage to healthy tissues and structures. Through 3D printing technology we demonstrated the feasibility of creating individualized applicators filled with radioisotopes, that have the potential to deliver therapeutic doses while ensuring patient comfort and affordability. This innovative approach, based on radiation delivery simulations, offers a promising future alternative to conventional radiation therapy methods. Clinical trials are required to validate its efficacy and safety, but the potential for reduced radiation exposure and enhanced treatment precision positions this personalized and targeted therapy as a valuable advancement in skin cancer treatment.

Rectal cancer presents a significant burden globally, often requiring multimodal therapy for locally advanced cases. Long-course chemoradiotherapy (LCRT) and short-course radiotherapy (SCRT) followed by surgery have been conventional neoadjuvant approaches. Recent trials favor LCRT due to improved local control. However, tumor distant recurrence remains a concern, prompting exploration of Total Neoadjuvant Therapy (TNT) as a comprehensive treatment strat-egy. Immune checkpoint inhibitors (ICIs) show promise, particularly in mismatch repair-deficient (dMMR) or microsatellite instability-high (MSI-H) tumors, potentially revolutionizing neoadjuvant regimens. Non-operative management (NOM) represents a viable alternative post-neoadjuvant therapy for selected patients achieving clinical complete response (cCR). Additionally, monitoring minimal residual disease (MRD) using circulating tumor DNA (ctDNA) emerges as a non-invasive method for treatment response assessment. This review synthesizes current evidence on TNT, ICIs, NOM and ctDNA, elucidating their implications for rectal cancer management and highlighting avenues for future research and clinical application.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (>200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene. Individuals with FXS experience a wide range of challenges related to social interaction (SI). Animal models, such as Drosophila melanogaster model for FXS where the only ortholog of human FMR1 (dFMR1) is mutated, have played a crucial role in understanding of FXS. The aim of this study was to investigate SI in the dFMR1B55 mutants using the novel Drosophila Shallow Chamber and a Python data processing pipeline based on social network analysis (SNA). In comparison with wild-type flies (w1118), SNA analysis in dFMR1B55 mutants revealed hypoactivity, fewer connections in their networks, a lower ability to efficiently transmit information, fewer alternative pathways for information transmission, a higher variability in the number of interactions they achieved, and tended to stay near the boundaries of the testing chamber. These observed alterations indicate the presence of characteristic strain-dependent social networks in dFMR1B55 flies, commonly referred to as the group phenotype. Finally, a combination of novel research tools is a valuable method for SI research in fruit flies.

Sweat rate and electrolyte losses have a large inter-individual variability, a personalized approach to hydration can overcome this issue to meet an individual’s needs. This study aimed to investigate the effects of personalized hydration strategy (PHS) on fluid balance and intermittent exercise performance. Twelve participants conducted 11 laboratory visits including a VO2max test and two 5-day trial arms under normothermic (NOR) or hyperthermic (HYP) environmental conditions. Each arm began with 3-day of familiarization exercise following by two random exercise trials with a either PHS or control (CON). Then, participants crossed-over to the second arm for: NOR+PHS, NOR+CON, HYP+PHS, and HYP+CON. The PHS was prescribed according to participants’ fluid and sweat sodium losses. CON drank ad-libitum of commercially-available electrolyte solution. Exercise trials consisted of two phases: 1) 45min constant workload; 2) high-intensity intermittent exercise (HIIT) until exhaustion. PHS had a significantly greater fluid intake compared to CON. HYP+CON produced the lowest sweat sodium concentration. HYP+PHS had a lower thirst perception and a longer HIIT compared to HYP+CON. Thus, PHS reinforces fluid intake and successfully optimizes hydration status, regardless of environmental conditions. PHS may be or is an important factor to preventing negative physiological consequences during high-intensity exercise in the heat.

Based on non-invasive neuroimaging techniques and graph theory-based network analyses, many researchers have tried to use small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed--randomization, regularization, stronger small-worldization and weaker small-worldization, we found that current results point to a significant trend toward more regularization in females and more randomization in males in the functional brain networks. As for particular topological properties, the most consistent findings include a significant shift toward higher global efficiency in males and higher local efficiency in females in the functional network studies. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks. Nevertheless, we noticed that the sample sizes in many published studies are limited, and future studies with larger samples are warranted to obtain more reliable results. We anticipate that the conclusions in this manuscript will contribute to a deeper understanding of neurobiological mechanisms underlying the sex differences in the brain.

It is estimated that pancreatic cancer will be the 2^nd leading cause of cancer-related deaths globally by 2030, highlighting the ongoing lack of effective treatment options in this devastating condition. There is a lack of reliable prognostic or predictive markers in pancreatic cancer to guide management decisions, whether for systemic chemotherapy, molecularly targeted therapies, or immunotherapies. To date, the results for targeted agents and immunotherapies in unselected populations of chemo-refractory pancreatic cancer have not met expectations. The reasons for this lack of efficacy of immunotherapy in pancreatic cancer are incompletely understood. The challenges in pancreatic cancer include the physical barrier created by the dense desmoplastic stroma surrounding the tumor, chemokine-mediated exclusion of T cells, poor antigenicity, paucity of infiltrating T cells within the tumor, ultimately leading to an immunosuppressive microenvironment. A better understanding of the role of inflammation in pancreatic cancer, its tumor microenvironment and individualized patient-related features, be they molecular, clinical or histopathological would enable a more effective tailored approach to the management of pancreatic cancer. In this review, the role of inflammation, the immune tumor microenvironment and potential immune biomarkers in pancreatic cancer are explored.

Although the dopamine hypothesis of schizophrenia explains the effects of all the available antipsychotics in clinical use, there is an increasing need for developing new drugs for the treatment of positive, negative, and cognitive symptoms of chronic psychoses. Xanomeline-trospium (KarXT) is a drug combination that is based on the essential role played by acetylcholine in the regulation of cognitive processes and the interactions between this neurotransmitter and other signaling pathways in the central nervous system, with a potential role in the onset of schizophrenia, Alzheimer’s disease, and substance use disorders. A systematic literature review that included four electronic databases (PubMed, Cochrane, Clarivate/Web of Science, and Google Scholar) and the US National Library of Medicine database for clinical trials detected 21 sources referring to 14 studies focused on KarXT, out of which only four have published results. Based on the results of these trials, the short-term efficacy and tolerability of xanomeline-trospium are good, but more data is needed before this drug combination may be recommended for clinical use. However, on a theoretical level, the exploration of KarXT is useful for increasing the interest of researchers in finding new, non-dopaminergic, antipsychotics that could be used either as monotherapy or as add-on drugs.

Psoriasis is a chronic, multisystem, inflammatory disease. The typical clinical cutaneous manifestation of psoriasis is scaly erythema or plaque, limited or widely distributed. However, psoriasis is far beyond the skin involvement and provides many challenges, including associated comorbidities. In this review, we classify psoriasis as cutaneous and systemic psoriasis, according to the clinical diversity and associated comorbid diseases, and recommend classification criteria for psoriasis. The key objective of this novel classification is to raise awareness of the complexity of this multifaceted disease and help to better understand and manage this complex disease comprehensively.

ADHD is a neurodevelopmental disorder that children and also adults can develop. A complex interplay of genetic and environmental factors may underlie interindividual variability in ADHD and potentially related aggressive behavior. Using high-resolution molecular biology techniques, we investigated the impact of some MAOA and SLC6A4 variations on ADHD and aggressive behavior in a group of 80 Italian children with ADHD and in 80 healthy controls.We found that homozygous genotypes of MAOA rs6323 and rs1137070 were associated with an increased risk of ADHD (p=0.02 and p=0.03, respectively), instead the heterozygous genotypes (GT of rs6323 and CT of rs1137030) (p=0.0002 and p=0.0006) were strongly linked to a lower risk of developing this disorder. In patients with aggressive behavior, we highligted only a weak negative association of both MAOA polymorphisms (heterozygous genotypes) with aggressiveness then, suggesting that these genotypes maybe protective towards specific changes in behavior (p=0.05). Interestingly, an increase of the GG genotype of rs6323 (p=0.01) and a decrease of GT genotype (p=0.0005) was also found in patients without aggressive behavior compared with controls. Regarding 5HTT gene genotyping, no allele and genotype differences have been detected among patients and controls. Our work shows that defining a genetic profile of ADHD may help to early detect those patients who are more vulnerable to ADHD and/or antisocial and aggressive behavior and to design precision-targeted therapies.

The aims of this systematic review and meta-analysis were to evaluate the risk of inmates with mental disorder of being placed into disciplinary confinement (DC) and its effect on mental health. A systematic search of studies was performed in PubMed, PsycINFO, Web of Science, and Google Scholar. The meta-analysis was conducted using random-effects models. Heterogeneity among study point estimates was assessed with Q statistics and quantified with I2 index. Publication bias was assessed with Egger’s test. Quality assessment was based on the GRADE Checklist for observational studies. Guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were followed throughout. First, a meta-analysis of 5 articles including 27,455 inmates showed than incarcerated individuals with a mental disorder were 1.23 times (OR=1.23, CI=1.10; 1.38) more likely to be placed in DC than incarcerated individuals without a mental disorder. Particularly, having a severe mental disorder (OR=1.31, p<0.001), a personality disorder (OR=1.66, p<0.001) and having previously received mental health services (OR=1.16, p=0.024) increased the risk of being placed in DC. Secondly, a systematic review of 5 articles including 171,300 inmates showed more psychological distress, psychiatric symptoms (self-harm, thought disorders, obsessive-compulsive symptoms), need for mental health services and hospitalizations in DC than the general correctional population. Considering the increased risk of placement in DC for incarcerated persons with a mental disorder and its deleterious effect on mental state, it is essential that correction officials create new safe interventions to manage these inmates and offer them proper mental health care to limit its use.

Background: Many clinical conditions are affected by meteorological factors and climate changes. Psychiatric disorders are large contributors to the global disease burden, but the literature on the impact of climate changes on mental health is limited. The aim of this study is to investigate the correlation between rising temperatures and admissions for acute exacerbations of psychiatric disorders. Methods: Data were collected for summer months of years 2013 to 2022. Mean monthly tempera-tures were obtained from the meteorological station of the Department of Physics of the Universi-ty of Turin. Data on patients admitted to the acute psychiatric unit of San Luigi Gonzaga Univer-sity Hospital (Turin, Italy) were extracted from the hospital registry. Linear regression analyses were used to investigate the correlation between temperature and number of admissions. Results: A total of 1600 admissions to our psychiatric ward were recorded. Mean monthly temper-ature and monthly number of admissions were directly correlated (P = 0.0020). The correlation between temperature and admissions was significant for the subgroup of admissions due to Bipo-lar Disorders (BD) (P=0.0011), but not for Schizophrenia (P=0.6070) (SCZ) or Major Depressive Disorder (MDD) (P=0.0588). Conclusions: These results confirm the impact of meteorological factors on mental disorders, and in particular on the course of BD. This provides new insights into etiopathological mechanisms and can contribute to improved follow up and relapse prevention.

This study analysed trends of first-time patients visiting the paediatric psychiatry clinic in a university hospital. The medical records from 2009 to 2016 of first-time patients visiting the Kyung Hee university hospital were reviewed, focusing on children in grades 1–12. We analysed the prevalence rate of psychiatric disorders per 100,000 general populations by gender and grade, and the characteristics of patients who sought outpatient care more than three times. The study included 1,467 participants, of which 931 were males (63.5%). The number of male patients per 100,000 populations significantly decreased from 4.14 in 2009 to 2.03 in 2016. While hyperkinetic disorders had the highest prevalence in males, neurotic disorders were most frequent in females. Prevalence of disruptive behaviour disorders in males and mental retardations in females decreased significantly during the study period. The factors affecting continuity were being female, studying in grades 7–12, and diagnosis of depressive, hyperkinetic, and tic disorders. Physicians should consider the new paediatric patients’ gender, grade, and expected diagnosis from their first visit to improve treatment compliance.

Gestational diabetes mellitus (GDM) triggers alterations in the maternal gut microbiome. Alongside metabolic shifts, microbial products may impact clinical factors and influence pregnancy outcomes. Here, we performed two separate analyses to integrate the maternal microbiome-metabolomic differences from a subset of the “Choosing Healthy Options in Carbohydrate Energy” (CHOICE) study. Women diagnosed with GDM were randomized to a diet higher in complex carbohydrates (CHOICE, n=18, 60% complex carbohydrate/25% fat/15% protein) or a conventional GDM diet (CONV, n=16, 40% carbohydrate/45% fat/15% protein). All meals were provided, diets were eucaloric, and fiber content was similar. CHOICE diet was associated with increases in TMAO, lysophosphotidylcholines, indoxyl sulfate, and several triglyceride (TG) species, while CONV diet was associated with higher plasma hippuric acid, aspartic acid, betaine, glutamic acid, indole propionic acid, and indoleacetic acid, suggesting that differences in lipid metabolism and tryptophan utilization pathways are linked to specific maternal diets. We also identified two latent metabolic groups not associated with either diet treatment: one metabolome associated with higher fasting glucose, and the other associated with higher fasting TGs after 7 weeks on either diet. Our study underscores the importance of utilizing dietary approaches to optimize maternal-fetal health outcomes in GDM treatment.

Informal dementia home caregiving is viewed negatively and can result in caregiver depression and anxiety from burnout, potentially compromising caregiving. Solutions are unclear, as caregiving relationships are unique—caregiver creation of a graphic memoir may help to mitigate the negative dementia narrative and reduce experienced burnout. This investigation examines the writing of a graphic memoir by one informal dementia home caregiver, created with the help of a cartoon illustrator and a publisher who edited, printed, and made the graphic memoir available at the psychiatric bookstore considered the largest in North America. Mother of the illustrator and the editor and publisher of this graphic memoir, this author provides the perspective taken in this investigation. The analysis, developed by this author, represents psychoanalytic narrative research, serving as the historical method. Aided by email threads between the publisher/caregiver, publisher/printer, and publisher/bookseller, and a published interview with the dementia caregiver are answers to how writing, illustrating, and publishing the graphic memoir affected the caregiver’s narrative reconstruction and burnout. Providing an example for other informal dementia home caregivers similarly to write and illustrate a publishable graphic memoir for their positive narrative reconstruction and to help decrease their depression and anxiety from burnout is the aim.

Children—Dependents in Social Construction Theory—are assessed within democracies regarding their ability to self-direct their behavior concerning their associated society. In this regard, childhood represents a lack of self-direction, a life period when people require the guidance and protection of adults. The assumed necessity of adults supervising children in democracies requires substantial social resources, reducing the self-direction of those charged with overseeing children. Given that self-direction defines the optimal experience of people within democratic society, finding ways to increase the self-direction of children is an individual and a social benefit. In this regard, smartphones have improved self-direction in children. How children have become empowered to self-direct their lives with smartphone use considering Social Construction Theory—as Advantaged, Contenders, or Deviants—is the focus of a scoping review conducted on “children, empowerment, smartphones” of the following databases: OVID, JSTOR, ProQuest, PubMed, Scopus, and Web of Science. The result is that children permitted to self-direct their activities aided by smartphone technology have comparable social behavior to adults positioned as Advantaged, Contenders, or Deviants and, as such they should be encouraged to utilize smartphone technology to improve their self-direction, as doing so will augment individual and caregiver self-direction, providing increased social benefit.

Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such “meta-prompt” is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning – based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.

Background: to assess the benefit of a deep inspiration breath hold (DIBH) over standard irradiation technique, and eventually to identify anatomical and/or treatment preplanning characteristics correlated with LAD dose. Methods: We retrospectively identified patients with left-sided breast cancer who underwent whole breast radiotherapy in DIBH. All patients had both plans in DIBH and free-breathing (FB). Receiving operating characteristics (ROC analysis) were performed to define the cut-off point of parameters to predict LAD maximum dose >10 Gy and LAD mean dose > 4 Gy. Areas under the curve (AUCs) were calculated for all variables. Post-test probability has been calculated to evaluate advantage for parameters combination. Results: One hundred ninety-seven patients were identified. LAD dose was significantly reduced in DIBH plans with maximum and mean dose reduced by 31.7% (mean value 3.5 Gy vs 4.8 Gy, p=<0.001) and 28.1% (mean value 8.2 Gy vs 12.8 Gy, p=<0.001) in DIBH plans compared to FB plans. The strongest predictor of LAD maximum dose > 10 Gy and a LAD mean dose > 4 Gy was the minimum distance of LAD from tangent open fields. Other parameters were lung volume and heart volume for LAD Dmax > 10 Gy and lung volume, heart volume and breast separation for LAD Dmean > 4 Gy. Conclusion: The dosimetric benefit of DIBH is valid for all patients and DIBH should be preferred for all left sided patients.

In today’s Web 2.0 era, online social media has become an integral part of our lives. In the course of the information revolution, the form of information has undergone a radical change, from simple text information to today’s integrated video, image, text and audio, and there has also been a great change in the way of dissemination and access, as people nowadays do not just rely on traditional media to passively receive information, but more actively and selectively obtain information from social media. Therefore, it has become a great challenge for us to effectively utilize these massive and integrated multi-modal media information to form an effective system of retrieval, browsing, analysis and usage. Unlike movies and traditional long-form video content, micro-videos are usually short in length, between a few seconds and tens of seconds, which allows users to quickly browse different contents and make full use of the fragmented time in their lives, while users can also share their micro-videos to their friends or the public, forming a unique social way. Video contains rich multimodal information, and fusing information from multiple modalities in a video recommendation task can improve the accuracy of the video recommendation task.According to the micro-video recommendation task, a new combinatorial network model is proposed to combine the discrete features of each modality into the overall features of various modalities through the network, and then fuse the various modal features to obtain the overall video features, which will be used for recommendation. In order to verify the effectiveness of the algorithm proposed in this paper, experiments are conducted in the public dataset, and it is shown the effectiveness of our model.

Background: Immunologic and neuro-inflammatory pathways have been found to play a major role in the pathogenesis of many neurological disorders such as epilepsy, proposing the use of novel therapeutic strategies. In the era of personalized medicine and in the face of the exhaustion of anti-seizure therapeutic resources it is worth looking at the current or future possibilities that neuroimmunomodulator or anti-inflammatory therapy can offer us in the management of patients with epilepsy. Methods: We performed a narrative review on the recent advances on the basic epileptogenic mechanisms related to the activation of immunity or neuroinflammation with special attention to current and future opportunities for novel treatments in epilepsy. Results: Neuroinflammation can be considered a universal phenomenon and occurs in structural, infectious, post-traumatic, autoimmune, or even genetically based epilepsies. The emerging research developed in recent years has allowed us to identify the main molecular pathways in-volved in these processes. These molecular pathways could constitute future therapeutic targets for epilepsy. Conclusions: Different drugs current or in development have demonstrated their capacity to inhibit or modulate molecular pathways involved in the immunologic or neuroinflammatory mechanisms described in epilepsy. Some of them should be tested in the future as possible antiepileptic drugs.

Limited understanding exists regarding the intricate dynamics between the levels of social inhi-bition exhibited by both wives and husbands concerning their perceived family support and de-pressive symptoms, particularly within couples who are parents of children diagnosed with au-tism spectrum disorder (ASD). This study used the actor-partner, interdependence mediation model to analyze data collected from 397 pairs of Chinese parents with children diagnosed with ASD. The findings of the study revealed significant indirect actor effects, indicating that the levels of social inhibition exhibited by both wives and husbands were associated with their own de-pressive symptoms through their respective perceptions of family support. In general, the study did not find significant partner effects, except for some indirect effect (IE) of wives on their hus-bands' depressive symptoms through the wives' perceived social support. This observation sug-gests that cultural norms and role expectations within the family context may influence these dy-namics. These results underscore the potential importance of interventions aimed at addressing social inhibition and enhancing perceived family support to alleviate depressive symptoms in this population. Additionally, interventions targeting fathers should emphasize the importance of their involvement in parenting and collaboration with their partners in caring for children with ASD. Such collaborative efforts may contribute to improved outcomes for both parents and chil-dren within families affected by ASD.

Diagnosis and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD) in women remain insufficient. Fluctuations of reproductive hormones during the premenstrual period, postpartum period and (peri)menopause are neglected, even though they impact ADHD symptoms and as-sociated mood disorders [1]. Therefore, we created a female-specific treatment group for women with ADHD and premenstrual worsening of ADHD and/or mood symptoms. We describe the group programme and underlying rationale, offering a qualitative analysis of the participants’ evaluation. The 7 two-weekly sessions foreground the menstrual cycle and address several ADHD-specific topics in relation to this cyclical pattern: emotional regulation; impulsivity; setting boundaries, and triggers. Concurrently, women track their menstrual cycle and (fluctuating) ADHD and mood symptoms with an adjusted premenstrual calendar. In total, 18 women (25-47yrs) participated in three consecutive groups. We analysed the evaluation of the last group. Participants experienced the group as a safe and welcoming space. Recognition was valued by all. The topics discussed were deemed valuable and the structure suited them well. Completing the premenstrual calendar augmented awareness and recognition of individual cyclical symptoms. Discussing ADHD throughout the lifespan increased self-understanding. Participants took their menstrual cycle more seriously, prioritising self-acceptance and self-care in the luteal phase of the cycle. Exploring a cyclical approach in a group setting seems to be a positive addition to treatment for female ADHD.

Cancer cells are adept at reprogramming energy metabolism and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism and ROS detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. For example, by using genotyping microarrays (e.g. collections of Single Nucleotide Polymorphism - SNP) it is possible to uncover the reasons (i.e. mutation in genes) because a treatment works properly in some patients (for example absence of mutated genes), but it does not work (presence of mutated genes) in others. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g. high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic software is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. Thus, to exploit all the potential of this massive amount of data in the short possible time (before that data becomes obsolete), the necessity to develop parallel software tools for efficient data collection and analysis arise. Moreover, due to the heterogeneity of the data produced by the different kinds of experimental platforms, it is necessary to automatize in a comprehensive software pipeline, the various steps that compose a bioinformatic analysis, such as: the preprocessing of raw data to remove noise or corrupted data; the annotation of data with external knowledge (e.g. Gene Ontology), and the integration of molecular data with clinical data. It should be noted that such steps are necessary to make statistical or data mining analysis more effective. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based SNP genotyping data. A case study in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data.

Recently, the Portuguese parliament approved the decriminalisation of euthanasia for incurable illnesses. As the experiences of other countries show us, it will be a matter of time before mental disorders are addressed. Studying the phenomenon, particularly in vulnerable groups, in advance is essential for proper law drafting. Therefore, instruments which allow an objective assessment and comparison between groups must be available. This study aims to explore the validation of Faria's Attitude about Euthanasia scale in Portuguese older adult patients with mixed anxiety-depressive disorder. A sample of 114 older adult patients with mixed anxiety-depressive disorder collected by convenience in the Psychiatry Department of Senhora da Oliveira Hospital in Portugal was included. The pre-final version of the scale was tested in a small group with good results. The validity of the internal structure was analysed using exploratory factorial analysis. The internal consistency study verified reliability. For construct validity, we assessed the correlation with other validated scales measuring attitudes toward euthanasia, cognitive performance, personality and empathy. Attitude about euthanasia scale showed good internal consistency. One factor was retained in the principal component analysis. Significant correlations verified construct validity. The results support the scale's usefulness and validity. This study makes available a unique instrument to assess the overall tendency of the attitudes towards euthanasia in European-Portuguese, which can be used, for example, to compare Portuguese with Brazilian older adult psychiatric patients suffering from the same disorder. Furthermore, the adapted scale paves the way for other cross-cultural translations, adaptations, validations, and comparative analyses.

Psychiatric disorders share the same pattern of longitudinal evolution and have courses that tend to be chronic and recurrent. These aspects of chronicity and longitudinal evolution of psychiatric disorders are currently studied under the neuroprogression framework. Interestingly, considering the plasticity of the brain, it is necessary to emphasize the bidirectional nature of neuroprogression. We review evidence highlighting alterations of the brain associated with the longitudinal evolution of psychiatric disorders from the framework of neuroplastic adaptation to pathology. This new framework highlights that substantial plasticity and remodelling may occur beyond the classic neuroprogressive framework, which is characterized only by loss of grey matter volume, decreased brain connectivity, and chronic inflammation. We also integrate the brain economy concept in the neuroplastic adaptation to pathology framework, emphasizing that to preserve its economy, i.e., function, the brain learns how to cope with the disease by adapting its architecture. This approach can disentangle both the specific pathophysiology of psychiatric symptoms and the adaptation to pathology, thus offering a new framework for both diagnosis and treatment.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

Patients with a psychotic disorder frequently report oral health problems, while mental health nurses (MHNs) seem not to be fully aware of these problems and the risk factors. Therefore, this study aims to develop supportive tools for MHNs regarding oral health to increase sensitization among MHNs and to provide MHNs with knowledge to recognize (potential) oral health problems in patients with a psychotic disorder. We used a human-centered design in which the user, MHNs as well as experts by experience, were placed in the centre of the research process. Problems and needs in MHNs working with patients with a psychotic disorder were addressed. To identify key issues of problem as well as the needs in terms of resources, we started with focus groups (n = 9). We analysed the data thematically based on context of patients and MHNs in practice regarding oral health, preferred oral health tools focused on MHN, and intended outcomes of tools. A Multi Criteria Decision Matrix was developed and analysed (n = 9) to identify the most optimal and viable solution based on established criteria and issues that are prevalent in the work of MHNs. Most promising result was the development of a brochure with an awareness screener. The brochure with awareness screener was developed as a low-fidelity prototype for MHNs regarding oral health in patients with a psychotic disorder, based on the latest scientific evidence. After testing it, the feasibility was tested through semi-structured interviews (n=19). MHNs and experts by experience were satisfied with the tool and provided recommendations for adjustments to the content. Significant augmentations to the brochure include a clinical lesson and a toothbrush with toothpaste for patients. We can conclude that a brochure with an awareness screener is feasible for MHNs. Future steps aiming to further refine and optimize care processes for oral health tools in MHNs, should take refining eligibility criteria for psychiatric populations and the language level of the target group of a tool into account.

Tumor cells evolve in a complex and heterogeneous environment composed of different cell types and an extracellular matrix. Current 2D culture methods are very limited in their ability to mimic the cancer cell environment. In recent years, various 3D models of cancer cells have been developed, notably in the form of spheroids/organoids, using scaffold or cancer-on-chip devices. However, these models have the disadvantage of not being able to precisely control the organization of multiple cell types in complex architecture and are sometimes not very reproducible in their production, and this is especially true for spheroids. Three-dimensional bioprinting can produce complex, multi-cellular, and reproducible constructs in which the matrix composition and rigidity can be adapted locally or globally to the tumor model studied. For these reasons, 3D bioprinting seems to be the technique of choice to mimic the tumor microenvironment in vivo as closely as possible. In this review, we discuss different 3D-bioprinting technologies, including bioinks and crosslinkers that can be used for in vitro cancer models, and the techniques used to study cells grown in hydrogels; finally, we provide some applications of bioprinted cancer models.

The value of landscape, as part of collective heritage, can be acquired by GIS due to the multilayer approach of the spatial configuration. Proficiency in geospatial technologies in order to collect, process, analyze, interpret, visualize and communicate geographic information is being increased by undergraduate and graduate students, but in particular by those who are training to become geography teachers at secondary education. This training can be carried out through personalized learning and distance learning methodology. Personalized GIS education aims to integrate students and enhance their understanding of landscape. Some teaching experiences are shown whereby opportunities offered by WebGIS will be described, through quantitative tools and techniques that will allow this modality of learning and improve its effectiveness. Results of this research show that students, through geospatial technologies, learn landscape as a diversity of elements but also the complexity of physical and human factors involved. Several conclusions will be highlighted: i) the contribution of geospatial training to education for sustainable development; ii) spatial analysis as a mean of skills acquisition about measures for landscape conservation; iii) expanding and applying acquired knowledge to other geographic spaces and different landscapes.

Burnout represents a concern for all healthcare providers, particularly those specializing in emergency medical care for whom burnout outcomes have been well-documented. What remains unknown is the effect of burnout on redeployed medical specialists during the COVID-19 pandemic from an appointment-centered practice to emergency care directed by public health considerations. This research aims to identify and assess the burnout responses of fourteen medical specialties noted in searched returns of the four most cited articles published since 2020 about non-emergency physicians regarding their burnout brought on by the unanticipated need for them to provide emergency care during the recent pandemic. The hypothesis is that medical specialists accustomed to planning for emergency possibilities in their appointment-centered practice would demonstrate the least burnout regarding COVID-19-related emergencies. Considering coping as a process based on Lazarus's research, comparing and ranking the COVID-19 emergency responses across the various normally appointment-centered medical specialties in their employed coping strategies determines the outcome. With the hypothesis supported, suggested interventions for future pandemics—when these specialists are, again, redeployed to emergency care directed by public health considerations—are those coping strategies identified as most effective in reducing burnout while maintaining the viability of the medical specialty and excellent patient care.

Geriatric decisional capacity assessment is a complex medico-legal activity that evaluates normal subjects involved in specific civil matter like wealth transfer, access to treatment or voluntary hospitalization. Our objective was to evaluate how geriatric extrajudicial capacity exams impacted a general psychiatric unit over a decade. We also assessed the value of a surrogate biomarker (neutrophils to lymphocyte ratio) as a screening factor in the capacity assessment process. 72 cases were assessed in one psychiatric unit over a decade (2009-2018). As population was fast ageing, the total number of capacity exams per year doubled in time. None of the subjects had an active diagnostic of severe cognitive impairment or active psychiatric disease. The percentage of subjects receiving an “absence of capacity” conclusion remained constant over time (approx. 20% of all cases). 81% of the subjects within this subgroup had a clinical diagnostic of mild cognitive impairment compared to 46% in the “presence of capacity” subgroup (p&lt;0.001). The surrogate biomarker neutrophils to lymphocyte ratio was significantly higher in the “absence of capacity” subgroup, mainly related to a cognitive impairment diagnostic. The ratio has to be validated by larger studies before to be included in the capacity assessment process as an early screening tool.

Regenerative endodontic procedures (REPs) aim at recreating dental pulp tissue using biomaterials such as hydrogels. Their bioactivity is mostly linked to the nature of biomolecules or chemical compounds that will compose the endodontic hydrogel. However, many other parameters, such as hydrogel concentration, type, bioactive molecules solubility, of apex size were reported to influence the hydrogel behavior, leading to the inability to predict the clinical outcome. This lack of knowledge and the various parameters that should be considered, suggest that biological activity of endodontic hydrogel is impossible to anticipate and could hinder the bench to bedside transition. We formulated in this article our opinion that most of these parameters could be identified, described, and studied. A second part of the document lists some challenges and perspectives, including development of future mathematical models able to explain and predict the bioactivity of these endodontic hydrogel in clinic.

Asthma is a prevalent disease that around 300 million worldwide, resulting in substantial morbidity, mortality, and economic burden on a global scale. New clinical and laboratory research has shed light on the immunology causing asthma. Asthma is now recognized as a heterogeneous disease. A personalized medicine is based on the classification of asthma by endotype by linking observable characteristics to immunological mechanisms. Identifying endotype mechanisms is essential for better characterizing patients and personalizing therapeutic approaches with novel biological agents that target specific immune pathways. This article provides a summary of the major immunological mechanisms involved in the pathogenesis and emergence of the disease's phenotypic features, as well as the individualized treatment for severe asthma subtypes

Discrete distribution estimation is a fundamental statistical tool, which is widely used to perform data analysis tasks in various applications involving sensitive personal information. Due to privacy concerns, individuals may not always provide their raw information, which leads to unpredictable biases in the final results of estimated distribution. Local Differential Privacy (LDP) is an advanced technique for privacy protection of discrete distribution estimation. Currently, typical LDP mechanisms provide same protection for all items in the domain, which imposes unnecessary perturbation on less sensitive items and thus degrades the utility of final results. Although, several recent works try to alleviate this problem, the utility can be further improved. In this paper, we propose a novel notion called Item-Oriented Personalized LDP (IPLDP), which independently perturbs different items with different privacy budgets to achieve personalized privacy protection. Furthermore, to satisfy IPLDP, we propose the Item-Oriented Personalized Randomized Response (IPRR) based on the observation that the sensitivity of data shows an inverse relationship with the population size of respective individuals. Theoretical analysis and experimental results demonstrate that our method can provide fine-grained privacy protection and improve data utility simultaneously.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

Given that metabolic syndrome, along with obesity as one of its key components, remains one of the leading yet insufficiently addressed public health issues, what contributes to the development and maintenance of the disorder must certainly attract the attention of professionals and scientists. Precisely because of this phenomenon, eating patterns associated with metabolic syndrome should be studied and attempts should be made to uncover patterns in occurrence. The aim of this study is to review the literature and consider the connection between metabolic syndrome and eating patterns such as emotional and compulsive eating, as well as through the lens of food addiction. Furthermore, we have attempted to uncover the role of psychiatry and psychotherapy in the treatment of metabolic syndrome and delve into the complexity of recognizing these patterns and emphasize the importance of a multidisciplinary approach in the treatment of metabolic syndrome.

This paper explores the increasing use of big data in education to create personalized learning experiences tailored to individual student needs. The trend, driven by vast amounts of educational data, requires the development of new techniques to optimize learning outcomes while addressing the ethical implications of data collection and utilization. The paper emphasizes the necessity for robust policy frameworks to ensure transparency and trust, based on clear guidelines and parental consent. It also discusses how educational institutions are updating curricula to prepare learners for a rapidly evolving workforce, incorporating contemporary AI technologies that impact education systems. The dual influence of AI presents both opportunities and challenges, necessitating a balanced and thoughtful approach. The paper advocates for a holistic strategy in integrating AI in education, emphasizing the importance of collaboration between the AI industry and other sectors to develop comprehensive policy frameworks for the ethical and effective use of AI in education.

In recent years, the FDA has approved numerous antitumor drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborated the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarized chemical drugs such as lorlatinib, osimertinib, and other natural products that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Purpose: To evaluate prognostic factors for visual outcome in patients with diabetes who have undergone vitrectomy (PPV) for severe proliferative diabetic vitreo-retinopathy (PDVR) in at least one eye in the past 15 years. Methods: Medical records of 132 eyes of 66 patients were analyzed (median age 52ys 21-80; patients with type 1/2 diabetes 40/26, median follow-up 38 months 9-125). Correlation between final good visual outcome defined as 0.5≤ best corrected visual acuity (BCVA) and prognostic factors (age, sex, type and duration of diabetes, metabolic status, BCVA, diabetic retinopathy status, data of preoperative management, data of vitrectomy, postoperative complications) were analyzed. Results: BCVA improved significantly in the entire study cohort (from median 0.05 min-max 0.001-1 to 0.32 0.001-1, p <0.000). Visual stabilization was achieved in the majority of patients, and relatively good visual acuity (0.5≤BCVA) was maintained in more than one third of eyes. Multivariable GEE statistics showed that in addition to duration of diabetes and stable HbA1c values, only preoperative tractional macular detachment proved to be an independent significant predictor of visual outcome. Conclusions: Pars plana vitrectomy is a useful tool when performed early before tractional macular detachment. However, long-term visual stability can only be achieved with good metabolic control.

Energy is needed by cancer cells to stay alive and communicate with their surroundings. The primary organelles for cellular metabolism and energy synthesis are mitochondria. Researchers recently proved that cancer cells can steal immune cells' mitochondria using nanoscale tubes. This finding demonstrates the dependence of cancer cells on normal cells for their living and function. It also denotes the importance of mitochondria in cancer cells’ biology. Emerging evidence has demonstrated how mitochondria are essential for cancer cells to survive in the harsh tumor microenvironment, evade the immune system, obtain more aggressive features, and resist treatments. For instance, functional mitochondria can improve cancer resistance against radiotherapy by scavenging the released reactive oxygen species. Therefore, targeting mitochondria can potentially enhance oncological outcomes, according to this notion. The patients' reactions to radiation are varied, ranging from a complete response to even cancer progression during treatment. This concept illustrates how different levels of mitochondrial metabolism might contribute to this heterogeneity. Considering this notion can help to improve personalized oncological treatments. This article outlines the importance of mitochondrial metabolism in cancer biology and personalized treatments.

Access to good nutritional health is one of the principal objectives of current society. Several e-services offer dietary advice. However, multifactorial and more individualized nutritional recommendations should be developed to recommend healthy menus according to the specific user's needs. In this article we present and validate a personalized nutrition system based on an application (APP) for smart devices with the capacity to offer an adaptable menu to the user. The APP was developed following a structured recommendation generation scheme, where the characteristics of the menus of 20 users were evaluated. Specific menus were generated for each user based on their preferences and nutritional requirements. These menus were evaluated by comparing their nutritional content versus the nutrient composition retrieved from dietary records. The generated menus showed great similarity to those obtained from the user dietary records. Furthermore, the generated menus showed less variability in micronutrient amounts and higher concentrations than the menus from the user records. The macronutrient deviations were also corrected in the generated menus, offering a better adaptation to the users. The presented system is a good tool for the generation of menus that are adapted to the user characteristics and a starting point to nutritional interventions.

Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involved and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, personalized e-learning system architecture is also presented which detects and responds teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective to analyze the big data to make education systems vigorous.

Traumatic brain injury (TBI) is an important global clinical issue, requiring not only prevention but also effective treatment. Following TBI, diverse parallel and intertwined pathological mechanisms affecting biochemical, neurochemical, and inflammatory pathways can have severe impact on the patient’s quality of life. The current review summarizes evidence for utility of amantadine in TBI in connection to its mechanism of action. Amantadine combining multiple mechanisms of action may offer both neuroprotective and neuroactivating effects in TBI patients. Indeed, use of amantadine in TBI has been encouraged by several clinical practice guidelines/recommendations. Amantadine is also available as infusion which may be of particular benefit in unconscious patients with TBI, due to immediate delivery to the central nervous system and the possibility of precise dosing. In other situations, orally administered amantadine may be used. There are several questions that remain to be addressed: Can amantadine be effective in disorders of consciousness requiring long-term treatment and in combination with drugs approved for treatment of TBI? Do the observed beneficial effects of amantadine extend to disorders of consciousness due to factors other than TBI? Well controlled clinical studies are warranted to ultimately confirm its utility in the TBI and provide answers these questions.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

Recent findings support an association between childhood maltreatment and the presence of elevated psychopathic traits in adulthood. Using a community sample recruited online (N = 210), we sought to (1) confirm the relationship between childhood traumas and psychopathic traits, and (2) investigate the role of parenting styles in psychopathic traits. Consistent with our predictions, we found an association between all types of childhood traumas and disinhibition. Age and gender moderated the relations between psychopathic traits and childhood maltreatments. Parental rejection and overprotection were positively correlated with the presence of psychopathic traits, while parental emotional warmth was negative correlated. Although our results converge with previous findings, the strength of the correlations observed was not as compelling as in research on undergraduate students. We discuss the numerous interpretations for our findings, and highlight the limitations of research in the field of psychopathy and childhood trauma in the general population.

The disruption of brain energy metabolism, leading to alterations in synaptic signaling, neural circuitry, and neuroplasticity, has been implicated in severe mental illnesses such as schizophrenia, bipolar disorder, and major depressive disorder. The therapeutic potential of ketogenic interventions in these disorders suggests a link between metabolic disturbances and disease pathology; however, the precise mechanisms underlying these metabolic disturbances and the therapeutic effects of metabolic ketogenic therapy remain poorly understood. In this study, we conducted an in silico analysis of transcriptomic data to investigate perturbations in metabolic pathways in the brain across severe mental illnesses via gene expression profiling. We also examined dysregulation of the same pathways in rodent or cell culture models of ketosis, comparing these expression profiles to those observed in the disease states. Our analysis revealed significant perturbations across all metabolic pathways, with the greatest perturbations in glycolysis, the tricarboxylic acid (TCA) cycle, and the electron transport chain (ETC) across all three disorders. Additionally, we observed some discordant gene expression patterns between disease states and ketogenic intervention studies, suggesting a potential role for ketone bodies in modulating pathogenic metabolic changes. Our findings highlight the importance of understanding metabolic dysregulation in severe mental illnesses and the potential therapeutic benefits of ketogenic interventions in restoring metabolic homeostasis. This study provides insights into the complex relationship between metabolism and neuropsychiatric disorders and lays the foundation for further experimental investigations aimed at appreciating the implications of the present transcriptomic findings as well as developing targeted therapeutic strategies.

Telemonitoring and telerehabilitation have shown promise in delivering individualized healthcare remotely. We introduce STASISM, a Sensor-Based Telerehabilitation and Telemonitoring System, in this work. This platform has been created to facilitate individualized telerehabilitation and telemonitoring for those who need rehabilitation or ongoing monitoring. To gather and analyze pertinent and validated physiological, kinematic, and environmental data, the system combines a variety of sensors and data analytic methodologies. The platform facilitates customized rehabilitation activities based on individual needs, allows for remote monitoring of patients' progress, and offers real-time feedback. To protect the security of patient data and to safeguard patient privacy, STASISM also provides secure data transmission and storage. The platform has the potential to significantly improve the accessibility and efficacy of telerehabilitation and telemonitoring programs, enhancing patients' quality of life and allowing healthcare professionals to provide individualized care outside of traditional clinical settings/

In recent years, there has been an increasing demand for personalized experiences in various domains, including the cultural and educational sectors. Museums, as custodians of art, history, and scientific knowledge, are seeking innovative ways to engage their visitors and provide tailored content that enhances their understanding and appreciation of the exhibits. This article presents ExhibitXplorer, a distributed architecture service that leverages geofencing, artificial intelligence, and microservices to enable personalized content delivery in museums. By combining implicit and explicit segmentation of museum visitors and utilizing the GPT API for content generation, ExhibitXplorer offers a dynamic experience to different visitor segments, including researchers, students, casual visitors, and children. The system utilizes push notifications triggered by visitor location changes, allowing seamless delivery of personalized information both indoors and outdoors. Tests were conducted to evaluate the user experience of visitors to an outdoor ethnographic museum. The results showed that 55% of the test participants were satisfied and 45% very satisfied with the way personalized content was delivered.

One of the main challenges for autonomous learning in virtual environments is finding the right material that fits students’ needs and supports their learning process. Personalized recommender systems partially solve this problem by suggesting online educational resources to students based on their preferences. However, in educational environments (which need a proper characterization of both users and educational resources), most existing recommendation algorithms either fail to include all the available information or use hybrid processes that do not exploit possible relationships between users and item features. This article presents a personalized recommender system for educational resources aimed at combining user and item information into a single mathematical model based on matrix factorization. As a result, estimated latent factors can provide insight into possible interactions between users and item features, improving the quality of the information retrieval process. We validated the proposed model on a real dataset that contains the ratings assigned by students from Universidad Nacional de Colombia and Universidade Feevale to educational resources in the Colombian Federation of Learning Object Repositories (FROAC in Spanish). User characterization included learning style and educational level, whereas item characterization (obtained from the objects’ metadata), included interactivity level, aggregation level and type, and resource format. These results, compared to those obtained when not all the available information is included, show that our method can improve the recommendation process.

Lithium is the superior first-line treatment for bipolar disorder (BD). Yet the percentage of patients receiving lithium is abysmally low, especially in the USA. Since psychiatrists have failed to place lithium in its appropriate role, we make the case that patients with BD themselves need to be better educated about the unique characteristics and preeminence of the drug so that it can be used more often and appropriately. Lithium has a highly unfavorable popular reputation among would-be patients and many psychiatrists. Thus, a direct appeal to patients with BD appears appropriate to try to remediate this situation. The unique assets of lithium are underappreciated or not well known. Conversely, the side effects profile of lithium are overestimated. Here, we make the case that lithium’s image needs to be revised not only with better and more accurate information but with a wholesale re-naming and rebranding of the drug. We will not only outline the unique qualities and new information about the side effects of the drug but attempt to change some of the terminology conventionally used to refer to lithium so that its use may be appropriately applied earlier and at an increased frequency for patients with BD.

Data can be illustrated in shapes, and the shapes could provide insight for data modeling and information extraction. Topological data analysis provides an alternative insight in biomedical data analysis and knowledge discovery with the algebra topology tools. In present work, we study the application of topological data analysis for personalized electrocardiographic signal classification toward arrhythmia analysis. Using phase space reconstruction technique, the signal samples are converted into point clouds for topological analysis facility. With topological techniques the persistence landscapes from the point clouds are extracted as features to perform the arrhythmia classification task. We find that the proposed method is robust to the training set size, with only a training set size of 20% percents, the normal heartbeat class are 100% recognized, ventricular beats for 97.13%, supra-ventricular beats for 94.27% and fusion beats for 94.27% within the corresponding experiments. The property of keeping high performance when using smaller training sample proves that the proposed method is especially applicable to personalized analysis. With the present study, we show that the topological data analysis technique could be a useful tool in biomedical signal analysis, and provide powerful ability in personalized analysis.

Concussion, also known as mild traumatic brain injury (mTBI), commonly causes transient neurocognitive symptoms, but in some cases, it causes cognitive impairment, including working memory (WM) deficit, which can be long-lasting and impede a patient’s return to work. The predictors of long-term cognitive outcomes following mTBI remain unclear because abnormality is often absent in structural imaging findings. The purpose of the study was to determine whether machine learning-based models using functional magnetic resonance imaging (fMRI) biomarkers and demographic or neuropsychological measures at baseline could effectively predict 1-year cognitive outcomes of concussion. We conducted a prospective, observational study of patients with mTBI who were compared with demographically-matched healthy controls enrolled between September 2015 to August 2020. Baseline assessments were collected within the first week of injury, and follow-ups were conducted at 6 weeks, 3 months, 6 months, and 1 year. Potential demographic, neuropsychological, and fMRI features were selected according to the significance of correlation with the estimated changes in WM ability. The support vector machine classifier was trained using these potential features and estimated changes in WM between the predefined time periods. Patients demonstrated significant cognitive recovery at the third month, followed by worsened performance after 6 months, which persisted until 1 year after concussion. Approximately half of the patients experienced prolonged cognitive impairment at 1-year follow up. Satisfactory predictions were achieved for patients whose WM function did not recover at 3 months (accuracy=87.5%), 6 months (accuracy=83.3%), 1 year (accuracy=83.3%), and performed worse at 1-year follow-up compared to baseline assessment (accuracy=83.3%). This study demonstrated the feasibility of personalized prediction for long-term postconcussive WM outcomes based on baseline fMRI and demographic features, opening a new avenue for early rehabilitation intervention in selected individuals with possible poor long-term cognitive outcomes.

BACKGROUND: Treatment failure in primary as well as metastatic cancer patients, caused by chemo and radio resistance, has truncated the research for the applicability of personalized medicine. The use of stem cells and cancer stem cells in such a treatment approach will be reviewed in this study. RESULTS: CRC stem cells prove to be a promising asset for CRC treatment optimization both by serving as biomarkers for the current therapy modalities by means of treatment personalization and patient/tumor stratification, as well as in the development of targeted therapies, selective for the stem cell population. Similar conclusions are drawn, regarding mesenchymal stromal cells and their effect in CRC therapy; while resident stromal cells of tumor microenvironment seem to promote the tumorigenic and metastatic processes in addition to conferring to the chemo- and radio resistance, under certain conditions they are able to improve the treatment outcome of CRC chemotherapy, e.g. by targeted enzyme/prodrug treatment of CRC cells. CONCLUSION: This review, truncates the dynamic potential of cancer stem cells and other stem cell types in CRC treatment personalization as well as, in the improvement of current treatment approaches opting to a higher therapeutic rate, improved prognosis, survival and quality of life for CRC patients.

In recent years, the field of radiotherapy has witnessed remarkable advancements with the integration of artificial intelligence (AI) technologies into clinical practice. Traditionally, radiotherapy treatment planning has been a labor-intensive process, requiring meticulous manual segmentation and optimization. With the advent of AI, particularly deep learning algorithms, the accuracy and efficiency of target delineation and organ-at-risk segmentation have significantly improved. AI-driven algorithms analyze voluminous medical imaging data, enabling rapid and precise contouring, thus expediting the planning phase and reducing inter-observer variability. Furthermore, AI's role extends to treatment plan optimization, where it intelligently explores vast parameter spaces to generate optimal plans tailored to individual patients. This not only saves dosimetrists' time but also enhances plan quality by accounting for complex anatomical variations and personalized clinical goals. In the realm of treatment delivery, AI-powered real-time image guidance enhances the accuracy of patient positioning, ensuring precise radiation targeting. Adaptive radiotherapy, enabled by AI, allows on-the-fly plan modifications in response to anatomical changes, significantly improving treatment accuracy in scenarios like tumor shrinkage or weight loss. Beyond planning and delivery, AI algorithms contribute to outcome prediction by analyzing historical patient data and treatment responses. This predictive capability aids clinicians in making informed decisions and refining treatment strategies for better prognoses. Despite the revolutionary potential, challenges remain in seamlessly integrating AI into clinical workflows. Ethical considerations, data privacy, and algorithm interpretability necessitate cautious implementation. Additionally, fostering interdisciplinary collaboration between AI experts and radiation oncologists is imperative to harness the technology's full potential. This paper explores the impact of AI in four key areas of radiotherapy: automated segmentation, dosimetric and machine quality assurance, adaptive radiation therapy, and clinical outcome prediction.

Background: We conducted research on the newly developed personalized recommendation service (PRS) of the global network of Korean scientists and engineers (KOSEN) in order to explore the information usage behavior and importance of the PRS used by Korean scientists and engineers. Methods: In order to understand information usage behavior, we gathered data from 513 survey results and analyzed them in terms of information usage behavior and the corresponding importance in each of the service quality areas. Results: We analyzed the 321 outcomes that indicated non-use of the PRS in order to understand the underlying reason(s); we employed 192 results that demonstrated the use of functionality to examine information usage behavior and importance. They found that the predominant motive for non-use of the service resulted from the respondents not knowing how to use it. According to demographic characteristics, the usage behavior of the PRS showed a difference regarding the purpose of using the service in the categories of gender and major field of study. Furthermore, users were concerned with various components of the PRS such as ease of use, design, relevance of content, user support, and interactivity. Conclusions: We suggest reinforcing user education degree and promotion to enhance the PRS. Since users were concerned with ease of use, design, relevance, user support, and interactivity, we recommend these as major points for improvement.

Academic stress affects students' psychological and physiological well-being. Dental undergraduate programs are known for their demanding curriculum, leading to significant stress symptoms. Objective was to determine if salivary cortisol levels were higher in students exposed to academic stress, assess the relationship between stress severity/temperament and cortisol values, and explore relaxation technique effects. Salivary cortisol was measured at two time points for all participants: before exams and during a relaxation period after summer break. A third measurement was conducted for students with high pre-test cortisol levels who received instructions on progressive muscle relaxation(PMR) before subsequent exams. Additionally, participants completed two questionnaires: Perceived Stress Scale(PSS) and Fisher's Temperament Questionnaire. The group analysis based on the PSS indicated that 39 participants reported high stress. Women demonstrated significantly higher stress than men(p=0.042054). A significant difference in stress levels was observed between Director and Builder temperament types(p=0.029276). Cortisol levels showed a significant decrease from the first measurement to the second measurement, and the third measurement after implementing PMR. The grade in the "Dermatovenereology" course correlated with stress level according to the PSS(k=0.578467). Pre-test cortisol levels correlated with the frequency of using PMR guidelines during winter test periods(k=0.416138). Stress negatively affects the immune system and poses health risks. Implementing stress reduction techniques in dental/medical education could benefit students and the healthcare system.

Federated learning trains a neural network model using the client's data to maintain the benefits of centralized model training, while maintaining the privacy. However, if the client data are not independently and identically distributed (non-IID) because of different environments, the accuracy of the model may suffer from client drift during training owing to discrepancies in each client's data. This study proposes a personalized federated learning algorithm based on the concept of multitask learning to divide each client model into two layers: a feature extraction layer and a category prediction layer. The feature extraction layer maps the input data to a low-dimensional feature vector space. Furthermore, the parameters of the neural network are aggregated with those of other clients using an adaptive method. The category prediction layer maps low-dimensional feature vectors to the label sample space, with its parameters remaining unaffected by other clients to maintain client uniqueness. The proposed personalized federated learning method produces faster learning model convergence rates and higher accuracy rates for the non-IID datasets in our experiments.

Glioblastoma (GBM) is one of the most aggressive and intricate forms of brain cancer, demanding novel therapeutic interventions. The role of transcription factors in epigenetic regulation has emerged as a promising avenue for targeting GBM. This comprehensive review seeks to explore the complex interplay between specific transcription factors such as SNAI2, FOXA1, YAP1, TWIST1, ZEB1, and NF-kB, and their role in GBM progression and therapy. The focus extends to innovative therapeutic strategies, including epigenetic modifiers, targeted therapies, and CRISPR/Cas9 technology. A unique aspect of this review highlights the connection between transcription factors, neoantigen presentation, and personalized immunotherapy in GBM. By delving into current research, clinical trials, and immunological data, this review emphasizes the significant potential of targeting transcription factors for personalized immunotherapy in GBM. The challenges, future perspectives, and ethical considerations are also critically discussed, offering a complete and thought-provoking insight into a vital and rapidly evolving field.

According to the International Agency for Research on Cancer (IARC) of the World Health Organization (data of year 2020), Breast Cancer (BC) is one of the most common cancer types worldwide, with large geographical variations occurring between countries and world regions and highest incidence rates in countries that have undergone economic transition. The risk factors for BC include women ageing, genetic mutations, reproductive history, dense breast tissues, personal history of BC or specific non-cancerous breast diseases, family history of breast or ovarian cancer, previous treatment using radiation therapy, and exposure to hormone-like drugs such as diethylstilbestrol (DES). Additional risk factors include being overweight or having obesity after menopause, and taking hormones. MicroRNAs (miRNAs or miRs) are small non-coding RNA molecules that have an essential role in gene expression and are significantly linked to the tumor development and angiogenesis process in different types of cancer, including BC. In the last years, the knowledge about miRNAs role in BC has significantly improved, and complex interactions among coding and non-coding RNA has been elucidated. In this context, an increasing number of papers had been published regarding the role of miR-125 in BC. In this review, we summarize the state-of-the-art about this research topic in addition to elaborating on the need to set novel ethical and legal standards for the governance of such innovations in healthcare.

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets of machine learning, such as reinforcement learning, and deep learning. This review emphasizes the significant application of AI algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of AI in plasma medicine, requires research efforts, data sharing and interdisciplinary collaborations. Unravelling the complex mechanisms, developing real-time diagnostics, and optimizing AI models will be crucial to harness the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.