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.

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.

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.

Traditional classroom teaching model can no longer meet the needs of students' ability development. As a new model actively advocated in the modern education concept, personalized teaching focuses on the development needs of students and emphasizes respect for students' individualized growth. In this work, we utilize deep learning method to recommend teaching strategies for different features of students. Specifically, the proposed method includes the construction of a multi-layer neural network model, which can analyze students' multi-dimensional learning data and behavior patterns, so as to recommend the most suitable teaching strategies for individual students. Through data collection and preprocessing, model architecture design, model training and optimization, the model gradually improves the accuracy of prediction and recommendation. Experimental analysis verifies the effectiveness of the model through field teaching experiments, and the results show that the personalized teaching strategy based on deep learning can significantly improve the learning effect and participation of students.

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.

An important aspect of PET imaging in clinical application is the localization and detection of tumors and lesions by administering a predetermined amount of radiotracer. This allows for example, a detailed view of what is going inside the patient body in cellular level. The quality of PET image is strongly dependent on the amount of administrated radiotracer and the patient’s body parameters. As the amount of injection radiotracer increases, the quality of resulting image increases and the lesion detection efficiency increases. The PET examiner society recognizes that any dose of radiotracer is associated with some possible radiation risks. It can harmful to the patient if essential PET imaging session is not made due to fear of radiation risk. In order to ensure the highest quality diagnosis and the smallest radiation risk, the patient should receive the smallest amount of radiotracer that provide image with sufficient quality. Our study is focused on proposing an efficient PET simulation tool that predict the smallest possible amount of administrated radiotracer to provides the appropriate diagnostic information based on significant patient’s body parameters (weight, age) at fixed scanning to improve the clinical diagnostic process in term of tumor detecting and localization. We have built a model of particular PET scanner and model of patient based on real MRI image and digital anthropomorphic phantom of our region of interest (brain). We have performed Monte Carlo simulation for whole PET procedure with special parameter In evaluating stage, a dataset of 60 patient is used and 11 independent dose prediction simulation for each patient are performed. We conclude that our simulator perform a desirable and efficient prediction of injection radiotracer amount that optimizes the current clinical amount up to 28%. In addition, we found that the total injected radiotracer dosage for adult patients are mostly affected when considering patient weight rather than patient age.

Phosphorus is essential and non-substitutable chemical element required for the cellular processes of all living organisms. The main source of phosphorus in the biosphere is phosphate rock. With more than 700 Mt P2O5, Estonia holds the largest sedimentary phosphate rock deposits in European Union. Estonian phosphate rock is principally sandstone that holds abundant remains of phosphatic brachiopod shells and compared to other sedimentary rocks, is particularly outstanding by its remarkably low content of hazardous heavy metals such as Cd and saturated by valuable elements present in the rock such as rear earth elements (REEs).

Recent advances in organoid technology have revolutionized cancer biology and thera-peutic interventions, offering personalized immunotherapy treatment. Organoids, three-dimensional cell cultures derived from patient tumors, accurately replicate the tumor microenvi-ronment, providing unprecedented insights into tumor-immune interactions and therapeutic re-sponses. In this literature-based study, we discuss various culture methods for the diverse appli-cations of organoids in cancer immunotherapy, including drug screening, personalized treatment strategies, and mechanistic studies. Additionally, we address the technological challenges asso-ciated with these methods and propose potential future solutions to accelerate the development of novel immunotherapeutic approaches. This review highlights the transformative potential of or-ganoid models in advancing preclinical cancer immunotherapy modeling, screening, and evalua-tion, paving the way for more effective and personalized cancer treatments.

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.

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.

This paper presents an innovative methodology for enhancing museum visitor experiences through personalized content delivery using a combination of explicit and implicit visitor profiling. The approach integrates Bluetooth Low Energy (BLE) smart badges to collect telemetry data, enabling precise visitor localization and dynamic group formation based on real-time proximity and shared interests. Initial profiling begins with OAuth registration and brief surveys, which is then refined through continuous tracking of exhibit interactions and time spent at each exhibit. An AI-driven system delivers content to individual and group profiles, fostering both personalized learning and social interaction. This methodology addresses the limitations of traditional profiling by adapting to visitor behaviors in real-time while maintaining a strong focus on data privacy and ethical considerations. The proposed system not only enhances engagement and satisfaction but also sets the stage for future advancements in personalized cultural experiences.

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.

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.

The leachate discharges generated in sanitary landfills contain many pollutants that are harmful to the environment; treatments are scarce and should be carried out better. The use of coagulation-flocculation processes has been one of the most widely used, but that; Due to the complexity of the characterization of the leachate, the dosing strategy of coagulants and biopolymers needs to be clarified. Therefore, the present study is carried out to determine the doses of coagulants and biopolymers suitable for coagulation-flocculation processes in the treatment of leachates by using computational models of machine learning techniques such as Artificial Neural Networks (ANN); these allow to decrease the operations of the tests of jars in the laboratory; optimizing resources. Through laboratory experimentation, there are real results of the effectiveness of applying biopolymers in leachate treatments at different concentration levels. The laboratory results were taken as input variables for the algorithms used; after the validation and calibration process, we proceeded to estimate predicted data with the computational model, obtaining predictions of optimal doses for treatment with high statistical adjustment indicators. It is verified that the applied coagulation-flocculation treatments reduce the turbidity values in the leachate and contaminants associated with suspended solids. In this way, the jar tests are optimized so that the operational costs decrease without affecting the results of adequate dosing.

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.

The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.

This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.

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.

For quantitative determination of honokiol (HNK) and metformin hydrochloride (M-HCl) by HPLC from formulations and from dissolution studies, a QbD approach using three factors - three levels design was implemented to optimize the chromatographic conditions. To study the impact of each variable on the peak area of both compounds and retention time of last compound eluted, the 3D response surface plots were developed. HNK and M-HCl were separated on a Thermoscientific ODS Hypersyl TM (250 x 4.6 mm, 5 μm) column mantained at 30 C, using 0.02 M acetate buffer (pH = 3) / methanol (15 / 85, v / v) as mobile phase with a flow rate of 1 mL/min. The injected volume was 20 L, and the detection wavelengths were 256 nm for HNK and 236 nm for M-HCl. The validation of proposed method showed a linearity range of 4 – 1200 μg/mL for M-HCl (r2 = 0.9992) and 1 – 300 μg/mL for HNK (r2 = 0.9998), good precision (RSD < 2% for both HNK and M-HCl, in intra- and inter-day studies) and accuracy (mean recovery = 100.09% in the range 99.80 – 100.43% for M-HCl and mean recovery = 100.12% in the range 99.70 – 100.38% for HNK). The environmentally friendly nature of the method was confirmed by the evaluation of the method’s greenness. The proposed method was applied for the determination of HNK and M-HCl from oral dosage forms and in dissolution studies.

Background: Number of joint infections caused by Vancomycin-resistant pathogens is emerging. Currently no PMMA cement is commercially available to cover VRE. Daptomycin shows promising results in infection treatment, offers a good safety profile and reduced risk for resistance development. Purpose of this in vitro study is to investigate the mechanical stability, handling properties, elution behavior and antimicrobial effectiveness of PMMA cement loaded with three different Daptomycin concentrations in comparison to commercially available ALBC; Methods: Mechanical properties and handling characteristics (ISO 5833, DIN 53435), HPLC elution, antimicrobial effectiveness with proliferation assay (DIN 17025) and inhibition zone testing were investigated; Results: All tested Daptomycin concentrations fulfilled ISO and DIN mechanical strength. Dosage of 0.5. g added Daptomycin did not show any antimicrobial effectiveness in contrast to 1.0 g and1.5 g. Cement with 1.5 g Daptomycin was best in elution and effectiveness, showed good mechanical ISO strength, ISO doughing was a little longer sticky and setting was faster as Vancomycin containing reference cement; Conclusion: PMMA cement containing 0.5 g Gentamicin and 1.5 g Daptomycin could be a good alternative to the already established COPAL® G+V for PJI treatment of VRE.

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.

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.

Coagulation is the most sensitive step in drinking water treatment. Underdosing may not yield the required water quality, whereas overdosing may result in higher costs and excess sludge. Traditionally, the coagulant dosage is set based on bath experiments performed manually. Therefore, this test does not allow real-time dosing control, and its accuracy is subject to operator experience. Alternatively, solutions based on machine-learning (ML) have been evaluated as a computer-aided alternative. Despite these advances, there is open debate on the most suitable ML method applied to the coagulation process, capable of the most highly accurate prediction. This study addresses this gap, where a comparative analysis between ML methods was performed. As a research hypothesis, a novel data-driven fuzzy inference system (FIS) should provide the best performance due to its ability to deal with uncertainties inherent to complex processes. Although ML methods have been widely investigated, only a few studies report hybrid neuro-fuzzy systems applied to coagulation. Thus, to the best of our knowledge, this is the first study thus far to address the accuracy of this novel data-driven FIS for such application. The novel FIS provided the smallest error (0.86), indicating a promising alternative tool for real-time and highly accurate coagulant dosing in drinking water treatment.

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.

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.

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.

Background/Objectives: The global burden of colorectal cancer (CRC) is projected to increase by 2040, highlighting the need for prevention strategies, early detection and adequate follow. The aim of this study was to provide an overview of the progress in personalized medicine of CRC in Serbia, with results and insights from the Institute for Oncology and Radiology of Serbia (IORS), and to propose guidance for tackling observed challenges in the future. Methods: Epidemiological data was derived from official global and national cancer registries and IORS electronic medical records. Germline genetic testing for Lynch syndrome was performed by Next Generation Sequencing. RAS and BRAF mutation analyses were performed using qPCR diagnostic kits. Results: Epidemiology and risk factors, prevention and early detection programs, as well as treatment options and scientific advances have been described in detail. Out of 103 patients who underwent germline testing for Lynch syndrome, 19 (18.4%) had a pathogenic or likely pathogenic mutation in one of the MMR genes, and 8 (7.8%) in other CRC-associated genes (APC, CHEK2, MUTYH). Of 6369 tested patients, 50.43% had a mutation in KRAS or NRAS genes, while 9.54% had the V600 mutation in the BRAF gene. Conclusions: Although significant improvements in CRC management have occurred globally in recent years, the high incidence of young-onset CRC and the growing elderly population due to a rise in life expectancy require a strategic approach leading to population-based systemic solutions, especially important for countries with limited health care resources as Serbia.

There is currently no effective treatment strategy for the recurrent/metastatic adenoid cystic carcinoma (R/M ACC). Furthermore, recent single-agent and combination immunotherapy trials have failed in unselected ACC cohorts unlike non-ACC salivary gland cancers. Genomic profiling revealed no actionable targets but NOTCH1 and KDM6A frameshift and CTCF splice site mutations (no MYB/L fusion) with a low TMB, MSS and negative PD-L1. We recommended anti-PD-1 plus anti-CTLA-4 combination based on TMB 2-fold greater than median TMB in ACC, tumor harboring multiple immunogenic frameshift or splice site mutations, and PD-L1 negativity. Accordingly, we achieved a complete response in a radiotherapy and chemotherapy-refractory patient with locally recurrent lacrimal gland ACC and lung metastasis following personalized immunotherapy in combination with integrative therapeutics. Therefore, it is crucial to assess not only conventional immune biomarkers but also patient-specific parameters.

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.

Currently, the number of approved veterinary medicines are limited, and human medications are used off-label. These approved human medications are of too high potencies for a cat or a small dog breed. Therefore, there is a dire demand for smaller doses of veterinary medicines. This study aims to investigate the use of three semi-solid extrusion 3D printers in a pharmacy or animal clinic setting for extemporaneous manufacturing of prednisolone containing orodispersible films for veterinary use. Orodispersible films with adequate content uniformity and acceptance values defined by the European Pharmacopoeia was produced with one of the studied printers, namely, the Allevi 2 bioprinter. Smooth and flexible films, with high mechanical strength, neutral pH, and low moisture content were produced with high correlation between prepared design and obtained drug amount, indicating that the Allevi 2 printer could successfully be used to extemporaneously manufacture personalized doses for animals at the point-of-care.

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.

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.

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.

This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model's adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences.

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.

Colorectal cancer (CRC) is characterized by a pro-inflammatory microenvironment and features high energy supply molecules that assure tumor growth. A still underestimated macromolecule is the Inorganic Polyphosphate (iPolyP), a high-energy linear polymer that is ubiquitous in all forms of life. Made up of hundreds of repeated orthophosphate units, iPolyP is essential for a wide variety of functions in mammalian cells, including the regulation of proliferative signaling pathways. Some evidence has suggested its involvement in carcinogenesis, although more studies need to be pursued. In this study, we tested the role of iPolyP on CRC proliferation, using in-vitro and ex-vivo approaches, in order to evaluate its effect on tumor growth. We found that iPolyP is significantly increased in tumor tissues compared to the corresponding peritumoral counterparts. In addition, iPolyP signaling occurs through the TRPM8 receptor, promoting CRC cell proliferation. Pharmacological inhibition of TRPM8 or RNA interference experiments showed that the involvement of TRPM8 is essential, greater than that of the other two known iPolyP receptors, P2Y1 and RAGE. The presence of iPolyP drives cancer cells towards the mitotic phase of the cell cycle by enhancing the expression of ccnb1, which encodes for the Cyclin B protein. In-vitro 2D and 3D data reflected the ex-vivo results, obtained by the generation of CRC-derived organoids, which increased in size. These results indicate that iPolyP may be considered a novel and unexpected early biomarker supporting colorectal cancer cell proliferation.

An HPLC method for indicating stability was developed, created, and confirmed for the quantification measurement of recombinant human erythropoietin in large quantities as well as dosage forms. An isocratic separation was accomplished using a Thermo Biobasic C18 (250 mm 4.6 mm i.d., 300Ao, 5 m) column with a flow rate of 1.0 mL min-1 as well as a UV detector to monitor the eluate at 278 nm. Acetonitrile, 0.05 mM potassium dihydrogen phosphate (80:20 v/v), and orthophosphoric acid adjusted to 4.0 made up the mobile phase. The drug was exposed to oxidative stress, hydrolysis, and photodegradation to simulate stress conditionsRecombinant Human Erythropoietin, the parent compound, was eluted at roughly 6.675 minutes, as well as all byproducts have been entirely disconnected inside an overall analytical run time of about 15 minutes. To identify quantification limits of 0.05 and 0.2 g mL-1, respectively, the method was linear over a concentration range of 1-6 g mL-1 (r = 0.9989). Recombinant Human Erythropoietin can be measured accurately, selectively, sensitively, and precisely using this method both in dosage form and in bulk. Stress-induced degradation products did not interact with the identification of Recombinant Human Erythropoietin, indicating that the assay is stable.

Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of MS, particularly when coupled with liquid chromatography (LC-MS), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized medicine advancement. MS-based technologies have transformed precision medicine, enabling comprehensive understanding of disease mechanisms and patient-specific treatment responses. LC-MS has shown exceptional utility in analyzing complex biological matrices, while high-resolution MS has expanded analytical capabilities, allowing detection of low-abundance molecules and elucidation of complex biological pathways. The integration of MS with other techniques, such as ion mobility spectrometry, has opened new avenues for biomarker discovery and validation. As we progress towards precision medicine, MS-based technologies will be crucial in addressing the challenges of individualized patient care, driving innovations in disease diagnosis, prognosis, and treatment strategies.

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.

German chamomile (Matricaria chamomilla L.) is an essential oil-content medicinal plant widely used worldwidely. The aim of this study was (1) to gain knowledge of the phytochemical composition and the analgesic and soporific activity of M. chamomilla flowers extract and its amino acid preparations, (2) to predict the mechanisms of their effects by molecular docking, and (3) to develop aqueous printing gels and novel 3D-printed oral dosage forms for M. chamomilla flowers extracts. In total 22 phenolics and 14 amino acids were identified and quantified in the M. chamomilla extracts. The in-vivo animal studies with rodents showed that the oral administration of such extracts revealed the potential for treating of sleep disorders and diseases accompanied by pain. Amino acids were found to potentiate these effects. Glycine enhanced the analgesic activity the most, while lysine and β-alanine improved the soporific activity. The molecular docking analysis revealed a high probability of GABAA and NMDA receptor antagonism and COX-1,2 inhibition by the extracts. A polyethylene oxide (PEO) based gel composition with the M. chamomilla extracts were proposed for preparing a novel 3D-printed dosage form for the oral administration. These 3D-printed extract preparations can be used for example in dietary supplement applications.

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.

The pharmaceutical industry usually utilizes either hydrophobic or hydrophilic substances extracted from raw plant materials for preparing a final product, However, the plant material waste still contain substances with an opposite solubility. The aim of this study was to enhance the comprehensive usability of plant materials by developing a new non-wasting extraction method for eucalypt leaves and by investigating the phytochemical and pharmacological properties of eucalypt extracts and their 3D-printed dosage forms. The present extraction method enabled to prepare both hydrophobic soft extracts and hydrophilic (aqueous) dry extracts. We identified total 28 terpenes in the hydrophobic soft extract. In the hydrophilic dry extract, total 57 substances were identified, and 26 of them were successfully isolated. The eucalypt extracts studied showed a significant antimicrobial activity against Staphylococcus aureus, Pseudomonas aeruginosa, Bacillus subtilis, Candida albicans, Corynebacterium diphtheriae gravis, and Corynebacterium diphtheriae mitis. The anti-inflammatory activity of the dry extract was studied with a formalin oedema model in mice. The maximum anti-exudative effect of the dry extract was 61.5% at a dose of 20 mg/kg. The composite gels of polyethylene oxide and eucalypt extract were developed, and the key process parameters for semi-solid extrusion (SSE) 3D printing of such gels were verified. The SSE 3D-printed preparations of novel synergistically acting eucalypt extracts could have uses in antimicrobial and anti-inflammatory medicinal applications.

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.

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.

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.

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.

Recent advancements in Intelligent Tutoring Systems (ITS), driven by Artificial Intelligence (AI), have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners' backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to support effective attainment of learning objectives has become a critical challenge. This paper introduces PerFuSIT (Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming), an innovative fuzzy logic-based module designed to select the most appropriate tutoring strategy from five available options, based on individual learner characteristics. The available strategies include revisiting previous content, progressing to the next topic, providing supplementary materials, assigning additional exercises, or advising the learner to take a break. PerFuSIT's decision-making process incorporates a range of learner-specific parameters, such as performance metrics, error types, indicators of carelessness, frequency of help requests, and the time required to complete tasks. Embedded within the traditional ITS framework, PerFuSIT introduces a sophisticated reasoning mechanism for dynamically determining the optimal instructional approach. Experimental evaluations demonstrate that PerFuSIT significantly enhances learner performance and improves the overall efficacy of interactions with the ITS. The findings highlight the potential of fuzzy logic to optimize adaptive tutoring strategies by customizing instruction to individual learners' strengths and weaknesses, thereby providing more effective and personalized educational support in programming instruction.

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.

The behavior of low concentrations of methanol (0.5 and 1.0 wt%) as a promoter for hydrate formation in seawater or pure water metastable solutions of methane was investigated using the classical molecular dynamics method at moderate temperature and pressure. The influence of methanol on the dynamics of the re-arrangement of the hydrogen bond network in seawater and pure water solutions of methane was studied by calculating the order parameters of the tetrahedral environment and intermolecular torsion angles for water molecules, as well as by calculating the number of hydrogen bonds, hydrate and hydrate-like cavities. It was found that hydrate nucleation can be considered a collective process in which the rate of hydrate growth is faster in systems with low concentrations of methanol and confident hydrate growth begins earlier in a metastable solution without sea salt with a small amount of methanol than in systems without methanol.

A new green-validated and highly sensitive electrochemical method for the determination of molnupiavir (MOV) has been developed using cyclic voltammetry. The proposed analytical platform involves the use of disposable screen-printed reduced graphene oxide 2.5% electrode (SPrGOE 2.5%) for the first time to measure MOV with high specificity. The surface morphology of the sensor was investigated by using a scanning electron microscope armed with an energy-dispersive X-ray probe. The fabricated sensor attains improved sensitivity when sodium dodecyl sulfate (SDS) surfactant was added to the supporting electrolyte solution. Thus, various factors were investigated in order to select the best supporting electrolyte for voltammetric determination of MOV, and the optimum conditions were achieved by employing 0.04 M Britton-Robinson at pH 2, 10-4 M SDS, and SPrGOE 2.5%. Well-defined MOV oxidation peaks were obtained with no reduction peaks in cyclic voltammetry using diffusion-controlled pathways. The method was validated using differential pulse voltammetry according to ICH guidelines, providing it to be precise, accurate, specific, and robust with linearity over a concentration range of 50-6017 ng/mL. The incorporation of the reduced graphene nanoparticles supported the in situ interaction between electrode surface and MOV, improved the selectivity, and reduced the detection limit to the concentration level of 15.98 ng/mL. The stability investigation demonstrated that SPrGOE 2.5% can provide high-stability behavior throughout a three-month period under refrigeration. The fabricated SPrGOE 2.5% was successfully employed for the measurement of MOV in pharmaceutical capsules and human bio-fluids without the interference of endogenous matrix components as well as the commonly used excipient.

The reinforcing effect of boehmite nanoparticles (BNP) in epoxy resins for fiber composite lightweight construction is related to the formation of a soft but bound interphase between filler and polymer. The interphase is able to dissipate crack propagation energy and consequently increases the fracture toughness of the epoxy resin. Usually, the nanoparticles are dispersed in the resin and then mixed with the hardener to form an applicable mixture to impregnate the fibers. If one wishes to locally increase the fracture toughness at particularly stressed positions of the fiber-reinforced polymer composites (FRPC), this could be done by spraying nanoparticles from a suspension. However, this would entail high costs for removing the nanoparticles from the ambient air. We propose that a fiber fleece containing bound nanoparticles be inserted at exposed locations. For the present proof-of-concept study, an electrospun polycarbonate nonwoven and taurine modified BNP are proposed. After fabrication of suitable PC/EP/BNP composites, the thermomechanical properties were tested by dynamic mechanical analysis (DMTA). Comparatively, the local nano-mechanical properties such as stiffness and elastic modulus were determined by atomic force microscopy (AFM). An additional investigation of the distribution of the nanoparticles in the epoxy matrix, which is a prerequisite for an effective nanocomposite, is carried out by scanning electron microscopy in transmission mode (TSEM). From the results it can be concluded that the concept of carrier fibers for nanoparticles is viable.

Persistent post market quality evaluation helps produce clear information on the current quality status of the different brands of a given drug and hence introduces biopharmaceutically and therapeutically equivalent list of the products to the prescribers and users of it. This in turn facilitates access to essential medicines by breaking the high cost barrier imposed by few expensive brands of the product. This study was aimed at determining the quality and evaluating equivalence of doxycycline hyclate capsules and tablets in Jimma, Ethiopia. Ten brands of doxycycline hyclate capsules and tablets were tested for physic-chemical parameters. All investigated brands of doxycycline complied with the USP for dosage unifor¬mity, assay of active ingredient and single-point dissolution tests. One brand, D09 failed both hardness and friabilty tests. Comparisons of dissolution profiles applying fit factors confirmed that only brands D04, D06 and D07 had similarity with innovator. Ratio test approaches also showed significant variability existence between test products and comparators. This may question the product’s being categorized as a <biowaiver> to mean in-vitro dissolution evaluation suffices its market approval. This work, however, found equivalence problems between ‘comparators’ and interchangeability issues among doxycycline different brands already distributed in the market.

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.

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.

Personalized surgery is not just a new trend but rather a patient-specific approach to therapy that makes it possible to adopt a targeted approach for a specific patient and closely mirrors the approach of personalized medicine. However, the application of tailored surgery in the context of cardiovascular replacement surgery was not systematically reviewed. The ability to customize a device is highly dependent on the collection of radiological image data for precise prosthesis modelling. These facts are essential to "tailor-made" device design for precise prosthesis implantation. According to this study, computed tomography (CT) was the most prominent imaging modality; however, transoesophageal echocardiography and echocardiography were also found to be helpful. Additionally, a dynamic finite element simulation is also an attractive alternative to the finite element analysis for an in-silico experiment. Nonetheless, there is a paucity of relevant publications and sporadic evidence. More clinical studies have been warranted, notwithstanding that the derived data and results from the insight into the use of therapeutic interventions may be of multiple directives in clinical practices and beyond. This study may help the integration of personalized devices for better comprehending predicted clinical outcomes, thus leading towards enhanced performance gains.

Boron Neutron Capture Therapy (BNCT) is a promising binary disease-targeted therapy, as neutrons preferentially kill cells labeled with boron (¹⁰B), which makes it a precision medicine treatment modality that provides a therapeutic effect exclusively on patient-specific tumor spread. Contrary to what is usual in radiotherapy, BNCT proposes cell-tailored treatment planning rather than to the tumor mass. The success of BNCT depends mainly on the sufficient spatial biodistribution of ¹⁰B located around or within neoplastic cells to produce a high-dose gradient between the tumor and healthy tissue. However, it is not yet possible to precisely determine the concentration of ¹⁰B in a specific tissue in real-time using noninvasive methods. Critical issues remain to be resolved if BNCT is to become a valuable, minimally invasive, and efficient treatment. Moreover, functional imaging technologies such as PET can be applied to determine biological information that can be used for the combined-modality radiotherapy protocol for each specific patient. Anyway, not only imaging methods but also proteomics and gene expression methods will facilitate BNCT becoming a modality of personalized medicine. This work provides an overview of the fundamental principles, recent advances, and future directions of BNCT as cell-targeted cancer therapy for personalized radiation treatment.

Many years and $$$ spent for research did not yet produced a universally effective gene therapy of prostate cancer (PCa). Our studies indicated that not only each human, but even each cancer nodule in the same tumor has unique and dynamic gene expression profile, control and coordination. The tumor heterogeneity of the transcriptome topology is a strong argument in favor of personalized gene therapies, tailored on patients’ primary tumor unique characteristics. Here, we propose a bioinformatics procedure by which to identify the Gene Master Regulators (GMR) of cancer cells from transcriptomic data and predict consequences of their experimental manipulation. The procedure, based on our Genomic Fabric Paradigm (GFP), can determine the most important gene in each cancer nodule whose controlled alteration would selectively kill the cancer cells. In this report, the method is applied to our microarray data on two men PCs (each with three distinct cancer nodules) and two standard human PCa cell lines (DU145 and LNCaP). We expect the industry to produce ready-to-use CRISPR constructs for all genes allowing the clinical oncologist to prescribe the adequate personalized CRISPR cocktail for his/her patient. Thus, the GMR approach may provide the most effective, yet affordable solution in fighting cancer.

In the age of digital revolution technology is being used more and more in education around the world to improve student learning. By enabling ongoing skill development and knowledge acquisition through digital platforms e-learning has become a crucial tool. Through E-Learning online learning is becoming increasingly popular and effective in addition to traditional methods of instruction as highlighted by the latest statistics from Badan Pusat Statistik Indonesia (BPS). In order to address the issues of scalability, interactivity and security in e-learning platforms. This study introduces a web-based learning environment that is focused on teaching programming. Leveraging the MERN stack, while also integrating TypeScript for improved code reliability, Next.js for accelerated React.js functionality, Tailwind CSS for responsive design. Backend features include media management with Cloudinary, secure video streaming with VdoCipher, data storage with MongoDB, and caching with Redis. Iterative development will be following agile methodology. In order to create a vibrant and encouraging learning environment the platform includes interactive course pages multimedia content real-time feedback mechanisms and community-building tools. Despite some early difficulties the implementation phase effectively achieved development goals. To further support international online learning initiatives further efforts will be directed toward improving platform security and functionality.

OBJECTIVE: Although 3D-printed anatomic models are not new to medicine, the high costs and lengthy production times entailed have limited their application. Our goal was developing a new and less costly 3D modeling method to depict organ-tumor relations at faster printing speeds. METHODS: We have devised a method of 3D modeling using DICOM images. Coordinates are extracted at a specified interval, connecting them to create mesh-work replicas. Adjacent constructs are depicted by density variations, showing anatomic targets (ie, tumors) in contrasting color. RESULTS: An array of organ solid-tumor models were printed via Fused Deposition Modeling 3D printer at significantly less cost ($0.05/cm3) and time expenditure (1.73 min/cm3; both, p<.001). Printed models helped promote visual appreciation of organ-tumor anatomy and adjacent tissues. Our mesh-work 3D thyroidal prototype reproduced glangular size/contour and tumor location, readily approximating the surgical specimen. CONCLUSIONS: This newly devised mesh-type 3D printing method may facilitate anatomic modeling for personalized care and improve patient awareness during informed surgical consent.

The prevalence of autism has been increasing at an alarming rate. Even accounting for the expansion of autism spectrum disorder diagnostic (ASD) criteria throughout the 1990’s, there has been an over 300% increase in ASD prevalence since the year 2000. The often debilitating personal, familial, and societal sequelae of autism are generally believed to be lifelong. However, there have been several encouraging case reports demonstrating reversal of autism diagnoses with a therapeutic focus on addressing the environmental and modifiable lifestyle factors believed to be largely underlying the condition. This case report describes the reversal of autism among dizygotic, female twin toddlers and provides a review of related literature describing associations between modifiable lifestyle factors, environmental exposures, and various clinical approaches to treating autism. The twins were diagnosed with Level 3 severity ASD “requiring very substantial support” at approximately 20 months of age following concerns of limited verbal and non-verbal communication, repetitive behaviors, rigidity around transitions, and extensive gastrointestinal symptoms, among other common symptoms. A parent-driven, multidisciplinary, therapeutic intervention involving a variety of licensed clinicians focusing primarily on addressing environmental and modifiable lifestyle factors was personalized to each of the twin’s symptoms, labs, and other outcome measures. Dramatic improvements were noted within several months in most domains of the twins’ symptoms, which was manifested in reductions of Autism Treatment Evaluation Checklist (ATEC) scores from 76 to 32 in one of the twins and from 43 to 4 in the other twin. The improvement in symptoms and ATEC scores has remained relatively stable for six months at last assessment. While prospective studies are required, this case offers further encouraging evidence of ASD reversal through a personalized, multidisciplinary approach focusing predominantly on addressing environmental and lifestyle risk factors.

This article aims to investigate the potential of artificial intelligence (AI) in revolutionizing the education sector and further advancing the vision of Education 6.0. This study explores how AI can foster personalized learning experiences and adaptive pedagogy. By analyzing recent developments and real-world applications of AI in education, this article offers insights into how AI can support educators in tailoring instruction to individual students' needs, promoting engagement, and optimizing learning outcomes. The article also discusses ethical considerations and challenges associated with the integration of AI in education, emphasizing the importance of responsible implementation and human supervision.

Background. Escalating global plastic production, expected to reach 34,000 million tons by 2050, poses a significant threat to human and environmental well-being, particularly in aquatic ecosystems. Microplastics (MP) and nanoplastics (NP), which originate from the degradation of plastics, are of concern due to their potential bioaccumulation and uptake of pollutants. This study addresses identification methods and focuses on insect meal, a raw material for aquaculture feed. Methods. By using different techniques, the study was able to detect MP and NP in insect meal samples. Chemical digestion with KOH at 60°C efficiently removed organic matter without affecting the synthetic polymer polyethylene (PE). Filtration, confocal Raman microscopy, SEM and TEM were used for comprehensive analysis and integrity tests on PE films were performed using Raman and FTIR spectroscopy. The results showed the presence of PE microplastic particles in the insect meal, which was confirmed by correlative Raman and SEM mapping on a positively charged surface. In addition, the increased resolution of the Raman microscope identified submicrometric PE NP (800 nm). Transmission electron microscopy (TEM) with energy dispersive X-ray spectroscopy (EDX) confirmed plastic-like structures in the insect meal, highlighting the presence of PE plastics characterized by irregular shapes and some agglomeration. The higher carbon concentration in the EDX analysis supported the plastic nature, which was also confirmed by Raman spectroscopy. Conclusions. The study provides a robust method for the detection of MP and NP in insect meal and provides valuable insight into the possible presence of plastics in insect-based aquafeeds. The combination of different analytical methods increases the reliability of the results and sets the stage for future investigations that could focus on the quantification of NP and the assessment of their potential environmental impact.

In the context of crowds innovation and the generative design driven by big language models, the personalized requirements has become the best engine for innovative design. The exploration of personalized requirements has become a key in significantly improving product innovation, concepts feasibility, and design interaction efficiency. To mine a large number of vague and unexpressed implicit requirements of personalized products, a domain knowledge graph-based method is proposed in this research. First, based on the classical theory of design science, the characteristics and categories of personalized implicit requirements are analyzed. Next, in order to improve the practicability and construction efficiency of the domain knowledge graph, a more informative ontology is constructed, and better performing NLP models are proposed. Then, a series of mining methods based on knowledge graph are proposed for the personalized implicit requirements of different categories. Finally, A platform was developed based on the technical solution proposed in this study, and an example verification was conducted in the field of electromechanical engineering. The efficiency improvement of the training model proposed in the research was analyzed, and the practicality of implicit requirement mining methods were discussed.

Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and Personal Health Knowledge Graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As proof of concept, we have demonstrated the versatility of our digital twin by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes.

De novo metastatic hormone-sensitive PC (mHSPC) accounts for 5-10% of all prostate cancer (PC) diagnoses but it is responsible for nearly 50% of PC-related deaths. Since 2015, the prognosis of mHSPC has slightly improved thanks to the introduction of new hormonal agents and chemotherapy combined with androgen deprivation therapy from the first-line setting. This review describes the current therapeutic opportunities in de novo mHSPC, focusing on potential molecular biomarkers identified in the main clinical trials that have changed the standard of care, the genomic features of de novo mHSPC, and the principal ongoing trials that are investigating new therapeutic approaches and the efficacy of a biomarker-guided treatment in this setting. The road towards a personalized treatment for de novo mHSPC is still long considering that the randomized clinical trials, which have furnished the basis of the current therapeutic options, stratified patients according to clinical criteria that not necessarily reflect the biological rationale of the chosen therapy. The role of transcriptomic profiling of mHSPC as predictive biomarker requires further validation, as well as it remains to ascertain how the genomic alterations detected in mHSPC, that are considered predictive in the castration-resistant disease, can be exploited in the mHSPC setting.

Prostate cancer (PCa) is the most commonly diagnosed cancer and the second most common cause of death due to cancer. About 30% of patients with PCa who have been castrated develop a castration-resistant form of the disease (CRPC), which is incurable. In the last decade, new treatments that control the disease have emerged, slowing progression and spread, and prolonging survival while maintaining the quality of life. These include immunotherapies; however, we do not yet know the optimal combination and sequence of these therapies with the standard ones. All therapies are not always suitable for every patient due to co-morbidities or adverse effects of therapies or both, so there is an urgent need for further work on new therapeutic options. Advances in cancer immunotherapy with an immune checkpoint inhibition mechanism (e.g., ipilimumab, an anti-CTLA-4 inhibitor) have not shown a survival benefit in patients with CRPC. Other immunological approaches have also not given clear results, which has indirectly prevented breakthrough for this type of therapeutic strategy into clinical use. Currently, the only approved form of immunotherapy for patients with CRPC is a cell-based medicine, but it is only available to patients in some parts of the world. Based on the experience gained from recently completed clinical research on immunotherapy with dendritic cell-based immunohybridomas, the aHyC dendritic cell vaccine, for patients with CRPC, we highlight the current status, and possible alternatives that should be considered in the future.

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.

Microsatellite instability (MSI) is a crucial feature in cancer biology, yet its prevalence and significance in canine cancers remain largely unexplored. This study conducted a comprehensive analysis of MSI across 10 distinct canine cancer histotypes using whole-exome sequencing data from 692 tumor-normal sample pairs. MSI was detected in 64% of tumors, with prevalence varying significantly among cancer types. B-cell lymphomas exhibited the highest MSI burden, contrasting with human studies. A novel "MSI-burden" score was developed, correlating significantly with tumor mutational burden. MSI-high (MSI-H) tumors showed elevated somatic mutation counts compared to MSI-low and microsatellite stable tumors. The study identified 3,632 recurrent MSI-affected genomic regions across cancer types. Notably, seven of the ten cancer types exhibited MSI-H tumors, with prevalence ranging from 1.5% in melanomas to 37% in B-cell lymphomas. These findings highlight the potential importance of MSI in canine cancer biology and suggest opportunities for targeted therapies, particularly immunotherapies. The high prevalence of MSI in canine cancers, especially in B-cell lymphomas, warrants further investigation into its mechanistic role and potential as a biomarker for prognosis and treatment response

Radiotherapy is the primary treatment modality for non-metastatic nasopharyngeal carcinoma (NPC) across all TNM-stages. Locoregional control rates have been impressive even from the 2D-radiotherapy (RT) era, except when the ability to deliver optimal dose coverage to the tumor is compromised. However, short- and long-term complications following head and neck RT are potentially debilitating, and thus, there had been much research investigating technological advances in RT delivery over the past decades, with the primary goal of limiting normal tissue damage. On this note, with a plateau in gains of therapeutic ratio by modern RT techniques, future advances have to be focused on individualization of RT, both in terms of dose prescription and delineation of target volumes. In this review, we analyzed the guidelines and evidence related to contouring methods, and dose prescription for early and locoregionally-advanced (LA-) NPC. Next, with the preference for induction chemotherapy (IC) in patients with LA-NPC, we assessed the evidence concerning radiotherapy adaptations guided by IC response, as well as functional imaging and contour changes during treatment. Finally, we discussed on RT individualization that is guided by EBV DNA assessment, and its importance in the era of combinatorial immune checkpoint blockade therapy with RT.

The co-occurrence of depression and obesity has become a significant public health concern worldwide. Recent studies have shown that metabolic dysfunction, which is commonly observed in obese individuals and characterized by inflammation, insulin resistance, leptin resistance, and hypertension, is a critical risk factor for depression. This dysfunction may induce structural and functional changes in the brain, ultimately contributing to depression's development. Given that obesity and depression mutually increase each other's risk of development by 50-60%, there is a need for effective interventions that address both conditions. The comorbidity of depression with obesity and metabolic dysregulation is thought to be related to chronic low-grade inflammation, characterized by increased circulating levels of pro-inflammatory cytokines, and C-reactive protein (CRP). As pharmacotherapy fails in at least 30-40% of cases to adequately treat major depressive disorder, a nutritional approach is emerging as a promising alternative. Omega-3 polyunsaturated fatty acids (n-3 PUFAs) are a promising dietary intervention that can reduce inflammatory biomarkers, particularly in patients with high levels of inflammation, including pregnant women with gestational diabetes, patients with type 2 diabetes mellitus, and overweight individuals with major depressive disorder. Further effort directed to implement these strategies in clinical practice could contribute to improved outcomes in patients with depression and comorbid obesity and/or metabolic dysregulation.

Currently, health tourists primarily rely on independent facilitators to manage and book their medical appointments and vacation plans. There is a notable absence of dedicated booking applications for health tourism packages. This paper proposes HealthTourismHub, an application designed to provide personalized packages that include medical appointments, accommodation options and recommended tourism activities. It also serves as a platform for medical experts and accommodation providers, allowing health tourists to discover and connect with them. To incorporate personalization, HealthTourismHub uses an ontology that organizes medical and tourism data, along with a reasoner that generates new knowledge. This approach enables the application to offer customized packages and identify the most suitable providers for each user. Providers are strategically paired and located in close proximity, with the package also including personalized tourism recommendations. A survey was conducted to assess the usability of the application and general perspectives towards health tourism, including motivations, concerns, and preferences. The results revealed an above-average SUS score, indicating that users found the application user-friendly and effective. Some areas for improvement were identified, such as error handling and additional functionalities. Nonetheless, HealthTourismHub shows great potential as a pioneer in the field of health tourism applications.

Circulating cell-free DNA (cfDNA) refers to small fragments of DNA molecules released after programmed cell death and necrosis in several body fluids such as blood, saliva, urine, and cerebrospinal fluid. The discovery of cfDNA has revolutionized the field of non-invasive diagnostics in the oncologic field, in prenatal testing, and in organ transplantation. Despite the potential of cfDNA and the solid results published in recent literature, several challenges remain, represented by low abundance, need for highly sensitive assays and analytical issues. In this review, the main technical advances in cfDNA analysis are presented and discussed, with a comprehensive examination of the current available methodologies applied in each field. Considering the potential advantages of cfDNA, this biomarker is increasing its consensus among clinicians, as it consents to monitor patients’ conditions in an easy and non-invasive way, offering a more personalized care. Nevertheless, cfDNA analysis is still considered a research marker to be further validated, and very few centers are implementing its analysis in the real-life assistance. As technical improvements are enhancing the performances of cfDNA analysis, its application will transversally improve patients’ quality of life.

COVID-19 severity is heterogeneously distributed over age strata, but current mitigation strategies are homogeneously applied to all population. Social-distancing and stay-home are effective conservative approaches but lack economic sustainability on long term. Conversely, herd-immunity is a nonrestrictive strategy which can cost remarkable number of human lives and can melt the healthcare system down. Here I propose an Age Adaptive Social Distancing (AASD) engineering strategy to mitigate COVID-19 outbreak. AASD is based on the scientific evidence that the fatality rate grows nonlinearly with age, hence also the containing strategy should adapt nonlinearly. Essentially, AASD suggests that ‘silent spreaders’ (age 0-39) should avoid/minimize direct and indirect contacts with individuals in ‘dangerous zone’ (age 40+). The rationale is: 0-19 should follow parents strategy, healthy 20-39 (low fatality rate) might conduct screened life under active surveillance, to sustain economy and acquire rational immunity; 40-59 should respect social distancing (waiting a therapy); 60+ should stay at home (waiting a vaccine). This might save human lives, reduce healthcare demand and improve economic sustainability. The final take-home message is that future studies should design precision and personalized strategies for specific contagious diseases that integrate different social constrains, active surveillance and contact tracing.

Epithelial ovarian cancer (EOC) is the deadliest gynecological malignancy worldwide. Despite the latest advances, a major clinical issue in EOC is the disappointing prognosis related to chemoresistance in almost one-third of cases. Drug resistance relies on heterogeneous cancer stem cells (CSCs), endowed with tumor-initiating potential, leading to relapse. No biomarkers of chemoresistance have been validated yet. Recently, major signaling pathways, micro ribonucleic acids (miRNAs), and circulating tumor cells (CTCs) have been advocated as putative biomarkers and potential therapeutic targets for drug resistance. However, further investigation is mandatory before their routine implementation. In accordance with the increasing rate of therapeutic efforts in EOC, the need for biomarker-driven personalized therapies is growing. This review aims to discuss the emerging hallmarks of drug resistance with an in-depth insight into the underlying molecular mechanisms lacking so far. Finally, a glimpse of novel therapeutic avenues and future challenges will be provided.

This paper examines the learning experience of an inter-faculty higher education course conducted in an expanded remote format, using digital space as facilitator. The study focuses on the implementation of personalized education, a pedagogical approach that emphasizes the diversification of resources, strategies, and actions to empower learners in assigning personal value and meaning to their learning journey. By presenting an action research project, this study highlights the pivotal role of personalized education in guiding the course design. To foster an engaging learning environment, the course incorporated open activities and leveraged tools and platforms of the social web. Through these means, the participants, forming a community of practice, were able to collaboratively generate knowledge before, during, and after synchronous sessions. Furthermore, the participants effectively disseminated this knowledge through networks and engaged in discussions with external stakeholders. The study underscores how personalized education facilitates situated and meaningful learning by fostering expanded, flexible, and distributed experiences. These experiences revolve around a transmedia narrative that seamlessly integrates digital culture into learning opportunities and processes, effectively naturalizing its presence. The results highlight the value of personalized education approaches in adapting to the complexities of the digital age and establishing meaningful learning experiences for students.

Metabolomics is an advanced technology, still under development, with multiple research applications, especially in the field of health. Individual metabolic profiles, the functionality of the body, as well as its interaction with the environment can be established using this technology. The body's response to various external factors, including the food consumed and the nutrients it contains, has increased researchers' interest in nutrimetabolomics. Establishing correlations between diet and the occurrence of various diseases or even the development of personalized nutrition plans, could contribute to advances in precision medicine. The interdependence between humans, animals and the environment are of particular importance today, with the dramatic emergence and spread of zoonotic diseases, food, water and soil contamination, and the degradation of resources and habitats. All these events have led to an increase of risk factors for functional diseases, burdening global health. Thus, this study aimed to highlight the importance of metabolomics, in particular of nutrimetabolomics, as a technical solution for a holistic, collaborative, and precise approach for the advancement of the One Health strategy.

Social Anxiety Disorder (SAD) is a common mental health condition characterized by an intense fear of social situations, leading to significant impairments in daily functioning. Traditional exposure therapies, including Cognitive-Behavioral Therapy (CBT), have shown efficacy in treating SAD but are often limited by logistical challenges and patient reluctance. Virtual Reality Exposure Therapy (VRET) has emerged as a promising alternative, providing a controlled and immersive environment for exposure to anxiety-provoking scenarios. This paper explores the integration of Generative Adversarial Networks (GANs) into VRET to enhance the personalization, realism, and adaptability of therapeutic scenarios. GANs, which consist of a generator and a discriminator network, allow for the creation of highly realistic virtual environments tailored to the specific fears and triggers of individual patients. By offering real-time adjustments and personalized exposure scenarios, GAN-enhanced VRET addresses key limitations of traditional therapies, increasing patient engagement and treatment adherence. This review synthesizes findings from key studies on GAN-enhanced VRET, evaluating its efficacy in reducing anxiety symptoms and discussing the potential for broader application across various anxiety disorders. The results indicate that GAN-enhanced VRET offers a significant advancement in the treatment of SAD, providing a more accessible, engaging, and effective approach to exposure therapy. Future directions include expanding accessibility through cost reduction, integrating additional AI techniques, and exploring applications beyond SAD to improve mental health outcomes across diverse patient populations.

The aim of this study was to determine the extent to which the Tyme Wear smart shirt is as reliable and valid in detecting personalized ventilatory thresholds when compared to the Parvo Medics TrueOne 2400. In this validation study, 19 subjects were recruited to conduct two graded exercise test (GXT) trials. Each GXT trial was separated by seven to ten days of rest. During the GXT, gas exchange and heart rate data were collected by the TrueOne 2400 (TRUE) in addition to the ventilation data collected by the Tyme Wear smart shirt (S-PRED). Gas exchange data from TRUE was used to detect VT1 and VT2. TRUE and S-PRED VT1 and VT2 were compared to determine the reliability and validity of the smart shirt. Of the 19 subjects, data from 15 subjects were used during analysis. S-PRED exhibited excellent (ICC &gt; 0.90) reliability for detection of VT1 and VT2 utilizing time point and workload and moderate (0.90 &gt; ICC &gt; 0.75 ) reliability utilizing heart rate. TRUE exhibited excellent reliability for detection of VT1 and VT2 utilizing time point, workload, and heart rate. When compared to TRUE, S-PRED appears to underestimate the VT1 workload (p &gt; 0.05) across both trials and heart rate (p &lt; 0.05) for trial 1. However, S-PRED appears to underestimate VT2 workload (p &lt; 0.05) and heart rate (p &lt; 0.05) across both trials. The result from this study suggests that the Tyme Wear smart shirt is less valid but is comparable in reliability when compared to the gold standard. Moreover, despite the underestimation of S-PRED VT1 and VT2, the S-PRED detected personalized ventilatory thresholds will provide an adequate training workload for most individuals. In conclusion, the Tyme Wear smart shirt provides easily accessible testing to establish threshold-guided training zones but does not devalue the long-standing laboratory equivalent.

Type 2 Diabetes (T2D) is a major public health concern. We have previously shown that the biochemical functions of the gut microbiome contribute to the postprandial glucose response and built a machine learned (ML) model that can personalize dietary choices using a stool metatranscriptomic test. We integrated this data-driven personalized nutrition process into the Viome Precision Nutrition Program (VPNP), which includes sample collection kits (capillary blood and stool), an AI/ML nutritional recommendation engine, and an app for digital delivery of dietary recommendations. To test the efficacy of VPNP, we conducted a decentralized, blinded, randomized, placebo-controlled trial in a prediabetic and diabetic adult population in the USA, with HbA1c as the primary endpoint. For this pilot trial, we recruited 27 participants and randomized them into two arms. The placebo arm included sample collection, digital delivery of the USDA dietary recommendations, and physical delivery of supplements filled with inert material. The VPNP (interventional) arm included digital delivery of data-driven and personalized dietary recommendations, and physical delivery of precision supplements. The nutritional intervention lasted ~90 days. The ANCOVA analysis method was employed to control for potential confounders measured at baseline. VPNP intervention (n=12) reduced HbA1c more than the placebo (n=15) in a clinically significant manner (HbA1c difference of 0.42%, p = 0.028). We demonstrate that a precision nutrition program based on stool and blood metatranscriptomic data and AI/ML analyses can significantly reduce HbA1c relative to USDA recommended nutrition in a prediabetic and diabetic population. These results suggest that integrating data-driven precision nutrition into the healthcare system could reduce the burden of T2D.

Frailty is a common condition in older adults that negatively impacts health and quality of life. This study evaluated a comprehensive, personalized, and coordinated intervention under the Value-Based Care approach to address frailty's multidimensional nature in older people. It em-ployed a pre-post randomized controlled design, involving frail individuals over 65 years in Valencia, Spain. Assessments were conducted at baseline, 12 months (immediately post-intervention), and 18 months. The intervention included a personalized care plan supported by technology, with monthly motivational follow-ups and plan updates by health professionals and participants. Outcomes were measured using an assessment questionnaire that included the International Consortium for Health Outcomes Measurement dataset for the older population: physical health, physical functioning, general mental health, satisfaction with social activities and relationships, ability to carry out usual social roles and activities, pain, general quality of life, loneliness, physical frailty, psychological frailty and social frailty. The study found signifi-cant improvements in physical frailty, quality of life, reduced health resource use and hospitali-zations, and lower levels of pain and depression/anxiety compared to baseline. The findings suggest further research into value-based care approaches, emphasizing the development and activation of personalized, comprehensive programs for older individuals with frailty.