Search Results (52,076)

Peripheral arterial disease (PAD) prevalence and diabetes mellitus (DM) prevalence are continuously increasing worldwide. The strong relationship between DM and PAD is highlighted by recent evidence. PAD diagnosis in diabetic patients is very important, particularly in patients with diabetic foot disease (DFD); however, it is often made difficult by the characteristics of such diseases. Diagnosing PAD makes it possible to identify patients at a very high cardiovascular risk who require intensive treatment in terms of risk factor modification and medical therapy. The purpose of this review is to discuss the diagnostic methods that allow for a diagnosis of PAD in diabetic patients. Non-invasive tests that address PAD diagnosis will be discussed, such as the ankle-brachial index (ABI), toe pressure (TP), and transcutaneous oxygen pressure (TcPO2). Furthermore, imaging methods, such as duplex ultrasound (DUS), computed tomography angiography (CTA), magnetic resonance angiography (MRA), and digital subtraction angiography (DSA), are described because they allow for diagnosing the anatomical localization and severity of artery stenosis or occlusion in PAD. Non-invasive tests will also be discussed in terms of their ability to assess foot perfusion. Foot perfusion assessment is crucial in the diagnosis of critical limb ischemia (CLI), the most advanced PAD stage, particularly in DFD patients. The impacts of PAD diagnosis and CLI identification in diabetic patients are clinically relevant to prevent amputation and mortality. Full article

Placenta accreta spectrum (PAS) refers to the abnormal adhesion of the placenta to the myometrium, with varying degrees of severity. Placenta accreta involves adhesion to the myometrium, placenta increta invades the myometrium, and placenta percreta extends through the serosa to adjacent organs. The condition is linked to deficient decidualization in scarred uterine tissue, and the risk increases when placenta previa is present and with each prior cesarean delivery. Other risk factors include advanced maternal age, IVF, short intervals between cesareans, and smoking. PAS incidence has risen due to the increase in cesarean deliveries. Placenta previa combined with PAS significantly raises the risk of severe peripartum bleeding, often necessitating a cesarean section with a total hysterectomy. Recognizing PAS prepartum is essential, with sonographic indicators including intraplacental lacunae and uterovesical hypervascularization. However, PAS can be present without sonographic signs, making clinical risk factors crucial for diagnosis. Effective management requires a multidisciplinary approach and proper infrastructure. This presentation covers PAS cases treated at University Hospital Freiburg, detailing patient conditions, diagnostic methods, treatments and outcomes. Full article

Objective:The objective of this study is to explore and enhance the diagnostic process of unipolar and bipolar disorders. The primary focus is on leveraging automated processes to improve the accuracy and accessibility of diagnosis. The study aims to introduce an audio corpus collected from patients diagnosed with these disorders, annotated using the Clinical Global Impressions Scale (CGI) by psychiatrists. Methods and procedures: Traditional diagnostic methods rely on the clinician’s expertise and consideration of co-existing mental disorders. However, this study proposes the implementation of automated processes in the diagnosis, providing quantitative measures and enabling prolonged observation of patients. The paper introduces a speech signal pipeline for CGI state classification, with a specific focus on selecting the most discriminative features. Acoustic features such as prosodies, MFCC, and LPC coefficients are examined in the study. The classification process utilizes common machine learning methods. Results: The results of the study indicate promising outcomes for the automated diagnosis of bipolar and unipolar disorders using the proposed speech signal pipeline. The audio corpus annotated with CGI by psychiatrists achieved a classification accuracy of 95% for the two-class classification. For the four- and seven-class classifications, the results were 77.3% and 73%, respectively, demonstrating the potential of the developed method in distinguishing different states of the disorders. Full article

Background/Objectives: Burden Syndrome, also known as Caregiver Syndrome, particularly affects those who serve in the role of informal caregiver in the presence of family members with conditions. The ABCX dual model examines the impact on the caregiver of the diagnosis of autism spectrum disorder (ASD) on the family. This model considers the severity of the stressor (A), the additional demands of life stress (aA), the family’s internal resources (B), the family’s external resources (bB), the family’s assessment of the situation (C), coping strategies (cC), and outcome (X). The purpose of the present study is to investigate the relationships between resilience, guilt, and burden of care in caregivers of children with ASD. Methods: Various assessment instruments were used, including the “Caregiver Burden Inventory” to measure burden, the “Brief Resilience Scale” to assess resilience, the “Guilt Sensitivity Questionnaire” to examine guilt sensitivity, and the “DA.L.I.A.” to collect information on parent and child characteristics. A total of 80 parents/caregivers participated in the research, including 53 women (Age M = 41.72; SD = 7.8) and 27 men (Age M = 43.35; SD = 6.29). Results: The findings indicate that individuals’ resilience to stressful events correlates negatively with burden, a developmental subtype. However, guilt seems not to play a significant role in the overall perception of burden. In contrast, it was found that the use of informal supports is associated with higher levels of guilt and emotional burden, whereas the use of formal supports is correlated with higher emotional burden, but not higher perceptions of guilt. Conclusions: This study provides important information about the support needed by caregivers and suggests how to address emotional burdens to prevent burnout and support families with children with ASD. Full article

Construction sites are highly unpredictable environments involving a wide variety of stakeholders with complex information exchanges, which lead to the well-known inefficiencies and unproductivity of the construction sector. The adoption of Building Digital Twins (BDT) in the construction site is a promising solution to this issue, by automating data acquisition and knowledge extraction processes and providing what-if scenario simulation capabilities. Furthermore, the current research sets the principles to define, replicate, and scale-up the architecture of a Building Digital Twin Platform (BDTP), conceived as a scalar ecosystem, which allows to seamlessly manage on-site construction processes, integrating cross-cutting domains for the construction site optimization (Progress monitoring, Quality control, Operational Health and Safety, Equipment control, and Production planning). The starting point of the research is a comprehensive diagnosis of on-site process inefficiencies and the barriers to its digitalization leading to the user requirements, which have been underpinned by questionnaires and interviews addressed within an open innovation user-centered approach around Living Labs. The research has been conceived following the Design Science Research (DSR) methodology and based on the Plan-Do-Check-Act (PDCA) analysis for the continuous improvement of the construction process. By means of the adoption of the standard Business Process Model and Notation (BPMN), based on the BDTP architecture, the research has resulted in BPMN workflows stemmed from the Digital Twin (DT) where the DT itself is an actor in a service-oriented data-exchange workflow. Moreover, the use of a BDTP can pave the way for the transition from user-driven construction management to hybrid management, coexisting with both human and digital actors and merging expert knowledge with artificial intelligence techniques. Full article

The liver is a vital organ in the human body, and CT images can intuitively display its morphology. Physicians rely on liver CT images to observe its anatomical structure and areas of pathology, providing evidence for clinical diagnosis and treatment planning. To assist physicians in making accurate judgments, artificial intelligence techniques are adopted. Addressing the limitations of existing methods in liver CT image segmentation, such as weak contextual analysis and semantic information loss, we propose a novel Dual Attention-Based 3D U-Net liver segmentation algorithm on CT images. The innovations of our approach are summarized as follows: (1) We improve the 3D U-Net network by introducing residual connections to better capture multi-scale information and alleviate semantic information loss. (2) We propose the DA-Block encoder structure to enhance feature extraction capability. (3) We introduce the CBAM module into skip connections to optimize feature transmission in the encoder, reducing semantic gaps and achieving accurate liver segmentation. To validate the effectiveness of the algorithm, experiments were conducted on the LiTS dataset. The results showed that the Dice coefficient and HD95 index for liver images were 92.56% and 28.09 mm, respectively, representing an improvement of 0.84% and a reduction of 2.45 mm compared to 3D Res-UNet. Full article

Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such as computed tomography colonography and stool tests are either too costly or less reliable than invasive ones. On the other hand, volatile organic compounds (VOCs) are potentially ideal non-invasive biomarkers for CRC detection and monitoring. The present review is a comprehensive presentation of the current state-of-the-art VOC-based CRC diagnostics, with a specific focus on recent advancements in biosensor design and application. Among them, breath-based chromatography pattern analysis and sampling techniques are overviewed, along with nanoparticle-based optical and electrochemical biosensor approaches. Limitations of the currently available technologies are also discussed with an outlook for improvement in combination with big data analytics and advanced instrumentation, as well as expanding the scope and specificity of CRC-related volatile biomarkers. Full article

Pregnancy-related morbidity and mortality remain high across the United States, with the majority of deaths being deemed preventable. Misdiagnosis and delay in diagnosis are thought to be significant contributors to preventable harm. These diagnostic errors in obstetrics are understudied. Presented here are five selected research methods to ascertain the rates of and harm associated with diagnostic errors and the pros and cons of each. These methodologies include clinicopathologic autopsy studies, retrospective chart reviews based on clinical criteria, obstetric simulations, pregnancy-related harm case reviews, and malpractice and administrative claim database research. We then present a framework for a future study of diagnostic errors and the pursuit of diagnostic excellence in obstetrics: (1) defining and capturing diagnostic errors, (2) targeting bias in diagnostic processes, (3) implementing and monitoring safety bundles, (4) leveraging electronic health record triggers for case reviews, (5) improving diagnostic skills via simulation training, and (6) publishing error rates and reduction strategies. Evaluation of the effectiveness of this framework to ascertain diagnostic error rates, as well as its impact on patient outcomes, is required. Full article

Avian leukosis viruses (ALVs) include a group of avian retroviruses primarily associated with neoplastic diseases in poultry, commonly referred to as avian leukosis. Belonging to different subgroups based on their envelope properties, ALV subgroups A, B, and J (ALV-A, ALV-B, and ALV-J) are the most widespread in poultry populations. Early identification and removal of virus-shedding birds from infected flocks are essential for the ALVs’ eradication. Therefore, the development of rapid, accurate, simple-to-use, and cost effective on-site diagnostic methods for the detection of ALV subgroups is very important. Cas13a, an RNA-guided RNA endonuclease that cleaves target single-stranded RNA, also exhibits non-specific endonuclease activity on any bystander RNA in close proximity. The distinct trans-cleavage activity of Cas13 has been exploited in the molecular diagnosis of multiple pathogens including several viruses. Here, we describe the development and application of a highly sensitive Cas13a-based molecular test for the specific detection of proviral DNA of ALV-A, B, and J subgroups. Prokaryotically expressed LwaCas13a, purified through ion exchange and size-exclusion chromatography, was combined with recombinase polymerase amplification (RPA) and T7 transcription to establish the SHERLOCK (specific high-sensitivity enzymatic reporter unlocking) molecular detection system for the detection of proviral DNA of ALV-A/B/J subgroups. This novel method that needs less sample input with a short turnaround time is based on isothermal detection at 37 °C with a color-based lateral flow readout. The detection limit of the assay for ALV-A/B/J subgroups was 50 copies with no cross reactivity with ALV-C/D/E subgroups and other avian oncogenic viruses such as reticuloendotheliosis virus (REV) and Marek’s disease virus (MDV). The development and evaluation of a highly sensitive and specific visual method of detection of ALV-A/B/J nucleic acids using CRISPR-Cas13a described here will help in ALV detection in eradication programs. Full article

Diastolic dysfunction, a prevalent condition characterized by impaired relaxation and filling of the left ventricle, significantly contributes to heart failure with preserved ejection fraction (HFpEF). Galectin-3, a β-galactoside-binding lectin, has garnered attention as a potential biomarker and mediator of fibrosis and inflammation in cardiovascular diseases. This comprehensive review investigates the impact of galectin-3 on diastolic dysfunction. We explore its molecular mechanisms, including its involvement in cellular signaling pathways and interaction with components of the extracellular matrix. Evidence from both animal models and clinical studies elucidates galectin-3’s role in cardiac remodeling, inflammation, and fibrosis, shedding light on the underlying pathophysiology of diastolic dysfunction. Additionally, we examine the diagnostic and therapeutic implications of galectin-3 in diastolic dysfunction, emphasizing its potential as both a biomarker and a therapeutic target. This review underscores the significance of comprehending galectin-3’s role in diastolic dysfunction and its promise in enhancing diagnosis and treatment approaches for HFpEF patients. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) and polycystic ovary syndrome (PCOS) are prevalent conditions that have been correlated with infertility through overlapped pathophysiological mechanisms. MASLD is associated with metabolic syndrome and is considered among the major causes of chronic liver disease, while PCOS, which is characterized by ovulatory dysfunction and hyperandrogenism, is one of the leading causes of female infertility. The pathophysiological links between PCOS and MASLD have not yet been fully elucidated, with insulin resistance, hyperandrogenemia, obesity, and dyslipidemia being among the key pathways that contribute to liver lipid accumulation, inflammation, and fibrosis, aggravating liver dysfunction. On the other hand, MASLD exacerbates insulin resistance and metabolic dysregulation in women with PCOS, creating a vicious cycle of disease progression. Understanding the intricate relationship between MASLD and PCOS is crucial to improving clinical management, while collaborative efforts between different medical specialties are essential to optimize fertility and liver health outcomes in individuals with MASLD and PCOS. In this review, we summarize the complex interplay between MASLD and PCOS, highlighting the importance of increasing clinical attention to the prevention, diagnosis, and treatment of both entities. Full article

Knee osteoarthritis is a chronic, progressive disease that rapidly progresses to severe stages. Reliable and accurate diagnosis, combined with the implementation of preventive lifestyle modifications before irreversible damage occurs, can effectively protect patients from becoming an inactive population. Artificial intelligence continues to play a pivotal role in computer-aided diagnosis with increasingly convincing accuracy, particularly in identifying the severity of knee osteoarthritis according to the Kellgren–Lawrence (KL) grading scale. The primary objective of this literature review is twofold. Firstly, it aims to provide a systematic analysis of the current literature on the main artificial intelligence models used recently to predict the severity of knee osteoarthritis from radiographic images. Secondly, it constitutes a critical review of the different methodologies employed and the key elements that have improved diagnostic performance. Ultimately, this study demonstrates that the considerable success of artificial intelligence systems will reinforce healthcare professionals’ confidence in the reliability of machine learning algorithms, facilitating more effective and faster treatment for patients afflicted with knee osteoarthritis. In order to achieve these objectives, a qualitative and quantitative analysis was conducted on 60 original research articles published between 1 January 2018 and 15 May 2024. Full article

The prevalence of allergic diseases has increased tremendously in recent decades, which can be attributed to growing exposure to environmental triggers, changes in dietary habits, comorbidity, and the increased use of medications. In this context, the multiplexed diagnosis of sensitization to various allergens and the monitoring of the effectiveness of treatments for allergic diseases become particularly urgent issues. The detection of allergen-specific antibodies, in particular, sIgE and sIgG, is a modern alternative to skin tests due to the safety and efficiency of this method. The use of allergen microarrays to detect tens to hundreds of allergen-specific antibodies in less than 0.1 mL of blood serum enables the transition to a deeply personalized approach in the diagnosis of these diseases while reducing the invasiveness and increasing the informativeness of analysis. This review discusses the technological approaches underlying the development of allergen microarrays and other protein microarrays, including the methods of selection of the microarray substrates and matrices for protein molecule immobilization, the obtainment of allergens, and the use of different types of optical labels for increasing the sensitivity and specificity of the detection of allergen-specific antibodies. Full article

Objective: We report findings from a U.S. mixed-methods pilot study of the Person-Centered Care in Fetal Care Centers (PCC-FCC) Scale. Methods: Participants, who received care at a U.S. Fetal Care Center (FCC) between 2017 and 2021, completed an online questionnaire providing sociodemographic details, specifics about the care received, qualitative experiences, and scores from the PCC-FCC Scale. Results: Participants’ (n = 247) PCC-FCC scores and qualitative feedback indicate high perceived person-centered care (PCC), particularly in areas of care coordination, respectful care, and patient education. However, 8% scored below the midpoint, and 38% of comments were negative, especially regarding expectation setting, preparation for post-intervention maternal health, and psychosocial support. Public insurance was associated with higher total PCC-FCC (p = 0.03) and Factor 2 scores (p = 0.02) compared to those with private insurance. The qualitative themes trust, clarity, comprehensive care, compassion, and belonging further elucidate the concept of PCC in FCCs. Conclusion: The PCC-FCC Scale pilot study revealed strong overall PCC in FCCs, yet variability in patient experiences suggests areas needing improvement, including expectation setting, preparation for post-intervention maternal health, and psychosocial support. Future research must prioritize diverse samples and continued mixed methodologies to better understand the role of insurance and identify other potential disparities, ensuring comprehensive representation of the FCC patient population. Full article

Partial discharge (PD) is one of the major causes of insulation accidents in oil-immersed transformers, generating a large number of signals that represent the health status of the transformer. In particular, acoustic signals can be detected by sensors to locate the source of the partial discharge. However, the array, type, and quantity of sensors play a crucial role in the research on the localization of partial discharge sources within transformers. Hence, this paper proposes a novel sensor array for the specific localization of PD sources using COMSOL Multiphysics software 6.1 to establish a three-dimensional model of the oil-immersed transformer and the different defect types of two-dimensional models. “Electric-force-acoustic” multiphysics field simulations were conducted to model ultrasonic signals of different types of PD by setting up detection points to collect acoustic signals at different types and temperatures instead of physical sensors. Subsequently, simulated waveforms and acoustic spatial distribution maps were acquired in the software. These simulation results were then combined with the time difference of arrival (TDOA) algorithm to solve a system of equations, ultimately yielding the position of the discharge source. Calculated positions were compared with the actual positions using an error iterative algorithm method, with an average spatial error about 1.3 cm, which falls within an acceptable range for fault diagnosis in transformers, validating the accuracy of the proposed method. Therefore, the presented sensor array and computational localization method offer a reliable theoretical basis for fault diagnosis techniques in transformers. Full article