Search

Background & Aims: Progressive familial intrahepatic cholestasis (PFIC) relates to a group of rare, debilitating, liver disorders which typically present in early childhood, but have also been reported in adults. Without early detection and effective treatment, PFIC can result in end-stage liver disease. The aim of the paper was to put forward recommendations that promote standardisation of the management of PFIC in clinical practice. Methods: A committee of six specialists came together to discuss the challenges faced by physicians in the management of PFIC. The committee agreed on two key areas where expert guidance is required to optimise care: (1) how to diagnose and treat patients with a clinical presentation of PFIC in the absence of clear genetic test results/whilst awaiting results, and (2) how to monitor disease progression and response to treatment. A systematic literature review was undertaken to contextualise and inform the recommendations. Results: An algorithm was developed for the diagnosis and treatment of children with suspected PFIC. The algorithm recommends the use of licensed inhibitors of ileal bile acid transporters as the first-line treatment for patients with PFIC and suggests that genetic testing be used to confirm genotype whilst treatment is initiated in patients in whom PFIC is suspected. The authors recommend referring patients to an experienced centre, and ensuring that monitoring includes measurements of pruritus, serum bile acid levels, growth, and quality of life following diagnosis and during treatment. Conclusions: The algorithm presented within this paper offers guidance to optimise the management of paediatric PFIC. The authors hope that these recommendations will help to standardise the management of PFIC in the absence of clear clinical guidelines. Impact and implications: This opinion paper outlines a consistent approach to the contemporaneous diagnosis, monitoring, referral and management of children with progressive familial intrahepatic cholestasis. This should assist physicians given the recent developments in genetic diagnosis and the availability of effective drug therapy. This manuscript will also help to raise awareness of current developments and educate health planners on the place for new drug therapies in progressive familial intrahepatic cholestasis.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases. This position paper focuses on the clinical management of valvular heart diseases with reference to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee tried to reflect the recently published results on the topic of valvular heart diseases and Korean data by a systematic literature search based on validity and relevance. In part I of this article, we will review and discuss the current position of aortic valve disease in Korea. [ABSTRACT FROM AUTHOR]

Summary This Good Practice Paper provides recommendations for the diagnosis and initial management of transplant‐eligible high‐risk myeloma patients. It describes recent updates to the genetic diagnostics of high‐risk myeloma and provides recommendations for treatment on the basis of recent prospective clinical trial evidence. [ABSTRACT FROM AUTHOR]

The recognition of constipation as a possible non‐Immunoglobulin E (IgE)‐mediated allergic condition is challenging because functional constipation (unrelated to food allergies) is a common health problem with a reported worldwide prevalence rate of up to 32.2% in children. However, many studies in children report challenge proven cow's milk allergy and constipation as a primary symptom and have found that between 28% and 78% of children improve on a cow's milk elimination diet. Due to the paucity of data and a focus on IgE‐mediated allergy, not all food allergy guidelines list constipation as a symptom of food allergy. Yet, it is included in all cow's milk allergy guidelines available in English language. The Exploring Non‐IgE‐Mediated Allergy (ENIGMA) Task Force (TF) of the European Academy for Allergy and Clinical Immunology (EAACI) considers in this paper constipation in the context of failure of standard treatment and discuss the role of food allergens as culprit in constipation in children. This position paper used the Delphi approach in reaching consensus on both diagnosis and management, as currently published data are insufficient to support a systematic review. [ABSTRACT FROM AUTHOR]

This article provides information on the diagnosis and management of smouldering myeloma, a precursor condition to multiple myeloma. It discusses various diagnostic tests and imaging techniques used to identify smouldering myeloma and emphasizes the importance of restaging if there is evidence of progression. The article also provides recommendations for screening and initial investigations for suspected myeloma. Additionally, it discusses risk stratification models and treatment options for smouldering myeloma, including the use of lenalidomide. The article concludes by highlighting ongoing clinical trials and the need for further research in the treatment of smouldering myeloma. [Extracted from the article]

This article summarizes several studies on various topics. The first study examines the relationship between internalizing symptoms and problematic pornography use in college students, finding that higher levels of internalizing symptoms are associated with higher levels of problematic pornography use. The second study focuses on sleep-wake disorders among veterans with opioid use disorder, revealing that 24.4% of veterans with opioid use disorder have a sleep disorder, with obstructive sleep apnea being the most common type. The third study explores the association between cannabis use and lifetime suicidal ideation and attempts, finding that cannabis use is associated with an increased risk of suicidal ideation and attempts. Another study examines the impact of mandatory buprenorphine training and the removal of the X-waiver on prescribing patterns for physicians, showing that while there was an increase in knowledge and a decrease in concern about legal consequences after training, there were no significant differences in buprenorphine prescribing patterns. The fifth study focuses on emergency department patients with illicit fentanyl use and co-occurring psychiatric disorders, highlighting the prevalence of illicit fentanyl use and the need for routine assessments and treatment for mental health and substance misuse in this population. Lastly, a study examines the burden of emergency admissions and hospitalizations for nonfatal acute iatrogenic events in people with sickle cell disease and opioid dependence or tolerance, emphasizing the need for improved interventions and better access to care for this population. The first article also highlights the need for further research to improve opioid treatment strategies and [Extracted from the article]

Delirium is a common complication in acute stroke patients, occurring in 15–35% of all stroke unit admissions and is associated with prolonged hospital stay and a poor post-stroke prognosis. Managing delirium in acute stroke patients necessitates an intensive and multiprofessional therapeutic approach, placing a significant burden on healthcare staff. However, dedicated practical recommendations for delirium management developed for the population of acute stroke patients are lacking. For this purpose, the Austrian Stroke Society, in cooperation with the Austrian Society of Neurology, the Austrian Society of Neurorehabilitation, and the Austrian Society of Psychiatry, Psychotherapy, and Psychosomatics has formulated an evidence-based position paper addressing the management of delirium in acute stroke patients. The paper outlines practical recommendations on the three pillars of care in stroke patients with delirium: (a) Key aspects of delirium prevention including stroke-specific delirium risk factors and delirium prediction scores are described. Moreover, a non-pharmacological delirium prevention bundle is presented. (b) The paper provides recommendations on timing and frequency of delirium screening to ensure early diagnosis of delirium in acute stroke patients. Moreover, it reports on the use of different delirium screening tools in stroke populations. (c) An overview of non-pharmacological and pharmacological treatment strategies in patients with delirium and acute stroke is presented and summarized as key recommendation statements. [ABSTRACT FROM AUTHOR]

Point-of-care testing (POCT) biosensors are rapidly developing in recent years because of the urgent needs for rapid test and home test. Among various types of POCT biosensors, paper-based biosensors have occupied large market share owing to the advantages of cost-effectiveness, portability and simplicity. To further meet the increasing requirement of POCT in various application scenes, the functionalization of paper is highly demanded. Metal-organic framework (MOF), a multifunctional porous material, is introduced in the design of paper-based biosensors, significantly improving the sensing property and application potential. This review provides a summary of the recent advances and emerging trends of MOF-functionalized paper-based biosensors (MOF@paper), including different types of substrates, fabrication methods, detection mechanisms and applications. On account of the multifunctionality and superior performance of MOF@paper biosensors, this field should possess promising prospect in scientific researches, translational medicine and clinical applications. • Multifunctional MOF endows paper-based biosensors with diverse detection properties. • The substrates and fabrication methods for MOF@paper biosensors are reviewed. • Various detection mechanisms are summarized and discussed. • MOF@paper biosensors are promising in diagnosis, food, environment and other related applications. [ABSTRACT FROM AUTHOR]

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care. [ABSTRACT FROM AUTHOR]

The use of microfluidic paper-based analytical devices (μPADs) for aiding medical diagnosis is a growing trend in the literature mainly due to their low cost, easy use, simple manufacturing, and great potential for application in low-resource settings. Many important biomarkers (proteins, ions, lipids, hormones, DNA, RNA, drugs, whole cells, and more) and biofluids are available for precise detection and diagnosis. We have reviewed the advances μPADs in medical diagnostics have achieved in the last few years, focusing on the most common human biofluids (whole blood/plasma, sweat, urine, tears, and saliva). The challenges of detecting specific biomarkers in each sample are discussed, along with innovative techniques that overcome such limitations. Finally, the difficulties of commercializing μPADs are considered, and future trends are presented, including wearable devices and integrating multiple steps in a single platform. • μPADs can be applied for simple and low-cost diagnosis in low-resource settings. • The challenges of applying μPADs to human biofluids are analyzed. • Recent advancements to overcome specific drawbacks for each fluid are discussed. • Difficulties in commercializing μPADs and future tendencies are presented. [ABSTRACT FROM AUTHOR]

Ketamine is one of the most commonly abused drugs globally, posing a severe risk to social stability and human health, not only it is being used for recreational purposes, but this tasteless, odourless, and colourless drug also facilitates sexual assaults when it is mixed with drinks. Ketamine abuse is a threat for safety, and this misuse is one of the main uses of the drug. The crucial role of ketamine detection is evident in its contributions to forensic investigations, law enforcement, drug control, workplace integrity, and public health. Electrochemical sensors have gained considerable interest among researchers due to their various advantages, such as low cost and specificity, and particularly screen-printed paper-based electrode (SPBE) biosensors have gained attention. Here, we reported an ePAD (electrochemical paper-based analytical device) for detecting the recreational drug ketamine. The advantages of using a paper-based electrode are that it reduces the electrode’s production costs and is disposable and environmentally friendly. At the same time, nanographite sheets (NGSs) assisted in amplifying the signals generated in the cyclic voltammetry system when ketamine was present. This ePAD was developed by immobilizing a ketamine aptamer on NGS electrodes. The characterization of proper synthesized NGSs was performed by Scanning Electron Microscopy (SEM), XRD (X-ray Diffraction), Fourier-transform infrared spectroscopy (FTIR), and UV-Vis spectroscopy. Electrochemical techniques, including cyclic voltammetry (CV) and linear sweep voltammetry (LSV), were employed to validate the results and confirm each attachment. Furthermore, the versatility of the proposed sensor was explored in both alcoholic and non-alcoholic beverages. The developed sensor showed a low LOD of about 0.01 μg/mL, and the linear range was between 0.01 and 5 μg/mL. This approach offers a valid diagnostic technique for onsite service with minimal resources. This cost effective and portable platform offers desirable characteristics like sensitivity and selectivity and can also be used for POC (point of care) testing to help in the quick identification of suspicious samples and for testing at trafficking sites, amusement parks, and by the side of the road.

Introduction: Antiretroviral therapy (ART) initiation in infants living with HIV before 12 weeks of age can reduce the risk of mortality by 75%. Point‐of‐care (POC) diagnostic testing is critical for prompt ART initiation; however, despite its availability, rates of ART initiation are still relatively low before 12 weeks of age. This systematic review describes the barriers to ART initiation in infants before 12 weeks of age, despite the availability of POC. Methods: This systematic review used a narrative synthesis methodology. We searched PubMed and Scopus using search strategies that combined terms of multiple variants of the keywords "early infant initiation on antiretroviral therapy," "barriers" and "sub‐Saharan Africa" (initial search 18th January 2023; final search 1st August 2023). We included qualitative, observational and mixed methods studies that reported the influences of early infant initiation on ART. We excluded studies that reported influences on other components of the Prevention of Mother to Child Transmission cascade. Using a deductive approach guided by the updated Consolidated Framework of Implementation Research, we developed descriptive codes and themes around barriers to early infant initiation on ART. We then developed recommendations for interventions for the identified barriers using the action, actor, target and time framework from the codes. Results: Of the 266 abstracts reviewed, 52 full‐text papers were examined, of which 12 papers were included. South Africa had most papers from a single country (n = 3) and the most reported study design was retrospective (n = 6). Delays in ART initiation beyond 12 weeks in infants 0–12 months were primarily associated with health facility and maternal factors. The most prominent barriers identified were inadequate resources for POC testing (including human resources, laboratory facilities and patient follow‐up). Maternal‐related factors, such as limited male involvement and maternal perceptions of treatment and care, were also influential. Discussion: We identified structural barriers to ART initiation at the health system, social and cultural levels. Improvements in the timely allocation of resources for POC testing operations, coupled with interventions addressing social and behavioural barriers among both mothers and healthcare providers, hold a promise for enhancing timely ART initiation in infants. Conclusions: This paper identifies barriers and proposes strategies for timely ART initiation in infants. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has significantly transformed the medical field, especially in the diagnosis, treatment, and management of oncological diseases. It has had a profound impact on clinical decision-making and has enhanced the quality of life for various populations. This study aims to comprehensively assess the inherent relationship between AI and medicine and to uncover both its positive and negative implications. To achieve a comprehensive understanding, a thorough systematic review of articles was conducted, examining a total of 80 papers published between 2017 and 2023. These articles were carefully selected from well-known open-access databases, such as Scopus, IOPscience, IEEE Xplore, Google Scholar, ResearchGate, and ProQuest. A key finding from this review is that the majority of research on this topic has been published in scientific journals ranked in the first-quartile (Q1), underscoring the importance and high quality of research in this field. The United States, China, India, the United Kingdom, and Canada are the foremost countries in publishing on this topic. Most of the research is published in first-quartile (Q1) journals, representing 51% of the studies. Only 1% of articles appear in third-quartile (Q3) journals. IEEE Xplore is renowned as the primary database for accessing high-impact studies in this field. Future research should prioritize investigating the long-term impact of AI on patient clinical outcomes. International collaborative research could promote innovation and fairness in the implementation of artificial intelligence (AI) in oncology. [ABSTRACT FROM AUTHOR]

Ketamine is one of the most commonly abused drugs globally, posing a severe risk to social stability and human health, not only it is being used for recreational purposes, but this tasteless, odourless, and colourless drug also facilitates sexual assaults when it is mixed with drinks. Ketamine abuse is a threat for safety, and this misuse is one of the main uses of the drug. The crucial role of ketamine detection is evident in its contributions to forensic investigations, law enforcement, drug control, workplace integrity, and public health. Electrochemical sensors have gained considerable interest among researchers due to their various advantages, such as low cost and specificity, and particularly screen-printed paper-based electrode (SPBE) biosensors have gained attention. Here, we reported an ePAD (electrochemical paper-based analytical device) for detecting the recreational drug ketamine. The advantages of using a paper-based electrode are that it reduces the electrode's production costs and is disposable and environmentally friendly. At the same time, nanographite sheets (NGSs) assisted in amplifying the signals generated in the cyclic voltammetry system when ketamine was present. This ePAD was developed by immobilizing a ketamine aptamer on NGS electrodes. The characterization of proper synthesized NGSs was performed by Scanning Electron Microscopy (SEM), XRD (X-ray Diffraction), Fourier-transform infrared spectroscopy (FTIR), and UV-Vis spectroscopy. Electrochemical techniques, including cyclic voltammetry (CV) and linear sweep voltammetry (LSV), were employed to validate the results and confirm each attachment. Furthermore, the versatility of the proposed sensor was explored in both alcoholic and non-alcoholic beverages. The developed sensor showed a low LOD of about 0.01 μg/mL, and the linear range was between 0.01 and 5 μg/mL. This approach offers a valid diagnostic technique for onsite service with minimal resources. This cost effective and portable platform offers desirable characteristics like sensitivity and selectivity and can also be used for POC (point of care) testing to help in the quick identification of suspicious samples and for testing at trafficking sites, amusement parks, and by the side of the road. [ABSTRACT FROM AUTHOR]

Background: Nutritional risk situations related to decreased food intake in the hospital environment hinder nutritional care and increase malnutrition in hospitalized patients and are often associated with increased morbidity and mortality. The objective of this study is to develop and test the reliability and data similarity of a mobile application as a virtual instrument to assess the acceptability and quality of hospital diets for inpatients. Methods: This intra- and interobserver development and reliability study investigated an in-hospital food intake monitoring application based on a validated instrument for patients with infectious diseases who were treated at the Evandro Chagas National Institute of Infectious Diseases (INI/FIOCRUZ). The instrument was sequentially administered to patients 48 h after admission to INI hospital units using the printed instrument (paper) and the digital application (ARIETI) simultaneously. The tested reliability factor was the consistency of the method in the digital platform, checking whether the application provided equivalent data to the paper instrument, and finally, a statistical analysis plan was performed in the R platform version 4.2.0. This project was authorized by the FIOCRUZ/INI Research Ethics Committee. Results: The ARIETI was developed and tested for reliability in 70 participants, showing a similar ability to calculate caloric intake in Kcal, protein intake (g), the proportion of caloric intake and protein intake relative to the prescribed goal. These instrument comparison analyses showed statistical significance (p < 0.001). The application was superior to the paper-based instrument, accelerating the time to perform the nutritional risk diagnosis based on food intake by approximately 250 s (average time). Conclusions: The ARIETI application has demonstrated equivalent reliability compared to the original instrument. Moreover, it optimized the time between the diagnosis of nutritional risk related to dietary intake and the nutritionist's decision making, showing an improved ability to maintain information quality compared to the paper-based instrument. [ABSTRACT FROM AUTHOR]

As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely used owing to the advantages of the easy collection, low cost, and non-invasiveness of CSs. However, in practical applications, the fault characteristics of the CS are weak, resulting in diagnostic performance that fails to meet the expected standards. In this paper, a diagnosis method is proposed to address this problem and enhance the diagnosis accuracy. Firstly, CSs from two phases are processed by periodic resampling to enhance data features, which are then fused through splicing operations. Subsequently, a feature enhancement module is constructed using multi-scale feature fusion for decomposing the input. Finally, a diagnosis model is constructed by using an improved channel attention module (CAM) for enhancing the diagnosis performance. The results from experiments containing two different types of bearing datasets show that the proposed method can extract high-quality fault features and improve the diagnosis accuracy, presenting great potential in intelligent fault diagnosis and the maintenance of electric vehicles. [ABSTRACT FROM AUTHOR]

Medical imaging methods such as optical imaging have advanced significantly in their ability to diagnose and monitor various illnesses without any invasive procedures. Optical imaging offers a detailed view of anatomical structures and molecular processes, utilizing the unique properties of light and its interactions with biological tissues. This paper provides a comprehensive overview of optical imaging principles, including techniques like Optical Coherence Tomography (OCT), fluorescence imaging, and diffuse optical imaging. Optical imaging methods provide non-invasive, detailed views of biological tissues, enabling early disease detection and monitoring, thereby significantly advancing diagnostic capabilities and improving patient outcomes. These methods are used in the detection of diseases such as cancer, cardiovascular conditions, and neurological disorders. [ABSTRACT FROM AUTHOR]

The emergency of esophageal button batteries is recognized. Foreign bodies in the ear or nose are treated as comparatively nonemergent. We present the case of a child who presented with suspected pen-nib in the nostril to highlight the importance of investigation and intervention in unwitnessed nasal foreign body and review the literature regarding X-rays in these cases. Fifteen papers were identified as per search parameters; 3 papers recommend the use of plain radiographs for nasal foreign bodies. We propose that in unwitnessed pediatric nasal foreign body insertion, a facial X-ray should be considered, especially if suspecting a metallic foreign body. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. [ABSTRACT FROM AUTHOR]

The 11th Revision of the International Statistical Classification of Diseases, Injuries, and Causes of Death (ICD-11) will replace its predecessor as international standard for cause-of death-statistics. The digitization of healthcare is a main motivation for its introduction. In parallel, the replacement of the paper-based death certificate with an electronic format is under evaluation. At the moment, the death certificate is used in paper-based format with ICD-10 for coding in Germany. To be prepared for the switch to ICD-11, the compatibility between ICD-11 and the electronic certificate should be assured. Objectives were to check the appropriateness of diagnosis-related information found on death certificates for an ICD-11 coding and to describe enhancements to the certificate's structure needed to fully utilize the strengths of ICD-11. As part of an exploratory test of a respective application, information from 453 electronic death certificates were provided by one local health authority. From a sample of 200 certificates, 433 diagnosis texts were coded into the German version of ICD-11. The appropriateness of the results as well as the further requirements of ICD-11, particularly with regard to post-coordination, were checked. For 430 diagnosis texts, 649 ICD-11 codes were used. Three hundred and sixty two diagnosis texts were rated as appropriately represented through the coding result. Almost all certificates contained diagnosis texts that lacked details required by ICD-11 for a precise coding. The distribution of diseases was very similar between ICD-10 and ICD-11 coding. A few gaps in ICD-11 were identified. Information requested by ICD-11 for a mandatory post-coordination were almost entirely absent from the death certificates. The structure and content of the death certificate are currently not well prepared for an ICD-11 coding. Necessary information was frequently missing. The line-oriented structure of death certificates has to be supplemented with a more flexible approach. Then, the semantic knowledge base of ICD-11 should better guide the content related input fields of a future electronic death certificate. [ABSTRACT FROM AUTHOR]

With the increasing complexity of integrated circuit design, the threat of a hardware Trojan (HT) is becoming more and more prominent. At present, the research mainly focuses on the detection of HTs, but the amount of research on the diagnosis of HTs is very small. The number of existing HT diagnosis methods is generally completed by detecting the HT nodes in the netlist. The main reason is the lack of consideration of the integrity of HTs, so the diagnosis accuracy is low. Based on the above reason, this paper proposes two implanted node search algorithms named layer-by-layer difference search (LDS) and layer-by-layer grouping difference search (LGDS). The LDS algorithm can greatly reduce the search time, and the LGDS algorithm can solve the problem of input node disorder. The two methods greatly reduce the number of nodes sorting and comparing, and therefore the time complexity is lower. Moreover, the relevance between implanted nodes is taken into account to improve the diagnosis rate. We completed experiments on an HT diagnosis; the HT implantation example is from Trust-Hub. The experimental results are shown as follows: (1) The average true positive rate (TPR) of the diagnosis using KNN, RF, or SVM with the LDS or LGDS algorithm is more than 93%, and the average true negative rate (TNR) is 100%. (2) The average proportion of implanted nodes obtained by the LDS or LGDS algorithm is more than 97%. The proposed method has a lower time complexity compared with other existing diagnosis methods, and the diagnosis time is shortened by nearly 75%. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of rough fermatean neutrosophic sets and investigates their properties. Additionally, a cosine similarity measure between these sets is proposed. By applying this measure to a medical diagnosis example, the paper illustrates how the method can be used in practical situations, highlighting its effectiveness in complex decision-making scenarios. This innovation holds promise for improving decision-making processes, especially in critical areas like medical diagnosis, where making accurate assessments amidst uncertainty is crucial. [ABSTRACT FROM AUTHOR]

Introduction: Skin disease is one of the most common diseases and can affect people of all ages and races. However, the diagnosis of skin diseases via observation is a highly challenging task for both doctors and patients, and would benefit from the use of an intelligent system. Building a large benchmark with professional dermatologists is resource-intensive, and we believe that few-shot learning (FSL) methods would be helpful in solving the problem of annotated data scarcity. In this paper, we propose CDD-Net (Context Feature Fusion and Dual Attention Dermatology Net), a plug-in module for FSL clinical skin disease classification. Methods: Current FSL methods used in skin disease classification are limited to nonuniversal approaches and few disease classes. Our CDD-Net has a flexible structure, including a context feature–fusion module and dual-attention module to extract discriminating texture feature and emphasize contributive regions and channels. The context feature–fusion module localizes discriminatory texture details of skin lesions by integrating features from different layers, while the dual-attention module highlights discriminative regions via channel-wise and pixel-wise depictions based on weight vectors and restrains the contributions of irrelevant areas. We also present Derm104, a new clinical skin disease data benchmark that has significant coverage of rare diseases and reliable annotation between primary species and subspecies for better validation of our approach. Results: Our experiments validated the versatility of CDD-Net for different FSL methods and achieved an improvement in accuracy of up to 9.14 percentage points compared with the vanilla network, which can be considered state of the art. The ablation study also showed that the dual-attention module and context feature–fusion module worked efficiently in CDD-Net. [ABSTRACT FROM AUTHOR]

When stroke occurs in pediatric age, it might be mistakenly interpreted as non-accidental head injury (NAHI). In these situations, a multidisciplinary approach is fundamental, including a thorough personal and familial history, along with accurate physical examination and additional investigations. Especially when the clinical picture is uncertain, it is important to remember that certain genetic conditions can cause bleeding inside the brain, which may resemble NAHI. Pediatric strokes occurring around the time of birth can also be an initial sign of undiagnosed genetic disorders. Hence, it is crucial to conduct a thorough evaluation, including genetic testing, when there is a suspicion of NAHI but the symptoms are unclear. In these cases, a characteristic set of symptoms is often observed. This study aims to summarize some of the genetic causes of hemorrhagic stroke in the pediatric population, thus mimicking non-accidental head injury, considering elements that can be useful in characterizing pathologies. A systematic review of genetic disorders that may cause ICH in children was carried out according to the Preferred Reporting Item for Systematic Review (PRISMA) standards. We selected 10 articles regarding the main genetic diseases in stroke; we additionally selected 11 papers concerning patients with pediatric stroke and genetic diseases, or studies outlining the characteristics of stroke in these patients. The disorders we identified were Moyamoya disease (MMD), COL4A1, COL4A2 pathogenic variant, Ehlers–Danlos syndrome (E-D), neurofibromatosis type 1 (Nf1), sickle cell disease (SCD), cerebral cavernous malformations (CCM), hereditary hemorrhagic telangiectasia (HHT) and Marfan syndrome. In conclusion, this paper provides a comprehensive overview of the genetic disorders that could be tested in children when there is a suspicion of NAHI but an unclear picture. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a chronic brain disorder affecting millions worldwide. It occurs when brain cells that produce dopamine, a chemical controlling movement, die or become damaged. This leads to PD, which causes problems with movement, balance, and posture. Early detection is crucial to slow its progression and improve the quality of life for PD patients. This paper proposes a handwriting-based prediction approach combining a cosine annealing scheduler with deep transfer learning. It utilizes the NIATS dataset, which contains handwriting samples from individuals with and without PD, to evaluate six different models: VGG16, VGG19, ResNet18, ResNet50, ResNet101, and Vit. This paper compares the performance of these models based on three metrics: accuracy, precision, and F1 score. The results showed that the VGG19 model, combined with the proposed method, achieved the highest average accuracy of 96.67%. [ABSTRACT FROM AUTHOR]

The diagnosis of neurodegenerative diseases (NDDs) remains challenging, and existing therapeutic approaches demonstrate little efficacy. NDD drug delivery can be achieved through the utilization of nanostructures, hence enabling multimodal NDD theranostics. Nevertheless, both biomembrane and non-biomembrane nanostructures possess intrinsic shortcomings that must be addressed by hybridization to create novel nanostructures with versatile applications in NDD theranostics. Hybrid nanostructures display improved biocompatibility, inherent targeting capabilities, intelligent responsiveness, and controlled drug release. This paper provides a concise overview of the latest developments in hybrid nanostructures for NDD theranostics and emphasizes various engineering methodologies for the integration of diverse nanostructures, including liposomes, exosomes, cell membranes, and non-biomembrane nanostructures such as polymers, metals, and hydrogels. The use of a combination technique can significantly augment the precision, intelligence, and efficacy of hybrid nanostructures, therefore functioning as a more robust theranostic approach for NDDs. This paper also addresses the issues that arise in the therapeutic translation of hybrid nanostructures and explores potential future prospects in this field. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Diagnosing a disease requires observing the affected tissues and drawing conclusions based on specific known features. Conventionally, a pathologist would diagnose the sample manually by placing it on a glass slide and viewing it under the microscope. These microscopes existed 400 years ago, but over the years, there have been modifications aimed at digitizing every possible diagnostic test. One of the major advantages of digitizing the process is the reduced time consumption for acquiring, processing, and analyzing the slides. Another positive aspect is the reduction in subjectivity achieved by utilizing artificial intelligence (AI) algorithms to classify and diagnose specific diseases. This is achieved by attaching a digital camera to the microscope, which captures images of the glass slides for subsequent processing and diagnosis. There has been a lot of research in this field, but its implementation has been hindered by challenges such as interoperability and high-resolution data, resulting in large file sizes. Various applications for whole slide imaging, such as disease diagnosis techniques, whole slide imaging (WSI) scanners, digital slide scanners, the Internet of Things (IoT), and AI, have been explored in this study. This paper reviews the trends and evolution of microscopes leading to present-day digital pathology scanners, with a major focus on one of the digital techniques, which is whole slide imaging. It also explores various areas where AI has been integrated into whole-slide imaging. [ABSTRACT FROM AUTHOR]

In the realm of disease prognosis and diagnosis, a plethora of medical images are utilized. These images are typically stored either within the local on-premises servers of healthcare providers or within cloud storage infrastructures. However, this conventional storage approach often incurs high infrastructure costs and results in sluggish information retrieval, ultimately leading to delays in diagnosis and consequential wastage of valuable time for patients. The methodology proposed in this paper offers a pioneering solution to expedite the diagnosis of medical conditions while simultaneously reducing infrastructure costs associated with data storage. Through this study, a high-speed biomedical image processing approach is designed to facilitate rapid prognosis and diagnosis. The proposed framework includes Deep learning QR code technique using an optimized database design aimed at alleviating the burden of intensive on-premises database requirements. The work includes medical dataset from Crawford Image and Data Archive and Duke CIVM for evaluating the proposed work suing different performance metrics, The work has also been compared from the previous research further enhancing the system's efficiency. By providing healthcare providers with high-speed access to medical records, this system enables swift retrieval of comprehensive patient details, thereby improving accuracy in diagnosis and supporting informed decision-making. [ABSTRACT FROM AUTHOR]

This paper explores a brand-new interdisciplinary approach applied to an enduring problem: the communication of severe diagnoses. The moment when physicians explain the diagnosis to patients and their relatives is sensitive, particularly for a disease that is rarely diagnosed early. The first part of the article is dedicated to the context of this delicate doctor-patient interaction. With this framework in mind, the paper delves into the innovative interdisciplinary methodology developed in the pilot study Communi.CARE, conducted in a hospital in Northern Italy, which focuses on the diagnosis of pancreatic ductal adenocarcinoma (PDAC). SARS-CoV-2 impact on the study development is highlighted. The study aims to explore the topic by combining different areas of expertise, including medicine, philosophy, sociology, and psychology. The contribution of philosophy is here presented as essential: it has a leading role in the conception of the study, its development, and the elaboration of results. It is shown throughout the study, from methodology to the analysis of results. Strengths and weaknesses of the methodology are discussed. In conclusion, further philosophical considerations on effective and ethical communication in this delicate context are recommended. [ABSTRACT FROM AUTHOR]

The advance of machine learning (ML) systems in real‐world scenarios usually expects safe deployment in high‐stake applications (e.g., medical diagnosis) for critical decision‐making process. To this end, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. Many studies have been done to enhance the robustness in recent years from different angles, such as variance‐regularized robust objective functions and conformal prediction (CP) for uncertainty quantification on testing data. Although these tools provably improve the robustness of ML model, there is still an inevitable gap to integrate them into an end‐to‐end deployment. For example, robust objectives usually require carefully designed optimization algorithms, while CP treats ML models as black boxes. This paper is a brief introduction to our recent research focusing on filling this gap. Specifically, for learning robust objectives, we designed sample‐efficient stochastic optimization algorithms that achieves the optimal (or faster compared to existing algorithms) convergence rates. Moreover, for CP‐based uncertainty quantification, we established a framework to analyze the expected prediction set size (smaller size means more efficiency) of CP methods in both standard and adversarial settings. This paper elaborates the key challenges and our exploration towards efficient algorithms with details of background methods, notions for robustness measure, concepts of algorithmic efficiency, our proposed algorithms and results. All of them further motivate our future research on risk‐aware ML that can be critical for AI–human collaborative systems. The future work mainly targets designing conformal robust objectives and their efficient optimization algorithms. [ABSTRACT FROM AUTHOR]

Simple Summary: Equine granulocytic anaplasmosis (EGA) is a tick-borne disease affecting horses worldwide, caused by Anaplasma phagocytophilum. The disease ranges from non-specific clinical signs to fatal outcomes. A large number of horses has been tested for serological and molecular surveys concerning EGA, but the number of clinical EGA reports is smaller and mostly as single or case series reports. Therefore, the aim of this paper was to analyze reported cases regarding clinico-pathology, diagnosis, and therapeutic management in equids that developed clinical signs of EGA and were confirmed to be infected with A. phagocytophilum. The variety of clinical and pathological findings and the challenging therapeutic approaches registered strongly suggest that EGA should be included in the differential diagnosis when fever occurs. In addition, the findings emphasize the importance of monitoring EGA to initiate proper medication in order to avoid complications. Equine granulocytic anaplasmosis (EGA) is a tick-borne disease affecting horses worldwide, caused by Anaplasma phagocytophilum. The disease ranges from non-specific clinical signs to fatal outcomes. This paper aimed to analyze EGA cases reported in peer-reviewed journals, particularly on clinico-pathological findings, diagnosis, and therapeutic management. Overall, 189 clinical cases from 31 publications were included in the study. Extensive symptomatology for the EGA cases was reported, of which mostly was fever (90.30%), followed by limb edema (48.51%), anorexia (41.79%), depression (32.84%), icterus (22.39%), ataxia (17.91), tachycardia (16.42%), and lethargy (15.67%). Laboratory tests revealed thrombocytopenia (90.32%), anemia (75%), decreased hematocrit (70.59%), leukopenia (55.88%), lymphopenia (58.14%), and neutropenia (41.67%) as the most common hematological abnormalities. For a subset of tested animals, hyperbilirubinemia (20/29), hyperfibrinogenemia (13/15), and hyponatremia (10/10) were also reported. The diagnosis was established by microscopic identification of morulae (in 153 cases), and/or PCR (120 cases), isolation (1 case), or serology (56 cases). For treatment, oxytetracycline was used in the majority (52.24%) of EGA cases, but recovery without antibiotherapy (10.34%) was also noted. In conclusion, the variety of clinical and pathological findings and the challenging therapeutic approaches reported suggest that EGA should be included in the differential diagnosis when fever occurs. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are highly prevalent, sudden onset, and relatively fatal, posing a significant public health burden. Long-term dynamic electrocardiography, which can continuously record the long-term dynamic ECG activities of individuals in their daily lives, has high research value. However, ECG signals are weak and highly susceptible to external interference, which may lead to false alarms and misdiagnosis, affecting the diagnostic efficiency and the utilization rate of healthcare resources, so research on the quality of dynamic ECG signals is extremely necessary. Aimed at the above problems, this paper proposes a dynamic ECG signal quality assessment method based on CNN and LSTM that divides the signal into three quality categories: the signal of the Q1 category has a lower noise level, which can be used for reliable diagnosis of arrhythmia, etc.; the signal of the Q2 category has a higher noise level, but it still contains information that can be used for heart rate calculation, HRV analysis, etc.; and the signal of the Q3 category has a higher noise level that can interfere with the diagnosis of cardiovascular disease and should be discarded or labeled. In this paper, we use the widely recognized MIT-BIH database, based on which the model is applied to realistically collect exercise experimental data to assess the performance of the model in dealing with real-world situations. The model achieves an accuracy of 98.65% on the test set, a macro-averaged F1 score of 98.5%, and a high F1 score of 99.71% for the prediction of Q3 category signals, which shows that the model has good accuracy and generalization performance. [ABSTRACT FROM AUTHOR]

In patients presenting in the emergency department with acute vertigo, a rapid and accurate differential diagnosis is crucial, as posterior circulation strokes can mimic acute vestibular losses, leading to inappropriate treatment. The diagnosis of vestibular neuritis is made based on the clinical manifestation and a bedside otoneurological assessment. In the clinical examination, an evaluation of the vestibulo-ocular reflex is the key element; however, the accuracy of the bedside head impulse test depends on the clinician's experience. Thus, new diagnostic methods are needed to objectify and facilitate such rapid vestibular evaluations. The aim of our paper is to provide a comprehensive review of the video head impulse test's application in the diagnosis of vestibular neuritis. Numerous studies have reported advantages that make this method helpful in detailed otoneurological evaluations; in contrast to the bedside head impulse test, it enables an analysis of all six semicircular canals function and records the covert corrective saccades, which are invisible to the naked eye. As a portable and easy diagnostic tool, it is known to improve the diagnostic accuracy in patients with acute vertigo presenting in the emergency department. Moreover, as it evaluates the vestibulo-ocular reflex across different frequencies, as compared to caloric tests, it can be used as an additional test that is complementary to videonystagmography. Recently, several papers have described the application of the video head impulse test in follow-up and recovery evaluations in patients with vestibular neuritis. [ABSTRACT FROM AUTHOR]

Background: A huge amount of research is carried out nowadays in Artificial Intelligence to propose automated ways to analyse medical data with the aim to support doctors in delivering medical diagnoses. However, a main issue of these approaches is the lack of transparency and interpretability of the achieved results, making it hard to employ such methods for educational purposes. It is therefore necessary to develop new frameworks to enhance explainability in these solutions. Results: In this paper, we present a novel full pipeline to generate automatically natural language explanations for medical diagnoses. The proposed solution starts from a clinical case description associated with a list of correct and incorrect diagnoses and, through the extraction of the relevant symptoms and findings, enriches the information contained in the description with verified medical knowledge from an ontology. Finally, the system returns a pattern-based explanation in natural language which elucidates why the correct (incorrect) diagnosis is the correct (incorrect) one. The main contribution of the paper is twofold: first, we propose two novel linguistic resources for the medical domain (i.e, a dataset of 314 clinical cases annotated with the medical entities from UMLS, and a database of biological boundaries for common findings), and second, a full Information Extraction pipeline to extract symptoms and findings from the clinical cases and match them with the terms in a medical ontology and to the biological boundaries. An extensive evaluation of the proposed approach shows the our method outperforms comparable approaches. Conclusions: Our goal is to offer AI-assisted educational support framework to form clinical residents to formulate sound and exhaustive explanations for their diagnoses to patients. [ABSTRACT FROM AUTHOR]

Anderson–Fabry disease is a lysosomal storage disorder caused by mutations in the GLA gene, which encodes the enzyme α-galactosidase A. The GLA gene is located on the X-chromosome, causing an X-linked pathology: due to lyonization, female patients usually manifest a variable symptomatology, ranging from asymptomatic to severe phenotypes. The confirmation of the clinical diagnosis of Fabry disease, achieved by measuring α-galactosidase A activity, which is usually the first test used, shows differences between male and female patients. This assay is reliable in male patients with causative mutations in the GLA gene, in whom the enzymatic activity is lower than normal values; on the other hand, in female Fabry patients, the enzymatic activity is extremely variable between normal and pathological values. These fluctuations are also found in female patients' blood levels of globotriaosylsphingosine (LysoGb3) for the same reason. In this paper, we present a retrospective study conducted in our laboratories on 827 Fabry patients with causative mutations in the GLA gene. Our results show that 100% of male patients had α-galactosidase A activity below the reference value, while more than 70% of female patients had normal values. It can also be observed that almost half of the female patients with pathogenic mutations in the GLA gene showed normal values of LysoGb3 in blood. Furthermore, in women, blood LysoGb3 values can vary over time, as we show in a clinical case presented in this paper. Both these tests could lead to missed diagnoses of Fabry disease in female patients, so the analysis of the GLA gene represents the main diagnostic test for Fabry disease in women to date. [ABSTRACT FROM AUTHOR]

This document is an editorial discussing the importance of understanding neurodevelopmental and neurodegenerative disorders for improved patient care and treatment. It highlights specific disorders such as Down syndrome, neural tube defects, inherited retinal diseases, Alzheimer's disease, and fatty acid hydroxylase-associated neurodegeneration. The editorial mentions the use of model organisms like Drosophila melanogaster and Xenopus laevis to study these diseases. It provides a summary of research papers included in the topic, covering various aspects of these disorders and potential therapeutic targets. This document is a note from the publisher of an article titled "Talents in the 'Prairie Talents' Project of Inner Mongolia." The note acknowledges an error in the article but states that it does not affect the scientific conclusions. It clarifies that the claims expressed in the article are solely those of the authors and do not necessarily represent their affiliated organizations or the publisher. The document does not provide any additional information about the content of the article. [Extracted from the article]

Deep neural networks (DNNs) are increasingly important in the medical diagnosis of electrocardiogram (ECG) signals. However, research has shown that DNNs are highly vulnerable to adversarial examples, which can be created by carefully crafted perturbations. This vulnerability can lead to potential medical accidents. This poses new challenges for the application of DNNs in the medical diagnosis of ECG signals. This paper proposes a novel network Channel Activation Suppression with Lipschitz Constraints Net (CASLCNet), which employs the Channel-wise Activation Suppressing (CAS) strategy to dynamically adjust the contribution of different channels to the class prediction and uses the 1-Lipschitz's ℓ∞ distance network as a robust classifier to reduce the impact of adversarial perturbations on the model itself in order to increase the adversarial robustness of the model. The experimental results demonstrate that CASLCNet achieves A C C r o b u s t scores of 91.03% and 83.01% when subjected to PGD attacks on the MIT-BIH and CPSC2018 datasets, respectively, which proves that the proposed method in this paper enhances the model's adversarial robustness while maintaining a high accuracy rate. [ABSTRACT FROM AUTHOR]

Background: Currently, the incidence of cerebral palsy is high in newborns. However, the current methods for diagnosing and treating patients with cerebral palsy are complex and poorly targeted. Moreover, these studies lack the support of bibliometric analysis results. Objective: Our study focused on a bibliometric analysis of published papers on the diagnosis and treatment of patients with cerebral palsy. This study identified the primary authors, institutions, and countries involved in analyzing the status and trends of research on the diagnosis and treatment of patients with cerebral palsy. Additionally, the study also involved screening pathways related to cerebral palsy. Methods: The PubMed database was searched for publications on the diagnosis and treatment of patients with cerebral palsy between 1990 and 2023. R v4.2.2 and VOSviewer v1.6.18 software tools were utilized to perform bibliometric analysis and visualization. Results: There were 1,965 publications on cerebral palsy diagnosis and 5,418 articles on the qualified treatment strategies, and the annual number of publications also increased. The United States dominated in this field of research. Gregory Y.H. Lip and Patrizio Lancellotti published the most number of papers. The Cleveland Clinic published the most number of papers in the field. According to the analysis of the co-occurrence of keywords, we found that the main research directions were age, sex, disease diagnosis, and treatment. Newly emerging research has focused mainly on heart failure, which is related to valvular heart disease. Conclusion: The findings presented in this study oer valuable insights into ongoing research and potential future directions pertaining to cerebral palsy. These insights can assist researchers in identifying suitable collaborators and enhancing their investigations aimed at identifying the underlying molecular mechanisms associated with cerebral palsy, encompassing its etiology, preventive measures, and therapeutic interventions. [ABSTRACT FROM AUTHOR]

As a significant reactive oxygen species (ROS), hypochlorous acid (HClO) plays a key role in the human immune system, ensuring the normal function of the organism. However, abnormal HClO levels have been linked to immune system disorders, neurodegenerative damage, and cancer. Therefore, rapid, and sensitive monitoring of HClO is important for the diagnosis of certain diseases. Here, we designed a probe XL for the detection of HClO in living cells, which is a mixture of phenothiazine and semi-cyanine. The probe XL is highly sensitive, selective and has an ultra-fast (<10 seconds) response to HClO in solution. The detection limit of probe XL for HClO was as low as 31 nM, and we also successfully used the test paper prepared with probe XL for the colorimetric fluorescence detection of HClO in real water samples. In addition, probe XL showed good response to both endogenous and exogenous HClO in cells. Finally, we successfully used probe XL for imaging HClO in zebrafish. [ABSTRACT FROM AUTHOR]