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To improve the storage and transport of clinical specimens for the diagnosis of Neisseria meningitidis (Nm) infections in resource-limited settings, we have evaluated the performance of dried blood spot (DBS) and dried cerebrospinal fluid spot (DCS) assays. DBS and DCS were prepared on filter paper from liquid specimens previously tested for Nm in the United Kingdom. Nm was detected and genogrouped by real-time PCR performed on crude genomic DNA extracted from the DBS (n = 226) and DCS (n = 226) specimens. Targeted whole-genome sequencing was performed on a subset of specimens, DBS (n = 4) and DCS (n = 6). The overall agreement between the analysis of liquid and dried specimens was (94.2%; 95% CI 90.8–96.7) for blood and (96.4%; 95% CI 93.5–98.0) for cerebrospinal fluid. Relative to liquid specimens as the reference, the DBS and DCS assays had sensitivities of (89.1%; 95% CI 82.7–93.8) and (94.2%; 95% CI 88.9–97.5), respectively, and both assays had specificities above 98%. A genogroup was identified by dried specimen analysis for 81.9% of the confirmed meningococcal infections. Near full-length Nm genome sequences (>86%) were obtained for all ten specimens tested which allowed determination of the sequence type, clonal complex, presence of antimicrobial resistance and other meningococcal genotyping. Dried blood and CSF filter spot assays offer a practical alternative to liquid specimens for the molecular and genomic characterisation of invasive meningococcal diseases in low-resource settings. [ABSTRACT FROM AUTHOR]

In vitro diagnosis (IVD) has become a hot topic in laboratory research and achievement transformation. However, due to the high cost, and time-consuming and complex operation of traditional technologies, some new technologies are being introduced into IVD, to solve the existing problems. As a result, IVD has begun to develop toward point-of-care testing (POCT), a subdivision field of IVD. The pandemic has made governments and health institutions realize the urgency of accelerating the development of POCT. Microfluidic paper-based analytical devices (μPADs), a low-cost, high-efficiency, and easy-to-operate detection platform, have played a significant role in advancing the development of IVD. μPADs are composed of paper as the core material, certain unique substances as reagents for processing the paper, and sensing devices, as auxiliary equipment. The published reviews on the same topic lack a comprehensive and systematic introduction to μPAD classification and research progress in IVD segmentation. In this paper, we first briefly introduce the origin of μPADs and their role in promoting IVD, in the introduction section. Then, processing and detection methods for μPADs are summarized, and the innovative achievements of μPADs in IVD are reviewed. Finally, we discuss and prospect the upgrade and improvement directions of μPADs, in terms of portability, sensitivity, and automation, to help researchers clarify the progress and overcome the difficulties in subsequent μPAD research. [ABSTRACT FROM AUTHOR]

Visceral leishmaniasis remains a serious public health issue, and Brazil was among the seven countries with the highest prevalence of this disease worldwide. The measures to control this disease are not easily developed, and the improvement of its diagnosis, surveillance, and control is still needed. This study aimed to carry out the polymerase chain reaction (PCR) diagnosis of Leishmania infantum in vector samples in some municipalities of the State of São Paulo, which included two municipalities with human disease transmission and two with dog transmission only. Vectors were collected in traps with luminous bait. Next, they were killed at −4 °C and kept in 70% alcohol. Groups of ten female insects (pools) were mashed on cation exchange paper (fine cellulose phosphate with 18 µEq/cm² ionic exchange capacity) for DNA extraction. The PCR was carried out to identify the natural infection of the Leishmania genus in female Lutzomyia longipalpis (Lu. Longipalpis). Out of the 3,880 Lu. longipalpis phlebotomines, 1060 were female and 2820 were male (3:1). The method used to extract the DNA in pools of ten phlebotomines and the PCR resulted in sensitivity, specificity, practicality, and faster analyses when compared to the individual analysis method. The procedure described can be used on a large scale in the leishmaniasis epidemiological surveillance, enabling a higher number of analyses and the optimization of human resources because the traditional diagnostic method is carried out via desiccation of the insect digestive system and microscopic examination, which is time-demanding and there is the need of manual skills. [ABSTRACT FROM AUTHOR]

Guidelines and recommendations for the diagnosis and management of cow's milk allergy (CMA) in childhood are based on scientific review of the available evidence. While this approach is the most rigorous, guidelines may not fully address all scenarios encountered by clinicians. Many symptoms of CMA overlap with other common childhood illnesses and are subjectively reported by the caregivers of the infant, as is the interpretation of the dietary interventions. Additionally, many healthcare professionals and caregivers do not follow the recommendations to perform an oral food challenge or reintroduction of cow's milk after a diagnostic elimination diet because (1) the infant is doing well and (2) the carer's fear of symptoms relapsing with this procedure. As a result, CMA in infants may be either under-diagnosed leading to reduced quality of life for families or over-diagnosed, resulting in unnecessary long-term elimination diets and increasing the risk for nutritional deficiencies. This paper discusses some of these controversial topics, focusing on misdiagnosis and mismanagement in clinical practice. The lack of objective diagnostic criteria can hamper the diagnosis and management of CMA in daily practice. [ABSTRACT FROM AUTHOR]

Lateral flow assay (LFA) is emerging as one of the most popular paper-based biosensors in the field of the diagnostic industry. LFA fills all the gaps between diagnosis and treatment as it provides beneficial qualities to users such as quick response, Point-of-care appeal, early detection, low cost, and effective and sensitive detections of various infectious diseases. These benefits increase LFA's dependability for disease management because rapid and accurate disease diagnosis is a prerequisite for effective medication. Only 2% of overall healthcare expenditures, according to Roche Molecular Diagnostics, are spent on in vitro diagnostics, even though 60% of treatment choices are based on this data. To make LFA more innovative, futuristic plans have been outlined in many reports. Thus, this review reports on very knowledgeable literature discussing LFA and its development along with recent futuristic plans for LFA-based biosensors that cover all the novel features of the improvement of LFA. LFA might therefore pose a very significant economic success and have a significant influence on medical diagnosis. [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 present study is a scientometrics evaluation of refereed publications on bacterial mastitis in sheep; the objectives were the evaluation of the relevant papers and the presentation of quantitative characteristics regarding their scientific content and bibliometric details. The Web of Science platform was used with search terms: [mastitis OR *mammary infection*] AND [sheep OR ewe* OR ovine] for papers from 1970 tο 2019; only 'articles', 'reviews', 'proceedings papers', or 'data papers' were evaluated, whilst documents related solely to contagious agalactia, mammary aspects of lentiviral infections, or infections of the teats and the udder skin were excluded. Finally, 580 papers were considered in detail. The number of published papers increased from 8 during the 1970s to 273 during the 2010s. These papers originated from 43 countries (most from Greece or Spain, n = 87 from each) and 240 institutions (145 universities and 95 other establishments), of which 35 produced ≥ 5 papers each. Most papers present original studies (n = 539) with a few reviews (n = 41). The original papers refer to dairy (n = 428), meat (n = 113), or wool (n = 1) production systems and present field (n = 329), laboratory (n = 163), or experimental (n = 67) work; the papers report aetiology (n = 146), risk factors (n = 100), pathogenesis (n = 92), diagnosis (n = 88), effects (n = 66), treatment (n = 50), control (n = 36), or descriptive epidemiology (n = 32) of the disease. Papers related to dairy production present more field and fewer experimental work than papers related to meat production; also, in papers describing work performed in dairy sheep, studies about aetiology, risk factors, and diagnosis of the disease predominate, whilst in papers performed in meat sheep, studies about aetiology, pathogenesis, and effects/diagnosis are reported more often. The papers were published in 175 scientific journals (most in Small Ruminant Research, n = 90, or Journal of Dairy Science, n = 54). On average, the papers received 16.8 total citations and 1.6 yearly citations (h-index = 47). Most papers were published in Scimago classification Q1 (n = 240) or Q2 (n = 230) journals and received 23.4 or 15.4 total citations, respectively. Reviews received more citations than original papers; among the latter, papers with work referring to dairy production received more yearly citations than papers referring to meat production; no differences in citations were seen according to type of work or mastitis aspect covered. Most citations were received by papers from France. Papers published in Journal of Dairy Science or Small Ruminant Research received the most citations. In total, there were 1558 individual authors of the papers, with 24 authors having co-authored > 10 papers each (max: 73 papers); on average, there were 5.2 co-authors per paper (min–max: 1–25). Average number of co-authors progressively increased from 2.1 in the 1970s to 6.3 in the 2010s, with original papers having a higher number of co-authors than reviews: 5.3 and 3.7, respectively. Papers from France had highers number of co-authors (7.9). The findings of this first ever scientometrics study into ovine mastitis indicate that the disease has not been studied as other sheep diseases and that future studies in it should be directed to its control. [ABSTRACT FROM AUTHOR]

In recent years, microfluidic lab-on-paper devices have emerged as a rapid and low-cost alternative to traditional laboratory tests. Additionally, they were widely considered as a promising solution for point-of-care testing (POCT) at home or regions that lack medical infrastructure and resources. This review describes important advances in microfluidic lab-on-paper diagnostics for human health monitoring and disease diagnosis over the past five years. The review commenced by explaining the choice of paper, fabrication methods, and detection techniques to realize microfluidic lab-on-paper devices. Then, the sample pretreatment procedure used to improve the detection performance of lab-on-paper devices was introduced. Furthermore, an in-depth review of lab-on-paper devices for disease measurement based on an analysis of urine samples was presented. The review concludes with the potential challenges that the future development of commercial microfluidic lab-on-paper platforms for human disease detection would face. [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]

The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found. [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]

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]

We present, for the very first time, the fabrication and electrochemical characterization of a paper-based experimental platform for dengue virus analysis. The paper-based device incorporates a screen-printing technology with the help of black carbon conductive ink. The paper-based device utilizes two styles of electrode setups, i.e., the two-electrode system and three-electrode system, and both setups effectively detected the dengue virus with an LOD of 0.1 µg/mL; however, these paper electrodes exhibit various current ranges, and the created sensor was encompassed and compared in this research based on current response. It is observed that the three-electrode system has a substantially higher current range, ranging from 55.53 µA to 322.21 µA, as compared to the two-electrode system, which has a current range of 0.85 µA to 4.54 µA. According to this study, the three-electrode system displayed a good range of current amplification that is roughly 50 times higher than the two-electrode system, which had a weak current response. As a result, the three-electrode method has emerged as a viable option for the very sensitive detection of the dengue virus, as well as for the diagnosis of other diseases. [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]

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]

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]

In recent years, the usage of wearable systems in healthcare has gained much attention, as they can be easily worn by the subject and provide a continuous source of data required for the tracking and diagnosis of multiple kinds of abnormalities or diseases in the human body. Wearable systems can be made useful in improving a patient's quality of life and at the same time reducing the overall cost of caring for individuals including the elderly. In this survey paper, the recent research in the development of intelligent wearable systems for the diagnosis of peripheral neuropathy is discussed. The paper provides detailed information about recent techniques based on different wearable sensors for the diagnosis of peripheral neuropathy including experimental protocols, biomarkers, and other specifications and parameters such as the type of signals and data processing methods, locations of sensors, the scales and tests used in the study, and the scope of the study. It also highlights challenges that are still present in order to make wearable devices more effective in the diagnosis of peripheral neuropathy in clinical settings. [ABSTRACT FROM AUTHOR]

Neuropsychiatric disorders affect the lives of individuals from cognitive, emotional, and behavioral aspects, impact the quality of their lives, and even lead to death. Outside the medical area, these diseases have also started to be the subject of investigation in the field of Artificial Intelligence: especially Natural Language Processing (NLP) and Computer Vision. The usage of NLP techniques to understand medical symptoms eases the process of identifying and learning more about language-related aspects of neuropsychiatric conditions, leading to better diagnosis and treatment options. This survey shows the evolution of the detection of linguistic markers specific to a series of neuropsychiatric disorders and symptoms. For each disease or symptom, the article presents a medical description, specific linguistic markers, the results obtained using markers, and datasets. Furthermore, this paper offers a critical analysis of the work undertaken to date and suggests potential directions for future research in the field. [ABSTRACT FROM AUTHOR]

Simple Summary: Diagnosing glioblastoma multiforme (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) in malignant tumors of the central nervous system using multi-modal magnetic resonance imaging (MRI) is significantly important in helping physicians develop treatment plans and enhance patient prognosis. In this paper, MFFC-Net is developed and validated using deep learning methods to predict these three tumor categories from multi-modal MRI without the manual region of interest (ROI). MFFC-Net first uses a multi-encoder with DenseBlocks to extract deep features from multi-modal MRI. Then, the feature fusion layer fuses the deep information between different modalities and tissues. Finally, the spatial-channel attention module suppresses redundant new information and activates tumor classification-related features. Compared with radiomics models, MFFC-Net demonstrated higher accuracy. In addition, the results in the different sequences provide important references for future clinical work on MRI image acquisition. We believe that MFFC-Net has the potential to assist in the diagnosis and treatment of brain tumors in the future. (1) Background: Diagnosis of glioblastoma (GBM), solitary brain metastases (SBM), and primary central nervous system lymphoma (PCNSL) plays a decisive role in the development of personalized treatment plans. Constructing a deep learning classification network to diagnose GBM, SBM, and PCNSL with multi-modal MRI is important and necessary. (2) Subjects: GBM, SBM, and PCNSL were confirmed by histopathology with the multi-modal MRI examination (study from 1225 subjects, average age 53 years, 671 males), 3.0 T T2 fluid-attenuated inversion recovery (T2-Flair), and Contrast-enhanced T1-weighted imaging (CE-T1WI). (3) Methods: This paper introduces MFFC-Net, a classification model based on the fusion of multi-modal MRIs, for the classification of GBM, SBM, and PCNSL. The network architecture consists of parallel encoders using DenseBlocks to extract features from different modalities of MRI images. Subsequently, an L 1 − n o r m feature fusion module is applied to enhance the interrelationships among tumor tissues. Then, a spatial-channel self-attention weighting operation is performed after the feature fusion. Finally, the classification results are obtained using the full convolutional layer (FC) and Soft-max. (4) Results: The ACC of MFFC-Net based on feature fusion was 0.920, better than the radiomics model (ACC of 0.829). There was no significant difference in the ACC compared to the expert radiologist (0.920 vs. 0.924, p = 0.774). (5) Conclusions: Our MFFC-Net model could distinguish GBM, SBM, and PCNSL preoperatively based on multi-modal MRI, with a higher performance than the radiomics model and was comparable to radiologists. [ABSTRACT FROM AUTHOR]

Background: Post-thrombotic syndrome (PTS) is a common and debilitating sequela of lower limb deep vein thrombosis (DVT). There is significant heterogeneity in reported PTS incidence due to lack of standardised diagnostic criteria. This review aimed to develop diagnostic criteria for PTS and subsequently refine the reported incidence and severity. Methods: PRISMA principles were followed; however, the review was not registered. The Cochrane CENTRAL database, MEDLINE, Embase, the NHS NICE Healthcare Databases Advanced Search interface, and trial registers including isrctn.com and clinicaltrials.gov were searched for studies addressing areas of interest (PTS definition, epidemiology, assessment). An experienced Clinical Librarian undertook the systematic searches, and two independent reviewers agreed on the relevance of the papers. Conflicts were resolved through panel review. Evidence quality was assessed using a modified Coleman scoring system and weighted according to their relevance to the aforementioned areas of interest. Results: A total of 339 abstracts were retrieved. A total of 33 full-text papers were included in this review. Following qualitative analysis, four criteria were proposed to define PTS: (1) a proven thrombotic event on radiological assessment; (2) a minimum 24-month follow-up period after an index DVT; (3) assessment with a validated score; and (4) evidence of progression of venous insufficiency from baseline. Four papers conformed to our PTS definition criteria, and the incidence of mild to moderate PTS ranged from 7 to 36%. On reviewing the studies which utilised the recommended Villalta scale, PTS incidence narrowed further to 23–36%. Incidence and severity reached a plateau at 24 months. Conclusions: Four diagnostic criteria were developed from qualitative synthesis. When these criteria were applied to the literature, the range of reported PTS incidence narrowed. These four criteria may standardise PTS diagnosis in future studies, facilitating the pooling of data for meta-analysis and synthesis of higher levels of evidence. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics. [ABSTRACT FROM AUTHOR]

This paper proposes a modular modeling towards a health system integration of fuel cells by considering not only the dynamics of the gases but also fault models that affect the PEMFC performances. The main goal is to simulate the faulty state in order to overcome data scarcity, since running a fuel cell to generate a database under faulty conditions is a costly process in time and resources. The degradation processes detailed in this paper allow to introduce a classification of faults that can occur, giving a better understanding of the performance losses necessary to simulate them. The faults that are detailed and modeled are the flooding, drying and aging processes. This modeling is based on a system approach, so it runs faster than real-time degradation tests, allowing the training and validation of online supervisors, such as the energy management strategy (EMS) method or diagnosis. The faults are reproduced according to the study requirements to be a very effective support tool to help design engineers to include faulty conditions in early design stages toward a diagnosis process and health-conscious energy management strategies. [ABSTRACT FROM AUTHOR]

Microfluidic chips have the advantages of miniaturization, integration, and portability, and are widely used in the early diagnosis of major diseases, personalized medical treatment, environmental detection, health quarantine, and other fields. The existing microfluidic chip manufacturing process is difficult to operate because of complex three-dimensional channels, complicated manufacturing steps, limited printing materials, the difficulty of operating the bonding process, and the need to purchase expensive new equipment. In this paper, an integrated molding method for microfluidic chips that integrates 3D printing and polymer dissolution technology is proposed. First, the channel mold of poly(vinyl alcohol) (PVA) or high impact polystyrene (HIPS) is dissolved to complete the manufacturing of the microfluidic chip channel. The integrated 3D-forming method of microfluidic chips proposed in this paper can manufacture microchannels inside the microfluidic chip, avoid the bonding process, and eliminate the need for rapid alignment of microchannels, material modification, and other operations, thus improving the stability of the process. Finally, by comparing the microchannels made by PVA and HIPS, it is concluded that the quality of the microchannels made by HIPS is obviously better than that made by PVA. This paper provides a new idea for the fabrication of microfluidic chips and the application of HIPS. [ABSTRACT FROM AUTHOR]

This paper describes a hardware/software strategy for the effective and efficient management of several distributed Memory Built-In Self-Test (MBIST) units orchestrated by a single CPU to enable the parallel testing of several memory banks. Experimental testing of the implementation on an Infineon chip shows up to a 25% test time reduction compared to traditional strategies, especially in cases for which there are a large number of failures affecting several banks. Additionally, it permits balanced failure collection from different banks in cases for which there are limitations to the storage of failure-related information. [ABSTRACT FROM AUTHOR]

Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike. [ABSTRACT FROM AUTHOR]

Complex organisations require coherence to achieve adaptive goals through agency. This paper introduces Mindset Agency Theory (MAT), a metatheoretical framework designed for modelling and diagnosing agency within culturally diverse populations. MAT, a cybernetic multi-ontology framework, delineates five formative traits defining agency character. Its cognitive style trait (with bipolar values of Patterning–Dramatising) elucidates how agencies acquire information. Examining diverse agencies requires an appreciation of the social relationships that exist there, but MAT is currently devoid of this capability. Using the configuration approach to enable the integration of Tönnies' social organisation theory into MAT, social relationships can be suitably explored, thus enhancing its capacity to investigate agency coherence. Tönnies' theory of social organisation (with bipolar values of Gemeinschaft-Gesellschaft) that frames inter-agent interactions is configured within MAT. This integration births a new formative trait, pairing cognitive style with social organisation, and is thus capable of indicating the likelihood of operative coherence. Configuration is applied by relating propositional attributes of a holding metatheory framework such as MAT, with an entry theory such as Tönnies' social organisation theory as determined from the literature. The elaborated MAT serves as a diagnostic tool, linking trait instabilities with agency pathologies that deliver dysfunction. A subsequent paper will apply this framework to ASEAN, a regional intergovernmental organisation addressing cultural diversity issues. The study aims to evaluate ASEAN's mindset and diagnose its pathologies, such as narcissism and paradoxical behaviour. [ABSTRACT FROM AUTHOR]

High-grade serous ovarian cancer (HGSOC) is the most lethal tumor of the female genital tract. Despite extensive studies and the identification of some precursor lesions like serous tubal intraepithelial cancer (STIC) or the deviated mutational status of the patients (BRCA germinal mutation), the pathophysiology of HGSOC and the existence of particular risk factors is still a puzzle. Moreover, a lack of screening programs results in delayed diagnosis, which is accompanied by a secondary chemo-resistance of the tumor and usually results in a high recurrence rate after the primary therapy. Therefore, there is an urgent need to identify the substantial risk factors for both predisposed and low-risk populations of women, as well as to create an economically and clinically justified screening program. This paper reviews the classic and novel risk factors for HGSOC and methods of diagnosis and prediction, including serum biomarkers, the liquid biopsy of circulating tumor cells or circulating tumor DNA, epigenetic markers, exosomes, and genomic and proteomic biomarkers. The novel future complex approach to ovarian cancer diagnosis should be devised based on these findings, and the general outcome of such an approach is proposed and discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount for implementing preventive measures and timely interventions. The World Health Organization (WHO) reports that cardiovascular diseases, responsible for an alarming 17.9 million annual fatalities, constitute a significant 31% of the global mortality rate. The intricate clinical landscape, characterized by inherent variability and a complex interplay of factors, poses challenges for accurately diagnosing the severity of cardiac conditions and predicting their progression. Consequently, early identification emerges as a pivotal factor in the successful treatment of heart-related ailments. This research presents a comprehensive framework for the prediction of cardiovascular diseases, leveraging advanced boosting techniques and machine learning methodologies, including Cat boost, Random Forest, Gradient boosting, Light GBM, and Ada boost. Focusing on "Early Heart Disease Prediction using Boosting Techniques", this paper aims to contribute to the development of robust models capable of reliably forecasting cardiovascular health risks. Model performance is rigorously assessed using a substantial dataset on heart illnesses from the UCI machine learning library. With 26 feature-based numerical and categorical variables, this dataset encompasses 8763 samples collected globally. The empirical findings highlight AdaBoost as the preeminent performer, achieving a notable accuracy of 95% and excelling in metrics such as negative predicted value (0.83), false positive rate (0.04), false negative rate (0.04), and false development rate (0.01). These results underscore AdaBoost's superiority in predictive accuracy and overall performance compared to alternative algorithms, contributing valuable insights to the field of cardiovascular health prediction. [ABSTRACT FROM AUTHOR]

Simple Summary: Severe equine asthma (SEA) is an important respiratory disease affecting a large number of horses worldwide. This chronic disease induces cough and respiratory distress compromising the animals' athletic ability and welfare. A wide variety of diagnostic tests have been described for researching and diagnosing SEA, but not all are commonly available outside of large research or specialized diagnostic centers. Moreover, in routine ambulatory practice the process of decision making is not always easy, especially in more complex medical cases, but its importance is pivotal for individualized patient care. Thus, the aim of this paper is to develop a flow-chart to assist equine practitioners in the process of decision making associated with diagnosing and monitoring SEA. Decision making consists of gathering quality data in order to correctly assess a situation and determine the best course of action. This process is a fundamental part of medicine and is what enables practitioners to accurately diagnose diseases and select appropriate treatment protocols. Despite severe equine asthma (SEA) being a highly prevalent lower respiratory disease amongst equids, clinicians still struggle with the optimization of routine diagnostic procedures. The use of several ancillary diagnostic tests has been reported for disease identification and monitoring, but many are only suitable for research purposes or lack practicality for everyday use. The aim of this paper is to assist the equine veterinarian in the process of decision making associated with managing SEA-affected patients. This review will focus on disease diagnosis and monitoring, while also presenting a flow-chart which includes the basic data that the clinician must obtain in order to accurately identify severely asthmatic horses in their everyday routine practice. It is important to note that European and American board-certified specialists on equine internal medicine can provide assistance in the diagnosis and treatment plan of SEA-affected horses. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, after conducting a comprehensive review of 1356 papers that evaluated the diagnostic performance of deep learning (DL) methods based on medical images for hepatocellular carcinoma (HCC), the findings showed a pooled sensitivity of 89% (95% CI: 87–91), a specificity of 90% (95% CI: 87–92), and an AUC of 0.95 (95% CI: 0.93–0.97). In addition, both the DL methods and human clinicians demonstrated similar levels of performance in HCC detection, with receiver operating characteristic curve (ROC) values of 0.97 (95% CI: 0.95–0.98) for both groups, indicating no discernible difference. Although the heterogeneity was obvious, the utilization of DL methods for diagnosing HCC through medical images has shown promising outcomes. (1) Background: The aim of our research was to systematically review papers specifically focused on the hepatocellular carcinoma (HCC) diagnostic performance of DL methods based on medical images. (2) Materials: To identify related studies, a comprehensive search was conducted in prominent databases, including Embase, IEEE, PubMed, Web of Science, and the Cochrane Library. The search was limited to studies published before 3 July 2023. The inclusion criteria consisted of studies that either developed or utilized DL methods to diagnose HCC using medical images. To extract data, binary information on diagnostic accuracy was collected to determine the outcomes of interest, namely, the sensitivity, specificity, and area under the curve (AUC). (3) Results: Among the forty-eight initially identified eligible studies, thirty studies were included in the meta-analysis. The pooled sensitivity was 89% (95% CI: 87–91), the specificity was 90% (95% CI: 87–92), and the AUC was 0.95 (95% CI: 0.93–0.97). Analyses of subgroups based on medical image methods (contrast-enhanced and non-contrast-enhanced images), imaging modalities (ultrasound, magnetic resonance imaging, and computed tomography), and comparisons between DL methods and clinicians consistently showed the acceptable diagnostic performance of DL models. The publication bias and high heterogeneity observed between studies and subgroups can potentially result in an overestimation of the diagnostic accuracy of DL methods in medical imaging. (4) Conclusions: To improve future studies, it would be advantageous to establish more rigorous reporting standards that specifically address the challenges associated with DL research in this particular field. [ABSTRACT FROM AUTHOR]

Handwriting is a complex perceptual motor task that requires years of training and practice before complete mastery. Its acquisition is crucial, since handwriting is the basis, together with reading, of the acquisition of higher-level skills such as spelling, grammar, syntax, and text composition. Despite the correct learning and practice of handwriting, some children never master this skill to a sufficient level. These handwriting deficits, referred to as developmental dysgraphia, can seriously impact the acquisition of other skills and thus the academic success of the child if they are not diagnosed and handled early. In this review, we present a non-exhaustive listing of the tools that are the most reported in the literature for the analysis of handwriting and the diagnosis of dysgraphia. A variety of tools focusing on either the final handwriting product or the handwriting process are described here. On one hand, paper-and-pen tools are widely used throughout the world to assess handwriting quality and/or speed, but no universal gold-standard diagnostic test exists. On the other hand, several very promising computerized tools for the diagnosis of dysgraphia have been developed in the last decade, but some improvements are required before they can be available to clinicians. Based on these observations, we will discuss the pros and cons of the existing tools and the perspectives related to the development of a universal, standardized test of dysgraphia combining both paper-and-pen and computerized approaches and including different graphomotor and writing tasks. [ABSTRACT FROM AUTHOR]

This article aims to assess, discuss and analyze the disturbances caused by electromagnetic field (EMF) noise of medical devices used near living tissues, as well as the corresponding functional control via the electromagnetic compatibility (EMC) of these devices. These are minimally invasive and non-ionizing devices allowing various healthcare actions involving monitoring, assistance, diagnoses and image-guided medical interventions. Following an introduction of the main items of the paper, the different imaging methodologies are conferred, accounting for their nature, functioning, employment condition and patient comfort and safety. Then the magnetic resonance imaging (MRI) components and their fields, the consequential MRI-compatibility concept and possible image artifacts are detailed and analyzed. Next, the MRI-assisted robotic treatments, the possible robotic external matter introductions in the MRI scaffold, the features of MRI-compatible materials and the conformity control of such compatibility are analyzed and conferred. Afterward, the embedded, wearable and detachable medical devices, their EMF perturbation control and their necessary protection via shielding technologies are presented and analyzed. Then, the EMC control procedure, the EMF governing equations and the body numerical virtual models are conferred and reviewed. A qualitative methodology, case study and simple example illustrating the mentioned methodology are presented. The last section of the paper discusses potential details and expansions of the different notions conferred in the paper, in the perspective of monitoring the disturbances due to EMF noise of medical devices working near living tissues. This contribution highlights the possibility of the proper functioning of medical instruments working close to the patient's body tissues and their protection by monitoring possible disturbances. Thanks to these commitments, various health recommendations have been taken into account. This concerns piezoelectric actuated robotics, assisted with MRI and the possible use of conductive materials in this imager under certain conditions. The safe use of onboard devices with EMF-insensitive or intelligently shielded materials with short exposure intervals is also of concern. Additionally, the need to monitor body temperature in case of prolonged exposure of onboard devices to EMF is analyzed in the Discussion section. Moreover, the use of virtual tissue models in EMC testing to achieve more realistic evaluation capabilities also features in the Discussion section. [ABSTRACT FROM AUTHOR]

Machine learning, powered by cloud servers, has found application in medical diagnosis, enhancing the capabilities of smart healthcare services. Research literature demonstrates that the support vector machine (SVM) consistently demonstrates remarkable accuracy in medical diagnosis. Nonetheless, safeguarding patients' health data privacy and preserving the intellectual property of diagnosis models is of paramount importance. This concern arises from the common practice of outsourcing these models to third-party cloud servers that may not be entirely trustworthy. Few studies in the literature have delved into addressing these issues within SVM-based diagnosis systems. These studies, however, typically demand substantial communication and computational resources and may fail to conceal classification results and protect model intellectual property. This paper aims to tackle these limitations within a multi-class SVM medical diagnosis system. To achieve this, we have introduced modifications to an inner product encryption cryptosystem and incorporated it into our medical diagnosis framework. Notably, our cryptosystem proves to be more efficient than the Paillier and multi-party computation cryptography methods employed in previous research. Although we focus on a medical application in this paper, our approach can also be used for other applications that need the evaluation of machine learning models in a privacy-preserving way such as electricity theft detection in the smart grid, electric vehicle charging coordination, and vehicular social networks. To assess the performance and security of our approach, we conducted comprehensive analyses and experiments. Our findings demonstrate that our proposed method successfully fulfills our security and privacy objectives while maintaining high classification accuracy and minimizing communication and computational overhead. [ABSTRACT FROM AUTHOR]

An automatic medical diagnosis service based on deep learning has been introduced in e-healthcare, bringing great convenience to human life. However, due to privacy regulations, insufficient data sharing among medical centers has led to many severe challenges for automated medical diagnostic services, including diagnostic accuracy. To solve such problems, swarm learning (SL), a blockchain-based federated learning (BCFL), has been proposed. Although SL avoids single-point-of-failure attacks and offers an incentive mechanism, it still faces privacy breaches and poisoning attacks. In this paper, we propose a new privacy-preserving Byzantine-resilient swarm learning (PBSL) that is resistant to poisoning attacks while protecting data privacy. Specifically, we adopt threshold fully homomorphic encryption (TFHE) to protect data privacy and provide secure aggregation. And the cosine similarity is used to judge the malicious gradient uploaded by malicious medical centers. Through security analysis, PBSL is able to defend against a variety of known security attacks. Finally, PBSL is implemented by uniting deep learning with blockchain-based smart contract platforms. Experiments based on different datasets show that the PBSL algorithm is practical and efficient. [ABSTRACT FROM AUTHOR]

Background: Spinal cord compression is a formidable complication of advanced cancer, and clinicians of copious specialities often have to encounter significant complex challenges in terms of diagnosis, management, and prognosis. Metastatic lesions from cancer are a common cause of spinal cord compression, affecting a substantial portion of oncology patients, and only in the US has the percentage risen to 10%. Acute metastasis-correlated spinal cord compression poses a considerable clinical challenge, necessitating timely diagnosis and intervention to prevent neurological deficits. Clinical presentation is often non-specific, emphasizing the importance of thorough evaluation and appropriate differential diagnosis. Diagnostic workup involves various imaging modalities and laboratory studies to confirm the diagnosis and assess the extent of compression. Treatment strategies focus on pain management and preserving spinal cord function without significantly increasing patient life expectancy, while multidisciplinary approaches are often required for optimal outcomes. Prognosis depends on several factors, highlighting the importance of early intervention. We provide an up-to-date overview of acute spinal cord compression in metastases, accentuating the importance of comprehensive management strategies. Objectives: This paper extensively explores the pathophysiology, clinical presentation, diagnostic strategies, treatment modalities, and prognosis associated with spinal cord metastases. Materials and Methods: A systematic literature review was conducted in accordance with the PRISMA guidelines. Conclusions: We aim to help healthcare professionals make informed clinical decisions when treating patients with spinal cord metastases by synthesizing current evidence and clinical insights. [ABSTRACT FROM AUTHOR]

Cancer is a significant global public health issue with increasing morbidity and mortality rates. To address this challenge, novel drug carriers such as nano-materials, liposomes, hydrogels, fibers, and microspheres have been extensively researched and utilized in oncology. Among them, polymer microspheres are gaining popularity due to their ease of preparation, excellent performance, biocompatibility, and drug-release capabilities. This paper categorizes commonly used materials for polymer microsphere preparation, summarizes various preparation methods (emulsification, phase separation, spray drying, electrospray, microfluidics, and membrane emulsification), and reviews the applications of polymer microspheres in cancer diagnosis, therapy, and postoperative care. The current status and future development directions of polymer microspheres in cancer treatment are analyzed, highlighting their importance and potential for improving patient outcomes. [ABSTRACT FROM AUTHOR]

Beyond the challenges of diagnosis, complexity measurement in clients with mental illness is an important but under-recognized area. Accurate and appropriate psychiatric diagnoses are essential, and further complexity measurements could contribute to improving patient understanding, referral, and service matching and coordination, outcome evaluation, and system-level care planning. Myriad conceptualizations, frameworks, and definitions of patient complexity exist, which are operationalized by a variety of complexity measuring tools. A limited number of these tools are developed for people with mental illness, and they differ in the extent to which they capture clinical, psychosocial, economic, and environmental domains. Guided by the PRISMA Extension for Scoping Reviews, this review evaluates the tools best suited for different mental health settings. The search found 5345 articles published until November 2023 and screened 14 qualified papers and corresponding tools. For each of these, detailed data on their use of psychiatric diagnostic categories, definition of complexity, primary aim and purpose, context of use and settings for their validation, best target populations, historical references, extent of biopsychosocial information inclusion, database and input technology required, and performance assessments were extracted, analyzed, and presented for comparisons. Two tools—the INTERMED, a clinician-scored and multiple healthcare data-sourced tool, and the VCAT, a computer-based instrument that utilizes healthcare databases to generate a comprehensive picture of complexity—are exemplary among the tools reviewed. Information on these limited but suitable tools related to their unique characteristics and utilities, and specialized recommendations for their use in mental health settings could contribute to improved patient care. [ABSTRACT FROM AUTHOR]

Autoimmune pancreatitis (AIP) is a unique form of chronic pancreatitis with a multifactorial pathogenesis. Historically, it has been classified as type 1 and type 2, according to its clinical and histological features. The diagnosis of AIP is challenging and relies on a combination of clinical, histopathologic, serologic, and imaging characteristics. In the available guidelines, the imaging hallmarks of AIP are based on cross-sectional imaging and cholangiopancreatography retrograde endoscopic findings. Endoscopic ultrasound (EUS) is generally used for pancreatic tissue acquisition to rule out pancreatic cancer and diagnose AIP with limited accuracy. Several papers reported the reliability of EUS for providing informative morphologic features of AIP. Nowadays, the improvement in the resolution of EUS conventional images and the development of new ancillary technologies have further increased the diagnostic yield of EUS: contrast-enhanced EUS and EUS elastography are non-invasive and real-time techniques that strongly support the diagnosis and management of pancreatic diseases. In this review article, we will present the role of conventional EUS and ancillary diagnostic techniques in the diagnosis of AIP to support clinicians and endosonographers in managing this condition. [ABSTRACT FROM AUTHOR]

Many ailments can be diagnosed while they are asymptomatic, meaning that the patient has no signs or symptoms of a progressing disease. If caught in their initial stage of formation, these disorders can be effectively treated, leading to successful outcomes; curative therapies can halt illnesses from advancing, thus improving the quality of life and long-term survival of the patient. Still, cutting-edge upgrades in precision technologies are necessary for early, reliable, affordable, and rapid disease detection, but also vital for the well-being of people and the future of global public health. The emerging role and utility of non-invasive and repeatable diagnostic test approaches for the detection of health conditions have been exemplified by liquid biopsies based on genomic biomarkers. As such, biological fluids permit any measurable molecular indicator or signature (e.g., proteins) to provide valuable information on an individual's wellness and/or disease. Among the bodily secretions used for non-invasive diagnostics is sputum, a complex viscous gel-like biopolymeric network that has gained growing recognition as a rich source of biomarkers of airway infections and pulmonary diseases, and serves as a determinant to reveal other illnesses. As per the World Health Organization, the burden of respiratory conditions is exacerbated by factors ranging from considerable subjection to air pollution and occupational contaminants to tobacco smoking and second-hand smoke, in addition to poor socio-economic status. Due to the likely increase in these determinants, respiratory tract ailments are on the rise, affecting the health of many individuals, in addition to putting stress on healthcare facilities and services worldwide. The aim of this study was to perform a narrative review of sputum constituents with an emphasis on proteins and glycoproteins assessed as possible biomarkers of lung and other organ diseases. A search was conducted using mucus, sputum proteomics, sputum biomarkers, and point-of-care testing as keywords employing Google, PubMed (MEDLINE), and Web of Science, selecting the most referenced and related papers of the last decade. We, therefore, highlight the need to use expectorated or induced sputum specimens as a routine sample source for testing valuable protein biomarkers to diagnose these chronic disorders, predict inflammation and disease progression, as well as monitor the effectiveness of treatments. Further, we discuss the urgent need for fast and reliable point-of-care methods to detect and quantify crucial protein biomarkers in sputum specimens, and the limitations faced when dealing with their complex matrices. [ABSTRACT FROM AUTHOR]

Adaptive optics (AO) has been used in many applications, including astronomy, microscopy, and medical imaging. In retinal imaging, AO provides real-time correction of the aberrations introduced by the cornea and the lens to facilitate diffraction-limited imaging of retinal microstructures. Most importantly, AO-based retinal imagers provide cellular-level resolution and quantification of changes induced by retinal diseases and systemic diseases that manifest in the eye enabling disease diagnosis and monitoring of disease progression or the efficacy of treatments. In this paper, we present an overview of our team efforts over almost two decades to develop high-resolution retinal imagers suitable for clinical use. Several different types of imagers for human and small animal eye imaging are reviewed, and representative results from multiple studies using these instruments are shown. These examples demonstrate the extraordinary power of AO-based retinal imaging to reveal intricate details of morphological and functional characteristics of the retina and to help elucidate important aspects of vision and of the disruptions that affect delicate retinal tissue. [ABSTRACT FROM AUTHOR]

Throughout the central nervous system, the spinal cord plays a very important role, namely, transmitting sensory and motor information inwardly so that it can be processed by the brain. There are many different ways this structure can be damaged, such as through traumatic injury or surgery, such as scoliosis correction, for instance. Consequently, damage may be caused to the nervous system as a result of this. There is no doubt that optical devices such as microscopes and cameras can have a significant impact on research, diagnosis, and treatment planning for patients with spinal cord injuries (SCIs). Additionally, these technologies contribute a great deal to our understanding of these injuries, and they are also essential in enhancing the quality of life of individuals with spinal cord injuries. Through increasingly powerful, accurate, and minimally invasive technologies that have been developed over the last decade or so, several new optical devices have been introduced that are capable of improving the accuracy of SCI diagnosis and treatment and promoting a better quality of life after surgery. We aim in this paper to present a timely overview of the various research fields that have been conducted on optical devices that can be used to diagnose spinal cord injuries as well as to manage the associated health complications that affected individuals may experience. [ABSTRACT FROM AUTHOR]

Pulmonologists may be involved in managing pulmonary diseases in children with complex clinical pictures without a diagnosis. Moreover, they are routinely involved in the multidisciplinary care of children with rare diseases, at baseline and during follow-up, for lung function monitoring. Lysosomal storage diseases (LSDs) are a group of genetic diseases characterised by a specific lysosomal enzyme deficiency. Despite varying pathogen and organ involvement, they are linked by the pathological accumulation of exceeding substrates, leading to cellular toxicity and subsequent organ damage. Less severe forms of LSDs can manifest during childhood or later in life, sometimes being underdiagnosed. Respiratory impairment may stem from different pathogenetic mechanisms, depending on substrate storage in bones, with skeletal deformity and restrictive pattern, in bronchi, with obstructive pattern, in lung interstitium, with altered alveolar gas exchange, and in muscles, with hypotonia. This narrative review aims to outline different pulmonary clinical findings and a diagnostic approach based on key elements for differential diagnosis in some treatable LSDs like Gaucher disease, Acid Sphingomyelinase deficiency, Pompe disease and Mucopolysaccharidosis. Alongside their respiratory clinical aspects, which might overlap, we will describe radiological findings, lung functional patterns and associated symptoms to guide pediatric pulmonologists in differential diagnosis. The second part of the paper will address follow-up and management specifics. Recent evidence suggests that new therapeutic strategies play a substantial role in preventing lung involvement in early-treated patients and enhancing lung function and radiological signs in others. Timely diagnosis, driven by clinical suspicion and diagnostic workup, can help in treating LSDs effectively. [ABSTRACT FROM AUTHOR]

Simple Summary: The diagnostic value of exosomes has been well recognized by researchers. The use of exosomal biomarkers as an adjunct diagnosis method not only improves diagnostic accuracy but can also be used for early diagnosis and disease progression differentiation, thus supporting personalized clinical treatment strategies for patients with neuropsychiatric disorders. In this paper, we summarize potential exosomal biomarkers in the diagnosis of neuropsychiatric diseases. Exosomes are 30–150 nm small extracellular vesicles (sEVs) which are highly stable and encapsulated by a phospholipid bilayer. Exosomes contain proteins, lipids, RNAs (mRNAs, microRNAs/miRNAs, long non-coding RNAs/lncRNAs), and DNA of their parent cell. In pathological conditions, the composition of exosomes is altered, making exosomes a potential source of biomarkers for disease diagnosis. Exosomes can cross the blood–brain barrier (BBB), which is an advantage for using exosomes in the diagnosis of central nervous system (CNS) diseases. Neuropsychiatric diseases belong to the CNS diseases, and many potential diagnostic markers have been identified for neuropsychiatric diseases. Here, we review the potential diagnostic markers of exosomes in neuropsychiatric diseases and discuss the potential application of exosomal biomarkers in the early and accurate diagnosis of these diseases. Additionally, we outline the limitations and future directions of exosomes in the diagnosis of neuropsychiatric diseases. [ABSTRACT FROM AUTHOR]

In recent years, the use of smartphones and other wireless technology in medical care has developed rapidly. However, in some cases, especially for pediatric medical problems, the reliability of information accessed by mobile health technology remains debatable. The main aim of this paper is to evaluate the relevance of smartphone applications in the detection and diagnosis of pediatric medical conditions for which the greatest number of applications have been developed. This is the case of smartphone applications developed for the diagnosis of acute otitis media, otitis media with effusion, hearing impairment, obesity, amblyopia, and vision screening. In some cases, the information given by these applications has significantly improved the diagnostic ability of physicians. However, distinguishing between applications that can be effective and those that may lead to mistakes can be very difficult. This highlights the importance of a careful application selection before including smartphone-based artificial intelligence in everyday clinical practice. [ABSTRACT FROM AUTHOR]

This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of "network structure" and "loss function", and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of "nnUNet" network structure and "Cross-Entropy + Iou + Boundary" hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services. [ABSTRACT FROM AUTHOR]

Bayesian networks (BNs) are probabilistic graphical models that leverage Bayes' theorem to portray dependencies and cause-and-effect relationships between variables. These networks have gained prominence in the field of health sciences, particularly in diagnostic processes, by allowing the integration of medical knowledge into models and addressing uncertainty in a probabilistic manner. Objectives: This review aims to provide an exhaustive overview of the current state of Bayesian networks in disease diagnosis and prognosis. Additionally, it seeks to introduce readers to the fundamental methodology of BNs, emphasising their versatility and applicability across varied medical domains. Employing a meticulous search strategy with MeSH descriptors in diverse scientific databases, we identified 190 relevant references. These were subjected to a rigorous analysis, resulting in the retention of 60 papers for in-depth review. The robustness of our approach minimised the risk of selection bias. Results: The selected studies encompass a wide range of medical areas, providing insights into the statistical methodology, implementation feasibility, and predictive accuracy of BNs, as evidenced by an average area under the curve (AUC) exceeding 75%. The comprehensive analysis underscores the adaptability and efficacy of Bayesian networks in diverse clinical scenarios. The majority of the examined studies demonstrate the potential of BNs as reliable adjuncts to clinical decision-making. The findings of this review affirm the role of Bayesian networks as accessible and versatile artificial intelligence tools in healthcare. They offer a viable solution to address complex medical challenges, facilitating timely and informed decision-making under conditions of uncertainty. The extensive exploration of Bayesian networks presented in this review highlights their significance and growing impact in the realm of disease diagnosis and prognosis. It underscores the need for further research and development to optimise their capabilities and broaden their applicability in addressing diverse and intricate healthcare challenges. [ABSTRACT FROM AUTHOR]