Search

The rarity and heterogeneity of liposarcomas (LPS) pose significant challenges in their diagnosis and management. In this work, a series of metal–organic frameworks (MOFs) engineering is designed and implemented. Through comprehensive characterization and performance evaluations, such as stability, thermal‐driven desorption efficiency, as well as energy‐ and charge‐transfer capacity, the engineering of group IV bimetallic MOFs emerges as particularly noteworthy. This is especially true for their derivative products, which exhibit superior performance across a range of laser desorption/ionization mass spectrometry (LDI MS) performance tests, including those involving practical sample assessments. The top‐performing product is utilized to enable high‐throughput recording of LPS metabolic fingerprints (PMFs) within seconds using LDI MS. With machine learning on PMFs, both the LPSrecognizer and LPSclassifier are developed, achieving accurate recognition and classification of LPS with area under the curves (AUCs) of 0.900–1.000. Simplified versions are also developed of the LPSrecognizer and LPSclassifier by screening metabolic biomarker panels, achieving considerable predictive performance, and conducting basic pathway exploration. The work highlights the MOFs engineering for the matrix design and their potential application in developing metabolic analysis and screening tools for rare diseases in clinical settings. [ABSTRACT FROM AUTHOR]

Recent advances in wireless networking, big data technologies, namely Internet of Things (IoT) 5G networks, health care big data analytics, and other technologies in artificial intelligence (AI) and wearables, have supported the progression of intellectual disease diagnosis methods. Medical data covers all patient data such as pharmacy texts, electronic health reports (EHR), prescriptions, study data from medical journals, clinical photographs, and diagnostic reports. Big data is a renowned method in the healthcare sector, with beneficial datasets that are highly difficult, voluminous, and rapid for healthcare providers for interpreting and computing using prevailing tools. This study combines concepts like deep learning (DL) and big data analytics in medical field. This article develops a new healthcare data analytics using optimal machine learning model in big data environment (HDAOML‐BDE) technique. The presented HDAOML‐BDE technique mainly aims to examine the healthcare data for disease detection and classification in the big data environment. For handling big data, the HDAOML‐BDE technique uses Hadoop MapReduce environment. In addition, the HDAOML‐BDE technique uses manta ray foraging optimization‐based feature selection (MRFO‐FS) technique to reduce high dimensionality problems. Moreover, the HDAOML‐BDE method uses relevance vector machine (RVM) model for the healthcare data environment. Furthermore, the arithmetic optimization algorithm (AOA) is utilized for the parameter tuning of the RVM classifier. The simulation results of the HDAOML‐BDE technique are tested on a healthcare dataset, and the outcomes portray the improved performance of the HDAOML‐BDE strategy over recent approaches in different measures. [ABSTRACT FROM AUTHOR]

In the era of rapidly advancing medical technologies, the segmentation of medical data has become inevitable, necessitating the development of privacy preserving machine learning algorithms that can train on distributed data. Consolidating sensitive medical data is not always an option particularly due to the stringent privacy regulations imposed by the Health Insurance Portability and Accountability Act. In this paper, I introduce a HIPAA compliant framework that can train from distributed data. I then propose a multimodal vertical federated model for Alzheimer's disease detection, a serious neurodegenerative condition that can cause dementia, severely impairing brain function and hindering simple tasks, especially without preventative care. This vertical federated learning model offers a distributed architecture that enables collaborative learning across diverse sources of medical data while respecting privacy constraints imposed by HIPAA. The VFL architecture proposed herein offers a novel distributed architecture, enabling collaborative learning across diverse sources of medical data while respecting statutory privacy constraints. By leveraging multiple modalities of data, the robustness and accuracy of AD detection can be enhanced. This model not only contributes to the advancement of federated learning techniques but also holds promise for overcoming the hurdles posed by data segmentation in medical research. [ABSTRACT FROM AUTHOR]

Healthcare data analysis has emerged as one of the most promising fields of study in recent years. There are different types of data in the healthcare industry, such as medical test results, blood reports, medical reports, X-rays, CT, MRI, ultrasound, clinical data, omics data, and sensor data. One of the most important and useful techniques for analysing this complicated medical data is machine learning (ML). ML is proving to be a useful artificial intelligence (AI) technique for data analysis. To accurately predict the outcomes of healthcare data, ML employs a variety of statistical techniques and cutting-edge algorithms. In recent years, different ML approaches have been applied to a variety of medical data for disease diagnosis. The paper provides a comprehensive literature survey based on ML techniques to diagnose various diseases. ML importance in the analysis of medical data is discussed with applications. This paper will motivate advanced research in machine intelligence-driven healthcare by showing its potential in healthcare data analysis. We also discuss the challenges that arise when applying ML to healthcare data. Furthermore, this study introduces a new approach to ensemble learning through explainable stacking. By integrating explainable artificial intelligence (XAI) techniques with the stacking method, we aim to not only enhance predictive accuracy but also improve the interpretability of the stacking model. The proposed predictive model outperforms the existing categorisation models, enhancing both the performance and efficiency of the diagnostic process. In addition, we suggest several future directions for further work in this area. [ABSTRACT FROM AUTHOR]

Metabolomics has come to the fore as an efficient tool in the search for biomarkers that are critical for precision health approaches and improved diagnostics. This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. In cancer, metabolomics provides evidence for unique oncometabolites that are important for early disease detection and monitoring of treatment responses. Metabolite profiling for conditions such as neurodegenerative and mental health disorders can offer early diagnosis and mechanisms into the disease especially in Alzheimer's and Parkinson's diseases. In addition to these, lipid biomarkers and other metabolites relating to cardiovascular and metabolic disorders are promising for patient stratification and personalized treatment. The gut microbiome and environmental exposure also feature among the influential factors in biomarker discovery because they sculpt individual metabolic profiles, impacting overall health. Further, we discuss technological advances in metabolomics, current clinical applications, and the challenges faced by metabolomics biomarker validation toward precision medicine. Finally, this review discusses future opportunities regarding the integration of metabolomics into routine healthcare to enable preventive and personalized approaches. [ABSTRACT FROM AUTHOR]

Developing a secured and accurate disease diagnosis framework in the healthcare cloud systems are still remains one of the crucial problems in recent times. Due to the rapid growth of information and technology, it is highly essential to protect the patient health information against the unauthenticated users for ensuring the privacy and security. For this purpose, the different types of security approaches are developed in the conventional works, which are mainly focused on increasing the privacy of medical data stored in the cloud systems. However, it lacks with the major issues of increased computational overhead, communication cost, lack of security, complex mathematical modeling, and increased time consumption. Therefore, the proposed work objects to implement an intelligent and advanced privacy preserving framework, named as, lightweight signing signature based secured jellyfish data aggregation (LS3JDA) for ensuring the privacy of medical data in the healthcare cloud systems. The main contribution of this research work is to develop a new and lightweight privacy preservation model by incorporating the functions of both AI and signing signature algorithms for assuring data security in cloud systems. Moreover, it simplified the process of entire privacy preservation system with low computational burden and high data security. It also objects to accurately predict the type of disease based on the patients' medical history by using an advanced random forest (RF) machine learning methodology. The novel contributions of this work are, a message signing signature generation algorithm is used to strengthen the security of patients' medical data, and a jelly fish optimization (JFO) methodology is used to improve the process of data aggregation. The primary advantages of the proposed system are reduced processing time, low computational burden, and simple to deploy. For validating the results of the proposed model, several parameters include level of security, time, throughput, latency, signature cost, and communication overhead are assessed during evaluation. Moreover, the results are contrasted with some of the recent privacy preservation models for assuring the superiority of the proposed framework. Here, the overall processing time is reduced up to 1.5 ms, and communication overhead is reduced up to 100 bytes with the use of optimization integrated data aggregation model. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) affects the whole life of children and leads their families to seek effective treatment and education. According to the Centres for Disease Control and Prevention, the disorder affects one in every 36 children today. Diagnosing this disease at an early age facilitates the treatment process and enables children to be reintegrated into society. The use of Artificial Neural Networks (ANN), one of the artificial intelligence methods used for prediction, has increased in the field of health in recent years and has become an important tool for early disease diagnosis. In this study, single layer perceptron neural networks were designed for the diagnosis of ASD. Data of 14 different parameters taken from children between 12-36 months of age were used, and as a result of the classification, the accuracy value of the neural network was 99.18%, the sensitivity value was 98.91%, the sensitivity value was 1 and the f1 score value was 99.45%. As a result, it is seen that the perceptron classification algorithm has a very high performance in terms of accuracy, precision, sensitivity and f1 score and successfully discriminates the data. [ABSTRACT FROM AUTHOR]

Optical biosensors are emerging as a promising technique for the sensitive and accurate detection of cancer biomarkers, enabling significant advancements in the field of early diagnosis. This study elaborates on the latest developments in optical biosensors designed for detecting cancer biomarkers, highlighting their vital significance in early cancer diagnosis. When combined with targeted nanoparticles, the bio‐fluids can help in the molecular stage diagnosis of cancer. This enhances the discrimination of disease from the normal subjects drastically. The optical sensor methods that are involved in the disease diagnosis and imaging of cancer taken for the present review are surface plasmon resonance, localized surface plasmon resonance, fluorescence resonance energy transfer, surface‐enhanced Raman spectroscopy and colorimetric sensing. The article meticulously describes the specific biomarkers and analytes that optical biosensors target. Beyond elucidating the underlying principles and applications, this article furnishes an overview of recent breakthroughs and emerging trends in the field. This encompasses the evolution of innovative nanomaterials and nanostructures designed to augment sensitivity and the incorporation of microfluidics for facilitating point‐of‐care testing, thereby charting a course towards prospective advancements. [ABSTRACT FROM AUTHOR]

BACKGROUND: Healthcare is crucial to patient care because it provides vital services for maintaining and restoring health. As healthcare technology evolves, cutting-edge tools facilitate faster diagnosis and more effective patient treatment. In the present age of pandemics, the Internet of Things (IoT) offers a potential solution to the problem of patient safety monitoring by creating a massive quantity of data about the patient through the linked devices around them and then analyzing it to estimate the patient's current status. Utilizing the IoT-based meta-heuristic algorithm allows patients to be remotely monitored, resulting in timely diagnosis and improved care. Meta-heuristic algorithms are successful, resilient, and effective in solving real-world enhancement, clustering, predicting, and grouping. Healthcare organizations need an efficient method for dealing with big data since the prevalence of such data makes it challenging to analyze for diagnosis. The current techniques used in medical diagnostics have limitations due to imbalanced data and the overfitting issue. OBJECTIVE: This study introduces the particle swarm optimization and convolutional neural network to be used as a meta-heuristic optimization method for extensive data analysis in the IoT to monitor patients' health conditions. METHOD: Particle Swarm Optimization is used to optimize the data used in the study. Information for a diabetes diagnosis model that includes cardiac risk forecasting is collected. Particle Swarm Optimization and Convolutional Neural Networks (PSO-CNN) results effectively make illness predictions. Support Vector Machine has been used to predict the possibility of a heart attack based on the classification of the collected data into projected abnormal and normal ranges for diabetes. RESULTS: The results of the simulations reveal that the PSO-CNN model used to predict diabetic disease increased in accuracy by 92.6%, precision by 92.5%, recall by 93.2%, F1-score by 94.2%, and quantization error by 4.1%. CONCLUSION: The suggested approach could be applied to identify cancer cells. [ABSTRACT FROM AUTHOR]

Heterogeneous graph neural networks have attracted significant attention in the field of disease diagnosis. Medical heterogeneous graphs encompass various types of nodes and edges, representing rich medical information and interconnections. However, there are limitations in applying inherited attention and multi-layer structures from graph neural networks to disease diagnosis tasks. Firstly, introducing attention to large medical heterogeneous graphs leads to significant computational complexity. Secondly, employing multi-layer structures when dealing with large medical heterogeneous graphs, with each layer performing semantic fusion, may cause semantic confusion and easily lead to issues such as vanishing or exploding gradients. To address these issues, a multi-length meta-path sematic fusion in medical heterogeneous graph for disease dignosis (MLM4DD) has been proposed. MLM4DD uses a lightweight average aggregator to precompute neighborhood aggregation, reducing computational complexity and improving information propagation efficiency. To better utilize semantic information and avoid issues like vanishing and exploding gradients, MLM4DD introduces a single-layer structure with multi-length meta-paths to expand the receptive field. It incorporates local attention and multi-scale attention fusion to capture features from different meta-paths, thus obtaining embedded representations of patient nodes. Extensive experiments on the MIMIC-IV dataset demonstrate that MLM4DD outperforms other baseline methods in terms of disease diagnostic performance, effectively improving the accuracy of disease diagnosis. [ABSTRACT FROM AUTHOR]

Sensitive and accurate detection of small molecules from complex matrix has aroused increasing interest in many fields, yet remains an open challenge. Recent years have witnessed a considerable advance of aptasensors for diagnostic assay development towards diverse small molecules because aptamer is one of the most powerful classes of molecular receptors with advanced affinity and specificity. Herein, we reviewed the small‐molecule aptasensors in the past five years, focusing on the principles to specific applications in clinical diagnosis, food safety, and environmental monitoring. The first introductory section on the development of aptasensors in historical view and its analytical features contextualizes essential health‐related small molecules. The second part highlights the basic components of aptasensor and the detection principles of different sensors based on signal output modes. The subsequent part systematically discusses various small‐molecule sensing platforms by interfacing aptamers with diverse signal amplification strategies. Finally, challenges and perspectives for improving the aptasensor performance are also discussed. By describing biochemical and analytical procedures, this review highlights the optimal use of aptamers in the detection, quantification, and imaging of important health‐related small molecules and presents new insights, technical advances, and engineering strategies for practical applications. [ABSTRACT FROM AUTHOR]

With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on a Convolutional Neural Network (CNN), but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA's methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through GrabCut. It shows a high performance through an image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images, instead of the actual input, as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed a higher performance than previous research, and image-based scores showed the best performance. [ABSTRACT FROM AUTHOR]

Copyright of China Journal of Oral & Maxillofacial Surgery is the property of Shanghai Jiao Tong University, College of Stomatology 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.)

Glaucoma stands out as a prevalent ocular ailment in the elderly population, causing substantial harm to the optic nerves and eventual vision impairment. Fundus photography plays a pivotal role in the clinical assessment of glaucoma, facilitating the exploration of associated morphological alterations. Computational algorithms, capable of processing fundus images, have emerged as indispensable tools in this diagnostic domain. Hence, the imperative development of an automated diagnostic system leveraging image processing techniques is underscored. In this study, a novel approach to the segmentation and classification of retinal optic nerve head images is introduced. This method concurrently executes both tasks through a deep learning framework, thereby enhancing the learning speed within the network. The proposed network encompasses approximately 29 million parameters and demonstrates an efficiency of 2.5 seconds for segmenting and classifying retinal images. Central to this strategy is a multi-task deep learning network, harmonizing segmentation and classification processes, and leveraging information from both tasks to optimize learning efficacy. Validation of the proposed method is conducted using the publicly available ORIGA dataset. The attained performance metrics for accuracy, sensitivity, specificity, and F1-score are 99.461, 93.46, 100, and 98.7006, respectively. These results collectively affirm the substantial advancement achieved by the proposed method in comparison to existing methodologies. [ABSTRACT FROM AUTHOR]

Electrochemical biosensors are characterized by rapid response, miniaturization, portability, and ease of operation. With tunable nanostructure, DNA has been comprehensively combined with electrochemical devices to design high-sensitivity and selectivity biosensors for the achievement of disease diagnosis, food safety, and environmental monitoring. In recent years, DNA-functionalized electrochemical biosensors have made significant research progress. In this article, the recent research progress of DNA-functionalized electrochemical biosensors for in vitro and in vivo disease diagnosis was reviewed. The structure and sensing principles of DNA-functionalized electrochemical biosensors were first summarized. The preparation of DNA-functionalized electrochemical biosensors based on nanomaterials was introduced in detail. Meanwhile, the latest evolution of integrated and portable DNA-functionalized electrochemical biosensors for in vitro disease diagnosis was summarized. For a further step, the construction of implantable DNA-functionalized electrochemical biosensors for in vivo and real-time disease monitoring was overviewed. Finally, the challenges and outlook of DNA-functionalized electrochemical biosensors were discussed to provide a guideline for the future development of DNA-functionalized electrochemical biosensors.

Abstract This study established a novel infield sensing approach based on detection of the volatile compound markers in skin secretions. This was based on analysis of volatile compounds in axillary sweat samples collected from RT-PCR-proven Coronavirus disease 2019 (COVID-19) positive and negative populations using gas chromatography-mass spectrometry (GC–MS). The analysis proposed the possible markers of the monoaromatic compounds and ethyl hexyl acrylate. A portable photo ionization detector (PID) incorporated with the selective material towards the marker compounds was then developed with the pressurized injection approach. This provided the accuracy of 100% in the research phase (n = 125). The developed approach was then applied for screening of 2207 COVID-19 related cases covering the periods of the Alpha, Beta, Delta and Omicron variants of SARS-CoV-2 infection in Bangkok, Thailand. This offered the sensitivity, specificity and accuracy ranges of 92–99, 93–98 and 95–97%, respectively.

Paddy is a major nutritional requirement for the human population. However, diseases such as bacterial leaf blight, brown spot, and leaf smut are significantly impacting the leaves of the paddy at various stages, resulting in significant losses. Consequently, this leads to an important decrease in production. An effective approach would involve assigning agricultural specialists to the affected area to provide remedies for the affected crops. This task is both time-consuming and tedious. The latest technological advancements will provide alternative solutions in a more efficient and quicker manner. We employed a deep transfer learning algorithm to assess the accuracy of six frequently utilised transfer learning algorithms. In order to improve the accuracy of the model, we boosted the quality of the images using adaptive filtering and Gaussian Mixture Model (GMM) clustering technique. This allows for the simple segmentation of images into diseased, normal, and surrounding regions, resulting in more accurate classification. We applied a deep transfer learning method to evaluate the accuracy of six widely used transfer learning algorithms, specifically VGG, ResNet, Inception, MobileNet, DenseNet, and EfficientNet. The DenseNet model achieved an average accuracy of 94%, with a precision of 92%, recall of 95%, and an F1-score of 93%. To improve performance, we implemented an ensemble technique, which involves combining two or more models. The Ensemble Convolutional Neural Network (ECNN) was designed by using three pre-trained architectures: ResNet, DenseNet, and EfficientNet.The ensemble model's final predict was determined by employing a weighted average methodology to enhance the prediction's accuracy. This technique shows a perfect balance between performance and accuracy in diagnosing diseases in paddy leaves. [ABSTRACT FROM AUTHOR]

The convergence of advanced nanotechnology with disease diagnosis has ushered in a transformative era in healthcare, empowering early and accurate detection of diseases and paving the way for timely interventions, improved treatment outcomes, and enhanced patient well‐being. The development of novel materials is frequently the impetus behind significant advancements in sensor technology. Among them, single‐walled carbon nanotubes (SWCNTSs) have emerged as promising nanomaterials for developing biosensors. Their unique optical, electrical, and biocompatibility properties make them promising candidates for enhancing the sensitivity and real‐time monitoring capabilities of biosensors, as well as for enabling various bioimaging techniques. Recent studies have demonstrated the utility of SWCNTS‐based biosensors in the real‐time monitoring of biological analytes, such as nitric oxide and hydrogen peroxide (H2O2), with potential implications for disease understanding and therapeutic response assessment. Moreover, SWCNTSs have shown promise in bioimaging applications, including fluorescence, Raman spectroscopy, and photoluminescence imaging of biological samples. This article delves into the core principles, design strategies, and operational mechanisms that underpin SWCNTS‐bioimaging techniques‐based biosensors. It emphasizes on their unique properties and versatile functionalization of carbon nanotubes, laying the foundation for their integration into biosensor platforms and applications aimed at diagnosing a wide spectrum of diseases including infectious diseases, cancer, neurological disorders, and metabolic conditions. [ABSTRACT FROM AUTHOR]

Polypharmacy is the simultaneous use of drugs with 5-9 drugs. Excessive polypharmacy is the simultaneous use of drugs with ≥ 10 drugs. Chronic diseases that are commonly suffered by geriatric patients are prone to causing a person to receive polypharmacy or excessive polypharmacy. This study aims to evaluate polypharmacy and excessive polypharmacy in inpatient geriatric patients, by knowing the factors that may trigger a person to receive polypharmacy. The research uses a cross-sectional study method, using medical record data for the period January to December 2023. The part studied was in the form of sociodemographics, disease history and treatment of patients, as well as length of stay. From the research conducted, the results were obtained from 295 patient data samples with 141 patients (47.8%) and 154 patients (52.5%) were female. The age of patients consisted of 60-74 years old 192 patients (65.1%), 75-90 years old 100 patients (33.9%), and ≥ 90 years 3 patients (1%). The last education of the highest patient with a high school background was 101 patients (34.2%), the highest marital status was married as many as 245 patients (83.1%). The diagnosis of the third disease had the most patients, namely diabetes mellitus as many as 95 patients (15.5%), anemia as many as 70 patients (11.5%), and hypertension as many as 56 patients (9.2%). The prevalence of polypharmacy in geriatric patients was 115 patients (39%) and excessive polypharmacy was 180 patients (61%). Broadly speaking, the sociodemographics of patients do not have a relationship with the occurrence of polypharmacy and excessive polypharmacy. The patient's clinical condition has a relationship with the occurrence of polypharmacy and excessive polypharmacy, this is proven by ap value of 0.000 (<0.05). The length of hospitalization and diagnosis of the patient's disease is a predictor of polypharmacy and excessive polypharmacy. [ABSTRACT FROM AUTHOR]

Background: This work explores the expansivetab realm of Artificial Intelligence (AI) applications in the dynamic landscape of animal health and veterinary sciences. Addressing challenges in conventional approaches, we delve into how AI is transforming diagnosis, treatment and healthcare practices for diverse animal species. Methods: Through a rigorous literature review and methodology, the study navigates the current state of AI in animal health, identifying gaps and emphasizing the need for further research. Looking ahead, the paper outlines future directions and opportunities, contributing to the discourse on technology's intersection with animal care. By providing a comprehensive overview, this research paves the way for innovative solutions, promising a brighter and healthier future for our animal companions. Result: In the domain of animal health, AI emerges as a powerful tool for early disease detection and intervention, offering personalized treatment plans and proactive disease management through continuous monitoring and surveillance. In veterinary sciences, AI accelerates drug discovery, enhances genetic research and reshapes surgical procedures with robotic assistance. However, ethical considerations and challenges, including data privacy and AI-driven decision-making and critical examination should be addressed to. [ABSTRACT FROM AUTHOR]

Changes in biomechanical properties such as elasticity modulus, viscosity, and poroelastic features are linked to the health status of biological tissues. Ultrasound elastography is a non-invasive imaging tool that quantitatively maps these biomechanical characteristics for diagnostic and treatment monitoring purposes. Mathematical models are essential in ultrasound elastography as they convert the raw data obtained from tissue displacement caused by ultrasound waves into the images observed by clinicians. This article reviews the available mathematical frameworks of continuum mechanics for extracting the biomechanical characteristics of biological tissues in ultrasound elastography. Continuum-mechanics-based approaches such as classical viscoelasticity, elasticity, and poroelasticity models, as well as nonlocal continuum-based models, are described. The accuracy of ultrasound elastography can be increased with the recent advancements in continuum modelling techniques including hyperelasticity, biphasic theory, nonlocal viscoelasticity, inversion-based elasticity, and incorporating scale effects. However, the time taken to convert the data into clinical images increases with more complex models, and this is a major challenge for expanding the clinical utility of ultrasound elastography. As we strive to provide the most accurate imaging for patients, further research is needed to refine mathematical models for incorporation into the clinical workflow. [ABSTRACT FROM AUTHOR]

Due to a series of problems in the diagnosis of liver disease, the mortality rate of liver disease patients is very high. Therefore, it is necessary for doctors and researchers to find a more effective non-invasive diagnostic method to meet clinical needs. We analyzed data from 416 patients with liver disease and 167 patients without liver disease from northeastern Andhra Pradesh, India. On the basis of considering age, gender and other basic data of patients, this paper uses total bilirubin and other clinical data as parameters to build a diagnostic model. In this paper, the accuracy of artificial intelligence method Random Forest (RF) and Support Vector Machine (SVM) model in the diagnosis of liver patients was compared. The results show that the support vector machine model based on Gaussian kernel function is more excellent in diagnostic accuracy, that is, SVM method is more suitable for the diagnosis of liver diseases. [ABSTRACT FROM AUTHOR]

In various fields, including medical science, datasets characterized by uncertainty are generated. Conventional clustering algorithms, designed for deterministic data, often prove inadequate when applied to uncertain data, posing significant challenges. Recent advancements have introduced clustering algorithms based on a possible world model, specifically designed to handle uncertainty, showing promising outcomes. However, these algorithms face two primary issues. First, they treat all possible worlds equally, neglecting the relative importance of each world. Second, they employ time-consuming and inefficient post-processing techniques for world selection. This research aims to create clusters of observed symptoms in patients, enabling the exploration of intricate relationships between symptoms. However, the symptoms dataset presents unique challenges, as it entails uncertainty and exhibits overlapping symptoms across multiple diseases, rendering the formation of mutually exclusive clusters impractical. Conventional similarity measures, assuming mutually exclusive clusters, fail to address these challenges effectively. Furthermore, the categorical nature of the symptoms dataset further complicates the analysis, as most similarity measures are optimized for numerical datasets. To overcome these scientific obstacles, this research proposes an innovative clustering algorithm that considers the precise weight of each symptom in every disease, facilitating the generation of overlapping clusters that accurately depict the associations between symptoms in the context of various diseases. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) can cause blindness and vision impairment. This degenerative eye condition may lead to an irreversible vision loss. The prevalence of vision impairment and blindness caused by DR emphasizes the critical need for better screening and therapy measures. DR aetiology involves persistent hyperglycemia-induced microvascular abnormalities, oxidative stress, inflammatory reactions, and retinal blood flow changes. Common screening methods for retinal issues include fundus photography, OCT, and fluorescein angiography. For those with diabetic macular edema (DME), it is a common cause of vision loss. Our goal is to develop an automated, cost-effective method for identifying diabetic retinal disease specimens. This study introduces a faster R-CNN method for detecting and classifying DR lesions in retinal images. Those are classified across five different classes. An extensive analysis of 88,704 images from a Kaggle dataset indicates the efficiency of the proposed model, with a reasonable accuracy of 98.38%. The proposed method is robust in disease localization and classification tasks and it has outperformed other existing studies in DR recognition. On evaluating cross-datasets in Kaggle and APTOS, the model has yield better results during training and testing phases. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is a prevalent disorder of the central nervous system, marked by the degeneration of dopamine (DA) neurons in the ventral midbrain. In the pathogenesis of PD, inflammation hypothesis has been concerned. This study aims to investigate clinical indicators of peripheral inflammation in PD patients and to explore the diagnostic value of neutrophilto-lymphocyte ratio (NLR), albumin-to-fibrinogen ratio (AFR), and lymphocyteto- monocyte ratio (LMR) in assessing PD risk. Methods: This study included 186 patients with PD and 201 matched healthy controls (HC) with baseline data. Firstly, the differences of hematological indicators between PD group and healthy participants were compared and analyzed. Univariate and multivariate regression analyses were then conducted. Smooth curve fitting was applied to further validate the relationships between NLR, LMR, AFR, and PD. Subsequently, subgroup analysis was conducted in PD group according to different duration of disease and Hoehn and Yahr (H&Y) stage, comparing differences in clinical indicators. Finally, the receiver operating characteristic (ROC) curve was employed to assess the diagnostic value of NLR, LMR, and AFR in PD. Results: Compared to the HC group, the PD group showed significantly higher levels of hypertension, diabetes, neutrophil count, monocyte count, CRP, homocysteine, fibrinogen, and NLR. Conversely, levels of LMR, AFR, lymphocyte count, HDL, LDL, TG, TC, uric acid, and albumin were significantly lower. The multivariate regression model indicated that NLR (OR = 1.79, 95% CI: 1.39-2.31, p < 0.001), LMR (OR = 0.75, 95% CI: 0.66-0.85, p < 0.001), and AFR (OR = 0.79, 95% CI: 0.73-0.85, p < 0.001) were significant factors associated with PD. Smooth curve fitting revealed that NLR was positively linked to PD risk, whereas AFR and LMR were inversely associated with it. In ROC curve analysis, the AUC of AFR was 0.7290, the sensitivity was 63.98%, and the specificity was 76.00%. The AUC of NLR was 0.6200, the sensitivity was 50.54%, and the specificity was 71.50%. The AUC of LMR was 0.6253, the sensitivity was 48.39%, and the specificity was 73.00%. The AUC of the combination was 0.7498, the sensitivity was 74.19%, and the specificity was 64.00%.Conclusion: Our findings indicate that NLR, LMR, and AFR are significantly associated with Parkinson's disease and may serve as diagnostic markers. [ABSTRACT FROM AUTHOR]

Simple Summary: Phytoplasmas are minute bacteria that infect many plant species, causing economic losses and impacting agriculture. Early diagnosis is crucial to effective disease management. A symptom database, iPhyDSDB, was developed by retrieving nearly 35,000 phytoplasma DNA sequences from the NCBI nucleotide database and identifying 945 plant species associated with phytoplasma diseases. The database includes curated links to symptom images and detailed disease information. Implemented on a web-based interface using MySQL and PHP, iPhyDSDB features advanced search functionality. This tool helps farmers, growers, researchers, and educators efficiently query based on plant host and symptom type, aiding in the identification and management of phytoplasma-related diseases. Phytoplasmas are small, intracellular bacteria that infect a vast range of plant species, causing significant economic losses and impacting agriculture and farmers' livelihoods. Early and rapid diagnosis of phytoplasma infections is crucial for preventing the spread of these diseases, particularly through early symptom recognition in the field by farmers and growers. A symptom database for phytoplasma infections can assist in recognizing the symptoms and enhance early detection and management. In this study, nearly 35,000 phytoplasma sequence entries were retrieved from the NCBI nucleotide database using the keyword "phytoplasma" and information on phytoplasma disease-associated plant hosts and symptoms was gathered. A total of 945 plant species were identified to be associated with phytoplasma infections. Subsequently, links to symptomatic images of these known susceptible plant species were manually curated, and the Phytoplasma Disease Symptom Database (iPhyDSDB) was established and implemented on a web-based interface using the MySQL Server and PHP programming language. One of the key features of iPhyDSDB is the curated collection of links to symptomatic images representing various phytoplasma-infected plant species, allowing users to easily access the original source of the collected images and detailed disease information. Furthermore, images and descriptive definitions of typical symptoms induced by phytoplasmas were included in iPhyDSDB. The newly developed database and web interface, equipped with advanced search functionality, will help farmers, growers, researchers, and educators to efficiently query the database based on specific categories such as plant host and symptom type. This resource will aid the users in comparing, identifying, and diagnosing phytoplasma-related diseases, enhancing the understanding and management of these infections. [ABSTRACT FROM AUTHOR]

Semi-supervised classification has gained widespread popularity because of their superior ability to handle unlabeled samples in practical problems. This paper has presented a novel estimation error-ranked LSSVM method with double Mahalanobis-kernel which is used for semi-supervised classification. The main point is to construct two Mahalanobis distances in Hilbert space to form double Mahalanobis-kernel by considering the relationship between the characteristics of two sorts of samples, so as to reduce the influence of non-informational dimensions. Furthermore, the implementation of the proposed method is required to solve the label security problem of unlabeled samples. The unlabeled sample with the minimum evaluated error is selected for labeling, which effectively ensures the accuracy of the unlabeled sample labeling. This method not only considers the similarity of sample features, but also focuses on the security of unlabeled samples. And based on the experimental results of four artificial data sets and several UCI data sets, it verifies the effectiveness of the semi-supervised method with double Mahalanobis-kernel. Especially considering the experimental results of five disease diagnosis data sets, it demonstrates the potential of the proposed semi-supervised classification method in medical diagnosis. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

X-ray is a fundamental technique in today's therapeutic management, diagnosing several illnesses and deciding the right dosage of prescriptions to enhance the lives of patients. This paper aims to explore the aspect of the functioning of X-rays as diagnostic tools in the disease process and the influence this aspect has on treatment procedures, with a special focus on the role of real images in the creation of pharmacological treatments. Through a literature review and studying case examples, we examine the role of X-ray diagnostics in pharma management of diverse conditions, including pneumonia, fractured bones, and osteoporosis. This study highlights the teamwork of pharmacists to individually adapt the medication regimen according to the characteristics of the radiographic images as a sign of the perfect link between accurate imaging and accurate drug therapy. This integrated function in healthcare settings not only improves the diagnostic functions of the devices but also the function of providing the best treatment to the patient emphasizing the combination of improved imaging techniques with the best pharma management strategies. [ABSTRACT FROM AUTHOR]

Appendicitis is a condition wherein the appendix becomes inflamed, and it can be difficult to diagnose accurately. The type of appendicitis can also be hard to determine, leading to misdiagnosis and difficulty in managing the condition. To avoid complications and reduce mortality, early diagnosis and treatment are crucial. While Alvarado's clinical scoring system is not sufficient, ultrasound and computed tomography (CT) imaging are effective but have downsides such as operator-dependency and radiation exposure. This study proposes the use of machine learning methods and a locally collected reliable dataset to enhance the identification of acute appendicitis while detecting the differences between complicated and non-complicated appendicitis. Machine learning can help reduce diagnostic errors and improve treatment decisions. This study conducted four different experiments using various ML algorithms, including K-nearest neighbors (KNN), DT, bagging, and stacking. The experimental results showed that the stacking model had the highest training accuracy, test set accuracy, precision, and F1 score, which were 97.51%, 92.63%, 95.29%, and 92.04%, respectively. Feature importance and explainable AI (XAI) identified neutrophils, WBC_Count, Total_LOS, P_O_LOS, and Symptoms_Days as the principal features that significantly affected the performance of the model. Based on the outcomes and feedback from medical health professionals, the scheme is promising in terms of its effectiveness in diagnosing of acute appendicitis. [ABSTRACT FROM AUTHOR]

Fundus photography (FP) is a crucial technique for diagnosing the progression of ocular and systemic diseases in clinical studies, with wide applications in early clinical screening and diagnosis. However, due to the nonuniform illumination and imbalanced intensity caused by various reasons, the quality of fundus images is often severely weakened, brings challenges for automated screening, analysis, and diagnosis of diseases. To resolve this problem, we developed strongly constrained generative adversarial networks (SCGAN). The results demonstrate that the quality of various datasets were more significantly enhanced based on SCGAN, simultaneously more effectively retaining tissue and vascular information under various experimental conditions. Furthermore, the clinical effectiveness and robustness of this model were validated by showing its improved ability in vascular segmentation as well as disease diagnosis. Our study provides a new comprehensive approach for FP and also possesses the potential capacity to advance artificial intelligence‐assisted ophthalmic examination. [ABSTRACT FROM AUTHOR]

This study established a novel infield sensing approach based on detection of the volatile compound markers in skin secretions. This was based on analysis of volatile compounds in axillary sweat samples collected from RT-PCR-proven Coronavirus disease 2019 (COVID-19) positive and negative populations using gas chromatography-mass spectrometry (GC–MS). The analysis proposed the possible markers of the monoaromatic compounds and ethyl hexyl acrylate. A portable photo ionization detector (PID) incorporated with the selective material towards the marker compounds was then developed with the pressurized injection approach. This provided the accuracy of 100% in the research phase (n = 125). The developed approach was then applied for screening of 2207 COVID-19 related cases covering the periods of the Alpha, Beta, Delta and Omicron variants of SARS-CoV-2 infection in Bangkok, Thailand. This offered the sensitivity, specificity and accuracy ranges of 92–99, 93–98 and 95–97%, respectively. [ABSTRACT FROM AUTHOR]

In recent decades, research on Extracellular Vesicles (EVs) has gained prominence in the life sciences due to their critical roles in both health and disease states, offering promising applications in disease diagnosis, drug delivery, and therapy. However, their inherent heterogeneity and complex origins pose significant challenges to their preparation, analysis, and subsequent clinical application. This review is structured to provide an overview of the biogenesis, composition, and various sources of EVs, thereby laying the groundwork for a detailed discussion of contemporary techniques for their preparation and analysis. Particular focus is given to state‐of‐the‐art technologies that employ both microfluidic and non‐microfluidic platforms for EV processing. Furthermore, this discourse extends into innovative approaches that incorporate artificial intelligence and cutting‐edge electrochemical sensors, with a particular emphasis on single EV analysis. This review proposes current challenges and outlines prospective avenues for future research. The objective is to motivate researchers to innovate and expand methods for the preparation and analysis of EVs, fully unlocking their biomedical potential. [ABSTRACT FROM AUTHOR]

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network's determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region of interest (ROI) cropping, and data augmentation steps to enhance classification accuracy. Later, a multi-layer Gradient CAM (ML-Grad-CAM) algorithm is integrated to generate a class-specific saliency map for improved visualization in CXR images. We also define and calculate a Severity Assessment Index (SAI) from the saliency map to quantitatively measure infection severity. The trained model achieved an accuracy score of 96.44% for the three-class CXR classification task, i.e., COVID-19, pneumonia, and normal (healthy patients), outperforming many existing techniques in the literature. The saliency maps generated from the proposed ML-GRAD-CAM algorithm are compared with the original Gran-CAM algorithm. [ABSTRACT FROM AUTHOR]

Accurate diagnosis of plant diseases by the assessment of pathogen presence to reduce disease-related production loss is one of the most fundamental issues for farmers and specialists. This will improve product quality, increase productivity, reduce the use of fungicides, and reduce the final cost of agricultural production. Today, new technologies such as image processing, artificial intelligence, and deep learning have provided reliable solutions in various fields of precision agriculture and smart farm management. In this research, microscopic image processing and machine learning have been used to identify the spores of four common tomato fungal diseases. A dataset including 100 microscopic images of spores for each disease was developed, followed by the extraction of the texture, color, and shape features from the images. The classification results using random forest revealed an accuracy higher than 98%. Besides, as a reliable feature selection algorithm, the butterfly optimization algorithm (BOA) was used to detect the effective image features to identify and classify diseases. This algorithm recognized image textural features as the most effective features in the diagnosis and classification of disease spores. Considering only the eight most effective features selected with BOA resulted in an accuracy of 95% in disease detection. To further investigate the performance of the proposed method, its accuracy was compared with the accuracies of convolutional neural networks and EfficientNet as two reliable deep learning algorithms. Not only the prediction accuracy of these methods was not favorable (65 and 83.55%, respectively), they were very time-consuming. According to the findings, the proposed framework has high reliability in disease diagnosis and can help in the management of tomato fungal diseases. [ABSTRACT FROM AUTHOR]

Goat production in Lao People's Democratic Republic (Lao PDR) is a small but rapidly growing sector owing to strong export demand, primarily from Vietnam. Disease has been identified as one of the major constraints to goat production but there are limited data on causes and effective control strategies. The situation is exacerbated by a lack of veterinary and extension services in rural areas. Information on the major disease and clinical syndromes of goats and their causative agents is needed to develop local and national control strategies and to improve animal welfare. Zoonotic diseases involving goats are also potentially important in terms of live goat trade and public health, albeit research is lacking. This review summarises and evaluates the available published data on caprine diseases in Lao PDR and provides possible disease control strategies to improve goat production in Lao PDR. Surveys and observations suggest that lip and facial dermatitis, eye conditions and diarrhoea are the most common clinical syndromes affecting the health of Lao goats. These clinical syndromes can be considered as priorities for Lao goats. Serological surveys conducted in limited geographical areas of the country have identified moderate seroprevalence of foot and mouth disease (FMD) and low seroprevalence of bluetongue, peste des petits ruminants (PPR), brucellosis and Q fever in goats. Accordingly, the clinical signs associated with the latter diseases were not commonly reported. Trichostrongylus spp., Haemonchus contortus and coccidia are the main gastro-intestinal parasites identified among Lao goats. Despite these studies, an understanding of the causation of the most common clinical syndromes in Lao goats is still lacking, similar to the situation in many other parts of Southeast Asia. Studies to determine the causation of common clinical syndromes need to be conducted in Lao goats if progress is to be made on overcoming the disease constraint. Similarly, studies are also needed to evaluate interventions that have been introduced to limit the impact of these disease and clinical syndromes. They will likely require changes to goat management and nutrition, in addition to disease-specific interventions. Disease is one of the major issues affecting goat production in Lao PDR. Although only a limited number of studies have been published on diseases in Lao goats, a review of the available literature is desirable, given that goat production is rapidly expanding among smallholder farmers. This review article brings together the disease-related studies for a better understanding of health and production in Lao goats. This article belongs to the Collection Sustainable Animal Agriculture for Developing Countries 2023 [ABSTRACT FROM AUTHOR]

Copyright of Biology Teaching is the property of East China Normal University 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.)