Diagnostic Analysis Research Articles

Reliability of neutrophil gelatinase-associated lipocalin in detecting acute tubular necrosis in decompensated cirrhosis: Systematic review and meta-analysis

BACKGROUND Acute kidney injury (AKI) in cirrhosis is common. The diagnosis of AKI in cirrhosis patients depends on clinical presentation and laboratory tests like serum creatinine. However, urine biomarkers could also be used to assess the type of AKI and the severity of the disease. We performed a systematic review with meta-analysis to evaluate the association with urine neutrophil gelatinase-associated lipocalin (NGAL) marker in identifying acute tubular necrosis (ATN) in patients with cirrhosis. AIM To assess the reliability of urine NGAL in the detection of ATN in patients with cirrhosis. METHODS We systematically searched MEDLINE and PubMed using keywords including “urine biomarkers”, “NGAL”, “kidney dysfunction”, and “cirrhosis” to identify relevant studies. Data was screened and extracted. Included studies assessed hospitalized cirrhosis patients with AKI using the urine NGAL biomarker. We synthesized the data using diagnostic odds ratio (DOR), comparative and descriptive analyses, and Cochran Mantel-Haenszel (CMH) statistics to evaluate heterogeneity. RESULTS Three thousand seven hundred and one patients with cirrhosis were analyzed from a total of 21 cohort studies. The DOR of 14 of those studies [pooled DOR: 22.150, (95%CI: 17.58-27.89), P < 0.0001] demonstrated a significant association between urine NGAL levels and its identification of ATN. Following stratification by cirrhosis status, heterogeneity was analyzed and showed a significant non-zero correlation between NGAL and AKI (CMH statistic = 702.19, P < 0.0001). CONCLUSION In patients with cirrhosis, the use of urine NGAL is a reliable biomarker for detecting ATN and identifying the etiology of AKI.

Retraction Note: Diagnostic analysis on different carcinoma to identify patients perception for QoL

Retraction Note: Diagnostic analysis on different carcinoma to identify patients perception for QoL

Parametric Quantile Autoregressive Conditional Duration Models With Application to Intraday Value‐at‐Risk Forecasting

ABSTRACTThe modeling of high‐frequency data that qualify financial asset transactions has been an area of relevant interest among statisticians and econometricians—above all, the analysis of time series of financial durations. Autoregressive conditional duration (ACD) models have been the main tool for modeling financial transaction data, where duration is usually defined as the time interval between two successive events. These models are usually specified in terms of a time‐varying mean (or median) conditional duration. In this paper, a new extension of ACD models is proposed which is built on the basis of log‐symmetric distributions reparametrized by their quantile. The proposed quantile log‐symmetric conditional duration autoregressive model allows us to model different percentiles instead of the traditionally used conditional mean (or median) duration. We carry out an in‐depth study of theoretical properties and practical issues, such as parameter estimation using maximum likelihood method and diagnostic analysis based on residuals. A detailed Monte Carlo simulation study is also carried out to evaluate the performance of the proposed models and estimation method in retrieving the true parameter values as well as to evaluate a form of residuals. Finally, we derive a semiparametric intraday value‐at‐risk (IVaR) model and then the proposed models are applied to two price duration data sets.

Inflammatory Bowel Disease in Activated PI3Kδ Syndrome: An Uncommon Complication of a Rare Condition

Background/Objectives: Monogenic primary immunodeficiencies represent a group of disorders with varying levels of severity, many of which remain poorly understood. Activated phosphoinositide-3 kinase delta syndrome (APDS) is a rare genetic condition resulting from dominant point mutations in the phosphoinositide-3 kinase delta (PI3Kδ) gene, which leads to hyperactivation of the PI3Kδ enzyme, primarily expressed in T and B lymphocytes. Children with this mutation often have recurrent sinopulmonary infections and immunodeficiency. Additional complications may include increased susceptibility to herpes virus infections, lymphoid hyperplasia, and autoimmune conditions. In this case, report, we describe the clinical course of a young boy diagnosed with APDS who developed unclassified inflammatory bowel disease (U-IBD) and explore a personalized treatment approach. Methods: We detail the clinical course of a 12-year-old boy with APDS who presented with fever, diarrhea, anemia, and significant weight loss. Diagnostic evaluations, including endoscopy and histological analysis, led to a diagnosis of U-IBD. Genetic testing confirmed a heterozygous PIK3CD mutation (c.G3061A, p.E1021K). Results: Although APDS is characterized by a broad spectrum of immune dysregulation, the occurrence of IBD in this context is rare. We managed the patient’s IBD with exclusively enteral nutrition to induce remission, followed by a maintenance regimen combining the Crohn’s Disease Exclusion Diet (CDED) and mesalamine, achieving sustained long-term clinical remission. Conclusions: This case underscores the importance of personalized treatment approaches in managing the complex manifestations of APDS.

Electrochemical Biosensors in Diagnostic Medicine: Detecting Lung Cancer Biomarkers – A Comprehensive Review

AbstractElectrochemical biosensors use biomolecules, such as proteins, enzymes, and antibodies, to translate the analytical signals detected in a sample. They have diverse applications including pesticide detection in agriculture, water analysis in various sectors, and biomedical and forensic diagnostics. With the estimated number of cancer cases in the US in 2024 being over two million, particularly lung cancer, which is notoriously difficult to diagnose early, the integration of biosensors into the Point‐of‐care Testing (PoCT) strategy can significantly improve the detection of cancer biomarkers, contributing to early diagnosis and successful treatment. Three‐dimensional (3D) printing is a promising alternative for reducing production costs and customizing devices in various ways. This review highlights recent trends and research on the development of electrochemical biosensors for early detection of lung cancer. These biosensors are expected to be more sensitive and selective for a variety of real samples and are precise, accurate, and stable during their commercialization. Significant progress has been made in the development of electrochemical devices for the early diagnosis of lung cancer, with various biomarker anchoring and detection strategies addressed throughout the study. Overcoming these challenges is key to advancing the use of these biosensors, thus improving diagnostic accuracy and enabling the successful treatment of lung cancer patients.

PHONOLOGICAL ANALYSIS OF LANGUAGE PRODUCTION IN PRESCHOOL CHILDREN

The paper presents an analysis of the phonological features of preschool children. The article aims to find patterns of typical and atypical errors. A method was created to conduct the study, which includes three levels of research - at the level of words, sentence repetition, and generating one's speech by picture (spontaneous speech). The results show that children's most common phonological errors are as follows: sound substitution, elision of sound, metathesis of sound, elision of syllable, and deletion of consonants clustered in a syllable. The data obtained from the noun generation task was informative enough about the phonological development of the children being studied. The repeated speech task was accessible for the children and of little value in analyzing the phonological processes. The sentence generation task provides sufficient information about sentence structure, prosody, rate, and force of speech. The test serves a diagnostic and therapeutic purpose. The diagnostic analysis is clear and informative enough to create a therapeutic program based on the examination results. Subsequently, it could be applied for re-testing to measure the progress in the child's output. In addition, the average time children take to administer the whole test is less than 18 minutes, which is an additional positive aspect of the test. The ultimate goal is to standardize this test to assist clinicians in establishing norms by examining Bulgarian-speaking children and assessing their phonological development.

Self-priming dumbbell probe-mediated cascade isothermal amplification for sensitive and label-free MICRORNA detection using fluorescence light-up silver nanocluster

Given the critical significance of microRNAs (miRNAs) in cancer diagnosis and classification (e.g., glioma), there is a need for the advancement of sensitive, specific, quantitative, and cost-effective approach for assessing miRNA expression levels. We present an effective amplification technology for ultrasensitive miRNA analysis by combining a self-priming dumbbell probe-mediated cascade isothermal amplification strategy with fluorescence-enhanced silver nanoclusters (DNA-AgNCs). The approach depends on the disassociation of the stem portion of the dumbbell probe through the binding of target miRNA, followed by self-priming mediated target recycling, DNA polymerase/endonuclease facilitated chain extension, and catalytic hairpin assembly (CHA) to provide an amplified signal. The CHA procedure generates an LP-AgNCs/LP-G duplex, wherein the fluorescence signals of DNA-AgNCs can be significantly amplified when in close proximity to guanine-rich (G-rich) DNA sequences. The proposed label-free assay, characterized by high sensitivity, a wide dynamic range (spanning 6 orders of magnitude), excellent specificity (capable of detecting single-base differences), and simple operation, has been successfully utilized for the detection of miRNA in real samples. This demonstrates its potential as a compelling alternative for miRNA analysis in gene expression profiling and molecular diagnostics, especially for early glioma cancer detection.

Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium.

The 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools. Each workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience. Key outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes. The symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

The Thoughtful program: a randomized controlled study of a mentalization-based mental health education intervention in a psychiatric outpatient population

Background P-factor and mentalizing theory and research set perspectives for transdiagnostic psychiatric treatments. Aims To test the effects of a low-cost mentalization-based health education program (the Thoughtful program) in an unselected waiting list population, from a psychiatric outpatient clinic in North Norway. Methods Waiting list patients were randomized (1:1 allocation): 79 patients in the control group were offered standard individual assessment and treatment. Seventy-nine patients in the intervention group were offered a one-day (six-hour) group-based Thoughtful course, plus individual assessment, and treatment. Results During a 6-month follow-up period, the number of patients in active assessment and treatment was 66% higher in the control group than in the intervention group. Self-reported patient questionnaire scores on mentalizing, well-being and suicidal ideation scores showed no significant changes in the control group. In the intervention group, significant changes were reported: Mentalizing scores improved by 72% and well-being scores improved by 55%. Suicidal ideation scores were not significantly changed. No adverse effects were registered. Limitations The results from this study should be interpreted with caution because of a small population size and low questionnaire response rate. The follow up period was limited to 6 months. Transfer of program information from intervention group patients to control group patients could not be guaranteed. It may be considered a limitation that diagnostic pattern analysis was not included in the study. Conclusions A low-cost transdiagnostic mentalization-based educational program in an outpatient psychiatry clinic may improve patient mentalizing and wellbeing without adverse effects and reduce the use of hospital services.

Experimental research on the TCV tokamak

Abstract Tokamak à configuration variable (TCV), recently celebrating 30 years of near-continual operation, continues in its missions to advance outstanding key physics and operational scenario issues for ITER and the design of future power plants such as DEMO. The main machine heating systems and operational changes are first described. Then follow five sections: plasma scenarios. ITER Base-Line (IBL) discharges, triangularity studies together with X3 heating and N2 seeding. Edge localised mode suppression, with a high radiation region near the X-point is reported with N2 injection with and without divertor baffles in a snowflake configuration. Negative triangularity (NT) discharges attained record, albeit transient, β N ∼ 3 with lower turbulence, higher low-Z impurity transport, vertical stability and density limits and core transport better than the IBL. Positive triangularity L-Mode linear and saturated ohmic confinement confinement saturation, often-correlated with intrinsic toroidal rotation reversals, was probed for D, H and He working gases. H-mode confinement and pedestal studies were extended to low collisionality with electron cyclotron heating obtaining steady state electron iternal transport barrier with neutral beam heating (NBH), and NBH driven H-mode configurations with off-axis co-electron cyclotron current drive. Fast particle physics. The physics of disruptions, runaway electrons and fast ions (FIs) was developed using near-full current conversion at disruption with recombination thresholds characterised for impurity species (Ne, Ar, Kr). Different flushing gases (D2, H2) and pathways to trigger a benign disruption were explored. The 55 kV NBH II generated a rich Alfvénic spectrum modulating the FI fas ion loss detector signal. NT configurations showed less toroidal Alfvén excitation activity preferentially affecting higher FI pitch angles. Scrape-off layer and edge physics. gas puff imaging systems characterised turbulent plasma ejection for several advanced divertor configurations, including NT. Combined diagnostic array divertor state analysis in detachment conditions was compared to modelling revealing an importance for molecular processes. Divertor physics. Internal gas baffles diversified to include shorter/longer structures on the high and/or low field side to probe compressive efficiency. Divertor studies concentrated upon mitigating target power, facilitating detachment and increasing the radiated power fraction employing alternative divertor geometries, optimised X-point radiator regimes and long-legged configurations. Smaller-than-expected improvements with total flux expansion were better modelled when including parallel flows. Peak outer target heat flux reduction was achieved (>50%) for high flux-expansion geometries, maintaining core performance (H 98 > 1). A reduction in target heat loads and facilitated detachment access at lower core densities is reported. Real-time control. TCV’s real-time control upgrades employed MIMO gas injector control of stable, robust, partial detachment and plasma β feedback control avoiding neoclassical tearing modes with plasma confinement changes. Machine-learning enhancements include trajectory tracking disruption proximity and avoidance as well as a first-of-its-kind reinforcement learning-based controller for the plasma equilibrium trained entirely on a free-boundary simulator. Finally, a short description of TCV’s immediate future plans will be given.

A redescription and new generic placement of the New Zealand marine-associated jumping spider Marpissa marina (Goyen, 1892) (Araneae: Salticidae: Euophryini)

ABSTRACT The jumping spider Marpissa marina (Goyen, 1892) is endemic to Aotearoa New Zealand. In this paper, we redescribe the species. Marpissa marina was placed in the Northern Hemisphere genus Marpissa due to its subjective similarity to spiders in that genus. The original description lacked modern morphological diagnoses and useful illustrations. The description was based largely on the female, and therefore lacked the taxonomically important characters seen in the male pedipalp. Previous literature has suggested M. marina may be related to the Australian genus, Maratus Karsch, 1878. We applied an integrative taxonomic approach based on diagnostic morphological characters and molecular analysis for a more detailed species description and assignment into an appropriate genus. Morphological features indicate that M. marina belongs to Maratus. This is supported by phylogenetic analyses using DNA sequences from the nuclear gene Actin 5C and the mitochondrial gene 16S ribosomal RNA. We transfer Marpissa marina into the genus Maratus, as Maratus marinus (Goyen, 1892), new combination.

East Asian monsoon and Pacific basin dynamics jointly regulate boreal winter marine cold spells in the Taiwan Strait

Marine cold spells (MCSs), characterized by pronounced seawater temperature declines, are representative marine responses to atmospheric and climatic extremes. Notably, during the boreal winter, MCSs in the Taiwan Strait (TS) have caused substantial economic losses due to the concurrence of juvenile development stages in mariculture and labor shortages during China’s New Year vacation. Understanding the spatio-temporal features of MCSs and the underlying mechanisms is imperative for mitigating the associated economic detriments. This study, for the first time, delineates two MCS hotspots in the northeastern and western TS, based on various sea surface temperature datasets. Our findings reveal a notable frequency and intensity of MCSs in the 1980s, followed by a period of suppressed phase, reaching a historic low point in 2002. However, contrary to expectations of the overall trend of global warming, MCSs have experienced a resurgence since 2002, with a pronounced focus on the western coast of the TS. Diagnostic analyses suggest that the Winter East Asian Monsoon-driven cold current and the Pacific basin scale dynamic-forced warm current jointly regulated the spatio-temporal features of the MCSs. This study contributes a novel perspective on the impacts of climate change on extreme marine events along subtropical coastal areas.

Identification of genes related to fatty acid metabolism in type 2 diabetes mellitus

AimFatty acid metabolism is pivotal for lipid synthesis, cellular signaling, and maintaining cell membrane integrity. However, its diagnostic significance in type 2 diabetes mellitus (T2DM) remains unclear. Materials and methodsThree datasets and fatty acid metabolism-related genes were retrieved. Differential expression analysis, WGCNA, machine learning algorithms, diagnostic analysis, and validation were employed to identify key feature genes. Functional analysis, ceRNA network construction, immune microenvironment assessment, and drug prediction were conducted to explore the underlying molecular mechanisms. ResultsSix feature genes were identified with strong diagnostic performance and were involved in processes such as ribosome function and fatty acid metabolism. Immune cells, including dendritic cells, eosinophils, and neutrophils, may play a role in the progression of T2DM. ceRNA and drug-target network analysis revealed potential interactions, such as RP11-miR-29a-YTHDF3 and BPA-MSANTD1. The expression patterns of the feature genes, except for YTHDF3, were consistently upregulated in T2DM, aligning with trends observed in the training set. ConclusionThis study investigated the potential molecular mechanisms of six fatty acid metabolism-related genes in T2DM, offering valuable insights that may guide future research and therapeutic development.

Reaching machine learning leverage to advance performance of electrocatalytic CO2 conversion in non-aqueous deep eutectic electrolytes

Deep eutectic electrolytes (DEEs) show promise for future electrochemical systems due to their adjustable buffer capacities. This study utilizes machine learning algorithms to analyse the carbon dioxide reduction reaction (CO2RR) in DEEs with a buffer capacity of approximately 10.21 mol/pH. The objective is to minimize undesired hydrogen evolution reactions (HER) and render CO2RR dominant in a membrane cell. The CO2RR process was found to be non-adiabatic, as the time of nuclear motion for CO32− in K2CO3 product, through CO2●− trapping, is 0.368 femtoseconds shorter than the 1.856 × 10−3s charge transfer relaxation time. Microkinetic analysis reveals that the rate of CO2RR to CO2●− is 2.14 × 103 mol/cm2/s2 with a rate constant of 2.1 × 1010 cm/s. Our findings demonstrate that ensemble and k-Nearest Neighbours algorithms learn the CO2RR dataset, achieving a prediction accuracy of over 99%. The models were verified visually and quantitatively by overlaying predicted and experimental dataset. Diagnostic and SHAP analyses highlighted the gradient boost ensemble algorithm, predicting asymptotic current densities of -4.114 mA/cm2 or -13.340 mA/cm2, with high turnover frequencies (TOF) of 3.79 × 1010 h-1 or 12.30 × 1010 h-1 for CO2●− or K2CO3 generation on silver electrodes, respectively. These results consider both accuracy and robustness against overfitting, providing an opportunity to optimize future non-aqueous electrolytes for convenient TOF measurements at industrially relevant current densities.

The Peculiar Disk Evolution of 4U 1630-472 Observed by Insight-HXMT During its 2022 and 2023 Outbursts

We study the spectral properties of the black hole X-ray transient binary 4U 1630–472 during its 2022 and 2023 outbursts with Insight-HXMT observations. We find that the outbursts are in peculiar soft states. The effect of the hardening factor on the disk temperature is taken into account by the kerrbb model, and the flux and temperature of the disk are found to follow F∝Teff3.92±0.13 and F∝Teff4.91±1.00 for the two outbursts, respectively. The flux–temperature relation is roughly consistent with a standard disk. By fitting with a p-free model, the p-value, the exponent of the radial dependence of the disk temperature, is found to have an anticorrelation with disk temperature. Combining a joint diagnostic analysis with a diagram of the relation between the nonthermal fraction and luminosity, we find a possible scenario that the disk evolves from 2022 to 2023 toward a slim one with a decreasing radiation efficiency, where such an evolution may depend on the fraction of the nonthermal emission in the high soft state.

Diagnostic Accuracy of Neurocognitive and Executive Functions to Discriminate Women With and Without Fibromyalgia Syndrome: A Secondary Analysis.

Objective: The aim of the current study was to determine the capability of neurocognitive variables and executive functions to differentiate women with and without fibromyalgia syndrome (FMS). Methods: A secondary diagnostic accuracy analysis was conducted. A battery of neurocognitive and executive function tests (the D2 Attention test, the Rey-Osterrieth Complex Figure for visual perception, "Digits D/R/I" tests of the WAIS-IV battery for working memory, the 5-Digit test for mental inhibition, the Symbol Search for processing speed, and the Zoo Test for planning/decision-making) were collected in 129 women with FMS and 111 without FMS. The area under the receiver operating characteristic (ROC) curve, optimal cut-off point, sensitivity, specificity, and positive and negative likelihood ratios (LR) for each variable were calculated. Results: Between-group differences were identified in ROCF_Copy (p = 0.043), ROCF_Recall (p = 0.004), d2_TR (p = 0.019), d2_TA (p = 0.007), d2_TOT (p = 0.005), d2_CON (p = 0.004), d2_C (p = 0.042), Symbol Search (p = 0.008), Decoding _FDT (p = 0.001), Retrieving_FDT (p = 0.001), and Inhibiting_FDT (p = 0.024). The result showed that FDT-based outcomes (Retrieving_FDT: ROC 0.739, sensitivity 85.3%, specificity 48.6%; Decoding_FDT: ROC 0.724, sensitivity 50.4%, specificity 16.2%; Inhibiting_FDT: ROC 0.708, sensitivity 56.6%, specificity 22.5%) were the variables able to differentiate between women with and without FMS. Conclusions: Although women with FMS exhibited deficits in attention, long-term visual memory, processing speed, and mental inhibition when compared with women without FMS, only mental inhibition scores showed moderate diagnostic accuracy to discriminate between women with and without FMS. Future studies investigating these results in clinical settings are needed to identify the clinical relevance of these findings.

DFC-Net: a dual-path frequency-domain cross-attention fusion network for retinal image quality assessment

Retinal image quality assessment (RIQA) is crucial for diagnosing various eye diseases and ensuring the accuracy of diagnostic analyses based on retinal fundus images. Traditional deep convolutional neural networks (CNNs) for RIQA face challenges such as over-reliance on RGB image brightness and difficulty in differentiating closely ranked image quality categories. To address these issues, we introduced the Dual-Path Frequency-domain Cross-attention Network (DFC-Net), which integrates RGB images and contrast-enhanced images using contrast-limited adaptive histogram equalization (CLAHE) as dual inputs. This approach improves structure detail detection and feature extraction. We also incorporated a frequency-domain attention mechanism (FDAM) to focus selectively on frequency components indicative of quality degradations and a cross-attention mechanism (CAM) to optimize the integration of dual inputs. Our experiments on the EyeQ and RIQA-RFMiD datasets demonstrated significant improvements, achieving a precision of 0.8895, recall of 0.8923, F1-score of 0.8909, and a Kappa score of 0.9191 on the EyeQ dataset. On the RIQA-RFMiD dataset, the precision was 0.702, recall 0.6729, F1-score 0.6869, and Kappa score 0.7210, outperforming current state-of-the-art approaches.

The need for health literacy in the 21st century

Health literacy is the ability of individuals to understand, evaluate and use health information effectively in their daily lives and should not be reduced to the simple ability to read or write about health-related topics. With appropriate health literacy, citizens will gain greater autonomy in their self-care, improve their quality of life and make better use of health services, thereby reducing health service overload, inequalities and costs. To increase health literacy, it is necessary to identify the specific health needs of today's society and to implement educational programmes that include content and pedagogical methods that address these needs. Addressing these needs requires a diagnostic analysis embedded in each culture and continuous re-evaluation of the scope and effectiveness of the programmes implemented from an interdisciplinary perspective. Currently, most community and school-based programmes are based on disease prevention and maintaining good sexual and environmental health. Since the pandemic, there has also been an increase in programmes to achieve and maintain good mental health, combined with the use of various applications to prevent anxiety and depression. However, there is still a large gap in the evaluation of the impact and effectiveness of these programmes. In general, most of these programmes need to be adapted to the digital age and have a comprehensive and holistic design and purpose. In conclusion, we believe that health literacy is essential in the 21st century and that it must be based on the promotion of healthy habits and behaviours as well as an understanding of one's own health.

CFD Analysis of Flow Characteristics and Diagnostics of Leaks in Water Pipelines

CFD Analysis of Flow Characteristics and Diagnostics of Leaks in Water Pipelines

An overview of current developments and methods for identifying diabetic foot ulcers: A survey

AbstractDiabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays a crucial role in automated health monitoring and diagnosis systems, where the integration of medical imaging, computer vision, statistical analysis, and gait information is essential for comprehensive understanding and effective management. Diagnosing DFU is imperative, as it plays a major role in the processes of diagnosis, treatment planning, and neuropathy research within automated health monitoring and diagnosis systems. To address this, various machine learning and deep learning‐based methodologies have emerged in the literature to support healthcare practitioners in achieving improved diagnostic analyses for DFU. This survey paper investigates various diagnostic methodologies for DFU, spanning traditional statistical approaches to cutting‐edge deep learning techniques. It systematically reviews key stages involved in diabetic foot ulcer classification (DFUC) methods, including preprocessing, feature extraction, and classification, explaining their benefits and drawbacks. The investigation extends to exploring state‐of‐the‐art convolutional neural network models tailored for DFUC, involving extensive experiments with data augmentation and transfer learning methods. The overview also outlines datasets commonly employed for evaluating DFUC methodologies. Recognizing that neuropathy and reduced blood flow in the lower limbs might be caused by atherosclerotic blood vessels, this paper provides recommendations to researchers and practitioners involved in routine medical therapy to prevent substantial complications. Apart from reviewing prior literature, this survey aims to influence the future of DFU diagnostics by outlining prospective research directions, particularly in the domains of personalized and intelligent healthcare. Finally, this overview is to contribute to the continual evolution of DFU diagnosis in order to provide more effective and customized medical care.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Artificial Intelligence