Search Results (14,035)

Background: Cardiac troponin testing is central to evaluating suspected acute coronary syndromes, yet its expanding use may increase resource utilization in low-risk emergency department populations. Methods: We conducted a multicenter retrospective cohort study across three tertiary hospitals in South Korea (2017–2023) using harmonized electronic health record data integrated with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) and the National Emergency Department Information System (NEDIS). Visits were stratified into low, intermediate, and high risk by age and chest pain presentation, and cardiac troponin T was categorized as normal (<0.014 ng/mL), borderline (0.014–0.052 ng/mL), or elevated (>0.052 ng/mL). Outcomes included emergency department length of stay, hospital admission, 30-day revisit, 30-day coronary revascularization, and 30-day mortality. Results: Among 727,772 visits, troponin testing increased from 29.8% in 2017 to 45.5% in 2023. High-risk patients were consistently tested, whereas testing rose substantially in intermediate- and low-risk groups. In high-risk patients, normal troponin values were associated with lower 30-day revascularization and mortality, without prolonging length of stay or increasing admissions. In contrast, in lower-risk groups, testing was associated with longer stays and higher admissions without clear short-term clinical benefit. Conclusions: These findings support more targeted troponin testing protocols to optimize emergency department resource use while preserving diagnostic performance in higher-risk presentations. Full article

Urinary tract infections (UTIs) are the most frequent infections in hospitalized and outpatient settings, where Escherichia coli is the predominant pathogen. Conventional diagnostic and antimicrobial susceptibility testing (AST) methods are time-consuming, often requiring 48 h, leading to empirical antibiotic therapy and contributing to antimicrobial resistance (AMR). FASTinov^® developed a rapid phenotypic method that enables AST directly from urine samples within two hours using flow cytometry. In this study, 154 inoculated urine samples were analyzed to evaluate the performance of two diagnostic panels: FASTgramneg for Gram-negative bacteria and FASTgrampos for Gram-positive bacteria. Data analysis was performed using bioFAST^® software (version 3.0), providing results in accordance with EUCAST guidelines. The FASTgramneg panel allows detection of resistance mechanisms, including extended-spectrum β-lactamases (ESBLs), and screening of AmpC β-lactamases and carbapenemases; the FASTgrampos panel additionally determines the minimal inhibitory concentration (MIC) of vancomycin for Staphylococcus aureus. Overall agreement with conventional AST methods was 97.5% for Gram-negative bacteria and 95.0% for Gram-positive bacteria. All resistance mechanisms were correctly identified with no false positives. The essential agreement for vancomycin’s MIC was 95.2%, with a BIAS of +14.3%. Reproducibility was 99.5% for FASTgramneg and 95.0% for FASTgrampos. These results demonstrate that the FASTinov^® kit significantly reduces turnaround time while maintaining high accuracy, supporting improved UTI management and antimicrobial stewardship. Full article

Background: Diabetes mellitus is a global health challenge, especially among homeless people. Early prediction of diabetes can reduce treatment costs and improve interventions. This study aimed to identify predictors of diabetes among homeless adults by utilizing artificial intelligence and providing recommendations for diabetes prevention. Methods: A case-control study of 150 homeless adults in Giza, Egypt (99 diabetes cases and 51 controls), analyzed 43 variables collected through interviews and physiological measures, with missing data imputed. Feature selection using recursive feature elimination and univariate and correlation analyses reduced the predictors to 13 variables. The class imbalance was addressed using synthetic minority over-sampling on the training set. Six models and a stacking ensemble with XGBoost as a meta-learner were evaluated using 5-fold cross-validation and performance metrics, including the accuracy, precision, recall, F1-score, and AUC-ROC. Results: The key predictors included BMI, systolic blood pressure, triceps skinfold thickness, waist circumference, lifestyle factors, comorbidities, diastolic blood pressure, age, medication adherence, educational level, marital status, duration of residence, and diabetes knowledge. Individual classifiers achieved a moderate performance (accuracy: 56.7–70.0%, F1-score: 0.686–0.781). The stacking ensemble substantially outperformed individual models, achieving a 95.45% accuracy, a 100% precision, a 93.75% recall, a 0.968 F1-score, and a 0.979 AUC-ROC on the test set. Conclusions: Machine learning models can reliably predict diabetes. The proposed hybrid stacking model outperformed conventional classifiers in terms of the prediction performance, highlighting the benefits of ensemble learning and sophisticated resampling strategies in dealing with imbalanced medical data. It is recommended that healthcare institutions integrate AI-powered diagnostic assistance technology into clinical processes to aid in the early detection and treatment of diabetes. Full article

Cassava brown streak disease (CBSD), caused by cassava brown streak virus (CBSV; Ipomovirus brunusmanihotis) and Ugandan cassava brown streak virus (UCBSV; Ipomovirus manihotis) (family Potyviridae, genus Ipomovirus), is increasingly becoming a threat to cassava production in several parts of Africa, especially in Eastern, Central and Southern Africa. In Kenya, the disease continues to wreak havoc on cassava production leading to a significant reduction in crop yields and economic losses of up to USD 1 billion. Variation in virus populations make the control of CBSD challenging as virus genomic variation can affect the accuracy of diagnostic tests, lead to resistance breaking isolates and jeopardize strategies of breeding for resistance. CBSV and UCBSV populations obtained from cassava fields in Kenya were characterized. In total, 44 new complete sequences of CBSV and UCBSV were assembled and 40 sequences successfully submitted to GenBank. Single Nucleotide Polymorphism (SNP) analysis revealed that the cylindrical inclusion protein (CI) is the most stable region across the genome of CBSV and UCBSV. In contrast, protein 1 (PI) and the coat protein (CP) were the most hypervariable regions. Phylogenetic analysis showed three major geographical groupings for both UCBSV and CBSV isolates, suggesting a continued spread of the viruses through human-mediated movement of infected planting materials. The data obtained in this study can support the development of disease management strategies through improved molecular diagnostic tests and targets for breeding for resistance against CBSD. Full article

(1) Brucellosis is a zoonotic infection that remains a significant public health concern in endemic regions. This study aimed to determine the seroprevalence of brucellosis in a tertiary care hospital, analyze associated risk factors, and evaluate the diagnostic performance of commonly used serological tests. (2) The study was based on the serological test results of 24,545 samples collected between 2020 and 2023. Rose Bengal, standard tube agglutination, and Brucellacapt tests were used for the diagnosis of brucellosis. Data were analyzed according to age, sex, clinical department, and seasonal distribution using SPSS version 25.0. (3) Overall, 367 cases (1.5%) tested positive. When the 367 seropositive cases were evaluated by year, the annual distribution showed a declining trend, decreasing from 2.5% in 2020 to 1.2% in 2023. Among the positive cases, 57.8% were female, and 36% were aged between 41 and 64 years. The infectious diseases department had the highest positivity rate (37.1%). Brucellacapt showed the highest positivity rate (90.2%), followed by Rose Bengal (76.2%). The highest monthly positivity rate was observed in October (11.4%), and seasonally in autumn (31.3%). (4) The Brucellacapt test has demonstrated high sensitivity and serves as a valuable supplementary diagnostic tool in the evaluation of brucellosis. However, its low specificity underscores the necessity for careful interpretation of positive results and supports its use in conjunction with other serological tests to enhance diagnostic accuracy. Considering seasonal and departmental variations, a combined testing approach may improve overall diagnostic accuracy. Full article

The term asphyxia refers to a disruption in brain function due to rapid and persistent cerebral hypoxia or anoxia as a consequence of accidental or non-accidental injury. Considering the different mechanisms that may determine asphyxiation, such injuries can be referred to different categories: strangulation (death by hanging, ligature or manual strangulation), suffocation (smothering, choking, confined spaces and vitiated atmosphere), mechanical asphyxia (positional and traumatic asphyxia) and drowning (submersion or immersion in liquid). In both human and veterinary forensic practice, fatal asphyxia is considered among the most diagnostically challenging categories of sudden death, as it often produces only subtle and non-pathognomonic macroscopic signs, which can be easily covered by post-mortem alterations. Therefore, a wide range of information is often needed for the diagnosis of asphyxiation, including medical history, crime scene analysis, testimonies and physical evidence, along with the macroscopic and histological findings. The following review addresses the main lesions, ancillary tests and diagnostic issues associated with non-drowning asphyxia in veterinary forensic pathology. Full article

Arcanobacterium haemolyticum is a facultative anaerobic, Gram-positive bacillus that has garnered attention due to its role in human infections, particularly among adolescents and young adults. Traditionally associated with pharyngitis, this organism is increasingly recognized for its involvement in systemic infections, including bacteremia, central nervous system abscesses, and Lemierre’s syndrome. The pathogenicity of A. haemolyticum is attributed to its production of hemolysins and neuraminidase, facilitating tissue invasion and immune evasion. Clinically, infections often present with sore throat, fever, and a characteristic scarlatiniform rash, which can lead to their misdiagnosis as streptococcal pharyngitis. Severe manifestations, though rare, have been documented, particularly in immunocompromised individuals. Diagnosis is challenging due to the organism’s slow growth and potential misidentification as diphtheroids in cultures. Accurate identification necessitates specific culture conditions and biochemical testing. Treatment typically involves beta-lactam antibiotics; however, the emergence of resistance patterns necessitate susceptibility testing to guide therapy. This review aims to consolidate current knowledge on A. haemolyticum, emphasizing its clinical presentations, diagnostic challenges, and management strategies, thereby enhancing recognition and treatment of infections caused by this emerging pathogen. Full article

As one of the world’s leading cotton-producing countries, China frequently experiences severe yield reductions due to crop diseases and pest infestations, with losses often exceeding 20%. Although computer vision models can identify diseased plants, they currently fail to connect visual symptoms to the diagnostic reasoning process used by agronomists. This leads to text descriptions that ignore the biological causes of the damage. To fix this, we built Multimodal Knowledge Graph-Enhanced Cross Vision Transformer-18-Dagger-408 and Text-to-Text Transfer Transformer for Cotton Disease and Pest Image Captioning (MKG-CottonCapT6), a model that uses a local knowledge database to generate professional diagnostic reports from field images. The technical core consists of a Multimodal Knowledge Graph (MMKG) containing 14 types of entities (such as Pathogens and Control Agents) and 12 types of relations. We use a Cross Vision-Transformer-18-Dagger-408 (CrossViT) encoder to capture both the overall leaf shape and microscopic details of pests. Through a Visual Entity Grounding (VEG) module, the model maps visual features directly to specific triplets in the graph. These triplets are then turned into text sequences and fused with image data in a Text-to-Text-Transfer-Transformer (T5) decoder. To train the model, we collected a dataset of cotton images paired with expert descriptions of lesions, colors, and affected plant parts. Tests show that MKG-CottonCapT6 performs better than standard models, reaching an Information-based Metric for Image Captioning (InfoMetIC) score of 72.6%. Results prove that by using a specific alignment loss (𝓛_align), the model generates reports that correctly name the disease stage and recommend specific chemicals, such as Carbendazim or Triadimefon. This framework provides a practical tool for farmers to record and treat cotton diseases with high precision. Full article

Background/Objectives: Radiological assessment has become indispensable for modern clinical decision-making. Image quality plays a critical role in the reliability of radiological interpretation. Unlike most previous studies, this study investigated the effect of monitor type on diagnostic accuracy and ease of diagnosis under physical conditions outside the radiology unit. Methods: Three image sets were prepared for the study, consisting of emergency radiological images, each containing 50 computed tomography, magnetic resonance imaging, and digital radiography images. The image sets were examined by five emergency specialists, who were blinded to each other’s work, under emergency service conditions on a standard monitor (SM), medical monitor (MM), and advanced monitor (AM). The accuracy and ease of diagnosis were analyzed statistically according to the type of monitor used. Results: Overall diagnostic accuracy rates were 98.7% for SM, 100% for AM, and 100% for MM. Cochran’s Q test demonstrated a statistically significant difference between monitor types (p = 0.002), with significant pairwise differences for SM–AM and SM–MM comparisons. The absolute risk difference between SM and AM/MM was 1.3%, corresponding to a relative risk of 1.013 and a number needed to benefit (NNB) of 77. Ease of diagnosis scores increased progressively across monitor types (SM: 7.6 [IQR 7–8], AM: 9.4 [IQR 9–9.8], MM: 9.8 [IQR 9.6–10]; p < 0.001), with a large overall effect size (Kendall’s W = 0.81). Multilevel modeling confirmed that these associations persisted after adjustment for clustering effects. Conclusions: In situations where medical monitors cannot be used due to cost and operational constraints, opting for advanced monitors instead of standard monitors may modestly improve diagnostic accuracy while substantially enhancing perceived ease of diagnosis. Full article

Molecular point-of-care testing (POCT) for respiratory infections has undergone remarkable advancement over the past two decades, driven by technological innovation and urgent clinical needs highlighted by the COVID-19 pandemic. This comprehensive systematic review was conducted following PRISMA 2020 guidelines, synthesizing evidence from 254 peer-reviewed studies published between 2006 and 2026, with detailed analysis of the 30 most relevant papers selected through a rigorous four-stage screening process. The review examines the evolution of molecular POCT technologies, including reverse transcription polymerase chain reaction (RT-PCR), loop-mediated isothermal amplification (LAMP), recombinase polymerase amplification (RPA), and CRISPR-based detection systems. Key findings demonstrate that modern molecular POCT platforms achieve diagnostic performance comparable to laboratory-based testing, with sensitivities ranging from 88% to 100% and specificities from 98% to 100%, while delivering results in 15 to 80 min. These technologies enable rapid, accurate detection of major respiratory pathogens, including SARS-CoV-2, influenza A/B, respiratory syncytial virus (RSV), and atypical bacteria. The integration of microfluidic systems, portable devices, and smartphone-based analysis has expanded access to testing in resource-limited settings, emergency departments, and wearable platforms. This review provides critical insights for clinicians, researchers, and policymakers regarding the current state, clinical applications, and future directions of molecular POCT for respiratory infections. Full article

Background/Objectives: Malaria remains a major cause of hospitalization in rural Madagascar, yet data on in-hospital clinical presentation, management, and patient outcomes remain limited. Methods: We conducted a three-year retrospective study (2023–2025) at a rural district hospital in Ambatoboeny, Madagascar, including patients of all ages hospitalized with malaria confirmed by rapid diagnostic testing and microscopy. Sociodemographic, clinical, laboratory, and treatment data were extracted from routine records. Length of hospital stay (LOS) was analyzed continuously and categorized as ≤2, 3–4, or ≥5 days. Seasonal admission patterns and factors associated with LOS were assessed using chi-square or Fisher’s exact tests, and associations with rainfall seasonality were explored using Spearman’s correlation. Results: Among 134 hospitalized patients, median age was 15 years (interquartile range (IQR) 7–25) and 52.2% were female. Plasmodium falciparum predominated (94.0%), while mixed-species infections were identified in 6.0% of cases; 20.1% of cases were classified as severe malaria, including 10.4% with cerebral malaria. Co-infections were frequent (52.2%), most commonly Schistosoma haematobium infection (14.2%) and typhoid fever (12.7%). Intravenous artesunate was initiated in 97.8% of patients; all received paracetamol and 94.8% received intravenous fluids. Median LOS was 2 days (IQR 2–3); 12.7% had prolonged hospitalization (≥5 days). Prolonged LOS was significantly associated with cerebral malaria, high parasitemia (≥5%), blood transfusion, and age < 15 years (all p ≤ 0.034), while co-infection and nutritional status were not. Conclusions: Hospitalized malaria in rural Madagascar presents with heterogeneous clinical phenotypes and a high burden of co-infections. Prolonged LOS is primarily driven by markers of severe disease and supportive care requirements, underscoring the need for early severity recognition and resource planning in low-resource hospitals. Full article

Background: Rapid diagnostic tests (RDTs) for bloodstream infections (BSIs) reduce time to pathogen identification, yet evidence on their real-world economic and clinical value remains inconsistent. This study aimed to compare clinical outcomes, antibiotic utilization, and hospital costs associated with different rapid microbiological identification methods versus standard culture. Methods: A retrospective observational study was conducted in a tertiary university hospital including 115 hospitalized patients with suspected or confirmed BSIs. Multiplex PCR (mPCR), fluorescence in situ hybridization (FISH), and MALDI-TOF MS were compared with conventional culture. Outcomes included mortality, length of stay, antibiotic-days, and direct and indirect hospital costs. Nonparametric and exploratory adjusted analyses were performed. Results: No significant differences were observed across diagnostic groups for age, sex, mortality, or length of stay. Patients tested with mPCR showed higher empirical and total antibiotic-days and increased antibiotic-related costs (p < 0.05). Median direct and indirect hospital costs were numerically lower with FISH and mPCR but did not reach statistical significance. Adjusted analyses confirmed that diagnostic modality was not independently associated with mortality or costs. Conclusions: Rapid diagnostics accelerate identification but demonstrate heterogeneous downstream clinical and economic effects. Their value appears to depend more on local implementation and antimicrobial stewardship integration than on diagnostic speed alone. Full article

Background: Mindray BC-6800 Plus ^TM (Mindray, Shenzhen, China) measures reticulocyte counts and provides the reticulocyte hemoglobin (RHe, reticulocyte Hb expression) and mean reticulocyte volume (MRV). We studied the performance of those reticulocyte-derived parameters for the detection of iron-restricted erythropoiesis in older patients, compared with standard laboratory tests. Methods: A total of 220 anemic patients, age > 65 years, were recruited in the context of routine health controls. Group differences were assessed using analysis of variance (ANOVA), with p values < 0.05 considered statistically significant. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance of RHe and MRV for detecting iron-restricted erythropoiesis. The reference standard for iron deficiency was sTfR > 52 nmol/L. A multivariable logistic regression model was constructed for iron-restricted erythropoiesis, including MRV, Ret-He and s-ferritin as independent covariates, and adjusted for inflammatory status and renal function. Results: Overall, 30.1% in the group had IDA and 29.0% had mixed IDA/ACD, so 59.1% had absolute or functional iron deficiency, while 40.9% had adequate iron supply. RHe and MRV values differed significantly between both groups (p = 0.0001). For s-ferritin, ROC analysis yielded an AUC of 0.685 (95% CI 0.606–0.767), with the best Youden index at a cut-off of 100 µg/L, corresponding to 72.5% sensitivity and 65.9% specificity. An MRV cut-off of 97.4 fL identified iron-restricted erythropoiesis with 88.2% sensitivity and 82.7% specificity (AUC 0.878, 95% CI 0.799–0.957); RHe AUC 0.860, 95% CI 0.777–0.947; cut-off 30.4 pg; sensitivity 82.4%, specificity 79.8%). In multivariable logistic regression adjusted for CRP and eGFR, s-ferritin was not an independent predictor of iron-restricted erythropoiesis, whereas MRV and RHe remained significant. The overall model demonstrated good discrimination, with an AUC 0.808 (95% CI 0.804–0.814). Conclusions: RHe and MRV are reliable parameters for assessing iron supply to erythropoiesis in older patients and can assist in distinguishing iron-restricted erythropoiesis in complex, inflammation-driven settings. Full article

Background/Objectives: Rapid and accurate classification of ring-enhancing brain lesions (REBLs) into infection or neoplasm is key to clinical triaging for expedited diagnostics in the former to enhance treatment outcomes, especially in the immunocompromised patients. High-resolution three-dimensional (3D) T1 post-contrast (T1+C) MRI provides high-dimensional volumetric data for radiomics analysis. While radiomics is useful in brain neoplasm characterization, its utility in central nervous system infection remains under-explored. In this exploratory study, we aim to determine if a radiomics-machine learning model, based solely on a 3D T1+C MRI dataset, can distinguish infective from neoplastic REBLs. Methods: 92 patients (infection, n = 26; neoplasm, n = 66) with 402 REBLs, who fulfilled criteria for “definite” or “probable” infective or neoplastic REBLs, were identified from scans performed at our hospital over four years and formed the training/validation dataset. All REBLs were manually annotated on T1+C MRI images under radiological supervision. In total, 1197 radiomics features were extracted, feature selection performed using mutual information, and nine machine learning classifiers applied to assess patient-level infection vs. neoplasm classification performance. End-to-end 2D CNN baselines and hybrid radiomics–CNN configurations were additionally evaluated under the same protocol for comparative benchmarking. Model performance was tested on an external holdout dataset of 57 patients (infection, n = 25; neoplasm, n = 32) with 454 REBLs from another hospital. Results: The Multi-layer Perceptron (MLP) model using the Original + LoG + Wavelet feature group demonstrated superior performance. In the cross-validation cohort, it achieved a mean AUC of 0.80 ± 0.02, sensitivity of 0.83 ± 0.09, specificity of 0.77 ± 0.08, and balanced accuracy of 0.80 ± 0.02. On external holdout data, the same configuration showed stable and sustainable performance with an AUC of 0.84, sensitivity of 0.84, specificity of 0.75, and balanced accuracy of 0.80. Conclusions: Our radiomics-machine learning model, based solely on a high-resolution 3D T1+C dataset, shows potential for distinguishing infective REBLs from neoplastic REBLs. Further study, with additional MR sequences and clinical data in a multimodal MRI radiomics-machine learning model, is warranted. Full article

Background/Objectives: Translating brain volumetric biomarkers to individual-level Alzheimer’s disease (AD) diagnosis remains challenging due to difficulty interpreting raw volumes without longitudinal monitoring or matched controls. We tested a classification model using population-referenced volumetric percentiles to distinguish AD from cognitively normal (CN) subjects and evaluated its generalization across independent cohorts. Methods: Brain volumes from 95 regions were extracted using an automated segmentation pipeline and converted to age and sex adjusted percentiles using a reference population (N = 1833). A logistic regression classifier was trained on ADNI subjects (N = 873; AD = 183, CN = 690) split into training (60%), validation (20%), and test (20%) sets. The model was evaluated on two independent validation datasets: the held-out ADNI validation set and an external Korean cohort (N = 72; AD = 36, CN = 36) acquired with different scanner protocols and demographic characteristics. Results: The model achieved excellent discrimination across all evaluation sets: ADNI validation (AUC = 0.963, accuracy = 90.3%), ADNI test (AUC = 0.960, accuracy = 89.7%), and Korean external validation (AUC = 0.981, accuracy = 87.5%). The minimal validation gap (0.018) demonstrated robust generalization. Positive coefficients for ventricular regions reflected AD-associated atrophy patterns, while negative coefficients for medial temporal structures indicated their contribution within multivariate patterns distinguishing AD from normal aging. Conclusions: Population-referenced brain volumetric percentiles enable accurate AD classification with robust generalization across populations and scanner protocols. By contextualizing individual brain structure relative to normative populations while accounting for age and sex, this approach demonstrates potential for clinical translation as an accessible neuroimaging-based diagnostic tool. Full article