Endocrine society 2025 diagnostic criteria increase primary aldosteronism detection in hypertensive patients: a comparative study with 2016 guidelines.

Identification of chalcopyrite-binding peptides for flotation applications using phage display and deep sequencing.

Performance evaluation of intelligent hybrid approach and ant colony optimisation for early-stage diabetes prediction in e-Health applications

Evaluation of the detection performance of the RaST-TAS β-lactamase screening reagent kit based based on the DSTM principle for ESBL production.

A quickest detection problem with false negatives

RAD-OAEnsemble: Residual attention DenseNet with radiograph-EHR fusion and dual-stream deep feature embedding for knee osteoarthritis risk prediction

Artificial intelligence in emergency skeletal X-ray: post-deployment monitoring and clinical impact of incorrect AI results.

Impact of delayed blood culture bottles incubation on the recovery and detection of microorganisms : Effect of ambient room temperature versus 4°C.

A neuromorphic safety monitor for verifiable runtime assurance in stochastic control loops

In the era of digital transformation, datacenters and High Performance Computing (HPC) Systems have emerged as the backbone of global technology infrastructure, powering essential services across various industries, including finance and healthcare. Therefore, ensuring the uninterrupted service of these datacenters has become a critical challenge. Thermal anomalies pose a significant risk to datacenter operation, potentially leading to hardware deterioration, system downtime, and catastrophic failures. This threat is exacerbated by the growing number of datacenters, increased power density, and heat waves fostered by global warming. Detecting thermal anomalies in datacenters involves several challenges. Large-scale data collection is difficult, requiring diverse monitoring signals from thousands of nodes over long periods. The absence of labeled data complicates the identification of normal and abnormal states. Establishing accurate classification thresholds to minimize false positives and negatives is another significant hurdle. Traditional statistical methods often fail to capture temporal dependencies and complex correlations in monitoring signals. Additionally, finding anomalies at both the system and subsystem levels adds to the complexity. Deploying machine learning models in production environments presents technical and operational challenges, making real-time anomaly detection a demanding task. This paper introduces ThermADNet, a Thermal Anomaly Detection framework that combines statistical rules-based methods with Deep Neural Network (DNN) techniques for thermal anomaly detection in datacenters. ThermADNet utilizes a semi-supervised learning approach by training on a ”semi-normal” dataset, addressing the challenges of large-scale data collection, semi-normal dataset identification, and classification threshold establishment. This framework’s efficacy is validated by its success in identifying real physical thermal failure events within a Tier-0 datacenter, pinpointing anomalies at both the system and subsystem levels, including compute nodes and datacenter infrastructure. In the critical evaluation window covering the July 28 failure, ThermADNet achieves precision and recall up to 0.97, with F1-scores as high as 0.97. By providing detailed information about anomalies, the framework clarifies the characteristics and reasoning behind the DNN outputs, thereby building trust in the AI model and ensuring that users can understand and rely on the system’s decisions. By offering a sophisticated method for thermal anomaly detection, ThermADNet significantly contributes to enhancing datacenter reliability and efficiency. This advancement supports the uninterrupted operation of critical HPC systems, averting considerable economic and societal losses.

Fault detection for switched systems based on reachable set estimation.

Enhancing SLN metastasis diagnosis in breast cancer: A comparative study of percutaneous and intravenous CEUS with FNA.

A new paradigm in tuberculosis diagnostics: Biosensing advances for early detection of Mycobacterium tuberculosis.

Assessing antibody stability in filter paper-preserved blood samples for wildlife disease surveillance in tropical forests.

Periodontitis is a common chronic disease associated with systemic conditions such as diabetes and cardiovascular disease. Diagnosis typically relies on dental examinations and radiographs, which may be underutilised by individuals who avoid, delay, or lack dental care. This study evaluated the potential of routine blood biomarkers and demographic data for screening moderate-to-severe periodontitis. Data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2011-2012 adults aged ≥ 30 years (N = 3,338). Periodontal status was classified using CDC/AAP definitions. An XGBoost classifier was trained on 77 features spanning demographics, complete blood count, glycemic and hemo-globin markers, heavy metals, and additional biochemical assays. Model performance was assessed with Accuracy, Precision, Recall, and F1 score. SHapley Additive exPlanations (SHAP) were used for interpretability. Subgroup analyses were conducted by age and gender. The model achieved 61.1% Accuracy, 57.4% Precision, 94.6% Recall, and an F1 score of 71.5%. Performance was higher in males (F1 = 78.2) than in females (F1 = 63.9%). SHAP identified age, gender, blood cadmium, blood lead, and glycohaemoglobin (HbA1c) as top predictors. Routine blood biomarkers and demographics can be leveraged for non-dental screening of periodontitis, offering a feasible preventive strategy in primary care. The model achieved high recall, minimising false negatives, and identified biologically plausible predictors. This approach may help flag at-risk individuals, particularly in underserved populations, and support integration of oral health into general medical care.

Hybrid fractional groupers and moray eels driven deep learning for pneumonia detection using multi-modal data in federated learning.

This study aims to evaluate the accuracy of an automated algorithm designed to identify catheter-associated urinary tract infections (CAUTIs) using electronic health records from a hospital. We assess the algorithm's effectiveness as a clinical decision support tool by analyzing its ability to accurately identify CAUTIs based on predefined clinical parameters. We conducted a retrospective analysis of all patients hospitalized in the acute internal medicine wards of the Cantonal Hospital of Baden between November 2022 and October 2024. Automated algorithm identifies potential CAUTI cases based on standard centers for disease control (CDC) criteria. The automated algorithm identified patients meeting these criteria as potential cases and the number of CAUTIs is visualized in a dash board. All records were manually reviewed and inconsistencies validated by an infection specialist. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), likelihood ratios (LR) and 95% confidence intervals were calculated to assess performance. Between November 2022 and October 2024, a cohort of 1424 patients with indwelling catheters was assessed, resulting in the identification of 11 manually diagnosed CAUTIs. The automated algorithm identified 6 of these cases, yielding 6 true positives (TP), 1,385 true negatives (TN), 28 false positives (FP), and 5 false negatives (FN). The algorithm demonstrated a sensitivity of 55% (95% CI: 27.3% - 81.8%) and a specificity of 98% (95% CI: 97.3% - 98.7%), positive likelihood ratio (LR+) of 27.3 and a negative likelihood ratio (LR-) of 0.5. AUC of 0.76 (95% CI: 0.73-0.80) with P-value of 0.0001. In this validation study, the automated algorithm demonstrated high specificity (98%) but limited sensitivity (55%) for the detection of CAUTIs. While a positive algorithm result was strongly associated with true infection, nearly half of confirmed CAUTI cases were not identified. Therefore, the algorithm may support surveillance by prioritizing cases for review; however, due to its limited sensitivity, it cannot reliably exclude non-cases and should not replace manual case adjudication. The primary reasons for missed cases were gaps in electronic health record (EHR) documentation. In hospitals with comprehensive EHR use across all departments, improved sensitivity is expected.

To systematically evaluate the diagnostic performance of artificial intelligence (AI) models for hepatocellular carcinoma (HCC) and to pool sensitivity and specificity using a diagnostic meta-analysis, with further assessment of the robustness of findings across different validation levels. Original studies on AI-based diagnosis of HCC were systematically searched. Literature screening, data extraction, and quality assessment were independently performed by two reviewers. Extracted data included basic study characteristics, model type, validation level, and diagnostic performance metrics, including sensitivity, specificity, area under the curve (AUC), and extractable true-positive (TP), false-positive (FP), false-negative (FN), and true-negative (TN) values. If a single study reported results from different validation levels or analytical scenarios, these were extracted as separate study records for quantitative synthesis. Methodological quality was assessed using the QUADAS-2 tool. Formal validation sets (validation, independent validation, or external validation) were included in the primary analysis, whereas development sets and internal validation sets were included in the sensitivity analysis. Because only one eligible external validation study was available for early HCC, this outcome was presented descriptively only. For studies with extractable 2 × 2 table data, pooled sensitivity and specificity were estimated using the Reitsma bivariate random-effects model, and a summary receiver operating characteristic (SROC) curve was constructed. An additional sensitivity analysis retaining a single highest-validation-level record per study was performed to assess the influence of potential non-independence among records from the same study. A total of 11 studies were included in the qualitative analysis. Because some studies reported results from different validation levels or analytical scenarios, separate study records were extracted for quantitative synthesis. Of these, four formal validation records were included in the primary analysis, seven records were included in the sensitivity analysis, and one early HCC record was included for descriptive presentation. The primary analysis showed that the pooled sensitivity and specificity of AI models for HCC diagnosis were 0.904 (95% CI: 0.845-0.942) and 0.971 (95% CI: 0.891-0.993), respectively. In the sensitivity analysis, after inclusion of development and internal validation datasets, the pooled sensitivity was 0.895 (95% CI: 0.831-0.936) and the pooled specificity was 0.935 (95% CI: 0.846-0.974). Overall, the currently available records reporting AUC values suggested that AI models had good discriminative ability for HCC diagnosis. In the early HCC analysis, a single external validation study reported a sensitivity of 0.88 and a specificity of 1.00; however, no pooled analysis was performed because of the limited evidence. In the additional study-level sensitivity analysis, the pooled sensitivity and specificity were 0.889 and 0.964, respectively, which were close to the record-level primary estimates. Exploratory assessment according to QUADAS-2 domains suggested that patient selection bias, particularly selected case-control comparisons, may have contributed to the high pooled specificity. Current evidence suggests that AI models may have promising potential as auxiliary diagnostic tools for HCC. However, because the primary pooled estimates were based on only four formal validation records with heterogeneous data modalities, reference standards, study populations, and validation designs, these results should be interpreted as preliminary and hypothesis-generating rather than definitive evidence of clinical readiness. Further high-quality, multicenter, prospective external validation studies with complete patient-level 2 × 2 data and clinically relevant comparator populations are needed to clarify the real-world clinical utility of AI models for HCC diagnosis, particularly for early HCC.

Association Between Immunosenescence and High Bacterial Load in Elderly Patients with Pulmonary Tuberculosis: A Retrospective Cohort Study Conducted in Southwest China, 2013-2020.

Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. However, this is often time-consuming and labour-intensive. Automation can ensure up-to-date evidence; reduce retrieval, screening time, and costs; and make information accessible to non-academic stakeholders. This study investigated whether a machine learning approach can perform any stages of a traditional scoping review on the health impacts, policies, and interventions related to air pollution due to domestic waste burning. A traditional approach was conducted in parallel with machine learning methods enabling a comparison of the efficiency and quality of the partially automated approach against the manual review. Findings from the two approaches were compared and the final set of included articles were considered for (1) reported impacts of waste burning on health outcomes and (2) recommendations on solutions and interventions to prevent/reduce adverse effects on health from waste burning. We found a range of health impacts associated with waste burning, including low birth weight, hypertensive disorders of pregnancy, adverse respiratory outcomes like asthma and wheeze, cancer risk, and mortality. Few studies proposed solutions or evaluated the effectiveness of interventions. The ML approach showed a tendency towards false positives, which are preferable to false negatives (where relevant papers were excluded). Results showed that the model can conduct initial searches and decisions for the review. However, the articles included in the model should be screened manually for final acceptance. Therefore, we propose a hybrid approach be used until the automated model can be further refined.