Background The purpose was to evaluate the diagnostic effectiveness of High-risk human papillomavirus(hrHPV) testing, DNA methylation, PAX1/ZNF671 methylation in triaging patients with abnormal cytology and/or abnormal cervical biopsy pathology in cervical cancer screening; And the detection performance of different screening strategies was compared among clinician-taken cervical scrapes and paired self-collected urine and vaginal samples. Methods A total of 136 urine-based,137self-collected vaginal and 140 cervical scrapes samples were analyzed. Samples were tested for hrHPV DNA and six methylation markers. Various screening strategies from different samples were compared under the definitive histopathology for their diagnostic accuracy against two standards: cervical intraepithelial neoplasia grade 2 or more severe lesions (CIN2+) and cervical intraepithelial neoplasia grade 3 or more severe lesions (CIN3+). Results For PAX1 and ZNF671, the areas under the ROC curve were 0.929 and 0.862 and the methylation-positive rate was 89.5% (77/86) and 80.2% (69/86) in CIN3+ lesions. The cutoff values were 7.95and 10.92, respectively, with the highest Youden index values being 0.763 and 0.684, respectively. The sensitivity of hrHPV testing for CIN2+ was 89.80% in cervical scrape to 92.63% in self-collected vaginal samples. And the optimal marker panel (PAX1/ZNF671) resulted in an 85.8% sensitivity and 5.8 PLR for CIN2+ detection in cervical scrapes, and reached a highest sensitivity for CIN3+ in cervical scrapes (91.9%), markedly exceeding that of vaginal (71.4%) and urine (48.1%) samples (P<0.001). The Negative Predictive Value (NPV) for cervical scrapes (85.7%) was higher than self-collected alternatives (P<0.001). As for hrHPV and DNA Methylation, a perfect sensitivity (96.51%), NPV (90.00%) and Negative Likelihood Ratio(NLR) (0.07) for CIN3+ were reached. For hrHPV negative population, trough PAX1/ZNF671 detection, Cervical scrapes showed a highest sensitivity (100.00%) and specificity (91.30%), a 77.78% PPV and 100% NPV were achieved for discriminating CIN3 +. Conclusion The prevalence of methylation for PAX1/ZNF671 genes exhibited a strong positive correlation with the severity of cervical lesions, and demonstrates a better diagnostic value for CIN2+ lesions. DNA methylation testing, especially PAX1/ZNF671 offers a promising strategy to detect CIN2/3 lesions or more serious disease. Cervical samples were the perfect candidates for DNA methylation. Furthermore, PAX1/ZNF671 methylation assays had a strong capacity in screening and excludingCIN2+ lesions among HPV-negative individuals.

Validity and reliability of the "Pendleton test": An innovative special test for intraarticular hip pathology.

Purpose This study aimed to develop and validate a CT-based nomogram incorporating three-dimensional fractal dimension (FD 3D) to noninvasively predict tumor spread through air spaces (STAS) in stage IA lung adenocarcinoma. Materials and methods A retrospective analysis was performed on 110 patients with stage IA lung adenocarcinoma who underwent surgical resection. CT morphological features and fractal-dimension metrics were collected. Patients were categorized into STAS-positive ( n = 48) and STAS-negative ( n = 62) groups based on pathology. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of STAS. Receiver operating characteristic (ROC) curve analysis evaluated predictive performance, and a nomogram model was constructed and internally validated. Based on the nomogram score, patients were further stratified into low- and high-risk STAS groups using the optimal cutoff value determined by the maximum Youden index. Results Univariate analysis showed significant differences in consolidation-to-tumor ratio (CTR) ( p < 0.001), morphological irregularity ( p = 0.006), lobulation ( p = 0.039), pleural indentation ( p = 0.004), vascular convergence ( p = 0.010), and FD 3D ( p < 0.001) between groups. Multivariate analysis identified CTR, morphological irregularity, lobulation, and FD 3D as independent predictors of STAS in stage IA lung adenocarcinoma. The nomogram model achieved an area under the curve (AUC) of 0.894 (95%CI: 0.821–0.944; p < 0.001), with a sensitivity of 75.00% and a specificity of 90.32%. At the optimal cutoff value of 0.56, the model demonstrated a positive predictive value (PPV) of 85.71% in the high-risk group ( n = 42, 38.18%) and a negative predictive value (NPV) of 82.35% in the low-risk group ( n = 68, 61.82%), with significant differences in STAS prevalence between groups (85.71% vs. 17.65%, χ 2 = 46.18, p < 0.001). Conclusion The CT-based nomogram integrating FD 3D and key imaging features can noninvasively predict STAS status in stage IA lung adenocarcinoma. This model shows promise for assisting surgical decision-making, though prospective studies are needed to validate its clinical utility.

Introduction The Bilistick is a handheld point-of-care device for measuring total bilirubin levels in small blood volumes. We assessed its diagnostic accuracy and user convenience in near-term neonates cared for at home. Methods A prospective cohort study was conducted in nine Dutch community midwifery practices. Neonates ≥35 weeks' gestation were eligible if they were at home between postnatal days 2-8 and had not received phototherapy. A Bilistick version 2.0 was used in parallel to laboratory-based bilirubin (LBB) quantification when significant visible jaundice was observed or the transcutaneous bilirubin reading was elevated. Results 2314 neonates were included in the study, with 423 blood samples analyzed across 13 laboratories. On 203 occasions, the Bilistick was not used. Among the remaining 220 Bilistick readings, 104 failed, and two lacked corresponding LBB results. A Bland-Altman plot of 114 paired measurements of Bilistick and LBB showed a mean difference of +9.7 µmol/L (0.57 mg/dL) with corresponding 95% limits of agreement of -179.7 to +199.2 µmol/L (-10.5 to 11.7 mg/dL). The positive predictive value of a Bilistick reading for having a TSB level above the phototherapy threshold was 36.4%. The negative predictive value was 90.1%, sensitivity 60% and specificity 77.6%. Hemolysis (24%) contributed to overestimations by the Bilistick. Community midwives expressed multiple barriers related to user convenience. Conclusion Diagnostic accuracy of the Bilistick when used in the home setting was limited. Its use was further hindered by a significant proportion of failed readings and low user-convenience when operated by midwives.

Background China is experiencing rapid population aging, accompanied by a rising prevalence of type 2 diabetes mellitus (T2DM) and its complex complications. Cognitive impairment is one of the major complications of T2DM and currently lacks effective treatment. These two conditions can interact and aggravate each other, forming a vicious cycle. Objective This study aimed to identify reliable early predictors of cognitive impairment among elderly individuals with T2DM, in order to facilitate early intervention and delay disease progression. Methods A total of 202 elderly patients with T2DM hospitalized at Tianyou Hospital, affiliated with Wuhan University of Science and Technology, between May and September 2025 were enrolled. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) with a cutoff score of 26. Seventy-two participants scoring ≥26 were assigned to the normal cognition group, and 130 participants scoring ≤25 were assigned to the cognitive impairment group. Demographic information, hematological and imaging parameters, and scale scores related to sleep quality, anxiety–depression status, and activities of daily living were collected. Statistical analyses were conducted using R version 4.5. Results Least absolute shrinkage and selection operator regression selected 14 predictors. After analyzing the data, four factors remained independently associated with T2DM related cognitive impairment: age (OR = 1.96, 95% CI: 1.31–2.95, P = 0.001), HADS-D score (OR = 1.87, 95% CI: 1.25–2.80, P = 0.002), WMD (OR = 2.44, 95% CI: 1.14–5.25, P = 0.022), and HbA1c (OR = 1.53, 95% CI: 1.01–2.30, P = 0.043). The model demonstrated an AUC of 0.812 (95% CI: 0.778–0.891) and was well-calibrated (Hosmer-Lemeshow P = 0.661). After bootstrap validation, the optimism-corrected AUC was 0.751, indicating minimal overfitting. At the optimal cut-off of 0.685, the model achieved a sensitivity of 69.2% and a specificity of 81.9%, with a positive predictive value of 87.4% and a negative predictive value of 59.6%. DCA demonstrated a positive net benefit across threshold probabilities from 0.02 to 0.86, supporting the model’s clinical value. Conclusion This study developed a prediction model for T2DM related cognitive impairment in elderly Chinese patients. The model showed good discrimination, calibration, and clinical value, supporting its potential role for identifying high-risk populations. However, before using this model, more research is needed to confirm it’s performance in different people.

Interstitial lung disease (ILD) is frequently complicated by pulmonary hypertension (PH) resulting in reduced functional capacity, diminished quality of life, and increased mortality. However, standardized screening for PH in ILD is lacking, causing delays in diagnosis and treatment. PHINDER (NCT05776225) is a prospective multicenter study that aims to identify parameters for the detection of PH in ILD. Data were collected prospectively in patients with ILD from predefined routine testing, including clinical, physiological, and imaging assessments. Precapillary PH was defined as mean pulmonary arterial pressure > 20mmHg, pulmonary artery wedge pressure ≤ 15mmHg, and pulmonary vascular resistance (PVR) > 2Wood units (WU). Investigators estimated probability of precapillary PH based on noninvasive evaluations before confirmation by right heart catheterization (RHC). Preliminary results included 190 participants; 105 (55%) had precapillary PH and 26 (14%) had severe PH (PVR > 5WU). Notable parameters associated with precapillary PH included supplemental oxygen use (OR 3.6, p = 0.004), diffusing capacity of the lung for carbon monoxide ([DLCO] OR 0.9, p = 0.005), forced vital capacity % to DLCO % ratio (OR 1.1, p = 0.008), tricuspid annular plane systolic excursion to right ventricular systolic pressure ratio (OR 0.8, p = 0.020), tricuspid regurgitant velocity (OR 4.4, p = 0.006), pulmonary artery (PA) enlargement (OR 10.6, p < 0.001), PA/aorta diameter ratio (OR 1.7, p = 0.004), and right to left ventricle diameter ratio (OR 1.5, p = 0.021). There was a trend toward higher likelihood of PH with higher clinician suspicion of PH before RHC, but gestalt-based assessment showed limited accuracy relative to hemodynamic confirmation (positive predictive value, 59%; negative predictive value, 68%; accuracy, 60%). Preliminary findings support the composite use of pulmonary function testing, lung imaging, and echocardiography to improve early detection of precapillary PH in ILD and guide structured screening strategies. The final data set from PHINDER will provide guidance on thresholds for continuous variables with application in diagnosing PH in ILD, facilitating the development of a validated evidence-based screening tool to aid the detection of PH in ILD. NCT05776225.

Postoperative complications frequently occur within 24 h of surgery and are challenging to detect early. Tools such as the Modified Aldrete Score (MAS) and National Early Warning Score (NEWS) support postanaesthesia care unit (PACU) discharge decisions; however, their prognostic performance is uncertain. Clinical nurse assessments may offer additional value. To compare the prognostic performance of MAS, NEWS, and PACU nurse clinical assessments in predicting early postoperative complications (within 72 h of PACU discharge) in high-risk surgical patients. Prospective, two-centre observational cohort. Conducted in two PACUs in the Capital Region of Denmark. Two hundred and forty high-risk adult surgical patients were included after elective or emergency procedures. Complications were defined according to Clavien-Dindo grade II to V. Patients with missing nurse assessments (n = 15) were excluded from analyses involving this variable. The primary outcome was the prognostic performance of MAS in identifying early postoperative complications. Secondary outcomes included the performance of NEWS and PACU nurse assessments. Early complications were defined as events occurring within 72 h of PACU discharge. MAS and NEWS showed limited predictive ability (AUC = 0.555 and 0.589, respectively). PACU nurse assessments were significantly associated with early complications (AUC = 0.652, P = 0.006). Patients rated as 'Potentially unstable' or 'Unstable' had a higher risk despite meeting MAS criteria: odds ratio (OR) = 3.65, 95% confidence interval (CI), 1.53 to 8.76; relative risk (RR) = 0.32, 95% CI, 0.15 to 0.70. The optimal threshold yielded 72.4% sensitivity, 61.2% specificity, 21.6% positive-predictive value (PPV), and 93.8% negative-predictive value (NPV). Early complications were associated with increased risk of subsequent complications: OR = 5.0, 95% CI, 1.8-14.1, P < 0.001. PACU nurse assessments outperformed MAS and NEWS in predicting early postoperative complications. Integrating structured clinical judgment into discharge decisions may enhance risk stratification and safety. ClinicalTrials.gov: NCT06013891.

ObjectivesPsychosis is a rare but severe neuropsychiatric manifestation of systemic lupus erythematosus (SLE). Its prevalence, clinical predictors, and immunopathogenesis remain incompletely understood. This study aimed to estimate the prevalence of psychosis in adult SLE patients and identify associated clinical and immunological risk factors.MethodsThis systematic review and meta-analysis was conducted following PRISMA guidelines. A comprehensive search of PubMed, Embase, and the Cochrane Library was conducted to identify all relevant studies with ≥10 adults through November 2024. Case-series, case reports, narrative reviews and conference abstracts were excluded. Quality assessment employed the Cochrane Risk of Bias Tool for randomized controlled trials and the Newcastle-Ottawa Scale for observational studies. Statistical analysis was performed using the random-effects model, with heterogeneity assessed via I2 statistics.ResultsA total of 65 studies, comprising 31,495 SLE patients, were included. The pooled prevalence of psychosis in SLE patients was 4.5% (95% CI: 3.6%-5.5%) and 20.5% (95% CI: 10.0%-37.6%) among neuropsychiatric SLE (NPSLE) patients. Psychosis frequently occurred within 2 years of SLE onset and was strongly associated with higher SLE disease activity, positive anti-ribosomal P antibodies, antiphospholipid antibodies, and complement consumption. Delusions and hallucinations predominated among clinical presentations. Heterogeneity among studies was substantial.ConclusionObservational studies have reported that psychosis in SLE is strongly associated with increased disease activity and immune dysregulation. Anti-ribosomal P antibodies demonstrate a high negative predictive value, offering a valuable diagnostic adjunct. Recognition of psychosis may prompt closer clinical evaluation and, where appropriate, consideration of immunosuppressive treatment.

Diagnostic performance of on-site, CT-derived fractional flow reserve in predicting invasive fractional flow reserve and absolute myocardial blood flow.

Brain gliomas represent one of the most aggressive cancers worldwide and remain difficult to diagnose accurately at an early stage. Although computer-aided diagnostic (CAD) approaches have progressed notably in recent years, distinguishing between high-grade glioma (HG-G), low-grade glioma (LG-G), and healthy brain tissue on magnetic resonance images is still a major challenge. To address this issue, we propose a multi-stage framework designed to push the boundaries of current classification methods. The framework begins with a preprocessing phase that integrates Adaptive Gamma Correction (AGC) for improved contrast adjustment with a Denoising Convolutional Neural Network (DnCNN) for noise removal. Feature extraction is then carried out from three representative layers across three fine-tuned transfer learning CNNs (TRCNNs), where each model is optimized by a different algorithm. These deep representations are combined with handcrafted texture measures based on the Gray-Level Co-occurrence Matrix (GLCM), producing nine unique CNN-GLCM Fused Feature (CGFF) sets. The resulting hybrid descriptors are evaluated using several strong classifiers such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM), along with a stacked ensemble to reinforce stability and robustness. Performance significance was verified through the Friedman statistical test, with p < 0.05, confirming the reliability of the improvements. The framework achieved 99.05% accuracy, 98.99% recall, 99.52% specificity, 99.08% positive predictive value (PPV), and 99.54% negative predictive value (NPV), consistently surpassed state-of-the-art (SOTA) methods across all reported metrics.

Objective: To evaluate the diagnostic accuracy of Protein Induced by Vitamin K Absence-II (PIVKA‑II; des-γ-carboxy prothrombin) in patients with suspected HCC who had nondiagnostic AFP or atypical imaging findings. Study Design: Retrospective Cross-sectional study. Setting: Rehman Medical Research Institute, Peshawar. Period: May 2018 and September 2024. Methods: Among 128 patients with suspected HCC the patients were 106 men and twenty two women, average age 60.3 years. Data from electronic medical records including demographics, hepatitis status, imaging results, laboratory markers and biopsy results were collected. PIVKA-II levels were analyzed and diagnostic accuracy has been calculated (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and overall accuracy). The performance of PIVKA-II was evaluated by receiver operating characteristic curve analysis. Results: Of 128 patients, 86 (67.2%) had HCC confirmed on biopsy. PIVKA‑II was abnormal in 81/128 patients (71 HCC, 10 non-HCC). Using these counts, PIVKA‑II showed sensitivity 82.6%, specificity 76.2%, PPV 87.7%, NPV 68.1%, and accuracy 80.5%. AUROC was 0.776 (p = 0.002). Conclusion: PIVKA-II is a useful biomarker for diagnosis of HCC when AFP is non-diagnostic or imaging results are inconclusive. It demonstrates high sensitivity and reasonable specificity to justify its use for early detection and differentiation of HCC from other liver lesions. Future studies should address the limitations of this single-institution, retrospective study to confirm the clinical utility of PIVKA-II.

BackgroundOpioid use disorder (OUD) remains a critical public health crisis in the United States. Despite widespread policy and clinical interventions, early identification of individuals at risk for developing OUD remains challenging due to limitations in traditional screening approaches and a lack of individualized risk stratification methods. Machine learning (ML) methods offer an opportunity to develop timely, high-performing, and explainable predictive models that can enhance OUD prevention strategies in clinical settings.ObjectiveThis study aims to develop and validate an ML model using electronic health record (EHR) data to predict the 3-month risk of incident OUD among adults initiating opioid therapy and to stratify patients into clinically actionable risk groups.MethodsThis prognostic modeling study used 2017‐2022 OneFlorida+ EHR data to develop and validate ML algorithms predicting 3-month incident OUD risk. We included 182,083 adults (≥18 y) without cancer, overdose, or OUD or hospice history who received ≥1 outpatient, noninjectable opioid prescription. Using 183 predictors measured in sequential 3-month intervals, we developed an elastic net, least absolute shrinkage and selection operator, gradient boosting machine (GBM), and random forest models on randomly split training, testing, and validation sets. Model performance was assessed using C-statistics, predictive values, and number needed to evaluate, with patients stratified into risk deciles for clinical applicability. Model explainability was assessed using Shapley additive explanations, and fairness was evaluated using standard metrics. We externally validated the best-performing model using an independent cohort from the 2018‐2020 UPMC (formerly University of Pittsburgh Medical Center) health system.ResultsIn the validation sample (n=60,694), GBM (C-statistics=0.879, 95% CI 0.874‐0.884) and elastic net (C-statistics=0.872, 95% CI 0.867‐0.877) outperformed least absolute shrinkage and selection operator (C-statistics=0.846, 95% CI 0.840‐0.851) and random forest (C-statistics=0.798, 95% CI 0.792‐0.804), with GBM model requiring the fewest predictors (n=75) for predicting 3-month incident OUD. Using the GBM algorithm to predict the subsequent 3-month OUD risk, the top decile subgroup had a positive predictive value of 3.26%, a negative predictive value of 99.8%, and a number needed to evaluate of 31. The top decile (n=6696) captured ~68% of patients with OUD. Shapley additive explanations analysis identified age, number of outpatient visits, history of back and other pain conditions, comorbidity burden, and opioid prescribing patterns as the strongest predictors of incident OUD. Fairness assessment showed an acceptable false negative rate parity across race, age, and sex. In external validation on the UPMC cohort, the GBM model maintained good discrimination (C-statistics=0.756, 95% CI 0.750‐0.762) and effective risk stratification.ConclusionsAn ML algorithm predicting incident OUD derived from OneFlorida+ EHR data performed well in external validation with data using UPMC. The algorithm might be valuable for incident OUD risk prediction and stratification across health systems, with potential to inform early intervention.

Recent studies using polymerase chain reaction (PCR) demonstrate that blood and pleural fluid (PF) cultures are inadequate to identify pneumococcus in children with pneumonia and empyema, leading to unnecessary utilization of broad-spectrum, often toxic antibiotics. Identification of pneumococcus facilitates the de-escalation of antibiotics. We compared a point-of-care immunochromatographic test that detects pneumococcal C-polysaccharide antigen (BINAX) performed in PF and urine to cultures (blood and PF) and 2 different PCR tests in PF of 79 children with empyema. BINAX in PF had 100% sensitivity and negative predictive value (NPV) to identify pneumococcus in children with empyema, compared with cultures (sensitivity 20.7%/NPV = 68.8%) or PCR (sensitivity 96.5%/NPV 98%). Urine BINAX demonstrated lower sensitivity (89%) and NPV (93%). BINAX in PF is superior to cultures (PF and blood), identifying pneumococcus in children with empyema. PCR has similar sensitivity and NPV to BINAX but is not readily available.

Herein, RATSGO (Real-time Automated Training and Sensing for Gas Odor), a fully automated live-animal olfactory training platform, for the detection of GBL as a sexual assault-facilitating drug is reported. The system integrates four distinct operant conditioning-based training paradigms, all executed without human intervention, to enhance learning speed, consistency, and scalability. Using this fully automated framework, four rats were trained to identify γ-butyrolactone (GBL). Three of the four animals successfully reached the predefined learning completion criterion, whereas one failed to meet the criterion. Across 320 automated trials, the GBL rats achieved a mean detection accuracy of 90%, with sensitivity and specificity values of 97% and 82%, respectively. The corresponding positive and negative predictive values (PPV and NPV) were 85% and 96%. When challenged with GBL diluted in drinking water (180 trials), performance remained high, yielding 88% accuracy, 89% sensitivity, 87% specificity, 85% PPV, and 90% NPV. Similarly, in experiments involving GBL mixed with whisky (200 trials), the rats demonstrated robust recognition capability, achieving 90% overall accuracy, perfect sensitivity (100%), 84% specificity, 79% PPV, and 100% NPV. Importantly, odor discrimination performance was preserved when reassessed four months after the completion of training, indicating strong long-term retention of the learned odor representations. Collectively, these findings confirm that the RATSGO system supports rapid, stable, and precise odor learning, underscoring its promise as a practical and extensible biological sensing platform for chemical detection applications.

Background &amp; Aims: The accurate, noninvasive assessment of hepatic steatosis is essential in living liver donor evaluation, where disease prevalence is low, and donor safety is paramount. This study evaluated commonly used noninvasive diagnostic tools for detecting hepatic steatosis in a real-world donor screening setting. Methods: We analyzed 108 living liver donor candidates (18–53 years) with complete MRI, CT, transient elastography (FibroScan®), and biochemical data obtained during routine donor evaluation. Hepatic steatosis was defined as an MRI-proton density fat fraction (PDFF) ≥5%, which served as the noninvasive reference standard. Diagnostic performance metrics, receiver operating characteristic (ROC) analyses, and correlations with serum fibrosis indices (FIB-4 and APRI) were assessed. Results: MRI-PDFF identified hepatic steatosis in 21 donors (19.4%). Controlled attenuation parameter (CAP), measured by transient elastography, demonstrated high sensitivity (90.5%) and negative predictive value (97.1%), supporting its role as a rule-out screening tool. CT showed excellent specificity (97.7%) but lower sensitivity (61.9%), consistent with a confirmatory role when MRI is unavailable. Serum fibrosis indices were generally low and did not correlate strongly with imaging-based steatosis. Conclusions: In the low-prevalence setting of living liver donor evaluation, CAP-based transient elastography provides effective noninvasive screening for hepatic steatosis, while MRI-PDFF serves as a confirmatory reference when indicated. These findings support a stepwise, clinically practical diagnostic approach that prioritizes donor safety and workflow efficiency.

Circulating tumor cells (CTCs) are detectable in early-stage cancer and may enable early cancer detection. We evaluated a CTC-based assay as a complementary biomarker for breast cancer detection in an Asian population with a high prevalence of dense breast tissue. In this single-center, prospective, blinded study, peripheral blood from Taiwanese women with breast cancer and healthy controls was analyzed using a CTC-enumeration platform (CMx) based on biomarker expression (cytokeratin 18 [CK18], mammaglobin [MGB], CD45), cell morphometry, and nuclear features. A machine-learning model integrating CTC biomarkers with age, white blood cell (WBC) count, and platelet count was developed to assess classification performance, providing proof-of-concept for combining CTC-derived and routine blood parameters in breast cancer risk assessment. A total of 228 breast cancer patients and 170 healthy controls were included. Age and CK18- and MGB-positive CTC counts differed significantly between groups, whereas WBC and platelet counts did not. An ensemble linear support vector machines model incorporating age and CTC features achieved an area under the curve of 0.85 (95% CI, 0.73-0.96) in the independent test cohort, with high sensitivity (0.93), positive predictive value (0.74), and negative predictive value (0.86), but modest specificity (0.57). In the exploratory BI-RADS 3/4 subgroup, the model identified all cancer cases (sensitivity 1.00), with a specificity of 0.44 and overall accuracy of 0.79. This study demonstrates the feasibility of combining CTC enumeration with machine learning for breast cancer detection and supports the need for future large-scale, multicenter, multiethnic prospective external validation.

Background Patients admitted with viral respiratory tract infections are at risk of bacterial co-infections, often requiring antibiotics. Standard bacterial cultures may take days to yield results, making early indicators of bacterial co-infection a potential asset. Methods In a retrospective regionwide cohort study, we included all patients admitted to a hospital in the Central Denmark Region with COVID-19, influenza, or respiratory syncytial virus (RSV) in the period February 2019 to February 2024. Further inclusion criteria were having a blood sample taken for procalcitonin measurement and blood cultures within 72 h of admission. We evaluated the diagnostic value of procalcitonin compared to other inflammatory markers. Results There were 1,670 patients fulfilling the inclusion criteria. The majority 1,556 (92.2%) were infected with SARS-CoV-2, 51 (3.7%) with influenza A, 1 (0.1%) with influenza B, 45 (2.7%) with RSV, and 17 (1.0%) with multiple viruses. Blood cultures were positive in 43 (2.6%) of the patients after a median of 67.4 h from time of admission. Median procalcitonin levels were higher in patients with bacteraemia (1.7 µg/L) than in patients without bacteraemia (0.2 µg/L. Overall, procalcitonin cut-off at >0.25 µg/L and >0.50 µg/L tended to have higher sensitivity, specificity, positive and negative predictive values than C-reactive protein (CRP) > 100 mg/L and total leukocyte count >10 × 109 cells/L, although the numerical differences were small. Overall diagnostic performance for bacterial pneumonia was lower. Conclusions All biomarkers had relatively low sensitivity for bacteraemia and bacterial pneumonia in patients with viral respiratory tract infection.

Introduction: Community-acquired urinary tract infection (CA-UTI) is the second most common infection in the community. The gold standard for diagnosing urinary tract infections (UTIs) is urine culture. A urine dipstick can also be used to diagnose UTIs. This study was designed to compare the urine dipstick test with urine culture in the diagnosis of community-acquired urinary tract infections in a tertiary care hospital in the Colombo district, Sri Lanka.Methods: Four hundred and eighty-nine urine samples were inoculated into cysteine-lactose-electrolyte-deficient agar. They were tested for the presence of nitrite and leukocytes using urine dipsticks, following the manufacturer's instructions.Results: In the entire group, the sensitivity, specificity, positive predictive value, and negative predictive value of the leukocyte esterase test were 91.92%, 76.15%, 91.41%, and 77.34%, respectively, and those of the nitrite test were 91.36%, 75.38%, 91.11%, and 75.97%, respectively.Conclusion: The dipstick is a low-cost, user-friendly test that can be used in an outpatient setting to identify patients who require antibiotics. This will help in establishing antibiotic stewardship by limiting unnecessary prescriptions.

INTRODUCTION. Evaluation of endodontic treatment outcomes through radiographic assessment is subject to interobserver variability and depends heavily on clinician experience. Artificial intelligence (AI) platforms offer potential for standardized, objective assessment of periapical healing. MATERIALS AND METHODS. This retrospective study analyzed 400 panoramic radiographs from patients who underwent root canal treatment between January 2023 and December 2024. An AI platform developed using TensorFlow and Keras, with model training in PyTorch and validation in MATLAB Deep Learning Toolbox, was employed. Three blinded expert endodontists independently assessed all radiographs, with consensus serving as the gold standard. Outcomes were classified as healed, healing, or diseased based on periapical index criteria. Diagnostic performance metrics including sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS. The AI platform demonstrated overall accuracy of 89.8% in classifying treatment outcomes. For detecting healed cases, sensitivity was 92.3%, specificity 87.6%, PPV 88.9%, and NPV 91.5%. For diseased / persistent pathology detection, sensitivity was 88.7%, specificity 93.2%, PPV 84.3%, and NPV 95.1%. Agreement between AI and expert consensus was substantial (Cohen’s κ = 0.834, p &lt; 0.001). AI performance was superior in anterior teeth (93.2% accuracy) compared to molars (86.4% accuracy, p = 0.008). Processing time per radiograph averaged 2.3 ± 0.4 seconds. CONCLUSIONS. The AI-driven platform demonstrated high diagnostic accuracy comparable to expert assessment, with potential for standardized, rapid evaluation of endodontic treatment outcomes. Further prospective validation and clinical integration studies are warranted.

The Joint Commission recommends universal suicide screening in emergency departments (EDs), which emphasizes the need to identify at-risk individuals. Existing suicide risk prediction models rely primarily on clinical data and demonstrate limited performance. The potential of incorporating psychosocial information to enhance predictive performance remains understudied. To evaluate whether augmenting clinical data-based risk scores with psychosocial factors improves the prediction of suicide attempt (SA). This retrospective prognostic study based on electronic health record data included 4661 ED patients discharged after presentation for suicidal ideation (SI) from middle Tennessee hospitals between June 1, 2018, and February 27, 2024. The primary outcome was SA within 90 days of ED admission and time-to-event in days. Clinical data-based Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) score and 6 psychosocial factors (homelessness, financial insecurity, chronic stress, social isolation, loneliness, and adverse childhood experiences) derived from clinical notes were integrated using a Cox proportional hazards regression model. Performance metrics included area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPRC), positive predictive value (PPV), negative predictive value, sensitivity, and specificity. Performance was evaluated for models trained on (1) VSAIL, (2) psychosocial factors, and (3) VSAIL plus psychosocial factors. This study included 3382 Vanderbilt University Hospital (VUH) (mean [SD] age, 26.1 [15.6] years; 1751 males [51.8%]) and 1279 Regional Health Systems (RHS) (mean [SD] age, 34.5 [18.0] years; 715 males [55.9%]) ED visits for SI. Within 90 days, SAs were reported in 160 (4.7%) VUH and 34 (2.7%) RHS ED visits for SI. Compared with VSAIL alone, VSAIL plus psychosocial factors was associated with significantly increased median AUROC (VUH: 0.645 [IQR, 0.645-0.645] vs 0.734 [IQR, 0.719-0.747]; P < .001; RHS: 0.547 [IQR, 0.547-0.547] vs 0.680 [IQR, 0.672-0.687]; P < .001), AUPRC (VUH: 0.083 [IQR, 0.083-0.083] vs 0.122 [IQR, 0.111-0.137]; P < .001; RHS: 0.029 [IQR, 0.029-0.029] vs 0.054 [IQR, 0.052-0.058]; P < .001), and PPV (VUH: 0.093 [IQR, 0.082-0.094] vs 0.143 [IQR, 0.123-0.161]; P < .001; RHS: 0.042 [IQR, 0.040-0.043] vs 0.112 [IQR, 0.096-0.129]; P < .001) while maintaining specificities above 0.90. Chronic stress emerged as the strongest predictor of SA (β = 0.643 [95% CI, 0.427-0.859]; P < .001). In this prognostic study of patients discharged from the ED after presentation for SI, augmenting a clinical data-based suicide risk prediction model with clinical note-extracted psychosocial factors was associated with significantly higher predictive performance. These findings suggest that psychosocial factors can enhance risk stratification and support targeted interventions, such as therapies addressing chronic stress.