Nevin Manimala

BMC Nephrol. 2025 Dec 6. doi: 10.1186/s12882-025-04669-0. Online ahead of print.

ABSTRACT

BACKGROUND: Acute kidney injury (AKI) is a prevalent and severe complication following non-cardiac surgery, often leading to poor outcomes. Despite the critical role of inflammation in AKI pathogenesis, reliable preoperative predictive models remain elusive. The pan-immune inflammation value (PIV), a novel index that integrates counts of neutrophils, platelets, lymphocytes, and monocytes, provides a comprehensive reflection of systemic inflammation. This study aimed to develop and validate a clinical prediction model for postoperative AKI (PO-AKI) in non-cardiac surgical patients.

METHODS: This retrospective study included adult patients who underwent non-cardiac surgery under general anaesthesia. The objective was to construct a model to predict PO-AKI. The statistical analysis focused on model construction and validation. LASSO regression was employed for variable selection to identify the most parsimonious set of predictors. The model’s performance was evaluated based on its discriminative ability (AUC), with calibration and decision curve analysis used to assess its clinical utility.

RESULTS: The cohort consisted of 1,164 adult patients. AKI was diagnosed in 8.4% of patients. The primary outcome, the performance of the prediction model, showed an AUC of 0.70. The model incorporated PIV and emergency surgery. The secondary outcome, the discriminative ability of PIV alone, yielded an AUC of 0.691. The model demonstrated good calibration and provided a clinical net benefit across a wide range of threshold probabilities.

CONCLUSION: We developed and validated a prediction model for PO-AKI. This model, which integrates PIV and emergency surgery, serves as an effective tool for preoperative risk stratification, facilitating the identification of high-risk patients and optimizing perioperative management.

PMID:41353543 | DOI:10.1186/s12882-025-04669-0

BMC Public Health. 2025 Dec 7. doi: 10.1186/s12889-025-25844-w. Online ahead of print.

ABSTRACT

BACKGROUND: Latent tuberculosis infection (LTBI) is a significant reservoir for active tuberculosis development. Identifying key risk factors is crucial for prevention strategies. Machine learning techniques can uncover complex relationships between risk factors and disease outcomes.

METHODS: Data were collected from China’s Tuberculosis Management Information System. LTBI was defined by positive tuberculin skin tests. A case-control design comparing LTBI (n = 669) with active tuberculosis (ATB, n = 669) patients was employed. Propensity score matching (1:1) was performed using age, gender, and education level. Four machine learning models (random forest, XGBoost, support vector machine, and neural network) were developed for feature importance analysis. Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression identified key risk factors. Bootstrap resampling (n = 1,000 iterations) assessed model stability with 95% confidence intervals. Shapley Additive Explanations (SHAP) analysis provided feature importance interpretation. A risk nomogram was constructed and evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis.

RESULTS: Among 1,338 matched participants, XGBoost demonstrated superior performance (AUC = 0.898, accuracy = 85.7%, sensitivity = 84.2%, specificity = 86.9%). SHAP analysis revealed age group (mean |SHAP value|=0.818) as the most influential predictor, followed by medical insurance type (0.599), income group (0.523), and education level (0.439). Logistic regression identified 11 significant risk factors: age (OR = 2.35, 95%CI: 1.86-2.96), BMI (OR = 0.81, 95%CI: 0.71-0.93), smoking status, occupational dust exposure, diabetes, medical insurance type, immunosuppressant use, education level, silicosis, anemia, and TB contact history. The nomogram showed good discrimination (AUC = 0.839) and clinical utility, identifying 64.44% of subjects as high-risk with 53.62% confirmed as true positives at 20% risk threshold.

CONCLUSION: This study successfully identified key LTBI risk factors using machine learning approaches. The developed nomogram provides a practical tool for targeted screening in resource-limited settings. Interventions targeting modifiable factors such as smoking cessation and occupational dust control may reduce LTBI and active TB burden.

PMID:41353539 | DOI:10.1186/s12889-025-25844-w

BMC Med Educ. 2025 Dec 6. doi: 10.1186/s12909-025-08336-8. Online ahead of print.

ABSTRACT

BACKGROUND: The rapid integration of blended learning (BL) into anatomy education has transformed traditional teaching. the preferences and perceptions of medical students toward BL, and its impact on anatomy learning, remain underexplored.

OBJECTIVE: This multi-institutional study aimed to assess medical students’ preferences and perceptions regarding BL in anatomy education, and to identify factors influencing their anatomy learning across three universities in Egypt and Oman.

METHODS: A comparative cross-sectional survey was conducted among 615 medical students from Alexandria University (Egypt), Mansoura University (Egypt), and National University (Oman). The validated Blended Learning Questionnaire (BLQ), adapted from Western Sydney University, was administered online. The BLQ evaluated preferences for learning modalities, satisfaction with BL, the role of self-regulated learning (SRL), small group activities. Data were analysed using descriptive statistics, chi-square tests, t-tests, and ANOVA, with significance set at p < 0.05.

RESULTS: students expressed a preference for BL and online modalities over traditional face-to-face lectures, with the highest preference for BL observed in National University. Female students favoured small group learning, while SRL was most valued by National students. The use of audio-visual resources and flexibility in accessing online materials were highly rated. Institutional differences were noted in preferred online tools and the value of small group activities.

CONCLUSION: Medical students across diverse settings prefer blended and online learning approaches for anatomy, highlighting the need for flexible, resource-rich, and student-cantered curricula. These highlight the importance of supporting SRL and using technology to optimize anatomy education, with implications for curriculum design and faculty development.

PMID:41353536 | DOI:10.1186/s12909-025-08336-8

BMC Med Imaging. 2025 Dec 6. doi: 10.1186/s12880-025-02094-1. Online ahead of print.

ABSTRACT

BACKGROUND: This study explores the feasibility and effectiveness of an interpretable machine learning model for assessing the pathological grading of pancreatic ductal adenocarcinoma (PDAC) using radiomics and topological features derived from contrast-enhanced CT habitat subregions.

METHODS: A retrospective study was conducted on a total of 306 patients with PDAC from two hospitals: a training cohort (n = 176), a validation cohort (n = 76), and a test cohort (n = 54). K-means clustering analysis was first used to segment portal venous phase CT images into three habitat regions. Radiomics features of the whole-tumour region, along with radiomics and topological features of each habitat region, were extracted respectively. LASSO regression was applied for feature dimensionality reduction to construct the radiomics score (Rad-score) for the whole-tumour region and the habitat score (H-score) for each habitat region. Meanwhile, logistic regression was used to identify statistically significant predictors from clinical and semantic features. Five machine learning algorithms were used to construct Habitat-TDA models, with interpretability analysis performed via SHAP analysis.

RESULTS: Total volume, diabetes, and M staging were identified as independent risk factors for predicting the pathological grading of PDAC, and were used to construct the Clinical model. 6 radiomics features with non-zero coefficients were selected to calculate the Rad-score, which was further used to construct the WholeRad model. In the three habitat regions, 6, 5, and 6 topological and radiomics features were included to generate the H-score. The logistic regression algorithm performed best in the validation and test cohorts and was ultimately selected as the classifier for constructing the Habitat-TDA model. SHAP analysis showed that H-score1, derived from Habitat Region 1 (the habitat region with the lowest average CT value), has the most significant average impact on the model output intensity. The AUC values of the Habitat-TDA model in the training, validation, and test cohorts were 0.894, 0.872, and 0.829, all outperforming the clinical model (0.784, 0.765, 0.731) and WholeRad model (0.817, 0.810, 0.773).

CONCLUSIONS: The Habitat-TDA model improves the accuracy and interpretability of preoperative predictions of PDAC grading, providing a promising tool for personalised management.

PMID:41353533 | DOI:10.1186/s12880-025-02094-1

BMC Anesthesiol. 2025 Dec 7. doi: 10.1186/s12871-025-03550-9. Online ahead of print.

ABSTRACT

BACKGROUND: Patients undergoing rotator cuff repair often experience moderate to severe postoperative pain. Interscalene brachial plexus block can provide analgesia and reduce opioid requirements. Bupivacaine is a standard anesthetic used for the brachial plexus block. Whether adding liposomal bupivacaine as an adjunct to bupivacaine-based brachial plexus block could improve analgesia and postoperative functional recovery remained unclear.

METHODS: A prospective, double-blind, randomized controlled clinical trial was conducted in patients scheduled for rotator cuff repair between September 2024 and March 2025. Eligible patients were randomized to receive either bupivacaine alone or a combination of bupivacaine with liposomal bupivacaine. The primary outcome was postoperative recovery measured by the quality of recovery-15 (QoR-15) scale at 24, 48, and 72 h after surgery. Secondary outcomes included perioperative measurements, pain intensity, muscle strength, and post-discharge functional evaluations. Complications were recorded.

RESULTS: A total of 113 patients (56 and 57 in the bupivacaine and combination groups, respectively) were included. Baseline characteristics were comparable between the two groups. Postoperative recovery was significantly better in the combination group than in the bupivacaine group at 48 h after surgery (P = 0.023). A greater proportion of patients in the combination group reported satisfaction than in the bupivacaine group (P = 0.023). The combination group also experienced significantly better pain relief at 12-, 24-, and 48-h but lower muscle strength between 6 and 48 h postoperatively than the bupivacaine group (all P < 0.01). Otherwise, there were no statistically significant differences between the two groups in intraoperative measurements, postoperative length of hospital stay, postoperative oxycodone consumption, heart rate, mean arterial pressure, complications, or post-discharge functional evaluations.

CONCLUSION: Adding liposomal bupivacaine to a standard bupivacaine-based brachial plexus block could safely improve postoperative recovery, pain relief, and patient satisfaction, but reduce muscle strength within 48 h after rotator cuff repair, without a significant impact on shoulder function after hospital discharge.

TRIAL REGISTRATION: Retrospectively registered, Chinese Clinical Trial Registry (registration number ChiCTR2500099378), registration date: March 21, 2025.

PMID:41353527 | DOI:10.1186/s12871-025-03550-9

BMC Infect Dis. 2025 Dec 6. doi: 10.1186/s12879-025-12288-1. Online ahead of print.

ABSTRACT

BACKGROUND: Culture-negative sepsis (CNNS) constitutes a significant proportion of neonates admitted to the NICU. However, the outcomes and factors influencing antimicrobial therapy in this group remain understudied.

METHODS: We prospectively enrolled preterm neonates (<34 weeks’ gestation) with clinical features of sepsis, with or without sepsis screen positive results. Primary outcome was a composite of death, bronchopulmonary dysplasia (BPD), retinopathy of prematurity requiring treatment, Intraventricular haemorrhage ≥ 2 and periventricular leukomalacia. Details of antimicrobial therapy were also collected.

RESULTS: Over an 18-month period, 172 neonates were enrolled. 104 had CNNS and 68 had culture-positive sepsis (CPNS). Primary outcome was observed in 19 (18.3%) neonates with CNNS and 25 (36.8%) with CPNS, with an adjusted odds ratio (aOR) of 0.50 (95% CI: 0.22-1.12, p = 0.095). Except for BPD, which was significantly lower in CNNS (aOR: 0.10; 95% CI: 0.02-0.52, p = 0.006), there was no statistically significant difference in other outcomes between groups. Multidrug-resistant organisms comprised 67.6% of the gram-negative bacterial isolates. Median (IQR) cumulative duration of antibiotic therapy was 5 (3-7) days in CNNS and 20.5 (15-24.3) days in CPNS. Prolonged cumulative antibiotic use was observed in 50 (48%) CNNS neonates (>5 days) and 50 (73.5%) CPNS neonates (>14 days). In CNNS group, 38 (36.5%) received second-line antibiotics, and 6 (5.7%) received third-line antibiotics.

CONCLUSION: In preterm neonates, composite outcome of mortality and major morbidities did not differ significantly between those with CNNS and CPNS. However, a considerable proportion of CNNS neonates received a prolonged course of higher antibiotics. Thus, there is a need for strategies to improve clinical outcomes and strengthen adherence to antibiotic stewardship principles.

PMID:41353525 | DOI:10.1186/s12879-025-12288-1

Sci Rep. 2025 Dec 6. doi: 10.1038/s41598-025-29419-y. Online ahead of print.

ABSTRACT

Rural areas worldwide face unprecedented challenges from rapid urbanization, necessitating transformative development pathways to rural revitalization. Here, we present a theoretical framework positioning new quality productivity (NQP)-a productivity paradigm driven by innovation-as a promising lever for rural revitalization. Using panel data from 282 cities in China spanning 2010-2021, we construct fixed effects models and two-stage least squares models to examine NQP’s causal impact on rural revitalization outcomes. Our analysis reveals that NQP significantly enhances rural revitalization with lasting effects. Moderating effect models demonstrate that NQP’s impact is more pronounced in economically developed eastern regions and under stronger government support. Spatial econometric models uncover substantial positive spillover effects extending to both geographically and economically proximate cities. These findings underscore the importance of tailoring NQP to local conditions, enhancing government guidance, and fostering cross-regional collaboration. Our study provides a systematic framework and robust evidence for the penetration of innovation-driven productivity into rural areas, offering actionable insights for public policies aimed at fostering sustainable rural development.

PMID:41353513 | DOI:10.1038/s41598-025-29419-y

Sci Rep. 2025 Dec 6. doi: 10.1038/s41598-025-31501-4. Online ahead of print.

ABSTRACT

The efficacy of three-dimensional (3D) magnetic resonance imaging (MRI)-based registration techniques in femur models of osteoarthritis (OA) with respect to OA severity was investigated in this study. MRI data of 58 OA femurs (23 Kellgren-Lawrence grade 2, 20 grade 3, and 15 grade 4) and 31 normal femurs were analyzed. Distal femurs were segmented and converted into 3D reconstructed models. Several registration techniques (fiducial registration and automated landmarking using point-cloud alignment and correspondence analysis [ALPACA]), were applied to OA femur models. Fit quality and volume differences between the reference and OA models were assessed with respect to OA severity. Generalized Procrustes analysis (GPA) and principal component analysis (PCA) explored important bone-shape features of OA femurs. Deformable ALPACA registration exhibited the best fit. Significant differences were observed in the quality of fit of our techniques and volume differences between the reference and OA models in the OA severity groups. The mean OA model demonstrated bony enlargement at the edges of the cartilage plate in 3D statistical shape analysis (SSA). This shape variation was a major component associated with OA severity in the GPA-aligned PCA. This novel 3D MRI-based registration technique and SSA is useful to differentiate OA severity grades.

PMID:41353498 | DOI:10.1038/s41598-025-31501-4

Eur Arch Otorhinolaryngol. 2025 Dec 6. doi: 10.1007/s00405-025-09852-5. Online ahead of print.

ABSTRACT

PURPOSE: Training and refining both custom and pre-trained convolutional neural network (CNN) models for calculation of intracochlear positional index (ICPI) is as effective as manual calculation. The ICPI is a position factor that is known to influence cochlear implant performance, however manual calculation on computed tomography (CT) imaging is labour-intensive and prone to calculation errors. Automation of this process with machine learning via a custom built CNN model aims to reduce the difficulty in obtaining this position factor. Increasing the number of training epochs will improve accuracy. Our study aims to develop a validated CNN for ICPI calculation, which may improve surgical electrode positioning.

METHODS: Custom built CNN model and pre-trained ResNet 50 model trained and validated on 34 images, and tested on eight CT images of temporal bones with cochlear implants. The ground truth was manually established by calculating the distance from modiolus to electrode (DE) and lateral wall (DL), and applied to derive the ICPI.

RESULTS: The pre-trained ResNet-50 model outperformed the custom-built CNN, with improvement statistically significant on evaluation metrics. The ResNet-50 model has lower mean absolute error and root mean squared error (RMSE). In both models, increasing the number of training epochs from ten to 100 improves accuracy of the ICPI calculation.

CONCLUSION: Our machine learning models successfully achieved automation of ICPI calculation, with increasing accuracy increasing training epochs to 100 iterations. Future studies should explore optimizing these models and validating them on broader datasets to enhance their applicability in real-world scenarios by comparison to speech and audiometric outcomes.

PMID:41353482 | DOI:10.1007/s00405-025-09852-5

Sci Rep. 2025 Dec 6. doi: 10.1038/s41598-025-30237-5. Online ahead of print.

ABSTRACT

Accelerometer data are commonly reduced into epoch summary measures (ESMs) for analysis, e.g. ENMO (Euclidean Norm Minus One), MAD (Mean Amplitude Deviation), MIMS (Monitor Independent Movement Summary) or Counts. We compared associations with all-cause mortality of the volume and intensity of physical activity when derived from those four measures in the Whitehall II and UK Biobank cohorts. Volume (Average Acceleration, AvAcc) and intensity (Intensity Gradient, IG) were derived from each ESM. Associations with mortality were estimated using Cox models. 3733 (25.1% female, median age 68.3 years) and 89,848 (56.4% female, 63.5 years) participants were included from Whitehall II and UK Biobank, respectively. Median (IQR) follow-up was 11.0 (10.7, 11.3) and 8.0 (7.5, 8.5) years, with 563 (15.1%) and 3656 (4.1%) deaths. Associations with mortality were largely consistent between ESMs with the lowest mortality risk for those high (above the median) in both AvAcc and IG (Whitehall: HR = 0.59-0.68; Biobank: 0.55-0.61, reference: low/low), and IG associated with lower mortality risk, irrespective of AvAcc. AvAcc was associated with lower mortality irrespective of IG in Biobank only. In conclusion, associations of AvAcc and IG with mortality are broadly consistent across common ESMs, supporting comparability of activity-health findings across studies using different ESMs.

PMID:41353463 | DOI:10.1038/s41598-025-30237-5