Tag: nevin manimala

Spec Care Dentist. 2026 Jan-Feb;46(1):e70144. doi: 10.1111/scd.70144.

ABSTRACT

OBJECTIVES: This study aimed to examine the association between oral health factors and Mild Cognitive Impairment (MCI), evaluating their independent effects after adjustments for sociodemographic, medical, and behavioral confounders.

METHODS: A cross-sectional analytical study was conducted among 248 older adults aged 60 years and above. Cognitive status was assessed using the Montreal Cognitive Assessment-Thai version (MoCA-T). Demographic, medical, and behavioral data were collected through structure interviews. Oral health assessments included active dental caries, periodontal disease, number of natural teeth, number of posterior occluding pairs, and masticatory performance, all measured through clinical examination. A multivariate logistic regression analysis was performed using the enter method, with statistical significance set at p < 0.05.

RESULTS: The mean age of participants was 68.7 years, and 73% were female. Of the 248 participants, 73 (29.4%) were identified as having MCI. After adjusting for age, marital status, education, occupation, income, hypertension, functional, and nutritional status, only periodontal disease remained significantly associated with MCI (adjusted OR = 2.01, 95% CI: 1.05-3.84, p = 0.035).

CONCLUSION: Among the oral health factors examined, periodontal disease emerged as the only factor independently associated with MCI after adjustment for demographic, medical, and behavioral confounders.

PMID:41635984 | DOI:10.1111/scd.70144

Chemistry. 2026 Feb 4:e03299. doi: 10.1002/chem.202503299. Online ahead of print.

ABSTRACT

Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms-deep evidential regression (DER) and deep ensembles-on the QM9 and WS22 datasets, with a specific emphasis on the role of post hoc calibration. Raw uncertainties from both methods were systematically miscalibrated: DER produced uncertainty estimates where data noise and model uncertainty were not cleanly separated, while ensembles produced sharper yet underconfident estimates. Applying calibration techniques such as isotonic regression (ISR), standard scaling, and GP-Normal corrected these deficiencies, aligning predicted variances with observed errors. On QM9, calibration enabled DER to filter high-confidence predictions more effectively than ensembles. On WS22, calibrated ensembles not only improved statistical reliability but also delivered substantial computational savings in active learning, reducing redundant ab initio evaluations by more than 20%. These results demonstrate that post hoc calibration is essential to transform uncertainty estimates from descriptive metrics into actionable signals, ensuring both trustworthy predictions and resource-efficient molecular modeling.

PMID:41635978 | DOI:10.1002/chem.202503299

Surg Laparosc Endosc Percutan Tech. 2026 Feb 1;36(1):e1423. doi: 10.1097/SLE.0000000000001423.

ABSTRACT

BACKGROUND: Postoperative intra-abdominal abscess (IAA) is the most feared complication after laparoscopic appendectomy (LA). The management of IAA measuring 2 to 4 cm remains controversial. We aimed to compare the effectiveness of antibiotic treatment versus percutaneous drainage for the treatment of IAA measuring 2 to 4 cm following LA.

METHODS: A consecutive series of patients with post-appendectomy IAA measuring 2 to 4 cm from January 2006 to April 2024 was included for analysis. The patient cohort was divided into 2 groups according to the treatment modality: antibiotic therapy alone (ATB) versus computed tomography-guided percutaneous drainage (PERC). The primary outcome was to compare the success rate between groups. Secondary endpoints included overall and major complications, length of stay (LOS), readmissions, and mortality.

RESULTS: During the study period, 2700 LA were performed, and 123 (4.5%) patients developed an IAA. Of these, 47 (38%) measured 2 to 4 cm: 25 (53%) received antibiotics only (ATB), and 22 (47%) underwent percutaneous drainage (PERC). The success rates were comparable between groups (ATB: 92% vs. PERC: 95.4%, P=0.6). Patients who failed conservative management in both groups underwent laparoscopic lavage without further complications. No readmissions, morbidity or mortality were observed. The mean LOS was longer in the PERC group (ATB: 2.0 vs. PERC: 3.5 d, P=0.03).

CONCLUSIONS: Antibiotic therapy and percutaneous drainage are both highly effective for treating IAA measuring 2 to 4 cm following LA. Given the less invasive nature of antibiotic therapy with shorter length of stay, it should be considered the initial treatment of choice.

PMID:41635964 | DOI:10.1097/SLE.0000000000001423

J Neuroimaging. 2026 Jan-Feb;36(1):e70127. doi: 10.1111/jon.70127.

ABSTRACT

BACKGROUND AND PURPOSE: Grading meningioma guides treatment choices from follow-up to surgical resection with adjuvant radiation. Radiomics may offer a non-invasive alternative to biopsies. We assessed radiomic features (RFs) for distinguishing Grade 1 and Grade 2 meningiomas on preoperative multiparametric MRI.

METHODS: Presurgical T1-weighted (T1), T2-weighted (T2), T2 gradient echo-weighted (T2GRE), fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC), and T1-weighted contrast-enhanced (T1CE). MRI sequences of histopathologically diagnosed meningiomas were collected retrospectively. Each volume had 75 RFs extracted from semimanually segmented tumors using MintLesion Research (Version 3.10). The Lasso method selected variables from imputed data, and 10-fold cross-validation determined the optimal regularization parameter. For Lasso-retained variables, multivariate effects were estimated.

RESULTS: Out of 150 patients (67.3% women), 110 (73.3%) had Grade 1 meningiomas, and 40 (26.7%) Grade 2. The strongest metrics to distinguish meningiomas Grade 1 versus Grade 2 were intensity histogram coefficient of variation on T1CE (odds ratio [OR] 0.47, 95% confidence interval [CI] 0.23-0.88; p = 0.028), maximum histogram gradient on T1 (OR 2.11, 95% CI 1.18-4.82; p = 0.043), and intensity histogram quartile coefficient of dispersion on FLAIR (OR 0.53, 95% CI 0.31-0.89; p = 0.021). The combined RFs achieved an area under the curve of 0.814 (95% CI, 0.732-0.896) for grading differentiation. Texture features and metrics extracted from T2, T2GRE, and ADC sequences did not discriminate meningioma grading.

CONCLUSIONS: Histogram-based first-order RFs from T1, FLAIR, and T1CE may predict meningioma grades preoperatively. Larger, multicenter studies are needed to confirm these findings, providing insights for clinical decision-making and personalized treatment.

PMID:41635960 | DOI:10.1111/jon.70127

Res Synth Methods. 2026 Mar;17(2):293-313. doi: 10.1017/rsm.2025.10050. Epub 2025 Nov 13.

ABSTRACT

Bayesian hierarchical models offer a principled framework for adjusting for study-level bias in meta-analysis, but their complexity and sensitivity to prior specifications necessitate a systematic framework for robust application. This study demonstrates the application of a Bayesian workflow to this challenge, comparing a standard random-effects model to a bias-adjustment model across a real-world dataset and a targeted simulation study. The workflow revealed a high sensitivity of results to the prior on bias probability, showing that while the simpler random-effects model had superior predictive accuracy as measured by the widely applicable information criterion, the bias-adjustment model successfully propagated uncertainty by producing wider, more conservative credible intervals. The simulation confirmed the model’s ability to recover true parameters when priors were well-specified. These results establish the Bayesian workflow as a principled framework for diagnosing model sensitivities and ensuring the transparent application of complex bias-adjustment models in evidence synthesis.

PMID:41635950 | DOI:10.1017/rsm.2025.10050

Res Synth Methods. 2026 Mar;17(2):314-331. doi: 10.1017/rsm.2025.10049. Epub 2025 Nov 13.

ABSTRACT

Ranking multiple interventions is a crucial task in network meta-analysis (NMA) to guide clinical and policy decisions. However, conventional ranking methods often oversimplify treatment distinctions, potentially yielding misleading conclusions due to inherent uncertainty in relative intervention effects. To address these limitations, we propose a novel Bayesian rank-clustering estimation approach, termed rank-clustering estimation (RaCE), specifically developed for NMA. Rather than identifying a single “best” intervention, RaCE enables the probabilistic clustering of interventions with similar effectiveness, offering a more nuanced and parsimonious interpretation. By decoupling the clustering procedure from the NMA modeling process, RaCE is a flexible and broadly applicable approach that can accommodate different types of outcomes (binary, continuous, and survival), modeling approaches (arm-based and contrast-based), and estimation frameworks (frequentist or Bayesian). Simulation studies demonstrate that RaCE effectively captures rank-clusters even under conditions of substantial uncertainty and overlapping intervention effects, providing more reasonable result interpretation than traditional single-ranking methods. We illustrate the practical utility of RaCE through an NMA application to frontline immunochemotherapies for follicular lymphoma, revealing clinically relevant clusters among treatments previously assumed to have distinct ranks. Overall, RaCE provides a valuable tool for researchers to enhance rank estimation and interpretability, facilitating evidence-based decision-making in complex intervention landscapes.

PMID:41635948 | DOI:10.1017/rsm.2025.10049

Res Synth Methods. 2026 Mar;17(2):378-388. doi: 10.1017/rsm.2025.10052. Epub 2025 Nov 24.

ABSTRACT

Accurate assessment of adverse event (AE) incidence is critical in clinical research for drug safety. While meta-analysis serves as an essential tool to comprehensively synthesize the evidence across multiple studies, incomplete AE reporting in clinical trials remains a persistent challenge. In particular, AEs occurring below study-specific reporting thresholds are often omitted from publications, leading to left-censored data. Failure to account for these censored AE counts can result in biased AE incidence estimates. We present an R Shiny application that implements a Bayesian meta-analysis model specifically designed to incorporate censored AE data into the estimation process. This interactive tool provides a user-friendly interface for researchers to conduct AE meta-analyses and estimate the AE incidence probability using an unbiased approach. It also enables direct comparisons between models that either incorporate or ignore censoring, highlighting the biases introduced by conventional approaches. This tutorial demonstrates the Shiny application’s functionality through an illustrative example on meta-analysis of PD-1/PD-L1 inhibitor safety and highlights the importance of this tool in improving AE risk assessment. Ultimately, the new Shiny app facilitates more accurate and transparent drug safety evaluations. The Shiny-MAGEC app is available at: https://zihanzhou98.shinyapps.io/Shiny-MAGEC/.

PMID:41635945 | DOI:10.1017/rsm.2025.10052

Clin Kidney J. 2025 Dec 8;19(2):sfaf370. doi: 10.1093/ckj/sfaf370. eCollection 2026 Feb.

ABSTRACT

BACKGROUND: Despite the high suicide rates among patients with end-stage kidney disease (ESKD), there is no suicide prediction model specifically designed for this vulnerable population. Herein, we aimed to develop and validate a novel suicide risk score for ESKD patients.

METHODS: We analyzed data from the National Health Insurance Service (NHIS) of South Korea, including 251 819 patients aged above 18 years diagnosed with ESKD between 2007 and 2022 in South Korea. The mean follow-up duration was 6.6 years. The cohort was randomly divided into derivation (70%) and validation (30%) sets. Using multivariate Cox proportional hazard regression, key variables were incorporated to develop the suicide risk score, which was converted into a 48-point scoring system, which is composed of easily identifiable clinical parameters.

RESULTS: Among 176 273 patients in the derivation cohort, 1126 (0.64%) patients committed suicide. The suicide risk score demonstrated moderate discrimination in both the derivation (C-statistic, 0.694) and validation (C-statistic, 0.709) cohorts, with good calibration. In the validation cohort, patients scoring below 16, 17-32 and 33-48 had predicted 10-year suicide risk of 0.2%, 1.2% and 7.7%, respectively, while the observed 10-year risk were 0.3%, 0.8% and 3.9%. These findings highlight the model’s ability to effectively stratify risk using routinely available clinical data.

CONCLUSIONS: The suicide risk score is a significant advancement in suicide risk prediction for ESKD patients. It is based on simple, routinely collected clinical indicators and provides an actionable tool for risk stratification and early intervention in daily practice.

PMID:41635920 | PMC:PMC12863073 | DOI:10.1093/ckj/sfaf370

Anim Microbiome. 2026 Feb 3;8(1):9. doi: 10.1186/s42523-026-00520-5.

ABSTRACT

BACKGROUND: Heat stress (HS) adversely affects poultry health and productivity. Recently, it has been suggested that the gut microbiota may play a role in host resilience to HS, although the details of its mechanism remain unclear. Here, the heat tolerance-related effects of dietary supplementation of compost fermented by the thermophile Bacillaceae were explored using a laying hen model (601,474 hens in total).

RESULTS: In a field study conducted during the summer (maximum temperatures of approximately 35 °C) in eleven hen houses, oral administration of the compost extract resulted in a statistically significant reduction in mortality. Difference-in-differences analysis revealed that the abundances of the genera Lachnospiraceae NK3A20 group, Enterococcus, Ruminococcus 2, Blautia, Lactobacillus, Christensenellaceae R-7 group, and Tyzzerella 4 were significantly increased by compost administration, whereas those of the Prevotellaceae NK3B31 group, Prevotella 9, Romboutsia, Turicibacter, and Escherichia-Shigella were significantly reduced. In addition, to evaluate the relationship between short-chain fatty acids (SCFAs) metabolic profiles and the gut bacterial population, factor analysis combined with feature selection based on multiple machine learning (ML) algorithms was performed. The resulting optimal structural equation model suggested that compost administration led to increases in the levels of the SCFAs acetate and butyrate, as well as decreases in the levels of the genera Romboutsia and Turicibacter.

CONCLUSION: Oral administration of thermophile-fermented compost to laying hens alleviated HS-induced mortality. Integrative computational evaluations further revealed that the reduction in mortality was linked to structural changes in the gut microbiota composition and SCFA concentrations.

PMID:41634863 | DOI:10.1186/s42523-026-00520-5

Cancer Imaging. 2026 Feb 3. doi: 10.1186/s40644-026-00996-z. Online ahead of print.

ABSTRACT

BACKGROUND: Accurate differentiation between luminal A and B subtypes of breast cancer is critical for selecting therapeutic strategies. However, current approaches rely predominantly on invasive biopsy and immunohistochemical (IHC) analysis. Therefore, the development of non-invasive imaging-based methods capable of reliably classifying tumor subtypes remains an urgent task.

METHODS: To develop and validate a hybrid classification model combining radiomic and deep learning features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate between luminal A and B subtypes of invasive breast cancer. The study included 312 women from China, Russia and Bulgaria with confirmed luminal subtypes of breast cancer. All patients underwent standardized pre-treatment DCE-MRI, and subtypes were determined using IHC. Tumors were semi-automatically segmented, and radiomic features were extracted using PyRadiomics. Additionally, deep features were extracted from DCE-MRI using a 3D ResNet-50 convolutional neural network. Three models were constructed: a radiomics-based model, a deep learning-based model, and a hybrid model that integrated both approaches using a stacking ensemble method. Model performance was evaluated using AUC, sensitivity, specificity, and other metrics on a test dataset and an independent external validation cohort (n = 148). SHAP and Grad-CAM techniques were applied for model interpretability.

RESULTS: The hybrid model significantly outperformed the individual approaches, achieving an AUC of 0.921, sensitivity of 88.6%, and specificity of 89.7% on the test dataset. Performance remained robust in the external validation cohort (AUC = 0.903). Statistical tests (DeLong and bootstrapping) confirmed the significance of these differences. The most important contributors were radiomic features related to shape and texture (e.g., entropy, sphericity) and high-level deep features. Visualizations highlighted clinically relevant model attention areas.

CONCLUSION: The proposed hybrid approach represents a clinically applicable, non-invasive method for classifying breast cancer subtypes, potentially complementing or partially replacing biopsy in selected cases. It enhances diagnostic accuracy while maintaining interpretability. Future work will focus on prospective validation and integration with genomic and clinical data within the framework of precision oncology.

PMID:41634854 | DOI:10.1186/s40644-026-00996-z