Category: Nevin Manimala

Mol Neurobiol. 2025 Dec 20;63(1):314. doi: 10.1007/s12035-025-05507-y.

ABSTRACT

Neuronal and glial biomarkers like glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), tau protein, and neurofilament light chain (NfL) in serum are reported to be beneficial in detecting sports-related concussions (SRC) or intracranial trauma sequelae. However, routine blood measurements are invasive, and a non-invasive approach using urine could be advantageous. This prospective study analyzed urine samples from athletes with varying head trauma risks: high-risk (boxing), moderate-risk (American football, soccer), and low-risk (endurance sports). Samples were collected before (pre) and 48-72 h after (post) competition. Consecutive matches per athlete were sampled. Biomarker concentrations were adjusted for urine dilution using the creatinine ratio (CR). Statistical differences between groups were assessed using the Kruskal-Wallis test. A total of 48 athletes (boxing 11, American football 18, soccer 10, endurance 9) provided 112 samples. Ten SRC were recorded (boxing 9, soccer 1). Boxing athletes had the highest biomarker-CR, with significant differences in tau-CR and UCH-L1-CR compared to American football and endurance athletes (p = 0.005 and p = 0.001, respectively). No significant differences were found for NfL-CR or GFAP-CR. No significant changes were observed for biomarker-CRs 48-72 h after SRC. However, in a pooled analysis of all subsequent samples after a SRC, irrespective of the latency of sampling, there were significantly higher values for tau-CR and UCH-L1-CR (p = 0.02). Significantly higher levels of tau-CR and UCH-L1 were found in high-risk sports, potentially reflecting increased head impacts. However, an early increase of biomarker-CR within 72 h after SRC was not observed.

PMID:41420097 | DOI:10.1007/s12035-025-05507-y

Sci Rep. 2025 Dec 19. doi: 10.1038/s41598-025-31406-2. Online ahead of print.

ABSTRACT

The aim of this study was to investigate influence of curing mode of universal adhesives and dentin sealing approach on microtensile bond strength (MTBS) of CAD/CAM composite to dentin. Occlusal surfaces of 36 human molars were ground to expose flat dentin and randomly assigned to 6 groups according to: (1) Adhesive curing mode [light-cured/LC (One coat 7 Universal, COLTENE); dual-cure/DC (One coat 7 Universal/Dual-cure Activator, COLTENE); and self-cure/SC (Palfique Universal Bond, Tokuyama); and (2) Dentin sealing approach (delayed sealing/DDS and immediate sealing/IDS). Following 1-week provisionalization, CAD/CAM composite blocks (Brilliant Crios, COLTENE), 4 mm², were cemented over conditioned dentin surfaces. After 24 h, bonded assemblies were thermo-cycled for 5000 cycles. Specimens were tested for MTBS. Failure mode was analyzed under stereomicroscope. Data were statistically analyzed using ANOVA. DDS showed significantly higher MTBS values than IDS when using LC and DC adhesives. While, IDS produced significantly higher MTBS values than DDS when using SC adhesive. Predominant failure mode was adhesive in all groups, except for SC adhesive in IDS group, mixed failure was the predominant mode. Light- and dual-cured universal adhesives improved bond strength in delayed dentin sealing approach. Self-cure universal adhesive produced better bond strength when applied to immediately sealed dentin.

PMID:41420076 | DOI:10.1038/s41598-025-31406-2

Sci Rep. 2025 Dec 19. doi: 10.1038/s41598-025-33035-1. Online ahead of print.

ABSTRACT

Pteridium aquilinum is a medicinally important fern with a limited range in northern Iran, increasingly threatened by climate change. Using morphological, genetic, and environmental data, we assessed differentiation, adaptive capacity, and vulnerability across 11 populations. Factor analysis of mixed data (FAMD) identified stipe indument, pinnule shape, and pinnae number as key traits distinguishing populations. Redundancy and association analyses (RDA/CCA) revealed strong links between both morphological and genetic variation and climatic gradients, particularly temperature and humidity, indicating local adaptation. Several SCoT loci were detected as adaptive outliers. Spatial PCA showed that variation is shaped by both global and local spatial factors, forming clines and local variants. Populations varied in sensitivity and adaptive capacity; populations 2, 3, 7, and 8 exhibited the lowest adaptive indices and highest vulnerability. Connectivity modeling suggested that while some populations (e.g., 2, 4, and 6) may maintain or slightly improve connectivity, others risk isolation under future climates. Structural equation modeling (SEM) indicated a positive genetic contribution to adaptation, while differential equation modeling (DEM) predicted logistic growth with temporary instability and genetic decline in vulnerable groups. Overall, findings highlight spatially uneven adaptive responses and recommend targeted conservation through connectivity enhancement, assisted gene flow, and ex-situ preservation of adaptive genotypes.

PMID:41420001 | DOI:10.1038/s41598-025-33035-1

Magn Reson Med. 2025 Dec 19. doi: 10.1002/mrm.70225. Online ahead of print.

ABSTRACT

Our goal was to understand the barriers and challenges to clinical translation of quantitative MR (qMR) as perceived by stakeholders in the UK. We conducted an electronic survey on seven key areas related to clinical translation of qMR, developed at the BIC-ISMRM workshop: “Steps on the path to clinical translation”. Based on the seven areas identified: (i) clinical workflow, (ii) changes in clinical practice, (iii) improving validation, (iv) standardization of data acquisition and analysis, (v) sharing of data and code, (vi) improving quality management, and (vii) end-user engagement, a 40-question survey was developed. Members of BIC-ISMRM, MR-PHYSICS, BSNR and institutional mailing lists were invited to respond to the online survey over a 5-week period between September and October 2022. The responses were analysed via descriptive statistics of multiple-choice questions, Likert scores and a thematic analysis of free text questions. A total of 69 responses were received from predominantly research imaging scientists (69%) in numerous centres across the UK. Three main themes were identified: (1) Consensus; the need to develop in terminology, decision making and validation; (2) Context Dependency; an appreciation of the uniqueness of each clinical situation, and (3) Product Profile; a clear description of the imaging biomarker and its intended use. Effective translation of qMR imaging and spectroscopic biomarkers to achieve their full clinical potential must address the differing needs and expectations of a wide range of stakeholders.

PMID:41419988 | DOI:10.1002/mrm.70225

BMC Chem. 2025 Dec 19;19(1):320. doi: 10.1186/s13065-025-01682-0.

ABSTRACT

A straightforward, precise, and rapid UV spectrophotometric method has been developed for the simultaneous determination of meclizine (MEC) and pyridoxine (PYR) in binary mixtures. The approach involved two calibration curves: the first was constructed to quantify PYR directly at 290 nm, while the second was used to calculate the concentration ratio (CR) of the mixture at 230 nm. This second curve was generated by plotting the absorbance ratio (AR) against the concentration ratio (CR = [MEC]/[PYR]). Using the experimentally determined concentration ratio (CRₑₓₚ) and the known PYR concentration, the MEC content was calculated as [MEC] = CRₑₓₚ × [PYR]. The method was validated in accordance with ICH Q2 guidelines and demonstrated reliable and consistent performance. When applied to a pharmaceutical formulation, the results expressed as a percentage of the labeled claim were 100.34% ± 0.47 for MEC and 100.04% ± 0.182 for PYR. Furthermore, statistical comparison with a previously published chromatographic method confirmed the equivalence of the two approaches. These results suggest that the developed method is suitable for routine quality control analysis of pharmaceutical preparations containing MEC and PYR. The linearity ranges were 4-20 µg/mL for meclizine and 6-30 µg/mL for pyridoxine and close to 1.00 for both analytes. Excellent precision was demonstrated, with percent Relative Standard Deviations (RSD%) well below 2%. The method also exhibited high accuracy, with analyte recoveries from tablets formulation 99.62 ± 0.34 and 98.92 ± 0.62 for MEC and PYR, respectively.

PMID:41419968 | DOI:10.1186/s13065-025-01682-0

Sci Rep. 2025 Dec 19. doi: 10.1038/s41598-025-31562-5. Online ahead of print.

ABSTRACT

Diabetes is one of the major health challenges in today’s world, since chronic elevation of blood sugar can cause serious and sometimes irreparable damage to organs such as the heart, kidneys, and nervous system. Early detection of this disease plays a vital role in reducing its complications. However, machine learning and deep learning models often face distrust in medical settings due to their opaque, “black-box” nature. The aim of this study was to combine three machine learning algorithms using stacking and voting methods to propose a model for type 2 diabetes detection, and to increase transparency by using the explainability techniques LIME and SHAP to identify important features. This study used medical data from 768 Pima Indians Diabetes samples, including 8 features such as age, BMI, glucose, insulin, blood pressure, skin thickness, pregnancies, and family history. Data preprocessing included mean imputation for missing or zero values, Min-Max normalization, and classification into “Normal”, “Prediabetes”, and “Diabetes” based on fasting glucose thresholds. Feature selection was performed using Spearman correlation to retain the most relevant variables. A hybrid machine learning model was developed using three base models Neural Network (NN), k-Nearest Neighbors (KNN), and Random Forest (RF) with automated hyperparameter tuning. The outputs of these models were combined via stacking using a logistic regression (LR) meta-model and in parallel using a soft voting method. Nested cross-validation (5 outer and 5 inner folds) was applied to prevent data leakage and ensure robust evaluation. Model interpretability was assessed using LIME for local explanations and SHAP for global feature importance. Decision thresholds and influential feature regions were identified, and model calibration and decision curves evaluated clinical reliability. Models performance was evaluated using accuracy, precision, recall, specificity, F1-score, AUROC, Brier Score (1-B), and Expected Calibration Error (1-E). Statistical reliability was assessed using bootstrap resampling to compute 95% confidence intervals, as well as paired tests to compare the hybrid model with the base models and voting ensemble. Based on the evaluation metrics, the stacking ensemble achieved perfect performance for Class 0, with 100% accuracy, precision, recall, specificity, F1 score, and AUROC, alongside the highest calibration metrics (Brier Score: 99.9, ECE: 98.7). The Random Forest model also excelled, achieving 100% accuracy, precision, recall, specificity, and F1 score for Class 0 and Class 2. In contrast, the KNN model consistently underperformed, particularly for Class 0 (F1: 83.3, Precision: 83.3, Recall: 83.3). The Neural Network demonstrated strong recall for Class 0 (100%), while the voting ensemble showed balanced results but was slightly outperformed by the top ensemble methods. Explainable AI analyses using LIME and SHAP revealed that glucose was the most influential predictor for identifying the Pre-diabetes state. Both methods consistently identified a decision band between 0.35 and 0.47 (corresponding to 100-125 mg/dL) as the transition zone between “Normal” and “Prediabetes”, confirming the model’s alignment with WHO/ADA diagnostic criteria. The stacking model achieved perfect performance and superior calibration, outperforming all other models in type 2 diabetes prediction and classification. Explainability techniques (LIME and SHAP) identified glucose level, body mass index, and blood pressure as key predictive factors. This approach provides an accurate and interpretable tool for clinical decision support in healthcare systems.

PMID:41419964 | DOI:10.1038/s41598-025-31562-5

Eur J Med Res. 2025 Dec 19. doi: 10.1186/s40001-025-03680-3. Online ahead of print.

ABSTRACT

BACKGROUND: This study investigates the association between TyG-related composite indices and the risk of gallstones and the history of cholecystectomy, using logistic regression and machine learning models to evaluate predictive performance and clinical utility. Additionally, the study explores the relationship between TyG-derived obesity indices and the age at the history of cholecystectomy.

METHODS: A total of 3737 participants were analyzed. Logistic regression models were used to assess the relationship between TyG, TyG.BMI, TyG.WC, and TyG.WHtR with gallstone prevalence and the history of cholecystectomy. Performance metrics for 11 machine learning models, including XGB, logistic regression (LR), and gradient boosting machine (GBM), were evaluated using AUC-ROC, accuracy, sensitivity, and specificity. Decision curve analysis (DCA), calibration plots, and SHAP (Shapley additive explanations) analysis were used to assess clinical utility and interpretability. Additionally, De Long test was applied to compare the AUC-ROC values of the machine learning models to identify statistically significant differences.

RESULTS: Among 3737 participants, 395 (10.6%) had gallstones. Individuals with gallstones were older (median 59 vs. 51 years, P < 0.01), predominantly female, and had higher levels of TyG and TyG-related indices (all P < 0.01). Logistic regression analyses revealed that while TyG was not significantly associated with gallstones after full adjustment, composite indices incorporating obesity measures-TyG.BMI, TyG.WC, and TyG.WHtR-remained robustly associated with gallstone risk in the fully adjusted model. Participants in the highest quartile (Q4) of these indices had higher odds of gallstones compared to those in the lowest quartile (Q1). Further analysis revealed that TyG.BMI, TyG.WC, and TyG.WHtR were associated with younger age at the history of cholecystectomy, with threshold effects identified at TyG-BMI = 184.35 and TyG-WC = 776.69, above which the association with younger cholecystectomy age became significant. In predicting the history of cholecystectomy, XGB outperformed other models with an AUC-ROC of 0.83, accuracy of 0.89, and F1-score of 0.73, showing balanced sensitivity (0.72) and specificity (0.82). The De Long test indicated that XGB demonstrated statistically significant superior performance compared to all other models (P < 0.01 for pairwise comparisons), reaffirming its high predictive capability. Supplementary Fig. 2 presents ROC curves for all models, where XGB achieved the highest AUC-ROC (0.827), outperforming other models such as LR (AUC-ROC = 0.746) and GBM (AUC-ROC = 0.742).

CONCLUSIONS: TyG-derived composite indices, particularly TyG.WHtR, are strong predictors of both gallstone prevalence and the history of cholecystectomy. The XGB model demonstrated the best performance in predicting cholecystectomy risk, with the De Long test confirming its superior AUC-ROC compared to other models. The combination of strong predictive performance, good calibration, and high interpretability makes XGB a valuable tool for clinical decision-making in managing gallbladder disease risk.

PMID:41419963 | DOI:10.1186/s40001-025-03680-3

Clin Epigenetics. 2025 Dec 19;17(1):202. doi: 10.1186/s13148-025-02019-3.

ABSTRACT

Orthostatic hypotension is a sharp decrease in blood pressure when an individual transitions from a supine to an upright position. OH affects at least 30% of older adults. It is attributed to the dysfunction of the autonomic innervation and decreased vascular bed capacity. Genomic (n = 2526), methylomic (n = 910), and transcriptomic (n = 391) data from centenarians aged 90 years and older were used to examine molecular and genetic factors for OH. No statistically significant genetic predictors of OH were identified. However, the study revealed numerous epigenetic markers of OH indicative of general aging, such as DNA hypomethylation. The predictive DNA methylation-based model for orthostatic hypotension demonstrated an average accuracy of 79%. The transcriptome analyses highlighted associations between OH and inflammation pathways, as well as other age-related biological processes. Integrated omics and clinical data have identified six key mechanisms associated with orthostatic hypotension: metabolic dysregulation, impaired muscle tone, altered cell proliferation, inflammation, humoral regulation, and neural regulation.

PMID:41419941 | DOI:10.1186/s13148-025-02019-3

J Transl Med. 2025 Dec 19. doi: 10.1186/s12967-025-07422-1. Online ahead of print.

NO ABSTRACT

PMID:41419940 | DOI:10.1186/s12967-025-07422-1

Cell Commun Signal. 2025 Dec 19. doi: 10.1186/s12964-025-02598-x. Online ahead of print.

ABSTRACT

RNA viruses can generate “defective” viral genomes during replication, which can interact with standard viral genomes affecting the course of infections. These non-standard viral genomes are related to milder clinical outcomes and are currently being tested as antivirals. Decades of research in influenza have focused on viral mechanisms affecting the production of deletion-containing viral genomes (DelVGs). Based on adaptations of influenza NS1 protein to manipulate host cell metabolism, we hypothesized host metabolic state could also alter the quantity and pattern of deletion-containing viral genomes and the particles that house them. To test this hypothesis, we manipulated host cell anabolic signaling activity and monitored the production of DelVGs and non-infectious particles by two influenza strains, using single-cell immunofluorescence and third-generation sequencing. We show that: 1) influenza infection activates PI3K signaling, with the A/H1N1 strain having roughly double the pAKT levels in single cells as the A/H3N2; 2) alpelisib, a PI3K receptor inhibitor, subverted the ability of both influenza strains to activate PI3K in a dose dependent manner; 3) DelVGs were increased roughly tenfold in polymerase complex segments and ~ 60% in the hemagglutinin segment of A/H1N1 at 20uM of alpelisib; and 4) the A/H3N2 strain did not show changes in DelVG production, but had a modest, statistically significant maximum increase of 11% in non-infectious particles. We find that host cell metabolism can increase the production of non-infectious particles and DelVGs during single rounds of infection, shifting potential interactions among virions. The differential results according to strain and alpelisib concentration suggest future directions examining strain differences in the NS1::p85β virus-host interaction and the specific metabolic state of the cell. Our study presents a new line of investigation into metabolic states associated with less severe flu infection and opens the possibility for potential induction of these states with metabolic drugs.

PMID:41419939 | DOI:10.1186/s12964-025-02598-x