Category: Nevin Manimala

Harm Reduct J. 2026 Mar 2. doi: 10.1186/s12954-026-01427-9. Online ahead of print.

ABSTRACT

BACKGROUND: Appropriate treatments for nonopioid substance use are currently unavailable. Venlafaxine may reduce withdrawal from nonopioid substances, but the effects have not been evaluated. We aimed to investigate the association between venlafaxine use and the risk of withdrawal from nonopioid substances.

METHODS: We linked Taiwan’s National Health Insurance Research Database and the Taiwan Illicit Drug Issue Database from January 2012 to December 2019. We used a case-case-time-control study involving a case-crossover analysis and a control-crossover analysis consisting of future cases. The outcomes were withdrawal from substances and all-cause mortality. For each individual, venlafaxine use during the hazard period (day – 8 to – 67 before the outcome) was compared with that during the 60-day reference period (between days – 248 and – 307). Conditional logistic regression was used to determine odds ratios with 95% confidence intervals to evaluate the associations between outcome events and the use of venlafaxine.

RESULTS: The participants’ average age on the index date was 39.5 years (standard deviation 8.7), with 84.1% men and 88.3% having low income. Venlafaxine was significantly associated with a lower risk of withdrawal from substances (odds ratio 0.35, 95% confidence interval 0.13 to 0.96). However, we found no association between the recent use of venlafaxine and all-cause mortality (1.08, 0.55 to 2.14). The point estimates were similar in a series of sensitivity analyses, though not all analyses statistical significance.

CONCLUSIONS: This study provides strong ground for clinicians to consider the use of venlafaxine to reduce patient experiencing withdrawal symptoms from substances.

PMID:41772603 | DOI:10.1186/s12954-026-01427-9

BMC Nurs. 2026 Mar 2. doi: 10.1186/s12912-026-04502-5. Online ahead of print.

ABSTRACT

INTRODUCTION: Digitalization in healthcare has reshaped nursing practice but also introduced psychosocial challenges. While well-designed digital systems can enhance workflow efficiency and satisfaction, excessive digital demands may lead to technostress and digital fatigue, reducing nurses’ motivation. This study aimed to examine the mediating role of digital fatigue in the relationship between job satisfaction and motivation among nurses.

METHODS: This descriptive, cross-sectional study was conducted between July and October 2025 with 350 nurses working at a hospital in İzmir, Türkiye. Data were collected using the Participant Information Form, Job Satisfaction Scale, Nurse Work Motivation Scale, and Technostress Creators Scale for Health Professionals. Analyses were performed using SPSS 27.0 with descriptive statistics, t-tests, ANOVA, correlation, and mediation analysis.

RESULTS: Most participants were female (86.3%) and under 30 years old (68.6%). Over half (52%) reported experiencing technology-related fatigue or burnout. Job satisfaction was positively associated with both intrinsic and extrinsic motivation (p < .001) and negatively associated with technostress (p < .05). While job satisfaction significantly predicted both technostress (β = 0.083, p = .033) and motivation (β = -0.156, p = .000), technostress did not significantly predict motivation (β = 0.004, p = .882), indicating that digital fatigue did not fully mediate this relationship. Older and more experienced nurses reported higher job satisfaction and lower technostress levels.

CONCLUSION: Job satisfaction remains a primary driver of motivation among nurses. However, in digitally intensive clinical environments, digital fatigue poses a significant risk to sustaining motivation. Healthcare institutions should address both organizational and technological factors by improving digital system usability, offering training, and implementing strategies to mitigate digital fatigue to enhance nurses’ motivation and retention.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:41772589 | DOI:10.1186/s12912-026-04502-5

Cardiovasc Diabetol. 2026 Mar 2. doi: 10.1186/s12933-026-03108-0. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with chronic kidney disease (CKD) and coronary artery diseases (CAD) have a poor long-term prognosis. Although insulin resistance (IR) and systemic inflammation are well-established drivers of cardiovascular risk, the prognostic value of their composite assessment across the glycemic spectrum in patients with CKD and CAD remains undetermined. This study aimed to evaluate the prognostic utility of composite IR-inflammation biomarkers for predicting mortality in patients with CKD and CAD stratified by glycemic status.

METHODS: 1353 patients with CKD and CAD were enrolled from National Health and Nutrition Examination Survey (NHANES) data (1999-2018). Composite biomarkers (TyG-hsCRP, TyG-CRP, and C-reactive Protein-Triglyceride Glucose Index [CTI]) were calculated. Patients were categorized by glycemic status (normoglycemia, prediabetes, diabetes) based on WHO/IEC criteria. The endpoint was all-cause and cardiovascular disease (CVD) death. Statistical analyses included Cox regression, Nelson-Aalen cumulative hazard plots with Log-rank test, restricted cubic splines, ROC curves, and reclassification metrics, adjusted for demographics, comorbidities, and treatments. Subgroup and sensitivity analyses ensured robustness.

RESULTS: Over a median follow-up of 63-months, 744 all-cause and 323 CVD deaths occurred. Adjusted models showed elevated composite indices linked to higher mortality (e.g., CTI HR 1.43 [95% CI 1.24-1.65] for all-cause; HR 1.32 [1.06-1.64] for CVD). CTI provided good discrimination (AUC 0.700) and reclassification (IDI 0.010; NRI 0.196 for all-cause). The predictive utility of all three composite biomarkers was most pronounced in patients with diabetes, whereas CTI retained the strong association with all-cause mortality in normoglycemic and prediabetic patients. Risk stratification using both CTI and glycemic status identified patients with diabetes and high CTI as having the highest all-cause (HR 1.63 [1.22-2.17]) and CVD (HR 1.37 [0.88-2.14]) death risk.

CONCLUSION: Composite biomarkers integrating IR and inflammation, particularly CTI, significantly enhance mortality prediction in patients with CKD and CAD. The predictive utility is modulated by underlying glycemic status, enabling refined risk stratification and potentially guiding tailored management strategies for this complex patient population.

PMID:41772566 | DOI:10.1186/s12933-026-03108-0

Cardiovasc Diabetol. 2026 Mar 2. doi: 10.1186/s12933-026-03119-x. Online ahead of print.

ABSTRACT

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is closely linked to cardiometabolic disorders, and cardiovascular disease is the leading cause of death among affected individuals. Identifying simple biomarkers that capture metabolic-inflammatory burden and predict long-term mortality in MASLD remains a clinical priority. The C-reactive protein-triglyceride-glucose index (CTI) integrates inflammation, dyslipidaemia, and glycaemic status, but its relevance to MASLD and long-term mortality has not been fully elucidated.

METHODS: We conducted a multi-stage investigation using two nationally representative cohorts from the United States and China. Cross-sectional associations between CTI and MASLD were assessed in the National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS). Prospective associations of CTI with cardiovascular and all-cause mortality among participants with MASLD were examined utilising multivariable Cox proportional hazards models, restricted cubic splines, threshold analyses, and competing risk models. Causal mediation analyses were performed to measure the mediating functions of diabetes, hypertension, and body mass index. Extensive sensitivity analyses using alternative MASLD definitions and analytic strategies were conducted to assess robustness. Results from NHANES were externally validated in CHARLS.

RESULTS: Higher CTI levels were strongly and nonlinearly associated with the presence of MASLD in both cohorts. Among individuals with MASLD, elevated CTI was associated with significantly increased risks of all-cause and cardiovascular mortality. Each unit increase in CTI in NHANES was linked to a 57% increased risk of cardiovascular death (HR 1.57, 95% CI 1.24-1.99) and a 47% increased risk of all-cause death (hazard ratio [HR] 1.47, 95% confidence interval [CI] 1.28-1.69) in fully adjusted models. A pronounced threshold effect was observed, with mortality risk rising sharply once CTI exceeded approximately 5.6. Consistent associations with all-cause mortality were observed in CHARLS. Mediation analyses indicated that diabetes accounted for a substantial proportion of the association between CTI and mortality, whereas body mass index played a minimal mediating role.

CONCLUSIONS: CTI is a robust metabolic-inflammatory marker associated with MASLD and long-term all-cause and cardiovascular mortality across diverse populations. The strong mediating role of diabetes underscores the central importance of glycaemic dysfunction in cardiometabolic risk. As a readily obtainable index, CTI may aid in cardiometabolic risk stratification among individuals with MASLD.

PMID:41772559 | DOI:10.1186/s12933-026-03119-x

BMC Med Educ. 2026 Mar 2. doi: 10.1186/s12909-026-08892-7. Online ahead of print.

ABSTRACT

BACKGROUND: Reliable assessment and meaningful feedback are essential to effective learning in medical education. However, conventional unstructured evaluation of essay-type responses is highly vulnerable to rater bias, inter-rater variability, and nonspecific feedback. To address these limitations, we developed an Assessment-cum-Feedback Checklist to provide a structured, criterion-based approach to scoring and feedback. In this study, we evaluated the checklist’s effectiveness in enhancing the reliability, consistency, and clarity of assessment while exploring student and faculty perceptions of its educational value.

METHODS: We used a mixed-methods design. Sixty-two first-year MBBS students and four faculty members (two junior < 5 years’ experience; two senior > 10 years’ experience) participated. Two essay-type questions were assessed independently by all four teachers using both the conventional unstructured method and the checklist-based method. Quantitative analyses included descriptive statistics, Wilcoxon signed-rank tests, Levene’s test for equality of variance, intraclass correlation coefficients (ICC), and Bland-Altman analysis to compare variability and agreement across methods. Data were analysed using JASP (version 0.18.3.0) at a 5% significance level. Student perceptions were gathered using a structured questionnaire, and faculty perceptions were explored through in-depth interviews. Qualitative data were analysed using QCAmap (2020). Institutional Ethics Committee approval was obtained.

RESULTS: Checklist-based scoring demonstrated lower standard errors, standard deviations, and coefficients of variation, indicating improved precision and reduced subjective variability compared with the conventional method. Mean scores were lower with the checklist, and Bland-Altman analysis showed a negative bias, reflecting greater scoring stringency due to explicit criteria. ICC values increased notably with the checklist-particularly among senior teachers-demonstrating improved inter-rater reliability and tighter limits of agreement. Teachers reported that the checklist enhanced objectivity, reduced bias, clarified performance expectations, and standardized assessment practices. Students expressed strong support, citing improved clarity, transparency, and usefulness of feedback.

CONCLUSIONS: The Assessment-cum-Feedback Checklist was associated with measurable improvements in the reliability and consistency of essay-type assessment. Both faculty and students perceived the checklist-based approach to enhance clarity, transparency, and the usefulness of feedback by making assessment criteria explicit. With appropriate faculty orientation and iterative refinement, the checklist represents a promising and potentially adaptable tool for strengthening assessment and feedback practices in constructed-response formats in medical education.

PMID:41772543 | DOI:10.1186/s12909-026-08892-7

J Environ Manage. 2026 Mar 1;402:129027. doi: 10.1016/j.jenvman.2026.129027. Online ahead of print.

ABSTRACT

During sewage sludge composting, the dynamic and nonlinear interactions among composting parameters and ammonia (NH₃) emissions present challenges to conventional statistical methods, hindering stage-specific mitigation of NH₃ emissions. This study employed multi-stage machine learning to investigate the associations among composting parameters and cumulative NH₃-N emissions (cNH₃) across different composting stages. The stage-specific models demonstrated high predictive accuracy (R² = 0.85-0.91) on independent test sets. Shapley Additive Explanations analysis identified composting time, aeration rate, and pH as the features most strongly associated with cNH₃ during the mesophilic and thermophilic stage, while aeration rate, pH, and organic matter were the predominant factors during the cooling and mature stage. Bivariate partial dependence plots revealed optimal parameter ranges and interactions, including a synergistic relationship between organic matter and nitrate levels, which was linked to lower cNH₃ in the model outputs. These findings illuminate the evolving relationship between key composting parameters and NH₃ emissions throughout the composting process, providing a scientific basis for developing stage-specific strategies to minimize NH₃ emissions based on model-derived patterns.

PMID:41771229 | DOI:10.1016/j.jenvman.2026.129027

Reprod Biomed Online. 2025 Aug 14;52(4):105221. doi: 10.1016/j.rbmo.2025.105221. Online ahead of print.

ABSTRACT

RESEARCH QUESTION: Is the quality, relevance and empathy of the answers provided by large language models (LLMs) in response to the most frequently asked patient questions in reproductive medicine comparable to those provided by human specialists?

DESIGN: This monocentric, double blind, prospective study involved two clinicians and two embryologists who answered 13 frequently asked questions in their respective field. The same questions were asked to a free online LLM, with the same constraint of text length as practitioners. All answers were blindly evaluated by four assessors (two gynaecologists and two embryologists depending on the topic) for quality and accuracy. A psychologist also evaluated empathy.

RESULTS: The mean number of words per answer was significantly higher (P < 0.001) for LLM than for humans. The average quality of answers was not statistically different between LLM and professionals. No answer provided by LLM was evaluated as completely aberrant, and only a minority contained false or inappropriate information or was scored as being very poor by assessors. Answers provided by embryologists, but not clinicians, ranked significantly higher (P = 0.02) than LLM. The psychologist chose LLM answers as most empathetic, clear, or both, in 14 out of 26 questions.

CONCLUSIONS: LLMs could be used as an educational tool within assisted reproductive technology centres to answer frequently asked patient questions. Although the potential applications of LLMs’ capabilities in answering medical questions are numerous, this should be carefully evaluated and regulated to prevent the dissemination of inaccurate information to patients.

PMID:41771212 | DOI:10.1016/j.rbmo.2025.105221

Int Dent J. 2026 Mar 1;76(3):109404. doi: 10.1016/j.identj.2025.109404. Online ahead of print.

ABSTRACT

INTRODUCTION AND AIMS: Automated dental report generation faces significant challenges in multimodal fusion, often resulting in suboptimal semantic quality and risks of hallucination, where AI generates clinically unsupported content. Current approaches that rely on simple feature concatenation or bidirectional attention mechanisms fail to effectively capture visual-textual relationships in medical imaging. This study aims to develop MedFusionT5, a unidirectional cross-modal alignment framework that (1) achieves superior clinical report quality through focused attention between visual patches and clinical text representations, and (2) ensures exceptional factual consistency by minimising hallucination rates.

METHODS: We implemented a novel architecture that integrates vision transformer (ViT) for patch-based visual feature extraction with Bio_ClinicalBERT for clinical text encoding. The core innovation is a unidirectional multihead attention alignment module that selectively maps textual embeddings to relevant visual patches before multimodal fusion. A T5-base decoder then generates diagnostic reports from the aligned representations. We evaluated performance on 700 dental panoramic radiographs using comprehensive metrics, including BLEU, ROUGE, CIDEr, clinical precision/recall, and specialised hallucination analysis, comparing against both concatenation and coattention baselines.

RESULTS: MedFusionT5 demonstrated superior performance across all evaluated metrics. Compared to the coattention baseline, CIDEr increased by 122% (5.65 vs 2.54) and by 320% over simple concatenation. BLEU-4 reached 0.865, outperforming both baselines, while maintaining the lowest hallucination rate at 2.42% (39% reduction vs coattention, 46% vs concatenation). The model achieved an optimal balance between precision (0.982) and recall (0.923), with 90% of reports exhibiting near-zero hallucination. Notably, MedFusionT5 showed consistent quality independent of report length (r = -0.022), unlike coattention’s length-dependent performance (r = +0.795).

CONCLUSION: MedFusionT5 establishes a new state-of-the-art in automated dental report generation, demonstrating that unidirectional cross-modal alignment achieves superior semantic quality and clinical precision while minimising hallucinations. This work identifies unidirectional attention as the optimal alignment strategy for medical AI, providing a foundation for trustworthy clinical deployment where both accuracy and reliability are paramount.

PMID:41771189 | DOI:10.1016/j.identj.2025.109404

J Breath Res. 2026 Mar 2. doi: 10.1088/1752-7163/ae4bfd. Online ahead of print.

ABSTRACT

Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei (Bp). Rapid diagnosis and appropriate antimicrobial treatment are critical to reduce mortality, yet diagnosis is hindered by diverse clinical manifestations, mimicry with other diseases, and reliance on slow culture-based methods. Detecting volatile compounds offers a non-invasive approach for rapid infection detection. In this study, we aim to identify volatile compounds in patients’ breath that can aid in diagnosing melioidosis and indicating response to treatment.&#xD;Methods: Breath samples were collected from 17 patients with culture-confirmed melioidosis and eight patients with other febrile illnesses. Longitudinal samples were collected from five of the 17 melioidosis patients over approximately one month of antibiotic treatment. Breath samples were analyzed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. Data analysis involved statistical comparison and machine learning-based feature selection. &#xD;Results: We identified three breath markers -camphene, 1-butanol, and 3-methylheptyl acetate -that discriminated melioidosis (n=7) from febrile controls (n=6) with an area under the receiver operating characteristic curve of 1.00. These three markers correctly classified 11 additional samples from 11 melioidosis patients, with one febrile control misclassified. Separately, we selected four breath markers, three of which were hydrocarbons, that differentiated samples associated with a positive Bp culture from those with a negative Bp culture, with a random forest model developed upon these four markers showing a sensitivity of 98% and specificity of 95%. Moreover, we identified a set of 16 volatile compounds that significantly correlated (correlation coefficient > 0.6) with blood C-reactive protein levels. Lastly, a panel of 144 volatile compounds was identified that corresponded to treatment time, indicating that the breath profile may reflect treatment response or shifts in disease severity.&#xD;Conclusion: This pilot study reports candidate breath-based markers for diagnosing melioidosis and assessing treatment outcome, supporting further validation in larger studies. &#xD.

PMID:41771178 | DOI:10.1088/1752-7163/ae4bfd

J Radiol Prot. 2026 Mar 2. doi: 10.1088/1361-6498/ae4be9. Online ahead of print.

ABSTRACT

Ionizing radiation elicits complex cellular responses that are influenced by both total dose and delivery rate. Understanding how dose rate modulates molecular outcomes is important for accurate risk assessment. In this study, we apply an integrative multi-omics approach combining transcriptomic and proteomic profiling, adjusting for covariates, to investigate how differential dose rates alter gene and protein expression in human lymphocytes, with emphasis on alterations in key molecular pathways.&#xD;Methods: Peripheral blood from 14 healthy donors (8 males, 6 females) was irradiated ex vivo with X-rays at 0.05 Gy/minute (DR1) and 1.0 Gy/minute (DR2) across a dose range 0-6 Gy. Gene expression was assessed using TempO-Seq™, and relative protein abundance was determined by mass spectrometry. Differential expression analysis was conducted using edgeR and limma, adjusting for sex, age, and leukocyte counts (false discovery rate (FDR) < 0.05). Multi-omics integration was performed using regularized canonical correlation analysis (rCCA) implemented in mixOmics, followed by Reactome pathway enrichment analysis.&#xD;Results: We identified 2,477 and 2,612 differentially expressed genes at DR1 and DR2, respectively, and 368 and 386 differentially expressed proteins. Using canonical variates from rCCA, we show that covariate adjustment improved dose discrimination, particularly above 0.5 Gy. Using a correlation cut-off threshold of 0.5 in rCCA, 212 (DR1) and 276 (DR2) highly correlated gene-protein pairs were identified. DR2 exposure was associated with stronger enrichment of stress-related pathways, including unfolded protein response, senescence and oncogenic kinase signaling. In contrast, DR1 induced enrichment of pathways associated with immune engagement, including antigen presentation. At both dose rates, transcriptomic changes highlighted upstream regulatory processes (chromatin modeling) and proteomic changes captured downstream functional pathways such as immune activity and apoptosis. &#xD;Conclusion: Multi-omics approach with covariate adjustment revealed key radiation-responsive pathways and dose-rate-dependent molecular differences, highlighting the value of integrating transcriptomic and proteomic data to better understand radiation effects.

PMID:41771177 | DOI:10.1088/1361-6498/ae4be9