Category: Nevin Manimala

BMC Med Res Methodol. 2026 Feb 21. doi: 10.1186/s12874-026-02793-5. Online ahead of print.

NO ABSTRACT

PMID:41723363 | DOI:10.1186/s12874-026-02793-5

BMC Geriatr. 2026 Feb 21. doi: 10.1186/s12877-026-07181-8. Online ahead of print.

NO ABSTRACT

PMID:41723350 | DOI:10.1186/s12877-026-07181-8

Ther Innov Regul Sci. 2026 Feb 21. doi: 10.1007/s43441-025-00894-9. Online ahead of print.

ABSTRACT

Medicinal products have benefits and risks that must be carefully balanced to inform decision making. The structured benefit-risk (BR) framework is a powerful approach not only to standardize a holistic BR assessment, but also to incorporate the patient perspective and guide the decisions and discussions of sponsors and regulatory agencies throughout the continuum of drug development. Structured BR assessment has been usually conducted using a qualitative approach during the late development stage. The use of quantitative models that can be applied throughout the drug development process may provide more objective BR information to support scientific recommendations to optimize and inform decisions for critical external and internal development opportunities. A new Multidimensional Benefit-Risk Integrated Evaluation (MBRIE) quantitative model was developed using key attributes of the structured BR assessment. Each attribute was evaluated by assigning a rating score ranging from 1 to 3 (low), 4-6 (medium), 7-10 (high). Also, two dimensions for comparative purposes were considered: standard of care (SOC) and probability of development success (PODS) (likelihood or favorability for development success). Graphical outputs were used to visualize and compare the ranking scores for each of the attributes across the two dimensions. This analysis implements a structured quantitative BR assessment approach earlier in drug development and through the drug lifecycle. The MBRIE model may be an innovative tool to facilitate solutions by fostering a collaborative culture that points to the true objective to improve patient outcomes.

PMID:41723330 | DOI:10.1007/s43441-025-00894-9

J Assist Reprod Genet. 2026 Feb 21. doi: 10.1007/s10815-026-03833-1. Online ahead of print.

ABSTRACT

PURPOSE: This study addressed the practical challenge of missing data in assisted reproductive technology by evaluating the reliability of predicting oocyte yield when anti-Müllerian hormone (AMH) values are unavailable. We examined the ability of AI-based models to recover missing biomarker data and maintain predictive accuracy despite data limitations.

METHOD: We conducted a retrospective analysis using data from 27,435 IVF cycles across multiple centers from 2018 to 2023. Various machine learning models were compared to serve as internal imputation models to fill data gaps and predict oocyte retrieval. We validated the models across a range of missingness rates (0% to 90%) using bootstrapping to ensure statistical robustness and evaluate generalizability across different clinical environments.

RESULTS: The best-performing model using actual AMH achieved an AUC of 0.838. Despite the relatively low explained variance in AMH recovery (R² ≈ 0.2), the imputed values captured enough clinical information to serve as reliable predictive proxies. The model’s performance remained above an illustrative benchmark of 0.80 AUC until the missing rate reached 35.5%. SHAP analysis confirmed that the AI model effectively used age and other clinical variables to compensate for missing AMH data.

CONCLUSIONS: AI-based imputation offers a practical solution for clinical infertility care, where missing data is caused by documentation issues or repeat-cycle workflows. This approach bridges the gap between ideal laboratory records and realistic data limitations, ensuring that data-driven decision support remains accessible even in the presence of incomplete records.

PMID:41723323 | DOI:10.1007/s10815-026-03833-1

Biol Trace Elem Res. 2026 Feb 21. doi: 10.1007/s12011-026-05030-8. Online ahead of print.

ABSTRACT

This study examined the geographical distribution of cadmium (Cd), copper (Cu), mercury (Hg), and lead (Pb) in the Monterrey Metropolitan Area (MMA), Mexico, using feral pigeons (Columba livia) as bioindicators. Trace metals were quantified in tail feathers collected from 74 individuals in nine municipalities using Anodic Stripping Voltammetry (ASV). Copper showed the highest concentrations (maximum mean 21.3 µg/g), followed by Pb (5.2 µg/g), Hg (0.7 µg/g), and Cd, which was detected in a few samples (< 0.6 µg/g). A consistent center-to-periphery gradient was observed for Cu, Hg, and Pb. Cluster analysis identified four spatially distinct groupings based on bioaccumulation, with the highest concentrations occurring in central municipalities characterized by higher human population density, suggesting heterogeneous exposure and increased combined risk from trace metals in more urbanized areas. Leukocyte frequencies varied across municipalities, suggesting site-specific physiological responses associated with differences in trace metal concentrations. Genotoxicity biomarkers showed spatial trends similar to trace metal levels, although differences were not statistically significant, indicating limited genotoxic effects at the observed exposure levels. The concordant spatial patterns between environmental and biological indicators highlight trace metal exposure as a relevant risk in the MMA and support the use of feral pigeons as effective bioindicators of trace metal pollution, with implications for human health in industrial and highly urbanized areas.

PMID:41723310 | DOI:10.1007/s12011-026-05030-8

Br Med Bull. 2026 Jan 2;157(1):ldag010. doi: 10.1093/bmb/ldag010.

ABSTRACT

BACKGROUND: Marathon running has evolved into a global phenomenon, with rising participation across age and experience groups. Training for a marathon requires adherence to well-established principles involving pacing, training volume, and periodization. With the increasing integration of artificial intelligence (AI) into healthcare and fitness, it remains unclear whether AI can reliably prescribe evidence-based training programs for such demanding endurance events.

SOURCES OF DATA: We conducted a descriptive study using outputs from leading AI models: Claude 3.5 Sonnet, Claude 3.5 Haiku (Free), ChatGPT 4.0 (o-model), ChatGPT 0.1, ChatGPT 4 (free), Gemini 2.0 Flash, Gemini 2.0 Flash Thinking, and DeepSeek R1. Each was prompted to generate a 6-month marathon training plan tailored to three athlete levels: Beginner, Intermediate, and Advanced. Outputs were compared with peer-reviewed literature on the determinants of marathon training.

AREAS OF AGREEMENT: Most AI systems identified key training components: weekly mileage progression, tapering, and intensity distribution (>80% at low intensity), which aligns with current endurance training theory.

AREAS OF CONTROVERSY: AI responses varied in accuracy and completeness. Some engines omitted key details (e.g. weekly mileage), failed to differentiate clearly between athlete levels (intermediate and advanced have been merged as if they were the same level), or offered inconsistent pacing data, especially for advanced runners. This descriptive analysis evaluated qualitative adherence to evidence-based training principles rather than quantitative outcomes requiring statistical inference.

GROWING POINTS: AI demonstrates strong potential in accessible, structured training content. When properly prompted, outputs often align with contemporary training principles, though significant limitations regarding personalization and professional oversight necessitate further validation before clinical implementation.

AREAS TIMELY FOR DEVELOPING RESEARCH: Future studies should evaluate the real-world outcomes of AI-generated programs in randomized trials including the integration of personal physiological data. Inizio moduloFine modulo.

PMID:41722095 | DOI:10.1093/bmb/ldag010

Rev Med Virol. 2026 Mar;36(2):e70114. doi: 10.1002/rmv.70114.

ABSTRACT

Azvudine is a nucleoside reverse transcriptase inhibitor (NRTI) and belongs to the family of 2′, 3′-dideoxynucleoside (ddNs) that can mimic natural nucleosides and block viral DNA or RNA chain synthesis and prevent viral replication. Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, Azvudine has been used to treat patients with COVID-19. Therefore, the objective of this meta-analysis study was to compare Azvudine and Nirmatrelvir-Ritonavir in hospitalised patients. The global online databases were used to identify relevant studies published between January 2019 and October 2024. The quality of all articles was determined using the Newcastle-Ottawa Scale (NOS) checklist. In this study, heterogeneity assay was assessed using the Cochran’s Q-test and the I2 index, and STATA software version.14 (StataCorp) was used for statistical analysis. Egger’s test, Begg’s test, and funnel plot were performed to estimate of the publication bias, and the impact of each study on the overall estimate was assessed using sensitivity analysis. In this study, 19 studies were included in this meta-analysis. The results of the meta-analysis showed that the relative risk of death in the Azvudine treatment group compared with the Nirmatrelvir-Ritonavir treatment group was 0.64 (95% CI: 0. 44, 0. 93). These results suggest that treatment with Azvudine may provide significant clinical benefit in patients hospitalised with COVID-19.

PMID:41722060 | DOI:10.1002/rmv.70114

Am J Clin Pathol. 2026 Jan 5;165(2):aqaf153. doi: 10.1093/ajcp/aqaf153.

ABSTRACT

OBJECTIVE: To evaluate the ability of 4 artificial intelligence large language models (LLMs) to create items that align with the item writing standards of the American Board of Pathology (ABPath) for continuing certification.

METHODS: An informatics item writing application was developed and used with prompts based on the ABPath item writing standards. Uniform prompts were used for the LLMs evaluated, with the content of the items generated tailored to the expertise of the reviewing subject matter experts (SMEs). The SMEs were blinded to the identity of the LLM that generated each item. The 14 SMEs graded 4 written items and 4 practical items, with 1 item from each set of 4 generated from each of the LLMs. The 19 questions used for grading concentrated on item anatomy (ie, item structure), accuracy, relevance, and level of item difficulty.

RESULTS: The overall scores for the 4 LLMs for the written items were as follows: Claude, 229 of 266 (86.1%); ChatGPT, 212 of 266 (79.7%); Llama, 175 of 266 (65.8%); and Titan, 162 of 266 (60.9%). The overall scores for the 4 LLMs for the practical items were as follows: Claude, 247 of 266 (92.9%); ChatGPT, 216 of 266 (81.2%); Llama, 175 of 266 (65.8%); and Titan, 151 of 266 (56.8%). Statistically significant differences existed between the LLMs.

CONCLUSIONS: We observed significant differences in the ability of the 4 LLMs evaluated to draft items consistent with the ABPath guidelines based on SME scoring. It is important to assess the various LLMs available to determine which model best meets the needs of the user for the proposed task and not to assume equivalence.

PMID:41722024 | DOI:10.1093/ajcp/aqaf153

Oncol Ther. 2026 Feb 21. doi: 10.1007/s40487-026-00418-x. Online ahead of print.

ABSTRACT

INTRODUCTION: Venetoclax represents a significant advancement in target anticancer therapy in the management of relapsed/refractory chronic lymphocytic leukemia (RR-CLL). This study aims to compare the efficacy and safety of the venetoclax + rituximab (VenR) regimen with other therapies approved in Brazil.

METHODS: A systematic review and network meta-analysis (NMA) was conducted to evaluate the efficacy and safety of treatments approved in Brazil for RR-CLL. Comprehensive literature searches were performed to identify randomized controlled trials. Risk of bias and certainty of evidence for each outcome were assessed across the included studies. The NMA was conducted using a frequentist framework. The primary efficacy outcomes were progression-free survival, overall survival, overall response rate, and time to next therapy. Safety was assessed on the basis of the incidence of serious adverse events.

RESULTS: A total of 24 publications related to 12 trials were identified and included. VenR was associated with better survival outcomes when compared with standard regimens such as rituximab (HR 0.16; 95% CI 0.03-0.74) and physician choice (HR 0.17; 95% CI 0.04-0.81). In terms of progression-free survival, VenR achieved HR < 0.20, supported by narrow confidence intervals, when compared to treatments such as bendamustine plus rituximab, ofatumumab and physician choice, in addition to significantly favorable results compared to ibrutinib and acalabrutinib. Approximately half of the studies presented a low risk of bias, and the certainty of evidence assessed using the GRADE-NMA approach resulted in very low certainty of evidence, mainly due to risk of bias, intransitivity, and imprecision.

CONCLUSION: This NMA provides valuable evidence to support rational therapeutic choices for RR-CLL in Brazil, highlighting VenR and Bruton tyrosine kinase inhibitors as leading treatment options across diverse clinical scenarios.

PMID:41722016 | DOI:10.1007/s40487-026-00418-x