Category: Nevin Manimala

Geriatr Gerontol Int. 2026 Jun;26(6):e70604. doi: 10.1111/ggi.70604.

ABSTRACT

BACKGROUND: Anticholinergic burden is common in older adults and has been associated with adverse outcomes, although its association with mortality is inconsistent. This study aimed to investigate the association between anticholinergic burden and time-to-death in older adults and to examine whether this association differs according to baseline frailty status.

METHODS: This retrospective cohort study analyzed 2739 adults (65-95 years) who attended a geriatric clinic between 2013 and 2020 and had a documented date of death during the study period. Anticholinergic burden was categorized by anticholinergic cognitive burden (ACB) scale scores as low (0-1) or high (≥ 2). Frailty was defined as a clinical frailty scale (CFS) score ≥ 4. Time-to-death was defined as the interval between the index geriatric visit and death. Subgroup analyses explored frailty, polypharmacy, and major comorbidities.

RESULTS: Among the study population, high ACB was associated with a significantly shorter time-to-death (log-rank p = 0.013). After adjustment for age, sex, and frailty status, high ACB remained independently associated with a higher hazard of death at both 1-year (HR 1.20, 95% CI 1.03-1.40) and 5-year follow-up (HR 1.15, 95% CI 1.05-1.28). In stratified analyses, this association was observed among frail individuals (log-rank p = 0.007), whereas no significant association was observed among non-frail participants (p = 0.767).

CONCLUSIONS: Higher anticholinergic burden was independently associated with shorter time-to-death in older adults, with the association predominantly observed in frail individuals. These findings underscore the need for routine frailty and anticholinergic burden assessment in geriatric care and support targeted deprescribing in frail populations.

PMID:42310484 | DOI:10.1111/ggi.70604

Cancer Med. 2026 Jun;15(6):e72052. doi: 10.1002/cam4.72052.

ABSTRACT

INTRODUCTION: Approximately 12.4% of patients with lung cancer experience depression after cancer diagnosis. Asian, Native Hawaiian, and Pacific Islanders (ANHPIs) constitute a large group with heterogeneity. The aim of this study is to examine the racial differences in depression risk, comparing overall ANHPI and ANHPI ethnic groups to non-Hispanic White (NHW) patients with lung cancer.

METHOD: We utilized the Surveillance, Epidemiology and End Results (SEER)-Medicare and SEER-Consumer Assessment of Healthcare Providers and System (CAHPS) dataset. We included patients with primary lung cancer who were aged 66 and older, diagnosed from 2000 to 2017 with full coverage of Medicare Part A and B. One ANHPI patient with lung cancer was matched to three NHW patients with lung cancer based on sex, diagnosis age, and diagnosis year. We used the Cox proportional hazards model to estimate the differences in depression incidence among ANHPI and NHW patients with lung cancer.

RESULTS: Overall ANHPI, Chinese, Japanese, Filipino, Asian Indian or Pakistani, and other Asian patients with lung cancer had a lower incidence of depression than NHW patients with lung cancer. Korean patients with lung cancer (HR 1.62, 95% CI 1.05-2.51) had a higher incidence of depression when compared to Chinese patients with lung cancer. ANHPI and NHW males had a lower incidence of depression compared to female lung cancer patients.

CONCLUSION: Korean patients with lung cancer may have a higher incidence of depression than Chinese comparisons. More research to investigate the heterogeneity and underdiagnosis of depression risk among older ANHPI groups is needed.

PMID:42310483 | DOI:10.1002/cam4.72052

Microbiologyopen. 2026 Jun;15(3):e70326. doi: 10.1002/mbo3.70326.

ABSTRACT

Colorectal cancer (CRC) is one of the most common types of cancer worldwide, and the gut microbiome plays a crucial role in its development. In the study, we examine the variation in gut microbial community composition among individuals diagnosed with CRC based on human fecal samples. A systematic search of online databases, including MEDLINE (PubMed), Web of Science, Embase, and Scopus up to March 2026, following the requirements outlined in the PRISMA guideline. The search strategy was based on a combination of keywords, including “colorectal cancer,” “gut microbiome”, and “feces.” The study analyzed 43 research articles on colorectal cancer microbiome. Most investigations utilized culture-independent techniques, revealing variations in microbial profiles between colorectal cancer cases and healthy controls. Fusobacterium and Porphyromonas emerged as potential colorectal cancer biomarkers, while multi-bacteria predictive models showed promise in enhancing colorectal cancer detection sensitivity and specificity. In this review, we will explore how advanced sequencing techniques have the potential to complement current non-invasive methods for early diagnosis and prevention of colorectal cancer. This includes conducting studies with robust statistical power and consistent, replicable methodologies, taking into consideration host factors, and performing external validation of predictive models.

PMID:42310475 | DOI:10.1002/mbo3.70326

CPT Pharmacometrics Syst Pharmacol. 2026 Jul;15(7):e70278. doi: 10.1002/psp4.70278.

ABSTRACT

The development of new methods for covariate model building (CMB) in population pharmacokinetics (popPK) highlights the need for a standardized evaluation framework for method benchmarking. This data paper introduces PMX-CovEval, a framework including a collection of datasets based on 127 distinct scenarios. These scenarios aim to reflect the diversity of models, available clinical studies, and covariates encountered in real-world applications, while remaining limited enough in number to encourage their practical use. To support the evaluation of standard CMB techniques available on PsN and Monolix, model files for NONMEM and Monolix are provided alongside the datasets. Additionally, empirical Bayes estimates (EBEs) from the true base models are included to facilitate the testing of EBE-based regression approaches. By offering pharmacokinetic (PK) datasets, model files, and EBEs in a unified resource, PMX-CovEval provides a standardized, reproducible framework for evaluation and systematic comparison of CMB strategies in popPK. Initial benchmarking results are also provided.

PMID:42310474 | DOI:10.1002/psp4.70278

Nature. 2026 Jun 17. doi: 10.1038/s41586-026-10664-8. Online ahead of print.

ABSTRACT

Climate-induced permafrost thaw unlocks large stores of organic carbon that are mineralized and emitted as carbon dioxide (CO₂) from rivers to the atmosphere¹. Concurrently, warming and permafrost thaw can increase mineral weathering rates, thus affecting the release and sequestration of inorganic carbon^2-4. Yet how these biological and geological carbon cycles interact and jointly affect CO₂ dynamics (emission compared with drawdown) in permafrost rivers remains unknown⁵. Here we combine CO₂ emissions, organic and inorganic solute concentrations, dual carbon isotopes (δ¹³C-Δ¹⁴C) and geochemical modelling to infer how permafrost thaw may affect river biogeochemistry over decades to centuries across the Qinghai-Tibet Plateau. Leveraging a gradient of thermal permafrost degradation, we find that river CO₂ emissions decline, whereas solute fluxes from rock weathering increase with decreasing permafrost cover. Across this region, net CO₂ drawdown fluxes from rock weathering are about 35% of river CO₂ emissions, varying from around 15% in catchments with continuous permafrost to more than 100% in catchments with discontinuous or isolated permafrost. Thus, carbon fluxes from chemical weathering may become increasingly important with ongoing permafrost thaw, potentially even outpacing river CO₂ emissions. Our findings disentangle the interplay between biological and geological carbon fluxes that are important for the cryosphere and the global carbon cycle.

PMID:42310459 | DOI:10.1038/s41586-026-10664-8

Sci Rep. 2026 Jun 18. doi: 10.1038/s41598-026-58302-7. Online ahead of print.

ABSTRACT

Financial time series forecasting faces significant challenges due to inherent nonlinearity, non-stationarity, and high levels of noise. To address these issues, this study proposes VMD-CSA-BiT, an integrated framework that combines variational mode decomposition (VMD), convolutional self-attention (CSA), and bidirectional transformers (BiT) to enhance prediction robustness. The methodology first decomposes raw price series into interpretable intrinsic mode functions via VMD. It then employs the CSA module to refine pointwise representations at individual time steps and applies the BiT network to model bidirectional long-term temporal dependencies. Evaluated on a range of financial assets using a comprehensive set of market features, the proposed framework demonstrates consistent performance improvements over multiple benchmark models, including traditional statistical methods, machine learning models, and advanced deep learning architectures, achieving significant reductions in key error metrics. The results indicate that VMD-CSA-BiT offers superior forecasting accuracy and stability, with visual analyses showing that its predictions generally align with actual market movements. This study shows that VMD-CSA-BiT is a promising and effective approach for financial time series forecasting. Future research will focus on further architectural optimizations and extending the framework to additional financial applications.

PMID:42310433 | DOI:10.1038/s41598-026-58302-7

Sci Rep. 2026 Jun 17. doi: 10.1038/s41598-026-58460-8. Online ahead of print.

ABSTRACT

Non-specific low back pain (NSLBP) represents about 80-95% of all LBP cases, yet the optimal lumbar traction position for effective treatment remains debated. To investigate the effect of different positions during lumbar traction on pain, function, and ROM in adults with NSLBP. Thirty participants were randomly allocated to one of three lumbar traction position groups: supine, side-lying, or prone. Outcome measures included the Oswestry Disability Index (ODI), International Physical Activity Questionnaire short form (IPAQ-SF), Finger-to-Floor Test (FFT), and Numerical Pain Rating Scale (NPRS). Measurements were obtained at baseline, after the third session, and after completion of the 3-week intervention. Within-group analysis showed significant improvements in ODI and NPRS scores over time in all three groups (p < 0.001). The supine group demonstrated earlier within-group improvement after the third session (p = 0.012). IPAQ improved significantly only in the side-lying group (p = 0.01), whereas FFT did not show significant changes in any group (p > 0.05). However, between-group analysis revealed no statistically significant differences among the three traction positions at any measurement point for any outcome (p > 0.05). Lumbar traction was associated with improvements in pain and disability regardless of position. However, no traction position demonstrated superiority over the others. The earlier improvement observed in the supine group should be interpreted cautiously because it was based on within-group change rather than between-group superiority. Future studies with larger samples are needed to clarify whether positional differences are clinically meaningful. Also, these findings are limited to male participants and should not be generalized to female populations.Trial registration: The trial was registered on 02/02/2025, clinicaltrials.gov, Identifier: (NCT06812338).

PMID:42310416 | DOI:10.1038/s41598-026-58460-8

Sci Rep. 2026 Jun 17;16(1):18859. doi: 10.1038/s41598-026-57128-7.

ABSTRACT

This study aimed to evaluate the effectiveness of intracorneal ring segments (Keraring) implantation for the management of high regular astigmatism (> 3.00 D) in eyes with non-ectatic corneas. Twenty eyes of twenty patients with high regular astigmatism and topographically normal corneas. All eyes underwent femtosecond laser-assisted implantation of SI-5 Keraring segments. Comprehensive preoperative and postoperative assessments included uncorrected and best-corrected distant visual acuity, manifest refraction, keratometric measurements, and anterior corneal higher-order aberrations (HOAs). Follow-up examinations were performed at 1 week, 1 month, and 6 months. Statistical analysis incorporated repeated-measures ANOVA and Pearson correlation coefficients. Significant improvement in UDVA was observed at all postoperative visits (p < 0.001). CDVA improved significantly during the early postoperative period but not at 6 months. Mean refractive astigmatism decreased markedly from – 8.19 ± 2.50 D preoperatively to – 3.14 ± 1.55 D at 6 months postoperatively, corresponding to a mean reduction of 61.3% (p < 0.000). Significant reductions were also observed in spherical equivalent, Keratometric indices, total HOAs and coma aberrations. Corneal thickness and spherical aberrations showed no significant changes throughout the follow-up period. No intraoperative or postoperative complications were observed. Keraring implantation may provide meaningful improvements in visual acuity, refractive outcomes, corneal regularity, and selected higher-order aberrations in eyes with high regular astigmatism and non-ectatic corneas. Larger studies with longer follow-up are warranted to confirm these preliminary findings.

PMID:42310410 | DOI:10.1038/s41598-026-57128-7

Nat Aging. 2026 Jun 17. doi: 10.1038/s43587-026-01130-1. Online ahead of print.

ABSTRACT

Although senolytics such as dasatinib and quercetin (D+Q) show promise in modulating aging, their tissue-specific efficacy and optimal intervention timing remain poorly understood. Given D+Q’s potential off-target effects, incomplete senescent cell clearance and associated hematologic side effects, we performed an unbiased multitissue single-cell analysis in aged mice across different aging phenotypes and tissue contexts. Here through integrative transcriptomics, single-cell technologies, histopathology and molecular profiling, we investigated the influence of D+Q treatment on aging-related phenotypes at the tissue and cellular levels. Specifically, D+Q remodeled immunity by enhancing immune cell function and maintaining population stability, alleviated tissue inflammation and improved metabolic profiles. Furthermore, intervention initiated during early aging and prolonged treatment showed a greater tendency to mitigate readouts of aging compared to shorter, late-stage treatment. Our findings reveal that D+Q systematically attenuates several aging hallmarks in a tissue- and cell-type-specific manner, and support the possibility that early-initiated, long-term intervention may amplify efficacy.

PMID:42310394 | DOI:10.1038/s43587-026-01130-1

Sci Rep. 2026 Jun 17. doi: 10.1038/s41598-026-57686-w. Online ahead of print.

ABSTRACT

In this paper, a distributed robust adaptive confined fault-tolerant optimal control method based on deep neural networks is proposed, aiming to solve the complexity and uncertainty problems in human height and weight prediction. Note that the term ‘control’ in this work refers to feedback regulation of the iterative learning/optimization dynamics of the predictor (iteration domain), rather than controlling the physical time evolution of human height/weight. In the field of public security technology, accurate prediction of individual physiological characteristics has important application value, especially in crime prevention, individual identification, and behavior analysis. Traditional prediction methods often perform erratically in the face of data noise, environmental changes, and outliers. To this end, this paper combines deep learning and fault-tolerant control theory to propose an efficient and reliable prediction framework by optimizing the robustness and adaptive ability of the predictor. By introducing a limited fault-tolerant mechanism, it can maintain high prediction accuracy and stability under various perturbations and incomplete data conditions. Moreover, we evaluate the proposed framework from three complementary dimensions-statistical similarity, overall predictive performance, and minority-class detection ability-and explicitly acknowledge that these criteria may exhibit trade-offs: improved distributional similarity does not necessarily translate into better decision boundaries, and optimizing overall performance can conflict with minority detection (e.g., recall/F1). Simulation and experimental results show that after 2000 rounds of iterative optimization, the normal and fault-tolerant prediction accuracies of human height for finger length of left and right hands are 98.4% and 97.7%, respectively, and the normal and fault-tolerant prediction accuracies of human body weight are 98.2% and 97.5%, respectively, by combining the 372 sets of data with 30% of data loss caused by human. The accuracy of normal and fault-tolerant prediction of human height was 90.8% and 89.2% for the finger length of the left hand, and the accuracy of normal and fault-tolerant prediction of human weight was 85.6% and 83.3%, respectively. The normal and fault-tolerant prediction accuracies of human height for the finger length of the right hand were 96% and 95.3%, and the normal and fault-tolerant prediction accuracies of human weight were 94.4% and 93.5%, respectively. These findings are most directly applicable to small-to-medium tabular datasets with moderate class imbalance and limited minority samples, which matches the regimes evaluated in this study. This study provides a new idea and technical path for biometric prediction and analysis in the field of public security technology, which has important theoretical significance and practical value.

PMID:42310389 | DOI:10.1038/s41598-026-57686-w