Tag: nevin manimala

Brief Bioinform. 2026 Mar 1;27(2):bbag103. doi: 10.1093/bib/bbag103.

ABSTRACT

Genome-wide association studies (GWASs) have been conducted primarily in European (EUR) populations, limiting insights into underrepresented groups such as East Asian (EAS), but cross-ancestry GWASs have demonstrated high trans-ethnic genetic similarity between EUR and non-EUR populations. To enhance association analysis power in EAS populations, we propose tranScore, a novel summary-statistics-based transfer learning method that leverages trans-ethnic genetic similarity through hierarchical modeling. By considering EUR as auxiliary population, tranScore performs joint testing of genetic effects in auxiliary and target populations via well-established P-value combination procedures. Simulations demonstrate that tranScore maintains control of type I error rates and provides substantial power gains for diverse genetic architectures, showing robustness against various challenges including incomplete SNP overlap and effect heterogeneity. In the real-data application of eight diseases from the China Kadoorie Biobank (CKB), after incorporating the genetic information of the EUR population, tranScore identified significantly more genes than the traditional score test which ignored such information. Approximately 41.9% of discovered genes were replicated in the Biobank Japan cohort. Overall, tranScore represents a flexible and powerful statistical approach for association analysis of complex diseases and traits through transfer learning of shared genetic similarities between the auxiliary and target populations.

PMID:41802284 | DOI:10.1093/bib/bbag103

Brief Bioinform. 2026 Mar 1;27(2):bbag090. doi: 10.1093/bib/bbag090.

ABSTRACT

While traditional imaging techniques, such as histopathology, are often part of clinical workflows, molecular profiling remains more difficult to conduct and is less cost-effective. Thus, the prediction of molecular ‘omics’ data directly from imaging has emerged as an appealing alternative. While existing reviews have mentioned image-based prediction of biomarkers within specific disease contexts, this review provides a comprehensive overview of current methods that leverage imaging to predict (i) DNA-based aberrations, (ii) bulk transcriptomic profiles, (iii) single-cell transcriptomics, and (iv) spatial transcriptomics across disease contexts and imaging modalities. To address the complexity of these predictive tasks, we find that many studies employ cutting-edge deep learning strategies for image processing, feature extraction, feature aggregation, and downstream molecular prediction. In this review, we highlight the diverse applications of both deep learning-based and modern statistical frameworks designed for image-based omics prediction. The insights gleaned from these inferred molecular data have broad clinical relevance and will continue to improve our understanding of the relationships between molecular and visual features, paving the way for new diagnostic and therapeutic applications.

PMID:41802282 | DOI:10.1093/bib/bbag090

Brief Bioinform. 2026 Mar 1;27(2):bbag102. doi: 10.1093/bib/bbag102.

ABSTRACT

As genome-wide association studies (GWAS) studies move from array-based genotyping to whole exome and genome sequencing, there is a significant increase in cost. Applying an appropriate technique for the selection of which controls to include, in large studies where more potential controls are available than needed for the study, may be a useful technique for minimizing resource intensity whilst maintaining statistical power. We evaluated three control selection strategies in prostate cancer GWAS using 15 250 UK Biobank cases: (a) all controls, (b) matched controls, and (c) random selection. Both (b) and (c) achieved comparable power in detecting significant loci relative to (a), but matched controls (b) showed greater consistency in identifying leading single nucleotide polymorphisms (SNPs). However, using (b) matched controls reduced discovery power by ~30% compared with (a) all controls, highlighting a trade-off. Matching controls (1:4 ratio) offers a cost-effective approach for targeted SNP analysis across phenotypes but may miss novel associations.

PMID:41802281 | DOI:10.1093/bib/bbag102

Mol Biol Evol. 2026 Mar 9:msag061. doi: 10.1093/molbev/msag061. Online ahead of print.

ABSTRACT

piqtree (pronounced pie-cue-tree) is an easy to use, open-source Python package that provides Python script based control of IQ-TREE’s phylogenetic inference engine. piqtree builds IQ-TREE as a Python package, presenting a library of Python functions for performing many of IQ-TREE’s capabilities including phylogenetic reconstruction, ultrafast bootstrapping, branch length optimization, model selection, rapid neighbor-joining, alignment simulation, and more. As piqtree explicitly uses IQ-TREE’s phylogenetic algorithms, the computational and statistical performance of piqtree equal that of IQ-TREE. Modestly higher memory usage may be expected owing to the Python runtime and the need to load the alignment in Python. By exposing IQ-TREE’s algorithms within Python, piqtree offers users a greatly simplified experience in development of phylogenetic workflows through seamless interoperability with other Python libraries and tools mediated by the cogent3 package. It enables users to perform interactive phylogenetic analyses and visualization using, for instance, Jupyter notebooks. We present the key features available in the piqtree library and a small case study that showcases its interoperability and highlight its potential for linking a high performance phylogenetic inference engine with more user friendly interfaces. piqtree is distributed for use as a standard Python package at https://pypi.org/project/piqtree/, documentation is available at https://piqtree.readthedocs.io, user contributed solutions at https://github.com/cogent3/c3codeshare, help forums at https://github.com/iqtree/piqtree/discussions and source code at https://github.com/iqtree/piqtree.

PMID:41802268 | DOI:10.1093/molbev/msag061

Clin Exp Dermatol. 2026 Mar 9:llag118. doi: 10.1093/ced/llag118. Online ahead of print.

ABSTRACT

BACKGROUND: Rosacea is a chronic skin disorder causing facial erythema, telangiectasias, papules, and pustules. Guidelines recommend sub-antimicrobial doses, such as doxycycline 40 mg. If symptoms persist, evaluating the efficacy and safety of sub-antimicrobial minocycline may expand therapeutic options.

AIM: The present systematic review and meta-analysis evaluates DFD-29 as a low-dose tetracycline alternative to treat rosacea.

METHODS: Our analysis included data from three RCTs sourced from PubMed, Embase, and Cochrane databases to compare the Investigator’s Global Assessment (IGA) success, reduction in inflammatory lesion counts, treatment-emergent adverse events (TEAEs), and serious adverse events (SAEs) in patients using DFD-29 versus modified-release doxycycline. Statistical analyses were conducted using RStudio.

RESULTS: In pooled analysis of 643 patients, DFD-29 significantly increased the likelihood of achieving a successful IGA score compared to doxycycline (OR 2.51; 95% CI 1.80 to 3.49; p < 0.001; I2 = 0%). The DFD-29 40 mg group had significantly greater reductions in IGA scores compared to the 20 mg group (p = 0.0210). The DFD-29 40 mg group also exhibited a higher reduction in inflammatory lesion counts than doxycycline (MD -4.56; 95% CI -6.18 to -2.93; p < 0.001), whereas the 20 mg group did not show significant results. There were no significant differences in TEAEs across groups (OR 0.96; 95% CI 0.55 to 1.66; p = 0.87; I2 = 0%), with only one SEA (atrial fibrillation) was reported with DFD-29.

CONCLUSION: Our meta-analysis showed that DFD-29 at 40 mg daily, is a superior and well-tolerated alternative to modified-release doxycycline for moderate-to-severe rosacea.

PMID:41802266 | DOI:10.1093/ced/llag118

Brain. 2026 Mar 9:awag084. doi: 10.1093/brain/awag084. Online ahead of print.

ABSTRACT

The skull bone marrow contributes to brain immune homeostasis via recently discovered skull-meningeal channels, enabling the bidirectional trafficking of immune cells between skull bone and underlying dura mater. In multiple sclerosis, autoreactive T cells migrate to the bone marrow and shift its hematopoietic output toward myeloid differentiation, contributing to disease progression. However, the role of the skull bone marrow in multiple sclerosis pathophysiology, and its relationship to brain damage and clinical disability remain largely unexplored. We utilized simultaneous MR-PET with the second-generation radioligand ¹¹C-PBR28 to characterize within the skull bone of multiple sclerosis patients the in vivo expression of the translocator protein (TSPO), an 18-kilodalton mitochondrial membrane protein largely expressed by microglia, astrocytes, and peripheral myeloid cells. Sixty-five multiple sclerosis subjects (46 relapsing-remitting, 19 secondary progressive) and 26 healthy controls underwent ¹¹C-PBR28 MR-PET to acquire 60-90-minute post-injection standardized uptake value maps and anatomical scans for brain volumetrics. Voxel-wise analyses of skull TSPO signal were conducted to assess group differences and associations with demographic, clinical, and brain volumetrics. Voxel-wise analyses revealed a divergent association between age and skull TSPO signal, with a negative correlation in healthy controls in bilateral frontal and right parietal regions (r=-0.67, p<0.001), and a positive correlation in multiple sclerosis patients in bilateral parietal and occipital skull bone regions (r=0.44, p<0.001). Compared to both healthy controls and relapsing-remitting multiple sclerosis, patients with secondary progressive multiple sclerosis showed widespread elevation in skull TSPO signal, in frontal, parietal, temporal, occipital, and skull base regions. No significant differences were detected between relapsing-remitting multiple sclerosis and healthy controls. Elevated skull TSPO signal was also observed in patients with more severe neurological disability irrespective of clinical phenotype. Widespread skull TSPO expression was observed to be positively correlated with EDSS scores (ρ=0.49, p<0.001) while a negative association was observed with white matter volume (r=-0.45, p<0.001) and SDMT z-scores (r=-0.48, p<0.001). In multivariable regression analysis, skull TSPO signal (β=6.63, SE=1.92, p=0.001) and T2-hyperintense white matter lesion volume (β=0.34, SE=0.14, p=0.020) were independently associated with disability, while white matter, cortical, and subcortical gray matter volumes did not retain statistical significance (all p>0.550). We provide in vivo evidence of skull TSPO overexpression in multiple sclerosis, observed in progressive disease and associated with clinical disability and structural brain damage. Overall, these findings suggest a role for the skull bone marrow in disease-related processes and highlight its potential as a novel radiological marker and therapeutic target.

PMID:41802262 | DOI:10.1093/brain/awag084

BJS Open. 2025 Mar 5;10(2):zraf178. doi: 10.1093/bjsopen/zraf178.

ABSTRACT

BACKGROUND: The incidence, aetiology, and outcome of unplanned intensive care unit admission after elective colon cancer surgery remain unclear. This study investigated the incidence of, and factors associated with, unplanned intensive care unit admission following elective colon cancer resection in Sweden.

METHODS: This nationwide retrospective registry study included adult patients undergoing elective colon cancer resection with curative intent in Sweden between 2010 and 2019. Patients with distant metastases, or rectal or appendiceal tumours were excluded. Data from the Swedish Colorectal Cancer Registry and the Swedish Intensive Care Registry were analysed. Patients not requiring intensive care unit admission served as controls.

RESULTS: Of 23 891 patients, 1343 (5.6%) required unplanned intensive care unit admission. These patients were older, had more co-morbidities, and were more likely to undergo open surgery and receive permanent stomas. Patients requiring surgical reintervention accounted for 43% of intensive care unit admissions and were identified later (day 5 versus day 1), had longer duration of stay in the intensive care unit (3 versus 1 day), and had worse outcomes than those with non-surgical complications, despite being younger, with less co-morbidity. Intensive care unit admission was linked to a higher unadjusted mortality rate at 30 days (13.9 versus 0.6%), 1 year (24.2 versus 4.6%), and 3 years (40.0 versus 15.3%). Laparoscopic surgery was associated with reduced intensive care unit admissions (odds ratio 0.59, 95% confidence interval 0.50 to 0.69) and lower 3-year mortality (odds ratio 0.79, 0.72 to 0.86), even after adjusting for patient- and surgery-related factors.

CONCLUSION: Unplanned intensive care unit admission was associated with increased short- and long-term mortality. Patients who had surgical reinterventions leading to intensive care unit admission were admitted later and had poorer outcomes than those with non-surgical complications, highlighting the need for earlier recognition and tailored postoperative monitoring strategies.

PMID:41802244 | DOI:10.1093/bjsopen/zraf178

J Med Internet Res. 2026 Mar 9;28:e78423. doi: 10.2196/78423.

ABSTRACT

BACKGROUND: Black veterans and veterans from lower socioeconomic backgrounds are more likely to have uncontrolled hypertension. One potential explanatory factor is reduced access to specific treatments that result in improved chronic disease management. In the Veterans Health Administration (VHA), veterans with hypertension may enroll in a remote patient monitoring (RPM) program, which consists of patient education, daily home blood pressure (BP) monitoring, health coaching, and case management. Barriers for socioeconomically disadvantaged patients may exist for similar programs in other health systems; however, the VHA is an integrated health care system, and these barriers may differ for veteran populations.

OBJECTIVE: The objective of this study was to assess the relationship between veteran race and neighborhood socioeconomic status and the likelihood of enrolling in the VHA RPM program.

METHODS: The study sample included VHA-enrolled veterans with a diagnosis of hypertension (average BP >130/80 mm Hg on ≥2 BP readings) between fiscal years 2020 and 2023. We ran random-effects logistic regression models to assess the relationship between veteran race and Area Deprivation Index and RPM enrollment each year, controlling for potential demographic and clinical confounders. For sensitivity analysis, we limited our sample to veterans with stage 2 hypertension (BP >140/90 mm Hg) and on antihypertensive medication.

RESULTS: Overall use of RPM was low, with only 4.1% (56,553/1,390,995; 95% CI 4%-4.1%) of veterans being enrolled in RPM. Black veterans, who represented 26.6% (n=35,096) of all veterans, were more likely (odds ratio [OR] 1.65, 95% CI 1.59-1.70) to enroll in RPM compared to White veterans. Asian American or Pacific Islander veterans were less likely to enroll (OR 0.83, 95% CI 0.74-0.94). We found no meaningful association between Area Deprivation Index and RPM enrollment (OR 1.00, 95% CI 0.99-1.00). When limiting our sample to those with stage or grade 2 hypertension, we found a similar association (OR 1.61, 95% CI 1.50-1.72) between Black race and RPM enrollment but no significant association with Asian or Pacific Islander race (OR 1.02, 95% CI 0.80-1.29).

CONCLUSIONS: Prior research on RPM in veterans has examined duration or outcomes of RPM enrollment but not the probability of initial enrollment. We found higher enrollment rates in the VHA RPM program among Black veterans but slightly lower enrollment among Asian American or Pacific Islander veterans. Higher enrollment among Black veterans and among those with higher comorbidity burden suggests that the VHA RPM program is successfully reaching those who could most benefit, despite low overall enrollment. Given the low enrollment in RPM, future research should focus on improving uptake among veterans who could additionally benefit from the program. Non-VHA systems, particularly those serving low-income or socioeconomically disadvantaged areas, should explore subsidized or free RPM programs for eligible patients similar to the VHA’s no-cost model for veterans.

PMID:41802237 | DOI:10.2196/78423

JMIR Form Res. 2026 Mar 9;10:e76838. doi: 10.2196/76838.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is increasingly influencing medical student education, with AI-driven chatbots, such as ChatGPT, emerging as powerful study tools. While these technologies offer numerous benefits, they also pose challenges that warrant the adaptation of medical school curricula.

OBJECTIVE: This study examines medical students’ perceptions and use of ChatGPT. We hypothesize that ChatGPT is widely used for academic support, but concerns remain regarding reliability and academic integrity.

METHODS: We conducted a cross-sectional study from August 25 to December 10, 2024, in the United States. Students in all years of medical training who were enrolled in accredited allopathic or osteopathic medical schools were eligible to participate. Data were collected using an anonymous online questionnaire, which was distributed through institutional mailing lists. Overall, 188 schools were reached, of which 14 (7.4%) responded and agreed to distribute the survey. A total of 177 participants completed the survey. Survey items consisted primarily of Likert-scale and multiple-choice questions. Primary outcome measures included self-reported frequency of ChatGPT use, perceived usefulness of ChatGPT, and ChatGPT use habits.

RESULTS: Overall, 98.9% (175/177) of participants had heard of ChatGPT, with 88.7% (157/177) reporting having used it; 62.7% (111/177) identified as female, and 52% (92/177) had completed at least 1 block of clinical rotations. Medical students most often used ChatGPT to understand complex medical concepts, prepare for exams, and generate study materials. Moreover, 46.5% (73/157) used it to help complete medical school assignments. Medical students also reported using it clinically, with the most common use being to generate differential diagnoses. Notably, 21.0% (33/157) of participants responded having used ChatGPT to help write clinical notes. Moreover, 73.9% (116/157) reported that their experience with ChatGPT improved their overall perception of AI’s potential to assist in medical practice, and 86.6% (135/157) believed that having ChatGPT as a resource would make them more effective physicians. Statistical analyses were performed using the Pearson chi-square test with α=.05. Students who reported moderate or advanced baseline understanding of AI were more likely to practice conscientious use habits, such as cross-checking (odds ratio [OR] 2.31, 95% CI 1.08-4.97) and editing (OR 2.45, 95% CI 1.05-5.71) ChatGPT output before using it, than those who reported a basic or limited understanding.

CONCLUSIONS: Our study is among the few to examine medical student perceptions of ChatGPT at a national level. We examined responsible use habits to identify areas in which reliance on this technology may lead users astray. We found that ChatGPT is being used to complete academic assignments and write clinical notes, raising concerns about information verification, AI literacy, patient confidentiality, and ethical use. Together, these findings highlight the need for structured AI education to help students leverage these technologies effectively while mitigating risks associated with misinformation and overreliance on AI.

PMID:41802232 | DOI:10.2196/76838

J Med Internet Res. 2026 Mar 9;28:e73812. doi: 10.2196/73812.

ABSTRACT

BACKGROUND: Digital biomarkers are gaining interest as proxy markers for mental health, as they enable passive and continuous data collection. However, the association between digital biomarkers of health and anxiety, both generalized anxiety disorder and anxiety symptoms, remains unknown.

OBJECTIVE: This systematic review and meta-analysis examined the association between digital biomarkers of health obtained from wrist-worn wearables and anxiety in adults.

METHODS: Systematic literature searches were conducted across 6 databases, including unpublished gray literature. The final search was done on September 21, 2025. Cross-sectional or longitudinal studies investigating the association between digital biomarkers from wrist-worn wearables and anxiety were eligible. Studies using inferential statistics or machine learning methods were both eligible. Studies were excluded if participants received diagnoses of neurodegenerative disorders or physical health conditions. Two risk-of-bias tools were used: the National Heart, Lung, and Blood Institute assessment tool for inferential statistical studies, and the modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 for machine learning studies. Whenever possible, effect sizes were combined across studies, for each digital biomarker of health separately, using random-effects meta-analyses. Sensitivity analyses were performed to assess whether results differed according to anxiety type (state or trait) and age group. Otherwise, studies were synthesized narratively.

RESULTS: A total of 44 studies from 42 articles were eligible. Among these, 36 studies used inferential statistical approaches for analysis (21 reporting sleep characteristics, 8 reporting physical activity, 2 reporting heart rate variability, and 5 reporting more than 1 type), and 8 studies used machine learning approaches. Sample size ranged from 17 to 170,320. Meta-analyses on 4 sleep metrics found no associations: sleep efficiency (Fisher z=-0.07, 95% CI -0.14 to 0.002; P=.06; PI -0.19 to 0.05), wake after sleep onset (Fisher z=0.13, 95% CI -0.04 to 0.30; P=.11; PI -0.15 to 0.41), total sleep time (Fisher z=0.009, 95% CI -0.01 to 0.03; P=.28; PI -0.02 to 0.03), and sleep onset latency (Fisher z=0.04, 95% CI -0.07 to 0.15; P=.08; PI -0.19 to 0.27). Qualitative syntheses revealed that lower physical activity levels and higher heart rate were associated with greater anxiety symptoms. Machine learning studies using wrist-worn wearable data alone showed varied performance, with predictive performance improving when wearable data were combined with other data sources.

CONCLUSIONS: This is the first review to synthesize evidence from inferential statistical (mostly fair quality) and machine learning studies examining association between wearable-derived digital biomarkers and anxiety. Meta-analyses found no associations between sleep metrics and anxiety. Although based on limited studies, lower physical activity levels and elevated heart rate were associated with greater anxiety symptoms. Digital biomarkers may be more useful when integrated with other data sources (eg, self-report and clinical data) rather than used as stand-alone screening tools.

TRIAL REGISTRATION: PROSPERO CRD42023409995; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023409995.

PMID:41802227 | DOI:10.2196/73812