Nevin Manimala

Optom Vis Sci. 2026 Jan;103(1):e70027. doi: 10.1002/ovs2.70027.

ABSTRACT

PURPOSE: The aim of this study is to evaluate and quantify model performance for commonly used statistical approaches when using data from both eyes of participants. These models highlight different methods of accounting for interocular correlation.

METHODS: We simulated a continuous outcome variable, a predictor variable measured per-eye (two correlated values per subject – termed bivariate), and a predictor measured once per subject (termed univariate). Both the outcome and the bivariate predictor shared the same correlation level in all simulations. Correlations were varied 0-0.9 in 0.1 steps, with sample sizes of 50, 100, and 150. Two thousand datasets were simulated under each correlation-sample size combination. The datasets were modeled using single-eye, averaged-eye, and assumed-independent two-eye approaches within linear regression, along with a mixed effects model and a generalized estimating equation (GEE).

RESULTS: Mixed effects models, modeling one eye per subject, and averaging eyes within subjects all controlled Type-I error at 0.05 across simulated conditions. GEEs slightly inflated Type-I error, especially with smaller sample sizes. Modeling both eyes independently inflated Type-I error as high as 0.194 in high correlation scenarios. This inflation increased with increasing correlation. For univariate predictors, GEEs, mixed effects modeling and averaging eyes within subjects attained similar power across scenarios. Single-eye modeling resulted in lower power, particularly in low correlation scenarios. For bivariate predictors, mixed effects modeling and GEEs yielded greater power than single-eye or averaged-eye modeling across scenarios.

CONCLUSIONS: Mixed effects models and GEEs out-perform other approaches when the predictor of interest is bivariate and correlated, assuming correlations are similar for the predictor and outcome. For univariate predictors, averaging the outcome across eyes within each subject performs similarly to mixed effects modeling. Treating correlated measurements as independent (such as when using data from both eyes without averaging or factored into a model) inflates Type-I error rates and yields inappropriately high power, especially as correlation increases; this modeling approach leads to inference errors and should be avoided.

PMID:41851051 | DOI:10.1002/ovs2.70027

Alzheimers Dement. 2026 Mar;22(3):e71279. doi: 10.1002/alz.71279.

ABSTRACT

INTRODUCTION: Polygenic risk scores (PRSs) for Alzheimer’s disease (AD) capture an individual’s genetic susceptibility to AD. Although thoroughly studied in older populations, there exists a notable gap in comprehensively exploring the association of AD PRS with early brain development.

METHODS: We examined longitudinal brain magnetic resonance imaging (MRI) data from 348 typically developing children in the RESONANCE cohort. Proportional cerebrospinal fluid (pCSF), white matter (pWM), and gray matter (pGM) volumes were analyzed using functional concurrent regression and Riemannian functional principal component analysis. AD-PRS scores (AD25 and AD54) were computed using genome-wide data.

RESULTS: Higher AD PRS was significantly associated with reduced pCSF during early childhood (ages 2.5 to 5.5 years for AD54). Energy and distance-based tests revealed overall significant differences in brain volume trajectories between moderate and low AD54 risk groups.

DISCUSSION: These findings suggest that genetic risk for late-onset AD is linked to early neurodevelopmental patterns, indicating that AD vulnerability may originate during critical windows of early brain maturation.

PMID:41851041 | DOI:10.1002/alz.71279

J Med Internet Res. 2026 Mar 18;28:e84349. doi: 10.2196/84349.

ABSTRACT

BACKGROUND: Cognitive decline across the mild cognitive impairment (MCI)-dementia continuum is a major driver of loss of independence and growing health- and social-care burden. Immersive technologies, such as virtual reality (VR), augmented reality (AR), and Cave Automatic Virtual Environment (CAVE) systems, are increasingly explored as tools to enhance engagement, personalization, and ecological validity in cognitive rehabilitation.

OBJECTIVE: This systematic review synthesizes current evidence on the usability, therapeutic effects, and implementation challenges of immersive technologies for cognitive rehabilitation in MCI and dementia.

METHODS: A systematic search of Scopus and Web of Science was conducted for peer-reviewed journal articles published between 2021 and 2026. Eligible studies investigated VR, AR, or CAVE interventions targeting cognitive rehabilitation outcomes in MCI and/or dementia and reported measures related to usability or acceptability, or cognitive, functional, or behavioral outcomes. Due to heterogeneity in technologies, intervention content, and outcome measures, findings were synthesized narratively with comparisons across modalities and study designs.

RESULTS: In total, 119 studies met the inclusion criteria. Across immersive VR interventions, signals of benefit were most consistently reported for memory, attention, and executive functioning, with several studies also targeting outcomes with higher ecological relevance (eg, everyday task performance and functional skills). AR approaches primarily support context-aware cueing and task guidance in real-world settings, aiming to strengthen daily functioning and independence. CAVE-based systems were frequently used for spatial navigation and embodied interaction, offering advantages for supervised clinical deployment. Key barriers included cybersickness and comfort issues, interface complexity, and onboarding demands in cognitively impaired users, limited accessibility and standardization of outcome measures, small samples and short follow-up periods, and practical constraints related to cost, space, staffing, and caregiver involvement.

CONCLUSIONS: Immersive VR, AR, and CAVE systems are feasible and often engaging for cognitive rehabilitation in MCI and dementia, with promising therapeutic signals but substantial uncertainty driven by methodological and implementation heterogeneity. Future work should prioritize standardized reporting (intervention components, dose, and adverse events), clinically meaningful outcomes (including functional end points), adequately powered comparative trials, and explicit evaluation of scalability and real-world deployment pathways.

PMID:41851030 | DOI:10.2196/84349

Stat Med. 2026 Mar;45(6-7):e70464. doi: 10.1002/sim.70464.

ABSTRACT

Mediation analysis is a fundamental tool for understanding biological mechanisms through which an exposure exerts its effect on an outcome via intermediate variables, or mediators. However, modern biomedical studies often involve multiple exposures and mediators with complex correlation structures, and may also involve multiple outcomes, as in multi-omics or imaging studies, where existing mediation analyses can suffer from instability and limited interpretability. In this work, we propose Envelope-Based Sparse Partial Least Squares for Mediation Analysis (ESPLSM), which integrates dimension reduction and sparsity enforcement via the sparse envelope model to improve estimation and interpretation of causal effects. We embed the envelope model within the causal mediation framework based on potential outcomes, which allows us to formally define and identify direct and indirect effects and to establish theoretical guarantees, including asymptotic efficiency and selection consistency. Through simulation studies, we show that ESPLSM outperforms existing methods in terms of estimation accuracy, statistical power, and variable selection. Finally, we apply ESPLSM to a cancer cell line dataset to investigate the role of RNA expression in mediating the effect of EGFR mutations on drug responses. Our results provide new insights into the molecular mechanisms underlying targeted cancer therapies. Overall, ESPLSM provides a statistically principled yet practical solution for interpretable and efficient mediation analysis in modern high-dimensional biomedical applications.

PMID:41851029 | DOI:10.1002/sim.70464

DNA Cell Biol. 2026 Mar 18:10445498261433751. doi: 10.1177/10445498261433751. Online ahead of print.

ABSTRACT

Cyclin-dependent kinase 17 (CDK17) is an understudied member of the PCTAIRE family of CDKs, with phosphorylation-guided molecular mechanism being underexplored. In this study, an in-depth mass spectrometry-based phosphoproteomics data integration and harmonization, coupled with replicable statistical analysis, was performed to understand the phosphorylation landscape of CDK17. High-confidence phosphorylation sites of CDK17 were derived from 711 phosphoproteomics profiling studies, where 176 datasets showed differential phosphorylation of CDK17. Among 13 identified phosphorylation sites of CDK17, S180, S137, and S146 were prominently detected in 75% of all the datasets. Notably, sequence conservation of CDK17 (S146, S137, and S180) with CDK16 (S119, S110, and S153) and CDK18 (S98, S89, and S132), respectively, was observed, where CDK16 (S119) is a part of the binding motif for multiple upstream kinases, 14-3-3 protein, and CCNYL1. Furthermore, conserved co-regulatory patterns of other proteins were identified as compared with CDK17 phosphorylation, which revealed 19 upstream kinases, 164 downstream substrates, and several interactors of CDK17, which conserved co-regulatory patterns across diverse biological contexts. Statistical analysis revealed phosphoregulation of CDK17 through other kinases, regulation of CDK17 substrates, protein-protein interactions, and conserved co-differential regulation in multiple datasets. Specifically, this analysis derived through global data integration with a replicable analytical framework lays a groundwork for experimental validation of CDK17 phosphorylation in its functional regulation.

PMID:41851027 | DOI:10.1177/10445498261433751

Future Cardiol. 2026 Mar 18:1-8. doi: 10.1080/14796678.2026.2645005. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with dialysis-dependent chronic kidney disease (CKD) have a high cardiovascular burden, prompting interest in fish oils or long-chain omega-3 polyunsaturated fatty acids (n-3 PUFAs) as potential risk-reducing therapies in this population.

METHODS: We conducted a systematic review and meta-analysis of studies in adults receiving dialysis that assessed associations between n-3 PUFA supplementation, baseline levels, or dietary intake and CV outcomes, or all-cause mortality. Hazard ratios (HRs) were pooled using random-effects models.

RESULTS: Twelve studies met inclusion criteria. In hemodialysis-dependent CKD, fish oil supplementation lowered cardiovascular events by 44% (HR 0.56; 95% CI 0.46-0.68) and myocardial infarction by 48% (HR 0.52; 95% CI 0.34-0.78). Higher baseline n-3 PUFA levels were associated with a 31% reduction in all-cause mortality (HR 0.69; 95% CI 0.54-0.88). Higher dietary n-3 PUFA intake showed a non-significant trend toward lower all-cause mortality (HR 0.92; 95% CI 0.79-1.08).

CONCLUSION: In dialysis-dependent CKD, higher n-3 PUFA exposure through fish oil supplementation or higher baseline levels was associated with fewer cardiovascular events and all-cause mortality. Appropriately dosed n-3 PUFA supplementation represents a promising cardiovascular risk reduction strategy in dialysis-dependent CKD, although confirmatory randomized trials are warranted.

PMID:41851014 | DOI:10.1080/14796678.2026.2645005

Mov Disord. 2026 Mar 18. doi: 10.1002/mds.70275. Online ahead of print.

ABSTRACT

BACKGROUND: Perinatal hypoxic-ischemic encephalopathy (HIE) is a severe condition resulting from impaired oxygen delivery to the developing brain, often leading to both motor deficits and dystonia-dyskinetic syndromes (DDS). In selected cases, deep brain stimulation of the globus pallidus internus (GPi-DBS) may provide a therapeutic option. However, predicting outcomes remains challenging because of clinical heterogeneity and variable responses.

OBJECTIVES: This retrospective study aims to identify preoperative imaging predictors of GPi-DBS efficacy in patients with DDS secondary to HIE, focusing on putaminal atrophy as a potential criterion.

METHODS: We retrospectively analyzed 73 patients with DDS secondary to HIE who underwent GPi-DBS at our institution from 2003 to 2023. Clinical outcomes were assessed using the Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS) and Barry-Albright Dystonia Scale (BADS) at baseline and up to 15 years post-surgery. Preoperative magnetic resonance imaging scans were qualitatively and quantitatively evaluated to assess putaminal atrophy. Statistical analyses explored the relationships between imaging findings, clinical severity, and DBS outcomes.

RESULTS: Patients with severe putaminal atrophy exhibited significantly higher preoperative BFMDRS motor and disability scores, correlating with a limited response to DBS at 1-year follow-up (P < 0.05). Volumetric analysis confirmed that greater putaminal atrophy was associated with poorer motor improvements post-surgery. The predictive value of putaminal volume for long-term outcomes remained significant at 5-year follow-up.

CONCLUSIONS: Putaminal atrophy is a key predictor of suboptimal outcomes following GPi-DBS in patients with HIE-related DDS. These findings highlight the importance of preoperative imaging in candidate selection and underscore the need for alternative strategies in patients with severe post-anoxic basal ganglia damage. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

PMID:41851006 | DOI:10.1002/mds.70275

Sci Bull (Beijing). 2026 Mar 4:S2095-9273(26)00234-3. doi: 10.1016/j.scib.2026.03.002. Online ahead of print.

ABSTRACT

The disordered quantum systems host three classes of quantum states, the extended, localized, and critical, which bring up seven distinct fundamental phases in nature: three pure phases and four coexisting ones with mobility edges, yet a unified theory built on universal mechanism and full realization of all these phases has not been developed. Here we propose a unified framework based on a spinful quasiperiodic (QP) system which realizes all the fundamental localization phases, with the exact and universal results being obtained for their characterization. First, we show that the pure phases are obtained when the chiral(-like) symmetry preserves in the proposed spinful QP model, giving a criterion for emergence of the pure phases and otherwise the coexisting ones. Further, we uncover a novel mechanism for the critical states that their emergence is protected by the generalized incommensurate matrix element zeros in the spinful QP model, which considerably broadens rigorous realizations of the exotic critical states. We then show criteria of exact solvability for the present spinful QP system, with which we construct various exactly solvable models for all distinct localization phases. In particular, we propose two novel models, dubbed spin-selective QP lattice model and QP optical Raman lattice model, to achieve all basic types of mobility edges and all the seven fundamental phases of Anderson localization physics, respectively. The experimental scheme is proposed and studied in detail to realize these models with high feasibility. This study establishes a complete and profound theoretical framework which enables an in-depth exploration of the broad classes of all fundamental localization phenomena in QP systems, and offers key insights for constructing their exactly solvable models with experimental feasibility.

PMID:41850988 | DOI:10.1016/j.scib.2026.03.002

J Prosthet Dent. 2026 Mar 17:S0022-3913(26)00139-3. doi: 10.1016/j.prosdent.2026.02.034. Online ahead of print.

ABSTRACT

STATEMENT OF PROBLEM: The accuracy of complete arch implant scans acquired using intraoral scanners (IOSs) has been extensively examined; however, these evaluations often disregard the influence of the scanning technique used to capture implant positions.

PURPOSE: The purpose of this systematic review was to assess the trueness and precision of complete arch implant scans recorded using nonsplinting techniques.

MATERIAL AND METHODS: A literature search was completed in 5 databases: PubMed/Medline, Scopus, Embase, Web of Science, and Cochrane. A manual search was also conducted. Studies analyzing the accuracy of complete arch scans using commercially available nonsplinting techniques recorded with IOSs were included. Two investigators evaluated the studies independently by applying the Joanna Briggs Institute critical appraisal. A third examiner was consulted to resolve any lack of consensus. The mean and standard deviation of the accuracy values reported were extracted, and a meta-analysis was performed. The mean difference between the analog and nonsplinting groups was calculated using the random effect model (a=.05). The I-squared (I²) statistic and its associated P value were used to assess the heterogeneity between studies. Publication bias was assessed using a funnel plot. The Egger test was used to determine the significance of the funnel plots.

RESULTS: A total of 100 articles were included. The linear discrepancy ranged from 22 to 1050 µm, with a mean linear discrepancy of 117 µm. The angular discrepancy values ranged from 0.01 to 1.75 degrees, with a mean angular discrepancy of 0.48 degrees. The reported linear trueness ranged from 41 to 557 µm, with a mean linear trueness of 153 µm. The linear precision ranged from 7 to 166 µm, with a mean linear precision of 57 µm. The angular trueness ranged from 0.20 to 1.69 degrees, with a mean angular trueness of 0.65 degrees. The angular precision ranged from 0.05 to 1.69 degrees, with a mean angular precision of 0.41 degrees. The mean RMS discrepancy ranged from 9 to 408 µm, with a mean RMS error of 101 µm. The RMS trueness ranged from 27 to 366 µm, with a mean RMS error of 60 µm. Lastly, the RMS precision ranged from 5 to 251 µm, with a mean RMS error of 35 µm. The funnel plots and Egger regression asymmetry tests revealed significant publication bias (P<.001).

CONCLUSIONS: Nonsplinting implant scanning techniques demonstrated high variability in the scanning accuracy outcomes. Further clinical studies are needed to evaluate the accuracy of implant scans, as substantial heterogeneity was observed among the included studies.

PMID:41850947 | DOI:10.1016/j.prosdent.2026.02.034

Curr Probl Diagn Radiol. 2026 Mar 11:S0363-0188(26)00042-3. doi: 10.1067/j.cpradiol.2026.03.001. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into radiology across multiple workflow levels, with its role as a simultaneous second reader holding particular promise.

METHODS: We performed an umbrella review of systematic reviews and meta-analyses reporting pooled diagnostic accuracy of AI models using clinician (including radiologist) interpretation as the reference standard. References were identified through queries of Pubmed, Scopus, Embase, and Google Scholar (last updated January 7th, 2025). Data were analyzed using metaumbrella tool within R statistical software with stratification of evidence by Ioannidis criteria. Study quality was assessed using the AMSTAR-2 tool.

RESULTS: From 1,719 unique references, ten meta-analyses met inclusion criteria, encompassing 147 primary studies with over 722,000 case and 3.6 million control images. Diagnostic odds ratios ranged from 30.67 (95% CI; 10.06-102.87), fracture detection on X-ray, to 273.60 (95% CI; 130.51-573.58), pulmonary nodule detection on CT. Most meta-analyses (n = 9) provided Class II evidence, reflecting highly suggestive findings limited by invariably substantial heterogeneity (I² = 89.9%-99.9%). The quality was assessed as critically low in nine reviews and low in one.

DISCUSSION: AI models have shown strong diagnostic performance across various radiologic applications. Due to our inclusion criteria requiring clinician/radiologist interpretation as the reference standard, these findings reflect AI-human agreement rather than AI accuracy using a more definitive ground truth (e.g. histopathology). Furthermore, the strength of this evidence is limited by substantial heterogeneity, variability in imaging modalities, and differences in model development and validation.

PMID:41850944 | DOI:10.1067/j.cpradiol.2026.03.001