Nevin Manimala

bioRxiv [Preprint]. 2026 Jul 21:2026.07.16.738889. doi: 10.64898/2026.07.16.738889.

ABSTRACT

BACKGROUND: Ductal carcinoma in situ (DCIS) is a noninvasive breast lesion with variable risk of progression to invasive breast cancer (IBC). Current transcription and cell marker investigations suggest ECM decreases in later events but are limited in details of ECM proteomic composition, including post-translational modifications. We investigated whether the extracellular matrix (ECM) proteome alters with later breast events of DCIS or IBC.

METHODS: ECM-targeted mass spectrometry imaging and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied to ten tissue microarrays from the Resource of Archival Human Breast Tissue cohort (RAHBT). Primary DCIS specimens (n=136) were analyzed in relation to later events of DCIS (n=40) or IBC(n=30), with a mean follow-up of 192.1 months 95% CI [179.1,205.1]. Statistical modeling, survival analyses, and exploratory machine learning approaches were used to identify ECM peptide signatures associated with later events.

RESULTS: Distinct ECM peptide profiles were associated with later events of DCIS or IBC. Fifteen peptides derived from fibrillar collagens (COL1A1, COL1A2, COL3A1) and elastin, showed significantly reduced abundance in patients who developed IBC. Lower expression of specific collagen peptides associated with overall 19.9% 95% CI [17.92, 21.81] decreased disease-free survival for IBC. Lower expression of these peptides was significantly associated with reduced disease-free survival (age-adjusted hazard ratio [HR] = 2.45, 95% CI: 2.33-2.57; P < 0.05). Patient-matched samples of primary DCIS, later DCIS, and later invasive breast cancer further demonstrated reduction in ECM peptide detection. Exploratory predictive modeling from patient-matched samples achieved high performance (AUROC >0.98, accuracy >93%) in distinguishing primary from later events. Following prior work in the RAHBT cohort, reduction of certain collagen peptides was also observed in primary DCIS samples from higher risk patient groups.

CONCLUSIONS: ECM proteomic remodeling, particularly decreases of specific collagen domains, is strongly associated with later events of DCIS and IBC. These findings highlight ECM proteome as a critical regulator of breast cancer emergence with potential as a prognosticator of risk stratification to guide clinical management of DCIS.

PMID:42539300 | PMC:PMC13420415 | DOI:10.64898/2026.07.16.738889

Neurotrauma Rep. 2026 Apr 30;7:2689288X261446234. doi: 10.1177/2689288X261446234. eCollection 2026 Jan-Dec.

ABSTRACT

The International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) in traumatic brain injury (TBI) only includes data from hospital admission as predictors. Including updated physiological data after hospital admission would likely improve prognostic ability above the IMPACT model alone. We sought to evaluate differences in the daily trajectory of clinical metabolic panels (e.g., glucose, sodium, platelets, hemoglobin) for the first 7 days post-severe TBI (sTBI). This is a prospective cohort study of patients with sTBI with Glasgow Coma Scale (GCS) ≤8. Intake GCS was conducted by a neurosurgeon or neurosurgical resident to ensure GCS at presentation was related to the brain injury and not other factors. We compared daily metabolic panel trajectories between survivors to 6 months and nonsurvivors to 6 months using ordinal mixed-effects models. Dichotomizing trajectories as “high” or “low” was set to 4+ of the first 7 days postinjury to represent a majority of the first postinjury week. We then examined the added prognostic value of these trajectories compared with the IMPACT-extended model for 6-month mortality. Included patients (n = 572) were 40.4 ± 17.1 years of age, 79.4% male, and 38.4% had died by 6 months postinjury. The likelihood ratio tests (LRT) comparing the ordinal trajectories of sodium and platelets were statistically significant after false discovery correction (sodium: likelihood ratio = 51.0; adj. p < 0.001; platelets: likelihood ratio = 16.0; adj. p = 0.003), indicating that the overall trajectory over 7 days differs between groups. The LRT comparing the trajectories of glucose and hemoglobin were not statistically significant (p = 0.21-0.98). The models identified a divergence in platelet values on days 6 and 7, where nonsurvivors to 6 months had lower odds (OR = 0.27-0.41) of being in higher platelet categories than survivors to 6 months on those days. The IMPACT-only model had an area under the curve (AUC) for 6-month mortality of 0.85 and a Hosmer-Lemeshow p value = 0.68. The IMPACT-biomarker trajectory model had an AUC for 6-month mortality of 0.87 (DeLong’s test p value of 0.005) and a Hosmer-Lemeshow p value of 0.16. Trajectory of metabolic panel labs in the first week postinjury yields meaningful improvements in prognostic ability for the individual patient.

PMID:42539293 | PMC:PMC13422602 | DOI:10.1177/2689288X261446234

Womens Health Rep (New Rochelle). 2026 Jul 3;7:26884844261465224. doi: 10.1177/26884844261465224. eCollection 2026 Jan-Dec.

ABSTRACT

BACKGROUND: This study examined the associations between specific adverse childhood experiences (ACEs) and hysterectomy among adult women and assesses the dose-response relationship with cumulative ACE exposure. It extends prior work by evaluating individual ACE categories while adjusting for gynecological cancers, diabetes, obesity, and selected health-risk behaviors.

METHODS: Data were drawn from the 2020, 2021, 2022, and 2024 Behavioral Risk Factor Surveillance System (BRFSS), including 119,165 women across 26 U.S. areas with available data on ACEs and hysterectomy. Multivariable logistic regression estimated associations between ACEs and hysterectomy, adjusting for covariates.

RESULTS: Approximately 24.2% of participants reported a history of hysterectomy, and 65.8% reported at least one ACE. All but one of the 11 ACE items were independently associated with higher odds of hysterectomy after adjustment for age, race/ethnicity, marital status, education, annual income, survey year, area, gynecological cancers, diabetes, obesity, physical activity, smoking, and alcohol consumption. The strongest associations were observed for childhood sexual abuse, including unwanted sexual touching and forced sexual intercourse. The ACE distribution was 34.2% for 0 ACEs, 33.5% for 1-2 ACEs, 15.5% for 3-4 ACEs, and 16.9% for ≥5 ACEs. The odds of hysterectomy increased in a dose-response manner with higher ACE exposure, becoming statistically significant at ≥3 ACEs after adjustment. The association between ACE count and hysterectomy did not differ significantly by age (p = 0.2464).

CONCLUSIONS: ACEs, particularly childhood sexual abuse, were associated with higher odds of hysterectomy. These associations persisted after adjustment for demographic, clinical, and behavioral factors, suggesting long-term gynecological consequences of early-life adversity.

PMID:42539250 | PMC:PMC13420389 | DOI:10.1177/26884844261465224

bioRxiv [Preprint]. 2026 Jul 26:2026.07.23.740264. doi: 10.64898/2026.07.23.740264.

ABSTRACT

Spatial proteomics provides single-cell protein measurements under highly constrained and heterogeneous protein panels across datasets, resulting in limited and partially overlapping measurement spaces for cellular characterization. Existing analyses predominantly rely on statistical or task-specific modeling, while learning scalable representations of spatial protein data remain underexplored. This gap motivates the need for models that can learn stable representations of cellular identity from constrained protein measurements. Here we introduce Spatium, a protein language foundation model trained on over 51 million cells across multiple spatial proteomics platforms. Spatium learns intrinsic co-expression hierarchies that capture cell identity in a manner robust to panel composition and measurement scale. Spatium builds a generalizable representation of cell states grounded in biologically interpretable protein expression patterns. We demonstrate that Spatium learns biologically meaningful cell representations across multiple downstream tasks. Spatium recovers accurate cell identities with marker expression patterns consistent with known biology and reveals functionally distinct spatial microenvironments characterized by coherent marker enrichment signatures. It further enables reconstruction of missing protein measurements while preserving biologically meaningful expression patterns. Across these analyses, Spatium demonstrates stable and interpretable performance with lightweight task-specific adaptation, highlighting the robustness of the learned representations across diverse biological and experimental contexts.

PMID:42539246 | PMC:PMC13419744 | DOI:10.64898/2026.07.23.740264

bioRxiv [Preprint]. 2026 Jul 20:2026.07.14.738506. doi: 10.64898/2026.07.14.738506.

ABSTRACT

Humans can rapidly extract summary statistics, such as the mean and variance of visual features, to efficiently represent complex visual environments despite limits in attention and working memory. For example, when viewing a field of flowers, we can perceive the average colour and overall variability of the display without individuating each flower. Although ensemble perception is central to visual cognition, two fundamental questions remain unresolved. First, it is unclear whether ensemble statistics for features represented at different levels of the visual hierarchy rely on a common neural system or on separate feature-specific systems. Second, it remains unknown whether different summary statistics, such as mean and variance, rely on shared or dissociable neural mechanisms. Here, we used fMRI and multivariate pattern analysis to examine the neural representation of ensemble mean and variance across three visual features spanning the processing hierarchy: orientation (low-level), shape (mid-level), and animacy (high-level) (N = 24; two fMRI sessions). By combining whole-brain searchlight and ROI-based approaches, we found a graded division of labour between ventral and dorsal visual pathways. Although mean and variance ensemble statistics were distributed across the visual cortex, mean decoding was stronger in ventral regions, whereas variance decoding was stronger in dorsal regions. Ensemble mean representations followed a posterior-anterior gradient within the ventral visual pathway, consistent with increasing abstraction from orientation to shape and animacy, and showed little anatomical overlap suggesting largely feature-specific. By contrast, ensemble variance was weighted toward dorsal parietal and frontoparietal regions, especially superior parietal cortex and intraparietal sulcus, decoding clusters largely overlap across features and generalized robustly across orientation, shape, and animacy. Together, these findings provide a more nuanced account of ensemble perception, showing that feature-specific and feature-independent neural codes can coexist across visual cortex and help reconcile previously conflicting evidence.

SIGNIFICANCE STATEMENT: This study shows that information about ensemble mean and variance is distributed throughout the visual cortex but weighted differently across visual pathways. Mean-related information was stronger in the ventral visual pathway and showed a largely feature-independent anatomical organization, whereas variance-related information was stronger in dorsal parietal regions and generalized across orientation, shape and animacy. Together, these findings provide clarifying neural evidence for longstanding debates about whether ensemble statistics rely on shared or dissociable mechanisms and whether ensemble representations are feature-specific or generalize across visual features. More broadly, the study offers a nuanced account of ensemble perception in which feature-specific and feature-general neural codes coexist across visual cortex, potentially reconciling previously conflicting evidence.

PMID:42539224 | PMC:PMC13419806 | DOI:10.64898/2026.07.14.738506

bioRxiv [Preprint]. 2026 Jul 22:2026.07.22.740142. doi: 10.64898/2026.07.22.740142.

ABSTRACT

A core motor function of the cerebellum is error-based learning: it uses prediction errors-mismatches between predicted and actual sensory feedback-to refine actions. One provocative hypothesis is that the cerebellum performs similar computations in cognitive domains. Here, we ask whether error-based learning computations in the cerebellum extend to a passive statistical learning task that requires neither action nor decision-making. Human participants viewed sequences of visual stimuli with varying probabilistic stimulus-stimulus transitions while undergoing fMRI. Subjects reported no explicit knowledge of the transitional probabilities after the scan. Nonetheless, putative cognitive regions of the cerebellum encoded prediction errors during statistical learning. Moreover, error signals in the cerebellum were encoded in a different manner than error signals in the anterior hippocampus, a well-known substrate of statistical learning: cerebellar activity covaried with trial-by-trial prediction errors that evolved slowly over time (echoing cerebellar computations in motor learning), whereas the hippocampus responded in a more fixed manner to the underlying transition structure. Computational modeling formalized this dissociation, with cerebellar activity explained by a delta-rule model that incrementally adjusted predictions based on recent experience, and hippocampal activity characterized as a form of Bayesian updating of a transition distribution held in memory. Our findings demonstrate that the cerebellum encodes prediction errors during passive nonmotor learning, and thus that it likely plays a domain-general role in error-based learning.

SIGNIFICANCE: The cerebellum is known to support motor learning by using prediction errors to refine behavior. Here, we test whether these computations extend to nonmotor learning contexts that require neither action nor decision-making. We report the discovery of prediction error signals in the cerebellum during statistical learning, an unsupervised process that extracts regularities from the sensory environment. These findings support a domain-general role for the cerebellum in error-based learning, offering a possible explanation for why cerebellar damage is implicated in such wide-ranging outcomes, from impaired motor coordination to complex disorders like autism.

PMID:42539180 | PMC:PMC13419723 | DOI:10.64898/2026.07.22.740142

medRxiv [Preprint]. 2026 Jul 20:2026.07.17.26358090. doi: 10.64898/2026.07.17.26358090.

ABSTRACT

People with Down syndrome have higher age-specific mortality rates compared to the general population as well as peers with other intellectual and developmental disabilities. While a large proportion of mortality is attributable to Alzheimer’s disease, many die prior to Alzheimer’s diagnosis and some live to old ages, dying without Alzheimer’s. Our objectives were to use 11 years of Medicaid and Medicare data to describe characteristics and factors related to death in adults with Down syndrome and use machine learning to identify which conditions most strongly predict death in the full population and stratified by age. We identified death using Center for Medicare and Medicaid Systems reported date of death health conditions using ICD 9 and 10 codes. We used a case-control design with risk set sampling to have that controls to mimic the distribution of times of incident Alzheimer’s disease. We trained gradient boosted trees to identify strongest predictors. Our cohort included 137,293 adults with Down syndrome. Among those, 30,894 (22.5%) died during the study period. Mean age at death among those who died was 55 years (SD=10). Mean age of death in those with Alzheimer’s disease was 59 (SD=7) and those without was 52 (SD=12). The most influential predictors of mortality were any claim for dementia, any claim for pneumonia, re-occurring claim for cardiovascular disease three years before index death, and any claim for heart failure and epilepsy. Our results align with previous clinical work and highlight intervenable areas to reduce mortality in the Down syndrome population.

PMID:42539115 | PMC:PMC13419647 | DOI:10.64898/2026.07.17.26358090

medRxiv [Preprint]. 2026 Jul 24:2026.07.22.26358355. doi: 10.64898/2026.07.22.26358355.

ABSTRACT

Traumatic brain injury (TBI) is a leading cause of death and long-term disability in children, with many experiencing persistent symptoms even after mild TBIs. Exposure to adverse childhood experiences (ACEs) can have physiological effects that may alter how the brain responds to injury, yet the effects of ACEs on white matter injury and recovery processes remain unclear. This study examined whether a history of ACEs is associated with patterns of longitudinal change in white matter microstructure in children with mTBI. Ninety-six concussed adolescents (mean age=14.9 years, range =11.4-17.9, 51% female) from the CARE4Kids consortium completed the Pediatric ACEs and Related Life-Events Screener and underwent diffusion-weighted MRI at baseline (7-35 days after injury) and follow-up (2 months later). Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), orientation dispersion index (ODI), and intracellular volume fraction (ICVF) were estimated using tract-based spatial statistics and harmonized across sites. Differences in the magnitude and direction of change in diffusion metrics over time were examined in 15 tracts of interest. Higher ACE exposure was associated with smaller absolute change in AD, MD, ODI, and ICVF across several white matter tracts, including the corpus callosum, internal and external capsules, corona radiata, and posterior thalamic radiation. Groups did not differ in the direction of white matter change for any tract-metric combination. These findings suggest that ACE exposure may blunt white matter reactivity to injury and/or reorganization during recovery processes.

PMID:42539109 | PMC:PMC13419653 | DOI:10.64898/2026.07.22.26358355

medRxiv [Preprint]. 2026 Jul 22:2026.07.20.26358527. doi: 10.64898/2026.07.20.26358527.

ABSTRACT

BACKGROUND: Peripheral artery disease (PAD) is an occlusive arterial disease primarily affecting the lower extremities. It impacts over 230 million people worldwide and is associated with significant morbidity and mortality. The ankle brachial index (ABI) test is a non-invasive method to detect PAD that compares the blood pressure in the ankle and arm to evaluate lower extremity blood flow. An estimated 20-50% of individuals with detectable PAD are asymptomatic and remain undiagnosed; however, ABI screening in high-risk, asymptomatic populations is not currently guideline-recommended. Few studies have evaluated change in ABI over time in asymptomatic populations. Therefore, we aimed to identify distinct trajectories of ABI values from mid-to late-life.

METHODS: We utilized data from the Atherosclerosis Risk in Communities (ARIC) study; a longitudinal cohort study initiated in 1987 that enrolled 15,792 participants aged 45-64. ABI measurements were collected at five visits over a 30-year period. We used group-based trajectory modeling to identify trajectories of ABI from mid-to late-life. Final model selection was based on visual fit, statistical criteria, group sizes, and substantive knowledge. Lastly, we compared baseline demographics, social determinants of health, and overall cardiovascular (CV) health, assessed using the American Heart Association’s Life’s Essential 8 (LE8) framework, across trajectory groups.

RESULTS: We identified 4,121 participants with ≥3 ABI measurements over the study period in at least one limb. At baseline, participants had an average age of 51.4 ± 4.9 years, were 57.3% female, 22.2% Black, and had an average overall LE8 score of 68.0 ± 13.9 points. Our final model identified three linear trajectories: low-normal, high-normal, and declining. Overall LE8 scores varied significantly across trajectory groups: 67.3 ± 10.7 (high-normal), 61.9 ± 13.3 (low-normal), and 50.1 ± 15.8 points (declining). Women had lower average ABI values, were more likely to experience a declining ABI trajectory, and had a delayed onset of decline compared to men. A greater proportion of Black participants experienced declining ABIs, with earlier, faster, and more severe declines than White participants.

CONCLUSIONS: Poor overall CV health and common CV risk factors are associated with ABI decline. Targeted ABI screening in middle age may help detect PAD in its beginning stages and support early intervention.

PMID:42539097 | PMC:PMC13419634 | DOI:10.64898/2026.07.20.26358527

medRxiv [Preprint]. 2026 Jul 22:2026.03.11.26348169. doi: 10.64898/2026.03.11.26348169.

ABSTRACT

BACKGROUND: CAG repeat expansions in ATXN2 are implicated as risk factors for several neurological diseases, including spinocerebellar ataxia type 2 (SCA2) when >=33 CAG repeats are present, and amyotrophic lateral sclerosis (ALS) when 27-33 CAG repeats are present. However, how haplotypes around the repeats and CAA interruptions within the repeats are associated with disease phenotypes remains poorly understood. Previous studies on haplotypes around ATXN2 were limited to SNPs very close to the repeats (<5kb) or were based on statistical inference only.

METHODS: Here, we used long-read sequencing on the Oxford Nanopore Technologies (ONT) platform to simultaneously infer haplotypes around ATXN2 , the number of CAG repeats, and the number of CAA interruptions, along with NYGC ALS Consortium NGS dataset. We further sequenced 41 individuals (EUR = 39) with neurological diseases with intermediate repeats by ONT.

RESULTS: We found that haplotypes around ATXN2 and the number of interruptions show ethnicity-specific and ALS-specific distribution. Three CAA interruptions are present at low prevalence (∼1%) in control populations in multiple ancestry groups, but high prevalence (∼55%) in ALS individuals with intermediate repeats. Furthermore, we examined 159 individuals with ALS (∼90% European ancestry) with intermediate ATXN2 repeats and found a unique haplotype in ALS individuals with three CAA interruptions, which can be tagged by an SNV, rs148019457. We also validated that the rs148019457-G allele is only present in haplotypes with three CAA interruptions.

CONCLUSIONS: In summary, our study shows that 3 CAA interruptions are rarely seen in healthy controls but are common in those with expanded ATXN2 CAG repeats who have neurological disorders, and that rs148019457 tags a specific haplotype with 3 CAA interruptions within expanded ATXN2 CAG repeats in individuals of European ancestry. These results have implications for the development of precision genomic medicine for neurological disorders, and the tag SNP may help identify those with interruptions from existing population genotyping data.

PMID:42539086 | PMC:PMC13419642 | DOI:10.64898/2026.03.11.26348169