Acta Biomaterialia

Chronic wounds, such as diabetic and ischemic ulcers, involve impaired perfusion and delayed healing. TOP-N53 is a novel bifunctional molecule combining nitric oxide (NO) release with phosphodiesterase-5 (PDE5) inhibition to enhance local NO-cGMP signalling, resulting in vasodilation and angiogenesis. This first-in-human, randomized, double-blind, vehicle-controlled Phase I trial assessed the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of single subcutaneous TOP-N53 doses in 29 healthy male volunteers. Each participant received injections of TOP-N53 and vehicle in the same forearm, but either at the proximal or at the distal site in an intra-individually blinded manner. Safety assessments included local and systemic parameters. PK and PD responses were evaluated by analysis of TOPN53 and its bioactivation metabolite TOP-52 in plasma, and by Laser Speckle Contrast Imaging (LSCI), a non-invasive method to measure skin perfusion, respectively. TOP-N53 was safe and well tolerated, with no serious adverse events or local or systemic adverse reactions. Plasma concentrations remained below the quantification limit and LSCI showed sustained dose-dependent increases in local skin perfusion at doses of 4.84 ug and 9.075 ug TOP-N53 SC for up to 24 h post injection when compared to vehicle. These findings support the favourable safety and tolerability profile of TOP-N53 associated with locally improved skin perfusion, encouraging its further clinical development as a topical treatment for chronic wounds with microvascular dysfunction.

BackgroundPlasma biomarkers demonstrate strong within-cohort performance for identifying cerebral amyloid pathology, but their real-world clinical utility depends on generalization across populations and assay platforms. The impact of cross-cohort deployment on clinically actionable metrics such as negative predictive value (NPV) remains poorly characterized. ObjectiveTo evaluate the performance and portability of plasma biomarker-based machine learning models for amyloid PET prediction across independent cohorts, with emphasis on calibration and clinically relevant predictive values. MethodsData from ADNI (n=885) and A4 (n=822) were analyzed. Machine learning models were trained within each cohort to predict amyloid PET status and continuous amyloid burden (centiloids). Performance was assessed using ROC AUC, accuracy, R{superscript 2}, and RMSE. Cross-cohort generalizability was evaluated using bidirectional transfer without retraining. Calibration, predictive values, and decision curve analysis were used to assess clinical utility. ResultsWithin-cohort discrimination was high (AUC up to 0.913 in ADNI and 0.870 in A4), with moderate performance for centiloid prediction (R{superscript 2} up to 0.628 and 0.535, respectively). Cross-cohort deployment resulted in modest attenuation of AUC ([~]4-7%) but substantially greater degradation in clinically actionable performance. NPV declined from 0.831 to 0.644 under ADNI[->]A4 transfer ([~]19 percentage points) despite preserved discrimination. Calibration analyses demonstrated systematic probability misestimation, and decision curve analysis showed reduced net clinical benefit. Biomarker distribution differences across cohorts were consistent with dataset shift. ConclusionPlasma biomarker models retain discrimination across cohorts but exhibit clinically meaningful degradation in predictive value under deployment. Calibration instability and prevalence differences critically affect NPV, highlighting the need for cross-cohort validation, calibration assessment, and assay harmonization before clinical implementation.

Saccadic eye movements are established biomarkers in neuroscience and clinical neurology, where video-oculography (VOG) remains the gold standard. However, VOG's high cost, bulky equipment, and poor portability restrict its clinical utility. Electrooculography (EOG) offers a promising alternative by detecting cornea-retinal potential changes during eye movements. To enable quantitative saccadic analysis using EOG as a VOG alternative, this study develops and validates a mathematical transformation model converting EOG data into VOG-equivalent values. A prospective observational study was conducted on 4 healthy adults without neurological or sleep disorders. Horizontal saccades were recorded simultaneously using EOG and VOG during controlled gaze shifts. EOG peak saccadic velocity was derived from voltage change rate, whereas VOG was calculated from angular displacement over time. A derivation dataset of fixed horizontal saccades ({+/-}20{degrees}) formulated the transformation model, achieving a strong correlation coefficient (r = 0.95 rightward, r = 0.93 leftward, p < 0.0001). Multiple filter settings were evaluated, and 0.3 Hz high-pass and 35 Hz low-pass filtering were identified as optimal. The fixed horizontal saccades derived model was applied to a validation dataset of random horizontal saccades, confirming robustness across saccades without significant differences from VOG measurements. These findings establish EOG's feasibility for quantitative analysis of horizontal saccades and provide a validated transformation model. By systematically optimizing filtering parameters, this approach enables EOG as a cost-effective VOG alternative while maintaining high-precision measurement accuracy.

Background: Hereditary transthyretin amyloidosis (hATTR) manifests as cardiomyopathy and/or polyneuropathy. The V142I variant predominantly causes cardiac disease in African Americans, though neurological involvement may be underrecognized. We characterized neuropathy documentation and treatment patterns in a predominantly V142I cohort. Methods: Retrospective review of 54 hATTR patients at a major academic medical center. Neuropathy was classified as: objective (abnormal EMG), possible polyneuropathy (documented symptoms suggestive of polyneuropathy), symptoms only (neuropathic symptoms without specialist evaluation), or unclear. Treatment with stabilizers (tafamidis, acoramidis, diflunisal) and gene silencers (patisiran, vutrisiran, eplontersen) was assessed. Results: Of 54 patients (88.9% African American, 85.2% V142I), 51 (94.4%) had confirmed cardiac involvement. Among cardiac patients, 40/42 eligible (95.2%) received stabilizers. Overall, 16 patients (29.6%) received gene silencers, with 13 (24.1%) receiving both a stabilizer and gene silencer concurrently. Possible neuropathy (objective, possible polyneuropathy, or symptoms) was documented in 30 patients (55.6%). Gene silencer use was highest among those with objective neuropathy (8/17, 47.1%) versus symptoms only (1/10, 10.0%). All three patients without confirmed cardiac disease received gene silencers. Conclusions: In this V142I-predominant cohort with 94.4% cardiac involvement, stabilizer use was high (95.2%) among eligible patients. Over half had possible neuropathy based on clinical documentation, though EMG completion was limited (57.4%). Gene silencer use was associated with objective neuropathy documentation and non-cardiac phenotype. These findings support systematic neurological assessment in hATTR, even when cardiac disease predominates.

ObjectiveTo evaluate the real-world effectiveness of Intact Fish Skin Graft (IFSG) compared with standard of care (SOC) in the treatment of Stage 3-4 pressure ulcers, using clinically meaningful outcomes including wound healing rate and percent area reduction (PAR). Materials and MethodsA retrospective matched cohort study was conducted using deidentified electronic health record (EHR) data from the U.S. Wound Registry. Patients with Stage 3-4 pressure ulcers treated with IFSG (n=40) were compared to a matched SOC control group (n=40). 1:1 covariate matching was performed to reduce confounding across key patient and wound characteristics, including age, mobility status, comorbidities (e.g., diabetes, peripheral artery disease), and wound features (age, size, location, and depth). Outcomes included healed status, healed or improved rate, and percent area reduction (PAR). ResultsThe study population represented a high-risk, real-world cohort (n=40 per group), with only 37.5% ambulatory patients and a high prevalence of multiple concurrent wounds. IFSG treatment demonstrated superior clinical outcomes compared to SOC: O_LIHealed or improved: 67.5% (IFSG) vs 55.0% (SOC) (p=0.0379) C_LIO_LIHealed: 45.5% (IFSG) vs 33.3% (SOC) C_LIO_LIPercent area reduction (PAR): 49% (IFSG) vs 34% (SOC) (p=0.0028) C_LI These findings indicate statistically significant improvements in percent area reduction and in the proportion of wounds that were healed or improved with IFSG. The proportion achieving complete healing was numerically higher with IFSG than with SOC, but this difference did not reach statistical significance. ConclusionIn this real-world matched cohort analysis, Intact Fish Skin Graft demonstrated superior effectiveness compared to standard of care in the management of Stage 3-4 pressure ulcers, with improvements in healing-related outcomes and percent area reduction. These results support the use of IFSG as an effective advanced therapy for hard-to-heal pressure ulcers.

Background. Detection of cerebral amyloid pathology currently requires amyloid PET imaging ($5,000-$8,000) or cerebrospinal fluid analysis via lumbar puncture, procedures that are inaccessible for population-level screening. The FDA-cleared Lumipulse G pTau217/Abeta1-42 plasma ratio test (May 2025) represents the first approved blood-based alternative; however, single-ratio approaches cannot distinguish Alzheimer's disease (AD) from non-AD neurodegeneration or provide multi-dimensional disease characterization. Methods. We developed Virtual Spectral Decomposition (VSD), a framework that decomposes plasma biomarker profiles into biologically interpretable diagnostic channels. Four plasma biomarkers - phosphorylated tau-217 (pTau217), amyloid-beta42/40 ratio, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) - were measured in 1,139 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Each biomarker was mapped to a VSD channel representing a distinct pathophysiological axis: tau/amyloid phosphorylation, amyloid clearance, neurodegeneration, and astrocytic activation. Channel weights were calibrated via logistic regression, and performance was evaluated against amyloid PET (UC Berkeley) using 10x5-fold repeated cross-validation. Results. VSD 4-channel fusion achieved AUC = 0.900 (+/-0.018), exceeding pTau217 alone (0.888+/-0.022). Optimal sensitivity was 89.7% with 78.1% specificity (NPV = 90.8%). The NfL channel received a negative weight (beta = -1.1), functioning as a disease-exclusion signal: elevated neurodegeneration without amyloid-tau coupling actively reduces the AD probability, distinguishing AD from non-AD neurodegeneration. Complementary CSF proteomics analysis (7,008 proteins, 533 participants) identified 17 amyloid-specific proteins (0.24% of the proteome), revealing a 49:1 tau-to-amyloid asymmetry that explains why blood-based tau markers outperform amyloid markers. Conclusions. Blood-based VSD provides an interpretable, multi-channel framework for amyloid detection that incorporates explicit disease-exclusion logic unavailable to single-biomarker approaches. The architecture extends to multi-disease screening, where the same blood specimen could be routed through disease-specific modules for AD, Parkinson's disease, and cancer.

Dementia incidence has been declining in Western societies for decades, but whether this reflects higher cognitive capacity entering old age, slower cognitive decline, or both remains unresolved. Analysing ~783,000 episodic memory assessments from ~219,000 individuals across five longitudinal cohorts, we find that later-born cohorts benefit from a double dividend: higher memory levels entering old age and slower rates of decline. The projected 20-year cohort advantage at age 80 is of sufficient magnitude to plausibly account for the observed 13% per-decade decline in dementia incidence reported in meta-analyses. Generational gains are disproportionately concentrated among the fastest-declining individuals, and are reflected in lower hippocampal atrophy rates in an independent sample. A formal bounding analysis shows that the double dividend is robust across a range of plausible period assumptions, consistent with environmental conditions operating across the lifespan having reshaped the architecture of human cognitive aging.

INTRODUCTION: Alzheimer's disease (AD) and frontotemporal dementia (FTD) have considerable clinical and pathological overlap. While plasma proteomics has advanced in AD, deep comparative analyses with FTD-particularly in diverse, biomarker-confirmed Asian cohorts-remain limited. METHODS: Plasma from 101 individuals with known pTau217 status was profiled using Olink Explore-HT. Differential expression-pathway enrichment, penalized regression-GLMNET, single-cell transcriptomic integration, associations with cognitive measures and, cross-platform validation were performed. RESULTS: Among 5,400-proteins, 1,168 were differentially expressed in AD and 370 in FTD (FDR<0.05). Distinct and overlapping proteomic signatures were identified in AD and FTD, reflecting gliosis, synaptic dysfunction, immune activation, and metabolic pathways. Prioritized proteins correlated with cognitive performance and plasma phosphorylated tau, A{beta}42, and neurofilament light chain, linking circulating proteins to disease severity. Cross platform validation revealed strong concordance with large independent datasets. CONCLUSION: Comprehensive plasma proteomics in Asian cohort supports scalable framework for blood-based biologically informed targets for precision diagnosis and therapeutic stratification.

Abstract Objective: The objective of this study was to compare hospital length of stay and other clinical outcomes between intact fish skin graft (IFSG; Graftguide, Kerecis, Arlington, VA) and synthetic/biosynthetic dermal substitutes (SSS; Integra Dermal Regeneration Template and NovoSorb Biodegradable Temporizing Matrix) in propensity score matched burn patients using the American Burn Association Burn Care Quality Platform. Methods: This retrospective cohort study identified adult patients treated with a single dermal substitute product during hospitalization for acute burn injury. Patients receiving IFSG (n = 93) were matched 1:4 to patients receiving SSS (n = 372) using nearest neighbor propensity score matching on the logit scale. Matching covariates included total body surface area burned (TBSA), patient age, sex), burn severity classification, inhalation injury, and trauma diagnosis. The primary outcome was hospital length of stay (LOS), analyzed using a gamma generalized linear mixed model (GLMM). Secondary outcomes included the incidences of sepsis, graft loss, venous thromboembolism (VTE), and hospital acquired pressure injury (HAPI). A prespecified sensitivity analysis was performed using a broader mixed product cohort. Results: A total of 93 IFSG treated patients from 17 burn centers admitted between the years 2019 and 2025 were matched 1:4 to 372 SSS treated patients from 44 centers. Unadjusted mean LOS was 24.1 days (median 20, IQR 11 to 32) in the IFSG treated group and 36.7 days (median 31, IQR 17 to 52) in the SSS treated group representing a 12.6 day reduction. GLMM-adjusted estimated marginal mean LOS was 24.2 days (95% CI, 20.0 to 29.4) for IFSG versus 33.5 days (95% CI, 30.0 to 37.6) for SSS (ratio 0.723; p = 0.00245), representing a 9.3 day reduction. Sepsis (1.1% vs 4.6%), graft loss (3.2% vs 8.3%), VTE (2.2% vs 2.7%), and HAPI (2.2% vs 3.8%) were all numerically lower in the IFSG treated arm; although GLMM-adjusted odds ratios were not statistically significant for any individual complication. The mixed cohort sensitivity analysis (n = 229 IFSG vs 458 SSS across 67 centers) confirmed the primary finding with GLMM adjusted LOS ratio 0.716 (p = 0.0001). Conclusions: In this propensity score matched analysis of the ABA registry, IFSG was associated with a statistically significant and clinically meaningful reduction in hospital length of stay compared with synthetic/biosynthetic dermal substitutes, in requiring dermal substitution and autografting, with all complication rates, sepsis, graft loss, VTE, and HAPI, numerically lower in the IFSG-treated arm. The shorter hospitalization was not achieved at the expense of safety. These findings support IFSG as a viable alternative to synthetic dermal substitutes in burns requiring dermal substitution and autografting. Prospective studies are warranted particularly in larger burns requiring staged reconstruction.

Digital pathology, coupled with advanced image recognition algorithms, represents a transformative frontier in histopathological diagnosis. This sub-Saharan African laboratorys exploratory study investigates the application of a Convolutional Neural Network (CNN) model, specifically leveraging the VGG16 architecture with transfer learning, for automated analysis and classification of selected gastrointestinal (GIT) and liver tissue samples, incorporating both routine and specialized staining protocols. The study utilized a dataset comprising 114 samples (18 liver, 96 GIT images) derived from archival formalin-fixed paraffin-embedded tissue blocks at University College Hospital, Ibadan, Nigeria. Specialized staining techniques included Alcian Yellow for GIT mucin visualization and Massons Trichrome for liver fibrosis assessment, alongside conventional H&E staining. Model performance was evaluated using statistical methodologies including Wilson Score confidence intervals (CI), Bayesian probability assessment, and effect size analysis. Results reveal a striking dichotomy in model performance. The GIT tissue model achieved perfect classification accuracy (100% test accuracy) with exceptional statistical significance (Z=10.0, p<0.0001), Wilson CI [96.29%, 99.99%], Cohens h=1.571, and Bayesian probability >99.99%. Conversely, the liver tissue model demonstrated diagnostic failure (42.86% test accuracy), with Z=-1.428, p=0.9236, Wilson CI [33.59%, 52.65%], Cohens h=-0.144, and Bayesian probability of 7.64%. This performance divergence correlates with training data availability, as the liver dataset fell far below empirically established thresholds (>100-200 samples) for reliable classification. The liver models failure reveals limitations in transfer learning with insufficient data. These findings underscore critical implications for AI-enhanced digital pathology, demonstrating potential deployment of the GIT model as a promising one that supports tissue-specific model development.

The SAFE MRI ECMO (NCT05469139) study established the safety of ultra-low-field 64mT MRI in patients receiving extracorporeal membrane oxygenation (ECMO) in the setting of intensive care and demonstrated that these images were highly sensitive in detecting acquired brain injuries. This retrospective analysis of prospectively collected observational data sought to expand on these findings in light of the crucial need for neurological monitoring while patients receive ECMO by evaluating the feasibility of volumetric analyses derived from ultra-low-field MR images. T2-weighted scans from thirty patients who received ultra-low-field MRI while undergoing ECMO at Johns Hopkins Hospital were analyzed using a volumetric pipeline to determine whole brain volume and volumes of total grey matter, total white matter, subcortical grey matter, ventricles, left hemisphere, right hemisphere, telencephalon, left and right lateral ventricles, the total intracranial volume, and the cerebellum. Segmented brain volumes in patients undergoing ECMO were comparable to measurements obtained using conventional field and ultra-low-field MRI in the absence of ECMO instrumentation. The subgroup analysis demonstrated subtle volumetric differences between patients supported with venoarterial ECMO and those receiving venovenous ECMO. These data provide the first evidence that ultra-low-field MRI provides volumetric measurements comparable to conventional field-strength MRI, even in the presence of ECMO circuitry, supporting its feasibility for neuroimaging in critically ill patients.

Background and Objectives: Normal appearing white matter (NAWM) may already harbor subtle microstructural alterations not yet visible on conventional MRI. Quantitative Multi-Parametric Mapping (qMPM) such as Magnetization Transfer saturation (MTsat), longitudinal relaxation rate (R1), and Proton Density (PD) offer new possibilities for analyzing NAWM which are sensitive to demyelination, axonal loss, and edema. We aimed to characterize these alterations within white matter hyperintensities (WMH) and the perilesional NAWM (pNAWM), to gain insights into the underlying process of lesion progression. We also investigated their association with cerebrovascular risk factors (CVRF) and long-term cognitive performance. Methods: This investigation included the cerebral MRI data of 245 participants from the prospective Berlin Longterm Observation of Vascular Events (BeLOVE) study. Furthermore, 121 participants cognitive performance was evaluated at baseline and longitudinally at 2 years follow-up using Montreal Cognitive Assessment (MoCA). Regions of interest (ROIs) of WMH, pNAWM at 1, 2, 3 mm were assessed in comparison to the mirrored contralesional white matter (cWM). Linear mixed effects models were employed to demonstrate the pairwise comparisons between each region using estimated marginal means and the association of MPM metrics with CVRFs. Linear regression was used to assess the association with cognitive performance. Results: In 245 participants, (mean age 62 years, SD: 12 years; 29.8% females), MPM metrics demonstrated a clear spatial gradient of microstructural injury. MTsat and R1 values were lower in WMH compared to cWM (lower case Greek beta = -0.48 (-0.52 - -0.44) and lower case Greek beta = -0.07 (-0.08 - -0.06), p<0.001, respectively) and showed gradual recovery with increasing distance indicating a microstructural gradient in pNAWM. Conversely, PD values were higher in WMH and decreased peripherally (lower case Greek beta = 2.32 (2.05 - 2.61, p<0.001). No substantial associations were found between MPM parameters and CVRFs in our cohort. At baseline and 2-year follow-up, cognitive performance was associated with higher pNAWM R1 values, whereas MTsat were only moderately associated. Discussion: Quantitative MPM reliably detects microstructural alterations not only within WMH, but also in pNAWM, confirming the high sensitivity of qMPM to subtle tissue pathology and support its utility as a promising biomarker for longitudinal studies and monitoring therapeutic effects.

Stroke-associated pneumonia (SAP) is a common, severe complication in acute ischemic stroke (AIS) patients receiving bridging therapy (intravenous thrombolysis + mechanical thrombectomy), worsening prognosis and increasing mortality. Current SAP prediction models rely heavily on subjective clinical factors, limiting accuracy. This study developed an interpretable machine learning (ML) model combining inflammatory biomarkers to stratify SAP risk in AIS patients undergoing bridging therapy. We retrospectively enrolled AIS patients who received bridging therapy, collected baseline clinical data and inflammatory biomarkers, and constructed ML models (including XGBoost, random forest) with SHAP analysis for interpretability. The model integrating inflammatory biomarkers achieved excellent predictive performance (AUC=0.XX, 95%CI: XX-XX), outperforming traditional clinical models. SHAP analysis identified key biomarkers driving SAP risk, enhancing model transparency. This interpretable ML model provides an objective, accurate tool for SAP risk stratification in AIS patients, helping clinicians identify high-risk individuals early and implement targeted interventions to improve outcomes.

Improving diversity in U.S. Alzheimers disease (AD) research is a pressing need. By 2050, Hispanic and Latino Americans will comprise 30% of the population. Hispanics are 1.5 times more likely and Blacks are twice as likely to develop AD compared to Whites, yet both remain vastly underrepresented in clinical trials research. Aging and AD research mentorship of underrepresented STEM undergraduates is designed to promote entry into related professions by students committed to decreasing disparities in AD research participation and clinical care. The NIA-funded MADURA program recruited 93 students from backgrounds historically underrepresented in STEM majors and/or from NIH-defined disadvantaged backgrounds. Trainees were placed in aging/AD research labs and received weekly training and mentorship from faculty research PIs and other types of supervisors (postdoctoral researchers, graduate students, research assistant staff...) Our study examined student ratings of the program and mentor behaviors, using a program-specific survey and the Mentoring Competency Assessment-21 (MCA-21). Trainees were highly satisfied with both mentoring relationships and the overall program. Student rated MCA-21 competency areas were quite high for both P.I.s and other types of research mentors. However, there were striking differences in associations between competencies and relationship and program satisfaction, by mentor type. For PI mentors, no MCA-21 competencies were associated with relationship satisfaction, but five of six competencies were associated with relationship satisfaction for other mentor types. Similarly, no PI mentor competencies were significantly correlated with overall placement satisfaction, but all six competencies were correlated with overall placement satisfaction for other mentor types. The authors discuss the likelihood of differing student expectations of faculty PI versus other types of research mentors, recommendations for assessing role-specific student expectations (including functions primarily possible only for senior faculty PIs), and utilizing nearer-peer plus PI faculty mentors to comprehensively address the gamut of mentee needs.

End-stage liver disease (ESLD) is one of the leading causes of death worldwide. Currently, the only curative option for patients with ESLD is liver transplantation. However, the demand for donor livers far exceeds the available supply, partly because many potentially viable livers are discarded following biopsy evaluation. While biopsy is the gold standard for assessing liver histological features related to graft quality and transplant suitability, it often leads to high discard rates due to its susceptibility to sampling errors and limited spatial coverage. Besides, biopsy is invasive, time-consuming, and unavailable in clinical facilities with limited resources. Here, we present an AI-assisted photoacoustic/ultrasound (PA/US) imaging framework for quantitative assessment of human donor liver graft quality and transplant suitablity at the whole-organ scale. With multimodal volumetric PA/US images as the input, our deep-learning (DL) model accurately predicted the risk level of fibrosis and steatosis, which indicate the graft quality and transplant suitability, when comparing with true pathological scores. DL also identified the imaging modes (PAI wavelength and B-mode USI) that correlated the most with prediction accuracy, without relying on ill-posed spectral unmixing. Our method was evaluated in six discarded human donor livers comprising sixty spatially matched regions of interest. Our study will pave the way for a new standard of care in organ graft quality and transplant suitability that is fast, noninvasive, and spatially thorough to prevent unnecessary organ discards in liver transplantation.

Purpose: To determine whether spatial decomposition of longitudinal retinal nerve fiber layer (RNFL) change maps reveals distinct modes of glaucomatous progression masked by conventional averaging, and to validate these modes through structure function mapping and genetic association analysis. Methods: Pixel wise RNFL rates of change were computed from longitudinal optic disc OCT scans of 15,242 eyes (8,419 adults with primary open angle glaucoma [POAG]; Massachusetts Eye and Ear, 1998 to 2023). A loss only constraint zeroed all thickening values, reflecting the biological prior that adult RNFL does not regenerate. Nonnegative matrix factorization decomposed these maps into spatial progression components (80% training set). Components were evaluated in a heldout set (20%) for retinotopic structure function concordance, visual field (VF) progressor classification against global and quadrant RNFL rates, and enrichment of genetic association signals at established POAG loci. Results: Six anatomically distinct progression patterns emerged, including diffuse circumferential loss, focal peripapillary defects, and arcuate bundle degeneration. Pattern based models significantly outperformed global RNFL rate for classifying VF progressors (area under the curve, 0.750 [95% CI, 0.709 to 0.790] vs. 0.702; P = .0096) and explained additional variance in functional decline (Nagelkerke pseudoR2, 0.301 vs. 0.198; P = .0011). Structure function mapping confirmed retinotopic coherence. Spatial phenotypes recovered stronger genetic signals than global rates at 85.3% of established POAG loci, suggesting they capture more biologically homogeneous endophenotypes of progression. Conclusions: Glaucomatous structural progression occurs through spatially distinct modes with independent structure function and genetic signatures that conventional RNFL averaging obscures.

Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, where a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential for faithfully capturing the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration.

In this work, the impact of outliers on the performance of machine learning and deep learning models is investigated, specifically for the case of histopathological images of colorectal cancer stained with Haematoxylin and Eosin. The evaluation of the impact is done through the systematic comparison of one machine learning model (Random Forests) and one deep learning model (ResNet-18). Both models were trained with the popular NCT-CRC-HE-VAL-100K dataset and tested on the CRC-HE-VAL-7K companion set. Then, a curation process was performed by analysing the divergence of patches based on chromatic, textural and topological features of the training set and removing outliers to repeat the training with a cleaned dataset. The results showed that machine learning models, can benefit more from improvements in the quality of data, than deep learning models. Further, the results suggest that deep learning models are more robust to outliers as, through the training process, the architectures can learn features other than those previously mentioned.

Walking speed is widely used to assess gait recovery following stroke, yet it provides limited insight into how walking performance is mechanically organized. This study examined how center of mass (COM) work organization and propulsion-support coupling vary across walking speeds in individuals with post stroke hemiparesis to distinguish recovery of gait organization from recovery of limb level mechanical function. Eleven individuals with post stroke hemiparesis performed treadmill walking across speeds ranging from 0.2 to 0.7 m/s while ground reaction forces were recorded. Limb specific COM power and work were computed using an individual limbs framework, and interlimb asymmetry in net and positive work, along with the propulsion-support ratio (PSR), were quantified. A qualitative transition in gait organization was observed: at lower walking speeds, COM power exhibited a simplified two phase pattern, whereas at higher walking speeds (approximately >=0.5 m/s), a structured four phase COM power pattern emerged, including identifiable push off and preload phases. Despite this recovery of gait organization, interlimb work asymmetry remained elevated and paretic PSR remained reduced across all speeds, indicating persistent limb level mechanical deficits. These findings demonstrate that increases in walking speed and the emergence of typical COM power structure reflect recovery of gait organization rather than restoration of underlying limb level mechanical capacity. Consequently, walking speed alone is insufficient to characterize gait recovery after stroke, and biomechanically informed measures of COM work organization and propulsion-support coupling provide complementary insight by distinguishing organizational recovery from limb-level mechanical recovery.

Single photon emission computed tomography (SPECT) is a highly specialized imaging modality that enables measurement of regional cerebral perfusion and, in particular, resting cerebral blood flow (rCBF). Recent technological advances have improved SPECT quantification and reliability, making it increasingly useful for studying rCBF abnormalities and perfusion network alterations in psychiatric and neurological disorders. To characterize large scale functional organization in SPECT data, data driven decomposition methods such as independent component analysis (ICA) have been used to extract covarying perfusion patterns that map onto interpretable brain networks. Blind ICA provides a data driven approach to estimate these networks without strong prior assumptions. More recently, a hybrid approach that leverages spatial priors to guide a spatially constrained ICA (sc ICA) have been used to fully automate the ICA analysis while also providing participant-specific network estimates. While this has been reliably demonstrated in fMRI with the NeuroMark template, there is currently no comparable SPECT template. A SPECT template would enable automatic estimation of functional SPECT networks with participant-specific expressions that correspond across participants and studies. The current study introduces a new replicable NeuroMark SPECT template for estimating canonical perfusion covariance patterns (networks). We first identify replicable SPECT networks using blind ICA applied to two large sample SPECT datasets. We then demonstrate the use of the resulting template by applying sc-ICA to an independent schizophrenia dataset. In sum, this work presents and shares the first NeuroMark SPECT template and demonstrating its utility in an independent cohort, providing a scalable and robust framework for network-based analyses.