Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression.

Purpose: Lymphocytes play critical roles in cancer immunity and tumor surveillance. Radiation-induced lymphopenia (RIL) is a common side effect observed in patients with cancer undergoing chemoradiation therapy (CRT), leading to impaired immunity and worse clinical outcomes. Although proton beam therapy (PBT) has been suggested to reduce RIL risk compared with intensity-modulated radiation therapy (IMRT), this study used Bayesian counterfactual machine learning to identify distinct patient profiles and inform personalized radiation modality choice.

Extrachromosomal DNA-Driven Oncogene Spatial Heterogeneity and Evolution in Glioblastoma.

Unlabelled: Oncogenes amplified on extrachromosomal DNA (ecDNA) contribute to treatment resistance and poor survival across cancers. Currently, the spatiotemporal evolution of ecDNA remains poorly understood. In this study, we integrate computational modeling with samples from 94 treatment-naive human glioblastomas (GBM) to investigate the spatiotemporal evolution of ecDNA.

Fine-grained multiclass nuclei segmentation with molecular empowered all-in-SAM model.

Purpose: Recent developments in computational pathology have been driven by advances in vision foundation models (VFMs), particularly the Segment Anything Model (SAM). This model facilitates nuclei segmentation through two primary methods: prompt-based zero-shot segmentation and the use of cell-specific SAM models for direct segmentation. These approaches enable effective segmentation across a range of nuclei and cells.

Upper limb motor recovery in chronic stroke-longitudinal aggregate analysis from control group outcomes.

Introduction: This study examines the effects of regular physical activity on upper extremity motor recovery during the late subacute and chronic phases of stroke.

Methods: Data were aggregated from 20 studies comprising 368 participants in control groups receiving usual care or general rehabilitation without specialized interventions. To isolate the impact of non-specific physical activity, studies involving robotics or task-specific therapies were excluded.

Complex technology requires cultural innovations for distributing cognition.

Over the last decade, new research has shown how human collectives can develop technologies that no single individual could discover on their own. However, this research often overlooks how technology can become so complex that individuals cannot operate it on their own. At this level of technological complexity, distributing cognition is a necessary process for reducing cognitive load on individuals.

Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.

Radiomics is a tool for medical imaging analysis that could have a relevant role in precision oncology by offering precise quantitative support for clinical decision-making. The Radiomics Quality Score (RQS) is a tool developed to assess the rigour of radiomics studies that has now been widely adopted by researchers. Although RQS version 1.

Computational Analysis of Threonine Ladders on Distinct β-Solenoid Scaffolds, with Implications for the Design of Novel Antifreeze Proteins.

Cold-adapted organisms frequently express antifreeze proteins (AFPs) that facilitate their survival at low temperatures, with some especially potent insect AFPs exhibiting β-solenoid structures with ice-binding threonine ladders. β-solenoids exist in nature in numerous forms and emerging protein design technologies may afford opportunities to diversify them further, suggesting the possibility of developing a variety of new AFPs by installing a threonine ladder on non-AFP natural or designed β-solenoids. However, early attempts at such engineering, combined with differences observed between AFPs and structurally similar ice-nucleating proteins, have raised a critical question: Will a threonine ladder show essentially the same behavior regardless of the β-solenoid scaffold that hosts it, or does the specific solenoid scaffold significantly affect a threonine ladder's structural characteristics (and thus potentially alter its suitability for ice binding)? We set out to address this question by creating distinct variants of a simplified model β-solenoid for analysis structure prediction and molecular dynamics simulations.

CLDN10-driven lineage decision in an amnion and primordial germ cell progenitor at the amnion-epiblast boundary in primates.

Background: A growing body of evidence from primate embryos as well as in vitro systems supports the notion that amnion and primordial germ cell (PGC) lineage progressing cells share a common precursor.

Results: To gain comprehensive transcriptomic insights into this critical but poorly understood precursor and its progeny, we examine the evolving transcriptome of a developing human pluripotent stem cell-derived model of amnion and PGC formation at the single cell level. This analysis reveals several continuous amniotic fate progressing states with state-specific markers.

GG in a chewable colonizes the nose and facilitates local immune benefits in allergic rhinoconjunctivitis patients.

Current treatments fall short in managing allergic rhinitis (AR), emphasizing the need for additional strategies. Beneficial bacteria application shows promise in AR; however, most studies focus on oral probiotic administration without monitoring the applied strains in the upper respiratory tract (URT) and their local effects. In this randomized, double-blind, placebo-controlled trial, the probiotic GG was administered via chewable tablets in seasonal AR patients, randomized to probiotic ( = 33) or placebo ( = 31) groups.

Learning and criticality in a self-organizing model of connectome growth.

The exploration of brain networks has reached an important milestone as relatively large and reliable information has been gathered for connectomes of different species. Analyses of connectome data sets reveal that the structural length follows the exponential rule, the distributions of in- and out-node strengths follow heavy-tailed lognormal statistics, while the functional network properties exhibit powerlaw tails, suggesting that the brain operates close to a critical point where computational capabilities and sensitivity to stimulus is optimal. Because these universal network features emerge from bottom-up (self-)organization, one can pose the question of whether they can be modeled via a common framework, particularly through the lens of criticality of statistical physical systems.

Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment.

Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its validity can be guaranteed by study design (e.g., randomized experiments) and does not require assuming specific outcome-generating distributions or super-population models.

Reticulated platelets in coronary artery disease: a multidimensional approach unveils prothrombotic signalling and novel therapeutic targets.

Background And Aims: Reticulated platelets (RPs), hyperreactive and RNA-rich, are associated with increased risk of cardiovascular events and suboptimal response to antiplatelet therapy in coronary artery disease (CAD). However, the underlying mechanisms remain poorly defined. This study aimed to characterise the molecular and functional phenotype of RPs in CAD and assess their potential as therapeutic targets.

Probiotics derived sodium benzoate improves social behavior of offspring exposed in the maternal immune activation through regulation of histone lysine benzoylation in astrocytes.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition increasingly linked to microbiota-gut-brain axis dysregulation, yet the causal microbial mediators and molecular mechanisms remain elusive. Based on our previously published ASD cohort, we discovered that depletion of Lactobacillus species in children with ASD correlates with exacerbated gastrointestinal symptoms and social deficits. Maternal immune activation (MIA) during pregnancy has been established as a critical environmental risk factor for ASD.

Interplay between maternal Tdap and infant pneumococcal vaccination in shaping infant pneumococcal vaccine serotype carriage.

Background: Tetanus, Diphtheria and acellular Pertussis (Tdap) vaccination during pregnancy blunts the infant humoral immune response following primary immunization with pneumococcal conjugate vaccines (PCVs). While this effect typically resolves after the booster dose for most vaccine serotypes, its impact on nasopharyngeal carriage of pneumococcal vaccine serotypes remains unclear.

Methods: A total of 3,298 nasopharyngeal swabs were collected from infants aged 6-30 months attending daycare centers in Belgium between 2018 and 2022, along with data on maternal Tdap vaccination status (clinicaltrials.

DropDAE: Denosing Autoencoder with Contrastive Learning for Addressing Dropout Events in scRNA-seq Data.

Single-cell RNA sequencing (scRNA-seq) has revolutionized molecular biology and genomics by enabling the profiling of individual cell types, providing insights into cellular heterogeneity. Deep learning methods have become popular in single cell analysis for tasks such as dimension reduction, cell clustering, and data imputation. In this work, we introduce DropDAE, a denoising autoencoder (DAE) model enhanced with contrastive learning, to specifically address the dropout events in scRNA-seq data, where certain genes show very low or even zero expression levels due to technical limitations.

Associations between gut microbiome and circulating cytokines: a cross-sectional analysis in the FINRISK 2002 population cohort.

Background: A growing body of evidence suggests a relationship between gut microbiome and circulating cytokines, yet there is still a lack of large-scale population-based studies investigating gut microbiome-cytokine associations. In this cross-sectional study, we aimed at investigating the associations of gut microbiome (exposure variable) with 45 cytokines and C-reactive protein (CRP) (outcome variables) in the population-based FINRISK 2002 cohort (N = 2,398). Our analyses focused mainly on gut microbiome alpha diversity, beta diversity, differentially abundant taxa, and predicted functions.

Graph Laplacian Learning with Exponential Family Noise.

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown.

A Digitally-Supported Shared Decision Making Approach for Patients during Cardiac Rehabilitation: a Randomized Controlled Trial.

Background And Aims: The aim of the study is to evaluate the effectiveness of combining center-based cardiac rehabilitation (CR) with shared decision making (SDM) based telerehabilitation (TR) on patients' quality of life, cardiorespiratory fitness, and physical activity level.

Methods: A randomized controlled trial (NCT05026957) was conducted with 80 patients with heart disease recruited from Jessa Hospital, Hasselt. Patients were randomized to either a control group receiving standard CR or an intervention group receiving standard CR with additional SDM-based TR via the SharedHeart smartphone application.

The winner's curse under dependence: repairing empirical Bayes using convoluted densities.

The winner's curse is a form of selection bias that arises when estimates are obtained for a large number of features, but only a subset of most extreme estimates is reported. It occurs in large scale significance testing as well as in rank-based selection, and imperils reproducibility of findings and follow-up study design. Several methods correcting for this selection bias have been proposed, but questions remain on their susceptibility to dependence between features since theoretical analyses and comparative studies are few.

Predictors of Morbidity and Mortality After Fall-related Traumatic Brain Injury.

Objective: The authors investigate predictors of morbidity and mortality in patients after fall-related Traumatic Brain Injury (TBI) in a retrospective cohort study of patients presenting to a single emergency department.

Methods: This study analyzed the predictors of a subset of patients who come to the emergency department (ED) of a Level 1 trauma center who sustained a TBI after a fall. The study also examines the utility of head Computed Tomography (CT) scan as a predictor in determining outcomes such as hospital admission, in-hospital death, and Intensive Care Unit (ICU) admission.

Invisible Scribes: Can Nurses Trust Ambient AI for Clinical Documentation?

Ambient artificial intelligence listening tools promise faster nursing documentation and improved patient engagement, yet they introduce risks of hallucinations, omission, and bias when nurses are excluded from the design and oversight process. Empowering nurses through continuing education and leadership in model development, deployment, and auditing is crucial for ensuring safe and equitable integration across care settings.

Lessons from the European Mpox Outbreak: Strengthening Cohort Research for Future Pandemic Preparedness.

Background: Well-designed cohort studies are crucial for pandemic preparedness informing evidence-based infection prevention and treatment strategies.

Objectives: Following the 2022 mpox outbreak in Europe, this scoping review critically evaluates the design, implementation, and characteristics of cohort studies focusing on mpox. The aim is to inform recommendations for the Cohort Coordination Board and CoMeCT to enhance cohort study research and improve preparedness.

From ageing clocks to human digital twins in personalising healthcare through biological age analysis.

Age is the most important risk factor for the majority human diseases, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (BA). These predictors offer promising insights into the ageing process and age-related diseases. With real-time, multi-modal data streams and continuous patient monitoring, these BA can also inform the construction of 'human digital twins', quantifying how age-related changes impact health trajectories.

Leveraging functional annotations to map rare variants associated with Alzheimer disease with gruyere.

Increased availability of whole-genome sequencing (WGS) has facilitated the study of rare variants (RVs) in complex diseases. Multiple RV association tests are available to study the relationship between genotype and phenotype, but most do not fully leverage the availability of variant-level functional annotations. We propose genome-wide rare variant enrichment evaluation (gruyere), an empirical Bayesian framework that complements existing methods by learning global, trait-specific weights for functional annotations to improve variant prioritization.