Mathematical Biosciences

Animals such as bees, ants, wasps, termites, and naked mole-rats live in colonies in which a single queen is the only female reproductive, an arrangement known as eusociality. Eusocial animals are known for their remarkably long lifespans. It has been argued that longevity becomes selected when queens are shielded from "external mortality". While such protection may contribute, we find a deeper reason: the eusocial reproduction strategy itself inherently creates selection for long lifespans. Lifespans typically reflect two processes: the baseline risk of death and the rate at which this risk increases with age. Each is a parameter in the Gompertz mortality equation. We show that the mathematical properties of eusocial reproduction lead to slowly-growing, older populations where selection acts more strongly on the rate at which risk increases than on the baseline risk. In addition, we show that channeling reproduction through a single female also selects for longevity, which we term the "queen effect". Thus, the dynamics of eusocial reproduction select for longer lifespan. More broadly, these results show that reproductive structure and population growth dynamics can fundamentally shape selection on lifespan, with implications outside eusocial systems as well.

Functional spinal cord repair in zebrafish is governed by regeneration-favorable biochemical and mechanical cues within the lesion microenvironment. Alterations in extracellular matrix composition and stiffness are closely associated with axon regeneration. However, experimentally dissecting the interplay between mechanical signals and axonal regrowth in vivo remains technically challenging. Here, we present an agent-based modeling framework to simulate stiffness-mediated axonal growth trajectories across the lesion. We use this model to explore potential mechanisms underlying the characteristic growth patterns observed during zebrafish spinal cord regeneration. Computational predictions were qualitatively compared with confocal imaging data obtained from larval zebrafish. These phenomenological comparisons revealed a close agreement between simulated and experimentally observed axon growth, indicating that experimentally observed patterns could be governed by transient changes in the stiffness profile of the spinal cord and lesion microenvironment. Hence, our computational framework provides an in silico platform for investigating the role of mechanical cues in axon regeneration in the injured spinal cord.

Stratified epithelial tissues such as the skin epidermis maintain barrier integrity during development and homeostasis through the coordinated action of cell proliferation, differentiation, delamination, and tissue-scale mechanical forces. During development, the orientation of cell division within the basal layer plays a pivotal role in tissue stratification; however, the mechanical principles linking the orientation of the division plane to these processes across developmental stages remain poorly understood. Here, we expand a recently developed three-dimensional vertex model for stratified epithelia, composed of the basement membrane, basal, and suprabasal layers, to study the mechanical and structural impact of cell divisions with a wider range of orientations. The model integrates developmental stage via specific changes in heterotypic interfacial tensions (arising from actomyosin cortical contractility and adhesion molecules at the basal-suprabasal interface) and tissue stiffness that have been quantified previously in experiments. By systematically varying background mechanical parameters, we investigate how heterotypic tension, division orientation, and tissue fluidity collectively influence the outcome of cell division. Our goal is to uncover the strategies that the embryo may employ to generate stratified phenotypes at different developmental stages, recognizing that these strategies might evolve over time. Although our focus is on the embryonic developmental stages of the epidermis, this framework may also be extended to investigate transformed cells, such as in cancer, to explore how altered division orientation contributes to precancerous or transformed phenotypes.

Patterns in the vegetation across arid and semiarid regions may be explained as a form of self-organization driven by water scarcity, and are often modeled through reaction-diffusion dynamics. Recent work has shown that similar mathematical models generate patterns on networks. However, these studies have focused on idealized topologies with no reference to natural pattern-forming systems. Our study aims at bridging these two fields: we employ a physical reaction-diffusion vegetation model, and gradually modify the topology of the diffusion network by adding random shortcuts over a 2-dimensional grid, interpolating between a regular lattice and a random network. We found that network topology strongly shapes both the resulting vegetation patterns and the precipitation range that supports them. Three behavioral regimes emerge. On a regular lattice, high-regularity patterns develop reflecting local diffusion processes. On a random network, the system is dominated by global pressure towards homogenization yielding either a uniform state or a single patch. In the intermediate shortcut density range, as the network topology resembles a small world network, the interaction between the two scales of diffusion generates two kinds of disordered patterns: low-regularity patterns with a well-defined characteristic wavelength, and irregular patterns characterized by a broad patch size distribution. These disordered patterns resemble real-world observations and, in our model, they show different responses to changing precipitation. Although we focused on dryland vegetation, we suggest that network-mediated diffusion could lead to similar mechanisms in a wide variety of pattern-forming systems. HighlightsO_LIWe study vegetation pattern formation over different diffusion network topologies. C_LIO_LITwo kinds of stable disordered patterns states develop over small world topologies. C_LIO_LILow-regularity patterns with a well-defined characteristic wavelength. C_LIO_LIIrregular patterns characterized by a broad patch size distribution. C_LIO_LIThese different kinds of disordered states show different relations to precipitation. C_LI

PurposeColonic motility disorders, including diarrhea-predominant irritable bowel syndrome and slow-transit constipation, impose a major clinical burden. Although high-resolution colonic manometry reveals characteristic spatiotemporal motor patterns, such as high-amplitude propagating contractions and cyclic motor pattern in healthy individuals, these patterns are often altered or absent in disease. Understanding how these patterns arise from underlying pacemaker, neural, and mechanical mechanisms is essential for improving treatment strategies. MethodsWe developed a biophysical whole-colon model that integrates an Interstitial Cells of Cajal-inspired oscillator network, enteric nervous system reflexes, a pressure-gated modulation element motivated by rectosigmoid brake behavior, and a nonlinear tube law describing colon wall mechanics. The model simulates spatiotemporal pressure patterns along the colon and allows systematic variation of physiological parameters associated with pacemaker activity, neural reflex control, and distal gating. ResultsA small set of parameters reproduces three illustrative motility patterns corresponding to healthy motility, diarrhea-predominant irritable bowel syndrome, and slow-transit constipation. The simulated pressure maps recapitulate key features observed in high-resolution manometry, including propagation direction, regional patterning of contractions, and case-specific changes in amplitude and coordination. Sensitivity analysis suggests that proximal excitation strength and waveform morphology strongly influence global motility metrics. ConclusionOur study presents a simple, biophysical framework for reproducing clinically observed colonic motor patterns and exploring their disruption in disease. More broadly, the model may help interpret clinical manometry in mechanistic terms and support hypothesis-driven in silico studies of colonic motility disorders.

Pathogens that persist subclinically across many wildlife populations can drive mass mortality in others. Mass mortality is often abrupt, and the timing can be difficult to predict from host or habitat features alone. In a recent field study tracking ranavirus epizootics in wood frog (Rana sylvatica) breeding ponds, we found that no environmental or biotic feature reliably predicted die-off occurrence or timing. Instead, the trajectory of viral accumulation in the water column was the strongest dynamic predictor of mass mortality. Infected hosts shed virus throughout epizootics, but the influence of waterborne viral concentration on disease progression was apparent only near die-off onset. This pattern suggests a potential threshold-dependent feedback operating through the shared viral environment. Here, we develop a compartmental model linking waterborne viral concentration to the rate at which subclinical infections progress to clinical, high-shedding states within already-infected hosts. We show that a dose-dependent progression model generates the two-phase epizootic trajectory observed in natural die-offs: prolonged subclinical circulation followed by abrupt clinical transition after environmental virus crosses an escalation threshold. The model exhibits a sharp phase transition between subclinical circulation and mass mortality, governed mainly by the clinical-to-subclinical shedding ratio, host density, and pond volume. Existing explanations for die-off variation emphasize individual-level susceptibility, but our model demonstrates that dose-dependent environmental feedback, a mechanism not previously formalized at the population level, can generate the transition from subclinical infection to mass mortality without invoking individual variation in host susceptibility. This mechanism may apply in any system where hosts share a bounded environment, pathogen dose influences disease severity, and pathogen shedding increases with disease progression.

Background and AimsFenofibrate is widely prescribed for hyperlipidaemia and has been associated with rare but severe cases of drug-induced liver injury (DILI), yet its effects on liver sinusoidal endothelial cells (LSECs) remain to be investigated. LSECs maintain a highly permeable specialized sinusoidal barrier characterized by transcellular pores (fenestrations), regulating the bidirectional transfer of circulating compounds to and from the hepatocytes. As drug-induced alterations in fenestration architecture could influence xenobiotic access to hepatocytes, these changes may modulate pathways associated with DILI. Understanding the effects of fenofibrate on LSEC ultrastructure may therefore provide insights into previously underexplored endothelial contributions to hepatic drug responses. MethodsBoth fenofibrate and its active metabolite, fenofibric acid, were evaluated for their effects on LSEC ultrastructure, mechanical properties, and functional markers. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) and were used to quantify fenestration architecture. AFM was additionally used to measure cellular mechanical properties, which were interpreted in the context of fluorescence-based quantification of cytoskeletal organization. Gene expression, viability, and cytotoxicity were assessed using PCR-based and biochemical assays. ResultsFenofibrate reduced fenestration number and porosity at both tested concentration (10, and 25 {micro}M). It also decreased the apparent Youngs modulus of LSECs, accompanied by changes in tubulin and actin architecture, without detectable cytotoxicity. In contrast, treatment with fenofibric acid did not result in significant structural or mechanical effects on LSECs, even at higher concentrations. ConclusionsTogether, these data identify LSECs as a drug-responsive hepatic cell type for fenofibrate, suggesting that LSECs could represent an underrecognized contributor to the complex, multifactorial processes underlying DILI. This work provides a framework for evaluating endothelial contributions to fenofibrate-associated liver effects in more complex models. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/718907v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1d3f60corg.highwire.dtl.DTLVardef@bea13aorg.highwire.dtl.DTLVardef@14b27d8org.highwire.dtl.DTLVardef@124e0d3_HPS_FORMAT_FIGEXP M_FIG Fenofibrate reduces LSEC fenestrations and metabolic activity at higher concentrations, while its metabolite, fenofibric acid, does not affect LSEC, regardless of its concentration. C_FIG

Road traffic accidents represent a global public safety crisis, necessitating advanced computational tools for accurate injury severity prediction and effective decision support. This study evaluates high-performing ensemble machine learning models, including AdaBoost, XGBoost, LightGBM, HistGBRT, CatBoost, Gradient Boosting, NGBoost, and Random Forest, using a comprehensive National Highway Traffic Safety Administration (NHTSA) dataset from 2018 to 2022. While all models demonstrated exceptional predictive accuracy, with HistGBRT achieving the highest overall accuracy of 92.26%, a defining achievement of this work is the perfect classification (100% precision and recall) of fatal injuries across all ensemble architectures. To bridge the gap between predictive performance and actionable intelligence, this research integrates SHapley Additive exPlanations (SHAP) to provide both global insights into dataset-wide risk factors and local, instance-specific rationales for individual crash events. The global analysis identified ethnicity, airbag deployment, and harmful event type as primary drivers of injury severity, while local force and waterfall plots revealed the precise "push and pull" of variables for specific incidents. The results offer a robust, interpretable framework for stakeholders tasked with improving traffic safety and mitigating crash-related harm.

Interpreting machine learning models typically relies on feature attribution methods that quantify the contribution of individual variables to model predictions. However, it remains unclear whether attribution magnitude reflects the true functional importance of features for model performance. Here, we present a unified interpretability framework integrating permutation-based attribution, feature ablation, and stability under perturbation across multiple feature spaces. Using nested cross-validation and permutation-based null diagnostics, we systematically evaluate the relationship between attribution magnitude and functional dependence in clinical and biomarker-based prediction models. Attribution magnitude is frequently misaligned with functional importance, with weak to strong negative correlations observed across feature spaces (Spearman {rho} ranging from -0.374 to -0.917). Features with high attribution often have limited impact on model performance when removed, whereas features with low attribution can be essential for maintaining predictive accuracy. These discrepancies define distinct classes of interpretability failure, including attribution excess and latent dependence. Interpretability further depends on feature space composition, and stable, functionally relevant features are not necessarily those with the highest attribution scores. By integrating attribution, functional impact, and stability into a composite Feature Reliability Score, we identify features that remain informative across perturbations and analytical contexts. These findings indicate that interpretability does not arise from attribution magnitude alone but is better characterized from stability under perturbation. This framework provides a basis for more robust model interpretation and highlights limitations of attribution-centric approaches in high-dimensional and correlated data settings.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved since its emergence in the human population in 2019. As of 1st August 2025, more than 1,700 Omicron subvariants have been designated by the Pango nomenclature system. The Pango nomenclature system designates a new lineage based on genetic and epidemiological information of SARS-CoV-2 strains. However, there is a possibility that strains that have similar genetic backgrounds and the same phenotype are given different Pango lineage names. In this paper, we propose a new algorithm, called FindPart-w, which can identify groups of viral lineages that share the same relative effective reproduction numbers. We introduced a new lineage replacement model, called the constrained RelRe model, which constrains groups of lineages to have the same relative effective reproduction numbers. The FindPart-w algorithm searches the equality constraints that minimise the Akaike Information Criterion of constrained RelRe models. Using hypothetical observation count data created by simulation, we found that the FindPart-w algorithm can identify groups of lineages having the same relative effective reproduction number in a practical computational time. Applying FindPart-w to actual real-world data of time-stamped lineage counts from the United States, we found that the Pango lineage nomenclature system may have given different lineage names to SARS-CoV-2 strains even if they have the same relative effective reproduction number and similar genetic backgrounds. In conclusion, this study showed that viruses that had the same relative effective reproduction number were identifiable from temporal count data of viral sequences. These findings will contribute to the future development of lineage designation systems that consider both genetic backgrounds and transmissibilities of lineages.

Routine ICU data offers valuable insights into daily physiological rhythms. While traditional methods assume these cycles maintain fixed periods and amplitudes, their inherent variability requires dynamic estimation of instantaneous trends. Wavelet transform effectively resolves circadian oscillations, especially for frequently measured vital parameters. We present novel extensions to the Continuous Wavelet Transform (CWT) power spectral analysis to better detect and segment subtle temporal patterns. Using this approach, we uncover hidden circadian patterns in cardiovascular vitals such as Heart Rate (HR) and Mean Blood Pressure (MBP) measured over five days in a retrospective cohort of 855 ICU patients. By quantifying non-stationary rhythms, we identified diurnal and semi-diurnal oscillations varying in period and power according to delirium and deep sedation. Notably, HR exhibits a clear diurnal and semi-diurnal rhythm when delirium is absent. Overall, our framework supports the CWT as a powerful tool for analyzing complex physiological signals, particularly vital signs. Crucially, our findings suggest that cardiovascular rhythm disruption can be associated with ICU-related delirium and deep sedation.

In dense urban settings, delays between diagnostic sampling and effective isolation can sustain transmission during peak infectiousness. We define a waiting-window transmission externality arising when infectious individuals remain mobile while awaiting results, formalized as E = N{middle dot}P{middle dot}TR{middle dot}D, where N is daily testing volume, P test positivity, TR transmission during the waiting period, and D turnaround time. Using Monte Carlo simulation and a susceptible-infectious-recovered (SIR) framework, we quantify excess infections per 1,000 tests/day under multiple diagnostic workflows. A surge scenario incorporates positive coupling between TR and D ({rho} = 0.45), reflecting co-occurrence of laboratory saturation and elevated contacts during system stress. Under centralized 48-hour workflows, excess infections reach [~]80 at P = 10% and [~]401 at P = 50%, increasing to [~]628 under surge conditions. In contrast, near-patient rapid testing and home sampling reduce this to [~]5 and [~]25-26, respectively. Workflows that eliminate the waiting window--either through immediate isolation at sampling or through home-based PCR that returns results at the point of collection--effectively collapse the transmission term. These findings identify diagnostic delay as a modifiable driver of epidemic dynamics. Operational redesign of testing workflows, including decentralized sampling and home-based molecular diagnostics, offers a scalable pathway to improve epidemic controllability and reduce inequities in dense urban environments.

A central goal of neuroscience is to understand how neural circuit architecture supports learning. While recent work has clarified the computational role of depth in sensory cortical hierarchies, it remains unclear why predominantly feedforward, non-convolutional circuits such as the cerebellum and olfactory system also contain multiple processing layers. Theoretical work in deep learning has shown that two-hidden-layer networks can achieve classification capacity that scales quadratically with the number of intermediate neurons, but these results rely on nonlocal synaptic optimization and are therefore difficult to reconcile with biological learning rules. Here, we show analytically and numerically that a two-hidden-layer network with feedforward gating can achieve quadratic capacity using local three-factor Hebbian learning when intermediate activity is sparse. This architecture supports efficient one-shot learning and, in settings where backpropagation requires many repeated weight updates, offers an advantage in learning speed. Beyond random perceptron tasks, the model also performs well on structured cerebellum-related tasks, including reinforcement-learning-based motor control. Mapping the model onto cerebellar microcircuitry further suggests functional roles for dendritic compartmentalization, branch-specific inhibition, and disinhibitory interneuron pathways. Together, these results extend the Marr-Albus-Ito framework by showing how the presence of multiple intermediate layers in cerebellum-like circuits can support fast, local, and high-capacity learning.

Increasing human activities along the US west coast are of concern for populations of cetaceans and particularly for a number of large whale species that are recovering from overexploitation during the era of commercial whaling. New rapid monitoring tools, such as satellite imagery analysis powered by recent advances in artificial intelligence, have potential to provide additional broad-scale and near real-time capacities for survey and monitoring. This paper investigates and demonstrates the feasibility of automatic detection of gray whales in sub-meter satellite imagery off the coast of California, USA. Observations and statistical analysis of regional imagery allowed not only an assessment of their detectability but also the development of robust signal processing and machine learning-based solutions for automated detection. To that end, a regional dataset of 221 gray whales was created using signal processing to inform a deep-learning-based detection framework, and 20 different large neural network architectures for feature extraction followed by a support vector machine algorithm for classification were evaluated for their detection performance. Neural network backbones included 19 convolutional neural networks and 1 transformer network. The best architecture generally achieved satisfying performance with an average balanced accuracy reaching up to 99.90%. It is also demonstrated that panchromatic imagery, in spite of the lesser amount of information provided, can be used to perform detection with a relatively high accuracy of 87.05%, allowing wider spatial and temporal coverage. Large-scale deployment of the best performing models over a broad range of regional satellite imagery resulted in the detection of 3353 gray whales, as well as opportunistic detections of humpback, blue and fin whales, in and going from December 28th 2009 to March 26th 2023. It also provided meaningful data points concerning the migration routes of gray whales within the Channel Islands and Southern California Bight. The large number of high-confidence detections indicates the capacity for a large-scale monitoring approach to support state and federal conservation policies such as gear mitigation, vessel speed reduction programs, or shipping lane redefinition that could also be expanded to other areas and for other species.

Target trial emulation (TTE) offers a principled way to estimate treatment effects using real-world observational data, but analyses of time-varying treatment strategies remain vulnerable to immortal time bias. The clone-censor-weight (CCW) approach is increasingly used to address this problem, yet key aspects of its causal interpretation and implementation remain unclear. In this work, we emulate a target trial using electronic health records (EHRs) to compare completion of a 3-dose 9-valent human papillomavirus vaccination (HPV) series within 12 months versus remaining partially vaccinated among vaccine initiators. We link CCW to the classic potential outcome framework in causal inference, evaluate the role of different weighting mechanisms, and account for within-subject correlation induced by cloning using cluster-robust variance estimation. Our study provides practical guidance for applying CCW in real-world comparative effectiveness studies to address immortal time bias and supports more rigorous and interpretable treatment effect estimation in TTE.

This report identifies a bidirectional signaling axis connecting lipid metabolism to nuclear mechanotransduction, with the potential to control fatty acid/triglyceride metabolism. The sterol regulatory element-binding (SREBP) family of transcription factors control fatty acid, triglyceride and cholesterol synthesis and metabolism. The family consists of three members: SREBP1a, SREBP1c, and SREBP2, that are regulated by intracellular cholesterol levels and insulin signaling. The SREBP2-dependent control of the LDL receptor gene is a well-established target for cholesterol-lowering therapeutics and the activity of SREBP1c is an attractive target in metabolic disease. In the current report, we identify SYNE4 (nesprin-4), a component of the Linker of Nucleoskeleton and Cytoskeleton (LINC) complex, as a direct target of the SREBP family of transcription factors, and show that nesprin-4 in turn supports SREBP1c function. We identify functional SREBP binding sites in the human SYNE4 promoter and demonstrate that these are required for the sterol- and SREBP-dependent regulation of the promoter. Furthermore, we show that the endogenous SYNE4 gene is also regulated by SREBP1/2 and intracellular sterol levels. Interestingly, SREBP2 is responsible for the sterol regulation of the SYNE4 gene in HepG2 cells, while SREBP1 is the major regulator in MCF7 cells, demonstrating that diberent cell types use diberent SREBP paralogs to regulate the same promoter/gene. Importantly, we find that nesprin-4 is a positive regulator of SREBP1c expression and function in HepG2 cells and during the diberentiation of human adipose-derived stem cells. In summary, the current report identifies a novel regulatory interaction between lipid metabolism and the LINC complex. Importantly, we demonstrate that this signaling axis is bidirectional, forming a closed loop that has the potential to control SREBP1c activity and thereby fatty acid and triglyceride synthesis/metabolism. Based on our data, we propose that the nesprin-4-dependent regulation of SREBP1c could represent a novel therapeutic target in metabolic disease.

O_LIThe management of invasive species often emphasizes removals to manage populations. However, evaluating the success of this management technique remains challenging, especially at large scales. Understanding the relationship between removal intensity and population growth is essential for determining when management achieves desired outcomes. C_LIO_LIWe used management removal data (removal resources [e.g. trapping] and relative effort [trap nights]) to estimate population density, demographic structure, and growth rates of invasive wild pigs (Sus scrofax domesticus) across a large landscape. From the management data and population estimates, we inferred population trajectories in the absence of removals and quantified the proportion of the population removed by the most widely used methods to control wild pigs. We then compared observed removal intensities and population growth rates to predict expected population trajectories immediately after management occurs. C_LIO_LIResults suggest substantial spatial and temporal variation in wild pig growth rates and variation in the effectiveness of removal efforts. Additionally, removing wild pigs at higher densities had a greater effect on limiting population growth than removals conducted at lower densities, though both are important. However, on large properties, removal intensity was often insufficient to offset population growth, indicating that management effort does not scale to large areas. C_LIO_LIThese results demonstrate how removal data and population modeling can provide robust inference on population dynamics and management effectiveness, offering a scalable framework for evaluating and improving invasive species control programs. We also discuss the current limitation of how effort is defined for different large-mammal removal techniques, and offer potential solutions for a more complete definition, such as going beyond trap nights and including constraints on personnel, equipment, and logistics. C_LI

Non-pharmaceutical interventions (NPIs) have been important for controlling SARS-CoV-2 transmission, particularly before and during initial vaccine rollout. During the pandemic, the US Centers for Disease Control and Prevention issued isolation and masking guidance in case of COVID-19-like illness, a positive SARS-CoV-2 test, or known exposure to SARS-CoV-2. However, the impact of this guidance on mitigating transmission in office workplaces is unclear. We used a network-based mathematical model to estimate the impact of this guidance on SARS-CoV-2 transmission among office workers and their communities. The model represented social contacts in the home, office, and community. We used data from the CorporateMix study to parametrize social contacts among office workers and calibrated the model to represent the COVID-19 epidemic in Georgia, USA from January 2021 through August 2022. In the reference scenario (58% adherence to guidance among office workers and the broader population), workplace transmission accounted for a small fraction of total infections. Reducing adherence among office workers to 0% increased workplace transmissions by 27.1% and increasing adherence to 75% reduced workplace transmission by 7.0%. Increasing adherence to 75% among office workers had minimal impact on symptomatic cases and deaths; increasing it among the broader population was more effective in reducing office worker cases and deaths. In our model, moderate adherence to recommended NPIs in workplaces was effective in reducing transmission, but increasing adherence had limited benefit given workplaces that have low contact intensity and hybrid work arrangements. These results underscore the public health benefits of community-wide adoption of recommended NPIs.

Abstract Background Spontaneous coronary artery dissection (SCAD) is a well-recognised cause of acute coronary syndrome particularly among women without conventional cardiovascular risk factors. Increasing evidence indicates a genetic contribution; however, the underlying genetic architecture of SCAD remains insufficiently understood. Objective The aim of this study was to assess the prevalence of rare variants in previously reported SCAD associated genes and to explore the potential presence of novel genetic alterations in well-characterised Swedish patients with SCAD. Methods The study comprised 201 patients enrolled in SweSCAD, a national project examining the clinical characteristics, aetiology, and outcomes of SCAD. All individuals had a confirmed diagnosis based on invasive coronary angiography. Comprehensive exome sequencing was performed to identify rare variants contributing to disease susceptibility. Results Genetic variants that have been associated with SCAD according to current clinical genetics practice for variant reporting were identified in approximately 4 % of patients. In addition, rare potentially relevant variants were detected in almost 60 % of patients in genes associated with vascular integrity and vascular remodelling. Conclusion This study supports SCAD as a genetically complex arteriopathy, driven by rare high?impact variants together with broader polygenic susceptibility. Variants in collagen, vascular extracellular matrix, and oestrogen?responsive pathways provide biologically plausible links to female?predominant disease. Although the diagnostic yield of clearly actionable variants is modest, these findings support broader genomic evaluation beyond overt syndromic presentations and highlight the need for larger integrative genomic and functional studies to refine risk stratification and management.

Purpose: To test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. Methods: We conducted a multi-cohort study including CALGB 9344 (anthracycline +/- paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrell's C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. Results: A total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. Conclusion: Histology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit.