q-bio
q-bio
11-25 00:00
arXiv:2511.17627v1 Announce Type: new Abstract: Benthic habitat is challenging due to the environmental complexity of the seafloor, technological limitations, and elevated operational costs, especially in under-explored regions. This generates knowledge gaps for the sustainable management of hydrobiological resources and their nexus with society. We developed ECOSAIC (Ecological Compression via Orthogonal Specialized Autoencoders for Interpretable Classification), an Artificial Intelligence framework for automatic classification of benthic habitats through interpretable latent representations using a customizable autoencoder. ECOSAIC compresses n-dimensional feature space by optimizing specialization and orthogonality between domain-informed features. We employed two domain-informed categories: biogeochemical and hydrogeomorphological, that together integrate biological, physicochemical, hydrological and geomorphological, features, whose constraints on habitats have been recognized in ecology for a century. We applied the model to the Colombian Pacific Ocean and the results revealed 16 benthic habitats, expanding from mangroves to deep rocky areas up to 1000 m depth. The candidate habitats exhibited a strong correspondence between their environmental constraints, represented in latent space, and their expected species composition. This correspondence reflected meaningful ecological associations rather than purely statistical correlations, where the habitat's environmental offerings align semantically with the species' requirements. This approach could improve the management and conservation of benthic habitats, facilitating the development of functional maps that support marine planning, biodiversity conservation and fish stock assessment. We also hope it provides new insights into how ecological principles can inform AI frameworks, particularly given the substantial data limitations that characterize ecological research.
cs.lgq-bio.pe
q-bio
q-bio
11-25 00:00
arXiv:2511.17641v1 Announce Type: new Abstract: Consider a population of organisms that harvest free energy from their environment to reproduce. This paper shows that if the organisms' reproductive rates are proportional to the amount of physical free energy that they can convert into reproductive work, then the implicit probabilities that the organisms assign to environmental states are updated according to Bayes' rule.
cond-mat.stat-mechq-bio.pe
q-bio
q-bio
11-25 00:00
arXiv:2511.17652v1 Announce Type: new Abstract: Advances in AI have introduced several strong models in computational pathology to usher it into the era of multi-modal diagnosis, analysis, and interpretation. However, the current pathology-specific visual language models still lack capacities in making diagnosis with rigorous reasoning paths as well as handling divergent tasks, and thus challenges of building AI Copilots for real scenarios still exist. Here we introduce TeamPath, an AI system powered by reinforcement learning and router-enhanced solutions based on large-scale histopathology multimodal datasets, to work as a virtual assistant for expert-level disease diagnosis, patch-level information summarization, and cross-modality generation to integrate transcriptomic information for the clinical usage. We also collaborate with pathologists from Yale School of Medicine to demonstrate that TeamPath can assist them in working more efficiently by identifying and correcting expert conclusions and reasoning paths. Overall, TeamPath can flexibly choose the best settings according to the needs, and serve as an innovative and reliable system for information communication across different modalities and experts.
cs.cvq-bio.qm
q-bio
q-bio
11-25 00:00
arXiv:2511.17685v1 Announce Type: new Abstract: Spatial Transcriptomics (ST) is a technology that measures gene expression profiles within tissue sections while retaining spatial context. It reveals localized gene expression patterns and tissue heterogeneity, both of which are essential for understanding disease etiology. However, its high cost has driven efforts to predict spatial gene expression from whole slide images. Despite recent advancements, current methods still face significant limitations, such as under-exploitation of high-level biological context, over-reliance on exemplar retrievals, and inadequate alignment of heterogeneous modalities. To address these challenges, we propose DKAN, a novel Dual-path Knowledge-Augmented contrastive alignment Network that predicts spatially resolved gene expression by integrating histopathological images and gene expression profiles through a biologically informed approach. Specifically, we introduce an effective gene semantic representation module that leverages the external gene database to provide additional biological insights, thereby enhancing gene expression prediction. Further, we adopt a unified, one-stage contrastive learning paradigm, seamlessly combining contrastive learning and supervised learning to eliminate reliance on exemplars, complemented with an adaptive weighting mechanism. Additionally, we propose a dual-path contrastive alignment module that employs gene semantic features as dynamic cross-modal coordinators to enable effective heterogeneous feature integration. Through extensive experiments across three public ST datasets, DKAN demonstrates superior performance over state-of-the-art models, establishing a new benchmark for spatial gene expression prediction and offering a powerful tool for advancing biological and clinical research.
q-bio.qmcs.ai
q-bio
q-bio
11-25 00:00
arXiv:2511.17794v1 Announce Type: new Abstract: Understanding how cooperation emerges and persists is a central challenge in evolutionary game theory. Existing models often rely on restricted, hand-picked strategy sets, which can overlook critical behavioural pathways. A recent four-strategy framework showed that cheap talk can promote cooperation through local interactions, yet it remained unclear whether modelled strategies might alter these conclusions. Here, we extend this framework to the complete set of eight strategies that naturally arise from communication and decision-making rules. We show that incorporating the full strategy space dramatically changes the evolutionary landscape. Cooperation becomes both more robust and more versatile, driven by novel pathways absent in the restricted model. In particular, we uncover a previously overlooked mechanism in which suspicious cooperation catalyses a cyclic dynamic that sustains cooperation. Conversely, the assumed role of strategic defection in the biased model is fragile, acting mainly as a spoiler rather than a genuine evolutionary attractor. The complete model further reveals a rich spectrum of long-term behaviours, including stable coexistence among up to seven strategies and time-varying patterns of partial coexistence. These results demonstrate that the full strategy space unlocks hidden routes to cooperative behaviour and highlight the importance of comprehensive modelling when explaining the emergence of cooperation.
cs.gtq-bio.pe
q-bio
q-bio
11-25 00:00
arXiv:2511.17905v1 Announce Type: new Abstract: Gene regulatory networks exhibit remarkable stability, maintaining functional phenotypes despite genetic and environmental perturbations. Discrete dynamical models, such as Boolean networks, provide systems biologists with a tractable framework to explore the mathematical underpinnings of this robustness. A key mechanism conferring stability is canalization. This perspective synthesizes historical insights, formal definitions of canalization in discrete dynamical models, quantitative measures of stability, illustrative applications, and emerging challenges at the interface of theory and experiment.
q-bio.mnmath.dscs.dm
q-bio
q-bio
11-25 00:00
arXiv:2511.17996v1 Announce Type: new Abstract: Collapse Lineage Tree (CLTree) is a software tool that annotates, roots, and evaluates phylogenetic trees by using lineages. A recursive algorithm was designed to annotate the branches by the common taxonomic lineage of its descendants in a rooted tree. For an unrooted tree, it determines the root that best conforms to the taxonomic system based on the aforementioned lineage annotations. Based on the lineage annotations of notes, CLTree infers the monophyly of taxonomic units and quantifies the concordance between the phylogenetic tree and the taxonomic system base on Shannon entropy. The core algorithm of CLTree is highly efficient with linear complexity, capable of processing phylogenetic trees with 17,955 terminal nodes within one second. We believe that CLTree will serve as a powerful tool for study of evolution and taxonomy.
q-bio.peq-bio.qm
q-bio
q-bio
11-25 00:00
arXiv:2511.18010v1 Announce Type: new Abstract: Deep learning has emerged as a powerful framework for analyzing biomolecular dynamics trajectories, enabling efficient representations that capture essential system dynamics and facilitate mechanistic studies. We propose a neural network architecture incorporating Fourier Transform analysis to process trajectory data, achieving dual objectives: eliminating high-frequency noise while preserving biologically critical slow conformational dynamics, and establishing an isotropic representation space through the last hidden layer for enhanced dynamical quantification. Comparative protein simulations demonstrate our approach generates more uniform feature distributions than linear regression methods, evidenced by smoother state similarity matrices and clearer classification boundaries. Moreover, by using saliency score, we identified key structural determinants linked to effective energy landscapes governing system dynamics. We believe that the fusion of neural network features with physical order parameters creates a robust analytical framework for advancing biomolecular trajectory analysis.
q-bio.qmq-bio.bm
q-bio
q-bio
11-25 00:00
arXiv:2511.18057v1 Announce Type: new Abstract: Current models treat physiological signals as noise corrupting neural computation. Previously, we showed that removing these "artifacts" eliminates 70% of predictive correlation, suggesting body signals functionally drive cognition. Here, we investigate the mechanism using high-density EEG (64 channels, 10 subjects, 500+ trials) during P300 target recognition. Phase Slope Index revealed zero-lag synchrony (PSI=0.000044, p=0.061) with high coherence (0.316, p<0.0001). Ridge-regularized Granger causality showed massive bidirectional coupling (F=100.53 brain-to-body, F=62.76 body-to-brain) peaking simultaneously at 78.1ms, consistent with mutually coupled resonance pairs. Time-resolved entropy analysis (200ms windows, 25ms steps) revealed triphasic dynamics: (1) constraint accumulation (0-78ms) building causal drive without entropy change (delta-S=-0.002 bits, p=0.75); (2) supercritical transition (100-600ms) triggering state expansion (58% directional increase, binomial p=0.002); (3) sustained metastability. Critically, transition magnitude was uncorrelated with resonance strength (r=-0.044, p=0.327), indicating binary threshold dynamics. Understanding emerges through a thermodynamic sequence: brain-body resonance acts as a discrete gate triggering non-linear information integration. This architecture may fundamentally distinguish biological from artificial intelligence. Keywords: embodied cognition, phase transitions, Granger causality, thermodynamics, neuromorphic computing, resonance dynamics, EEG artifacts
eess.spq-bio.nc
q-bio
q-bio
11-25 00:00
arXiv:2511.18142v1 Announce Type: new Abstract: Population heterogeneity is a key factor in epidemic dynamics, influencing both transmission and final epidemic size. While heterogeneity is often modeled through age structure, spatial location, or contact patterns, differences in host susceptibility have recently gained attention, particularly during the COVID-19 pandemic. Building on the framework of Diekmann and Inaba (Journal of Mathematical Biology, 2023), we focus on the special case of SEIR-models, which are widely used for influenza and other respiratory infections. We derive the model equations under two distinct assumptions linking susceptibility and infectiousness. Analytical results show that heterogeneity in susceptibility reduces the epidemic final size compared to homogeneous models with the same basic reproduction number $\Ro$. In the case of gamma-distributed susceptibility, we obtain stronger results on the epidemic final size. The resulting model captures population heterogeneity through a single parameter, which makes it practical for fitting epidemic data. We illustrate its use by applying it to seasonal influenza in Italy.
q-bio.pemath.ds
q-bio
q-bio
11-25 00:00
arXiv:2511.18325v1 Announce Type: new Abstract: Psychedelics, such as psilocybin, reorganise large-scale brain connectivity, yet how these changes are reflected across electrophysiological (electroencephalogram, EEG) and haemodynamic (functional magnetic resonance imaging, fMRI) networks remains unclear. We present Brain-MGF, a multimodal graph fusion network for joint EEG-fMRI connectivity analysis. For each modality, we construct graphs with partial-correlation edges and Pearson-profile node features, and learn subject-level embeddings via graph convolution. An adaptive softmax gate then fuses modalities with sample-specific weights to capture context-dependent contributions. Using the world's largest single-site psilocybin dataset, PsiConnect, Brain-MGF distinguishes psilocybin from no-psilocybin conditions in meditation and rest. Fusion improves over unimodal and non-adaptive variants, achieving 74.0% accuracy and 76.5% F1 score on meditation, and 76.0% accuracy with 85.8% ROC-AUC on rest. UMAP visualisations reveal clearer class separation for fused embeddings. These results indicate that adaptive graph fusion effectively integrates complementary EEG-fMRI information, providing an interpretable framework for characterising psilocybin-induced alterations in large-scale neural organisation.
cs.lgq-bio.nc
q-bio
q-bio
11-25 00:00
arXiv:2511.18626v1 Announce Type: new Abstract: T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable specificity and sensitivity. Quantitatively understanding how T cell receptors (TCRs) discriminate among antigens requires biophysical models and theoretical analysis of signaling networks. Here, we review current theoretical frameworks of antigen recognition in the context of modern experimental and computational advances. Antigen potency spans a continuum and exhibits nonlinear effects within complex mixtures, challenging discrete classification and simple threshold-based models. This complexity motivates the development of models such as adaptive kinetic proofreading, which integrate both activating and inhibitory signals. Advances in high-throughput technologies now generate large-scale, quantitative datasets, enabling the refinement of such models through statistical and machine learning approaches. This convergence of theory, data, and computation promises deeper insights into immune decision-making and opens new avenues for rational immunotherapy design.
q-bio.mnq-bio.cbq-bio.qm
q-bio
q-bio
11-25 00:00
arXiv:2511.18745v1 Announce Type: new Abstract: Cell migration often exhibits long-range temporal correlations and anomalous diffusion, even in the absence of external guidance cues such as chemical gradients or topographical constraints. These observations raise a fundamental question: do such correlations simply reflect internal cellular processes, or do they enhance a cell's ability to navigate complex environments? In this work, we explore how temporally correlated noise (modeled using fractional Brownian motion) influences chemotactic search dynamics. Through computational experiments, we show that superdiffusive motion, when combined with gradient-driven migration, enables robust exploration of the chemoattractant landscape. Cells reliably reach the global maximum of the concentration field, even in the presence of spatial noise, secondary cues, or irregular signal geometry. We quantify this behavior by analyzing the distribution of first hitting times under varying degrees of temporal correlation. Notably, our results are consistent across diverse conditions, including flat and curved substrates, and scenarios involving both primary and self-generated chemotactic signals. Beyond biological implications, these findings also offer insight into the design of optimization and sampling algorithms that benefit from structured stochasticity.
stat.apcs.ceq-bio.qm
q-bio
q-bio
11-25 00:00
arXiv:2511.18883v1 Announce Type: new Abstract: Autocatalysis is an important feature of metabolic networks, contributing crucially to the self-maintenance of organisms. Autocatalytic subsystems of chemical reaction networks (CRNs) are characterized in terms of algebraic conditions on submatrices of the stoichiometric matrix. Here, we derive sufficient conditions for subgraphs supporting irreducible autocatalytic systems in the bipartite K\"onig representation of the CRN. On this basis, we develop an efficient algorithm to enumerate autocatalytic subnetworks and, as a special case, autocatalytic cores, i.e., minimal autocatalytic subnetworks, in full-size metabolic networks. The same algorithmic approach can also be used to determine autocatalytic cores only. As a showcase application, we provide a complete analysis of autocatalysis in the core metabolism of E. coli and enumerate irreducible autocatalytic subsystems of limited size in full-fledged metabolic networks of E. coli, human erythrocytes, and Methanosarcina barkeri (Archea). The mathematical and algorithmic results are accompanied by software enabling the routine analysis of autocatalysis in large CRNs.
q-bio.mnq-bio.cbmath.co
q-bio
q-bio
11-25 00:00
arXiv:2511.19068v1 Announce Type: new Abstract: Modern genomic analyses increasingly rely on pangenomes, that is, representations of the genome of entire populations. The simplest representation of a pangenome is a set of individual genome sequences. Compared to e.g. sequence graphs, this has the advantage that efficient exact search via indexes based on the Burrows-Wheeler Transform (BWT) is possible, that no chimeric sequences are created, and that the results are not influenced by heuristics. However, such an index may report a match in thousands of positions even if these all correspond to the same locus, making downstream analysis unnecessarily expensive. For sufficiently similar sequences (e.g. human chromosomes), a multiple sequence alignment (MSA) can be computed. Since an MSA tends to group similar strings in the same columns, it is likely that a string occurring thousands of times in the pangenome can be described by very few columns in the MSA. We describe a method to tag entries in the BWT with the corresponding column in the MSA and develop an index that can map matches in the BWT to columns in the MSA in time proportional to the output. As a by-product, we can efficiently project a match to a designated reference genome, a capability that current pangenome aligners based on the BWT lack.
cs.dsq-bio.gn
q-bio
q-bio
11-25 00:00
arXiv:2511.19093v1 Announce Type: new Abstract: Collective vigilance describes how animals in groups benefit from the predator detection efforts of others. Empirical observations typically find either a many-eyes strategy with all (or many) group members maintaining a low level of individual vigilance, or a sentinel strategy with one (or a few) individuals maintaining a high level of individual vigilance while others do not. With a general analytical treatment that makes minimal assumptions, we show that these two strategies are alternate solutions to the same adaptive problem of balancing the costs of predation and vigilance. Which strategy is preferred depends on how costs scale with the level of individual vigilance: many-eyes strategies are preferred where costs of vigilance rise gently at low levels but become steeper at higher levels (convex; e.g. an open field); sentinel strategies are preferred where costs of vigilance rise steeply at low levels and then flatten out (concave; e.g. environments with vantage points). This same dichotomy emerges whether individuals act selfishly to optimise their own fitness or cooperatively to optimise group fitness. The model is extended to explain discrete behavioural switching between strategies and differential levels of vigilance such as edge effects.
q-bio.pecs.maphysics.bio-ph
q-bio
q-bio
11-25 00:00
arXiv:2511.19184v1 Announce Type: new Abstract: Designing new protein structures is fundamental to computational biology, enabling advances in therapeutic molecule discovery and enzyme engineering. Existing diffusion-based generative models typically operate in Cartesian coordinate space, where adding noise disrupts strict geometric constraints such as fixed bond lengths and angles, often producing physically invalid structures. To address this limitation, we propose a Torsion-Space Diffusion Model that generates protein backbones by denoising torsion angles, ensuring perfect local geometry by construction. A differentiable forward-kinematics module reconstructs 3D coordinates with fixed 3.8 Angstrom backbone bond lengths while a constrained post-processing refinement optimizes global compactness via Radius of Gyration (Rg) correction, without violating bond constraints. Experiments on standard PDB proteins demonstrate 100% bond-length accuracy and significantly improved structural compactness, reducing Rg error from 70% to 18.6% compared to Cartesian diffusion baselines. Overall, this hybrid torsion-diffusion plus geometric-refinement framework generates physically valid and compact protein backbones, providing a promising path toward full-atom protein generation.
cs.aiq-bio.bm
q-bio
q-bio
11-25 00:00
arXiv:2511.19423v1 Announce Type: new Abstract: We present Genie-CAT, a tool-augmented large-language-model (LLM) system designed to accelerate scientific hypothesis generation in protein design. Using metalloproteins (e.g., ferredoxins) as a case study, Genie-CAT integrates four capabilities -- literature-grounded reasoning through retrieval-augmented generation (RAG), structural parsing of Protein Data Bank files, electrostatic potential calculations, and machine-learning prediction of redox properties -- into a unified agentic workflow. By coupling natural-language reasoning with data-driven and physics-based computation, the system generates mechanistically interpretable, testable hypotheses linking sequence, structure, and function. In proof-of-concept demonstrations, Genie-CAT autonomously identifies residue-level modifications near [Fe--S] clusters that affect redox tuning, reproducing expert-derived hypotheses in a fraction of the time. The framework highlights how AI agents combining language models with domain-specific tools can bridge symbolic reasoning and numerical simulation, transforming LLMs from conversational assistants into partners for computational discovery.
q-bio.qmcs.ai
q-bio
q-bio
11-25 00:00
arXiv:2511.12135v2 Announce Type: cross Abstract: Aligning molecular sequence representations (e.g., SMILES notations) with textual descriptions is critical for applications spanning drug discovery, materials design, and automated chemical literature analysis. Existing methodologies typically treat molecular captioning (molecule-to-text) and text-based molecular design (text-to-molecule) as separate tasks, relying on supervised fine-tuning or contrastive learning pipelines. These approaches face three key limitations: (i) conventional metrics like BLEU prioritize linguistic fluency over chemical accuracy, (ii) training datasets frequently contain chemically ambiguous narratives with incomplete specifications, and (iii) independent optimization of generation directions leads to bidirectional inconsistency. To address these issues, we propose RTMol, a bidirectional alignment framework that unifies molecular captioning and text-to-SMILES generation through self-supervised round-trip learning. The framework introduces novel round-trip evaluation metrics and enables unsupervised training for molecular captioning without requiring paired molecule-text corpora. Experiments demonstrate that RTMol enhances bidirectional alignment performance by up to 47% across various LLMs, establishing an effective paradigm for joint molecule-text understanding and generation.
cs.lgcs.aiq-bio.bm
q-bio
q-bio
11-25 00:00
arXiv:2511.17695v1 Announce Type: new Abstract: Motivation: Drug synergy is strongly influenced by cellular context. Variations in protein interaction landscapes and pathway activities across cell types can reshape how drugs act in combination. However, most existing models overlook this heterogeneity and rely on static or bulk level protein protein interaction networks that ignore cell specific molecular wiring. With the availability of single cell transcriptomic data, it is now possible to reconstruct cell line specific interactomes, offering a new foundation for contextualized drug synergy modeling. Results: We present SynCell, a contextualized drug synergy framework that integrates drug protein, protein protein, and protein cell line relations within a unified graph architecture. SynCell leverages single cell derived, cell line specific PPI networks to embed the molecular context in which drugs act, and employs graph convolutional learning to model how pharmacological effects propagate through cell specific signaling networks. This formulation treats synergy prediction as a cell line contextualized drug drug interaction problem. Across two large scale benchmarks (NCI ALMANAC and ONeil), SynCell consistently outperforms state of the art baselines including DeepDDS, HypergraphSynergy, and HERMES, especially in predicting synergies involving unseen drugs or novel cell lines. Ablation analyses show that contextualizing PPIs with single cell resolution yields substantial gains in generalization and biological interpretability.
q-bio.qm
q-bio
q-bio
11-25 00:00
arXiv:2511.18238v1 Announce Type: new Abstract: Alzheimer's disease (AD) emerges from a complex interplay of molecular, cellular, and network-level disturbances that are not easily captured by traditional reductionist frameworks. Conventional analyses of gene expression often rely on thresholded correlation networks or clustering-based module detection, approaches that may obscure nonlinear structure and higher-order organization. Here, we introduce a comparative topological framework that makes use of topological data analysis (TDA) and the Mapper algorithm to detect discontinuities - localized disruptions in the topology of gene co-expression space between healthy and AD brain tissue. Using gene expression data from 3 brain regions, we mapped how AD reshapes the global topology of gene-gene relationships. Discontinuity hotspots were identified via variability-based node scoring and subjected to Gene Ontology Biological Process enrichment analysis. This work illustrates the potential of TDA to uncover disease-relevant structure in high-dimensional transcriptomic data and motivates broader application of shape-based comparative methods in neurodegeneration research and other areas that benefit from comparative analysis.
q-bio.qm
q-bio
q-bio
11-25 00:00
arXiv:2511.18598v1 Announce Type: new Abstract: The urban habitat provides a landscape that increases the chances of human-animal interactions, which can lead to increased human-animal conflict, but also coexistence. Some species show high levels of socio-cognitive abilities that enable them to perceive communicational gestures of humans and use them for their own benefit. This study investigated the ability of Indian free-ranging dogs (Canis lupus familiaris) to utilise human social-referential cues (pointing and gazing) to locate hidden food, focusing on the relative effectiveness of unimodal versus multimodal cues. A total of 352 adult free-ranging dogs were tested in an object-choice task involving six different cue conditions: control (no cue), negative control (one baited bowl, no cue), combined pointing and gazing, pointing-only, gazing-only, and conflicting cues (pointing and gazing at opposite bowls). The dogs successfully chose the correct target only in the combined pointing and gazing condition, while performance under unimodal and conflicting cue conditions did not differ significantly from chance. This highlights the importance of signal redundancy and clarity in interspecific communication for this population. A dog's demeanor was a significant predictor of its willingness to engage: affiliative dogs were significantly more likely to succeed in the overall experiment and displayed a significantly shorter approach latency compared to anxious and neutral dogs. While demeanor affected the approach latency, it did not affect the accuracy of the choice, decoupling the dogs' personality from its cognitive ability to comprehend the clear cue. Neither the dogs' sex nor the experimental condition significantly predicted approach latency.
q-bio.ot
q-bio
q-bio
11-25 00:00
arXiv:2511.18853v1 Announce Type: new Abstract: Stroke is a leading cause of disability and death worldwide, with ischemic strokes accounting for nearly 80% of cases. Fewer than 5% of patients receive the sole validated pharmacotherapy, intravenous thrombolysis, highlighting the urgent need for novel therapies. Within this landscape, the exploration of natural molecules emerges as a promising avenue, particularly as a means to address limitations associated with conventional drugs. Nutraceuticals, bioactive compounds derived from food sources, offer a compelling prospect for health and wellness. The term nutraceutical reflects their dual potential in nutrition and pharmacotherapy, emphasizing their relevance to both disease prevention and treatment. Interestingly, many were initially recognized as ''natural preconditioners'', substances that prime the body for protection against stress or damage. In fact, numerous nutraceuticals have been shown to activate protective pathways similar to those triggered by preconditioning across various organs. Among nutraceuticals, omega-3 polyunsaturated fatty acids sourced from plants or fish, along with polyphenols, have emerged as particularly promising. Their consumption has been associated with a reduced risk of ischemic stroke, supported by numerous preclinical studies demonstrating their beneficial effects on cellular components within the neurovascular unit. This review explores the shared protective mechanisms of various nutraceuticals against key drivers of ischemic injury, including excitotoxicity, oxidative stress, apoptosis, and inflammation. By delineating these actions, the review highlights the potential of nutraceuticals as brain preconditioners that enhance neuroprotection, thereby mitigating the impact of cerebral ischemia in both preventive and therapeutic contexts.
q-bio.nc
q-bio
q-bio
11-25 00:00
arXiv:2511.18893v1 Announce Type: new Abstract: Neurodegenerative diseases are driven by the accumulation of protein aggregates in the brain of affected individuals. The aggregation behaviour in vitro is well understood and driven by the equilibration of a super-saturated protein solution to its aggregated equilibrium state. However, the situation is altered fundamentally in living systems where active processes consume energy to remove aggregates. It remains unclear how and why cells transition from a state with predominantly monomeric protein, which is stable over decades, to one dominated by aggregates. Here, we develop a simple but universal theoretical framework to describe cellular systems that include both aggregate formation and removal. Using a two-dimensional phase-plane representation, we show that the interplay of aggregate formation and removal generates cell-level bistability, with a bifurcation structure that explains both the emergence of disease and the effects of therapeutic interventions. We explore a wide range of aggregate formation and removal mechanisms and show that phenomena such as seeding arise robustly when a minimal set of requirements on the mechanism are satisfied. By connecting in vitro aggregation mechanisms to changes in cell state, our framework provides a general conceptual link between molecular-level therapeutic interventions and their impact on disease progression.
q-bio.bm