Endothelial dysfunction is a critical initiating event in atherosclerosis. The regulatory role of the AKT signaling pathway in the transition between injury and repair remains unclear. This study developed a multiscale model to elucidate how AKT signaling modulates endothelial repair under shear stress. The framework integrates hemodynamic stimuli, cellular behaviors, and nonlinear AKT/NO/ROS biochemical kinetics coupled with endothelial phenotypic transitions. Results demonstrate that endothelial dysfunction is driven by a spatial mismatch between NO production and ROS consumption rather than global deficiency. Phenotypic restoration is governed by the nonlinear interactions within the NO-ROS network. Systematic simulations identified a golden window, where Day 1 intervention and moderate AKT activation effectively restored endothelial cells and normalized redox status. Conversely, excessive activation caused supra-physiological over-suppression. These findings suggest that repair is determined by the tight coupling of redox homeostasis and immune dynamics, highlighting intervention timing and dosage as critical factors for precision vascular therapy.
Tuberculosis (TB), caused by Mycobacterium tuberculosis ( Mtb ), remains a global health crisis, further complicated by increasing drug resistance. The mycobacterial membrane protein MmpL3, which is essential for transporting cell wall precursors such as trehalose monomycolate (TMM), is a promising therapeutic target. However, the mechanistic link between proton-driven dynamics and substrate transport in MmpL3 is not fully understood. In this study, we use molecular dynamics simulations to investigate how protonation of conserved Asp-Tyr dyads (Asp256-Tyr646 and Asp645-Tyr257) in Mycobacterium smegmatis ( Msmg ) MmpL3 regulates conformational changes and TMM binding. Protonation at Asp256 induces an open dyad conformation, increases local water accessibility, and is associated with a tendency toward more open configurations of the periplasmic binding pocket. In contrast, protonation at Asp645 stabilizes a closed dyad conformation, reduces water accessibility, and narrows the transmembrane binding pocket. These findings support a proposed sequential protonation model in which protonation-dependent conformational changes are allosterically coupled to substrate translocation.
Real-world distractors occur in environments whose states change at different rates. We asked whether such volatility alters initial overt oculomotor allocation, later target verification and response commitment, or both. Observers performed an additional-singleton search task with concurrent eye tracking while distractor presence followed high- or low-volatility sequences, with overall distractor prevalence held constant. Trial-pooled oculomotor capture was higher under high volatility, a pattern that appears to indicate altered filtering. Matching the same-location run position removed that difference, with no detectable difference in capture across volatility regimes at matched run position. The pooled capture effect was therefore consistent with a structural consequence of the volatility manipulation, which enriched high-volatility blocks with early-run positions where capture is intrinsically high. The volatility effect itself appeared on distractor-absent trials. Under high volatility, response times were longer, fixations more frequent, final-target dwell longer, and errors fewer, while target-acquisition latency itself was unchanged. Same-location repetition learning showed no detectable difference in slope across regimes. A hierarchical drift-diffusion model (DDM) and a complementary volatility Kalman-filter (VKF) comparison converged in showing that manual responses carried both boundary-related and drift-related structure. With distractor frequency held constant, the per-trial effect of volatility therefore emerged after the first saccade as an increase in response caution rather than in oculomotor capture. This pattern is consistent with precision-weighted adjustment to environmental uncertainty.
Motivation Predicting drug sensitivity across diverse cancer samples is a fundamental challenge in personalized therapy and drug discovery. This task requires comprehensive characterization of both drug and cancer cell line features. Current approaches often fail to fully capture the complex structural characteristics and interactions of drug compounds and struggle to integrate multi-omics features derived from the same sample, limiting their feature representation capacity. Consequently, there is a need for modeling frameworks that can jointly model molecular structural features and multi-modal cellular contexts in a unified manner. Results We present SADRP, a structurally aware multi-modal framework for drug response prediction, designed to model complex molecular structures and multi-omics data. For drug representation, SADRP incorporates Shortest Distance Path (SDP) encoding and edge encoding to capture both topological relationships and bond-type chemical semantics. For cell line profiling, a multi-view interactive encoder integrates gene expression and DNA methylation, capturing cross-omics synergistic complementarity. SADRP consistently outperforms state-of-the-art methods, while also identifying potential therapeutic candidates and biomarkers, providing mechanistic insights and supporting applications in drug discovery and personalized medicine.
Mineralization is a critical process in the formation of hard tissues such as bones and teeth, yet the cellular mechanisms underlying this process remain incompletely understood. To address this, we constructed a comprehensive single-cell atlas of tooth development, integrating data from 261,929 cells across 15 projects and encompassing both odontogenesis and amelogenesis. We developed a novel algorithm, TrajDTW, to identify genes with concordant trajectory dynamics across these large-scale datasets, allowing us to detect robust developmental signals. To define a shared mineralization trajectory architecture, we then integrated our tooth atlas with a previously constructed bone atlas. Applying TrajDTW to this combined resource revealed common molecular pathways governing the formation of distinct hard tissues, including bone, enamel, and dentin. Furthermore, cross-species analysis between human and mouse data uncovered cross-species conserved mesenchymal/odontoblast programs. This study provides an unprecedented resource for developmental biology and defines a molecular framework for mineralization that is shared across tissues and species.
