中国人民解放军军事科学院军事医学研究院

Respiratory vaccinology has long prioritized the discovery of increasingly potent immune adjuvants, yet this strategy has failed to resolve the core limitation of mucosal vaccination: the lack of spatiotemporal control over innate immune activation. NanoCF501, a precision-engineered inhalable STING nanoadjuvant recently reported by Liu et al., signals a paradigm shift. Its transformative value lies not in molecular potency, but in the rational integration of particle architecture, size tuning (-18 nm for optimal mucus penetration), polymer chemistry, and pharmacokinetic compartmentalization to program immune outcomes. By achieving coordinated mucosal-systemic immunity at one-twentieth the systemic dose while eliminating systemic exposure, NanoCF501 demonstrates that mucosal vaccine design is fundamentally a challenge of precision engineering rather than empirical adjuvant discovery. We distill four design principles from this work and contextualize them within a broader emerging framework of sequential biological barrier engineering, from aerosol stability to intracellular delivery.

Postoperative cognitive dysfunction (POCD) is a common neurological complication severely affecting patient prognosis. Preoperative acute sleep deprivation, a frequent perioperative stressor, significantly exacerbates POCD severity. Sigma-1 receptor (σ-1R), an endoplasmic reticulum molecular chaperone, plays a crucial role in modulating neuroinflammation and oxidative stress. However, whether σ-1R can serve as an interventional target to mitigate sleep deprivation-aggravated cognitive impairment remains insufficiently elucidated. This study aimed to clarify the regulatory role of σ-1R in a mouse model combining preoperative sleep deprivation with exploratory laparotomy, focusing on Keap1/Nrf2 signaling, and to evaluate its clinical translational potential. Behavioral, molecular biological, and morphological methods were utilized. Sleep deprivation exacerbated postoperative cognitive impairment, accompanied by a compensatory upregulation of σ-1R expression in hippocampal microglia. Notably, exogenous potentiation of σ-1R with the agonist PRE-084 reversed cognitive deficits, suppressed neuroinflammation and oxidative stress, promoted Nrf2 nuclear translocation, and upregulated downstream genes HO-1 and NQO1; conversely, the antagonist BD-1047 worsened the damage. Mechanistically, σ-1R physically associated with Keap1, promoting Nrf2 release. The Nrf2 inhibitor ML385 blocked PRE-084’s protective effects. In vitro experiments confirmed PRE-084 alleviated inflammation and oxidative stress via an Nrf2-dependent pathway. Moreover, σ-1R signaling may partially contribute to the neuroprotective effects of dexmedetomidine. In conclusion, this study reveals that σ-1R activation is associated with protective effects in sleep deprivation-exacerbated POCD, potentially through regulating the Keap1/Nrf2 signaling pathway, a mechanism rarely reported in this context, suggesting σ-1R as a potential avenue for further investigation in POCD prevention.

Mapping information flow along the central dogma across genome organization, chromatin accessibility, transcription, translation, and protein abundance is fundamental to understanding cellular heterogeneity and function. However, single-cell multi-omics datasets remain fragmented across assays, and most existing cross-modality methods focus on a limited range of vector-valued molecular readouts, making it difficult to connect heterogeneous modalities with distinct data geometries. Here, we present MIGO, a mutual-information-based fusion framework applied across modality-paired datasets spanning scRNA-seq, scATAC-seq, scHi-C, ribosome profiling, and protein abundance. MIGO separates biological-state representations from technical variation, aligns paired biological states, and organizes the aligned representation through vector quantization, while modality-specific decoders support both vector-valued and structured outputs. Across 20 dataset-level benchmarks, MIGO demonstrates strong cross-modal performance and batch-robust representation learning while preserving modality-specific biological structure. Notably, MIGO generates scHi-C contact maps from scRNA-seq and recovers chromatin contact-decay patterns and higher-order structural features. It also recovers cell-type-specific translational-efficiency programs and protein-abundance profiles that preserve marker identity and cellular organization. Together, MIGO provides a common information-fusion framework for transcriptome-centered generation across heterogeneous molecular readouts.

Accurate prediction of organ-specific toxicity with mechanistic interpretability remains a central challenge in chemical safety assessment and translational toxicology. Although animal-based assays provide biologically relevant information, they are low throughput and increasingly constrained by ethical and regulatory considerations. In parallel, data-driven computational approaches-particularly deep learning-have achieved strong predictive performance but often lack transparency, limiting their utility for mechanistic interpretation and regulatory decision-making. Here, we present ToxiGuard, a mechanistically informed deep learning framework that embeds organ-specific Adverse Outcome Pathway (AOP) structures directly into the model architecture. By explicitly encoding Molecular Initiating Event-Key Event-Adverse Outcome (MIE-KE-AO) connectivity, ToxiGuard constrains representation learning to biologically plausible causal pathways rather than relying solely on post hoc explanation. The framework integrates molecular descriptors, functional-class fingerprints, and curated AOP networks to enable complementary interpretability at both chemical and biological levels. Across four organ toxicity endpoints-hepatotoxicity, cardiotoxicity, nephrotoxicity, and respiratory toxicity-ToxiGuard demonstrates robust and consistent predictive performance, outperforming traditional machine learning models and AOP-agnostic neural network baselines. SHAP-based analyses reveal concentrated contributions from specific physicochemical properties, molecular substructures, and mechanistic AOP components, including CAR/PXR-associated pathways in hepatotoxicity and hERG-related mechanisms in cardiotoxicity. These findings are consistent with established toxicological knowledge and support the biological plausibility of model predictions. Overall, this study demonstrates that embedding mechanistic toxicological structure into deep learning architectures can enhance both predictive reliability and interpretability. ToxiGuard provides a transparent, AOP-anchored computational approach that complements existing experimental and in silico methods for early-stage organ toxicity assessment and chemical safety evaluation.

Advanced Oxidation Processes (AOPs) based on peroxymonosulfate activation hold promise for degrading organic pollutants, nevertheless challenges remain in enhancing free radical utilization and achieving deep water purification. Herein, we designed a Co-doped W18O49 (CWO) catalyst to address these challenges by boosting the generation of SO4 center dot- while reducing their interaction distance with pollutants. Under light excitation, the incorporation of Co sites enhanced photoelectron transfer, promoted the desorption of *SO4 intermediates, accordingly increase the generation of SO4 center dot-. Simultaneously, the distribution of asymmetric charges enhances the local dipole moment of the catalyst, shortening the interaction distance between pollutants and SO4 center dot- through dipoledipole interactions. Based on this, the k-value of carbamazepine degradation in the CWO system was 20-fold compared to the WO system. Furthermore, in the CWO@PVA-AL hydrogel system, the COD removal rate for mixed wastewater reached up to 92%. The results of Electrical Energy per Order (EE/O) metric and Life Cycle Assessment (LCA) of the system driven by sunlight demonstrated the practical applicability of this highly efficient free radical system.