ObjectivePost-ischemic acute inflammation and the subsequent persistent dysregulation of the immune microenvironment represent major pathological drivers that aggravate neuronal injury and severely restrict functional recovery following ischemic stroke. Although current reperfusion therapies partially restore blood flow, they fail to effectively modulate the secondary inflammatory cascade and oxidative stress, which remain critical barriers to neurological restoration. To address this challenge, this study aimed to engineer and systematically evaluate a biomimetic nanosystem composed of transforming growth factor-β1 (TGF-β1)-loaded platelet membrane-camouflaged lipid nanoparticles (PLP). This nanosystem was designed to achieve dual lesion-targeted delivery and immune microenvironment remodeling. By verifying its spatiotemporal accumulation, anti-inflammatory activity, and neuroprotective efficacy, we sought to establish an integrated therapeutic strategy that simultaneously enables lesion targeting, immune regulation, and functional recovery after ischemic injury. MethodsThe physicochemical properties of PLP, including hydrodynamic particle size, zeta potential, structural stability, and morphology, were characterized using dynamic light scattering, zeta potential analysis, and transmission electron microscopy. The preservation of platelet membrane-derived adhesion and immunoregulatory proteins was confirmed by SDS-PAGE through comparative analysis of protein band profiles between PLP and native platelet membranes. The in vitro biological activities of PLP were evaluated using two complementary cellular models. LPS-induced M1-polarized RAW264.7 macrophages were employed to assess inflammatory modulation, while oxygen glucose deprivation/reperfusion (OGD/R)-induced BV2 microglial cells and SH-SY5Y neuronal cells were utilized to investigate neuroinflammatory regulation and neuronal protection. For in vivo validation, a transient middle cerebral artery occlusion (tMCAO) mouse model was established to mimic ischemia-reperfusion injury. The spatiotemporal biodistribution and lesion-targeting capability of the PLP were monitored through live fluorescence imaging. Therapeutic efficacy was comprehensively evaluated by triphenyltetrazolium chloride (TTC) staining, glial fibrillary acidic protein (GFAP) immunofluorescence analysis, body weight monitoring, and neurological severity score (NSS) assessment. ResultsPLP nanoparticles displayed a uniform spherical morphology, nanoscale particle size distribution, and stable negative surface charge, indicating favorable colloidal stability and circulation potential. SDS-PAGE results confirmed the effective retention of key platelet membrane proteins associated with endothelial adhesion, immune evasion, and inflammatory regulation, demonstrating the successful biomimetic construction. Optimal therapeutic concentrations were determined in OGD/R-induced BV2 cells, where PLP exhibited excellent cytocompatibility and anti-inflammatory activity.In vitro experiments demonstrated that PLP significantly inhibited the polarization of RAW264.7 macrophages toward the pro-inflammatory M1 phenotype and markedly reduced neuronal apoptosis under ischemia-reperfusion conditions. In vivo fluorescence imaging revealed that PLP rapidly accumulated in the ischemic brain hemisphere and maintained prolonged retention for up to 7 d, suggesting enhanced lesion-specific targeting and sustained drug release. Compared with control group, PLP treatment significantly reduced cerebral infarct volume, attenuated reactive astrogliosis, improved weight recovery, and accelerated neurological functional restoration, as reflected by significantly improved NSS scores. ConclusionThis study establishes a multifunctional biomimetic nanoplatform that integrates platelet membrane-mediated active targeting with the anti-inflammatory, antioxidative, and neuroprotective properties of TGF-β1. The PLP system enables rapid lesion homing and long-term retention while synergistically regulating the post-stroke inflammatory microenvironment by suppressing pro-inflammatory immune activation, reducing neuronal apoptosis, and limiting excessive astrocyte reactivity. Importantly, this study proposes a conceptually therapeutic paradigm that combines targeted delivery with immune microenvironment remodeling to achieve comprehensive neurovascular protection. These findings provide strong experimental evidence supporting the translational potential of biomimetic nanotherapeutics as next-generation precision interventions for ischemic stroke.

Colorectal cancer （CRC） is one of the most common cancers， with its incidence ranking high among cancers. It stands as the second leading cause of cancer-related death worldwide. In the early stages， CRC lacks specific symptoms， and most patients are diagnosed at advanced stages， making it a major research focus in the field of gastrointestinal tumors. Currently， clinical CRC treatments face several common challenges， including high surgical risks， frequent metastasis and recurrence， drug resistance， and significant side effects from chemotherapy and radiation therapy. With the development and application of traditional Chinese medicine （TCM）， it has been found that TCM and its active ingredients can effectively inhibit CRC cell proliferation， invasion， migration， and angiogenesis， and promote apoptosis and autophagy， thereby slowing the progression of CRC. This has become a key focus of CRC treatment research. Salvia Miltiorrhiza has multiple pharmacological effects， including activating blood circulation to dispel blood stasis， unlocking meridians to relieve pain， clearing heat to calm irritability， and cooling blood to reduce abscesses. It contains a variety of chemical components， including diterpenoids， phenolic acids， flavonoids， polysaccharides， nitrogen-containing compounds， steroids， and lactone compounds. This review summarized the molecular mechanisms of Salvia miltiorrhiza and its active ingredients in the treatment of CRC. It is found that these ingredients exert anti-CRC effects through various molecular mechanisms， including cell cycle arrest， promotion of apoptosis， inhibition of cell invasion and migration， induction of autophagy， suppression of tumor angiogenesis， and remodeling of the tumor microenvironment. The review aims to provide new insights for the drug development and clinical application of Salvia miltiorrhiza in CRC treatment.

Abstract The prevention and control of myopia in Chinese children and adolescents has become a major public health issue. While maintaining increased outdoor activity as a cornerstone intervention, there is an urgent need to explore new complementary approaches that can be effectively implemented in both indoor and outdoor settings. In recent years, environmental spatial frequency has gained increasing attention as one of the key environmental factors influencing the development and progression of myopia. Both animal studies and human research have confirmed that indoor environments lacking mid to high spatial frequency components, often characterized as "visually impoverished", can promote axial elongation and myopia through mechanisms such as disruption of retinal neural signaling, impaired accommodative function, and altered expression of related molecules. Based on the scientific consensus, it is recommended that "enriching of environmental spatial frequency" should be integrated into the myopia prevention and control framework. Following the principles of schoolled organization, family cooperation, community involvement, and student participation, specific measures are put forward in three areas：optimizing school visual settings, improving home spatial environments, and promoting healthy visual behavior. The aim is to create "visually friendly" indoor environments as an important supplement to outdoor activity, thereby providing a novel perspective and strategy for comprehensively advancing myopia prevention and control among children and adolescents.

ObjectiveTaking classical herbal pair compatibility research as the entry point， this study aimed to deeply investigate the material basis and compatibility rules underlying the antidepressant effects of the traditional Chinese medicine （TCM） formula Zhizi Houpotang， and to elucidate its antidepressant mechanism， with a particular focus on its regulation of neuroinflammatory responses mediated by the NOD-like receptor protein 3 （NLRP3）/gasdermin D （GSDMD） signaling pathway and the consequent improvement of neuronal synaptic plasticity. MethodsC57BL/6J mice were randomly divided into a blank control group， a chronic unpredictable mild stress （CUMS） depression model group， a Zhizi Houpotang full-formula group （6 g·kg^-1·d^-1）， a Magnoliae Officinalis Cortex （MOC）-Aurantii Fructus Immaturus （AFI） herbal pair group （4.2 g·kg^-1·d^-1）， a Gardeniae Fructus （GF）-MOC herbal pair group （4.2 g·kg^-1·d^-1）， a GF-AFI herbal pair group （3.6 g·kg^-1·d^-1）， and a positive drug group （fluoxetine， 12 mg·kg^-1·d^-1）. Depressive-like behaviors in mice were evaluated using behavioral tests. Immunofluorescence staining was used to label and quantify the expression of the microglial marker ionized calcium-binding adaptor molecule 1 （Ibal） and the purinergic receptor P2X ligand-gated ion channel 7 （P2RX7） in the prefrontal cortex （PFC）. Enzyme-linked immunosorbent assay （ELISA） was applied to detect the levels of inflammatory cytokines interleukin-1β （IL-1β） and interleukin-18 （IL-18） in serum and PFC tissues. Western blot was employed to determine the expression of pannexin 1 （Panx1）， P2RX7， NLRP3， apoptosis-associated speck-like protein containing a CARD （ASC）， Caspase-1， GSDMD， postsynaptic density protein 95 （PSD95）， and the presynaptic protein Synapsin 1 in PFC tissues. Golgi staining was used to assess dendritic spine density of neurons in the PFC. ResultsCompared with the blank control group， the depression model group exhibited significant depressive-like behaviors. In addition， the immunofluorescence areas of Ibal and P2RX7 in the PFC were significantly increased （P<0.01）， the levels of IL-1β and IL-18 in serum and the PFC were significantly elevated （P<0.01）， and the protein expression levels of Panx1， P2RX7， NLRP3， ASC， Caspase-1， and GSDMD in the PFC were significantly upregulated （P<0.01）. In contrast， the protein expression levels of PSD95 and Synapsin 1 were significantly downregulated （P<0.01）， and neuronal dendritic spine density was significantly reduced （P<0.01）. Compared with the model group， the Zhizi Houpotang full-formula group and the GF-MOC herbal pair group showed significant improvement in all the above indicators （P<0.01）. The GF-AFI herbal pair group improved all the above indicators except P2RX7， Caspase-1， GSDMD， and PSD95 （P<0.05， P<0.01）. In contrast， the MOC-AFI herbal pair group showed no statistically significant improvement in any of the above indicators compared with the model group. ConclusionZhizi Houpotang and its key herbal pair， GF-MOC， can effectively ameliorate CUMS-induced depressive-like behaviors in mice. Its core antidepressant mechanism may involve inhibition of P2RX7/Panx1 signaling， thereby blocking the NLRP3/GSDMD-mediated pyroptosis pathway and significantly reducing the release of inflammatory cytokines IL-1β and IL-18. Simultaneously， it upregulates the expression of synapse-related proteins PSD95 and Synapsin 1 and increases dendritic spine density， promoting the recovery of synaptic plasticity. These results suggest that GF plays a key role in the antidepressant effects of this formula， and that the compatibility of GF with MOC may represent the principal herbal pair combination responsible for its core therapeutic action.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective: To analyze the relationship between body mass index(BMI) during pre pregnancy, maternal lipid levels during early pregnancy and childhood overweight and obesity, as well as the mediating role of maternal lipid levels during early pregnancy in pre pregnancy BMI and childhood overweight and obesity, providing scientific evidence for developing obesity prevention strategies in preschool children. Methods: Using data from Peking University Birth Cohort in Tongzhou (PKUBC-T) collected between June 2018 and September 2022, the study included 1 292 mother-child pairs. Participants were stratified into two groups based on children s BMI Z scores at age 3: an overweight/obesity risk group (BMI Z >1, n =173) and a non overweight/obesity risk group (BMI Z ≤1, n =1 119).Multivariate Logistic regression was conducted to analyze the associations between pre pregnancy BMI, maternal lipid levels[total cholesterol(TC),triglyceride（TG）,high density lipoprotein cholesterol(HDL-C),low density lipoprotein cholesterol(LDL-C),TC/HDL-C,TG/HDL-C,LDL-C/HDL-C] during early pregnancy and childhood overweight and obesity. The mediating effect of maternal lipid levels during early pregnancy on pre pregnancy BMI and childhood overweight and obesity was further explored. Results: There were statistically significant differences in pregnancy BMI levels, early pregnancy blood LDL-C ,TC/HDL-C,LDL-C/HDL-C levels between the overweight and obesity risk group and the non overweight and obesity risk group ( χ 2/Z =19.01, 2.48, 2.48, 2.71, all P <0.05). Multivariate Logistic regression analysis showed that pre pregnancy BMI, LDL-C, TC/HDL-C and LDL-C/HDL-C in early pregnancy were significantly associated with childhood overweight and obesity ( OR =1.09, 1.42, 1.49, 1.60, all P <0.05). LDL-C, TC/HDL-C and LDL-C/HDL-C in early pregnancy played a significant mediating role on pre pregnancy BMI and childhood obesity and the mediating effects accounted for 7.3%, 10.2%, 23.5% of the total effects, respectively (all P <0.05). Conclusions Maternal hyperlipidemia during early pregnancy partially mediated the association between pre pregnancy obesity and childhood obesity. Both pre pregnancy obesity and maternal hyperlipidemia during early pregnancy are risk factors for obesity in preschool children.

Abstract Modern western medicine typically focuses on treating specific symptoms or diseases, and traditional Chinese medicine (TCM) emphasizes the interconnections of the body’s various systems under external environment and takes a holistic approach to preventing and treating diseases. Phenomics was initially introduced to the field of TCM in 2008 as a new discipline that studies the laws of integrated and dynamic changes of human clinical phenomes under the scope of the theories and practices of TCM based on phenomics. While TCM Phenomics 1.0 has initially established a clinical phenomic system centered on Zhenghou (a TCM definition of clinical phenome), bottlenecks remain in data standardization, mechanistic interpretation, and precision intervention. Here, we systematically elaborates on the theoretical foundations, technical pathways, and future challenges of integrating digital medicine with TCM phenomics under the framework of “TCM phenomics 2.0”, which is supported by digital medicine technologies such as artificial intelligence, wearable devices, medical digital twins, and multi-omics integration. This framework aims to construct a closed-loop system of “Zhenghou–Phenome–Mechanism–Intervention” and to enable the digitization, standardization, and precision of disease diagnosis and treatment. The integration of digital medicine and TCM phenomics not only promotes the modernization and scientific transformation of TCM theory and practice but also offers new paradigms for precision medicine. In practice, digital tools facilitate multi-source clinical data acquisition and standardization, while AI and big data algorithms help reveal the correlations between clinical Zhenghou phenomes and molecular mechanisms, thereby improving scientific rigor in diagnosis, efficacy evaluation, and personalized intervention. Nevertheless, challenges persist, including data quality and standardization issues, shortage of interdisciplinary talents, and insufficiency of ethical and legal regulations. Future development requires establishing national data-sharing platforms, strengthening international collaboration, fostering interdisciplinary professionals, and improving ethical and legal frameworks. Ultimately, this approach seeks to build a new disease identification and classification system centered on phenomes and to achieve the inheritance, innovation, and modernization of TCM diagnostic and therapeutic patterns.

Myelodysplastic syndrome (MDS), a myeloid tumor derived from the malignant clones of hematopoietic stem cells, has an annually increasing incidence. The contemporary research direction has shifted to analyzing the synergistic effect of immune surveillance collapse and abnormal bone marrow microenvironment in the pathological process of MDS. Against this backdrop, the immune checkpoint molecule TIM3 has emerged as a key target because of its persistently high expression on the surface of important immune cells such as T and NK cells. The abnormal activation of the TIM3 pathway is the mechanism by which solid tumors and hematological malignancies achieve immune escape and is a key hub in the formation of immune exhaustion phenotypes. This work integrates the original discoveries of our team with the latest international progress, systematically demonstrating the bidirectional regulatory network of TIM3 between the malignant clone proliferation of MDS and the immunosuppressive microenvironment. Integrating the evidence from emerging clinical trials allows us to consider the clinical significance of TIM3-targeted blocking for MDS, providing a transformative path to overcome the resistance of traditional treatments and marking a new chapter in the active immune reconstitution of MDS treatment.

ObjectiveTo investigate the alleviating effect of calcium cyanamide （CaCN₂） soil fumigation on replanting problems of Rehmannia glutinosa. MethodsNewly soil （NP） was used as the control group， while three treatment groups were established： replanted soil （RP）， newly soil treated with CaCN₂ （120 g·m²， tillage depth 25 cm） （NPCC）， and replanted soil treated with CaCN₂ （RPCC）. R. glutinosa was cultivated in all groups. At harvest， the tuber agronomic traits （number of enlarged roots， maximum root diameter， fresh weight， dry weight） were measured. The content of catalpol and rehmannioside D was quantified by ultra-high-performance liquid chromatography （UPLC） to evaluate medicinal quality. Rhizosphere soil available nutrients and enzyme activities were analyzed by assay kits. The community structure and composition of fungi and bacteria in rhizosphere soil were assessed via internal transcribed spacer 2 （ITS2） sequencing and 16S rDNA sequencing， respectively. ResultsCompared with NP， the RP group showed obviously reduced in tuber agronomic traits and quality indicators （P0.05）. However， the RPCC group showed significant improvement in agronomic traits and a notable increase in rehmannioside D content compared to RP （P0.05）. The contents of available phosphorus and potassium in RPCC and NP groups were obviously lower than those in RP （P0.05）. The polyphenol oxidase soil （S-PPO） activity in RP was obviously lower than in NP （P0.05）， while sucrose soil （S-SC）， acid phosphatase soil （S-ACP）， and S-PPO activities in RPCC were obviously higher than in RP （P0.05）. Microbial richness and diversity in RP were obviously higher than in NP （P0.05）， whereas no significant differences were observed between the RPCC and NP. The relative abundances of fungal genera Nectria， Myrothecium， Tomentella， and bacterial genus Skermanella were obviousl lower in RPCC and NP than in RP （P0.05）. Correlation analysis that S-ACP activity was positively correlated with the content of rehmannioside D （P0.05）. Fungal genera Engyodontium and Alternaria， and bacterial genera Pir4 lineage， Pirellula， Methyloversatilis， Brevundimonas， Ralstonia， and Acidibacter were obviously positively correlated with tuber dry weight （P0.05）. Conversely， fungal genera Pseudaleuria， Nectria， Haematonectria， Ceratobasidium， and bacterial genera Streptomyces， Skermanella， RB41， Gemmatimonas， and Bacillus were obviously negatively correlated with dry weight （P0.05）. The fungal genus Alternaria and bacterial genera Brevundimonas， Ralstonia， Acidibacter， and Dongia showed positive correlations with medicinal quality of R.glutinosa tuber， while fungal genera Pseudaleuria， Nectria， Stachybotrys， Fusarium， Gibberella， Ceratobasidium， and bacterial genera Sphingomonas， Skermanella， RB41， Gemmatimonas， and Bacillus were obviously negatively correlated （P0.05）. ConclusionCaCN₂ soil fumigation can significantly improve enzyme activities in replanted Rehmannia rhizosphere soil， enhance the utilization of available nutrients， reshape microbial community structure of replanted R.glutinosa at the family and genus level， and notably improve tuber agronomic traits and medicinal quality. This study provides a novel approach to alleviating replanting problems and offers insights for the integrated development of standardized cultivation techniques， including soil disinfection， nutrient-targeted regulation， and microbial inoculant application.

Nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 (NLRP3) is a cytosolic pattern recognition receptor that recognizes multiple pathogen-associated molecular patterns and damage-associated molecular patterns. It is a cytoplasmic immune factor that responds to cellular stress signals, and it is usually activated after infection or inflammation, forming an NLRP3 inflammasome to protect the body. Aberrant NLRP3 inflammasome activation is reportedly associated with some inflammatory diseases and metabolic diseases. Recently, there have been mounting indications that NLRP3 inflammasomes play an important role in liver injuries caused by a variety of diseases, specifically hepatic ischemia/reperfusion injury, hepatitis, and liver failure. Herein, we summarize new research pertaining to NLRP3 inflammasomes in hepatic injury, hepatitis, and liver failure. The review addresses the potential mechanisms of action of the NLRP3 inflammasome, and its regulation in these liver diseases.
Humans ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/physiology* ; Animals ; Liver Diseases/metabolism* ; Liver/metabolism* ; Reperfusion Injury/metabolism*