Osteoarthritis (OA), a highly prevalent degenerative joint disease worldwide, is defined by articular cartilage degradation, abnormal bone remodeling, and persistent chronic inflammation. It severely compromises patients’ quality of life, and currently, there is no radical cure. Abnormal mechanical stress is widely regarded as a core driver of OA pathogenesis, and the exploration of mechanical signal perception and transduction mechanisms has become crucial for deciphering OA’s pathophysiological processes. Piezo1, a key mechanosensitive cation channel belonging to the Piezo protein family, has recently gained significant attention due to its pivotal role in mediating cellular responses to mechanical stimuli in joint tissues. This review systematically examines Piezo1’s expression patterns, regulatory mechanisms, and pathological functions in OA, with a particular focus on its dual roles in modulating chondrocyte homeostasis and bone metabolism disorders, while also delving into the underlying molecular signaling pathways and potential therapeutic implications. Piezo1, consisting of approximately 2 500 amino acids and forming a unique trimeric propeller-like structure, is widely expressed in chondrocytes, osteocytes, mesenchymal stem cells, and synovial cells. It exhibits permeability to cations such as Ca²⁺, K⁺, and Na⁺, and directly responds to membrane tension changes induced by mechanical stimuli like fluid shear stress and mechanical overload. In OA patients and animal models, Piezo1 expression is significantly upregulated, especially in cartilage regions subjected to abnormal mechanical stress (e.g., human temporomandibular joint cartilage). This overexpression is closely associated with aggravated cartilage degeneration, increased chondrocyte apoptosis, accelerated cellular senescence, and intensified inflammatory responses. Mechanical overload and pro-inflammatory cytokines (e.g., IL-1β) are key inducers of Piezo1 upregulation: IL-1β activates the PI3K/AKT/mTOR signaling pathway to enhance Piezo1 expression, forming a pathogenic positive feedback loop that inhibits chondrocyte autophagy, promotes apoptosis, and further accelerates joint degeneration. Mechanistically, Piezo1 mediates OA progression through multiple interconnected pathways. When activated by mechanical stress, Piezo1 triggers excessive Ca²⁺ influx, leading to endoplasmic reticulum stress (ERS) and mitochondrial dysfunction, which directly induce chondrocyte apoptosis. This process involves the activation of downstream signaling cascades such as cGAS-STING and YAP-MMP13/ADAMTS5. YAP, a transcriptional regulator, upregulates the expression of matrix metalloproteinase 13 (MMP13) and aggrecanase (ADAMTS5), thereby accelerating cartilage matrix degradation. Additionally, Piezo1-driven Ca²⁺ overload promotes the accumulation of reactive oxygen species (ROS) and upregulates senescence markers (p16 and p21), accelerating chondrocyte senescence via the p38MAPK and NF-κB pathways. Senescent chondrocytes secrete senescence-associated secretory phenotype (SASP) factors (e.g., IL-6, IL-1β), further amplifying joint inflammation. In terms of bone metabolism, Piezo1 maintains joint homeostasis by promoting the differentiation of fibrocartilage stem cells into chondrocytes and balancing bone formation and resorption through regulating the FoxC1/YAP axis and RANKL/OPG ratio. Therapeutically, targeting Piezo1 shows promising potential. Preclinical studies have demonstrated that Piezo1 inhibitors (e.g., GsMTx4) can reduce joint damage and alleviate pain in OA mice. Simultaneously, siRNA-mediated co-silencing of Piezo1 and TRPV4 (another mechanosensitive channel) decreases intracellular Ca²⁺ concentration, inhibits chondrocyte apoptosis, and promotes cartilage repair. Conditional knockout of Piezo1 using Gdf5-Cre transgenic mice alleviates cartilage degeneration in post-traumatic OA models by downregulating MMP13 and ADAMTS5 expression. Despite existing challenges, such as off-target effects of inhibitors, inefficient local drug delivery, and interindividual genetic variability, strategies like developing selective Piezo1 antagonists, optimizing targeted nanocarriers, and combining Piezo1-targeted therapy with physical therapy provide viable avenues for clinical translation. The authors propose that Piezo1 serves as a critical therapeutic target for OA, and future research should focus on deciphering its context-dependent regulatory networks, developing tissue-specific intervention strategies, and validating their efficacy and safety in clinical trials to address the unmet medical needs of OA patients.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Gastric cancer remains a highly prevalent malignancy worldwide, with surgical resection currently constituting the cornerstone of treatment aimed at improving long-term patient survival. Owing to their notable advantages, including reduced surgical trauma and accelerated postoperative recovery, minimally invasive procedures are progressively supplanting conventional open surgery and have become the mainstream approach in gastric cancer management. Concurrently, the rapid advancement of artificial intelligence (AI) technologies has enabled real-time intraoperative monitoring of surgical scenes, thereby furnishing novel technical support for adjunctive decision-making, surgical navigation, and skill assessment during gastrectomy. This article provides a systematic review of the current status of AI applications in minimally invasive gastric cancer surgery, with a particular focus on research progress pertaining to instrument recognition, surgical phase identification, delineation of normal anatomical structures, detection of metastatic foci, and early warning of intraoperative adverse events. Furthermore, we discuss the potential value of AI in enhancing surgical efficiency, ensuring patient safety, and optimizing surgical education. On this basis, we further analyze the principal challenges and inherent risks confronting current AI systems, with the aim of informing future technological innovation and facilitating clinical translation.

This study identified 8 members including NjBIN2 of the GSK3 family in Nardostachys jatamansi by bioinformatics analysis. Moreover, the phylogenetic tree revealed that the GKS3 family members of N. jatamansi had a close relationship with those of Arabidopsis. RT-qPCR results showed that NjBIN2 presented a tissue-specific expression pattern with the highest expression in roots, suggesting that NjBIN2 played a role in root growth and development. In addition, the application of epibrassinolide or the brassinosteroid(BR) synthesis inhibitor(brassinazole) altered the expression pattern of NjBIN2 and influenced the photomorphogenesis(cotyledon opening) and root development of N. jatamansi, which provided direct evidence about the functions of NjBIN2. In conclusion, this study highlights the roles of BIN2 in regulating the growth and development of N. jatamansi by analyzing the expression pattern and biological function of NjBIN2. It not only enriches the understanding about the regulatory mechanism of the growth and development of N. jatamansi but also provides a theoretical basis and potential gene targets for molecular breeding of N. jatamansi with improved quality in the future.
Brassinosteroids/metabolism* ; Steroids, Heterocyclic/metabolism* ; Gene Expression Regulation, Plant/drug effects* ; Plant Proteins/metabolism* ; Phylogeny ; Nardostachys/metabolism* ; Plant Growth Regulators/pharmacology* ; Plant Roots/drug effects*

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

This study aims to explore the effects and mechanisms of the traditional Chinese medicine Cordyceps sinensis(CS) in ameliorating heart aging and injury in mice based on animal experiments and network pharmacology. A mouse model of heart aging was established by continuously subcutaneous injection of D-galactose(D-gal). Thirty mice were randomly assigned into a normal group, a model group, a low-dose CS(CS-L) group, a high-dose CS(CS-H) group, and a vitamin E(VE) group. Mice in these groups were administrated with normal saline, different doses of CS suspension, or VE suspension via gavage daily. After 60 days of treatment with D-gal and various drugs, all mice were euthanized, and blood and heart tissue samples were collected for determination of the indicators related to heart aging and injury in mice. Experimental results showed that both high and low doses of CS and VE ameliorated the aging phenotype, improved the heart index and myocardial enzyme spectrum, restored the expression levels of proteins associated with cell cycle arrest and senescence-associated secretory phenotypes(SASP), and alleviated the fibrosis and histopathological changes of the heart tissue in model mice. From the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),259 active ingredients of CS were retrieved. From Gene Cards and OMIM, 2 568 targets related to heart aging were identified, and 133common targets shared by CS and heart aging were obtained. The Gene Ontology(GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes( KEGG) pathway enrichment revealed that the pathways related to heart aging involved oxidative stress,apoptosis, inflammation-related signaling pathways, etc. The animal experiment results showed that both high and low doses of CS and VE ameliorated oxidative stress and apoptosis in the heart tissue to varying degrees in model mice. Additionally, CS-H and VE activated the nuclear factor E2-related factor 2(Nrf2)/heme oxygenase-1(HO-1) pathway and inhibited the expression of key proteins in the nuclear factor-κB(NF-κB) pathway in the heart tissue of model mice. In conclusion, this study demonstrated based on network pharmacology and animal experiments that CS may alleviate heart aging and injury in aging mice by reducing oxidative stress,apoptosis, and inflammation in the heart via the Nrf2/HO-1/NF-κB pathway.
Animals ; Cordyceps/chemistry* ; Mice ; NF-E2-Related Factor 2/genetics* ; NF-kappa B/genetics* ; Aging/genetics* ; Male ; Signal Transduction/drug effects* ; Network Pharmacology ; Drugs, Chinese Herbal/pharmacology* ; Heme Oxygenase-1/genetics* ; Heart/drug effects* ; Humans ; Myocardium/metabolism* ; Membrane Proteins/genetics*

As an innovative dosage form, traditional Chinese medicine(TCM) dry powder inhalers have emerged as a focal point in the research and development of new preparations due to its high efficiency, safety, and bioavailability. This paper systematically reviewed the relevant literature and patents associated with TCM dry powder inhalers to analyze the origins and the current research and development status. Furthermore, this paper probed into the research and development ideas of TCM dry powder inhalers regarding clinical positioning, prescription screening, and druggability. Additionally, the paper thoroughly analyzed the technical barriers in druggability studies and elaborated on corresponding research techniques and coping measures. Furthermore, it emphasized the need for improved regulations and policies governing TCM dry powder inhalers, advocated for strengthened oversight, and called for the establishment of a scientific quality evaluation system. Measures such as promoting production-education-research collaboration, enhancing personnel training, and fostering international exchanges were proposed to provide a scientific and systematic reference for the future research, development, and application of TCM dry powder inhalers, thereby facilitating the rapid modernization of TCM.
Humans ; Dry Powder Inhalers/trends* ; Drugs, Chinese Herbal/chemistry* ; Medicine, Chinese Traditional/instrumentation* ; Administration, Inhalation