ObjectiveZheng’s San Qi San (ZSQS) power, a classic traditional Chinese medicine (TCM) formula, is used for treating soft tissue injuries involving muscles, tendons, and ligaments. However, its underlying therapeutic mechanisms remain unclear. This study aimed to screen and identify pharmaceutically active ingredients and their candidate biomolecule targets, and further elucidate the molecular mechanism of ZSQS in the treatment of skeletal muscle injury. MethodsNetwork pharmacology was employed to construct “ZSQS-component-target”, “protein-protein interaction (PPI)” and “active ingredient-core protein-pathway” networks to predict the key active ingredients and potential core targets of ZSQS for skeletal muscle injury. The predicted results were then validated via microarray data from the GEO database. Molecular docking was then performed to assess the binding ability between the screened active ingredients of ZSQS and the candidate core targets. Moreover, liquid chromatography-mass spectrometry (LC-MS) was used for qualitative and quantitative analysis to verify the active components of the drug and ZSQS serum. Finally, an animal model of eccentric exercise-induced skeletal muscle injury and a myotube cell model of oxidative stress-induced injury were established to validate the effects of ZSQS and its interventional effects on the biological functions of critical targets, thereby demonstrating the potential therapeutic mechanism of ZSQS. ResultsAmong the 111 active components identified in ZSQS and their corresponding 204 targets related to the skeletal muscle injury repair process, 14 core targets (including AKT1) and 4 core active components (quercetin, luteolin, kaempferol, and β‑sitosterol) were screened out, while the corresponding metabolites of quercetin, luteolin and kaempferol were detected in the ZSQS serum. Among these targets, 5 candidate genes (IL-6, CASP3, HIF1A, STAT3, and JUN) overlapped with the differential expression screening results with GEO data, and IL-6 was confirmed to be enriched in the PI3K/AKT pathway. Combined with the prediction results of the AKT expression levels, these findings suggest that the phosphorylation level of AKT1 plays a core role in the therapeutic mechanism of ZSQS. Molecular docking analysis further revealed that the PH domain of AKT1 had high binding energy with all 4 core active components, as verified by LC-MS. Finally, animal model studies have shown the promoting effect of ZSQS administration on skeletal muscle injury repair and its possible antioxidant damage mechanism. Cell model studies further demonstrated that ZSQS-containing serum, core active ingredient combination therapy, and quercetin monomer could increase the phosphorylation level of AKT, promote the nuclear translocation of Nrf2, upregulate the expression of downstream antioxidant enzymes (SOD, GPx, and GR), and inhibit the expression of inflammatory factors (IL-6 and TNF-α), thereby alleviating oxidative stress and the inflammatory response. ConclusionZSQS alleviates skeletal muscle injury mainly by activating the AKT/Nrf2 signaling pathway, enhancing cellular antioxidant and anti-inflammatory capabilities. The results of this study provide a scientific basis for the clinical application and modernized development of ZSQS.

Objective: To characterize perioperative dynamic changes in immune-cell phenotypes and inflammatory cytokines in patients undergoing CPB (cardiopulmonary bypass) cardiac surgery, and to explore their associations with postoperative outcomes. Methods: In this prospective cohort study, 120 adult patients who underwent elective cardiac surgery under CPB at West China Hospital from May 2022 to March 2023 were enrolled. Perioperative immune-cell phenotypes and concentrations of 40 inflammation-related cytokines were measured. The primary outcomes were the sequential organ failure assessment (SOFA) score at 24 h after surgery and ΔSOFA (the peak SOFA score within 48 h after surgery minus the preoperative SOFA score). Secondary outcomes included major adverse cardiovascular events (MACE), acute kidney injury (AKI), respiratory failure, severe liver injury, and infection. Results: The mean age of enrolled patients was 57±10 years. Of these, 52% (62/120) were male and 90% (108/120) underwent valve surgery. During the rewarming to the end of CPB, neutrophil counts rapidly increased (7.39×10 /L vs preoperative 3.07×10 /L, P<0.001), with significant upregulation of CD11b (7.30×10 /L vs preoperative 3.05×10 /L, P<0.001) and CD54 (7.15×10 /L vs preoperative 2.99×10 /L, P<0.001). Lymphocyte counts increased at the end of CPB (1.75×10 /L vs preoperative 1.12×10 /L, P<0.001) but decreased significantly at 24 h after surgery (0.59×10 /L vs preoperative 1.12×10 /L, P<0.001). Plasma analysis showed that multiple pro-inflammatory cytokines increased during CPB and remained elevated up to 24 h after surgery; five chemokines and the anti-inflammatory cytokine IL-10 peaked at the end of CPB. The SOFA score increased from 1 (1, 2) preoperatively to 7 (5, 10) at 24 h after surgery, with a ΔSOFA of 6 (4, 8). Within 30 days after surgery, 48 patients (40.0%) developed AKI, 17 (14.2%) developed infection, 4 (3.3%) developed severe liver injury, 3 (2.5%) developed respiratory failure, and 3 (2.5%) experienced MACE. During the 2-year follow-up, 8 patients (6.7%) experienced MACE and 5 (4.2%) died. Conclusion: Multi-organ dysfunction is common after cardiac surgery under CPB (median ΔSOFA, 6), accompanied by perioperative activation of multiple immune-cell subsets and upregulation of pro-inflammatory, anti-inflammatory, and chemotactic mediators. This study provides data-driven evidence and research clues for further investigation of the associations between CPB-related immune perturbations and postoperative organ dysfunction and clinical outcomes.

Microorganisms are closely associated with human health, and their pathogenicity is a key factor in various infectious diseases, particularly in dentistry, where they contribute to common conditions such as dental caries, periodontitis, and oral mucosal diseases. Accurate and rapid microbial detection is crucial for early diagnosis, targeted therapy, and disease prevention. Conventional methods, including bacterial culture and molecular biological assays, offer specificity but are limited by long detection cycles, complex procedures, and dependence on laboratory conditions. Terahertz (THz) spectroscopy has emerged as a promising tool in microbial detection due to its non-ionizing nature, high sensitivity, and specific responses to water molecules and biomacromolecules. Integrating THz time-domain spectroscopy, near-field imaging, and metamaterial-enhanced techniques, studies have demonstrated the ability of this approach to effectively distinguish bacteria, fungi, and yeast, differentiate gram-positive and gram-negative bacteria, and even assess bacterial viability. Machine learning has further enhanced feature extraction and classification accuracy, and THz-based methods have shown notable advantages in multi-class microbial identification, detection of antibiotic-resistant strains, and quantitative analysis of microbial concentrations. However, current THz technologies are still constrained by strong water absorption, limited penetration depth, and the lack of standardized spectral databases. Future efforts should focus on mitigating water background interference, improving detection in complex samples, and establishing unified microbial spectral standards. This review systematically summarizes the latest advances of THz technologies in microbial detection, analyzes their mechanisms, advantages, and translational challenges, and proposes directions for future research.

Early caries confined to the enamel layer represent a critical window for achieving noninvasive intervention in caries management. Caries management has shifted from the traditional “drill-and-fill” model toward a modern paradigm centered on caries risk and lesion management. Based on contemporary concepts, this review systematically summarizes recent advances in early caries management, including caries risk assessment, early diagnosis, treatment strategy selection, and follow-up monitoring, while highlighting the major challenges currently being faced, and further reviewing and discussing the application of artificial intelligence (AI) in early caries management. In terms of risk management, conventional systems including the American Dental Association, Caries Management by Risk Assessment, Cariogram, and the Caries-Risk Assessment Tool remain mainstays in clinical practice. However, AI offers predictive capability through higher-dimensional data processing and the integration of numerous influencing factors, with the potential to improve the accuracy of risk stratification. For diagnosis, visual inspection, tactile examination, and bitewing radiography remain fundamental methods, yet their sensitivity for early caries—particularly proximal lesions—is limited. The application of optical technologies, including quantitative light-induced fluorescence, optical coherence tomography, near-infrared light transillumination, fiber-optic transillumination, and laser-induced fluorescence, enables digital characterization of caries lesions, providing a data foundation for demineralization assessment, lesion activity evaluation, and AI model development. The management of early caries primarily relies on noninvasive and minimally invasive approaches. Remineralization therapy is suitable for superficial lesions, resin infiltration offers the dual advantages of inhibiting lesion progression and improving aesthetics, and microabrasion and bleaching may serve as adjunctive aesthetic treatments. Emerging modalities such as laser, ozone, and photodynamic therapy have also demonstrated potential. Treatment decision-making should comprehensively consider lesion activity, patient caries risk status, demineralization depth, patient compliance, and treatment preferences. However, precise quantification of demineralization depth remains challenging, and standardized decision-making criteria are still lacking. Follow-up management should be individualized based on risk stratification, with attention to lesion changes, patient compliance, and the risk of recurrence. In summary, intelligent and precision-based approaches are expected to define the future of early caries management, and the application of AI in risk prediction, image analysis, and clinical decision support is anticipated to further enhance the efficiency and effectiveness of early caries diagnosis and treatment.

ObjectiveTo compare the differences in fungal community composition between lesional and non-lesional scalp areas in patients suffering from severe alopecia areata （AA）， and compare these with healthy scalp areas in control subjects. Additionally， to preliminarily explore the changes in scalp fungal communities in severe AA patients and their potential underlying immunological mechanisms. MethodsA total of 20 severe AA patients and 18 healthy controls were enrolled. Skin swab samples were collected from lesional and non-lesional scalp areas of severe AA patients， as well as from the normal scalp of healthy controls. The fungal internal transcribed spacer （ITS） region was amplified and analyzed using high-throughput sequencing. ResultsThe lesional scalp areas of severe AA patients exhibited higher α-diversity and species richness in fungal communities. Notably， the relative abundance of Ascomycota， along with genera such as Mycosphaerella， Aspergillus， Penicillium， and Wallemia， significantly increased in the bald regions. In contrast， Acremonium and Schizophyllum were more predominant in the non-lesional areas of severe AA patients. ConclusionDistinct region-specific differences in scalp fungal microbiota in severe AA patients suggests that fungal dysbiosis may play a potential role in the pathogenesis of alopecia areata. These findings provide new insights into the disease characteristics of severe AA from the perspective of scalp microecology.

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.

Cell Biology is one of the most rapidly developing branches of modern life sciences, characterized by distinct interdisciplinary integration. It provides theoretical foundations, experimental skills, and cutting-edge perspectives for undergraduate and graduate students in bioscience and related majors. Against the backdrop of higher education reform in the new era, the Cell Biology teaching team at the University of Chinese Academy of Sciences (UCAS) has restructured the curriculum. The course focuses on the fundamental structures and life processes of cells while incorporating ideological and political elements to foster students’ scientific mindset, patriotic sentiment, and social responsibility. By optimizing teaching design, enhancing practical components, and innovating assessment methods, the course integrates knowledge transfer, skill development, and value education. This paper summarizes preliminary experiences from the teaching development and educational practice of the undergraduate Cell Biology course at UCAS, serving as a reference for collaborative research- and teaching-oriented courses in science and engineering.

Objective: To analyze the occupational health literacy (OHL) level and its influencing factors among key populations in the tertiary industry in Lu'an City, Anhui Province, so as to provide a basis for developing targeted health interventions and improving regional occupational health policies. Methods: A stratified cluster random sampling method was employed to select five categories of key populations from the tertiary industry in Lu'an City as study subjects from August to September 2024. Data on gender, age, education level, and OHL were collected through the National OHL Monitoring Questionnaire for Key Populations. The OHL levels were analyzed, and influencing factors of OHL levels among key populations were analyzed using a multivariable logistic regression model. Results: A total of 1 243 individuals were surveyed, comprising 700 (56.32%) males and 543 (43.68%) females. The median age was 42.00 (interquartile range, 17.00) years. There were 609 individuals with OHL, and the OHL level was 48.99%. The OHL levels in fundamental knowledge of occupational health protection, healthy work styles and behaviors, knowledge of occupational health laws, and basic skills for occupational health protection were 84.71%, 60.34%, 43.93%, and 37.09%, respectively. Multivariable logistic regression analysis showed that educational level (primary school and below, OR=0.149, 95%CI: 0.064-0.344; junior high school, OR=0.340, 95%CI: 0.184-0.629; high school, OR=0.408, 95%CI: 0.230-0.723), average monthly personal income (3 000-<5 000 yuan, OR=1.655, 95%CI: 1.092-2.508; 5 000-<7 000 yuan, OR=2.195, 95%CI: 1.302-3.699; ≥7 000 yuan, OR=2.062, 95%CI: 1.016-4.183), employer nature (private enterprises, OR=2.992, 95%CI: 1.569-5.443), and industry category (education, OR=3.423, 95%CI: 1.407-8.327; courier / food delivery services, OR=0.459, 95%CI: 0.268-0.787; healthcare, OR=7.539, 95%CI: 3.255-17.461) were statistically associated with the OHL level among key population. Conclusion The OHL level among key population in the tertiary industry of Lu'an City can be further enhanced, with educational level, average monthly personal income, employer nature, and industry category identified as the primary influencing factors.

Objective To analyze the epidemiological characteristics and spatiotemporal clustering of varicella in Lu'an City from 2005 to 2023, and to provide a scientific basis for optimizing varicella prevention and control strategies. Methods Data on varicella cases were collected through the Chinese Center for Disease Control and Prevention Information System. Descriptive epidemiology, temporal trend analysis, seasonal analysis, spatiotemporal clustering analysis, and spatial autocorrelation analysis were conducted using QGIS, JoinPoint, SaTScan and GeoDa software. Results The average annual reported incidence rate of varicella in Lu'an City from 2005 to 2023 was 34.55/100,000, showing a trend of initial increase followed by a decrease. The peak incidence occurred from October to January of the following year (RR=1.97, LLR=1743.95, P=0.001). Students aged 0 to 19 was the primary affected group. Spatiotemporal scan analysis revealed four types of spatiotemporal clusters, with the cluster in Jin'an District from October 2017 to December 2023 being particularly prominent (RR=2.87，LLR=1734.15，P<0.001). Spatial autocorrelation analysis indicated significant clustering of varicella cases in the main urban area (Moran's I=0.216，Z=4.786，P=0.003). Conclusion The incidence of varicella in Lu'an City exhibits distinct seasonal and spatial clustering, and schools and kindergartens in the main urban area are the key to varicella prevention and control. It is necessary to enhance the monitoring of disease outbreaks during peak periods and in key areas, and to increase the two-dose vaccination rate for varicella in areas with case aggregation and among key populations.