ObjectiveHistorically documented Zhejiang Atractylodis Macrocephalae Rhizoma（Baizhu） possesses premium characteristics such as phoenix-like head and crane-like neck， pronounced sweetness， and fragrant aroma. However， its current market circulation is low， and the processed products with Zhejiang-style characteristics are at the risk of being lost. This study aims to preserve the ancient Zhejiang-style processing techniques and evaluate them using modern scientific methods. MethodsMultidimensional intelligent sensory evaluation was used to digitally characterize the "quality-structure" of the external appearance of nine-steamed and nine-processed Baizhu medicinal materials（intermediate processed products） and the "odor-taste" of the internal quality of its decoction pieces（slices）， and the appearance parameters were digitally characterized by colorimeter， texture analyzer， electronic nose and electronic tongue， the chemical composition was analyzed via ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）. Then， cluster analysis on the differences in odor between the medicinal materials（intermediate processed products） and decoction pieces（slices） of nine-steamed and nine-processed Baizhu was conducted， as well as the differences in taste between water-soluble and alcohol-soluble extracts of the decoction pieces（slices）， and the correlation analysis of chroma value-alcohol-soluble extract content-component response value. ResultsThe nine-steamed and nine-processed Baizhu had a dark brown to black epidermis， a brownish-yellow to brownish-gray cross-section， a slightly tough texture， a faint odor， and a slightly sweet， bitter and pungent taste. Texture analyzer measurements revealed minimal adhesion and maximum recovery in the middle section of the characteristic processed Baizhu， consistent with the processing endpoint of thorough steaming and cooking. The head section showed the highest internal hardness， elasticity and chewiness， indicating a denser texture in this area. The electronic nose sensor could clearly distinguish the difference between the medicinal materials and its decoction pieces， with a more significant clustering effect at 60 ℃ for 30 minutes compared to ambient temperature headspace for 2 hours， highlighting the significant impact of the baking degree before slicing on the quality. The electronic tongue taste signal map clearly distinguished the differences between water-soluble and alcohol-soluble extracts of nine-steamed and nine-processed Baizhu decoction pieces， and the addition of auxiliary materials during processing could enhance its alcohol-soluble extract content. A total of 82 chemical components were identified in the characteristic processed Baizhu. After processing， the contents of 58 components increased， while 24 components decreased. Correlation analysis revealed significant negative correlations（P<0.01） between ethanol-soluble extract content and colorimetric values of brightness（L^*）， yellow-bule value（b^*）， and total color difference（E^*_ab）. E^*_ab showed marked negative correlations（P<0.05） with the response values of isochlorogenic acid A and C. ConclusionThis study establishes a modern intelligent sensory evaluation model for multidimensional holographic characterization of nine-steamed and nine-processed Baizhu， clarifying the correlation between increased isochlorogenic acid content and the visual color appearance after different steaming cycles， as well as its intrinsic alcohol-soluble extracts. This provides a reference for quality evaluation and processing standards of the Zhejiang-style characteristic processed products.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

ObjectiveTo evaluate the efficacy and safety of Janus kinase （JAK） inhibitors combined with Chinese herbal medicine （CHM） in treating rheumatoid arthritis （RA）. MethodsClinical data from 169 RA patients were retrospectively collected. Among them， 71 cases received JAK inhibitors as the control group， while 98 cases received JAK inhibitors plus CHM as the observation group， both treated for 24 weeks. The rheumatoid factor （RF）， cyclic citic peptide antibody （anti-CCP）， erythrocyte sedimentation rate （ESR）， C-reactive protein （CRP）， and white blood cell count （WBC） were recorded before and after treatment. Databases including CNKI， Wanfang， VIP， PubMed and Web of Science were searched from inception till August 31st， 2025 for randomized controlled trials （RCTs） on the combined use of JAK inhibitors and CHM for RA. The methodological quality of the included studies was evaluated using the risk of bias assessment tool. Meta-analyses were performed for RF， anti-CCP， ESR， CRP， 28-joint disease activity score （DAS28）， overall clinical effective rate， and incidence of adverse events. Sensitivity analysis were also performed. ResultsThe retrospective study demonstrated that after treatment， ESR， CRP， and anti-CCP levels decreased in the observation group， while ESR and CRP levels decreased in the control group （P＜0.05）. Moreover， ESR and RF levels in the observation group were lower than those in the control group （P＜0.05）. A total of 9 RCTs involving 770 patients were included in the meta-analysis. The results indicated that the JAK inhibitors plus CHM group was superior to the JAK inhibitors group in reducing RF （MD=-8.97， 95%CI -15.01 to -2.94， P=0.004）， CRP （MD=-3.34， 95%CI -3.82 to -2.86， P＜0.001）， ESR （MD=-5.33， 95%CI -7.98 to -2.69， P＜0.001）， and DAS28 score （MD=-0.54， 95%CI -0.74 to -0.34， P＜0.001）， as well as in improving the overall clinical effective rate （OR=4.53， 95%CI 2.55 to 8.03， P＜0.001）. No statistically significant differences were observed between groups in anti-CCP levels （SMD=-2.08， 95%CI -4.41 to 0.24， P=0.080） or incidence of adverse events （OR=0.93， 95%CI 0.55 to 1.57， P=0.790）. ConclusionThe combination of JAK inhibitors and CHM demonstrates remarkable efficacy in treating RA， contributing to improved disease activity and reduced inflammatory markers with a favorable safety profile.

ObjectiveObesity, a global chronic metabolic disease, is closely associated with disruptions in lipid metabolism and gut microbiota. Current intervention strategies still have limitations in terms of safety and microecological regulation, necessitating the exploration of novel natural regulatory approaches. Based on the early pathological characteristics of obesity, this study innovatively employs a rectal delivery method alongside a high-fat diet (HFD)-induced obesity model to systematically evaluate the inhibitory effects, safety, and gut microbiota regulation mechanisms of leek-derived and konjac-derived extracellular vesicles on obesity development. By simulating early clinical intervention scenarios, this study aims to explore the preventive potential of plant-derived extracellular vesicles during the initial stages of obesity onset. MethodsExtracellular vesicles from leek and konjac were isolated using ultracentrifugation combined with density gradient centrifugation. Their nanoscale properties were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). Male C57BL/6J mice were randomly divided into four groups: normal control (NC), high-fat diet (HFD), leek-derived extracellular vesicles (LEVs), and konjac-derived extracellular vesicles (KEVs). Beginning simultaneously with HFD feeding, mice in the intervention groups received 20 g/L vesicles rectally every 3 d for 4 weeks. Body mass and body composition were monitored throughout. At endpoint, mouse serum, adipose tissue, and colonic contents were collected. Serum biochemical indices (lipid profile, liver and kidney function, cardiac markers) were assessed to evaluate safety and metabolic efficacy, while 16S rRNA sequencing was employed to analyze gut microbial structure and diversity. ResultsDLS, NTA, and TEM confirmed that both LEVs and KEVs exhibited typical cup-shaped nanostructures with average particle sizes of approximately 284 nm and 223 nm, respectively. LEVs and KEVs treatment significantly suppressed HFD-induced weight gain and elevation of body-fat percentage (P<0.05), and reduced accumulation of abdominal white and epididymal adipose tissue. Serological analyses showed that both vesicles lowered total cholesterol, triglycerides and LDL-cholesterol, and ameliorated liver enzyme profiles (ALT, AST), demonstrating lipid-metabolic regulation and hepatoprotective effects. No hepatic, renal or cardiac dysfunction was observed, indicating favorable safety. Gut microbiota analyses revealed that vesicle intervention partially restored HFD-depleted microbial diversity and reshaped community structure. Notably, LEVs markedly increased the relative abundance of the beneficial taxon Lachnospiraceae at the family level, which is known for producing short-chain fatty acids and enhancing intestinal barrier function. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional prediction suggested that LEVs and KEVs modulated gut microbial functions through distinct mechanisms: LEVs downregulated pathways related to ribosomes and DNA replication while enhancing xenobiotic degradation, whereas KEVs tended to upregulate energy metabolism and protein synthesis toward healthy levels. ConclusionRectally administered LEVs and KEVs exhibit excellent safety and pronounced metabolic benefits during the early phase of obesity, suppressing weight gain, correcting lipid dysregulation, and exerting effects via modulation of gut microbial composition and function. This study provides systematic experimental evidence supporting plant-derived exosome-like vesicles as an early intervention strategy against obesity.

ObjectiveObesity, a global chronic metabolic disease, is closely associated with disruptions in lipid metabolism and gut microbiota. Current intervention strategies still have limitations in terms of safety and microecological regulation, necessitating the exploration of novel natural regulatory approaches. Based on the early pathological characteristics of obesity, this study innovatively employs a rectal delivery method alongside a high-fat diet (HFD)-induced obesity model to systematically evaluate the inhibitory effects, safety, and gut microbiota regulation mechanisms of leek-derived and konjac-derived extracellular vesicles on obesity development. By simulating early clinical intervention scenarios, this study aims to explore the preventive potential of plant-derived extracellular vesicles during the initial stages of obesity onset. MethodsExtracellular vesicles from leek and konjac were isolated using ultracentrifugation combined with density gradient centrifugation. Their nanoscale properties were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), and nanoparticle tracking analysis (NTA). Male C57BL/6J mice were randomly divided into four groups: normal control (NC), high-fat diet (HFD), leek-derived extracellular vesicles (LEVs), and konjac-derived extracellular vesicles (KEVs). Beginning simultaneously with HFD feeding, mice in the intervention groups received 20 g/L vesicles rectally every 3 d for 4 weeks. Body mass and body composition were monitored throughout. At endpoint, mouse serum, adipose tissue, and colonic contents were collected. Serum biochemical indices (lipid profile, liver and kidney function, cardiac markers) were assessed to evaluate safety and metabolic efficacy, while 16S rRNA sequencing was employed to analyze gut microbial structure and diversity. ResultsDLS, NTA, and TEM confirmed that both LEVs and KEVs exhibited typical cup-shaped nanostructures with average particle sizes of approximately 284 nm and 223 nm, respectively. LEVs and KEVs treatment significantly suppressed HFD-induced weight gain and elevation of body-fat percentage (P<0.05), and reduced accumulation of abdominal white and epididymal adipose tissue. Serological analyses showed that both vesicles lowered total cholesterol, triglycerides and LDL-cholesterol, and ameliorated liver enzyme profiles (ALT, AST), demonstrating lipid-metabolic regulation and hepatoprotective effects. No hepatic, renal or cardiac dysfunction was observed, indicating favorable safety. Gut microbiota analyses revealed that vesicle intervention partially restored HFD-depleted microbial diversity and reshaped community structure. Notably, LEVs markedly increased the relative abundance of the beneficial taxon Lachnospiraceae at the family level, which is known for producing short-chain fatty acids and enhancing intestinal barrier function. Furthermore, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) functional prediction suggested that LEVs and KEVs modulated gut microbial functions through distinct mechanisms: LEVs downregulated pathways related to ribosomes and DNA replication while enhancing xenobiotic degradation, whereas KEVs tended to upregulate energy metabolism and protein synthesis toward healthy levels. ConclusionRectally administered LEVs and KEVs exhibit excellent safety and pronounced metabolic benefits during the early phase of obesity, suppressing weight gain, correcting lipid dysregulation, and exerting effects via modulation of gut microbial composition and function. This study provides systematic experimental evidence supporting plant-derived exosome-like vesicles as an early intervention strategy against obesity.

T7 RNA polymerase (T7 RNAP) is one of the simplest known RNA polymerases. Its unique structural features make it a critical model for studying the mechanisms of RNA synthesis. This review systematically examines the static crystal structure of T7 RNAP, beginning with an in-depth examination of its characteristic “thumb”, “palm”, and “finger” domains, which form the classic “right-hand-like” architecture. By detailing these structural elements, this review establishes a foundation for understanding the overall organization of T7 RNAP. This review systematically maps the functional roles of secondary structural elements and their subdomains in transcriptional catalysis, progressively elucidating the fundamental relationships between structure and function. Further, the intrinsic flexibility of T7 RNAP and its applications in research are also discussed. Additionally, the review presents the structural diagrams of the enzyme at different stages of the transcription process, and through these diagrams, it provides a detailed description of the complete transcription process of T7 RNAP. By integrating structural dynamics and kinetics analyses, the review constructs a comprehensive framework that bridges static structure to dynamic processes. Despite its advantages, T7 RNAP has a notable limitation: it generates double-stranded RNA (dsRNA) as a byproduct. The presence of dsRNA not only compromises the purity of mRNA products but also elicits nonspecific immune responses, which pose significant challenges for biotechnological and therapeutic applications. The review provides a detailed exploration of the mechanisms underlying dsRNA formation during T7 RNAP catalysis, reviews current strategies to mitigate this issue, and highlights recent progress in the field. A key focus is the semi-rational design of T7 RNAP mutants engineered to minimize dsRNA generation and enhance catalytic performance. Beyond its role in transcription, T7 RNAP exhibits rapid development and extensive application in fields, including gene editing, biosensing, and mRNA vaccines. This review systematically examines the structure-function relationships of T7 RNAP, elucidates the mechanisms of dsRNA formation, and discusses engineering strategies to optimize its performance. It further explores the engineering optimization and functional expansion of T7 RNAP. Furthermore, this review also addresses the pressing issues that currently need resolution, discusses the major challenges in the practical application of T7 RNAP, and provides an outlook on potential future research directions. In summary, this review provides a comprehensive analysis of T7 RNAP, ranging from its structural architecture to cutting-edge applications. We systematically examine: (1) the characteristic right-hand domains (thumb, palm, fingers) that define its minimalistic structure; (2) the structure-function relationships underlying transcriptional catalysis; and (3) the dynamic transitions during the complete transcription cycle. While highlighting T7 RNAP’s versatility in gene editing, biosensing, and mRNA vaccine production, we critically address its major limitation—dsRNA byproduct formation—and evaluate engineering solutions including semi-rationally designed mutants. By synthesizing current knowledge and identifying key challenges, this work aims to provide novel insights for the development and application of T7 RNAP and to foster further thought and progress in related fields.

ObjectiveTo develop a nomogram-based risk prediction model for chronic obstructive pulmonary disease (COPD) incidence among the community-dwelling population aged 40 years old and above, so as to provide targeted references for the screening and prevention of COPD. MethodsBased on a natural population cohort in suburban Shanghai, a total of 3 381 randomly selected participants aged ≥40 years underwent pulmonary function tests between July and October 2021. Cox stepwise regression analysis was used to develop overall and gender-specific risk prediction models, along with the construction of corresponding risk nomograms. Model predictive performance was evaluated using the C-indice, area under the curve (AUC) values, and Brier score. Stability was assessed through 10-fold cross-validation and sensitivity analysis. ResultsA total of 3 019 participants were included, with a median follow-up duration of 4.6 years. The COPD incidence density was 17.22 per 1 000 person-years, significantly higher in males (32.04/1 000 person-years) than that in females (7.38/1 000 person-years) (P<0.001). The overall risk prediction model included the variables such as gender, age, education level, BMI, smoking, passive smoking, and respiratory comorbidities. The male-specific model incorporated the variables such as age, BMI, respiratory comorbidities, and smoking, while the female-specific model included age, marital status, respiratory comorbidities, and pulmonary tuberculosis history. The C-indices for the overall, male-specific, and female-specific models were 0.829, 0.749, and 0.807, respectively. The 5-year AUC values were 0.785, 0.658, and 0.811, with Brier scores of 0.103, 0.176, and 0.059, respectively. Both 10-fold cross-validated C-indices and sensitivity analysis (excluding participants with a follow-up duration of <6 months) yielded C-indices were above 0.740. ConclusionThis study developed concise and practical overall and gender-specific COPD risk prediction models and corresponding nomograms. The models demonstrated robust performance in predicting COPD incidence, providing a valuable reference for identifying high-risk populations and formulating targeted screening and personalized management strategies.

With the emergence of deep learning techniques based on convolutional neural networks, artificial intelligence (AI) has driven transformative developments in the field of medical image analysis. Recently, large language models (LLMs) such as ChatGPT have also started to achieve distinction in this domain. Increasing research shows the undeniable role of AI in reshaping various aspects of medical image analysis, including processes such as image enhancement, segmentation, detection in image preprocessing, and postprocessing related to medical diagnosis and prognosis in clinical settings. However, despite the significant progress in AI research, studies investigating the recent advances in AI technology in the aforementioned aspects, the changes in research hotspot trajectories, and the performance of studies in addressing key clinical challenges in this field are limited. This article provides an overview of recent advances in AI for medical image analysis and discusses the methodological profiles, advantages, disadvantages, and future trends of AI technologies.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Deep Learning ; Diagnostic Imaging/methods*

Lung cancer is one of the most common and deadliest cancers globally, with its incidence and mortality rates rising each year. Therefore, finding new, safe, and effective alternative therapies poses a significant research challenge in this field. Programmed cell death refers to the process by which cells actively self-destruct in response to specific stimuli, regulated by genetic mechanisms. Modern research indicates that dysregulation of programmed cell death is widespread in the occurrence and progression of lung cancer, allowing cancer cells to evade death while continuing to proliferate and metastasize. Thus, inducing the death of lung cancer cells can be considered a novel therapeutic strategy for treating the disease. In recent years, research on traditional Chinese medicine(TCM) in the field of oncology has gained widespread attention, becoming a focal point. An increasing number of studies have demonstrated that TCM can inhibit the progression of lung cancer and exert anti-cancer effects by inducing apoptosis, necroptosis, pyroptosis, autophagy, and ferroptosis. This paper provided a comprehensive review of the molecular mechanisms of programmed cell death in lung cancer, along with the potential mechanisms and research advancements related to the regulation of these processes by TCM, so as to establish a theoretical foundation and direction for future basic and clinical research on lung cancer.
Humans ; Lung Neoplasms/pathology* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use* ; Apoptosis/drug effects* ; Animals ; Autophagy/drug effects*