ObjectiveTo investigate the therapeutic effects and mechanism of decoctions from four kinds of processed products of Aconiti Radix Lateralis Praeparata（ARLP） in deficiency-cold syndrome. MethodsA total of 36 SD rats were randomly divided into the control group， model group， Shengfupian（SFP） group， Paofuzi（PFZ） group， Heishunpian（HSP） group and Paofupian（PFP） group with 6 rats in each group. Except for the control group， rats in other groups were administered hydrocortisone sodium succinate via intramuscular injection to induce a cold deficiency syndrome model. After 14 consecutive days， each ARLP decoction pieces was administered via continuous gastric lavage at a dose of 12 g·kg^-1·d^-1 for 7 d， while the control and model groups received an equivalent volume of physiological saline. After the end of administration， body weight， spleen weight and thymus weight were measured for calculating the spleen and thymus indexes. Hematoxylin-eosin（HE） staining was used to observe the pathological morphology of adrenal tissue. The fully automatic biochemistry analyzer was used to measure the total cholesterol（TC）， triglyceride（TG）， lactic dehydrogenase（LDH） and lactate（LAC） levels in serum. Enzyme-linked immunosorbent assay（ELISA） was employed to measure the contents of 17-hydroxycorticosteroid（17-OHCS）， cortisol（CORT）， triiodothyronine（T₃）， thyroxine（T₄）， thyrotropin（TSH）， immunoglobulin（Ig） M， IgG， cyclic adenosine monophosphate（cAMP） and cyclic guanosine monophosphate（cGMP）. Western blot was used to measure the protein expression levels of protein kinase A（PKA）， cAMP response element-binding protein（CREB）， silent information regulator 1（Sirt1） and peroxisome proliferator-activated receptor γ coactivator-1α（PGC-1α）. And high performance liquid chromatography（HPLC） was used to determine the content of major alkaloids， followed by Pearson correlation analysis with pharmacodynamic indicators. ResultsAfter modeling， compare with the control group， the model rats exhibited symptoms such as lethargy and loose stools， mild abnormalities were observed in adrenal tissue structure， and both spleen and thymus indices were significantly reduced（P<0.01）. Thyroid， adrenal and immune system functions were suppressed， with decreased serum cAMP level and significantly elevated cGMP level（P<0.01）. Compared with the model group， the adrenal injury by hydrocortisone sodium succinate were repaired and the spleen index were increased significantly in all four ARLP groups（P<0.05， P<0.01）. The thymus index in SFP and PFZ groups were increased significantly（P<0.05）. The contents of T₃， TSH， 17-OHCS and CORT were increased significantly in SFP and PFZ groups（P<0.05）. In addition， the content of IgG in SFP， PFZ and PFP groups were increased significantly（P<0.01）， while the content of IgM in PFZ and HSP groups were also increased significantly（P<0.05）. Regarding the cyclic nucleotide system， PFZ significantly elevated cAMP level while reducing cGMP level（P<0.05）， exhibiting the most pronounced effect among the four decoction pieces. For energy metabolism indicators， PFZ significantly improved abnormal markers including TC， TG， LDH， and LAC（P<0.05）. HSP showed marked improvement effects on TG， LDH， and LAC（P<0.05）. Both PFZ and SFP significantly elevated the expression levels of PKA， CREB， Sirt1， and PGC-1α proteins（P<0.01）. Additionally， the diester alkaloids in ARLP showed a strong positive correlation with TG， IgG， and CORT， a strong negative correlation with LAC， a moderate positive correlation with T₄， and moderate negative correlations with cAMP and spleen index. Monomeric alkaloids showed strong positive correlations with TG and IgG， strong negative correlations with LAC， moderate positive correlations with CORT and T₄， and moderate negative correlations with cAMP and spleen index. However， the content of water-soluble alkaloids showed strong positive correlations with TC， LDH， 17-OHCS， T₃， TSH， and thymus index， moderate positive correlations with cAMP， CORT， T₄， and spleen index， and moderate negative correlation with cGMP. ConclusionAmong different processed ARLP decoction pieces， PFZ processed according to ZHANG Zhongjing's method exhibits the most potent warming and cold-dispelling effects. Its pharmacological actions are mediated through regulating the thyroid， adrenal， immune， cyclic nucleotide systems， and material-energy metabolism pathways. Among these， water-soluble alkaloids show strong or moderate correlations with more indicators of deficiency-cold syndrome and exhibit the highest content in PFZ. Therefore， PFZ processed according to ZHANG Zhongjing's method may exert its warming and cold-dispelling effects through water-soluble alkaloids.

Stroke is one of the leading causes of disability and mortality worldwide. Research into its mechanisms and the development of therapeutic strategies heavily rely on animal models that accurately replicate the pathological features of human disease. An ideal animal model for stroke should not only reproduce the neurological deficits and pathological changes observed in clinical patients but also demonstrate good reproducibility and translational value. This review focuses on the preparation and evaluation methods of ischemic stroke animal models. Firstly, it elaborates on the selection criteria, advantages, and disadvantages of experimental animals, including rodents (rats, mice) and non-rodents (non-human primates, miniature pigs, rabbits, zebrafish). Secondly, it provides a detailed overview of the modeling principles, key procedures, and application scopes for ischemic stroke models and hemorrhagic stroke models. Furthermore, the review summarizes advances in the applications of emerging technologies—including gene editing [e.g., clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) gene editing], multimodal imaging (e.g., two-photon microscopy, photoacoustic imaging), artificial intelligence, optogenetics, 3D bioprinting, organoid models, and multi-omics–in model optimization, precise assessment, and mechanistic investigation. Finally, based on a systematic analysis of relevant domestic and international literature from 2019 to 2024, this review discusses model selection strategies based on research objectives, a multidimensional evaluation system encompassing behavioral, imaging, and molecular pathological assessments, and envisions future directions involving technological integration to achieve model precision and individualization. This article aims to provide a comprehensive methodological reference to help researchers select appropriate animal models of stroke according to specific scientific questions.

Esophageal squamous cell carcinoma is the main histological type of esophageal cancer in China, which seriously threatens the health of people. The application of immunotherapy, mainly immune checkpoint inhibitors, has greatly improved the prognosis of patients with esophageal squamous cell carcinoma, but the efficacy of treatment is still limited. Tertiary lymphoid structure (TLS) is an ectopic organized lymphoid structure that accumulates in non-lymphoid organs. Previous studies have found that TLS in esophageal squamous cell carcinoma is associated with better patient outcomes and enhanced immunotherapy efficacy. Based on current researches about TLS in esophageal squamous cell carcinoma, this paper reviews the relationship between TLS and the prognosis and immunotherapy of patients. We hope to provide reference for the precise immunotherapy of esophageal squamous cell carcinoma.

Chronic post-surgical pain (CPSP) is a common long-term complication following lung surgery. Its high incidence significantly impacts patients’ quality of life and functional recovery, and imposes a substantial socioeconomic burden. This consensus aims to systematically establish a standardized integrated Chinese and Western medicine diagnostic and treatment framework for chronic post-lung surgery pain (CPLSP). Based on the latest domestic and international evidence-based medical research and multidisciplinary clinical experience, the working group comprehensively elaborates on core issues regarding CPLSP, including its definition, epidemiology, pathogenesis, clinical assessment, Western medical treatment, traditional Chinese medicine (TCM) treatment, and integrated strategies. The consensus emphasizes a patient-centered approach, adhering to the principles of multimodality, individualization, and stepwise management, highlighting the synergistic advantages of integrating Chinese and Western medicine throughout the entire perioperative management cycle encompassing "perioperative anti-inflammation, acute analgesia, and chronic rehabilitation." Through systematic literature retrieval and evidence integration, a total of 9 core recommendations were established to provide scientifically sound and clinically practical guidance.

Liver fibrosis is caused by various factors such as viral infection， alcohol intake， and metabolism-related damage， leading to the replacement of normal tissue by fibrous scars. As a regulatory factor for cell proliferation， insulin-like growth factor 1 （IGF-1） participates in the regulation of cell cycle， the promotion of cell proliferation and differentiation， and the inhibition of cell apoptosis by binding to its receptor insulin-like growth factor-1 receptor （IGF-1R）. Studies have shown that the IGF-1/IGF-1R signaling pathway can regulate the process of liver fibrosis by affecting the senescence and apoptosis of hepatocytes， the activation and proliferation of hepatic stellate cells， and the dysfunction of endothelial cells. In addition， the IGF-1/IGF-1R signaling system can also regulate multiple mechanisms such as DNA damage repair， cell proliferation， lipid metabolism， cell senescence， and oxidative stress， thereby providing new strategies and potential targets for the prevention and treatment of liver fibrosis. This article summarizes the mechanism of action of IGF-1/IGF-1R and its signal transduction system in mediating liver fibrosis by regulating DNA damage repair in different cells， in order to provide a theoretical basis for the treatment of liver fibrosis.

OBJECTIVE To introduce the development of an intelligent prescription review decision-support system for anti-tumor drugs and assess its clinical application outcomes. METHODS Relevant data sources， including national and local pharmaceutical administration policies， clinical practice guidelines/consensus， hospital information systems data， and genetic testing results， were integrated. Adhering to the principles of structure， standardization and dynamic updating， a knowledge base covering chemotherapeutic， targeted and immunotherapeutic agents was constructed using a dual-dimensional modeling approach that combined “drug attributes” and “clinical contexts”. This knowledge base was then embedded into the hospital’s electronic medical order system to establish the prescription review decision-support system. The application and performance of the system were evaluated at Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS A knowledge base containing 18 318 prescription review rules for anti-tumor drugs was constructed， and a closed-loop prescription review system was successfully established， encompassing pre-prescription real-time intervention， in-process interactive review， and post-prescription evaluation and analysis. From 2021 to 2024， the system generated a total of 57 879 alerts for prescriptions of five typical categories of anti-tumor drugs. For platinum-containing prescriptions， 22 577 alerts were generated， with Cisplatin for injection （lyophilized） being the most frequently alerted drug （13 445 alerts）， and “ototoxicity risk due to combined use” alerts remained high （7 682 alerts）. For methotrexate-containing prescriptions， 3 721 alerts were recorded， primarily related to “precaution-related issues” （76.4%， 2 843/3 721）. For doxorubicin-containing prescriptions， 17 301 alerts were triggered， primarily related to “dosage and administration” （14 315 alerts）. For human epidermal growth factor receptor 2-targeted agents-containing prescriptions， 1 007 alerts were issued， mostly related to “reimbursement restrictions” （956 alerts）. For programmed death-1/programmed death-ligand 1 inhibitors-containing prescriptions， the alerts increased year by year， totaling 13 273 alerts， primarily related to “inappropriate indication” （9 118 alerts）. Over the 4 years， the physician response rates to system alerts were 21.4%， 27.1%， 33.5% and 51.6%， respectively. CONCLUSIONS An intelligent decision-support system for anti-tumor drug prescription review， encompassing a closed-loop process of “real-time pre-event intervention， interactive in-event prescription review， post-event evaluation and analysis”， has been successfully constructed and implemented throughout the entire workflow. There is a discernible trend in this hospital， where the focus on monitoring anti-tumor drugs is shifting towards immunotherapy drugs. Additionally， the acceptance rate of physicians regarding prescription review opinions has been steadily increasing year by year.

Background Lead is a typical persistent environmental pollutant that can accumulate in bones for decades. During pregnancy, alterations in calcium metabolism promote the mobilization of bone lead, resulting in secondary exposure; however, the mechanisms by which pregnancy-associated bone lead mobilization affects maternal renal function remain unclear. Objective To investigate the role of mitochondrial dysfunction in pregnancy-related bone lead mobilization-induced renal injury. Methods Newly weaned female Wistar rats were randomly assigned to a control or a lead-exposed group administered either 0.05% sodium acetate or 0.05% lead acetate in drinking water. Following a 4-week lead exposure and a 4-week washout period, the females were co-housed with healthy age-matched males for mating. Rats were sacrificed at early (gestational day 3) and late (gestational day 17) pregnancystages, respectively. Renal histopathology was assessed using hematoxylin and eosin staining staining. Mitochondria-related indicators, including oxidative stress, inflammatory responses, and energy metabolism, were measured. Differential metabolites were identified using serum metabolomics. Results Renal injury in the lead-exposed pregnant rats progressed in a time-dependent manner, characterized by degeneration of proximal tubular epithelial cells, glomerular hyaline changes, and interstitial inflammatory cell infiltration. Repeated measures ANOVA indicated a significant interaction between the treatment factor (lead exposure) and the temporal factor (gestational stage) on renal injury (P<0.001). Further analysis of mitochondrial function-related indicators in late-pregnancy renal tissue revealed that the lead exposure group exhibited significantly increased levels of malondialdehyde (MDA) and reactive oxygen species (ROS) (P<0.05), accompanied by a reduction in superoxide dismutase (SOD) and reduced glutathione (GSH) activities (P<0.05); regarding inflammatory markers, levels of interleukin-18 (IL-18) and interleukin-1β (IL-1β) were elevated (P<0.01), whereas interleukin-33 (IL-33) was decreased in the lead-exposed group (P<0.05); energy metabolism-related indicators, including adenosine triphosphate (ATP) level, Na⁺-K⁺-ATPase and Ca²⁺-Mg²⁺-ATPase activities, and mitochondrial respiratory chain complexes I, III, and V activities, were significantly reduced (P<0.05) in the lead-exposed gorup. The typical differential metabolite N-methylisoleucine, identified through serum metabolomics analysis, was negatively correlated with blood lead levels, kidney injury scores, and IL-1β, while positively correlated with catalase (CAT) activity and Ca²⁺-Mg²⁺-ATPase. Conclusions Mitochondrial dysfunction may play a critical role in renal injury induced by bone lead mobilization during late gestation.

Objective: To address systemic challenges in the blood system, such as supply-demand imbalance, inefficient allocation, and inappropriate clinical use, while bridging gaps in current theories regarding the integration of social mobilization, institutional practice, and policy coordination. It sought to construct a system dynamics model spanning from macro to micro levels to analyze the blood management system holistically. Methods: Key variables were identified by a systematic literature search in both Chinese and English databases. An improved Delphi method was then employed to conduct expert consultations across different fields, leading to the selection and determination of core variable sets for each sub-model. Furthermore, by defining their logical relationships, a system dynamics model was constructed to systematically analyze the operation mechanism of the blood management system. Results: Four core sub-models were developed: 1) A macro "Dynamic Balance" model quantifying regional supply-demand equilibrium and inventory control; 2) a mobilization "Three-Layer Funnel" model analyzing how socio-cultural factors, service channels, and policy incentives influence donation behavior; 3) an institutional "Dual-Cycle Regulation" model revealing hospital blood usage is driven by both disease burden (demand cycle) and management practices (regulation cycle); and 4) an individual "Three-Layer Filter" model standardizing clinical transfusion decisions based on necessity, risk-benefit, and context. These were integrated into a "Multi-Layer Linkage and Feedback" model, elucidating bidirectional interactions among five levels: policy environment, regional supply, blood station mobilization, hospital application, and clinical decision-making. Conclusion: This study constructed a system dynamics model for blood management. By defining key variables and their logical relationships, it systematically analyzes the system′s operational mechanism. The integrated framework connects multiple levels—regional supply, voluntary donation, hospital blood use, and clinical decision-making—revealing their intrinsic linkages. Future efforts should employ systems thinking to synergistically enhance supply-side mobilization, demand-side management, systemic regulation, and decision standardization to build a safe, efficient, and sustainable blood security system.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective: To explore the related factors of college students social network addiction based on problem behavior theory, so as to provide a basis for improving social networking addiction in this population. Methods: From May to June 2023, a method combining convenient sampling and cluster random sampling was used to select 1 768 college students from five universities in Shanghai for a questionnaire survey on social networking addiction, self esteem, loneliness, depressive symptoms, social support, interpersonal needs, sense of distress and frustration; at the same time, the physical exercise, smoking and drinking of college students were investigated. Multivariate Logistic regression analysis was applied to explore the association between the three systems of the problem behavior theory (personality, behavior, and social environment) and social networking addiction among college students. Results: The score on the Social Network Addiction Tendency Scale was (21.08±6.29) among college students, and the detection rate of social networking addiction was 66.29%. After adjusting for gender, family economic status, parental divorce status, and whether being an only child, multivariate Logistic regression analysis showed that in the personality system, higher loneliness ( OR =1.66) and higher depressive symptoms ( OR =2.18) were associated with increased risk of social networking addiction among college students; in the behavior system, alcohol consumption ( OR =1.42) was associated with higher risk of soical networking addiction compared to non drinkers; and in the social environment system, low social support ( OR =1.43) was associated with increased risk of social networking addiction (all P <0.05). Conclusions The rate of social networking addiction among college students is relatively high, and the systems of personality, behavior, and social environment are all related to social network addiction. Providing social support, cultivating healthy lifestyle habits, and increasing interpersonal interactions may help reduce excessive dependence on social networking among college students.