Community-acquired pneumonia （CAP） refers to an infectious inflammation of the lung parenchyma （including the alveolar wall，that is，the broad pulmonary interstitium） acquired outside the hospital. Its common pathogens include streptococcus pneumoniae，respiratory viruses， mycoplasma pneumoniae， and so on. The related factors for the occurrence and development of CAP include patient characteristics （immune function，mucus production and clearance function，coagulation function，physical condition， and comorbidity） and pathogen characteristics （susceptibility，virulence，and antibiotic resistance）. The pathogenesis of CAP lies in immune deficiency，pathogen invasion，inflammatory response disorder，mucus production and clearance disorder， coagulation disorder， and so on. The pathogenesis of CAP in traditional Chinese medicine can be described as "vital Qi deficiency and toxic stasis". Vital Qi deficiency （lack of immunity） is the potential pathogenesis of the disease and easy to be invaded by external pathogens （respiratory pathogens）. Toxic stasis （inflammatory disorder，mucus production and clearance disorder，and coagulation dysfunction） is the key pathogenic factor. Vital Qi deficiency and toxic stasis are intermingled in a state of deficiency and excess，which suggests that the treatment of CAP lies in strengthening vital Qi and eliminating pathogenic factors. This involves strengthening vital Qi in the whole process to consolidate body resistance and nourish promordial Qi. It also involves clearing heat，eliminating phlegm，removing dampness，and dispelling stasis to dispel pathogenic toxins based on the syndrome differentiation. Its action mechanism is to regulate immune and inflammatory responses，resist pathogens，and improve mucus production and clearance， as well as coagulation disorders. Starting from the key pathogenesis of CAP，"vital Qi deficiency and toxic stasis"， this paper discussed the pathogenesis of CAP and summarized the action mechanism of vital Qi strengthening for detoxification in its treatment. It is intended to complement the theoretical system by identifying "vital Qi deficiency and toxic stasis" as the key pathogenesis underlying CAP and the scientific connotation of treating CAP with vital Qi strengthening for detoxification，thereby providing insights for its clinical application.

Perimenopausal depression （PMD） is an affective disorder that occurs in women during the transition from sexual maturity to old age. It can induce various complications， such as insomnia and cognitive decline. The etiology of PMD is complex. Although multiple hypotheses have been proposed， there is still no unified theory that fully explains its pathogenesis. Research into its mechanisms relies heavily on animal experiments， and establishing reliable animal models is crucial for experimental studies. Appropriate animal models can better simulate human pathophysiological states， rapidly evaluate the efficacy and safety of drugs and intervention methods， grasp the essence of the disease， and uncover its intrinsic connections， thereby exploring more advanced intervention strategies. However， there is a lack of systematic review and summarization of literature related to model construction. Additionally， traditional Chinese medicine （TCM）， adhering to the principles of ''syndrome differentiation and treatment'' and ''holistic concept''， has shown significant efficacy in treating PMD. In recent years， research exploring and analyzing its therapeutic mechanisms has been increasing. Therefore， to gain a clearer and more comprehensive understanding of PMD animal modeling methods and the mechanisms of TCM， this paper reviewed Chinese and English literature on PMD animal models and mechanisms of TCM in PMD treatment. It summarized the construction methods of single-factor and multi-factor PMD models， and discussed the advantages and disadvantages of each modeling approach. Furthermore， it delved into the mechanisms of TCM intervention in PMD， revealing that TCM formulas primarily exert their effects by regulating the hypothalamic-pituitary-adrenal axis， hypothalamic-pituitary-ovarian axis， gut-brain axis， cell signaling pathways， neural circuits， hormone levels， and neurotransmitter levels. This review aims to provide a reference for future research in this field. In summary， by summarizing the progress in the methods for PMD animal model construction and the mechanisms of TCM， the paper seeks to offer new insights into the mechanistic research of TCM intervention in PMD.

Human umbilical cord mesenchymal stem cell （hUC-MSC） as a cell-based therapeutic strategy have demonstrated significant application potential in the field of intervention for neurodegenerative disease （NDD） due to their advantages such as self-renewal， multi-directional differentiation， and low immunogenicity. hUC-MSC effectively intervenes in the pathological features and neurological functions of various disease models such as Alzheimer disease， Parkinson’s disease， amyotrophic lateral sclerosis， and multiple sclerosis primarily through multiple mechanisms such as homing and differentiation， mediating paracrine actions and releasing exosomes， as well as immune regulation and anti-inflammation. Some clinical studies have also preliminarily verified their safety and effectiveness. Currently， its research still faces challenges such as immune rejection reactions requiring further observation， long-term safety needing evaluation， mechanisms of action not being fully elucidated， and slow progress in clinical trials. Future research needs to establish pharmaceutical standards for hUC-MSC， deepen their pharmacological mechanisms and clinical trials， ultimately providing new and effective drug treatment options for patients with NDD.

This article systematically reviews the current research status as well as diagnosis and treatment strategies of traditional Chinese medicine （TCM） for gastroesophageal reflux disease （GERD）. Studies demonstrate that TCM， based on the "disease-syndrome combination" approach， exhibits multi-target advantages in alleviating symptoms of various GERD subtypes， promoting mucosal repair， regulating emotions， and facilitating the reduction of western medication. To address clinical challenges such as symptom overlap and limited therapeutic efficacy， strategies have been proposed including "treating different diseases with the same method" and integrated regulation based on viscera correlation. Future efforts should focus on elucidating the mechanisms of compound prescriptions， promoting TCM drug development under the "three-combination" evaluation framework that integrates TCM theory， human experience and clinical trial evidence， and optimizing integrated traditional and western medicine models to enhance GERD management.

OBJECTIVE To confirm trigger items for adverse drug reaction （ADR） induced by bevacizumab， to identify and analyze the occurrence of related ADR， and to establish a predictive model for severe adverse reaction （SAR） caused by this drug. METHODS Based on the global trigger tool （GTT） theory， and referencing the GTT White Paper， drug package inserts and relevant literature， trigger items for bevacizumab-related ADR were confirmed using a single-round Delphi method. Utilizing these established items， electronic medical records of relevant patients at Guilin People’s Hospital from January 2020 to September 2024 were actively screened via the China Hospital Pharmacovigilance System. Pharmacists then identified and tallied the occurrence of bevacizumab-induced ADR. Data from patients with any positive trigger item served as the study subjects （divided into training and test sets at a ratio of 7∶3）， candidate feature variables were selected from 39 related variables using the Boruta algorithm， and the multivariable Logistic regression analysis was performed with the occurrence of SAR as the dependent variable. Based on these candidate features， Logistic Regression， Extreme Gradient Boosting， Light Gradient Boosting Machine， Random Forest， and Categorical Boosting models were constructed. Model performance was evaluated using metrics including the area under the curve （AUC） of receiver operating characteristic curve and recall rate. The Shapley Additive exPlanations （SHAP） method was applied to analyze and interpret the contribution of each variable. A nomogram was constructed based on the optimal model. RESULTS A total of 38 trigger items for active monitoring of bevacizumab-related ADR were determined， comprising 17 laboratory indicators， 13 clinical manifestations， and 8 intervention measures. In total， 483 patients with positive trigger items were included， and 318 patients with bevacizumab-induced ADR were identified， including 83 SARs. The positive predictive values for the trigger items and cases were 43.57% （708/1 625） and 63.84% （318/483）， respectively. Bevacizumab-induced ADR involved 7 systems/organs， with the hematological system being the most frequently involved （64.15%）. The Boruta algorithm selected 7 vari ables： serum potassium， hematocrit， albumin-to-globulin ratio， prealbumin， hypertension history， age and red blood cell count. Multivariable Logistic regression showed that elevated serum potassium levels were associated with a decreased risk of bevacizumab-induced SAR （OR＝0.234， P ＝0.002）， while a history of hypertension （OR＝2.642， P ＝0.006） and increased age （OR＝1.040， P ＝0.025） were associated with an increased risk. The Logistic Regression model demonstrated superior performance with higher AUC， F1 score and recall rate （0.761， 0.447， 0.607）， compared to other models. SHAP evaluation results indicated that variables such as serum potassium， hematocrit， and age ranked highest in importance. CONCLUSIONS Totally 38 trigger entries have been successfully identified for active screening of bevacizumab-related ADR. Elevated serum potassium levels are a protective factor against bevacizumab-induced SAR， whereas the hypertension history and increased age are risk factors. The Logistic Regression model is the optimal predictive model.

OBJECTIVE To investigate the therapeutic effect and mechanism of Qiwei dongqingye powder （QDP） on bronchial asthma in mice. METHODS The mice were divided into blank group （normal saline）， model group （normal saline）， dexamethasone group （2 mg/kg）， and QDP low-， medium-， and high-dose groups （200， 400， 800 mg/kg）， with 14 mice in each group. Except for the blank group， mice in all other groups were given ovalbumin via intraperitoneal injection followed by aerosol inhalation to induce a bronchial asthma model. During the modeling process， mice in each group were administered corresponding drug solutions or normal saline intragastrically/intraperitoneally. After the last medication， the number of cells in the bronchoalveolar lavage fluid （BALF） of the mice was observed and counted； the pathological changes of the bronchus and lung tissue were observed； the levels of malondialdehyde （MDA）， nitric oxide （NO）， total superoxide dismutase （T-SOD）， and glutathione peroxidase （GSH-Px） in the lung tissue of the mice were determined， and the level of interleukin-17 （IL-17） in the BALF and serum was determined. Transcriptomics was employed to predict and validate the mechanism of action of QDP against bronchial asthma. RESULTS Compared with the model group， the total cell count， neutrophil count， lymphocyte count， and macrophage counts in the BALF of the QDP high-dose group were all significantly reduced （ P ＜0.05）； the levels of MDA and NO in the lung tissue， and the levels of IL-17 in the BALF and serum were all decreased significantly （ P ＜0.05）； the levels of T-SOD and GSH-Px were significantly increased （ P ＜0.05）； the arrangement of lung tissue cells tended to normalize， with reduced infiltration of inflammatory cells and decreased exfoliation of bronchial simple columnar epithelial cells. The transcriptomic results revealed that the differentially expressed genes were B-cell receptor signaling pathway， nuclear factor κB （NF-κB） signaling pathway， ferroptosis signaling pathway， and others. Further validation revealed that， compared with the model group， the expression levels of NF-κB p65 and chemokine ligand 20， as well as the phosphorylation level of NF-κB inhibitor protein α， were significantly decreased in the lung tissues of the mice in all QDP groups （ P ＜0.05）. Conversely， the protein expression of nuclear factor erythroid 2-related factor 2 （Nrf2） and heme oxygenase 1 （HO-1） were significantly increased （ P ＜0.05）. CONCLUSIONS QDP can effectively alleviate bronchial asthma by inhibiting the NF-κB signaling pathway， activating the Nrf2/HO-1 signaling pathway， regulating oxidative stress， and reducing inflammatory responses.

ObjectiveThis study aims to explore the effect of Feiyanning granules on ferroptosis in lung cancer cells and its regulatory function within the nuclear transcription factor E₂-related factor 2 （Nrf2）/mouse solute carrier family 7 member 11 （SLC7A11）/glutathione peroxidase 4 （GPX4） signaling pathway. MethodsThe cell counting kit-8 （CCK-8） method was used to detect the effect of Feiyanning granule on the proliferation of A549 lung cancer cells. A549 lung cancer cells were categorized into a blank group， a ferroptosis inhibitor-1 （Fer-1） group （10 μmol·L^-1）， a Feiyanning granules （600 mg·L^-1） group， and a Feiyanning granules + Fer-1 group. After 48 hours of intervention， the activity and morphology of the cells were observed. The CCK-8 method was employed to measure cell viability. Biochemical assays were carried out to measure the levels of reactive oxygen species （ROS）， malondialdehyde （MDA）， glutathione （GSH）， and ferrous ions （Fe²⁺） in A549 cells. Western blot was utilized to evaluate the expression levels of Kelch-like ECH-associated protein 1 （Keap1）， Nrf2， SLC7A11， and GPX4 proteins. A549 lung cancer cells were categorized into a blank group and a Feiyanning Granule group （600 mg·L^-1）， and mitochondrial morphology was examined via transmission electron microscopy （TEM）. ResultsAfter the intervention of Feiyaning granules， the proliferation of A549 cells was significantly inhibited in a concentration-dependent manner compared with that in the blank group （P<0.01）. Compared with the blank group， the Feiyanning granules group exerted an significantly inhibitory effect on the viability of lung cancer cells （P<0.01）. Compared with that in the Feiyanning granules group， the cell viability in the Feiyanning granules +Fer-1 group was obviously restored （P<0.05）. Compared with the blank group， the Feiyanning Granule group showed a significant increase in the levels of ROS， MDA， and Fe²⁺ （P<0.01）， a significant decrease in the GSH level （P<0.01）， and facilitated ferroptosis. Compared with the blank group， the Feiyanning granules group showed significantly decreased expression of Nrf2， SLC7A11， and GPX4 proteins and enhanced expression of Keap1 （P<0.01）. Compared with those in the Feiyanning Granule group， the protein levels of Nrf2， SLC7A11， and GPX4 increased significantly （P<0.01）， and the expression of Keap1 decreased significantly in the Feiyanning granules + Fer-1 group （P<0.01）. Compared with the blank group， the Feiyaning granules group exhibited reduced mitochondrial size and increased matrix electron density. ConclusionFeiyanning granules can induce ferroptosis in lung cancer cells， and its underlying mechanism might be associated with the inhibition of the Nrf2/SLC7A11/GPX4 signaling pathway.

Chronic obstructive pulmonary disease （COPD） is a chronic respiratory disease that poses a significant threat to global health， exhibiting high morbidity， disability and mortality rate， with its prevention and treatment situation becoming increasingly critical. The pathogenesis of COPD is complex， and the underlying cellular and molecular biological mechanisms remain incompletely elucidated. Programmed cell death （PCD） is the process wherein cells actively undergo demise to maintain internal environmental stability in response to certain signals or specific stimuli. Contemporary medical research indicates that the dysregulation of PCD patterns such as apoptosis， necroptosis， pyroptosis， autophagy， and ferroptosis is closely related to the onset and progression of COPD. Clarifying the molecular mechanisms of PCD in COPD may provide novel perspectives for in-depth understanding and prevention of the disease. Traditional Chinese medicine （TCM） is characterized by holistic regulation. In recent years， extensive research has been conducted in the TCM field focusing on modulating apoptosis， necroptosis， pyroptosis， autophagy， and ferroptosis for the treatment of COPD， yielding remarkable achievements. Therefore， this study systematically explored the molecular mechanism of PCD in COPD and reviewed the potential mechanisms and intervention status of TCM targeting PCD in COPD， aiming to provide insights and references for the clinical prevention， treatment and in-depth research of COPD.

Chronic obstructive pulmonary disease （COPD） is a chronic respiratory disease that poses a significant threat to global health， exhibiting high morbidity， disability and mortality rate， with its prevention and treatment situation becoming increasingly critical. The pathogenesis of COPD is complex， and the underlying cellular and molecular biological mechanisms remain incompletely elucidated. Programmed cell death （PCD） is the process wherein cells actively undergo demise to maintain internal environmental stability in response to certain signals or specific stimuli. Contemporary medical research indicates that the dysregulation of PCD patterns such as apoptosis， necroptosis， pyroptosis， autophagy， and ferroptosis is closely related to the onset and progression of COPD. Clarifying the molecular mechanisms of PCD in COPD may provide novel perspectives for in-depth understanding and prevention of the disease. Traditional Chinese medicine （TCM） is characterized by holistic regulation. In recent years， extensive research has been conducted in the TCM field focusing on modulating apoptosis， necroptosis， pyroptosis， autophagy， and ferroptosis for the treatment of COPD， yielding remarkable achievements. Therefore， this study systematically explored the molecular mechanism of PCD in COPD and reviewed the potential mechanisms and intervention status of TCM targeting PCD in COPD， aiming to provide insights and references for the clinical prevention， treatment and in-depth research of COPD.

Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.