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.

ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.

ObjectiveTo explore the role of cucurbitacin B （CuB） in inducing ferroptosis in 4T1 cells and its mechanism. MethodsThe effects of CuB（0.2， 0.4， 0.8 μmol·L^-1）on the proliferation ability of 4T1 cells in vitro were detected using the methyl thiazolyl tetrazolium （MTT） assay. The clonogenic ability of 4T1 cells was detected by the plate cloning assay， and the levels of lactate dehydrogenase （LDH） in 4T1 cells were detected by the use of a kit. The mitochondrial membrane potential and reactive oxygen species （ROS） levels in 4T1 cells were detected by flow cytometry， and the mitochondrial ultrastructure of 4T1 cells was observed by transmission electron microscopy. The western blot was used to detect the expression of ferroptosis-related protein p53 in 4T1 cells， solute carrier family 7 member 11 （SCL7A11）， glutathione peroxidase 4 （GPX4）， long-chain acyl-CoA synthetase 4 （ACSL4）， transferrin receptor protein 1 （TFR1）， and ferritin heavy chain 1 （FTH1）. ResultsCompared with that in the blank group， the survival rate of 4T1 cells in CuB groups was significantly decreased （P<0.05）， and the number of cell clones in CuB groups was significantly reduced （P<0.01）. In addition， compared with that in the blank group， the leakage of LDH in cells in CuB groups was significantly increased （P<0.01）， and the mitochondrial membrane potential of cells in CuB groups decreased significantly （P<0.01）. Cellular ROS levels were significantly elevated in CuB groups （P<0.01）. The mitochondria of cells in CuB groups were obviously wrinkled， and the mitochondrial cristae were reduced or even disappeared. Compared with that in the blank group， the protein expression of p53， ACSL4， and TFR1 were significantly up-regulated in CuB groups （P<0.05）， and that of SLC7A11， GPX4， and FTH1 were significantly down-regulated （P<0.05）. ConclusionCuB may inhibit SLC7A11 and GPX4 expression by up-regulating the expression of p53， which in turn regulates the p53/SLC7A11/GPX4 signaling pathway axis and accelerates the generation of lipid peroxidation substrate by up-regulating the expression of ACSL4. It up-regulates TFR1 expression to promote cellular uptake of Fe³⁺ and down-regulates the expression of FTH1 to reduce the ability of iron storage， resulting in an elevated free Fe²⁺ level. It catalyzes the Fenton reaction， generates excess ROS， imbalances the antioxidant system and iron metabolism， and then induces ferroptosis in 4T1 cells.

ObjectiveTo explore the role of cucurbitacin B （CuB） in inducing ferroptosis in 4T1 cells and its mechanism. MethodsThe effects of CuB（0.2， 0.4， 0.8 μmol·L^-1）on the proliferation ability of 4T1 cells in vitro were detected using the methyl thiazolyl tetrazolium （MTT） assay. The clonogenic ability of 4T1 cells was detected by the plate cloning assay， and the levels of lactate dehydrogenase （LDH） in 4T1 cells were detected by the use of a kit. The mitochondrial membrane potential and reactive oxygen species （ROS） levels in 4T1 cells were detected by flow cytometry， and the mitochondrial ultrastructure of 4T1 cells was observed by transmission electron microscopy. The western blot was used to detect the expression of ferroptosis-related protein p53 in 4T1 cells， solute carrier family 7 member 11 （SCL7A11）， glutathione peroxidase 4 （GPX4）， long-chain acyl-CoA synthetase 4 （ACSL4）， transferrin receptor protein 1 （TFR1）， and ferritin heavy chain 1 （FTH1）. ResultsCompared with that in the blank group， the survival rate of 4T1 cells in CuB groups was significantly decreased （P<0.05）， and the number of cell clones in CuB groups was significantly reduced （P<0.01）. In addition， compared with that in the blank group， the leakage of LDH in cells in CuB groups was significantly increased （P<0.01）， and the mitochondrial membrane potential of cells in CuB groups decreased significantly （P<0.01）. Cellular ROS levels were significantly elevated in CuB groups （P<0.01）. The mitochondria of cells in CuB groups were obviously wrinkled， and the mitochondrial cristae were reduced or even disappeared. Compared with that in the blank group， the protein expression of p53， ACSL4， and TFR1 were significantly up-regulated in CuB groups （P<0.05）， and that of SLC7A11， GPX4， and FTH1 were significantly down-regulated （P<0.05）. ConclusionCuB may inhibit SLC7A11 and GPX4 expression by up-regulating the expression of p53， which in turn regulates the p53/SLC7A11/GPX4 signaling pathway axis and accelerates the generation of lipid peroxidation substrate by up-regulating the expression of ACSL4. It up-regulates TFR1 expression to promote cellular uptake of Fe³⁺ and down-regulates the expression of FTH1 to reduce the ability of iron storage， resulting in an elevated free Fe²⁺ level. It catalyzes the Fenton reaction， generates excess ROS， imbalances the antioxidant system and iron metabolism， and then induces ferroptosis in 4T1 cells.

As the biggest tissue of human body, skin is the first barrier of resisting external aggression. Collagen is one of important parts of the skin, which could not only affect the aesthetics of skin, but also influence the health and normal function of skin. It is the great significance to find ways that could inhibit the loss of collagen. The mechanisms of the collagen degradation in skin are complex and multifaceted. Natural bioactive products have unique advantages in treating the loss of collagen, which have multi-targets and mechanisms. In this review, the mechanisms of skin collagen degradation are discussed, and the research progress of natural bioactive products in resisting skin aging through promoting collagen synthesis are reviewed, in order to provide references for futural research.

Objective: To classify the ABO blood group phenotypes of 5 cases from a family, and to explore the molecular mechanism for reduced B antigen expression in ABO blood group system. Methods: Serological identification of the ABO blood group was performed using microcolumn gel assay and saline tube method. The soluble antigens in saliva were detected by the agglutination inhibition assay. The full-length sequences and upstream promoter regions of ABO gene were sequenced for genotyping using PacBio SMRT sequencing technology. Results: The results of serological tests indicated the expression of B antigen decreased in 3 out of 5 blood samples. A mixed-field agglutination was observed with anti-B antibody. B antigen was not detected in all 5 saliva samples. The ABO genotype for all samples were ABO B.01/ABO O.01.02, and a novel mutation c. 28+5875C>T within the DNA-binding region of RUNX1 in +5.8-kb site were found in the B allele for 3 samples with reduced expression of B antigen. Conclusion: Results of serological and genetic analyses classify the 3 cases with reduced B antigen expression as B phenotype. The novel mutation c. 28+5875C>T of RUNX1 could be the key reason for reduced B antigen expression in 3 cases with B phenotype.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.