Objective:To evaluate the efficacy and safety of transdermal delivery of compound glycyrrhizin injection as an adjunctive treatment for erythematotelangiectatic rosacea (ETR).Methods:This was a randomized controlled trial conducted from March to October 2024. At Sichuan Provincial People′s Hospital, 60 patients with newly diagnosed ETR were prospectively enrolled and randomized by a random number table into study group [ n=30; 6 male and 24 female; aged 18-60 (38.9±9.8) years] and control group [ n=30; 4 male and 26 female; aged 18-60 (35.7±10.1) years]. The study group received transdermal delivery of compound glycyrrhizin injection by a medium-frequency drug-delivery therapeutic apparatus together with oral azithromycin and hydroxychloroquine sulfate, whereas the control group received oral azithromycin and hydroxychloroquine sulfate. The efficacy evaluations were conducted at baseline and at weeks 2, 4, 6, and 8 post-treatment. Outcome measures included percentage of erythema area, stratum corneum hydration, transepidermal water loss (TEWL), clinician′s erythema assessment (CEA), erythema and telangiectasia scores, dermatology life quality index (DLQI), and efficacy rate. Adverse reactions during treatment were also recorded. Results:Compared with baseline, both groups exhibited significant reductions in percentage of erythema area, TEWL, CEA, erythema and telangiectasia score, and DLQI, and significant increases in stratum corneum hydration at each post-treatment time point (all P<0.05). After 2, 4, 6, and 8 weeks of treatment, the percentage of erythema area, erythema and telangiectasia scores in the study group were all lower than those in the control group, while the stratum corneum hydration level in the study group was higher than that in the control group (all P<0.05). After 4, 6, and 8 weeks of treatment, TEWL, CEA, and DLQI in the study group were all lower than those in the control group (all P<0.05). After 4 weeks of treatment, the efficacy rate in the study group was 56.7% (17/30), which was higher than that of the control group at 23.3% (7/30, P=0.046). After 6 weeks of treatment, the efficacy rate in the study group was 83.3% (25/30), higher than that of the control group at 50.0% (15/30, P=0.020). After 8 weeks of treatment, the efficacy rate in the study group was 86.7% (26/30), higher than that of the control group at 66.7% (20/30, P<0.001). No severe adverse reactions were observed in either group. Conclusion:Transdermal delivery of compound glycyrrhizin injection as an adjunctive treatment for ETR demonstrates favorable efficacy and good safety.

Objective:To analyze the composition and diversity characteristics of facial microbial communities in patients with moderate acne.Methods:This prospective study enrolled 30 patients with moderate acne [12 males, 18 females; aged 21-30 (25.4±2.5) years] from the Department of Dermatology, Sichuan Provincial People′s Hospital from March to July 2021. Thirty healthy controls [13 males, 17 females; aged 24-29 (25.2±1.4) years] were included during the same period. Facial skin swabs were collected from both groups. Total DNA was extracted, followed by PCR amplification, library preparation, and PE250 sequencing. After splicing, filtering, denoising, and chimera removal, amplicon sequence variants (ASV) feature tables and representative sequences were generated to compare microbial community differences between the two groups.Results:A total of 60 samples were sequenced, yielding 2 021 342 valid sequences. The 16S rRNA gene sequences were clustered into 8 379 ASV, with 589 ASV shared between the two groups, while 6 445 ASV were uniquely identified in healthy controls. At the phylum level, both groups showed similar dominant phyla: Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. At the genus level, the acne group was predominantly colonized by Ralstonia (relative abundance 31.85%) and Staphylococcus (28.04%), while healthy controls exhibited more balanced distributions, primarily Staphylococcus (9.18%) and Enhydrobacter (7.37%). Alpha diversity analysis, Beta diversity analysis, and LefSe analysis revealed statistically significant differences in microbial communities between groups ( R2=0.157, P<0.001). The acne group showed lower microbial richness and evenness compared to healthy controls (both P<0.001). Conclusion:Patients with moderate acne exhibit microecological imbalance in facial microbial communities, characterized by reduced microbial richness and evenness.

Rapid developments in biotechnology have led researchers to seek new method to improve the efficiency and accuracy of biomedical research and drug development,promoting interdisciplinary integration.Recent advancements in artificial intelligence(AI)technologies have brought unprecedented opportunities to this field.The integration of various AI models allows researchers to better utilize multi-omics data,identify disease phenotypes,interpret animal behavior,assess treatment effects,improve experimental designs,reduce the use of experimental animals,enhance animal facility management,and improve animal welfare.This article reviews the advancements in AI biomedical research over the past decade and discusses its contributions to disease phenotype identification,the selection and design of experimental animal models,animal behavior analysis,and animal facility management.It also points out the challenges related to data standardization,AI model selection and interpretability,the extrapolation process from AI models to animal experiments and clinical practice,as well as ethical considerations in using AI in sensitive areas involving human genetics and personalized medicine.This review aims to help researchers and practitioners in relevant fields understand the current state and opportunities of AI development,thus providing support for its broader application.

Objective: To comprehensively evaluate and analyze the transfusion outcomes of patients with acute upper gastrointestinal bleeding (UGIB). Methods: The transfusion management system and hospital information system (HIS) were used to retrospectively collect clinical data of 230 patients with UGIB admitted to Zhejiang Provincial People's Hospital and its branches from June 2018 to June 2021. 101 cases were screened and categorized into transfusion group (n=56) and non-transfusion group (n=45) based on transfusion outcomes. The cohort comprised 68 males and 33 females. A univariate model based on the AIMS65 score, a logistic multiple regression model, and multivariate transfusion models using machine learning methods (including Random Forest, Support Vector Machine, and Artificial Neural Network) were established. The sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curves of each model were compared. Results: For the univariate model based on the AIMS65 scoring, the optimal threshold was 1.5. This model demonstrated a sensitivity of 0.446, a specificity of 0.822, an AUC of 0.67, an accuracy (ACC) of 0.614, a Kappa value of 0.256, and an F1-score of 0.655. For logistics regression model (optimal critical probability: 0.459), the sensitivity was 0.929, specificity was 0.889, AUC was 0.96, ACC was 0.911, Kappa was 0.819, and F1-score was 0.899. For the Random Forest model (optimal critical probability: 0.458), the sensitivity was 0.964, specificity was 0.956, AUC was 0.99, ACC was 0.960, Kappa was 0.920, and F1-score was 0.956. For the Support Vector Machine model (optimal critical probability: 0.474), the sensitivity was 0.875, specificity was 0.933, AUC was 0.94, ACC was 0.901, Kappa was 0.801, and F1-score was 0.894. For the Artificial Neural Network model (optimal critical probability: 0.797), the sensitivity was 0.804, specificity was 0.956, AUC was 0.96, ACC was 0.871, Kappa was 0.745, and F1-score was 0.869. Ten-fold cross validation also confirmed the reliability of the results. Conclusion: Based on integrated various clinical test indicators of patients, we could establish logistic regression model and multiple machine learning models. These models hold significant value for predicting the need for blood transfusion in patients, indicating a promising application prospect for machine learning algorithms in transfusion prediction.

Objective:To develop and validate a diagnostic model for pancreatic benign and malignant tumors using radiomics technology.Methods:A retrospective analysis was conducted on the clinical data of 113 patients with pancreatic tumors who underwent surgical treatment at the Hepatobiliary and Pancreatic Surgery Departments of the Second Hospital and the First Hospital of Hebei Medical University from January 2020 to December 2022. There were 59 male and 54 female patients, aged (55.3±16.8) years. Preoperative enhanced thin-slice computed tomography (CT) data and postoperative pathological diagnosis results were collected. Data from 74 patients at the Second Hospital were selected, and according to the random classification principle of 7∶3, the data of 52 patients were determined as the training set for model construction, while the remaining 22 patients' data served as the internal validation set. Data from 39 patients at the First Hospital of Hebei Medical University were used as the external validation set to assess the generali-zability of the established model. The region of interest in the lesions on CT images was analyzed using three-dimensional radiomics feature extraction, and the top 5 features were selected using feature selection methods. Radiomics models were established for the selected features using 17 classifiers. The performance of the models was evaluated using the area under curve (AUC) of the receiver operating characteristic (ROC).Results:Two hundred and fifty-five models were established with 15 feature selection methods and 17 classifiers. 7 models with the AUC greater than 0.7 were selected, among which the best one was LASSO-K neighbors classifier model, constructed using the LASSO feature selection method and the k-nearest neighbors algorithm, achieving AUC values of 0.933 (95% CI: 0.859-0.984) in the training set, 0.973 (95% CI: 0.896-1.000) in the validation set, and 0.774 (95% CI: 0.624-0.908) in the external validation set, with satisfactoryclassification and generalization ability. Conclusion:The radiomics-based diagnostic model for pancreatic benign and malignant tumors can effectively distinguish the benignancy and malignancy of tumors. The LASSO-K neighbors classifier model demonstrated high accuracy and reliability in this study.

Objective:To explore the aperiodic components(1/f slopes)and their associations with cognitive impairment in patients with schizophrenia.Methods:Nineteen patients with schizophrenia according to the Interna-tional Statistical Classification of Diseases and Related Health Problem,Tenth Revision(ICD-10)and 21 normal controls were administrated the total Brief Assessment of Cognition in Schizophrenia(BACS)to measure the cogni-tive performance.The 5-minute eyes-closed and eyes-open resting EEG signals were collected and parameterized in-to aperiodic components(1/f slope).Finally,Pearson correlation was used to examine the relationships between the 1/f slope and cognition assessment scores.Results:The patients with schizophrenia had higher 1/f slope compared to HC on central location of scalp(P＜0.05).The vocal memory scores showed a significantly positive relation with 1/f slopes in patients with schizophrenia(anterior location:r=-0.68,P＜0.05;central location:r=-0.44,P＜0.05),but a significantly negative relation in normal controls(anterior location:r=0.57,P＜0.05;posterior lo-cation:r=0.54,P＜0.05).Conclusion:The 1/f slopes of EEG in schizophrenia were steeper than normal control,suggesting its strong cognitive functional significance and complex mechanisms in schizophrenia.

Objective To investigate the regulation of synaptic plasticity by cannabinoid receptor 1(CB1R)and its effects on autism spectrum disorder(ASD)-like behavior.Methods CB1R-knockout(KO)mice and valproic acid(VPA)-induced ASD model mice(VPA mice)were used as study subjects.Behavioral experiments were used to assess the effects of CB1R on ASD-like behavior in mice,neuronal structural integrity and dendritic density were detected by microtubule-associated protein 2(MAP2)staining experiments,and the expression of synapse-associated proteins was detected by Western blot,to assess the effects of CB1R on synaptic plasticity.Results Behavioral result showed that VPA mice demonstrated significant ASD-like behavior,while CB1R-/-mice spent a significantly smaller proportion of residence time in the central region of the open field(P＜0.0001),showed significant increases in the number of marbles buried and self-grooming time(P＜0.01),significantly less time spent socializing with unfamiliar mice 2 and exploring unfamiliar objects(P＜0.001),and significantly more time exploring old objects(P＜0.05).The relative dwelling time was significantly reduced in CB1R+/-mice(P＜0.001),and the number of marbles buried and self-grooming time were significantly increased(P＜0.05).Synaptic plasticity assays revealed significant synaptic plasticity impairment in VPA mice.Hippocampal MAP2-positive neuron densities were significantly reduced in CB1R-/-and CB1R+/-mice,and expression levels of synapsin-1 were significantly increased(P＜0.05).Conclusions CB1R KO leads to ASD-like behavior such as anxiety and repetitive stereotyped behavior,social and cognitive impairments,as well as neuronal damage,dendritic dysplasia and disrupted synaptic protein expression in mice,suggesting that CB1R is involved in regulating synaptic plasticity as a pathological mechanism for the development of ASD-like behavior.

Rapid developments in biotechnology have led researchers to seek new method to improve the efficiency and accuracy of biomedical research and drug development,promoting interdisciplinary integration.Recent advancements in artificial intelligence(AI)technologies have brought unprecedented opportunities to this field.The integration of various AI models allows researchers to better utilize multi-omics data,identify disease phenotypes,interpret animal behavior,assess treatment effects,improve experimental designs,reduce the use of experimental animals,enhance animal facility management,and improve animal welfare.This article reviews the advancements in AI biomedical research over the past decade and discusses its contributions to disease phenotype identification,the selection and design of experimental animal models,animal behavior analysis,and animal facility management.It also points out the challenges related to data standardization,AI model selection and interpretability,the extrapolation process from AI models to animal experiments and clinical practice,as well as ethical considerations in using AI in sensitive areas involving human genetics and personalized medicine.This review aims to help researchers and practitioners in relevant fields understand the current state and opportunities of AI development,thus providing support for its broader application.

Objective:To explore the aperiodic components(1/f slopes)and their associations with cognitive impairment in patients with schizophrenia.Methods:Nineteen patients with schizophrenia according to the Interna-tional Statistical Classification of Diseases and Related Health Problem,Tenth Revision(ICD-10)and 21 normal controls were administrated the total Brief Assessment of Cognition in Schizophrenia(BACS)to measure the cogni-tive performance.The 5-minute eyes-closed and eyes-open resting EEG signals were collected and parameterized in-to aperiodic components(1/f slope).Finally,Pearson correlation was used to examine the relationships between the 1/f slope and cognition assessment scores.Results:The patients with schizophrenia had higher 1/f slope compared to HC on central location of scalp(P＜0.05).The vocal memory scores showed a significantly positive relation with 1/f slopes in patients with schizophrenia(anterior location:r=-0.68,P＜0.05;central location:r=-0.44,P＜0.05),but a significantly negative relation in normal controls(anterior location:r=0.57,P＜0.05;posterior lo-cation:r=0.54,P＜0.05).Conclusion:The 1/f slopes of EEG in schizophrenia were steeper than normal control,suggesting its strong cognitive functional significance and complex mechanisms in schizophrenia.

Objective To investigate the regulation of synaptic plasticity by cannabinoid receptor 1(CB1R)and its effects on autism spectrum disorder(ASD)-like behavior.Methods CB1R-knockout(KO)mice and valproic acid(VPA)-induced ASD model mice(VPA mice)were used as study subjects.Behavioral experiments were used to assess the effects of CB1R on ASD-like behavior in mice,neuronal structural integrity and dendritic density were detected by microtubule-associated protein 2(MAP2)staining experiments,and the expression of synapse-associated proteins was detected by Western blot,to assess the effects of CB1R on synaptic plasticity.Results Behavioral result showed that VPA mice demonstrated significant ASD-like behavior,while CB1R-/-mice spent a significantly smaller proportion of residence time in the central region of the open field(P＜0.0001),showed significant increases in the number of marbles buried and self-grooming time(P＜0.01),significantly less time spent socializing with unfamiliar mice 2 and exploring unfamiliar objects(P＜0.001),and significantly more time exploring old objects(P＜0.05).The relative dwelling time was significantly reduced in CB1R+/-mice(P＜0.001),and the number of marbles buried and self-grooming time were significantly increased(P＜0.05).Synaptic plasticity assays revealed significant synaptic plasticity impairment in VPA mice.Hippocampal MAP2-positive neuron densities were significantly reduced in CB1R-/-and CB1R+/-mice,and expression levels of synapsin-1 were significantly increased(P＜0.05).Conclusions CB1R KO leads to ASD-like behavior such as anxiety and repetitive stereotyped behavior,social and cognitive impairments,as well as neuronal damage,dendritic dysplasia and disrupted synaptic protein expression in mice,suggesting that CB1R is involved in regulating synaptic plasticity as a pathological mechanism for the development of ASD-like behavior.