AIM To investigate the variations in mercury dissolution from HgS-containing traditional medicines in three kinds of simulated gastrointestinal dissolution media.METHODS 39 batches of 15 types of HgS-containing traditional medicines were collected,total mercury content and dissolved mercury concentrations in simulated gastric fluid,simulated intestinal fluid,and L-cysteine-containing simulated intestinal fluid were measured.The maximum daily intake of total mercury and soluble mercury was calculated based on the maximum daily clinical dosage.RESULTS Among the 15 types of medicines,the maximum daily intake of total mercury varied by 156 times,the daily intake of soluble mercury varied by 3 502 times in simulated gastric fluid,313 times in simulated intestinal fluid,and 10 663 times in L-cysteine-containing simulated intestinal fluid,approximately.CONCLUSION For the 15 types of HgS-containing traditional medicines,the daily maximum intake of soluble mercury showed greater variations than that of total mercury.Soluble mercury concentration is more closely correlated with intestinal absorption of mercury and thus represents a more rational quality control indicator for HgS-containing traditional medicines.

BACKGROUND:Knee osteoarthritis is a common degenerative disease that significantly impacts patients'quality of life and increases the societal healthcare burden.Early and accurate diagnosis of knee osteoarthritis is crucial for the treatment and prognosis of patients.Traditional diagnostic methods are not only subjective and time-consuming but also do not guarantee consistently high accuracy.OBJECTIVE:To develop an automatic diagnostic method for knee osteoarthritis based on deep learning,utilizing deep learning networks to improve diagnostic accuracy and efficiency.METHODS:A new network model,YOLOV8-ViT,was proposed by replacing the backbone network of YOLOv8n with the Efficient-ViT network and incorporating attention mechanisms for the automatic identification and classification of X-ray images of knee osteoarthritis.The experimental dataset included 5 078 X-ray images of patients with knee osteoarthritis obtained from the Third Affiliated Hospital of Guangzhou University of Chinese Medicine.Three imaging physicians annotated the sites of knee osteoarthritis and classified them according to the Kellgren-Lawrence grading standard using Labelme software,and the results were combined.The evaluation indicators used in this study included Precision,F1 score,mean average precision(mAP),Recall,val/box_loss,val/cls_loss,and val/dfl_loss.RESULTS AND CONCLUSION:The experimental results showed that the YOLOV8-ViT model outperformed the YOLOv5n,YOLOv8n,and YOLOv9n models in terms of precision,mAP50,mAP50-95,F1 score,and Recall,while lowering val/box_loss,val/cls_loss,and val/dfl_loss by 0.496,0.45,and 0.523;1.037,0.305,and 0.728;and 0.267,0.654,and 0.854,respectively.These experimental data validate that this model has high detection accuracy.

BACKGROUND:Knee osteoarthritis is a common degenerative disease that significantly impacts patients'quality of life and increases the societal healthcare burden.Early and accurate diagnosis of knee osteoarthritis is crucial for the treatment and prognosis of patients.Traditional diagnostic methods are not only subjective and time-consuming but also do not guarantee consistently high accuracy.OBJECTIVE:To develop an automatic diagnostic method for knee osteoarthritis based on deep learning,utilizing deep learning networks to improve diagnostic accuracy and efficiency.METHODS:A new network model,YOLOV8-ViT,was proposed by replacing the backbone network of YOLOv8n with the Efficient-ViT network and incorporating attention mechanisms for the automatic identification and classification of X-ray images of knee osteoarthritis.The experimental dataset included 5 078 X-ray images of patients with knee osteoarthritis obtained from the Third Affiliated Hospital of Guangzhou University of Chinese Medicine.Three imaging physicians annotated the sites of knee osteoarthritis and classified them according to the Kellgren-Lawrence grading standard using Labelme software,and the results were combined.The evaluation indicators used in this study included Precision,F1 score,mean average precision(mAP),Recall,val/box_loss,val/cls_loss,and val/dfl_loss.RESULTS AND CONCLUSION:The experimental results showed that the YOLOV8-ViT model outperformed the YOLOv5n,YOLOv8n,and YOLOv9n models in terms of precision,mAP50,mAP50-95,F1 score,and Recall,while lowering val/box_loss,val/cls_loss,and val/dfl_loss by 0.496,0.45,and 0.523;1.037,0.305,and 0.728;and 0.267,0.654,and 0.854,respectively.These experimental data validate that this model has high detection accuracy.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

Objective:To investigate the effects of moxibustion at Tianshu(ST25)acupoint on 5-fluorouracil(5-FU)-induced intestinal mucositis(IM)and its underlying mechanisms.Methods:Eighteen C57BL/6 male mice were randomly divided into four groups:normal control(NC),IM model(IM),moxibustion 15 min(MO 15 min),and moxibustion 30 min(MO 30 min).The IM model was established via intraperitoneal injection of 5-FU.Pathological changes in colon tissues were observed using hematoxylin and eosin(HE)staining.Protein expression levels of peroxisome proliferator-activated receptor alpha(PPARα),nuclear factor kappa-B(NF-κB),phosphorylated NF-κB(p-NF-κB),NOD-like receptor thermal protein domain associated protein 3(NLRP3),caspase-1,interleukin-1β(IL-1β),and interleukin-18(IL-18)were analyzed via Western blot,ELISA,and immunohistochemistry.Results:Compared with the NC group,the IM group showed significantly shortened colon length(P＜0.05),exhibited mucosal damage,inflammatory cell infiltration,and glandular disorder,along with upregulated protein expression of p-NF-κB,NLRP3,IL-1β,IL-18,and caspase-1(P＜0.05),and downregulated PPARα expression(P＜0.05).After moxibustion intervention,the MO 15 min group demonstrated increased intestinal length(P＜0.05),reduced pathological scores(P＜0.05),significantly downregulated expression of NLRP3,p-NF-κB,IL-1β,and IL-18(P＜0.05),and elevated PPARα expression(P＜0.05),while total NF-κB protein levels remained unchanged.Conclusion:Moxibustion at Tianshu(ST25)acupoint may alleviate 5-FU-induced intestinal mucosal inflammatory responses by activating PPARα to suppress the NF-κB/NLRP3 inflammasome signaling pathway.

Objective To assess the effectiveness of the Gasdermin D C-terminal fragment(GSDMD-CT)as a novel plasma biomarker for the clinical diagnosis of sepsis.Methods Between July 2021 and November 2024,245 patients from Tangshan Gongren Hospital were enrolled in this study.In accordance with the diagnostic criteria for sepsis and the systemic inflammatory response syndrome(SIRS),patient samples were classified into the sepsis group and the SIRS group.Meanwhile,healthy individuals were selected as the healthy control(HC)group.Sepsis patients were further categorized into the Gram-positive bacterial group,the Gram-negative bacterial group,and the fungal group based on the type of pathogen infection.The levels of GSDMD-CT,C-reactive protein(CRP),and procalcitonin(PCT)were measured in all subjects.Nonparametric tests were employed to compare the differences in various indices among different groups.The diagnostic value of GSDMD-CT in sepsis was evalu-ated by constructing the receiver operating characteristic(ROC)curve.Spearman's correlation analysis was used to examine the relationships among GSDMD-CT,CRP,and PCT.Results The plasma GSDMD-CT levels in the sepsis group 23.02(16.71,33.01)pg/mL and in the SIRS group 16.52(11.26,22.22)pg/mL were significantly higher than those in the healthy control group 7.02(4.42,11.43)pg/mL(U=-10.175,-7.890,P<0.001).Moreover,the plasma GSDMD-CT levels in the sepsis group were significantly higher than those in the SIRS group(U=-2.941,P<0.05).In the Gram-positive bacterial group,the Gram-negative bacterial group,and the fungal group,the GSDMD-CT levels were 23.01(17.16,27.51)pg/mL,23.41(16.78,35.50)pg/mL,and 16.29(14.53,56.27)pg/mL,respectively.When compared with the healthy control group,the GSDMD-CT levels in these three groups were all significantly higher(P<0.05).However,there were no significant differences in GSDMD-CT levels among these three groups(P>0.05).The area under the curve(AUC)of plasma GSDMD-CT for diagnosing sepsis was 0.881(95%confidence interval:0.833～0.929),with a Youden index(YI)of 0.695,a sensitivity of 85.0%,and a specificity of 84.5%.Spearman correlation analysis indicated a weak correlation between GSDMD-CT and C-reactive protein(CRP)(r=0.32,P<0.001)and a positive correlation between GSDMD-CT and procalci-tonin(PCT)(r=0.65,P<0.001).Conclusion GSDMD-CT exhibits significant clinical value in the diagnosis of sepsis and holds great potential as a biomarker in the diagnostic process of sepsis.

A growing body of research points out that gut microbiota plays a key role in tumor immunotherapy. By optimizing the composition of intestinal microbiota, it is possible to effectively improve immunotherapy resistance and enhance its therapeutic effect. This article comprehensively analyzes the mechanism of intestinal microbiota influencing tumor immunotherapy resistance, expounds the current strategies for targeted regulation of intestinal microbiota, such as traditional Chinese medicine and plant components, fecal microbiota transplantation, probiotics, prebiotics and dietary therapy, and explores the potential mechanisms of these strategies to improve patients' resistance to tumor immunotherapy. At the same time, the article also briefly discusses the prospects and challenges of targeting intestinal microbiota to improve tumor immunotherapy resistance, which provides a reference for related research to help the strategy research of reversing tumor immunotherapy resistance.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Objective:To investigate the effectiveness and safety of lacosamide (LCM) in pregnant women with epilepsy.Methods:A retrospective study was conducted involving 6 pregnant women with epilepsy who were treated with LCM at the Electroencephalogram Monitoring Center of the Department of Neurology, Xijing Hospital of Air Force Military Medical University from January 2022 to June 2023. Their electroclinical characteristics, seizures during pregnancy, breastfeeding, and follow-up were summarized.Results:The 6 patients were aged 22 to 30 years at the time of pregnancy. Three patients were treated with monotherapy, with a daily dose of LCM ranging from 150 mg to 200 mg, while the other 3 patients were treated with combination therapy, with a daily dose of 150 mg. The seizures of 5 patients decreased during pregnancy compared with progestation except for the case 2 without adherence to Medication. No malformations were observed in the newborns, with the Apgar scores of 9-10 at 1 minute and 5 minutes after birth. The infants showed normal growth, development, intelligence, and motor skills in subsequent assessments. Two patients breastfed their infants, 1 for 6 months and the other for 14 months by the last follow-up, with a daily LCM dose of 150 mg to 300 mg during the breastfeeding. No adverse reactions were observed in the infants.Conclusion:The addition of LCM during pregnancy and lactation showed good effectiveness and safety, with no observed birth malformations.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.