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.

We reported a case of eosinophilic meningoencephalitis,presenting with symptoms such as depression,headache and memory decline.Physical examination revealed mental tension,slightly slurred speech,and cognitive decline.Laboratory tests indicated a significant increase in eosinophils in both peripheral blood and cerebrospinal fluid,suggesting eosinophilic meningoencephalitis.After treatment with glucocorticoids and cognitive function improvement measures,the patient's symptoms improved.Here,we summarized its clinical features,laboratory examination,diagnosis and treatment.Then,we discussed the pathogenesis,treatment and differential diagnosis of eosinophilia manifested as meningoencephalitis.In order to improve clinicians'understanding of eosinophilia complicated with meningoencephalitis and provide reference for clinical diagnosis and treatment of these diseases.

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 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 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.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

According to the World Health Organization (WHO) manual, sperm concentration should be measured using an improved Neubauer hemocytometer, while sperm motility should be measured by manual assessment. However, in China, thousands of laboratories do not use the improved Neubauer hemocytometer or method; instead, the Makler counting chamber is one of the most widely used chambers. To study sources of error that could impact the measurement of the apparent concentration and motility of sperm using the Makler counting chamber and to verify its accuracy for clinical application, 67 semen samples from patients attending the Department of Andrology, West China Second University Hospital, Sichuan University (Chengdu, China) between 13 September 2023 and 27 September 2023, were included. Compared with applying the cover glass immediately, delaying the application of the cover glass for 5 s, 10 s, and 30 s resulted in average increases in the sperm concentration of 30.3%, 74.1%, and 107.5%, respectively (all P < 0.0001) and in the progressive motility (PR) of 17.7%, 30.8%, and 39.6%, respectively (all P < 0.0001). However, when the semen specimens were fixed with formaldehyde, a delay in the application of the cover glass for 5 s, 10 s, and 30 s resulted in an average increase in the sperm concentration of 6.7%, 10.8%, and 14.6%, respectively, compared with immediate application of the cover glass. The accumulation of motile sperm due to delays in the application of the cover glass is a significant source of error with the Makler counting chamber and should be avoided.
Humans ; Male ; Sperm Motility/physiology* ; Sperm Count ; Semen Analysis/methods* ; Spermatozoa/physiology* ; Time Factors