Objective To establish a fluorescent recombinase-aided amplification (RAA) assay for detection of Strongyloides stercoralis nucleic acid and to preliminarily evaluate its performance. Methods Six sets of specific primers targeting S. stercoralis 18S ribosomal RNA (18S rRNA) gene and one fluorescent probe were designed and synthesized. The optimal primer-probe set was determined through systematic screening and optimization to establish the fluorescent RAA assay. The assay was evaluated using S. stercoralis genomic DNA at concentrations of 100, 10, and 1 pg/μL, and 100, 10, and 1 fg/μL, as well as recombinant pUC57 plasmids containing the target gene fragments at 1 × 10⁵, 1 × 10⁴, 1 × 10³, 1 × 10², 1 × 10¹, 1 × 10⁰ copies/reaction, to determine the analytical sensitivity. Genomic DNA from Ascaris lumbricoides, Ancylostoma duodenale, Enterobius vermicularis, Angiostrongylus cantonensis, Trichinella spiralis, Clonorchis sinensis, Schistosoma japonicum, and Taenia saginata was used to assess assay specificity. A total of 25 stool samples from patients suspected of S. stercoralis infection were tested by the modified Baermann funnel technique, PCR, and the established fluorescent RAA assay. The sensitivity, specificity, concordance rate and their 95% confidence intervals (CI) of these three techniques were estimated, and agreement between methods was evaluated using the Kappa coefficient. Results Exo-4 was identified as the optimal primer set screened from the six primer sets, and the best amplification performance was achieved when the final concentrations of the forward and reverse primers were 0.44 μmol/L and a probe concentration was 0.20 μmol/L. The limit of detection of the fluorescent RAA assay was 100 fg/μL for genomic DNA of S. stercoralis and 1 × 10⁰ copies/reaction for recombinant plasmids. Specific fluorescence signals were detected within 5 min, with no cross-reactivity observed with A. lumbricoides, A. duodenale, E. vermicularis, A. cantonensis, T. spiralis, C. sinensis, S. japonicum, or T. saginata. Among the 25 clinical stool samples from patients suspected of S. stercoralis infections, the modified Baermann funnel technique and fluorescent RAA assay detected 19 positives and 6 negatives, whereas PCR detected 18 positives and 7 negatives. The fluorescent RAA assay showed a sensitivity of 100.00% [95% CI: (82.35%, 100.00%)], specificity of 100.00% [95% CI: (54.07%, 100.00%)], concordance rate of 100.00% [95% CI: (86.28%, 100.00%)], and a Kappa coefficient of 1.00 [95% CI: (1.00, 1.00)] (P < 0.001) relative to the modified Baermann funnel technique, and a sensitivity of 100.00% [95% CI: (81.47%, 100.00%)], specificity of 85.71% [95% CI: (42.13%, 99.64%)], concordance rate of 96.00% [95% CI: (79.65%, 99.90%)], and a Kappa coefficient of 0.90 [95% CI: (0.70, 1.00)] (P < 0.001). Positive amplification products emitted green fluorescence under a portable blue-light device, enabling visual interpretation of results. Conclusions The fluorescent RAA assay established in this study is rapid, highly sensitive, and highly specific. It enables detection of S. stercoralis nucleic acid under isothermal conditions and allows visual interpretation of results, providing a novel tool for rapid clinical diagnosis and field screening of S. stercoralis infections.

To provide in-service medical technicians and nurses with convenient access to continuing education resources in transfusion medicine, reduce transfusion-related adverse events, and ensure the safety, rationalization, and effectiveness of clinical transfusion, we designed and developed an online transfusion continuing education platform. The platform was based on the new managed code programming model.NET Core and the powerful functions of hypertext preprocessor PHP 7.4, addressing current issues in transfusion online continuing education. Through in-depth analysis of student attributes, learning behaviors, and teaching behaviors, a comprehensive online continuous teaching quality evaluation index system was established. This system not only facilitates the quantitative assessment of teaching quality but also successfully integrates the two core functions of teaching and management, thereby achieving unified online teaching.

To train applied stomatology students, a stomatology practice education system was created and practiced in conjunction with post competency in the context of New Medicine. The practice teaching system is featured by six progressive stages and integration of knowledge and practice, uses student-centered and ability-oriented teaching model throughout the education, integrates the curriculum ideology system of working together in the same direction and cultivating talents with high moral standards, and forms a multi-cooperative and people-oriented evaluation system. The stomatology practice education system led by the New Medicine with collaboration of multiple elements such as "medicine, teaching, research, and innovation" has contributed to the comprehensive improvement of post competency in stomatology students. This system has played an important supportive role in the training of applied stomatology talents in the new era.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

Purpose To investigate the differential diagnosis of diffusion-weighted imaging(DWI),perfusion-weighted imaging(PWI)and DWI combined with PWI in supratentorial pilocytic astrocytoma(PA)and WHO grade 2 pleomorphic xanthoastrocytoma(PXA).Materials and Methods The conventional MRI,DWI and PWI data of 23 cases of supratentorial PA and 11 cases of PXA(WHO grade 2)in the First Affiliated Hospital of Fujian Medical University from January 2010 to February 2022 were retrospectively analyzed.Supratentorial PA was further divided into lobar PA and non-lobar PA based on the lesion location.The basic data(gender,age,cystic degeneration,homogeneous enhancement,lesion size and meningeal involvement),minimum apparent diffusion coefficient,relative apparent diffusion coefficient and relative maximum cerebral blood volume were compared and analyzed between the two groups.Results The probability of leptomeningeal involvement in PXA(WHO grade 2)(36.3%)was higher than that in supratentorial PA(4.3%,P=0.029).Compared with supratentorial PA and lobar PA,PXA(WHO grade 2)had lower minimum apparent diffusion coefficient,relative apparent diffusion coefficient and higher relative maximum cerebral blood volume(t=-4.398-5.828,all P<0.05).The threshold value of minimum apparent diffusion coefficient to discriminate between supratentorial PA and PXA(WHO grade 2)was 1.09×10-3 mm2/s,and its sensitivity,specificity and area under the curve were 90.91%,83.33%and 0.947,respectively.When the differential diagnosis threshold of relative maximum cerebral blood volume was 1.79,its sensitivity,specificity and area under the curve were 90.00%,100.00%and 0.950,respectively.The area under the curve of DWI combined with PWI was 0.993,which did not significantly improve the diagnostic performance compared with DWI and PWI respectively(Z=1.371,0.928,both P>0.05).In subgroup analysis,the area under the curve of DWI combined with PWI in lobar PA and PXA(WHO grade 2)was 0.988,which did not improve the diagnostic performance compared with DWI and PWI respectively(Z=1.322,0.882,both P>0.05).Conclusion DWI and PWI are helpful in the differential diagnosis of PXA(WHO grade 2)from supratentorial PA and lobar PA,but combining DWI and PWI does not significantly improve the efficacy of differential diagnosis.

This study aims to explore protective effects of Lycium barbarum polysaccharides(LBP)on intestinal damage caused by lipopolysaccharide(LPS)-induced inflammatory bowel disease(IBD)in chicks.Network pharmacology was initially employed to determine the target proteins of wolfberry in the prevention and treatment of IBD.Following this,protein-protein interaction analy-sis,GO and KEGG pathway enrichment analysis,and molecular docking studies were conducted.Subsequently,an animal study was conducted:a total of 100 one-day-old male Hy-line brown lay-ing hens were randomly divided into five groups:a blank control group(CON),an LPS treatment group(LPS),a low-dose LBP group(LPS+LBP 0.25 g/L,L-LBP),a medium-dose LBP group(LPS+LBP 0.5 g/L,M-LBP),and a high-dose LBP group(LPS+LBP 1 g/L,H-LBP).Upon reac-hing 21 days old,duodenal,jejunal,ileal,and cecal tissues were collected to determine SOD and GSH-Px levels.Furthermore,the mRNA expression levels of TNF-α,AKT1,IL-6,IL-1β and TP53 in the intestinal tissues were measured using quantitative real-time PCR.The results demonstrated that network pharmacology identified 45 active ingredients in wolfberry that target 116 key protein sites,including TNF,AKT1 and IL6.The primary objectives focus on signaling pathways including AGE-RAGE,IL-17,TNF,HIF-1,and NF-κB.Molecular docking showed excellent ligand-receptor docking scores,with stable binding facilitated by hydrogen bonds and hydrophobic interactions.Compared to the LPS group,the 0.5 g/L LBP exhibited notably higher levels of SOD and T-AOC.In comparison with the LPS group,the medium and high-dose LBP experimental groups showed notably decreased the mRNA expressions of TNF-α,AKT1,IL-6,and IL-1β,while TP53 mRNA expression was significantly upregulated(P＜0.01).In summary,wolfberry exerts preventive and therapeutic effects on IBD through a multi-component,multi-target,and multi-pathway mecha-nism.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.

This study aims to explore protective effects of Lycium barbarum polysaccharides(LBP)on intestinal damage caused by lipopolysaccharide(LPS)-induced inflammatory bowel disease(IBD)in chicks.Network pharmacology was initially employed to determine the target proteins of wolfberry in the prevention and treatment of IBD.Following this,protein-protein interaction analy-sis,GO and KEGG pathway enrichment analysis,and molecular docking studies were conducted.Subsequently,an animal study was conducted:a total of 100 one-day-old male Hy-line brown lay-ing hens were randomly divided into five groups:a blank control group(CON),an LPS treatment group(LPS),a low-dose LBP group(LPS+LBP 0.25 g/L,L-LBP),a medium-dose LBP group(LPS+LBP 0.5 g/L,M-LBP),and a high-dose LBP group(LPS+LBP 1 g/L,H-LBP).Upon reac-hing 21 days old,duodenal,jejunal,ileal,and cecal tissues were collected to determine SOD and GSH-Px levels.Furthermore,the mRNA expression levels of TNF-α,AKT1,IL-6,IL-1β and TP53 in the intestinal tissues were measured using quantitative real-time PCR.The results demonstrated that network pharmacology identified 45 active ingredients in wolfberry that target 116 key protein sites,including TNF,AKT1 and IL6.The primary objectives focus on signaling pathways including AGE-RAGE,IL-17,TNF,HIF-1,and NF-κB.Molecular docking showed excellent ligand-receptor docking scores,with stable binding facilitated by hydrogen bonds and hydrophobic interactions.Compared to the LPS group,the 0.5 g/L LBP exhibited notably higher levels of SOD and T-AOC.In comparison with the LPS group,the medium and high-dose LBP experimental groups showed notably decreased the mRNA expressions of TNF-α,AKT1,IL-6,and IL-1β,while TP53 mRNA expression was significantly upregulated(P＜0.01).In summary,wolfberry exerts preventive and therapeutic effects on IBD through a multi-component,multi-target,and multi-pathway mecha-nism.

Objective:To construct and validate a predictive model and application program for stone recurrence after endoscopic retrograde cholangiopancreatography (ERCP) based on machine learning algorithms in patients with common bile duct stones (CBDS).Methods:A multicenter retrospective cohort study was conducted, 862 CBDS patients underwent ERCP from June 2020 to September 2023 in Changshu First People′s Hospital (data set 1, 759 cases, including a training set of 588 cases and a validation set of 171 cases) and Changshu Hospital of Traditional Chinese Medicine (data set 2, 103 cases, used as a test set). The demographics, medical history, ERCP procedural records and laboratory indices were collected. All patients were followed up for 1 year, and the stone recurrence was recorded. In training set, the feature selection was conducted by the least absolute shrinkage and selection operator (LASSO) algorithm, and a conventional Logistic regression model was constructed based on selected features. The 3 machine learning algorithms (gradient boosting machine model, extreme gradient boosting model and random forest model) and a conventional Logistic regression model (LASSO model) were trained to fit predictive models. The model performance was assessed by area under curve (AUC) of receiver operating characteristic curve. The model interpretability was analyzed by feature importance evaluation, Shapley additive explanations (SHAP) and force plots. The best-performing model was deployed as an online application by Streamlit framework (V1.36.0).Results:Among the 862 patients, 158 patients (18.33%) developed stone recurrence after ERCP. There were no statistical difference in demographics, medical history, ERCP procedural records and laboratory indices between training set and a validation set ( P>0.05). LASSO regression analysis result showed that 6 key variables (in descending order of significance: endoscopic sphincterotomy, common bile duct angulation, stone diameter, stone count, common bile duct diameter, and periampullary diverticulum) influencing stone recurrence. ROC curve analysis result showed that the random forest model exhibited the highest predictive performance (it had the largest AUC of 0.900). SHAP analysis result showed that common bile duct angulation, common bile duct diameter, stone diameter, endoscopic sphincterotomy and stone count were the top 5 contributing factors in the random forest model. Using Python, the random forest model was implemented into a Streamlit-based application with a user-friendly visual interface, providing predictive outcomes, confidence levels, SHAP force diagram and health recommendations. In the test set, the application program achieved an accuracy of 84.5% (87/103), sensitivity of 82.6% (19/23), and specificity of 85.0% (68/80). SHAP plots and force diagram intuitively illustrated the impact of key features on stone recurrence prediction, offering a clear visualization of each variable′s role within the model. Conclusions:The predictive model and application program based on the random forest machine learning algorithms demonstrate excellent predictive performance and practical usability in predicting stone recurrence after ERCP in patients with CBDS.

Objective To construct YOLOv10 based artificial intelligence(AI)models for the automatic detection in small bowel capsule endoscopy(SBCE)images.Methods SBCE data from two centers was collected,including 23 115 images and 35 412 annotated labels covering 11 categories of small bowel lesions.The images were annotated using the LabelMe tool and converted into the YOLO format required for deep learning model development.The pre-trained YOLOv10 and YOLOv8 models were used for transfer learning training on the constructed dataset.Model performance was comprehensively evaluated using metrics such as precision,accuracy,sensitivity,specificity,false-positive rate,and detection speed.Finally,the models were deployed on local computers for real-time detection of SBCE images and videos.Results Six different versions of YOLO object detection models were developed,namely YOLOv8n,YOLOv8s,YOLOv8m,YOLOv10n,YOLOv10s,and YOLOv10m.On the validation set,YOLOv10s model achieved the best mAP50(0.795);although its inference latency was not the fastest(4.803 ms/img),it met the requirements for clinical application.On the test set,YOLOv10s performed well,with an accuracy of 92.69%,a sensitivity of 89.23%,and a false-positive rate of 4.78%.Especially,in category-specific inference,the highest sensitivity was for"bleeding"at 96.41%,while the lowest was for"narrowing"at 82.29%.Conclusion The model constructed based on YOLOv10 neural network can rapidly and accurately detect and classify various small bowel lesions,exhibiting significant clinical application potential.