Objective To evaluate the effectiveness of schistosomiasis surveillance in Jiangsu Province during the stage moving from transmission control to transmission interruption, and to analyze the current risk and challenges, so as to provide the evidence for achieving the target of schistosomiasis elimination. Methods Schistosomiasis surveillance data were collected from Jiangsu Province from 2012 to 2024, and the endemic areas, Schistosoma japonicum infections in humans and livestock, Oncomelania hupensis snail distribution and implementation of integrated interventions were descriptively analyzed. In addition, the trends in areas with snails, seroprevalence of human S. japonicum infections and numbers of advanced schistosomiasis cases were assessed using a Joinpoint regression model. Results The endemic areas of schistosomiasis continued to shrink in Jiangsu Province from 2012 to 2024, with the number of schistosomiasis-eliminated counties (cities, districts) increasing from 53 (75.71%) to 63 (96.92%), and interruption of schistosomiasis transmission was achieved across the province. A total of 4 600 300 person-times were tested for serum antibodies against S. japonicum, with 28 719 person-times positive detected; and 616 500 person-times were tested S. japonicum infections among local residents in Jiangsu Province from 2012 to 2024, with only 3 egg-positives detected, and no egg-positives found since 2017. A total of 187 600 herd-times were tested for schistosomiasis in livestock, and no S. japonicum infections were found. O. hupensis snail survey was performed covering 1 018 408.97 hm², and a total of 35 556.35 hm² was found with snail-infested habitats, including 174.40 hm2 of emerging snail-infested habitats. A total of 1 102 800 O. hupensis snails were identified for S. japonicum infections, and no infections were found. The areas of snail-infested habitats appeared a tendency towards a rise in Jiangsu Province from 2019 to 2023 (APC = 23.67%, P < 0.05), and the actual areas of snail-infested habitats appeared a tendency towards a decline from 2012 to 2015 (APC = −22.77%, P < 0.05), and towards a rise from 2015 to 2023 (APC = 9.76%, P < 0.01). The seroprevalence of anti-S. japonicum antibodies appeared a tendency towards a decline among residents in Jiangsu Province from 2017 to 2023 (APC = −14.92%, P < 0.01). In addition, the number of newly diagnosed advanced schistosomiasis cases appeared a tendency towards a decline from 2012 to 2024 (APC = −12.02%, P < 0.01), and the numbers of advanced schistosomiasis patients requiring treatment showed a tendency towards a decline from 2012 to 2021 (APC = −10.56%, P < 0.01) and from 2021 to 2023 (APC = −20.06%, P < 0.01). Conclusions Great progresses had been achieved in schistosomiasis control in Jiangsu Province following transmission control, and transmission interruption had been achieved; however, there are still snail-infested habitats. High-intensity surveillance and integrated control are required to be maintained to advance the achievement of the target of schistosomiasis elimination in Jiangsu Province.

ObjectiveTo investigate the current professional competency among full-time and part-time personnel of the nosocomial infection control administration in Shanghai, so as to provide a scientific basis for future training programmes. MethodsIn December 2024, a questionnaire survey was conducted by the Shanghai Nosocomial Infection Quality Control Center among full-time and part-time personnel of the nosocomial infection control administration across medical institutions at various levels and types in Shanghai using convenience sampling method. The questionnaire consisted of two parts: demographic information and professional competency assessment. The professional competency scale comprised four dimensions: fundamental cognition, basic skills, professional expertise, and personal qualities, totaling 35 items. ResultsA total of 1 179 questionnaires were distributed, with 1 144 valid responses collected, yielding an effective response rate of 97.03%. Statistically significant differences were observed among full-time and part-time personnel of the nosocomial infection control administration in terms of age (t=5.32, P=0.021), professional background (χ²=9.90, P=0.019), educational qualifications (χ²=19.10, P<0.001), professional titles (χ²=12.60, P=0.002), and the levels of medical institutions (χ²=111.08, P<0.001). The scores of full-time personnel of the nosocomial infection control administration in fundamental cognition [92 (82, 99) points] and basic skills [88 (78, 96) points] were significantly higher than those of part-time personnel(Z=-2.21, P=0.027;Z=-2.74, P=0.006). Statistically significant differences were found in fundamental cognition scores between full-time and part-time personnel of the nosocomial infection control administration regarding occupational safety protection, definition of healthcare-associated infection outbreaks, types of drug-resistant bacteria and their prevention and control strategies, and transmission routes of different infectious diseases (all P<0.05). Statistically significant differences were also observed in basic skills scores including proficient use of monitoring platforms, formulation and revision of standard operating procedures (SOPs), independent completion of targeted surveillance, guidance on basic infection control skills, guidance for key departments, and follow-up of personnel with occupational exposure (all P<0.05). However, no statistically significant differences were found in scores of professional knowledge and personal qualities (P>0.05). ConclusionThere are certain differences in professional competency between full-time and part-time personnel of the nosocomial infection control administration in Shanghai in terms of fundamental cognition and basic skills. Part-time personnel can effectively improve their professional competency through systematic training on basic infection control knowledge and practical skills, thereby comprehensively enhancing the overall quality of the nosocomial infection administration team.

BACKGROUND: Peripheral helper T (T PH ) cells are uniquely positioned within pathologically inflamed non-lymphoid tissues to stimulate B-cell responses and antibody production. However, the phenotype, function, and clinical relevance of T PH cells in hepatocellular carcinoma (HCC) are currently unknown. METHODS: Blood, tumor, and peritumoral liver tissue samples from 39 HCC patients (Sep 2016-Aug 2017) and 101 HCC patients (Sep 2011-Dec 2012) at the Third Affiliated Hospital of Sun Yat-sen University were used. Flow cytometry was used to quantify the expression, phenotype, and function of T PH cells. Log-rank tests were performed to evaluate disease-free survival and overall survival in samples from 39 patients and 101 patients with HCC. T PH cells, CD19 + B cells, and T follicular helper (T FH ) cells were cultured separately in vitro or isolated from C57/B6L mice in vivo for functional assays. RESULTS: T PH cells highly infiltrated tumor tissues, which was correlated with tumor size, early recurrence, and shorter survival time. The tumor-infiltrated T PH cells showed a unique ICOS hi CXCL13 + IL-21 - MAF + BCL-6 - phenotype and triggered naïve B-cell differentiation into regulatory B cells. Triggering programmed cell death protein 1 (PD-1) induced the production of C-X-C motif chemokine ligand 13 (CXCL13) by T PH cells, which then suppressed tumor-specific immunity and promoted disease progression. CONCLUSION Our study reveals a novel regulatory mechanism of T PH cell-regulatory B-cell-mediated immunosuppression and provides an important perspective for determining the balance between the differentiation of protumorigenic T PH cells and that of antitumorigenic T FH cells in the HCC microenvironment.
Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Humans ; T-Lymphocytes, Helper-Inducer/metabolism* ; Animals ; Mice ; Male ; Female ; Mice, Inbred C57BL ; Middle Aged ; B-Lymphocytes, Regulatory/metabolism* ; Flow Cytometry ; Interleukin-21 ; Aged ; Chemokine CXCL13/metabolism*

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Objective Based on the clinical data and imaging manifestations of patients with ischemic stroke to establish a simple clinical prediction model that is used for identifying intracranial atherosclerotic stenosis-acute large vessel occlusion(ICAS-LVO in posterior circulation before surgery.Methods The clinical data of patients with acute large vessel occlusion(LVO in the posterior circulation,who received endovascular intervention at the First Affiliated Hospital of Nanjing Medical University of China from January 2019 to September 2022,were retrospectively analyzed.According to the intraoperative angiographic findings,the patients were divided into ICAS-LVO group and non-ICAS-LVO group.Univariate analysis and multivariate logistic regression analysis were used to analyze the patient's demographic characteristics,clinical history,imaging findings,and laboratory results,based on which a clinical prediction model for ICAS-LVO was established,and according to the relevant parameters a nomogram prediction model was plotted.Results A total of 110 patients with LVO in the posterior circulation who received endovascular treatment were included in the final analysis.In 51 patients(49.6％)the cause of vascular occlusion was the atherosclerotic stenosis of the intracranial arteries.Compared with non-ICAS-LVO group,in ICAS-LVO group the patients were younger,the incidence of atrial fibrillation was lower,and the level of plasma D-dimer was lower.Three factors,including atrial fibrillation,occlusion site and collateral circulation status,were finally screened out to establish the prediction model for ICAS-LVO.This model demonstrated acceptable calibration(Hosmer-Lemeshow test,P=0.562)and good discrimination ability(AUC=0.956;95％CI:0.906-0.986).Conclusion The clinical prediction model for ICAS-LVO,which is established on the three predictive factors(absence of atrial fibrillation,occlusion located at the V4 segment of the vertebral artery or at the proximal to mid segment of the basilar artery,and a favorable collateral circulation),carries high sensitivity and accuracy.This model can help neurointervention physicians to make early identification of ICAS-LVO and to promptly formulate vascular recanalization treatment strategies.

Kidney transplantation is an effective treatment for end-stage renal disease.However,post transplantation diabetes mellitus(PTDM)is a common complication after kidney transplantation,affecting 10%to 40%of recipients and increasing the risk of cardiovascular disease,infections,sepsis and other conditions.The pathogenesis of PTDM is complex,including pancreatic β-cell dysfunction and insulin resistance.Tacrolimus,a commonly used immunosuppressive drug,is an independent risk factor for PTDM.Its mechanisms include damaging pancreatic β-cells,mediating impaired mitochondrial autophagy,etc.In addition,tacrolimus also raises blood glucose levels through various pathways,such as affecting gut microbiota metabolism and activating bile acid signaling pathways.In recent years,some new anti-diabetic drugs have shown certain application prospects in kidney transplant recipients,but the evidence-based medical evidence for their combined use still needs further exploration.In the future,it is necessary to conduct in-depth research on the multiple sites of action of tacrolimus to reduce the occurrence of PTDM and improve the prognosis of kidney transplant recipients.

Three typical failures of large-aperture 16-slice spiral Siemens SOMATOM Sensation Open CT were introduced in terms of phenomenon,cause and treatment method.References were provided for medical engineers to treat similar failures.

Three typical failures of large-aperture 16-slice spiral Siemens SOMATOM Sensation Open CT were introduced in terms of phenomenon,cause and treatment method.References were provided for medical engineers to treat similar failures.

Kidney transplantation is an effective treatment for end-stage renal disease.However,post transplantation diabetes mellitus(PTDM)is a common complication after kidney transplantation,affecting 10%to 40%of recipients and increasing the risk of cardiovascular disease,infections,sepsis and other conditions.The pathogenesis of PTDM is complex,including pancreatic β-cell dysfunction and insulin resistance.Tacrolimus,a commonly used immunosuppressive drug,is an independent risk factor for PTDM.Its mechanisms include damaging pancreatic β-cells,mediating impaired mitochondrial autophagy,etc.In addition,tacrolimus also raises blood glucose levels through various pathways,such as affecting gut microbiota metabolism and activating bile acid signaling pathways.In recent years,some new anti-diabetic drugs have shown certain application prospects in kidney transplant recipients,but the evidence-based medical evidence for their combined use still needs further exploration.In the future,it is necessary to conduct in-depth research on the multiple sites of action of tacrolimus to reduce the occurrence of PTDM and improve the prognosis of kidney transplant recipients.

Objective To construct a visual intelligent recognition model for Oncomelania hupensis robertsoni in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of O. hupensis robertsoni. Methods A total of 400 O. hupensis robertsoni and 400 Tricula snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 O. hupensis robertsoni and 300 Tricula snails. A total of 925 O. hupensis robertsoni images and 1 062 Tricula snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 O. hupensis robertsoni and 354 images from the remaining 100 Tricula snails served as an external test set. All acquired images were subjected to preprocessing, including cropping and resizing. Three data augmentation approaches were employed, including baseline, Mixup and Gaussian blurring, and model hyperparameters included two optimization algorithms of adaptive moment estimation (Adam) and stochastic gradient descent (SGD), two loss functions of focal loss and cross entropy loss, and two learning rate decay strategies of cosine annealing and multi-step. The intelligent recognition models of O. hupensis robertsoni and Tricula snails were constructed based on the EfficientNet-B4 model, and 7 training strategy groups were generated by combinations of different data augmentation approaches and hyperparameters. The performance of intelligent recognition models was tested with external test sets, and evaluated with accuracy, precision, recall, F1 score, loss, Youden’s index, and the area under the receiver operating characteristic curve (AUC) under different training strategies. Results The variation of loss values was comparable among intelligent recognition models with different data augmentation approaches. The Group 4 model constructed with Mixup and Gaussian blurring data augmentation approaches showed the optimal performance, with an accuracy of 90.38%, precision of 90.07%, F1 score of 89.44%, Youden’s index of 0.81 and AUC of 0.961 in the external test set. The accuracy of models using the SGD optimizer reduced by 29.16% as compared to those using the Adam optimizer (χ² = 81.325, P < 0.001), and the accuracy of models using the cross entropy loss function reduced by 0.80% as compared to the Group 4 model (χ² = 3.147, P > 0.05), while the accuracy of models using the multi-step learning rate decay strategy increased by 0.65% as compared to the Group 4 model (χ² = 0.208, P > 0.05). In addition, the model with the baseline + Mixup + Gaussianblurring data augmentation approach and hyperparameters of Adam optimizer, focal loss function and multi-step learning rate decay strategy showed the highest performance, with an accuracy of 91.03%, precision of 91.97%, recall of 88.11%, F1 score of 90.00%, Youden’s index of 0.82 and AUC values of 0.969 in external test set, respectively. Conclusions The intelligent recognition model of O. hupensis robertsoni based on EfficientNet-B4 model is accurate for identification of O. hupensis robertsoni and Tricula snails in Yunnan Province.