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.

Purpose/Significance The paper discusses the application of artificial intelligence technology to the key entity recognition ofunstructured text data in the electronic medical records of lymphedema patients.Method/Process It expounds the solution of model fine-tuning training under the background of sample scarcity,a total of 594 patients admitted to the department of lymphatic surgery of Beijing Shijitan Hospital,Capital Medical University are selected as the research objects.The prediction layer of the GlobalPointer model is fine-tuned according to 15 key entity categories labeled by clinicians,nested and non-nested key entities are identified with its glob-al pointer.The accuracy of the experimental results and the feasibility of clinical application are analyzed.Result/Conclusion After fine-tuning,the average accuracy rate,recall rate and Macro_F1 ofthe model are 0.795,0.641 and 0.697,respectively,which lay a foundation for accurate mining of lymphedema EMR data.

With the rapid development of information technology， research on ancient TCM books has shifted from the traditional collation and digitization into intelligent knowledge service， thereby achieving the deep integration of ancient TCM books collation and clinical needs. Based on the clinical problem and knowledge element theory， we implemented in-depth indexing and knowledge mining for 600 kinds of ancient TCM books， built a knowledge sharing service platform for ancient TCM books by integrating database， cloud platform， knowledge graph and other technologies， and carried out the thematic literature research and developed databases for four major diseases including stroke， heart failure， liver cirrhosis， and diabetes. The digital intelligence products have been applied in hundreds of hospitals for evaluation and feedback. Finally， through "digital processing plus intelligent application"， the two-way interaction between ancient TCM books and current clinical practice is realized， and the path and paradigm of ancient TCM books knowledge serving the modern prevention and control of major diseases is formed， providing reference for the innovative utilization of ancient TCM books.

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.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To study the application of 3D printing technology in multi-center fenestrated/branched endovascular repair (F/B-EVAR) for endovascular repair of abdominal aortic diseases.Methods:From Feb 2018 to Mar 2023, The clinical and followup data of 316 cases of abdominal aortic lesions undergoing repair with F/B-EVAR at 69 medical centers nationwide using 3D printing technology to guide physician-modified stent graft were retrospectively analyzed.Results:The mean follow-up time of the patients was 23 months (2-60 months), and 24 cases were lost to follow up, the follow-up rate was 92.4% (292/316), the mean postoperative hospitalization time was (8.2±4.9) days. A total of 944 main abdominal branch arteries were reconstructed. Intraoperative reconstruction of 11 branches failed, with a success rate of 98.8% (933/944). Within 30 days after surgery, 8 patients died (2.5%), and 6 patients died during follow-up, a total of 14 patients died (4.4%). There were 11 cases (3.5%) of spinal cord ischemia and no patient suffered from permanent paraplegia. There were 19 patients (6.0%) with postoperative renal function injury. Internal leakage was found in 26 patients, and the rate of internal leakage was 8.2%.Conclusion:3D printing technology can accurately locate the location of branch arteries, simplifing the surgical process, shortening the learning curve , and improving clinical efficacy.

Objective : To investigate the molecular mechanism of Toll⁃like receptor 4 ( TLR4)/Ras homologue A (RhoA) signaling pathway involved in regulating the effect of septic serum on vascular endothelial cell permeability before and after continuous hemofiltration. Methods : The serum of 5 patients with sepsis before and after continuous hemofiltration treatment was collected , and the levels of inflammatory cytokines in serum before and after hemofiltration were detected. Human umbilical vein endothelial cells (HUVEC) were treated with serum before and after continuous hemofiltration for 24 hours. The expression of VE⁃cadherin , F ⁃actin , TLR4 and RhoA in vascular endothelial cells were detected by Western blot. A TLR4 low expression cell line was constructed to detect the effect of TLR4 low expression on the expression of VE⁃cadherin , F ⁃actin and RhoA and the permeability of endothelial cells. Results : After continuous blood treatment , the serum levels of TLR4 , RhoA , interleukin⁃1 (IL⁃1) , interleukin⁃6 (IL⁃6) and tumor necrosis factor⁃α (TNF⁃α ) significantly decreased. The expression levels of VE⁃cadherin , F ⁃actin , TLR4 and RhoA in the serum intervention group after continuous hemofiltration treatment significantly decreased , and the cell permeability significantly decreased. Low expression of TLR4 significantly promoted the expression of VE⁃cadherin and F ⁃actin , and inhibited the expression of RhoA protein. Conclusion TLR4/RhoA signaling pathway is involved in the regulation of changes in vascular endothelial cell permeability induced by septic serum after continuous hemofiltration treatment.

Due to the complexity of bioactive ingredients in biological samples,the screening of target proteins is a complex process.Herein,a feasible strategy for directing protein immobilization on silica magnetic beads for ligand fishing based on SpyTag/SpyCatcher(ST/SC)-mediated anchoring is presented.Carboxyl functional groups on the surface of silica-coated magnetic beads(SMBs)were coupled with SC using the 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride/N-hydroxysulfosuccinimide method,named SC-SMBs.The green fluorescent protein(GFP),as the capturing protein model,was ST-labeled and anchored at a specific orientation onto the surface of SC-SMBs directly from relevant cell lysates via ST/SC self-ligation.The characteristics of the SC-SMBs were studied via electron microscopy,energy dispersive spectroscopy,and Fourier transform infrared spectroscopy.The spontaneity and site-specificity of this unique reaction were confirmed via electrophoresis and fluorescence analyses.Although the alkaline stability of ST-GFP-ligated SC-SMBs was not ideal,the formed isopeptide bond was unbreakable under acidic conditions(0.05 M glycine-HCl buffer,pH 1-6)for 2 h,under 20％ethanol solution within 7 days,and at most temperatures.We,therefore,present a simple and universal strategy for the preparation of diverse protein-functionalized SMBs for ligand fishing,prompting its usage on drug screening and target finding.

Animals ; Coronary Vessels ; Diabetes Mellitus ; Muscle, Smooth, Vascular ; Myocytes, Smooth Muscle ; Rats

Humans ; COVID-19 ; Consensus ; SARS-CoV-2 ; China