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.

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Objective To compare the external morphological characteristics and movement patterns between Schistosoma japonicum and S. sinensis cercariae. Methods S. japonicum and S. sinensis cercariae were heat-fixed, and well-extended cercariae, of 50 each species, were randomly selected for measurement of body length, body width, tail stem length, and tail fork length. The external morphological characteristics of S. japonicum and S. sinensis cercariae were compared. In addition, S. japonicum-infected Oncomelania snails and S. sinensis-infected Tricula snails were observed under a microscope and the movement patterns of S. japonicum and S. sinensis cercariae were compared. Results The mean body length, body width, tail stem length, and tail fork length were (0.16 ± 0.01), (0.05 ± 0.01), (0.14 ± 0.01) mm and (0.06 ± 0.01) mm for S. japonicum cercariae, and (0.13 ± 0.01), (0.05 ± 0.01), (0.13 ± 0.01) mm and (0.06 ± 0.01) mm for S. sinensis cercariae, respectively, and there were significant differences in terms of cercaria body length (t = 14.583, P < 0.05) and tail stem length (t = 3.861, P < 0.05), while no significant differences were seen in terms of body width (t = 0.896, P > 0.05) or tail fork length (t = −0.454, P > 0.05). Microscopy revealed that the tails of both S. japonicum and S. sinensis cercariae swung from side to side and there was no significant difference in their movement pattern. Conclusion S. sinensis and S. japonicum cercariae share highly similar external external morphological characteristics and movement patterns.

Schistosomiasis was once hyper-endemic in Yunnan Province. Following concerted efforts for over 70 years, remarkable achievements have been made for schistosomiasis control in the province. In 2004, the Mid- and Long-term Plan for Schistosomiasis Prevention and Control in Yunnan Province was initiated in Yunnan Province, and the target for transmission control of schistosomiasis was achieved in the province in 2009. Following the subsequent implementation of the Outline for Key Projects in Integrated Schistosomiasis Control Program (2009—2015) and the 13th Five - year Plan for Schistosomiasis Control in Yunnan Province, no acute schistosomiasis had been identified in Yunnan Province for successive 12 years, and no local Schistosoma japonicum infections had been detected in humans, animals or Oncomelania hupensis snails for successive 6 years in the province by the end of 2020. The transmission of schistosomiasis was interrupted in Yunnan Province in 2020. This review summarizes the history of schistosomiasis, changes in schistosomiasis prevalence and progress of schistosomiasis control in Yunnan Province, and proposes the future priorities for schistosomiasis control in the province.

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.

Objective To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. Methods The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Results Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Conclusions Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Objective To analyze the expression level of YEATS2 in hepatocellular carcinoma(HCC)about its clinical prognosis and therapeutic value based on biological information from the cancer genome atlas(TCGA)and human protein atlas(HPA)databases.Methods The mRNA expression data and clinical information of HCC were downloaded from the TCGA database,the expression of YEATS2 between HCC tissues and normal tissues was analyzed by using the R software,and the protein expression differences were preliminary verified by the HPA database.The expression differences of YEATS2 between various clinical features of HCC were compared,and their effects on the survival of HCC patients by Kaplan-Meier method and COX regression analysis were then evaluated.Receiver operating characteristic(ROC)curves were plotted to evaluate their diagnostic values.The biological functions of YEATS2 in HCC were analyzed using gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis.The relationship between YEATS2 expression and tumor microenvironment(TME)was analyzed by the"ESTIMATE"algorithm,and its relationship with tumor-infiltrating immune cells(TIICs)was assessed by CIBERSORT.Analysis of YEATS2 expression levels to immune checkpoints and drug sensitivity was performed using the R package.Results The expression of YEATS2 was increased in HCC tissues(P=4.96e-21),and its expression level was correlated with age,clinical stage,pathological grade and T stage(all P＜0.05).Overall survival(OS)(P＜0.001)and progression-free survival(FPS)(P=0.016)were decreased in HCC patients with high expression of YEATS2,COX regression results showed that the expression level ofYEATS2 was associated with poor prognosis in HCC patients(OS:HR=2.167,95％CI:1.441～3.261,P=2.06e-04),and it was an independent risk factor for predicting poor prognosis in HCC patients(OS:HR=1.891,95％CI:1.243～2.877,P=0.003).The ROC curve suggested the AUCs for 1,3 and 5 years were 0.677,0.622 and 0.612,respectively,indicating good predictive ability.The TCGA database screened a total of 6 764 differential genes in the YEATS2 high and low expression groups,of which 4 094 genes were up-regulated and 2 670 genes were down-regulated in the YEATS2 high expression group.The results of GO and KEGG enrichment analyses showed that the differentially differentiated genes in the YEATS2 high expression group were mainly enriched in immunoregulation,and cell cycle regulation drug resistance pathway.The results of the TME score showed that the YEATS2 high expression group caused a decrease in immunity score(P＜0.01).The correlation between YEATS2 and TIICs showed that YEATS2 expression was positively correlated with the level of M0-type macrophage infiltration levels(r=0.48,P＜0.001)and 23 immune checkpoint genes(r=0.20～0.46,all P＜0.05),and was negatively correlated with the CD8+T-cells,plasma cells and monocyte(r=-0.26,-0.29,-0.30,P=0.021,0.011,0.008).Drug sensitivity analysis showed that the half maximal inhibitory concentration(IC50)of cabozantinib,lincitinib,doxorubicin,and cyclobenzaprine in patients with high expression of YEATS2 was higher than those in patients with low expression(all P＜0.01).Conclusion YEATS2 was highly expressed in HCC,and the expression level was associated with poor prognosis in HCC patients.YEATS2 can be used as a biomarker for the clinical early diagnosis,prognosis and immunotherapy of HCC,which may provide new ideas for clinical diagnosis and treatment.

Objective:To investigate the clinical manifestations of population following COVID-19 by using questionnaires.Methods:COVID-19 among anesthesia workers and the surrounding population was investigated between 11 November 2022 and 31 December 2022 in China.The Tencent electronic questionnaire(ID.11492813) was sent to different WeChat groups of the Association of Anesthesiologists or Society of Anesthesiologists via the WeChat platform of the medical personnel in China. The survey was conducted between January 7 and January 15, 2023. Results:A total of 17 000 questionnaires were issued for this survey, 11 060 valid questionnaires from 31 provinces and autonomous regions were collected all over the country, with a recovery rate of 65.059%.There were 10068 (91.037%) participants diagnosed as having COVID-19, and among of them, 47.606% were male and 52.394% were female. The main post-COVID-19 clinical manifestations included fever (85.777%), cough (83.731%), fatigue (75.338%), parasomnia (64.352%), limb soreness (58.890%), dizziness, headache, tinnitus (38.617%), loss or abnormality of taste (37.763%), and loss or abnormality of smell (30.960%); peripheral neuralgia was usually found within 3 days after positive nucleic acid test or positive antigen test; there were 2 963 cases accompanied with sweating, and among of them, 47.25% were male and 52.75% were female, and 37.80% of these participants continued to sweat after the nucleic acid test or antigen test became negative. There were 1 151 cases with premature heart beats among the study population, and the symptoms aggravated following COVID-19 were found in 34.32% of these patients.Conclusions:In addition to the respiratory system, the central and peripheral nerves of patients are also affected following COVID-19, and the peripheral and central nerve disorders last until several days after negative nucleic acid test or antigen test, suggesting that anesthesiologists should pay more attention to monitoring of various nerve function and impact of surgery and anesthetic drugs on the stress response of the body in such patients.

Objective:A high performance liquid chromatography (HPLC) method was developed to determine the enzymatic activity of CD38 in blood, which was the major enzyme responsible for consuming nicotinamide adenine dinucleotide (NAD). Additionally, the study aimed to detect the differences in CD38 enzymatic activity among individuals of varying ages and health statuses.Methods:A 50 μl whole blood matrix and enzyme reaction substrate of 150 μl β-NAD at a concentration of 500 μmol/L were selected for the analysis. To eliminate the impact of endogenous β-NAD, the whole blood sample was pre-incubated at 37 ℃ for 20 minutes before adding the substrate. The reaction was terminated by perchloric acid (PCA) after incubation at 37 ℃ for 40 min. The change in product nicotinamide (NAM) before and after the enzymatic reaction was measured by HPLC to calculate the CD38 activity. The linearity, limit of detection, limit of quantification, precision, and stability of the method were evaluated. The CD38 enzymatic activities in 60 healthy volunteers and 30 colorectal cancer patients in blood were determined by the developed method.Results:Pre-incubation at 37 ℃ for 20 minutes eliminated the effect of endogenous β-NAD. The correlation coefficient of NAM was 0.999 in the concentration range of 0.1-3.2 μmol/L, with limit of detection of 0.5 nmol/L and limit of quantification of 2.1 nmol/L. The average within-run imprecision ( CV) and total CV were 3.22%-4.03% and 2.91%-4.70%, respectively. The recovery rate ranged from 94.82% to 96.81%. The CD38 activity of whole blood was stable by storage at 4 ℃ for 48 hours, storage at room temperature for 8 hours, thawing of frozen whole blood at room temperature for 2 hours, or repeated freeze-thawing three times. NAM, NAD standards, and pre-treatment samples were stable after 48 hours at 4 ℃ and 8 hours at room temperature. CD38 activity gradually decreased with increasing concentration of the added CD38 inhibitor 4-aminoquinoline derivative (78c). Measurement of 60 healthy physical examination population samples showed significantly higher CD38 enzyme activity in the elderly group than that in the young group ( t=-2.776, P=0.007) and measurement of 30 colorectal cancer patients showed significantly higher CD38 enzyme activity than that in healthy people ( t=-2.572, P=0.012). Conclusion:The established HPLC method for determining CD38 enzymatic activity is characterized by its simplicity, efficiency, accuracy, and reproducibility. This technique serves as a valuable tool for investigating aging and aging-related diseases.

Objective:To explore the clinical characteristics of pneumocystis carinii pneumonia (PCP) after kidney transplantation.Methods:From January 2020 to January 2022, clinical data were retrospectively reviewed for 13 renal transplant recipients with pneumocystis pneumonia diagnosed by metagenomics next generation sequencing (mNGS). There were 3 females and 10 males with an age range of (46±10) years.The median time of postoperative onset was 10(2-21) months; The major clinical manifestations included fever ( n=11), cough ( n=7), expectoration ( n=6) and dyspnea ( n=11). Paired t-test was employed for analyzing the laboratory results at admission and discharge. Results:The diagnosis was confirmed by the detection of NGS in alveolar lavage fluid or venous blood.The levels of G test, LDH test, total T lymphocyte absolute count (CD3+ Abs), inhibitory/cytotoxic T lymphocyte count (CD3+ CD8+ Abs) and auxiliary/induced T lymphocyte absolute count (CD3+ CD4+ Abs) were (543.27±440.49) pg/ml, (529.98±222.43)U/L and (191.92±119.42)/μl, (87.33±50.59)/μl and (106.92±87.42)/μl at admission and (69.58±50.21) pg/ml, (285.38±46.62 U/L), (888.58±672.99)/μl, (336.83±305.21)/μl and (520.08±388.76)/μl at discharge.The differences were statistically significant ( P<0.001, P=0.002, 0.006, 0.017, 0.005). All of them received compound sulfamethoxazole and caspofungin.Except for one death due to septic shock after 21-day treatment, 12 cases were cured. Conclusions:mNGS test is one of the important tool for an early diagnosis of PCP.Combined use of compound sulfamethoxazole and caspofungin is an effective anti-infective regimen.And immune function monitoring is vital for adjusting antibiotic and immunosuppressive regimens.