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 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 develop a rapid method for detection of activated RhoA protein using the high content imaging system(HCIS).METHODS Hek293 or CHO cells were seeded in 96-well plates and subjected to starvation treatment after attachment.Hek293 cells were incubated with nocodazole,a RhoA agonist,at concentrations of 0(vehicle control),10-11,10-10,10-9,10-8,10-7,10-6,10-5 and 10-4 mol·L-1 for 3,10 and 30 min respectively.CHO cells were incubated with nocodazole,lyso-phosphatidic acid(LPA)and calpain at the same concentrations for 3,10 and 30 min respectively.Imme-diately after incubation,the cells were fixed with 3.7%formaldehyde solution and stained using Hoechst and rhodamin phallodin at room temperature and protected from light.Images were captured using HCIS and analyzed statistically.Changes in the mean fluorescence intensity(MFI)were used to assess the activation of RhoA protein by the drugs.RESULTS Compared with the vehicle control group,the MFI of Hek293 cells treated with nocodazole for 3 min significantly increased at concentra-tions ranging from 10-10 to 10-6 mol·L-1(P<0.01).When the treatment duration was extended to 30 min,MFI elevations were observed at concentrations between 10-10 and 10-4 mol·L-1(P<0.01),indicating the activation of RhoA protein.In CHO cells,compared with the vehicle control group,MFI was increased after 10-10-10-6 mol·L-1 nocodazole treatment of 10 min and 30 min(P<0.05,P<0.01).Similarly,MFI was also increased under various conditions of LPA and calpeptin treatment.LPA 10-11-10-4 mol·L-1 treatment of 3 min and 10-11,10-8-10-4 mol·L-1 treatment of 10 min and 10-11-10-9,10-7,10-6,10-4 mol·L-1 treatment of 30 min all resulted in an elevated MFI(P<0.05,P<0.01).Calpeptin 10-11-10-6,10-4 mol·L-1 treatment of 10 min and 10-11 and 10-4 mol·L-1 treatment of 30 min also resulted in an elevated MFI(P<0.05,P<0.01).These results indicated that RhoA protein was effectively activated.CONCLUSION A method for rapid detection of RhoA protein activation has been established,which is capable high-throughput,rapid and easy detection of activated RhoA protein.

Culture and purification, identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometer and biochemical assay, drug sensitive test, resistance gene detection and multilocus sequence typing were conducted for 4 strains of serotype 2 Streptococcus suis isolated from clinical specimens in 2021, Hainan Province. The results showed that Strain 1 and Strain 2 were ST7 type, Strain 3 was ST1 type, and Strain 4 was ST2302 type. The strains are sensitive to levofloxacin, penicillin, meropenem, linezolid, ceftriaxone, cefepime, and daptomycin. Three strains are resistant to tetracycline, erythromycin, and azithromycin, two strains are resistant to clindamycin and ampicillin, and one strain is resistant to chloramphenicol and vancomycin. There may be sources of Streptococcus suis infection in multiple regions of Hainan Province, and the ST molecular typing of Streptococcus suis is diverse.

BACKGROUND: China bears the biggest atrial fibrillation (AF) burden in the world. However, little is known about the incidence and predictors of AF. This study aimed to investigate the current incidence of AF and its electrocardiographic (ECG) predictors in general community individuals aged over 60 years in China. METHODS: This was a prospective cohort study, recruiting subjects who were aged over 60 years and underwent annual health checkups from April to July 2015 in four community health centers in Songjiang District, Shanghai, China. The subjects were then followed up from 2015 to 2019 annually. Data on sociodemographic characteristics, medical history, and the resting 12-lead ECG were collected. Kaplan-Meier curve was used for showing the trends in AF incidence and calculating the predictors of AF. Associations of ECG abnormalities and AF incidence were examined using Cox proportional hazard models. RESULTS: This study recruited 18,738 subjects, and 351 (1.87%) developed AF. The overall incidence rate of AF was 5.2/1000 person-years during an observation period of 67,704 person-years. Multivariable Cox regression analysis indicated age (hazard ratio [HR], 1.07; 95% confidence interval [CI]: 1.06-1.09; P < 0.001), male (HR, 1.30; 95% CI: 1.05-1.62; P = 0.018), a history of hypertension (HR, 1.55; 95% CI: 1.23-1.95; P < 0.001), a history of cardiac diseases (HR, 3.23; 95% CI: 2.34-4.45; P < 0.001), atrial premature complex (APC) (HR, 2.82; 95% CI: 2.17-3.68; P < 0.001), atrial flutter (HR, 18.68; 95% CI: 7.37-47.31; P < 0.001), junctional premature complex (JPC) (HR, 3.57; 95% CI: 1.59-8.02; P = 0.002), junctional rhythm (HR, 18.24; 95% CI: 5.83-57.07; P < 0.001), ventricular premature complex (VPC) (HR, 1.76; 95% CI: 1.13-2.75, P = 0.012), short PR interval (HR, 5.49; 95% CI: 1.36-22.19; P = 0.017), right atrial enlargement (HR, 6.22; 95% CI: 1.54-25.14; P = 0.010), and pacing rhythm (HR, 3.99; 95% CI: 1.57-10.14; P = 0.004) were independently associated with the incidence of AF. CONCLUSIONS The present incidence of AF was 5.2/1000 person-years in the studied population aged over 60 years in China. Among various ECG abnormalities, only APC, atrial flutter, JPC, junctional rhythm, short PR interval, VPC, right atrial enlargement, and pacing rhythm were independently associated with AF incidence.
Humans ; Male ; Middle Aged ; Aged ; Atrial Fibrillation/epidemiology* ; Prospective Studies ; Incidence ; Atrial Flutter/complications* ; Risk Factors ; China/epidemiology* ; Electrocardiography

Glioblastoma multiforme (GBM), a highly malignant and heterogeneous brain tumor, contains various types of tumor and non-tumor cells. Whether GBM cells can trans-differentiate into non-neural cell types, including mural cells or endothelial cells (ECs), to support tumor growth and invasion remains controversial. Here we generated two genetic GBM models de novo in immunocompetent mouse brains, mimicking essential pathological and molecular features of human GBMs. Lineage-tracing and transplantation studies demonstrated that, although blood vessels in GBM brains underwent drastic remodeling, evidence of trans-differentiation of GBM cells into vascular cells was barely detected. Intriguingly, GBM cells could promiscuously express markers for mural cells during gliomagenesis. Furthermore, single-cell RNA sequencing showed that patterns of copy number variations (CNVs) of mural cells and ECs were distinct from those of GBM cells, indicating discrete origins of GBM cells and vascular components. Importantly, single-cell CNV analysis of human GBM specimens also suggested that GBM cells and vascular cells are likely separate lineages. Rather than expansion owing to trans-differentiation, vascular cell expanded by proliferation during tumorigenesis. Therefore, cross-lineage trans-differentiation of GBM cells is very unlikely to occur during gliomagenesis. Our findings advance understanding of cell lineage dynamics during gliomagenesis, and have implications for targeted treatment of GBMs.
Mice ; Animals ; Humans ; Glioblastoma/pathology* ; Endothelial Cells/pathology* ; DNA Copy Number Variations ; Brain/metabolism* ; Brain Neoplasms/pathology*

Objective:To explore the common signaling pathways of Helicobacter pylori(Hp) infection and rosacea and screen Hub genes by bioinformatic analysis. Methods:Gene expression data sets related to Hp (GSE70394) and rosacea (GSE65914) were downloaded from GEO database. The common differentially expressed genes (DEGs) were screened by using the limma package of R and Venn diagram. Metascape database was used for gene ontology(GO) enrichment analysis, and clusterProfiler package of R was used for Kyoto encyclopedia of genes and genomes(KEGG) analysis on up-regulated and down-regulated genes. Subsequently, STRING and Cytoscape software were used to establish protein-protein interaction(PPI) network and its internal plug-in MCODE and Cytohubba were applied to screen key functional modules and Hub genes, then Hub genes were imported into GeneMANIA to construct Hub genes co-expression network. Finally, GO and KEGG enrichment analysis of Hub genes were performed again.Results:GSE70394 and GSE65914 data sets included three samples of AGS cells without Hp infection and three samples of AGS cells 24 hours after Hp infection, skin tissues from nineteen rosacea patients and ten healthy volunteers, respectively. Finally, 139 common DEGs were obtained including 93 up-regulated genes and 46 down-regulated genes. GO enrichment analysis mainly focused on smooth muscle cell regulation, vascular development and lipid metabolism. KEGG enrichment analysis mainly involved lipid and atherosclerosis, inflammatory response and immune-related pathways, such as PPAR signaling pathway, cytokine-cytokine receptor interaction, Th17 cell differentiation, IL-17 signaling pathway and NF-κB signaling pathway. Total of 16 Hub genes were identified by Cytohubba, including SPRR1B, GCLM, KRT16, GPX2, S100A2, SOD2, MMP1, MSMO1, HMOX1, GLRX, IL-1β, CXCL1, PPARγ, HMGCS1, SRXN1 and SPRR3.Conclusion:There is a link existing between Hp infection infection and rosacea. Hp may be involved in the occurrence and development of rosacea by mediating inflammatory immune response and regulating lipid metabolism, the selected Hub genes and related signaling pathways may provide a theoretical reference for subsequent correlation analysis.

Objective:To explore the common signaling pathways of Helicobacter pylori(Hp) infection and rosacea and screen Hub genes by bioinformatic analysis. Methods:Gene expression data sets related to Hp (GSE70394) and rosacea (GSE65914) were downloaded from GEO database. The common differentially expressed genes (DEGs) were screened by using the limma package of R and Venn diagram. Metascape database was used for gene ontology(GO) enrichment analysis, and clusterProfiler package of R was used for Kyoto encyclopedia of genes and genomes(KEGG) analysis on up-regulated and down-regulated genes. Subsequently, STRING and Cytoscape software were used to establish protein-protein interaction(PPI) network and its internal plug-in MCODE and Cytohubba were applied to screen key functional modules and Hub genes, then Hub genes were imported into GeneMANIA to construct Hub genes co-expression network. Finally, GO and KEGG enrichment analysis of Hub genes were performed again.Results:GSE70394 and GSE65914 data sets included three samples of AGS cells without Hp infection and three samples of AGS cells 24 hours after Hp infection, skin tissues from nineteen rosacea patients and ten healthy volunteers, respectively. Finally, 139 common DEGs were obtained including 93 up-regulated genes and 46 down-regulated genes. GO enrichment analysis mainly focused on smooth muscle cell regulation, vascular development and lipid metabolism. KEGG enrichment analysis mainly involved lipid and atherosclerosis, inflammatory response and immune-related pathways, such as PPAR signaling pathway, cytokine-cytokine receptor interaction, Th17 cell differentiation, IL-17 signaling pathway and NF-κB signaling pathway. Total of 16 Hub genes were identified by Cytohubba, including SPRR1B, GCLM, KRT16, GPX2, S100A2, SOD2, MMP1, MSMO1, HMOX1, GLRX, IL-1β, CXCL1, PPARγ, HMGCS1, SRXN1 and SPRR3.Conclusion:There is a link existing between Hp infection infection and rosacea. Hp may be involved in the occurrence and development of rosacea by mediating inflammatory immune response and regulating lipid metabolism, the selected Hub genes and related signaling pathways may provide a theoretical reference for subsequent correlation analysis.

Objective:To evaluate the survival outcomes of radiotherapy in patients with newly-diagnosed metastatic head and neck squamous cell carcinoma (HNSCC) based on data from the Surveillance, Epidemiology and End Results (SEER) database.Methods:A total of 1226 patients newly-diagnosed with metastatic HNSCC between 2010 and 2015 were selected from the SEER database. There were 762 patients (62.1%) in the radiotherapy group and 464 patients (37.9%) in the non-radiotherapy group. Kaplan-Meier method was used to calculate the cancer-specific survival (CSS) and overall survival (OS). The effect of radiotherapy on survival was assessed by Cox multivariate regression and Propensity score-matched analyses (PSM). According to the results of multivariate analysis, the patients were further divided into low-, intermediate- and high-risk groups, and the effect of radiotherapy on survival was analyzed in different risk groups.Results:The median CSS and OS time of the whole group was 11.0 months and 10.0 months, respectively. For patients in the radiotherapy group and non-radiotherapy group, the median CSS time was 13.0 months and 6.0 months, and the median OS time was 12.0 months and 6.0 months, respectively. Multivariate analysis showed that age (CSS, P=0.045;OS, P=0.002), primary tumor site (CSS, P=0.021;OS, P<0.001), T stage (CSS, P=0.001;OS, P=0.002), N stage (CSS, P=0.002;OS, P<0.001), number of metastatic organs (CSS, P<0.001;OS, P<0.001), surgery (CSS, P<0.001;OS, P<0.001), radiotherapy (CSS, P<0.001;OS, P<0.001), and chemotherapy (CSS, P<0.001;OS, P<0.001)were the independent prognostic factors. After PSM, patients with and without radiotherapy in the low-,intermediate-,and high-risk groups, the 3-year CSS rates were 62.5% vs 23.5%( P=0.008), 22.4% vs 15.7%( P=0.001)and 10.5% vs 9.6%( P=0.203), respectively; the 3-year OS were 58.0% vs 20.8%( P=0.002), 19.8% vs 12.7%( P=0.001)and 7.0% vs 6.1%( P=0.166), respectively. Conclusion:Radiotherapy significantly improves CSS and OS in the low- and intermediate-risk groups, but patients in the high-risk group do not benefit from radiotherapy.

Objective: To investigate the effect of elective neck dissection on the 5-year survival rate of patients with early oral squamous cell carcinoma. Methods: The data of 100 patients with early oral squamous cell carcinoma (cT1-2N0M0) were retrospectively analyzed. In 61 cases, the primary tumor was subjected to elective neck dissection (END). Neck observation and follow-up (NOF) were performed in 39 cases with enlarged resection of primary lesions. Clinicopathological data such as pT staging, pathology classification,the rate of cervical lymph node metastasis and the 5-year survival rate of the patients were statistically analyzed. Results: The 5-year survival rates of the END and NOF groups were 86.9% and 69.2%, respectively, and the difference was statistically significant (P=0.028). END treatment was significantly better than NOF in controlling cervical lymph node metastasis in early oral squamous cell carcinoma (P=0.009). After stratified analysis of histopathological features, the 5-year survival rate of patients with pathological T2 (pT2) stage OSCC in the END group was significantly higher than that in the NOF group (P=0.020). The 5-year survival rate of patients with moderate and poorly differentiated pathological grade OSCC in the END group was significantly higher than that in the NOF group (P=0.013). Conclusion END is effective for the management of the cervical lymph node metastasis rate in early OSCC patients. For patients with pT2 stage or low differentiation pathological grade, active END can significantly improve the 5-year survival rate.