The aim of this experiment was to study the effects of different feeding modes on growth performance,blood biochemical indexes and intestinal flora of lactating Holstein male calves.Twenty-four newborn Holstein male calves with body mass of(40.00±1.01)kg and similar day old were selected and randomly divided into four groups of six calves each.The subgroups were low-milk group(LM),high-milk group(HM),high-milk milk replacer feeding group(HMR),and low-milk switching to high-milk milk replacer feeding group(CMR).The results showed that:At 45 d,the body mass of calves in the HM group was significantly higher than that of calves in the other groups(P＜0.05),and at 60 d,the body mass of calves in the HM group was significantly higher than that of calves in the LM &.CMR groups(P＜0.05).At 90 d,the body mass of calves in the LM group was significantly higher than that of calves in the HM group.Throughout the ex-perimental period,the average daily weight gain and average pellet feed intake of calves in the LM group were significantly higher than that of calves in the HM group(P＜0.05).The calf globulin level in the HMR group was significantly higher than that in the LM and HM groups(P＜0.05);the plasma immunoglobulin A level of calves in the HM group was significantly lower than that of calves in the LM and HMR groups(P＜0.05);and the plasma immunoglobulin M level of calves in the HM group was significantly higher than that of calves in the LM and CMR groups(P＜0.05),and HMR group was also significantly higher than that of LM group(P＜0.05);plasma glutathione peroxidase level of calves in HMR group was significantly higher than that of LM group(P＜0.05);plasma malondialdehyde level of calves in LM group was significantly higher than that of calves in HMR and HM groups(P＜0.05),and CMR group was also significantly higher than that of HM group(P＜0.05).Relative abundance of Thermodesulfovibrio was higher in the HM group(P＜0.05),relative abundance of Bacteroidetes in the LM group was significantly higher than that in the HMR and HM groups(P＜0.05),relative abundance of Blautia in the HM group(P＜0.05),and relative abundance of Corynebacterium in the CMR group was significantly higher than that in the LM and HM groups(P＜0.05).In summary,calves in the LM group had better weaning weights and pellet feed intake;calves in the CMR group could compensate for growth by supplemental feeding of milk replacer to obtain more optimal weaning weights and pel-let feed intake;the HMR group proved that milk-free feeding could ensure stable growth of calves;and calves in the HM group had a better pre-lactation growth performance,lower levels of oxida-tive stress,and a healthier fecal flora.

The aim of this experiment was to study the effects of different feeding modes on growth performance,blood biochemical indexes and intestinal flora of lactating Holstein male calves.Twenty-four newborn Holstein male calves with body mass of(40.00±1.01)kg and similar day old were selected and randomly divided into four groups of six calves each.The subgroups were low-milk group(LM),high-milk group(HM),high-milk milk replacer feeding group(HMR),and low-milk switching to high-milk milk replacer feeding group(CMR).The results showed that:At 45 d,the body mass of calves in the HM group was significantly higher than that of calves in the other groups(P＜0.05),and at 60 d,the body mass of calves in the HM group was significantly higher than that of calves in the LM &.CMR groups(P＜0.05).At 90 d,the body mass of calves in the LM group was significantly higher than that of calves in the HM group.Throughout the ex-perimental period,the average daily weight gain and average pellet feed intake of calves in the LM group were significantly higher than that of calves in the HM group(P＜0.05).The calf globulin level in the HMR group was significantly higher than that in the LM and HM groups(P＜0.05);the plasma immunoglobulin A level of calves in the HM group was significantly lower than that of calves in the LM and HMR groups(P＜0.05);and the plasma immunoglobulin M level of calves in the HM group was significantly higher than that of calves in the LM and CMR groups(P＜0.05),and HMR group was also significantly higher than that of LM group(P＜0.05);plasma glutathione peroxidase level of calves in HMR group was significantly higher than that of LM group(P＜0.05);plasma malondialdehyde level of calves in LM group was significantly higher than that of calves in HMR and HM groups(P＜0.05),and CMR group was also significantly higher than that of HM group(P＜0.05).Relative abundance of Thermodesulfovibrio was higher in the HM group(P＜0.05),relative abundance of Bacteroidetes in the LM group was significantly higher than that in the HMR and HM groups(P＜0.05),relative abundance of Blautia in the HM group(P＜0.05),and relative abundance of Corynebacterium in the CMR group was significantly higher than that in the LM and HM groups(P＜0.05).In summary,calves in the LM group had better weaning weights and pellet feed intake;calves in the CMR group could compensate for growth by supplemental feeding of milk replacer to obtain more optimal weaning weights and pel-let feed intake;the HMR group proved that milk-free feeding could ensure stable growth of calves;and calves in the HM group had a better pre-lactation growth performance,lower levels of oxida-tive stress,and a healthier fecal flora.

Objectives:To examine the proportion and trends of cervical adenocarcinoma in cervical cancer (ICC), mainly including cervical adenocarcinoma (CADC) and squamous cervical cancer (SCC) in China, and to analyze the distribution of human papilloma virus (HPV) in CADC and SCC.Methods:Published studies reporting HPVs distribution in various histological types or relative proportions of CADC in ICC in China were identified manually and searched systematically in Medline, Embase, Cochrane Library databases, CNKI and Wanfang since the databases were established until October 2022. Meta-analysis was performed using Stata 16.0 software. And we applied the random-effects models to estimate the combined effect values due to the high heterogeneity.Results:Twenty-three studies were eligible. The relative prevalence of CADC was 9.0% (95% CI, 7.7%－10.3%). According to the diagnosis time of ICC, the patients were divided into three time periods, which is 1979-2005, 2006-2011, 2012-2022 respectively. The prevalence of CADC by time was: 6.0% in 1979-2005, 8.1% in 2006-2011, and 9.5% in 2012-2022, respectively, with no statistically significant trend in proportions over time (χ 2=5.03, P=0.081). Meanwhile, the percentage of CADC also varies by regions, and the highest percentage of CADC was found in the eastern region (11.2%), followed by the western region (7.3%) and the central region (5.9%). The total prevalence of HPV infection in CADC was 72.3%, which was lower than 92.0% in SCC, and the difference was statistically significant (χ 2=300.89, P＜0.01). To be specific, the top three HPV types prevalent in CADC were HPV18 (45.0%), HPV16 (22.0%), and HPV52 (7.3%), and those prevalent in the SCC were HPV16 (64.2%), HPV52 (5.6%), HPV18 (5.4%). The results of the Egger's test, and Begg's test showed that there was no publication bias in this study and sensitivity analysis showed that the results of this study were fairly stable. Conclusions:The proportion of CADC in China has increased in a limited way in the past decades, and there are regional differences in the proportion of CADC. The predominant type is HPV18 in CADC and HPV16 in SCC. To eliminate the limitations of the secondary literature, a multicenter study with consistent diagnostic levels and identical HPV genotyping tests is still needed in the future to better characterize the relative proportion of cervical adenocarcinoma and the trend of HPV changes, which will provide a basis for the improvement of HPV vaccine and screening policies.

Objective:To evaluate the value of metagenomic Next Generation Sequencing (mNGS) for the pathogenetic diagnosis of bloodstream infections in patients with sepsis.Methods:A retrospective analysis was performed on 105 sepsis cases who received blood mNGS and blood culture tests during their hospitalization in the Intensive Care Department of the Affiliated Hospital of North Sichuan Medical College from September 2021 to August 2023, and the results were compared and analyzed. According to mNGS and blood culture results, the cases were divided into the positive group and negative group.The distribution of pathogens in the enrolled patients were analyzed, and the diagnostic performance and consistency of blood culture and mNGS were compared. The differences in clinical characteristics of patients in each group were analyzed, and the pathogenic diagnostic value of mNGS for bloodstream infections in patients with sepsis was evaluated.Results:①Among 105 blood samples, 61 cases (58.10%) had positive results of mNGS, and 32 cases (30.48%) had positive results of blood culture. The positive rate of mNGS was higher than that of blood culture, and the difference was statistically significant ( P < 0.05).The accuracy of mNGS was lower than that of blood culture②The mean values of ESR, PCT and CRP in positive group were higher than those in negative group, and the proportion of septic shock was higher than that in negative group, and the differences were statistically significant ( P < 0.05). There was no significant difference in 28-day mortality rate between the two groups ( P > 0.05). Conclusions:mNGS is beneficial to the etiological diagnosis and treatment of bloodstream infection in patients with sepsis.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.