Seroprevalence and risk factors of Streptococcus ovis infection in Tibet, China.

Sheep septicemic streptococcosis is an acute, febrile, and septic zoonotic disease caused by Streptococcus ovis. There are few reports of this disease especailly at high-altitude areas of Tibet, China. A cross-sectional study was conducted at five different counties of Shigatse area (a city in Tibet territory) from 2021 to 2022. Sera from 1458 randomly selected sheep and goats from 50 flocks were tested for antigens of Streptococcus ovis using a commercial ELISA kit. The individual animal level prevalence was 6.93% (95% CI: 5.7-8.4), and the flock prevalence level was 84% (95% CI: 70.9-92.8). At the individual animal level, year and management type were significantly associated with seropositivity by binary logistic regression analysis. Prevalence of Streptococcus in 2021 significantly differed from 2022 (OR = 2.16, 95% CI: 1.39-3.39). The prevalence of Streptococcus varied between management types with herds having higher odds of disease than intensive farms (OR = 2.64, 95% CI: 1.71-4.10). Hence, our study reports for the first time on seroepidemiology and risk factors of Streptococcus ovis infection, laying a theoretical foundation for preventing and controlling streptococcosis in Tibet area.

A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimization is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography. We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images and gene information were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system. In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings. Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimization and early prevention before patients show severe symptoms. Take-home messageFully automatic deep learning system provides a convenient method for COVID-19 diagnostic and prognostic analysis, which can help COVID-19 screening and finding potential high-risk patients with worse prognosis.

Effect of catgut embedding in the Liumai acupuncture point on myocardial potential, myoelectric potential and tension in the rumen of Tibet sheep / 中国兽医学报

One of the purposes for the improvement of forages efficient utilization in rumens was to adjust the physiological function of ruminant in Tibet,and to improve the disadvantage influences under the altitude environment.The electrocardiography,rumen electromyography and tension in the rumen were drawn the before and after catgut embedding in the liumai acupuncture point to the Tibet sheep.The result showed that catgut embedding in the liumai acupuncture point could promoted the blood circulation,alleviated the overload of myocardial and remedied the right deviation of heart electrical axis up to 40°.The VPP and RA of rumen were significantly lower and the tension of rumen decreased 1/3 after catgut embedding.The result indicated that it could relaxed the right ventricular hypertrophy,reduced the tension of rumen,improved the function of gastrointestinal tract and blood circulation,ensured sufficient time to digest the forage in rumen.

Mammogram texture analysis in prediction of axillary lymph node metastasis for breast carcinoma / 中国医学影像技术

Objective To explore the value of mammogram texture analysis in prediction of metastasis of axillary lymph nodes for breast carcinoma.Methods Mammograms and clinical data of 171 patients with breast carcinoma confirmed by pathology were retrospectively analyzed,and all patients underwent axillary lymph node dissection (ALND).Then the patients were divided into axillary lymph node metastasis group and non-metastasis group according to the result of ALND.The texture features of these lesions were statistically analyzed,including gray-level histogram texture parameters (mean value,standard deviation,skewness,kurtosis and variance) and gray-level co-occurrence matrix texture parameters (energy,entropy,correlation,inertia,inverse difference moment and contrast).Results In all of 171 breast cancer patients,96patients had axillary lymph node metastasis,while 75 patients had no metastasis.Mammograms showed negative axillary lymph nodes in 119 patients and positive axillary lymph nodes in 52 patients,and the sensitivity and specificity of mammograms in the diagnosis of positive axillary lymph nodes was 48.96％ (47/96) and 93.33％ (70/75),respectively.Mammogram texture analysis showed the values of energy,entropy,inverse difference moment and correlation in axillary lymph node metastasis group were higher than those in non-metastasis group,while the values of inertia and contrast in the axillary lymph node metastasis group were lower than those in non-metastasis group (all P＜0.05).The rest texture parameters had no significant differences between two groups (all P＞0.05).Area under curve (AUC) for texture parameters of energy,entropy,inertia,inverse difference moment,correlation and contrast was 0.610,0.610,0.374,0.599,0.612 and 0.421 (all P＜0.05),respectively.AUC of mammography,mammogram texture features,and the combination of mammography and texture features was 0.711,0.676 and 0.787 (all P＜0.05),respectively.The sensitivity and specificity of mammogram texture features,the combination of mammography and texture features in diagnosis of axillary lymph nodes metastasis was 62.5％ and 64.6％,66.7％ and 82.7％,respectively.Conclusion Mammogram texture parameters are helpful for predicting axillary lymph node metastasis,and the combination of mammography and texture features can improve diagnostic efficiency of axillary lymph node metastasis.