Efficacy and immunogenicity of AS04-HPV-16/18 vaccine in females with existing cervical HR-HPV infection at first vaccination: A pooled analysis of four large clinical trials worldwide.

Females with existing high-risk HPV (HR-HPV) infections remain at risk of subsequent multiple or recurrent infections, on which benefit from HPV vaccines was under-reported. We pooled individual-level data from four large-scale, RCTs of AS04-HPV-16/18 vaccine to evaluate efficacy and immunogenicity in females DNA-positive to any HR-HPV types at first vaccination. Females receiving the AS04-HPV-16/18 vaccine in the original RCTs constituted the vaccine group in the present study, while those unvaccinated served as the control group. Vaccine efficacy (VE) against new infections and associated cervical intraepithelial neoplasia (CIN) 2+ in females DNA-negative to the considered HR-HPV type but positive to any other HR-HPV types, VE against reinfections in females DNA-positive to the considered HR-HPV type but cleared naturally during later follow-up, and levels of anti-HPV-16/18 IgG were assessed. Our final analyses included 5137 females (vaccine group = 2532, control group = 2605). The median follow-up time was 47.88 months (IQR: 45.72-50.04). For the prevention of precancerous lesions related to the non-infected HR-HPV types at baseline, VE against HPV-16/18 related CIN 2+ was 82.70% (95% CI: 63.70-93.00%). For the prevention of reinfections related to the infected HR-HPV types following natural clearance, VE against HPV-16/18 12MPI was non-significant (p > .05), albeit robust immunity persisted for at least 48 months. Females with existing HR-HPV infections at first vaccination still benefit from vaccination in preventing precancers related to the non-infected types at baseline. VE against reinfections related to the infected types following natural clearance remains to be further investigated.

In vivo prediction of abdominal fat and breast muscle in broiler chicken using live body measurements based on machine learning.

The purpose of this study was to predict the carcass characteristics of broilers using support vector regression (SVR) and artificial neural network (ANN) model methods. Data were obtained from 176 yellow feather broilers aged 100-day-old (90 males and 86 females). The input variables were live body measurements, including external measurements and B-ultrasound measurements. The predictors of the model were the weight of abdominal fat and breast muscle in male and female broilers, respectively. After descriptive statistics and correlation analysis, the datasets were randomly divided into train set and test set according to the ratio of 7:3 to establish the model. The results of this study demonstrated that it is feasible to use machine learning methods to predict carcass characteristics of broilers based on live body measurements. Compared with the ANN method, the SVR method achieved better prediction results, for predicting breast muscle (male: R2 = 0.950; female: R2 = 0.955) and abdominal fat (male: R2 = 0.802; female: R2 = 0.944) in the test set. Consequently, the SVR method can be considered to predict breast muscle and abdominal fat of broiler chickens, except for abdominal fat in male broilers. However, further revaluation of the SVR method is suggested.