Human Growth Hormone

Purpose Accurate prediction of postoperative remission is beneficial for effective patient-physician communication in acromegalic patients. This study aims to train and validate machine learning prediction models for early endocrine remission of acromegalic patients. Methods The training cohort included 833 patients with growth hormone (GH) secreting pituitary adenoma from 2010 to 2018. We trained a partial model (only using pre-operative variables) and a full model (using all variables) to predict off-medication endocrine remission at six-month follow-up after surgery using multiple algorithms. The models were validated in 99 prospectively collected patients from a second campus and 52 patients from a third institution. Results C-statistic and the accuracy of the best partial model was 0.803 (95% CI 0.757-0.849) and 72.5% (95% CI 67.6-77.5%), respectively. C-statistic and the accuracy of the best full model was 0.888 (95% CI 0.861-0.914) and 80.3% (95% CI 77.5-83.1%), respectively. The c-statistics (and accuracy) of using only Knosp grade, total resection, or postoperative day 1 GH level as the single predictor were lower than our partial model or full model (p < 0.001). C-statistics remained similar in the prospective cohort (partial model 0.798, and full model 0.903) and in the external cohort (partial model 0.771, and full model 0.871). A web-based application integrated with the trained models was published at . Conclusion We developed and validated interpretable and applicable machine learning models to predict early endocrine remission after surgical resection of a GH-secreting pituitary adenoma. Predication accuracy of the trained models were better than those using single variables.

The introduction of transgenic cotton (Bt-cotton) for controlling bollworms has resulted in increased production; however, the residual effects of mulches fromBt-cotton are poorly understood. Therefore, the current study evaluated the impact ofBtand non-Btcotton mulches on soil properties, weed dynamics and yield of winter crops sown after cotton. Three different winter crops, i.e., wheat (Triticum aestivumL.), canola (Brassica napusL.) and Egyptian clover (Trifolium alexandrinumL.) and two mulch types, i.e.,Btmulch (obtained fromBt-cotton cultivars, i.e., 'CIM-616' and 'GH-Mubarik') and non-Btmulch (obtained from non-Btcultivars, i.e., 'CIM-620' and 'N-414') were included in the study. The mulches were applied at a rate of 2 t ha(-1)before planting the winter crops. TheBtand non-Btmulches differentially affected soil properties, weed dynamics and productivity of winter crops. The non-Btmulches decreased the soil bulk density and penetration resistance, while increased the soil porosity. Wheat crop increased the soil porosity, pH, available N and soil organic matter content. Overall, non-Btmulches improved the productivity of winter crops compared withBtmulches. The toxins released byBtmulches lowered the weed density; however, it negatively influenced soil properties (bulk density and available nitrogen) and productivity of winter crops. Therefore, appropriate crop rotation measures may be opted for the soils cultivated withBt-cotton to conserve soil and achieve yield sustainability for the crops sown after cotton. Nonetheless, non-Btmulches can be used for improving soil properties and productivity of winter crops.