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New avenues of technological opportunities in agriculture are opening as we are further delving deeper into the 21st century, but at the same time, new challenges are emerging. One of these challenges is the growing quantity of food demand, which is highly vital for regional trade, food security, and meeting the nutritious requirements of the population. A timely prediction with accuracy about crop yield could be valuable for greater food production and maintainability of sustainable agricultural growth. This paper presents a predictive model of wheat production using machine learning. The northern areas of Pakistan which grow wheat are selected as a case study due to their importance in the country's agricultural sector. We collected data of five years and selected the best attribute subset related to crop production. We applied twelve (12) algorithms by dividing data samples into three sets. Experimental results helped to shortlist three algorithms for the final analysis i.e. Sequential Minimal Optimization Regression (SMOreg), Multilayer Processing (MLP) and Gaussian Process (GP). The Root Mean Square (RMSE) and Percentage Absolute Difference (PAD) metrics were used to validate the results. The SMOreg obtained the lowest PAD (0.0093) and RMSE (0.5552) values. MLP was a little closer with second-lowest PAD (0.0116) and RMSE (0.737) value. The performance of GP was found lowest due to higher PAD (0.2203) and RMSE (17.7423) values. Our findings confirm the predictive ability of machine learning algorithms on a crop dataset recorded in a localized environment, which could be replicated on other crops and regions. • Explored the predicting ability of machine learning in crop production. • Selected the best attribute subset related to wheat cultivation. • Tested the model on a localized wheat dataset of five years in northern areas of Pakistan. • Model performance found was comparable with existing research. • Demonstrated a way of using an emerging trend of tech-based agricultural innovations in a developing country Pakistan. [ABSTRACT FROM AUTHOR]

The severe neurologic tumor known as glioblastoma (GBM), also referred to as a grade IV astrocytoma, is rapidly progressive and debilitating. Supratotal resection (SpTR) is an emerging concept within glioma surgery, which aims to achieve a more extensive resection of the tumor than is possible with conventional techniques. We performed a language-independent search of PubMed, Scopus, and Cochrane CENTRAL to identify all available literature up to August 2022 of patients undergoing SpTR assessing survival outcomes in comparison to other surgical modalities. After screening for exclusion, a total of 13 studies, all retrospective in design, were identified and included in our meta-analysis. SpTR was associated with significantly increased overall survival (hazard ratio 0.77, 95% CI 0.71–0.84; P < 0.01, I2 = 96%) and progression-free survival (hazard ratio 0.2, 95% CI 0.07–0.56; P = 0.002, I2 = 88%). SpTR is associated with greater overall survival and PFS when compared with other glioblastoma surgeries like GTR or SubTR. [ABSTRACT FROM AUTHOR]