Present trends, sustainable strategies and energy potentials of crop residue management in India: A review.

India generating huge amount of agricultural waste, especially crop residues. In India, around 141 MT of crop residue is generated each year, in which 92 MT burned due to inadequate sustainable management practices, which results in rise in emissions of particulate matter as well as quality of air pollution. Burning crop residues raises mortality rates and substantially decreases crop production while posing a major risk of threatening the environment, condition of the soil, human health, and air quality.

Exploring intranasal drug delivery via nanocarriers: A promising glioblastoma therapy.

Glioblastoma is one of the most recurring types of glioma, having the highest mortality rate among all other gliomas. Traditionally, the standard course of treatment for glioblastoma involved maximum surgical resection, followed by chemotherapy and radiation therapy. Nanocarriers have recently focused on enhancing the chemotherapeutic administration to the brain to satisfy unmet therapeutic requirements for treating brain-related disorders.

Federated benchmarking of medical artificial intelligence with MedPerf.

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain.

Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

Background And Purpose: Artificial intelligence models in radiology are frequently developed and validated using data sets from a single institution and are rarely tested on independent, external data sets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multicenter artificial intelligence competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency.

Materials And Methods: In total, 1201 anonymized, full-head NCCT clinical scans from 5 institutions were pooled to form the data set.

Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

Background And Purpose: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthesize virtual gadolinium contrast-enhanced T1-weighted MR images from noncontrast multiparametric MR images in patients with primary brain tumors by using deep learning.

Materials And Methods: We trained and validated a deep learning network by using MR images from 335 subjects in the Brain Tumor Segmentation Challenge 2019 training data set.