Perioperative Respiratory Outcome of Patients with Eosinophilia: A Cohort Study in a Tertiary Care Hospital

Background. A respiratory adverse event is one of the main causes of critical events in the perioperative period. Perioperative distress symptoms like cough and stridor have been reported to occur in patients with hyperreactive airways. Objective. This study was conducted to determine the relationship between blood eosinophil count and perioperative adverse respiratory events among different age groups of patients who require general anesthesia for different types of surgeries. Methods. A cohort study was conducted on 197 patients of either gender, aged 3 years and above, belonging to ASA classes I–II, who were scheduled to undergo surgery requiring general anesthesia and intubation. Patients were stratified according to absolute eosinophil count into two groups: Group A (AEC 0 to 499/mm3) and Group B (AEC 500 to 1000/mm3). Patients were monitored for 24 hours in the perioperative period for adverse respiratory events such as bronchospasm, laryngospasm, a fall in SPO2 < 95%, and cough and stridor. Results. A total of 197 patients were evaluated, with a median age of 37 ± 14.4 years. The percentage range of adverse respiratory events across different age groups was 35% in adults to 60% in children. Major complications noted were a fall in SPO2 < 95% (62.5%) and cough (27.7%) as per CTCAE v5.0 (November 27, 2017). The Naranjo score of adverse respiratory events was categorized as possible with mild level 1 severity. Adverse respiratory events were managed with humidified oxygen, antitussives, and bronchodilators. Conclusions. Eosinophilia is seen in one-third of the patients undergoing surgical interventions. Patients with a blood eosinophil count of ≥400/mm3 had an increased risk of exacerbations of respiratory adverse events in the perioperative period

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as “message-passing paradigms”, “spatial-symmetry-preserving networks”, “hybrid de novo design”, and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation