No abstract

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.

Scrub typhus is an acute infectious illness, distributed throughout the Asia Pacific rim. In India, it has been reported from northern, eastern, and southern India. However, cases of scrub typhus have not been well-documented from Vidarbha, an eastern region of Maharashtra state in central India. We report two cases of complicated scrub typhus from Vidarbha region. These cases admitted in unconscious state with 8-10 days history of fever, body ache, cough, and progressive breathlessness. The diagnosis in both cases was based on presence of eschar, a positive Weil-Felix test, and a positive rapid diagnostic test (immunochromatographic assay). Both cases were complicated by acute respiratory distress syndrome (ARDS) and multiorgan failure. Both of them presented in their 2nd week of illness and died during the hospital course in spite of intensive supportive care. The main cause of mortality was delayed referral leading to delay in diagnosis and treatment.

Kikuchi's disease (KD) also known as histiocytic necrotizing lymphadenitis is rare, idiopathic, generally self-limited cause of lymphadenitis. We present a case of twenty year young female who presented in critically ill state with fever, cervical and axillary lymphadenopathy, rash, vomiting and altered sensorium and found to have neurological, hepatic, renal and dermatological involvement. Kikuchi's disease should be considered in differential diagnosis of fever and lymphadenopathy and though benign can sometimes present with multi-organ involvement. It is because of rarity of this disease with unusual complications, present case is reported.

Hematological parameters like total leukocyte count (TLC), neutrophil, lymphocyte, and absolute eosinophil counts (AEC), and neutrophil-to-lymphocyte ratio (NLR) are known to predict the severity of novel coronavirus disease 2019 (COVID-19) patients. In the present study, we aimed to study the role of complete blood count parameters in triaging these patients requiring intensive care unit (ICU) admission. A retrospective study was done over a period of 2 months. Patients, who were ≥ 18 years of age with COVID-19 confirmed on SARS-CoV-2 reverse transcription-polymerase chain reaction (RT-PCR) and whose routine hematology counts were sent within 24 h of admission, were included in the study. Cut-off values of 47.5 years for age, 11.3 × 10 9 /L for TLC, and 9.1 for NLR were predictive of disease severity among COVID-19 patients. Relative neutrophilia ≥ 70% ( p < 0.007), relative lymphopenia ≤ 20% ( p < 0.002), AEC ≤ 40/cumm ( p < 0.001), and NLR ≥ 9.1 ( p < 0.001) were significantly associated with ICU admission. Routine hematological parameters are cost-effective and fast predictive markers for severe COVID-19 patients, especially in resource-constrained health care settings to utilize limited ICU resources more effectively.