To elucidate the role of artificial intelligence (AI) in therapeutics for coronavirus disease 2019 (COVID-19). Five databases were searched (December 2019–May 2020). We included both published and pre-print original articles in English that applied AI, machine learning or deep learning in drug repurposing, novel drug discovery, vaccine and antibody development for COVID-19. Out of 31 studies included, 16 studies applied AI for drug repurposing, whereas 10 studies utilized AI for novel drug discovery. Only four studies used AI technology for vaccine development, whereas one study generated stable antibodies against SARS-CoV-2. Approx. 50% of studies exclusively targeted 3CLpro of SARS-CoV-2, and only two studies targeted ACE/TMPSS2 for inhibiting host viral interactions. Around 16% of the identified drugs are in different phases of clinical evaluation against COVID-19. AI has emerged as a promising solution of COVID-19 therapeutics. During this current pandemic, many of the researchers have used AI-based strategies to process large databases in a more customized manner leading to the faster identification of several potential targets, novel/repurposing of drugs and vaccine candidates. A number of these drugs are either approved or are in a late-stage clinical trial and are potentially effective against SARS-CoV2 indicating validity of the methodology. However, as the use of AI-based screening program is currently in budding stage, sole reliance on such algorithms is not advisable at this current point of time and an evidence based approach is warranted to confirm their usefulness against this life-threatening disease. Communicated by Ramaswamy H. Sarma

The most common non-Hodgkin lymphoma (NHL) subtype is diffuse large B-cell lymphoma (DLBCL). It accounts for roughly 30% of all cases of NHL affecting both nodal and extra nodal sites. There are molecular subtypes of DLBCL, germinal centre subtype (GCB), and activated B-cell (ABC), based on gene expression profiling (GEP), in accumulation to distinct morphological and clinicopathological subtypes. To prognosticate patients, the International Prognostication Index (IPI) and its variants are used. In ABC type DLBCL, limited stage disease is treated with a combination of abbreviated systemic chemotherapy (three cycles) and field radiation therapy. Although advanced stage disease is treated with a full course of chemotherapy as well as novel agents (Bortezomib, Ibrutinib, Lenalidomide). In this review study, we looked at the role of multiple aspects of genetic and microenvironment changes which have effects in DLBCL tumours.

To compare the effects of metformin alone versus combined therapy of metformin with myoinositol (MI) plus D-chiro-inositol (DCI) in women with polycystic ovary syndrome (PCOS). This is a prospective, non-blinded randomized controlled trial conducted in newly diagnosed PCOS women aged 18 to 45 years. Group I received metformin 500 mg twice a day orally for 6 months while group II received metformin 500 mg twice a day orally along with MI 550 mg plus DCI 150 mg twice daily orally for six months. The primary outcome was a change in clinical, metabolic and hormonal parameters of the two groups from baseline to the end of six months of treatment. A total of 72 patients were randomized into two groups of 36 patients each. Statistically, a significant difference was seen in terms of mean global acne score (p=0.004) and cycle regularity (p=0.034) after six months of treatment in group II. A significant difference in values of luteinizing hormone (LH) (p=0.002), luteinizing hormone/follicle-stimulating hormone (LH/FSH) ratio (p=0.007), mean cholesterol (p=0.040), mean high-density lipoprotein (HDL) (p=0.049), mean low-density lipoprotein (LDL) (p=0.0001) and postprandial insulin (p=0.005) was also seen in group II at the end of treatment duration. No significant difference was seen between the two groups in terms of mean FSH, mean testosterone, mean dehydroepiandrosterone sulfate (DHEAS), mean triglyceride, mean fasting and postprandial blood sugar, fasting insulin and homeostatic model assessment of insulin resistance (HOMA-IR) index. Combined therapy with metformin and MI plus DCI in women with PCOS and insulin resistance seems promising with the need for further studies with a greater sample size to evaluate the efficacy of this treatment.

Children with cleft lip and/or palate can be undernourished due to feeding difficulties after birth. A vicious cycle ensues where malnutrition and low body weight precludes the child from having the corrective surgery, in the absence of which the child fails to gain weight. This study aimed to identify the proportion of malnutrition, including the deficiency of major micronutrients, namely iron, folate and vitamin B12, in children with cleft lip and/or palate and thus help in finding out what nutritional interventions can improve the scenario for these children. All children less than 5 years with cleft lip and/or cleft palate attending our institute were included. On their first visit, following were recorded: demographic data, assessment of malnutrition, investigations: complete blood count and peripheral blood film examination; serum albumin, ferritin, iron, folate, and vitamin B12 levels. Eighty-one children with cleft lip and/or palate were included. Mean age was 25.37± 21.49 months (range, 3-60 months). In 53% of children suffered from moderate to severe wasting, according to World Health Organization (WHO) classification. Iron deficiency state was found in 91.6% of children. In 35.80% of children had vitamin B12 deficiency and 23.45% had folate deficiency. No correlation was found between iron deficiency and the type of deformity. Iron deficiency state is almost universally present in children with cleft lip and palate. Thus, iron and folic acid supplementation should be given at first contact to improve iron reserve and hematological parameters for optimum and safe surgery.

Insulin resistance is an integral component of a multi-organ dysfunction syndrome (MODS) associated with increased mortality. We determined a cutoff value for the homeostatic model assessment of insulin resistance (HOMA-IR) during an ICU admission that could predict 28-day mortality of nondiabetic MODS patients. In this prospective, outcome assessor blinded cohort design, we evaluated 82 such patients for fasting blood glucose (FBG)/insulin levels (FIL) during an ICU admission and followed their outcome for 28 days. The primary outcome variable was the HOMA-IR score calculated from the above variables. The statistical tool included receiver operating characteristic curve, Youden index, and correlation and regression analysis. Overall, 38 patients succumbed to their illness. The optimal cutoff value for HOMA-IR was ≥1.61 (area under curve: 0.684, sensitivity: 36.8%, specificity: 95.5%). The 28-day survival was significantly lower (p = 0.001) at HOMA-IR threshold ≥1.61 (odds ratio: 12.25, hazard ratio: 2.98). The mean HOMA-IR among survivors vs nonsurvivors was 0.76 ± 0.61 and 1.38 ± 1.14, respectively (p = 0.004). Except for FIL and FBG, HOMA-IR values did not correlate with any other baseline or outcome parameters (demographics, APACHE II/sequential organ failure assessment score, vasopressor needs, or ICU/hospital stay). On comparing these parameters across the HOMA-IR threshold, only FIL and the hospital stay varied significantly. Most of the outcome parameters, however, varied significantly among nonsurvivors vs survivors. The HOMA-IR is a significant predictor of mortality in MODS. Its cutoff value may assist in determining a reference range for critically ill patients. Its routine use in the light of other disease severity scores may serve in their better prognostication. Sama S, Jain G, Kant R, Bhadoria AS, Naithani M, Kumar A. Quantifying the Homeostatic Model Assessment of Insulin Resistance to Predict Mortality in Multi-organ Dysfunction Syndrome. Indian J Crit Care Med 2021;25(12):1364–1369.