Clinico-demographic Profile and Predictors of Intensive Care Need in Children with Respiratory Syncytial Virus-associated Acute Lower Respiratory Illness during Its Recent Outbreak alongside Ongoing COVID-19 Pandemic: An Eastern Indian Perspective.

Introduction: The objective was to delineate the clinico-epidemiological characteristics of hospitalized children with respiratory syncytial virus (RSV)-associated acute lower respiratory tract infection (RSV-ALRI) during its recent outbreak and to find out the independent predictors of pediatric intensive care unit (PICU) admission. Materials and methods: Children aged between 1 month and 12 years who tested positive for RSV were included. Multivariate analysis was performed to identify the independent predictors and predictive scores were developed from the ß-coefficients. Receiver operating characteristic curve (ROC) was generated and the area under the curve (AUC) was calculated to assess the overall precision. The performance of sum scores in predicting PICU need, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive and negative likelihood ratios (LR+ and LR-) were calculated for each cutoff value. Results: The proportion of RSV positivity was 72.58%. A total of 127 children were included with a median [interquartile range (IQR)] age of 6 (2-12) months, of whom 61.42% were males and 33.07% had underlying comorbidity. Tachypnea, cough, rhinorrhea, and fever were predominant clinical presentations while hypoxia and extrapulmonary manifestations were present in 30.71% and 14.96% of children, respectively. About 30% required PICU admission, and 24.41% developed complications. Premature birth, age below 1 year, presence of underlying CHD, and hypoxia were independent predictors. The AUC [95% confidence interval (CI)] was 0.869 (0.843-0.935). Sum score below 4 had 97.3% sensitivity and 97.1% NPV whereas sum score above 6 had 98.9% specificity, 89.7% PPV, 81.3% NPV, 46.2 LR+, and 0.83 LR- to predict PICU needs. Conclusion: Awareness of these independent predictors and application of the novel scoring system will be beneficial for busy clinicians in planning the level of care needed, thereby optimizing PICU resource utilization. How to cite this article: Ghosh A, Annigeri S, Hemram SK, Dey PK, Mazumder S. Clinico-demographic Profile and Predictors of Intensive Care Need in Children with Respiratory Syncytial Virus-associated Acute Lower Respiratory Illness during Its Recent Outbreak alongside Ongoing COVID-19 Pandemic: An Eastern Indian Perspective. Indian J Crit Care Med 2022;26(11):1210-1217.

Graphene-Based Field-Effect Transistor for Ultrasensitive Immunosensing of SARS-CoV-2 Spike S1 Antigen.

Coronavirus disease (COVID-19) is an infectious disease that has posed a global health challenge caused by the SARS-CoV-2 virus. Early management and diagnosis of SARS-CoV-2 are crucial for the timely treatment, traceability, and reduction of viral spread. We have developed a rapid method using a Graphene-based Field-Effect Transistor (Gr-FET) for the ultrasensitive detection of SARS-CoV-2 Spike S1 antigen (S1-Ag). The in-house developed antispike S1 antibody (S1-Ab) was covalently immobilized on the surface of a carboxy functionalized graphene channel using carbodiimide chemistry. Ultraviolet-visible spectroscopy, Fourier-Transform Infrared Spectroscopy, X-ray Photoelectron Spectroscopy (XPS), Atomic Force Microscopy (AFM), Optical Microscopy, Raman Spectroscopy, Scanning Electron Microscopy (SEM), Enzyme-Linked Immunosorbent Assays (ELISA), and device stability studies were conducted to characterize the bioconjugation and fabrication process of Gr-FET. In addition, the electrical response of the device was evaluated by monitoring the change in resistance caused by Ag-Ab interaction in real time. For S1-Ag, our Gr-FET devices were tested in the range of 1 fM to 1 µM with a limit of detection of 10 fM in the standard buffer. The fabricated devices are highly sensitive, specific, and capable of detecting low levels of S1-Ag.

Network approaches for modeling the effect of drugs and diseases.

The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug's MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).