Analytics Pipeline for Visualization of Single Cell RNA Sequencing Data from Brochoaveolar Fluid in COVID-19 Patients: Assessment of Neuro Fuzzy-C-Means and HDBSCAN.

Since the mutation in SARS-COV2 poses new challenges in designing vaccines, it is imperative to develop advanced tools for visualizing the genetic information. Specially, it remains challenging to address the patient-to-patient variability and identify the signature for severe/critical conditions. In this endeavor we analyze the large-scale RNA-sequencing data collected from broncho-alveolar fluid. In this work, we have used PCA and tSNE for the dimension-reduction. The novelty of the current work is to depict a detailed comparison of k-means, HDBSAN and neuro-fuzzy method in visualization of high-dimension data on gene expression. Clinical Relevance- The subpopulation profiling can be used to study the patient-to patient variability when infected by SARS-COV-2 and its variants. The distribution of cell types can be relevant in designing new drugs that are targeted to control the distribution of epithelial cells T cells and macrophages.

Mapping of structural arrangement of cells and collective calcium transients: an integrated framework combining live cell imaging using confocal microscopy and UMAP-assisted HDBSCAN-based approach.

Live cell calcium (Ca2+) imaging is one of the important tools to record cellular activity during in vitro and in vivo preclinical studies. Specially, high-resolution microscopy can provide valuable dynamic information at the single cell level. One of the major challenges in the implementation of such imaging schemes is to extract quantitative information in the presence of significant heterogeneity in Ca2+ responses attained due to variation in structural arrangement and drug distribution. To fill this gap, we propose time-lapse imaging using spinning disk confocal microscopy and machine learning-enabled framework for automated grouping of Ca2+ spiking patterns. Time series analysis is performed to correlate the drug induced cellular responses to self-assembly pattern present in multicellular systems. The framework is designed to reduce the large-scale dynamic responses using uniform manifold approximation and projection (UMAP). In particular, we propose the suitability of hierarchical DBSCAN (HDBSCAN) in view of reduced number of hyperparameters. We find UMAP-assisted HDBSCAN outperforms existing approaches in terms of clustering accuracy in segregation of Ca2+ spiking patterns. One of the novelties includes the application of non-linear dimension reduction in segregation of the Ca2+ transients with statistical similarity. The proposed pipeline for automation was also proved to be a reproducible and fast method with minimal user input. The algorithm was used to quantify the effect of cellular arrangement and stimulus level on collective Ca2+ responses induced by GPCR targeting drug. The analysis revealed a significant increase in subpopulation containing sustained oscillation corresponding to higher packing density. In contrast to traditional measurement of rise time and decay ratio from Ca2+ transients, the proposed pipeline was used to classify the complex patterns with longer duration and cluster-wise model fitting. The two-step process has a potential implication in deciphering biophysical mechanisms underlying the Ca2+ oscillations in context of structural arrangement between cells.

Mathematical modeling of viral infection dynamics and immune response in SARS-CoV-2: A computational framework for testing drug efficacy.

SARS-CoV-2 has emerged to cause the outbreak of COVID-19, which has expanded into a worldwide human pandemic. Although detailed experimental data on animal experiments would provide insight into drug efficacy, the scientists involved in these experiments would be exposed to severe risks. In this context, we propose a computational framework for studying infection dynamics that can be used to capture the growth rate of viral replication and lung epithelial cell in presence of SARS-CoV-2. Specifically, we formulate the model consisting of a system of non-linear ODEs that can be used for visualizing the infection dynamics in a cell population considering the role of T cells and Macrophages. The major contribution of the proposed simulation method is to utilize the infection progression model in testing the efficacy of the drugs having various mechanisms and analyzing the effect of time of drug administration on virus clearance.Clinical Relevance-The proposed computational framework incorporates viral infection dynamics and role of immune response in Covid-19 that can be used to test the impact of drug efficacy and time of drug administration on infection mitigation.

Quantitative Confocal Microscopy for Grouping of Dose-Response Data: Deciphering Calcium Sequestration and Subsequent Cell Death in the Presence of Excess Norepinephrine.

Fluorescent calcium (Ca2+) imaging is one of the preferred methods to record cellular activity during in vitro preclinical studies, high-content drug screening, and toxicity analysis. Visualization and analysis for dose-response data obtained using high-resolution imaging remain challenging, due to the inherent heterogeneity present in the Ca2+ spiking. To address this challenge, we propose measurement of cytosolic Ca2+ ions using spinning-disk confocal microscopy and machine learning-based analytics that is scalable. First, we implemented uniform manifold approximation and projection (UMAP) for visualizing the multivariate time-series dataset in the two-dimensional (2D) plane using Python. The dataset was obtained through live imaging experiments with norepinephrine-induced Ca2+ oscillation in HeLa cells for a large range of doses. Second, we demonstrate that the proposed framework can be used to depict the grouping of the spiking pattern for lower and higher drug doses. To the best of our knowledge, this is the first attempt at UMAP visualization of the time-series dose response and identification of the Ca2+ signature during lytic death. Such quantitative microscopy can be used as a component of a high-throughput data analysis workflow for toxicity analysis.

Leave against Medical Advice in Children: Rural Indian Perspective.

OBJECTIVES: To determine the burden and etiological factors of leave against medical advice (LAMA) in Indian children. METHODOLOGY: In this prospective study, legal guardians of 528 patients who took the decision of LAMA were interviewed (using structured question answers based multi-option) and data were captured over a period of 16 months. The resultant database was analyzed using standard statistical methods. RESULTS: About 6.12% of childhood LAMA cases were dealt out of total pediatric admission including newborns. Neonatal preponderance noted in cases of LAMA from intensive care unit (ICU; 57.14%, p < 0.05). The overall male (n = 293) to female (n = 235) ratio in this study was 1.25:1. Number of LAMA patients was higher from rural area (83.33%), mostly admitted in ICU (93.65%, Pearson's chi-squared test, p < 0.05). Higher proportion (29.47%) of patients with infection availed LAMA from neonatal age group but overall LAMA patients fall under category of respiratory disorders (22.35%). Interest of the domestics issues other than suffering child was considered primary during LAMA for those admitted in ward as compared with ICU patients [odds ratio (OR): 1.73, CI: 1.02-2.94, p < 0.05]. ICU patients were reportedly to be taken to private health care facility (OR: 1.93, CI: 1.06-3.49, p < 0.05). Duration of stay before taking LAMA from ward was <7 days (85.59%, OR: 0.19, CI: 0.11-0.35, p < 0.05). Upper-lower socio-economic class attributed financial constraint as the main reason for LAMA (Pearson's chi-squared test, Chi-square value: 152.23, p < 0.05). CONCLUSIONS: This study tried to elucidate the determinants of childhood LAMA in rural Indian setting.

Multiorgan dysfunction syndrome in sepsis: Is macrophage activation syndrome secondary to infection?

OBJECTIVE: To assess macrophage activation syndrome (MAS) in septic shock leading to multiorgan dysfunction syndrome (MODS). METHODS: A prospective observational study was conducted at a tertiary care hospital to evaluate the MAS criteria in different stages of sepsis. Children aged 6 months to 12 years in different stages of septic shock were recruited. The Paediatric Rheumatology International Trials Organisation Collaborative Initiative (PRINTO) criteria of MAS were applied initially at the stage of septic shock and subsequently at the stage of MODS (MODS cohort) or following recovery from septic shock without going through MODS (non-MODS cohort). RESULTS: A total of 127 subjects were studied, with 53 comprising the MODS cohort and the rest 74 the non-MODS cohort. At the initial assessment, a comparable proportion of subjects in the MODS and non-MODS groups satisfied the MAS criteria (20.75% and 25.68%, respectively; p=0.529). However, by the time of progression to MODS, 81.13% of the subjects satisfied the MAS criteria in the MODS group, whereas only 16.18% subjects in the non-MODS group continued to satisfy the MAS criteria (p<0.001). Thus, there was a definite increase in the proportion of subjects showing MAS by the time they progressed to multiorgan dysfunction (p<0.001). In contrast, the proportion declined significantly (25.68% to 16.18%; p=0.008) in the subjects who had recovered. CONCLUSION: The findings bear out the hypothesis that MODS in sepsis is a reflection of MAS secondary to sepsis. However, studies in larger cohorts are needed to validate these findings and explore the therapeutic implications.

Detection of Specific Templates in Calcium Spiking in HeLa Cells Using Hierarchical DBSCAN: Clustering and Visualization of CellDrug Interaction at Multiple Doses.

One of the major challenges in analyzing large scale intracellular calcium spiking data obtained through fluorescent imaging is to identify various patterns present in time series data. Such an analysis identifying the distinct frequency and amplitude encoding during cell-drug interaction study is expected to provide new insights into the drug action patterns over a time course. Here, we present the HDBSCAN clustering algorithm to find a clustering pattern present in calcium spiking obtained by confocal imaging of single cells. Our methodology uncovers the specific templates present in dynamic responses obtained through treatment with multiple doses of the drug. First, we attempt to visualize the clustering pattern present in time-series data corresponding to various doses of the drug. Secondly, we show that the HDBSCAN can be used for the detection of specific signatures corresponding to low and high cell density regions selected from in vitro experiments. To the best of our knowledge, this is the first attempt to optimize the clustering of calcium dynamics using HDBSCAN. Finally, we emphasize that HDBSCAN offers a high-level grasp on systems biology data, including complex spiking pattern and can be used as a visual analytic tool for drug dose selection.