Effectiveness of community-based treatment programs for treatment of uncomplicated severe acute malnourished children aged 6-59 months using locally produced nutrient dense foods: protocol for a multicentric longitudinal quasi-experimental study.

BACKGROUND: Severe acute malnutrition (SAM) is a major underlying cause of mortality among children. Around one third of the world's acutely malnourished children live in India. The WHO recommends community-based management of acute malnutrition (CMAM) for managing children with SAM. In India, different states are implementing community-based SAM treatment programme, hereinafter called CSAM, using varieties of locally produced nutrient dense food items with different nutrient compositions. The study will assess the effectiveness of these state specific CSAM interventions. METHODS: The longitudinal quasi-experimental study will be undertaken in two purposively selected blocks of one district each in the four intervention states and one comparison state. From each state, 200 SAM children identified using weight-for-length/height z-score (WHZ) < - 3 criteria will be enrolled in the study. Their anthropometric data and skinfold thickness will be taken on admission, at sixth week and at discharge by trained field investigators. Other child details, incidence of morbidity and socio-economic details will be collected on admission. To assess food consumption pattern including consumption of locally produced nutrient dense food supplements, dietary assessment, using 24-h dietary recall will be conducted on admission, at sixth week and at discharge. In addition, body composition parameters will be assessed for a sub-set of children using bio-electrical impedance analysis on admission and at discharge to analyse changes in total body water, fat-free mass, and fat mass. Post discharge, all study participants will be followed up monthly until 6 months. Atleast 10% of the sample will be checked for quality assessment. The study's primary outcome is cure rate defined as children attaining WHZ ≥ -2. Secondary outcomes include mean weight gain, mean length of stay, body composition parameters, relapse and mortality rates. Additionally, process evaluation and cost effectiveness analysis will be conducted. DISCUSSION: There is a shortage of robust evidence regarding the effectiveness of locally produced nutrient dense food supplements provided as part of the CSAM intervention in India. This study will contribute to evidence on effective strategies to manage children with uncomplicated SAM in India. The study protocol has all necessary ethical approvals. Written informed consent will be obtained from caregivers of the children. TRIAL REGISTRATION: The study is registered with Clinical Trial Registration of India (Registration No.: CTRI/2020/09/028013 ) Date of registration 24/09/2020.

A Geometric Clustering Tool (AGCT) to robustly unravel the inner cluster structures of time-series gene expressions.

Systems biology aims at holistically understanding the complexity of biological systems. In particular, nowadays with the broad availability of gene expression measurements, systems biology challenges the deciphering of the genetic cell machinery from them. In order to help researchers, reverse engineer the genetic cell machinery from these noisy datasets, interactive exploratory clustering methods, pipelines and gene clustering tools have to be specifically developed. Prior methods/tools for time series data, however, do not have the following four major ingredients in analytic and methodological view point: (i) principled time-series feature extraction methods, (ii) variety of manifold learning methods for capturing high-level view of the dataset, (iii) high-end automatic structure extraction, and (iv) friendliness to the biological user community. With a view to meet the requirements, we present AGCT (A Geometric Clustering Tool), a software package used to unravel the complex architecture of large-scale, non-necessarily synchronized time-series gene expression data. AGCT capture signals on exhaustive wavelet expansions of the data, which are then embedded on a low-dimensional non-linear map using manifold learning algorithms, where geometric proximity captures potential interactions. Post-processing techniques, including hard and soft information geometric clustering algorithms, facilitate the summarizing of the complete map as a smaller number of principal factors which can then be formally identified using embedded statistical inference techniques. Three-dimension interactive visualization and scenario recording over the processing helps to reproduce data analysis results without additional time. Analysis of the whole-cell Yeast Metabolic Cycle (YMC) moreover, Yeast Cell Cycle (YCC) datasets demonstrate AGCT's ability to accurately dissect all stages of metabolism and the cell cycle progression, independently of the time course and the number of patterns related to the signal. Analysis of Pentachlorophenol iduced dataset demonstrat how AGCT dissects data to identify two networks: Interferon signaling and NRF2-signaling networks.

Multi-dimensional computational pipeline for large-scale deep screening of compound effect assessment: an in silico case study on ageing-related compounds.

Designing alternative approaches to efficiently screen chemicals on the efficacy landscape is a challenging yet indispensable task in the current compound profiling methods. Particularly, increasing regulatory restrictions underscore the need to develop advanced computational pipelines for efficacy assessment of chemical compounds as alternative means to reduce and/or replace in vivo experiments. Here, we present an innovative computational pipeline for large-scale assessment of chemical compounds by analysing and clustering chemical compounds on the basis of multiple dimensions-structural similarity, binding profiles and their network effects across pathways and molecular interaction maps-to generate testable hypotheses on the pharmacological landscapes as well as identify potential mechanisms of efficacy on phenomenological processes. Further, we elucidate the application of the pipeline on a screen of anti-ageing-related compounds to cluster the candidates based on their structure, docking profile and network effects on fundamental metabolic/molecular pathways associated with the cell vitality, highlighting emergent insights on compounds activities based on the multi-dimensional deep screen pipeline.

Digital health revolution: perfect storm or perfect opportunity for pharmaceutical R&D?

The convergence of technology and medicine has pushed healthcare to the brink of a major disruption that pharma has, until recently, been slow to recognize. Tech players have pioneered the emerging field of digital wellness and health, and pharma is ideally placed to use its expertise in drug development and embrace these technologies to create digital applications that address major medical needs. This review describes digital innovation from a pharma R&D perspective, outlining principal drivers, digital components, opportunities and challenges as well as a sustainable new business model predicated on empowered patients and achieving therapeutic outcomes.

Connecting the dots: role of standardization and technology sharing in biological simulation.

The role of biological modeling and simulation in enhancing productivity across the drug discovery pipeline has been increasingly appreciated over the past decade by the pharmaceutical industry. However, adoption of in silico modeling and simulation techniques has been sparse due to skepticism in the associated pay-offs and knowledge gap in research. While biological simulations have been successfully applied in specific projects, a standardized, community-wide platform is imperative for making the final leap of faith across the domain. This review outlines the issues and challenges involved in fostering a private-public collaborative effort for the development of standard modeling and biosimulation platforms and concludes with insights into possible mechanisms for integrating an in silico pipeline into the drug discovery and development process.

A markov model based analysis of stochastic biochemical systems.

The molecular networks regulating basic physiological processes in a cell are generally converted into rate equations assuming the number of biochemical molecules as deterministic variables. At steady state these rate equations gives a set of differential equations that are solved using numerical methods. However, the stochastic cellular environment motivates us to propose a mathematical framework for analyzing such biochemical molecular networks. The stochastic simulators that solve a system of differential equations includes this stochasticity in the model, but suffer from simulation stiffness and require huge computational overheads. This paper describes a new markov chain based model to simulate such complex biological systems with reduced computation and memory overheads. The central idea is to transform the continuous domain chemical master equation (CME) based method into a discrete domain of molecular states with corresponding state transition probabilities and times. Our methodology allows the basic optimization schemes devised for the CME and can also be extended to reduce the computational and memory overheads appreciably at the cost of accuracy. The simulation results for the standard Enzyme-Kinetics and Transcriptional Regulatory systems show promising correspondence with the CME based methods and point to the efficacy of our scheme.