Altaf-Ul-Amin, M. | Search bioRxiv

bioRxiv2021

Integrated Metabolomics to Reveal the Impacts of Common Antibiotics based on Drug Resistance Prediction of Gut Microbiota

Due to the close interaction between the host and the gut microbiota, the alterations in gut microbiota metabolism may therefore contribute to various diseases. How to use antibiotics more wisely in clinical practice is a promising task in the field of pathophysiology related to gut microbiota. The hope fueling this research is that the alteration of gut microbial communities are paralleled by their capacity on metabolomic from the combined perspective of microbiome and metabolomics. In order to reveal the impacts of antibiotics on microbiota-associated host metabolomic phenotypes, a feasible methodology should be well developed to assess the pervasive effects of antibiotics on the population structure of gut microbial communities. Our attempt starts from predicting specific resistance phenotypes of the individuals in isolation from the rest of the gut microbiota community, according to their resistant genotypes. Once resistance phenotypes of microbiome is determined, we integrated metabolomics with machine learning by applying various analysis algorithms to explore the relationship between the predicted resistance and metabolites, including what the microbial community is after medication, which microbes produce metabolites, and how these metabolites enrich.

bioinformatics↗

bioRxiv2019

The Feasibility of Predicting Impending Malignant Ventricular Arrhythmias on the basis of Signal Complexity of Heartbeat Intervals

Malignant ventricular arrhythmias (MAs), such as ventricular tachycardia (VT) that presages cardiac arrest, present the highest hurdle for the healthcare community to overcome. Given that MAs occur unpredictably and lead to emergencies, convenient tracking devices, e.g. photoplethysmogram (PPG), that could predict MAs would be irreplaceably valuable. Since the use of heartbeat intervals (HbI) to predict the occurrence of arrhythmias is becoming more feasible, a further attempt to establish a new convenient approach for predicting impending MAs with HbI is worth trying. Assuming that intrinsic characteristics of MAs (VT and ventricular fibrillation: VF) can be revealed by a suitable approach on the basis of signal complexity, we propose an approach that first expresses the physiological status of the heart by HbI; then delineates the patterns of HbI by a new complexity metric (refined composite multi-scale entropy: RCMsEn); and finally trains a nonlinear machine learning model (random forest: RF) to learn the specific patterns of MAs so as to differentiate them from the normal sinus heart rhythm(N) and other prevalent arrhythmias (atrial fibrillation: AF, and premature ventricular contraction: V). For calculating entropy values and predicting MAs as early as possible (which is the aim of this study), two specifications are of interest: the minimal length of HbI needed to delineate the MAs patterns sufficiently (lenmin), and the maximum time length at which our model can predict impending MAs (timemax). We compared the RF model with support vector machine (SVM) models based on linear and Gaussian kernels. Results show that the RF model performs the best, reaching a 99.24% recall and a 99.87% precision for a HbI of 500 heartbeats (the lenmin) 374 seconds (the timemax) preceding the occurrence of MAs. The HbI samples in this study were extracted from an electrocardiograph (ECG). However, given the subtle difference (0.1 ms typically) between the R-R interval of ECG and the P-P interval of PPG, this approach could be extended to HbI acquired by the PPG sensor and thus should be of substantial theoretical and practical significance in cardiac arrest prevention.

bioinformatics↗