3 results about "Median lethal dose" patented technology

The invention relates to the technical field of drug side reaction risk prediction, in particular to a tumor patient drug side reaction risk prediction system based on multi-source data. The system comprises a data acquisition module used for integrating multi-source time series data, including medication dosage, pharmacokinetic parameters and clinical index changes, and constructing a multi-dimensional data set; the cumulative effect analysis module is used for calculating a drug attenuation factor, a binding dose and median lethal dose quantitative cumulative effect intensity based on first-order elimination kinetics, and capturing drug metabolic dynamics; due to the fact that the interaction influence effect possibly exists between the drugs, the influence coefficient analysis module is used for adopting elastic network regression and analyzing the drug main effect and the drug combination interaction effect, and determining the main influence coefficient and the interaction influence coefficient of the drugs on each clinical index; and the risk prediction module is used for weighting and optimizing decision tree sorting through the abnormal contribution degree, and outputting the risk probability of each side reaction in combination with a random forest algorithm.

This invention discloses a method and its application for creating stress-resistant mutants of common bean using chemical mutagenesis. The main steps include: determining the screening concentration of hydroxyproline (Hyp), determining the median lethal dose of ethyl methanesulfonate (EMS), and subculturing the stress-resistant mutants. After 3-5 subcultures, the stress-resistant mutants screened by this invention ultimately yielded 31 cotyledonary nodes of common bean resistant to hydroxyproline (Hyp), with a mutation rate of 8.16%. This invention can induce mutagenesis and directionally screen for stress-resistant common bean mutants, providing material support for stress-resistant breeding of common bean.

The present application relates to the technical field of drug side reaction risk prediction, and particularly relates to a tumor patient drug side reaction risk prediction system based on multi-source data. The system comprises: a data acquisition module for integrating multi-source time series data, including drug dosage, pharmacokinetic parameters and clinical index changes, and constructing a multi-dimensional data set; a cumulative effect analysis module for calculating a drug attenuation factor based on first-order elimination kinetics, quantifying the cumulative effect intensity in combination with the dosage and median lethal dose, and capturing the drug metabolism dynamics; since there may be interactive effects between drugs, an influence coefficient analysis module is used to analyze the drug main effect and the joint drug interaction effect by using elastic network regression, and to determine the main influence coefficient and the interaction influence coefficient of the drug on each clinical index; a risk prediction module for optimizing the decision tree sorting by the abnormal contribution degree weighting, and outputting the risk probability of each side reaction in combination with the random forest algorithm.