Identification of meat adulteration in minced meat samples labeled as beef and mutton in Tehran stores using duplex PCR.

Food fraud and profiteering are becoming increasingly common in the meat industry. Therefore, it is essential to identify such practices to prevent consumer deception and maintain food safety. This study aimed to determine the contents of minced meat samples labeled as beef and mutton in retail stores across Tehran province, Iran, to identify instances of meat adulteration.

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

Background And Aim: Staging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning-Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C patients.

Methods: We propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning.