Multivariate Regression in Conjunction with GA-BP for Optimization of Data Processing of Trace NO Gas Flow in Active Pumping Electronic Nose

Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation

In areas where livestock are bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. However, existing electronic nose systems have slow response times and limited detection accuracy. In this study, we introduce a novel solution: the bionic chamber construction of the electronic nose is optimized, and the sensor response data in the chamber are analyzed using an intelligent algorithm. We analyze the structure of the biomimetic chamber and the surface airflow of the sensor array to determine the sensing units of the system. The system employs an electronic nose to detect ammonia and ethanol gases in a circulating airflow within a closed box. The captured signals are processed, followed by the application of classification and regression models for data prediction. Our results suggest that the system, leveraging the biomimetic chamber, offers rapid gas detection response times. A high classification prediction accuracy, with a determination coefficient R2 value of 0.99 for single-output regression and over 0.98 for multi-output regression predictions, is achieved by incorporating a backpropagation (BP) neural network algorithm. These outcomes demonstrate the effectiveness of the electronic nose, based on an optimized bionic chamber combined with a BP neural network algorithm, in accurately detecting ammonia emitted during livestock excreta fermentation, satisfying the ammonia detection requirements of breeding farms.

Deciphering treatment resistance in metastatic colorectal cancer: roles of drug transports, EGFR mutations, and HGF/c-MET signaling

In 2023, colorectal cancer (CRC) is the third most diagnosed malignancy and the third leading cause of cancer death worldwide. At the time of the initial visit, 20% of patients diagnosed with CRC have metastatic CRC (mCRC), and another 25% who present with localized disease will later develop metastases. Despite the improvement in response rates with various modulation strategies such as chemotherapy combined with targeted therapy, radiotherapy, and immunotherapy, the prognosis of mCRC is poor, with a 5-year survival rate of 14%, and the primary reason for treatment failure is believed to be the development of resistance to therapies. Herein, we provide an overview of the main mechanisms of resistance in mCRC and specifically highlight the role of drug transports, EGFR, and HGF/c-MET signaling pathway in mediating mCRC resistance, as well as discuss recent therapeutic approaches to reverse resistance caused by drug transports and resistance to anti-EGFR blockade caused by mutations in EGFR and alteration in HGF/c-MET signaling pathway.