ABSTRACT
Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in the brain is insufficient due to atherosclerosis or stenosis of the coronary arteries in the patient's heart, which leads to neurological deficits. To predict the pathogenic factors of cardiogenic cerebral infarction, this paper proposes a machine learning based analytical prediction model. 494 patients with CCI who were hospitalized for the first time were consecutively included in the study between January 2017 and December 2021, and followed up every three months for one year after hospital discharge. Clinical, laboratory and imaging data were collected, and predictors associated with relapse and death in CCI patients at six months and one year after discharge were analyzed using univariate and multivariate logistic regression methods, meanwhile established a new machine learning model based on the enhanced moth-flame optimization (FTSAMFO) and the fuzzy K-nearest neighbor (FKNN), called BITSAMFO-FKNN, which is practiced on the dataset related to patients with CCI. Specifically, this paper proposes the spatial transformation strategy to increase the exploitation capability of moth-flame optimization (MFO) and combines it with the tree seed algorithm (TSA) to increase the search capability of MFO. In the benchmark function experiments FTSAMFO beat 5 classical algorithms and 5 recent variants. In the feature selection experiment, ten times ten-fold cross-validation trials showed that the BITSAMFO-FKNN model proved actual medical importance and efficacy, with an accuracy value of 96.61%, sensitivity value of 0.8947, MCC value of 0.9231, and F-Measure of 0.9444. The results of the trial showed that hemorrhagic conversion and lower LVDD/LVSD were independent risk factors for recurrence and death in patients with CCI. The established BITSAMFO-FKNN method is helpful for CCI prognosis and deserves further clinical validation.
ABSTRACT
The fall armyworm Spodoptera frugiperda (Smith) (FAA) is responsible for considerable losses in grain production, and chemical control is the most effective strategy. However, frequent insecticide application can lead to the development of resistance. In insects, cytochrome P450 plays a crucial role in insecticide metabolism. CYP6K2 is related to FAA resistance to chlorantraniliprole. However, the regulatory mechanism of CYP6K2 expression is poorly understood. In this study, a conserved target of isolated miRNA-190-5p was located in the 3' UTR of CYP6K2 in FAA. A luciferase reporter analysis showed that in FAA, miRNA-190-5p can combine with the 3'UTR of CYP6K2 to suppress its expression. Injected miRNA-190-5p agomir significantly reduced CYP6K2 abundance by 54.6% and reduced tolerance to chlorantraniliprole in FAA larvae, whereas injected miRNA-190-5p antagomir significantly increased CYP6K2 abundance by 1.77-fold and thus improved chlorantraniliprole tolerance in FAA larvae. These results provide a basis for further research on the posttranscriptional regulatory mechanism of CYP6K2 and will facilitate further study on the function of miRNAs in regulating tolerance to chlorantraniliprole in FAA.
