Risk assessment, fitness cost and transcriptome analysis of cyantraniliprole resistance in Spodoptera frugiperda.

Spodoptera frugiperda is a notorious invasive pest causing substantial yield losses of crops and has developed resistance to various types of insecticides. In this study, a cyantraniliprole-resistant strain, SfCYAN-R, was obtained from a susceptible strain, SfCYAN-S, after 13 generations of selection with cyantraniliprole. The fitness cost in SfCYAN-R strain was evaluated, and the putative resistance-related genes were explored by RNA-seq analysis. The results showed that SfCYAN-R strain developed 23.97-fold resistance to cyantraniliprole with the realistic heritability of 0.127. The development time of eggs, larvae, prepupae and pupae in SfCYAN-R strain was significantly prolonged than that in SfCYAN-S strain, but no difference in pupation rate, emergence rate and female fecundity was observed between SfCYAN-R and SfCYAN-S strains. Comparative gene expression analysis between SfCYAN-R and SfCYAN-S strains identified 776 significant differentially expressed genes (DEGs), among which several DEGs associated with xenobiotic metabolism were upregulated in SfCYAN-R strain. These results provide insights into the resistance mechanisms of cyantraniliprole and would be helpful for resistance management of S. frugiperda.

Low concentrations of cyantraniliprole negatively affects the development of Spodoptera frugiperda by disruption of ecdysteroid biosynthesis and carbohydrate and lipid metabolism.

In addition to the acute lethal toxicity, insecticides might affect population dynamics of insect pests by inducing life history trait changes under low concentrations, however, the underlying mechanisms remain not well understood. Here we examined systemic impacts on development and reproduction caused by low concentration exposures to cyantraniliprole in the fall armyworm (FAW), Spodoptera frugiperda, and the putative underlying mechanisms were investigated. The results showed that exposure of third-instar larvae to LC10 and LC30 of cyantraniliprole significantly extended larvae duration by 1.46 and 5.41 days, respectively. Treatment with LC30 of cyantraniliprole significantly decreased the pupae weight and pupation rate as well as the longevity, fecundity and egg hatchability of female adults. Consistently, we found that exposure of FAW to LC30 cyantraniliprole downregulated the mRNA expression of four ecdysteroid biosynthesis genes including SfNobo, SfShd, SfSpo and SfDib and one ecdysone response gene SfE75 in the larvae as well as the gene encoding vitellogenin (SfVg) in the female adults. We also found that treatment with LC30 of cyantraniliprole significantly decreased the whole body levels of glucose, trehalose, glycogen and triglyceride in the larvae. Our results indicate that low concentration of cyantraniliprole inhibited FAW development by disruption of ecdysteroid biosynthesis as well as carbohydrate and lipid metabolism, which have applied implications for the control of FAW.

Potential clinical treatment prospects behind the molecular mechanism of alternative lengthening of telomeres (ALT).

Normal somatic cells inevitably experience replicative stress and senescence during proliferation. Somatic cell carcinogenesis can be prevented in part by limiting the reproduction of damaged or old cells and removing them from the cell cycle [1, 2]. However, Cancer cells must overcome the issues of replication pressure and senescence as well as preserve telomere length in order to achieve immortality, in contrast to normal somatic cells [1, 2]. Although telomerase accounts for the bulk of telomere lengthening methods in human cancer cells, there is a non-negligible portion of telomere lengthening pathways that depend on alternative lengthening of telomeres (ALT) [3]. For the selection of novel possible therapeutic targets for ALT-related disorders, a thorough understanding of the molecular biology of these diseases is crucial [4]. The roles of ALT, typical ALT tumor cell traits, the pathophysiology and molecular mechanisms of ALT tumor disorders, such as adrenocortical carcinoma (ACC), are all summarized in this work. Additionally, this research compiles as many of its hypothetically viable but unproven treatment targets as it can (ALT-associated PML bodies (APB), etc.). This review is intended to contribute as much as possible to the development of research, while also trying to provide a partial information for prospective investigations on ALT pathways and associated diseases.

Identification of Pulmonary Hypertension Animal Models Using a New Evolutionary Machine Learning Framework Based on Blood Routine Indicators.

Pulmonary Hypertension (PH) is a global health problem that affects about 1% of the global population. Animal models of PH play a vital role in unraveling the pathophysiological mechanisms of the disease. The present study proposes a Kernel Extreme Learning Machine (KELM) model based on an improved Whale Optimization Algorithm (WOA) for predicting PH mouse models. The experimental results showed that the selected blood indicators, including Haemoglobin (HGB), Hematocrit (HCT), Mean, Platelet Volume (MPV), Platelet distribution width (PDW), and Platelet-Large Cell Ratio (P-LCR), were essential for identifying PH mouse models using the feature selection method proposed in this paper. Remarkably, the method achieved 100.0% accuracy and 100.0% specificity in classification, demonstrating that our method has great potential to be used for evaluating and identifying mouse PH models.

An evolutionary machine learning for pulmonary hypertension animal model from arterial blood gas analysis.

Pulmonary hypertension (PH) is a rare and fatal condition that leads to right heart failure and death. The pathophysiology of PH and potential therapeutic approaches are yet unknown. PH animal models' development and proper evaluation are critical to PH research. This work presents an effective analysis technology for PH from arterial blood gas analysis utilizing an evolutionary kernel extreme learning machine with multiple strategies integrated slime mould algorithm (MSSMA). In MSSMA, two efficient bee-foraging learning operators are added to the original slime mould algorithm, ensuring a suitable trade-off between intensity and diversity. The proposed MSSMA is evaluated on thirty IEEE benchmarks and the statistical results show that the search performance of the MSSMA is significantly improved. The MSSMA is utilised to develop a kernel extreme learning machine (MSSMA-KELM) on PH from arterial blood gas analysis. Comprehensively, the proposed MSSMA-KELM can be used as an effective analysis technology for PH from arterial Blood gas analysis with an accuracy of 93.31%, Matthews coefficient of 90.13%, Sensitivity of 91.12%, and Specificity of 90.73%. MSSMA-KELM can be treated as an effective approach for evaluating mouse PH models.