Role and mechanism of COL3A1 in regulating the growth, metastasis, and drug sensitivity in cisplatin-resistant non-small cell lung cancer cells.

Non-small cell lung cancer (NSCLC) is among the most difficult malignancies to treat. Type III collagen (COL3A1) can affect the progression and chemoresistance development of NSCLC. We herein explored the mechanism that drives COL3A1 dysregulation in NSCLC. Potential RNA-binding proteins (RBPs) and transcription factors (TFs) that could bind to COL3A1 were searched by bioinformatics. mRNA expression was detected by quantitative PCR. Protein expression was evaluated using immunoblotting and immunohistochemistry. The effects of the variables were assessed by gauging cell growth, invasiveness, migratory capacity, apoptosis, and cisplatin (DDP) sensitivity. The direct YY1/COL3A1 relationship was confirmed by ChIP and luciferase reporter experiments. Xenograft experiments were done to examine COL3A1's function in DDP efficacy. COL3A1 showed enhanced expression in DDP-resistant NSCLC. In H460/DDP and A549/DDP cells, downregulation of COL3A1 exerted inhibitory functions in cell growth, invasiveness, and migration, as well as promoting effects on cell DDP sensitivity and apoptosis. Mechanistically, ELAV-like RNA binding protein 1 (ELAVL1) enhanced the mRNA stability and expression of COL3A1, and Yin Yang 1 (YY1) promoted the transcription and expression of COL3A1. Furthermore, upregulation of COL3A1 reversed ELAVL1 inhibition- or YY1 deficiency-mediated functions in DDP-resistant NSCLC cells. Additionally, COL3A1 downregulation enhanced the anti-tumor efficacy of DDP in vivo. Our investigation demonstrates that COL3A1 upregulation, induced by both RBP ELAVL1 and TF YY1, exerts important functions in phenotypes of NSCLC cells with DDP resistance, offering an innovative opportunity in the treatment of drug-resistant NSCLC.

Triptolide restrains the growth, invasion, stemness, and glycolysis of non-small cell lung cancer cells by PFKFB2-mediated PI3K/AKT pathway.

Triptolide (TP) has been found to have anti-tumor effects. However, more potential molecular mechanisms of TP in the progression of non-small cell lung cancer (NSCLC) deserve further investigation. Cell proliferation, apoptosis, invasion, and stemness were detected by cell counting kit 8 assay, EdU assay, flow cytometry, transwell assay, and sphere formation assay. Cell glycolysis was evaluated by corresponding assay kits. 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 (PFKFB2) expression was measured by western blot (WB), qRT-PCR and immunohistochemical staining. PI3K/AKT pathway-related markers were determined by WB. Besides, xenograft tumor model was conducted to evaluate the anti-tumor effect of TP in NSCLC. Our results revealed that TP treatment suppressed NSCLC cell proliferation, invasion, stemness, glycolysis, and enhanced apoptosis. PFKFB2 was upregulated in NSCLC tissues and cells, and its expression was decreased by TP. PFKFB2 knockdown restrained NSCLC cell functions, and its overexpression also eliminated TP-mediated NSCLC cell functions inhibition. TP decreased PFKFB2 expression to inactivate PI3K/AKT pathway. Moreover, PI3K/AKT pathway inhibitor LY294002 also could reverse the promoting effect of PFKFB2 on NSCLC cell functions. In addition, TP suppressed NSCLC tumorigenesis by inhibiting PFKFB2/PI3K/AKT pathway. In conclusion, TP exerted anti-tumor role in NSCLC, which was achieved by reducing PFKFB2 expression to inactivate PI3K/AKT pathway.

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a Hierarchical Reinforcement Learning decision framework for multi-mode Epidemic Control with multiple interventions called HRL4EC. We devise an epidemiological model, referred to as MID-SEIR, to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.

Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19.

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

Performance optimization of salp swarm algorithm for multi-threshold image segmentation: Comprehensive study of breast cancer microscopy.

Multi-threshold image segmentation (MIS) is now a well known image segmentation technique, and many researchers have applied intelligent algorithms to it, but these methods suffer from local optimal drawbacks. This paper presented a novel approach to improve the Salp Swarm Algorithm (SSA), namely EHSSA, and applied it to MIS. Knowing the inaccuracies and discussions on implementation of this method, a new efficient mechanism is proposed to improve global search capability of the algorithm and avoid falling into a local optimum. Moreover, the excellence of the proposed algorithm was proved by comparative experiments at IEEE CEC2014. Afterward, the performance of EHSSA was demonstrated by testing a set of images selected from the Berkeley segmentation data set 500 (BSDS500), and the experimental results were analyzed by evaluating the parameters, which proved the efficiency of the proposed algorithm in MIS. Furthermore, EHSSA was applied to the microscopic image segmentation of breast cancer. Medical image segmentation is the study of how to quickly extract objects of interest (human organs) from various images to perform qualitative and quantitative analysis of diseased tissues and improve the accuracy of their diagnosis, which assists the physician in making more informed decisions and patient rehabilitation. The results of this set of experiments also proved its superior performance. For any info about this paper, readers can refer to https://aliasgharheidari.com.

Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi's entropy for chronic obstructive pulmonary disease.

Image segmentation is an essential pre-processing step and is an indispensable part of image analysis. This paper proposes Renyi's entropy multi-threshold image segmentation based on an improved Slime Mould Algorithm (DASMA). First, we introduce the diffusion mechanism (DM) into the original SMA to increase the population's diversity so that the variants can better avoid falling into local optima. The association strategy (AS) is then added to help the algorithm find the optimal solution faster. Finally, the proposed algorithm is applied to Renyi's entropy multilevel threshold image segmentation based on non-local means 2D histogram. The proposed method's effectiveness is demonstrated on the Berkeley segmentation dataset and benchmark (BSD) by comparing it with some well-known algorithms. The DASMA-based multilevel threshold segmentation technique is also successfully applied to the CT image segmentation of chronic obstructive pulmonary disease (COPD). The experimental results are evaluated by image quality metrics, which show the proposed algorithm's extraordinary performance. This means that it can help doctors analyze the lesion tissue qualitatively and quantitatively, improve its diagnostic accuracy and make the right treatment plan. The supplementary material and info about this article will be available at https://aliasgharheidari.com.

[Rapid nondestructive detection of water content in fresh pork based on spectroscopy technique combined with support vector machine].

Visible near infrared reflectance spectra in the range of 350 nm to 1700 nm were collected from 98 pork samples to develop online, rapid and nondestructive detection system for water content in fresh pork Median smoothing filter (M-filter), multiplication scatter correlation (MSC) and first derivative (FD) were used as compound preprocessing method to reduce noise present in the original spectrum. Seventy four samples were randomly selected to develop training model and remaining 24 samples were used to test the model. The optimal punishment parameters for the support vector machine (SVM) were determined by using cross--validation and grid--search in the training set. SVM prediction model was developed with the radial basis function (RBF) and the developed model was compared with the model developed by partial least squares regression (PLSR) method. SVM prediction model based on RBF had the correlation coefficient and root mean standard error of 0.96 and 0.32 respectively in the training set. The model obtained correlation coefficient of 0.87 and root mean square error of 0.67 in the test set. The result thus obtained demonstrates the applicability of SVM model for rapid, nondestructive detection of water content in pork.