Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer.

Cervical cancer is one of the most dangerous and widespread illnesses afflicting women throughout the globe, particularly in East Africa and South Asia. In industrialised nations, the incidence of cervical cancer has consistently decreased over the past few decades. However, in developing countries, the reduction in incidence has been considerably slower, and in some instances, the incidence has increased. Implementing routine screenings for cervical cancer is something that has to be done to protect the health of women. Cervical cancer is famously difficult to diagnose and cure due to the slow rate at which it spreads and develops into more advanced stages of the disease. Screening for cervical cancer using a Pap smear, more often referred to as a Pap test, has the potential to detect the illness in its earlier stages. For the purpose of selecting features for this article, a gray level co-occurrence matrix (GLCM) technique was used. Following this step, classification is performed with methods such as convolutional neural network (CNN), support vector machine, and auto encoder. According to the findings of this experiment, the GLCM-CNN classifier proved to be the one with the highest degree of precision.

miR-199a-3p suppresses neuroinflammation by directly targeting MyD88 in a mouse model of bone cancer pain.

AIMS: Pain is a profoundly debilitating symptom in cancer patients, leading to disability, immobility, and a marked decline in their quality of life. This study aimed to investigate the potential roles of miR-199a-3p in a murine model of bone cancer pain induced by tumor cell implantation in the medullary cavity of the femur. MATERIALS AND METHODS: We assessed pain-related behaviors, including the paw withdrawal mechanical threshold (PWMT) and the number of spontaneous flinches (NSF). To investigate miRNA expression and its targets in astrocytes, we employed a combination of RNA-seq analysis, qRT-PCR, Western blotting, EdU, TUNEL, ChIP, ELISA, and luciferase reporter assays in mice (C3H/HeJ) with bone cancer pain and control groups. KEY FINDINGS: On days 10, 14, 21, and 28 post-surgery, we observed significant differences in PWTL, PWMT, and NSF when compared to the sham group (P < 0.001). qRT-PCR assays and miRNA sequencing results confirmed reduced miR-199a-3p expression in astrocytes of mice with bone cancer pain. Gain- and loss-of-function experiments demonstrated that miR-199a-3p suppressed astrocyte activation and the expression of inflammatory cytokines. In vitro investigations revealed that miR-199a-3p mimics reduced the levels of inflammatory factors in astrocytes and MyD88/NF-κB proteins. Furthermore, treatment with a miR-199a-3p agonist resulted in reduced expression of MyD88, TAK1, p-p65, and inflammatory mediators, along with decreased astrocyte activation in the spinal cord. SIGNIFICANCE: Collectively, these findings demonstrate that upregulation of miR-199a-3p may offer a therapeutic avenue for mitigating bone cancer pain in mice by suppressing neuroinflammation and inhibiting the MyD88/NF-κB signaling pathway.

Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman's premature death from the disease. False-positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time-consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c-means approach is applied. The images are segmented using the fuzzy c-means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.

Therapeutic potential of mesenchymal stem/stromal cells (MSCs)-based cell therapy for inflammatory bowel diseases (IBD) therapy.

Recently, mesenchymal stem/stromal cells (MSCs) therapy has become an emerging therapeutic modality for the treatment of inflammatory bowel disease (IBD), given their immunoregulatory and pro-survival attributes. MSCs alleviate dysregulated inflammatory responses through the secretion of a myriad of anti-inflammatory mediators, such as interleukin 10 (IL-10), transforming growth factor-ß (TGFß), prostaglandin E2 (PGE2), tumor necrosis factor-stimulated gene-6 (TSG-6), etc. Indeed, MSC treatment of IBD is largely carried out through local microcirculation construction, colonization and repair, and immunomodulation, thus alleviating diseases severity. The clinical therapeutic efficacy relies on to the marked secretion of various secretory molecules from viable MSCs via paracrine mechanisms that are required for gut immuno-microbiota regulation and the proliferation and differentiation of surrounding cells like intestinal epithelial cells (IECs) and intestinal stem cells (ISCs). For example, MSCs can induce IECs proliferation and upregulate the expression of tight junction (TJs)-associated protein, ensuring intestinal barrier integrity. Concerning the encouraging results derived from animal studies, various clinical trials are conducted or ongoing to address the safety and efficacy of MSCs administration in IBD patients. Although the safety and short-term efficacy of MSCs administration have been evinced, the long-term efficacy of MSCs transplantation has not yet been verified. Herein, we have emphasized the illumination of the therapeutic capacity of MSCs therapy, including naïve MSCs, preconditioned MSCs, and also MSCs-derived exosomes, to alleviate IBD severity in experimental models. Also, a brief overview of published clinical trials in IBD patients has been delivered.

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment.

As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country's economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.