Spotlight on the impact of viral infections on Hematopoietic Stem Cells (HSCs) with a focus on COVID-19 effects.

Hematopoietic stem cells (HSCs) are known for their significant capability to reconstitute and preserve a functional hematopoietic system in long-term periods after transplantation into conditioned hosts. HSCs are thus crucial cellular targets for the continual repair of inherited hematologic, metabolic, and immunologic disorders. In addition, HSCs can undergo various fates, such as apoptosis, quiescence, migration, differentiation, and self-renewal. Viruses continuously pose a remarkable health risk and request an appropriate, balanced reaction from our immune system, which as well as affects the bone marrow (BM). Therefore, disruption of the hematopoietic system due to viral infection is essential. In addition, patients for whom the risk-to-benefit ratio of HSC transplantation (HSCT) is acceptable have seen an increase in the use of HSCT in recent years. Hematopoietic suppression, BM failure, and HSC exhaustion are all linked to chronic viral infections. Virus infections continue to be a leading cause of morbidity and mortality in HSCT recipients, despite recent advancements in the field. Furthermore, whereas COVID-19 manifests initially as an infection of the respiratory tract, it is now understood to be a systemic illness that significantly impacts the hematological system. Patients with advanced COVID-19 often have thrombocytopenia and blood hypercoagulability. In the era of COVID-19, Hematological manifestations of COVID-19 (i.e., thrombocytopenia and lymphopenia), the immune response, and HSCT may all be affected by the SARS-CoV-2 virus in various ways. Therefore, it is important to determine whether exposure to viral infections may affect HSCs used for HSCT, as this, in turn, may affect engraftment efficiency. In this article, we reviewed the features of HSCs, and the effects of viral infections on HSCs and HSCT, such as SARS-CoV-2, HIV, cytomegalovirus, Epstein-Barr virus, HIV, etc. Video Abstract.

A comprehensive survey into the role of exosomes in pancreatic cancer; from the origin of cancer to the progress and possibility of diagnosis and treatment.

Pancreatic cancer is the fourth most common malignant tumor in the world, which has a high mortality rate due to high invasiveness, early metastases, lack of specific symptoms, and high invasiveness. Recent studies have shown that exosomes can be essential sources of biomarkers in pancreatic cancer. Over the past ten years, exosomes have been implicated in multiple trials to prevent the growth and metastasis of many cancers, including pancreatic cancer. Exosomes also play essential roles in immune evasion, invasion, metastasis, proliferation, apoptosis, drug resistance, and cancer stemness. Exosomes help cells communicate by carrying proteins and genetic material, such as non-coding RNAs, including mRNAs and microRNAs. This review examines the biological significance of exosomes in pancreatic cancer and their functions in tumor invasion, metastasis, treatment resistance, proliferation, stemness, and immune evasion. We also emphasize recent advances in our understanding of the main functions of exosomes in diagnosing and treating pancreatic cancer.

Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer Diagnosis.

Cervical cancer is one of the most common types of cancer among women, which has higher death-rate than many other cancer types. The most common way to diagnose cervical cancer is to analyze images of cervical cells, which is performed using Pap smear imaging test. Early and accurate diagnosis can save the lives of many patients and increase the chance of success of treatment methods. Until now, various methods have been proposed to diagnose cervical cancer based on the analysis of Pap smear images. Most of the existing methods can be divided into two groups of methods based on deep learning techniques or machine learning algorithms. In this study, a combination method is presented, whose overall structure is based on a machine learning strategy, where the feature extraction stage is completely separate from the classification stage. However, in the feature extraction stage, deep networks are used. In this paper, a multi-layer perceptron (MLP) neural network fed with deep features is presented. The number of hidden layer neurons is tuned based on four innovative ideas. Additionally, ResNet-34, ResNet-50 and VGG-19 deep networks have been used to feed MLP. In the presented method, the layers related to the classification phase are removed in these two CNN networks, and the outputs feed the MLP after passing through a flatten layer. In order to improve performance, both CNNs are trained on related images using the Adam optimizer. The proposed method has been evaluated on the Herlev benchmark database and has provided 99.23 percent accuracy for the two-classes case and 97.65 percent accuracy for the 7-classes case. The results have shown that the presented method has provided higher accuracy than the baseline networks and many existing methods.

Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet.

Cell phenotype classification is a critical task in many medical applications, such as protein localization, gene effect identification, and cancer diagnosis in some types. Fluorescence imaging is the most efficient tool to analyze the biological characteristics of cells. So cell phenotype classification in fluorescence microscopy images has received increased attention from scientists in the last decade. The visible structures of cells are usually different in terms of shape, texture, relationship between intensities, etc. In this scope, most of the presented approaches use one type or joint of low-level and high-level features. In this paper, a new approach is proposed based on a combination of low-level and high-level features. An improved version of local quinary patterns is used to extract low-level texture features. Also, an innovative multilayer deep feature extraction method is performed to extract high-level features from DenseNet. In this respect, an output feature map of dense blocks is entered in a separate way to pooling and flatten layers, and finally, feature vectors are concatenated. The performance of the proposed approach is evaluated on the benchmark dataset 2D-HeLa in terms of accuracy. Also, the proposed approach is compared with state-of-the-art methods in terms of classification accuracy. Comparison of results demonstrates higher performance of the proposed approach in comparison with some efficient methods.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.