Role of hematopoietic cells in Mycobacterium tuberculosis infection

Tuberculosis remains one of the most significant causes of mortality worldwide and the current situation shows a re-emergence of TB due to the emergence of new antibiotic-resistant strains and the widespread of disease caused by immunodeficiencies. For these reasons, a big effort is made to improve the therapeutic strategies against Mycobacterium tuberculosis and to perform new therapeutic and diagnostic strategies. This review analyzes the various hematopoietic populations, their role and the different changes they undergo during Mycobacterium tuberculosis infection or disease. We have examined the population of lymphocytes, monocytes, neutrophils, eosinophils and platelets, in orderto understand how each of them is modulated during the course of infection/disease. In this way it will be possible to highlight the correlations between these cell populations and the different stages of tubercular infection. In fact, Mycobacterium tuberculosis is able to influence both proliferation and differentiation of hematopoietic stem cells. Several studies have highlighted that Mycobacterium tuberculosis can also infect progenitor cells in the bone marrow during active disease driving towards an increase of myeloid differentiation. This review focuses how the different stages of tubercular infection could impact on the different hematopoietic populations, with the aim to correlate the changes of different populations as biomarkers useful to discriminate infection from disease and to evaluate the effectiveness of new therapies

A rapid and simple multiparameter assay to quantify spike-specific CD4 and CD8 T cells after SARS-CoV-2 vaccination: A preliminary report

mRNA and Adenovirus vaccines for COVID-19 are used to induce humoral and cell-mediated immunity, with the aim to generate both SARS-CoV-2 B and T memory cells. In present study, we described a simple assay to detect and quantify Spike-specific CD4+ and CD8+ T cell responses induced by vaccination in healthy donors and in subjects with B cell compart impairment, in which antibody response is absent due to primary immunodeficiencies or CD20 depleting therapy. We detect and quantified memory T cell immune responses against SARS-CoV-2 evocated by vaccination in both groups, irrespective to the humoral response. Furthermore, we identified TNF-α as the main cytokine produced by T memory cells, after antigen-specific stimulation in vitro, that could be considered, other than IFN-γ, an additional biomarker of induction of T memory cells upon vaccination. Further studies on the vaccine-induced T cell responses could be crucial, not only in healthy people but also in immunocompromised subjects, where antigen specific T cells responses play a protective role against SARS-CoV-2

Real-time and intelligent flood forecasting using UAV-assisted wireless sensor network

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers' water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (R2), correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and BIAS are provided

Immunity and Nutrition: The Right Balance in Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) is an increasingly urgent medical problem that strongly impairs quality of life for patients. A global rise in incidence has been observed over the last few decades, with the highest incidence rates recorded in North America and Europe. Still, an increased incidence has been reported in the last ten years in newly industrialized countries in Asia, including China and India, both with more than one billion inhabitants. These data underline that IBD is an urgent global health problem. In addition, it is estimated that between 20% and 30% of IBD patients will develop colorectal cancer (CRC) within their lifetime and CRC mortality is approximately 50% amongst IBD patients. Although the exact etiology of IBD is still being defined, it is thought to be due to a complex interaction between many factors, including defects in the innate and adaptive immune system; microbial dysbiosis, i.e., abnormal levels of, or abnormal response to, the gastrointestinal microbiome; a genetic predisposition; and several environmental factors. At present, however, it is not fully understood which of these factors are the initiators of inflammation and which are compounders. The purpose of this review is to analyze the complex balance that exists between these elements to maintain intestinal homeostasis and prevent IBD or limit adverse effects on people’s health

Real-time and intelligent flood forecasting using UAV-assisted wireless sensor network

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers' water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (R2), correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and BIAS are provided

Metabolic Reprogramming of Innate Immune Cells as a Possible Source of New Therapeutic Approaches in Autoimmunity

Immune cells undergo different metabolic pathways or immunometabolisms to interact with various antigens. Immunometabolism links immunological and metabolic processes and is critical for innate and adaptive immunity. Although metabolic reprogramming is necessary for cell differentiation and proliferation, it may mediate the imbalance of immune homeostasis, leading to the pathogenesis and development of some diseases, such as autoimmune diseases. Here, we discuss the effects of metabolic changes in autoimmune diseases, exerted by the leading actors of innate immunity, and their role in autoimmunity pathogenesis, suggesting many immunotherapeutic approaches

PD-1/PD-L1 immune-checkpoint blockade induces immune effector cell modulation in metastatic non-small cell lung cancer patients: A single-cell flow cytometry approach

Peripheral immune-checkpoint blockade with mAbs to programmed cell death receptor-1 (PD-1) (either nivolumab or pembrolizumab) or PD-Ligand-1 (PD-L1) (atezolizumab, durvalumab, or avelumab) alone or in combination with doublet chemotherapy represents an expanding treatment strategy for metastatic non-small cell lung cancer (mNSCLC) patients. This strategy lays on the capability of these mAbs to rescue tumor-specific cytotoxic T lymphocytes (CTLs) inactivated throughout PD-1 binding to PD-L1/2 in the tumor sites. This inhibitory interactive pathway is a physiological mechanism of prevention against dangerous overreactions and autoimmunity in case of prolonged and/or repeated CTL response to the same antigen peptides. Therefore, we have carried out a retrospective bioinformatics analysis by single-cell flow cytometry to evaluate if PD-1/PD-L1-blocking mAbs modulate the expression of specific peripheral immune cell subsets, potentially correlated with autoimmunity triggering in 28 mNSCLC patients. We recorded a treatment-related decline in CD4+ T-cell and B-cell subsets and in the neutrophil-to-lymphocyte ratio coupled with an increase in natural killer T (NKT), CD8+PD1+ T cells, and eosinophils. Treatment-related increase in autoantibodies [mainly antinuclear antibodies (ANAs) and extractable nuclear antigen (ENA) antibodies] as well as the frequency of immune-related adverse events were associated with the deregulation of specific immune subpopulations (e.g., NKT cells). Correlative biological/clinical studies with deep immune monitoring are badly needed for a better characterization of the effects produced by PD-1/PD-L1 immune-checkpoint blockade