Elucidating the clinical and immunological value of m6A regulator-mediated methylation modification patterns in adrenocortical carcinoma

N6-methyladenosine methylation (m6A) is a common type of epigenetic alteration that prominently affects the prognosis of tumor patients. However, it is unknown how the m6A regulator affects the tumor microenvironment (TME) cell infiltration in adrenocortical carcinoma (ACC) and how it affects the prognosis of ACC patients yet. The m6A alteration patterns of 112 ACC patients were evaluated, furthermore, the association with immune infiltration cell features was investigated. The unsupervised clustering method was applied to typify the m6A alteration patterns of ACC patients. The principal component analysis (PCA) technique was taken to create the m6A score to assess the alteration pattern in specific malignancies. We found two independent patterns of m6A alteration in ACC patients. The TME cell infiltration features were significantly in accordance with phenotypes of tumor immune-inflamed and immune desert in both patterns. The m6Ascore also served as an independent predictive factor in ACC patients. The somatic copy number variation (CNV) and patients prognosis can be predicted by m6A alteration patterns. Moreover, the ACC patients with high m6A scores had better overall survival (OS) and higher efficiency in immune checkpoint blockade therapy. Our work demonstrated the significance of m6A alteration to the ACC patients immunotherapy. The individual m6A alteration patterns analysis might contribute to ACC patients prognosis prediction and immunotherapy choice

Elucidating the clinical and immunological value of m6A regulator-mediated methylation modification patterns in adrenocortical carcinoma

N6-methyladenosine methylation (m6A) is a common type of epigenetic alteration that prominently affects the prognosis of tumor patients. However, it is unknown how the m6A regulator affects the tumor microenvironment (TME) cell infiltration in adrenocortical carcinoma (ACC) and how it affects the prognosis of ACC patients yet. The m6A alteration patterns of 112 ACC patients were evaluated, furthermore, the association with immune infiltration cell features was investigated. The unsupervised clustering method was applied to typify the m6A alteration patterns of ACC patients. The principal component analysis (PCA) technique was taken to create the m6A score to assess the alteration pattern in specific malignancies. We found two independent patterns of m6A alteration in ACC patients. The TME cell infiltration features were significantly in accordance with phenotypes of tumor immune-inflamed and immune desert in both patterns. The m6Ascore also served as an independent predictive factor in ACC patients. The somatic copy number variation (CNV) and patients prognosis can be predicted by m6A alteration patterns. Moreover, the ACC patients with high m6A scores had better overall survival (OS) and higher efficiency in immune checkpoint blockade therapy. Our work demonstrated the significance of m6A alteration to the ACC patients immunotherapy. The individual m6A alteration patterns analysis might contribute to ACC patients prognosis prediction and immunotherapy choice

Transcriptomics-based characterization of the toxicity of ZnO nanoparticles against chronic myeloid leukemia cells

Introduction: Zinc oxide nanoparticles (ZnO NPs) have recently attracted attention as potential anti-cancer agents. To the best of our knowledge, the toxicity of ZnO NPs against human chronic myeloid leukemia cells (K562 cell line) has not been studied using transcriptomics approach. Objective: The goals of this study were to evaluate the capability of ZnO NPs to induce apoptosis in human chronic myeloid leukemia cells (K562 cells) and to investigate the putative mechanisms of action. Methods: We used viability assay and flowcytometry coupled with Annexin V-FITC and propidium iodide to investigate the toxicity of ZnO NPs on K562 cells and normal peripheral blood mononuclear cells. Next we utilized a DNA microarray-based transcriptomics approach to characterize the ZnO NPs-induced changes in the transcriptome of K562 cells. Results: ZnO NPs exerted a selective toxicity (mainly by apoptosis) on the leukemic cells (p≤ 0.005) and altered their transcriptome; 429 differentially expressed genes (DEGs) with fold change (FC)≥ 4 and p≤ 0.008 with corrected p≤ 0.05 were identified in K562 cells post treatment with ZnO NPs. The over-expressed genes were implicated in “response to zinc”, “response to toxic substance” and “negative regulation of growth” (corrected p≤ 0.05). In contrast, the repressed genes positively regulated “cell proliferation”, “cell migration”, “cell adhesion”, “receptor signaling pathway via JAK-STAT” and “phosphatidylinositol 3-kinase signaling” (corrected p≤ 0.05). Lowering the FC to ≥ 1.5 with p≤ 0.05 and corrected p≤ 0.1 showed that ZnO NPs over-expressed the anti-oxidant defense system, drove K562 cells to undergo mitochondrial-dependent apoptosis, and targeted NF-κB pathway. Conclusion: Taken together, our findings support the earlier studies that reported anti-cancer activity of ZnO NPs and revealed possible molecular mechanisms employed by ZnO NPs to induce apoptosis in K562 cells

Considerations for Novel COVID-19 Mucosal Vaccine Development

Mucosal surfaces are the first contact sites of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most SARS-CoV-2 vaccines induce specific IgG responses but provide limited mucosal immunity. Cytokine B-cell activation factor (BAFF) and A proliferation-inducing ligand (APRIL) in the tumor necrosis factor (TNF) superfamily play key immunological functions during B cell development and antibody production. Furthermore, homeostatic chemokines, such as C-X-C motif chemokine ligand 13 (CXCL13), chemokine (C–C motif) ligand 19 (CCL19), and CCL21, can induce B- and T-cell responses to infection and promote the formation of inducible bronchus-associated lymphoid tissues (iBALT), where specific local immune responses and memory cells are generated. We reviewed the role of BAFF, APRIL, CXCL13, CCL19, and CCL21 in the activation of local B-cell responses and antibody production, and the formation of iBALT in the lung following viral respiratory infections. We speculate that mucosal vaccines may offer more efficient protection against SARS-CoV-2 infection than systematic vaccines and hypothesize that a novel SARS-CoV-2 mRNA mucosal vaccine using BAFF/APRIL or CXCL13 as immunostimulants combined with the spike protein-encoding mRNA may enhance the efficiency of the local immune response and prevent the early stages of SARS-CoV-2 replication and the rapid viral clearance from the airways

Antifungal Efficacy of Marine Macroalgae against Fungal Isolates from Bronchial Asthmatic Cases

Fungal sensitization is very common in bronchial asthmatic cases, and the connection with airway colonization by fungi remains uncertain. Antifungal therapy failure is a significant fraction of the cost and morbidity and mortality in the majority of the asthmatic cases. Hence, the present study aimed to investigate the antifungal activity of five marine macroalgae—Acanthaophora specifera, Cladophoropsis sp., Laurencia paniculata, Tydemania sp., and Ulva prolifera—which were tested on selected fungal pathogens isolated from 15 sputum of 45 bronchial asthmatic patients. The highest antifungal activity was observed in ethanol fractions of L. paniculata followed by U. prolifera, Cladophoropsis sp., A. specifera, and Tydemania sp. The minimum fungicidal concentration and minimum inhibitory concentration values of the ethanolic fractions of algal species were found to be 125⁻1000 µg/mL and 125⁻500 µg/mL, respectively. The algal extracts contained terpene alcohol, diterpene, steroids, sesquiterpene, and sesquiterpene alcohol, as determined by GC⁻MS/MS analyses. The present study shows that the marine macroalgae containing bioactive compounds had excellent inhibitory activity against a variety of fungal pathogens, which may be useful for combating fungal infections and recovering from chronic asthmatic states

Middle East Respiratory Syndrome Coronavirus (MERS-CoV): Infection, Immunological Response, and Vaccine Development

Middle East respiratory syndrome coronavirus (MERS-CoV) first emerged in late 2012. Since its emergence, a total of 2279 patients from 27 countries have been infected across the globe according to a World Health Organization (WHO) report (Feb. 12th, 2019). Approximately 806 patients have died. The virus uses its spike proteins as adhesive factors that are proinflammatory for host entry through a specific receptor called dipeptidyl peptidase-4 (DPP4). This receptor is considered a key factor in the signaling and activation of the acquired and innate immune responses in infected patients. Using potent antigens in combination with strong adjuvants may effectively trigger the activation of specific MERS-CoV cellular responses as well as the production of neutralizing antibodies. Unfortunately, to date, there is no effective approved treatment or vaccine for MERS-CoV. Thus, there are urgent needs for the development of novel MERS-CoV therapies as well as vaccines to help minimize the spread of the virus from infected patients, thereby mitigating the risk of any potential pandemics. Our main goals are to highlight and describe the current knowledge of both the innate and adaptive immune responses to MERS-CoV and the current state of MERS-CoV vaccine development. We believe this study will increase our understanding of the mechanisms that enhance the MERS-CoV immune response and subsequently contribute to the control of MERS-CoV infections

The pivotal roles of the host immune response in the fine-tuning the infection and the development of the vaccines for SARS-CoV-2

SARS-CoV2 infection induces various degrees of infections ranging from asymptomatic to severe cases and death. Virus/host interplay contributes substantially to these outcomes. This highlights the potential roles of the host immune system in fighting virus infections. SARS-CoV-2. We highlighted the potential roles of host immune response in the modulation of the outcomes of SARS-CoV infections. The newly emerged SARS-CoV-2 mutants complicated the control and mitigation strategies measures. We are highlighting the current progress of some already deployed vaccines worldwide as well as those still in the pipelines. Recent studies from the large ongoing global vaccination campaign are showing promising results in reducing the hospitality rates as well as the number of severe SARS-CoV-2 infected patients. Careful monitoring of the genetic changes of the virus should be practiced. This is to prepare some highly sensitive diagnostic assays as well as to prepare some homologous vaccines matching the circulating strains in the future

Production of Anti-Camel IgY for diagnosis of infectious diseases affecting camels located in Kingdom of Saudi Arabia

Chicken can be used for the production of hyper-immune serum for many type of antigens specially, the antigens of mammals and other species. Therefore, the current study aimed to use the chickens for the productions of anti-camel IgY- antibodies using ammonium sulphate and ammonium sulphate - caprylic acid methods for detection of any infectious disease affecting camels.One ml of purified camel IgG of mixed with complete freund's adjuvant was injected intramuscularly and S/C to four groups of laying hens (Hisex strain, 140 days old). The first booster dose was administrated two week after first immunization with one ml of purified camel IgG mixed with incomplete freund's adjuvant and the hens were repeatedly boostered (three times) at two weeks intervals. During the immunization periods we collect the eggs every day and the anti-camel immunoglobulins Y was separated by using two extraction methods; the ammonium sulphate precipitation method and the ammonium sulphate caprylic acid method, the sensitivity and the specificity of the recovered antibody against the camel IgG were evaluated using SDS-PAGE and ELISA.Two weeks after the initial dose of immunizing the hens the anti-camels IgY-antibodies were detected then the mean antibody titer increased significantly (P  0.05) up to 12 weeks after the first dose of immunization then started to decrease.Therefore, there are several benefits of using immunized chicken to produce IgY antibodies rather than using other species such as mammals in order to prepare anti-camels antibodies conjugates for diagnosis of infectious disease affecting camels

Prediction and Classification of COVID-19 Admissions to Intensive Care Units (ICU) Using Weighted Radial Kernel SVM Coupled with Recursive Feature Elimination (RFE)

Healthcare systems have been under immense pressure since the beginning of the COVID-19 pandemic; hence, studies on using machine learning (ML) methods for classifying ICU admissions and resource allocation are urgently needed. We investigated whether ML can propose a useful classification model for predicting the ICU admissions of COVID-19 patients. In this retrospective study, the clinical characteristics and laboratory findings of 100 patients with laboratory-confirmed COVID-19 tests were retrieved between May 2020 and January 2021. Based on patients’ demographic and clinical data, we analyzed the capability of the proposed weighted radial kernel support vector machine (SVM), coupled with (RFE). The proposed method is compared with other reference methods such as linear discriminant analysis (LDA) and kernel-based SVM variants including the linear, polynomial, and radial kernels coupled with REF for predicting ICU admissions of COVID-19 patients. An initial performance assessment indicated that the SVM with weighted radial kernels coupled with REF outperformed the other classification methods in discriminating between ICU and non-ICU admissions in COVID-19 patients. Furthermore, applying the Recursive Feature Elimination (RFE) with weighted radial kernel SVM identified a significant set of variables that can predict and statistically distinguish ICU from non-ICU COVID-19 patients. The patients’ weight, PCR Ct Value, CCL19, INF-β, BLC, INR, PT, PTT, CKMB, HB, platelets, RBC, urea, creatinine and albumin results were found to be the significant predicting features. We believe that weighted radial kernel SVM can be used as an assisting ML approach to guide hospital decision makers in resource allocation and mobilization between intensive care and isolation units. We model the data retrospectively on a selected subset of patient-derived variables based on previous knowledge of ICU admission and this needs to be trained in order to forecast prospectively