Trained innate immunity and diseases: Bane with the boon

Emerging research shows that innate immunity can also keep the memory of prior experiences, challenging the long-held notion that immunological memory is only the domain of the adaptive immune cells. However, the absence of immunological memory in innate immune responses has recently been brought into question. Now it is known that after a few transient activations, innate immune cells may acquire immunological memory phenotype, resulting in a stronger response to a subsequent secondary challenge. When exposed to particular microbial and/or inflammatory stimuli, trained innate immunity is characterized by the enhanced non-specific response, which is regulated by substantial metabolic alterations and epigenetic reprogramming. Trained immunity is acquired by two main reprogramming, namely, epigenetic reprogramming and metabolic adaptation/reprogramming. Epigenetic reprogramming causes changes in gene expression and cell physiology, resulting in internal cell signaling and/or accelerated and amplified cytokine release. Metabolic changes due to trained immunity induce accelerated glycolysis and glutaminolysis. As a result, trained immunity can have unfavorable outcomes, such as hyper inflammation and the development of cardiovascular diseases, autoinflammatory diseases, and neuroinflammation. In this review, the current scenario in the area of trained innate immunity, its mechanisms, and its involvement in immunological disorders are briefly outlined.Copyright © 2022

Deep CNN based Multi Classification of Respiratory Disease using X-Ray Images

COVID-19 debuted in Wuhan, China on December 19, 2019. In a brief period, deadly virus now migrated to practically every country. To avoid the causative agent COVID-19 disease, governments implement a number of strict restrictions, notably prohibiting people from leaving their homes. This paper focused on detecting and classifying disease such as viral pneu-monia, covidand normal from x-ray images using deep learning methods along with pre-trained models. Moreover, validation accuracy of CNN model attained around 91 % while performing layers in neural network. Several investigations examined that identifying disease of covid reached more accuracy around 98% with hybrid and other algorithms without removing noise from particular images. But this work mainly focused on normalizing images to make the computation very efficient, convergence faster too. © 2022 IEEE.

Increasing access for biochemistry research in undergraduate education: The malate dehydrogenase CURE community.

Integrating research into the classroom environment is an influential pedagogical tool to support student learning, increase retention of STEM students, and help students identify as scientists. The evolution of course-based undergraduate research experiences (CUREs) has grown from individual faculty incorporating their research in the teaching laboratory into well-supported systems to sustain faculty engagement in CUREs. To support the growth of protein-centric biochemistry-related CUREs, we cultivated a community of enthusiastic faculty to develop and adopt malate dehydrogenase (MDH) as a CURE focal point. The MDH CURE Community has grown into a vibrant and exciting group of over 28 faculty from various institutions, including community colleges, minority-serving institutions, undergraduate institutions, and research-intensive institutions in just 4 years. This collective has also addressed important pedagogical questions on the impact of CURE collaboration and the length of the CURE experience in community colleges, undergraduate institutions, and research-intensive institutions. This work provided evidence that modular or partial-semester CUREs also support student outcomes, especially the positive impact it had on underrepresented students. We are currently focused on expanding the MDH CURE Community network by generating more teaching and research materials, creating regional hubs for local interaction and increasing mentoring capacity, and offering mentoring and professional development opportunities for new faculty adopters.

Credit Card Fraud Detection Web Application using Streamlit and Machine Learning

Credit Card Fraud is one of the major threads in the financial industry. Due to the covid-19 pandemic and the advance in technologies, the number of users is increasing, with the increased use of credit cards. Due to more use of credit cards, Fraud cases also increase day by day. The research community striving hard to explore myriad credit card fraud detection techniques, but changes in technology and the varying nature of credit card fraud make it difficult to develop an effective technique for the detection of credit card fraud. This research work used a real-world credit card dataset. To detect the fraud transaction within this dataset three machine learning algorithms are used (i.e. Random Forest, Logistic regression, and AdaBoost) and compared the machine learning algorithms based on their Accuracy and Mathews Correlation Coefficient (MCC) Score. In these three algorithms, the Random Forest Algorithm achieved the best Accuracy and MCC score. The Streamlit framework is used to create the machine learning web application. © 2022 IEEE.

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil.

The large amount of data generated during the COVID-19 pandemic requires advanced tools for the long-term prediction of risk factors associated with COVID-19 mortality with higher accuracy. Machine learning (ML) methods directly address this topic and are essential tools to guide public health interventions. Here, we used ML to investigate the importance of demographic and clinical variables on COVID-19 mortality. We also analyzed how comorbidity networks are structured according to age groups. We conducted a retrospective study of COVID-19 mortality with hospitalized patients from Londrina, Parana, Brazil, registered in the database for severe acute respiratory infections (SIVEP-Gripe), from January 2021 to February 2022. We tested four ML models to predict the COVID-19 outcome: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. We also constructed a comorbidity network to investigate the impact of co-occurring comorbidities on COVID-19 mortality. Our study comprised 8358 hospitalized patients, of whom 2792 (33.40%) died. The XGBoost model achieved excellent performance (ROC-AUC = 0.90). Both permutation method and SHAP values highlighted the importance of age, ventilatory support status, and intensive care unit admission as key features in predicting COVID-19 outcomes. The comorbidity networks for old deceased patients are denser than those for young patients. In addition, the co-occurrence of heart disease and diabetes may be the most important combination to predict COVID-19 mortality, regardless of age and sex. This work presents a valuable combination of machine learning and comorbidity network analysis to predict COVID-19 outcomes. Reliable evidence on this topic is crucial for guiding the post-pandemic response and assisting in COVID-19 care planning and provision.

Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention-based neural networks.

Viral infections represent a major health concern worldwide. The alarming rate at which SARS-CoV-2 spreads, for example, led to a worldwide pandemic. Viruses incorporate genetic material into the host genome to hijack host cell functions such as the cell cycle and apoptosis. In these viral processes, protein-protein interactions (PPIs) play critical roles. Therefore, the identification of PPIs between humans and viruses is crucial for understanding the infection mechanism and host immune responses to viral infections and for discovering effective drugs. Experimental methods including mass spectrometry-based proteomics and yeast two-hybrid assays are widely used to identify human-virus PPIs, but these experimental methods are time-consuming, expensive, and laborious. To overcome this problem, we developed a novel computational predictor, named cross-attention PHV, by implementing two key technologies of the cross-attention mechanism and a one-dimensional convolutional neural network (1D-CNN). The cross-attention mechanisms were very effective in enhancing prediction and generalization abilities. Application of 1D-CNN to the word2vec-generated feature matrices reduced computational costs, thus extending the allowable length of protein sequences to 9000 amino acid residues. Cross-attention PHV outperformed existing state-of-the-art models using a benchmark dataset and accurately predicted PPIs for unknown viruses. Cross-attention PHV also predicted human-SARS-CoV-2 PPIs with area under the curve values >0.95. The Cross-attention PHV web server and source codes are freely available at https://kurata35.bio.kyutech.ac.jp/Cross-attention_PHV/ and https://github.com/kuratahiroyuki/Cross-Attention_PHV, respectively.

Efficacy and Safety of ELOM-080 as Add-On Therapy in COVID-19 Patients with Acute Respiratory Insufficiency: Exploratory Data from the Prospective Placebo-Controlled COVARI Trial.

INTRODUCTION: Enhancement of mucociliary clearance (MCC) might be a potential target in treating COVID-19. The phytomedicine ELOM-080 is an MCC enhancer that is used to treat inflammatory respiratory diseases. PATIENTS/METHODS: This randomised, double-blind exploratory study (EudraCT number 2020-003779-17) evaluated 14 days' add-on therapy with ELOM-080 versus placebo in patients with COVID-19 hospitalised with acute respiratory insufficiency. RESULTS: The trial was terminated early after enrolment of 47 patients as a result of poor recruitment. Twelve patients discontinued prematurely, leaving 35 in the per-protocol set (PPS). Treatment with ELOM-080 had no significant effect on overall clinical status versus placebo (p = 0.49). However, compared with the placebo group, patients treated with ELOM-080 had less dyspnoea in the second week of hospitalisation (p = 0.0035), required less supplemental oxygen (p = 0.0229), and were more often without dyspnoea when climbing stairs at home (p < 0.0001). CONCLUSION: These exploratory data suggest the potential for ELOM-080 to improve respiratory status during and after hospitalisation in patients with COVID-19.

Mobile Cloud Computing: Issues, Applications and Scope in COVID-19

As the world is transitioning into a tech-savvy era, the twenty-first century is evidence of many technological advancements in the field of AI, IoT, ML, etc. Mobile Cloud computing (MCC) is one such emerging technology, providing services regardless of the time and place, contours the limitations of mobile devices to process bulk data, providing multi-platform support and dynamic provisioning. Not only there is an enhancement in computation speed, energy efficiency, execution, integration, but also incorporates considerate issues in terms of client-to-cloud and cloud-to-client authentication, privacy, trust, and security. Reviewing and overcoming addressed concerns is essential to provide reliable yet efficient service in nearing future. Mobile Cloud Computing has the potential to bring wonders in the fields such as education, medical science, biometry, forensics, and automobiles, which could counter the challenges faced in the ongoing COVID-19 Pandemic. To combat the prevailing challenges due to COVID-19, it has become critical that more efficient and specialized technologies like Mobile Cloud Computing are accepted that enable appropriate reach and delivery of vital services by involving gamification, cloud rendering, and collaborative practices. This paper provides a detailed study about MCC, mitigated security and deployment attacks, issues, applications of MCC, providing developers and practitioners opportunities for future enhancements. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Using bioinformatics and systems biology to discover common pathogenetic processes between sarcoidosis and COVID-19.

The coronavirus disease (COVID-19) pandemic caused by SARS-CoV-2 is ongoing. Individuals with sarcoidosis tend to develop severe COVID-19; however, the underlying pathological mechanisms remain elusive. To determine common transcriptional signatures and pathways between sarcoidosis and COVID-19, we investigated the whole-genome transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with COVID-19 and sarcoidosis and conducted bioinformatic analysis, including gene ontology and pathway enrichment, protein-protein interaction (PPI) network, and gene regulatory network (GRN) construction. We identified 33 abnormally expressed genes that were common between COVID-19 and sarcoidosis. Functional enrichment analysis showed that these differentially expressed genes were associated with cytokine production involved in the immune response and T cell cytokine production. We identified several hub genes from the PPI network encoded by the common genes. These hub genes have high diagnostic potential for COVID-19 and sarcoidosis and can be potential biomarkers. Moreover, GRN analysis identified important microRNAs and transcription factors that regulate the common genes. This study provides a novel characterization of the transcriptional signatures and biological processes commonly dysregulated in sarcoidosis and COVID-19 and identified several critical regulators and biomarkers. This study highlights a potential pathological association between COVID-19 and sarcoidosis, establishing a theoretical basis for future clinical trials.

Specific inhibition of the NLRP3 inflammasome suppresses immune overactivation and alleviates COVID-19 like pathology in mice.

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has been a great threat to global public health since 2020. Although the advance on vaccine development has been largely achieved, a strategy to alleviate immune overactivation in severe COVID-19 patients is still needed. The NLRP3 inflammasome is activated upon SARS-CoV-2 infection and associated with COVID-19 severity. However, the processes by which the NLRP3 inflammasome is involved in COVID-19 disease remain unclear. METHODS: We infected THP-1 derived macrophages, NLRP3 knockout mice, and human ACE2 transgenic mice with live SARS-CoV-2 in Biosafety Level 3 (BSL-3) laboratory. We performed quantitative real-time PCR for targeted viral or host genes from SARS-CoV-2 infected mouse tissues, conducted histological or immunofluorescence analysis in SARS-CoV-2 infected mouse tissues. We also injected intranasally AAV-hACE2 or intraperitoneally NLRP3 inflammasome inhibitor MCC950 before SARS-CoV-2 infection in mice as indicated. FINDINGS: We have provided multiple lines of evidence that the NLRP3 inflammasome plays an important role in the host immune response to SARS-CoV-2 invasion of the lungs. Inhibition of the NLRP3 inflammasome attenuated the release of COVID-19 related pro-inflammatory cytokines in cell cultures and mice. The severe pathology induced by SARS-CoV-2 in lung tissues was reduced in Nlrp3-/- mice compared to wild-type C57BL/6 mice. Finally, specific inhibition of the NLRP3 inflammasome by MCC950 alleviated excessive lung inflammation and thus COVID-19 like pathology in human ACE2 transgenic mice. INTERPRETATION: Inflammatory activation induced by SARS-CoV-2 is an important stimulator of COVID-19 related immunopathology. Targeting the NLRP3 inflammasome is a promising immune intervention against severe COVID-19 disease. FUNDING: This work was supported by grants from the Bureau of Frontier Sciences and Education, CAS (grant no. QYZDJ-SSW-SMC005 to Y.G.Y.), the key project of the CAS "Light of West China" Program (to D.Y.) and Yunnan Province (202001AS070023 to D.Y.).

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds.

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID-19 antiviral SAR dataset whose activity was reported based on IC50 value. A total 1157 two-dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree-based, Correlation-based and Mutual information-based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies.

[Mucociliary clearance in COVID-19 - an underestimated danger area in early phase of disease?] / Mukoziliäre Clearance bei COVID-19-Erkrankungen : Ein unterschätztes Gefahrengebiet in der Frühphase?

BACKGROUND: As with other inflammatory diseases, the (dry) cough in COVID-19 patients indicates that mucociliary clearance (MCC) is at least at risk, if not overloaded, damaged or largely inoperable. Coughing is an important secondary mechanism that only takes over bronchial cleansing as a replacement if the MCC has failed. METHOD: The review article describes the physiology and pathophysiology of MCC and its possible role in the pathogenesis of COVID-19. RESULTS AND CONCLUSIONS: Human and animal studies as well as autopsy reports indicate that MCC could also be important for the COVID-19 pathogenesis. In primary care, MCC plays a major role in inflammatory respiratory diseases. In Germany, drugs for self-medication are approved for treatment and, due to the high quality of studies, are also recommended in the respective guidelines. A symptomatic approach to stabilize the airway barrier would also be conceivable in the early outpatient phase of COVID-19.

Coordinating and Assisting Research at the SARS-CoV-2/Microbiome Nexus.

Although the COVID-19 pandemic is caused by a single virus, the rest of the human microbiome appears to be involved in the disease and could influence vaccine responses while offering opportunities for microbiome-directed therapeutics. The newly formed Microbiome Centers Consortium (MCC) surveyed its membership and identified four ways to leverage the strengths and experience of microbiome centers in the response to the COVID-19 pandemic. To meet these needs, the MCC will provide a platform to coordinate clinical and environmental research, assist with practical obstacles, and help communicate the connections between the microbiome and COVID-19. We ask that microbiome researchers join us in these efforts to address the ongoing pandemic.