Association studies between COVID-19 and SSc-ILD

Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases. © 2023 Editorial Office of Journal of Shenzhen University. All rights reserved.

The impact of patient and public involvement on COVID-19 immunology research: experiences from the UK Coronavirus Immunology Consortium.

BACKGROUND: Patient and Public Involvement (PPI) in clinical trial research is recognised as relevant but the active involvement of patients and the public in basic science or laboratory-based research is seen as more challenging and not often reported. PPI within the UK Coronavirus Immunology Consortium (UK-CIC), a translational research project aimed at tackling some of the key questions about the immune system's response to SARS-CoV-2, is an example of overcoming negative perceptions and obstacles. Given the widespread impact of COVID-19, it was important to consider the impact of UK-CIC research on patients and the public throughout, and the PPI panel were an integral part of the consortium. FINDINGS: Building in funding for a PPI panel to value involvement and ensuring effective expert administrative support and management of PPI were crucial to success. Facilitating relationships and quality interactions between public contributors and researchers required time and commitment to the project from all parties. Through creating a platform and open space to explore diverse views and a wide range of perspectives, PPI was able to influence researchers' ways of thinking about their research and impact future research questions about COVID-19 immunology. Moreover, there was long-term impact from the involvement of the PPI panel in COVID-19 research and their value was reflected in invitations to contribute to additional immunology projects. CONCLUSION: The ability to conduct meaningful PPI with basic immunology research has been shown possible through the UK-CIC in the context of the fast-moving COVID-19 pandemic. The UK-CIC project has laid the foundations for PPI in immunology and this should now be built upon for the advantage of future basic scientific research; PPI can impact greatly on laboratory-based research when given the opportunity to do so.

Patient public involvement (PPI) in health literacy research: Engagement of adults with literacy needs in the co-creation of a hospital-based health literacy plan.

BACKGROUND: People with literacy needs can experience many challenges in accessing, understanding and using health services and health information. Such challenges can adversely impact patient-provider interactions and ultimately, health outcomes. Healthcare providers need to be aware of health literacy (HL) to address the demands of healthcare systems, improve their interactions with communities and patients and promote patient engagement for improved health outcomes. METHODS: This paper reports on a process of patient and public involvement (PPI) with participants in an adult literacy programme acting as PPI contributors to identify priority areas for a local hospital HL action plan and to develop a protocol for a PPI process with other groups. A qualitative community-based participatory research study design informed by principles of PPI was undertaken, drawing on the tools of participatory and visual methods, open discussion and workshop format to facilitate a process of co-creation. Three workshops with six PPI contributors took place to identify issues to be included in the hospital action plan. PPI contributors identified issues and grouped these into priority areas using discussion and ranking procedures. RESULTS: Key areas prioritised for HL action by the PPI contributors were: verbal communication, emphasising the patient's right to understand, and improved understanding of medication use. These were incorporated into the action plan. The workshop format and process were deemed acceptable to the group and input on improvements will be incorporated into further work in this area. CONCLUSION: PPI acts as a lever in the knowledge translation process. Genuine engagement with service users can meaningfully contribute to relevant and sustainable changes to services as well as foster the empowerment of service users. PATIENT OR PUBLIC CONTRIBUTION: Members of the public with literacy needs actively participated in the co-creation of a HL action plan for a local hospital and in the development of a protocol for a patient and public process for HL research.

Adapting Patient and Public Involvement processes in response to the Covid-19 pandemic.

BACKGROUND: The COVID-19 pandemic brought rapid and major changes to research, and those wishing to carry out Patient and Public Involvement (PPI) activities faced challenges, such as restrictions on movement and contact, illness, bereavement and risks to potential participants. Some researchers moved PPI to online settings during this time but remote consultations raise, as well as address, a number of challenges. It is important to learn from PPI undertaken in this period as face-to-face consultation may no longer be the dominant method for PPI. METHODS: UK stay-at-home measures announced in March 2020 necessitated immediate revisions to the intended face-to-face methods of PPI consultation for the ESORT Study, which evaluated emergency surgery for patients with common acute conditions. PPI plans and methods were modified to all components being online. We describe and reflect on: initial plans and adaptation; recruitment; training and preparation; implementation, contextualisation and interpretation. Through first-hand accounts we show how the PPI processes were developed, experienced and viewed by different partners in the process. DISCUSSION AND CONCLUSIONS: While concerns have been expressed about the possible limiting effects of forgoing face-to-face contact with PPI partners, we found important benefits from the altered dynamic of the online PPI environment. There were increased opportunities for participation which might encourage the involvement of a broader demographic, and unexpected benefits in that the online platform seemed to have a 'democratising' effect on the meetings, to the benefit of the PPI processes and outcomes. Other studies may however find that their particular research context raises particular challenges for the use of online methods, especially in relation to representation and inclusion, as new barriers to participation may be raised. It is important that methodological challenges are addressed, and researchers provide detailed examples of novel methods for discussion and empirical study. PATIENT AND PUBLIC CONTRIBUTION: We report a process which involved people with lived experience of emergency conditions and members of the public. A patient member was involved in the design and implementation, and two patients with lived experience contributed to the manuscript.

Neem and Turmeric in the management of Covid Associated Mucormycosis (CAM) derived through network pharmacology.

Mucormycosis or 'Black Fungus' has been known to target immunocompromised individuals even before the emergence of COVID-19. Nevertheless, the present circumstances provide the best opening for Covid Associated Mucormycosis (CAM), as the global pandemic is engulfing a large part of human population making them immunocompromised. This drastic increase in Mucormycosis infections has to be addressed as early as possible. There is a growing tendency of relying upon herbal drugs that have minimal side effects and does not compromise our immune system. Recently, the concept of network pharmacology has grabbed the attention of modern science, especially advanced medical sciences. This is a new discipline that can use computational power to systematically catalogue the molecular interactions between botanical formulations and the human body. In this study, Neem and Turmeric was considered as the target plants and an attempt was made to reveal various aspects through which phytocompounds derived from them may effectively manage CAM menace. We have taken a step-by-step approach for identifying the target proteins and ligands associated with Mucormycosis treatment. Functional network analysis and Molecular docking approaches were applied to validate our findings. Quercetin derived from both Neem and Turmeric was found to be one of the main phytocompounds working against Mucormycosis. Along with that, Caffeic acid, Curcumin, Kaempferol, Tetrahydrocurcumin and Myricetin also play a pivotal role in fighting against Black-Fungus. A thorough analysis of our result suggested a triple-front attack on the fungal pathogens and the approaches are necrosis inhibition, iron chelation and immuno-boosting.Communicated by Ramaswamy H. Sarma.

Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction.

Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human-SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.

The benefits of continuing patient and public involvement as part of a randomised controlled trial during the Covid-19 global pandemic

Patient and public involvement (PPI) in clinical research strengthens the quality and relevance of research, and has been crucial to ensure that researchers continue to investigate relevant and important topics during the global Covid-19 pandemic. The MICE (Mental Health Intervention for Children with Epilepsy) randomised controlled trial relies upon PPI to steer the direction and delivery of the trial, and the PPI Research Advisory Group (RAG) adapted to remote online meetings during the pandemic. This article first describes how the PPI RAG supported the research trial during the course of the pandemic, particularly with key trial stages of recruitment, retention and follow-up. It considers how the PPI tasks were adapted to ensure that they remained meaningful throughout this period, particularly for children and young people. Second, the article explores the acceptability of PPI in research using teleconferencing methods, via a co-produced survey of the PPI group members. Survey results indicated that, while participants valued face-to-face meetings, having remote PPI meetings was preferable to having nothing. There was some suggestion that teleconferencing platforms make it challenging for reserved members of the group, and for children, to contribute. Our findings emphasise the importance of continuing PPI even when circumstances are sub-optimal. We hope that our findings will contribute to the wider conversation about what makes PPI effective, particularly in a digital world. [ FROM AUTHOR] Copyright of Research for All is the property of UCL Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Network pharmacology research of mechanism of Maxing Shigan Decoction in treating COVID-19

Background: The COVID-19 has a huge negative impact on people's health. Traditional Chinese Medicine (TCM) has a good effect on viral pneumonia. It is of great practical significance to study its pharmacology. Methods: The ingredients and targets of each herb in Maxing Shigan Decoction which obtained from Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and the related targets of COVID-19 were screened by GeneCards database based on the network pharmacology. Venn was used to analyze the intersection target between active ingredients and diseases. Cytoscape software was used to construct an active ingredient-disease target network. The protein-protein interaction network was constructed by STRING database and Cytohubba was used to screen out the key targets. Gene Ontology (GO) functional enrichment analysis and KEGG pathway analysis were performed by DAVID database. Results: In this study, a total of 134 active ingredients and 229 related targets, 198 targets of COVID-19 and 48 common targets of drug-disease were chosen. Enrichment items and pathways were obtained through GO and KEGG pathway analysis. The predicted active ingredients were quercetin, kaempferol, luteolin, naringenin, glycyrol, and the key targets involved IL6, MAPK3, MAPK8, CASP3, IL10, etc. The results showed that the active ingredients of Maxing Shigan Decoction acted on multiple targets which played roles in the treatment of COVID-19 by regulating inflammation, immune system and other pathways. Conclusions: The main contribution of this paper is to use data to mine the principles of the treatment of COVID-19 from the pharmacology of these prescriptions, and the results can be provided theoretical reference for medical workers. © The Author (s) 2023. Published by Higher Education Press.

Network-based methods with heterogeneous data to identify severe COVID immune-related genes

Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method. © 2022 IEEE.

Identification of phytochemical compounds of Fagopyrum dibotrys and their targets by metabolomics, network pharmacology and molecular docking studies.

Acute lung injury (ALI) is a clinically severe lung illness with high incidence rate and mortality. Especially, coronavirus disease 2019 (COVID-19) poses a serious threat to world wide governmental fitness. It has distributed to almost from corner to corner of the universe, and the situation in the prevention and control of COVID-19 remains grave. Traditional Chinese medicine plays a vital role in the precaution and therapy of sicknesses. At present, there is a lack of drugs for treating these diseases, so it is necessary to develop drugs for treating COVID-19 related ALI. Fagopyrum dibotrys (D. Don) Hara is an annual plant of the Polygonaceae family and one of the long-history used traditional medicine in China. In recent years, its rhizomes (medicinal parts) have attracted the attention of scholars at home and abroad due to their significant anti-inflammatory, antibacterial and anticancer activities. It can work on SARS-COV-2 with numerous components, targets, and pathways, and has a certain effect on coronavirus disease 2019 (COVID-19) related acute lung injury (ALI). However, there are few systematic studies on its aerial parts (including stems and leaves) and its potential therapeutic mechanism has not been studied. The phytochemical constituents of rhizome of F. dibotrys were collected using TCMSP database. And metabolites of F. dibotrys' s aerial parts were detected by metabonomics. The phytochemical targets of F. dibotrys were predicted by the PharmMapper website tool. COVID-19 and ALI-related genes were retrieved from GeneCards. Cross targets and active phytochemicals of COVID-19 and ALI related genes in F. dibotrys were enriched by gene ontology (GO) and KEGG by metscape bioinformatics tools. The interplay network entre active phytochemicals and anti COVID-19 and ALI targets was established and broke down using Cytoscape software. Discovery Studio (version 2019) was used to perform molecular docking of crux active plant chemicals with anti COVID-19 and ALI targets. We identified 1136 chemicals from the aerial parts of F. dibotrys, among which 47 were active flavonoids and phenolic chemicals. A total of 61 chemicals were searched from the rhizome of F. dibotrys, and 15 of them were active chemicals. So there are 6 commonly key active chemicals at the aerial parts and the rhizome of F. dibotrys, 89 these phytochemicals's potential targets, and 211 COVID-19 and ALI related genes. GO enrichment bespoken that F. dibotrys might be involved in influencing gene targets contained numerous biological processes, for instance, negative regulation of megakaryocyte differentiation, regulation of DNA metabolic process, which could be put down to its anti COVID-19 associated ALI effects. KEGG pathway indicated that viral carcinogenesis, spliceosome, salmonella infection, coronavirus disease - COVID-19, legionellosis and human immunodeficiency virus 1 infection pathway are the primary pathways obsessed in the anti COVID-19 associated ALI effects of F. dibotrys. Molecular docking confirmed that the 6 critical active phytochemicals of F. dibotrys, such as luteolin, (+) -epicatechin, quercetin, isorhamnetin, (+) -catechin, and (-) -catechin gallate, can combine with kernel therapeutic targets NEDD8, SRPK1, DCUN1D1, and PARP1. In vitro activity experiments showed that the total antioxidant capacity of the aerial parts and rhizomes of F. dibotrys increased with the increase of concentration in a certain range. In addition, as a whole, the antioxidant capacity of the aerial part of F. dibotrys was stronger than that of the rhizome. Our research afford cues for farther exploration of the anti COVID-19 associated ALI chemical compositions and mechanisms of F. dibotrys and afford scientific foundation for progressing modern anti COVID-19 associated ALI drugs based on phytochemicals in F. dibotrys. We also fully developed the medicinal value of F. dibotrys' s aerial parts, which can effectively avoid the waste of resources. Meanwhile, our work provides a new strategy for integrating metabonomics, network pharmacology, and molecular docking techniques which was an efficient way for recognizing effective constituents and mechanisms valid to the pharmacologic actions of traditional Chinese medicine.

Recent computational drug repositioning strategies against SARS-CoV-2.

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

A framework for implementing Patient and Public Involvement in mental health research: The PATHWAY research programme benchmarked against NIHR standards.

BACKGROUND: Patient and Public Involvement (PPI) in research has become a key component recommended by research commissioners, grant award bodies and specified in government policies. Despite the increased call for PPI, few studies have demonstrated how to implement PPI within large-scale research studies. OBJECTIVE: The aim of the current study was to provide a case example of the implementation of a patient advisory group in a large-scale mental health research programme (PATHWAY) and to benchmark this against UK standards. METHOD: A PPI group was incorporated throughout the PATHWAY research programme, from grant development to dissemination. The group attended regular meetings and supported participant recruitment, evaluated patient-facing documents, supported the piloting of the research intervention and co-developed the dissemination and impact strategy. The implementation of PPI throughout the project was benchmarked against the UK standards for PPI. RESULTS: The inclusion of PPI in the PATHWAY project provided tangible changes to the research project (i.e., improving study documents, co-developing dissemination materials) but also proved to be a beneficial experience to PPI members through the development of new skills and the opportunity to provide a patient voice in research. We show how PPI was involved across seven study phases and provide examples of implementation of the six UK standards. The study did not include PPI in data analysis but met all the UK standards for PPI. Challenges regarding practical components (i.e., meeting frequency, language use), increasing diversity and PPI members' knowledge of research were highlighted as areas for further improvement. CONCLUSIONS: We provide a case example of how PPI can be implemented throughout a research lifecycle and we note the barriers faced and make suggestions for PPI in future implementation and research. PATIENT AND PUBLIC CONTRIBUTION: PPI members were involved throughout the lifecycle of the research programme. The PPI lead was a co-author on the manuscript and contributed to report writing.

Infection prevention and control programme and COVID-19 measures: Effects on hospital-acquired infections in patients with cirrhosis.

Background & Aims: Bacterial infections affect survival of patients with cirrhosis. Hospital-acquired bacterial infections present a growing healthcare problem because of the increasing prevalence of multidrug-resistant organisms. This study aimed to investigate the impact of an infection prevention and control programme and coronavirus disease 2019 (COVID-19) measures on the incidence of hospital-acquired infections and a set of secondary outcomes, including the prevalence of multidrug-resistant organisms, empiric antibiotic treatment failure, and development of septic states in patients with cirrhosis. Methods: The infection prevention and control programme was a complex strategy based on antimicrobial stewardship and the reduction of patient's exposure to risk factors. The COVID-19 measures presented further behavioural and hygiene restrictions imposed by the Hospital and Health Italian Sanitary System recommendations. We performed a combined retrospective and prospective study in which we compared the impact of extra measures against the hospital standard. Results: We analysed data from 941 patients. The infection prevention and control programme was associated with a reduction in the incidence of hospital-acquired infections (17 vs. 8.9%, p <0.01). No further reduction was present after the COVID-19 measures had been imposed. The impact of the infection prevention and control programme remained significant even after controlling for the effects of confounding variables (odds ratio 0.44, 95% CI 0.26-0.73, p = 0.002). Furthermore, the adoption of the programme reduced the prevalence of multidrug-resistant organisms and decreased rates of empiric antibiotic treatment failure and the development of septic states. Conclusions: The infection prevention and control programme decreased the incidence of hospital-acquired infections by nearly 50%. Furthermore, the programme also reduced the prevalence of most of the secondary outcomes. Based on the results of this study, we encourage other liver centres to adopt infection prevention and control programmes. Impact and implications: Infections are a life-threatening problem for patients with liver cirrhosis. Moreover, hospital-acquired infections are even more alarming owing to the high prevalence of multidrug-resistant bacteria. This study analysed a large cohort of hospitalised patients with cirrhosis from three different periods. Unlike in the first period, an infection prevention programme was applied in the second period, reducing the number of hospital-acquired infections and containing multidrug-resistant bacteria. In the third period, we imposed even more stringent measures to minimise the impact of the COVID-19 outbreak. However, these measures did not result in a further reduction in hospital-acquired infections.

Integrative Multiomics and Regulatory Network Analyses Uncovers the Role of OAS3, TRAFD1, miR-222-3p, and miR-125b-5p in Hepatitis E Virus Infection.

The hepatitis E virus (HEV) is a long-ignored virus that has spread globally with time. It ranked 6th among the top risk-ranking viruses with high zoonotic spillover potential; thus, considering its viral threats is a pressing priority. The molecular pathophysiology of HEV infection or the underlying cause is limited. Therefore, we incorporated an unbiased, systematic methodology to get insights into the biological heterogeneity associated with the HEV. Our study fetched 93 and 2016 differentially expressed genes (DEGs) from chronic HEV (CHEV) infection in kidney-transplant patients, followed by hub module selection from a weighted gene co-expression network (WGCN). Most of the hub genes identified in this study were associated with interferon (IFN) signaling pathways. Amongst the genes induced by IFNs, the 2'-5'-oligoadenylate synthase 3 (OAS3) protein was upregulated. Protein-protein interaction (PPI) modular, functional enrichment, and feed-forward loop (FFL) analyses led to the identification of two key miRNAs, i.e., miR-222-3p and miR-125b-5p, which showed a strong association with the OAS3 gene and TRAF-type zinc finger domain containing 1 (TRAFD1) transcription factor (TF) based on essential centrality measures. Further experimental studies are required to substantiate the significance of these FFL-associated genes and miRNAs with their respective functions in CHEV. To our knowledge, it is the first time that miR-222-3p has been described as a reference miRNA for use in CHEV sample analyses. In conclusion, our study has enlightened a few budding targets of HEV, which might help us understand the cellular and molecular pathways dysregulated in HEV through various factors. Thus, providing a novel insight into its pathophysiology and progression dynamics.

Integrated Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unravels the Influences of SARS-CoV-2 Infections to Cancer Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious and pathogenic coronavirus that emerged in late 2019 and caused a pandemic of respiratory illness termed as coronavirus disease 2019 (COVID-19). Cancer patients are more susceptible to SARS-CoV-2 infection. The treatment of cancer patients infected with SARS-CoV-2 is more complicated, and the patients are at risk of poor prognosis compared to other populations. Patients infected with SARS-CoV-2 are prone to rapid development of acute respiratory distress syndrome (ARDS) of which pulmonary fibrosis (PF) is considered a sequelae. Both ARDS and PF are factors that contribute to poor prognosis in COVID-19 patients. However, the molecular mechanisms among COVID-19, ARDS and PF in COVID-19 patients with cancer are not well-understood. In this study, the common differentially expressed genes (DEGs) between COVID-19 patients with and without cancer were identified. Based on the common DEGs, a series of analyses were performed, including Gene Ontology (GO) and pathway analysis, protein-protein interaction (PPI) network construction and hub gene extraction, transcription factor (TF)-DEG regulatory network construction, TF-DEG-miRNA coregulatory network construction and drug molecule identification. The candidate drug molecules (e.g., Tamibarotene CTD 00002527) obtained by this study might be helpful for effective therapeutic targets in COVID-19 patients with cancer. In addition, the common DEGs among ARDS, PF and COVID-19 patients with and without cancer are TNFSF10 and IFITM2. These two genes may serve as potential therapeutic targets in the treatment of COVID-19 patients with cancer. Changes in the expression levels of TNFSF10 and IFITM2 in CD14+/CD16+ monocytes may affect the immune response of COVID-19 patients. Specifically, changes in the expression level of TNFSF10 in monocytes can be considered as an immune signature in COVID-19 patients with hematologic cancer. Targeting N6-methyladenosine (m6A) pathways (e.g., METTL3/SERPINA1 axis) to restrict SARS-CoV-2 reproduction has therapeutic potential for COVID-19 patients.

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach.

Corona Virus Disease 2019 (COVID-19), an acute respiratory infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has spread rapidly worldwide, resulting in a pandemic with a high mortality rate. In clinical practice, we have noted that many critically ill or critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. In addition, it has been demonstrated that severe COVID-19 has some pathological similarities with sepsis, such as cytokine storm, hypercoagulable state after blood balance is disrupted and neutrophil dysfunction. Considering the parallels between COVID-19 and non-SARS-CoV-2 induced sepsis (hereafter referred to as sepsis), the aim of this study was to analyze the underlying molecular mechanisms between these two diseases by bioinformatics and a systems biology approach, providing new insights into the pathogenesis of COVID-19 and the development of new treatments. Specifically, the gene expression profiles of COVID-19 and sepsis patients were obtained from the Gene Expression Omnibus (GEO) database and compared to extract common differentially expressed genes (DEGs). Subsequently, common DEGs were used to investigate the genetic links between COVID-19 and sepsis. Based on enrichment analysis of common DEGs, many pathways closely related to inflammatory response were observed, such as Cytokine-cytokine receptor interaction pathway and NF-kappa B signaling pathway. In addition, protein-protein interaction networks and gene regulatory networks of common DEGs were constructed, and the analysis results showed that ITGAM may be a potential key biomarker base on regulatory analysis. Furthermore, a disease diagnostic model and risk prediction nomogram for COVID-19 were constructed using machine learning methods. Finally, potential therapeutic agents, including progesterone and emetine, were screened through drug-protein interaction networks and molecular docking simulations. We hope to provide new strategies for future research and treatment related to COVID-19 by elucidating the pathogenesis and genetic mechanisms between COVID-19 and sepsis.

Corrigendum: Targeting the N-terminus domain of the coronavirus nucleocapsid protein induces abnormal oligomerization via allosteric modulation.

[This corrects the article DOI: 10.3389/fmolb.2022.871499.].

User involvement throughout development of technology to support diabetes foot care: a case illustration of the devices for dignity PPIE model.

The successful development and implementation of any healthcare technology requires input from multiple stakeholders including clinical leads, trust information technology directorates as well as project management and procurement. In this process however, a key stakeholder that is often overlooked is the patient.This paper illustrates the crucial importance of patient involvement to avoid poor design and poor uptake of technology and subsequently poor health outcomes.To highlight this, we share a case example evidencing involvement of people with lived experience of foot ulcers resulting from Diabetic foot neuropathy throughout identification of unmet technology needs, design requirements for the device and iterative device development and evaluation.

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.