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1.
Gastroenterol Hepatol Bed Bench ; 15(2): 158-163, 2022.
Article in English | MEDLINE | ID: covidwho-1940023

ABSTRACT

Aim: Analysis of networks of digestive disorder and their relationship with Covid-19 based on systems biology methods, evaluation similarity, and usefulness of networks to give a new treatment approach. Background: Digestive disorders are typically complex diseases associated with high treatment costs. They are related to the immune system and inflammation. With the outbreak of Covid-19, this disease was shown to have signs like diarrhea. Some signs of Covid-19 are similar to those of digestive disorders, like IBD and diarrhea. Both of them are accompanied by inflammation and induce disorders in the digestive system. Methods: DisGeNET and STRING databases were sources of disease genes and constructing networks and were used to construct the network of digestive diseases and Covid-19. Three plugins of Cytoscape software, namely ClusterONE, ClueGO, and CluePedia, were used to analyze cluster networks and enrichment pathways. To describe the interaction of proteins, information from KEGG pathway and Reactome was used. Results: According to the results, IBD, gastritis, and diarrhea have common pathways. The CXCL8, IL-6, IL-1ß, TNF-α, TLR4, and MBL2 molecules were identified as inflammatory molecules in all networks. Conclusion: It seems that detecting genes and pathways can be useful in applying new approaches for treating these diseases.

2.
Pathogens ; 11(7)2022 Jun 29.
Article in English | MEDLINE | ID: covidwho-1917672

ABSTRACT

COVID-19 vaccines have been instrumental tools in reducing the impact of SARS-CoV-2 infections around the world by preventing 80% to 90% of hospitalizations and deaths from reinfection, in addition to preventing 40% to 65% of symptomatic illnesses. However, the simultaneous large-scale vaccination of the global population will indubitably unveil heterogeneity in immune responses as well as in the propensity to developing post-vaccine adverse events, especially in vulnerable individuals. Herein, we applied a systems biology workflow, integrating vaccine transcriptional signatures with chemogenomics, to study the pharmacological effects of mRNA vaccines. First, we derived transcriptional signatures and predicted their biological effects using pathway enrichment and network approaches. Second, we queried the Connectivity Map (CMap) to prioritize adverse events hypotheses. Finally, we accepted higher-confidence hypotheses that have been predicted by independent approaches. Our results reveal that the mRNA-based BNT162b2 vaccine affects immune response pathways related to interferon and cytokine signaling, which should lead to vaccine success, but may also result in some adverse events. Our results emphasize the effects of BNT162b2 on calcium homeostasis, which could be contributing to some frequently encountered adverse events related to mRNA vaccines. Notably, cardiac side effects were signaled in the CMap query results. In summary, our approach has identified mechanisms underlying both the expected protective effects of vaccination as well as possible post-vaccine adverse effects. Our study illustrates the power of systems biology approaches in improving our understanding of the comprehensive biological response to vaccination against COVID-19.

3.
J Pers Med ; 12(7)2022 Jun 23.
Article in English | MEDLINE | ID: covidwho-1911443

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic causes many morbidity and mortality cases. Despite several developed vaccines and antiviral therapies, some patients experience severe conditions that need intensive care units (ICU); therefore, precision medicine is necessary to predict and treat these patients using novel biomarkers and targeted drugs. In this study, we proposed a multi-level biological network analysis framework to identify key genes via protein-protein interaction (PPI) network analysis as well as survival analysis based on differentially expressed genes (DEGs) in leukocyte transcriptomic profiles, discover novel biomarkers using microRNAs (miRNA) from regulatory network analysis, and provide candidate drugs targeting the key genes using drug-gene interaction network and structural analysis. The results show that upregulated DEGs were mainly enriched in cell division, cell cycle, and innate immune signaling pathways. Downregulated DEGs were primarily concentrated in the cellular response to stress, lysosome, glycosaminoglycan catabolic process, and mature B cell differentiation. Regulatory network analysis revealed that hsa-miR-6792-5p, hsa-let-7b-5p, hsa-miR-34a-5p, hsa-miR-92a-3p, and hsa-miR-146a-5p were predicted biomarkers. CDC25A, GUSB, MYBL2, and SDAD1 were identified as key genes in severe COVID-19. In addition, drug repurposing from drug-gene and drug-protein database searching and molecular docking showed that camptothecin and doxorubicin were candidate drugs interacting with the key genes. In conclusion, multi-level systems biology analysis plays an important role in precision medicine by finding novel biomarkers and targeted drugs based on key gene identification.

4.
Clin Infect Dis ; 2022 Jun 09.
Article in English | MEDLINE | ID: covidwho-1890896

ABSTRACT

Within two years, novel SARS-CoV-2 vaccines have been developed, rigorously evaluated in large phase 3 trials, and administered to over 5 billion individuals globally. However, adverse events of special interest (AESIs) have been described post-implementation, including myocarditis after mRNA vaccines and thrombosis with thrombocytopenia syndrome (TTS) after adenoviral vector vaccines. AESIs are rare (<1-10/100,000 vaccinees) and less frequent than COVID-19 complications, though they have associated morbidity and mortality. The diversity of: 1) COVID-19 vaccine platforms (e.g., mRNA, viral vector, protein), and 2) rates of AESIs both between and within platforms (e.g., higher rate of myocarditis after mRNA-1273 versus BNT162b2 vaccines) present an important opportunity to advance vaccine safety science. The International Network of Special Immunization Services (INSIS) has been formed with experts in vaccine safety, systems biology, and other relevant disciplines to study cases of AESIs and matched controls to uncover the pathogenesis of rare AESIs and inform vaccine development.

5.
Cell Rep Med ; 3(6): 100652, 2022 06 21.
Article in English | MEDLINE | ID: covidwho-1889958

ABSTRACT

Disease recovery dynamics are often difficult to assess, as patients display heterogeneous recovery courses. To model recovery dynamics, exemplified by severe COVID-19, we apply a computational scheme on longitudinally sampled blood transcriptomes, generating recovery states, which we then link to cellular and molecular mechanisms, presenting a framework for studying the kinetics of recovery compared with non-recovery over time and long-term effects of the disease. Specifically, a decrease in mature neutrophils is the strongest cellular effect during recovery, with direct implications on disease outcome. Furthermore, we present strong indications for global regulatory changes in gene programs, decoupled from cell compositional changes, including an early rise in T cell activation and differentiation, resulting in immune rebalancing between interferon and NF-κB activity and restoration of cell homeostasis. Overall, we present a clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.


Subject(s)
COVID-19 , NF-kappa B , Cell Differentiation , Humans , Interferons/metabolism , NF-kappa B/genetics , Neutrophils/metabolism , Signal Transduction
6.
Current Signal Transduction Therapy ; 16(3):269-279, 2021.
Article in English | EMBASE | ID: covidwho-1869302

ABSTRACT

Background: As the outbreak of COVID-19 has accelerated, an urgent need for finding strategies to combat the virus is growing. Thus, gaining more knowledge on the pathogenicity mechanism of SARS-CoV-2, i.e. the causing agent of COVID-19, and its interaction with the immune system is of utmost importance. Although this novel virus is not well known yet, its structural and genetic similarity with SARS-CoV as well as the comparable pattern of age-mortality relations suggest that some previous findings on SARS could be applicable for COVID-19. Objective: The aim of this study was to investigate the most important signaling pathways activated by coronaviruses to better understand the viral pathogenesis and host immune responses. Method: Here, a systems biological study was conducted on a SARS database. It was followed by a literature review on the cognate subject. Results: It was proved that interferons may possess a crucial role in the defense against coronavirus diseases. The literature supported the validity of the employed approach and the notion that interferon induction could play a key role in the body defense against coronavirus infections. Conclusion: Altogether, administration of interferons or interferon-inducing agents in a prophylactic manner or at the early stages of the disease, could mimic the effective antiviral responses against SARS-CoV-2 and reduce the disease severity. At later stages of the disease, however, the balance of the immune reactions would be disrupted and the responses would shift toward immunopathogenic over-reactions, which could be exacerbated by the interferon usage. Moderating the activity of the immune system by anti-inflammatory agents, might be the optimum approach in such conditions.

7.
STAR Protoc ; 3(3): 101460, 2022 09 16.
Article in English | MEDLINE | ID: covidwho-1867903

ABSTRACT

We describe a protocol to identify physicochemical properties using amino acid sequences of spike (S) proteins of SARS-CoV-2. We present an S protein prediction technique named SPIKES, incorporating an inheritable bi-objective combinatorial genetic algorithm to determine the host species specificity. This protocol addresses the S protein amino acid sequence data collection, preprocessing, methodology, and analysis. For complete details on the use and execution of this protocol, please refer to Yerukala Sathipati et al. (2022).


Subject(s)
SARS-CoV-2 , Spike Glycoprotein, Coronavirus , Host Specificity , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics
8.
Elife ; 112022 05 17.
Article in English | MEDLINE | ID: covidwho-1847655

ABSTRACT

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.


While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.


Subject(s)
COVID-19 , SARS-CoV-2 , Hospitalization , Hospitals , Humans , Machine Learning , Retrospective Studies
9.
Methods Mol Biol ; 2452: 317-351, 2022.
Article in English | MEDLINE | ID: covidwho-1844274

ABSTRACT

The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.


Subject(s)
COVID-19 , SARS-CoV-2 , Antiviral Agents/pharmacology , Antiviral Agents/therapeutic use , Artificial Intelligence , COVID-19/drug therapy , Humans , Pandemics/prevention & control , Systems Biology
10.
Int J Mol Sci ; 23(7)2022 Mar 26.
Article in English | MEDLINE | ID: covidwho-1834806

ABSTRACT

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world at a rapid speed. Among COVID-19 patients, SARS-CoV-2-associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS are quite common, and their therapeutic treatments are limited because the intricated pathophysiology having been not fully understood. In this study, to investigate the pathogenic mechanism of SARS-CoV-2-associated ARDS and non-SARS-CoV-2-associated ARDS, first, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining. With the help of host-pathogen RNA sequencing (RNA-Seq) data, real HPI-GWGEN of COVID-19-associated ARDS and non-viral ARDS were obtained by system modeling, system identification, and Akaike information criterion (AIC) model order selection method to delete the false positives in candidate HPI-GWGEN. For the convenience of mitigation, the principal network projection (PNP) approach is utilized to extract core HPI-GWGEN, and then the corresponding core signaling pathways of COVID-19-associated ARDS and non-viral ARDS are annotated via their core HPI-GWGEN by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-associated ARDS and non-viral ARDS, we identified essential biomarkers as drug targets of pathogenesis by comparing the core signal pathways between COVID-19-associated ARDS and non-viral ARDS. The deep neural network of the drug-target interaction (DNN-DTI) model could be trained by drug-target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates to repurpose potential multiple-molecule drugs by the filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity, and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-associated ARDS and non-viral ARDS but also provide novel therapeutic options for COVID-19-associated ARDS and non-viral ARDS.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , Biomarkers , COVID-19/complications , COVID-19/drug therapy , Drug Design , Drug Repositioning , Humans , Respiratory Distress Syndrome/drug therapy , Respiratory Distress Syndrome/etiology , SARS-CoV-2 , Systems Biology
11.
Vasc Biol ; 4(1): R15-R34, 2022 Feb 01.
Article in English | MEDLINE | ID: covidwho-1825406

ABSTRACT

During sepsis, defined as life-threatening organ dysfunction due to dysregulated host response to infection, systemic inflammation activates endothelial cells and initiates a multifaceted cascade of pro-inflammatory signaling events, resulting in increased permeability and excessive recruitment of leukocytes. Vascular endothelial cells share many common properties but have organ-specific phenotypes with unique structure and function. Thus, therapies directed against endothelial cell phenotypes are needed to address organ-specific endothelial cell dysfunction. Omics allow for the study of expressed genes, proteins and/or metabolites in biological systems and provide insight on temporal and spatial evolution of signals during normal and diseased conditions. Proteomics quantifies protein expression, identifies protein-protein interactions and can reveal mechanistic changes in endothelial cells that would not be possible to study via reductionist methods alone. In this review, we provide an overview of how sepsis pathophysiology impacts omics with a focus on proteomic analysis of mouse endothelial cells during sepsis/inflammation and its relationship with the more clinically relevant omics of human endothelial cells. We discuss how omics has been used to define septic endotype signatures in different populations with a focus on proteomic analysis in organ-specific microvascular endothelial cells during sepsis or septic-like inflammation. We believe that studies defining septic endotypes based on proteomic expression in endothelial cell phenotypes are urgently needed to complement omic profiling of whole blood and better define sepsis subphenotypes. Lastly, we provide a discussion of how in silico modeling can be used to leverage the large volume of omics data to map response pathways in sepsis.

12.
Neuroepidemiology ; 56(SUPPL 1):94, 2022.
Article in English | EMBASE | ID: covidwho-1813111

ABSTRACT

There are currently 449,368,894 confirmed cases and 6,033,022 deaths from the coronavirus COVID- 19 outbreak as of March 08, 2022, leaving 443,335,872 survivors. The actual number of global COVID- 19 cases is likely to be two to three times higher than reported. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel virus responsible for the coronavirus disease 2019 (COVID-19) pandemic, resulting in unprecedented global health and economic crises with massive social impacts and massively strained health resources globally. COVID-19 is well recognized as a multi-organ disease with a broad spectrum of manifestations. During the early phases of the pandemic, patient advocacy groups, many members of which identify themselves as long COVID, have helped contribute to the recognition of post-acute COVID-19 Neurological Syndrome (PCNS), a syndrome characterized by persistent symptoms and delayed or long-term complications beyond four weeks from the onset of symptoms. This paper provides a comprehensive review of the current literature on post-acute COVID- 19 Neurological Syndrome, its pathophysiology, and its organ-specific sequelae. We explore the shared pathobiological profiles of stroke and COVID-19 involvement in the brain with a significant impact on the long-term care of both groups of survivors. The paper will discuss the role of universal biomarker, serial systemic immune-inflammatory indices (SSIIi) in the context of PCNS and potential implications in post-stroke neurological complications to introduce a systems biology approach to promote brain health for all globally.

13.
J Virol ; 96(8): e0003422, 2022 04 27.
Article in English | MEDLINE | ID: covidwho-1779310

ABSTRACT

With the much-debated exception of the modestly reduced acquisition reported for the RV144 efficacy trial, HIV-1 vaccines have not protected humans against infection, and a vaccine of similar design to that tested in RV144 was not protective in a later trial, HVTN 702. Similar vaccine regimens have also not consistently protected nonhuman primates (NHPs) against viral acquisition. Conversely, experimental vaccines of different designs have protected macaques from viral challenges but then failed to protect humans, while many other HIV-1 vaccine candidates have not protected NHPs. While efficacy varies more in NHPs than humans, vaccines have failed to protect in the most stringent NHP model. Intense investigations have aimed to identify correlates of protection (CoPs), even in the absence of net protection. Unvaccinated animals and humans vary vastly in their susceptibility to infection and in their innate and adaptive responses to the vaccines; hence, merely statistical associations with factors that do not protect are easily found. Systems biological analyses, including artificial intelligence, have identified numerous candidate CoPs but with no clear consistency within or between species. Proposed CoPs sometimes have only tenuous mechanistic connections to immune protection. In contrast, neutralizing antibodies (NAbs) are a central mechanistic CoP for vaccines that succeed against other viruses, including SARS-CoV-2. No HIV-1 vaccine candidate has yet elicited potent and broadly active NAbs in NHPs or humans, but narrow-specificity NAbs against the HIV-1 isolate corresponding to the immunogen do protect against infection by the autologous virus. Here, we analyze why so many HIV-1 vaccines have failed, summarize the outcomes of vaccination in NHPs and humans, and discuss the value and pitfalls of hunting for CoPs other than NAbs. We contrast the failure to find a consistent CoP for HIV-1 vaccines with the identification of NAbs as the principal CoP for SARS-CoV-2.


Subject(s)
AIDS Vaccines , HIV-1 , AIDS Vaccines/standards , Animals , Antibodies, Neutralizing , Artificial Intelligence , COVID-19 Vaccines/standards , Data Interpretation, Statistical , HIV Infections/prevention & control , Humans , SARS-CoV-2
14.
Elife ; 112022 03 16.
Article in English | MEDLINE | ID: covidwho-1766127

ABSTRACT

Publications are essential for a successful academic career, and there is evidence that the COVID-19 pandemic has amplified existing gender disparities in the publishing process. We used longitudinal publication data on 431,207 authors in four disciplines - basic medicine, biology, chemistry and clinical medicine - to quantify the differential impact of COVID-19 on the annual publishing rates of men and women. In a difference-in-differences analysis, we estimated that the average gender difference in publication productivity increased from -0.26 in 2019 to -0.35 in 2020; this corresponds to the output of women being 17% lower than the output of men in 2109, and 24% lower in 2020. An age-group comparison showed a widening gender gap for both early-career and mid-career scientists. The increasing gender gap was most pronounced among highly productive authors and in biology and clinical medicine. Our study demonstrates the importance of reinforcing institutional commitments to diversity through policies that support the inclusion and retention of women in research.


Subject(s)
COVID-19 , Efficiency , Female , Humans , Male , Pandemics , Publishing , Sex Factors
15.
Non-conventional in English | National Technical Information Service, Grey literature | ID: grc-753594

ABSTRACT

Biotechnology, the engineering and application of the science of biology to meet human goals, is critical to economic success in the twenty-first century. In the United States, revenues generated by biotechnology (principally drugs, crops, and chemicals) are already larger than 2 percent of gross domestic product and are growing approximately twice as fast as the economy as a whole. Individuals and news articles from China describe similarly sized biotechnology revenues there, but in both nations, the accuracy and precision of estimates is limited by the paucity of data. Revenues to date have been achieved using first-generation technologies. Second-generation technologies will be more powerful and could supply up to 60 percent of physical inputs to the global economy, with a direct economic impact of $4 trillion a year. Chinese leaders have identified biotechnology in writings and in pronouncements as critical to their vision of China as a dominant global economic power. To that end, they are pursuing a long-term strategy of climbing up the value chain and using a familiar set of tactics that includes the following: financial support for industry champions, intellectual property licensing from abroad, infrastructure spending (laboratories, technology parks, academic research), as well as IT hacking and industrial espionage. By contrast, the United States has adopted a laissez-faire approach and has little strategy or policy regarding biotechnology.

16.
Cells ; 11(5)2022 03 01.
Article in English | MEDLINE | ID: covidwho-1715131

ABSTRACT

Severe COVID-19 patients present a clinical and laboratory overlap with other hyperinflammatory conditions such as hemophagocytic lymphohistiocytosis (HLH). However, the underlying mechanisms of these conditions remain to be explored. Here, we investigated the transcriptome of 1596 individuals, including patients with COVID-19 in comparison to healthy controls, other acute inflammatory states (HLH, multisystem inflammatory syndrome in children [MIS-C], Kawasaki disease [KD]), and different respiratory infections (seasonal coronavirus, influenza, bacterial pneumonia). We observed that COVID-19 and HLH share immunological pathways (cytokine/chemokine signaling and neutrophil-mediated immune responses), including gene signatures that stratify COVID-19 patients admitted to the intensive care unit (ICU) and COVID-19_nonICU patients. Of note, among the common differentially expressed genes (DEG), there is a cluster of neutrophil-associated genes that reflects a generalized hyperinflammatory state since it is also dysregulated in patients with KD and bacterial pneumonia. These genes are dysregulated at the protein level across several COVID-19 studies and form an interconnected network with differentially expressed plasma proteins that point to neutrophil hyperactivation in COVID-19 patients admitted to the intensive care unit. scRNAseq analysis indicated that these genes are specifically upregulated across different leukocyte populations, including lymphocyte subsets and immature neutrophils. Artificial intelligence modeling confirmed the strong association of these genes with COVID-19 severity. Thus, our work indicates putative therapeutic pathways for intervention.


Subject(s)
COVID-19 , Lymphohistiocytosis, Hemophagocytic , Artificial Intelligence , COVID-19/complications , COVID-19/genetics , Child , Humans , Lymphohistiocytosis, Hemophagocytic/complications , Neutrophil Activation , SARS-CoV-2 , Systemic Inflammatory Response Syndrome
17.
2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021 ; : 52-59, 2021.
Article in English | Scopus | ID: covidwho-1708365

ABSTRACT

This paper reports on the development of a model of COVID-19 transmission dynamics that takes into account a comprehensive mitigation protocol. This is necessary for public health decision support and making actionable recommendations on COVID-19 response. The comprehensive mitigation protocol includes (1) personal protection and social distancing, (2) use of smart applications for symptom reporting and contact tracing, (3) targeted testing based on identification of individuals with possible exposure and/or infection via symptom reporting and contact tracing, (4) surveillance testing, and (5) shelter, quarantine and isolation procedures. The proposed model (1) extends a common epidemiological discrete dynamic model with the comprehensive mitigation protocol, (2) uses Bayesian probability analysis to estimate the conditional probabilities of being in non-circulating epidemiological sub-compartments as a function of the mitigation protocol parameters, based on which it (3) estimates transition ratios among the compartments, and (4) computes a range of key performance indicators including health outcomes, mitigation cost and productivity loss. The proposed model can serve as a critical component for COVID-19 mitigation decision support and recommender systems, as part of a broader effort to support urgent pandemic response. © 2021 IEEE.

18.
Front Genet ; 12: 812853, 2021.
Article in English | MEDLINE | ID: covidwho-1703625

ABSTRACT

De novo pathway enrichment is a systems biology approach in which OMICS data are projected onto a molecular interaction network to identify subnetworks representing condition-specific functional modules and molecular pathways. Compared to classical pathway enrichment analysis methods, de novo pathway enrichment is not limited to predefined lists of pathways from (curated) databases and thus particularly suited for discovering novel disease mechanisms. While several tools have been proposed for pathway enrichment, the integration of de novo pathway enrichment in end-to-end OMICS analysis workflows in the R programming language is currently limited to a single tool. To close this gap, we have implemented an R package KeyPathwayMineR (KPM-R). The package extends the features and usability of existing versions of KeyPathwayMiner by leveraging the power, flexibility and versatility of R and by providing various novel functionalities for performing data preparation, visualization, and comparison. In addition, thanks to its interoperability with a plethora of existing R packages in e.g., Bioconductor, CRAN, and GitHub, KPM-R allows carrying out the initial preparation of the datasets and to meaningfully interpret the extracted subnetworks. To demonstrate the package's potential, KPM-R was applied to bulk RNA-Seq data of nasopharyngeal swabs from SARS-CoV-2 infected individuals, and on single cell RNA-Seq data of aging mice tissue from the Tabula Muris Senis atlas.

19.
2021 International Automatic Control Conference, CACS 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1685061

ABSTRACT

The coronavirus disease 2019 (COVID-19) epidemic is currently raging around the world with a rapid speed. Among the COVID-19 patients, SARS-CoV-2 associated acute respiratory distress syndrome (ARDS) is the main contribution to the high ratio of morbidity and mortality. However, clinical manifestations between SARS-CoV-2-caused-ARDS and non-SARS-CoV-2-caused-ARDS are quite common and their therapy is limited owing to the intricated pathophysiology are not fully understood. In this study, we constructed a candidate host-pathogen interspecies genome-wide genetic and epigenetic network (HPI-GWGEN) via database mining at first. With the help of host-pathogen microarray data, real HPI-GWGEN of COVID-19-ARDS and Non-Viral-ARDS were obtained by system modeling, system identification and Akaike information criterion (AIC) of model order selection method to delete the false positives in candidate HPI-GWGEN. Afterwards, principal network projection (PNP) approach is utilized to extract core HPI-GWGEN and their core signaling pathways of COVID-19-ARDS and Non-Viral-ARDS annotated by KEGG pathways. In order to design multiple-molecule drugs of COVID-19-ARDS and Non-Viral-ARDS, we identified essential biomarkers of pathogenesis by comparing the core signal pathways between COVID-19-ARDS and Non-Viral-ARDS. The deep neural network of drug-target interaction model (DNN-DTI) would be trained by drug-target interaction databases in advance to predict candidate drugs for the identified biomarkers. We further narrowed down these predicted drug candidates as potential multiple-molecule drug by filters of drug design specifications, including regulation ability, sensitivity, excretion, toxicity and drug-likeness. Taken together, we not only enlighten the etiologic mechanisms under COVID-19-ARDS but also provided novel therapeutic options for COVID-19-ARDS and Non-viral-ARDS. © 2021 IEEE

20.
Annales Medico-Psychologiques ; 2022.
Article in English | EMBASE | ID: covidwho-1664644

ABSTRACT

Introduction: Psychiatry is challenged by a plurality of complementary approaches. These challenges stem from the existence of multiple levels of understanding, i.e. systems of representations, tools, methodologies and objectives in psychiatry–ranging from computational approaches and systems dynamics to the multiplicity of emerging nosographies, such as the NIMH Research Domain Criteria project or staging models. In this plurality, a significant number of clinicians have adopted the biopsychosocial model. However, such a model has been widely criticized for more than twenty years. In parallel, science has declined a set of different pluralistic frameworks. Thus, through the challenges of computational modeling in psychiatry, we will see how the enactive approach of psychiatry could respond to this multiplicity. Indeed, such an enactive approach considers that perception is a (predictive) activity, which gives sense to the environment (i.e., sense making). Perception and, by extension, cognitive processes are not internal representations of the outside world, but they are deployed according to the 5E approach, i.e., an embodied, embedded, enacted, emotive and extended approach. Methods: In this article, we first study the pluralist framework in psychiatry, in order to show its contributions in the clinical practice. Secondly, we analyze the contributions of the enactive approach for clinical practice in psychiatry. Results: Two forms of pluralisms can be described: a non-integrative pluralism and an integrative pluralism. The first examines the coexistence of different potentially incompatible or untranslatable systems in the scientific or clinical landscape. The second proposes the development of a general framework, bringing together the different levels of understanding and systems of representations. However, pluralism has many pitfalls and limitations. Especially by allowing computational modeling, the enactive framework, anchored both in cognitive sciences, theory of dynamic systems, systems biology and phenomenology, has recently been proposed as an answer to the challenge of integrative psychiatry. Conclusions: A significant number of mental health professionals are already working accepting such a variety of clinical and scientific approaches. We show that the enactive approach allows psychiatry: (1) to consider the subjectivity and the patient's experience, (2) to articulate different “granularities” within the clinical consultation, (3) to explain the benefits the creation of meaning for the patient, (4) to provide concrete models, (5) to support pedagogy in psychiatry. The enactive approach provides a conception for understanding psychiatric disorders as embodied, embedded, enacted, emotional and extended. In that way, the manifestations experienced by the patients are sense making experiences and can be conceived according to various levels of granularity.

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