Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies

COVID-19 in patients with myasthenia gravis: a single-center retrospective study in China

BACKGROUND

Coronavirus disease 2019 (COVID-19) has been a great concern since 2019. Patients with myasthenia gravis (MG) may be at higher risk of COVID-19 and a more severe disease course. We examined the associations between COVID-19 and MG.

METHODS

This single-center retrospective cohort study involved 134 patients who were diagnosed with MG from June 2020 to November 2022 and followed up until April 2023. They were divided into a COVID-19 group and non-COVID-19 group. Logistic regression analysis was used to detect factors potentially associating COVID-19 with MG.

RESULTS

Of the 134 patients with MG, 108 (80.6%) had COVID-19. A higher number of comorbidities was significantly associated with an increased risk of COVID-19 (p = 0.040). A total of 103 patients (95.4%) had mild/moderate COVID-19 symptoms, and 4 patients (3.7%) were severe/critical symptoms (including 2 deaths). Higher age (p = 0.036), use of rituximab (p = 0.037), tumors other than thymoma (p = 0.031), Hashimoto's thyroiditis (p = 0.011), more comorbidities (p = 0.002), and a higher baseline MG activities of daily living (MG-ADL) score (p = 0.006) were risk factors for severe COVID-19 symptoms. The MG-ADL score increased by ≥ 2 points in 16 (15.7%) patients. Dry cough and/or expectoration (p = 0.011), use of oral corticosteroids (p = 0.033), and use of more than one kind of immunosuppressant (p = 0.017) were associated with the increase of the post-COVID-19 MG-ADL score.

CONCLUSION

Most patients with MG have a mild course of COVID-19. However, patients with older age, many comorbidities, a high MG-ADL score, and use of a variety of immunosuppressants during COVID-19 may be more prone to severe symptoms.

ATF4 Signaling in HIV-1 Infection: Viral Subversion of a Stress Response Transcription Factor

Cellular integrated stress response (ISR), the mitochondrial unfolded protein response (UPRmt), and IFN signaling are associated with viral infections. Activating transcription factor 4 (ATF4) plays a pivotal role in these pathways and controls the expression of many genes involved in redox processes, amino acid metabolism, protein misfolding, autophagy, and apoptosis. The precise role of ATF4 during viral infection is unclear and depends on cell hosts, viral agents, and models. Furthermore, ATF4 signaling can be hijacked by pathogens to favor viral infection and replication. In this review, we summarize the ATF4-mediated signaling pathways in response to viral infections, focusing on human immunodeficiency virus 1 (HIV-1). We examine the consequences of ATF4 activation for HIV-1 replication and reactivation. The role of ATF4 in autophagy and apoptosis is explored as in the context of HIV-1 infection programmed cell deaths contribute to the depletion of CD4 T cells. Furthermore, ATF4 can also participate in the establishment of innate and adaptive immunity that is essential for the host to control viral infections. We finally discuss the putative role of the ATF4 paralogue, named ATF5, in HIV-1 infection. This review underlines the role of ATF4 at the crossroads of multiple processes reflecting host-pathogen interactions.

In silico based analysis to explore genetic linkage between atherosclerosis and its potential risk factors

Atherosclerosis (ATH) is a chronic cardiovascular disease characterized by plaque formation in arteries, and it is a major cause of illness and death. Although therapeutic advances have significantly improved the prognosis of ATH, missing therapeutic targets pose a significant residual threat. This research used a systems biology approach to identify the molecular biomarkers involved in the onset and progression of ATH, analysing microarray gene expression datasets from ATH and tissues impacted by risk factors such as high cholesterol, adipose tissue, smoking, obesity, sedentary lifestyle, stress, alcohol consumption, hypertension, hyperlipidaemia, high fat, diabetes to find the differentially expressed genes (DEGs). Bioinformatic analyses of Protein-Protein Interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted on differentially expressed genes, revealing metabolic and signaling pathways (the chemokine signaling pathway, cytokine-cytokine receptor interaction, the cytosolic DNA-sensing pathway, the peroxisome proliferator-activated receptors signaling pathway, and the nuclear factor-kappa B signaling pathway), ten hubs proteins (CCL5, CCR1, TLR1, CCR2, FCGR2A, IL1B, CD163, AIF1, CXCL-1 and TNF), five transcription factors (YY1, FOXL1, FOXC1, SRF, and GATA2), and five miRNAs (mir-27a-3p, mir-124-3p, mir-16-5p, mir-129-2-3p, mir-1-3p). These findings identify potential biomarkers that may increase knowledge of the mechanisms underlying ATH and their connection to risk factors, aiding in the development of new therapies.

Subtyping Social Determinants of Health in All of Us: Network Analysis and Visualization Approach

Background

Social determinants of health (SDoH), such as financial resources and housing stability, account for between 30-55% of people's health outcomes. While many studies have identified strong associations among specific SDoH and health outcomes, most people experience multiple SDoH that impact their daily lives. Analysis of this complexity requires the integration of personal, clinical, social, and environmental information from a large cohort of individuals that have been traditionally underrepresented in research, which is only recently being made available through the All of Us research program. However, little is known about the range and response of SDoH in All of Us, and how they co-occur to form subtypes, which are critical for designing targeted interventions.

Objective

To address two research questions: (1) What is the range and response to survey questions related to SDoH in the All of Us dataset? (2) How do SDoH co-occur to form subtypes, and what are their risk for adverse health outcomes?

Methods

For Question-1, an expert panel analyzed the range of SDoH questions across the surveys with respect to the 5 domains in Healthy People 2030 (HP-30), and analyzed their responses across the full All of Us data (n=372,397, V6). For Question-2, we used the following steps: (1) due to the missingness across the surveys, selected all participants with valid and complete SDoH data, and used inverse probability weighting to adjust their imbalance in demographics compared to the full data; (2) an expert panel grouped the SDoH questions into SDoH factors for enabling a more consistent granularity; (3) used bipartite modularity maximization to identify SDoH biclusters, their significance, and their replicability; (4) measured the association of each bicluster to three outcomes (depression, delayed medical care, emergency room visits in the last year) using multiple data types (surveys, electronic health records, and zip codes mapped to Medicaid expansion states); and (5) the expert panel inferred the subtype labels, potential mechanisms that precipitate adverse health outcomes, and interventions to prevent them.

Results

For Question-1, we identified 110 SDoH questions across 4 surveys, which covered all 5 domains in HP-30. However, the results also revealed a large degree of missingness in survey responses (1.76%-84.56%), with later surveys having significantly fewer responses compared to earlier ones, and significant differences in race, ethnicity, and age of participants of those that completed the surveys with SDoH questions, compared to those in the full All of Us dataset. Furthermore, as the SDoH questions varied in granularity, they were categorized by an expert panel into 18 SDoH factors. For Question-2, the subtype analysis (n=12,913, d=18) identified 4 biclusters with significant biclusteredness (Q=0.13, random-Q=0.11, z=7.5, P<0.001), and significant replication (Real-RI=0.88, Random-RI=0.62, P<.001). Furthermore, there were statistically significant associations between specific subtypes and the outcomes, and with Medicaid expansion, each with meaningful interpretations and potential targeted interventions. For example, the subtype Socioeconomic Barriers included the SDoH factors not employed, food insecurity, housing insecurity, low income, low literacy, and low educational attainment, and had a significantly higher odds ratio (OR=4.2, CI=3.5-5.1, P-corr<.001) for depression, when compared to the subtype Sociocultural Barriers. Individuals that match this subtype profile could be screened early for depression and referred to social services for addressing combinations of SDoH such as housing insecurity and low income. Finally, the identified subtypes spanned one or more HP-30 domains revealing the difference between the current knowledge-based SDoH domains, and the data-driven subtypes.

Conclusions

The results revealed that the SDoH subtypes not only had statistically significant clustering and replicability, but also had significant associations with critical adverse health outcomes, which had translational implications for designing targeted SDoH interventions, decision-support systems to alert clinicians of potential risks, and for public policies. Furthermore, these SDoH subtypes spanned multiple SDoH domains defined by HP-30 revealing the complexity of SDoH in the real-world, and aligning with influential SDoH conceptual models such as by Dahlgren-Whitehead. However, the high-degree of missingness warrants repeating the analysis as the data becomes more complete. Consequently we designed our machine learning code to be generalizable and scalable, and made it available on the All of Us workbench, which can be used to periodically rerun the analysis as the dataset grows for analyzing subtypes related to SDoH, and beyond.

Comorbidities among persons living with HIV (PLWH) in Florida: a network analysis

People living with HIV (PLWH) experience a higher rate of age-related comorbidities at younger ages. Understanding common comorbidities among PLWH and their relationship to one another could be significant in improving aging for PLWH. The goal of the present study is to identify the most common comorbidities among PLWH and the relationship between them using network analysis. We used abstracted electronic medical record (EMR) data of PLWH from the Florida Cohort study, a prospective cohort study conducted in eight cities in Florida, USA. We used International Classification of Diseases (10th revision, ICD-10) code to classify comorbidities and organ systems. Network analysis was conducted to determine the degree and betweenness centrality among comorbidities. We included 756 PLWH with an average age of 46.4 years (SD 11.3) in the analysis. Infectious diseases (A00-B99, 50.8%), mental and behavioural (F01-F99, 47.0%), endocrine, nutritional and metabolic (E00-E88, 45.2%), and circulatory (I00-I99, 39%) disorders were the most prevalent system comorbidities among PLWH. Hypertensive disorder (I10-I1635.8%), dyslipidaemia (E78, 25.7%) and major depressive disorder (F32-F33, 23.9%) were the most common non-infectious conditions affecting PLWH. Viral hepatitis (B15-B19, 17.1%) and syphilis (A15-A53, 12%) were the most common coinfections among PLWH. Hypertension, dyslipidaemia and major depressive disorder were the most central of the comorbidities among PLWH. Comorbidities among PLWH were most prevalent for chronic disease and mental illness. Targeting shared disease risk factors in addition to monitoring known pathological pathways may prevent comorbidities among PLWH.

Insights into the Impact of Hesitancy on Cancer Care and COVID-19

World Health Organization findings indicate that the COVID-19 pandemic adversely affected cancer diagnosis and management. The COVID-19 pandemic disrupted the optimal management of outpatient appointments, scheduled treatments, and hospitalizations for cancer patients because of hesitancy among patients and health-care providers. Travel restrictions and other factors likely affected medical, surgical, and radiation treatments during the COVID-19 pandemic. Cancer patients were more likely to be affected by severe illness and complications if they contracted COVID-19. A compromised immune system and comorbidities in cancer patients may have contributed to this increased risk. Hesitancy or reluctance to receive appropriate therapy or vaccination advice might have played a major role for cancer patients, resulting in health-care deficits. The purpose of this review is to evaluate the impact of COVID-19 on screening, entry into clinical trials, and hesitancy among patients and health-care professionals, limiting adjuvant and metastatic cancer treatment.

Lycopene: a therapeutic strategy against coronavirus disease 19 (COVID- 19)

Lycopene is a group of phytochemicals found in nature, primarily in fruits and vegetables. Lycopene is thought to protect against a variety of diseases attributed to its antioxidant capabilities. Lycopene has anti-inflammatory, anti-cancer, and immunity-boosting qualities, among other biological and pharmacological benefits. COVID-19 (coronavirus disease 19) is an infectious disease caused by the SARS-CoV-2 virus, which has recently emerged as one of the world's leading causes of death. Patients may be asymptomatic or show signs of respiratory, cytokine release syndrome, gastrointestinal, or even multiple organ failure, all of which can lead to death. In COVID-19, inflammation, and cytokine storm are the key pathogenic mechanisms, according to SARS-CoV-2 infection symptoms. ARDS develops in some vulnerable hosts, which is accompanied by an inflammatory "cytokine syndrome" that causes lung damage. Immunological and inflammatory markers were linked to disease severity in mild and severe COVID-19 cases, implying that inflammatory markers, including IL-6, CRP, ESR, and PCT were significantly linked with COVID-19 severity. Patients with severe illness have reduced levels of several immune subsets, including CD4 + T, NK, and CD8 + cells. As a result, lycopene can be commended for bolstering physiological defenses against COVID-19 infections.

Cardiovascular Complications in Community-Acquired Pneumonia

Community-acquired pneumonia (CAP) is accountable for high mortality in both pediatric and adult populations worldwide, about one-third of hospitalized patients pass away within a year of being discharged from the facility. The high mortality and morbidity rates are closely related to cardiovascular complications that are consequent or concomitant to the acute episode of pneumonia. An updated perspective on the major pathophysiological mechanisms, prevalence, risk factors, outcomes, and relevant treatments of cardiovascular events in CAP patients is provided in the current study. It is possible to evaluate the pathophysiology of cardiac disease in this population based on plaque-related events, such as acute myocardial infarction, or events unrelated to plaque, such as arrhythmias and heart failure. With an absolute rate of cardiovascular problems ranging broadly from 10% to 30%, CAP raises the risk of both plaque-related and plaque-unrelated events. Both in- and out-patients may experience these issues at admission, throughout hospitalization, or even up to a year following discharge. At long-term follow-up, cardiac events account for more than 30% of deaths in CAP patients, making them a significant cause of mortality. If patients at risk for cardiac events are stratified, diagnostic tools, monitoring, and preventive measures may be applied to these patients. A prospective evaluation of cardioprotective treatments is urgently required from a research point of view.

Post-COVID-19 manifestations: A study of analyzing symptoms, complications following hospitalization

Background

Post-COVID-19 symptoms and diseases appeared on recovered from COVID-19. Hence, the study aims to investigate and characterize the manifestations which appear after recovery from the corona virus infection.

Objectives

To investigate the post-COVID-19 Manifestation, to demonstrate different symptoms or signs that appeared during COVID and after recovery from the disease and to see association of independent factors (like age, sex, BMI, Comorbidities) with Post-COVID complication.

Methods

The study was conducted using cross-sectional study among COVID positive patients admitted and then recovered in Bangur Hospital, Pali, Rajasthan, including ICU and Isolation wards from March to December 2020. Sample size calculated was 423 with simple random sampling.

Findings

In our study of these 421 COVID-19 cases, median age was 36 year (Interquartile Range: 26-55 years). Post-COVID manifestation (at least one symptom) significantly associated with age of subjects (p = 0.001), subjects who were in ICU during COVID-19 positive (p = 0.003), symptomatic subjects (p = 0.009) during COVID positive and SPO₂ level at the time of admission during COVID positive (p = 0.01).

Conclusion

The recovered subjects should be highly vigilant in maintaining and monitoring their health status as there is a risk of future complications after recovery.

Early macrophage response to obesity encompasses Interferon Regulatory Factor 5 regulated mitochondrial architecture remodelling

Adipose tissue macrophages (ATM) adapt to changes in their energetic microenvironment. Caloric excess, in a range from transient to diet-induced obesity, could result in the transition of ATMs from highly oxidative and protective to highly inflammatory and metabolically deleterious. Here, we demonstrate that Interferon Regulatory Factor 5 (IRF5) is a key regulator of macrophage oxidative capacity in response to caloric excess. ATMs from mice with genetic-deficiency of Irf5 are characterised by increased oxidative respiration and mitochondrial membrane potential. Transient inhibition of IRF5 activity leads to a similar respiratory phenotype as genomic deletion, and is reversible by reconstitution of IRF5 expression. We find that the highly oxidative nature of Irf5-deficient macrophages results from transcriptional de-repression of the mitochondrial matrix component Growth Hormone Inducible Transmembrane Protein (GHITM) gene. The Irf5-deficiency-associated high oxygen consumption could be alleviated by experimental suppression of Ghitm expression. ATMs and monocytes from patients with obesity or with type-2 diabetes retain the reciprocal regulatory relationship between Irf5 and Ghitm. Thus, our study provides insights into the mechanism of how the inflammatory transcription factor IRF5 controls physiological adaptation to diet-induced obesity via regulating mitochondrial architecture in macrophages.

Interferon Regulatory Factor 5 levels have been shown to increase in adipose tissue macrophages in diet-induced obesity. Here authors show that IRF5 transcriptionally represses the Growth Hormone Inducible Transmembrane Protein gene encoding a mitochondrial protein important for oxidative respiration in macrophages, thus driving the detrimental metabolic changes observed in obesity.

The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study

Background

Network analysis, a technique for describing relationships, can provide insights into patterns of co-occurring chronic health conditions. The effect that co-occurrence measurement has on disease network structure and resulting inferences has not been well studied. The purpose of the study was to compare structural differences among multimorbidity networks constructed using different co-occurrence measures.

Methods

A retrospective cohort study was conducted using four fiscal years of administrative health data (2015/16 – 2018/19) from the province of Manitoba, Canada (population 1.5 million). Chronic conditions were identified using diagnosis codes from electronic records of physician visits, surgeries, and inpatient hospitalizations, and grouped into categories using the Johns Hopkins Adjusted Clinical Group (ACG) System. Pairwise disease networks were separately constructed using each of seven co-occurrence measures: lift, relative risk, phi, Jaccard, cosine, Kulczynski, and joint prevalence. Centrality analysis was limited to the top 20 central nodes, with degree centrality used to identify potentially influential chronic conditions. Community detection was used to identify disease clusters. Similarities in community structure between networks was measured using the adjusted Rand index (ARI). Network edges were described using disease prevalence categorized as low (< 1%), moderate (1 to < 7%), and high (≥7%). Network complexity was measured using network density and frequencies of nodes and edges.

Results

Relative risk and lift highlighted co-occurrences between pairs of low prevalence health conditions. Kulczynski emphasized relationships between high and low prevalence conditions. Joint prevalence focused on highly-prevalent conditions. Phi, Jaccard, and cosine emphasized associations involving moderately prevalent conditions. Co-occurrence measurement differences significantly affected the number and structure of identified disease clusters. When limiting the number of edges to produce visually interpretable graphs, networks had significant dissimilarity in the percentage of co-occurrence relationships in common, and in their selection of the highest-degree nodes.

Conclusions

Multimorbidity network analyses are sensitive to disease co-occurrence measurement. Co-occurrence measures should be selected considering their intrinsic properties, research objectives, and the health condition prevalence relationships of greatest interest. Researchers should consider conducting sensitivity analyses using different co-occurrence measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01607-8.

Association of Pre-Existing Comorbidities with Disease Severity Among COVID-19 Patients in Eastern Ethiopia

Background

Comorbidities and advanced age increase the risk of severe outcomes of COVID-19. In order to shift the possible unfavorable treatment outcome in patients with chronic illnesses, information related to the prevalence of chronic illness and its effect on severity of COVID-19 infection has paramount importance.

Objective

This study was aimed at assessing the prevalence of comorbidities and associated severity among COVID-19 patients admitted to COVID-19 treatment center, eastern Ethiopia.

Methods

An institution-based cross-sectional study design was employed among 422 COVID-19 patients admitted to COVID-19 treatment center, eastern Ethiopia from April 10, 2020, to August 10, 2021. Binary logistic regression was fitted to identify comorbidities and other factors associated with severe clinical outcome, associations were presented with adjusted odds ratios (AORs) and 95% confidence intervals (CIs). In all analyses statistical significance were declared at p-value <0.05.

Results

More than half (52.4%) of the COVID-19 patients were presented with comorbid conditions. One third (34.6%) of the admitted COVID-19 patients were in severe clinical stages. Marital status (AOR=4.56; 95% CI: 1.40, 14.76), hypertension (AOR=2.08; 95% CI: 1.09, 3.97), diabetes mellitus (AOR=3.31; 95%:1.84, 5.98), and cardiovascular diseases (AOR=4.22; 95% CI: 2.18, 8.15) were identified as factors associated with severe clinical stages.

Conclusion

The comorbid conditions such as diabetes, hypertension, and cardiovascular diseases, and marital status were identified as significant predictors of severe outcomes of COVID-19. Therefore, identifying the people with chronic comorbidities as a risk group would help to anticipate and prevent the serious outcomes of COVID-19 infection.

Immune-related biomarkers shared by inflammatory bowel disease and liver cancer

It has been indicated that there is an association between inflammatory bowel disease (IBD) and hepatocellular carcinoma (HCC). However, the molecular mechanism underlying the risk of developing HCC among patients with IBD is not well understood. The current study aimed to identify shared genes and potential pathways and regulators between IBD and HCC using a system biology approach. By performing the different gene expression analyses, we identified 871 common differentially expressed genes (DEGs) between IBD and HCC. Of these, 112 genes overlapped with immune genes were subjected to subsequent bioinformatics analyses. The results revealed four hub genes (CXCL2, MMP9, SPP1 and SRC) and several other key regulators including six transcription factors (FOXC1, FOXL1, GATA2, YY1, ZNF354C and TP53) and five microRNAs (miR-124-3p, miR-34a-5p, miR-1-3p, miR-7-5p and miR-99b-5p) for these disease networks. Protein-drug interaction analysis discovered the interaction of the hub genes with 46 SRC-related and 11 MMP9- related drugs that may have a therapeutic effect on IBD and HCC. In conclusion, this study sheds light on the potential connecting mechanisms of HCC and IBD.

Post-Immune Antibodies in HIV-1 Infection in the Context of Vaccine Development: A Variety of Biological Functions and Catalytic Activities

Unlike many other viruses, HIV-1 is highly variable. The structure of the viral envelope changes as the infection progresses and is one of the biggest obstacles in developing an HIV-1 vaccine. HIV-1 infection can cause the production of various natural autoantibodies, including catalytic antibodies hydrolyzing DNA, myelin basic protein, histones, HIV-integrase, HIV-reverse transcriptase, β-casein, serum albumin, and some other natural substrates. Currently, there are various directions for the development of HIV-1 vaccines: stimulation of the immune response on the mucous membranes; induction of cytotoxic T cells, which lyse infected cells and hold back HIV-infection; immunization with recombinant Env proteins or vectors encoding Env; mRNA-based vaccines and some others. However, despite many attempts to develop an HIV-1 vaccine, none have been successful. Here we review the entire spectrum of antibodies found in HIV-infected patients, including neutralizing antibodies specific to various viral epitopes, as well as antibodies formed against various autoantigens, catalytic antibodies against autoantigens, and some viral proteins. We consider various promising targets for developing a vaccine that will not produce unwanted antibodies in vaccinated patients. In addition, we review common problems in the development of a vaccine against HIV-1.

Patient-centric characterization of multimorbidity trajectories in patients with severe mental illnesses: A temporal bipartite network modeling approach

Multimorbidity is a major factor contributing to increased mortality among people with severe mental illnesses (SMI). Previous studies either focus on estimating prevalence of a disease in a population without considering relationships between diseases or ignore heterogeneity of individual patients in examining disease progression by looking merely at aggregates across a whole cohort. Here, we present a temporal bipartite network model to jointly represent detailed information on both individual patients and diseases, which allows us to systematically characterize disease trajectories from both patient and disease centric perspectives. We apply this approach to a large set of longitudinal diagnostic records for patients with SMI collected through a data linkage between electronic health records from a large UK mental health hospital and English national hospital administrative database. We find that the resulting diagnosis networks show disassortative mixing by degree, suggesting that patients affected by a small number of diseases tend to suffer from prevalent diseases. Factors that determine the network structures include an individual's age, gender and ethnicity. Our analysis on network evolution further shows that patients and diseases become more interconnected over the illness duration of SMI, which is largely driven by the process that patients with similar attributes tend to suffer from the same conditions. Our analytic approach provides a guide for future patient-centric research on multimorbidity trajectories and contributes to achieving precision medicine.

Epidemic dreams: dreaming about health during the COVID-19 pandemic

The continuity hypothesis of dreams suggests that the content of dreams is continuous with the dreamer's waking experiences. Given the unprecedented nature of the experiences during COVID-19, we studied the continuity hypothesis in the context of the pandemic. We implemented a deep-learning algorithm that can extract mentions of medical conditions from text and applied it to two datasets collected during the pandemic: 2888 dream reports (dreaming life experiences), and 57 milion tweets (waking life experiences) mentioning the pandemic. The health expressions common to both sets were typical COVID-19 symptoms (e.g. cough, fever and anxiety), suggesting that dreams reflected people's real-world experiences. The health expressions that distinguished the two sets reflected differences in thought processes: expressions in waking life reflected a linear and logical thought process and, as such, described realistic symptoms or related disorders (e.g. nasal pain, SARS, H1N1); those in dreaming life reflected a thought process closer to the visual and emotional spheres and, as such, described either conditions unrelated to the virus (e.g. maggots, deformities, snake bites), or conditions of surreal nature (e.g. teeth falling out, body crumbling into sand). Our results confirm that dream reports represent an understudied yet valuable source of people's health experiences in the real world.

COVID-19, the Pandemic of the Century and Its Impact on Cardiovascular Diseases

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection likely ranks among the deadliest diseases in human history. As with other coronaviruses, SARS-CoV-2 infection damages not only the lungs but also the heart and many other organs that express angiotensin-converting enzyme 2 (ACE2), a receptor for SARS-CoV-2. COVID-19 has upended lives worldwide. Dietary behaviors have been altered such that they favor metabolic and cardiovascular complications, while patients have avoided hospital visits because of limited resources and the fear of infection, thereby increasing out-hospital mortality due to delayed diagnosis and treatment. Clinical observations show that sex, age, and race all influence the risk for SARS-CoV-2 infection, as do hypertension, obesity, and pre-existing cardiovascular conditions. Many hospitalized COVID-19 patients suffer cardiac injury, acute coronary syndromes, or cardiac arrhythmia. SARS-CoV-2 infection may lead to cardiomyocyte apoptosis and necrosis, endothelial cell damage and dysfunction, oxidative stress and reactive oxygen species production, vasoconstriction, fibrotic and thrombotic protein expression, vascular permeability and microvascular dysfunction, heart inflammatory cell accumulation and activation, and a cytokine storm. Current data indicate that COVID-19 patients with cardiovascular diseases should not discontinue many existing cardiovascular therapies such as ACE inhibitors, angiotensin receptor blockers, steroids, aspirin, statins, and PCSK9 inhibitors. This review aims to furnish a framework relating to COVID-19 and cardiovascular pathophysiology.

Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders

Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.

Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection

The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.

Higher coronavirus disease-19 mortality linked to comorbidities: A comparison between low-middle income and high-income countries

BACKGROUND

Global burden of disease (GBD) provides the estimates of mortality and morbidity, while case fatality rate (CFR) helps in understanding the severity of the disease. People infected with severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) with underlying medical conditions have shown higher levels of unfavorable outcomes including mortality. We assessed the association of SARS-CoV-2 CFR with disability-adjusted life years (DALY) of various comorbidities in the low-middle income countries (LMIC) and high-income countries (HIC) to study the relationship of coronavirus disease-19 (COVID-19) mortality with GBDs and to understand the linkage between COVID-19 mortality and comorbidities.

MATERIALS AND METHODS

This was an ecological study with secondary data analysis comparing the DALY of various morbidities from GBD with CFR of COVID-19. Gross domestic product was the basis of stratifying 177 countries into low-middle income (LMIC) and high-income groups (HIC). The mortality was analyzed using Pearson correlation and linear regression.

RESULTS

The median global CFR of SARS-CoV-2 was 2.15. The median CFR among LMIC (n = 60) and HIC (n = 117) was 2.01 (0.00-28.20) and 2.29 (0.00-17.26), respectively. The regression analysis found that, in both LMIC and HIC, maternal disorders were associated with higher SARS-CoV-2 CFR, while tuberculosis, mental health disorders, and were associated with lower CFR. Further, in LMIC, musculoskeletal disorders and nutritional deficiencies were associated with higher CFR, while respiratory disorders were associated with lower CFR.

CONCLUSIONS

SARS-CoV-2 infection appears to be a systemic disease. Individuals with comorbidities, such as maternal disorders, neurological diseases, musculoskeletal disorders, and nutritional deficiencies, have poorer outcomes with COVID-19, leading to higher mortality.

Mining microbe-disease interactions from literature via a transfer learning model

Background

Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe–disease interactions are hidden in the biomedical literature. The structured databases for microbe–disease interactions are in limited amounts. In this paper, we aim to construct a large-scale database for microbe–disease interactions automatically. We attained this goal via applying text mining methods based on a deep learning model with a moderate curation cost. We also built a user-friendly web interface that allows researchers to navigate and query required information.

Results

Firstly, we manually constructed a golden-standard corpus and a sliver-standard corpus (SSC) for microbe–disease interactions for curation. Moreover, we proposed a text mining framework for microbe–disease interaction extraction based on a pretrained model BERE. We applied named entity recognition tools to detect microbe and disease mentions from the free biomedical texts. After that, we fine-tuned the pretrained model BERE to recognize relations between targeted entities, which was originally built for drug–target interactions or drug–drug interactions. The introduction of SSC for model fine-tuning greatly improved detection performance for microbe–disease interactions, with an average reduction in error of approximately 10%. The MDIDB website offers data browsing, custom searching for specific diseases or microbes, and batch downloading.

Conclusions

Evaluation results demonstrate that our method outperform the baseline model (rule-based PKDE4J) with an average \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}F1-score of 73.81%. For further validation, we randomly sampled nearly 1000 predicted interactions by our model, and manually checked the correctness of each interaction, which gives a 73% accuracy. The MDIDB webiste is freely avaliable throuth http://dbmdi.com/index/

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

Network-Based Analysis of Fatal Comorbidities of COVID-19 and Potential Therapeutics

COVID-19 is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The case-fatality rate is significantly higher in older patients and those with diabetes, cancer or cardiovascular disorders. The human proteins, angiotensin-converting enzyme 2 (ACE2), transmembrane protease serine 2 (TMPRSS2) and basigin (BSG), are involved in high-confidence host-pathogen interactions with SARS-CoV-2 proteins. We considered these three proteins as seed nodes and applied the random walk with restart method on the human interactome to construct a protein-protein interaction sub-network, which captures the effects of viral invasion. We found that 'Insulin resistance', 'AGE-RAGE signaling in diabetic complications' and 'adipocytokine signaling' were the common pathways associated with diabetes, cancer and cardiovascular disorders. The association of these critical pathways with aging and its related diseases explains the molecular basis of COVID-19 fatality. We further identified drugs that have effects on these proteins/pathways based on gene expression studies. We particularly focused on drugs that significantly downregulate ACE2 along with other critical proteins identified by the network-based approach. Among them, COL-3 had earlier shown activity against acute lung injury and acute respiratory distress, while entinostat and mocetinostat have been investigated for non-small-cell lung cancer. We propose that these drugs can be repurposed for COVID-19.

Network-Based Analysis of Fatal Comorbidities of COVID-19 and Potential Therapeutics

COVID-19 is a highly contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The case-fatality rate is significantly higher in older patients and those with diabetes, cancer or cardiovascular disorders. The human proteins, angiotensin-converting enzyme 2 (ACE2), transmembrane protease serine 2 (TMPRSS2) and basigin (BSG), are involved in high-confidence host-pathogen interactions with SARS-CoV-2 proteins. We considered these three proteins as seed nodes and applied the random walk with restart method on the human interactome to construct a protein-protein interaction sub-network, which captures the effects of viral invasion. We found that 'Insulin resistance', 'AGE-RAGE signaling in diabetic complications' and 'adipocytokine signaling' were the common pathways associated with diabetes, cancer and cardiovascular disorders. The association of these critical pathways with aging and its related diseases explains the molecular basis of COVID-19 fatality. We further identified drugs that have effects on these proteins/pathways based on gene expression studies. We particularly focused on drugs that significantly downregulate ACE2 along with other critical proteins identified by the network-based approach. Among them, COL-3 had earlier shown activity against acute lung injury and acute respiratory distress, while entinostat and mocetinostat have been investigated for non-small-cell lung cancer. We propose that these drugs can be repurposed for COVID-19.

Vulnerability, Disability, and Public Health Crises

This article suggests that those individuals typically acknowledged as vulnerable during public health crises, such as pandemics, are often-times doubly so. I suggest that individuals can be vulnerable in a person-affecting way (in a way that suggests they are at greater risk to their physical person) as well as in a personhood-affecting way (in a manner that results in individuals being at risk of having their personhood or status as valuable members of a society challenged). I suggest that the former notion of vulnerability coincides with many existing accounts of vulnerability and that subsequently, many of the more standard arguments for moral and justice-based obligations to minimize such vulnerability, hold. I also suggest that the latter notion of vulnerability adds another layer of vulnerability to those that we typically view to be at risk. I argue that personhood-vulnerability constitutes a novel interpretation of vulnerability than expands our ideas of the kinds of harm that emerge during public health crises.

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR.

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

Clinical features, comorbidities, complications and treatment options in severe and non-severe COVID-19 patients: A systemic review and meta-analysis

OBJECTIVES

The aim of this analysis was to assess the prevalence of clinical features, comorbidities, complications and treatment options in the patients with COVID-19 and compare incidence of these clinical data in severe and non-severe patients.

DESIGN

Systemic review and Meta-analysis.

METHODS

PubMed, Embase, Scopus and Web of Sciences databases were searched to identify relevant papers until 20 July 2020. All studies comparing clinical data of severe and non-severe patients of COVID-19 were included. Heterogeneity across included studies was determined using Cochrane's Q test and the I2 statistic. Results were expressed as odds ratio with accompanying 95% confidence intervals.

RESULTS

Twelve studies with 3,046 patients were included. The result showed the most prevalent clinical symptoms were fever 88.3%, cough 62.2%, fatigue 39.5% and dyspnoea 31.5%. Further meta-analysis showed incidence of fever, cough, fatigue and dyspnoea was higher in severe patients. The most prevalent comorbidities were hypertension 22.6%, diabetes 11.5%, cardiovascular disease 10.3% and cancer 2.5%. We found that compared with non-severe patients, the symptoms, existing comorbidities and complications are prevalent in severe COVID-19 patients. Future well-methodologically designed studies from other populations are strongly recommended.

Pathogenetic profiling of COVID-19 and SARS-like viruses

The novel coronavirus (2019-nCoV) has recently emerged, causing COVID-19 outbreaks and significant societal/global disruption. Importantly, COVID-19 infection resembles SARS-like complications. However, the lack of knowledge about the underlying genetic mechanisms of COVID-19 warrants the development of prospective control measures. In this study, we employed whole-genome alignment and digital DNA-DNA hybridization analyses to assess genomic linkage between 2019-nCoV and other coronaviruses. To understand the pathogenetic behavior of 2019-nCoV, we compared gene expression datasets of viral infections closest to 2019-nCoV with four COVID-19 clinical presentations followed by functional enrichment of shared dysregulated genes. Potential chemical antagonists were also identified using protein-chemical interaction analysis. Based on phylogram analysis, the 2019-nCoV was found genetically closest to SARS-CoVs. In addition, we identified 562 upregulated and 738 downregulated genes (adj. P ≤ 0.05) with SARS-CoV infection. Among the dysregulated genes, SARS-CoV shared ≤19 upregulated and ≤22 downregulated genes with each of different COVID-19 complications. Notably, upregulation of BCL6 and PFKFB3 genes was common to SARS-CoV, pneumonia and severe acute respiratory syndrome, while they shared CRIP2, NSG1 and TNFRSF21 genes in downregulation. Besides, 14 genes were common to different SARS-CoV comorbidities that might influence COVID-19 disease. We also observed similarities in pathways that can lead to COVID-19 and SARS-CoV diseases. Finally, protein-chemical interactions suggest cyclosporine, resveratrol and quercetin as promising drug candidates against COVID-19 as well as other SARS-like viral infections. The pathogenetic analyses, along with identified biomarkers, signaling pathways and chemical antagonists, could prove useful for novel drug development in the fight against the current global 2019-nCoV pandemic.

Frequency of Comorbidities in Admitting COVID-19 Pneumonia Patients in a Tertiary Care Setup: An Observational Study

Background

The novel coronavirus disease 2019 (COVID-19) is a highly infectious and pandemic disease with a variable mode of action. Patients with underlying illnesses such as diabetes, hypertension, and other diseases are more prone to infection. An understanding of the different comorbidities that place patients at the highest risk of COVID-19 pneumonia and other fatal complications associated with COVID-19 is necessary for healthcare professionals. This study aimed to determine the frequency of different comorbid illnesses among COVID-19 patients admitted to a tertiary care hospital in Karachi, Pakistan.

Methodology

All patients diagnosed with COVID-19 who required admission for the care of their symptoms were included in this observational, cross-sectional study conducted from May 1 to July 30, 2020. The patients were treated at a specialized COVID-19 isolation ward built at the Dow University of Health Sciences at the Ojha campus. The patients were referred from the emergency department, medical and allied wards, and COVID-19 screening units. A detailed history and clinical examination were performed, and comorbidities were evaluated.

Results

A total of 212 patients were admitted during the study with a mean age of 52 ± 16 years. The study population consisted of 120 (56.6%) males and 92 (43.39%) females, and the most common comorbidities were uncontrolled diabetes with hypertension (n = 56; 26.4%), controlled diabetes (n = 22; 10.37%), obstructive airway disease (n = 16; 7.5%), and interstitial lung disease (n = 14; 6.6%). A total of 48 (22.64%) patients had no comorbidities.

Conclusions

Most COVID-19-positive patients with pneumonia were male, and common comorbidities included uncontrolled diabetes, hypertension, and obstructive and restrictive lung disease. The presence of comorbidities was associated with a marked increase in the risk of morbidity and mortality. Further studies are warranted to confirm these findings.

Novel signaling pathways regulate SARS-CoV and SARS-CoV-2 infectious disease

Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 induces severe infection, and it is responsible for a worldwide disease outbreak starting in late 2019. Currently, there are no effective medications against coronavirus. In the present study, we utilized a holistic bioinformatics approach to study gene signatures of SARS-CoV- and SARS-CoV-2-infected Calu-3 lung adenocarcinoma cells. Through the Gene Ontology platform, we determined that several cytokine genes were up-regulated after SARS-CoV-2 infection, including TNF, IL6, CSF2, IFNL1, IL-17C, CXCL10, and CXCL11. Differentially regulated pathways were detected by the Kyoto Encyclopedia of Genes and Genomes, gene ontology, and Hallmark platform, including chemokines, cytokines, cytokine receptors, cytokine metabolism, inflammation, immune responses, and cellular responses to the virus. A Venn diagram was utilized to illustrate common overlapping genes from SARS-CoV- and SARS-CoV-2-infected datasets. An Ingenuity pathway analysis discovered an enrichment of tumor necrosis factor- (TNF-) and interleukin (IL)-17-related signaling in a gene set enrichment analysis. Downstream networks were predicted by the Database for Annotation, Visualization, and Integrated Discovery platform also revealed that TNF and TNF receptor 2 signaling elicited leukocyte recruitment, activation, and survival of host cells after coronavirus infection. Our discovery provides essential evidence for transcript regulation and downstream signaling of SARS-CoV and SARS-CoV-2 infection.

Clinical and Demographic Characteristics Including Comorbidities and Their Outcomes Among Patients Hospitalized With COVID-19 in Four Tertiary Care Hospitals Across Lahore

Background

The first case of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was diagnosed in Wuhan, China, in 2019. By the first half of 2020, coronavirus disease 2019 (COVID-19) turned into a global pandemic.

Objectives

The aim of this study is to describe the clinical and demographic characteristics including comorbidities and their outcomes among patients hospitalized with COVID-19 in four tertiary care hospitals across Lahore. This retrospective study was conducted at Fatima Memorial Hospital, Sir Ganga Ram Hospital, Lahore General Hospital, and Jinnah Hospital, all in Lahore, Pakistan, from May 1, 2020, to June 30, 2020. The sample size was 445, which was derived using the convenient sampling method. Clinical outcomes during hospitalization included the requirement of invasive positive pressure ventilation, need for renal replacement therapy (RRT), and death. Data regarding demographics, baseline comorbidities, important vital signs on reporting, and initial workup with results were also collected.

Results

A total of 445 patients’ data were studied, of whom 291 (65.4%) were male patients and 154 (34.6%) female patients. The median age was 54 years (interquartile range [IQR]: 24). The most common comorbidities were hypertension (HTN) (195; 43.8%) followed by diabetes mellitus (DM) (168; 37.8%) and cardiovascular disease (CVD) (61; 13.7%). The median length of hospital stay was eight days (IQR: 3). Of the total patients, 137 (30.7%) were treated in intensive care unit settings, 40 (9%) received invasive mechanical ventilation, 40 (9%) patients had acute kidney injury, 38 (8.5%) received RRT, and 37 (8.3%) died. It was seen that more patients who were either diabetic or hypertensive received invasive mechanical ventilation as compared to those who did not have these comorbidities. The most common radiological finding on chest X-ray was the classical ground-glass appearance of COVID-19, which was found in 318 (71.4%) patients.

Conclusions

Patients with one or more underlying comorbidities had poor clinical outcomes compared to those with no comorbidities, with the most vulnerable group being patients with chronic kidney disease, DM, HTN, and CVD in descending order.

Cardiovascular disease in patients with COVID-19: evidence from cardiovascular pathology to treatment

The coronavirus disease-2019 (COVID-19) caused by the novel coronavirus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has rapidly developed into a global pneumonia pandemic. Cardiovascular disease is the major comorbidity of COVID-19 patients and is closely related to the severity of COVID-19. SARS-CoV-2 infection can directly or indirectly cause a series of cardiac complications, including acute myocardial injury and myocarditis, heart failure and cardiac arrest, arrhythmia, acute myocardial infarction, cardiogenic shock, Takotsubo cardiomyopathy, and coagulation abnormalities. Intensive research on the SARS-CoV-2-associated cardiovascular complications is urgently needed to elucidate its exact mechanism and to identify potential drug targets, which will help to formulate effective prevention and treatment strategies. Hence, this review will summarize recent progress regarding the effects of COVID-19 on the cardiovascular system and describe the underlying mechanism of cardiovascular injury caused by SARS-CoV-2.

Comparative transcriptome analysis of SARS-CoV, MERS-CoV, and SARS-CoV-2 to identify potential pathways for drug repurposing

The ongoing COVID-19 pandemic caused by the coronavirus, SARS-CoV-2, has already caused in excess of 1.25 million deaths worldwide, and the number is increasing. Knowledge of the host transcriptional response against this virus and how the pathways are activated or suppressed compared to other human coronaviruses (SARS-CoV, MERS-CoV) that caused outbreaks previously can help in the identification of potential drugs for the treatment of COVID-19. Hence, we used time point meta-analysis to investigate available SARS-CoV and MERS-CoV in-vitro transcriptome datasets in order to identify the significant genes and pathways that are dysregulated at each time point. The subsequent over-representation analysis (ORA) revealed that several pathways are significantly dysregulated at each time point after both SARS-CoV and MERS-CoV infection. We also performed gene set enrichment analyses of SARS-CoV and MERS-CoV with that of SARS-CoV-2 at the same time point and cell line, the results of which revealed that common pathways are activated and suppressed in all three coronaviruses. Furthermore, an analysis of an in-vivo transcriptomic dataset of COVID-19 patients showed that similar pathways are enriched to those identified in the earlier analyses. Based on these findings, a drug repurposing analysis was performed to identify potential drug candidates for combating COVID-19.

How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach

BACKGROUND

When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx.

OBJECTIVE

This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions.

METHODS

We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions.

RESULTS

The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups.

CONCLUSIONS

The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.

Comorbidities in Patients with COVID-19 and Their Impact on the Severity of the Disease

Infection with COVID-19 is associated with significant morbidity, especially in patients with chronic medical conditions. At least one-fifth of cases require supportive care in intensive care units, which have limited availability in most developing countries. A literature search was conducted on PubMed, Medline, Scopus, Embase, and Google Scholar to find articles published by May 7, 2020 on the role of comorbidities in patients with COVID-19 and the impact of comorbidities on the disease. This review highlighted that patients with comorbidities are more likely to experience severe disease than those with no other conditions; that is, comorbidities correlated with greater disease severity in patients with COVID-19. Proper screening of COVID-19 patients should include careful inquiries into their medical history; this will help healthcare providers identify patients who are more likely to develop serious disease or experience adverse outcomes. Better protection should also be given to patients with COVID-19 and comorbidities upon confirmation of the diagnosis. This literature review showed that the comorbidities most often associated with more severe cases of COVID-19 are hypertension, cardiovascular disease, and diabetes. Individuals with these comorbidities should adopt restrictive measures to prevent exposure to COVID-19, given their higher risk of severe disease.

A Testimony of the Surgent SARS-CoV-2 in the Immunological Panorama of the Human Host

The resurgence of SARS in the late December of 2019 due to a novel coronavirus, SARS-CoV-2, has shadowed the world with a pandemic. The physiopathology of this virus is very much in semblance with the previously known SARS-CoV and MERS-CoV. However, the unprecedented transmissibility of SARS-CoV-2 has been puzzling the scientific efforts. Though the virus harbors much of the genetic and architectural features of SARS-CoV, a few differences acquired during its evolutionary selective pressure is helping the SARS-CoV-2 to establish prodigious infection. Making entry into host the cell through already established ACE-2 receptor concerted with the action of TMPRSS2, is considered important for the virus. During the infection cycle of SARS-CoV-2, the innate immunity witnesses maximum dysregulations in its molecular network causing fatalities in aged, comorbid cases. The overt immunopathology manifested due to robust cytokine storm shows ARDS in severe cases of SARS-CoV-2. A delayed IFN activation gives appropriate time to the replicating virus to evade the host antiviral response and cause disruption of the adaptive response as well. We have compiled various aspects of SARS-CoV-2 in relation to its unique structural features and ability to modulate innate as well adaptive response in host, aiming at understanding the dynamism of infection.

Implications of Sex Differences in Immunity for SARS-CoV-2 Pathogenesis and Design of Therapeutic Interventions

Men present more frequently with severe manifestations of coronavirus disease 2019 (COVID-19) and are at higher risk for death. The underlying mechanisms for these differences between female and male individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are insufficiently understood. However, studies from other viral infections have shown that females can mount stronger immune responses against viruses than males. Emerging knowledge on the basic biological pathways that underlie differences in immune responses between women and men needs to be incorporated into research efforts on SARS-CoV-2 pathogenesis and pathology to identify targets for therapeutic interventions aimed at enhancing antiviral immune function and lung airway resilience while reducing pathogenic inflammation in COVID-19.

Coronavirus disease 2019 (COVID-19) outcomes in HIV/AIDS patients: a systematic review

OBJECTIVES

The aim of the study was to systematically review current studies reporting on clinical outcomes in people living with HIV (PLHIV) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

METHODS

We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. A comprehensive literature search was conducted in Global Health, SCOPUS, Medline and EMBASE using pertinent key words and Medical Subject Headings (MeSH) terms relating to coronavirus disease 2019 (COVID-19) and HIV. A narrative synthesis was undertaken. Articles are summarized in relevant sections.

RESULTS

Two hundred and eighty-five articles were identified after duplicates had been removed. After screening, eight studies were analysed, totalling 70 HIV-infected patients (57 without AIDS and 13 with AIDS). Three themes were identified: (1) controlled HIV infection does not appear to result in poorer COVID-19 outcomes, (2) more data are needed to determine COVID-19 outcomes in patients with AIDS and (3) HIV-infected patients presenting with COVID-19 symptoms should be investigated for superinfections.

CONCLUSIONS

Our findings suggest that PLHIV with well-controlled disease are not at risk of poorer COVID-19 disease outcomes than the general population. It is not clear whether those with poorly controlled HIV disease and AIDS have poorer outcomes. Superimposed bacterial pneumonia may be a risk factor for more severe COVID-19 but further research is urgently needed to elucidate whether PLHIV are more at risk than the general population.

Advances in the relationship between coronavirus infection and cardiovascular diseases

The outbreak of coronavirus disease 2019 (COVID-19) has once again aroused people's concern about coronavirus. Seven human coronaviruses (HCoVs) have been discovered so far, including HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU115, severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus and severe acute respiratory syndrome coronavirus 2. Existing studies show that the cardiovascular disease increased the incidence and severity of coronavirus infection. At the same time, myocardial injury caused by coronavirus infection is one of the main factors contributing to poor prognosis. In this review, the recent clinical findings about the relationship between coronaviruses and cardiovascular diseases and the underlying pathophysiological mechanisms are discussed. This review aimed to provide assistance for the prevention and treatment of COVID-19.

Effect of coronavirus infection on the human heart: A scoping review

BACKGROUND

The global coronavirus disease 2019 pandemic has highlighted the importance of understanding the cardiovascular implications of coronavirus infections, with more severe disease in those with cardiovascular co-morbidities, and resulting cardiac manifestations such as myocardial injury, arrhythmias, and heart failure.

DESIGN

A systematic review of the current knowledge on the effects of coronavirus infection on the cardiovascular system in humans was performed and results were summarized.

METHODS

Databases such as MEDLINE, EMBASE, CENTRAL, Scopus, Web of Science, ClinicalTrials.gov, Chinese Knowledge Resource Integrated Database and Chinese Clinical Trial Registry were searched on 20 March 2020.

RESULTS

In total, 135 studies were included, involving severe acute respiratory syndrome, Middle East respiratory syndrome, coronavirus disease 2019 and other coronaviruses. Most were case reports, case series and cohort studies of poor to fair quality. In post-mortem examinations of subjects who died from infection, around half had virus identified in heart tissues in severe acute respiratory syndrome, but none in Middle East respiratory syndrome and coronavirus disease 2019. Cardiac manifestations reported include tachycardia, bradycardia, arrhythmias, and myocardial injury, secondary to both systemic infection and treatment. Cardiac injury and arrhythmias are more prevalent in coronavirus disease 2019, and elevated cardiac markers are associated with intensive care unit admission and death. In severe acute respiratory syndrome, Middle East respiratory syndrome, and coronavirus disease 2019, comorbidities such as hypertension, diabetes mellitus, and heart disease are associated with intensive care unit admission, mechanical ventilation, and mortality. There were cases of misdiagnosis due to overlapping presentations of cardiovascular diseases and coronavirus infections, leading to hospital spread and delayed management of life-threatening conditions.

CONCLUSION

This review highlighted the ways in which coronaviruses affect cardiovascular function and interacts with pre-existing cardiovascular diseases.

Diabetes and metabolic syndrome as risk factors for COVID-19

BACKGROUND AND AIMS

Clinical evidence exists that patients with diabetes are at higher risk for Coronavirus disease 2019 (COVID-19). We investigated the physiological origins of this clinical observation linking diabetes with severity and adverse outcome of COVID-19.

METHODS

Publication mining was applied to reveal common physiological contexts in which diabetes and COVID-19 have been investigated simultaneously. Overall, we have acquired 1,121,078 publications from PubMed in the time span between 01-01-2000 and 17-04-2020, and extracted knowledge graphs interconnecting the topics related to diabetes and COVID-19.

RESULTS

The Data Mining revealed three pathophysiological pathways linking diabetes and COVID-19. The first pathway indicates a higher risk for COVID-19 because of a dysregulation of Angiotensin-converting enzyme 2. The other two important physiological links between diabetes and COVID-19 are liver dysfunction and chronic systemic inflammation. A deep network analysis has suggested clinical biomarkers predicting the higher risk: Hypertension, elevated serum Alanine aminotransferase, high Interleukin-6, and low Lymphocytes count.

CONCLUSIONS

The revealed biomarkers can be applied directly in clinical practice. For newly infected patients, the medical history needs to be checked for evidence of a long-term, chronic dysregulation of these biomarkers. In particular, patients with diabetes, but also those with prediabetic state, deserve special attention.

Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach

Emerging evidence indicates IBD is a risk factor for the increasing incidence of colorectal cancer (CRC) development. We used a system biology approach to identify common molecular signatures and pathways that interact between IBD and CRC and the indispensable pathological mechanisms. First, we identified 177 common differentially expressed genes (DEGs) between IBD and CRC. Gene set enrichment, protein-protein, DEGs-transcription factors, DEGs-microRNAs, protein-drug interaction, gene-disease association, Gene Ontology, pathway enrichment analyses were conducted to these common genes. The inclusion of common DEGs with bimolecular networks disclosed hub proteins (LYN, PLCB1, NPSR1, WNT5A, CDC25B, CD44, RIPK2, ASAP1), transcription factors (SCD, SLC7A5, IKZF3, SLC16A1, SLC7A11) and miRNAs (mir-335-5p, mir-26b-5p, mir-124-3p, mir-16-5p, mir-192-5p, mir-548c-3p, mir-29b-3p, mir-155-5p, mir-21-5p, mir-15a-5p). Analysis of the interaction between protein and drug discovered ASAP1 interacts with cysteine sulfonic acid and double oxidized cysteine drug compounds. Gene-disease association analysis retrieved ASAP1 also associated with pulmonary and bladder neoplasm diseases.

Why does COVID-19 disproportionately affect older people?

The severity and outcome of coronavirus disease 2019 (COVID-19) largely depends on a patient's age. Adults over 65 years of age represent 80% of hospitalizations and have a 23-fold greater risk of death than those under 65. In the clinic, COVID-19 patients most commonly present with fever, cough and dyspnea, and from there the disease can progress to acute respiratory distress syndrome, lung consolidation, cytokine release syndrome, endotheliitis, coagulopathy, multiple organ failure and death. Comorbidities such as cardiovascular disease, diabetes and obesity increase the chances of fatal disease, but they alone do not explain why age is an independent risk factor. Here, we present the molecular differences between young, middle-aged and older people that may explain why COVID-19 is a mild illness in some but life-threatening in others. We also discuss several biological age clocks that could be used in conjunction with genetic tests to identify both the mechanisms of the disease and individuals most at risk. Finally, based on these mechanisms, we discuss treatments that could increase the survival of older people, not simply by inhibiting the virus, but by restoring patients' ability to clear the infection and effectively regulate immune responses.

Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis

Background

The coronavirus disease 2019 (COVID-19) outbreak is evolving rapidly worldwide.

Objective

To evaluate the risk of serious adverse outcomes in patients with COVID-19 by stratifying the comorbidity status.

Methods

We analysed data from 1590 laboratory confirmed hospitalised patients from 575 hospitals in 31 provinces/autonomous regions/provincial municipalities across mainland China between 11 December 2019 and 31 January 2020. We analysed the composite end-points, which consisted of admission to an intensive care unit, invasive ventilation or death. The risk of reaching the composite end-points was compared according to the presence and number of comorbidities.

Results

The mean age was 48.9 years and 686 (42.7%) patients were female. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached the composite end-points. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD (HR (95% CI) 2.681 (1.424–5.048)), diabetes (1.59 (1.03–2.45)), hypertension (1.58 (1.07–2.32)) and malignancy (3.50 (1.60–7.64)) were risk factors of reaching the composite end-points. The hazard ratio (95% CI) was 1.79 (1.16–2.77) among patients with at least one comorbidity and 2.59 (1.61–4.17) among patients with two or more comorbidities.

Conclusion

Among laboratory confirmed cases of COVID-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.

The presence and number of comorbidities predict clinical outcomes of COVID-19http://bit.ly/3b9ibw5

Scoping review of prevalence of neurologic comorbidities in patients hospitalized for COVID-19

OBJECTIVE

The emergence of coronavirus disease 2019 (COVID-19) presents a challenge for neurologists caring for patients with preexisting neurologic conditions hospitalized for COVID-19 or for evaluation of patients who have neurologic complications during COVID-19 infection. We conducted a scoping review of the available literature on COVID-19 to assess the potential effect on neurologists in terms of prevalent comorbidities and incidence of new neurologic events in patients hospitalized with COVID-19.

METHODS

We searched MEDLINE/PubMed, CINAHL (EBSCO), and Scopus databases for adult patients with preexisting neurologic disease who were diagnosed and hospitalized for COVID-19 or reported incidence of secondary neurologic events following diagnosis of COVID-19. Pooled descriptive statistics of clinical data and comorbidities were examined.

RESULTS

Among screened articles, 322 of 4,014 (8.0%) of hospitalized patients diagnosed and treated for COVID-19 had a preexisting neurologic illness. Four retrospective studies demonstrated an increased risk of secondary neurologic complications in hospitalized patients with COVID-19 (incidence of 6%, 20%, and 36.4%, respectively). Inconsistent reporting and limited statistical analysis among these studies did not allow for assessment of comparative outcomes.

CONCLUSION

Emerging literature suggests a daunting clinical relationship between COVID-19 and neurologic illness. Neurologists need to be prepared to reorganize their consultative practices to serve the neurologic needs of patients during this pandemic.

Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis

BACKGROUND

The coronavirus disease 2019 (COVID-19) outbreak is evolving rapidly worldwide.

OBJECTIVE

To evaluate the risk of serious adverse outcomes in patients with COVID-19 by stratifying the comorbidity status.

METHODS

We analysed data from 1590 laboratory confirmed hospitalised patients from 575 hospitals in 31 provinces/autonomous regions/provincial municipalities across mainland China between 11 December 2019 and 31 January 2020. We analysed the composite end-points, which consisted of admission to an intensive care unit, invasive ventilation or death. The risk of reaching the composite end-points was compared according to the presence and number of comorbidities.

RESULTS

The mean age was 48.9 years and 686 (42.7%) patients were female. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached the composite end-points. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD (HR (95% CI) 2.681 (1.424-5.048)), diabetes (1.59 (1.03-2.45)), hypertension (1.58 (1.07-2.32)) and malignancy (3.50 (1.60-7.64)) were risk factors of reaching the composite end-points. The hazard ratio (95% CI) was 1.79 (1.16-2.77) among patients with at least one comorbidity and 2.59 (1.61-4.17) among patients with two or more comorbidities.

CONCLUSION

Among laboratory confirmed cases of COVID-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.

Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma

Molecular mechanisms underlying the pathogenesis and progression of malignant thyroid cancers, such as follicular thyroid carcinomas (FTCs), and how these differ from benign thyroid lesions, are poorly understood. In this study, we employed network-based integrative analyses of FTC and benign follicular thyroid adenoma (FTA) lesion transcriptomes to identify key genes and pathways that differ between them. We first analysed a microarray gene expression dataset (Gene Expression Omnibus GSE82208, n = 52) obtained from FTC and FTA tissues to identify differentially expressed genes (DEGs). Pathway analyses of these DEGs were then performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) resources to identify potentially important pathways, and protein-protein interactions (PPIs) were examined to identify pathway hub genes. Our data analysis identified 598 DEGs, 133 genes with higher and 465 genes with lower expression in FTCs. We identified four significant pathways (one carbon pool by folate, p53 signalling, progesterone-mediated oocyte maturation signalling, and cell cycle pathways) connected to DEGs with high FTC expression; eight pathways were connected to DEGs with lower relative FTC expression. Ten GO groups were significantly connected with FTC-high expression DEGs and 80 with low-FTC expression DEGs. PPI analysis then identified 12 potential hub genes based on degree and betweenness centrality; namely, TOP2A, JUN, EGFR, CDK1, FOS, CDKN3, EZH2, TYMS, PBK, CDH1, UBE2C, and CCNB2. Moreover, transcription factors (TFs) were identified that may underlie gene expression differences observed between FTC and FTA, including FOXC1, GATA2, YY1, FOXL1, E2F1, NFIC, SRF, TFAP2A, HINFP, and CREB1. We also identified microRNA (miRNAs) that may also affect transcript levels of DEGs; these included hsa-mir-335-5p, -26b-5p, -124-3p, -16-5p, -192-5p, -1-3p, -17-5p, -92a-3p, -215-5p, and -20a-5p. Thus, our study identified DEGs, molecular pathways, TFs, and miRNAs that reflect molecular mechanisms that differ between FTC and benign FTA. Given the general similarities of these lesions and common tissue origin, some of these differences may reflect malignant progression potential, and include useful candidate biomarkers for FTC and identifying factors important for FTC pathogenesis.