Abstract. Far-infrared (FIR) is a form of thermal radiation, which may have beneficial effects on cardiovascular health. Clinical studies suggest that FIR irradiation may have therapeutic effects in heart failure, myocardial ischaemia and may improve flow and survival of arteriovenous fistula. Animal studies have suggested a wide range of potential mechanisms involving endothelial nitric oxide synthase and nitric oxide bioavailability, oxidative stress, heat shock proteins and endothelial precursor cells. However, the exact cellular and molecular mechanism of FIR on the cardiovascular system remains elusive. The purpose of this review is to discuss the current literature, focusing on mechanistic studies involving the cardiovascular system, and with a view to highlighting areas for future investigation.

Objectives To clarify the work done by using AI for identifying the genomic sequences, development of drugs and vaccines for COVID-19 and to recognize the advantages and challenges of using such technology. Methods A non-systematic review was done. All articles published on Pub-Med, Medline, Google, and Google Scholar on AI or digital health regarding genomic sequencing, drug development, and vaccines of COVID-19 were scrutinized and summarized. Results The sequence of SARS- CoV-2 was identified with the help of AI. It can help also in the prompt identification of variants of concern (VOC) as delta strains and Omicron. Furthermore, there are many drugs applied with the help of AI. These drugs included Atazanavir, Remdesivir, Efavirenz, Ritonavir, and Dolutegravir, PARP1 inhibitors (Olaparib and CVL218 which is Mefuparib hydrochloride), Abacavir, Roflumilast, Almitrine, and Mesylate. Many vaccines were developed utilizing the new technology of bioinformatics, databases, immune-informatics, machine learning, and reverse vaccinology to the whole SARS-CoV-2 proteomes or the structural proteins. Examples of these vaccines are the messenger RNA and viral vector vaccines. AI provides cost-saving and agility. However, the challenges of its usage are the difficulty of collecting data, the internal and external validation, ethical consideration, therapeutic effect, and the time needed for clinical trials after drug approval. Moreover, there is a common problem in the deep learning (DL) model which is the shortage of interpretability. Conclusion The growth of AI techniques in health care opened a broad gate for discovering the genomic sequences of the COVID-19 virus and the VOC. AI helps also in the development of vaccines and drugs (including drug repurposing) to obtain potential preventive and therapeutic agents) for controlling the COVID-19 pandemic.

Tuberculosis (TB) is the leading cause of death from a single infectious agent. The estimated total global TB deaths in 2019 were 1.4 million. The decline in TB incidence rate is very slow, while the burden of noncommunicable diseases (NCDs) is exponentially increasing in low- and middle-income countries, where the prevention and treatment of TB disease remains a great burden, and there is enough empirical evidence (scientific evidence) to justify a greater research emphasis on the syndemic interaction between TB and NCDs. The current study was proposed to build a disease-gene network based on overlapping TB with NCDs (overlapping means genes involved in TB and other/s NCDs), such as Parkinson’s disease, cardiovascular disease, diabetes mellitus, rheumatoid arthritis, and lung cancer. We compared the TB-associated genes with genes of its overlapping NCDs to determine the gene-disease relationship. Next, we constructed the gene interaction network of disease-genes by integrating curated and experimentally validated interactions in humans and find the 13 highly clustered modules in the network, which contains a total of 86 hub genes that are commonly associated with TB and its overlapping NCDs, which are largely involved in the Inflammatory response, cellular response to cytokine stimulus, response to cytokine, cytokine-mediated signaling pathway, defense response, response to stress and immune system process. Moreover, the identified hub genes and their respective drugs were exploited to build a bipartite network that assists in deciphering the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drugs combination or drug repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs, and give a synergistic effect with better outcomes. Thus, understanding the Mycobacterium tuberculosis (Mtb) infection and associated NCDs is a high priority to contain its short and long-term effects on human health. Our network-based analysis opens a new horizon for more personalized treatment, drug-repurposing opportunities, investigates new targets, multidrug treatment, and can uncover several side effects of unrelated drugs for TB and its overlapping NCDs.

The renin–angiotensin system (RAS) plays a pivotal role in blood pressure regulation. In some cases, this steering mechanism is affected by various deleterious factors (mainly via the overactivation of the RAS) causing cardiovascular damage, including coronary heart disease (CHD) that can ultimately lead to chronic heart failure (CHF). This not only causes cardiovascular disability and absenteeism from work but also imposes significant healthcare costs globally. The incidence of cardiovascular diseases has escalated exponentially over the years with the major outcome in the form of CHD, stroke, and CHF. The involvement of the RAS in various diseases has been extensively researched with significant limelight on CHD. The RAS may trigger a cascade of events that lead to atherosclerotic mayhem, which causes CHD and related aggravation by damaging the endothelial lining of blood vessels via various inflammatory and oxidative stress pathways. Although there are various diagnostic tests and treatments available in the market, there is a constant need for the development of procedures and therapeutic strategies that increase patient compliance and reduce the associated side effects. This review highlights the advances in the diagnostic and treatment domains for CHD, which would help in subjugating the side effects caused by conventional therapy.

In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.

Sepsis is a clinical syndrome with high mortality and morbidity rates. In sepsis, the abrupt release of cytokines by the innate immune system may cause multiorgan failure, leading to septic shock and associated complications. In the presence of a number of systemic disorders, such as sepsis, infections, diabetes, and systemic lupus erythematosus (SLE), cardiorenal syndrome (CRS) type 5 is defined by concomitant cardiac and renal dysfunctions Thus, our study suggests that certain mRNAs and unexplored pathways may pave a way to unravel critical therapeutic targets in three debilitating and interrelated illnesses, namely, sepsis, SLE, and CRS. Sepsis, SLE, and CRS are closely interrelated complex diseases likely sharing an overlapping pathogenesis caused by erroneous gene network activities. We sought to identify the shared gene networks and the key genes for sepsis, SLE, and CRS by completing an integrative analysis. Initially, 868 DEGs were identified in 16 GSE datasets. Based on degree centrality, 27 hub genes were revealed. The gProfiler webtool was used to perform functional annotations and enriched molecular pathway analyses. Finally, core hub genes (EGR1, MMP9, and CD44) were validated using RT-PCR analysis. Our comprehensive multiplex network approach to hub gene discovery is effective, as evidenced by the findings. This work provides a novel research path for a new research direction in multi-omics biological data analysis.

Pentalogy of Cantrell (PC) is a rare congenital anomaly involving defects in the anterior diaphragm, supraumbilical abdominal wall, diaphragmatic pericardium, and lower sternum, and other congenital intracardiac abnormalities. Here, we report the case of a newborn infant who was born at 32 weeks of gestation and had all 5 features of PC, in addition to absent kidneys and a deformed left hand. Medical intervention would not be able to save the patient, so we allowed her to die in peace. We discuss here the etiology, prenatal diagnosis, and severity of and the mortality associated with this condition. To our knowledge, this was the first reported case of PC in Saudi Arabia.

Objective: To present various types of vaccines and viral infections which can induce cross-reactive immunity against COVID-19. In addition, this article discusses the role of herd immunity and convalescent serum therapy in preventing and controlling SARS CoV-2. The study also determined the claims and counterclaims about their protective and therapeutic effects. Method: Non-systematic review was done using different articles done on cross-reactive immunity against COVID-19 through vaccinations, previous infections, herd immunity and the therapeutic effects of convalescence serum. The search was done on the PubMed, Google Scholar, and Science Direct, WHO, Euro-surveillance, CDC databases.   Results: Many observational correlational studies reported that BCG decreases the incidence and mortality from COVID-19. Furthermore, homology between the COVID-19 virus and the measles, mumps, and rubella (MMR) viruses was discovered. Few studies suggested the presence of cross-immunity between MMR vaccine and SARS-CoV-2. Similarly, few studies suggested protective effects of Oral Polio Vaccine (OPV) against SARS-CoV-2; since both viruses are positive-single-strand RNA (+ssRNA). Diphtheria, pertussis, and tetanus (DPT) vaccines, particularly those that include inactivated whole pertussis vaccine, might induce B and T cell cross-reactive immunity against SARS-CoV-2. Other vaccines against Streptococcus pneumonia, Haemophilus influenza, and Meningococcal meningitis vaccines are suggested also to induce some immunity against Covid-19.  It is hypothesized that infections with other Coronaviruses may cause protection against SARS-CoV-2. However, the studies done on these suggestions were mostly observational that can carry a high chance of inherent biases. There are also claims and counterclaims about the effect of herd immunity and convalescence serum on the prevention and control of Covid-19. So, appropriately designed RCTs are needed to prove or disprove their protective and therapeutic effects. Conclusions: There are claims and counterclaims about the protective effects of different vaccines, previous infections, and herd immunity and regarding the therapeutic effects of convalescence serum. Comparing with other vaccines, BCG was suggested to have the highest cross-reactive epitopes against SARS-Cov-2 virus. MMR, OPV, DPT, Influenza, Pneumococcal and meningococcal vaccines are suggested to protect against Covid-19. Previous infection with other Corona viruses, herd immunity and convalescence serum may play roles in the prevention and control of Covid-19. Many large clinical trials are undergoing nowadays and their results are needed to prove or disprove the cross-immunity related to SARS-CoV-2 and the effect of convalescence serum.