Knowledge domain and emerging trends in HIV-MTB co-infection from 2017 to 2022: A scientometric analysis based on VOSviewer and CiteSpace.

Co-infection with Mycobacterium tuberculosis (MTB) in human immunodeficiency virus (HIV)-infected individuals is one of the leading causes of death. Also, research on HIV and MTB (HIV-MTB) co-infection was found to have a downward trend. In this work, we performed the knowledge domain analysis and visualized the current research progress and emerging trends in HIV-MTB co-infection between 2017 and 2022 by using VOSviewer and CiteSpace. The relevant literatures in this article were collected in the Web of Science (WoS) database. VOSviewer and CiteSpace bibliometric software were applied to perform the analysis and visualization of scientific productivity and frontier. Among all the countries, USA was dominant in the field, followed by South Africa, and England. Among all the institutions, the University of Cape Town (South Africa) had more extensive collaborations with other research institutions. The Int J Tuberc Lung Dis was regarded as the foremost productive journal. Survival and mortality analysis, pathogenesis, epidemiological studies, diagnostic methods, prognosis improvement of quality of life, clinical studies and multiple infections (especially co-infection with COVID-19) resulted in the knowledge bases for HIV-MTB co-infection. The clinical research on HIV-MTB co-infection has gradually shifted from randomized controlled trials to open-label trials, while the cognition of HIV-TB has gradually shifted from cytokines to genetic polymorphisms. This scientometric study used quantitative and qualitative methods to conduct a comprehensive review of research on HIV-MTB co-infection published over the past 5 years, providing some useful references to further the study of HIV-MTB co-infection.

Immunoinformatic-Based Multi-Epitope Vaccine Design for Co-Infection of Mycobacterium tuberculosis and SARS-CoV-2.

(1) Background: Many co-infections of Mycobacterium tuberculosis (MTB) and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have emerged since the occurrence of the SARS-CoV-2 pandemic. This study aims to design an effective preventive multi-epitope vaccine against the co-infection of MTB and SARS-CoV-2. (2) Methods: The three selected proteins (spike protein, diacylglycerol acyltransferase, and low molecular weight T-cell antigen TB8.4) were predicted using bioinformatics, and 16 epitopes with the highest ranks (10 helper T lymphocyte epitopes, 2 CD8+ T lymphocytes epitopes, and 4 B-cell epitopes) were selected and assembled into the candidate vaccine referred to as S7D5L4. The toxicity, sensitization, stability, solubility, antigenicity, and immunogenicity of the S7D5L4 vaccine were evaluated using bioinformatics tools. Subsequently, toll-like receptor 4 docking simulation and discontinuous B-cell epitope prediction were performed. Immune simulation and codon optimization were carried out using immunoinformatics and molecular biology tools. (3) Results: The S7D5L4 vaccine showed good physical properties, such as solubility, stability, non-sensitization, and non-toxicity. This vaccine had excellent antigenicity and immunogenicity and could successfully simulate immune responses in silico. Furthermore, the normal mode analysis of the S7D5L4 vaccine and toll-like receptor 4 docking simulation demonstrated that the vaccine had docking potential and a stable reaction. (4) Conclusions: The S7D5L4 vaccine designed to fight against the co-infection of MTB and SARS-CoV-2 may be safe and effective. The protective efficacy of this promising vaccine should be further verified using in vitro and in vivo experiments.

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.

BACKGROUND: Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014-2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. METHODS: Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, "caret" R package, "e1071" R package and "Tensorflow" Python package, respectively. RESULTS: Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83-93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. CONCLUSIONS: Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

Immunobiology of tubercle bacilli and prospects of immunomodulatory drugs to tackle tuberculosis (TB) and other non-tubercular mycobacterial infections.

The COVID-19 pandemic has set back progress made on antimicrobial resistance (AMR). Without urgent re-focus, we risk slowing down drug discovery and providing treatment for drug resistant Mycobacterium tuberculosis. Unique in its immune evasion, dormancy and resuscitation, the causal pathogens of tuberculosis (TB) have demonstrated resistance to antibiotics with efflux pumps and the ability to form biofilms. Repurposing drugs is a prospective avenue for finding new anti-TB drugs. There are many advantages to discovering novel targets of an existing drug, as the pharmacokinetic and pharmacodynamic properties have already been established, they are cost-efficient and can be commercially accelerated for the new development. One such group of drugs are non-steroidal anti-inflammatory drugs (NSAIDs) that are originally known for their ability to supress the host proinflammatory responses. In addition to their anti-inflammatory properties, some NSAIDs have been discovered to have antimicrobial modes of action. Of particular interest is Carprofen, identified to inhibit the efflux mechanism and disrupt biofilm formation in mycobacteria. Due to the complexities of host-pathogens interactions in the lung microbiome, inflammatory responses must carefully be controlled alongside the in vivo actions of the prospective anti-infectives. This critical review explores the potential dual role of a selection of NSAIDs, as an anti-inflammatory and anti-tubercular adjunct to reverse the tide of antimicrobial resistance in existing treatments.

Mycogenic Metal Nanoparticles for the Treatment of Mycobacterioses.

Mycobacterial infections are a resurgent and increasingly relevant problem. Within these, tuberculosis (TB) is particularly worrying as it is one of the top ten causes of death in the world and is the infectious disease that causes the highest number of deaths. A further concern is the on-going emergence of antimicrobial resistance, which seriously limits treatment. The COVID-19 pandemic has worsened current circumstances and future infections will be more incident. It is urgent to plan, draw solutions, and act to mitigate these issues, namely by exploring new approaches. The aims of this review are to showcase the extensive research and application of silver nanoparticles (AgNPs) and other metal nanoparticles (MNPs) as antimicrobial agents. We highlight the advantages of mycogenic synthesis, and report on their underexplored potential as agents in the fight against all mycobacterioses (non-tuberculous mycobacterial infections as well as TB). We propose further exploration of this field.