Are mRNA based transcriptomic signatures ready for diagnosing tuberculosis in the clinic? - A review of evidence and the technological landscape

Advancement in diagnostic approaches for latent tuberculosis: distinguishing recent from remote infections

Tuberculosis (TB) remains as a significant global health threat to date, with latent TB infection (LTBI) serving as a major reservoir for future active disease cases. A practical approach to an effective control and eradication of TB hence, requires an explicit identification of infected patient whom are at high risk of progressing from latent to active TB, particularly in those recently infected individuals. Current diagnostic tools however, including Tuberculin Skin Test and Interferon-Gamma Release Assays, are still lacking for their ability to critically distinguish between recent and remote infections, leading to insufficiency in optimizing targeted preventive treatment strategies. This review examines the limitations of current diagnostic tools and explores novel biomarkers to enhance distinction within the infection timeline in LTBI diagnostics. Advancement in immune profiling, dormancy antigen, along with molecular and transcriptomic approaches holds great promise to develop a diagnostic tools with better accuracy to differentiate recent from remote infections, thereby optimizing targeted interventions to improve TB control strategies. These underscores the need for further research into these emerging diagnostic tools to facilitate an effective public health strategies and contribute to the united efforts in End TB Strategy.

Assessing the risk of TB progression: Advances in blood-based biomarker research

This review addresses the significant advancements in the identification of blood-based prognostic biomarkers for tuberculosis (TB), highlighting the importance of early detection to prevent disease progression. The manuscript discusses various biomarker categories, including transcriptomic, proteomic, metabolomic, immune cell-based, cytokine-based, and antibody response-based markers, emphasizing their potential in predicting TB incidence. Despite promising results, practical application is hindered by high costs, technical complexities, and the need for extensive validation across diverse populations. Transcriptomic biomarkers, such as the Risk16 signature, show high sensitivity and specificity, while proteomic and metabolic markers provide insights into protein-level changes and biochemical alterations linked to TB. Immune cell and cytokine markers offer real-time data on the body's response to infection. The manuscript also explores the role of single-nucleotide polymorphisms in TB susceptibility and the challenges of implementing novel RNA signatures as point-of-care tests in low-resource settings. The review concludes that, while significant progress has been made, further research and development are necessary to refine these biomarkers, improve their practical application, and achieve the World Health Organization's TB elimination goals.

Backtranslation of human RNA biosignatures of tuberculosis disease risk into the preclinical pipeline is condition dependent

It is unclear whether human progression to active tuberculosis disease (TB) risk signatures are viable endpoint criteria for evaluations of treatments in development. TB is the deadliest infectious disease globally and more efficacious vaccines are needed to reduce this mortality. However, the immune correlates of protection for either preventing infection with Mycobacterium tuberculosis or preventing TB disease have yet to be completely defined, making the advancement of candidate vaccines through the pipeline slow, costly, and fraught with risk. Human-derived correlate of risk (COR) gene signatures, which identify an individual's risk of progressing to active TB disease, provide an opportunity for evaluating new therapies for TB with clear and defined endpoints. Though prospective clinical trials with longitudinal sampling are prohibitively expensive, the characterization of COR gene signatures is practical with preclinical models. Using a 3Rs (replacement, reduction, and refinement) approach we reanalyzed heterogeneous publicly available transcriptional data sets to determine whether a specific set of COR signatures are viable endpoints in the preclinical pipeline. We selected RISK6, Sweeney3, and BATF2 human-derived blood-based RNA biosignatures because they require relatively few genes and have been carefully evaluated across several clinical cohorts. These data suggest that in certain experimental designs and in several tissue types, human COR signatures correlate with disease progression as measured by the bacterial burden in the preclinical TB model pipeline. We observed the best performance when the model most closely reflected human infection or disease conditions. Human-derived COR signatures offer an opportunity for high-throughput preclinical endpoint criteria of vaccine and drug therapy evaluations.

IMPORTANCE

Understanding the strengths or limitations of back-translating human-derived correlate of risk (COR) RNA signatures into the preclinical pipeline may help streamline down-selection of therapeutic vaccine and drug candidates and better align preclinical models with proposed clinical trial efficacy endpoints.

Artificial intelligence in tuberculosis: a new ally in disease control

The challenges to effective tuberculosis (TB) disease control are considerable, and the current global targets for reductions in disease burden seem unattainable. The combination of complex pathophysiology and technical limitations results in difficulties in achieving consistent, reliable diagnoses, and long treatment regimens imply serious physiological and socioeconomic consequences for patients. Artificial intelligence (AI) applications in healthcare have significantly improved patient care regarding diagnostics, treatment and basic research. However, their success relies on infrastructures prioritising comprehensive data generation and collaborative research environments to foster stakeholder engagement. This viewpoint article briefly outlines the current and potential applications of advanced AI models in global TB control and the considerations and implications of adopting these tools within the public health community.

Combination of exhaled breath condensate samples with lipoarabinomannan point of care assay for pulmonary tuberculosis (TB) diagnosis: protocol for a diagnostic accuracy study

INTRODUCTION

The WHO estimates a gap of about 30% between the incident (10.6 million) and notified (7.5 million) cases of tuberculosis (TB). Combined with the growing recognition in prevalence surveys of the high proportion of cases identified who are asymptomatic or paucisymptomatic, these data underscore how current symptom screening approaches and use of diagnostic tests with suboptimal performance on sputum miss large numbers of cases. Thus, the development of sputum-free biomarker-based tests for diagnosis is becoming necessary, which the WHO has already identified as a priority for new TB diagnostics.The objective of this study is to evaluate a combination of exhaled breath condensate (EBC) samples and mycobacterial lipoarabinomannan (LAM) as point-of-care (POC) assays to identify TB patients.

METHODS AND ANALYSIS

This prospective diagnostic accuracy study is conducted at the TB Screening and Treatment Centre of International Center for Diarrhoeal Disease Research, Bangladesh, on a cohort of adults and adolescents >11 years of age. A total of 614 individuals with presumptive pulmonary TB based on TB signs, symptoms and radiography are being recruited from 28 August 2023. Spot sputum is collected for standard reference testing (L-J culture, GeneXpert MTB/Rif, acid-fast Bacilli microscopy) to fine-tune categorisation of TB disease status for each participant, defined as (1) definite TB (at least one positive standard reference test); (2) probable TB (not microbiologically confirmed but under TB treatment); (3) possible TB (no TB treatment but signs, symptoms and radiography suggestive of TB); (4) other respiratory disease (microbiologically not confirmed and no radiography presenting abnormalities compatible with TB); and (5) unknown (no microbiological evidence with normal/no TB abnormalities with radiography). Urine and EBC specimens will be subjected to LAM POC testing and biobanked for further investigation. Statistical analyses will include an assessment of diagnostic accuracy by constructing receiver operating curves and calculating sensitivity and specificity, as well as post-test probabilities.

ETHICS AND DISSEMINATION

The study protocol was approved by the Research Review Committee as well as the Ethical Review Committee of icddr,b and recorded under a protocol reference number, PR-2301. Results will be submitted to open-access peer-reviewed journals, presented at academic meetings, and shared with national and international policymaking bodies.

Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

BACKGROUND

Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression.

METHODS

We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics.

RESULTS

Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822-0.972]; DB from DV with AUC of 0.825 [0.691-0.959] (signature-1) and 0.867 [0.753-0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787-0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808-1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%).

CONCLUSIONS

Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.

Blood transcriptomic signatures for symptomatic tuberculosis in an African multicohort study

BACKGROUND

Multiple host blood transcriptional signatures have been developed as non-sputum triage tests for tuberculosis (TB). We aimed to compare the diagnostic performance of 20 blood transcriptomic TB signatures for differentiating between symptomatic patients who have TB versus other respiratory diseases (ORD).

METHODS

As part of a nested case-control study, individuals presenting with respiratory symptoms at primary healthcare clinics in Ethiopia, Malawi, Namibia, Uganda, South Africa and The Gambia were enrolled. TB was diagnosed based on clinical, microbiological and radiological findings. Transcriptomic signatures were measured in whole blood using microfluidic real-time quantitative PCR. Diagnostic performance was benchmarked against the World Health Organization Target Product Profile (TPP) for a non-sputum TB triage test.

RESULTS

Among 579 participants, 158 had definite, microbiologically confirmed TB, 32 had probable TB, while 389 participants had ORD. Nine signatures differentiated between ORD and TB with equivalent performance (Satproedprai7: area under the curve 0.83 (95% CI 0.79-0.87); Jacobsen3: 0.83 (95% CI 0.79-0.86); Suliman2: 0.82 (95% CI 0.78-0.86); Roe1: 0.82 (95% CI 0.78-0.86); Kaforou22: 0.82 (95% CI 0.78-0.86); Sambarey10: 0.81 (95% CI 0.77-0.85); Duffy9: 0.81 (95% CI 0.76-0.86); Gliddon3: 0.8 (95% CI 0.75-0.85); Suliman4 0.79 (95% CI 0.75-0.84)). Benchmarked against a 90% sensitivity, these signatures achieved specificities between 44% (95% CI 38-49%) and 54% (95% CI 49-59%), not meeting the TPP criteria. Signature scores significantly varied by HIV status and country. In country-specific analyses, several signatures, such as Satproedprai7 and Penn-Nicholson6, met the minimal TPP criteria for a triage test in Ethiopia, Malawi and South Africa.

CONCLUSION

No signatures met the TPP criteria in a pooled analysis of all countries, but several signatures met the minimum criteria for a non-sputum TB triage test in some countries.

A computational framework to improve cross-platform implementation of transcriptomics signatures

The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications.

Backtranslation of human RNA biosignatures of tuberculosis disease risk into the preclinical pipeline is condition dependent

It is not clear whether human progression to active tuberculosis disease (TB) risk signatures are viable endpoint criteria for evaluations of treatments in clinical or preclinical development. TB is the deadliest infectious disease globally and more efficacious vaccines are needed to reduce this mortality. However, the immune correlates of protection for either preventing infection with Mycobacterium tuberculosis or preventing TB disease have yet to be completely defined, making the advancement of candidate vaccines through the pipeline slow, costly, and fraught with risk. Human-derived correlate of risk (COR) gene signatures, which identify an individual's risk to progressing to active TB disease, provide an opportunity for evaluating new therapies for TB with clear and defined endpoints. Though prospective clinical trials with longitudinal sampling are prohibitively expensive, characterization of COR gene signatures is practical with preclinical models. Using a 3Rs (Replacement, Reduction and Refinement) approach we reanalyzed heterogeneous publicly available transcriptional datasets to determine whether a specific set of COR signatures are viable endpoints in the preclinical pipeline. We selected RISK6, Sweeney3 and BATF2 human-derived blood-based RNA biosignatures because they require relatively few genes to assign a score and have been carefully evaluated across several clinical cohorts. Excitingly, these data provide proof-of-concept that human COR signatures seem to have high fidelity across several tissue types in the preclinical TB model pipeline and show best performance when the model most closely reflected human infection or disease conditions. Human-derived COR signatures offer an opportunity for high-throughput preclinical endpoint criteria of vaccine and drug therapy evaluations.

One Sentence Summary

Human-derived biosignatures of tuberculosis disease progression were evaluated for their predictive fidelity across preclinical species and derived tissues using available public data sets.

State-of-the-art detection of Mycobacterium tuberculosis in blood during tuberculosis infection using phage technology

Tuberculosis (TB), an aerosol-transmitted infection caused by Mycobacterium tuberculosis (Mtb), remains the commonest cause of death globally, from an infectious bacterial disease. Nine years on from the launch of the World Health Organization (WHO)'s END-TB strategy, disease incidence rates are stubbornly unchanged [1]. While this represents, in part, a reversal of improving trends caused by the COVID-19 pandemic, it also reflects the fragility and inadequacy of healthcare systems to sustain TB control [2]. Although multifactorial, a key reason for this is the ineffectiveness of existing clinical tools to meet the two key objectives of the END-TB strategy-(i) early diagnosis and treatment of TB disease (to limit onward transmission); and (ii) disease prevention through screening for asymptomatic TB infection (TBI). Meeting both objectives will rely on the development of new biomarkers with high accuracy, but the global nature of the TB problem also requires that new tests are rapid, low cost and can be measured in patients by sampling from universally accessible sites. In this review, we will present the accumulating evidence for circulating Mtb in both TB disease and asymptomatic TBI and discuss the potential utility of novel bacteriophage-based technology for blood-based detection of Mtb.

An integrative multi-omics approach to characterize interactions between tuberculosis and diabetes mellitus

Tuberculosis-diabetes mellitus (TB-DM) is linked to a distinct inflammatory profile, which can be assessed using multi-omics analyses. Here, a machine learning algorithm was applied to multi-platform data, including cytokines and gene expression in peripheral blood and eicosanoids in urine, in a Brazilian multi-center TB cohort. There were four clinical groups: TB-DM(n = 24), TB only(n = 28), DM(HbA1c ≥ 6.5%) only(n = 11), and a control group of close TB contacts who did not have TB or DM(n = 13). After cross-validation, baseline expression or abundance of MMP-28, LTE-4, 11-dTxB2, PGDM, FBXO6, SECTM1, and LINCO2009 differentiated the four patient groups. A distinct multi-omic-derived, dimensionally reduced, signature was associated with TB, regardless of glycemic status. SECTM1 and FBXO6 mRNA levels were positively correlated with sputum acid-fast bacilli grade in TB-DM. Values of the biomarkers decreased during the course of anti-TB therapy. Our study identified several markers associated with the pathophysiology of TB-DM that could be evaluated in future mechanistic investigations.

Activin A levels are raised during human tuberculosis and blockade of the activin signaling axis influences murine responses to M. tuberculosis infection

Activin A strongly influences immune responses; yet, few studies have examined its role in infectious diseases. We measured serum activin A levels in two independent tuberculosis (TB) patient cohorts and in patients with pneumonia and sarcoidosis. Serum activin A levels were increased in TB patients compared to healthy controls, including those with positive tuberculin skin tests, and paralleled severity of disease, assessed by X-ray scores. In pneumonia patients, serum activin A levels were also raised, but in sarcoidosis patients, levels were lower. To determine whether blockade of the activin A signaling axis could play a functional role in TB, we harnessed a soluble activin type IIB receptor fused to human IgG1 Fc, ActRIIB-Fc, as a ligand trap in a murine TB model. The administration of ActRIIB-Fc to Mycobacterium tuberculosis-infected mice resulted in decreased bacterial loads and increased numbers of CD4 effector T cells and tissue-resident memory T cells in the lung. Increased frequencies of tissue-resident memory T cells corresponded with downregulated T-bet expression in lung CD4 and CD8 T cells. Altogether, the results suggest a disease-exacerbating role of ActRIIB signaling pathways. Serum activin A may be useful as a biomarker for diagnostic triage of active TB or monitoring of anti-tuberculosis therapy.

IMPORTANCE

Tuberculosis remains the leading cause of death by a bacterial pathogen. The etiologic agent of tuberculosis, Mycobacterium tuberculosis, can remain dormant in the infected host for years before causing disease. Significant effort has been made to identify biomarkers that can discriminate between latently infected and actively diseased individuals. We found that serum levels of the cytokine activin A were associated with increased lung pathology and could discriminate between active tuberculosis and tuberculin skin-test-positive healthy controls. Activin A signals through the ActRIIB receptor, which can be blocked by administration of the ligand trap ActRIIB-Fc, a soluble activin type IIB receptor fused to human IgG1 Fc. In a murine model of tuberculosis, we found that ActRIIB-Fc treatment reduced mycobacterial loads. Strikingly, ActRIIB-Fc treatment significantly increased the number of tissue-resident memory T cells. These results suggest a role for ActRIIB signaling pathways in host responses to Mycobacterium tuberculosis and activin A as a biomarker of ongoing disease.

Blazing the trail for innovative tuberculosis diagnostics

The COVID-19 pandemic brought diagnostics into the spotlight in an unprecedented way not only for case management but also for population health, surveillance, and monitoring. The industry saw notable levels of investment and accelerated research which sparked a wave of innovation. Simple non-invasive sampling methods such as nasal swabs have become widely used in settings ranging from tertiary hospitals to the community. Self-testing has also been adopted as standard practice using not only conventional lateral flow tests but novel and affordable point-of-care molecular diagnostics. The use of new technologies, including artificial intelligence-based diagnostics, have rapidly expanded in the clinical setting. The capacity for next-generation sequencing and acceptance of digital health has significantly increased. However, 4 years after the pandemic started, the market for SARS-CoV-2 tests is saturated, and developers may benefit from leveraging their innovations for other diseases; tuberculosis (TB) is a worthwhile portfolio expansion for diagnostics developers given the extremely high disease burden, supportive environment from not-for-profit initiatives and governments, and the urgent need to overcome the long-standing dearth of innovation in the TB diagnostics field. In exchange, the current challenges in TB detection may be resolved by adopting enhanced swab-based molecular methods, instrument-based, higher sensitivity antigen detection technologies, and/or artificial intelligence-based digital health technologies developed for COVID-19. The aim of this article is to review how such innovative approaches for COVID-19 diagnosis can be applied to TB to have a comparable impact.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers

Background

The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear.

Methodology

In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response.

Results and discussion

The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers.

Conclusions

Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

Detection of Mycobacterium tuberculosis in human tissue via RNA in situ hybridization

Rationale

Accurate TB diagnosis is hampered by the variable efficacy of the widely-used Ziehl-Neelsen (ZN) staining method to identify Mycobacterium tuberculosis ( Mtb ) acid-fast bacilli (AFB). Here, we sought to circumvent this current limitation through direct detection of Mtb mRNA.

Objectives

To employ RNAscope to determine the spatial distribution of Mtb mRNA within tuberculous human tissue, to appraise ZN-negative tissue from confirmed TB patients, and to provide proof-of-concept of RNAscope as a platform to inform TB diagnosis and Mtb biology.

Methods

We examined ante- and postmortem human TB tissue using RNAscope to detect Mtb mRNA and a dual ZN/immunohistochemistry staining approach to identify AFB and bacilli producing antigen 85B (Ag85B).

Measurements and main results

We adapted RNAscope for Mtb and identified intact and disintegrated Mtb bacilli and intra- and extracellular Mtb mRNA. Mtb mRNA was distributed zonally within necrotic and non-necrotic granulomas. We also found Mtb mRNA within, and adjacent to, necrotic granulomas in ZN-negative lung tissue and in Ag85B-positive bronchial epithelium. Intriguingly, we observed accumulation of Mtb mRNA and Ag85B in the cytoplasm of host cells. Notably, many AFB were negative for Ag85B staining. Mtb mRNA was observed in ZN-negative antemortem lymph node biopsies.

Conclusions

RNAscope has diagnostic potential and can guide therapeutic intervention as it detects Mtb mRNA and morphology in ZN-negative tissues from TB patients, and Mtb mRNA in ZN-negative antemortem biopsies, respectively. Lastly, our data provide evidence that at least two phenotypically distinct populations of Mtb bacilli exist in vivo .

Challenges and the Way forward in Diagnosis and Treatment of Tuberculosis Infection

Globally, it is estimated that one-quarter of the world's population is latently infected with Mycobacterium tuberculosis (Mtb), also known as latent tuberculosis infection (LTBI). Recently, this condition has been referred to as tuberculosis infection (TBI), considering the dynamic spectrum of the infection, as 5-10% of the latently infected population will develop active TB (ATB). The chances of TBI development increase due to close contact with index TB patients. The emergence of multidrug-resistant TB (MDR-TB) and the risk of development of latent MDR-TB has further complicated the situation. Detection of TBI is challenging as the infected individual does not present symptoms. Currently, there is no gold standard for TBI diagnosis, and the only screening tests are tuberculin skin test (TST) and interferon gamma release assays (IGRAs). However, these tests have several limitations, including the inability to differentiate between ATB and TBI, false-positive results in BCG-vaccinated individuals (only for TST), false-negative results in children, elderly, and immunocompromised patients, and the inability to predict the progression to ATB, among others. Thus, new host markers and Mtb-specific antigens are being tested to develop new diagnostic methods. Besides screening, TBI therapy is a key intervention for TB control. However, the long-course treatment and associated side effects result in non-adherence to the treatment. Additionally, the latent MDR strains are not susceptible to the current TBI treatments, which add an additional challenge. This review discusses the current situation of TBI, as well as the challenges and efforts involved in its control.