The Lungs Unveiled: Current Trends in Pulmonary Tuberculosis Research

Wyan Rdams^*
*Correspondence: Wyan Rdams, Department of Pulmonary Medicine, Golden Gate University, San Francisco, CA, USA, Email:

Introduction

Pulmonary Tuberculosis (TB) is a global health concern, with millions of people affected each year. Despite advancements in medical science, this infectious disease continues to be a significant cause of morbidity and mortality worldwide. Research into pulmonary tuberculosis is ongoing, and current trends in this field aim to unveil new insights, diagnostic tools, and treatment strategies. This article will delve into the current state of pulmonary tuberculosis research, highlighting the latest developments, challenges, and prospects for the future.

Before discussing the latest trends in pulmonary tuberculosis research, it is essential to understand the current epidemiological landscape of the disease. Tuberculosis is primarily caused by the bacterium Mycobacterium tuberculosis and primarily affects the lungs, resulting in pulmonary tuberculosis. According to the World Health Organization (WHO), TB remains one of the top ten causes of death worldwide, and in 2020, an estimated 10 million people fell ill with TB, leading to 1.5 million deaths. Notably, the COVID-19 pandemic has further complicated the TB situation, causing disruptions in diagnosis and treatment services. Furthermore, the global burden of TB varies significantly by region, with the highest incidence rates observed in countries with limited resources and inadequate healthcare infrastructure. The emergence of drug-resistant TB strains has also posed significant challenges to TB control and treatment. These epidemiological factors underscore the importance of ongoing research into pulmonary tuberculosis [1].

Liquid Biopsies: Liquid biopsies, which involve analyzing blood or other bodily fluids for tuberculosis-related biomarkers, are a promising avenue. Research in this area is exploring the use of specific proteins and nucleic acids as markers for TB infection, offering the potential for non-invasive and rapid diagnosis. Machine learning and artificial intelligence are being applied to chest radiography and computed tomography scans to enhance the detection of TB-related abnormalities. AI algorithms can help radiologists identify TB lesions more accurately, speeding up the diagnostic process. The emergence of drug-resistant TB strains, particularly Multidrug-Resistant (MDR) and Extensively Drug-Resistant (XDR) TB, has heightened the need for novel treatments. Research efforts have focused on developing new drugs and repurposing existing ones to combat drug-resistant TB [2].

Description

These two drugs were approved in recent years for the treatment of MDR-TB. Research is ongoing to optimize their use and explore their efficacy against different strains. The development of shorter treatment regimens for drug-susceptible and drug-resistant TB is a significant research focus. Reducing the treatment duration can improve adherence and reduce the likelihood of resistance. Researchers are investigating therapies that modulate the host immune response to enhance the body's ability to control TB infection. These therapies may complement existing drug treatments. The combination of smoking and asthma can be life-threatening in such situations. Smoking significantly accelerates the decline in lung function over time. For individuals with asthma, this decline can be even more pronounced, leading to greater impairment in daily activities and reduced quality of life. Vaccination is a cornerstone of tuberculosis control. The Bacillus Calmette-Guérin (BCG) vaccine, while effective against severe forms of TB in children, offers limited protection in adults. Research is ongoing to develop a more efficacious vaccine. Several vaccine candidates are in various stages of clinical development. The M72/AS01E vaccine, for example, has shown promise in reducing the risk of TB infection in adults [3-5].

Conclusion

Pulmonary tuberculosis remains a formidable global health challenge, with millions of lives affected each year. Despite ongoing efforts to combat this infectious disease, recent trends in pulmonary tuberculosis research reveal a commitment to unraveling its complexities and finding innovative solutions. From improved diagnostics to cutting-edge treatment regimens and promising vaccine candidates, the field is witnessing significant progress. However, numerous challenges persist, including the emergence of drug-resistant strains, funding shortages, stigma, and disparities in healthcare access. These challenges underscore the urgency of continued research and global collaboration. Looking to the future, personalized medicine, integrated care, novel therapies, data integration, enhanced surveillance, and global cooperation will be critical in the fight against pulmonary tuberculosis. By addressing these areas, the scientific community, healthcare providers, and policymakers can work together to reduce the burden of this disease and move closer to a world where tuberculosis is no longer a major global health threat.

Acknowledgement

None.

Conflict of Interest

The authors declare that there is no conflict of interest associated with this manuscript.

References

1. Li Guangdi and Erik De Clercq. "Therapeutic options for the 2019 novel coronavirus (2019-nCoV)." Nat Rev Drug Discov 19 (2020): 149-150.

Google Scholar, Crossref, Indexed at

2. Nemati, Shamim, Andre Holder, Fereshteh Razmi and Matthew D. Stanley, et al. "An interpretable machine learning model for accurate prediction of sepsis in the ICU." ICU Crit Care Med 46 (2018): 547.

Google Scholar, Crossref, Indexed at

3. Mohammed, Akram, Franco Van Wyk, Lokesh K. Chinthala and Anahita Khojandi, et al. "Temporal differential expression of physiomarkers predicts sepsis in critically ill adults." Shock 56 (2021): 58.

Google Scholar, Crossref, Indexed at

4. Han, Daiwei, Marjolein A. Heuvelmans, Rozemarijn Vliegenthart and Mieneke Rook, et al. "Influence of lung nodule margin on volume-and diameter-based reader variability in CT lung cancer screening." Br J Radiol 91 (2018): 20170405.

Google Scholar, Crossref, Indexed at

5. Goo, Jin Mo. "Computer-aided detection of lung nodules on chest CT: Issues to be solved before clinical use." Korean J Radiol 6 (2005): 62-63.

Google Scholar, Crossref, Indexed at