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Multidrug resistant tuberculosis - Diagnostic challenges and its conquering by nanotechnology approach - An overview. Chem Biol Interact 2021; 337:109397. [PMID: 33508305 DOI: 10.1016/j.cbi.2021.109397] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 11/27/2020] [Accepted: 12/30/2020] [Indexed: 12/15/2022]
Abstract
One of the leading killer diseases that target the parenchymal tissues of lungs is Tuberculosis. Although antimycobacterial drugs are available, there are increased incidences of drug resistance encountered in Mycobacterium sp. They have been categorized into MDR (Multidrug resistant) and XDR (Extensively drug-resistant) strains exhibiting resistance toward successive treatment regimen. This situation threatens the futuristic containment of TB with the dearth of anti-TB drugs. Nanotechnology, the emerging multidisciplinary science has presented an excellent opportunity for timely and accurate diagnosis and discrimination of Mycobacteria via its unique physio-chemical and optical characteristics. The delayed and misdiagnosis of TB and lack of sensitive diagnostic method(s) has seen a paradigm shift toward nanoparticulate system for improved diagnosis, drug delivery and reduced treatment frequency. This review article highlights the evolution of tuberculosis and its transformation to multidrug resistant strain. Further, the conventional methods for diagnosing TB and the challenges encountered in their analytical performance have been highlighted and the strategies to overcome those challenges have been briefly discussed. Smart approaches encompassing metal nanoparticles, Quantum Dots (QDs) and Field Effect Transistors (FET) based biosensor for accurate diagnosis have been critically reviewed. A decade long state-of-the-art knowledge on TB nanodiagnostics, fabrication concepts and performance characteristics has been reviewed.
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Xu K, Ding C, Mangan CJ, Li Y, Ren J, Yang S, Wang B, Ruan B, Sheng J, Li L. Tuberculosis in China: A longitudinal predictive model of the general population and recommendations for achieving WHO goals. Respirology 2017; 22:1423-1429. [PMID: 28556405 DOI: 10.1111/resp.13078] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Revised: 03/13/2017] [Accepted: 03/30/2017] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND OBJECTIVE Tuberculosis (TB) remains a major public health challenge. China accounts for more than 10% of the global TB burden, and effective modelling of TB trends remains limited. METHODS We used data drawn primarily from two Chinese nation-wide cross-sectional epidemiological surveys combined with data from China's National Disease Reporting Network to construct an eight-state Markov model that simulates TB prevalence. By adjusting the relevant parameters, we evaluated which characteristics have the greatest bearing upon prevalence and efficacy of the response measures. RESULTS If current trends continue, the prevalence of TB in China will enter an 8-year period of decline from approximately 390 to 200 cases per 100 000 population and stabilize at 163 cases per 100 000 population, which is a figure well above the World Health Organization (WHO) goal of eliminating TB by 2050. We find that the proportion of notified cases in the population, the rate of progression from latent to active and the overall treatment success rate are the chief factors affecting disease progression. CONCLUSION We suggest a 90-90-90 strategy, wherein the proportion of notified cases in the population reaches 90%, the risk of progression from latent to active is decreased by 90% compared with the current level and the overall treatment success rate is increased to 90%. This strategy could reduce TB prevalence to less than 10 cases per 100 000 population within 5 years and to 1.77 cases per 100 000 population within 50 years.
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Affiliation(s)
- Kaijin Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Cheng Ding
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Connor J Mangan
- Department of Neurobiology, Harvard University, Cambridge, Massachusetts, USA
| | - Yiping Li
- Zhejiang Institute of Medical Care Information Technology, Hangzhou, China
| | - Jingjing Ren
- Department of General Practice, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Shigui Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Bing Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Bing Ruan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Jifang Sheng
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
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