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Antimicrobial Applications of Nanodevices Prepared from Metallic Nanoparticles and Their Role in Controlling Infectious Diseases
Published in Suvardhan Kanchi, Rajasekhar Chokkareddy, Mashallah Rezakazemi, Smart Nanodevices for Point-of-Care Applications, 2022
Existing research works reported that silver nanoparticles enter the microbial cell wall and cause structural damage and result in cell death. Some other researchers reported that silver nanoparticles deal with the production of free radicals which exhibit the destruction of cells [19,88,89]. Silver nanoparticles have been accounted to be powerful for the treatment of bacterial diseases like tuberculosis, gonorrhea, chlamydia, syphilis and infections that occur in the urinary tract. Tuberculosis is a disease that affects the lungs and it is caused by the bacterium Mycobacterium tuberculosis. The treatment of tuberculosis is disturbed by drug resistance which occurs due to the usage of antibiotics throughout an extensive period. Metal-based nanoparticles have been reported to control antibiotic resistance. In previous studies, silver and zinc oxide nanoparticles are combined at specific ratios which resulted in exhibiting potential antibacterial activity against Mycobacterium tuberculosis [90].
Biological Hazards
Published in W. David Yates, Safety Professional’s Reference and Study Guide, 2020
Tuberculosis, also referred to as TB, is a disease caused by a bacterium called Mycobacterium tuberculosis. The bacteria usually attack the lungs, but TB bacteria can attack any part of the body such as the kidney, spine, and brain. If not treated properly, TB disease can be fatal. TB disease was once the leading cause of death in the United States. It is spread through the air, when people who have the disease cough, sneeze, or spit. Most infections in humans result in an asymptomatic (without symptoms), latent infection, and approximately one in ten latent infections eventually progresses to active disease, which, if left untreated, kills more than 50% of its victims. Occupations at greatest risk include health care workers, prison employees and inmates, homeless shelter employees, and drug treatment center employees.
Tuberculosis Detection from Conventional Sputum Smear Microscopic Images Using Machine Learning Techniques
Published in Siddhartha Bhattacharyya, Václav Snášel, Indrajit Pan, Debashis De, Hybrid Computational Intelligence, 2019
Rani Oomman Panicker, Biju Soman, M.K. Sabu
Tuberculosis (TB) is a serious contagious disease that spreads from one person to another through air. Every second, a new person in the world gets infected with TB disease [25]. A type of slow growing bacterium, Mycobacterium tuberculosis (MTB) causes TB and these bacilli are rod shaped, with varying lengths from 1 μm to 10 μm [13]. It mainly affects the lungs, but it can also attack other parts of the human body such as bone marrow, kidney, spinal cord, brain, etc. [13]. The former type of TB disease is called pulmonary TB and the latter type is called extra pulmonary TB [14]. The symptoms (fever, cough, weight loss, night sweats, etc.) of TB may not be serious for several months, and an infected person can spread it to 10 to 15 other people yearly through direct communication. As per the World Health Organization (WHO) 2018 report, 10 million people are affected with TB, among them 1.3 million died in 2017 [7]. Also, two-thirds of global TB load is concentrated in eight countries including India (27%), China (9%), Indonesia (8%), etc. [14]. So, WHO treated the TB disease as a global emergency [14,25] and also recommended the urgent need of adoption of new technology for reducing TB deaths and new TB incidence [7].
Approaches for designing and delivering solid lipid nanoparticles of distinct antitubercular drugs
Published in Journal of Biomaterials Science, Polymer Edition, 2023
Mallikarjun Vasam, Rama Krishna Goulikar
Conventional therapeutic systems came into existence for the treatment of TB with anti-tubercular drugs. The five main types of drugs used in chemotherapy for tuberculosis are: First-line anti-tubercular drugs (Isoniazid, Rifampicin, Rifabutin, Ethambutol, and Pyrazinamide) work effectively against tuberculosis, while second-line anti-tubercular drugs (Ethionamide, Cycloserine) work well when first-line drugs fail due to drug resistance [4, 9]. In tuberculosis treatment, patients need to take four oral antibiotics every day for six to nine months. Generally, TB is treated in two steps: during the initial phase, most of the live bacilli are killed by treatment with four first-line antibiotics for two months. The second phase is called the continuation phase, in which rifampicin and isoniazid are used daily or three times a week for 4–6 months to kill the bacteria that survived the initiation phase [10], which has complicated long-term conventional treatment has lethal side effects of anti-tubercular drugs may cause poor patients’ compliance, which can lead to the growth of drug-resistant strains.
Design of artificial neural networks optimized through genetic algorithms and sequential quadratic programming for tuberculosis model
Published in Waves in Random and Complex Media, 2022
Muhammad Shoaib, Saba Kainat, Muhammad Asif Zahoor Raja, Kottakkaran Sooppy Nisar
The tuberculosis model is discussed in this paper using GA-SQP. The symptoms of tuberculosis include cough, sputum production, fever, weight loss, night sweats, hemoptysis, anorexia, and dyspnea. The global tuberculosis (TB) epidemic has created an urgent need for early detection and effective therapy of tuberculosis patients, especially those with pulmonary TB who transmit infection. The discovery of TB's cause by German physician Robert Koch, which he revealed on March 24, 1882, Mycobacterium tuberculosis was identified as the agent. TB remains a substantial cause of illness and death around the world; it is estimated that one-third of the world's population is attacked with Mycobacterium tuberculosis, with an estimated nine million people diagnosed with the disease each year and nearly two million dying from it [26,27]. According to a recent systematic study, those with diabetes mellitus (DM) had around three times the chance of having tuberculosis infection as people without the condition [28].
Molecular characterization and antimicrobial resistance profiles of Mycobacterium tuberculosis complex in environmental substrates from three dairy farms in Eastern Cape, South Africa
Published in International Journal of Environmental Health Research, 2021
Athini Ntloko, Martins Ajibade Adefisoye, Ezekiel Green
Tuberculosis (TB) is a contagious disease caused by a consortium of closely-associated bacteria, referred to as Mycobacterium tuberculosis complex or MTBC (Sharma et al. 2016). The classification of the bacterial strains within the complex has been made difficult due to taxonomic and nomenclature changes. However, Riojas et al. (2018) have recently suggested that the various MTBC strains within the complex should rather be described as variants of MTBC based on the genetic relatedness of these strains, which largely exceeds the respective species declination threshold. The World Health Organisation (WHO) ranked TB as one of the first ten global causes of death, with an estimated 10.4 million individuals falling ill with TB in 2016 alone, while about 1.7 million people, including 0.4 million individuals living with the human immunodeficiency virus (HIV) died of the disease. More than 95% of TB-associated deaths are recorded in low-income and middle-income countries, with China, India, Indonesia, Nigeria, Pakistan, Philippines and South Africa accounting for about 64% of the total death cases (WHO 2018).