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Transmission Model of Tuberculosis Disease with Age Structure of Human in Thailand
(2021-08-20)
Pongsumpun, Puntani
Mycobacterium tuberculosis caused Tuberculosis. This bacteria attack the lungs and the other parts of the body. Tuberculosis distributed from human to human through the air. The symptoms of Tuberculosis in the lungs may include a bad cough that lasts >= 3 weeks, weight loss, fever and night sweats .The tuberculosis(TB) is appeared in about one-quarter of the world's population. TB cases appeared in different age group. In this research, we formulate the model can explain the spread of TB with age group. We separated human into 3 age groups and each human group is classified unto 5 groups such as susceptible, exposed, infectious, quarantined and recovered. The analyzation and simulation of our model are shown.
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Local stability analysis of mathematical model of Tuberculosis disease in Thailand
(2021-01-15)
Pongsumpun, Puntani
Tuberculosis (TB) is a contagious disease that is caused by Mycobacterium. It can be transmitted by air. When infected Tuberculosis speaks, coughs or sneezes. TB is present in the sputum droplets and rises into the air. Large aerosol particles often fall on the ground and dry out. The main symptom of tuberculosis is a chronic cough that lasts 2 weeks or more. Other symptoms may include loss of appetite, weight loss, fatigue, fever, chest pain, shortness of breath. This disease is transmitted between human. In this paper, we find the dynamical equations of this disease. We analyzed our mathematical model to find the equilibrium points of our mathematical model. Numerical solutions are analyzed to see the distribution of each group of population. The basic reproduction number of the disease is derived. The influence of each factor is analyzed.
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Hybrid learning of hand-crafted and deep-activated features using particle swarm optimization and optimized support vector machine for tuberculosis screening
(2020-09-01)
Win, Khin Yadanar
;
Maneerat, Noppadol
;
Hamamoto, Kazuhiko
;
Sreng, Syna
Tuberculosis (TB) is a leading infectious killer, especially for people with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Early diagnosis of TB is crucial for disease treatment and control. Radiology is a fundamental diagnostic tool used to screen or triage TB. Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled experts in remote areas. We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model. Then, six sets of hand-crafted features from statistical textures, local binary pattern, GIST, histogram of oriented gradients (HOG), pyramid histogram of oriented gradients and bags of visual words (BoVW), and nine sets of deep-activated features from AlexNet, GoogLeNet, InceptionV3, XceptionNet, ResNet-50, SqueezeNet, ShuffleNet, MobileNet, and DenseNet, were extracted. The dominant features of each feature set were selected using particle swarm optimization, and then separately input to an optimized support vector machine classifier to label 'normal' and 'TB' x-rays. GIST, HOG, BoVW from hand-crafted features, and MobileNet and DenseNet from deep-activated features performed better than the others. Finally, we combined these five best-performing feature sets to build a hybrid-learning algorithm. Using the Montgomery County (MC) and Shenzen datasets, we found that the hybrid features of GIST, HOG, BoVW, MobileNet and DenseNet, performed best, achieving an accuracy of 92.5% for the MC dataset and 95.5% for the Shenzen dataset.
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Overexpression of eis without a mutation in promoter region of amikacin- and kanamycin-resistant Mycobacterium tuberculosis clinical strain
(2018-07-16)
Sowajassatakul, Angkanang
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Prammananan, Therdsak
;
Chaiprasert, Angkana
;
Phunpruch, Saranya
Background: Aminoglycosides such as amikacin and kanamycin are effective injectable second-line drugs for treatment of multidrug-resistant tuberculosis. Molecular mechanisms underlying aminoglycoside resistance are not well understood. We have previously identified the amikacin- and kanamycin-resistant M. tuberculosis MT433 clinical strain, of which all known mutations related to resistance have not been found. Drug efflux pump is one of reported resistance mechanisms that might play a role in aminoglycoside resistance. Methods: The expression levels of sixteen putative efflux pump genes, including eis and one regulator gene, whiB7, of MT433 in the presence of kanamycin were determined using the reverse transcription-quantitative PCR method. The effects of upregulated genes on amikacin and kanamycin resistance were investigated by overexpression in M. tuberculosis H37Ra strain. Results: Upon kanamycin exposure, other than whiB7 and eis that were found extremely overexpressed, two drug efflux pump genes, namely Rv1877 and Rv2846c, showed specifically high-level of expression in M. tuberculosis MT433 strain. However, direct effect of overexpressed Rv1877 and Rv2846c on amikacin and kanamycin resistance could not be demonstrated in M. tuberculosis H37Ra overexpressed strain. Conclusions: Our finding demonstrated that overexpression of eis could occur without any mutations in the promoter region and be detectable in clinical isolate. This might be a consequence of overexpressed whiB7, resulting in amikacin and kanamycin resistance in M. tuberculosis MT433 strain.
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Molecular characterization of amikacin, kanamycin and capreomycin resistance in M/XDR-TB strains isolated in Thailand
(2014-06-22)
Sowajassatakul, Angkanang
;
Prammananan, Therdsak
;
Chaiprasert, Angkana
;
Phunpruch, Saranya
Background: The emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) makes the treatment and control of tuberculosis difficult. Rapid detection of drug-resistant strains is important for the successful treatment of drug-resistant tuberculosis; however, not all resistance mechanisms to the injectable second-line drugs such as amikacin (AK), kanamycin (KM), and capreomycin (CAP) are well understood. This study aims to validate the mechanisms associated with AK, KM, and CAP resistance in M. tuberculosis clinical strains isolated in Thailand. Results: A total of 15,124 M. tuberculosis clinical strains were isolated from 23,693 smear-positive sputum samples sent from 288 hospitals in 46 of 77 provinces of Thailand. Phenotypic analysis identified 1,294 strains as MDR-TB and second-line drugs susceptibility was performed in all MDR-TB strains and revealed 58 XDR-TB strains. Twenty-nine KM-resistant strains (26 XDR-TB and 3 MDR-TB) could be retrieved and their genes associated with AK, KM, and CAP resistance were investigated compared with 27 KM-susceptible strains. Mutation of the rrs (A1401G) was found in 21 out of 29 KM-resistant strains whereas mutations of eis either at C-14 T or at G-37 T were found in 5 strains. Three remaining KM-resistant strains did not contain any known mutations. Capreomycin resistance was determined in 28 of 29 KM-resistant strains. Analysis of tlyA revealed that the A33G mutation was found in all CAP-resistant strains and also in susceptible strains. In contrast, the recently identified tlyA mutation T539G and the novel Ins49GC were found in two and one CAP-resistant strains, respectively. In addition, our finding demonstrated the insertion of cytosine at position 581 of the tap, a putative drug efflux encoding gene, in both KM-resistant and KM-susceptible strains. Conclusions: Our finding demonstrated that the majority of KM resistance mechanism in Thai M. tuberculosis clinical strains was rrs mutation at A1401G. Mutations of the eis promoter region either at C-14 T or G-37 T was found in 5 of 29 strains whereas three strains did not contain any known mutations. For CAP resistance, 3 of 28 CAP-resistant strains contained either T539G or Ins49GC mutations at tlyA that might be associated with the resistant phenotype. © 2014 Sowajassatakul et al.; licensee BioMed Central Ltd.
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