Bacterial etiology of sputum from tuberculosis suspected patients and antibiogram of the isolates

Regmi, Ramesh Sharma; Khadka, Sujan; Adhikari, Sanjib; Dhakal, Khuma Kumari; Dhakal, Bishnu P.; Lamsal, Bandana; Kafle, Sarad Chandra

doi:10.1186/s13104-020-05369-8

Cited by 13 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As depicted in Table 2 , 66.6% of the isolated micro-organisms were bacteria, and P. aeruginosa was the most frequently isolated bacterium. Similar findings have been reported in Nepal,[ 21 ] Cambodia,[ 14 ] and Pakistan. [ 22 ] The ability of P. aeruginosa to colonize a wide range of ecological niches, such as air polluting agents, animal hosts, and people, may explain the high prevalence of P. aeruginosa in the sputum of patients with PTB.…”

Section: Discussionsupporting

confidence: 90%

Coinfection of pulmonary tuberculosis with other lower respiratory tract infections

Aldriwesh¹,

Alaqeel²,

Mashraqi³

et al. 2022

Annals of Thoracic Medicine

View full text Add to dashboard Cite

BACKGROUND: Little attention has been given to the development of lower respiratory tract infections (LRTIs) in patients with pulmonary tuberculosis (PTB) during their anti-tuberculosis (anti-TB) treatment and how that might affect patients’ health status. Here, the prevalence and etiologies of other LRTIs in a cohort of PTB patients were determined, and the clinical features and outcomes were described. METHODS: Adult patients with PTB between 2015 and 2020 were recruited and monitored during their anti-TB treatment for the presence of LRTIs. Clinical data were retrospectively collected from patients’ medical records. RESULTS: Data from 76 PTB patients (57 [75%] males) were reviewed. The median age was 61.0 (interquartile range 83.5–35.5) years, and other LRTIs were detected in 45 (59.2%) PTB patients. Of the 126 episodes of LRTIs, 84 (66.7%) were due to bacterial infections, 37 (29.4%) were due to fungal infections, and 5 (3.9%) were due to viral infections. The development of LRTIs was significantly more common in older ( P = 0.012) and hypertensive patients with PTB ( P = 0.019). Patients with PTB and LRTIs experienced significantly more frequent extrapulmonary infections ( P = 0.0004), bloodstream infections ( P = 0.001), intensive care unit stays ( P = 0.001), and invasive mechanical ventilation use ( P = 0.03) than patients who did not develop LRTI. CONCLUSIONS: The identification of host-related risk factors for LRTI development among patients with PTB could be used to develop a prediction model for LRTI development. Hence, initiating antimicrobials early, in parallel with appropriate anti-TB treatment, may mitigate PTB-related health and economic consequences.

show abstract

Section: Discussionsupporting

confidence: 90%

Coinfection of pulmonary tuberculosis with other lower respiratory tract infections

Aldriwesh¹,

Alaqeel²,

Mashraqi³

et al. 2022

Annals of Thoracic Medicine

View full text Add to dashboard Cite

show abstract

“…During this period the individual is in compartment E (for exposed), resulting in SEIR models (for Susceptible–Exposed–Infectious–Removed) which have been employed in numerous studies of COVID-19 (e.g. Annas et al, 2020 ; Brauner et al, 2020 ; He et al, 2020 ; Hsiang et al, 2020 ; Lai et al, 2020 ; Ma, 2020 ; Mwalili et al, 2020 ). If τ E denotes the mean latent period, i.e., time from infection to onset of infectiousness, the sum τ I + τ E is called the serial interval ( Lipsitch et al, 2003 ).…”

Section: Epidemiology Models and Hinderingmentioning

confidence: 99%

The impact of policy timing on the spread of COVID-19

Elitzur¹,

Kaplan²,

Ivezić³

et al. 2021

Infectious Disease Modelling

View full text Add to dashboard Cite

We model COVID-19 data for 89 nations and US states with a recently developed formalism that describes mathematically any pattern of growth with the minimum number of parameters. The results show that the disease has a typical duration of 18 days, with a significant increase in fatality when it lasts longer than about 4 months. Searching for correlations between “flattening of the curve” and preventive public policies, we find strong statistical evidence for the impact of the first implemented policy on decreasing the pandemic growth rate; a delay of one week in implementation nearly triples the size of the infected population, on average. Without any government action, the initial outburst still slows down after 36 days, possibly thanks to changes in public behavior in response to the pandemic toll. Stay-at-home (lockdown) was not the first policy of any sample member, and we could not find statistically meaningful evidence for its added impact, similar to a recent study that employed an entirely different approach. However, lockdown was mostly imposed only shortly before the exponential rise was arrested by other measures, too late for a meaningful impact. A third of the sample members that did implement lockdown imposed it only after the outburst had already started to slow down. The possibility remains that lockdown might have significantly shortened the initial exponential rise had it been employed as first resort rather than last.

show abstract

“…were the dominant bacteria. 24 Regarding the bacterial isolates recovered from the blood samples; Streptococcus spp., Staphylococcus aureus, E. coli, and the Enterobacter sp. were detected as reported by.…”

Section: Resultsmentioning

confidence: 99%

Antibacterial Resistance Pattern in Clinical and Non-clinical Bacteria by Phenotypic and Genotypic Assessment

Shawky¹,

Suleiman²,

Farrag³

2021

J. Pure Appl. Microbiol.

View full text Add to dashboard Cite

Bacterial infections represent a very serious problem that threatens human health, antibiotics were designed to attack the causative agents of infectious diseases, but some bacterial pathogens became virulent and resistant to antibiotics by different mechanisms, resistance genes represented one of those mechanisms. This study attempts to screen the existence of five different resistance genes (mecA, TEM, FemA, MexD, and AmpC) among 25 bacterial isolates divided into two groups the first was non-clinical bacterial type strains including Bacillus subtilis, Escherichia coli, Klebsiella pneumonia, Pseudomonas aeruginosa, Salmonella typhi, and the other group includes some clinical bacterial isolates. Evaluation of their susceptibilities to different 12 antibiotic discs and attempting to find the relationship between genotype and phenotype assessment. Different responses were reported which varied from slightly susceptible to multidrug-resistant such as P. aeruginosa and K. pneumonia which could be considered as multidrug-resistant strains. Therefore, detection of resistance gene became crucial and critical to recognize the mechanism of resistance, five pairs of primers were included to investigate five responsible genes belonging to beta-lactamases, efflux pump, and methicillin resistance. Conclusively, the PCR technique is a very accurate tool to check the genetic resistance whether being expressed to phenotype or not. Moreover, the clinical bacterial isolates appeared more resistant that reflecting the impact of the surrounding environment on bacterial behavior.

show abstract

Bacterial etiology of sputum from tuberculosis suspected patients and antibiogram of the isolates

Cited by 13 publications

References 13 publications

Coinfection of pulmonary tuberculosis with other lower respiratory tract infections

Coinfection of pulmonary tuberculosis with other lower respiratory tract infections

The impact of policy timing on the spread of COVID-19

Antibacterial Resistance Pattern in Clinical and Non-clinical Bacteria by Phenotypic and Genotypic Assessment

Contact Info

Product

Resources

About