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Infection and Drug Resistance 2021We investigated the clonal diversity of carbapenemase-producing isolates from the Shenzhen Children's Hospital, China, and drew conclusions on the clinical and public...
AIM
We investigated the clonal diversity of carbapenemase-producing isolates from the Shenzhen Children's Hospital, China, and drew conclusions on the clinical and public health impact of these isolates as multidrug-resistant.
METHODS
From January 2014 to December 2018, a total number of 36 unique carbapenemase-producing clinical isolates of were collected out of 900 clinical isolates in paediatric patients from the Shenzhen Children's Hospital, China. After carbapenemase production confirmation, antimicrobial susceptibility, resistance determinants and phylogenetic relationship were determined.
RESULTS
The isolates showed resistance to ceftazidime, ertapenem, ampicillin, cefazolin, ceftriaxone, cefotetan, ticarcillin, cefaclor, cefpodoxime, azlocillin, cefcapene, mezlocillin and ampicillin-sulbactam. Of the 36 carbapenemase genes coding isolates, was the mostly detected 50% (n=18) followed by and 19% (n=7), 17% (n=6), 8% (n=3) and 5% (n=2), whereas extended-spectrum β-lactamase ( ) was predominantly detected 92% (n=33) followed by 53% (n=19) and 28% (n=10). Pulsed-field gel electrophoresis typing showed eight different patterns, and twenty-five distinct sequences types were observed with ST307 being predominantly identified 11% (n=4), followed by ST2407 8% (n=3). Plasmid replicon typing results indicated that IncFIS, IncHI2, IncFIC and IncFIA plasmids carry and genes.
CONCLUSION
This study reports on the occurrence and spread of carbapenemase and extended-spectrum β-lactamase encoding genes co-existence in sporadic ST307 in paediatric patients from the Shenzhen Children's Hospital, China.
PubMed: 34511949
DOI: 10.2147/IDR.S324018 -
Applied and Environmental Microbiology Jul 2016This study aimed to isolate nontuberculous mycobacterial species from environmental samples obtained from some selected communities in Ghana. To optimize...
UNLABELLED
This study aimed to isolate nontuberculous mycobacterial species from environmental samples obtained from some selected communities in Ghana. To optimize decontamination, spiked environmental samples were used to evaluate four decontamination solutions and supplemented media, after which the best decontamination solution and media were used for the actual analysis. The isolates obtained were identified on the basis of specific genetic sequences, including heat shock protein 65, IS2404, IS2606, rpoB, and the ketoreductase gene, as needed. Among the methods evaluated, decontamination with 1 M NaOH followed by 5% oxalic acid gave the highest rate of recovery of mycobacteria (50.0%) and the lowest rate of contamination (15.6%). The cultivation medium that supported the highest rate of recovery of mycobacteria was polymyxin B-amphotericin B-nalidixic acid-trimethoprim-azlocillin-supplemented medium (34.4%), followed by isoniazid-supplemented medium (28.1%). Among the 139 samples cultivated in the main analysis, 58 (41.7%) yielded mycobacterial growth, 70 (50.4%) had no growth, and 11 (7.9%) had all inoculated tubes contaminated. A total of 25 different mycobacterial species were identified. Fifteen species (60%) were slowly growing (e.g., Mycobacterium ulcerans, Mycobacterium avium, Mycobacterium mantenii, and Mycobacterium malmoense), and 10 (40%) were rapidly growing (e.g., Mycobacterium chelonae, Mycobacterium fortuitum, and Mycobacterium abscessus). The occurrence of mycobacterial species in the various environmental samples analyzed was as follows: soil, 16 species (43.2%); vegetation, 14 species (38.0%); water, 3 species (8.0%); moss, 2 species (5.4%); snail, 1 species (2.7%); fungi, 1 species (2.7%). This study is the first to report on the isolation of M. ulcerans and other medically relevant nontuberculous mycobacteria from different environmental sources in Ghana.
IMPORTANCE
Diseases caused by mycobacterial species other than those that cause tuberculosis and leprosy are increasing. Control is difficult because the current understanding of how the organisms are spread and where they live in the environment is limited, although this information is needed to design preventive measures. Growing these organisms from the environment is also difficult, because the culture medium becomes overgrown with other bacteria that also live in the environment, such as in soil and water. We aimed to improve the methods for growing these organisms from environmental sources, such as soil and water samples, for better understanding of important mycobacterial ecology.
Topics: Bacterial Proteins; Bacteriological Techniques; Buruli Ulcer; Culture Media; DNA Transposable Elements; Decontamination; Endemic Diseases; Environmental Microbiology; Ghana; Humans; Nontuberculous Mycobacteria; Specimen Handling
PubMed: 27208141
DOI: 10.1128/AEM.01002-16 -
Clinical Pharmacokinetics Nov 2021Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex...
BACKGROUND
Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.
OBJECTIVE
The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.
METHODS
Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.
RESULTS
The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.
CONCLUSION
A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
Topics: Drug Elimination Routes; Humans; Infant, Newborn; Machine Learning; Metabolic Clearance Rate; Models, Biological; Vancomycin
PubMed: 34041714
DOI: 10.1007/s40262-021-01033-x -
FEBS Letters Jan 2016Penicillin-binding protein 3 (PBP3) from Pseudomonas aeruginosa is the molecular target of β-lactam-based antibiotics. Structures of PBP3 in complexes with azlocillin...
Penicillin-binding protein 3 (PBP3) from Pseudomonas aeruginosa is the molecular target of β-lactam-based antibiotics. Structures of PBP3 in complexes with azlocillin and cefoperazone, which are in clinical use for the treatment of pseudomonad infections, have been determined to 2.0 Å resolution. Together with data from other complexes, these structures identify a common set of residues involved in the binding of β-lactams to PBP3. Comparison of wild-type and an active site mutant (S294A) showed that increased thermal stability of PBP3 following azlocillin binding was entirely due to covalent binding to S294, whereas cefoperazone binding produces some increase in stability without the covalent link. Consistent with this, a third crystal structure was determined in which the hydrolysis product of cefoperazone was noncovalently bound in the active site of PBP3. This is the first structure of a complex between a penicillin-binding protein and cephalosporic acid and may be important in the design of new noncovalent PBP3 inhibitors.
Topics: Acylation; Azlocillin; Cefoperazone; Crystallography, X-Ray; Models, Molecular; Molecular Structure; Penicillin-Binding Proteins
PubMed: 26823174
DOI: 10.1002/1873-3468.12054 -
EBioMedicine Apr 2019Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a...
BACKGROUND
Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment.
METHODS
In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions.
FINDINGS
We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line.
INTERPRETATION
Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.
Topics: Cell Line, Tumor; Cell Survival; Drug Discovery; Drug Repositioning; Gene Expression Profiling; Genes, Reporter; Genome, Human; Genomics; Humans; Male; Metabolic Networks and Pathways; Metabolomics; Piperidines; Prostatic Neoplasms; Proteome; Proteomics
PubMed: 30905848
DOI: 10.1016/j.ebiom.2019.03.009