Background: The rapid identification of pathogenic bacteria is important for determining an appropriate antimicrobial therapy for pneumonia, but traditional bacterial culture is time-consuming and labourious. The aim of this study was to develop and evaluate a DNA microarray assay for the simultaneous detection of fifteen bacterial species directly from respiratory tract specimens in patients with pneumonia. These species included Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Mycoplasma pneumoniae, Enterococcus faecalis, Enterococcus faecium, Enterobacter cloacae, Stenotrophomonas maltophilia, Burkholderia cepacia, Legionella pneumophila and Chlamydia pneumoniae. The 16S rDNA genes and other specific genes of each pathogen were chosen as the amplification targets, amplified via multiplex polymerase chain reaction (PCR), and hybridized to oligonucleotide probes in a microarray.
Results: The DNA microarray detection limit was 10 copies/μL. Nineteen standard strains and 119 clinical isolates were correctly detected with our microarray, and 3 nontarget species from 4 clinical isolates were not detected. Additionally, bacterial pathogens were accurately identified when two or three bacterial targets were mixed together. Furthermore, the results for 99.4% (156/157) of clinical specimens were the same as those from a conventional assay.
Conclusions: We developed a DNA microarray that could simultaneously detect various bacterial pathogens in pneumonia. The method described here has the potential to provide considerable labour and time savings due to its ability to screen for 15 bacterial pathogens simultaneously.
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http://dx.doi.org/10.1186/s12866-020-01842-3 | DOI Listing |
World J Oncol
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The First Clinical Medical School, Jinan University, Guangzhou 510632, Guangdong, China.
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January 2025
Xuanwu Hospital, China-America Institute of Neurology, Capital Medical University, No. 45, Changchun Street, Xicheng District, Beijing, China.
It is unclear how steroid hormones contribute to stroke, and conducting randomized controlled trials to obtain related evidence is challenging. Therefore, Mendelian randomization (MR) technique was employed in this study to examine this association. Through genome-wide association meta-analysis, the genetic variants of steroid hormones, including testosterone/17β-estradiol (T/E2) ratio, aldosterone, androstenedione, progesterone, and hydroxyprogesterone, were acquired as instrumental variables.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Background: Genome-wide association studies (GWAS) provide a powerful method for identifying the loci and genes that contribute to disease. However, in many cases, the specific cell types and states that confer disease risk through these genes remain unknown. Determining this relationship is crucial for identifying pathogenic processes and developing therapeutic strategies.
View Article and Find Full Text PDFBMC Cardiovasc Disord
January 2025
Department of Respiratory and Critical Care Medicine, The Second Hospital of Hebei Medical University, 215 Heping West Road, Shijiazhuang, Hebei, China.
Background: Pulmonary arterial hypertension (PAH) is a severe and progressive cardiovascular disease. While potential links between clonal hematopoiesis (CH) and cardiovascular diseases have been identified, the causal relationship between CH and PAH remains unclear. This study aims to investigate the causal effect of CH on the risk of PAH using a two-sample Mendelian randomization (MR) approach.
View Article and Find Full Text PDFBMC Genomics
January 2025
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Background: There is increasing need for effective incorporation of high-dimensional genetics data from individuals with varied ancestry in genome-wide association (GWAS) analyses. Classically, multi-ancestry GWAS analyses are performed using statistical meta-analysis to combine results conducted within homogeneous ancestry groups. The emergence of cosmopolitan reference panels makes collective preprocessing of GWAS data possible, but impact on downstream GWAS results in a mega-analysis framework merits investigation.
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