METHODOLOGY: One hundred and twenty clinical isolates of S. pneumoniae were obtained from patients of University Malaya Medical Centre (UMMC). The strains were screened using a multiplex real-time PCR method for the presence of alterations in the genes encoding the penicillin binding proteins: pbp2b, macrolide resistance determinant ermB and the pneumolysin gene, ply. Dual-labelled Taqman probes were used in the real-time detection method comprising three different genes labeled with individual fluorophores at different wavelengths. One hundred and twenty isolates from bacterial cultures and isolates directly from blood cultures samples were analyzed using this assay.
RESULTS: A multiplex PCR comprising the antibiotic resistance genes, ermB and and pneumolysin gene (ply), a S. pneumoniae species specific gene, was developed to characterize strains of S. pneumoniae. Out of the 120 pneumococcal isolates, 58 strains were categorized as Penicillin Sensitive Streptococcus pneumoniae (PSSP), 36 as Penicillin Intermediate Streptococcus pneumoniae (PISP) and 26 as Penicillin Resistant Streptococcus pneumoniae (PRSP). All the 58 PSSP strains harboured the pbp2b gene while the 36 PISP and 26 PRSP strains did not harbour this gene, thus suggesting reduced susceptibility to penicillin. Resistance to erythromycin was observed in 47 of the pneumococcal strains while 15 and 58 were intermediate and sensitive to this drug respectively. Susceptibility testing to other beta-lactams (CTX and CRO) also showed reduced susceptibility among the strains within the PISP and PRSP groups but most PSSP strains were sensitive to other antibiotics.
CONCLUSION: The characterization of pneumococcal isolates for penicillin and erythromycin resistance genes could be useful to predict the susceptibility of these isolates to other antibiotics, especially beta-lactams drugs. We have developed an assay with a shorter turnaround time to determine the species and resistance profile of Streptococcus pneumoniae with respect to penicillin and macrolides using the Real Time PCR format with fluorescent labeled Taqman probes, hence facilitating earlier and more definitive antimicrobial therapy which may lead to better patient management.
STUDY DESIGN: Eighty-six postnasal biopsy samples and 71 fine-needle aspirate samples of neck masses were obtained from patients who were clinically suspect for NPC. Genomic DNA was extracted from the samples, and EBNA1, EBNA2, and LMP genes of EBV were detected by PCR. PCR results were compared with NPC histopathology findings.
RESULTS: The sensitivity of PCR to detect EBNA1 (97.14%), EBNA2 (88.57%), and LMP (91.43%) genes of EBV in nasopharyngeal biopsy samples were higher than those in fine-needle aspirate samples.
CONCLUSION: Detection of EBV by PCR in tissue obtained from nasopharyngeal biopsy and fine-needle aspirate samples of neck masses is a relatively inexpensive, reliable, and accurate method of diagnosing NPC. Detection of EBV genes is on par with histopathological examination (HPE) and superior to fine-needle aspirate cytology.
SIGNIFICANCE: PCR is an ideal tool for suggesting NPC and guiding the diagnostic workup in occult primary tumors, facilitating earlier diagnosis and reducing morbidity and mortality.
PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation).
EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers.
KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%.
CONCLUSIONS AND IMPLICATIONS: We have shown that the pharmacometabonomics technique was able to differentiate between unstable and stable INR with good accuracy. NMR-based pharmacometabonomics has the potential to identify novel biomarkers in plasma, which can be useful in individualizing treatment and controlling warfarin side effects, thus, minimizing undesirable effects in the future.