MATERIALS AND METHODS: We studied the samples of the pharyngeal mucosa smears taken from children aged 1-15 years with X-ray confirmed pneumonia. The selection of DNA probes for specific detection of community-acquired pneumonia pathogens (S. pneumoniae, H. influenzae, M. pneumoniae, C. pneumonia, and L. pneumophila) and development of the microarray design were carried out using the disprose program. The nucleotide sequences of pathogens were obtained from NCBI Nucleotide database. In the research we used CustomArray microarrays (USA). For a pooled sample containing S. pneumoniae and H. influenzae DNA, we performed a sequential selection of the best combinations of hybridization parameters: DNA fragment size, DNA amount, hybridization temperature. The selection criteria were: the percentage of effective probes with a standardized hybridization signal (SHS) ≥3 Z, and the excess of SHS levels of effective specific probes compared to SHS of effective nonspecific probes. We selected the probes to detect of S. pneumoniae and H. influenzae characterized by an effective hybridization signal under optimal conditions. The developed microarray was tested under the selected conditions on clinical samples containing S. pneumoniae or H. influenzae DNA. Using ROC analysis there were established threshold values for the signals of specific probes at optimal sensitivity points and the test specificity, the excess of which was interpreted as the evidence of pathogen presence in a sample.

RESULTS: A microarray design included 142 DNA probes to detect S. pneumoniae, H. influenzae, M. pneumoniae, C. pneumoniae, and L. pneumophila, the probes being synthesized onto slides. Using the example of clinical samples containing S. pneumoniae and/or H. influenza DNA, we selected optimal parameters for DNA hybridization on microarrays, which enabled to identify bacterial pathogens of community-acquired pneumonia with sufficient efficiency, specificity and reproducibility: the amount of hybridized DNA was 2 μg, the DNA fragment size: 300 nt, hybridization temperature: 47°C. There was selected a list of probes for specific detection of S. pneumoniae and H. influenzae characterized by an effective hybridization signal under the identified conditions. We determined the threshold values of standardized probe signals for specific detection of S. pneumoniae (4.5 Z) and H. influenzae (4.9 Z) in clinical samples.

DESIGN: Studies on the association between CT values and smear status were included in a descriptive systematic review. Authors of studies including smear, culture and Xpert results were asked for individual-level data, and receiver operating characteristic curves were calculated.

RESULTS: Of 918 citations, 10 were included in the descriptive systematic review. Fifteen data sets from studies potentially relevant for individual-level data meta-analysis provided individual-level data (7511 samples from 4447 patients); 1212 patients had positive Xpert results for at least one respiratory sample (1859 samples overall). ROC analysis revealed an area under the curve (AUC) of 0.85 (95%CI 0.82-0.87). Cut-off CT values of 27.7 and 31.8 yielded sensitivities of 85% (95%CI 83-87) and 95% (95%CI 94-96) and specificities of 67% (95%CI 66-77) and 35% (95%CI 30-41) for smear-positive samples.

METHODS AND FINDINGS: The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed.