Genetic diversity of Mycobacterium avium isolates recovered from clinical samples and from the environment: molecular characterization for diagnostic purposes

Isolation of Mycobacterium avium complex (MAC) organisms from clinical samples may occur in patients without clinical disease, making the interpretation of results difficult. The clinical relevance of MAC isolates from different types of clinical samples (n = 47) from 39 patients in different sections of a hospital was assessed by comparison with environmental isolates (n = 17) from the hospital. Various methods for identification and typing (commercial probes, phenotypic characteristics, PCR for detection of IS1245 and IS901, sequencing of the hsp65 gene, and pulsed-field gel electrophoresis) were evaluated. The same strain was found in all the environmental isolates, 21 out of 23 (91.3%) of the isolates cultured from urine samples, and 5 out of 19 (26.3%) isolates from respiratory specimens. This strain did not cause disease in the patients. Testing best characterized the strain as M. avium subsp. hominissuis, with the unusual feature that 81.4% of these isolates lacked the IS1245 element. Contamination of certain clinical samples with an environmental strain was the most likely event; therefore, characterization of the environmental mycobacteria present in health care facilities should be performed to discard false-positive isolations in nonsterile samples, mainly urine samples. Molecular techniques applied in this study demonstrated their usefulness for this purpose.

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography

Purpose: The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT). Methods: Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed. Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model. Results: The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma. The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911–0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group. Conclusions: Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.