Panoramic radiographic findings as predictors of mandibular nerve exposure following third molar extraction: digital versus conventional radiographic techniques.

OBJECTIVE: The aim was to compare digital and conventional panoramic imaging techniques for identifying high-risk radiographic markers associated with mandibular nerve (MN) injury after mandibular third molar (M3) removal. STUDY DESIGN: The study used a retrospective cohort model. The predictor variable was the presence or absence of radiographic signs associated with MN exposure during M3 removal. The outcome variable was MN exposure. Intraexaminer variability was estimated using a kappa statistic. Logistic regression modeling was used to measure the association between radiographic signs and MN exposure and determine if imaging technique modified that association. The level of statistical significance was set at P < .05. RESULTS: The sample was composed of 571 patients having 1,017 mandibular M3s removed. The MN was visualized in 66 extraction sites (6.5%). In the adjusted logistic model, radiographic signs were statistically associated with MN exposure (P < .001) and imaging technique was a statistically insignificant effect modifying variable (P = .4). CONCLUSION: The results of this study suggest that imaging technique does not modify the relationship between high-risk panoramic radiographic signs and MN exposure.

A Heuristic method for assigning a false-discovery rate for protein identifications from Mascot database search results.

MS/MS and database searching has emerged as a valuable technology for rapidly analyzing protein expression, localization, and post-translational modifications. The probability-based search engine Mascot has found widespread use as a tool to correlate tandem mass spectra with peptides in a sequence database. Although the Mascot scoring algorithm provides a probability-based model for peptide identification, the independent peptide scores do not correlate with the significance of the proteins to which they match. Herein, we describe a heuristic method for organizing proteins identified at a specified false-discovery rate using Mascot-matched peptides. We call this method PROVALT, and it uses peptide matches from a random database to calculate false-discovery rates for protein identifications and reduces a complex list of peptide matches to a nonredundant list of homologous protein groups. This method was evaluated using Mascot-identified peptides from a Trypanosoma cruzi epimastigote whole-cell lysate, which was separated by multidimensional LC and analyzed by MS/MS. PROVALT was then compared with the two traditional methods of protein identification when using Mascot, the single peptide score and cumulative protein score methods, and was shown to be superior to both in regards to the number of proteins identified and the inclusion of lower scoring nonrandom peptide matches.