Accuracy of the central landmark for catheterization of the right internal jugular vein after placement of the ProSeal laryngeal mask airway.

OBJECTIVE: Catheterization of the internal jugular vein (IJV) after placement of a laryngeal mask airway (LMA) has been reported to be difficult. The purpose of this study was to evaluate the accuracy of the central landmark for catheterization of the right IJV after placement of a ProSeal LMA. METHODS: We enrolled 80 patients (30 men and 50 women) who were scheduled to undergo surgery under general anesthesia conveyed by a size 3 ProSeal LMA. A needle pathway based on the central landmark for right IJV catheterization was simulated. Ultrasound images were obtained, which we contrasted with the simulated pathway to evaluate whether the landmark accuracy remained unchanged after placement of the ProSeal LMA. Both frequency of simulated right carotid artery (CA) puncture and overlap between the right IJV and right CA were also investigated. RESULTS: The simulated needle pathway ran along the course of the right IJV in 60% (48/80) of subjects, and transected the CA in 31.3% (25/80) of subjects. Both events together occurred in 20% (16/80) of subjects. The central landmark had a medial bias of 6.8 mm (95% confidence interval, 5.3-8.4). In 83.8% (67/80) of subjects, the center of the right IJV was lateral to the central landmark. The possibility of overlap of the right IJV and CA was high after ProSeal LMA placement. CONCLUSION: After placement of the ProSeal LMA, the central landmark could not offer a good success rate at the first puncture attempt. When using the central landmark to catheterize the IJV after a ProSeal LMA placement, medial deviation of the central landmark should be considered. Ultrasound guidance may be helpful in difficult cases.

Pairagon: a highly accurate, HMM-based cDNA-to-genome aligner.

MOTIVATION: The most accurate way to determine the intron-exon structures in a genome is to align spliced cDNA sequences to the genome. Thus, cDNA-to-genome alignment programs are a key component of most annotation pipelines. The scoring system used to choose the best alignment is a primary determinant of alignment accuracy, while heuristics that prevent consideration of certain alignments are a primary determinant of runtime and memory usage. Both accuracy and speed are important considerations in choosing an alignment algorithm, but scoring systems have received much less attention than heuristics. RESULTS: We present Pairagon, a pair hidden Markov model based cDNA-to-genome alignment program, as the most accurate aligner for sequences with high- and low-identity levels. We conducted a series of experiments testing alignment accuracy with varying sequence identity. We first created 'perfect' simulated cDNA sequences by splicing the sequences of exons in the reference genome sequences of fly and human. The complete reference genome sequences were then mutated to various degrees using a realistic mutation simulator and the perfect cDNAs were aligned to them using Pairagon and 12 other aligners. To validate these results with natural sequences, we performed cross-species alignment using orthologous transcripts from human, mouse and rat. We found that aligner accuracy is heavily dependent on sequence identity. For sequences with 100% identity, Pairagon achieved accuracy levels of >99.6%, with one quarter of the errors of any other aligner. Furthermore, for human/mouse alignments, which are only 85% identical, Pairagon achieved 87% accuracy, higher than any other aligner. AVAILABILITY: Pairagon source and executables are freely available at http://mblab.wustl.edu/software/pairagon/