A prospective randomized study assessing optimal method for teaching vascular anastomoses.

BACKGROUND: Laboratory skills training is now required for general surgery residents. The optimal method of teaching vascular anastomosis (VA) is not well defined. Teaching VA skills one-on-one with a faculty instructor will result in a more rapid accumulation of skills than teaching in a large group setting. METHODS: Residents were shown an instructional video on how to perform a VA using a standardized model (cadaver saphenous vein and porcine aorta). Each resident then performed a baseline VA. Sixteen first- and second-year surgical residents were then randomized to 2 VA teaching sessions that consisted of either 1) group teaching (GT, 8 residents in a room with 1 faculty instructor circulating) or 2) one-on-one teaching (1-on-1, faculty member focused on individual resident). After each of these sessions, residents performed a standardized VA. The anastomoses were video recorded. Performance was evaluated using a standardized scoring system by a separate expert who viewed the video recordings in a blinded fashion. Outcome measures included total errors, total time, global rating scale, and an anastomosis-specific end-product evaluation (leak and passage of coronary dilator). RESULTS: Overall, significant decreases in total errors (21 to 15, P=0.001) and time to complete anastomoses (42 to 38 min, P=0.02) and an increase in global rating scales (7 to 11, P=0.003) were noted in both groups from baseline after 2 VA teaching session. The 1-on-1 group demonstrated significantly greater improvement in terms of reduced anastomotic time (30 vs. 42 min, P=0.007) and in reduction of errors (13 vs. 19 errors, P=0.09) than the GT group. CONCLUSIONS: The high-fidelity VA model is a useful tool for junior general surgery residents. Both GT and 1-on-1 groups demonstrated significant improvement in total errors and time after only 2 sessions. Greater improvement was noted using the 1-on-1 model.

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control.

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Clipper: p-value-free FDR control on high-throughput data from two conditions.

High-throughput biological data analysis commonly involves identifying features such as genes, genomic regions, and proteins, whose values differ between two conditions, from numerous features measured simultaneously. The most widely used criterion to ensure the analysis reliability is the false discovery rate (FDR), which is primarily controlled based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions. Clipper is a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper outperforms existing methods for a broad range of applications in high-throughput data analysis.