Consistency of Thyroid Imaging Reporting and Data System Reporting in Community-Based Imaging Centers Versus a Large Tertiary Hospital

ObjectiveIn our country, thyroid nodules are sonographically evaluated in health maintenance organization (HMO) imaging centers, and patients are referred to tertiary hospitals for ultrasound-guided fine-needle aspiration (FNA) biopsy when indicated. We evaluated the concordance in Thyroid Imaging Reporting and Data System (TI-RADS) classification reporting between these sites.MethodsWe conducted a retrospective cohort study reviewing the sonographic features of thyroid nodules evaluated both at the HMO and a large tertiary center between January 2018 and December 2019. The primary outcome was concordance between the TI-RADS classification at both sites. Additional endpoints included correlation of TI-RADS to the Bethesda category following FNA and correlation of TI-RADS with malignancy on final pathology at each site.ResultsThe records of 336 patients with 370 nodules were reviewed. The level of concordance was poor (19.8%), with 277 (74.8%) nodules demonstrating higher TI-RADS and 20 (5.4%) lower TI-RADS at the HMO compared to the hospital (P < .001; weighted κ = 0.120). FNA results were available for 236 (63.8%) nodules. The Bethesda category strongly correlated with the hospital TI-RADS (P < .001), yet not with HMO TI-RADS (P = .123). In the surgically removed 57 nodules, a strong correlation was identified between the malignancy on final pathology and TI-RADS documented at the hospital (P < .001), yet not at the HMO (P = .259).ConclusionsThere is poor agreement between TI-RADS classification on ultrasound performed in the HMO compared to a tertiary hospital. The hospital’s TI-RADS strongly correlated with the Bethesda category and the final risk of malignancy, unlike the HMO.     

Systematic Mapping of Protein Mutational Space by Prolonged Drift Reveals the Deleterious Effects of Seemingly Neutral Mutations

Systematic mappings of the effects of protein mutations are becoming increasingly popular. Unexpectedly, these experiments often find that proteins are tolerant to most amino acid substitutions, including substitutions in positions that are highly conserved in nature. To obtain a more realistic distribution of the effects of protein mutations, we applied a laboratory drift comprising 17 rounds of random mutagenesis and selection of M.HaeIII, a DNA methyltransferase. During this drift, multiple mutations gradually accumulated. Deep sequencing of the drifted gene ensembles allowed determination of the relative effects of all possible single nucleotide mutations. Despite being averaged across many different genetic backgrounds, about 67% of all nonsynonymous, missense mutations were evidently deleterious, and an additional 16% were likely to be deleterious. In the early generations, the frequency of most deleterious mutations remained high. However, by the 17th generation, their frequency was consistently reduced, and those remaining were accepted alongside compensatory mutations. The tolerance to mutations measured in this laboratory drift correlated with sequence exchanges seen in M.HaeIII’s natural orthologs. The biophysical constraints dictating purging in nature and in this laboratory drift also seemed to overlap. Our experiment therefore provides an improved method for measuring the effects of protein mutations that more closely replicates the natural evolutionary forces, and thereby a more realistic view of the mutational space of proteins.     

A Method for Removing Effects of Nonspecific Binding on the Distribution of Binding Stoichiometries: Application to Mass Spectroscopy Data

There is often an interest in knowing, for a given ligand concentration, how many protein molecules have one, two, three, etc. ligands bound in a specific manner. This is a question that cannot be addressed using conventional ensemble techniques. Here, a mathematical method is presented for separating specific from nonspecific binding in nonensemble studies. The method provides a way to determine the distribution of specific binding stoichiometries at any ligand concentration when using nonensemble (e.g., single-molecule) methods. The applicability of the method is demonstrated for ADP binding to creatine kinase using mass spectroscopy data. A major advantage of our method, which can be applied to any protein-ligand system, is that no previous information regarding the mechanism of ligand interaction is required.