Usefulness of Routine Fractional Flow Reserve for Clinical Management of Coronary Artery Disease in Patients With Diabetes

Importance: Approximately one-third of patients considered for coronary revascularization have diabetes, which is a major determinant of clinical outcomes, often influencing the choice of the revascularization strategy. The usefulness of fractional flow reserve (FFR) to guide treatment in this population is understudied and has been questioned. Objective: To evaluate the usefulness and rate of major adverse cardiovascular events (MACE) of integrating FFR in management decisions for patients with diabetes who undergo coronary angiography. Design, setting, and participants: This cross-sectional study used data from the PRIME-FFR study derived from the merger of the POST-IT study (Portuguese Study on the Evaluation of FFR-Guided Treatment of Coronary Disease [March 2012-November 2013]) and R3F study (French Study of FFR Integrated Multicenter Registries Implementation of FFR in Routine Practice [October 2008-June 2010]), 2 prospective multicenter registries that shared a common design. A population of all-comers for whom angiography disclosed ambiguous lesions was analyzed for rates, patterns, and outcomes associated with management reclassification, including revascularization deferral, in patients with vs without diabetes. Data analysis was performed from June to August 2018. Main outcomes and measures: Death from any cause, myocardial infarction, or unplanned revascularization (MACE) at 1 year. Results: Among 1983 patients (1503 [77%] male; mean [SD] age, 65 [10] years), 701 had diabetes, and FFR was performed for 1.4 lesions per patient (58.2% of lesions in the left anterior descending artery; mean [SD] stenosis, 56% [11%]; mean [SD] FFR, 0.81 [0.01]). Reclassification by FFR was high and similar in patients with and without diabetes (41.2% vs 37.5%, P = .13), but reclassification from medical treatment to revascularization was more frequent in the former (142 of 342 [41.5%] vs 230 of 730 [31.5%], P = .001). There was no statistical difference between the 1-year rates of MACE in reclassified (9.7%) and nonreclassified patients (12.0%) (P = .37). Among patients with diabetes, FFR-based deferral identified patients with a lower risk of MACE at 12 months (25 of 296 [8.4%]) compared with those undergoing revascularization (47 of 257 [13.1%]) (P = .04), and the rate was of the same magnitude of the observed rate among deferred patients without diabetes (7.9%, P = .87). Status of insulin treatment had no association with outcomes. Patients (6.6% of the population) in whom FFR was disregarded had the highest MACE rates regardless of diabetes status. Conclusions and relevance: Routine integration of FFR for the management of coronary artery disease in patients with diabetes may be associated with a high rate of treatment reclassification. Management strategies guided by FFR, including revascularization deferral, may be useful for patients with diabetes.info:eu-repo/semantics/publishedVersio

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Partial purification and characterization of the specific protein-lysine N-methyltransferase of YL32, a yeast ribosomal protein.

YL23 and YL32 are two of the three most heavily methylated ribosomal proteins of Saccharomyces cerevisiae. Using an in vitro assay, it was determined that they are methylated by two distinct enzymes. The protein-lysine N-methyltransferase that methylates YL32 was partially purified by affinity and ion-exchange chromatography. Its molecular mass was estimated to be 82 kDa, and its isoelectric point to be 4.45. Optimum activity was expressed at pH 7.5, and the enzyme was irreversibly inactivated at pH lower than 5.0. The Km of the enzyme for AdoMet is 1.7 +/- 0.4 microM, and the Ki toward AdoHcy was 0.71 microM. Formation of epsilon-N-dimethyllysine was observed to occur in two steps via epsilon-N-monomethyllysine. Like other protein-lysine N-methyltransferases, the methylase of YL32 exhibits a high substrate specificity