RESUMO
Although lowering low-density lipoprotein cholesterol (LDL-C) levels following acute myocardial infarction (MI) is the cornerstone of secondary prevention, the attainment of recommended LDL-C goals remains suboptimal in real-world practice. We sought to investigate recurrent adverse events in post-MI patients. From the Korea Acute Myocardial Infarction-National Institutes of Health registry, a total of 5049 patients with both measurements of plasma LDL-C levels at index admission and at the one-year follow-up visit were identified. Patients who achieved an LDL-C reduction ≥ 50% from the index MI and an LDL-C level ≤ 70 mg/dL at follow-up were classified as target LDL-C achievers. The primary endpoint was a two-year major adverse cardiac and cerebrovascular event (MACCE), including cardiovascular mortality, recurrent MI, and ischemic stroke. Among the 5049 patients, 1114 (22.1%) patients achieved the target LDL-C level. During a median follow-up of 2.1 years, target LDL-C achievers showed a significantly lower incidence (2.2% vs. 3.5%, log-rank p = 0.022) and a reduced adjusted hazard of MACCE (0.63; p = 0.041). In patients with acute MI, achieving a target LDL-C level was associated with a lower incidence and a reduced hazard of recurrent clinical events. These results highlight the need to improve current practices for managing LDL-C levels in real-world settings.
RESUMO
Heterogeneity in intratumoral cancers leads to discrepancies in drug responsiveness, due to diverse genomics profiles. Thus, prediction of drug responsiveness is critical in precision medicine. So far, in drug responsiveness prediction, drugs' molecular "fingerprints", along with mutation statuses, have not been considered. Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular fingerprints. As a result, DeepIC50 showed better cell viability IC50 prediction accuracy in pan-cancer cell lines over two independent cancer cell line datasets. Gastric cancer (GC) is not only one of the lethal cancer types in East Asia, but also a heterogeneous cancer type. Currently approved targeted therapies in GC are only trastuzumab and ramucirumab. Responsive GC patients for the drugs are limited, and more drugs should be developed in GC. Due to the importance of GC, we applied DeepIC50 to a real GC patient dataset. Drug responsiveness prediction in the patient dataset by DeepIC50, when compared to the other models, were comparable to responsiveness observed in GC cell lines. DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.
