Supplementary data are available at Bioinformatics online.

Background and Purpose-We sought to determine the incidence and severity of bleeding events in patients with stroke and cardiovascular diseases who were taking oral antithrombotic agents in Japan, where the incidence of hemorrhagic stroke is higher than in Western countries. Methods-A prospective, multicenter, observational study was conducted; 4009 patients who were taking oral antithrombotic agents for stroke and cardiovascular diseases were enrolled. The patients were classified into 4 groups according to their antithrombotic treatment: the single antiplatelet agent group (47.2%); the dual antiplatelet agent group (8.7%); the warfarin group (32.4%); and the warfarin plus antiplatelet agent group (11.7%). The primary end point was life-threatening or major bleeding according to the MATCH trial definition. Results-During a median follow-up of 19 months, there were 57 life-threatening and 51 major bleeding events, including 31 intracranial hemorrhages. The annual incidence of the primary end point was 1.21% in the single antiplatelet agent group, 2.00% in the dual antiplatelet agent group, 2.06% in the warfarin group, and 3.56% in the warfarin plus antiplatelet agent group (PϽ0.001). After adjustment for baseline characteristics, adding an antiplatelet agent to warfarin increased the risk of the primary end point (relative riskϭ1.76; 95% CI, 1.05 to 2.95), and adding another antiplatelet agent to single antiplatelet agent therapy increased the secondary end point of any bleeding, including minor events (relative riskϭ1.37; 95% CI, 1.07 to 1.76). Conclusions-The incidence of bleeding events during antithrombotic therapy in Japan was similar to that reported for Western countries, although the trials used different study designs. Dual antithrombotic therapy was independently related to an increased risk of bleeding events.

Background: To determine whether the use of oral antithrombotic agents before the onset of intracerebral hemorrhage (ICH) affects hematoma features and early patient outcome. Methods: A retrospective, multicenter study involving 1,006 consecutive Japanese patients (607 men, 67 ± 12 years of age) hospitalized within 24 h after the onset of nontraumatic ICH was conducted. Results: One hundred and eighty patients were taking oral antiplatelet agents (17.9%, AP group), 67 were taking warfarin (6.7%, W group), and 21 were taking both (2.1%, W + AP group). After adjustment for age, sex, and known confounders, the taking of each kind of antithrombotic therapy was independently related to cerebellar hemorrhage; the odds ratios (OR) and 95% CI, with patients taking no antithrombotic agents as the reference group, were 2.31 (1.23–4.32) for the AP group, 2.90 (1.26–6.63) for the W group, and 3.43 (1.02–11.59) for the W + AP group. Similarly, the taking of each kind of antithrombotic therapy was independently related to hematoma enlargement within the initial 24 h (OR and 95% CI: AP group, 1.92, 1.10–3.34; W group, 4.80, 2.12–10.87; W + AP group, 4.94, 1.31–18.61) and mortality at 3 weeks post-ICH (OR and 95% CI: AP group, 2.70, 1.56–4.68; W group, 2.50, 1.05–5.96; W + AP group, 9.41, 2.78–31.88). Conclusions: Prior medication with antiplatelet agents, warfarin, or both was predictive of cerebellar hemorrhage, hematoma enlargement, and early death in Japanese ICH patients.

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Accurate protein side-chain conformation prediction is crucial for protein modeling and existing methods for the task are widely used; however, faster and more accurate methods are still required. Here we present a new machine learning approach to the problem where an energy function for each rotamer in a structure is computed additively over pairs of contacting atoms. A family of 156 neural networks indexed by amino acid and contacting atom types is used to compute these rotamer energies as a function of atomic contact distances. Although direct energy targets are not available for training, the neural networks can still be optimized by converting the energies to probabilities and optimizing these probabilities using Markov Chain Monte Carlo methods. The resulting predictor SIDEpro makes predictions by initially setting the rotamer probabilities for each residue from a backbone dependent rotamer library, then iteratively updating these probabilities using the trained neural networks. After convergences of the probabilities, the side-chains are set to the highest probability rotamer. Finally, a post-processing clash reduction step is applied to the models. SIDEpro represents a significant improvement in speed and a modest, but statistically significant, improvement in accuracy when compared to the state-of-the-art for rapid side-chain prediction method SCWRL4 on the following datasets: (1) 379 protein test set of SCWRL4; (2) 94 proteins from CASP9; (3) a set of 7 large protein-only complexes; and (4) a ribosome with and without the RNA. Using the SCWRL4 test set, SIDEpro's accuracy (χ1 86.14%, χ1+2 74.15%) is slightly better than SCWRL4-FRM (χ1 85.43%, χ1+2 73.47%) and it is 7.0 times faster. On the same test set SIDEpro is clearly more accurate than SCWRL4-RRM (χ1 84.15%, χ1+2 71.24%) and 2.4 times faster. Evaluation on the additional test sets yield similar accuracy results with SIDEpro being slightly more accurate than SCWRL4-FRM and clearly more accurate than SCWRL4-RRM; however, the gap in CPU time is much more significant when the methods are applied to large protein complexes. SIDEpro is part of the SCRATCH suite of predictors and available from: http://scratch.proteomics.ics.uci.edu/.

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