Effect of intra-arrest trans-nasal evaporative cooling in out-of-hospital cardiac arrest: a pooled individual participant data analysis

Background: Randomized trials have shown that trans-nasal evaporative cooling initiated during CPR (i.e. intra-arrest) effectively lower core body temperature in out-of-hospital cardiac arrest patients. However, these trials may have been underpowered to detect significant differences in neurologic outcome, especially in patients with initial shockable rhythm. Methods: We conducted a post hoc pooled analysis of individual data from two randomized trials including 851 patients who eventually received the allocated intervention and with available outcome (“as-treated” analysis). Primary outcome was survival with favourable neurological outcome at hospital discharge (Cerebral Performance Category [CPC] of 1–2) according to the initial rhythm (shockable vs. non-shockable). Secondary outcomes included complete neurological recovery (CPC 1) at hospital discharge. Results: Among the 325 patients with initial shockable rhythms, favourable neurological outcome was observed in 54/158 (34.2%) patients in the intervention and 40/167 (24.0%) in the control group (RR 1.43 [confidence intervals, CIs 1.01–2.02]). Complete neurological recovery was observed in 40/158 (25.3%) in the intervention and 27/167 (16.2%) in the control group (RR 1.57 [CIs 1.01–2.42]). Among the 526 patients with initial non-shockable rhythms, favourable neurological outcome was in 10/259 (3.8%) in the intervention and 13/267 (4.9%) in the control group (RR 0.88 [CIs 0.52–1.29]; p = 0.67); survival and complete neurological recovery were also similar between groups. No significant benefit was observed for the intervention in the entire population. Conclusions: In this pooled analysis of individual data, intra-arrest cooling was associated with a significant increase in favourable neurological outcome in out-of-hospital cardiac arrest patients with initial shockable rhythms. Future studies are needed to confirm the potential benefits of this intervention in this subgroup of patients

Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy

Abstract For understanding a chemical compound’s mechanism of action and its side effects, as well as for drug discovery, it is crucial to predict its possible protein targets. This study examines 15 developed target-centric models (TCM) employing different molecular descriptions and machine learning algorithms. They were contrasted with 17 third-party models implemented as web tools (WTCM). In both sets of models, consensus strategies were implemented as potential improvement over individual predictions. The findings indicate that TCM reach f1-score values greater than 0.8. Comparing both approaches, the best TCM achieves values of 0.75, 0.61, 0.25 and 0.38 for true positive/negative rates (TPR, TNR) and false negative/positive rates (FNR, FPR); outperforming the best WTCM. Moreover, the consensus strategy proves to have the most relevant results in the top $$20\%$$ 20 % of target profiles. TCM consensus reach TPR and FNR values of 0.98 and 0; while on WTCM reach values of 0.75 and 0.24. The implemented computational tool with the TCM and their consensus strategy at: https://bioquimio.udla.edu.ec/tidentification01/ . Scientific Contribution: We compare and discuss the performances of 17 public compound-target interaction prediction models and 15 new constructions. We also explore a compound-target interaction prioritization strategy using a consensus approach, and we analyzed the challenging involved in interactions modeling. Graphical Abstrac