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Background: Drug repositioning, the strategy of unveiling novel targets of existing drugs could reduce costs and accelerate the pace of drug development. To elucidate the novel molecular mechanism of known drugs, considering the long time and high cost of experimental determination, the efficient and feasible computational methods to predict the potential associations between drugs and targets are of great aid.Methods: A novel calculation model for drug-target interaction (DTI) prediction based on network representation learning and convolutional neural networks, called DLDTI， was generated. The proposed approach simultaneously fuses the topology of complex networks and diverse information from heterogeneous data sources, and copes with the noisy, incomplete, and high-dimensional nature of large-scale biological data by learning the low-dimensional and rich depth features of drugs and proteins. The low-dimensional feature vectors were used to train DLDTI to obtain the optimal mapping space and to infer new DTIs by ranking candidates according to their proximity to the optimal mapping space. More specifically, based on the results from the DLDTI, we experimentally validate the predicted targets of tetramethylpyrazine (TMPZ) on atherosclerosis progression in vivo.Results: The experimental results show that the DLDTI model achieves promising performance under 5-fold cross-validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. For the validation study of TMPZ on atherosclerosis, a total of 288 targets were identified and 190 of them were involved in platelet activation. The pathway analysis indicated signaling pathways, namely PI3K/Akt, cAMP and calcium pathways might be the potential targets. Effects and molecular mechanism of TMPZ on atherosclerosis were experimentally confirmed in animal models.Conclusions: DLDTI model can serve as a useful tool to provide promising DTI candidates for experimental validation. Based on the predicted results of DLDTI model, we found TMPZ could attenuate atherosclerosis by inhibiting signal transductions in platelets. The source code and datasets explored in this work are available at https://github.com/CUMTzackGit/DLDTI.

Objective Clinical guidelines recommend an optimal serum potassium concentration between 4.0 and 5.0 mmol/L in patients with acute myocardial infarction (AMI), which was based on lower‐quality evidence from more than 20 years ago. Therefore, it is essential to re‐evaluate the range of optimal potassium levels in patients with AMI in intensive care unit (ICU). Methods This was a retrospective study based on Philips eICU Collaborative Research Database, which covered 9776 patients with AMI between 2014 and 2015. All patients had more than or equal to 2 serum potassium measurements and were categorized by the mean serum potassium level (<3.5, 3.5–4.5, 4.5–5.5, ≥5.5 mmol/L) and potassium variability (1st, 2nd, and ≥3rd standard deviation (SD)). Binary logistic regression was used to determine the association between mean potassium levels, variability and in‑hospital mortality in AMI. Results Of all 9776 AMI patients in ICU, 8731 (89.3%) patients were included. A total of 69847 potassium measurements were performed in these patients. There was a J‐shaped relationship between mean serum potassium level and in‐hospital mortality. The lowest mortality (mortality rate, 7.2%; 95% CI, 6.57%–7.76%) was observed in patients with mean potassium level between 3.5 and 4.5 mmol/L and a low potassium variability within the 1st SD. Logistic regression showed that the risk of in‑hospital mortality is highest when the mean potassium level ≥5.5 mmol/L (57.6%; 95% Cl, 45.02%–70.24%; multivariable adjusted OR, 14.8; 95% CI, 8.4–26.2) compared to the reference group of 3.5–4.5 mmol/L and potassium variability within the 3rd SD (16.5%; 95% Cl, 15.19%–17.88%; multivariable adjusted OR, 3.3; 95% CI, 2.7–4.1) compared to 1st SD. Several sensitivity analyses confirmed these results. Conclusion Among AMI patients in ICU, the minimum risk of in‑hospital mortality was observed in those with mean potassium levels between 3.5 and 4.5 mmol/L or a minimal potassium variability compared to those who had higher or lower values.