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Based on the advantages of azole molecules and fluoroquinolone drugs, we designed and synthesized 34 clinafloxacin-azole conjugates using fragment-based drug design and drug combination principles. The in vitro activities of the synthesized conjugates against Mycobacterium tuberculosis (H37Rv), Hela cell as well as Gram-positive and Gram-negative bacteria were assayed. The bioassay results revealed that most of the target molecules had anti-tuberculosis (anti-TB) activity, of which 14 compounds had very strong anti-TB activity [minimum inhibitory concentration (MIC) 2 μM]. In addition, the compounds with strong activity towards H37Rv had weak activity towards Gram-negative and Gram-positive bacteria, showing obvious selectivity towards H37Rv. Predicted toxicity data indicated that 27 molecules were less toxic or equivalent to that of the original drug (clinafloxacin). Especially, it is demonstrated that compound TM2l exhibited the strongest anti-TB activity (MIC = 0.29 μM), low antibacterial activity, negligible toxicity, and good drug-likeness values, which can be considered as an ideal lead molecule for future optimization.

Background: We pioneered a Deep Learning-based Coronary computed tomography–angiography Automatic Reconstruction and Diagnosis System (D-CARDS) for optimizing the workflow of diagnosis in coronary artery disease. Its efficiency and performance in clinical settings were verified. Method: In the model establishment stage, D-CARDS was trained with coronary computed tomography–angiography (CCTA) imaging data from 10,410 patients divided into training, tuning, and external validation test sets in a ratio of 7:2:1. A total of 685 patients were included in the study of clinical validation. 350 CCTA cases were collected for comparison of time efficiency. Another 335 CCTA cases were selected to reveal the diagnostic performance of D-CARDS with paired invasive coronary angiography (ICA) as the reference standard. Stenosis of 50% was considered to be obstructive and 70% or more to be significantly obstructive. The diagnostic performance of D-CARDS was evaluated as the receiver operating characteristic curve (ROC) and the corresponding area under the curve (AUC) at patient, vascular and segmental bases compared to both unilateral expert and arbitrated expert results. Findings: The average time taken of CCTA procedure after scanning (post-processing and diagnostic reporting) by D-CARDS was decreased by 73.3% from an average of 16.1 min with the conventional approach to 4.3 min (p=0.000). A total of 335 patients with 1,222 vessels and 3,559 segments were included in the final comparison of diagnostic performance. D-CARDS showed greater sensitivity (89.3% and 72.4%) than arbitrated expert results(82.6% and 62.0%)for detecting stenosis at both the 50% and 70% thresholds on patient-base, whereas its specificities were lower on every bases. The AUC showed that its diagnostic performance was equivalent to that of either of a unilateral expert on patient-based analysis, although slightly inferior to the arbitrated expert results. Interpretation: D-CARDS greatly improves the efficiency of CCTA procedure. Its diagnostic performance in detecting coronary stenosis is closer to that of an attending radiologist on patient-based analysis. The system can be used to optimize CCTA workflow. Funding Statement: This study was funded by Beijing science and technology committee. (grant reference number, Z201100005620009). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: The study was approved by the Institutional Review Board (IRB)/Ethics Committee. The work was conducted in a manner compliant with the Measures for the Ethical Review of Biomedical Research Involving Humans and was adherent to the tenets of the Declaration of Helsinki.