No abstract

Anticancer drugs that bind to DNA and inhibit DNA-processing enzymes represent an important class of anticancer drugs. In order to find stronger DNA binding and more potent cytotoxic compounds, a series of ester-coupled bisanthrapyrazole derivatives of 7-chloro-2-[2-[(2-hydroxyethyl)methylamino]ethyl]anthra[1,9-cd]pyrazol-6(2H)-one (AP9) were designed and evaluated by molecular docking techniques. Because the anthrapyrazoles are unable to be reductively activated like doxorubicin and other anthracyclines, they should not be cardiotoxic like the anthracyclines. Based on the docking scores of a series of bisanthrapyrazoles with different numbers of methylene linkers (n) that were docked into an X-ray structure of double-stranded DNA, five bisanthrapyrazoles (n = 1 to 5) were selected for synthesis and physical and biological evaluation. The synthesized compounds were evaluated for DNA binding and bisintercalation by measuring the DNA melting temperature increase, for growth inhibitory effects on the human erythroleukemic K562 cell line, and for DNA topoisomerase IIα-mediated cleavage of DNA and inhibition of DNA topoisomerase IIα decatenation activities. The results suggest that the bisanthrapyrazoles with n = 2 to 5 formed bisintercalation complexes with DNA. In conclusion, a novel group of bisintercalating anthrapyrazole compounds have been designed, synthesized and biologically evaluated as possible anticancer agents.

A series of amide-coupled bisanthrapyrazole derivatives of 7-chloro-2-[2-[(2-hydroxyethyl)methylamino]ethyl]anthra[1,9-cd]pyrazol-6(2H)-one (AP9) were designed using molecular modeling and docking and synthesized in order to develop an anticancer drug that formed a strongly binding bisintercalation complex with DNA. Concentration dependency for the increase in the DNA melting temperature was used to determine the DNA binding strength and whether bisintercalation occurred for the newly synthesized analogs. The ability of the compounds to inhibit the growth of the human erythroleukemic K562 cell line and inhibit the decatenation activity of DNA topoisomerase IIalpha was also measured. Finally, the compounds were evaluated for their ability to act as topoisomerase II poisons by measuring the topoisomerase IIalpha-mediated double strand cleavage of DNA. All of the bisanthrapyrazoles inhibited K562 cell growth and topoisomerase IIalpha in the low micromolar range. Compounds with either two or three methylene linkers formed bisintercalation complexes with DNA and bound as strongly as, or more strongly than, doxorubicin. In conclusion, a novel group of amide-coupled bisintercalating anthrapyrazole compounds were designed, synthesized, and evaluated for their physico-chemical and biologic properties as potential anticancer agents.

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients’ presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.

Background: Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke. Methods: We conducted our modeling on consecutive patients who were evaluated in our health system with an initial diagnosis of TIA in a 9-month period. We established the final diagnoses after the clinical evaluation by independent verification from two stroke neurologists. We used Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) for prediction modeling. Results: The RFE-based classifier correctly predicts 78% of the overall observations. In particular, the classifier correctly identifies 68% of the cases labeled as "TIA mimic" and 83% of the "TIA" discharge diagnosis. The LASSO classifier had an overall accuracy of 74%. Both the RFE and LASSO-based classifiers tied or outperformed the ABCD2 score and the Diagnosis of TIA (DOT) score. With respect to predicting TIA, the RFE-based classifier has 61.1% accuracy, the LASSO-based classifier has 79.5% accuracy, whereas the DOT score applied to the dataset yields an accuracy of 63.1%. Conclusion: The results of this pilot study indicate that a multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, can be used to effectively differentiate between TIA, TIA mimics, and minor stroke.