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Acetylcholinesterase (AChE) is a proven target for control of the malaria mosquito (Anopheles gambiae). Unfortunately, a single amino acid mutation (G119S) in An. gambiae AChE-1 (AgAChE) confers resistance to the AChE inhibitors currently approved by the World Health Organization for indoor residual spraying. In this report, we describe several carbamate inhibitors that potently inhibit G119S AgAChE and that are contact-toxic to carbamate-resistant An. gambiae. PCR-RFLP analysis was used to confirm that carbamate-susceptible G3 and carbamate-resistant Akron strains of An. gambiae carry wild-type (WT) and G119S AChE, respectively. G119S AgAChE was expressed and purified for the first time, and was shown to have only 3% of the turnover number (k cat) of the WT enzyme. Twelve carbamates were then assayed for inhibition of these enzymes. High resistance ratios (>2,500-fold) were observed for carbamates bearing a benzene ring core, consistent with the carbamate-resistant phenotype of the G119S enzyme. Interestingly, resistance ratios for two oxime methylcarbamates, and for five pyrazol-4-yl methylcarbamates were found to be much lower (4- to 65-fold). The toxicities of these carbamates to live G3 and Akron strain An. gambiae were determined. As expected from the enzyme resistance ratios, carbamates bearing a benzene ring core showed low toxicity to Akron strain An. gambiae (LC50>5,000 μg/mL). However, one oxime methylcarbamate (aldicarb) and five pyrazol-4-yl methylcarbamates (4a–e) showed good to excellent toxicity to the Akron strain (LC50 = 32–650 μg/mL). These results suggest that appropriately functionalized “small-core” carbamates could function as a resistance-breaking anticholinesterase insecticides against the malaria mosquito.

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As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer.