The pharmacophore of the neonicotinoid insecticide imidacloprid, nitroiminoimidazolidine, was modified to heterocycles such as thiazolidine, pyrrolidine, dihydroimidazole, dihydrothiazole, and pyridone conjugated to nitroimine (=NNO2) or nitromethylene (=CHNO2). Their 6-chloro-3-pyridylmethyl or 5-chloro-3-thiazolylmethyl derivatives were examined for insecticidal activity against the American cockroach by injection and for neuroblocking activity using the cockroach ganglion. Most of the compounds having the neonicotinoidal pharmacophore exhibited insecticidal activity at the nanomolar level, which was enhanced in the presence of synergists, and high neuroblocking activity at the micromolar level. Quantitative analysis for the compounds showed that the neuroblocking potency is proportional both to the Mulliken charge on the nitro oxygen atom and to the partition coefficient log P value. The equation for the insecticidal versus neuroblocking potencies indicated that both potencies are related proportionally with each other when the other factors are the same.

Background and Aim Contrast‐enhanced harmonic endoscopic ultrasonography (CH‐EUS) is useful for the diagnosis of lesions inside and outside the digestive tract. This study evaluated the value of artificial intelligence (AI) in the diagnosis of gastric submucosal tumors by CH‐EUS. Methods This retrospective study included 53 patients with gastrointestinal stromal tumors (GISTs) and leiomyomas, all of whom underwent CH‐EUS between June 2015 and February 2020. A novel technology, SiamMask, was used to track and trim the lesions in CH‐EUS videos. CH‐EUS was evaluated by AI using deep learning involving a residual neural network and leave‐one‐out cross‐validation. The diagnostic accuracy of AI in discriminating between GISTs and leiomyomas was assessed and compared with that of blind reading by two expert endosonographers. Results Of the 53 patients, 42 had GISTs and 11 had leiomyomas. Mean tumor size was 26.4 mm. The consistency rate of the segment range of the tumor image extracted by SiamMask and marked by the endosonographer was 96% with a Dice coefficient. The sensitivity, specificity, and accuracy of AI in diagnosing GIST were 90.5%, 90.9%, and 90.6%, respectively, whereas those of blind reading were 90.5%, 81.8%, and 88.7%, respectively (P = 0.683). The κ coefficient between the two reviewers was 0.713. Conclusions The diagnostic ability of CH‐EUS results evaluated by AI to distinguish between GISTs and leiomyomas was comparable with that of blind reading by expert endosonographers.