Functional impact of subunit composition and compensation on Drosophila melanogaster nicotinic receptors-targets of neonicotinoids.

Neonicotinoid insecticides target insect nicotinic acetylcholine receptors (nAChRs) and their adverse effects on non-target insects are of serious concern. We recently found that cofactor TMX3 enables robust functional expression of insect nAChRs in Xenopus laevis oocytes and showed that neonicotinoids (imidacloprid, thiacloprid, and clothianidin) exhibited agonist actions on some nAChRs of the fruit fly (Drosophila melanogaster), honeybee (Apis mellifera) and bumblebee (Bombus terrestris) with more potent actions on the pollinator nAChRs. However, other subunits from the nAChR family remain to be explored. We show that the Dα3 subunit co-exists with Dα1, Dα2, Dß1, and Dß2 subunits in the same neurons of adult D. melanogaster, thereby expanding the possible nAChR subtypes in these cells alone from 4 to 12. The presence of Dα1 and Dα2 subunits reduced the affinity of imidacloprid, thiacloprid, and clothianidin for nAChRs expressed in Xenopus laevis oocytes, whereas the Dα3 subunit enhanced it. RNAi targeting Dα1, Dα2 or Dα3 in adults reduced expression of targeted subunits but commonly enhanced Dß3 expression. Also, Dα1 RNAi enhanced Dα7 expression, Dα2 RNAi reduced Dα1, Dα6, and Dα7 expression and Dα3 RNAi reduced Dα1 expression while enhancing Dα2 expression, respectively. In most cases, RNAi treatment of either Dα1 or Dα2 reduced neonicotinoid toxicity in larvae, but Dα2 RNAi enhanced neonicotinoid sensitivity in adults reflecting the affinity-reducing effect of Dα2. Substituting each of Dα1, Dα2, and Dα3 subunits by Dα4 or Dß3 subunit mostly increased neonicotinoid affinity and reduced efficacy. These results are important because they indicate that neonicotinoid actions involve the integrated activity of multiple nAChR subunit combinations and counsel caution in interpreting neonicotinoid actions simply in terms of toxicity.

Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond.

There have been prior attempts to utilize machine learning to address issues in the medical field, particularly in diagnoses using medical images and developing therapeutic regimens. However, few cases have demonstrated the usefulness of machine learning for enhancing health consciousness of patients or the public in general, which is necessary to cause behavioral changes. This paper describes a novel case wherein the uptake rate for colorectal cancer examinations has significantly increased due to the application of machine learning and nudge theory. The paper also discusses the effectiveness of social impact bonds (SIBs) as a scheme for realizing these applications. During a healthcare SIB project conducted in the city of Hachioji, Tokyo, machine learning, based on historical data obtained from designated periodical health examinations, digitalized medical insurance receipts, and medical examination records for colorectal cancer, was used to deduce segments for whom the examination was recommended. The result revealed that out of the 12,162 people for whom the examination was recommended, 3264 (26.8%) received it, which exceeded the upper expectation limit of the initial plan (19.0%). We conclude that this was a successful case that stimulated discussion on potential further applications of this approach to wider regions and more diseases.