Interactive parallel sex pheromone circuits that promote and suppress courtship behaviors in the cockroach.

Many animals use multicomponent sex pheromones for mating, but the specific function and neural processing of each pheromone component remain unclear. The cockroach Periplaneta americana is a model for studying sex pheromone communication, and an adult female emits major and minor sex pheromone components, periplanone-B and -A (PB and PA), respectively. Attraction and courtship behaviors (wing-raising and abdominal extension) are strongly expressed when adult males are exposed to PB but weakly expressed when they are exposed to PA. When major PB is presented together with minor PA, behaviors elicited by PB were impaired, indicating that PA can both promote and suppress courtship behaviors depending on the pheromonal context. In this study, we identified the receptor genes for PA and PB and investigated the effects of knocking down each receptor gene on the activities of PA- and PB-responsive sensory neurons (PA- and PB-SNs), and their postsynaptic interneurons, and as well as effects on courtship behaviors in males. We found that PB strongly and PA weakly activate PB-SNs and their postsynaptic neurons, and activation of the PB-processing pathway is critical for the expression of courtship behaviors. PA also activates PA-SNs and the PA-processing pathway. When PA and PB are simultaneously presented, the PB-processing pathway undergoes inhibitory control by the PA-processing pathway, which weakens the expression of courtship behaviors. Our data indicate that physiological interactions between the PA- and PB-processing pathways positively and negatively mediate the attraction and courtship behaviors elicited by sex pheromones.

Predictive Insights Into Exocrine Pancreatic Insufficiency in Chronic Pancreatitis and Autoimmune Pancreatitis: A Decision Tree Approach.

OBJECTIVE: Exocrine pancreatic insufficiency (EPI) is a common manifestation of chronic pancreatitis (CP) and autoimmune pancreatitis (AIP). This study aimed to estimate the presence of EPI in patients with CP or AIP using alternative clinical markers. MATERIALS AND METHODS: A machine learning analysis employing a decision tree model was conducted on a retrospective training cohort comprising 57 patients with CP or AIP to identify EPI, defined as fecal elastase-1 levels less than 200 µg/g. The outcomes were then confirmed in a validation cohort of 26 patients. RESULTS: Thirty-nine patients (68%) exhibited EPI in the training cohort. The decision tree algorithm revealed body mass index (≤21.378 kg/m 2 ) and total protein level (≤7.15 g/dL) as key variables for identifying EPI. The algorithm's performance was assessed using 5-fold cross-validation, yielding area under the receiver operating characteristic curve values of 0.890, 0.875, 0.750, 0.625, and 0.771, respectively. The results from the validation cohort closely replicated those in the training cohort. CONCLUSIONS: Decision tree analysis revealed that EPI in patients with CP or AIP can be identified based on body mass index and total protein. These findings may help guide the implementation of appropriate treatments for EPI.