This is the first study to examine the relationship between type D personality and adherence to MAD treatment. Type D patients reported a significantly higher discontinuation rate when compared to patients without type D personality.

Autism spectrum disorders are severe neurodevelopmental disorders, marked by impairments in reciprocal social interaction, delays in early language and communication, and the presence of restrictive, repetitive and stereotyped behaviors. Accumulating evidence suggests that dysfunction of the amygdala may be partially responsible for the impairment of social behavior that is a hallmark feature of ASD. Our studies suggest that a valproic acid (VPA) rat model of ASD exhibits an enlargement of the amygdala as compared to controls rats, similar to that observed in adolescent ASD individuals. Since recent research suggests that altered neuronal development and morphology, as seen in ASD, may result from a common post-transcriptional process that is under tight regulation by microRNAs (miRs), we examined genome-wide transcriptomics expression in the amygdala of rats prenatally exposed to VPA, and detected elevated miR-181c and miR-30d expression levels as well as dysregulated expression of their cognate mRNA targets encoding proteins involved in neuronal system development. Furthermore, selective suppression of miR-181c function attenuates neurite outgrowth and branching, and results in reduced synaptic density in primary amygdalar neurons in vitro. Collectively, these results implicate the small non-coding miR-181c in neuronal morphology, and provide a framework of understanding how dysregulation of a neurodevelopmentally relevant miR in the amygdala may contribute to the pathophysiology of ASD.

Flooding is a compound stress, imposing strong limitations on plant development. The expression of adaptive traits that alleviate flooding stress may be constrained if floodwater levels are too deep. For instance, adventitious root outgrowth is typically less profound in completely submerged plants than in partially submerged plants, suggesting additional constraints in full submergence. As both oxygen and carbohydrates are typically limited resources under submergence, we tested the effects of oxygen concentration in the floodwater and carbohydrate status of the plants on flooding-induced adventitious root formation in Solanum dulcamara L. Partially submerged plants continued to form adventitious roots in low-oxygen floodwater, whereas completely submerged plants developed hardly any roots, even in floodwater with twice the ambient oxygen concentration. This suggests that contact with the atmosphere, enabling internal aeration, is much more important to optimal adventitious root formation than floodwater oxygen concentrations. If plants were depleted of carbohydrates before flooding, adventitious root formation in partial submergence was poor, unless high light was provided. Thus, either stored or newly produced carbohydrates can fuel adventitious root formation. These results imply that the impact of an environmental stress factor like flooding on plant performance may strongly depend on the interplay with other environmental factors.

Background: Manual data collection is still the gold standard for disease-specific patient registries. However, CAPRI-3 uses text mining (an artificial intelligence (AI) technology) for patient identification and data collection. The aim of this study is to demonstrate the reliability and efficiency of this AI-driven approach. Methods: CAPRI-3 is an observational retrospective multicenter cohort registry on metastatic prostate cancer. We tested the patient-identification algorithm and automated data extraction through manual validation of the same patients in two pilots in 2019 and 2022. Results: Pilot one identified 2030 patients and pilot two 9464 patients. The negative predictive value of the algorithm was maximized to prevent false exclusions and reached 94.8%. The completeness and accuracy of the automated data extraction were 92.3% or higher, except for date fields and inaccessible data (images/pdf) (10–88.9%). Additional manual quality control took over 3 h less time per patient than the original fully manual CAPRI registry (105 vs. 300 min). Conclusions: The CAPRI-3 patient-identification algorithm is a sound replacement for excluding ineligible candidates. The AI-driven data extraction is largely accurate and complete, but manual quality control is needed for less reliable and inaccessible data. Overall, the AI-driven approach of the CAPRI-3 registry is reliable and timesaving.