We examined the effects of three allelochemicals found in tomato (chlorogenic acid, rutin, and tomatine) and two thermal regimes (21Њ:10ЊC and 26Њ:15ЊC, representing spring and summer, respectively) on the performance of a generalist insect predator (Podisus maculiventris: Pentatomidae) fed prey containing those allelochemicals. The prey were Manduca sexta (Sphingidae) caterpillars, Solanaceae specialists with a preference for tomato. Whether an allelochemical had a negative, neutral, or positive effect on developmental time or mass gained by the predators depended on thermal regime and the combination of allelochemicals in the prey's diet. The effects of multiple allelochemicals were not always additive. For the most part, the allelochemicals had greater negative effects at the warmer thermal regime. Effects of allelochemicals also depended on the stage of the predator. Individually, chlorogenic acid and rutin prolonged developmental time of secondinstar nymphs. In contrast, only rutin and tomatine together affected developmental time of fourth-instar nymphs, and this combination of allelochemicals reduced developmental time. Tomatine substantially reduced mass gained by second-instar nymphs but had no effect on mass gained by fourth-instar nymphs. Rutin and tomatine together had no effect on second-instar nymphs but increased the mass of fourth-instar nymphs. There were no allelochemical by temperature interactions for second-instar nymphs, whereas allelochemical by temperature interactions influenced stadium duration, final dry mass, and relative growth rate of fourth-instar nymphs. Rutin and tomatine together eliminated the negative effect of chlorogenic acid on consumption of prey by the fourth-instar nymphs. Chlorogenic acid by itself and rutin and tomatine together increased the efficiency of conversion of ingested prey to nymphal biomass. Comparison with an earlier study revealed that the effects of these thermal and dietary conditions were distinctly different for prey and predators, which suggests that such conditions would promote developmental asynchrony between prey and predator populations.
Background Acute kidney injury (AKI) stage 3, one of the most severe complications in patients with heart transplantation (HT), is associated with substantial morbidity and mortality. We aimed to develop a machine learning (ML) model to predict post-transplant AKI stage 3 based on preoperative and perioperative features. Methods Data from 107 consecutive HT recipients in the provincial center between 2018 and 2020 were included for analysis. Logistic regression with L2 regularization was used for the ML model building. The predictive performance of the ML model was assessed using the area under the curve (AUC) in tenfold stratified cross-validation and was compared with that of the Cleveland-clinical model. Results Post-transplant AKI occurred in 76 (71.0%) patients including 15 (14.0%) stage 1, 18 (16.8%) stage 2, and 43 (40.2%) stage 3 cases. The top six features selected for the ML model to predicate AKI stage 3 were serum cystatin C, estimated glomerular filtration rate (eGFR), right atrial long-axis dimension, left atrial anteroposterior dimension, serum creatinine (SCr) and FVII. The predictive performance of the ML model (AUC: 0.821; 95% confidence interval [CI]: 0.740–0.901) was significantly higher compared with that of the Cleveland-clinical model (AUC: 0.654; 95% [CI]: 0.545–0.763, p < 0.05). Conclusions The ML model, which achieved an effective predictive performance for post-transplant AKI stage 3, may be helpful for timely intervention to improve the patient’s prognosis.
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