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Background The rapid development in big data analytics and the data-rich environment of intensive care units together provide unprecedented opportunities for medical breakthroughs in the field of critical care. We developed and validated a machine learning-based model, the Pediatric Risk of Mortality Prediction Tool (PROMPT), for real-time prediction of all-cause mortality in pediatric intensive care units. Methods Utilizing two separate retrospective observational cohorts, we conducted model development and validation using a machine learning algorithm with a convolutional neural network. The development cohort comprised 1445 pediatric patients with 1977 medical encounters admitted to intensive care units from January 2011 to December 2017 at Severance Hospital (Seoul, Korea). The validation cohort included 278 patients with 364 medical encounters admitted to the pediatric intensive care unit from January 2016 to November 2017 at Samsung Medical Center. Results Using seven vital signs, along with patient age and body weight on intensive care unit admission, PROMPT achieved an area under the receiver operating characteristic curve in the range of 0.89–0.97 for mortality prediction 6 to 60 h prior to death. Our results demonstrated that PROMPT provided high sensitivity with specificity and outperformed the conventional severity scoring system, the Pediatric Index of Mortality, in predictive ability. Model performance was indistinguishable between the development and validation cohorts. Conclusions PROMPT is a deep model-based, data-driven early warning score tool that can predict mortality in critically ill children and may be useful for the timely identification of deteriorating patients.

Stagnation of contents at the anastomotic site for intestinal flows after anastomotic operation is a critical issue in neonates. Although various anastomosis methods have been developed, in the clinical field, poor passage at the anastomotic site in cases of jejunal atresia is still observed. A CFD study was carried out to clarify the reasons for the stagnation and to find favorable anastomosis methods from a fluid dynamical point of view. Direct numerical simulations were performed using OpenFOAM. The boundaries of the computational domain were peristaltically moved to reproduce flow. The results reveal that the peristaltic motion on the distal side dominates the flow and that on the proximal side has a negligible influence. In particular, the contents do not pass the anastomotic site when the peristaltic motion on the distal side is not active. The flow rate as a measure of the driving force of the flow on the proximal side is large when the amplitude of the peristaltic motion is large and the diameter is small. It was also found that anastomosis methods do not affect flow resistance. [Display omitted] •Reason of flow stagnation at anastomotic site after operation is identified.•Peristaltic motion on distal side dominates the flow.•Anastomosis methods do not affect flow resistance.•Driving force of flow on proximal side depends on diameter and peristaltic amplitude.

Multiple studies have yielded conflicting findings regarding the impact of incorporating triticale as a feed ingredient on laying hens’ production parameters. This article used a meta-analysis to assess the factors influencing its effects on layers’ performance. According to the PRISMA guidelines, articles examining the influence of triticale on layers’ egg production (EP), egg weight (EW), egg yolk color (EYC), feed intake (FI), and feed conversion ratio (FCR) were identified across Google Scholar, PubMed, and Science Direct. As a result, six articles were selected and categorized into 16 experiments for inclusion in our meta-analysis. Overall, the trim-and-fill method indicated that triticale had comparable effects to conventional cereals on the performance of laying hens. However, the meta-ANOVA emphasized that the Hy-Line Brown hen strain and Joesong and Juanilo triticale strains induced the best laying hen performance. Moreover, the meta-regression emphasized a positive correlation between the triticale inclusion percentage and the EW in Juanilo triticale diets and a negative correlation between the triticale inclusion percentages and the EYC in the triticale and laying hens strains studied. Therefore, this meta-analysis makes a valuable contribution to comprehending the factors that may influence the effects of triticale on the performance of layers.

Eating addiction is the primary cause of modern obesity. Although the causal role of the lateral hypothalamic area (LHA) for eating is demonstrated in rodents, there is no evidence in primates regarding naturalistic eating behaviors. We investigated the role of LHA GABAergic (LHAGABA) neurons in eating by chemogenetics in three macaques. LHAGABA neuron activation significantly increased naturalistic goal-directed behaviors and food motivation, which was specific for palatable food. PET and MRS validated the chemogenetic activation. Rs-fMRI result revealed that functional connectivity (FC) between the LHA and frontal areas was increased, while the FC between the frontal cortices was decreased after the LHAGABA neuron activation. Thus, our study elucidates the role of LHAGABA neurons in eating and obesity therapeutics for primates and humans.