After radical cystectomy (RC), patients are at risk for complications including infections. The expansion of myeloid-derived suppressor cells (MDSCs) after surgery may contribute to the lower resistance to infection. Immune response and postoperative complications were compared in men consuming either specialized immunonutrition (SIM; n = 14) or an oral nutrition supplement (ONS; n = 15) before and after RC. MDSC count (Lin− CD11b+ CD33+) was significantly different between the groups over time (p = 0.005) and significantly lower in SIM 2 d after RC (p < 0.001). MDSC count expansion from surgery to 2 d after RC showed a weak association with an increase in infection rate 90 d after surgery (p = 0.061). Neutrophil:lymphocyte ratio was significantly lower in SIM compared with ONS 3 h after the first incision (p = 0.039). Participants receiving SIM had a 33% reduction in postoperative complication rate (95% confidence interval [CI], 1–64; p = 0.060) and a 39% reduction in infection rate (95% CI, 8–70; p = 0.027) during late-phase recovery. The small sample size limits the study findings. Patient summary Results show that the immune response to surgery and late infection rates differ between radical cystectomy patients receiving specialized immunonutrition versus oral nutrition supplement in the perioperative period. Trial registration ClinicalTrials.gov NCT01868087.

Although the reflux resolution rates at initial postoperative voiding cystourethrogram approach those of open surgery, there is a significant failure rate at 1 year, which warrants long-term followup.

Th1-to-Th2 ratios, peak interleukin-6 levels and plasma arginine suggest that consuming specialized immunonutrition counteracts the disrupted T-helper balance, lowers the inflammatory response and prevents arginine depletion due to radical cystectomy.

Traditional drug discovery is an inefficient process. Human pluripotent stem cell‐derived cardiomyocytes can potentially fill the gap between animal and clinical studies, but conventional two‐dimensional cultures inadequately recapitulate the human cardiac phenotype. Here, we systematically examined the pharmacological responses of engineered human ventricular‐like cardiac tissue strips (hvCTS) and organoid chambers (hvCOC) to 25 cardioactive compounds covering various drug classes. While hvCTS effectively detected negative and null inotropic effects, the sensitivity to positive inotropes was modest. We further quantified the predictive capacity of hvCTS in a blinded screening, with accuracies for negative, positive, and null inotropic effects at 100%, 86%, and 80%, respectively. Interestingly, hvCOC, with a pro‐maturation milieu that yields physiologically complex parameters, displayed enhanced positive inotropy. Based on these results, we propose a two‐tiered screening system for avoiding false positives and negatives. Such an approach would facilitate drug discovery by leading to better overall success.

SummaryAccurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS) electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug.