Background: Hyperhomocysteinemia might be at least partially due to compromised B vitamin status in critically ill patients and has been linked with critical illness. This study was conducted to examine the association between plasma homocysteine with B vitamins and clinical outcomes in critically ill surgical patients. Methods: Thirty‐two patients in the surgical intensive care unit (SICU) were enrolled. Disease severity (Acute Physiology and Chronic Health Evaluation II score), hematological values, serum and erythrocyte folate, serum vitamin B12, plasma, and erythrocyte pyridoxal 5′‐phosphate (PLP) were determined within 24 hours of admission and again after 7 days. Results: The prevalence of hyperhomocysteinemia in the patients was either 46.9% (plasma homocysteine ≥12 µmol/L) or 31.3% (plasma homocysteine ≥15 µmol/L) on day 1 in the SICU and increased to 62.5% (plasma homocysteine ≥12 µmol/L) and 37.5% (plasma homocysteine ≥15 µmol/L) on day 7 after admission to the SICU. Plasma homocysteine, serum folate, and vitamin B12 significantly increased by day 7, whereas plasma and erythrocyte PLP remained constant throughout the study. Plasma homocysteine was not correlated with serum folate and vitamin B12. However, plasma and erythrocyte PLP on day 1 were adversely associated with day 1 levels of plasma homocysteine after adjusting for potential confounders. Plasma homocysteine on day 1 or changes (Δ day 7 – day 1) did not show any association with clinical outcomes. Conclusions: Lower plasma PLP might be a significant factor for increased plasma homocysteine in critically ill surgical patients. The association between plasma homocysteine and clinical outcomes was not found.
ObjectivesWe investigated whether chronological changes in portal flow and clinical factors play a role in the liver regeneration (LR) process after right donor-hepatectomy.Materials and methodsParticipants in this prospective study comprised 58 donors who underwent right donor-hepatectomy during the period February 2014 to February 2015 at a single medical institution. LR was estimated using two equations: remnant left liver (RLL) growth (%) and liver volumetric recovery (LVR) (%). Donors were classified into an excellent regeneration (ER) group or a moderate regeneration (MR) group based on how their LR on postoperative day 7 compared to the median value.ResultsMultivariate analysis revealed that low residual liver volume (OR = .569, 95% CI: .367– .882) and high portal venous velocity in the immediate postoperative period (OR = 1.220, 95% CI: 1.001–1.488) were significant predictors of LR using the RLL growth equation; high portal venous velocity in the immediate postoperative period (OR = 1.325, 95% CI: 1.081–1.622) was a significant predictor of LR using the LVR equation. Based on the two equations, long-term LR was significantly greater in the ER group than in the MR group (p < .001).ConclusionPortal venous velocity in the immediate postoperative period was an important factor in LR. The critical time for short-term LR is postoperative day 7; it is associated with long-term LR in donor-hepatectomy.
<p style='text-indent:20px;'>Public bike sharing systems have become the most popular shared economy application in transportation. The convenience of this system depends on the availability of bikes and empty racks. One of the major challenges in operating a bike sharing system is the repositioning of bikes between rental sites to maintain sufficient bike inventory in each station at all times. Most systems hire trucks to conduct dynamic repositioning of bikes among rental sites. We have analyzed a commonly used repositioning scheme and have demonstrated its ineffectiveness. To realize a higher quality of service, we proposed a crowdsourced dynamic repositioning strategy: first, we analyzed the historical rental data via the random forest algorithm and identified important factors for demand forecasting. Second, considering 30-minute periods, we calculated the optimal bike inventory via integer programming for each rental site in each time period with a sufficient crowd for repositioning bikes. Then, we proposed a minimum cost network flow model in a time-space network for calculating the optimal voluntary rider flows for each period based on the current bike inventory, which is adjusted according to the forecasted demands. The results of computational experiments on real-world data demonstrate that our crowdsourced repositioning strategy may reduce unmet rental demands by more than 30% during rush hours compared to conventional trucks.</p>
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