Assessment of intradialysis calcium mass balance by a single pool variable‐volume calcium kinetic model

Introduction: A reliable method of intradialysis calcium mass balance quantification is far from been established. We herein investigated the use of a single‐pool variable‐volume Calcium kinetic model to assess calcium mass balance in chronic and stable dialysis patients. Methods: Thirty‐four patients on thrice‐weekly HD were studied during 240 dialysis sessions. All patients were dialyzed with a nominal total calcium concentration of 1.50 mmol/L. The main assumption of the model is that the calcium distribution volume is equal to the extracellular volume during dialysis. This hypothesis is assumed valid if measured and predicted end dialysis plasma water ionized calcium concentrations are equal. A difference between predicted and measured end‐dialysis ionized plasma water calcium concentration is a deviation on our main hypothesis, meaning that a substantial amount of calcium is exchanged between the extracellular volume and a nonmodeled compartment. Findings: The difference between predicted and measured values was 0.02 mmol/L (range ?0.08:0.16 mmol/L). With a mean ionized dialysate calcium concentration of 1.25 mmol/L, calcium mass balance was on average negative (mean ± SD ?0.84 ± 1.33 mmol, range ?5.42:2.75). Predialysis ionized plasma water concentration and total ultrafiltrate were the most important predictors of calcium mass balance. A significant mobilization of calcium from the extracellular pool to a nonmodeled pool was calculated in a group of patients. Discussion: The proposed single pool variable‐volume Calcium kinetic model is adequate for prediction and quantification of intradialysis calcium mass balance, it can evaluate the eventual calcium transfer outside the extracellular pool in clinical practice.     

Effects of nitric oxide on the alleviation of postharvest disease induced by Penicillium italicum in navel orange fruits

The present study evaluated the effects of 15 μL L^-1 nitric oxide (NO) on the fruit defence response of ‘Newhall’ navel orange. The decay rate of NO-treated navel orange fruits was significantly lower than that of control fruits during storage at 20 °C storage (P < 0.05). Treatment with NO suppressed the increase in disease incidence and lesion area in orange fruits inoculated with Penicillium italicum (P. italicum); significantly increased the activities of phenolic metabolism-associated enzymes and pathogenesis-related (PR) proteins, including polyphenoloxidase (PPO), phenylalanine ammonia-lyase (PAL), β-1,3-glucanase (GLU) and chitinase (CHT); and enhanced the activities of key enzymes, including 4-coumarate: CoA ligase (4CL), cinnamate-4-hydroxylase (C4H) and chalcone isomerase (CHI) in the phenylpropanoid pathway. The contents of total phenolics, flavonoids and lignin were also higher in NO-treated fruits than in control fruits. The findings suggest that exogenous NO could induce disease resistance against P. italicum in navel orange fruits.     

Lipids and Fatty Acids of the Gonads of Sea Urchin Diadema setosum (Echinodermata) From the Coastal Area of the Nha Trang Bay,Central Vietnam

The sea urchin Diadema setosum is edible and desirable as food by locals in central Vietnam and a promising target for potential fishing. The lipid profiles of the gonads of the sea urchin inhabiting the coastal area in Nha Trang Bay are studied for the first time. The determination of the content of the total lipids (TL), total phospholipids (PL), monoacylglycerols (MAG), diacylglycerols (DAG), triacylglycerols (TAG), cholesterol (Chol), sterol esters, and free fatty acids (FFA) is analyzed by high-performance thin layer chromatography (HPTLC); the phosphatidylserine (PS), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and sphingomyelin (SM) are determined by high-pressure liquid chromatography (HPLC); and the fatty acid levels of TLs are identified using gas chromatography (GC). Non-polar TAG, FFA, Chol, and DAG dominated. The content of total PL is significant. PC is abundant among PL fractions, followed by PE, PI, and PS. The TLs contain a high proportion of PUFA, mainly due to arachidonic fatty acid and eicosapentanoic fatty acid. The (n−6)/(n−3) ratio is 1.68, and the atherogenic and thrombogenic indexes of D. setosum are 1.13 and 0.73. The results can be used in the development of marine bioprospecting and methodological approaches for the creation of functional substances.     

Microfluidic Tumor-on-a-Chip Model to Study Tumor Metabolic Vulnerability

Tumor-specific metabolic adaptations offer an interesting therapeutic opportunity to selectively destroy cancer cells. However, solid tumors also present gradients of nutrients and waste products across the tumor mass, forcing tumor cells to adapt their metabolism depending on nutrient availability in the surrounding microenvironment. Thus, solid tumors display a heterogenous metabolic phenotype across the tumor mass, which complicates the design of effective therapies that target all the tumor populations present. In this work, we used a microfluidic device to study tumor metabolic vulnerability to several metabolic inhibitors. The microdevice included a central chamber to culture tumor cells in a three-dimensional (3D) matrix, and a lumen in one of the chamber flanks. This design created an asymmetric nutrient distribution across the central chamber, generating gradients of cell viability. The results revealed that tumor cells located in a nutrient-enriched environment showed low to no sensitivity to metabolic inhibitors targeting glycolysis, fatty acid oxidation, or oxidative phosphorylation. Conversely, when cell density inside of the model was increased, compromising nutrient supply, the addition of these metabolic inhibitors disrupted cellular redox balance and led to tumor cell death.     

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10–50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8–82.9% during wk 1 to 7) and negative predictive value (range: 89.5–93.8%) but lower specificity (range: 76.7–88.5%) and positive predictive value (range: 58.1–78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.     

Sulfite Action in Glycolytic Inhibition: In Vivo Real‐Time Observation by Hyperpolarized 13C NMR Spectroscopy

Detecting the molecular targets of xenobiotic substances in vivo poses a considerable analytical challenge. Here, we describe the use of an NMR‐based tracer methodology for the instantaneous in vivo observation of sulfur(IV) action on cellular metabolism. Specifically, we find that glycolytic flux is directed towards sulfite adducts of dihydroxyacetone phosphate and pyruvate as off‐pathway intermediates that obstruct glycolytic flux. In particular, the pyruvate–sulfite association hinders the formation of downstream metabolites. The apparent in vivo association constant of pyruvate and sulfite agrees with the apparent inhibition constant of CO₂ formation, thus supporting the importance of pyruvate interception in disturbing central metabolism and inhibiting NAD regeneration.     

Composition and Structure of the Bovine Milk Fat Globule Membrane—Some Nutritional and Technological Implications

Milk fat is dispersed in milk as small, spherical globules, stabilized in the form of emulsion by its surrounding membrane. This membrane, called the milk fat globule membrane (MFGM), is created in the secretory cells of the mammary gland, and represents an ordered and unique biophysical system. This review characterizes the main milk fat globule components, their structure, and intracellular origin. The milk fat globule membrane has many potentially bioactive components. These are discussed in terms of their health effects for the native and processed globules. Because of their functional and nutritional properties, MFGM components can be used as valuable ingredients in the manufacture of new functional foods.     

A theoretical physiologically based pharmacokinetic approach for modeling the fate of anthocyanins in vivo

Recent studies on the pharmacokinetics of anthocyanins (ACNs) and their metabolites have uncovered evidence for hitherto unknown physiological effects affecting the fate of these compounds in vivo. In particular, it has been shown that the stomach, in addition to the small intestine, has an important role in absorption. Most studies still use a noncompartmental or one-compartmental approach to determine the pharmacokinetic parameters of ACNs, which does not represent the anatomical and physiological conditions that a compound is subject to in the organism. Thus, the objective of this study was to review the current knowledge of the different processes involved in the metabolism of ACNs once ingested and, based on this information, propose a theoretical physiologically based, multicompartmental pharmacokinetic (PBMK) model to describe their fate in vivo. This is the first study that reports a PBMK model for ACNs; the model provides a more physiologically representative approach for ANC metabolism, which could be used as a basis for experimental designs and interspecies scale-up.