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.     

School attendance and daily respiratory symptoms in children: influence of moisture damage

We investigated the effect of weekends and school holidays on the daily frequency and severity of respiratory and other symptoms among children attending schools with (index) or without (reference) moisture damage in Spain, the Netherlands, and Finland. Throughout 1 year, parents of 419 children with a respiratory condition attending index (n=15) or reference (n=10) primary schools completed three symptom diaries. We assessed associations between lower respiratory tract, upper respiratory tract or allergy, and other symptom scores and school day, weekend, or summer holiday using mixed regression models stratified by country and moisture damage. We evaluated interactions between moisture damage and type of day. We combined country‐specific estimates (incidence rate ratios [IRRs] and 95% confidence interval [CI]) in meta‐analyses. Symptom scores were lower during weekends and holiday. Lower respiratory tract symptoms were statistically significantly less common during holiday with strongest effect in index schools (IRR=0.7; CI=0.6–0.8). Reporting of other symptoms was more reduced during holiday in index (IRR=0.6; CI=0.4–0.9) than in reference (IRR=0.95; CI=0.8–1.2) schools (interaction P<.01). In conclusion, symptoms were less frequent and/or severe during summer holiday and weekends. This pattern was stronger among children attending moisture‐damaged schools, suggesting potential improvement in moisture damage‐related symptoms during school breaks.     

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.     

豆腐果苷及其代谢产物时辰药动学研究

目的：研究不同时辰给药对豆腐果苷及其代谢产物药代动力学行为的影响。方法：建立并验证豆腐果苷及其3种Ⅰ相代谢产物高效液相色谱-质谱联用同时检测方法。分别在8∶00,14∶00及0∶00灌胃给予大鼠豆腐果苷50 mg/kg,眼底静脉丛采集血样,测得其血药浓度,对比在不同时间给药后原药及代谢产物的药物代谢动力学行为。结果：所建立的高效液相色谱串联质谱检测方法成功应用于灌胃给药后的大鼠血浆豆腐果苷及其3种代谢产物的同时检测。以AUC(0-t)做为吸收程度评价指标,原药、氧化产物、还原产物均为8∶00给药组>0∶00给药组>14∶00给药组;同一时间点原药与代谢产物比较,8∶00给药组、14∶00给药组、0∶00给药组均为：原药>还原产物>氧化产物。Cmax原药0∶00给药组>8∶00给药组>14∶00给药组,还原产物与氧化产物为8∶00给药组>14∶00给药组>0∶00给药组;Tmax原药与还原产物8∶00给药组>14∶00给药组>0∶00给药组,氧化产物8∶00给药组>0∶00给药组>14∶00给药组;以CLz/F为表征的清除情况,原药、还原产物与氧化产物均为14∶00给药组>0∶00给药组>8∶00给药组。 结论：本研究所选择的三个特定时间点,大鼠体内豆腐果苷原药及代谢产物在吸收与代谢过程显示出明显差异。8∶00给药吸收程度要大于其他两个时间点（P<0.01）,14∶00给药原药及代谢产物体内清除快于其他时间点（P<0.01）。     

一种新型睡眠呼吸暂停检测系统的设计

为了监测睡眠呼吸暂停低通气综合征,基于光栅编码传感器,设计了可穿戴式的呼吸暂停检测仪。光栅编码传感器被嵌入在一条系在患者腰部的腹带内,在呼吸过程中,由于腹部体积的变化引起腹带中光栅的位置发生变化。通过光电二极管传感器阵列对光栅的运动进行检测,采用单片机对信号进行分析处理,从而判断呼气和吸气的过程,计算出呼吸周期,若周期超时则为呼吸暂停。经多次实验,并与呼吸检测设备的检测结果进行比较,本系统的平均精确度高于95%。     

微透析-液相色谱-质谱联用技术在中药研究中的应用

微透析-液相色谱-质谱联用技术不仅可以实现活体、实时、连续地微创取样,还具有高灵敏度、分析速度快、检测范围宽等特点,是中药研究的利器,已广泛应用于中药体内过程及作用机制研究等方面。本文对微透析技术进行了简要介绍,并结合相关研究实例,着重介绍了微透析-液相色谱-质谱联用技术在中药活性成分筛选、体内外代谢、药代动力学、代谢组学等研究中的策略及应用。