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Since every biological system requires capillaries to support its oxygenation, design of engineered preclinical models of such systems, for example, vascularized microphysiological systems (vMPS) have gained attention enhancing the physiological relevance of human biology and therapies. But the physiology and function of formed vessels in the vMPS is currently assessed by non‐standardized, user‐dependent, and simple morphological metrics that poorly relate to the fundamental function of oxygenation of organs. Here, a chained neural network is engineered and trained using morphological metrics derived from a diverse set of vMPS representing random combinations of factors that influence the vascular network architecture of a tissue. This machine‐learned algorithm outputs a singular measure, termed as vascular network quality index (VNQI). Cross‐correlation of morphological metrics and VNQI against measured oxygen levels within vMPS revealed that VNQI correlated the most with oxygen measurements. VNQI is sensitive to the determinants of vascular networks and it consistently correlates better to the measured oxygen than morphological metrics alone. Finally, the VNQI is positively associated with the functional outcomes of cell transplantation therapies, shown in the vascularized islet‐chip challenged with hypoxia. Therefore, adoption of this tool will amplify the predictions and enable standardization of organ‐chips, transplant models, and other cell biosystems.

Knowing concentrations of lipids is essential for understanding their physiological functions and discovering new disease biomarkers. However, it is highly challenging to accurately quantify lipids due to structural diversity and multiple isomeric forms of lipids. To address these critical gaps, we have developed a novel aziridine‐based isobaric tag labelling strategy that allows (i) determination of lipid double‐bond positional isomers, (ii) accurate relative quantification of unsaturated lipids, and (iii) improvement of ionization efficiencies of nonpolar lipids. The power of this method is demonstrated in characterization and quantification of various categories of lipids such as fatty acids, phosphoglycerol lipids, cholesteryl esters (CE), and glycerides. 17 CE lipid isomers were identified and quantified simultaneously from Alzheimer's disease (AD) mouse serum without using lipid standards. Among them, 6 CE isomers showed significant changes in concentrations in AD serum.

Knowing concentrations of lipids is essential for understanding their physiological functions and discovering new disease biomarkers. However, it is highly challenging to accurately quantify lipids due to structural diversity and multiple isomeric forms of lipids. To address these critical gaps, we have developed a novel aziridine‐based isobaric tag labelling strategy that allows (i) determination of lipid double‐bond positional isomers, (ii) accurate relative quantification of unsaturated lipids, and (iii) improvement of ionization efficiencies of nonpolar lipids. The power of this method is demonstrated in characterization and quantification of various categories of lipids such as fatty acids, phosphoglycerol lipids, cholesteryl esters (CE), and glycerides. 17 CE lipid isomers were identified and quantified simultaneously from Alzheimer's disease (AD) mouse serum without using lipid standards. Among them, 6 CE isomers showed significant changes in concentrations in AD serum.