Reconstructed Human Skin with Hypodermis Shows Essential Role of Adipose Tissue in Skin Metabolism.

BACKGROUND: Dysregulation of skin metabolism is associated with a plethora of diseases such as psoriasis and dermatitis. Until now, reconstructed human skin (RhS) models lack the metabolic potential of native human skin, thereby limiting their relevance to study human healthy and diseased skin. We aimed to determine whether incorporation of an adipocyte-containing hypodermis into RhS improves its metabolic potential and to identify major metabolic pathways up-regulated in adipose-RhS. METHODS: Primary human keratinocytes, fibroblasts and differentiated adipose-derived stromal cells were co-cultured in a collagen/fibrin scaffold to create an adipose-RhS. The model was extensively characterized structurally in two- and three-dimensions, by cytokine secretion and RNA-sequencing for metabolic enzyme expression. RESULTS: Adipose-RhS showed increased secretion of adipokines. Both RhS and adipose-RhS expressed 29 of 35 metabolic genes expressed in ex vivo native human skin. Addition of the adipose layer resulted in up-regulation of 286 genes in the dermal-adipose fraction of which 7 were involved in phase I (CYP19A1, CYP4F22, CYP3A5, ALDH3B2, EPHX3) and phase II (SULT2B1, GPX3) metabolism. Vitamin A, D and carotenoid metabolic pathways were enriched. Additionally, pro-inflammatory (IL-1ß, IL-18, IL-23, IL-33, IFN-α2, TNF-α) and anti-inflammatory cytokine (IL-10, IL-12p70) secretion was reduced in adipose-RhS. CONCLUSIONS: Adipose-RhS mimics healthy native human skin more closely than traditional RhS since it has a less inflamed phenotype and a higher metabolic activity, indicating the contribution of adipocytes to tissue homeostasis. Therefore it is better suited to study onset of skin diseases and the effect of xenobiotics.

Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines.

Machine learning is considered to be one of the most promising applications of quantum computing. Therefore, the search for quantum advantage of the quantum analogues of machine learning models is a key research goal. Here, we show that variational quantum classifiers and support vector machines with quantum kernels can solve a classification problem based on the k-FORRELATION problem, which is known to be PROMISEBQP-complete. Because the PROMISEBQP complexity class includes all Bounded-Error Quantum Polynomial-Time (BQP) decision problems, our results imply that there exists a feature map and a quantum kernel that make variational quantum classifiers and quantum kernel support vector machines efficient solvers for any BQP problem. Hence, this work implies that their feature map and quantum kernel, respectively, can be designed to have a quantum advantage for any classification problem that cannot be classically solved in polynomial time but contrariwise by a quantum computer.

Inkjet-Printed Temperature Sensors Characterized according to Standards.

This paper describes the characterization of inkjet-printed resistive temperature sensors according to the international standard IEC 61928-2. The goal is to evaluate such sensors comprehensively, to identify important manufacturing processes, and to generate data for inkjet-printed temperature sensors according to the mentioned standard for the first time, which will enable future comparisons across different publications. Temperature sensors were printed with a silver nanoparticle ink on injection-molded parts. After printing, the sensors were sintered with different parameters to investigate their influences on the performance. Temperature sensors were characterized in a temperature range from 10 °C to 85 °C at 60% RH. It turned out that the highest tested sintering temperature of 200 °C, the longest dwell time of 24 h, and a coating with fluoropolymer resulted in the best sensor properties, which are a high temperature coefficient of resistance, low hysteresis, low non-repeatability, and low maximum error. The determined hysteresis, non-repeatability, and maximum error are below 1.4% of the full-scale output (FSO), and the temperature coefficient of resistance is 1.23-1.31 × 10-3 K-1. These results show that inkjet printing is a capable technology for the manufacturing of temperature sensors for applications up to 85 °C, such as lab-on-a-chip devices.