Endometriosis detection and localization in laparoscopic gynecology

Multimedia Tools and Applications - Endometriosis is a common gynecologic condition typically treated via laparoscopic surgery. Its visual versatility makes it hard to identify for non-specialized...     

Bewertung alternativer Arbeitsmittel für Wärmepumpen im Gebäudesektor

Forschung im Ingenieurwesen - Das Arbeitsmittel in Wärmepumpen unterliegt einer Vielzahl von Anforderungen, welche für einen effizienten und sicheren Betrieb eingehalten werden...     

Lighting the Path: Light Delivery Strategies to Activate Photoresponsive Biomaterials In Vivo

Photoresponsive biomaterials are experiencing a transition from in vitro models to in vivo demonstrations that point toward clinical translation. Dynamic hydrogels for cell encapsulation, light-responsive carriers for controlled drug delivery, and nanomaterials containing photosensitizers for photodynamic therapy are relevant examples. Nonetheless, the step to the clinic largely depends on their combination with technologies to bring light into the body. This review highlights the challenge of photoactivation in vivo, and presents strategies for light management that can be adopted for this purpose. The authors’ focus is on technologies that are materials-driven, particularly upconversion nanoparticles that assist in “direct path” light delivery through tissue, and optical waveguides that “clear the path” between external light source and in vivo target. The authors’ intention is to assist the photoresponsive biomaterials community transition toward medical technologies by presenting light delivery concepts that can be integrated with the photoresponsive targets. The authors also aim to stimulate further innovation in materials-based light delivery platforms by highlighting needs and opportunities for in vivo photoactivation of biomaterials.     

Organofluorine Hydrazone Derivatives as Multifunctional Anti-Alzheimer's Agents with CK2 Inhibitory and Antioxidant Features

A set of novel hydrazone derivatives were synthesized and analyzed for their biological activities. The compounds were tested for their inhibitory effect on the phosphorylating activity of the protein kinase CK2, and their antioxidant activity was also determined in three commonly used assays. The hydrazones were evaluated for their radical scavenging against the DPPH, ABTS and peroxyl radicals. Several compounds have been identified as good antioxidants as well as potent protein kinase CK2 inhibitors. Most hydrazones containing a 4-N(CH₃)₂ residue or perfluorinated phenyl rings showed high activity in the radical-scavenging assays and possess nanomolar IC₅₀ values in the kinase assays.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Effects of feed composition,protein denaturation and storage of milk serum protein/lactose powders on lactosylation

During whey powder production, the feed is subjected to several heat treatments which can cause lactosylation of proteins. In this study, lactosylation of whey proteins was evaluated in spray-dried powders before and after storage by varying the native protein fraction as well as the serum protein/lactose ratio in the powders. The lactosylation of native α-lactalbumin and β-lactoglobulin in the powders before storage was not affected to a large extent by the protein denaturation or if the feed had been heat treated in a high or low lactose environment. After storage (relative humidity of 23.5%, 30 °C, 25 days), the kinetic of lactosylation tended to increase with increasing native protein fraction and bulk protein content in the powders. An explanation could be that proteins dissolved in the lactose glassy structure might have a lower reactivity, while proteins present in the protein glassy structure with dissolved lactose may display higher lactosylation reactivity.     

Role of Endocrine-Disrupting Chemicals in the Pathogenesis of Non-Alcoholic Fatty Liver Disease: A Comprehensive Review

Non-alcoholic fatty liver disease (NAFLD) is considered the most common liver disorder, affecting around 25% of the population worldwide. It is a complex disease spectrum, closely linked with other conditions such as obesity, insulin resistance, type 2 diabetes mellitus, and metabolic syndrome, which may increase liver-related mortality. In light of this, numerous efforts have been carried out in recent years in order to clarify its pathogenesis and create new prevention strategies. Currently, the essential role of environmental pollutants in NAFLD development is recognized. Particularly, endocrine-disrupting chemicals (EDCs) have a notable influence. EDCs can be classified as natural (phytoestrogens, genistein, and coumestrol) or synthetic, and the latter ones can be further subdivided into industrial (dioxins, polychlorinated biphenyls, and alkylphenols), agricultural (pesticides, insecticides, herbicides, and fungicides), residential (phthalates, polybrominated biphenyls, and bisphenol A), and pharmaceutical (parabens). Several experimental models have proposed a mechanism involving this group of substances with the disruption of hepatic metabolism, which promotes NAFLD. These include an imbalance between lipid influx/efflux in the liver, mitochondrial dysfunction, liver inflammation, and epigenetic reprogramming. It can be concluded that exposure to EDCs might play a crucial role in NAFLD initiation and evolution. However, further investigations supporting these effects in humans are required.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.