Christophersenziegler8913

Fascia is a fibrous connective tissue present all over the body. At the lower limb level, the deep fascia that is overlying muscles of the outer thigh and sheathing them (fascia lata) is involved in various pathologies. However, the understanding and quantification of the mechanisms involved in these sheathing effects are still unclear. The aim of this study is to observe and quantify the strain field of the fascia lata, including the iliotibial tract (ITT), during a passive movement of the knee. Three fresh postmortem human subjects were studied. To measure hip and knee angles during knee flexion-extension, passive movements from 0° to around 120° were recorded with a motion analysis system and strain fields of the fascia were acquired using digital image correlation. Strains were computed for three areas of the fascia lata anterior fascia, lateral fascia, and ITT. Mean principal strains showed different strain mechanisms depending on location on the fascia and knee angle. For anterior and lateral fascia, a tension mechanism was mainly observed with major strain greater than minor strain in absolute value. While for the ITT, two strain mechanisms were observed depending on knee movement tension is observed when the knee is extended relatively to reference position of 47°, however, pure shear can be observed when the knee is flexed. In some cases, minor strain can also be higher than major strain in absolute value, suggesting high tissue compression probably due to microstructural fiber rearrangements. This in situ study is the first attempt to quantify the superficial strain field of fascia lata during passive leg movement. The study presents some limitations but provides a step in understanding strain mechanism of the fascia lata during passive knee movement.Sequencing-based identification of tumor tissue-of-origin (TOO) is critical for patients with cancer of unknown primary lesions. Even if the TOO of a tumor can be diagnosed by clinicopathological observation, reevaluations by computational methods can help avoid misdiagnosis. In this study, we developed a neural network (NN) framework using the expression of a 150-gene panel to infer the tumor TOO for 15 common solid tumor cancer types, including lung, breast, liver, colorectal, gastroesophageal, ovarian, cervical, endometrial, pancreatic, bladder, head and neck, thyroid, prostate, kidney, and brain cancers. Celastrol order To begin with, we downloaded the RNA-Seq data of 7,460 primary tumor samples across the above mentioned 15 cancer types, with each type of cancer having between 142 and 1,052 samples, from the cancer genome atlas. Then, we performed feature selection by the Pearson correlation method and performed a 150-gene panel analysis; the genes were significantly enriched in the GO2001242 Regulation of intrinsic apoptotic signaling pathway and the GO0009755 Hormone-mediated signaling pathway and other similar functions. Next, we developed a novel NN model using the 150 genes to predict tumor TOO for the 15 cancer types. The average prediction sensitivity and precision of the framework are 93.36 and 94.07%, respectively, for the 7,460 tumor samples based on the 10-fold cross-validation; however, the prediction sensitivity and precision for a few specific cancers, like prostate cancer, reached 100%. We also tested the trained model on a 20-sample independent dataset with metastatic tumor, and achieved an 80% accuracy. In summary, we present here a highly accurate method to infer tumor TOO, which has potential clinical implementation.The gastrointestinal (GI) tract is a complex system responsible for nutrient absorption, digestion, secretion, and elimination of waste products that also hosts immune surveillance, the intestinal microbiome, and interfaces with the nervous system. Traditional in vitro systems cannot harness the architectural and functional complexity of the GI tract. Recent advances in organoid engineering, microfluidic organs-on-a-chip technology, and microfabrication allows us to create better in vitro models of human organs/tissues. These micro-physiological systems could integrate the numerous cell types involved in GI development and physiology, including intestinal epithelium, endothelium (vascular), nerve cells, immune cells, and their interplay/cooperativity with the microbiome. In this review, we report recent progress in developing micro-physiological models of the GI systems. We also discuss how these models could be used to study normal intestinal physiology such as nutrient absorption, digestion, and secretion as well as GI infection, inflammation, cancer, and metabolism.Starting in 1969 laser scissors have been used to study and manipulate chromosomes in mitotic animal cells. Key studies demonstrated that using the "hot spot" in the center of a focused Gaussian laser beam it was possible to delete the ribosomal genes (secondary constriction), and this deficiency was maintained in clonal daughter cells. It wasn't until 2020 that it was demonstrated that cells with focal-point damaged chromosomes could replicate due to the cell's DNA damage repair molecular machinery. A series of studies leading up to this conclusion involved using cells expressing different GFP DNA damage recognition and repair molecules. With the advent of optical tweezers in 1987, laser tweezers have been used to study the behavior and forces on chromosomes in mitotic and meiotic cells. The combination of laser scissors and tweezers were employed since 1991 to study various aspects of chromosome behavior during cell division. These studies involved holding chromosomes in an optical while gradually reducing the laser power until the chromosome recovered their movement toward the cell pole. It was determined in collaborative studies with Prof. Arthur Forer from York University, Toronto, Canada, cells from diverse group vertebrate and invertebrates, that forces necessary to move chromosomes to cell poles during cell division were between 2 and 17pN, orders of magnitude below the 700 pN generally found in the literature.The immobilization of enzymes in biocatalytic flow reactors is a common strategy to increase enzyme reusability and improve biocatalytic performance. Extrusion-based 3D bioprinting has recently emerged as a versatile tool for the fabrication of perfusable hydrogel grids containing entrapped enzymes for the use in such reactors. This study demonstrates the suitability of water-in-oil high internal phase emulsions (HIPEs) as 3D-printable bioinks for the fabrication of composite materials with a porous polymeric scaffold (polyHIPE) filled with enzyme-laden hydrogel. The prepared HIPEs exhibited excellent printability and are shown to be suitable for the printing of complex three-dimensional structures without the need for sacrificial support material. An automated activity assay method for the systematic screening of different material compositions in small-scale batch experiments is presented. The monomer mass fraction in the aqueous phase and the thickness of printed objects were found to be the most important parameters determining the apparent activity of the immobilized enzyme. Mass transfer limitations and enzyme inactivation were identified as probable factors reducing the apparent activity. The presented HIPE-based bioinks enable the fabrication of flow-optimized and more efficient biocatalytic reactors while the automated activity assay method allows the rapid screening of materials to optimize the biocatalytic efficiency further without time-consuming flow-through experiments involving whole printed reactors.Cancer of unknown primary site (CUPS) is a type of metastatic tumor for which the sites of tumor origin cannot be determined. Precise diagnosis of the tissue origin for metastatic CUPS is crucial for developing treatment schemes to improve patient prognosis. Recently, there have been many studies using various cancer biomarkers to predict the tissue-of-origin (TOO) of CUPS. However, only a very few of them use copy number alteration (CNA) to trance TOO. In this paper, a two-step computational framework called CNA_origin is introduced to predict the tissue-of-origin of a tumor from its gene CNA levels. CNA_origin set up an intellectual deep-learning network mainly composed of an autoencoder and a convolution neural network (CNN). Based on real datasets released from the public database, CNA_origin had an overall accuracy of 83.81% on 10-fold cross-validation and 79% on independent datasets for predicting tumor origin, which improved the accuracy by 7.75 and 9.72% compared with the method published in a previous paper. Our results suggested that the autoencoder model can extract key characteristics of CNA and that the CNN classifier model developed in this study can predict the origin of tumors robustly and effectively. link2 CNA_origin was written in Python and can be downloaded from https//github.com/YingLianghnu/CNA_origin.Microalgae can produce high-value-added products such as lipids and carotenoids using light or sugars, and their biosynthesis mechanism can be triggered by various stress conditions. Under nutrient deprivation or environmental stresses, microalgal cells accumulate lipids as an energy-rich carbon storage battery and generate additional amounts of carotenoids to alleviate the oxidative damage induced by stress conditions. Though stressful conditions are unfavorable for biomass accumulation and can induce oxidative damage, stress-based strategies are widely used in this field due to their effectiveness and economy. For the overproduction of different target products, it is required and meaningful to deeply understand the effects and mechanisms of various stress conditions so as to provide guidance on choosing the appropriate stress conditions. Moreover, the underlying molecular mechanisms under stress conditions can be clarified by omics technologies, which exhibit enormous potential in guiding rational genetic engineering for improving lipid and carotenoid biosynthesis.Besides the outstanding potential in biomedical applications, extracellular vesicles (EVs) are also promising candidates to expand our knowledge on interactions between vesicular surface proteins and small-molecules which exert biomembrane-related functions. link3 Here we provide mechanistic details on interactions between membrane active peptides with antimicrobial effect (MAPs) and red blood cell derived EVs (REVs) and we demonstrate that they have the capacity to remove members of the protein corona from REVs even at lower than 5 μM concentrations. In case of REVs, the Soret-band arising from the membrane associated hemoglobins allowed to follow the detachment process by flow-Linear Dichroism (flow-LD). Further on, the significant change on the vesicle surfaces was confirmed by transmission electron microscopy (TEM). Since membrane active peptides, such as melittin have the affinity to disrupt vesicles, a combination of techniques, fluorescent antibody labeling, microfluidic resistive pulse sensing, and flow-LD were employed to distinguish between membrane destruction and surface protein detachment. The removal of protein corona members is a newly identified role for the investigated peptides, which indicates complexity of their in vivo function, but may also be exploited in synthetic and natural nanoparticle engineering. Furthermore, results also promote that EVs can be used as improved model systems for biophysical studies providing insight to areas with so far limited knowledge.