FOXP3+ T cells in uterine sarcomas are associated with favorable prognosis, low extracellular matrix expression and reduced YAP activation

Uterine sarcomas are rare but deadly malignancies without effective treatment. Immunotherapy is a promising new approach to treat these tumors but has shown heterogeneous effects in sarcoma patients. With the goal of identifying key factors for improved patient treatment, we characterized the tumor immune landscape in 58 uterine sarcoma cases with full clinicopathological annotation. Immune cell characterization revealed the overall prevalence of FOXP3+ cells and pro-tumor M2-like macrophages. Hierarchical clustering of patients showed four tumor type-independent immune signatures, where infiltration of FOXP3+ cells and M1-like macrophages associated with favorable prognosis. High CD8+/FOXP3+ ratio in UUS and ESS correlated with poor survival, upregulation of immunosuppressive markers, extracellular matrix (ECM)-related genes and proteins, and YAP activation. This study shows that uterine sarcomas present distinct immune signatures with prognostic value, independent of tumor type, and suggests that targeting the ECM could be beneficial for future treatments.

Age group and gender classification using convolutional neural networks with a fuzzy logic-based filter method for noise reduction

Biometry is the science that enables living things to be distinguished by examining their physical and behavioral characteristics. The facial recognition system (FCS) is a kind of biometric system. FCS provides a unique mathematical model by determining the distance between the cheekbones, chin, nose, eyes, jawline, and similar positions using the facial features of the persons. Determining the gender and age group of chosen persons’ from face images is the main purpose of this study. It is targeted to distinguish the gender of the person and to obtain information about the person is children or adults by making essential works on the images. Convolutional neural network (CNN) is one of the deep face recognition algorithms that widely used to recognize facial images. This study is suggested as a study that detects noise in images using the fuzzy logic-based filter method and classifies this cleared data by gender using the matrix completion and CNN. TensorFlow which is a machine learning library that used to train and tests deep learning methods is used for experiments. The customer photographs taken during using the system are transformed into a matrix expression through a system trained using this algorithm. The obtained results indicated that the offered technique detects age and gender with a 96% accuracy value and 1.145 seconds time.

High CXCL6 drives matrix expression and correlate with markers of poor outcome in IPF

Signaling via G protein-coupled receptors (GPCRs) can modulate levels of cyclic adenosine monophosphate (cAMP) and shape the functions of fibroblasts in idiopathic pulmonary fibrosis (IPF). We have identified Chemokine (C-X-C) Motif Ligand 6 (CXCL6) as a potential pro-fibrotic GPCR ligand. We tested the function of CXCL6 in ex vivo human donor and fibrotic lung fibroblasts and in an animal model of pulmonary fibrosis. We also measured levels of CXCL6 in the blood and bronchoalveolar lavage (BAL) of patients with IPF. CXCL6 decreased cAMP levels in a dose-dependent manner in Donor and IPF Fibroblasts. CXCL6 mRNA and protein were localized to epithelial cells. Administration of mCXCL5 (LIX, murine CXCL6 homologue) to mice increased collagen synthesis with and without bleomycin. CXCL6 increased Collagen I and α-SMA levels in Donor and IPF Fibroblasts. Silencing of CXCR1/2 as well as Reparixin, a CXCR1/2 inhibitor, blocked effects of CXCL6. Treprostinil blocked effects of CXCL6 only on levels of α-SMA but not on Collagen I. CXCL6 levels in the BAL of two separate cohorts of patients with IPF was associated with poor survival. We conclude that high CXCL6 drives fibroblast function and correlates with poor outcomes in IPF.

Iterative learning control for nonlinear nonaffine networked systems with stochastic noise in communication channels

This article investigates the convergence analysis of networked iterative learning control for nonlinear nonaffine systems firstly by considering stochastic noise introduced by the network channels. The convergence analysis is under a data-driven framework, which does not rely on any mechanism model information. To deal with the nonlinearity, both the state transition technique and the differential mean value principle are used to formulate the iterative dynamics of system states, tracking errors and input signals using a lifted matrix expression, respectively. In terms of the contraction mapping principle, the tracking error is shown to be iteratively convergent under the sense of mathematical expectation. Since the [Formula: see text]-norm is not used in the analysis, the convergence property of the tracking error is not affected by the operation interval and a good transient performance can be ensured in theory. Simulation studies test the theoretical results.

Effect of Vitamin D on Elastin and Collagen Expression: In Vitro Study of Pelvic Organ Prolapse Prevention

Introduction: Pelvic organ prolapse is defined as abnormal protrusion of the pelvic organ beyond its normal anatomical site. It occurs due to the structural weakness of the connective tissue that plays a role in supporting the uterus on the pelvic floor, specifically elastin and collagen. Our study evaluated the effect of vitamin D [1,25(OH)2D3] in preventing pelvic organ prolapse by aggregating elastin and collagen expression. Material and Methods: A true experimental research was carried out by assessing the cell cultures of sacro-uterine ligament from female patients who underwent hysterectomy. The cell cultures were divided into groups that were exposed to vitamin D at different concentrations of 100 µM, 200 µM, 400 µM, 800 µM, and control without any exposure. The expression of elastin and collagen was subsequently analyzed using immunofluorescence and ELISA method. Results: This study showed that exposure to vitamin D significantly affected elastin expression (p-value <0.05). The concentration found to be the most effective to induce elastin expression is at 400 µM. Vitamin D also significantly affected the collagen expression (p-value <0.05), with the concentration found to be the most effective to induce collagen expression is at 800 µM. Conclusion: This study suggested that vitamin D had a significant positive effect of increasing extracellular matrix expression and potentially become a preventive agent for pelvic organ prolapse. Vitamin D is widely available in tropical countries like Indonesia, so this preparation is considered very easy for Indonesian women to apply.