The molecular biological features of the eutopic and ectopic endometrium were studied in 46 patients with adenomyosis, 44 with endometrioid cysts in the ovaries, and 34 with disseminated mixed forms of genital endometriosis. Reproductive-aged patients with the eutopic endometrium in a proliferation phase with hyperplastic or inflammatory changes were selected. Ten samples of the endometrium in a phase proliferation, which had been obtained at medicolegal autopsy of women without reproductive disorders, were studied as a control group. Both the glandular and stromal components of the ectopic and eutopic endometrium in different forms of endometriosis were shown to differ from the intact endometrium in their molecular biological features (the expression of Ki-67, Bcl-2, Bax, vascular endothelial growth factor, transforming growth factor-beta1, matrix metalloproteinases 2 and 10, matrix metalloproteinase-2 inhibitor, the enzyme cytochrome P450 aromatase.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies.
PLoS Pathog
January 2025
Biotechnology and Bioengineering, Sandia National Laboratories, Livermore, California, United States of America.
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2.
View Article and Find Full Text PDFPhysiology-informed regularisation enables training of universal differential equation systems for biological applications.
PLoS Comput Biol
January 2025
Deparment of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Systems biology tackles the challenge of understanding the high complexity in the internal regulation of homeostasis in the human body through mathematical modelling. These models can aid in the discovery of disease mechanisms and potential drug targets. However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data.
View Article and Find Full Text PDFThe tumour histopathology "glossary" for AI developers.
PLoS Comput Biol
January 2025
Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom.
The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective translation of these computational methods requires computational researchers to have at least a basic understanding of histopathology. In this work, we aim to bridge that gap by introducing essential histopathology concepts to support AI developers in their research.
View Article and Find Full Text PDFData-driven discovery and parameter estimation of mathematical models in biological pattern formation.
PLoS Comput Biol
January 2025
Department of Anatomy and Cell Biology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, Japan.
Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have been made empirically. In the present study, we propose a data-driven approach to validate the applicability of mathematical models. Specifically, we developed methods to automatically select the appropriate mathematical models based on the patterns of interest and to estimate the model parameters.
View Article and Find Full Text PDFMEGA-GO: Functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network.
Bioinformatics
January 2025
Guangdong Provincial Key Laboratory IRADS, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China.
Motivation: The increasing accessibility of large-scale protein sequences through advanced sequencing technologies has necessitated the development of efficient and accurate methods for predicting protein function. Computational prediction models have emerged as a promising solution to expedite the annotation process. However, despite making significant progress in protein research, graph neural networks face challenges in capturing long-range structural correlations and identifying critical residues in protein graphs.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!