A5101634468- Endometriosis Research and Treatment
- Mesenchymal stem cell research
- Hematopoietic Stem Cell Transplantation
- Reproductive System and Pregnancy
- Endometrial and Cervical Cancer Treatments
- Cytomegalovirus and herpesvirus research
- Hormonal Regulation and Hypertension
- Immunotherapy and Immune Responses
- Genomics and Phylogenetic Studies
- Immune Cell Function and Interaction
- Systemic Lupus Erythematosus Research
- Influenza Virus Research Studies
- T-cell and B-cell Immunology
- Machine Learning in Bioinformatics
- Adrenal Hormones and Disorders
Hanshan Normal University
2023
Shantou University Medical College
2023
Shantou University
2023
Jilin University
2018
Anhui Provincial Hospital
2016
Vecna Technologies (United States)
2016
The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research development vaccines, diagnostics therapeutics against by comprehensive collection influenza-related data integrated from various sources, growing suite analysis visualization tools mining hypothesis generation, personal workbench spaces...
Endometriosis (EMT) is an aggressive disease of the reproductive system, also called "benign cancer". However, effective treatments for EMT are still lacking in clinical practice. Interestingly, immune infiltration significantly involved pathogenesis. Currently, no studies have shown involvement cuproptosis-related genes (CRGs) regulating EMT. This study identified three CRGs such as GLS, NFE2L2, and PDHA1, associated with using machine learning algorithms. These were upregulated endometrium...
Abstract Endometriosis (EMT) is a chronic hormone-dependent disease where in viable endometrial tissue transplanted outside the uterus. Interestingly, immune infiltration significantly involved EMT pathogenesis. Currently, no studies have shown involvement of cuproptosis-related genes (CRGs) regulating EMT. This study identified three CRGs such as GLS, NFE2L2, and PDHA1, associated with using machine learning algorithms. These were upregulated endometrium patients moderate/severe...