Purpose Of Review: Patients with endometrial cavity fluid (ECF) in assisted reproductive techniques (ARTs) are poor in prognosis. This review presents the research development of ECF during ARTs, particularly in treatment.
Recent Findings: ECF patients with or without tubal infertility may represent a different clinical entity. ECF impairs the ART outcome in tubal factor, but not polycystic ovarian syndrome, patients. Actually, it was tubal infertility, not only hydrosalpinx, that was related to the development of ECF. Both appearance time and accumulation amount of ECF are critical in the impact of ECF on the ART outcome. Since excessive ECF (equal to or higher than 3.5 mm in the anterior-posterior diameter) usually had a negative impact on the ART outcome, postponing embryo transfer should be considered. A nonexcessive ECF usually disappeared by the time of embryo transfer. The routine embryo transfer in these ECF patients could yield the same ART outcome as in patients without ECF. If a nonexcessive ECF persisted until the day of embryo transfer, particularly in patients with nontube infertility, transvaginal sonographic aspiration could be an alternative of treatment.
Summary: The treatment of ECF during ARTs should be individual according to the causes, appearance time and accumulation amount of ECF.
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http://dx.doi.org/10.1097/GCO.0b013e3283468b94 | DOI Listing |
Infect Chemother
December 2024
Department of Infectious Diseases, Chonnam National University Hospital, Gwangju, Korea.
Background: The life expectancy of people living with human immunodeficiency virus (PLWH) has significantly improved with advancements in antiretroviral therapy (ART). However, aging PLWH face a growing burden of non-communicable diseases (NCDs), polypharmacy, and drug-drug interactions (DDIs), which pose challenges in their management. This study investigates the prevalence of NCDs, polypharmacy, and DDIs among PLWH aged ≥50 years in Korea and their impact on quality of life (QOL).
View Article and Find Full Text PDFBMC Emerg Med
January 2025
Emergency department, CHR Metz-Thionville, Metz, 57000, France.
Introduction: Overcrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.
Aim: The main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.
Nat Rev Clin Oncol
January 2025
Department of Thoracic/Head and Neck Medical Oncology, the University of Texas, MD Anderson Cancer Center, Houston, TX, USA.
Immune-checkpoint inhibitors (ICIs) have transformed the treatment paradigm for advanced-stage squamous non-small-cell lung cancer (LUSC), a histological subtype associated with inferior outcomes compared with lung adenocarcinoma. However, only a subset of patients derive durable clinical benefit. In the first-line setting, multiple ICI regimens are available, including anti-PD-(L)1 antibodies as monotherapy, in combination with chemotherapy, or with an anti-CTLA4 antibody with or without chemotherapy.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Predicting protein-protein interactions (PPIs) is crucial for advancing drug discovery. Despite the proposal of numerous advanced computational methods, these approaches often suffer from poor usability for biologists and lack generalization. In this study, we designed a deep learning model based on a coattention mechanism that was capable of both PPI and site prediction and used this model as the foundation for PPI-CoAttNet, a user-friendly, multifunctional web server for PPI prediction.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
School of Biomedical Engineering, Anhui Medical University, Hefei, China.
Synonymous mutations, once considered neutral, are now understood to have significant implications for a variety of diseases, particularly cancer. It is indispensable to identify these driver synonymous mutations in human cancers, yet current methods are constrained by data limitations. In this study, we initially investigate the impact of sequence-based features, including DNA shape, physicochemical properties and one-hot encoding of nucleotides, and deep learning-derived features from pre-trained chemical molecule language models based on BERT.
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