N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), an environmental pollutant derived from the ozonolysis of the widely used tire rubber antioxidant 6PPD, has been found to accumulate in air, dust, and water, posing significant health risks. While its reproductive toxicity in male organisms has been established, its effects on female reproductive health remain unclear. Polycystic ovary syndrome (PCOS), a common endocrine disorder in premenopausal women, is known to be influenced by environmental pollutants.
View Article and Find Full Text PDFContext: Polycystic ovary syndrome (PCOS) is a prevalent hormonal imbalance that predominantly affects women in their reproductive years. Previous studies have yielded conflicting conclusions.
Objective: This is an updated meta-analysis aiming to explore the connection between flavonoid supplementation and PCOS.
Objective: Qu's formula 3 (QUF3) is a patented Chinese herbal medicine used to alleviate anxiety disorders during in vitro fertilization-embryo transfer (IVF-ET). This study aimed to identify the potential active constituents and molecular mechanisms of action of QUF3 in alleviating anxiety disorders during IVF-ET.
Methods: The active constituents of QUF3 were identified from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and literatures.
Phthalates (PAEs), a group of environmental endocrine disruptors, are associated with oxidative stress and have adverse effects on female ovarian reserves. However, this association has been poorly investigated, particularly with respect to clinical evidence. In this study, we provided clinical evidence of a relationship between exposure levels of PAEs, oxidative stress and decreased ovarian reserve (DOR).
View Article and Find Full Text PDFBackground: Polycystic ovary syndrome (PCOS), as a common endocrine disease in reproductive-age women, which is characterized by both reproductive and metabolic disorders. Cang-Fu-Dao-Tan Formula (CFDTF) is an effective and relatively safe treatment for PCOS. However, the underlying mechanism is poorly understood.
View Article and Find Full Text PDFBackground: Clinical severity scores, such as acute physiology, age, chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), Pitt Bacteremia Score (PBS), and European Confederation of Medical Mycology Quality (EQUAL) score, may not reliably predict candidemia prognosis owing to their prespecified scorings that can limit their adaptability and applicability.
Objectives: Unlike those fixed and prespecified scorings, we aim to develop and validate a machine learning (ML) approach that is able to learn predictive models adaptively from available patient data to increase adaptability and applicability.
Methods: Different ML algorithms follow different design philosophies and consequently, they carry different learning biases.
Ethnopharmacological Relevance: Polycystic ovary syndrome (PCOS) is a common gynecological endocrine and metabolic disorder. Chinese herbal medicine has some advantages in the treatment of PCOS with its unique theoretical system and rich clinical practice experiences.
Aim Of The Study: The present study was to investigate the potential mechanisms of Bu-Shen-Jian-Pi Formula (BSJPF) on the treatment of PCOS.
The global outbreak of the coronavirus disease 2019 (COVID-19) led to the suspension of most treatments with assisted reproductive technique (ART). However, with the recent successful control of the pandemic in China, there is an urgent public need to resume full reproductive care. To determine whether the COVID-19 pandemic had any adverse effects on female fertility and the pregnancy outcomes of women undergoing ART, a systematic review and meta-analysis was conducted using the electronic Chinese and English databases.
View Article and Find Full Text PDFIn the era of bathing in big data, it is common to see enormous amounts of data generated daily. As for the medical industry, not only could we collect a large amount of data, but also see each data set with a great number of features. When the number of features is ramping up, a common dilemma is adding computational cost during inferring.
View Article and Find Full Text PDFBackground: The criteria outlined in the International Consensus Meeting (ICM) in 2018, which were prespecified and fixed, have been commonly practiced by clinicians to diagnose periprosthetic joint infection (PJI). We developed a machine learning (ML) system for PJI diagnosis and compared it with the ICM scoring system to verify the feasibility of ML.
Methods: We designed an ensemble meta-learner, which combined 5 learning algorithms to achieve superior performance by optimizing their synergy.