Flavonoid Supplementation Is Beneficial for Polycystic Ovary Syndrome: A Systematic Review and Meta-analysis.

CONTEXT: 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. DATA SOURCES: Seven databases were searched: Cochrane Library, PubMed, Web of Science, Embase, Wanfang, China Science and Technology Journal, and China National Knowledge Infrastructure, spanning from their inception to April 15, 2024. DATA EXTRACTION: Two authors independently searched the databases using the search terms. DATA ANALYSIS: Following strict inclusion criteria, 8 papers were ultimately included. This updated meta-analysis suggests that flavonoid supplementation could enhance follicular development, promote the proliferation and differentiation of follicular granulosa cells, elevate estradiol levels, and mitigate testosterone, C-reactive protein, and ovarian index levels. CONCLUSION: This analysis suggests that dietary flavonoids could potentially alleviate symptoms associated with PCOS. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42022382912.

Network pharmacology analysis of a patented Chinese herbal medicine for alleviating anxiety disorder in in vitro fertilization-embryo transfer.

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. Potential targets of anxiety disorder and IVF-ET were identified using GeneCards, Online Mendelian Inheritance in Man, and the UniProt Database. Protein-protein interaction (PPI) network, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the potential mechanisms. Molecular docking and molecular dynamics (MD) simulations were performed to visualize and verify the results. Results: Quercetin, sophoranol, luteolin, kaempferol, and neurotoxin inhibitors were identified as the TOP 5 active constituents of QUF3. Forty common targets were shared among QUF3, anxiety disorders, and IVF-ET. Tumour necrosis factor, interleukin-6, vascular endothelial growth factor A, epidermal growth factor, interleukin-1B, cellular tumour antigen p53, matrix metalloproteinase-9, and oestrogen receptor were identified as the TOP 8 potential targets through PPI analysis. A total of 697 biological processes, 20 cellular components, and 54 molecular functions were identified. Further, 91 KEGG pathways were revealed to be enriched. The TOP 5 active constituents were verified to have good binding activity with the TOP 8 potential targets using molecular docking and MD simulations. Conclusions: The mechanism of QUF3 in alleviating anxiety disorders in patients undergoing IVF-ET may be related to the interleukin-17 and tumour necrosis factor signalling pathways, inhibiting inflammatory responses and antioxidants, which may provide a solid foundation for the clinical application and further study of QUF3.

Relationship between phthalates exposure, risk of decreased ovarian reserve, and oxidative stress levels.

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). Firstly, the urinary concentrations of metabolites of PAEs were measured by high performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). The serum concentrations of follicle-stimulating hormone (FSH), luteinizing hormone (LH), and anti-Mullerian hormone (AMH), and the biomarkers of oxidative stress, malondialdehyde (MDA), superoxide dismutase (SOD), and total antioxidant capacity (T-AOC), were determined. Finally, statistical analyses were conducted to describe the relationship between the PAEs exposure, oxidative stress and DOR. We found that the levels of monomethyl phthalate (MMP), monoisobutyl phthalate (MiBP), mono-(2-ethylhexyl) phthalate (MEHP), and mono-(2-ethyl-5-hydroxypentyl) phthalate (MECPP) in the DOR group were significantly higher than those in the control group. There was a significant negative association between AMH and MMP, MiBP levels. and a significant positive association between FSH and MMP levels. PAEs exposure was also associated with a significant increase in MDA levels and decrease in SOD levels. In conclusion, the exposure of PAEs was closely associated with DOR, potentially mediated by oxidative stress pathways; however, small sample size was a limitation in this study.

Application of machine learning for mortality prediction in patients with candidemia: Feasibility verification and comparison with clinical severity scores.

BACKGROUND: 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. We have designed an ensemble meta-learner based on stacked generalisation to integrate multiple learners as a team to work at its best in a synergy to improve predictive performances. RESULTS: In the multicenter retrospective study, we analysed 512 patients with candidemia from January 2014 to July 2019 and compared a stacked generalisation model (SGM) with APACHE II, SOFA, PBS and EQUAL score to predict the 14-day mortality. The cross-validation results showed that the SGM significantly outperformed APACHE II, SOFA, PBS, and EQUAL score across several metrics, including F1-score (0.68, p < .005), Matthews correlation coefficient (0.54, p < .05 vs. SOFA, p < .005 vs. the others) and the area under the curve (AUC; 0.87, p < .005). In addition, in an independent external test, the model effectively predicted patients' mortality in the external validation cohort, with an AUC of 0.77. CONCLUSIONS: ML models show potential for improving mortality prediction amongst patients with candidemia compared to clinical severity scores.

The effective compounds and mechanisms of Cang-Fu-Dao-Tan Formula in treating polycystic ovary syndrome based on UPLC/Q-TOF-MS/MS, network pharmacology and molecular experiments.

BACKGROUND: 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. PURPOSE: To explore the effective compounds and mechanisms of CFDTF in treating PCOS based on UPLC/Q-TOF-MS/MS, network pharmacology and molecular experiments. METHODS: The UPLC/Q-TOF-MS/MS and TCMSP, SwissTargetPrediction databases were used to identify the active ingredients of CFDTF. Then GeneCards, Disgenet, Drugbank databases were used to obtain the PCOS related targets. Based above, the Drug-component-target (D-C-T) network and protein-protein-interaction (PPI) network were built to analysis the key targets. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis were performed to find the potential mechanisms. Finally, molecular docking analysis, molecular dynamics (MD) simulations and molecular experiments were used to confirm the interactions among the active compounds, targets and explore the potential mechanisms. RESULTS: A total of 20 compounds were identified by UPLC/Q-TOF-MS/MS, and 136 active compounds by TCMSP from CFDTF. After removing the duplicate results, there were 370 targets related to both CFDTF and PCOS, among which, MAPK3, AKT1, RELA, EGF, TP53 and MYC were proved to have high interactions with the components. The mechanisms of CFDTF against PCOS were related to PI3K-Akt, mTOR, MAPK signaling pathways, and the in vitro experiments proved that the CFDTF positively regulated the cell proliferation and inhibited the apoptosis levels in PCOS cell model. CONCLUSIONS: The combination of UPLC/Q-TOF-MS/MS, systematic network pharmacology and molecular experiments identified that the quercetin, hesperidin, and glycyrrhizin disaccharide are the TOP 3 effective compounds of CFDTF in treating PCOS and the potential mechanisms may involve in regulating proliferation and apoptosis of granulosa cells.

Impact of COVID-19 pandemic on the pregnancy outcomes of women undergoing assisted reproductive techniques (ARTs): a systematic review and meta-analysis.

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. Dichotomous outcomes were summarized as prevalence, and odds ratios (ORs) and continuous outcomes as standardized mean difference (SMD) with 95% confidence interval (CI). The risk of bias and subgroup analyses were assessed using Stata/SE 15.1 and R 4.1.2. The results showed that compared with women treated by ART in the pre-COVID-19 time frame, women undergoing ART after the COVID-19 pandemic exhibited no significant difference in the clinical pregnancy rate (OR 1.07, 95% CI 0.97 to 1.19; I2=0.0%), miscarriage rate (OR 0.95, 95% CI 0.79 to 1.14; I2=38.4%), embryo cryopreservation rate (OR 2.90, 95% CI 0.17 to 48.13; I2=85.4%), and oocyte cryopreservation rate (OR 0.30, 95% CI 0.03 to 3.65; I2=81.6%). This review provided additional evidence for gynecologists to guide the management of women undergoing ART treatment during the COVID-19 pandemic timeframe.

Random RotBoost: An Ensemble Classification Method Based on Rotation Forest and AdaBoost in Random Subsets and Its Application to Clinical Decision Support.

In 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. To address this concern, the data rotational method by PCA in tree-based methods shows a path. This work tries to enhance this path by proposing an ensemble classification method with an AdaBoost mechanism in random, automatically generating rotation subsets termed Random RotBoost. The random rotation process has replaced the manual pre-defined number of subset features (free pre-defined process). Therefore, with the ensemble of the multiple AdaBoost-based classifier, overfitting problems can be avoided, thus reinforcing the robustness. In our experiments with real-world medical data sets, Random RotBoost reaches better classification performance when compared with existing methods. Thus, with the help from our proposed method, the quality of clinical decisions can potentially be enhanced and supported in medical tasks.

Periprosthetic Joint Infection Prediction via Machine Learning: Comprehensible Personalized Decision Support for Diagnosis.

BACKGROUND: 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. To increase the comprehensibility of ML, we developed an explanation generator that produces understandable explanations of individual predictions. We performed stratified 5-fold cross-validation on a cohort of 323 patients to compare the ML meta-learner with the ICM scoring system. RESULTS: Cross-validation demonstrated ML's superior predictive performance to that of the ICM scoring system for various metrics, including accuracy, precision, recall, F1 score, Matthews correlation coefficient, and area under receiver operating characteristic curve. Moreover, the case study showed that ML was capable of identifying personalized important features missing from ICM and providing interpretable decision support for individual diagnosis. CONCLUSION: Unlike ICM, ML could construct adaptive diagnostic models from the available patient data instead of making diagnoses based on prespecified criteria. The experimental results suggest that ML is feasible and competitive for PJI diagnosis compared with the current widely used ICM scoring criteria. The adaptive ML models can serve as an auxiliary system to ICM for diagnosing PJI.