Article
Medicine, General & Internal
Hela Elmannai, Nora El-Rashidy, Ibrahim Mashal, Manal Abdullah Alohali, Sara Farag, Shaker El-Sappagh, Hager Saleh
Summary: Polycystic ovary syndrome (PCOS) is a common and severe health problem among women globally. Early detection and treatment can reduce the risk of long-term complications, including developing type 2 diabetes and gestational diabetes. Therefore, effective and early diagnosis of PCOS is important to reduce the problems and complications of the disease, and model explanations are provided to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations.
Article
Reproductive Biology
Zhijing Na, Wen Guo, Jiahui Song, Di Feng, Yuanyuan Fang, Da Li
Summary: In this study, novel biomarkers for polycystic ovary syndrome (PCOS) were identified and their potential roles in immune infiltration during PCOS pathogenesis were analyzed. Two diagnostic biomarkers, HDDC3 and SDC2, were identified and validated using gene expression data and clinical samples. These biomarkers were correlated with immune infiltration in PCOS.
JOURNAL OF OVARIAN RESEARCH
(2022)
Article
Obstetrics & Gynecology
Mengge Gao, Xiaohua Liu, Mengxuan Du, Heng Gu, Hang Xu, Xingming Zhong
Summary: In this study, immune cell subsets and gene expression in patients with PCOS were evaluated, and TMEM54 and PLCG2 were identified as potential biomarkers of PCOS. Immune cell infiltration analysis showed that certain T cell subsets may affect the occurrence of PCOS, and PLCG2 was highly correlated with these T cell subsets.
BMC PREGNANCY AND CHILDBIRTH
(2023)
Article
Endocrinology & Metabolism
I. S. Silva, C. N. Ferreira, L. B. X. Costa, M. O. Soter, L. M. L. Carvalho, J. de C. Albuquerque, M. F. Sales, A. L. Candido, F. M. Reis, A. A. Veloso, K. B. Gomes
Summary: Using machine learning algorithms, important clinical and laboratory variables related to PCOS diagnosis were identified, and patients were classified into two phenotypically different clusters.
JOURNAL OF ENDOCRINOLOGICAL INVESTIGATION
(2022)
Review
Computer Science, Information Systems
Samia Ahmed, Md. Sazzadur Rahman, Ismate Jahan, M. Shamim Kaiser, A. S. M. Sanwar Hosen, Deepak Ghimire, Seong-Heum Kim
Summary: Polycystic Ovary Syndrome (PCOS) is a hormone disorder that significantly impacts women's lives, especially in the modern generation. Early and accurate detection of PCOS is crucial to minimize complications such as infertility. Machine Learning (ML) has shown excellent performance in PCOS detection due to its feature extraction capability.
Article
Computer Science, Artificial Intelligence
Shamik Tiwari, Lalit Kane, Deepika Koundal, Anurag Jain, Adi Alhudhaif, Kemal Polat, Atef Zaguia, Fayadh Alenezi, Sara A. Althubiti
Summary: Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder that can be diagnosed non-invasively using machine learning algorithms, with Random Forest showing the best performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Nutrition & Dietetics
Dong-Dong Wang, Ya-Feng Li, Yi-Zhen Mao, Su-Mei He, Ping Zhu, Qun-Li Wei
Summary: This study explored the effect of carnitine supplementation on body weight in patients with polycystic ovary syndrome (PCOS) and predicted an appropriate dosage schedule using a machine-learning approach. The study found that a dosage of 250 mg/day of carnitine supplementation for at least 14.4 weeks could achieve optimal therapeutic effect.
FRONTIERS IN NUTRITION
(2022)
Article
Cell Biology
Xin Huang, Ling Hong, Yuanyuan Wu, Miaoxin Chen, Pengcheng Kong, Jingling Ruan, Xiaoming Teng, Zhiyun Wei
Summary: This study evaluated the potential of follicular fluid (FF) Raman spectra in predicting embryo development and pregnancy outcome for PCOS patients. Specific Raman bands in PCOS FF were identified to have biomarker potential for predicting oocyte developmental potential and clinical pregnancy. Machine-learning algorithms achieved high accuracies in correctly assigning oocyte developmental potential and clinical pregnancy based on Raman spectra of PCOS FF.
FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY
(2021)
Article
Integrative & Complementary Medicine
Jiekee Lim, Jieyun Li, Xiao Feng, Lu Feng, Yumo Xia, Xinang Xiao, Yiqin Wang, Zhaoxia Xu
Summary: This study used machine learning methods to classify and predict polycystic ovary syndrome (PCOS), and found that radial pulse wave signals can accurately identify most PCOS patients, which is valuable for early detection and monitoring of PCOS. The study provides evidence for the objective quantification of pulse diagnosis in traditional Chinese medicine (TCM).
BMC COMPLEMENTARY MEDICINE AND THERAPIES
(2023)
Review
Cell Biology
Xue-Ling Xu, Shou-Long Deng, Zheng-Xing Lian, Kun Yu
Summary: Female infertility is mainly caused by ovulation disorders, with polycystic ovary syndrome (PCOS) being the most prevalent. PCOS is associated with abnormal function of estrogen and estrogen receptors (ERs), affecting cellular activities. Selective estrogen receptor modulators (SERMs) like tamoxifen and clomiphene have shown clinical applications for subfertility with PCOS, but further understanding of the role of ER in PCOS is needed.
Article
Computer Science, Information Systems
Shazia Nasim, Mubarak Saad Almutairi, Kashif Munir, Ali Raza, Faizan Younas
Summary: Polycystic ovary syndrome (PCOS) is a critical disorder in women during their reproduction phase, commonly caused by excess male hormone and androgen levels, resulting in complications such as miscarriage, infertility issues, and complications during pregnancy. This study aims to predict PCOS using advanced machine learning techniques and achieved satisfactory results through the proposed optimized chi-squared feature selection and gaussian naive bayes model.
Review
Medicine, General & Internal
Pawel Dybciak, Dorota Raczkiewicz, Ewa Humeniuk, Tomasz Powrozek, Mariusz Gujski, Teresa Malecka-Massalska, Artur Wdowiak, Iwona Bojar
Summary: This meta-analysis utilized research utilizing the Hospital Anxiety and Depression Scale (HADS) to determine the prevalence, mean level, standardized mean difference, and probability of depression in patients with PCOS. The results indicate an increased risk of depressive symptoms in women with PCOS.
JOURNAL OF CLINICAL MEDICINE
(2023)
Review
Endocrinology & Metabolism
Siyu Zhou, Shu Wen, Yongcheng Sheng, Meina Yang, Xiaoyang Shen, Yan Chen, Deying Kang, Liangzhi Xu
Summary: The study found no significant associations between ESR1 and ESR2 gene polymorphisms and PCOS susceptibility, even after considering ethnicity as a major source of heterogeneity.
FRONTIERS IN ENDOCRINOLOGY
(2021)
Article
Cell Biology
Xinyi Zhang, Bo Liang, Jun Zhang, Xinyao Hao, Xiaoyan Xu, Hsun-Ming Chang, Peter C. K. Leung, Jichun Tan
Summary: Raman spectroscopy combined with machine-learning algorithms can detect changes in the metabolic profiles of PCOS patients, aiding in diagnosis and characterization.
MOLECULAR AND CELLULAR ENDOCRINOLOGY
(2021)
Article
Engineering, Multidisciplinary
Xiaoke Wu, Chi Chiu Wang, Yijuan Cao, Jian Li, Zhiqiang Li, Hongli Ma, Jingshu Gao, Hui Chang, Duojia Zhang, Jing Cong, Yu Wang, Qi Wu, Xiaoxiao Han, Pui Wah Jacqueline Chung, Yiran Li, Xu Zheng, Lingxi Chen, Lin Zeng, Astrid Borchert, Hartmut Kuhn, Zi-Jiang Chen, Ernest Hung Yu Ng, Elisabet Stener-Victorin, Heping Zhang, Richard S. Legro, Ben Willem J. Mol, Yongyong Shi
Summary: This study identified common genetic variants and rare mutations associated with the failure of ovulation induction in infertile patients with polycystic ovary syndrome (PCOS). Furthermore, a prediction model was developed using machine learning techniques. The study revealed the significant associations of common variants in ZNF438 and rare mutations in REC114 with ovulation outcomes in PCOS patients.
Article
Obstetrics & Gynecology
Alice Newman-Sanders, Jackson C. Kirkman-Brown, Meurig T. Gallagher
Summary: This study revealed a significant lack of awareness among young adults in the UK regarding the potential impacts of gym lifestyles and supplementation on male infertility. Men were found to have a concerning lack of concern for their own fertility, with differences in awareness levels between men and women. It was also observed that men were more likely to consider making changes to their behavior if it had a long-term impact on their fertility compared to short-term effects.
REPRODUCTIVE BIOMEDICINE ONLINE
(2024)
Article
Obstetrics & Gynecology
Shachar Reuvenny, Michal Youngster, Almog Luz, Rohi Hourvitz, Ettie Maman, Micha Baum, Ariel Hourvitz
Summary: Using a machine-learning model to determine the optimal trigger days can improve the outcomes of antagonist protocol cycles in freeze-all or fresh transfer cycles, for all age groups. Implementing these models can more accurately predict the number of retrieved oocytes, optimizing physicians' decisions, balancing workloads, and creating more standardized yet patient-specific protocols.
REPRODUCTIVE BIOMEDICINE ONLINE
(2024)
Article
Obstetrics & Gynecology
Chao Chen, Qi Wen, Feng Deng, Rong Li, Ying Wang, Xiumei Zhen, Jing Hang
Summary: This study investigates the proteomic and phosphoproteomic differences in the endometrium of women with recurrent pregnancy loss (RPL) compared to healthy control women during different phases of the menstrual cycle. The results identify differentially expressed proteins and phosphorylated proteins, and highlight the insulin/cyclic nucleotide signalling pathway and AMPK/mTOR signalling pathway as major contributors to the abnormality of RPL endometrium. The findings provide insights into potential proteins associated with the pathogenesis of RPL and contribute to the identification of potential targets for RPL treatment.
REPRODUCTIVE BIOMEDICINE ONLINE
(2024)
Article
Obstetrics & Gynecology
Jaime Guerrero, Juan Carlos Castillo, Jorge Ten, Jose Antonio Ortiz, Belen Lledo, Domingo Orozco, Francisco Quereda, Andrea Bernabeu, Rafael Bernabeu
Summary: The study found no significant differences in clinical outcomes between using oocytes obtained from random-start protocols and those from conventional ovarian stimulation in oocyte donation treatments. Luteal-phase stimulation required longer stimulation and higher FSH consumption.
REPRODUCTIVE BIOMEDICINE ONLINE
(2024)