Pelvic floor disorder (PFD) is a common disease affecting the quality of life of middle-aged and elderly women. Pelvic floor muscle (PFM) damage is related to delivery mode, fetal size, and parity. Spontaneous vaginal delivery causes especially great damage to PFM. The purpose of this study was to summarize the characteristics of PFM action during the second stage of labor by collecting female pelvic MRI (magnetic resonance imaging) data and, further, to try to investigate the potential pathogenetic mechanism of PFD. A three-dimensional model was established to study the influence factors and characteristics of PFM strength. In the second stage of labor, the mechanical responses, possible damage, and the key parts of postpartum lesions of PFM due to the different fetal biparietal diameter (BPD) sizes were analyzed by finite element simulations. The research results showed that the peak stress and strain of PFM appeared at one-half of the delivery period and at the attachment point of the pubococcygeus to the skeleton. In addition, during the simulation process, the pubococcygeus was stretched by about 1.2 times and the levator ani muscle was stretched by more than two-fold. There was also greater stress and strain in the middle area of the levator ani muscle and pubococcygeus. According to the statistics, either being too young or in old maternal age will increase the probability of postpartum PFM injury. During delivery, the entire PFM underwent the huge deformation, in which the levator ani muscle and the pubococcygeus were seriously stretched and the attachment point between the pubococcygeus and the skeleton were the places with the highest probability of postpartum lesions.
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http://dx.doi.org/10.3390/ijerph182010821 | DOI Listing |
Int Urogynecol J
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Clin Pract
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Faculty of Medicine, Ovidius University of Constanta, 900470 Constanta, Romania.
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View Article and Find Full Text PDFJ Transl Med
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Department of General Surgery (Colorectal Surgery), The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Accurate and fast histological diagnosis of cancers is crucial for successful treatment. The deep learning-based approaches have assisted pathologists in efficient cancer diagnosis. The remodeled microenvironment and field cancerization may enable the cancer-specific features in the image of non-cancer regions surrounding cancer, which may provide additional information not available in the cancer region to improve cancer diagnosis.
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January 2025
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JAMA
January 2025
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