Bacterial vaginosis (BV) is a highly prevalent infectious disease causing numerous complications in obstetrics, gynecology, and in the newborns. The necessity of investigating BV is explained by its increasing incidence, including its asymptomatic forms, its probable sexual transmission, and its possible role in the etiology and pathogenesis of diseases of the cervix uteri because of exposure of the squamous epithelium of the cervix uteri, permanently regenerating and therefore highly sensitive to unfavorable effects, to high concentrations of opportunistic microorganisms. Our findings indicate that women with BV, particularly those with signs of another infection, are at risk of diseases of the cervix uteri. The risk group can be detected by simple and available methods for detecting BV with obligatory cytological studies, particularly in cases with inflammatory processes, and additional tests for associated infections (chlamydiosis, herpes simplex, etc.), whose role in the pathogenesis of cervical diseases is well known.
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J Med Virol
February 2025
Department of Medical Technology, Faculty of Health Sciences, Kyorin University, Tokyo, Japan.
In cervical cancer screening, cytology is used as a triage test to refer high-risk human papillomavirus (HR-HPV)-positive women for colposcopy, but its accuracy is inadequate. The present study aimed to demonstrate that the presence of atypical cells with large vacuoles in the cytoplasm of parabasal cells, referred to as vacuolated parabasal cells (VPCs), which are observed in the Pap smears of HPV-positive women, is associated with specific HPV genotypes. Among 2175 patients, 310 with a single HR-HPV infection and cytological diagnosis of high-grade squamous intraepithelial lesions (HSIL) or atypical squamous cells not excluding HSIL (ASC-H) were included, of which 86 were infected with HPV16.
View Article and Find Full Text PDFJ Med Virol
February 2025
Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu, Taiwan.
This study examined the relationship between the vaginal microbiome, HPV infection, and cervical intraepithelial neoplasia (CIN) in 173 women. Subjects were grouped by HPV status and cervical lesion severity, ranging from HPV-negative to CIN Grade 2 or higher. Using VALENCIA classification, the study identified different community state types (CSTs) of vaginal microbiota, with CST IV subtypes (Staphylococcus dominated) showing high diversity and increased pathogenic bacteria.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Biomedical Engineering, School of Life Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.
The cervical cell classification technique can determine the degree of cellular abnormality and pathological condition, which can help doctors to detect the risk of cervical cancer at an early stage and improve the cure and survival rates of cervical cancer patients. Addressing the issue of low accuracy in cervical cell classification, a deep convolutional neural network A2SDNet121 is proposed. A2SDNet121 takes DenseNet121 as the backbone network.
View Article and Find Full Text PDFInt J Surg Case Rep
January 2025
Addis Ababa University, College of Health Sciences, Department of Internal Medicine, Addis Ababa, Ethiopia.
Introduction And Importance: Uterine didelphys is a Müllerian duct anomaly with two uteri and cervices, with or without a vaginal septum. A di-cavitary twin pregnancy in a uterus didelphys is an infrequent occurrence.
Case Presentation: A 27-year-old woman, gravida 3, para 2, at a gestational age of 37 weeks and 4 days, presented with pushing-down pain.
Sci Rep
January 2025
School of Computer and Information Science, Chongqing Normal University, Chongqing, 401331, China.
Cervical cancer poses a significant health risk to women. Deep learning methods can assist pathologists in quickly screening images of suspected lesion cells, greatly improving the efficiency of cervical cancer screening and diagnosis. However, existing deep learning methods rely solely on single-scale features and local spatial information, failing to effectively capture the subtle morphological differences between abnormal and normal cervical cells.
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