Factor Associated to Bacterial Vaginosis in Non-pregnant Women of North Indian Population

Amita, Singh Hari Om Singh; Nain, Dhole TN Sumitra

doi:10.4172/2155-952x.1000195

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…The diagnosis of BV is complicated by the lack of consensus over what is defined as BV, the natural variation of the VMB in women of different racial backgrounds and the elusive polymicrobial etiology of BV. Several risk factors have been identified in the pathogenesis of BV, such as age, socioeconomical status, antibiotic usage, sexual behavior and ethnicity (Brumley, 2012;Singh et al, 2015;Ranjit et al, 2018). Lactobacillus bacteria are traditionally linked with a healthy VMB, but L. iners may be more pathogen than friend.…”

Section: Factors Complicating the Diagnosis Of Bvmentioning

confidence: 99%

Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities

Redelinghuys

Geldenhuys

Jung

et al. 2020

Front. Cell. Infect. Microbiol.

117

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A healthy female genital tract harbors a microbiome dominated by lactic acid and hydrogen peroxide producing bacteria, which provide protection against infections by maintaining a low pH. Changes in the bacterial compositions of the vaginal microbiome can lead to bacterial vaginosis (BV), which is often associated with vaginal inflammation. Bacterial vaginosis increases the risk of acquiring sexually transmitted infections (STIs) like human immunodeficiency virus (HIV) and affects women's reproductive health negatively. In pregnant women, BV can lead to chorioamnionitis and adverse pregnancy outcomes, including preterm premature rupture of the membranes and preterm birth. In order to manage BV effectively, good diagnostic procedures are required. Traditionally clinical and microscopic methods have been used to diagnose BV; however, these methods require skilled staff and time and suffer from reduced sensitivity and specificity. New diagnostics, including highly sensitive and specific point-of-care (POC) tests, treatment modalities and vaccines can be developed based on the identification of biomarkers from the growing pool of vaginal microbiome and vaginal metabolome data. In this review the current and future diagnostic avenues will be discussed.

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Section: Factors Complicating the Diagnosis Of Bvmentioning

confidence: 99%

Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities

Redelinghuys

Geldenhuys

Jung

et al. 2020

Front. Cell. Infect. Microbiol.

117

View full text Add to dashboard Cite

show abstract

“…Prior studies also proposes the role of vaginal douching in increasing the risk of BV and reccurent BV in non-pregnant females. 10,11,25,26 Our Findings stated that BV patient have a history of vaginal douching (34.3%). Research by Ranjit et al showed that 32.1% of BV patients (p=0.015) had a frequent vaginal douching habit compared with those who performed it occasionally.…”

Section: Discussionmentioning

confidence: 65%

“…11 Different research by Om et al and Bitew et al also suggest that vagina douching is increasing BV risk (OR=1.33: 95% CI 0.8 to 2.0) and (OR=1.73: 95% CI 0.99 to 3.04), respectively. 10,26 Vaginal douching can affect the ecosystem of vaginal microbiome, possibly promoting the growth of opportunistic pathogen bacteria. Discontinuation of vaginal douching is believed to reduce the risk of BV recurrence.…”

Section: Discussionmentioning

confidence: 99%

The Profile of Bacterial Vaginosis in Academic Hospital Surabaya: A Retrospective Study

Manuputty

Matodiharjo

2020

BIKK

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Background: Bacterial vaginosis (BV) is a lower genital tract infection, which occur as a result of vaginal microbiome dysbiosis. This vaginal infection can lead to symptoms such as vaginal discharge, malodor and irritation. It is the most common condition occurred in reproductive age but may give 50–75% of asymptomatic signs. Purpose: To overview new BV patients at the Sexually Transmitted Infection Division of Dermatology and Venereology Outpatient Clinic Dr. Soetomo General Hospital Surabaya in 2015–2017. Methods: A retrospective study was performed by evaluating medical records of new BV patients, including identity, history taking, examination, diagnosis, treatment, follow up and counselling. Result: The number of new BV cases was 35 (1,38%). The most infected age group was 25–44 years old (51%), and 65.7% of them were married. The most common chief complaint was vaginal discharge without subjective complaints (74.3%), the most common type of complaint was recurrence (48.6%), the most partner roles which affected by BV patients were husbands (51.4%), history of vaginal douching (34.2%), and the most vaginal discharges were severe (75.8%). The whiff test showed positive results and pH > 4.5 in 48.6% of patients. All microscopical preparations showed clue cells. The most prescribed drug was metronidazole. Conclusion: The overview new BV cases showed a small incidence with commonly occurred among women in childbearing age and were asymptomatic. A routine high vaginal swab helps the proper diagnosis and management to prevent the recurrence and complication of BV.

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Detect feature edges for diagnosis of bacterial vaginosis

Li¹,

Li²

2023

PeerJ

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One of the most common diseases among women of reproductive age is bacterial vaginosis (BV). However, the etiology of BV remains unknown. In this study, we modeled the temporal sample of the vaginal microbiome as a network and investigated the relationship between the network edges and BV. Furthermore, we used feature selection algorithms including decision tree (DT) and ReliefF (RF) to select the network feature edges associated with BV and subsequently validated these feature edges through logistic regression (LR) and support vector machine (SVM). The results show that: machine learning can distinguish vaginal community states (BV, ABV, SBV, and HEA) based on a few feature edges; selecting the top five feature edges of importance can achieve the best accuracy for the feature selection and classification model; the feature edges selected by DT outperform those selected by RF in terms of classification algorithm LR and SVM, and LR with DT feature edges is more suitable for diagnosing BV; two feature selection algorithms exhibit differences in the importance of ranking of edges; the feature edges selected by DT and RF cannot construct sub-network associated with BV. In short, the feature edges selected by our method can serve as indicators for personalized diagnosis of BV and aid in the clarification of a more mechanistic interpretation of its etiology.

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Factor Associated to Bacterial Vaginosis in Non-pregnant Women of North Indian Population

Cited by 3 publications

References 44 publications

Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities

Bacterial Vaginosis: Current Diagnostic Avenues and Future Opportunities

The Profile of Bacterial Vaginosis in Academic Hospital Surabaya: A Retrospective Study

Detect feature edges for diagnosis of bacterial vaginosis

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