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Zarmakoupi P, Psarris A, Karasmani C, Antsaklis P, Theodora M, Syndos M, Pampanos A, Pappa KI, Domali E, Thomakos N, Akinosoglou K, Tsiakalos A, Daskalakis G. Cracking the Code: Investigating the Correlation between Aerobic Vaginitis and Preterm Labor. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:648. [PMID: 38674294 PMCID: PMC11052301 DOI: 10.3390/medicina60040648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/16/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024]
Abstract
Aerobic vaginitis (AV) is a distinct clinical entity characterized by inflammation and abnormal vaginal microflora. Often mistaken for bacterial vaginosis, AV remains relatively unknown and underdiagnosed. AV's understanding is evolving, with some experts suggesting it may primarily be an immunological disorder, the prevalence of which has a range of 7-13% in non-pregnant women and 4.1-8.3% during pregnancy. Pregnancy can affect susceptibility to vaginal infections, leading to adverse outcomes for the woman and the newborn. This review summarizes the correlation between AV and adverse pregnancy outcomes, particularly preterm birth, the leading cause of morbidity and mortality among neonates. An improved understanding of AV's impact on pregnancy outcomes can lead to early recognition, proper management, and effective interventions. While some studies support an association between AV and preterm labor, the existing knowledge of this relationship remains limited. The evidence suggests that AV may contribute to adverse pregnancy outcomes, mainly preterm birth, but further research is needed to establish a definitive link. Further studies are needed to investigate the underlying mechanisms and clarify AV's role in premature labor. A comprehensive understanding of AV's impact on pregnancy outcomes is crucial for early recognition, appropriate management, and effective interventions.
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Affiliation(s)
- Panagiota Zarmakoupi
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Alexandros Psarris
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Christina Karasmani
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Panagiotis Antsaklis
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Marianna Theodora
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Michael Syndos
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Andreas Pampanos
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Kalliopi I. Pappa
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Ekaterini Domali
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Nikolaos Thomakos
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
| | - Karolina Akinosoglou
- Department of Internal Medicine and Infectious Diseases, Medical School University of Patras, 26504 Patras, Greece;
| | | | - George Daskalakis
- 1st Department of Obstetrics and Gynecology, Alexandra Hospital, 11527 Athens, Greece; (P.Z.); (A.P.); (P.A.); (M.T.); (M.S.); (A.P.); (K.I.P.); (E.D.); (N.T.); (G.D.)
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Ene A, Banerjee S, Wolfe AJ, Putonti C. Exploring the genotypic and phenotypic differences distinguishing Lactobacillus jensenii and Lactobacillus mulieris. mSphere 2023; 8:e0056222. [PMID: 37366621 PMCID: PMC10449518 DOI: 10.1128/msphere.00562-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 05/10/2023] [Indexed: 06/28/2023] Open
Abstract
Lactobacillus crispatus, Lactobacillus gasseri, Lactobacillus iners, and Lactobacillus jensenii are dominant species of the urogenital microbiota. Prior studies suggest that these Lactobacillus species play a significant role in the urobiome of healthy females. In our prior genomic analysis of all publicly available L. jensenii and Lactobacillus mulieris genomes at the time (n = 43), we identified genes unique to these two closely related species. This motivated our further exploration here into their genotypic differences as well as into their phenotypic differences. First, we expanded genome sequence representatives of both species to 61 strains, including publicly available strains and nine new strains sequenced here. Genomic analyses conducted include phylogenetics of the core genome as well as biosynthetic gene cluster analysis and metabolic pathway analyses. Urinary strains of both species were assayed for their ability to utilize four simple carbohydrates. We found that L. jensenii strains can efficiently catabolize maltose, trehalose, and glucose, but not ribose, and L. mulieris strains can utilize maltose and glucose, but not trehalose and ribose. Metabolic pathway analysis clearly shows the lack of treB within L. mulieris strains, indicative of its inability to catabolize external sources of trehalose. While genotypic and phenotypic observations provide insight into the differences between these two species, we did not find any association with urinary symptom status. Through this genomic and phenotypic investigation, we identify markers that can be leveraged to clearly distinguish these two species in investigations of the female urogenital microbiota. IMPORTANCE We have expanded upon our prior genomic analysis of L. jensenii and L. mulieris strains, including nine new genome sequences. Our bioinformatic analysis finds that L. jensenii and L. mulieris cannot be distinguished by short-read 16S rRNA gene sequencing alone. Thus, to discriminate between these two species, future studies of the female urogenital microbiome should employ metagenomic sequencing and/or sequence species-specific genes, such as those identified here. Our bioinformatic examination also confirmed our prior observations of differences between the two species related to genes associated with carbohydrate utilization, which we tested here. We found that the transport and utilization of trehalose are key distinguishing traits of L. jensenii, which is further supported by our metabolic pathway analysis. In contrast with other urinary Lactobacillus species, we did not find strong evidence for either species, nor particular genotypes, to be associated with lower urinary tract symptoms (or the lack thereof).
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Affiliation(s)
- Adriana Ene
- Bioinformatics Program, Loyola University Chicago, Chicago, Illinois, USA
| | - Swarnali Banerjee
- Department of Mathematics and Statistics, Loyola University Chicago, Chicago, Illinois, USA
| | - Alan J. Wolfe
- Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
| | - Catherine Putonti
- Bioinformatics Program, Loyola University Chicago, Chicago, Illinois, USA
- Department of Microbiology and Immunology, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA
- Department of Biology, Loyola University Chicago, Chicago, Illinois, USA
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Microbial Diversity and Pathogenic Properties of Microbiota Associated with Aerobic Vaginitis in Women with Recurrent Pregnancy Loss. Diagnostics (Basel) 2022; 12:diagnostics12102444. [PMID: 36292132 PMCID: PMC9600244 DOI: 10.3390/diagnostics12102444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/24/2022] [Accepted: 09/26/2022] [Indexed: 12/02/2022] Open
Abstract
Recurrent pregnancy loss (RPL) is a major reproductive problem that affects approximately 5% of couples. The objective of this study was to assess vaginal flora dysbiosis in women suffering from unexplained RPL and to investigate the pathogenic properties of the microbiota associated with aerobic vaginitis (AV). The study included one hundred fifteen women, 65 with RPL and 50 controls. The diversity of vaginal microbiota isolated was evaluated by molecular sequencing. Then, pathogenic factors, such as acid-resistance, antibiotics susceptibility, and biofilm formation were evaluated. The prevalence of AV was five-fold higher in the RPL group than in the controls (64.6% vs. 12.0%). The most prevalent isolates in the case group were Enterococcus spp. (52%) and Staphylococcus spp. (26%). All bacterial strains tolerate low pH. The prevalence of multidrug resistance (MDR) among all bacteria was 47.7%. Of all strains, 91.0% were biofilm producers. The presence of MDR was found to be related to biofilm formation. The results provide evidence supporting an increased presence of dysbiosis of the vaginal flora, especially AV, in women with RPL in Tunisia. The viability of the AV-associated bacteria and their persistence in the genitals may be due to their ability to resist low pH and to produce a biofilm.
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Montella E, Ferraro A, Sperlì G, Triassi M, Santini S, Improta G. Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052498. [PMID: 35270190 PMCID: PMC8909182 DOI: 10.3390/ijerph19052498] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 12/22/2022]
Abstract
Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy).
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Affiliation(s)
- Emma Montella
- Department of Public Health, University of Naples “Federico”, 80125 Naples, Italy; (E.M.); (M.T.); (G.I.)
| | - Antonino Ferraro
- Department of Information Technology and Electrical Engineering, University of Naples “Federico”, Via Claudio 21, 80125 Naples, Italy; (A.F.); (S.S.)
| | - Giancarlo Sperlì
- Department of Information Technology and Electrical Engineering, University of Naples “Federico”, Via Claudio 21, 80125 Naples, Italy; (A.F.); (S.S.)
- CINI-ITEM National Lab, Complesso Universitario di Monte S. Angelo Via Cinthia Edificio Centri Comuni, 80126 Naples, Italy
- Correspondence:
| | - Maria Triassi
- Department of Public Health, University of Naples “Federico”, 80125 Naples, Italy; (E.M.); (M.T.); (G.I.)
- Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico”, 80131 Naples, Italy
| | - Stefania Santini
- Department of Information Technology and Electrical Engineering, University of Naples “Federico”, Via Claudio 21, 80125 Naples, Italy; (A.F.); (S.S.)
- CINI-ITEM National Lab, Complesso Universitario di Monte S. Angelo Via Cinthia Edificio Centri Comuni, 80126 Naples, Italy
| | - Giovanni Improta
- Department of Public Health, University of Naples “Federico”, 80125 Naples, Italy; (E.M.); (M.T.); (G.I.)
- Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico”, 80131 Naples, Italy
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Aerobic Vaginitis Diagnosis Criteria Combining Gram Stain with Clinical Features: An Establishment and Prospective Validation Study. Diagnostics (Basel) 2022; 12:diagnostics12010185. [PMID: 35054351 PMCID: PMC8775230 DOI: 10.3390/diagnostics12010185] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 01/06/2022] [Accepted: 01/10/2022] [Indexed: 02/05/2023] Open
Abstract
Wet-mount microscopy aerobic vaginitis (AV) diagnostic criteria need phase-contrast microscopy and keen microscopists, and the preservation of saline smears is less common in clinical practice. This research work developed new AV diagnostic criteria that combine Gram stain with clinical features. We enrolled 325 AV patients and 325 controls as a study population to develop new AV diagnostic criteria. Then, an independent group, which included 500 women, was used as a validation population. AV-related microscopic findings on Gram-stained and wet-mount smears from the same participants were compared. The accuracy of bacterial indicators from the two methods was verified by bacterial 16S rRNA V4 sequencing (n = 240). Logistic regression was used to analyse AV-related clinical features. The screened clinical features were combined with Gram-stain microscopic indicators to establish new AV diagnostic criteria. There were no significant differences in the leukocyte counts or the parabasal epitheliocytes (PBC) proportion between the Gram-stain and wet-mount methods (400×). Gram stain (1000×) satisfied the ability to identify bacteria as verified by 16S rRNA sequencing but failed to identify toxic leukocytes. The new criteria included: Lactobacillary grades (LBG) and background flora (Gram stain, 1000×), leukocytes count and PBC proportion (Gram stain, 400×), and clinical features (vaginal pH > 4.5, vagina hyperemia, and yellow discharge). These criteria satisfied the accuracy and reliability for AV diagnosis (Se = 86.79%, Sp = 95.97%, and Kendall’s W value = 0.899) in perspective validation. In summary, we proposed an alternative and valuable AV diagnostic criteria based on the Gram stain, which can make it possible to diagnose common vaginitis like AV, BV, VVC, and mixed infections on the same smear and can be available for artificial intelligence diagnosis in the future.
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