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The aetiology of bacterial vaginosis (BV), a biofilm-associated vaginal infection, remains unknown. Epidemiologic data suggest that it is sexually transmitted. BV is characterised by loss of lactic acid-producing lactobacilli and an increase in facultative and strict anaerobic bacteria. Gardnerella spp are present in 95%–100% of cases; Gardnerella vaginalis has been found to be more virulent than other BV-associated bacteria (BVAB) in vitro. However, G. vaginalis is found in women with normal vaginal microbiota and colonisation is not sufficient for BV development. We hypothesise that Gardnerella spp initiate BV biofilm formation, but incident BV (iBV) requires incorporation of other key BVAB (ie, Prevotella bivia, Fannyhessea vaginae) into the biofilm that alter the transcriptome of the polymicrobial consortium. This study will investigate the sequence of microbiologic events preceding iBV.
This study will enrol 150 women aged 18–45 years with normal vaginal microbiota and no sexually transmitted infections at a sexual health research clinic in Birmingham, Alabama. Women will self-collect twice daily vaginal specimens up to 60 days. A combination of 16S rRNA gene sequencing, qPCR for Gardnerella spp, P. bivia and F. vaginae, and broad range 16S rRNA gene qPCR will be performed on twice daily vaginal specimens from women with iBV (Nugent score 7–10 on at least 2 consecutive days) and controls (with comparable age, race, contraceptive method and menstrual cycle days) maintaining normal vaginal microbiota to investigate changes in the vaginal microbiota over time for women with iBV. Participants will complete daily diaries on multiple factors including sexual activity.
This protocol is approved by the University of Alabama at Birmingham Institutional Review Board (IRB-300004547) and written informed consent will be obtained from all participants. Findings will be presented at scientific conferences and published in peer-reviewed journals as well as disseminated to providers and patients in communities of interest.
by Rebecca H. Correia, Darly Dash, Aaron Jones, Meredith Vanstone, Komal Aryal, Henry Yu-Hin Siu, Aquila Gopaul, Andrew P. Costa
We established consensus on practice-based metrics that characterize quality of care for older primary care patients and can be examined using secondary health administrative data. We conducted a two-round RAND/UCLA Appropriateness Method (RAM) study and recruited 10 Canadian clinicians and researchers with expertise relevant to the primary care of elderly patients. Informed by a literature review, the first RAM round evaluated the appropriateness and importance of candidate quality measures in an online questionnaire. Technical definitions were developed for each endorsed indicator to specify how the indicator could be operationalized using health administrative data. In a virtual synchronous meeting, the expert panel offered feedback on the technical specifications for the endorsed indicators. Panelists then completed a second (final) questionnaire to rate each indicator and corresponding technical definition on the same criteria (appropriateness and importance). We used statistical integration to combine technical expert panelists’ judgements and content analysis of open-ended survey responses. Our literature search and internal screening resulted in 61 practice-based quality indicators for rating. We developed technical definitions for indicators endorsed in the first questionnaire (n = 55). Following the virtual synchronous meeting and second questionnaire, we achieved consensus on 12 practice-based quality measures across four Priority Topics in Care of the Elderly. The endorsed indicators provide a framework to characterize practice- and population-level encounters of family physicians delivering care to older patients and will offer insights into the outcomes of their care provision. This study presented a case of soliciting expert feedback to develop measurable practice-based quality indicators that can be examined using administrative data to understand quality of care within population-based data holdings. Future work will refine and operationalize the technical definitions established through this process to examine primary care provision for older adults in a particular context (Ontario, Canada).by Aaron C. Drake, Christopher R. Sears
Research has found that humour styles can moderate the relationship between various facets of mental health and well-being. Most of these studies have used college student samples, however, and the generalizability of these findings has not been firmly established. This study examined how humour styles moderate the relationship between hopelessness and suicide ideation in both student and community samples. Community participants from the U.S. and Canada (n = 554) and student participants from a Canadian university (n = 208) completed several self-report measures including the Humor Styles Questionnaire. Analyses revealed differences in humour styles between the samples, as well as differences in humour styles between men and women. Regression analyses showed that self-defeating humour moderated the relationship between hopelessness and suicide ideation for student participants but not for community participants. Conversely, self-enhancing humour moderated the relationship between hopelessness and suicide ideation for community participants but not for student participants. These results suggest that high levels of self-defeating humour and self-enhancing humour may be uniquely maladaptive for these respective samples. These and other findings point to the necessity of recruiting diverse samples to better understand the beneficial and detrimental associations between humour styles and mental health. The potential to use measures of humour style as a tool to help identify at-risk individuals and to inform the development of intervention programs is discussed.by Bushra Jamil, Divya Nair, Pruthu Thekkur, Neelofar Laeeq, Anum Adil, Mohammed Khogali, Rony Zachariah, Selma Dar Berger, Srinath Satyanarayana, Ajay M. V. Kumar, Aaron Bochner, Amanda McClelland, Razia Fatima, Anthony D. Harries
IntroductionScreening household contacts of TB patients and providing TB preventive therapy (TPT) is a key intervention to end the TB epidemic. Global and timely implementation of TPT in household contacts, however, is dismal. We adapted the 7-1-7 timeliness metric designed to evaluate and respond to infectious disease outbreaks or pandemics, and assessed the feasibility, enablers and challenges of implementing this metric for screening and management of household contacts of index patients with bacteriologically-confirmed pulmonary TB in Karachi city, Pakistan.
MethodsWe conducted an explanatory mixed methods study with a quantitative component (cohort design) followed by a qualitative component (descriptive design with focus group discussions).
ResultsFrom January-June 2023, 92% of 450 index patients had their household contacts line-listed within seven days of initiating anti-TB treatment (“first 7”). In 84% of 1342 household contacts, screening outcomes were ascertained within one day of line-listing (“next 1”). In 35% of 256 household contacts eligible for further evaluation by a medical officer (aged ≤5 years or with chest symptoms), anti-tuberculosis treatment, TPT or a decision for no drugs was made within seven days of symptom screening (“second 7”). The principal reason for not starting anti-tuberculosis treatment or TPT was failure to consult a medical officer: only 129(50%) of 256 contacts consulted a medical officer. Reasons for poor performance in the “second 7” component included travel costs to see a medical officer, loss of daily earnings and fear of a TB diagnosis. Field staff reported that timeliness metrics motivated them to take prompt action in household contact screening and TPT provision and they suggested these be included in national guidelines.
ConclusionsField staff found “7-1-7” timeliness metrics to be feasible and useful. Integration of these metrics into national guidelines could improve timeliness of diagnosis, treatment and prevention of TB within households of index patients.
by Linda J. Paul, Aaron C. Ericsson, Frank M. Andrews, Zachary McAdams, Michael L. Keowen, Michael P. St Blanc, Heidi E. Banse
Equine glandular gastric disease (EGGD) is a common disease among athletic horses that can negatively impact health and performance. The pathophysiology of this EGGD remains poorly understood. Previous studies using controlled populations of horses identified differences in the gastric glandular mucosal microbiome associated with disease. The objective of this study was to compare the gastric microbiome in horses with EGGD and those without across multiple barns and differing management practices. We hypothesized that alterations in the microbiome of the gastric glandular mucosa are associated with EGGD. A secondary objective was to perform a risk factor analysis for EGGD using the diet and management data collected. Microbial populations of biopsies from normal pyloric mucosa of horses without EGGD (control biopsies), normal pyloric mucosa of horses with EGGD (normal biopsies) and areas of glandular mucosal disruption in horses with EGGD (lesion biopsies) were compared. Lesion biopsies had a different microbial community structure than control biopsies. Control biopsies had a higher read count for the phylum Actinomycetota compared to lesion biopsies. Control biopsies also had an enrichment of the genera Staphylococcus and Lawsonella and the species Streptococcus salivarius. Lesion biopsies had an enrichment of the genera Lactobacillus and Actinobacillus and the species Lactobacillus equigenerosi. These results demonstrate differences in the gastric glandular microbiome between sites of disrupted mucosa in horses with EGGD compared to pyloric mucosa of horses without EGGD. Risk factor analysis indicated that exercise duration per week was a risk factor for EGGD.by Yufeng Zhang, Keith D. Aaronson, Jonathan Gryak, Emily Wittrup, Cristian Minoccheri, Jessica R. Golbus, Kayvan Najarian
BackgroundTimely referral for advanced therapies (i.e., heart transplantation, left ventricular assist device) is critical for ensuring optimal outcomes for heart failure patients. Using electronic health records, our goal was to use data from a single hospitalization to develop an interpretable clinical decision-making system for predicting the need for advanced therapies at the subsequent hospitalization.
MethodsMichigan Medicine heart failure patients from 2013–2021 with a left ventricular ejection fraction ≤ 35% and at least two heart failure hospitalizations within one year were used to train an interpretable machine learning model constructed using fuzzy logic and tropical geometry. Clinical knowledge was used to initialize the model. The performance and robustness of the model were evaluated with the mean and standard deviation of the area under the receiver operating curve (AUC), the area under the precision-recall curve (AUPRC), and the F1 score of the ensemble. We inferred membership functions from the model for continuous clinical variables, extracted decision rules, and then evaluated their relative importance.
ResultsThe model was trained and validated using data from 557 heart failure hospitalizations from 300 patients, of whom 193 received advanced therapies. The mean (standard deviation) of AUC, AUPRC, and F1 scores of the proposed model initialized with clinical knowledge was 0.747 (0.080), 0.642 (0.080), and 0.569 (0.067), respectively, showing superior predictive performance or increased interpretability over other machine learning methods. The model learned critical risk factors predicting the need for advanced therapies in the subsequent hospitalization. Furthermore, our model displayed transparent rule sets composed of these critical concepts to justify the prediction.
ConclusionThese results demonstrate the ability to successfully predict the need for advanced heart failure therapies by generating transparent and accessible clinical rules although further research is needed to prospectively validate the risk factors identified by the model.
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