scholarly journals Predicting future ocular Chlamydia trachomatis infection prevalence using serological, clinical, molecular, and geospatial data

2021 ◽  
Author(s):  
Christine Tedijanto ◽  
Solomon Aragie ◽  
Zerihun Tadesse ◽  
Mahteme Haile ◽  
Taye Zeru ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlamydia Trachomatis ◽  
Public Health Problem ◽  
Predictive Performance ◽  
Infection Prevalence ◽  
Chlamydia Trachomatis Infection ◽  
Ensemble Machine Learning ◽  
High Infection ◽  
Future Prediction ◽  
Intensive Control

Trachoma is an infectious disease characterized by repeated exposures to Chlamydia trachomatis (Ct) that may ultimately lead to blindness. Certain areas, particularly in Africa, pose persistent challenges to elimination of trachoma as a public health problem. Efficiently identifying communities with high infection burden could help target more intensive control efforts. We hypothesized that IgG seroprevalence in combination with geospatial layers, machine learning, and model-based geostatistics would be able to accurately predict future community-level ocular Ct infections detected by PCR. We used measurements from 40 communities in the hyperendemic Amhara region of Ethiopia. Median Ct infection prevalence among children 0-5 years old increased from 6% at enrollment to 29% by month 36. At baseline, correlation between seroprevalence and Ct infection was stronger among children 0-5 years old (ρ = 0.77) than children 6-9 years old (ρ = 0.48), and stronger than the correlation between clinical trachoma and Ct infection (0-5y ρ = 0.56; 6-9y ρ = 0.40). Seroprevalence was the strongest concurrent predictor of infection prevalence at month 36 among children 0-5 years old (cross-validated R2 = 0.75, 95% CI: 0.58-0.85), though predictive performance declined substantially with increasing temporal lag between predictor and outcome measurements. Geospatial variables, a spatial Gaussian process, and stacked ensemble machine learning did not meaningfully improve predictions. Serological markers among children 0-5 years old may be an objective, programmatic tool for identifying communities with high levels of active ocular Ct infections, but accurate, future prediction in the context of changing transmission remains an open challenge.

scholarly journals Genomics of Ocular Chlamydia trachomatis After 5 Years of SAFE Interventions for Trachoma in Amhara, Ethiopia

2020 ◽  
Author(s):  
Harry Pickering ◽  
Ambahun Chernet ◽  
Eshetu Sata ◽  
Mulat Zerihun ◽  
Charlotte A Williams ◽  
...  
Keyword(s):  
Chlamydia Trachomatis ◽  
Treatment Success ◽  
Public Health Problem ◽  
Control Program ◽  
Genomic Variation ◽  
Macrolide Resistance ◽  
World Health ◽  
Infection Prevalence ◽  
Improvement Strategy ◽  
Health Organization

Abstract Background To eliminate trachoma as a public health problem, the World Health Organization recommends the SAFE (surgery, antibiotics, facial cleanliness, and environmental improvement) strategy. As part of the SAFE strategy in the Amhara Region, Ethiopia, the Trachoma Control Program distributed >124 million doses of antibiotics between 2007 and 2015. Despite this, trachoma remained hyperendemic in many districts and a considerable level of Chlamydia trachomatis (Ct) infection was evident. Methods We utilized residual material from Abbott m2000 Ct diagnostic tests to sequence 99 ocular Ct samples from Amhara and investigated the role of Ct genomic variation in continued transmission of Ct. Results Sequences were typical of ocular Ct at the whole-genome level and in tissue tropism–associated genes. There was no evidence of macrolide resistance in this population. Polymorphism around the ompA gene was associated with village-level trachomatous inflammation–follicular prevalence. Greater ompA diversity at the district level was associated with increased Ct infection prevalence. Conclusions We found no evidence for Ct genomic variation contributing to continued transmission of Ct after treatment, adding to evidence that azithromycin does not drive acquisition of macrolide resistance in Ct. Increased Ct infection in areas with more ompA variants requires longitudinal investigation to understand what impact this may have on treatment success and host immunity.

Diagnostic utility of urethral smears in predicting urethral chlamydia in HIV-infected men

2008 ◽  
Vol 19 (11) ◽  
pp. 741-743 ◽  
Author(s):  
C C Iwuji ◽  
I Reeves ◽  
K Nambiar ◽  
D Richardson
Keyword(s):  
Chlamydia Trachomatis ◽  
Odds Ratio ◽  
Chlamydial Infection ◽  
Adjusted Odds Ratio ◽  
Polymorphonuclear Leucocyte ◽  
Hiv Positive ◽  
Infection Prevalence ◽  
Diagnostic Utility ◽  
Chlamydia Trachomatis Infection ◽  
Asymptomatic Men

We collected data from 218 HIV-infected men to assess the usefulness of the urethral smear and symptoms in predicting Chlamydia trachomatis infection. Prevalence of urethral chlamydia was 9%. A polymorphonuclear leucocyte (PMNL) count ≥5 was 73% sensitive and 71% specific for C. trachomatis infection. Adjusted odds ratio for risk of chlamydial infection was significant for urethral irritation (7.48; 1.54–36.4), a PMNL count of 20 or more (9.83; 2.52–8.4) and a PMNL count of 5–19 (4.10; 1.34–12.5). We had to perform 50 urethral smears in HIV-positive men without symptoms to treat one case of C. trachomatis at the time of visit. Findings suggest that the presence of symptoms, in particular urethral irritation may be associated with chlamydial urethritis and that the higher the urethral PMNL count, the more likely it is for C. trachomatis to be detected. The findings in this study also lend further support to recent guidelines that urethral microscopy is not useful in asymptomatic men and hence should be abandoned.

scholarly journals Trachoma, Anti-Pgp3 Serology, and Ocular Chlamydia trachomatis Infection in Papua New Guinea

2020 ◽  
Author(s):  
Colin K Macleod ◽  
Robert Butcher ◽  
Sarah Javati ◽  
Sarah Gwyn ◽  
Marinjho Jonduo ◽  
...  
Keyword(s):  
Public Health ◽  
Chlamydia Trachomatis ◽  
Papua New Guinea ◽  
Clinical Signs ◽  
Public Health Problem ◽  
New Guinea ◽  
Dried Blood Spots ◽  
Causative Organism ◽  
Ocular Infection ◽  
Chlamydia Trachomatis Infection

Abstract Background In Melanesia, the prevalence of trachomatous inflammation–follicular (TF) suggests that public health–level interventions against active trachoma are needed. However, the prevalence of trachomatous trichiasis is below the threshold for elimination as a public health problem and evidence of conjunctival infection with trachoma’s causative organism (Chlamydia trachomatis [CT]) is rare. Here, we examine the prevalence of ocular infection with CT and previous exposure to CT in three evaluation units (EUs) of Papua New Guinea. Methods All individuals aged 1–9 years who were examined for clinical signs of trachoma in 3 Global Trachoma Mapping Project EUs were eligible to take part in this study (N = 3181). Conjunctival swabs were collected from 349 children with TF and tested by polymerase chain reaction to assess for ocular CT infection. Dried blood spots were collected from 2572 children and tested for anti-Pgp3 antibodies using a multiplex assay. Results The proportion of children with TF who had CT infection was low across all 3 EUs (overall 2%). Anti-Pgp3 seroprevalence was 5.2% overall and there was no association between anti-Pgp3 antibody level and presence of TF. In 2 EUs, age-specific seroprevalence did not increase significantly with increasing age in the 1- to 9-year-old population. In the third EU, there was a statistically significant change with age but the overall seroprevalence and peak age-specific seroprevalence was very low. Conclusions Based on these results, together with similar findings from the Solomon Islands and Vanuatu, the use of TF to guide antibiotic mass drug administration decisions in Melanesia should be reviewed.

scholarly journals Chlamydia trachomatis infection Prevalence varies in general practice

BMJ ◽  
1994 ◽  
Vol 308 (6930) ◽  
pp. 716-716 ◽  
Author(s):  
P Owen ◽  
T Crowley ◽  
P Horner ◽  
J Nelki ◽  
E O Caul
Keyword(s):  
General Practice ◽  
Chlamydia Trachomatis ◽  
Infection Prevalence ◽  
Chlamydia Trachomatis Infection

scholarly journals Interpreting tree ensemble machine learning models with endoR

2022 ◽  
Author(s):  
Albane Ruaud ◽  
Niklas A Pfister ◽  
Ruth E Ley ◽  
Nicholas D Youngblut
Keyword(s):  
Machine Learning ◽  
Predictive Performance ◽  
R Package ◽  
Metagenomic Data ◽  
Learning Models ◽  
Model Interpretation ◽  
Ensemble Machine Learning ◽  
Fermenting Bacteria ◽  
Microbiome Data ◽  
Machine Learning Models

Background: Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa or genomic content may be associated. Results: We developed endoR, a method to interpret a fitted tree ensemble model. First, endoR simplifies the fitted model into a decision ensemble from which it then extracts information on the importance of individual features and their pairwise interactions and also visualizes these data as an interpretable network. Both the network and importance scores derived from endoR provide insights into how features, and interactions between them, contribute to the predictive performance of the fitted model. Adjustable regularization and bootstrapping help reduce the complexity and ensure that only essential parts of the model are retained. We assessed the performance of endoR on both simulated and real metagenomic data. We found endoR to infer true associations with more or comparable accuracy than other commonly used approaches while easing and enhancing model interpretation. Using endoR, we also confirmed published results on gut microbiome differences between cirrhotic and healthy individuals. Finally, we utilized endoR to gain insights into components of the microbiome that predict the presence of human gut methanogens, as these hydrogen-consumers are expected to interact with fermenting bacteria in a complex syntrophic network. Specifically, we analyzed a global metagenome dataset of 2203 individuals and confirmed the previously reported association between Methanobacteriaceae and Christensenellales. Additionally, we observed that Methanobacteriaceae are associated with a network of hydrogen-producing bacteria. Conclusion: Our method accurately captures how tree ensembles use features and interactions between them to predict a response. As demonstrated by our applications, the resultant visualizations and summary outputs facilitate model interpretation and enable the generation of novel hypotheses about complex systems. An implementation of endoR is available as an open-source R-package on GitHub (https://github.com/leylabmpi/endoR).

scholarly journals Chlamydia trachomatis infection prevalence and serovar distribution in a high-density urban area in the north of Italy

2016 ◽  
Vol 65 (6) ◽  
pp. 510-520 ◽  
Author(s):  
Claudio Foschi ◽  
Paola Nardini ◽  
Nicoletta Banzola ◽  
Antonietta D'Antuono ◽  
Monica Compri ◽  
...  
Keyword(s):  
Chlamydia Trachomatis ◽  
Urban Area ◽  
High Density ◽  
Infection Prevalence ◽  
Chlamydia Trachomatis Infection ◽  
The North

scholarly journals An ensemble prediction model for COVID-19 mortality risk

2022 ◽  
Author(s):  
Jie Li ◽  
Xin Li ◽  
John Hutchinson ◽  
Mohammad Asad ◽  
Yadong Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Features ◽  
Early Stage ◽  
Predictive Performance ◽  
Ensemble Prediction ◽  
Death Risk ◽  
Risk Of Death ◽  
Ensemble Machine Learning ◽  
Key Features ◽  
Independent Cohort

Background: It's critical to identify COVID-19 patients with a higher death risk at early stage to give them better hospitalization or intensive care. However, thus far, none of the machine learning models has been shown to be successful in an independent cohort. We aim to develop a machine learning model which could accurately predict death risk of COVID-19 patients at an early stage in other independent cohorts. Methods: We used a cohort containing 4711 patients whose clinical features associated with patient physiological conditions or lab test data associated with inflammation, hepatorenal function, cardiovascular function and so on to identify key features. To do so, we first developed a novel data preprocessing approach to clean up clinical features and then developed an ensemble machine learning method to identify key features. Results: Finally, we identified 14 key clinical features whose combination reached a good predictive performance of AUC 0.907. Most importantly, we successfully validated these key features in a large independent cohort containing 15,790 patients. Conclusions: Our study shows that 14 key features are robust and useful in predicting the risk of death in patients confirmed SARS-CoV-2 infection at an early stage, and potentially useful in clinical settings to help in making clinical decisions.

scholarly journals Completing the Market

2019 ◽  
Vol 19 (292) ◽  
Author(s):  
Nan Hu ◽  
Jian Li ◽  
Alexis Meyer-Cirkel
Keyword(s):  
Machine Learning ◽  
Credit Risk ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Predictive Performance ◽  
Gradient Boosting ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Ensemble Machine Learning ◽  
Out Of Sample Prediction

We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.

scholarly journals Genomics of Ocular Chlamydia trachomatis after 5 years of SAFE interventions for trachoma in Amhara, Ethiopia

2020 ◽  
Author(s):  
Harry Pickering ◽  
Ambahun Chernet ◽  
Eshetu Sata ◽  
Mulat Zerihun ◽  
Charlotte A. Williams ◽  
...  
Keyword(s):  
Chlamydia Trachomatis ◽  
Treatment Success ◽  
Public Health Problem ◽  
Control Program ◽  
Genomic Variation ◽  
Macrolide Resistance ◽  
Infection Prevalence ◽  
Safe Strategy ◽  
Genome Level

AbstractBackgroundTo eliminate trachoma as a public health problem, the WHO recommends the SAFE strategy. As part of the SAFE strategy in the Amhara Region, Ethiopia, the Trachoma Control Program distributed over 124 million doses of antibiotic between 2007 and 2015. Despite these interventions, trachoma remained hyperendemic in many districts and a considerable level of Chlamydia trachomatis (Ct) infection was evident.MethodsWe utilised residual material from Abbott m2000 Ct diagnostic tests to sequence 99 ocular Ct samples from Amhara and investigated the role of Ct genomic variation in the continued transmission of Ct following 5 years of SAFE.FindingsSequences were typical of ocular Ct, at the whole-genome level and in tissue tropism-associated genes. There was no evidence of macrolide-resistance in this Ct population. Polymorphism in a region around ompA gene was associated with village-level TF prevalence. Additionally, greater ompA diversity at the district-level was associated with increased Ct infection prevalence.InterpretationWe found no evidence for Ct genomic variation contributing to continued transmission of Ct after treatment, adding to previous evidence that azithromycin does not drive acquisition of macrolide resistance alleles in Ct. Increased Ct infection in villages and in districts with more ompA variants requires longitudinal investigation to understand what impact this may have on treatment success and host immunity.FundingEuropean Commission; Neglected Tropical Disease Support Center; International Trachoma Initiative

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