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- Barnabas, SL, et al.
(author)
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Converging epidemics of sexually transmitted infections and bacterial vaginosis in southern African female adolescents at risk of HIV
- 2018
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In: International journal of STD & AIDS. - : SAGE Publications. - 1758-1052 .- 0956-4624. ; 29:6, s. 531-539
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Journal article (peer-reviewed)abstract
- Adolescents in Africa are at high risk for HIV infection, other sexually transmitted infections (STIs) and bacterial vaginosis (BV). Since behavior and burden of STIs/BV may influence HIV risk, behavioral risk factors and prevalence of STIs/BV were compared in HIV-seronegative adolescent females (n = 298; 16–22 years) from two South African communities (Soweto and Cape Town). STIs ( Chlamydia trachomatis, Neisseria gonorrhoeae, Trichomonas vaginalis, Mycoplasma genitalium, herpes simplex virus (HSV)-1, HSV-2, Treponema pallidum, and Haemophilus ducreyi) were detected by multiplex polymerase chain reaction, human papillomavirus (HPV) by Roche Linear Array, and BV by Nugent scoring. Rates of BV (Nugent ≥7; 46.6%) and HPV (66.8%) were high in both communities. Prevalence of C. trachomatis and N. gonorrhoeae were >2-fold higher in Cape Town than Soweto (Chlamydia: 42% [62/149] versus 18% [26/148], p < 0.0001; gonorrhoea 11% [17/149] versus 5% [7/148], p = 0.05). Only 24% of adolescents with vaginal discharge-causing STIs or BV were symptomatic. In South African adolescents, clinical symptoms compatible with vaginal discharge syndrome had a sensitivity of 23% and specificity of 85% for the diagnosis of discharge-causing STI or BV. In a region with high HIV prevalence and incidence, >70% of young women with treatable conditions that could enhance HIV risk would have been missed because they lacked symptoms associated with syndromic management.
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- Romagnoni, A, et al.
(author)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Journal article (peer-reviewed)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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