| 2. |
- Garnaud, C., et al.
(författare)
-
Toxoplasma gondii-specific IgG avidity testing in pregnant women
- 2020
-
Ingår i: Clinical Microbiology and Infection. - : Elsevier. - 1198-743X .- 1469-0691. ; 26:9, s. 1155-1160
-
Forskningsöversikt (refereegranskat)abstract
- Background: The parasite Toxoplasma gondii can cause congenital toxoplasmosis following primary infection in a pregnant woman. It is therefore important to distinguish between recent and past infection when both T. gondii-specific IgM and IgG are detected in a single serum in pregnant women. Toxoplasma gondii-specific IgG avidity testing is an essential tool to help to date the infection. However, interpretation of its results can be complex.Objectives: To review the benefits and limitations of T. gondii-specific avidity testing in pregnant women, to help practitioners to interpret the results and adapt the patient management.Sources: PubMed search with the keywords avidity, toxoplasmosis and Toxoplasma gondii for articles published from 1989 to 2019.Content: Toxoplasma gondii-specific IgG avidity testing remains a key tool for dating a T. gondii infection in immunocompetent pregnant women. Several commercial assays are available and display comparable performances. A high avidity result obtained on a first-trimester serum sample is indicative of a past infection, which occurred before pregnancy. To date, a low avidity result must still be considered as non-informative to date the infection, although some authors suggest that very low avidity results are highly suggestive of recent infections depending on the assay. Interpretation of low or grey zone avidity results on a first-trimester serum sample, as well as any avidity result on a second-trimester or third-trimester serum sample, is more complex and requires recourse to expert toxoplasmosis laboratories. Implications: Although used for about 30 years, T. gondii-specific avidity testing has scarcely evolved. The same difficulties in interpretation have persisted over the years. Some authors have proposed additional thresholds to exclude an infection of <9 months, or in contrast to confirm a recent infection. Such thresholds would be of great interest to adapt management of pregnant women and avoid unnecessary treatment; however, they need confirmation and further studies.
|
|
| 3. |
- Pham, Quoc Bao, et al.
(författare)
-
A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping
- 2021
-
Ingår i: Geomatics, Natural Hazards and Risk. - : Taylor & Francis. - 1947-5705 .- 1947-5713. ; 12:1, s. 1741-1777
-
Tidskriftsartikel (refereegranskat)abstract
- Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naive Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.
|
|
