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- Asgardoon, MH, et al.
(författare)
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Monogenic Primary Immunodeficiency Disorder Associated with Common Variable Immunodeficiency and Autoimmunity
- 2020
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Ingår i: International archives of allergy and immunology. - : S. Karger AG. - 1423-0097 .- 1018-2438. ; 181:9, s. 706-714
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Tidskriftsartikel (refereegranskat)abstract
- <b><i>Background:</i></b> Common variable immunodeficiency (CVID) is the most frequent primary immunodeficiency disorder mainly characterized by recurrent bacterial infections besides other immunological defects including loss of or dysfunction of B cells and decreased immunoglobulin levels. In this study, our aim is to evaluate clinical, immunological, and molecular data of patients with a primary clinical diagnosis of CVID and autoimmune phenotype with a confirmed genetic diagnosis. <b><i>Methods:</i></b> Among 297 patients with CVID, who were registered in the Iranian Primary Immunodeficiency Registry at Children’s Medical Center Hospital in Iran, 83 patients have been genetically examined and 27 patients with autoimmunity and confirmed genetic mutations were selected for analysis. Whole-exome sequencing and confirmatory Sanger sequencing methods were used for the study population. A questionnaire was retrospectively filled for all patients to evaluate demographic, laboratory, clinical, and genetic data. <b><i>Results:</i></b> In the 27 studied patients, 11 different genetic defects were identified, and the most common mutated gene was <i>LRBA</i>, reported in 17 (63.0%) patients. Two patients (7.7%) showed autoimmune complications as the first presentation of immunodeficiency. Eleven patients (40.7%) developed one type of autoimmunity, and 16 patients (59.3%) progressed to poly-autoimmunity. Most of the patients with mono-autoimmunity (<i>n</i> = 9, 90.0%) primarily developed infectious complications, while in patients with poly-autoimmunity, the most common first presentation was enteropathy (<i>n</i> = 6, 37.6%). In 13 patients (61.9%), the diagnosis of autoimmune disorders preceded the diagnosis of primary immunodeficiency. The most frequent autoimmune manifestations were hematologic (40.7%), gastrointestinal (48.1%), rheumatologic (25.9%), and dermatologic (22.2%) disorders. Patients with poly-autoimmunity had lower regulatory T cells than patients with mono-autoimmunity. <b><i>Conclusion:</i></b> In our cohort, the diagnosis of autoimmune disorders preceded the diagnosis of primary immunodeficiency in most patients. This association highlights the fact that patients referring with autoimmune manifestations should be evaluated for humoral immunity.
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- Darabi, H., et al.
(författare)
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Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood
- 2021
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Ingår i: Geocarto International. - : Taylor and Francis Ltd.. - 1010-6049 .- 1752-0762.
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Tidskriftsartikel (refereegranskat)abstract
- In this study, a new hybridized machine learning algorithm for urban flood susceptibility mapping, named MultiB-MLPNN, was developed using a multi-boosting technique and MLPNN. The model was tested in Amol City, Iran, a data-scarce city in an ungauged area which is prone to severe flood inundation events and currently lacks flood prevention infrastructure. Performance of the hybridized model was compared with that of a standalone MLPNN model, random forest and boosted regression trees. Area under the curve, efficiency, true skill statistic, Matthews correlation coefficient, misclassification rate, sensitivity and specificity were used to evaluate model performance. In validation, the MultiB-MLPNN model showed the best predictive performance. The hybridized MultiB-MLPNN model is thus useful for generating realistic flood susceptibility maps for data-scarce urban areas. The maps can be used to develop risk-reduction measures to protect urban areas from devastating floods, particularly where available data are insufficient to support physically based hydrological or hydraulic models.
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- Rahmati, O., et al.
(författare)
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Development of novel hybridized models for urban flood susceptibility mapping
- 2020
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Floods in urban environments often result in loss of life and destruction of property, with many negative socio-economic effects. However, the application of most flood prediction models still remains challenging due to data scarcity. This creates a need to develop novel hybridized models based on historical urban flood events, using, e.g., metaheuristic optimization algorithms and wavelet analysis. The hybridized models examined in this study (Wavelet-SVR-Bat and Wavelet-SVR-GWO), designed as intelligent systems, consist of a support vector regression (SVR), integrated with a combination of wavelet transform and metaheuristic optimization algorithms, including the grey wolf optimizer (GWO), and the bat optimizer (Bat). The efficiency of the novel hybridized and standalone SVR models for spatial modeling of urban flood inundation was evaluated using different cutoff-dependent and cutoff-independent evaluation criteria, including area under the receiver operating characteristic curve (AUC), Accuracy (A), Matthews Correlation Coefficient (MCC), Misclassification Rate (MR), and F-score. The results demonstrated that both hybridized models had very high performance (Wavelet-SVR-GWO: AUC = 0.981, A = 0.92, MCC = 0.86, MR = 0.07; Wavelet-SVR-Bat: AUC = 0.972, A = 0.88, MCC = 0.76, MR = 0.11) compared with the standalone SVR (AUC = 0.917, A = 0.85, MCC = 0.7, MR = 0.15). Therefore, these hybridized models are a promising, cost-effective method for spatial modeling of urban flood susceptibility and for providing in-depth insights to guide flood preparedness and emergency response services.
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