Adverse Events among HIV/MDR-TB Co-Infected Patients Receiving Antiretroviral and Second Line Anti-TB Treatment in Mumbai, India.

Significant adverse events (AE) have been reported in patients receiving medications for multidrug- and extensively-drug-resistant tuberculosis (MDR-TB & XDR-TB). However, there is little prospective data on AE in MDR- or XDR-TB/HIV co-infected patients on antituberculosis and antiretroviral therapy (ART) in programmatic settings

Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches

Landslide is a type of slope process causing a plethora of economic damage and loss of lives worldwide every year. This study aimed to analyze spatial landslide susceptibility mapping in the Khalkhal-Tarom Basin by integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches, i.e., the best-worst method (BWM) and the stepwise weight assessment ratio analysis (SWARA) techniques. For this purpose, the first step was to prepare a landslide inventory map, which was then divided randomly into the ratio of 70/30% for model training and validation. Thirteen conditioning factors were selected based on the previous studies and available data. In the next step, the BWM and the SWARA methods were utilized to determine the relationships between the sub-criteria and landslides. Finally, landslide susceptibility maps were generated by implementing ANFIS-BWM and ANFIS-SWARA ensemble models, and then several quantitative indices such as positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root-mean-square-error, and the ROC curve were employed to appraise the predictive accuracy of each model. The results indicated that the ANFIS-BWM ensemble model (AUC = 75%, RMSE = 0.443) has better performance than ANFIS-SWARA (AUC = 73.6%, RMSE = 0.477). At the same time, the ANFIS-BWM model had the maximum sensitivity, specificity, and accuracy with values of 87.1%, 54.3%, and 40.7%, respectively. As a result, the BWM method was more efficient in training the ANFIS. Evidently, the generated landslide susceptibility maps (LSMs) can be very efficient in managing land use and preventing the damage caused by the landslide phenomenon. Graphical abstract: [Figure not available: see fulltext.

High Rate of Hypothyroidism in Multidrug-Resistant Tuberculosis Patients Co-Infected with HIV in Mumbai, India.

Adverse events (AEs) among HIV-infected patients with multidrug-resistant tuberculosis (MDR-TB) receiving anti-TB and antiretroviral treatments (ART) are under-researched and underreported. Hypothyroidism is a common AE associated with ethionamide, p-aminosalicylic acid (PAS), and stavudine. The aim of this study was to determine the frequency of and risk factors associated with hypothyroidism in HIV/MDR-TB co-infected patients

CD4 T Cell Immunity Is Critical for the Control of Simian Varicella Virus Infection in a Nonhuman Primate Model of VZV Infection

Primary infection with varicella zoster virus (VZV) results in varicella (more commonly known as chickenpox) after which VZV establishes latency in sensory ganglia. VZV can reactivate to cause herpes zoster (shingles), a debilitating disease that affects one million individuals in the US alone annually. Current vaccines against varicella (Varivax) and herpes zoster (Zostavax) are not 100% efficacious. Specifically, studies have shown that 1 dose of varivax can lead to breakthrough varicella, albeit rarely, in children and a 2-dose regimen is now recommended. Similarly, although Zostavax results in a 50% reduction in HZ cases, a significant number of recipients remain at risk. To design more efficacious vaccines, we need a better understanding of the immune response to VZV. Clinical observations suggest that T cell immunity plays a more critical role in the protection against VZV primary infection and reactivation. However, no studies to date have directly tested this hypothesis due to the scarcity of animal models that recapitulate the immune response to VZV. We have recently shown that SVV infection of rhesus macaques models the hallmarks of primary VZV infection in children. In this study, we used this model to experimentally determine the role of CD4, CD8 and B cell responses in the resolution of primary SVV infection in unvaccinated animals. Data presented in this manuscript show that while CD20 depletion leads to a significant delay and decrease in the antibody response to SVV, loss of B cells does not alter the severity of varicella or the kinetics/magnitude of the T cell response. Loss of CD8 T cells resulted in slightly higher viral loads and prolonged viremia. In contrast, CD4 depletion led to higher viral loads, prolonged viremia and disseminated varicella. CD4 depleted animals also had delayed and reduced antibody and CD8 T cell responses. These results are similar to clinical observations that children with agammaglobulinemia have uncomplicated varicella whereas children with T cell deficiencies are at increased risk of progressive varicella with significant complications. Moreover, our studies indicate that CD4 T cell responses to SVV play a more critical role than antibody or CD8 T cell responses in the control of primary SVV infection and suggest that one potential mechanism for enhancing the efficacy of VZV vaccines is by eliciting robust CD4 T cell responses

Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms

Landslide is a natural phenomenon that can turn into a natural disaster. The main goal of this research was to spatial prediction of a high-risk region located in the Zagros mountains, Iran, using hybrid machine learning and metaheuristic algorithms, namely the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), the Harris hawks optimisation (HHO), and the bat algorithm (BA). The landslide occurrences were first divided into training and testing datasets with a 70/30 ratio. Fourteen landslide-related factors were considered, and the stepwise weight assessment ratio analysis (SWARA) were employed to determine the correlation between landslides and factors. After that, the hybrid models of ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were applied to generate landslide susceptibility maps (LSMs). Finally, in order to validation and comparison of the applied models, two indexes, namely mean square error (MSE) and area under the ROC curve (AUROC), were used. According to the validation results, the AUROC values for the ANFIS-HHO, ANFIS-BA, SVR-HHO and SVR-BA were 0.849, 0.82, 0.895, and 0.865, respectively. The SVR-HHO showed the highest accuracy, with AUROC of 0.895 and lowest MSE of 0.147, and ANFIS-BA showed the least accuracy with an AUROC value of 0.82 and MSE value of 0.218. Based on the results, although four hybrid models with more than 80% accuracy can generate very good results, the SVR is superior to the ANFIS model, whereas the HHO algorithm outperformed the bat algorithm. The map generated in this study can be employed by land use planners in more efficient management

GIS-based comparison of the GA-LR ensemble method and statistical models at Sefiedrood Basin, Iran

© 2020, Saudi Society for Geosciences. Landslide is a natural phenomenon which occurs on mountainous areas and causes a lot of damages every year. Decision makers, engineers, and urban planners can mitigate or prevent loss of lives as well as potential economic losses in the future through using the landslide susceptibility map. This study aims to identify the areas susceptible to landslide occurrence in Sefidrood basin located in Alborz Mountains in the north of Iran using bivariate models, i.e., frequency ratio (FR), weights of evidence (WoE), and Dempster-Shafer theory (DST), and compare them with ensemble method of multivariate logistic regression (LR) and genetic algorithm (GA). In the first step, 265 landslide locations were identified as the inventory map. From these identified landslides, 70% (186 landslide locations) was randomly selected as the training data and the remaining 30% (79 landslide locations) was used for validation purposes. In the next step, considering the region’s condition and the experts’ opinion, twelve discrete and continuous conditioning factors were prepared including slope angle, slope aspect, altitude, distance from streams, distance from roads, distance from faults, land use, rainfall, NDVI, lithology, profile curvature, plan curvature, and rainfall. After establishing the database, the proposed models were analyzed using conditional factors and inventory map. Finally, after preparing landslide susceptibility maps, the performance of each method was investigated and compared using ROC curve. The results from validation process showed that the area under the curve (AUC) for the models of FR, WoE, DST, and GA-LR was 0.741, 0.814, 0.826, and 0.938, respectively. According to the results, GA-LR model exhibited higher accuracy in prediction of landslide susceptibility in the study area compared with other models. Therefore, the map produced by this model can help engineers, land use planners, and crisis management personnel make more accurate decisions

Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping

© 2020, Springer Nature B.V. Abstract: Many landslides occur in the Karun watershed in the Zagros Mountains. In the present study, we employed a novel comparative approach for spatial modeling of landslides given the high potential of landslides in the region. The aim of the study was to combine adaptive neuro-fuzzy inference system (ANFIS) with grey wolf optimizer (GWO) and particle swarm optimizer (PSO) algorithms using the outputs of qualitative stepwise weight assessment ratio analysis (SWARA) and quantitative certainty factor (CF) models. To this end, 264 landslide positions and twelve conditioning factors including slope, aspect, altitude, distance to faults, distance to rivers, distance to roads, land use, lithology, rainfall, plan and profile curvature and TWI were then extracted considering regional characteristics, literature review and available data. In the next step, the multi-criteria SWARA decision-making model and CF probability model were used to evaluate a correlation between landslide distribution and conditioning factors. Ultimately, landslide susceptibility maps were generated by ANFIS-GWO and ANFIS-PSO hybrid models and the accuracy of models was assessed by ROC curve. According to the results, the area under the curve (AUC) for the hybrid models ANFIS - GWO SWARA, ANFIS - PSO SWARA, ANFIS - GWO CF and ANFIS - PSO CF was 0.789, 0.838, 0.850 and 0.879, respectively. The hybrid models ANFIS - PSO CF and ANFIS - GWO SWARA showed the highest and lowest prediction rate, respectively. Moreover, CF outperformed the SWARA method in terms of evaluating correlation between conditioning factors and landslides. The map produced in this study can be used by regional authorities to manage landslide risk. Graphic abstract: [Figure not available: see fulltext.]