Allergenicity of latex rubber products used in South African dental schools

Background: Allergens from latex products in healthcare settings have been known to trigger latex induced allergic reactions in healthcare workers (HCWs). There is a need to quantify individual latex allergens in products in order to assess the allergenicity of latex products used in health care settings, so as to minimize the risk of sensitisation to these proteins. Methods: Fourteen latex examination gloves representing six brands (powdered and non-powdered) and five dental rubber dams from five dental academic institutions were analysed for latex allergens and total protein. Total protein content was determined using the BIORAD DC protein assay kit and natural rubber allergen levels using a capture ELISA assay specific for hev b 1, hev b 3, hev b 5 and hev b 6.02. Results: Hev b 6.02 was found in higher concentrations than other NRL allergens in the products analysed. Hev b 5 content ranged from 0 to 9.2µg/g and hev b 6.02 from 0.09 to 61.5µg/g of sample. Hev b 1 levels were below the detection limit (DL) for 79% of the samples (15/19). Dental dams showed higher allergen levels (median: 80.91µg/g) in comparison to latex gloves (median: 11.34µg/g). Powdered rubber samples also showed higher allergen levels (median: 40.54µg/g) compared to non-powdered samples (median: 5.31µg/g). A statistically significant correlation was observed between total protein and total allergen (r=0.74, p<0.001) concentrations. Conclusion Natural rubber latex (NRL) allergen concentrations differ significantly by product and brand. This study has demonstrated that NRL allergens in latex containing products used in South African dental institutions are present at sufficiently high levels to pose an allergic health risk

Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa

Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete