Recent advances in hearing conservation programmes: A systematic review

Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program

Background

Though the artificial neural network (ANN) technique has been used to predict noise-induced hearing loss (NIHL), the established prediction models have primarily relied on cross-sectional datasets, and hence, they may not comprehensively capture the chronic nature of NIHL as a disease linked to long-term noise exposure among workers.

Methods

A comprehensive dataset was utilized, encompassing eight-year longitudinal personal hearing threshold levels (HTLs) as well as information on seven personal variables and two environmental variables to establish NIHL predicting models through the ANN technique. Three subdatasets were extracted from the afirementioned comprehensive dataset to assess the advantages of the present study in NIHL predictions.

Results

The dataset was gathered from 170 workers employed in a steel-making industry, with a median cumulative noise exposure and HTL of 88.40 dBA-year and 19.58 dB, respectively. Utilizing the longitudinal dataset demonstrated superior prediction capabilities compared to cross-sectional datasets. Incorporating the more comprehensive dataset led to improved NIHL predictions, particularly when considering variables such as noise pattern and use of personal protective equipment. Despite fluctuations observed in the measured HTLs, the ANN predicting models consistently revealed a discernible trend.

Conclusions

A consistent correlation was observed between the measured HTLs and the results obtained from the predicting models. However, it is essential to exercise caution when utilizing the model-predicted NIHLs for individual workers due to inherent personal fluctuations in HTLs. Nonetheless, these ANN models can serve as a valuable reference for the industry in effectively managing its hearing conservation program.

Oridonin employs interleukin 1 receptor type 2 to treat noise-induced hearing loss by blocking inner ear inflammation

NOD-like receptor protein 3 (NLRP3) inflammasomes trigger the inflammatory cascades and participate in various inflammatory diseases, including noise-induced hearing loss (NIHL) caused by oxidative stress. Recently, the anti-inflammatory traditional medicine oridonin (Ori) has been reported to provide hearing protection in mice after noise exposure by blocking the NLRP3-never in mitosis gene A-related kinase 7 (NEK7)-inflammasome complex assembly. Using RNA sequencing analysis, we further elucidated that interleukin 1 receptor type 2 (IL1R2) may be another crucial factor regulated by Ori to protect NIHL. We observed that IL1R2 expression was localized in spiral ganglion neurons, inner and outer hair cells, in Ori-treated mouse cochleae. Additionally, we confirmed that ectopic overexpression of IL1R2 in the inner ears of healthy mice using an adeno-associated virus delivery system significantly reduced noise-induced ribbon synapse lesions and hearing loss by blocking the "cytokine storm" in the inner ear. This study provides a novel theoretical foundation for guiding the clinical treatment of NIHL.

Quantifying traffic noise pollution levels: a cross-sectional survey in South Africa

Despite the alarming increase in environmental noise pollution, particularly road traffic noise, in developing countries, there seems to be no awareness regarding the long-term impacts of noise, specifically traffic noise, on the health outcomes of individuals exposed to excessive noise. Additionally, there is a dearth of studies on noise and its effects utilising the pollution modelling technique known as Pollution Standard Index (PSI) to analyse the impact of noise pollution on exposed individuals. The aim of this study was to investigate the noise levels commuters are exposed to and to apply PSI to determine the level of exposure. We conducted a cross-sectional study at two taxi ranks, over 28 days. Eighty-four noise measurements were collected using a sound level meter and a dosimeter at different times of the day and month, peak vs off-peak hours and busy days vs quiet days. Data were collected between April and July 2019. We used the Pollution Standard Index to analyse the data. Noise levels were above the permissible commercial noise levels as they fell within the extremely dangerous noise sensitivity zone as determined by the PSI. Furthermore, the noise levels fell below the WHO maximum permissible level of 90 dB. There was no statistical difference between the means of the open and closed ranks. Dosimeter noise level recordings fell within the satisfactory zone as measurements were below 300 PSI, which is considered unhealthy. There is a need to raise awareness on the dangers and effects of noise pollution in developing countries, as their populations are exposed to road traffic noise.