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Noise pollution in developing countries such as Nigeria and Ghana is causing adverse effects on citizens, including hearing impairment, sleep disturbances, adverse social behavior, and cardiovascular diseases. This study assessed noise levels at the Kejetia Market in Ghana and the perceptions of health impact. A sound level meter (JD‐801A) was used to measure the noise levels at the various points in the market. Results showed that noise exposure levels were not within Ghana Environmental Protection Agency standards 2008, with sources including loud music, advertisements, human congestion, and vehicles. Respondents perceived noise pollution sources as annoyance, mental stress, sleep disturbances, lack of concentration, hearing, and cardiovascular effects. The study suggests that stakeholders and authorities should educate the public on the health effects of noise pollution. [ABSTRACT FROM AUTHOR]

The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplasmic variants, are critical for determining human disease severity. While there are approaches for obtaining mitochondrial DNA variants from NGS data, these software do not account for the unique characteristics of mitochondrial genetics and can be inaccurate even for homoplasmic variants. We introduce MitoScape, a novel, big-data, software for extracting mitochondrial DNA sequences from NGS. MitoScape adopts a novel departure from other algorithms by using machine learning to model the unique characteristics of mitochondrial genetics. We also employ a novel approach of using rho-zero (mitochondrial DNA-depleted) data to model nuclear-encoded mitochondrial sequences. We showed that MitoScape produces accurate heteroplasmy estimates using gold-standard mitochondrial DNA data. We provide a comprehensive comparison of the most common tools for obtaining mtDNA variants from NGS and showed that MitoScape had superior performance to compared tools in every statistically category we compared, including false positives and false negatives. By applying MitoScape to common disease examples, we illustrate how MitoScape facilitates important heteroplasmy-disease association discoveries by expanding upon a reported association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men (adjusted p-value = 0.003). The improved accuracy of mitochondrial DNA variants produced by MitoScape will be instrumental in diagnosing disease in the context of personalized medicine and clinical diagnostics. Author summary: Recent studies have highlighted the importance of mitochondrial DNA variation in both primary mitochondrial disease and complex, human pathology including COVID-19, and space-flight stress. The vast amount of existing, next-generation sequencing (NGS) data can be leveraged to interrogate both nuclear and mitochondrial DNA (mtDNA) sequence simultaneously, allowing for analysis of the interplay between mitochondrial and nuclear encoded genes in mitochondrial function. Identifying mtDNA sequence accurately is complicated by the presence of nuclear encoded mitochondrial sequences (NUMTs), which are homologous to mtDNA. Current software for analyzing mtDNA from NGS do not accurately model the unique characteristics of mitochondrial genetics. We introduce MitoScape, a novel, big-data, software which models mitochondrial genetics through machine learning to accurately identify mtDNA sequence from NGS data. MitoScape takes advantage of rho-zero cell data to model the characteristics of NUMTs. We show that MitoScape produces more accurate heteroplasmy estimates compared to published software. We provide an example of applying MitoScape in replicating an association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men. MitoScape is an important contribution to mitochondrial genomics allowing for accurate mtDNA variants, and the ability to tailor mtDNA analysis in different population and disease contexts, which is not available in other software. [ABSTRACT FROM AUTHOR]

Background: China has become the world's second largest healthcare market based on a recent report by the World Health Organization. Eventhough China achieved universal health insurance coverage in 2011, representing the largest expansion of insurance coverage in human history achieved; health inequality remains endemic in China. Lessons from the effect of market crisis on health equity in Europe and other places has reignited interest in exploring the potential healthcare market aberrations that can trigger distributive injustice in healthcare resource allocation among China's provinces. Recently, many healthcare investors in China have become more concerned about capital preservation, and are responding by abandoning long term investments strategies in healthcare. This investment withdrawal en mass is perceived to be influenced by herding tendencies and can trigger or consolidate endemic health inequality. Methods: Our study simultaneously employs four testing models (two state spaced models and two return dispersion models) to establish the existence of procyclical (herding) behavior among the stocks and its health equity implications. These are applied to a large set of data to compare and contrast results of herd formation among investors in fourteen healthcare sectors in China. Results: The study reveals that apart from the cross sectional standard deviation (CSSD) model, the remaining two models and our augmented state space model yields significant evidence of herding in all subsectors of the healthcare market. We also find that the herding effect is more prominent during down movements of the market. Conclusion: Herding behavior may lead to contemporaneous loss of investor confidence and capital withdrawal and thereby deprive the healthcare sector of the much needed capital for expansion. Thus there may be obvious delay in efforts to bridge the gap in access to healthcare facilities, medical support services, medical supplies, pharmaceuticals, biotechnology, diagnostic substances, medical laboratory and advanced medical equipment across China. Moreover, a potential crash in the healthcare market is possible in the healthcare sector as a result of persistent herding tendencies among investors and that may have more damaging consequences for health inequality in China. [ABSTRACT FROM AUTHOR]

Noise pollution in developing countries such as Nigeria and Ghana is causing adverse effects on citizens, including hearing impairment, sleep disturbances, adverse social behavior, and cardiovascular diseases. This study assessed noise levels at the Kejetia Market in Ghana and the perceptions of health impact. A sound level meter (JD-801A) was used to measure the noise levels at the various points in the market. Results showed that noise exposure levels were not within Ghana Environmental Protection Agency standards 2008, with sources including loud music, advertisements, human congestion, and vehicles. Respondents perceived noise pollution sources as annoyance, mental stress, sleep disturbances, lack of concentration, hearing, and cardiovascular effects. The study suggests that stakeholders and authorities should educate the public on the health effects of noise pollution., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2024 Lyndon N. A. Sackey et al.)