A Comprehensive Review of Auditory and Non-Auditory Effects of Noise on Human Health.

OBJECTIVE: Excessive noise is unpleasant and induces several physiological and psychological effects. Noise pollution is a potential threat to humans, particularly those continuously exposed for extended periods throughout the day over many years. This review aims to examine the various auditory and non-auditory outcomes associated with prolonged exposure to noise pollution. MATERIALS AND METHODS: The review utilized a combination of relevant keywords to search the electronic databases. After screening based on the applied selection criteria for title, abstract, and full text, 44 articles were finally selected for critical review. RESULTS: We identified and analyzed research findings related to noise-induced hearing loss, tinnitus, and sleep disturbances along with non-auditory issues such as annoyance, cognitive impairments, and mental stress associated with cardiovascular disorders. Furthermore, the existing studies were compared and collated to highlight the unique challenges and significance of noise pollution as a distinctive environmental concern and to explore the ongoing efforts in its research and prevention, including the early detection and potential reversal of noise-induced hearing loss. CONCLUSION: The fundamental health consequences of noise pollution underscore the need for extensive research encompassing emerging noise sources and technologies to establish a health management system tailored to address noise-related health concerns and reduce noise exposure risk among populations. Finally, further research is warranted to ensure improved measurement of noise exposure and related health outcomes, especially in the context of occupational noise.

Early detection of glaucoma integrated with deep learning models over medical devices.

The early detection of some diseases can be a decisive factor in postponing or stabilizing their most adverse effects on the people who suffer from them. In the case of glaucoma, which is an ocular pathology that is the second leading cause of blindness in the world, early detection can make the difference between a patient's complete losses of vision, or preserve their sight, as well as improve their subsequent treatment. It is for this reason that there are currently medical campaigns for the early detection of pathologies with these characteristics in a certain study population, called screening, which have shown very good results. In addition, the application of telemedicine to these processes has allowed remote evaluation of cases by clinical experts and numerous initiatives have emerged for its use in new screening strategies. On the other hand, biomedical image processing techniques based on deep learning have undergone great development in recent years, and there are several works that have demonstrated their possible application in the automatic detection of glaucoma with fundus images. The article has consisted of the development of a web platform that integrates both scenarios: on the one hand, the remote evaluation of fundus images by medical specialists, and on the other, the application of a tool based on Deep Learning for the automatic detection of glaucoma in the case studies.

Support vector machine based prediction of P. falciparum proteasome inhibitors and development of focused library by molecular docking.

The emergence and spread of Plasmodium falciparum resistance to existing antimalarials emphasize the impelling search for novel drug targets and chemotherapeutic compounds. The ubiquitin-proteasome system plays a major role in overall protein turnover, in eukaryotic cells including plasmodia. 20S ß subunit is the catalytic core of this proteolytic machinery, and hence most of the inhibitors developed are being targeted towards this component. Inhibition of the proteasome is established as a promising strategy to develop novel antimalarial drugs. The present study reports identification of novel drug-like 20S proteasome inhibitors with potential activity against the 20S ß subunit of P. falciparum using a combination of ligand based (Support Vector Machines) and receptor based (molecular docking) techniques. The robust learning and generalizing capability of Support Vector Machines (SVM) has been exploited to classify proteasome inhibitors and non-inhibitors, targeted towards P. falciparum 20S proteasome. SVM model has been trained using 170 molecular descriptors of 64 inhibitors and 208 putative non-inhibitors of 20S proteasome. The non-linear classifier based on Radial Basis Function (RBF) kernel yielded highest classification accuracy in comparison to the linear classifier. The best classifier had 5-fold Cross-Validation (CV) accuracy of 97% and Area Under Curve (AUC) of 0.99 reflecting good accuracy of the model. The SVM model rapidly classified compounds with potential proteasomal activity. Subsequently, molecular docking studies aided the generation of focused collection of compounds with good binding affinity towards the substrate-binding site of 20S ß subunit. The novel drug-like 20S proteasome inhibitors identified in this study can be a good starting point to develop novel antimalarial drugs.

Virtual high throughput screening (vHTS)--a perspective.

With the exponential rise in the number of viable novel drug targets, computational methods are being increasingly applied to accelerate the drug discovery process. Virtual High Throughput Screening (vHTS) is one such established methodology to identify drug candidates from large collection of compound libraries. Although it complements the expensive and time consuming High Throughput Screening (HTS) of compound libraries, vHTS possess inherent challenges. The successful vHTS requires the careful implementation of each phase of computational screening experiment right from target preparation to hit identification and lead optimization. This article discusses some of the important considerations that are imperative for designing a successful vHTS experiment.