Search Results (6)

Background: The study evaluates the immunomodulatory potential of secukinumab (SECU) and honokiol (HONK) in a murine model of allergic asthma complicated by acute lung injury (ALI), with an emphasis on modulating key inflammatory pathways. The rationale is driven by the necessity to attenuate Th17-mediated cytokine cascades, wherein IL-17 plays a critical role, as well as to explore the adjunctive anti-inflammatory effects of HONK on Th1 cytokine production, including IL-6, TNF-α, and Th2 cytokines. Methods: Mice were sensitized and challenged with ovalbumin (OVA) and lipopolysaccharide (LPS) was administrated to exacerbate pulmonary pathology, followed by administration of SECU, HONK (98% purity, C₁₈H₁₈O₂), or their combination. Quantitative analyses incorporated OVA-specific IgE measurements, differential cell counts in bronchoalveolar lavage fluid (BALF), and extensive cytokine profiling in both BALF and lung tissue homogenates, utilizing precise immunoassays and histopathological scoring systems. Results: Both SECU and HONK, when used alone or in combination, display significant immunomodulatory effects in a murine model of allergic asthma concomitant with ALI. The combined therapy synergistically reduced pro-inflammatory mediators, notably Th1 cytokines, such as TNF-α and IL-6, as measured in both BALF and lung tissue homogenates. Conclusions: The combined therapy showed a synergistic attenuation of pro-inflammatory mediators, a reduction in goblet cell hyperplasia, and an overall improvement in lung histoarchitecture. While the data robustly support the merit of a combinatorial approach targeting multiple inflammatory mediators, the study acknowledges limitations in cytokine diffusion and the murine model’s translational fidelity, thereby underscoring the need for further research to optimize clinical protocols for severe respiratory inflammatory disorders. Full article

Electric bicycle (EB) riders, being vulnerable road users (VRUs), are increasingly becoming victims of road traffic injuries (RTIs). This study aimed to determine the current status and epidemiological characteristics of RTIs among EB riders through a questionnaire survey and roadside observations in Shantou to provide a scientific basis for the prevention and control of electric bicycle road traffic injuries (ERTIs). A total of 2412 EB riders were surveyed, and 34,554 cyclists were observed in the study. To analyze the relationship between riding habits and injuries among EB riders, chi-square tests and multi-factor logistic regression models were employed. The findings reveal that the prevalence of ERTIs in Shantou was 4.81%, and the most affected group was children under 16 years old, accounting for 9.84%. Risky behavior was widespread among EB riders, such as the infrequent wearing of safety helmets, carrying people on EBs, riding on sidewalks, and listening to music with headphones while bicycling. Notably, over 90% of those who wore headphones while bicycling engaged in this risky behavior. The logistic regression analysis showed that honking the horn (odds ratio (OR): 2.009, 95% CI: 1.245–3.240), riding in reverse (OR: 4.210, 95% CI: 2.631–6.737), and continuing to ride after a fault was detected (OR: 2.010, 95% CI: 1.188–3.402) all significantly increased the risk of ERTIs (all p < 0.05). Risky riding behavior was significantly less observed at traffic intersections with traffic officers than at those without (all p < 0.001). Full article

Microseismic monitoring has become a well-known technique for predicting the mechanisms of rock failure in deeply buried energy exploration, in which noise has a great influence on microseismic monitoring results. We proposed an improved microseismic denoising method based on different wavelet coefficients of useful signal and noise components. First, according to the selection of an appropriate wavelet threshold and threshold function, the useful signal part of original microseismic signal was decomposed many times and reconstructed to achieve denoising. Subsequently, synthetic signals of different types (microseismic noise, microseismic current, microseismic noise current) and with various signal-to-noise ratios (SNRs, −10~10) were used as test data. Evaluation indicators (mean absolute error μ and standard deviation error σ) were established to compare the denoising effect of different denoising methods and verify that the improved method is more effective than the traditional denoising methods (wavelet global threshold, empirical mode decomposition and wavelet transform–empirical mode decomposition). Finally, the proposed method was applied to actual field microseismic data. The results showed that the microseismic signal (with different types of noise) could be fully denoised (car honk, knock, current and construction noise, etc.) without losing useful signals (pure microseismic), suggesting that the proposed approach provides a good basis for the subsequent evaluation and classification of rock burst disasters. Full article

Car horns were originally installed in vehicles for safety. However, many urban areas in several countries face noise problems related to the use of car and motorbike horns. To propose measures to suppress the use of horns, relationships between horn use and factors including driver awareness and behavior, traffic environment, and the transportation system should be investigated. The present study therefore conducted surveys to grasp the current circumstances of horn use and traffic at urban intersections in Taiwan. The relationship between horn use and the traffic volume of standard-sized vehicles was found. According to an analysis of horn use during traffic signal cycles, in many cases, horns were honked after entering intersections to turn left. In particular, horns were honked when the driver waited more than 4 s for the car in front to start moving after the green light allowing left turns was turned on. An analysis of noise levels at intersections showed that the maximum noise level value (L_Amax) could be reduced if vehicle horns were not used. Multiple regression analysis also indicated that L_Amax values increased with the frequency of horn use. The equivalent continuous A-weighted sound pressure level (L_Aeq,10min) did not change with driver horn use, and increased with the traffic volume of motorcycles. Full article

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. L_eq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models. Full article

Hearing-impaired people do not hear indoor and outdoor environment sounds, which are important for them both at home and outside. By means of a wearable device that we have developed, a hearing-impaired person will be informed of important sounds through vibrations, thereby understanding what kind of sound it is. Our system, which operates in real time, can achieve a success rate of 98% when estimating a door bell ringing sound, 99% success identifying an alarm sound, 99% success identifying a phone ringing, 91% success identifying honking, 93% success identifying brake sounds, 96% success identifying dog sounds, 97% success identifying human voice, and 96% success identifying other sounds using the audio fingerprint method. Audio fingerprint is a brief summary of an audio file, perceptively summarizing a piece of audio content. In this study, our wearable device is tested 100 times a day for 100 days on five deaf persons and 50 persons with normal hearing whose ears were covered by earphones that provided wind sounds. This study aims to improve the quality of life of deaf persons, and provide them a more prosperous life. In the questionnaire performed, deaf people rate the clarity of the system at 90%, usefulness at 97%, and the likelihood of using this device again at 100%. Full article