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This study delves into the influence of traffic-related pollution on respiratory diseases within the United States. While prior research has established a connection between air pollution and mortality or morbidity in sensitive age groups, this association has been predominantly observed in highly polluted areas, particularly in California. In this paper, we extend this line of investigation by examining adult patients with asthma symptoms across various metropolitan areas throughout the country. To address potential endogeneity concerns in our empirical framework, we adopt highway density as an instrumental variable (IV) for NO₂ concentrations. This approach allows us to gain a more robust understanding of the relationship between traffic-related pollution and respiratory health outcomes. Our findings indicate that NO₂ does not have a significant impact on patients with asthma symptoms in the overall sample. However, we observed that it can exacerbate asthma conditions in patients residing in warm areas. A back-of-the-envelope calculation estimates a $6.758 billion economic loss due to increasing asthma attack on adults in the selected study area. This regional disparity outside of California underscores the necessity for adjusting current regulations on vehicle emissions based on unique regional characteristics. Such adjustments could help mitigate adverse health effects associated with traffic-related pollution in different parts of the United States.

COVID-19 is a pandemic disease, which massively affected human lives in more than 200 countries. Caused by the coronavirus SARS-CoV-2, this acute respiratory illness affects the human lungs and can easily spread from person to person. Since the disease heavily affects human lungs, analyzing the X-ray images of the lungs may prove to be a powerful tool for disease investigation. In this research, we use the information contained within the complex structures of X-ray images between the cases of COVID-19 and other respiratory diseases, whereas the case of healthy lungs is taken as the reference point. To analyze X-ray images, we benefit from the concept of Shannon’s entropy and fractal theory. Shannon’s entropy is directly related to the amount of information contained within the X-ray images in question, whereas fractal theory is used to analyze the complexity of these images. The results, obtained in this study, show that the method of fractal analysis can detect the level of infection among different respiratory diseases and that COVID-19 has the worst effect on the human lungs. In other words, the complexity of X-ray images is proportional to the severity of the respiratory disease. The method of analysis, employed in this study, can be used even further to analyze how COVID-19 progresses in affected patients.

Japanese Scientists Develop New Method to Decipher Gene Sequences.

Korean Scientists Discover New Cholesterol-Reducing Compound.

Taiwan's Academia Sinica Pushes for Genome Research.

New Biochip to Diagnose Respiratory Diseases.

Simple Technique Boosts China's Sweet Potato Harvest.

Chemicals from Cockroaches in Fight against AIDS.

Cardiff and Guangdong Universities Collaborate on Nasal Cancer Research.

Indian Scientist Couple Discover New Malaria Drug.

Breakthrough for Fish Oil Products.

DeltaHealth Inspires a New Standard of Cardiovascular Care in China with the Opening of its First Hospital in Shanghai.

International Science Community Welcomes China National GeneBank Opening.

Shanghai Newsummit Biopharma Group and Indiso Sign Collaboration Agreement for Clinician Trials and Commercial Development in China.

Merck Strengthens Display Materials R&D in China.

Shuwen Biotech's Clinical Lab Receives Accreditation from College of American Pathologists.

Booming Chinese DNA Sequencing Market Rife with Opportunities Reports BCC Research.

China Issues Plan on Turning Beijing Into a Tech Innovation Hub.

Study Shows Sound-induced Fear Can Be Treated.

Respiratory diseases have a significant impact on modern society, posing considerable public health risks. With the emergence of the COVID-19 pandemic, addressing respiratory issues has become increasingly urgent and important. Recently, artificial intelligence-based methods utilizing the acoustic recordings of suspected patients have shown promise in the diagnosis of various respiratory diseases, enabling localized treatment and containment of their spread. As the existing methods cannot efficiently extract subtle differences between the sound samples, thereby limiting their generalization ability. To improve the diagnosis accuracy, this paper proposes a novel multi-channel, multi-modal deep learning architecture based on the attention mechanism. The proposed framework combines a deep convolutional neural network (DCNN) with a bidirectional long short-term memory (BLSTM) network, and also utilizes the attention scheme to extract temporal and spectral features from different modalities of speech data (e.g. coughs, counting sounds and sustained vowel articulation). The proposed method effectively classifies COVID-19 patients, asthma patients and healthy individuals, with a test accuracy of 89.27 ± 0.1%, and an F1 score of 85.42 ± 0.2%. The experimental results validate the feasibility of our method, and also indicate that it is competitive with the existing deep networks.

In epidemiological studies associations have been observed consistently and coherently between ambient concentrations of particulate matter and morbidity and mortality. With improvement of measurement techniques, the effects became clearer when smaller particle sizes were considered. Therefore, it seems worthwhile to look at the smallest size fraction available today, namely ultrafine particles (UPs, diameter below 0.1 μm) and to compare their health effects with those of fine particles (FPs, diameter below 2.5 μm). However, there are only few studies available which allow such a comparison.

Four panel studies with asthma patients have been performed in Germany and Finland. A decrease of peak expiratory flow and an increase of daily symptoms and medication use was found for elevated daily particle concentrations, and in three of these studies it was strongest for UPs. One large study on daily mortality is available from Germany. It showed comparable effects of fine and ultrafine particles in all size classes considered. However, FPs showed more immediate effects while UPs showed more delayed effects with a lag of four days between particulate concentrations and mortality. Furthermore, immediate effects were clearer in respiratory cases, whereas delayed effects were clearer in cardiovascular cases.

In total, the limited body of studies suggests that there are health effects, due to both UPs and FPs, which might be independent from each other. If this is confirmed in further investigations, it might have important implications for monitoring and regulation, which until now does not exist for UPs. Data from Germany show that FPs cannot be used as indicator for UPs: the time trends for FPs decreased, while UPs was stable and the smallest size fraction of UPs has continually increased since 1991/92.