[Adaptation to altitude in respiratory diseases]

High altitude air pollution and respiratory disease: Evaluating compounded exposure events and interactions

Today, air pollution remains a significant issue, particularly in high-altitude areas where its impact on respiratory disease remains incompletely explored. This study aims to investigate the association between various air pollutants and outpatient visits for respiratory disease in such regions, specifically focussing on Xining from 2016 to 2021. By analysing over 570,000 outpatient visits using a time-stratified case-crossover design and conditional logistic regression, we assessed the independent effects of pollutants like PM2.5, PM10, SO2, NO2, and CO, as well as their interactions. The evaluation of interactions employed measures such as relative excess odds due to interaction (REOI), attributable proportion due to interaction (AP), and synergy index (S). We also conducted a stratified analysis to identify potentially vulnerable populations. Our findings indicated that exposure to PM2.5, PM10, SO2, NO2, and CO significantly increased outpatient visits for respiratory disease, with odds ratios (ORs) of 2.40 % (95 % CI: 2.05 %, 2.74 %), 1.07 % (0.98 %, 1.16 %), 3.86 % (3.23 %, 4.49 %), 4.45 % (4.14 %, 4.77 %), and 6.37 % (5.70 %, 7.04 %), respectively. However, exposure to O3 did not show a significant association. We found significant interactions among PM2.5, SO2, NO2, and CO, where combined exposure further exacerbated the risk of respiratory diseases. For example, in the combination of PM2.5 and SO2, the REOI, AP, and S were 0.07 (95 % CI: 0.06, 0.09), 0.07 (0.06, 0.07), and 1.07 (1.05, 1.09), respectively. Additionally, elderly individuals and females were more sensitive to these pollutants, but no statistically significant interaction effects were observed between different age and gender groups. In conclusion, our study highlights the strong link between air pollution and respiratory disease in high-altitude areas, with combined pollutant exposure posing an even greater risk. It underscores the need for enhanced air quality monitoring and public awareness campaigns, particularly to protect vulnerable populations like the elderly and females.

On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories

The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to the three classes mentioned above for both imaging modalities: radiography at 99.81% and CT at 99.88%. This work implements an accurate computer-aided system for the analysis of radiographic and CT medical images. The results of the classification are promising and will certainly improve the diagnosis and decision making of lung diseases that keep appearing over time.

Characteristics of oral microbiota in plateau and plain youth‐positive correlations between blood lipid level, metabolism and specific microflora in the plateau group

Objectives

To analyze the characteristics of oral microbiota in plateau and plain youth and the possible function of the microbiome.

Materials and methods

A total of 120 healthy young males (80 on the plateau, 40 on the plain) completed this cross-sectional study. Oral microflora samples were collected from all participants. The bacterial 16S rDNA was amplified using PCR and sequenced using Illumina MiSeq high-throughput sequencing. The data were analyzed to determine the microbial distribution and community structure of the oral microflora from the two groups. Metastats was used to test differences in relative species abundance between the groups. The correlation between the abundance of specific bacteria and blood indicators was also analyzed.

Results

As demonstrated by alpha and beta diversity, the plateau group had lower microbial richness and a less even distribution of oral microbiota than the plain group. All predominant phyla and genera were qualitatively similar between the two groups, but their relative abundances differed. The relative abundance of bacteria in the phylum Firmicutes was significantly higher in the plateau group than in the plain group. At the genus level, Streptococcus spp. and Gemella spp. were also more abundant in the plateau group. The functional prediction indicated vigorous microbial metabolism in the oral bacterial community. We also found that the relative abundance of Streptococcus spp., the dominant genus, was positively correlated with triglyceride levels in the plateau group.

Conclusions

With increasing altitude, the diversity of oral microbiota and the relative proportion of predominant bacteria were altered. The distribution and related function of Streptococcus spp. were prominent in plateau samples. This comprehensive study of the relationship between oral microecology and elevation provides a point of reference for studying the human body’s adaptability or inadaptability to high altitude.