Analysis of care-seeking and diagnosis delay among pulmonary tuberculosis patients in Beijing, China.

Background: Tuberculosis (TB) remains a significant public health challenge in China. Early detection and diagnosis of TB cases are crucial to interrupt disease transmission and prevent its progression. This study aims to describe the delay in seeking care and diagnosis among patients with pulmonary tuberculosis (PTB) and identify the influencing factors in two counties in Beijing. Methods: A retrospective analysis was carried out to investigate care-seeking and diagnosis delay in two counties in Beijing. Basic information of PTB patients from January 1 to December 31, 2021, was extracted from the Tuberculosis Information Management System of China (TBIMS), and all enrolled patients were interviewed via telephone using a standard questionnaire. Statistical description was performed using the median and interquartile range (IQR). Chi-square test and multivariate logistic regression model were used to analyze the influencing factors. Results: 537 patients were enrolled. The median duration of care-seeking and diagnosis delay was 11 (IQR: 5-26) days and 8 (IQR: 0-18) days, with 41.71 and 35.20% of patients experiencing delays (>14 days). The study found that being asymptomatic (OR = 2.791, 95%CI: 1.710-4.555) before seeking medical care and not attending work during treatment (OR = 2.990, 95%CI: 1.419-6.298) were identified as risk factors for care-seeking delay. Patients who were tracked (OR = 2.632, 95%CI: 1.062-6.521) and diagnosed at tuberculosis control and prevention institutions (OR = 1.843, 95%CI: 1.061-3.202) had higher odds of diagnostic delays. 44.69% of patients presented a total delay (>28 days), with a median duration of 25 (IQR: 13-39) days. A multivariate logistic regression analysis showed that healthy examination (OR = 0.136, 95%CI: 0.043-0.425) was a protective factor for total delay. Conclusion: Public interventions are necessary to improve the efficiency of PTB patients detection and treatment in Beijing. Medical services should focus on the target population and improve access to medical care to further reduce delays for PTB patients.

Reprogramming of arachidonic acid metabolism using α-terpineol to alleviate asthma: insights from metabolomics.

Asthma is a chronic inflammatory disorder in airways with typical pathologic features of airway inflammation and mucus hypersecretion. α-Terpineol is a monocyclic terpene found in many natural plants and foods. It has been reported to possess a wide range of pharmacological activities including anti-inflammatory and expectorant effects. However, the role of α-terpineol in asthma and its potential protective mechanism have not been well elucidated. This study is designed to investigate the pharmacological effect and mechanism of α-terpineol on asthmatic mice using the metabolomics platform. A murine model of asthma was established using ovalbumin (OVA) sensitization and then challenged for one week. The leukocyte count and inflammatory cytokines in the bronchoalveolar lavage fluid (BALF), lung histopathology, inflammatory  infiltrate and mucus secretion were evaluated. An ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based metabolomics study was performed on lung tissues and serum to explore endogenous small molecule metabolites affected by α-terpineol in asthmatic mice. After α-terpineol treatment, leukocyte count, inflammatory cytokines in the BALF, and peribronchial inflammation infiltration were significantly downregulated. Goblet cell hyperplasia and mucus secretion were attenuated, with the level of Muc5ac in BALF decreased. These results proved the protective effect of α-terpineol against airway inflammation, mucus hypersecretion and Th1/Th2 immune imbalance. To further investigate the underlying mechanisms of α-terpineol in asthma treatment, UPLC-MS/MS-based metabolomics analysis was performed. 26 and 15 identified significant differential metabolites were found in the lung tissues and serum of the control, model and α-terpineol groups, respectively. Based on the above differential metabolites, enrichment analysis showed that arachidonic acid (AA) metabolism was reprogrammed in both mouse lung tissues and serum. 5-Lipoxygenase (5-LOX) and cysteinyl leukotrienes (CysLTs) are the key enzyme and the end product of AA metabolism, respectively. In-depth studies have shown that pretreatment with α-terpineol can alleviate asthma by decreasing the AA level, downregulating the expression of 5-LOX and reducing the accumulation of CysLTs in mouse lung tissues. In summary, this study demonstrates that α-terpineol is a potential agent that can prevent asthma via regulating disordered AA metabolism.

Insight gained via ultra-performance liquid chromatography tandem mass spectrometry-based metabolomics: Protective effect of Artemisia argyi H.Lév. & Vaniot essential oil on lipopolysaccharide-induced liver injury mice.

Artemisia argyi H.Lév. & Vaniot essential oil (AAEO) has shown pharmacological effects such as anti-inflammation, antioxidant, and anti-tumor properties. However, the protective effect of AAEO on lipopolysaccharide (LPS)-induced liver injury and its potential protective mechanism are still unclear. In this study, we used ultra-performance liquid chromatography tandem mass spectrometry metabolomics techniques to investigate the changes in liver tissue metabolites in mice exposed to LPS with or without AAEO treatment for 14 days. The biochemical results showed that compared with the control group, AAEO significantly reduced the levels of liver functional enzymes, suggesting a significant improvement in liver injury. In addition, the 18 differential metabolites identified by metabolomics were mainly involved in the reprogramming of arachidonic acid metabolism, tryptophan metabolism, and purine metabolism. AAEO could significantly inhibit the expression of COX-2, IDO1, and NF-κB; enhance the body's anti-inflammatory ability; and alleviate liver injury. In summary, our study identified the protective mechanism of AAEO on LPS-induced liver injury at the level of small molecular metabolites, providing a potential liver protective agent for the treatment of LPS-induced liver injury.

Analysis of Human Exercise Health Monitoring Data of Smart Bracelet Based on Machine Learning.

The smart bracelet has become a hot-selling commodity, according to a daily consumption survey. Based on people's interest and concern for their health, the smart bracelet, as a design and application for achieving healthy weight loss monitoring, is quickly becoming a popular new favorite. This bracelet detects fat using the near-infrared diffuse reflection principle, with the goal of assisting people in controlling and maintaining a healthy weight. A large amount of data has been accumulated in all walks of life due to the development of the Internet network and data storage technology. As a result, the emergence of machine learning plays a critical role in the data analysis of human sports health monitoring of smart bracelets. Based on machine learning, this paper investigates the data analysis of human sports health monitoring smart bracelets. When the population index reaches 50 in the analysis of health monitoring data, the average accuracy of data mining is 86.8 percent, the average accuracy of the association rule algorithm is 85.9 percent, the average accuracy of the collaborative filtering algorithm is 84.3 percent, and the average accuracy of the machine learning algorithm is 90.1 percent in this paper. Among the four algorithms, the method presented in this paper is clearly the most effective, stable, and accurate. The system's stability and accuracy have been greatly improved by the addition of GPS-assisted and hand-up misjudgment algorithms. Because the smart bracelet is inexpensive, easy to wear, and consistent with consumer psychology, it is becoming increasingly popular to use it to monitor the human body's sports health.

Monitoring of Maximum Oxygen Intake of Breathing and Heart Rate in Exercise Training Based on Regression Equations.

In order to provide a new reference method and basis for the physical testing study in healthy adults aged 40∼and 49, this paper proposes a regression equation-based monitoring study of exercise training breathing and heart rate. Sixty four subjects (30 males and 34 females), aged 40∼and 49 years, were selected. First, the subjects were screened by relevant health test and medical questionnaire to exclude the subjects with exercise contraindications and high-intensity exercise; then, the incremental load test was directly measured by gas analysis, and then, the subjects who completed the maximum oxygen intake test were tested twice, the corresponding heart rate value was recorded, and walking time were averaged for calculation. We show that the regression analysis of each index yielded regression equations and cross-validation regression equations show correlation statistics for measured and inferred maximum oxygen intake of RLOOCV = 0.826 and SEELOOCV = 0.378 (L/min). The equation can effectively speculate on the maximum oxygen intake of 40∼in healthy adults aged 49, with the advantages of efficient, low cost, fast, and convenient.