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Background: Pulmonary infections are significant global health burdens, and conventional diagnostic methods (culture and polymerase chain reaction), are often limited by slow results and low sensitivity. Metagenomic next-generation sequencing (mNGS) offers a rapid, comprehensive alternative for identifying diverse pathogens, including rare and mixed infections. Thus, we assessed the diagnostic performance of mNGS in pulmonary infections, compared the findings with those of traditional pathogen detection methods, and explored its potential to enhance clinical diagnostics and patient care. Methods: We collected samples from 125 immunocompromised patients diagnosed with pulmonary infection at the Department of Respiratory Medicine of Shenzhen Longgang Central Hospital from March 2020 to July 2022. We compared the rate of pathogen positivity and pathogen distribution between conventional pathogen detection methods and mNGS using samples including sputum, blood, and bronchoalveolar lavage fluid. Results: Among the 125 cases of unexplained pulmonary infection, 82 (65.6%) and 40 (32.0%) tested positive for pathogens using mNGS and routine culture, respectively (P < 0.05). Both methods of pathogen detection were positive in 28 (22.4%) cases (complete match, 9; complete mismatch, 13; partial match, 6). However, 43.2% of cases only tested positive using mNGS, 9.4% only tested positive using routine tests, and 24.8% tested negative using both methods. A viral infection was present in 55.2% of cases. The detection rate of mycobacteria using mNGS (12.8%) was higher than that using conventional pathogen detection methods (5.6%). Conclusion: mNGS technology enhances pathogen detection in unexplained pulmonary infections, enabling targeted antimicrobial therapy and consequently helping to reduce broad-spectrum antibiotic use, aligning treatments more closely with the causative pathogens. Thus, mNGS offers significant clinical value by improving treatment efficacy and potentially reducing antibiotic resistance in pulmonary infection cases.
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@#【Objective 】 To explore the influencing factors of Yang deficiency constitution in traditional Chinese medicine (TCM) from the perspective of mathematics with the use of calculation formulas, so as to protect patients from getting diseases caused by Yang deficiency constitution and provide suggestions for TCM disease prevention. 【Methods】 Based on the classification and determination criteria of TCM constitution implemented by China Association of Chinese Medicine, data for 24 solar terms from May 5, 2020(Start of Summer) to April 20, 2021 (Grain Rain) for the identification of Yang deficiency were collected by mobile constitution identification system. The grey correlation analysis method was used to determine the grey correlation degree of the factors influencing Yang deficiency constitution. In addition, a random forest model was constructed for the verification of the results from the grey correlation analysis, and for the evaluation of correlation degree between Yang deficiency constitution and its influencing factors. 【Results】 A total of 16 259 sets of data were collected from healthy or sub-healthy individuals aged from 18 to 60 years living in the central and northeastern parts of Sichuan Province(China) for the identification of TCM constitutions. After screening and preprocessing, a total of 544 sets of data for the identification of Yang deficiency constitution, involving 18 aspects of factors influencing Yang deficiency constitution. The results of the grey correlation analysis showed that there were 12 influencing factors whose grey correlation degree with Yang deficiency constitution was greater than 0.6. The accuracy of these 12 influencing factors with the training set and validation set of the Yang deficiency constitution random forest model were 98.39% and 93.12%, respectively. 【Conclusion】 In the sample data selected in this paper, grey correlation analysis is the appropriate technology to analyze the influencing factors of Yang deficiency constitution. It provides a new idea and a new methodological reference for the research and analysis of the influencing factors of TCM constitution.
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@#Objective To study the influencing factors of blood stasis constitution and provide a basis for treating blood stasis-related diseases by traditional Chinese medicine (TCM) constitution identification. Methods Data were collected using the self-developed TCM constitution identification platform based on B/S model by the project team. The obtained data were divided into blood stasis constitution and normal constitution groups. The differences of the categorical type influencing factors (gender, birth mode, feeding mode within four months of birth, family history, marital status, eating habits, sleeping habits, exercise habits, emotional state, stress situation, and living environment) and the quantitative type influencing factors (sleep time, age, and mother's age at birth) on the constitution of the two groups were analyzed. In the single-factor analysis, the Pearson's chi-square test was selected for the categorical variable, and the independent sample t test and Mann-Whitney U nonparametric test were selected for the quantitative variables according to whether they conformed to the positive-terrestrial distribution; the binary logistic stepwise regression method was selected for the multi-factor analysis. Results The data of 318 cases were collected from the TCM composition identification platform, and 159 cases of blood stasis constitution were used as the experimental group and 159 cases of normal constitution were used as the control group. The Pearson's chi-square test yielded significant differences (P < 0.05) in the effects of gender, pressure situation, family history, living environment, emotional state, exercise habits, and dietary habits on blood stasis constitution. The independent samples t test yielded differences in sleep duration between the blood stasis constitution and normal constitution populations (P < 0.05), which meant sleep duration of the blood stasis constitution population was less than that of the normal constitution population. The Mann-Whitney U nonparametric test results accepted the original hypothesis that there was no difference in the distribution of age and mother’s age at birth across constitution types (P > 0.05). Binary logistic regression analysis showed that gender, family history, marital status, living environment, exercise habits, and emotional state were risk factors for blood stasis constitution (P < 0.05). Conclusion Gender, family history, living environment, emotional state, and exercise habits were significant influencing factors of blood stasis constitution. Blood stasis constitution populations can pay more attention to these influencing factors in their daily life for the prevention and reconciliation of blood stasis constitution.
