Objective The relationship between asthma and hypertension was explored by using the least absolute shrinkage and selection operator(LASSO)regression and Mendelian randomization(MR).Methods Data from the National Health and Nutrition Examination Survey(NHANES)of the United States from 2001 to 2018 were selected.LASSO regression was used to screen variables,and a nomogram prediction model for hypertension risk was established.The model was verified to evaluate its predictive ability and stability for the risk of hypertension.Finally,two-sample MR analysis was used to verify the causal relationship.Results The study ultimately included 26 456 cases,including 8833 hypertensive patients.LASSO regression screened out 9 variables,including age,educational level,race,marital status,smoking history,asthma,diabetes,cancer and body mass index,and a nomogram model was constructed based on these variables.The calibration curve indicated that there was a good fit between two groups.After adjusting for the measured confounding factors,the inverse variance weighted analysis showed that asthma was associated with an increased risk of hypertension(OR=2.67,95％CI:1.31-5.43,P＜0.01).Conclusion There is a causal relationship between asthma and hypertension,and controlling asthma conditions may help reduce the risk of developing hypertension.

Objective The relationship between asthma and hypertension was explored by using the least absolute shrinkage and selection operator(LASSO)regression and Mendelian randomization(MR).Methods Data from the National Health and Nutrition Examination Survey(NHANES)of the United States from 2001 to 2018 were selected.LASSO regression was used to screen variables,and a nomogram prediction model for hypertension risk was established.The model was verified to evaluate its predictive ability and stability for the risk of hypertension.Finally,two-sample MR analysis was used to verify the causal relationship.Results The study ultimately included 26 456 cases,including 8833 hypertensive patients.LASSO regression screened out 9 variables,including age,educational level,race,marital status,smoking history,asthma,diabetes,cancer and body mass index,and a nomogram model was constructed based on these variables.The calibration curve indicated that there was a good fit between two groups.After adjusting for the measured confounding factors,the inverse variance weighted analysis showed that asthma was associated with an increased risk of hypertension(OR=2.67,95％CI:1.31-5.43,P＜0.01).Conclusion There is a causal relationship between asthma and hypertension,and controlling asthma conditions may help reduce the risk of developing hypertension.

Objective:To analyze the characteristics of infectious pathogens in burn patients with sepsis based on metagenomic next-generation sequencing (mNGS) technology.Methods:This study was a retrospective observational study. From July 2021 to December 2023, 109 burn patients with sepsis who met the inclusion criteria were admitted to the Department of Burns of the First People's Hospital of Zhengzhou, including 68 males aged 57 to 92 years and 41 females aged 48 to 83 years. Blood, bronchoalveolar lavage fluid, cerebrospinal fluid, sputum, or other fluid specimens were collected from the patients during their hospital stay for microbiological culture (86 patients) and mNGS technology detection (109 patients). The types of specimens and pathogens detected by mNGS technology were counted. Patients were divided into intensive care unit (ICU) group (78 cases) who were admitted to the ICU and non-ICU group (31 cases) who were not admitted to the ICU, and the pathogens for infection in the two groups of patients were analyzed. In addition, the detection of pathogens in the specimens of 86 patients who underwent both mNGS technology detection and microbiological culture detection was analyzed.Results:Among the 109 specimens detected by mNGS technology, there were 42 blood specimens, 17 bronchoalveolar lavage fluid specimens, 4 sputum specimens, 6 cerebrospinal fluid specimens, 16 pus specimens, and 24 tissue fluid specimens; a total of 39 pathogens were detected, including 13 bacteria, 12 fungi, 10 viruses, 2 parasites, and 2 mycoplasmas. The overall positive rate of pathogen detection was 88.99% (97/109). Ranked by the detection rate, the top three Gram-negative bacteria were Klebsiella pneumoniae, Acinetobacter baumannii, and Pseudomonas spp, the top three Gram-positive bacteria were Streptococcus pneumoniae, Staphylococcus aureus, and Enterococcus faecalis; the top three viruses were human herpesvirus, cytomegalovirus, and circovirus; the top three fungi were Aspergillus fumigatus, Candida albicans, and Aspergillus flavus. Twenty-seven patients were infected with one pathogen, 45 patients with two pathogens, and 25 patients with three or more pathogens. Compared with those in non-ICU group, the proportions of Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas spp, Streptococcus pneumoniae, Aspergillus fumigatus, and cytomegalovirus detected in the patients in ICU group were significantly higher (with χ2 values of 8.62, 7.93, 3.93, 5.48, 4.28, and 5.58, respectively, P<0.05). In the pathogens detected by mNGS technology and microbiological culture method, the most common bacteria were Klebsiellapneumoniaeand Acinetobacter baumannii, and the most common fungi were strains of Aspergillus and Candida. There were 19 pathogens those could only be detected by mNGS technology, such as Lichtheimia ramosa, Pneumocystis jirovecii, Mycobacterium tuberculosis, viruses, etc.; there were no pathogens detected by microbiological culture method that couldn't be detected by mNGS technology. Compared with those detected by microbiological culture method, the overall positive rate, bacterial positive rate, and fungal positive rate detected by mNGS technology were significantly increased (with χ2 values of 45.52, 5.88, and 4.94, respectively, P<0.05). The 27.91% (24/86) of patients were detected positive by both methods, and 72.09% (62/86) of the patients were detected positive by mNGS technology but negative by microbiological culture method. The consistency test of the results obtained by the two detection methods showed that the difference was not statistically significant ( κ=0.02, P>0.05). Conclusions:The positive rate of pathogen detection in specimens using mNGS technology is higher than that detected by using conventional microbiological culture method, and it can detect pathogens those cannot be detected by the latter, such as Lichtheimia ramosa, Pneumocystis jirovidii, Mycobacterium tuberculosis, viruses, etc. Detection using mNGS technology can help clarify the types of infectious pathogens in burns patients with sepsis, and provide basis and guidance for clinical medication.

Objective To analyze the risk factors of poor prognosis in patients with extended-spectrum β-lactamase producing enterobacterales(ESBL-E)bloodstream infection,and establish a nomogram prediction model to provide help for clinical diagnosis and treatment.Methods A total of 235 patients with ESBL-E bloodstream infection were collected from the First People's Hospital of Jiande City.According to their prognosis,the patients were divided into survival group(n=211)and death group(n=224).The clinical data of the patients were collected,and the independent risk factors of poor prognosis were screened by multivariate Logistic regression analysis.The nomogram was established and verified.Results The mortality of ESBL-E bloodstream infection patients with shock,respiratory failure,diabetes and leukemia,ICU admission,hypoproteinemia,increased or decreased white blood cells,and thrombocytopenia was higher(P＜0.05).Multivariate Logistic regression analysis showed that combined shock,respiratory failure and leukemia were independent risk factors for death from ESBL-E bloodstream infection.Conclusion The nomogram prediction model of adverse prognostic risk factors in patients with ESBL-E bloodstream infection can provide help for clinicians to judge the poor prognosis in the early stage,and it is of reference significance to take early intervention measures to reduce the mortality of patients.