Research on the Corrosion Detection of Rebar in Reinforced Concrete Based on SMFL Technology.

The corrosion damage of rebars is a leading cause of structural failure in reinforced concrete structures. Timely detection and evaluation of corrosion damage are crucial for ensuring structural safety. The self-magnetic flux leakage (SMFL) technology is often used due to its unique advantages in detecting corrosion damage of rebars. However, challenges persist in theoretically characterizing corrosion damage and exploring influencing factors. Therefore, the magnetic dipole theory model coupled with multiple-shaped defects is proposed and the influence of corrosion expansion force on the detection of corrosion damage is analyzed. The results show that the standard deviation of the magnetic field intensity induced by corrosion varied by up to 833%, while that induced by corrosion expansion force did not exceed 10%. So the changes in the SMFL field induced by corrosion damage play the dominant role and the influence of corrosion expansion force can be ignored. In addition, corrosion damage experiments on reinforced concrete based on the SMFL technology were conducted. The results indicate that the SFML curves of rebars change monotonically with the increasing corrosion degree. Significant variations in the curves correspond well with the locations of severe corrosion on the rebars. There is a positive relationship between the proposed magnetic parameters and the corrosion degree of the rebars. Furthermore, a corrosion damage evaluation model considering multiple parameters is developed to predict the corrosion degree of rebars. The prediction results demonstrate high accuracy, with an average absolute error of only 8.33%, which is within 10%.

The roles of bacteria and viruses in COPD-Bronchiectasis association: A prospective cohort study.

BACKGROUND: Exacerbations are implicated in bronchiectasis and COPD, which frequently co-exist [COPD-Bronchiectasis association (CBA)]. We aimed to determine the bacterial and viral spectrum at stable-state and exacerbation onset of CBA, and their association with exacerbations and clinical outcomes of CBA as compared with bronchiectasis. METHODS: We prospectively collected spontaneous sputum from adults with CBA, bronchiectasis with (BO) and without airflow obstruction (BNO) for bacterial culture and viral detection at stable-state and exacerbations. RESULTS: We enrolled 76 patients with CBA, 58 with BO, and 138 with BNO (711 stable and 207 exacerbation visits). Bacterial detection rate increased from BNO, CBA to BO at steady-state (P = 0.02), but not at AE onset (P = 0.91). No significant differences in viral detection rate were found among BNO, CBA and BO. Compared with steady-state, viral isolations occurred more frequently at exacerbation in BNO (15.8 % vs 32.1 %, P = 0.001) and CBA (19.5 % vs 30.6 %, P = 0.036) only. In CBA, isolation of viruses, human metapneumovirus and bacteria plus viruses was associated with exacerbation. Repeated detection of Pseudomonas aeruginosa (PA) correlated with higher modified Reiff score (P = 0.032) in CBA but not in BO (P = 0.178). Repeated detection of PA yielded a shorter time to the first exacerbation in CBA [median: 4.3 vs 11.1 months, P = 0.006] but not in BO (median: 8.4 vs 7.6 months, P = 0.47). CONCLUSIONS: Isolation of any viruses, human metapneumovirus and bacterialplus viruses was associated with CBA exacerbations. Repeated detection of PA confers greater impact of future exacerbations on CBA than on BO.

Clinical Characteristics and Outcomes of the Phenotypes of COPD-Bronchiectasis Association.

INTRODUCTION: Although COPD may frequently co-exist with bronchiectasis [COPD-bronchiectasis associated (CBA)], little is known regarding the clinical heterogeneity. We aimed to identify the phenotypes and compare the clinical characteristics and prognosis of CBA. METHODS: We conducted a retrospective cohort study involving 2928 bronchiectasis patients, 5158 COPD patients, and 1219 patients with CBA hospitalized between July 2017 and December 2020. We phenotyped CBA with a two-step clustering approach and validated in an independent retrospective cohort with decision-tree algorithms. RESULTS: Compared with patients with COPD or bronchiectasis alone, patients with CBA had significantly longer disease duration, greater lung function impairment, and increased use of intravenous antibiotics during hospitalization. We identified five clusters of CBA. Cluster 1 (N=120, CBA-MS) had predominantly moderate-severe bronchiectasis, Cluster 2 (N=108, CBA-FH) was characterized by frequent hospitalization within the previous year, Cluster 3 (N=163, CBA-BI) had bacterial infection, Cluster 4 (N=143, CBA-NB) had infrequent hospitalization but no bacterial infection, and Cluster 5 (N=113, CBA-NHB) had no hospitalization or bacterial infection in the past year. The decision-tree model predicted the cluster assignment in the validation cohort with 91.8% accuracy. CBA-MS, CBA-BI, and CBA-FH exhibited higher risks of hospital re-admission and intensive care unit admission compared with CBA-NHB during follow-up (all P<0.05). Of the five clusters, CBA-FH conferred the worst clinical prognosis. CONCLUSION: Bronchiectasis severity, recent hospitalizations and sputum culture findings are three defining variables accounting for most heterogeneity of CBA, the characterization of which will help refine personalized clinical management.

Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction-a retrospective analysis of the MIMIC database.

Background: This study aims to investigate the clinical outcome between high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) therapy in mild to moderate hypoxemic patients on the first ICU day and to develop a predictive model of 48-h intubation. Methods: The study included adult patients from the MIMIC III and IV databases who first initiated HFNC or NIV therapy due to mild to moderate hypoxemia (100 < PaO2/FiO2 ≤ 300). The 48-h and 30-day intubation rates were compared using cross-sectional and survival analysis. Nine machine learning and six ensemble algorithms were deployed to construct the 48-h intubation predictive models, of which the optimal model was determined by its prediction accuracy. The top 10 risk and protective factors were identified using the Shapley interpretation algorithm. Result: A total of 123,042 patients were screened, of which, 673 were from the MIMIC IV database for ventilation therapy comparison (HFNC n = 363, NIV n = 310) and 48-h intubation predictive model construction (training dataset n = 471, internal validation set n = 202) and 408 were from the MIMIC III database for external validation. The NIV group had a lower intubation rate (23.1% vs. 16.1%, p = 0.001), ICU 28-day mortality (18.5% vs. 11.6%, p = 0.014), and in-hospital mortality (19.6% vs. 11.9%, p = 0.007) compared to the HFNC group. Survival analysis showed that the total and 48-h intubation rates were not significantly different. The ensemble AdaBoost decision tree model (internal and external validation set AUROC 0.878, 0.726) had the best predictive accuracy performance. The model Shapley algorithm showed Sequential Organ Failure Assessment (SOFA), acute physiology scores (APSIII), the minimum and maximum lactate value as risk factors for early failure and age, the maximum PaCO2 and PH value, Glasgow Coma Scale (GCS), the minimum PaO2/FiO2 ratio, and PaO2 value as protective factors. Conclusion: NIV was associated with lower intubation rate and ICU 28-day and in-hospital mortality. Further survival analysis reinforced that the effect of NIV on the intubation rate might partly be attributed to the other impact factors. The ensemble AdaBoost decision tree model may assist clinicians in making clinical decisions, and early organ function support to improve patients' SOFA, APSIII, GCS, PaCO2, PaO2, PH, PaO2/FiO2 ratio, and lactate values can reduce the early failure rate and improve patient prognosis.

Bacteria and viruses and clinical outcomes of asthma-bronchiectasis overlap syndrome: A cohort study.

BACKGROUND: Despite the high prevalence of co-existing bronchiectasis and asthma (asthma-bronchiectasis overlap syndrome [ABOS]), little is known regarding the dominant pathogens and clinical correlates. OBJECTIVE: To investigate the bacteria and viruses which differentially dominate in ABOS (including its subtypes) compared with bronchiectasis alone, and determine their relevance with bronchiectasis severity and exacerbations. METHODS: This was a prospective observational cohort study conducted between March 2017 and August 2023. We included 81 patients with ABOS and 107 patients with bronchiectasis alone. At steady-state baseline, patients underwent comprehensive assessments and sputum collection for bacterial culture and viral detection (quantitative polymerase-chain-reaction). Patients were followed-up to record exacerbation and spirometry. RESULTS: Patients with ABOS had significantly higher symptom burden and exacerbation frequency than those with bronchiectasis alone. Despite similar pathogen spectrum, the rate of bacteria-virus co-detection increased less substantially at acute exacerbations (AE) onset than at steady-state compared with bronchiectasis alone. Pathogenic bacteria (particularly Pseudomonas aeruginosa) were detected fairly common (exceeding 50%) in ABOS and were associated with greater severity of bronchiectasis when stable and conferred greater exacerbation risks at follow-up. Viral but not bacterial compositions changed substantially at AE onset compared with clinical stability. Higher blood eosinophil count, moderate-to-severe bronchiectasis and non-atopy were associated with higher odds of bacterial, but not viral, detection (all p < 0.05). CONCLUSION: Detection of bacteria or virus is associated with bronchiectasis severity or clinical outcomes in ABOS. This highlights the importance of integrating sputum microbial assessment for ascertaining the dominant pathophysiology (atopy vs. infection) and longitudinal trajectory prediction in ABOS.