Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals.

Background and Objective: The 6-min walk test (6MWT) is a common functional assessment test, but adverse events during the test can be potentially dangerous and can lead to serious consequences and low quality of life. This study aimed to predict the occurrence of adverse events during 6MWT, using continuous physiological parameters combined with demographic variables. Methods: 578 patients with respiratory disease who had performed standardized 6MWT with wearable devices from three hospitals were included in this study. Adverse events occurred in 73 patients (12.6%). ECG, respiratory signal, tri-axial acceleration signals, oxygen saturation, demographic variables and scales assessment were obtained. Feature extraction and selection of physiological signals were performed during 2-min resting and 1-min movement phases. 5-fold cross-validation was used to assess the machine learning models. The predictive ability of different models and scales was compared. Results: Of the 16 features selected by the recursive feature elimination method, those related to blood oxygen were the most important and those related to heart rate were the most numerous. Light Gradient Boosting Machine (LightGBM) had the highest AUC of 0.874 ± 0.063 and the AUC of Logistic Regression was AUC of 0.869 ± 0.067. The mMRC (Modified Medical Research Council) scale and Borg scale had the lowest performance, with an AUC of 0.733 and 0.656 respectively. Conclusion: It is feasible to predict the occurrence of adverse event during 6MWT using continuous physiological parameters combined with demographic variables. Wearable sensors/systems can be used for continuous physiological monitoring and provide additional tools for patient safety during 6MWT.

Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards.

Physiological signals can contain abundant personalized information and indicate health status and disease deterioration. However, in current medical practice, clinicians working in the general wards are usually lack of plentiful means and tools to continuously monitor the physiological signals of the inpatients. To address this problem, we here presented a medical-grade wireless monitoring system based on wearable and artificial intelligence technology. The system consists of a multi-sensor wearable device, database servers and user interfaces. It can monitor physiological signals such as electrocardiography and respiration and transmit data wirelessly. We highly integrated the system with the existing hospital information system and explored a set of processes of physiological signal acquisition, storage, analysis, and combination with electronic health records. Multi-scale information extracted from physiological signals and related to the deterioration or abnormality of patients could be shown on the user interfaces, while a variety of reports could be provided daily based on time-series signal processing technology and machine learning to make more information accessible to clinicians. Apart from an initial attempt to implement the system in a realistic clinical environment, we also conducted a preliminary validation of the core processes in the workflow. The heart rate veracity validation of 22 patient volunteers showed that the system had a great consistency with ECG Holter, and bias for heart rate was 0.04 (95% confidence interval: -7.34 to 7.42) beats per minute. The Bland-Altman analysis showed that 98.52% of the points were located between Mean ± 1.96SD. This system has been deployed in the general wards of the Hyperbaric Oxygen Department and Respiratory Medicine Department and has collected more than 1000 cases from the clinic. The whole system will continue to be updated based on clinical feedback. It has been demonstrated that this system can provide reliable physiological monitoring for patients in general wards and has the potential to generate more personalized pathophysiological information related to disease diagnosis and treatment from the continuously monitored physiological data.

Vaporized perfluorocarbon inhalation attenuates primary blast lung injury in canines by inhibiting mitogen-activated protein kinase/nuclear factor-κB activation and inducing nuclear factor, erythroid 2 like 2 pathway.

Blast lung injury is associated with high morbidity and mortality. Vaporized perfluorocarbon (PFC) inhalation has been reported to attenuate acute respiratory distress syndrome in humans and animal models. However, the effect of vaporized PFC on blast lung injury is still unknown. In this study, we investigated the protective effects and potential underlying mechanisms of action of vaporized PFC on blast lung injury in a canine model. This was a prospective, controlled, animal study in adult male hybrid dogs randomized to sham, blast (B), blast plus mechanical ventilation (B + M), and blast plus PFC (B + P) groups. All groups except for the sham were exposed to blast wave. The B + P group was treated with vaporized PFC for 1.5 h followed by 5.5 h mechanical ventilation. B + M group received 7.5 h mechanical ventilation and B group was observed for 7.5 h. Blast lung injury was induced using a shock tube. Blood gas, inflammatory cytokines, and oxidative stress were measured. Expression of nuclear factor (NF)-κB activation, mitogen-activated protein kinase (MAPK) and nuclear factor, erythroid 2 like 2 (Nrf2) were measured using western blot. Lung injury observed after blast exposure was marked by increased histopathological scores, ratio of lung wet to dry weight. PFC treatment attenuated blast lung injury as indicated by histopathological scores and ratio of lung wet to dry weight. PFC treatment downregulated interleukin (IL)-6, tumor necrosis factor (TNF)-α, and malondialdehyde (MDA), and upregulated superoxide dismutase (SOD) activity. PFC also suppressed expression of MAPK/NF-κB and Nrf2 protein levels. Our results suggest that PFC attenuated blast-induced acute lung injury by inhibiting MAPK/NF-κB activation and inducing Nrf2 expression in dogs.

Identification of differentially expressed proteins in the injured lung from zinc chloride smoke inhalation based on proteomics analysis.

BACKGROUND: Lung injury due to zinc chloride smoke inhalation is very common in military personnel and leads to a high incidence of pulmonary complications and mortality. The aim of this study was to uncover the underlying mechanisms of lung injury due to zinc chloride smoke inhalation using a rat model. METHODS: Histopathology analysis of rat lungs after zinc chloride smoke inhalation was performed by using haematoxylin and eosin (H&E) and Mallory staining. A lung injury rat model of zinc chloride smoke inhalation (smoke inhalation for 1, 2, 7 and 14 days) was developed. First, isobaric tags for relative and absolute quantization (iTRAQ) and weighted gene co-expression network analysis (WGCNA) were used to identify important differentially expressed proteins. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to study the biological functions of differentially expressed proteins. Then, analysis of lung injury repair-related differentially expressed proteins in the early (day 1 and day 2) and middle-late stages (day 7 and day 14) of lung injury after smoke inhalation was performed, followed by the protein-protein interaction (PPI) analysis of these differentially expressed proteins. Finally, the injury repair-related proteins PARK7 and FABP5 were validated by immunohistochemistry and western blot analysis. RESULTS: Morphological changes were observed in the lung tissues after zinc chloride smoke inhalation. A total of 27 common differentially expressed proteins were obtained on days 1, 2, 7 and 14 after smoke inhalation. WGCNA showed that the turquoise module (which involved 909 proteins) was most associated with smoke inhalation time. Myl3, Ckm, Adrm1 and Igfbp7 were identified in the early stages of lung injury repair. Gapdh, Acly, Tnni2, Acta1, Actn3, Pygm, Eno3 and Tpi1 (hub proteins in the PPI network) were identified in the middle-late stages of lung injury repair. Eno3 and Tpi1 were both involved in the glycolysis/gluconeogenesis signalling pathway. The expression of PARK7 and FABP5 was validated and was consistent with the proteomics analysis. CONCLUSION: The identified hub proteins and their related signalling pathways may play crucial roles in lung injury repair due to zinc chloride smoke inhalation.