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In order to study the influence of ventilation parameters on the ventilation of plateau highway construction tunnels, a highway tunnel construction section in Yunnan is taken as the research background, and Fluent software is used for simulation. The results of the study show that: under the conditions of press-in ventilation, the wind speed in the center of the vortex area in the wind flow field is smaller than the wind speed in the surrounding area, and with the diffusion of the flow field, the average wind speed in the tunnel section gradually decreases, and ultimately stabilizes at the level of 0.5 m/s. After blasting, the dust mass concentration on the return side of the tunnel is higher than that on the duct side. Dust with a particle size of 30 µm or more settled rapidly within 100 m from the boring face, while dust with a particle size of 30 µm or less gradually diffused outward under the action of the wind flow. In the vicinity of the tunnel boring face, reducing the distance from the air outlet to the boring face and increasing the air velocity can improve the dust removal effect. This conclusion can provide theoretical basis and certain guidance for the evolution of dust and dust prevention in the tunnel construction process in plateau area.

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BACKGROUND: Mechanical ventilation, as an important respiratory support, plays an important role in general anesthesia and it is the cornerstone of intraoperative management of surgical patients. Different from spontaneous respiration, intraoperative mechanical ventilation can lead to postoperative lung injury, and its impact on surgical mortality cannot be ignored. Postoperative lung injury increases hospital stay and is related to preoperative conditions, anesthesia time, and intraoperative ventilation settings. METHOD: Through reading literature and research reports, the relationship between perioperative input parameters and output parameters related to mechanical ventilation and ventilator-related complications was reviewed, providing reference for the subsequent setting of input parameters of mechanical ventilation and new ventilation strategies. RESULTS: The parameters of inspiratory pressure rise time and inspiratory time can change the gas distribution, gas flow rate and airway pressure into the lungs, but there are few clinical studies on them. It can be used as a prospective intervention to study the effect of specific protective ventilation strategies on pulmonary complications after perioperative anesthesia. CONCLUSION: There are many factors affecting lung function after perioperative mechanical ventilation. Due to the difference of human body, the ventilation parameters suitable for each patient are different, and the deviation of each ventilation parameter can lead to postoperative pulmonary complications. Inspiratory pressure rise time and inspiratory time will be used as the new ventilation strategy.

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BACKGROUND: Prolonged mechanical ventilation (PMV), mostly defined as mechanical ventilation > 72 h after lung transplantation with or without tracheostomy, is associated with increased mortality. Nevertheless, the predictive factors of PMV after lung transplant remain unclear. The present study aimed to develop a novel scoring system to identify PMV after lung transplantation. METHODS: A total of 141 patients who underwent lung transplantation were investigated in this study. The patients were divided into PMV and non-prolonged ventilation (NPMV) groups. Univariate and multivariate logistic regression analyses were performed to assess factors associated with PMV. A risk nomogram was then established based on the multivariate analysis, and model performance was further examined regarding its calibration, discrimination, and clinical usefulness. RESULTS: Eight factors were finally identified to be significantly associated with PMV by the multivariate analysis and therefore were included as risk factors in the nomogram as follows: the body mass index (BMI, P = 0.036); primary diagnosis as idiopathic pulmonary fibrosis (IPF, P = 0.038); pulmonary hypertension (PAH, P = 0.034); primary graft dysfunction grading (PGD, P = 0.011) at T0; cold ischemia time (CIT P = 0.012); and three ventilation parameters (peak inspiratory pressure [PIP, P < 0.001], dynamic compliance [Cdyn, P = 0.001], and P/F ratio [P = 0.015]) at T0. The nomogram exhibited superior discrimination ability with an area under the curve of 0.895. Furthermore, both calibration curve and decision-curve analysis indicated satisfactory performance. CONCLUSION: A novel nomogram to predict individual risk of receiving PMV for patients after lung transplantation was established, which may guide preventative measures for tackling this adverse event.

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The airflow exchange between the mine ventilation system and the surface atmosphere can influence ventilation effectiveness and emit more greenhouse gas from the mine for shallow cover mines. This airflow exchange also changes the airflow dynamics at the ventilation system, specially at the tailgate corner of the longwall. In this study, the fix-point traverse technique was employed to conduct a comprehensive survey at a coal mine longwall face and tailgate region. The air velocity, oxygen and carbon monoxide concentrations, barometric pressure, and temperature were measured and surveyed. Based on the survey data, the airflow pattern and gas concentration were analyzed at return air corner on the tailgate side. Based on the measurement and analyses, it was found that the airflow at the face can be broken into two compartments by the hydraulic cylinder. These two compartments can periodically exchange the air at the face. This can influence the abnormal gas concentration for the release of carbon monoxide from the gob attributed from coal spontaneous combustion. Also, our study provided detailed information for more understanding of airflow in working face and gob by simulation method in the future work.

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BACKGROUND: Currently, the primary graft dysfunction (PGD) score is used to measure allograft function in the early post-lung transplant period. Although PGD grades at later time points (T48 hours and T72 hours) are useful to predict mid- and long-term outcomes, their predictive value is less relevant within the first 24 hours after transplantation. This study aimed to evaluate the capability of PGD grades to predict prolonged mechanical ventilation (MV) and compare it with a model derived from ventilation parameters measured on arrival at the intensive care unit (ICU). METHODS: A retrospective single-center analysis of 422 double lung transplantations (LTxs) was performed. PGD was assessed 2 hours after arrival at ICU, and grades were associated with length of MV (LMV). In addition, peak inspiratory pressure (PIP), ratio of the arterial partial pressure of oxygen to fraction of inspired oxygen (P/F ratio), and dynamic compliance (cDyn) were collected, and a logistic regression model was created. The predictive capability for prolonged MV was calculated for both (the PGD score and the model). In a second step, the created model was externally validated using a prospective, international multicenter cohort including 102 patients from the lung transplant centers of Vienna, Toronto, and Budapest. RESULTS: In the retrospective cohort, a high percentage of extubated patients was reported at 24 hours (35.1%), 48 hours (68.0%), and 72 hours (80.3%) after transplantation. At T0 (time point defined as 2 hours after arrival at the ICU), patients with PGD grade 0 had a shorter LMV with a median of 26 hours (interquartile range [IQR]: 16-47 hours) than those with PGD grade 1 (median: 42 hours, IQR: 27-50 hours), PGD grade 2 (median: 37.5 hours, IQR: 15.5-78.5 hours), and PGD grade 3 (median: 46 hours, IQR: 27-86 hours). However, IQRs largely overlapped for all grades, and the value of PGD to predict prolonged MV was poor. A total of 3 ventilation parameters (PIP, cDyn, and P/F ratio), determined at T0, were chosen on the basis of clinical reasoning. A logistic regression model including these parameters predicted prolonged MV (>72 hours) with an optimism-corrected area under the curve (AUC) of 0.727. In the prospective validation cohort, the model proved to be stable and achieved an AUC of 0.679. CONCLUSIONS: The prediction model reported in this study combines 3 easily obtainable variables. It can be employed immediately after LTx to quantify the risk of prolonged MV, an important early outcome parameter.

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BACKGROUND: The evaluation of donor lungs heavily depends on the subjective judgment of the retrieval surgeon. As a consequence, acceptance rates vary significantly among transplant centers. We aimed to determine donor ventilation parameters in a prospective study and test if they could be used as objective quality criteria during organ retrieval. METHODS: A prospective evaluation of lung donors was performed in 3 transplant centers. Ventilation parameters were collected at the time of retrieval using a standardized ventilation protocol. Recipient length of mechanical ventilation (LMV) was defined as the primary end point, and collected data was used to build linear models predicting LMV. RESULTS: In total, 166 donors were included in this study. Median LMV after transplantation was 32 hours (interquartile range: 20-63 hours). Peak inspiratory pressure and dynamic compliance (Cdyn) at the time of retrieval, but not the partial pressure of oxygen/fraction of inspired oxygen (P/F) ratio, correlated with recipient LMV in Spearman correlations (r = 0.280, p = 0.002; r = -0.245, p = 0.003; and r = 0.064, p = 0.432, respectively). Linear models were built to further evaluate the impact of donor ventilation parameters on LMV. The first model was based on donor P/F ratio, donor age, donor intubation time, donor smoking history, donor partial pressure of carbon dioxide, aspiration, chest trauma, and pathologic chest X-ray. This model performed poorly (multiple R-squared = 0.063). In a second model, donor ventilation parameters were included, and Cdyn was identified as the strongest predictor for LMV. The third model was extended by recipient factors, which significantly improved the robustness of the model (multiple R-squared = 0.293). CONCLUSION: In this prospective evaluation of donor lung parameters, currently used donor quality criteria poorly predicted recipient LMV. Our data suggest that Cdyn is a strong donor-bound parameter to predict short-term graft performance; however, recipient factors are similarly relevant.

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OBJECTIVE: Despite technical specifications of neonatal mechanical ventilators (MVs) guarantee clinically irrelevant discrepancies between the set and the delivered values of ventilation parameters, previous studies reported large deviations. Most studies characterized performances of a given model/brand by studying a single device, disregarding possible intramodel differences, and leaving the accuracy of the ventilation parameters effectively delivered in clinical settings unknown. The aim of this study was to evaluate the real-life accuracy of pressure and volume parameters delivered by neonatal ventilators ready to be used on patients in neonatal intensive care units (NICUs). STUDY DESIGN: In vitro study. SUBJECTS SELECTION: Neonatal ventilators (n = 33 of 8 different models) available in four European NICUs. METHODOLOGY: The MVs were connected to a test lung (resistance = 50 cmH2 O*s/L, compliance = 0.35 mL/cmH2 O) provided with pressure and flow sensors. MVs were tested over two different ventilation modes randomly: (a) pressure controlled (PC) with a peak inspiratory pressure (PIP) of 22 cmH2 O, and (b) PC with volume targeted ventilation (VTV) with a tidal volume (VT ) of 6 mL. In all tests, positive end-expiratory pressure (PEEP) was set to 6 cmH2 O, respiratory rate to 45 breaths/min, inspiratory time to 0.33 seconds, and oxygen fraction to 0.3. RESULTS: During PC the median (min-max) values delivered were: PEEP = 5.84(4.95-6.48) cmH2 O, PIP = 21.63(20.04-22.62) cmH2 O. During VTV, VT was 5.94(4.63-8.01) mL. VT was considerably variable, ranging from -22% to +33% of the set and displayed values. Differences in accuracy among devices of the same model were comparable to those found among different models. CONCLUSIONS: Our findings suggest that loss of accuracy in ventilation variables is likely related to daily use of the devices rather than weakness in the design or manufacturing process, urging the improvement of maintenance and quality control procedures to preserve the performances of neonatal MVs during their entire lifespan.

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Emergency department (ED) lenght of stay of the patients requiring admission to the intensive care units has increased gradually in recent years. Mechanical ventilation is an integral part of critical care and mechanically ventilated patients have to be managed and monitored by emergency physicians for longer than expected in EDs. This early period of care has significant impact on the outcomes of these patients. Therefore, emergency physicians should have comprehensive knowledge of mechanical ventilation. This review will summarize the current literature of the basic concepts, appropriate clinical applications, monitoring parameters, components and mechanisms of mechanical ventilation in the ED.

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Differential lung ventilation treatment is a mechanical ventilation strategy used for unilateral lung disease or injury. This treatment can be provided to patients who fail to respond to conventional mechanical ventilation to both lungs and is technically challenging to medical personnel. An effective computerised decision support system (CDSS) can be used as a support system to intensivists in providing this treatment to their patients. In this study, a CDSS for differential lung ventilation is presented. By using this system, the mode of ventilation to each lung can be pressure controlled or volume controlled and all ventilation parameters including the peak inspiratory pressure (P insp), tidal volume (V t), positive end-expiratory pressure, fraction of inspired oxygen ( F I O 2 ), and the respiratory rate (f) can be assigned individually to each lung. The proposed CDSS has the potential to be used as a support system to clinicians in providing differential lung ventilation treatments to patients.

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In this study, passenger comfort and the air pollution status of the micro-environmental conditions in an air-conditioned bus were investigated through questionnaires, field measurements, and a numerical simulation. As a subjective analysis, passengers' perceptions of indoor environmental quality and comfort levels were determined from questionnaires. As an objective analysis, a numerical simulation was conducted using a discrete phase model to determine the diffusion and distribution of pollutants, including particulate matter with a diameter < 10 µm (PM10), which were verified by experimental results. The results revealed poor air quality and dissatisfactory thermal comfort conditions in Jinan's air-conditioned bus system. To solve these problems, three scenarios (schemes A, B, C) were designed to alter the ventilation parameters. According to the results of an improved simulation of these scenarios, reducing or adding air outputs would shorten the time taken to reach steady-state conditions and weaken the airflow or lower the temperature in the cabin. The airflow pathway was closely related to the layout of the air conditioning. Scheme B lowered the temperature by 0.4 K and reduced the airflow by 0.01 m/s, while scheme C reduced the volume concentration of PM10 to 150 µg/m3. Changing the air supply angle could further improve the airflow and reduce the concentration of PM10. With regard to the perception of airflow and thermal comfort, the scheme with an airflow provided by a 60° nozzle was considered better, and the concentration of PM10 was reduced to 130 µg/m3.

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