Objective:To verify the clinical safety and efficacy of new intelligent ventilation mode adaptive minute ventilation (AMV)+IntelliCycle ventilation in patients with mild-to-moderate acute respiratory distress syndrome (ARDS).Methods:The patients with mild-to-moderate ARDS, admitted to intensive care unit (ICU) of the First Affiliated Hospital of Jinzhou Medical University from February 2018 to February 2019, were enrolled in the study. The patients were divided into synchronous intermittent mandatory ventilation+pressure support ventilation (SIMV+PSV) group and AMV+IntelliCycle group according to the random number table method. All patients were given mechanical ventilation, anti-infection, analgesia and sedation, nutritional support and symptomatic treatment of primary disease after admission. SV800 ventilator was used for mechanical ventilation. In the AMV+IntelliCycle group, after setting the minute ventilation volume (VE), inhaled oxygen concentration (FiO 2) and positive end expiratory pressure (PEEP), the ventilator was turned on the full-automatic mode, and the preset value of VE percentage was 120%. In the SIMV+PSV group, the ventilator parameters were set as follows: the ventilation frequency was 12-20 times/min, the inspiratory expiratory ratio was 1∶1-2, the peak inspiratory pressure (PIP) limit level was 35-45 cmH 2O (1 cmH 2O = 0.098 kPa), and the setting of FiO 2 and PEEP was as the same as that of AMV+IntelliCycle group, the triggering flow was set to 2 L/min. All of the clinical parameters between the two groups were compared. The main outcomes were duration of mechanical ventilation, ventilator alarm times, manual operation times, and the mechanical power; the secondary outcomes were respiratory rate (RR), VE, tidal volume (VT), PIP, mouth occlusion pressure (P0.1), static compliance (Cst), work of breathing (WOB), and time constant at 0, 6, 12, 24, 48, 72, and 120 hours; and the blood gas analysis parameters of patients before and after ventilation were recorded. Results:A total of 92 patients with mild-to-moderate ARDS were admitted during the study period, excluding those who quit the study due to death, abandonment of treatment, accidental extubation of tracheal intubation and so on. Eighty patients were finally enrolled in the analysis, with 40 patients in SIMV+PSV group and AMV+IntelliCycle group respectively. ① Results of main outcomes: compared with the SIMV+PSV mode, AMV+IntelliCycle ventilation mode could shorten the duration of mechanical ventilation (hours: 106.35±55.03 vs. 136.50±73.78), reduce ventilator alarm times (times: 10.35±5.87 vs. 13.93±6.87) and the manual operations times (times: 4.25±2.01 vs. 6.83±3.75), and decrease the mechanical power (J/min: 12.88±4.67 vs. 16.35±5.04, all P < 0.05). But the arterial partial pressure of carbon dioxide (PaCO 2) of AMV+IntelliCycle group was significantly higher than that of SIMV+PSV group [mmHg (1 mmHg = 0.133 kPa): 41.58±6.81 vs. 38.45±5.77, P < 0.05]. ② Results of secondary outcomes: the RR of both groups was improved significantly with the prolongation of ventilation time which showed a time effect ( F = 4.131, P = 0.005). Moreover, compared with SIMV+PSV mode, AMV+IntelliCycle mode could maintain a better level of RR, with intervention effect ( F = 5.008, P = 0.031), but no interaction effect was found ( F = 2.489, P = 0.055). There was no significant difference in VE, PIP, P0.1 or Cst between the two groups, without intervention effect ( F values were 3.343, 2.047, 0.496, 1.456, respectively, all P > 0.05), but they were significantly improved with the prolongation of ventilation time in both groups, with time effect ( F values were 2.923, 12.870, 23.120, 7.851, respectively, all P < 0.05), but no interaction effect was found ( F values were 1.571, 1.291, 0.300, 0.354, respectively, all P > 0.05). The VT, WOB or time constant in both groups showed no significant changes with the prolongation of ventilation time, and no significant difference was found between the two groups, there was neither time effect ( F values were 0.613, 1.049, 2.087, respectively, all P > 0.05) nor intervention effect ( F values were 1.459, 0.514, 0.923, respectively, all P > 0.05). Conclusion:AMV+IntelliCycle ventilation mode can shorten the ventilation time of patients with mild-to-moderate ARDS, reduce mechanical power, and reduce the workload of medical care, but PaCO 2 in the patients with AMV+IntelliCycle mode is higher than that in the patients with SIMV+PSV mode.

Objective:To investigate the clinical efficacy of conversion therapy with XELOX regimen in the treatment of stage IV advanced gastric cancer.Methods:The diagnosis and treatment process of two patients with stage IV gastric cancer who were diagnosed and treated in Southern Central Hospital of Yunnan Province (The First People's Hospital of Honghe State) in September 2018 and July 2019 were retrospectively analyzed. The performance of conversion therapy with XELOX regimen in the treatment of stage IV gastric cancer was analyzed based on relevant literature.Results:Pathological complete remission of stage IV gastric cancer was achieved in both patients after conversion therapy with XELOX regimen.Conclusion:Conversion therapy with XELOX regimen is effective on stage IV gastric cancer and is worthy of clinical promotion.

Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.
Electroencephalography