A risk prediction model for efficient intubation in the emergency department: A 4-year single-center retrospective analysis.

Objective: To analyze the risk factors associated with intubated critically ill patients in the emergency department (ED) and develop a prediction model by machine learning algorithms. Methods: This study was conducted in an academic tertiary hospital in Hangzhou, China. Critically ill patients admitted to the ED were retrospectively analyzed from May 2018 to July 2022. The demographic characteristics, distribution of organ dysfunction, parameters for different organs' examination, and status of mechanical ventilation were recorded. These patients were assigned to the intubation and non-intubation groups according to ventilation support. We used the eXtreme Gradient Boosting (XGBoost) algorithm to develop the prediction model and compared it with other algorithms, such as logistic regression, artificial neural network, and random forest. SHapley Additive exPlanations was used to analyze the risk factors of intubated critically ill patients in the ED. Results: Of 14,589 critically ill patients, 10,212 comprised the training group and 4377 comprised the test group; 2289 intubated patients were obtained from the electronic medical records. The mean age, mean scores of vital signs, parameters of different organs, and blood oxygen examination results differed significantly between the two groups (p < 0.05). The white blood cell count, international normalized ratio, respiratory rate, and pH are the top four risk factors for intubation in critically ill patients. Based on the risk factors in different predictive models, the XGBoost model showed the highest area under the receiver operating characteristic curve (0.84) for predicting ED intubation. Conclusions: For critically ill patients in the ED, the proposed model can predict potential intubation based on the risk factors in the clinically predictive model.

Screening and Molecular Mechanisms of Novel ACE-Inhibitory Peptides from Gracilariopsis lemaneiformis.

Candidate peptides with novel angiotensin-I-converting enzyme (ACE) inhibitor activity were obtained from hydrolysates of Gracilariopsis lemaneiformis by virtual screening method. Our results showed that G. lemaneiformis peptides (GLP) could significantly lower blood pressure in spontaneously hypertensive rats (SHR). At least 101 peptide sequences of GLP were identified by LC-MS/MS analysis and subjected to virtual screening. A total of 20 peptides with the highest docking score were selected and chemically synthesized in order to verify their ACE-inhibitory activities. Among them, SFYYGK, RLVPVPY, and YIGNNPAKG showed good effects with IC50 values of 6.45 ± 0.22, 9.18 ± 0.42, and 11.23 ± 0.23 µmoL/L, respectively. Molecular docking studies revealed that three peptides interacted with the active center of ACE by hydrogen bonding, hydrophobic interactions, and electrostatic forces. These peptides could form stable complexes with ACE. Furthermore, SFYYGK, RLVPVPY, and YIGNNPAKG significantly reduced systolic blood pressure (SBP) in SHR. YIGNNPAKG exhibited the highest antihypertensive effect, with the largest decrease in SBP (approximately 23 mmHg). In conclusion, SFYYGK, RLVPVPY, and YIGNNPAKG can function as potent therapeutic candidates for hypertension treatment.