Effects of Combined Use of Salbutamol/Budesonide in Thoracic Surgery on Postoperative Myocardial Injury (MINS) - A Prospective Randomized Clinical Trial.

Purpose: This study aims to investigate whether the administration of salbutamol/budesonide reduced the incidence of myocardial injury in thoracic surgery. Methods: The randomized controlled trial included 298 patients over 45 and at high-risk for cardiovascular complications after lobectomy. Patients in the experimental group were treated with salbutamol/budesonide after anesthesia induction with fiberoptic bronchoscope. The primary outcome was the incidence rates of myocardial injury, assessed before and three days after the operation. The secondary outcome was respiratory function at each time point during the operation, including lung compliance and arterial partial pressure of oxygen, postoperative pulmonary and cardiovascular complications, hospital stay, pain score, and analgesic dosage. Results: In the control group, the incidence of myocardial injury was 57/150 (38%), while that in the experimental group was 33/148 (22%); compared between the two groups, the difference in the incidence of myocardial injury was statistically significant. The dynamic compliance and static compliance at half an hour after the start of surgery in the experimental group were significantly improved. Before leaving the operating room, the difference in arterial oxygen partial pressure between the two groups was statistically significant. Conclusion: Intraoperative administration of salbutamol/budesonide reduced the incidence of myocardial injury after thoracic surgery, improved lung function, and reduced the incidence of postoperative pulmonary complications.

A positive feedback loop of heparanase/syndecan1/nerve growth factor regulates cancer pain progression.

Background: The purpose of this research was to assess the role of heparanase (HPSE)/syndecan1 (SDC1)/nerve growth factor (NGF) on cancer pain from melanoma. Methods: The influence of HPSE on the biological function of melanoma cells and cancer pain in a mouse model was evaluated. Immunohistochemical staining was used to analyze HPSE and SDC1. HPSE, NGF, and SDC1 were detected using western blot. Inflammatory factors were detected using ELISA assay. Results: HPSE promoted melanoma cell viability, proliferation, migration, invasion, and tumor growth, as well as cancer pain, while SST0001 treatment reversed the promoting effect of HPSE. HPSE up-regulated NGF, and NGF feedback promoted HPSE. High expression of NGF reversed the inhibitory effect of HPSE down-regulation on melanoma cell phenotype deterioration, including cell viability, proliferation, migration, and invasion. SST0001 down-regulated SDC1 expression. SDC1 reversed the inhibitory effect of SST0001 on cancer pain. Conclusions: The results showed that HPSE promoted melanoma development and cancer pain by interacting with NGF/SDC1. It provides new insights to better understand the role of HPSE in melanoma and also provides a new direction for cancer pain treatment.

Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features.

Preoperative assessment of the difficulty of tracheal intubation is of great importance in anesthesia practice because failed intubation can lead to severe complications and even death. The Mallampati score is widely used as a critical assessment criterion in combination with other measures to assess the difficulty of tracheal intubation. The performance of existing methods for Mallampati classification with artificial intelligence (AI) is unreliable to the extent that the current clinical judgment of the Mallampati score relies entirely on doctors' experience. In this paper, we propose a new method for automatic Mallampati classification. Our method extracts deep features that are more favorable for the Mallampati classification task by introducing an attention mechanism into the basic deep convolutional neural network (DCNN) and then further improves the classification performance by jointly using attention-based deep features with handcrafted features. We conducted experiments on a dataset consisting of 321 oral images collected online. The proposed method has a classification accuracy of 97.50%, a sensitivity of 96.52%, a specificity of 98.05%, and an F1 score of 96.52% after five-fold cross-validation. The experimental results show that our proposed method is superior to other methods, can assist doctors in determining Mallampati class objectively and accurately, and provide an essential reference element for assessing the difficulty of tracheal intubation.