Notes

Spinal Mix together with Sacral Alar Iliac Pelvic Fixation inside Extreme Neuromuscular Scoliosis.
hypertension on left maximum-baseline (t=-4.530, P less then 0.01) was statistically significant. The influence of hypertension on right maximum-baseline was statistically significant (t=-4.629,P less then 0.01). Conclusion Preoperative diabetes and hypertension are factors significantly affecting the intraoperative rSctO2 of elderly patients with laparoscopic hysterectomy.Objective To investigate the outcome of hip fracture patients associated with hyponatremia. Methods From January 2012 to December 2016, the data of 1 001 elderly patients with hip fracture treated in the Seventh Medical Center of PLA General Hospital were analyzed retrospectively. According to the level of serum sodium, the patients were divided into hyponatremia group (sodium less then 135 mmol/L) and non-hyponatremia group (sodium≥135 mmol/L), in which≥130-135 mmol/L was mild hyponatremia, ≥125-130 mmol/L was moderate hyponatremia, and less then 125 mmol/L was severe hyponatremia. find more The length of hospital stay, incidence of complications and mortality were compared between patient with hyponatremia and without; and the above three indexes between patients with mild hyponatremia and moderate severe hyponatremia were also analyzed. Results There were 126 patients with hyponatremia, including 98 with mild hyponatremia (9.8%, 98/1 001), 18 with moderate hyponatremia (1.8%, 18/1 001), and 10 with severe hyponatremvely; only the difference for 30 days mortality was statistically different between two groups (χ²=4.278, P=0.039). The length of hospital stay for mild hyponatremia patients were 11 (9,16) d, and it was 12(10,18) d in patients with moderate and severe hyponatremia patients, and there was no significant difference between the two groups (Z=1.613, P=0.107). The incidence of complications was 22.9% (200/875) in non-hyponatremia group and 32.5%(41/126) in hyponatremia group, and there was significant difference between the two groups (χ²=5.649, P=0.017). Conclusions Compared with non-hyponatremia, patients with hyponatremia have higher incidence of perioperative complications, longer hospital stay and higher mortality. With the increasing degree of hyponatremia, the above indicators tend to be serious.Objective To investigate the diagnostic efficacy and potential application value of deep learning-based chest CT auxiliary diagnosis system in emergency trauma patients. Methods A total of 403 patients, including 254 males and 149 females aged from 16 to 100 (50±19) years, who received emergency treatment for trauma and chest CT examination in the Eastern Theater General Hospital from September 2019 to November 2019 were retrospectively analyzed. Dr. Wise Lung Analyzer's chest CT auxiliary diagnosis system was applied to detect 5 types of injuries, including pneumothorax, pleural effusion/hemothorax, pulmonary contusion (shown as consolidation and ground glass opacity), rib fractures, and other fractures (including thoracic vertebrae, sternum, scapula and clavicle, etc.) and 6 other abnormalities (bullae, emphysema, pulmonary nodules, stripe, reticulation, pleural thickening). The diagnostic reference standards were labeled by two radiologists independently. The sensitivity and specificity of the auxiliary diulation and pleural thickening. Conclusions The deep learning-based chest CT auxiliary diagnosis system could effectively assist chest CT to detect injuries in emergency trauma patients, which was expected to optimize the clinical workflow.Objective To evaluate the diagnostic value of the lung nodule classification and segmentation algorithm based on deep learning among different CT reconstruction algorithms. Methods Chest CT of 363 patients from June 2019 to September 2019 in Radiology Department of Tianjin Medical University Chu Hsien-I Memorial Hospital were retrospectively collected in this study, each of which consisted of images by three different reconstruction methods (lung reconstruction, mediastinal reconstruction, bone reconstruction).These collected data were used as testing set and a total of 4 185 Chest CTs including the public data set and the constructed private data set were used as the training set. A model combines 3D deep convolutional neural network and recurrent neural network under a multi-task joint learning algorithm for lung nodule classification and segmentation were constructed. The well-trained method was tested on 363 test cases using two metrics, i.e., the accuracy of the density classification and the Dice coeffih 3D convolutional neural network and recurrent neural network has demonstrated relatively stable in classification and segmentation of lung nodules under different CT reconstruction method.Objective To investigate the segmentation effects of the deep learning method on CT in the arterial phase and venous phase respectively by using subjective and objective evaluation system, and to investigate the factors that affect the difference between arterial phase and venous phase pancreas segmentation and the related factors affecting the venous pancreas segmentation. Method A total of 218 cases of pancreatic CT scan data in the Department of Radiology of Peking Union Medical College Hospital from January to November 2019 were retrospectively collected. Each case contained images of arterial and venous phases, and the data were randomly divided into training set (139 cases), validation set (20 cases) and test set (59 cases) according to the ratio of the training and verification set to the test set of 7∶3. The two-stage global local progressive fusion network was trained on the training set, the model parameters of the optimal segmentation effect were found on the validation set, and the test set was pr 0.05). Conclusion Dual-phase CT was used to construct a deep learning automatic pancreas segmentation model, and the segmentation effect was evaluated subjectively and objectively. Subjective evaluation was helpful to improve the ability to segment the critical regions of the pancreas in the future.Objective To investigate the role of artificial intelligence-based coronary CT blood flow reserve score (FFRCT) in assessing hemodynamic relevance in patients with deep myocardial bridge (MB) of the left anterior descending coronary artery. Methods A total of 113 patients diagnosed with deep MB of the left anterior descending coronary artery by coronary CT angiography (CCTA) at the Department of Radiology of Tongji Hospital Affiliated to Tongji University from January 2017 to December 2019 were retrospectively analyzed. The location, length, depth, and degree of systolic compression of the MB were measured. The artificial intelligence-based coronary FFRCT software was employed to calculate the FFRCT value of the deep MB of the left anterior descending coronary artery. With the boundary of 0.80, all patients were divided into FFRCT normal group (FFRCT>0.80) and FFRCT abnormal group (FFRCT≤0.80), and the relationship between FFRCT abnormality and the location, length, depth, and degree of systolic stenosis of the deep MB of the left anterior descending branch was analyzed.
Homepage: https://www.selleckchem.com/products/darapladib-sb-480848.html