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Dexmedetomidine takes away early on brain injury subsequent upsetting injury to the brain through curbing autophagy along with neuroinflammation with the ROS/Nrf2 signaling pathway.
Objective This study aims to review the histopathologic results of tonsillectomy specimens, determine the rates of the tonsillectomy indications, and investigate the characteristics of asymmetric hypertrophy. Materials and Methods Medical records of 484 patients who underwent tonsillectomy were reviewed retrospectively. Descriptive data of adult and pediatric patients were presented as percentage. Comparisons between asymmetric and symmetric hypertrophy groups were performed to determine the features of asymmetric hypertrophy. Results The mean age of 484 patients who underwent tonsillectomy was 13 years (range 3-69 years). While 372 (76.85%) patients were operated for infection, 100 (20.66%) were operated for tonsillar hypertrophy, 1 (0.21%) for a suspicion of malignancy, and 11 (2.27%) for other various reasons. Asymmetric hypertrophy was seen in 25 (5.16%) patients, whereas symmetric hypertrophy was seen in 75 (15.49%) patients. Malignancy was detected in three (0.61%) adult patients with asymmetric hypertrophy. Tonsillar tuberculosis was observed in one foreign patient with asymmetric hypertrophy. Veliparib The presence of malignancy was higher in the asymmetric hypertrophy group (three patients [12%]) compared with the symmetric hypertrophy group (none) (p=0.002; X2=9.27). Median maximum specimen diameter was 3 cm (range 1.15-5.5 cm) in the asymmetric hypertrophy group and 2.4 cm (range 1.25-4.8 cm) in the symmetric hypertrophy group (p=0.08). The Friedman grade was significantly (p less then 0.001), positively, and strongly (r=0.885) correlated with the maximum specimen diameter. Conclusion Routine histopathologic examination of the tonsillectomy specimens might not be necessary for all patients, but it is recommended for the patients with a real asymmetry. Copyright © 2020, Kayabasi et al.Introduction The cemented polyethylene cup has remained the standard acetabular implant for 50 years although there has been little research into cementing techniques. In the past, cement was previously inserted by sequential manual pressurisation (thumbing) but this technique was prone to contamination of the cement leading to weakening of fixation. In recent times, third-generation techniques using sealed pressurisation with rim preparation have been espoused with similar results. We were interested in establishing whether repeated cycles of compression of cement allowing adequate time for relaxation increases its depth of penetration, and the optimum period of relaxation required to achieve this goal. Method A single mix of polymethylmethacrylate cement at dough stage was inserted into a model of the reamed acetabulum. Cyclical pressurisation of the cement with 50 N followed nine different patterns to simulate thumbing, constant pressure, and the application of a sealed and unsealed acetabular cup implant. Results A constant load was as effective as all variations of repeated cycles of load and relaxation except for 50 N pressure applied for four seconds with four second intervals. A four second interval of relaxation achieved significantly more penetration than five or three seconds. Following two minutes of constant pressure, the application of a sealed or unsealed thrust of the plunger had no effect on cement penetration. Conclusion This study suggests that optimal polymethylmethacrylate cement penetration into the acetabulum occurs with cycled application of load for four seconds followed by four seconds of relaxation. The subsequent pressurisation with either a flanged or unflanged acetabular implant does not appear to improve cement penetration. Copyright © 2020, Rocos et al.OBJECTIVE Autonomic Dysreflexia (AD) is a potentially life-threatening syndrome which occurs in individuals with higher level spinal cord injuries (SCI). AD is caused by triggers which can lead to rapid escalation of pathophysiological responses and if the trigger is not removed, AD can be fatal. There is currently no objective, non-invasive and accurate monitoring system available to automatically detect the onset of AD symptoms in real time in a non-clinical setting. Technology or Method We developed a user-independent method of symptomatic AD detection in real time with a wearable physiological telemetry system (PTS) and a machine learning model using data from eleven participants with SCI. RESULTS The PTS could detect onset of AD symptoms with an average accuracy of 94.10% and a false negative rate of 4.89%. CONCLUSIONS The PTS can detect the onset of the symptoms AD with high sensitivity and specificity to assist people with SCIs in preventing the occurrence of AD. It would enable persons with high level SCIs to be more independent and pursue vocational activities while granting continuous medical oversight. Clinical Impact The PTS could serve as a supplementary tool to current solutions to detect the onset of AD and prepare individuals who are newly injured to be better prepared for AD episodes. Moreover, it could be translated into a system to encourage individuals to practice better healthcare management to prevent future occurrences.BACKGROUND Cardiovascular diseases (CVD) are the leading cause of death globally. Electrocardiogram (ECG) analysis can provide thoroughly assessment for different CVDs efficiently. We propose a multi-task group bidirectional long short-term memory (MTGBi-LSTM) framework to intelligent recognize multiple CVDs based on multi-lead ECG signals. METHODS This model employs a Group Bi-LSTM (GBi-LSTM) and Residual Group Convolutional Neural Network (Res-GCNN) to learn the dual feature representation of ECG space and time series. GBi-LSTM is divided into Global Bi-LSTM and Intra-Group Bi-LSTM, which can learn the features of each ECG lead and the relationship between leads. Then, through attention mechanism, the different lead information of ECG is integrated to make the model to possess the powerful feature discriminability. Through multi-task learning, the model can fully mine the association information between diseases and obtain more accurate diagnostic results. In addition, we propose a dynamic weighted loss function to better quantify the loss to overcome the imbalance between classes. RESULTS Based on more than 170,000 clinical 12-lead ECG analysis, the MTGBi-LSTM method achieved accuracy, precision, recall and F1 of 88.86%, 90.67%, 94.19% and 92.39%, respectively. The experimental results show that the proposed MTGBi-LSTM method can reliably realize ECG analysis and provide an effective tool for computer-aided diagnosis of CVD.
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