Objective To investigate the correlated risk factors of deep venous thrombosis (DVT) after the laparoscopic surgery. Methods Clinical records of 16 patients with DVT and 148 patients without DVT after laparoscopic surgery in Huashan hospital from Mar.2007 to Jan.2008 were analyzed retrospectively. Results There were several factors which could induce DVT after the laparoscopic surgery, including operation time ＞1 h 〔OR=4.15 （95% CI： 1.36-12.68）〕, operative site located in hypogastrium 〔OR=2.94 （95% CI： 1.07-8.08）〕 and the number of high risk factors ≥3 〔OR=3.94 （95% CI： 1.38-11.23）〕. Conclusions The long time of operation， hypogastric operation of laparoscopic surgery and high risk factors could induce DVT. Prevention measures should be made in preoperative period.
Objective To study the effect of interleukin-6,10 (IL-6,10), C-reactive protein (CRP), and fibrinogen (FIB) on inflammatory response of lower limbs deep vein thrombosis (DVT). Methods Thirty patients with acute lower limb DVT (DVT group) and 30 volunteers (normal control group) were included in this study, and then the concentrations of serum IL-6, IL-10, CRP, and FIB were detected. Results The concentrations of serum IL-6, IL-10, CRP, and FIB of patients in DVT group before treatment were higher than those in normal control group (Plt;0.001). Compared with before treatment, the concentrations of serum IL-6, CRP, and FIB of patients after treatment were lower in DVT group (Plt;0.001), however, the concentration of serum IL-10 was higher (Plt;0.001). There was no difference of the concentrations of serum FIB between DVT group after treatment and normal control group (Pgt;0.05), but the concentrations of serum IL-6, IL-10, and CRP of patients in DVT group after treatment were higher than those in normal control group (Plt;0.05). Conclusion Inflammatory factors may involve in DVT. Therein IL-6, CRP, and FIB play important roles in acute stage of DVT, and IL-10 may have an anti-inflammatory effect.
ObjectiveTo investigate the technique of optimizing the location of femoral attachment in medial patellofemoral ligament (MPFL) reconstruction assisted with arthroscopy and evaluate the effectiveness.MethodsBetween January 2014 and September 2018, 35 patients with patellar dislocation were admitted. There were 14 males and 21 females with an average age of 22.6 years (range, 16-38 years). All patients had a history of knee sprain. The disease duration ranged from 1 to 7 days (mean, 2.8 days). Patellar dislocation occurred 2-4 times (mean, 2.5 times). The preoperative Lysholm score and Kujala score were 47.60±11.24 and 48.37±9.79, respectively. The patellar congruence angle was (31.40±6.81)°, the patellar tilt angle was (29.95±5.44)°, the lateral patellofemoral angle was (−11.46±5.18)°, and the tibial tubercle-trochlear groove distance was (16.66±1.28) mm. All patients were treated by MPFL reconstruction with the semitendinosus tendon under arthroscopy. During operation, the suture anchors were inserted into the midpoint and the 1/3 point of superomedial edge of the patella. Then, the femoral tunnels were created in medial femoral condyle through limited excision. For tendon fixation, the Kirschner wires were inserted into adductor tubercle, medial epicondyle of femur, and the midpoint between the two points, as well as the anteriorly and posteriorly. Afterwards, the changes of ligament length and tension, patellar tracking, and the relationship of patella and femoral trochlea were evaluated, thereby determining the optimized femoral attachment for MPFL reconstruction. Finally, the patellar congruence angle, patellar tilt angle, and lateral patellofemoral angle were measured by imaging to assess the relationship of patella and femoral trochlea. Moreover, Lysholm score and Kujala score were used to evaluate the knee joint function.ResultsAll incisions healed by first intention without infection. All patients were followed up 12-18 months (mean, 15.4 months). At 12 months, the Lysholm score was 94.40±3.99 and the Kujala score was 92.28±4.13, which were significant higher than those before operation (P<0.05). No patellar dislocation occurred during follow-up. At 12 months, the patellar congruence angle was (6.57±4.59)°, the patellar tilt angle was (9.73±2.82)°, the lateral patellofemoral angle was (7.14±4.63)°, which were superior to those before operation (P<0.05).ConclusionDuring the MPFL reconstruction under arthroscopy, a higher positioning accuracy for the femoral attachment and satisfactory effectiveness can be obtained by evaluating MPFL length and tension, patellofemoral joint kinematics, and patellar tracking.
Objective To assess the combined management of lower limb chronic venous diseases according to the CEAP classification. Methods One hundred and twenty patients were classified according to the CEAP classification. Based on clinical presentation and image study, all patients were treated with combined management plan including oppression, medication and surgery. Results All 120 patients (135 limbs) were followed up in clinic, the local recurrence rate was 18.52%(25/135). Conclusion CEAP classification expounds the developing process of lower limb chronic venous diseases. With CEAP, we can avoid the blind spot in the treatment and expand the extent of combined therapy. Accordingly, CEAP classification is useful in the treatment and diagnosis of chronic venous diseases.
ObjectiveTo detect expressions of cell programmed death ligand 1 (PD-L1) and adenosine 2a receptor (A2aR) proteins in colorectal cancer tissues and investigate its relationship with clinicopathologic features of patients with colorectal cancer.MethodsThe colorectal cancer tissues and corresponding paracancerous tissues of 106 patients with colorectal cancer were collected, the patients underwent surgery in the Affiliated Hospital of Xuzhou Medical University from August 2013 to August 2015. The immunohistochemical staining was used to detect the expressions of A2aR and PD-L1 proteins.ResultsThe positive rates of A2aR and PD-L1 protein expression in the colorectal cancer tissues were significantly higher than those in the corresponding paracancerous tissues, respectively [A2aR: 74 (69.8%) versus 35 (33.0%), χ2=28.721, P<0.001; PD-L1: 57 (53.8%) versus 28 (26.4%), χ2=16.516, P<0.001], which in the colorectal cancer tissues were correlated with the Broders grading (A2aR: χ2=9.198, P=0.010; PD-L1: χ2=8.354, P=0.015), T staging (A2aR: χ2=6.737, P=0.009; PD-L1: χ2=6.437, P=0.011), and TNM staging (A2aR: χ2=4.884, P=0.027; PD-L1: χ2=8.246, P=0.004) and were not correlated with the gender, age, tumor portion, lymph node metastasis and CA19-9 (P>0.05), but the positive rates of A2aR protein expression were correlated with the tumor diameter (χ2=4.386, P=0.036) and CEA positive (χ2=6.315, P=0.012), and the positive rates of PD-L1 protein expression were not correlated with them (P>0.05). The expression of PD-L1 protein was positively correlated with the expression of A2aR in the colorectal cancer tissues (rs=0.237, P=0.027).ConclusionPD-L1 and A2aR protein expressions are higher in colorectal cancer tissues, it is provided that both of them might play an important role in promoting occurrence and development of colorectal cancer.
When performing eye movement pattern classification for different tasks, support vector machines are greatly affected by parameters. To address this problem, we propose an algorithm based on the improved whale algorithm to optimize support vector machines to enhance the performance of eye movement data classification. According to the characteristics of eye movement data, this study first extracts 57 features related to fixation and saccade, then uses the ReliefF algorithm for feature selection. To address the problems of low convergence accuracy and easy falling into local minima of the whale algorithm, we introduce inertia weights to balance local search and global search to accelerate the convergence speed of the algorithm and also use the differential variation strategy to increase individual diversity to jump out of local optimum. In this paper, experiments are conducted on eight test functions, and the results show that the improved whale algorithm has the best convergence accuracy and convergence speed. Finally, this paper applies the optimized support vector machine model of the improved whale algorithm to the task of classifying eye movement data in autism, and the experimental results on the public dataset show that the accuracy of the eye movement data classification of this paper is greatly improved compared with that of the traditional support vector machine method. Compared with the standard whale algorithm and other optimization algorithms, the optimized model proposed in this paper has higher recognition accuracy and provides a new idea and method for eye movement pattern recognition. In the future, eye movement data can be obtained by combining it with eye trackers to assist in medical diagnosis.
Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.