Objective:To evaluate the effectiveness of individual operative design for unilateral cleft lip repair.Methods:Individual operation design was applied for the repair of unilateral cleft lip in 40 cases.The operations were conducted according the designs and the secondary nasal deformities were corrected at the same time.One week after operation,dermal sutures were removed,the effects were evaluated by 3 professional doctors.7 labial morphometric lines were measured.The ratio of the line length on healthy side to the correspondence on the cleft side(RLL)were calculated for objective evaluation.Results:First intention was found in all cases,the labial bow of both sides was symmetried after operation.In dynamic state and static state,the effects of the operations were satisfactory.RLL of 0.91-1.10 of the 7 lines was observed in 67.5%-95.0% of the patients.Conclusion:The individual operation design is feasible for unilateral cleft lip repair.

Objective To summarize the experience in endovascular repair of De Bakey type Ⅲ aortic dissection in recent years and summarize the prevention and management of the related perioperative complications.Methods From January 2009 to January 2011,49 cases of endovascular repair for De Bakey type Ⅲ aortic dissection were performed under general anesthesia in our department.There were 45 male and 4 female.The follow-up was performed in the outpatient department or by telephone.Results There was no inhospital death and no paraplegia events.Severe complication included:coma,2 cases ( 4.1％ ) ; endoleak,2 cases (4.1％ ) ; upper limb ischemia,2 cases (4.1％ ).Recurrent proximal aortic dissection,1 case.Fever was occurred in most of those cases.Conclusion Endovascular repair of aortic dissection improves the outcome of aortic dissection patients.But more attention should be pay to prevent the severe complications,It will help to improve the prognosis and life quality by reducing the risk of retrograde dissection,acute brain ischemia and endoleak.

Objective To evaluate the value of MRI in qualitative and quantitative diagnosis of bone marrow involvement in patients with lymphoma. Methods 28 diagnosed lymphoma with bone marrow infiltration and 31 healthy spines as controls were included.MRI performance and the signal intensity ratio on T1WI (SIR1) of the spine bone marrow and cerebrospinal fluid were analyzed and statistical analyses were performed.Results Qualitative diagnosis among these patients,was shown than MRI results indicated that 27 cases had abnormal signal. The sensitivity of MRI was 96.4 ％. There were four main patterns of marrow infiltration in MRI,containing nodular pattern 21.4 ％ (6/28),scattered pattern 53.6 ％ (15/28),mottled pattern 14.3 ％ (4/28) and uniform pattern 7.1％ (2/28) respectively.By quantitative diagnosis the SIR1 of study group (1.251±0.253) was apparently lower than that in the control group (2.625±0.434) with statistical significance (t =15.022,P ＜ 0.001).The results of multiple comparisons showed that the SIR1 of mild degree (1.390±0.172),moderate degree (0.982±0.790) and severe degree (0.908±0.122) patients with lymphoma compared with the normal controls had significant differences (all P =0.000),and difference between mild and moderate degree had statistical significance (LSD,P =0.012),so did the difference between mild and severe degree (LSD,P =0.025).However,no significant difference could be seen between moderate and severe degree (LSD, P =0.757). Positive linear correlation was existed between the persentages of SIR1 and tumor cells in bone marrow (r =-0.765, P ＜ 0.001). Conclusion As a noninvasive and direct-viewing technique, MRI presents a global view of bone marrow with high sensitivity in detecting bone marrow involvement in lymphoma. To a certain extent, it could be possible to estimate the infiltration degree and evaluation of the tumor burden in bone marrow by quantitative measurement of MRI.

The research of sleep staging is an important basis of evaluating sleep quality and diagnosing diseases. In order to achieve automatic sleep staging, we proposed a new method which combines with principal component analysis (PCA) and support vector machine (SVM) for automatic sleep staging. Firstly, we used PCA to reduce dimension of time-frequency-space domains and nonlinear dynamical characteristics of sleep EEG from 5 subjects to reduce data redundancy. Secondly, we used 1-a-1 SVM to classify sleep stages. The results showed that the correct rate can reach 89.9%, which was better than those of many other similar studies.
Electroencephalography ; Humans ; Nonlinear Dynamics ; Principal Component Analysis ; Sleep Stages ; Support Vector Machine

We applied Lempel-Ziv complexity (LZC) combined with brain electrical activity mapping (BEAM) to study the change of alertness under sleep deprivation in our research. Ten subjects were involved in 36 hours sleep deprivation (SD), during which spontaneous electroencephalogram (EEG) experiments and auditory evoked EEG experiments-Oddball were recorded once every 6 hours. Spontaneous and evoked EEG data were calculated and BEAMs were structured. Results showed that during the 36 hours of SD, alertness could be divided into three stages, i. e. the first 12 hours as the high stage, the middle 12 hours as the rapid decline stage and the last 12 hours as the low stage. During the period SD, LZC of Spontaneous EEG decreased over the whole brain to some extent, but remained consistent with the subjective scales. By BEAMs of event related potential, LZC on frontal cortex decreased, but kept consistent with the behavioral responses. Therefore, LZC can be effective to reflect the change of brain alertness. At the same time LZC could be used as a practical index to monitor real-time alertness because of its simple computation and fast calculation.
Attention ; physiology ; Brain Mapping ; Electroencephalography ; Evoked Potentials ; Humans ; Nonlinear Dynamics ; Sleep Deprivation

Objective: To investigate the differences of gemstone spectral curve and CT value of gastric cancer with different pathological types and differentiation degrees. Methods: 91 cases of preoperative gemstone CT images with gastric cancer were collected, including 24 cases of mucinous carcinoma, 67 cases of non-mucinous carcinoma, 16 cases of signet ring cell carcinoma, 8 cases of mucinous adenocarcinoma, 32 cases of moderately differentiated adenocarcinoma and 35 cases of poorly differentiated adenocarcinoma. Gemstone CT spectral imaging was performed preoperatively, and the spectral curve of the lesion in venous phase was obtained by using GSI Viewer software, the slope of the curve was calculated, and 11 monoenergetic CT values of 40~140 keV (10 keV interval) were measured. The gemstone spectral curves and CT values of gastric cancer with different pathological types and differentiation degrees are compared. Results: The curve slopes of non-mucinous carcinoma, signet ring cell carcinoma and poorly differentiated adenocarcinoma were -1.92±0.53, -1.73±0.37 and -2.14±0.54, respectively. The absolute values were higher than those of mucinous carcinoma (-1.45±0.54), mucinous adenocarcinoma (-0.90±0.34) and moderately differentiated adenocarcinoma (-1.67±0.41), and the differences were all statistically significant (P<0.05). There were significant differences in monoenergetic CT values between mucinous and non-mucinous carcinomas at 40-140 keV (all P<0.05). The former was lower than the latter in different degrees, and the lower the energy, the greater the difference was. There were significant differences in monoenergetic CT values between signet ring cell carcinoma and mucinous adenocarcinoma at 40-100 keV (all P<0.05); monoenergetic CT values between poorly differentiated adenocarcinoma and moderately differentiated adenocarcinoma at 40-90 keV showed statistically significant differences (P<0.05). Conclusions Gastric cancer with different pathological types and differentiation degrees have their characteristic spectral curves in venous phase, and the monoenergetic CT values are significantly different at low energy. The spectral curve of gemstone CT may be helpful to evaluate the pathological type and differentiation degree of gastric cancer before operation.

Objective:To investigate the value of preoperative prediction of Ki-67 expression status in breast cancer based on multi-phase enhanced MRI combined with clinical imaging characteristics prediction model.Methods:This study was retrospective. A total of 213 breast cancer patients who underwent surgical treatment at Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between June 2016 and May 2017 were enrolled. All patients were female, aged 24-78 (51±10) years, and underwent routine breast MRI within 2 weeks prior to surgery. According to the different Ki-67 expression of postoperative pathological results, patients were divided into high expression group (Ki-67≥20%, 153 cases) and low expression group (Ki-67<20%, 60 cases). The radiomic features of breast cancer lesions were extracted from phase 2 (CE-2) and phase 7 (CE-7) images of dynamic contrast enhanced (DCE)-MRI, and all cases were divided into training and test sets according to the ratio of 7∶3. The radiomic features were first selected using ANOVA and Wilcoxon signed-rank test, followed by the least absolute shrinkage and selection operator method regression model. The same method of parameters selection was applied to clinical information and conventional imaging features [including gland classification, degree of background parenchymal enhancement, multifocal/multicentric, lesion location, lesion morphology, lesion long diameter, lesion short diameter, T 2WI signal characteristics, diffusion-weighted imaging (DWI) signal characteristics, apparent diffusion coefficient (ADC) values, time-signal intensity curve type, and axillary lymph nodes larger than 1 cm in short axis]. Support vector machine (SVM) was then used to construct prediction models for Ki-67 high and low expression states. The predictive performance of the models were evaluated using receiver operating characteristic (ROC) curves and area under cueve(AUC). Results:Totally 1 029 radiomic features were extracted from CE-2 and CE-7 images, respectively, and 9 and 7 best features were obtained after selection, respectively. And combining the two sets of features for a total of 16 features constituted the CE-2+CE-7 image best features. Five valuable parameters including lesion location, lesion short diameter, DWI signal characteristics, ADC values, and axillary lymph nodes larger than 1 cm in short axis, were selected from all clinical image features. The SVM prediction models obtained from the radiomic features of CE-2 and CE-7 images had a high AUC in predicting Ki-67 expression status (>0.70) in both the training set and the test set. The models were constructed by combining the CE-2, CE-7, and CE-2+CE-7 radiomic features with clinical imaging features, respectively, and the corresponding model performance in predicting Ki-67 expression status was improved compared with the models obtained by using the CE-2, CE-7, and CE-2+CE-7 radiomic features alone. The SVM prediction model obtained from CE-2+CE-7 radiomic features combined with clinical imaging features had the best prediction performance, with AUC of 0.895, accuracy of 84.6%, sensitivity of 87.9%, and specificity of 76.2% for predicting Ki-67 expression status in the training set and AUC of 0.822, accuracy of 70.3%, sensitivity of 76.1%, and specificity of 55.6% in test sets.Conclusion:The SVM prediction model based on DCE-MRI radiomic features can effectively predict Ki-67 expression status, and the combination of radiomic features and clinical imaging features can further improve the model prediction performance.

Keratinized mucosa in oral cavity plays an important role in periodontal health. The defect of keratinized mucosa may increase the risks of complication of oral implant surgery and restoration. Graft of keratinized tissue and connective tissue are still the gold standard for treating keratinized mucosa defect now. The current research focus on how to modulate non-keratinized mucosa to highly-efficient and minimally-invasive keratinized mucosa. Keratinocytes are critical components of oral mucosa and its final differentiation into keratinized mucosa is controlled by the connective tissue microenvironment involving a variety of molecules and ions. To fully understand keratinized differentiation of keratinocyte, this review focuses on its influence factors and possible mechanisms under the differentiation.