To study the influence of maggot secretion on expression of bFGF and connective tissue growth factor(CTGF) in ulcer tissue of diabetes mell itus(DM)rats and its antibacterial function. Methods There were 40 3-month-old SD male rats (weighing 300-350 g) which were randomly divided into 2 groups: control group and experimental maggot secretion group. The model of ulcer wound of DM rats was made. The ulcer wound of DM rats in maggot secretiongroup spread maggot secretions, but no secretion on ulcer wound was found in control group. The morphological and tissue changes of ulcer wound were observed at different times, and the conditions of bacterial infection on ulcer wound in the two groups were checked. Tissue sl ices were prepared on 7, 14 and 21 days, respectively; immunohistological detection of bFGF and CTFG in ulcer wound of the two groups was done; and the cell number of positive expression of bFGF and CTFG was counted. Results It was found that the heal ing of ulcer was dominant in experimental group; the wound was clean; the tissue regenerated and no Staphylococcus aureus infection was seen. Bad heal ing was obtained in control group; tissue necrosis was found and the rate of Staphylococcus aureus infection was 60%. Positive expression cell number of bFGF in ulcer wound was detected on 7 and 14 days after operation with 23.76 ± 3.34 and 52.76 ± 4.84 in experimental group, and 18.88 ± 2.16 and 46.04 ± 4.00 in control group. Positive expression cell number of CTGF in ulcer wound was detected on 7 and 14 days after operation with 18.76 ± 3.24 and 46.52 ± 4.07 in experimental group, and 12.52 ± 3.03 and 40.52 ± 3.96 in control group. There was significant difference between positive expressions of bFGF and CTFG in the two groups (P﹤0.05). Conclusion The maggot secretion can elevate the expressions of bFGF and CTFG in ulcers, promote heal ing and prevent bacterial infection.
Fibropolycystic liver diseases (FLDs) is a rare genetic disorder, including bile duct hamartomas, congenital hepatic fibrosis, polycystic liver disease, Caroli’s disease, and choledochal cysts. Fibropolycystic liver diseases has received little clinical attention and exhibits a variety of imaging manifestations, leading to a high likelihood of missed diagnosis and misdiagnosis. Through this case, we delineate the characteristic imaging manifestations of the disease and its underlying pathological mechanisms. Our objective is to enhance readers' comprehension of the disease and thereby reduce the rate of missed diagnosis and misdiagnosis of the disease.
Objective To assess the effectiveness of the auto-continuous positive airway pressure (Auto-CPAP) versus the fixed-continuous positive airway pressure (Fixed-CPAP) in patients with obstructive sleep Apnea syndrome (OSAS). Methods Such databases as PubMed (1990 to 2010), SpringerLink (1995 to 2010), CNKI (1990 to 2010), WanFang Data (1995 to 2010), and Google academic (1994 to 2010) were searched, the relevant conference theses were retrieved, and the experts in this field were enquired to collect the randomized controlled trials (RCTs) on Auto-CPAP versus Fixed-CPAP for patients with OSAS. Two reviewers independently screened the trials according to inclusion and exclusion criteria, abstracted the data, and assessed the methodology quality. Meta-analyes was performed using RevMan 5.0 software. Results A total of 11 RCTs involving 327 patients were included. The results of meta-analyses showed that, compared with the Fixed-CPAP group after treatment, the Auto-CPAP group significantly reduced the mean effective therapeutic pressure (WMD=-1.79, 95%CI -3.39 to -0.20), won much better treatment adherence (WMD=0.43, 95%CI 0.30 to 0.56), but got much higher scores of the Apnea-hypopnea index (AHI) (WMD=1.17, 95%CI 0.25 to 2.08) and Epworth Sleepiness Scale (ESS) (WMD=0.88, 95%CI 0.42 to 1.33) as well. There was no significant difference between those two groups in patients’ subjective preference for treatment (OR=2.06, 95%CI 0.46 to 9.10). Conclusion Compared to the Fixed-CPAP, the Auto-CPAP significantly reduces the mean effective therapeutic pressure and improves the treatment adherence of the patients, but is inferior in decreasing AHI and ESS. However, more high-quality and large-scale RCTs are required to verify the above conclusion because of the limitation of research quality and sample at present.
The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.
This study aims to predict expression of Ki67 molecular marker in pancreatic cystic neoplasm using radiomics. We firstly manually segmented tumor area in multi-detector computed tomography (MDCT) images. Then 409 high-throughput features were automatically extracted and the least absolute shrinkage selection operator (LASSO) regression model was used for feature selection. After 200 bootstrapping repetitions of LASSO, 20 most frequently selected features made up the optimal feature set. Then 200 bootstrapping repetitions of support vector machine (SVM) classifier with 10-fold cross-validation were used to avoid overfitting and accurately predict the Ki67 expression. The highest prediction accuracy could achieve 85.29% and the highest area under the receiver operating characteristic curve (AUC) was 91.54% with a sensitivity (SENS) of 81.88% and a specificity (SPEC) of 86.75%. According to the results of experiment, the feasibility of predicting expression of Ki67 in pancreatic cystic neoplasm based on radiomics was verified.
Objective To analyze the clinical intervention effect of multi-disciplinary team (MDT) nursing mode on patients after transcatheter aortic valve implantation (TAVI). Methods A total of 89 patients who were admitted to our hospital and underwent TAVI surgery from April to December 2021 were selected, including 64 males and 25 females, with an average age of 64.7±11.8 years. The subjects were divided into a MDT intervention group (n=42) and a control group (n=47) according to different postoperative nursing intervention methods. Clinical effectivenesses were compared between the two groups. Results The left ventricular ejection fraction in the two groups significantly increased on the 7th day after the operation, and the increase in the MDT intervention group was more obvious, with no statistical difference between the two groups (P=0.14). On the 7th day after surgery, forced vital capacity/predicated value and forced expiratory volume in one second/predicated value significantly decreased, and decreased more significantly in the control group than those in the MDT intervention group with statistical differences (P=0.01). The ICU stay time (P=0.01), hospital stay time (P<0.01) and total postoperative pulmonary complications rate (P=0.03) in the MDT intervention group were significantly shorter or lower than those in the control group The evaluation results of the anxiety and depression status of the patients before and after nursing intervention showed that the scores of anxiety and depression in the two groups were significantly lower than before, and the scores of each scale in the MDT intervention group were lower. The score of quality of life of the two groups significantly improved at the end of 6 months after surgery, and in the MDT intervention group it was significantly higher than that in the control group (P=0.02). Conclusion MDT intervention mode can promote the rapid recovery of patients after TAVI, effectively reduce the risk of postoperative pulmonary complications, and improve the postoperative quality of life.
This study aims to predict expression of estrogen receptor (ER) in breast cancer by radiomics. Firstly, breast cancer images are segmented automatically by phase-based active contour (PBAC) method. Secondly, high-throughput features of ultrasound images are extracted and quantized. A total of 404 high-throughput features are divided into three categories, such as morphology, texture and wavelet. Then, the features are selected by R language and genetic algorithm combining minimum-redundancy-maximum-relevance (mRMR) criterion. Finally, support vector machine (SVM) and AdaBoost are used as classifiers, achieving the goal of predicting ER by breast ultrasound image. One hundred and four cases of breast cancer patients were conducted in the experiment and optimal indicator was obtained using AdaBoost. The prediction accuracy of molecular marker ER could achieve 75.96% and the highest area under the receiver operating characteristic curve (AUC) was 79.39%. According to the results of experiment, the feasibility of predicting expression of ER in breast cancer using radiomics was verified.
It is of great clinical significance in the differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) because there are enormous differences between them in terms of therapeutic regimens. In this paper, we propose a system based on sparse representation for automatic classification of PCNSL and GBM. The proposed system distinguishes the two tumors by using of the different texture detail information of the two tumors on T1 contrast magnetic resonance imaging (MRI) images. First, inspired by the process of radiomics, we designed a dictionary learning and sparse representation-based method to extract texture information, and with this approach, the tumors with different volume and shape were transformed into 968 quantitative texture features. Next, aiming at the problem of the redundancy in the extracted features, feature selection based on iterative sparse representation was set up to select some key texture features with high stability and discrimination. Finally, the selected key features are used for differentiation based on sparse representation classification (SRC) method. By using ten-fold cross-validation method, the differentiation based on the proposed approach presents accuracy of 96.36%, sensitivity 96.30%, and specificity 96.43%. Experimental results show that our approach not only effectively distinguish the two tumors but also has strong robustness in practical application since it avoids the process of parameter extraction on advanced MRI images.
Kidney tumor is one of the diseases threatening human health. Ultrasound is widely applied in kidney tumor diagnosis due to its high popularization, low price and no radiation. Accurate segmentation of kidney tumor is the basis of precise treatment. Kidney tumors often grow in the middle of cortex, so that segmentation is easy disturbed by nearby organs. Besides, ultrasound images own low contrast and large speckle, leading to difficult segmentation. This paper proposed a novel kidney tumor segmentation method in ultrasound images using adaptive sub-regional evolution level set models (ASLSM). Regions of interest are firstly divided into subareas. Secondly, object function is designed by integrating inside and outside energy and gradient, in which the ratio of these two parts are adjusted adaptively. Thirdly, ASLSM adapts convolution radius and curvature according to centroid principle and similarity inside and outside zero level set. Hausdorff distance (HD) of (8.75 ± 4.21) mm, mean absolute distance (MAD) of (3.26 ± 1.69) mm, dice-coefficient (DICE) of 0.93 ± 0.03 were obtained in the experiment. Compared with traditional ultrasound segmentation method, ASLSM is more accurate in kidney tumor segmentation. ASLSM may offer convenience for doctor to locate and diagnose kidney tumor in the future.
ObjectiveTo systematically review the prevalence and risk factors of the chronic post-cesarean section pain (CPCSP). MethodsPubMed, EMbase, The Cochrane Library, CINAHL, PsycInfo, CBM, WanFang Data, VIP, and CNKI databases were electronically searched to collect studies on the prevalence and risk factors of CPCSP from inception to August 2021. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies. Meta-analysis was then performed using Stata 15.1 software. ResultsA total of 43 studies involving 12 435 participants were included. The results of meta-analysis showed that the prevalence of CPCSP for 2 to 5 months, 6 to 11 months, and at least 12 months were 19% (95%CI 15% to 23%), 13% (95%CI 9% to 17%), and 8% (95%CI 6% to 10%), respectively. The risk factors included preoperative pain present elsewhere, postoperative severe acute pain, low abdominal transverse incision, non-intrathecal administration of morphine, preoperative anxiety, postpartum depression, etc. ConclusionsThe current evidence shows that the overall prevalence of CPCSP is high. Preoperative pain presents elsewhere, postoperative severe acute pain, low abdominal transverse incision, non-intrathecal administration of morphine, preoperative anxiety and postpartum depression may increase the risk of CPCSP.