目的 探讨嵌顿性食管旁疝的诊断和外科治疗。方法 对我院手术治疗的4例嵌顿性食管旁疝患者的临床资料进行分析。结果 2例急诊剖腹探查确诊，2例经胸部X线平片和CT检查确诊； 4例均行经腹Hill胃背侧固定术，术后均无并发症，无疝复发。结论 胸部X线平片及CT检查是诊断本病的主要手段； 一旦获得诊断或高度怀疑，应及早手术治疗； 经腹Hill胃背侧固定术式简单、可靠、复发率低，适合基层医院医生掌握。
Portal hypertension (PHT) is a common complication of liver cirrhosis, which could be measured by the means of portal vein pressure (PVP). However, there is no report about an effective and reliable way to achieve noninvasive assessment of PVP so far. In this study, firstly, we collected ultrasound images and echo signals of different ultrasound contrast agent (UCA) concentrations and different pressure ranges in a low-pressure environment based on an in vitro simulation device. Then, the amplitudes of the subharmonics in the echo signal were obtained by ultrasound grayscale image construction and fast Fourier transform (FFT). Finally, we analyzed the relationship between subharmonic amplitude (SA) and bionic portal vein pressure (BPVP) through linear regression. As a result, in the pressure range of 7.5–45 mm Hg and 8–20 mm Hg, the linear correlation coefficients (LCC) between SA and BPVP were 0.927 and 0.913 respectively when the UCA concentration was 1∶3 000, and LCC were 0.737 and 0.568 respectively when the UCA concentration was 1∶6 000. Particularly, LCC was increased to 0.968 and 0.916 respectively while the SAs of two UCA concentrations were used as the features of BPVP. Therefore, the results show a good performance on the linear relationship between SA and BPVP, and the LCC will be improved by using SAs obtained at different UCA concentrations as the features of BPVP. The proposed method provides reliable experimental verification for noninvasive evaluation of PVP through SA in clinical practice, which could be a guidance for improving the accuracy of PVP assessment.
Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.
Ultrasound is the best way to diagnose thyroid nodules. To discriminate benign and malignant nodules, calcification is an important characteristic. However, calcification in ultrasonic images cannot be extracted accurately because of capsule wall and other internal tissue. In this paper, deep learning was first proposed to extract calcification, and two improved methods were proposed on the basis of Alexnet convolutional neural network. First, adding the corresponding anti-pooling (unpooling) and deconvolution layers (deconv2D) made the network to be trained for the required features and finally extract the calcification feature. Second, modifying the number of convolution templates and full connection layer nodes made feature extraction more refined. The final network was the combination of two improved methods above. To verify the method presented in this article, we got 8 416 images with calcification, and 10 844 without calcification. The result showed that the accuracy of the calcification extraction was 86% by using the improved Alexnet convolutional neural network. Compared with traditional methods, it has been improved greatly, which provides effective means for the identification of benign and malignant thyroid nodules.
Objective To explore the correlation between the imaging features of peripheral ground-glass pulmonary nodules and the invasion degree of lung adenocarcinoma, and the high risk factors for infiltrating lung adenocarcinoma under thin-slice CT, which provides some reference for clinicians to plan the surgical methods of pulmonary nodules before operation and to better communicate with patients, and assists in building a clinical predictive model for invasive adenocarcinoma. MethodsClinical data of the patients with peripheral ground-glass pulmonary nodules (diameter≤3 cm) in thin-slice chest CT in the First Affiliated Hospital of Soochow University from January 2019 to January 2020 were continuously collected. All patients underwent thin-slice CT scan and thoracoscopic surgery in our center. According to the pathological examination results, they were divided into two groups: an adenocarcinoma lesions before infiltration group, and an invasive lung adenocarcinoma group. The thin-slice CT imaging parameters of pulmonary nodules were collected. The nodular diameter, mean CT value, consolidation tumor ratio (CTR), nodular shape, vacuolar sign, bronchial air sign, lobulation sign, burr sign, lesion boundary, pleural depression sign, vascular cluster sign and other clinical data were collected. Univariate and multivariate analyses were conducted to analyze the independent risk factors for the infiltrating lung adenocarcinoma, and to analyze the threshold value and efficacy of each factor for the identification of infiltrating lung adenocarcinoma. Results Finally 190 patients were enrolled. There were 110 patients in the adenocarcinoma lesions before infiltration group, including 21 males and 89 females with a mean age of 53.57±10.90 years, and 80 patients in the invasive lung adenocarcinoma group, including 31 males and 49 females with a mean age of 56.45±11.30 years. There was a statistical difference in the mean CT value, nodular diameter, CTR, gender, smoking, nodular type, nodular shape, vacuolar sign, lobulation sign, burr sign, lesion boundary, pleural depression sign, vascular cluster sign between the two groups (P＜0.05). However, there was no statistical difference between the two groups in age (P=0.081), lesion site (P=0.675), and bronchial air sign (P=0.051). Multiple logistic regression analysis showed that nodular diameter, mean CT value, CTR and lobulation sign were independent risk factors for differentiating preinvasive adenocarcinoma from invasive adenocarcinoma. At the same time, the threshold value was calculated by Youden index, indicating that the CTR was 0.45, the nodal diameter was 10.5 mm and the mean CT value was –452 Hu. Conclusion In the peripheral ground-glass pulmonary nodules, according to the patient's CT imaging features, such as mixed ground-glass nodules, irregular shapes, vacuoles, short burrs, clear boundaries, pleural indentations, and vascular clusters, have a certain reference value in the discrimination of the invasion degree of ground-glass pulmonary nodules. At the same time, it is found in this research that peripheral ground-glass pulmonary nodules with diameter greater than 10.5 mm, CT value greater than –452 Hu, CTR greater than 0.45 and lobulation sign are more likely to be infiltrating lung adenocarcinoma.