Elevated Intraocular Pressure Moderated Brain Morphometry in High-tension Glaucoma: a Structural MRI Study.

High-tension glaucoma (HTG) is one of the most common forms of primary open angle glaucoma. The purpose of this study was to assess in HTG brain, whether the elevated intraocular pressure (IOP) had an effect on the brain morphological alterations via structural MRI. We acquired T1WI structural MRI images from 56 subjects including 36 HTG patients and 20 healthy controls. We tested whether the brain morphometry was associated with the mean IOP in HTG patients. Moreover, we conducted moderation analysis to assess the interactions between subject type (HTG - healthy controls) and IOP. In HTG group, cortical thickness was negatively correlated with the mean IOP in the left rostral middle frontal gyrus, left pars triangularis, right precentral gyrus, left postcentral gyrus, left superior temporal gyrus (p < 0.05, FDR corrected). Four of the five regions negatively correlated with mean IOP showed reduced cortical thickness in HTG group compared with healthy controls, which were the left rostral middle frontal gyrus, left pars triangularis, left postcentral gyrus and left superior temporal gyrus (p < 0.05, FDR corrected). IOP moderated the interaction between subject type and cortical thickness of the left rostral middle frontal gyrus (p = 0.0017), left pars triangularis (p = 0.0011), left postcentral gyrus (p = 0.0040) and left superior temporal gyrus (p = 0.0066). Elevated IOP may result brain morphometry alterations such as cortical thinning. The relationship between IOP and brain morphometry underlines the importance of the IOP regulation for HTG patients.

Segmentation of lung nodules based on a refined segmentation network.

BACKGROUND: Accurate segmentation of lung nodules is of great significance for early screening and diagnosis of lung cancer. PURPOSE: However, the heterogeneity of lung nodules and the similarities between them and other lung tissues make it difficult to accurately segment these nodules. As regards the use of deep learning to segment lung nodules, convolutional neural networks would gradually lead to errors accumulating at the network layer due to the presence of multiple upsampling and downsampling layers, resulting in poor segmentation results. METHODS: In this study, we developed a refined segmentation network (RS-Net) for lung nodule segmentation to solve this problem. Accordingly, the proposed RS-Net was first used to locate the core region of the lung nodules and to gradually refine the segmentation results of the core region. In addition, to solve the problem of misdetection of small-sized nodules owing to the imbalance of positive and negative samples, we devised an average dice-loss function computed on nodule level. By calculating the loss of each nodule sample to measure the overall loss, the network can address the misdetection problem of lung nodules with smaller diameters more efficiently. RESULTS: Our method was evaluated based on 1055 lung nodules from Lung Image Database Consortium data and a set of 120 lung nodules collected from Shanghai Chest Hospital for additional validation. The segmentation dice coefficients of RS-Net on these two datasets were 85.90% and 81.13%, respectively. The analysis of the segmentation effect of different properties and sizes of nodules indicates that RS-Net yields a stable segmentation effect. CONCLUSIONS: The results show that the segmentation strategy based on gradual refinement can considerably improve the segmentation of lung nodules.

Correlation between the CEM imaging characteristics and different molecular subtypes of breast cancer.

PURPOSE: To investigate the correlation between the contrast-enhanced mammography (CEM) imaging characteristics and different molecular subtypes of breast cancer (BC). METHODS: We retrospectively included 313 eligible female patients who underwent CEM examination and surgery in our hospital from July 2017 to July 2021. Their lesions were confirmed on histopathological examination and immunohistochemical analysis. BC was divided into luminal A, luminal B, HER2-enriched, and triple-negative BC (TNBC) subtypes according to immunohistochemical markers. Nine features were extracted from CEM images, including tumor shape, margins, spiculated mass, lobulated mass, malignant calcification, lesion conspicuity, internal enhancement pattern, multifocal mass, and swollen axillary lymph nodes. Statistical analysis was performed using SPSS 25.0. Univariate analysis and binomial regression were used to analyze the correlation between CEM imaging features and BC molecular subtypes. RESULTS: There were 184 (58.8 %) Luminal A, 44 (14.1 %) Luminal B, 47 (15.0 %) HER-2-enriched and 38 (12.1 %) TNBC, respectively. Molecular subtypes were significantly related to the tumor shape, margins, spiculated mass, internal enhancement pattern, malignant calcification and swollen axillary lymph nodes. Spiculated and calcified tumors were associated with Luminal subtypes, especially Luminal B (P < 0.05). Irregular tumor shape and malignant calcification were associated with HER-2-enriched subtype (P < 0.05). Oval or round tumor shape, rim enhancement, and swollen axillary lymph nodes were associated with TNBC (P < 0.05). CONCLUSION: CEM imaging features could distinguish BC molecular subtypes. In particular, TNBC showed oval or round tumor shape, rim enhancement, and swollen axillary lymph nodes, providing insights into the diagnosis and prognosis of TNBC.

Molecular-subtype guided automatic invasive breast cancer grading using dynamic contrast-enhanced MRI.

BACKGROUND AND OBJECTIVES: Histological grade and molecular subtype have presented valuable references in assigning personalized or precision medicine as the significant prognostic indicators representing biological behaviors of invasive breast cancer (IBC). To evaluate a two-stage deep learning framework for IBC grading that incorporates with molecular-subtype (MS) information using DCE-MRI. METHODS: In Stage I, an innovative neural network called IOS2-DA is developed, which includes a dense atrous-spatial pyramid pooling block with a pooling layer (DA) and inception-octconved blocks with double kernel squeeze-and-excitations (IOS2). This method focuses on the imaging manifestation of IBC grades and performs preliminary prediction using a novel class F1-score loss function. In Stage II, a MS attention branch is introduced to fine-tune the integrated deep vectors from IOS2-DA via Kullback-Leibler divergence. The MS-guided information is weighted with preliminary results to obtain classification values, which are analyzed by ensemble learning for tumor grade prediction on three MRI post-contrast series. Objective assessment is quantitatively evaluated by receiver operating characteristic curve analysis. DeLong test is applied to measure statistical significance (P < 0.05). RESULTS: The molecular-subtype guided IOS2-DA performs significantly better than the single IOS2-DA in terms of accuracy (0.927), precision (0.942), AUC (0.927, 95% CI: [0.908, 0.946]), and F1-score (0.930). The gradient-weighted class activation maps show that the feature representations extracted from IOS2-DA are consistent with tumor areas. CONCLUSIONS: IOS2-DA elucidates its potential in non-invasive tumor grade prediction. With respect to the correlation between MS and histological grade, it exhibits remarkable clinical prospects in the application of relevant clinical biomarkers to enhance the diagnostic effectiveness of IBC grading. Therefore, DCE-MRI tends to be a feasible imaging modality for the thorough preoperative assessment of breast biological behavior and carcinoma prognosis.

Predictive value of lesion morphology in rectal cancer based on MRI before surgery.

OBJECTIVE: To explore the relationship of MRI morphology of primary rectal cancer with extramural vascular invasion (EMVI), metastasis and local recurrence. MATERIALS AND METHODS: This retrospective study included 153 patients with rectal cancer. Imaging factors and histopathological index including nodular projection (NP), cord sign (CS) at primary tumor margin, irregular nodules (IN) of mesorectum, MRI-detected peritoneal reflection invasion (PRI), range of rectal wall invasion (RRWI), patterns and length of tumor growth, maximal extramural depth (EMD), histologically confirmed local node involvement (hLN), MRI T stage, MRI N stage, MRI-detected extramural vascular invasion (mEMVI) and histologically confirmed extramural vascular invasion (hEMVI) were evaluated. Determining the relationship between imaging factors and hEMVI, synchronous metastasis and local recurrence by univariate analysis and multivariable logistic regression, and a nomogram validated internally via Bootstrap self-sampling was constructed based on the latter. RESULTS: Thirty-eight cases of hEMVI, fourteen cases of synchronous metastasis and ten cases of local recurrence were observed among 52 NP cases. There were 50 cases of mEMVI with moderate consistency with hEMVI (Kappa = 0.614). NP, CS, EMD and mEMVI showed statistically significant differences in the negative and positive groups of hEMVI, synchronous metastasis, and local recurrence. Compared to patients with local mass growth, the rectal tumor with circular infiltration had been found to be at higher risk of synchronous metastasis and local recurrence (P < 0.05). NP and IN remained as significant predictors for hEMVI, and mEMVI was a predictor for synchronous metastasis, while PRI and mEMVI were predictors for local recurrences. The nomogram for predicting hEMVI demonstrated a C-index of 0.868, sensitivity of 86.0%, specificity of 79.6%, and accuracy of 81.7%. CONCLUSION: NP, CS, IN, large EMD, mEMVI, and circular infiltration are significantly associated with several adverse prognostic indicators. The nomogram based on NP has good predictive performance for preoperative EMVI. mEMVI is a risk factor for synchronous metastasis. PRI and mEMVI are risk factors for local recurrence.

Automatic Alberta Stroke Program Early Computed Tomographic Scoring in patients with acute ischemic stroke using diffusion-weighted imaging.

Alberta Stroke Program Early Computed Tomographic Scoring (ASPECTS) is a reliable method for assessing early ischemic changes in the blood supply area of the middle cerebral artery in patients with acute ischemic stroke. This study aims to propose a deep learning based automatic evaluation strategy for DWI-ASPECTS to serve as a reference for clinicians in urgent decision making for endovascular thrombectomy. Ten ASPECTS regions are extracted from the DWI series to train the independent classification network for each region, the accurate training labels of which are confirmed by neuroradiologists. Two classical convolutional neural networks (VGG-16 and ResNet-50) are validated. Subsequently, the innovative CBAM-VGG is designed to improve the accurate scoring of four small-volume DWI-ASPECTS regions, including caudate nucleus, lenticular nucleus, internal capsule, and insular lobe. Average F1-score of 0.929 and 0.840 and the average accuracy of 94.75% and 84.99% are obtained when scoring on six cortical regions M1-M6 and four small ASPECTS regions, respectively. In addition, the modified algorithm CBAM-VGG shows a significant improvement in the accuracy of estimating the four ASPECTS regions with smaller volumes. The experimental results demonstrate that the deep learning methods facilitate the efficiency and robustness of automatic DWI-ASPECTS scoring, which can provide a reference for clinical decision-making.

Weakly Supervised Breast Lesion Detection in Dynamic Contrast-Enhanced MRI.

Currently, obtaining accurate medical annotations requires high labor and time effort, which largely limits the development of supervised learning-based tumor detection tasks. In this work, we investigated a weakly supervised learning model for detecting breast lesions in dynamic contrast-enhanced MRI (DCE-MRI) with only image-level labels. Two hundred fifty-four normal and 398 abnormal cases with pathologically confirmed lesions were retrospectively enrolled into the breast dataset, which was divided into the training set (80%), validation set (10%), and testing set (10%) at the patient level. First, the second image series S2 after the injection of a contrast agent was acquired from the 3.0-T, T1-weighted dynamic enhanced MR imaging sequences. Second, a feature pyramid network (FPN) with convolutional block attention module (CBAM) was proposed to extract multi-scale feature maps of the modified classification network VGG16. Then, initial location information was obtained from the heatmaps generated using the layer class activation mapping algorithm (Layer-CAM). Finally, the detection results of breast lesion were refined by the conditional random field (CRF). Accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were utilized for evaluation of image-level classification. Average precision (AP) was estimated for breast lesion localization. Delong's test was used to compare the AUCs of different models for significance. The proposed model was effective with accuracy of 95.2%, sensitivity of 91.6%, specificity of 99.2%, and AUC of 0.986. The AP for breast lesion detection was 84.1% using weakly supervised learning. Weakly supervised learning based on FPN combined with Layer-CAM facilitated automatic detection of breast lesion.

Weakly supervised breast lesion detection in DCE-MRI using self-transfer learning.

BACKGROUND: Breast cancer is a typically diagnosed and life-threatening cancer in women. Thus, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast lesion detection and diagnosis because of the high resolution of soft tissues. Moreover, supervised detection methods have been implemented for breast lesion detection. However, these methods require substantial time and specialized staff to develop the labeled training samples. PURPOSE: To investigate the potential of weakly supervised deep learning models for breast lesion detection. METHODS: A total of 1003 breast DCE-MRI studies were collected, including 603 abnormal cases with 770 breast lesions and 400 normal subjects. The proposed model was trained using breast DCE-MRI considering only the image-level labels (normal and abnormal) and optimized for classification and detection sub-tasks simultaneously. Ablation experiments were performed to evaluate different convolutional neural network (CNN) backbones (VGG19 and ResNet50) as shared convolutional layers, as well as to evaluate the effect of the preprocessing methods. RESULTS: Our weakly supervised model performed better with VGG19 than with ResNet50 (p < 0.05). The average precision (AP) of the classification sub-task was 91.7% for abnormal cases and 88.0% for normal samples. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.939 (95% confidence interval [CI]: 0.920-0.941). The weakly supervised detection task AP was 85.7%, and the correct location (CorLoc) was 90.2%. A sensitivity of 84.0% at two-false positives per image was assessed based on free-response ROC (FROC) curve. CONCLUSIONS: The results confirm that a weakly supervised CNN based on self-transfer learning is an effective and promising auxiliary tool for detecting breast lesions.

Ischemic stroke subtyping method combining convolutional neural network and radiomics.

BACKGROUND: Cardiogenic embolism (CE) and large-artery atherosclerosis embolism (LAA) are the two most common ischemic stroke (IS) subtypes. OBJECTIVE: In order to assist doctors in the precise diagnosis and treatment of patients, this study proposed an IS subtyping method combining convolutional neural networks (CNN) and radiomics. METHODS: Firstly, brain embolism regions were segmented from the computed tomography angiography (CTA) images, and radiomics features were extracted; Secondly, the extracted radiomics features were optimized with the L2 norm, and the feature selection was performed by combining random forest; then, the CNN Cap-UNet was built to extract the deep learning features of the last layer of the network; Finally, combining the selected radiomics features and deep learning features, 9 small-sample classifiers were trained respectively to build and select the optimal IS subtyping classification model. RESULTS: The experimental data include CTA images of 82 IS patients diagnosed and treated in Shanghai Sixth People's Hospital. The AUC value and accuracy of the optimal subtyping model based on the Adaboost classifier are 0.9018 and 0.8929, respectively. CONCLUSION: The experimental results show that the proposed method can effectively predict the subtype of IS and has potential to assist doctors in making timely and accurate diagnoses of IS patients.

STEMI in an adolescent boy due to anomalous left main coronary artery arising from the right sinus of Valsalva: Case report and brief review of the literature.

Congenital anomalous origin of coronary artery is a rare cardiovascular malformation and the most common anomaly is the left circumflex (LCX) arising from the right sinus of Valsalva (RSV). Other forms include both coronary arteries from RSV, the left anterior descending coronary artery from RSV, and a single coronary artery from the left sinus of Valsalva. Despite being rare, anomalous origin of left main coronary artery (LMCA) from RSV carries a high risk of sudden cardiac death. Here, we report a case of 13-year-old boy with chest pain and acute extensive anterior ST-segment elevation myocardial infarction (STEMI) who was initially diagnosed as acute myocarditis in the emergency department. A bedside echocardiogram showed severe global hypokinesia of left ventricle (LV) and normal right ventricle (RV) function. Coronary computed tomography angiography (CCTA) examination showed LMCA originated from the RSV. The patient underwent coronary artery bypass grafting surgery and was discharged without complications. A timely correct diagnosis of an anomalous coronary artery is critical in symptomatic patients, CCTA plays an important role in clinical decision making.

A method for distinguishing benign and malignant pulmonary nodules based on 3D dual path network aided by K-means clustering analysis.

In the USA, there were about 1 |806 |590 new cancer cases in 2020, and 606 520 cancer deaths are expected to have occurred in 2021. Lung cancer has become the leading cause of death from cancer in both men and women (Siegel et al., 2020). Clinical studies show that the five-year survival rate of lung cancer patients after early diagnosis and treatment intervention can reach 80%, compared with that of patients having advanced lung cancer. Thus, the early diagnosis of lung cancer is a key factor to reduce mortality.

Comparison of Contrast-Enhanced Spectral Mammography and Contrast-Enhanced MRI in Screening Multifocal and Multicentric Lesions in Breast Cancer Patients.

Objectives: We aimed to determine the difference between contrast-enhanced spectral mammography (CESM) and contrast-enhanced magnetic resonance imaging (CE-MRI) in detecting multifocal and multicentric breast cancer (MMBC). Methods: : This study was conducted among breast cancer patients between July 1, 2017, and May 30, 2021. The sensitivity, specificity, and accuracy of CESM and CE-MRI in the diagnosis of MMBC were evaluated with pathological results as the gold standard. Results: A total of 188 lesions were detected in 54 patients with MMBC, including 177 breast cancer and 11 benign lesions. Based on CESM and CE-MRI, 4 false-positive cases and 3 false-negative cases and 7 false-positive cases and 1 false-negative case, respectively, were found. The accuracy of CESM was higher than that of MRI (96.3% vs 95.7%), and the specificity was higher than that of MRI (63.6% vs 36.4%). There were no significant differences in the sensitivity, specificity, and accuracy for the detection of MMBC between CESM and CE-MRI (p = 0.500; p = 0.250; p = 0.792). Conclusion: CESM is an effective method for the detection of MMBC, which is consistent with the sensitivity and accuracy of CE-MRI.

Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast-Enhanced MRI.

BACKGROUND: Imaging-driven deep learning strategies focus on training from scratch and transfer learning. However, the performance of training from scratch is often impeded by the lack of large-scale labeled training data. Additionally, owing to the differences between source and target domains, analyzing medical image tasks satisfactorily via transfer learning based on ImageNet is difficult. PURPOSE: To investigate two transfer learning algorithms for breast cancer molecular subtype prediction (luminal and non-luminal) based on unsupervised pre-training and ensemble learning: M_EL and B_EL, using malignant and benign datasets as the source domain, respectively. STUDY TYPE: Retrospective. POPULATION: Eight hundred and thirty-three female patients with histologically confirmed breast lesions (567 benign and 266 malignant cases) were selected. In the 5-fold cross-validation, the malignant cohort was randomly divided into 5 subsets to form a training set (80%) and a validation set (20%). FIELD STRENGTH/SEQUENCE: 3.0 T, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using T1-weighted high-resolution isotropic volume examination. ASSESSMENT: First, three datasets acquired at different times post-contrast were preprocessed as unlabeled source domains. Second, three baseline networks corresponding to the different MRI post-contrast phases were built, optimized by a combination of mutual information maximization between high- and low-level representations and prior distribution constraints. Next, the pre-trained networks were fine-tuned on the labeled target domain. Finally, prediction results were integrated using weighted voting-based ensemble learning. STATISTICAL TESTS: Mean accuracy, precision, specificity, and area under receiver operating characteristic curve (AUC) were obtained with 5-fold cross-validation. P < 0.05 was considered to be statistically significant. RESULTS: Compared with a convolutional long short-term memory network, pre-trained VGG-16, VGG-19, and DenseNet-121 from ImageNet, M_EL and B_EL exhibited significantly more optimized prediction performance (specificity: 90.5% and 89.9%; accuracy: 82.6% and 81.1%; precision: 91.2% and 90.9%; AUC: 0.836 and 0.823, respectively). DATA CONCLUSION: Transfer learning based on unsupervised pre-training may facilitate automatic prediction of breast cancer molecular subtypes. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Altered fMRI-derived functional connectivity in patients with high-tension glaucoma.

BACKGROUND: High-tension glaucoma (HTG) is associated with functional changes in the brain, and elevated intraocular pressure (IOP) is one of the major causes. PURPOSE: To evaluate the effects of high IOP on the brain in patients with HTG by using resting-state functional magnetic resonance imaging (rs-fMRI). MATERIALS AND METHODS: Thirty-six patients with HTG and 20 age- and gender-matched healthy controls (HCs) were recruited and underwent IOP examination and rs-fMRI scan. Voxel-wise functional connectivity (FC) values were obtained between the Brodmann Area (BA) 17 (primary visual cortex) and the rest of the brain, two-sample t test was performed between HTG group and HCs. Correlation analysis was performed between FC and clinical information. RESULTS: Compared with HCs, HTG patients demonstrated decreased FC between BA 17 and the right precuneus gyrus, decreased FC between BA 17 and the right superior frontal gyrus (SFG) (GRF corrected at voxel level P<0.001 and cluster level P<0.05, two-tailed). FC between BA 17 and the right SFG showed significantly negative correlation with right eyes' IOP and mean IOP. CONCLUSION: HTG patients had abnormal FC changes between the visual cortex and multiple functional brain regions related to visual sense, memory consolidation and cognitive processing, which provided image support for the pathophysiology research of HTG, and revealed new targets for the accurate treatment of HTG.

Clinical and imaging features of pediatric COVID-19.

BACKGROUND: Pediatric COVID-19 is relatively mild and may vary from that in adults. This study was to investigate the epidemic, clinical, and imaging features of pediatric COVID-19 pneumonia for early diagnosis and treatment. METHODS: Forty-one children infected with COVID-19 were analyzed in the epidemic, clinical and imaging data. RESULTS: Among 30 children with mild COVID-19, seven had no symptoms, fifteen had low or mediate fever, and eight presented with cough, nasal congestion, diarrhea, headache, or fatigue. Among eleven children with moderate COVID-19, nine presented with low or mediate fever, accompanied with cough and runny nose, and two had no symptoms. Significantly (P < 0.05) more children had a greater rate of cough in moderate than in mild COVID-19. Thirty children with mild COVID-19 were negative in pulmonary CT imaging, whereas eleven children with moderate COVID-19 had pulmonary lesions, including ground glass opacity in ten (90.9%), patches of high density in six (54.5%), consolidation in three (27.3%), and enlarged bronchovascular bundles in seven (63.6%). The lesions were distributed along the bronchus in five patients (45.5%). The lymph nodes were enlarged in the pulmonary hilum in two patients (18.2%). The lesions were presented in the right upper lobe in two patients (18.1%), right middle lobe in one (9.1%), right lower lobe in six (54.5%), left upper lobe in five (45.5%), and left lower lobe in eight (72.7%). CONCLUSIONS: Children with COVID-19 have mild or moderate clinical and imaging presentations. A better understanding of the clinical and CT imaging helps ascertaining those with negative nucleic acid and reducing misdiagnosis rate for those with atypical and concealed symptoms.

Functional Alterations in Resting-State Visual Networks in High-Tension Glaucoma: An Independent Component Analysis.

BACKGROUND: High-tension glaucoma (HTG) is the most common type of primary open angle glaucoma and elevated intraocular pressure (IOP) is the major risk factor of the disease. The aim of this study was to assess alterations in resting-state visual networks in patients with HTG and investigate the effect of elevated IOP on the visual networks. METHODS: T1-weighted and resting-state functional MRI images were acquired from 36 HTG patients (aged 49.22 ± 15.26 years) and 20 healthy controls (aged 49.90 ± 5.62 years). Group independent component analysis (ICA) was utilized to evaluate altered functional connectivity (FC) in resting-state visual networks between HTG patients and healthy controls. Pearson correlation analysis between mean IOP and altered FCs in the visual networks was performed. RESULTS: ICA demonstrated decreased FCs in HTG group in the left calcarine cortex of the lateral visual network, in the bilateral lingual gyrus of the medial visual network and in the bilateral lingual gyrus of the occipital visual network compared with healthy controls. Furthermore, correlation analysis revealed negative correlation between mean IOP and altered FC within the lateral visual network. CONCLUSION: The results suggested reduced FCs between primary and higher visual cortices in HTG brain. The IOP elevation might be responsible for the functional alterations in the visual networks.

Effects of the NF­κB/p53 signaling pathway on intervertebral disc nucleus pulposus degeneration.

The incidence of intervertebral disc degeneration (IDD) is increasing, especially among elderly individuals. The present study aimed to investigate the effects of the NF­κB/p53 signaling pathway on IDD and its regulatory effect on associated cytokines. In the present study, human nucleus pulposus cells were isolated from patients with thoracic­lumbar fractures and patients with IDD to observe cellular morphology and detect phosphorylated (p)­p65/p53 expression levels. The locality and expression levels of p65 in interleukin (IL)­1ß­stimulated nucleus pulposus cells, with or without the addition of ammonium pyrrolidinedithiocarbamate (PDTC; a NF­κB signaling pathway­specific blocker), were measured. Furthermore, the effects of IL­1ß stimulation on the protein and gene expression levels of IDD­related cytokines were determined following p53 knockdown and inhibition of the NF­κB signaling pathway. The results suggested that p­p65 and p53 expression was significantly increased in IDD cells compared with normal nucleus pulposus cells. Moreover, nucleus pulposus cells isolated from patients with IDD contained less cytoplasm compared with normal nucleus pulposus cells, and p65 expression levels were higher in the cytoplasm than the nucleus of IL­1ß­stimulated PDTC­treated healthy nucleus pulposus cells. Moreover, the p53 expression levels were significantly decreased following transfection with sip53. PDTC treatment and p53 knockdown significantly decreased matrix metallopeptidase (MMP)­3, MMP­13, metallopeptidases with thrombospondin type 1 motif (ADAMTS)­4 and ADAMTS­5 expression levels, and increased aggrecan and collagen type II expression levels in IL­1ß­stimulated cells. The present study indicated that activation of the NF­κB/p53 signaling pathway might be related to the occurrence of IDD; therefore, the NF­κB/p53 signaling pathway may serve as a therapeutic target for IDD.

CT imaging features of 34 patients infected with COVID-19.

OBJECTIVE: To retrospectively analyze the CT findings in patients infected with Coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: The thirty-four cases, 15 females and 19 males, with an age ranging from 7 to 88 years old, confirmed by real-time reverse-transcriptase-polymerase chain reaction (RT-PCR), were used for our study. All thin-section CT scans of the lungs were performed in all of patients. The clinical, laboratory and CT imaging were available to evaluate in all patients. RESULTS: The patients present with fever (85.29%, n = 29), cough (67.65%, n = 23), fatigue or myalgia (26.47%, n = 9), and pharyngalgia (8.82%, n = 3). The 4 patients (11.76%) with no symptoms were identified during screening for close contacts, who had typical CT findings. On initial CT scans, the bilateral lung involved was shown in 24 cases (70.59%), while 29 (82.35%) cases were distributed in peripheral. The pure ground glass opacity (GGO) was shown in 18 cases (52.94%), the GGO with consolidation was in 12 cases (35.29%), and full consolidation only in 3 cases. The lesion with air bronchogram was seen in 14 (41.18%) cases, with enlarged blood vessel in 17 (50.00%) cases, with crazy-paving pattern in 8 (23.53%) cases, with fine reticular pattern in 4 (11.77%) cases, and with intralesional vacuole sign in 6 (17.65%) cases. The pleural effusion was seen in one patient. Follow-up imaging in 19 patients during the study time window demonstrated mild, moderate or severe progression of disease, as manifested by increasing extent and density of lung opacities. CONCLUSIONS: The bilateral GGO with air bronchogram, enlarged blood vessel, fine reticular pattern, and peripheral distribution are the early CT findings of COVID-19. The crazy-paving pattern and intralesional vacuole sign are the features of progressive stage.

Hippocampal sulcal cavities in healthy individual: high resolution magnetic resonance imaging using 3.0 MRI.

Most of the studies on hippocampal sulcal cavities (HSCs) have been focused on the hippocampal lesions, lacking of systemic investigations on the normal individuals. In this study, we aimed to investigate the detection rate and number of HSCs together with the correlation between HSCs and the gender, sides, hippocampal volume, and age. In total, 187 healthy subjects underwent 3.0 Tesla magnetic resonance scan. Chi square test was utilized for the comparison of HSCs detection rate among the male and female individuals. Student's t-test was used to compare the HSCs number between the subjects with different ages as well as different body sides. Analysis of variance was performed for the comparison of primary hippocampal volume, corrected hippocampal volume, and age. Person regression analysis was utilized to analyze the correlation between HSCs number and the primary hippocampal volume, corrected hippocampal volume, and age. The incidence of HSCs was 95% among the 187 subjects. There was no significant gender difference in the incidence of HSCs (P=0.448). There was no statistically significant difference in the number of HSCs on the left and right sides (P=0.093). There were statistically significant differences in mean age between groups (P<0.01). Pearson correlation analysis was performed on HSCs with age, hippocampal original volume and corrected volume, and the correlation coefficients were 0.316, -0.005 and 0.055. Healthy population has a high HSCs incidence, which had no significant gender-related difference. There is no significant difference of HSCs number between the left and right sides. HSCs showed a low correlation with age and no correlation with hippocampal volume.

Brain morphological alterations of cerebral cortex and subcortical nuclei in high-tension glaucoma brain and its associations with intraocular pressure.

PURPOSE: To investigate brain morphological alterations of high-tension glaucoma patients and explore the association between brain morphological changes and elevated intraocular pressure. METHODS: Thirty-six patients with high-tension glaucoma and 20 healthy controls were collected and underwent structural MRI scan. Surface-based morphometry and voxel-based morphometry were applied to assess cortical thickness and subcortical gray matter volume of the enrolled subjects. The association between brain morphometry and intraocular pressure was assessed by partial correlation. RESULTS: Compared with healthy controls, high-tension glaucoma patients showed decreased cortical thickness in the bilateral superior temporal gyrus, bilateral superior parietal gyrus, bilateral lateral occipital gyrus, left fusiform gyrus, left medial orbitofrontal gyrus, right precentral gyrus, and right superior frontal gyrus (p < 0.05). High-tension glaucoma patients also showed reduced gray matter volume in the right hippocampus, bilateral putamen, and bilateral thalamus (p < 0.05). In addition, brain morphological correlates of mean intraocular pressure were found in the left rostral middle frontal gyrus, right precentral gyrus, and left postcentral gyrus in high-tension glaucoma group (p < 0.05). CONCLUSION: High-tension glaucoma patients experienced morphological reduction in the visual and nonvisual areas throughout the entire brain. Elevated intraocular pressure may contribute to the reduction of cortical thickness in certain areas in the progression of the disease.