O-GlcNAcylation regulates the stability of transferrin receptor (TFRC) to control the ferroptosis in hepatocellular carcinoma cells.

Ferroptosis is an iron-dependent programmed cell death (PCD) enforced by lipid peroxidation accumulation. Transferrin receptor (TFRC), one of the signature proteins of ferroptosis, is abundantly expressed in hepatocellular carcinoma (HCC). However, post-translational modification (PTM) of TFRC and the underlying mechanisms for ferroptosis regulation remain less understood. In this study, we found that TFRC undergoes O-GlcNAcylation, influencing Erastin-induced ferroptosis sensitivity in hepatocytes. Further mechanistic studies found that Erastin can trigger de-O-GlcNAcylation of TFRC at serine 687 (Ser687), which diminishes the binding of ubiquitin E3 ligase membrane-associated RING-CH8 (MARCH8) and decreases polyubiquitination on lysine 665 (Lys665), thereby enhancing TFRC stability that favors labile iron accumulation. Therefore, our findings report O-GlcNAcylation on an important regulatory protein of ferroptosis and reveal an intriguing mechanism by which HCC ferroptosis is controlled by an iron metabolism pathway.

Role of epidural fat in the local milieu: what we know and what we don't.

PURPOSE: Traditionally, the epidural fat (EF) is known as a physical buffer for the dural sac against the force and a lubricant facilitating the relative motion of the latter on the osseous spine. Along with the development of the studies on EF, controversies still exist on vital questions, such as the underlying mechanism of the spinal epidural lipomatosis. Meanwhile, the scattered and fragmented researches hinder the global insight into the seemingly dispensable tissue. METHODS: Herein, we reviewed literature on the EF and its derivatives to elucidate the dynamic change and complex function of EF in the local milieu, especially at the pathophysiological conditions. We start with an introduction to EF and the current pathogenic landscape, emphasizing the interlink between the EF and adjacent structures. We generally categorize the major pathological changes of the EF into hypertrophy, atrophy, and inflammation. RESULTS AND CONCLUSIONS: It is acknowledged that not only the EF (or its cellular components) may be influenced by various endogenic/exogenic and focal/systematic stimuli, but the adjacent structures can also in turn be affected by the EF, which may be a hidden pathogenic clue for specific spinal disease. Meanwhile, the unrevealed sections, which are also the directions the future research, are proposed according to the objective result and rational inference. Further effort should be taken to reveal the underlying mechanism and develop novel therapeutic pathways for the relevant diseases.

Determination of p53abn endometrial cancer: a multitask analysis using radiological-clinical nomogram on MRI.

OBJECTIVES: We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MRI. METHODS: We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the nonendometrioid, high-risk, and P53abn EC group. RESULTS: The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). CONCLUSION: In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC. ADVANCES IN KNOWLEDGE: (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).

Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi-Parametric MRI by Deep Learning.

BACKGROUND: Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi-parametric MRI (mpMRI) images. PURPOSE: To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images. STUDY TYPE: Retrospective. POPULATION: Six hundred twenty-one patients with histologically proven EC from two institutions, including 111 LNM-positive and 168 LVSI-positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI), contrast-enhanced T1WI (CE-T1WI), and diffusion-weighted imaging (DWI) were scanned with turbo spin-echo, gradient-echo, and two-dimensional echo-planar sequences, using either a 1.5 T or 3 T system. ASSESSMENT: EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU-Net model for automatic segmentation. A multi-task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE-T1WI, and DWI images as inputs. The performance of the model for LNM-positive diagnosis was compared with those of three radiologists in the external test cohort. STATISTICAL TESTS: Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant. RESULTS: EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674). DATA CONCLUSION: The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Histogram Feature Analysis of Tumor Body on Diffusion-weighted MR Imaging in Differentiation between Granulosa Cell Tumors and Other Sex-cord Tumors in Ovary: Comparison with Histological Results.

OBJECTIVE: We aimed to differentiate granulosa cell tumors (GCT) from other ovarian sex-cord tumors (OSCs) based on feature analysis of the tumor body on MR imaging. METHODS: We retrospectively enrolled 27 patients with pathologically proven sex-cord tumours (14 GSTs, 8 fibromas, 4 fibrothecomas, and 1 sclerosing stromal tumour) from our institution. All MRI examinations were performed at least one month prior to surgery. MR image features were recorded by two radiologists with consensus readings. Histogram analysis was performed using FeAture Explorer software. The differences in histogram parameters between GCT (38.1 ± 14.6 years) and OSC (43.7 ± 18.0 years) groups were compared. Fourteen randomly selected cellular-type myomas who also underwent MRI in our hospital were considered as the control group. The intra-operator consistency of ADC value was evaluated across measurements twice. RESULTS: The repeatability of conventional ADC measurements on the tumor body was good. The values of ADC-mean, ADC-min, and ADC-max significantly differed across three groups (p < 0.001). The histogram variance on DWI, histogram percentage on T2WI, and ADC min showed the best discriminative performance in determining GCTs from other OSCs with an area under the receiver operator curve (AUC) of 0.997, 0.882, and 0.795, respectively. The histogram variance on DWI yielded a sensitivity of 92.3%, a specificity of 100%, and an accuracy of 96.6% in discriminating GSTs from other OSCs. CONCLUSION: In the present study, feature analysis of tumor body MR imaging has helped to differentiate GST from OSC with better performance than conventional ADC measurements.

Identification of glycogene-based prognostic signature and validation of B3GNT7 as a potential biomarker and therapeutic target in breast cancer.

BACKGROUND: Breast cancer is the most common cancer worldwide, with the fifth highest mortality rate among all cancers and high risk of metastasis. However, potential biomarkers and molecular mechanisms underlying the stratification of breast cancer in terms of clinical outcomes remain to be investigated. Therefore, we aimed to find a novel prognostic biomarker and therapeutic target for breast cancer patients. METHODS: Unsupervised hierarchical clustering was used to perform comprehensive transcriptomic study of total 185 glycogenes in public datasets of breast cancer with clinicopathological and survival information. A glycogene-based signature for subtype classification was discovered using Limma packages, and relevance to four known molecular features was identified by GSVA. Experimental verification was performed and biological functions of B3GNT7 were characterized by quantitative RT-PCR, western blot, transwell assays, and lectin immunofluorescence staining in breast cancer cells. RESULTS: A 23-glycogene signature was identified for the classification of breast cancer. Among the 23 glycogenes, B3GNTs showed significantly positive associations with ER-/Her2- subtype in breast cancer patients (n = 2655). Overexpressed B3GNT7 were correlated with poor prognosis in breast cancer patients based on public datasets. B3GNT7 depletion inhibited cell proliferation, migration, and invasion, and decreased global fucosylation in MDA-MB-231 and HCC1937 breast cancer cells. CONCLUSIONS: Herein, we discovered a unique 23-gene signature for breast cancer patient glycogene-type classification. Among these genes, B3GNT7 was shown to be a potential biomarker for unfavorable outcomes and therapeutic target of breast cancer.

Dynamic contrast-enhanced magnetic resonance imaging in epithelial ovarian tumor categorization: comparison with apparent diffusion coefficient histogram analysis and the tumor cell proliferation marker.

OBJECTIVE: To compare the capability of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) histogram analysis in epithelial ovarian tumor categorization. METHODS: We retrospectively recruited 52 patients with pathologically proven ovarian serous epithelial cancer from our institution. ADC histogram analysis was performed using FeAture Explorer software after outlining the whole lesion area on the ADC map. The ADC histogram parameter difference between subgroups was compared; the correlation between the quantitative parameters on MRI and Ki-67 expression was calculated for both groups. RESULTS: The repeatability of ADC measurements across the two methods was good; the area method (ADCarea) had better performance in repeatability than the ROI method (ADCroi). The ADCroi, ADCarea, Ktrans, and Kep values significantly differed between the groups (P < 0.05). The histogram parameters (percent10, entropy, minimum, range and variance) and DCE parameter (Ktrans) were strongly correlated with Ki-67 expression (P = 0.000), while the conventional ADC measurements were not significantly correlated with Ki-67 expression (P > 0.05). Overall, Ktrans had the best diagnostic performance for discriminating type I with type II ovarian cancers (AUC = 0.826). CONCLUSION: In the present study, both diffusion-weighted imaging (DWI) and DCE MRI could help classify ovarian cancer patients with high accuracy. ADC histogram analysis could accurately reflect the proliferative capability of tumor cells to some extent.

Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging.

To establish a deep learning (DL) model in differentiating borderline ovarian tumor (BOT) from epithelial ovarian cancer (EOC) on conventional MR imaging. We retrospectively enrolled 201 patients of 102 pathologically proven BOTs and 99 EOCs at OB/GYN hospital Fudan University, between January 2015 and December 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) MR images were used for lesion area determination. We trained a U-net++ model with deep supervision to segment the lesion area on MR images. Then, the segmented regions were fed into a classification model based on DL network to categorize ovarian masses automatically. For ovarian lesion segmentation, the mean dice similarity coefficient (DSC) of the trained U-net++ model in the testing dataset achieved 0.73 [Formula: see text] 0.25, 0.76 [Formula: see text] 0.18, and 0.60 [Formula: see text] 0.24 in the sagittal T2WI, coronal T2WI, and axial T1WI images, respectively. The DL model by combined T2WI computerized network could differentiate BOT from EOC with a significantly higher AUC of 0.87, an accuracy of 83.7%, a sensitivity of 75.0% and a specificity of 87.5%. In comparison, the AUC yielded by radiologist was only 0.75, with an accuracy of 75.5%, a sensitivity of 96.0% and specificity of 54.2% (P < 0.001).The trained DL network model derived from routine MR imaging could help to distinguish BOT from EOC with a high accuracy, which was superior to radiologists' assessment.

Histogram Analysis of Apparent Diffusion Coefficient on Diffusion Weighted Magnetic Resonance Imaging in Differentiation between Low and High Grade Serous Ovarian Cancer.

BACKGROUND: Ovarian cancer is a leading cause of death in gynecological malignancies. Being the most common subtype in OEC, ovarian serous cancer also includes two subtypes: low grade serous ovarian cancer (LGSC) and high grade serous ovarian cancer (HGSC) (1). PURPOSE: The study aims to assess the capability of apparent diffusion coefficient (ADC) histogram analysis and conventional measurements on magnetic resonance imaging (MRI) in differentiating between LGSC and HGSC. METHODS: We retrospectively recruited 38 patients with pathologically proven ovarian serous epithelial cancer. The mean ADC value was measured by one technician using two methods on post-processed workstation. The ADC value and histogram parameter difference between LGSC and HGSC groups were compared. The correlation between the ADC value and the Ki-67 expression was calculated across both groups. RESULTS: The repeatability of ADC measurements across two methods was good; the ROI method (ADC-roi) had better performance repeatability than the area method (ADC-area). The value of ADC-mean , ADC-min, ADC-max, and ADC-area significantly differed between both groups (p < 0.001). The value of ADC-area correlated inversely with ki-67 expression in the whole group (Pearson coefficient = -0.382, p = 0.02). The 3D computerized-diagnostic model had the best discriminative performance in determining HGSC than 2D and conventional ADC measurements. The 3D model yielded a sensitivity of 100%, a specificity of 95.45%, and an accuracy of 97.73%. CONCLUSION: In the present study, the 3D ADC histogram model help differentiate HGSC from LGSC with a better performance than conventional ADC measurements.

MicroRNA-223 Attenuates Stretch-Injury-Induced Apoptosis in Brain Microvascular Endothelial Cells by Regulating RhoB Expression.

MiR-223 is a miRNA with important functions in apoptosis, carcinogenesis, and inflammation, and it was demonstrated to be over-expressed in brain tissue after traumatic brain injury (TBI). However, few studies have focused on its role in protecting brain microvascular endothelial cells (BMECs). This study evaluated the protective effect of miR-223 on BMECs after stretch injury (SI). bEnd.3 cells (BMECs of mouse) were transfected with overexpressing and blocking lentivirus of miR-223, then were subjected to SI. After immunofluorescence assay, it was demonstrated that miR-223 overexpression significantly rescued the SI-induced loss of ZO-1 (Zonula Occludens 1, tight junction protein) (p < 0.01), while miR-223 blocking exacerbated the loss of ZO-1 (p < 0.05). Flow cytometry confirmed a significant increase in the proportion of apoptotic bEnd.3 cells after SI, and miR-223 overexpression reduced this proportion (p < 0.001). The result of Western blot revealed that miR-223 overexpression significantly reduced the expression of cleaved caspase-3 (cl-caspase 3) (p < 0.05) and RhoB (p < 0.01), while miR-223 blocking increased the expression of these proteins (p < 0.05, p < 0.001). Additionally, knockdown of RhoB significantly reduced the expression of cl-caspase 3 (p < 0.001). These findings suggested that miR-223 can alleviate SI-induced apoptosis of BMECs, and this anti-apoptotic effect is at least partially achieved by inhibiting the expression of RhoB. Moreover, miR-223 may play a role in maintaining the integrity of BBB during TBI.

Activation of Atg7-dependent autophagy by a novel inhibitor of the Keap1-Nrf2 protein-protein interaction from Penthorum chinense Pursh. attenuates 6-hydroxydopamine-induced ferroptosis in zebrafish and dopaminergic neurons.

The death of dopaminergic neurons is a dominant factor during the occurrence and development of Parkinson's disease (PD). Previous studies demonstrated that ferroptosis is implicated in the death of dopaminergic neurons. Besides, polyphenols have been proven to be effective in preventing the death of dopaminergic neurons. This work aims to explore the neuroprotective effect and mechanism of thonningianin A (Th A), a polyphenolic compound in natural plant foods, against 6-hydroxydopamine (6-OHDA)-induced ferroptosis in dopaminergic cells. The results of molecular docking and other binding assays collectively demonstrated that Th A can strongly target the Kelch domain of Keap1. Th A treatment significantly facilitated the nuclear factor erythroid 2-like 2 (Nrf2) nuclear translocation and subsequently increased the heme oxygenase-1 (HO-1) protein level through inhibiting the protein-protein interaction (PPI) of Keap1 and Nrf2. Compared with the nomifensine (Nomi) treatment, Th A had a more potent protective effect on 6-OHDA-induced ferroptosis during PD pathology in zebrafish, which was associated with assuaging the reduction of the total swimming distance, glutathione (GSH) depletion, iron accumulation, lipid peroxidation, and aggregation of α-synuclein (α-syn). Furthermore, Th A also exhibited a strong protective effect against 6-OHDA-induced ferroptosis in vitro in the human neuroblastoma cell line SH-SY5Y. Th A degraded Keap1 protein through activating Atg7-dependent autophagy. Additionally, Th A treatment facilitated the degradation of Keap1 protein by promoting the interaction between p62/SQSTM1 (sequestosome 1, hereafter referred to as p62) and Keap1. Taken together, our findings indicated that Th A protects dopaminergic cells against 6-OHDA-induced ferroptosis through activating the Nrf2-based cytoprotective system, thus enabling a potential application of Keap1-Nrf2 PPI inhibitors in the restraint of ferroptosis and treatment of PD.

MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols.

BACKGROUND: Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. METHODS: A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. RESULTS: The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. CONCLUSION: MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.

Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation.

OBJECTIVES: To develop and validate a radiomics nomogram for differentiating between malignant pulmonary nodules and benign nodules. METHODS: 56 benign and 51 malignant nodules from 96 patients were analyzed using manual segmentation of the T2-fBLADE-TSE, while the nodules signal intensity (SIlesion), lesion muscle ratio (LMR) and nodule size were all measured and recorded. The maximum relevance and minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select nonzero coefficients and develop the model in pulmonary nodules diagnosis. The radiomics nomogram was also developed. The clinical prediction value was determined by the decision curve analysis (DCA). RESULTS: The nodule size, SIlesion and LMR of the benign group were 1.78 ± 0.57 cm, 227.50 ± 81.39 and 2.40 ± 1.27 respectively, in contrast to the 2.00 ± 0.64 cm, 232.87 ± 82.21 and 2.17 ± 0.91, respectively, in the malignant group (P = 0.09, 0.60 and 0.579). A total of 13 radiomics features were retained. The Rad-score of the benign nodules group was lower than that of the malignant nodules group (P < 0.001 & 0.049, training & test set). The AUC of radiomics signature for nodules diagnosis was 0.82 (95% CI, 0.73-0.91) in the training set and 0.71 (95% CI, 0.51-0.90) in the test set. A nomogram, consisting of 13 radiomics features and nodule size, produced good prediction in the training set (AUC, 0.82; 95% CI, 0.73-0.91), which was significantly better than that of T2-based quantitative parameters (AUC, 0.62; 95% CI, 0.50-0.75, P = 0.003). In the test set, the performance of radiomics nomogram (AUC, 0.70; 95% CI, 0.51-0.90) was also better than that of T2-based quantitative parameters (AUC, 0.46; 95% CI, 0.25-0.67) (P = 0.145). The DCA showed that radiomics nomogram and T2-based quantitative parameter had overall net benefits, while the performance of nomogram was better. CONCLUSION: We constructed and validated a T2-fBLADE-TSE-based radiomics nomogram that can help to differentiate between malignant pulmonary nodules and benign nodules.

Radiomics for the Prediction of Epilepsy in Patients With Frontal Glioma.

OBJECTIVE: This study was conducted in order to investigate the association between radiomics features and frontal glioma-associated epilepsy (GAE) and propose a reliable radiomics-based model to predict frontal GAE. METHODS: This retrospective study consecutively enrolled 166 adult patients with frontal glioma (111 in the training cohort and 55 in the testing cohort). A total 1,130 features were extracted from T2 fluid-attenuated inversion recovery images, including first-order statistics, 3D shape, texture, and wavelet features. Regions of interest, including the entire tumor and peritumoral edema, were drawn manually. Pearson correlation coefficient, 10-fold cross-validation, area under curve (AUC) analysis, and support vector machine were adopted to select the most relevant features to build a clinical model, a radiomics model, and a clinical-radiomics model for GAE. The receiver operating characteristic curve (ROC) and AUC were used to evaluate the classification performance of the models in each cohort, and DeLong's test was used to compare the performance of the models. A two-sided t-test and Fisher's exact test were used to compare the clinical variables. Statistical analysis was performed using SPSS software (version 22.0; IBM, Armonk, New York), and p <0.05 was set as the threshold for significance. RESULTS: The classification accuracy of seven scout models, except the wavelet first-order model (0.793) and the wavelet texture model (0.784), was <0.75 in cross-validation. The clinical-radiomics model, including 17 magnetic resonance imaging-based features selected among the 1,130 radiomics features and two clinical features (patient age and tumor grade), achieved better discriminative performance for GAE prediction in both the training [AUC = 0.886, 95% confidence interval (CI) = 0.819-0.940] and testing cohorts (AUC = 0.836, 95% CI = 0.707-0.937) than the radiomics model (p = 0.008) with 82.0% and 78.2% accuracy, respectively. CONCLUSION: Radiomics analysis can non-invasively predict GAE, thus allowing adequate treatment of frontal glioma. The clinical-radiomics model may enable a more precise prediction of frontal GAE. Furthermore, age and pathology grade are important risk factors for GAE.

Ccdc134 deficiency impairs cerebellar development and motor coordination.

Coiled-coil domain containing 134 (CCDC134) has been shown to serve as an immune cytokine to exert antitumor effects and to act as a novel regulator of hADA2a to affect PCAF acetyltransferase activity. While Ccdc134 loss causes abnormal brain development in mice, the significance of CCDC134 in neuronal development in vivo is controversial. Here, we report that CCDC134 is highly expressed in Purkinje cells (PCs) at all developmental stages and regulates mammalian cerebellar development in a cell type-specific manner. Selective deletion of Ccdc134 in mouse neural stem cells (NSCs) caused defects in cerebellar morphogenesis, including a decrease in the number of PCs and impairment of PC dendritic growth, as well as abnormal granule cell development. Moreover, loss of Ccdc134 caused progressive motor dysfunction with deficits in motor coordination and motor learning. Finally, Ccdc134 deficiency inhibited Wnt signaling but increased Ataxin1 levels. Our findings provide evidence that CCDC134 plays an important role in cerebellar development, possibly through regulating Wnt signaling and Ataxin1 expression levels, and in controlling cerebellar function for motor coordination and motor learning, ultimately making it a potential contributor to cerebellar pathogenesis.

High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules.

OBJECTIVE: To investigate whether 3D convolutional neural network (CNN) is able to enhance the classification performance of radiologists in classifying pulmonary non-solid nodules (NSNs). MATERIALS AND METHODS: Data of patients with solitary NSNs and diagnosed as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC) in pathological after surgical resection were analyzed retrospectively. Ultimately, 532 patients in our institution were included in the study: 427 cases (144 AIS, 167 MIA, 116 IAC) were assigned to training dataset and 105 cases (36 AIS, 41 MIA and 28 IAC) were assigned to validation dataset. For external validation, 177 patients (60 AIS, 69 MIA and 48 IAC) from another hospital were assigned to testing dataset. The clinical and morphological characteristics of NSNs were established as radiologists' model. The trained classification model based on 3D CNN was used to identify NSNs types automatically. The evaluation and comparison on classification performance of the two models and CNN + radiologists' model were performed via receiver operating curve (ROC) analysis and integrated discrimination improvement (IDI) index. The Akaike information criterion (AIC) was calculated to find the best-fit model. RESULTS: In external testing dataset, radiologists' model showed inferior classification performance than CNN model both in discriminating AIS from MIA-IAC and AIS-MIA from IAC (the area under the ROC curve (Az value), 0.693 vs 0.820, P = 0.011; 0.746 vs 0.833, P = 0.026, respectively). However, combining CNN significantly enhanced the classification performance of radiologists and exhibited higher Az values than CNN model alone (Az values, 0.893 vs 0.820, P < 0.001; 0.906 vs 0.833, P < 0.001, respectively). The IDI index further confirmed CNN's contribution to radiologists in classifying NSNs (IDI = 25.8 % (18.3-46.1 %), P < 0.001; IDI = 30.1 % (26.1-45.2 %), P < 0.001, respectively). The CNN + radiologists' model also provided the best fit over radiologists' model and CNN model alone (AIC value 63.3 % vs. 29.5 %, 49.5 %, P < 0.001; 69.2 % vs. 34.9 %, 53.6 %, P < 0.001, respectively). CONCLUSION: CNN successfully classified NSNs based on CT images and its classification performance were superior to radiologists' model. But the classification performance of radiologists can be significantly enhanced when combined with CNN in classifying NSNs.

Chinese and global burdens of gastric cancer from 1990 to 2019.

BACKGROUND: Gastric cancer is a common cancer in China. This project investigated the disease burden of gastric cancer from 1990 to 2019 in China and globally. METHODS: The global age-standardized rates (ASRs) were extracted from the Global Burden of Disease. Moreover, the estimated annual percentage changes (eAPCs) in the ASRs of incidence (ASIR), mortality (ASMR), and disability-adjusted life-years (DALYs) were calculated to determine the trends by countries and regions. RESULTS: In China, the ASIR declined from 37.56 to 30.64 per 100,000 and the ASMR declined from 37.73 to 21.72 per 100,000. The global ASIR decreased from 22.44 to 15.59 and the ASMR declined from 20.48 to 11.88 per 100,000 persons from 1990 to 2019. The ASIR was the lowest in Malawi (3.28 per 100,000) and the highest in Mongolia (43.7 per 100,000), whereas the ASMR was the lowest in the United States of America (3.40 per 100,000) and the highest in Mongolia (40.04 per 100,000) in 2019. The incidence of early-onset gastric cancer increased in China. The DALYs attributed to gastric cancer presented a slight decrease during the period. China had a higher mortality/incidence ratio (0.845) and 5-year prevalence (27.6/100,000) than most developed countries. CONCLUSION: China presented a steady decline in the incidence and mortality rates for gastric cancer. The global ASIR, ASMR, and DALYs showed a slight rise decrease. Different patterns of gastric cancer rates and temporal trends have been identified in different geographical regions, indicating that specific strategies are needed to prevent the increase in some countries.

Plasma cytokines interleukin-18 and C-X-C motif chemokine ligand 10 are indicative of the anti-programmed cell death protein-1 treatment response in lung cancer patients.

BACKGROUND: Although programmed cell death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) checkpoint inhibitors have shown prominent efficacy for treatment of advanced lung cancer, the outcomes of metastatic lung cancer remain poor throughout the world. Although progression-free survival (PFS) and overall survival (OS) have improved in the first- and second-line therapy settings for advanced lung cancer, the response rates to PD-1/PD-L1 inhibition range from 20% to 40%. Furthermore, patients may be at risk for immune-related adverse events (irAEs); hence, appropriate patient selection is crucial. This study aimed to identify a panel of plasma cytokines representing prognostic and predictive biomarkers of the response to anti-PD-1/PD-L1 treatment. METHODS: We prospectively studied 32 lung cancer patients who received anti-PD-1/PD-L1 antibody immunotherapy. Plasma cytokines in peripheral blood samples were evaluated and analyzed using flow cytometry at the time of diagnosis and at 2 months after the initiation of PD-1/PD-L1 inhibition. RESULTS: The baseline plasma concentrations of interleukin-18 (IL-18) and C-X-C motif chemokine ligand 10 (CXCL10) were correlated with the degree of tumor response. Moreover, the magnitude of plasma IL-18 and CXCL10 level fluctuations were correlated significantly with the objective tumor response to anti-PD-1/PD-L1 immunotherapy, and patients with high CXCL10 expression had significantly shorter PFS than those with low CXCL10 expression. A strong positive correlation between the fluctuation of IL-18 and interleukin-8 (IL-8) levels was detected, as was a negative correlation between the fluctuation of IL-18 and CXCL10 levels. The level of plasma C-C motif chemokine ligand 5 (CCL5) was significantly higher in patients with irAEs than in those without irAEs. CONCLUSIONS: Plasma cytokines are related to the clinical efficacy of PD-1/PD-L1 inhibitors. IL-18 and CXCL10 are potential predictive markers for anti-PD-1/PD-L1 therapy in lung cancer patients and may play an important role in selecting patients who would benefit from PD-1/PD-L1 inhibitors.

Identification of Potential Inhibitors Against the TGF-ß/BMPs-Activin Receptor- like Kinase 1 Signal Pathway.

INTRODUCTION: In many diseased states, especially fibrosis and cancer, TGF-ß family members are overexpressed and the outcome of signaling is diverted toward disease progression. As the result of activin receptor-like kinase 1 (ALK1) plays a key role in TGF-ß signaling, discovering inhibitors of ALK1 to block TGF-ß signaling for a therapeutic benefit has become an effective strategy. METHODS: In this work, ZINC15894217 and ZINC12404282 were identified as potential ALK1 inhibitors using molecular docking, molecular dynamics simulation and MM/PBSA calculations studies. The analysis of energy decomposition found that Val208, Val216, Lys229, Gly283, Arg334 and Leu337 acted as crucial residues for ligand binding and system stabilizing. RESULTS: In addition, these compounds displayed excellent pharmacological and structural properties, which can be further evaluated through in vitro and in vivo experiments for the inhibition of ALK1 to be developed as drugs against fibrosis and tumor. CONCLUSION: Overall, our study illustrated a time- and cost-effective computer aided drug design procedure to identify potential ALK1 inhibitors. It would provide useful information for further development of ALK1 inhibitors to improve disease related to TGF-ß signal pathway.

Postoperative antibiotic management strategy for febrile patient with posterior cranial fossa tumor resection: Retrospective clinical study.

OBJECTIVE: Postoperative fever (POF), associated with posterior cranial fossa (PCF) surgery, occurs commonly and is a potential intracranial infection indicator of perioperative antibiotics prolongation and advancement. The existing prophylactic approaches to balancing the risk between intracranial infection and antibiotics abuse are debatable. METHODS: We retrospectively assessed 100 patients subjected to PCF tumor resection between December 2015 and December 2018 at a single institution. Forty febrile patients were selected for further analysis. Of them, 16 received basic and 24 advanced antibiotics and were subjected to prophylactic antibiotic assessment. RESULTS: The total POF rate of PCF tumor resection was 49.4%. POF occurred from day 1 to day 5, along with the abnormalities of cerebrospinal fluid (CSF) profiles and the mild meningeal irritation symptom. CSF cultures of all selected patients were negative. In the comparison between the basic and advanced antibiotic therapy, we found no statistically significant differences in the results of the average and dynamic analysis of the body temperature and CSF profiles. Negative results of outcome studies were also obtained in the duration of fever, duration of hospitalization, and total hospitalization expenses. However, the expenses were substantially increased in the advanced antibiotic treatment. CONCLUSIONS: Although POF is a common symptom after PCF tumor resection, definite intracranial infection is rare. A high body temperature and significant abnormal CSF profiles at an early stage may not be a specific and sufficient indicator of intracranial infection to upgrade antibiotics therapy when standard prophylactic protocols have been accurately achieved.