Radiogenomic analysis based on lipid metabolism-related subset for non-invasive prediction for prognosis of renal clear cell carcinoma.

PURPOSE: Multiple lipid metabolism pathways alterations are associated with clear cell renal cell carcinoma (ccRCC) development and aggressiveness. In this study, we aim to develop a novel radiogenomics signature based on lipid metabolism-related genes (LMRGs) that may accurately predict ccRCC patients' survival. MATERIALS AND METHODS: First, 327 ccRCC were used to screen survival-related LMRGs and construct a gene signature based on The Cancer Genome Atlas (TCGA) database. Then, 182 ccRCC were analyzed to establish radiogenomics signature linking LMRGs signature to radiomic features in The Cancer Imaging Archive (TCIA) database included enhanced CT images and transcriptome sequencing data. Lastly, we validated the prognostic power of the identified radiogenomics signature using these patients of TCIA and the Third Xiangya Hospital. RESULTS: We identified the LMRGs signature, consisting of 13 genes, which could efficiently discriminate between low-risk and high-risk patients and serve as an independent and reliable predictor of overall survival (OS). Radiogenomics signature, comprised of 9 radiomic features, was created and could accurately predict the expression level of LMRGs signature (low- or high-risk) for patients. The predictive performance of this radiogenomics signature was demonstrated through AUC values of 0.75 and 0.74 for the training and validation sets (at a ratio of 7:3), respectively. Radiogenomics signature was proven to be an independent risk factor for OS by multivariable analysis (HR = 4.98, 95 % CI:1.72-14.43, P = 0.003). CONCLUSIONS: The LMRGs radiogenomics signature could serve as a novel prognostic predictor.

A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights.

Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved.

Development and validation of MRI-based scoring models for predicting placental invasiveness in high-risk women for placenta accreta spectrum.

OBJECTIVES: To develop and validate MRI-based scoring models for predicting placenta accreta spectrum (PAS) invasiveness. MATERIALS AND METHODS: This retrospective study comprised a derivation cohort and a validation cohort. The derivation cohort came from a systematic review of published studies evaluating the diagnostic performance of MRI signs for PAS and/or placenta percreta in high-risk women. The significant signs were identified and used to develop prediction models for PAS and placenta percreta. Between 2016 and 2021, consecutive high-risk pregnant women for PAS who underwent placental MRI constituted the validation cohort. Two radiologists independently evaluated the MRI signs. The reference standard was intraoperative and pathologic findings. The predictive ability of MRI-based models was evaluated using the area under the curve (AUC). RESULTS: The derivation cohort included 26 studies involving 2568 women and the validation cohort consisted of 294 women with PAS diagnosed in 258 women (88%). Quantitative meta-analysis revealed that T2-dark bands, placental/uterine bulge, loss of T2 hypointense interface, bladder wall interruption, placental heterogeneity, and abnormal intraplacental vascularity were associated with both PAS and placenta percreta, and myometrial thinning and focal exophytic mass were exclusively associated with PAS. The PAS model was validated with an AUC of 0.90 (95% CI: 0.86, 0.93) for predicting PAS and 0.85 (95% CI: 0.79, 0.90) for adverse peripartum outcome; the placenta percreta model showed an AUC of 0.92 (95% CI: 0.86, 0.98) for predicting placenta percreta. CONCLUSION: MRI-based scoring models established based on quantitative meta-analysis can accurately predict PAS, placenta percreta, and adverse peripartum outcome. CLINICAL RELEVANCE STATEMENT: These proposed MRI-based scoring models could help accurately predict PAS invasiveness and provide evidence-based risk stratification in the management of high-risk pregnant women for PAS. KEY POINTS: • Accurately identifying placenta accreta spectrum (PAS) and assessing its invasiveness depending solely on individual MRI signs remained challenging. • MRI-based scoring models, established through quantitative meta-analysis of multiple MRI signs, offered the potential to predict PAS invasiveness in high-risk pregnant women. • These MRI-based models allowed for evidence-based risk stratification in the management of pregnancies suspected of having PAS.

CT enterography-based radiomics combined with body composition to predict infliximab treatment failure in Crohn's disease.

PURPOSE: Accurately predicting the treatment response in patients with Crohn's disease (CD) receiving infliximab therapy is crucial for clinical decision-making. We aimed to construct a prediction model incorporating radiomics and body composition features derived from computed tomography (CT) enterography for identifying individuals at high risk for infliximab treatment failure. METHODS: This retrospective study included 137 patients with CD between 2015 and 2021, who were divided into a training cohort and a validation cohort with a ratio of 7:3. Patients underwent CT enterography examinations within 1 month before infliximab initiation. Radiomic features of the intestinal segments involved were extracted, and body composition features were measured at the level of the L3 lumbar vertebra. A model that combined radiomics with body composition was constructed. The primary outcome was the occurrence of infliximab treatment failure within 1 year. The model performance was evaluated using discrimination, calibration, and decision curves. RESULTS: Fifty-two patients (38.0%) showed infliximab treatment failure. Eight significant radiomic features were used to develop the radiomics model. The model incorporating radiomics model score, skeletal muscle index (SMI), and creeping fat showed good discrimination for predicting infliximab treatment failure, with an area under the curve (AUC) of 0.88 (95% CI 0.81, 0.95) in the training cohort and 0.83 (95% CI 0.66, 1.00) in the validation cohort. The favorable clinical application was observed using decision curve analysis. CONCLUSIONS: We constructed a comprehensive model incorporating radiomics and muscle volume, which could potentially be used to facilitate the individualized prediction of infliximab treatment response in patients with CD.

Establishment and application of the BRP prognosis model for idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial lung disease. Clinical models to accurately evaluate the prognosis of IPF are currently lacking. This study aimed to construct an easy-to-use and robust prediction model for transplant-free survival (TFS) of IPF based on clinical and radiological information. METHODS: A multicenter prognostic study was conducted involving 166 IPF patients who were followed up for 3 years. The end point of follow-up was death or lung transplantation. Clinical information, lung function tests, and chest computed tomography (CT) scans were collected. Body composition quantification on CT was performed using 3D Slicer software. Risk factors in blood routine examination-radiology-pulmonary function (BRP) were identified by Cox regression and utilized to construct the "BRP Prognosis Model". The performance of the BRP model and the gender-age-physiology variables (GAP) model was compared using time-ROC curves, calibration curves, and decision curve analysis (DCA). Furthermore, histopathology fibrosis scores in clinical specimens were compared between the different risk stratifications identified by the BRP model. The correlations among body composition, lung function, serum inflammatory factors, and profibrotic factors were analyzed. RESULTS: Neutrophil percentage > 68.3%, pericardial adipose tissue (PAT) > 94.91 cm3, pectoralis muscle radiodensity (PMD) ≤ 36.24 HU, diffusing capacity of the lung for carbon monoxide/alveolar ventilation (DLCO/VA) ≤ 56.03%, and maximum vital capacity (VCmax) < 90.5% were identified as independent risk factors for poor TFS among patients with IPF. We constructed a BRP model, which showed superior accuracy, discrimination, and clinical practicability to the GAP model. Median TFS differed significantly among patients at different risk levels identified by the BRP model (low risk: TFS > 3 years; intermediate risk: TFS = 2-3 years; high risk: TFS ≈ 1 year). Patients with a high-risk stratification according to the BRP model had a higher fibrosis score on histopathology. Additionally, serum proinflammatory markers were positively correlated with visceral fat volume and infiltration. CONCLUSIONS: In this study, the BRP prognostic model of IPF was successfully constructed and validated. Compared with the commonly used GAP model, the BRP model had better performance and generalization with easily obtainable indicators. The BRP model is suitable for clinical promotion.

Wireless Electrical Signals Induce Functional Neuronal Differentiation of BMSCs on 3D Graphene Framework Driven by Magnetic Field.

Bone marrow mesenchymal stem cells (BMSCs) are suggested as candidates for neurodegeneration therapy by autologous stem cells to overcome the lack of neural stem cells in adults. However, the differentiation of BMSCs into functional neurons is a major challenge for neurotherapy. Herein, a methodology has been proposed to induce functional neuronal differentiation of BMSCs on a conductive three-dimensional graphene framework (GFs) combined with a rotating magnetic field. A wireless electrical signal of about 10 µA can be generated on the surface of GFs by cutting the magnetic field lines based on the well-known electromagnetic induction effect, which has been proven to be suitable for inducing neuronal differentiation of BMSCs. The enhanced expressions of the specific genes/proteins and apparent Ca2+ intracellular flow indicate that BMSCs cultured on GFs with 15 min/day rotating magnetic field stimulation for 15 days can differentiate functional neurons without any neural inducing factor. The animal experiments confirm the neural differentiation of BMSCs on GFs after transplantation in vivo, accompanied by stimulation of an external rotating magnetic field. This study overcomes the lack of autologous neural stem cells for adult neurodegeneration patients and provides a facile and safe strategy to induce the neural differentiation of BMSCs, which has potential for clinical applications of neural tissue engineering.

High-power, sub-100-fs, 1600-1700-nm all-fiber laser for deep multiphoton microscopy.

The 1600-1700-nm ultrafast fiber lasers attract great interests in the deep multiphoton microscopy, due to the reduced levels of the tissue scattering and absorption. Here, we report on the 86.7-MHz, 717-mW, 91.2-fs, all-fiber laser located in the spectral range from 1600 nm to 1700nm. The soliton self-frequency shift (SSFS) was introduced into the Er:Yb co-doped fiber amplifier (EYDFA) to generate the high-power, 1600-1700-nm Raman soliton. Detailed investigations of the nonlinear fiber amplification process were implemented in optimizing the generated Raman soliton pulses. The miniature multiphoton microscopy was further realized with this home-built laser source. The clearly imaging results can be achieved by collecting the generated harmonic signals from the mouse tail skin tissue with a penetration depth of ∼500 µm. The experimental results indicate the great potential in utilizing this 1600-1700-nm fiber laser in the deep multiphoton microscopy.

Trimanganese Tetroxide Nanozyme protects Cartilage against Degeneration by Reducing Oxidative Stress in Osteoarthritis.

Osteoarthritis, a chronic degenerative cartilage disease, is the leading cause of movement disorders among humans. Although the specific pathogenesis and associated mechanisms remain unclear, oxidative stress-induced metabolic imbalance in chondrocytes plays a crucial role in the occurrence and development of osteoarthritis. In this study, a trimanganese tetroxide (Mn3 O4 ) nanozyme with superoxide dismutase (SOD)-like and catalase (CAT)-like activities is designed to reduce oxidative stress-induced damage and its therapeutic effect is investigated. In vitro, Mn3 O4 nanozymes are confirmed to reprogram both the imbalance of metabolism in chondrocytes and the uncontrolled inflammatory response stimulated by hydrogen peroxide. In vivo, a cross-linked chondroitin sulfate (CS) hydrogel is designed as a substrate for Mn3 O4 nanozymes to treat osteoarthritis in mouse models. As a result, even in the early stage of OA (4 weeks), the therapeutic effect of the Mn3 O4 @CS hydrogel is observed in both cartilage metabolism and inflammation. Moreover, the Mn3 O4 @CS hydrogel maintained its therapeutic effects for at least 7 days, thus revealing a broad scope for future clinical applications. In conclusion, these results suggest that the Mn3 O4 @CS hydrogel is a potentially effective therapeutic treatment for osteoarthritis, and a novel therapeutic strategy for osteoarthritis based on nanozymes is proposed.

SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening and Lesion Segmentation.

Insufficient annotated data and minor lung lesions pose big challenges for computed tomography (CT)-aided automatic COVID-19 diagnosis at an early outbreak stage. To address this issue, we propose a Semi-Supervised Tri-Branch Network (SS-TBN). First, we develop a joint TBN model for dual-task application scenarios of image segmentation and classification such as CT-based COVID-19 diagnosis, in which pixel-level lesion segmentation and slice-level infection classification branches are simultaneously trained via lesion attention, and individual-level diagnosis branch aggregates slice-level outputs for COVID-19 screening. Second, we propose a novel hybrid semi-supervised learning method to make full use of unlabeled data, combining a new double-threshold pseudo labeling method specifically designed to the joint model and a new inter-slice consistency regularization method specifically tailored to CT images. Besides two publicly available external datasets, we collect internal and our own external datasets including 210,395 images (1,420 cases versus 498 controls) from ten hospitals. Experimental results show that the proposed method achieves state-of-the-art performance in COVID-19 classification with limited annotated data even if lesions are subtle, and that segmentation results promote interpretability for diagnosis, suggesting the potential of the SS-TBN in early screening in insufficient labeled data situations at the early stage of a pandemic outbreak like COVID-19.

Fault Diagnosis for Complex Equipment Based on Belief Rule Base with Adaptive Nonlinear Membership Function.

Fault diagnosis of complex equipment has become a hot field in recent years. Due to excellent uncertainty processing capability and small sample problem modeling capability, belief rule base (BRB) has been widely used in the fault diagnosis. However, previous BRB models almost did not consider the diverse distributions of observation data which may reduce diagnostic accuracy. In this paper, a new fault diagnosis model based on BRB is proposed. Considering that the previous triangular membership function cannot address the diverse distribution of observation data, a new nonlinear membership function is proposed to transform the input information. Then, since the model parameters initially determined by experts are inaccurate, a new parameter optimization model with the parameters of the nonlinear membership function is proposed and driven by the gradient descent method to prevent the expert knowledge from being destroyed. A fault diagnosis case of laser gyro is used to verify the validity of the proposed model. In the case study, the diagnosis accuracy of the new BRB-based fault diagnosis model reached 95.56%, which shows better fault diagnosis performance than other methods.

Eclampsia with hypothyroidism complicated with posterior reversible encephalopathy syndrome-a case report.

BACKGROUND: Posterior reversible encephalopathy syndrome (PRES) is a rare neurological disorder with complex physiopathological mechanisms that have not been fully understood. Early identification is of great prognostic significance, of which the symptoms and radiological abnormalities can be completely reversed. If the diagnosis and treatment are delayed, ischemia and massive infarction may be developed in some patients. Posterior reversible encephalopathy syndrome (PRES) has been reported mainly in association with postpartum eclampsia, which have been rarely reported, while the association with hypothyroidism has not been reported at home or abroad. CASE PRESENTATION: Here we report on a pregnant 29-year-old with multipara and a chief complication of hypothyroidism. She presented in the emergency department with frequent attacks of severe headache symptoms resulting from reversible cerebral vasoconstriction syndrome (RCVS), accompanied with prenatal eclampsia. PRES was determined by radiological examination. CONCLUSION: To the best of our knowledge, this is the first case of PRES complicated by hypothyroidism and prepartum eclampsia.Clinicians should be alert for the co-occurence of eclampsia, PRES, and RCVS when patients have convulsions after a typical throbbing headache. Moreover, regular monitoring of thyroid function during pregnancy should also occupy certain special attention.

CT Radiomics to Predict Macrotrabecular-Massive Subtype and Immune Status in Hepatocellular Carcinoma.

Background Macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) is an aggressive variant associated with angiogenesis and immunosuppressive tumor microenvironment, which is expected to be noninvasively identified using radiomics approaches. Purpose To construct a CT radiomics model to predict the MTM subtype and to investigate the underlying immune infiltration patterns. Materials and Methods This study included five retrospective data sets and one prospective data set from three academic medical centers between January 2015 and December 2021. The preoperative liver contrast-enhanced CT studies of 365 adult patients with resected HCC were evaluated. The Third Xiangya Hospital of Central South University provided the training set and internal test set, while Yueyang Central Hospital and Hunan Cancer Hospital provided the external test sets. Radiomic features were extracted and used to develop a radiomics model with machine learning in the training set, and the performance was verified in the two test sets. The outcomes cohort, including 58 adult patients with advanced HCC undergoing transarterial chemoembolization and antiangiogenic therapy, was used to evaluate the predictive value of the radiomics model for progression-free survival (PFS). Bulk RNA sequencing of tumors from 41 patients in The Cancer Genome Atlas (TCGA) and single-cell RNA sequencing from seven prospectively enrolled participants were used to investigate the radiomics-related immune infiltration patterns. Area under the receiver operating characteristics curve of the radiomics model was calculated, and Cox proportional regression was performed to identify predictors of PFS. Results Among 365 patients (mean age, 55 years ± 10 [SD]; 319 men) used for radiomics modeling, 122 (33%) were confirmed to have the MTM subtype. The radiomics model included 11 radiomic features and showed good performance for predicting the MTM subtype, with AUCs of 0.84, 0.80, and 0.74 in the training set, internal test set, and external test set, respectively. A low radiomics model score relative to the median value in the outcomes cohort was independently associated with PFS (hazard ratio, 0.4; 95% CI: 0.2, 0.8; P = .01). The radiomics model was associated with dysregulated humoral immunity involving B-cell infiltration and immunoglobulin synthesis. Conclusion Accurate prediction of the macrotrabecular-massive subtype in patients with hepatocellular carcinoma was achieved using a CT radiomics model, which was also associated with defective humoral immunity. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Yoon and Kim in this issue.

A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading.

Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOLO version 3 (YOLOv3) for automatic simultaneous localization of knee joints and quantification of radiographic knee OA. As an advanced deep convolutional neural network (CNN) algorithm for target detection, YOLOv3 enables simultaneous small object detection and quantification due to its unique residual connection and feature map merging. Hence, a unified CNN model is built for the elegant integration of knee joint detection and corresponding OA severity grading using the YOLOv3 framework. We achieve desirable accuracy in knee OA grading using the public and clinical datasets. It provides improvements in the precision, recall, F1 score and diagnostic accuracy of knee OA as well. Because of the fully automatic target detection and quantification, the time of handling an image is merely 40 ms from inputting the image to getting its label, supporting quick clinic decisions. It, thus, affords convenient and efficient image analysis for daily clinical diagnosis.

A New Topology-Switching Strategy for Fault Diagnosis of Multi-Agent Systems Based on Belief Rule Base.

Effective fault-diagnosis strategies have been the focus of research on multi-agent systems (MASs). In this paper, the belief rule base (BRB)-based distributed fault-diagnosis problem for MASs is investigated, and a topology-switching strategy is developed to increase the reliability of fault-diagnosis model. Firstly, a BRB-based distributed fault-diagnosis model is constructed for the MAS with multiple faults, then expert knowledge is used to judge whether the agent is faulty. Then, considering that the system may be influenced by the fault or some other factors and thus leading to a decrease in the accuracy of the fault-diagnosis results, a topology-switching strategy based on the average distance of the output diagnosis accuracy is proposed to update the topology of the agent so that the fault-diagnosis results can be more reliable. Note that the topology-switching threshold is designed based on the average distance between the accuracy of the fault diagnosis of each agent. The method proposed in this paper can solve the problem when the fault-diagnosis accuracy of the model is affected by some common factors and thus decreases, and can improve the reliability of the fault-diagnosis model very well. Finally, the effectiveness of the BRB-based distributed fault-diagnosis model and the proposed topology-switching strategy to improve the fault-diagnosis accuracy is verified by simulation examples.

Treatment-related adverse events of PD-1/PD-L1 inhibitors combined with CTLA-4 inhibitors in clinical trials: a meta-analysis.

AIM: PD-1/PD-L1 inhibitors in combination with CTLA-4 inhibitors are being tested in a number of ongoing clinical trials. As a result, it is critical to fully comprehend the toxicity characteristics of adverse events in combination therapy. This study aims to extensively compare the incidences and ORs of treatment-related adverse events between two combination strategies. METHODS: The eligible articles were searched from PubMed, EMBASE and Cochrane databases for studies published between 1 January 2010 and 1 May 2021, investigating PD-1/PD-L1 inhibitors plus CTLA-4 inhibitor-based combined clinical therapies. The mean incidences and pooled ORs of all-grade and grade 3 or higher adverse events were calculated by random-effects model using Stata 12.1. Heterogeneity between studies was assessed with I2 statistics and Chi square-based Q statistic. The overall risk of bias was assessed by Review Manager 5.3. RESULTS: A total of 26 eligible studies of 3607 patients were selected; 2852 patients developed at least one all-grade adverse event. PD-L1 inhibitors plus CTLA-4 inhibitors regimen (incidence 0.67, 95% CI: 0.57-0.77) had marked advantage over PD-1 inhibitors plus CTLA-4 inhibitors regimen (incidence 0.89, 95% CI: 0.86-0.93). CONCLUSION: PD-L1 inhibitors plus CTLA-4 inhibitors shows better safety in treatment-related adverse events than PD-1 inhibitors plus CTLA-4 inhibitors.

Influence of different region of interest sizes on CT-based radiomics model for microvascular invasion prediction in hepatocellular carcinoma. / æ„Ÿå ´è¶£åŒºèŒƒå›´å¯¹CT影像组学模型预测肝细胞癌微血管侵犯的影响.

OBJECTIVES: Microvascular invasion (MVI) is an important predictor of postoperative recurrence or poor outcomes of hepatocellular carcinoma (HCC). Radiomics is able to predict MVI in HCC preoperatively. This study aims to investigate the influence of different region of interest (ROI) sizes on CT-based radiomics model for MVI prediction in HCC. METHODS: Patients with HCC with or without MVI confirmed by pathology and those who underwent preoperative plain or enhanced abdominal CT scans in the Third Xiangya Hospital of Central South University from January 2010 to December 2020 were retrospectively and consecutively included. According to the ratio of 7 to 3, the patients were randomly assigned into a training set and a validation set. Clinical data were collected from medical records, and radiomics features were extracted from the arterial phase (AP) and portal venous phase (PVP) of preoperatively acquired CT in all patients. Six different ROI sizes were employed. The original ROI (OROI) was manually delineated along the visible borders of the tumor layer-by-layer. The OROI was expanded out by 1-5 mm. The OROI was combined with 5 different peritumoral regions to generate the other 5 ROIs, named Plus1-Plus5. Feature extraction, dimension reduction, and model development were conducted in 6 different ROIs separately. Supporter vector machine (SVM) was used for model construction. Model performance was assessed via receiver operating characteristic (ROC) curve. RESULTS: A total of 172 HCC patients were included, in which 83 (48.3%) were MVI positive, and 89 (51.7%) were MVI negative. Three hundred and ninety-six features based on AP or PVP images were extracted from each ROI. After feature selection and dimension reduction, 4, 5, 15, 11, 6, and 3 features of OROI, Plus1, Plus2, Plus 3, Plus4, and Plus5 were selected for model construction, respectively. In the training set, the sensitivity, specificity, and area under the curve (AUC) of OROI were 0.759, 0.806, and 0.855, respectively. The AUC values of Plus2 (0.979) and Plus3 (0.954) were higher than that of OROI. The AUC values of Plus1 (0.802), Plus4 (0.792), and Plus5 (0.774) were not significantly different from those of OROI. In the validation set, the sensitivity, specificity, and AUC value of OROI were 0.640, 0.630, and 0.664, respectively. The AUC value of Plus3 was 0.903, which was higher than that of OROI. The AUC values of Plus1 (0.679), Plus2 (0.536), Plus4 (0.708), and Plus5 (0.757) were not significantly different from that of OROI (P>0.05). CONCLUSIONS: The size of ROI significantly inflluences on the performance of CT-based radiomics model for MVI prediction in HCC. Including appropriate area around the tumor into ROI could improve the predictive performance of the model, and 3 mm might be appropriate distance.

Overlapping syndrome of recurrent anti-N-methyl-D-aspartate receptor encephalitis and anti-myelin oligodendrocyte glycoprotein demyelinating diseases: A case report.

BACKGROUND: Anti-N-methyl-D-aspartate receptor encephalitis (NMDARe) is capable of presenting a relapsing course and coexisting with myelin oligodendrocyte glycoprotein antibody disease, whereas it has been relatively rare. We describe a man with no history of tumor who successively developed anti-NMDARe and anti-myelin oligodendrocyte glycoprotein antibody disease. CASE SUMMARY: A 29-year-old man was initially admitted with headache, fever, intermittent abnormal behavior, decreased intelligence, limb twitching and loss of consciousness on July 16, 2018. On admission, examination reported no abnormality. During his presentation, he experienced aggravated symptoms, and the re-examination of cranial magnetic resonance imaging (MRI) indicated punctate abnormal signals in the left parietal lobe. External examination of cerebrospinal fluid and serum results revealed serum NMDAR antibody (Ab) (-), cerebrospinal fluid NMDAR-Ab (+) 1:10 and Epstein-Barr virus capsid antigen antibody IgG (+). Due to the imaging findings, anti-NMDARe was our primary consideration. The patient was treated with methylprednisolone and gamma globulin pulse therapy, mannitol injection dehydration to reduce intracranial pressure, sodium valproate sustained-release tablets for anti-epilepsy and olanzapine and risperidone to mitigate psychiatric symptoms. The patient was admitted to the hospital for the second time for "abnormal mental behavior and increased limb movements" on December 14, 2018. Re-examination of electroencephalography and cranial MRI showed no abnormality. The results of autoimmune encephalitis antibody revealed that serum NMDAR-Ab was weakly positive and cerebrospinal fluid NMDAR-Ab was positive. Considering comprehensive recurrent anti-NMDARe, the patient was treated with propylene-hormone pulse combined with immunosuppressive agents (mycophenolate mofetil), and the symptoms were relieved. The patient was admitted for "hoarseness and double vision" for the third time on August 23, 2019. Re-examination of cranial MRI showed abnormal signals in the medulla oblongata and right frontal lobe, and synoptophore examination indicated concomitant esotropia. The patient's visual acuity further decreased, and the re-examination of cranial MRI + enhancement reported multiple scattered speckled and patchy abnormal signals in the medulla oblongata, left pons arm, left cerebellum and right midbrain, thalamus. The patient was diagnosed with an accompanying demyelinating disease. Serum anti-myelin oligodendrocyte glycoprotein 1:10 and NMDAR antibody 1:10 were both positive. The patient was diagnosed with myelin oligodendrocyte glycoprotein antibody-related inflammatory demyelinating disease of the central nervous system complicated with anti-NMDARe overlap syndrome. The patient was successfully treated with methylprednisolone, gamma globulin pulse therapy and rituximab treatment. The patient remained asymptomatic and follow-up MRI scan 6 mo later showed complete removal of the lesion. CONCLUSION: We emphasize the rarity of this antibody combination and suggest that these patients may require longer follow-up due to the risk of recurrence of two autoimmune disorders.

Assessment of Solid Pulmonary Nodules or Masses Using Zero Echo Time MR Lung Imaging: A Prospective Head-to-Head Comparison With CT.

Objective: The aim of this study is to determine the potential of zero echo time (ZTE) MR lung imaging in the assessment of solid pulmonary nodules or masses and diagnostic consistency to CT in terms of morphologic characterization. Methods: Our Institutional Review Board approved this prospective study. Seventy-one patients with solid pulmonary nodules or masses larger than 1 cm in diameter confirmed by chest CT were enrolled and underwent further lung ZTE-MRI scans within 7 days. ZTE-MRI and CT images were compared in terms of image quality and imaging features. Unidimensional diameter and three-dimensional volume measurements on both modalities were manually measured and compared using the Wilcoxon signed-rank test, intraclass correlation coefficient (ICC), Pearson's correlation analysis, and Bland-Altman analysis. Multivariable logistic regression analysis was used to identify the factors associated with significant inter-modality variation of volume. Results: Fifty-four of 71 (76.1%) patients were diagnosed with lung cancer. Subjective image quality was superior in CT compared with ZTE-MRI (p < 0.001). Inter-modality agreement for the imaging features was moderate for emphysema (kappa = 0.50), substantial for fibrosis (kappa = 0.76), and almost perfect (kappa = 0.88-1.00) for the remaining features. The size measurements including diameter and volume between ZTE-MRI and CT showed no significant difference (p = 0.36 for diameter and 0.60 for volume) and revealed perfect inter-observer (ICC = 0.975-0.980) and inter-modality (ICC = 0.942-0.992) agreements. Multivariable analysis showed that non-smooth margin [odds ratio (OR) = 6.008, p = 0.015] was an independent predictor for the significant inter-modality variation of volume. Conclusion: ZTE lung imaging is feasible as a part of chest MRI in the assessment and surveillance for solid pulmonary nodules or masses larger than 1 cm, presenting perfect agreement with CT in terms of morphologic characterization.

Stem Cell Membrane-Encapsulated Zeolitic Imidazolate Framework-8: A Targeted Nano-Platform for Osteogenic Differentiation.

Mesenchymal stem cells (MSCs) have been recognized as one of the most promising pharmaceutical multipotent cells, and a key step for their wide application is to safely and efficiently regulate their activities. Various methods have been proposed to regulate the directional differentiation of MSCs during tissue regeneration, such as nanoparticles and metal ions. Herein, nanoscale zeolitic imidazolate framework-8 (ZIF-8), a Zn-based metal-organic framework, is modified to direct MSCs toward an osteoblast lineage. Specifically, ZIF-8 nanoparticles are encapsulated using stem cell membranes (SCMs) to mimic natural molecules and improve the biocompatibility and targeted ability toward MSCs. SCM/ZIF-8 nanoparticles adjust the sustained release of Zn2+ , and promote their specific internalization toward MSCs. The internalized SCM/ZIF-8 nanoparticles show excellent biocompatibility, and increase MSCs' osteogenic potentials. Moreover, RNA-sequencing results elucidate that the activated cyclic adenosine 3,5-monophosphate (cAMP)-PKA-CREB signaling pathway can be dominant in accelerating osteogenic differentiation. In vivo, SCM/ZIF-8 nanoparticles greatly promote the formation of new bone tissue in the femoral bone defect detected by 3D micro-CT, hematoxylin and eosin staining, and Masson staining after 4 weeks. Overall, the SCM-derived ZIF-8 nanostructures achieve the superior targeting ability, biocompatibility, and enhanced osteogenesis, providing a constructive design for tissue repair.

Biomimetic Metal-Organic Frameworks as Targeted Vehicles to Enhance Osteogenesis.

Although engineered nanoparticles loaded with specific growth factors are used to regulate differentiation of stem cells, the low loading efficiency and biocompatibility are still great challenges in tissue repair. A nature-inspired biomimetic delivery system with targeted functions is attractive for enhancing cell activity and controlling cell fate. Herein, a stem cell membrane (SCM)-wrapped dexamethasone (DEX)-loaded zeolitic imidazolate framework-8 (ZIF-8) is constructed, which integrates the synthetic nanomaterials with native plasma membrane, to achieve efficient DEX delivery and DEX-mediated bone repair. The DEX@ZIF-8-SCM enables high DEX loading capacity, modulates the sustained release, and facilitates the specific uptake of mesenchymal stem cells (MSCs), owing to the porous property of ZIF-8 and the innate targeting capability of SCM. The endocytosed DEX@ZIF-8-SCM shows high cytocompatibility and greatly enhances the osteogenic differentiation of MSCs. Furthermore, RNA-sequencing data reveal that the phosphoinositide 3-kinase (PI3K)-Akt signaling pathways are activated and dominantly involved in the accelerated osteogenesis. In the bone defect model, the administrated DEX@ZIF-8-SCM exerts excellent biocompatibility and effectively promotes bone regeneration. Overall, the SCM-derived biomimetic nanoplatform achieves targeted delivery, excellent biosafety, and enhanced osteogenic differentiation and bone repair, which provides a new and valid strategy for treating various tissue injuries.