Establishment and verification of a prognostic model based on coagulation and fibrinolysis-related genes in hepatocellular carcinoma.

BACKGROUND: Studies have shown that coagulation and fibrinolysis (CFR) are correlated with Hepatocellular carcinoma (HCC) progression and prognosis. We aim to build a model based on CFR-correlated genes for risk assessment and prediction of HCC patient. METHODS: HCC samples were selected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases respectively. The Molecular Signatures Database (MSigDB) was used to select the CFR genes. RiskScore model were established by single sample gene set enrichment analysis (ssGSEA), weighted correlation network analysis (WGCNA), multivariate Cox regression analysis, LASSO regression analysis. RESULTS: PCDH17, PGF, PDE2A, FAM110D, FSCN1, FBLN5 were selected as the key genes and designed a RiskScore model. Those key genes were Differential expressions in HCC cell and patients. Overexpression PDE2A inhibited HCC cell migration and invasion. The higher the RiskScore, the lower the probability of survival. The model has high AUC values in the first, third and fifth year prediction curves, indicating that the model has strong prediction performance. The difference analysis of clinicopathological features found that a great proportion of high clinicopathological grade samples showed higher RiskScore. RiskScore were positively correlated with immune scores and TIDE scores. High levels of immune checkpoints and immunomodulators were observed in high RiskScore group. High RiskScore groups may benefit greatly from taking traditional chemotherapy drugs. CONCLUSIONS: We screened CFR related genes to design a RiskScore model, which could accurately evaluate the prognosis and survival status of HCC patients, providing certain value for optimizing the clinical treatment of cancer in the future.

CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis.

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

Exploring the Application of Large-Scale Pre-Trained Models on Adverse Weather Removal.

Image restoration under adverse weather conditions (e.g., rain, snow, and haze) is a fundamental computer vision problem that has important implications for various downstream applications. Distinct from early methods that are specially designed for specific types of weather, recent works tend to simultaneously remove various adverse weather effects based on either spatial feature representation learning or semantic information embedding. Inspired by various successful applications incorporating large-scale pre-trained models (e.g., CLIP), in this paper, we explore their potential benefits for leveraging large-scale pre-trained models in this task based on both spatial feature representation learning and semantic information embedding aspects: 1) spatial feature representation learning, we design a Spatially Adaptive Residual (SAR) encoder to adaptively extract degraded areas. To facilitate training of this model, we propose a Soft Residual Distillation (CLIP-SRD) strategy to transfer spatial knowledge from CLIP between clean and adverse weather images; 2) semantic information embedding, we propose a CLIP Weather Prior (CWP) embedding module to enable the network to adaptively respond to different weather conditions. This module integrates the sample-specific weather priors extracted by the CLIP image encoder with the distribution-specific information (as learned by a set of parameters) and embeds these elements using a cross-attention mechanism. Extensive experiments demonstrate that our proposed method can achieve state-of-the-art performance under various and severe adverse weather conditions. The code will be made available.

LViT: Language Meets Vision Transformer in Medical Image Segmentation.

Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the prohibitive data annotation cost. To alleviate this limitation, we propose a new text-augmented medical image segmentation model LViT (Language meets Vision Transformer). In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data. In addition, the text information can guide to generate pseudo labels of improved quality in the semi-supervised learning. We also propose an Exponential Pseudo label Iteration mechanism (EPI) to help the Pixel-Level Attention Module (PLAM) preserve local image features in semi-supervised LViT setting. In our model, LV (Language-Vision) loss is designed to supervise the training of unlabeled images using text information directly. For evaluation, we construct three multimodal medical segmentation datasets (image + text) containing X-rays and CT images. Experimental results show that our proposed LViT has superior segmentation performance in both fully-supervised and semi-supervised setting. The code and datasets are available at https://github.com/HUANGLIZI/LViT.

Large-scale pancreatic cancer detection via non-contrast CT and deep learning.

Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986-0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes.

INTRODUCTION: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. MATERIALS AND METHODS: Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3-5 images in each group were used for the results and correlated against weight changes. RESULTS: Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026). CONCLUSIONS: The best scanning conditions are 3-5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.

Temperature Resistance Properties of Unidirectional Laminated Cf/SiC-Al Prepared by PIP and Vacuum Pressure Infiltration.

Material used for aero-engine fan blade requires excellent mechanical properties at high temperature (300 °C). Continuous carbon-fiber-reinforced silicon carbide ceramic matrix composites (Cf/SiC) are necessary candidates in this field, possessing low density, high strength, high modulus, and excellent high-temperature resistance. However, during the preparation process of Cf/SiC, there were inevitably residual pores and defects inside, resulting in insufficient compressive strength and reliability. The vacuum pressure melting infiltration process was used to infiltrate low melting point and high wettability aluminum alloys into the porous Cf/SiC composite material prepared by the precursor impregnation cracking process, repairing the residual pore defects inside the body. The porosity of porous Cf/SiC decreased from 49.65% to 5.1% after aluminum alloy repair and strengthening. The mechanical properties of Cf/SiC-Al composite materials strengthened by aluminum alloy repair after heat treatment were studied. The tensile strength of the as-prepared Cf/SiC-Al was 166 ± 10 MPa, which were degraded by 13~22% after heat treatment. The nonlinear sections of stress-displacement curve of as-treated samples were shorter than that of as-prepared sample. The hardness of aluminum alloy matrix after 300 °C 1 h heat treatment was 58 Hv, which was not obviously reduced compared with the sample without heat treatment. The vacuum infiltration of aluminum alloy is expected to have guiding significance for repairing and strengthening internal defects in ceramic matrix composites.

Targeted co-delivery of PD-L1 monoclonal antibody and sorafenib to circulating tumor cells via platelet-functionalized nanocarriers.

BACKGROUND: Circulating tumor cells (CTCs) can adsorb and activate platelets to form a microthrombus protective barrier around them, so that therapeutic drugs and immune cells cannot effectively kill CTCs. The platelet membrane (PM) bionic carrying drug system has the powerful ability of immune escape, and can circulate in the blood for a long time. MATERIALS AND METHODS: we developed platelet membrane coated nanoparticles (PM HMSNs) to improve the precise delivery of drugs to tumor sites and to achieve more effective immunotherapy combined with chemotherapy strategy. RESULTS: Successfully prepared aPD-L1-PM-SO@HMSNs particles, whose diameter is 95-130 nm and presenting the same surface protein as PM. Laser confocal microscopy and flow cytometry experimental results showed that the fluorescence intensity of aPD-L1-PM-SO@HMSNs was greater than SO@HMSNs that are not coated by PM. Biodistribution studies in H22 tumor-bearing mice showed that due to the combined action of the active targeting effect and the EPR effect, the high accumulation of aPD-L1-PM-SO@HMSNs in the local tumor was more effective in inhibiting tumor growth than other groups of therapeutic agents. CONCLUSION: Platelet membrane biomimetic nanoparticles have a good targeted therapeutic effect, which can effectively avoid immune clearance and have little side effects. It provides a new direction and theoretical basis for further research on targeted therapy of CTCs in liver cancer.

Synthesis of Cyclopenta[c]quinolines by Palladium-Catalyzed Cyclization of 3-Bromoindoles with Internal Alkynes via Spirocyclic Cyclopentadiene Intermediates.

A novel method for the construction of a cyclopenta[c]quinoline ring via cyclization of 3-bromoindoles with internal alkynes in the presence of palladium is described. The formation of the cyclopenta[c]quinoline ring is proposed from a double [1,5] carbon sigmatropic rearrangement of the spirocyclic cyclopentadiene intermediate, which is generated in situ from the cyclization of 3-bromoindoles with internal alkynes involving a sequential double alkyne insertion into the carbon-palladium bond and dearomatization of indole. The present studies have developed a novel ring-expansion reaction of the pyrrole ring to pyridine via one carbon insertion into the C2-C3 bond of indoles and provided a simple and distinct route for the construction of tricyclic fused-quinoline derivatives that are not easy to access with conventional methods.

Evaluating the efficacy of endodontic microsurgery for teeth with an undeveloped root apex and periapical periodontitis after nonsurgical treatment failure.

AIM: To determine the efficacy of endodontic microsurgery for teeth with an undeveloped root apex and periapical periodontitis caused by an abnormal central cusp fracture after failed nonsurgical treatment. METHODOLOGY: Eighty teeth in 78 patients were subjected to endodontic microsurgery. All patients were clinically and radiologically examined 1 year postoperatively. The data were statistically analyzed using SPSS 27.0 software. RESULTS: Of the 80 teeth in 78 patients, periapical lesions had disappeared in 77 teeth at 1-year postoperative follow-up, with a success rate of approximately 96.3% (77/80). The efficacy of endodontic microsurgery was not affected by sex, age, extent of periapical lesions, and presence of the sinus tract. Between-group differences were not statistically significant (P > 0.05). CONCLUSIONS: Endodontic microsurgery can be an effective alternative treatment option for teeth with an undeveloped root apex and periapical periodontitis caused by an abnormal central cusp fracture after nonsurgical treatment failure.

Acoustic radiation force impulse predicts long-term outcomes in a large-scale cohort: High liver cancer, low comorbidity in hepatitis B virus.

BACKGROUND: Acoustic radiation force impulse (ARFI) is used to measure liver fibrosis and predict outcomes. The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus (HBV) than in other etiologies of chronic liver disease. AIM: To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease. METHODS: Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled. After excluding dual infection, alcoholism, autoimmune hepatitis, and others with incomplete data, this retrospective cohort were divided into hepatitis B (HBV, n = 1064), hepatitis C (HCV, n = 507), and non-HBV, non-HCV (NBNC, n = 391) groups. The indexed cases were linked to cancer registration (1987-2020) and national mortality databases. The differences in morbidity and mortality among the groups were analyzed. RESULTS: At the enrollment, the HBV group showed more males (77.5%), a higher prevalence of pre-diagnosed hepatocellular carcinoma (HCC), and a lower prevalence of comorbidities than the other groups (P < 0.001). The HCV group was older and had a lower platelet count and higher ARFI score than the other groups (P < 0.001). The NBNC group showed a higher body mass index and platelet count, a higher prevalence of pre-diagnosed non-HCC cancers (P < 0.001), especially breast cancer, and a lower prevalence of cirrhosis. Male gender, ARFI score, and HBV were independent predictors of HCC. The 5-year risk of HCC was 5.9% and 9.8% for those ARFI-graded with severe fibrosis and cirrhosis. ARFI alone had an area under the receiver operating characteristic curve (AUROC) of 0.742 for prediction of HCC in 5 years. AUROC increased to 0.828 after adding etiology, gender, age, and platelet score. No difference was found in mortality rate among the groups. CONCLUSION: The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups. Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study.

OBJECTIVE: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. BACKGROUND: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. METHODS: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. RESULTS: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk. CONCLUSIONS: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma.

Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (n = 545) and validation (n = 189) sets. Patients who had LN metastasis (LN-positive group) accounted for 340 of 734 (46%) patients. In the training set, the AI model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.91) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.58, 0.76, and 0.71, respectively. In the validation set, the AI model showed the highest performance (AUC, 0.92) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.65, 0.77, and 0.68, respectively (P < .001). AI model-predicted positive LN metastasis was associated with worse survival (hazard ratio, 1.46; 95% CI: 1.13, 1.89; P = .004). Conclusion An artificial intelligence model outperformed radiologists and clinical and radiomics models for prediction of lymph node metastasis at CT in patients with pancreatic ductal adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Lumbar Bone Mineral Density Estimation From Chest X-Ray Images: Anatomy-Aware Attentive Multi-ROI Modeling.

Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. The proposed method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. The proposed method is evaluated on 13719 CXR patient cases with ground truth BMD measured by the gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.894 on lumbar 1). When applied in osteoporosis screening, it achieves a high classification performance (average AUC of 0.968). As the first effort of using CXR scans to predict the BMD, the proposed algorithm holds strong potential to promote early osteoporosis screening and public health.

Bibliometric analysis study on cognitive function in developmental coordination disorder from 2010 to 2022.

Objective: To identify the research hotspots on cognitive function in developmental coordination disorder (DCD) in recent years, predict the research frontier and development trend, and provide more perspectives for the study of the DCD population. Methods: Using CiteSpace and VOSviewer software to draw charts, 1,082 pieces of literature about DCD and cognitive function in the Web of Science core collection database from 2010 to 2022 were visually analyzed. Results and conclusion: Interest in the cognitive function of DCD has been on the rise in the past 10 years. Over 40 countries and regions, 117 institutions and 200 researchers have participated in the corresponding research, mainly in the United States, and their institutions have published more highly influential results. The hot keywords are DCD, children, attention, working memory, performance, and attention-deficit/hyperactivity disorder (ADHD), and the main research hot topics include functional performance, population, cognitive psychology. The research directions include "DCD," "Asperger syndrome," "memory," "infant," "clumsiness," "neurodevelopmental disorder," "occupational therapy," "preschool children," "motor competence," "model," and "online control." Future research should focus on motor imagery and intrinsic models and use more neurophysiological techniques to reveal the cognitive characteristics of children with DCD and develop intervention programs.

Ginsenoside Rg1 Nanoparticles Induce Demethylation of H3K27me3 in VEGF-A and Jagged 1 Promoter Regions to Activate Angiogenesis After Ischemic Stroke.

Background: Compared with traditional drugs, nanomaterial drugs have the benefits of improving the solubility, bioavailability, and absorption rate of insoluble drugs. Nanoporous complexes can increase the efficiency with which drugs can penetrate the blood-brain barrier and reach target organs. Ginsenoside Rg1 is an effective drug that promotes angiogenesis. Ginsenoside Rg1 composite nanoparticles were employed to induce the expression of several key epigenetic enzymes and then activate the VEGF and Notch pathways after the onset of ischemic brain lesions. Methods: We constructed nanoparticles to fully encapsulate the therapeutic drug (ginsenoside Rg1), which can be transferred into brain tissue via the receptor-mediated transfer of drug-encapsulated nanoparticles. Evaluation of the therapeutic effect of ginsenoside Rg1 complex nanovesicles (CNV) was performed by in vitro and in vivo experiments. Real-time polymerase chain reaction (RT- PCR), Western blot, immunohistochemistry staining (IHC), and Co-immunoprecipitation (co-IP) were employed to screen for epigenetic enzymes with an up-regulated expression post ginsenoside Rg1-CNV intervention. RNA sequencing, shRNA knockdown, and chromatin Immunoprecipitation (ChIP) sequencing were performed to detect the target genes of ginsenoside Rg1-CNV that regulate angiogenesis. Then, bioinformatic analysis was performed to investigate the mechanism of action of epigenetic modifying enzymes in regulating target genes. Results: The average of the synthesized ginsenoside Rg1-CNV was 203.78±6.83 nm, the polydispersion index was 0.135±0.007, and the Zeta potential was 23.13±1.65 mV. Through in vivo and in vitro experiments, we found that it promotes the proliferation, migration, and tubular formation of brain microvascular endothelial cells (BMECs). Meanwhile, the intervention of ginsenoside Rg1-CNV promoted the demethylation of H3K27me3 within the promoter region of VEGF-A and Jagged1 genes and reduced the H3K27me3 modification within this region. Conclusion: The ginsenoside Rg1 nanoparticles may be an available blood-brain barrier penetrating agent for ischemic stroke.