An Immunocompetent Mongolian Gerbil Model for Hepatitis E Virus Genotype 1 Infection.

BACKGROUND AND AIMS: Hepatitis E virus (HEV), primarily genotype 1 (HEV-1), causes approximately 20.1 million infections, 44,000 deaths, and 3000 stillbirths annually. Current evidence indicates that HEV-1 is only transmitted in humans. Here, we evaluated whether Mongolian gerbils can serve as animal models for HEV-1 infection. METHODS: Mongolian gerbils were used for HEV-1 and hepatitis E virus genotype 3 infection experiments. HEV infection parameters, including detection of HEV RNA and HEV antigen, liver function assessment, and histopathology, were evaluated. RESULTS: We adapted a clinical isolate of HEV-1 for Mongolian gerbils by serial passaging in feces of aged male gerbils. The gerbil-adapted strain obtained at passage 3 induced a robust, acute HEV infection, characterized by stable fecal virus shedding, elevated liver enzymes, histopathologic changes in the liver, and seroconversion to anti-HEV. An infectious complementary DNA clone of the adapted virus was generated. HEV-1-infected pregnant gerbils showed a high rate of maternal mortality and vertical transmission. HEV RNA or antigens were detected in the liver, kidney, intestine, placenta, testis, and fetus liver. Liver and placental transcriptomic analyses indicated activation of host immunity. Tacrolimus prolonged HEV-1 infection, whereas ribavirin cleared infection. The protective efficacy of a licensed HEV vaccine was validated using this model. CONCLUSIONS: HEV-1 efficiently infected Mongolian gerbils. This HEV-1 infection model will be valuable for investigating hepatitis E immunopathogenesis and evaluating vaccines and antivirals against HEV.

Deep learning in the precise assessment of primary Sjögren's Syndrome based on ultrasound images.

OBJECTIVES: This study aimed to investigate the value of a deep learning (DL) model based on greyscale ultrasound (US) images for precise assessment and accurate diagnosis of primary Sjögren's syndrome (pSS). METHODS: This was a multicentre prospective analysis. All pSS patients were diagnosed according to 2016 ACR/EULAR criteria. 72 pSS patients and 72 sex- and age-matched healthy controls recruited between January 2022 and April 2023, together with 41 patients and 41 healthy controls recruited from June 2023 to February 2024 were used for DL model development and validation, respectively. DL model was constructed based on the ResNet 50, input with preprocessed all participants' bilateral submandibular glands (SMGs), parotid glands (PGs), and lacrimal glands (LGs) greyscale US images. Diagnostic performance of the model was compared with two radiologists. The accuracy of prediction and identification performance of DL model were evaluated by calibration curve. RESULTS: 864 and 164 greyscale US images of SMGs, PGs, and LGs were collected for development and validation of the model. The AUCs of DL model in the SMG, PG, and LG were 0.92, 0.93, 0.91 in the model cohort, and were 0.90, 0.88, 0.87 in the validation cohort respectively, outperforming both radiologists. Calibration curves showed the prediction probability of DL model were consistent with the actual probability in both model cohort and validation cohort. CONCLUSION: DL model based on greyscale US images showed diagnostic potential in the precise assessment of pSS patients in the SMG, PG, and LG, outperforming conventional radiologist evaluation.

MRI-Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Growing up without health insurance: Evidence from rural China.

This paper examines the long-term effects of health insurance on children's educational attainment in a developing country. Utilizing the county-by-county rollout of the New Rural Cooperative Medical Scheme in rural China, we find that exposure to the health insurance program in early life leads to improved educational attainment in adulthood. Empirical tests suggest that a short-term increase in health care utilization is unlikely to be a potential channel. We provide some evidence for the channel that health insurance reduces household financial burdens and increases household expenditure on children's nutrition.

Development of an atmospheric plasma jet device for versatile treatment of electron microscope sample grids.

Atmospheric-pressure plasmas have been widely applied for surface modification and biomedical treatment because of their ability to generate highly reactive radicals and charged particles. In negative-stain electron microscopy (Neg-EM) and cryogenic electron microscopy (cryo-EM), plasmas have been used to generate hydrophilic surfaces and eliminate surface contaminants to embed specimens onto grids. In addition, plasma treatment is a prerequisite for negative-stain and Quantifoil grids, whose surfaces are coated with hydrophobic amorphous carbon. Although the conventional glow discharge system has been used successfully in this purpose, there has been no further effort to take an advantage from the recent progress in the plasma field. Here, we developed a nonthermal atmospheric plasma jet system as an alternative tool for treatment of surfaces. The low-temperature plasma is a nonequilibrium system that has been widely used in biomedical area. Unlike conventional glow discharge systems, the plasma jet system successfully cleans and introduces hydrophilicity on the grid surface in the ambient environment without a vacuum. Therefore, we anticipate that the plasma jet system will have numerous benefits, such as convenience and versatility, as well as having potential applications in surface modification for both negative-stain and cryo-EM grid treatment.

Predicting Microvascular Invasion in Hepatocellular Carcinoma Using CT-based Radiomics Model.

Background Prediction of microvascular invasion (MVI) may help determine treatment strategies for hepatocellular carcinoma (HCC). Purpose To develop a radiomics approach for predicting MVI status based on preoperative multiphase CT images and to identify MVI-associated differentially expressed genes. Materials and Methods Patients with pathologically proven HCC from May 2012 to September 2020 were retrospectively included from four medical centers. Radiomics features were extracted from tumors and peritumor regions on preoperative registration or subtraction CT images. In the training set, these features were used to build five radiomics models via logistic regression after feature reduction. The models were tested using internal and external test sets against a pathologic reference standard to calculate area under the receiver operating characteristic curve (AUC). The optimal AUC radiomics model and clinical-radiologic characteristics were combined to build the hybrid model. The log-rank test was used in the outcome cohort (Kunming center) to analyze early recurrence-free survival and overall survival based on high versus low model-derived score. RNA sequencing data from The Cancer Image Archive were used for gene expression analysis. Results A total of 773 patients (median age, 59 years; IQR, 49-64 years; 633 men) were divided into the training set (n = 334), internal test set (n = 142), external test set (n = 141), outcome cohort (n = 121), and RNA sequencing analysis set (n = 35). The AUCs from the radiomics and hybrid models, respectively, were 0.76 and 0.86 for the internal test set and 0.72 and 0.84 for the external test set. Early recurrence-free survival (P < .01) and overall survival (P < .007) can be categorized using the hybrid model. Differentially expressed genes in patients with findings positive for MVI were involved in glucose metabolism. Conclusion The hybrid model showed the best performance in prediction of MVI. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Summers in this issue.

An imaging-based machine learning model outperforms clinical risk scores for prognosis of cirrhotic variceal bleeding.

OBJECTIVES: To develop and validate a machine learning model based on contrast-enhanced CT to predict the risk of occurrence of the composite clinical endpoint (hospital-based intervention or death) in cirrhotic patients with acute variceal bleeding (AVB). METHODS: This retrospective study enrolled 330 cirrhotic patients with AVB between January 2017 and December 2020 from three clinical centers. Contrast-enhanced CT and clinical data were collected. Centers A and B were divided 7:3 into a training set and an internal test set, and center C served as a separate external test set. A well-trained deep learning model was applied to segment the liver and spleen. Then, we extracted 106 original features of the liver and spleen separately based on the Image Biomarker Standardization Initiative (IBSI). We constructed the Liver-Spleen (LS) model based on the selected radiomics features. The performance of LS model was evaluated by receiver operating characteristics and calibration curves. The clinical utility of models was analyzed using decision curve analyses (DCA). RESULTS: The LS model demonstrated the best diagnostic performance in predicting the composite clinical endpoint of AVB in patients with cirrhosis, with an AUC of 0.782 (95% CI 0.650-0.882) and 0.789 (95% CI 0.674-0.878) in the internal test and external test groups, respectively. Calibration curves and DCA indicated the LS model had better performance than traditional clinical scores. CONCLUSION: A novel machine learning model outperforms previously known clinical risk scores in assessing the prognosis of cirrhotic patients with AVB CLINICAL RELEVANCE STATEMENT: The Liver-Spleen model based on contrast-enhanced CT has proven to be a promising tool to predict the prognosis of cirrhotic patients with acute variceal bleeding, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS: • The Liver-Spleen machine learning model (LS model) showed good performance in assessing the clinical composite endpoint of cirrhotic patients with AVB (AUC ≥ 0.782, sensitivity ≥ 80%). • The LS model outperformed the clinical scores (AUC ≤ 0.730, sensitivity ≤ 70%) in both internal and external test cohorts.

The social scar of the pandemic: Impacts of COVID-19 exposure on interpersonal trust.

This paper employs a difference-in-differences strategy to examine the causal effect of exposure to the COVID-19 pandemic on interpersonal trust amidst zero-COVID policies in China. Using a nationally representative panel survey, we find that COVID-19 exposure leads to a decrease in the levels of generalized trust. We also show that the change in interpersonal trust varies across domains. Specifically, COVID-19 exposure significantly decreases trust in parents, neighbors, and local government officials, but has small and insignificant effects on trust in doctors, strangers, and Americans. Empirical tests suggest that changes in income and physical health status are not likely to be potential channels. We provide some evidence for the mechanism of deteriorated mental health status and pessimistic expectations.

One-Step Synthesis of Multi-Core-Void@Shell Structured Silicon Anode for High-Performance Lithium-Ion Batteries.

The core-void@shell architecture shows great advantages in enhancing cycling stability and high-rate performance of Si-based anodes. However, it is usually synthesized by template methods which are complex and environmentally unfriendly and would lead to low-efficiency charge and mass exchange because of the single-point van der Waals contact between the Si core and the shell. Here, a facile and benign one-step method to synthesize multi-Si-void@SiO2 structure, where abundant void spaces exist between multiple Si cores that are multi-point attached to a SiO2 shell through strong chemical bonding, is reported. The corresponding electrode exhibits highly stable cycling stability and excellent electrochemical performance. After 200 cycles at a current density of 0.1 A g-1 and then another 200 cycles at 1.2 A g-1 , the electrode outputs a specific capacity of 1440 mAh g-1 . Even at 2.0 A g-1 , it outputs a specific capacity as high as 1182 mAh g-1 . Such an anode can match almost all the cathode materials presently used in lithium-ion batteries. These results demonstrate the multi-Si-void@SiO2 as a promising anode to be used in future commercial lithium-ion batteries of high energy density and high power density.

Preoperative Microvascular Invasion Prediction to Assist in Surgical Plan for Single Hepatocellular Carcinoma: Better Together with Radiomics.

BACKGROUND: Prediction models with or without radiomic analysis for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) have been reported, but the potential for model-predicted MVI in surgical planning is unclear. Therefore, we aimed to explore the effect of predicted MVI on early recurrence after anatomic resection (AR) and non-anatomic resection (NAR) to assist surgical strategies. METHODS: Patients with a single HCC of 2-5 cm receiving curative resection were enrolled from 2 centers. Their data were used to develop (n = 230) and test (n = 219) two prediction models for MVI using clinical factors and preoperative computed tomography images. The two prediction models, clinico-radiologic model and clinico-radiologic-radiomic (CRR) model (clinico-radiologic variables + radiomic signature), were compared using the Delong test. Early recurrence based on model-predicted high-risk MVI was evaluated between AR (n = 118) and NAR (n = 85) via propensity score matching using patient data from another 2 centers for external validation. RESULTS: The CRR model showed higher area under the curve values (0.835-0.864 across development, test, and external validation) but no statistically significant improvement over the clinico-radiologic model (0.796-0.828). After propensity score matching, difference in 2-year recurrence between AR and NAR was found in the CRR model predicted high-risk MVI group (P = 0.005) but not in the clinico-radiologic model predicted high-risk MVI group (P = 0.31). CONCLUSIONS: The prediction model incorporating radiomics provided an accurate preoperative estimation of MVI, showing the potential for choosing the more appropriate surgical procedure between AR and NAR.

Insulin Resistance and Cognitive Impairment: Evidence From Neuroimaging.

Insulin is a peptide well known for its role in regulating glucose metabolism in peripheral tissues. Emerging evidence from human and animal studies indicate the multifactorial role of insulin in the brain, such as neuronal and glial metabolism, glucose regulation, and cognitive processes. Insulin resistance (IR), defined as reduced sensitivity to the action of insulin, has been consistently proposed as an important risk factor for developing neurodegeneration and cognitive impairment. Although the exact mechanism of IR-related cognitive impairment still awaits further elucidation, neuroimaging offers a versatile set of novel contrasts to reveal the subtle cerebral abnormalities in IR. These imaging contrasts, including but not limited to brain volume, white matter (WM) microstructure, neural function and brain metabolism, are expected to unravel the nature of the link between IR, cognitive decline, and brain abnormalities, and their changes over time. This review summarizes the current neuroimaging studies with multiparametric techniques, focusing on the cerebral abnormalities related to IR and therapeutic effects of IR-targeting treatments. According to the results, brain regions associated with IR pathophysiology include the medial temporal lobe, hippocampus, prefrontal lobe, cingulate cortex, precuneus, occipital lobe, and the WM tracts across the globe. Of these, alterations in the temporal lobe are highly reproducible across different imaging modalities. These structures have been known to be vulnerable to Alzheimer's disease (AD) pathology and are critical in cognitive processes such as memory and executive functioning. Comparing to asymptomatic subjects, results are more mixed in patients with metabolic disorders such as type 2 diabetes and obesity, which might be attributed to a multifactorial mechanism. Taken together, neuroimaging, especially MRI, is beneficial to reveal early abnormalities in cerebral structure and function in insulin-resistant brain, providing important evidence to unravel the underlying neuronal substrate that reflects the cognitive decline in IR. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.

Disturbed Interhemispheric Functional and Structural Connectivity in Type 2 Diabetes.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with cognitive decline and altered brain structure and function. However, the interhemispheric coordination of T2DM patients is unclear. PURPOSE: To investigate interhemispheric functional and anatomic connectivity in T2DM, and their associations with cognitive performance and endocrine parameters. STUDY TYPE: Prospective. SUBJECTS: 38 T2DM patients and 42 matched controls. FIELD STRENGTH/SEQUENCES: 3.0 T magnetic resonance imaging (MRI) scanner; magnetization-prepared rapid acquisition gradient echo sequence; fluid-attenuated inversion recovery sequence; single-shot, gradient-recalled echo-planar imaging sequence (resting-state functional MRI); and diffusion-weighted spin-echo-based echo-planar sequence (diffusion tensor imaging). ASSESSMENT: Voxel-mirrored homotopic connectivity (VMHC) value was calculated based on the functional images. Fibers passing through the regions with significant VMHC differences were identified using an atlas-guided track recognition. The mean fractional anisotropy (FA), mean diffusivity (MD), and fiber length were extracted and compared between the two groups. Finally, correlational analyses were performed to examine the relationships between abnormal interhemispheric connectivity, cognitive performances, and endocrine parameters. STATISTICAL TESTS: Two-sample t-tests were performed controlling for confounding factors, with partial correlation analysis. False discovery rate (FDR) correction was used for multiple comparisons. A P value <0.05 was considered statistically significant. RESULTS: T2DM patients exhibited significantly decreased VMHC between bilateral lingual gyrus and sensorimotor cortex. The fibers connecting lingual gyrus in patients showed significantly lower FA (P = 0.011) and shorter fiber length (P < 0.001), while the differences in sensorimotor fibers were insignificant (P = 0.096 for FA, P = 0.739 for fiber length and P = 0.150 for MD). The FA value in the lingual fibers was negatively correlated with insulin resistance (IR) level in T2DM group after FDR correction (R = -0.635). DATA CONCLUSION: We noted disruptions in interhemispheric coordination in T2DM patients, involving both functional and anatomical connectivities. IR might be a promising therapeutic target in the intervention of T2DM-related cognitive impairment. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Beyond collaterals: brain frailty additionally improves prediction of clinical outcome in acute ischemic stroke.

OBJECTIVES: We aimed to investigate the additional significance of cerebral small vessel disease (SVD) beyond collaterals in determining the clinical outcome after acute ischemic stroke (AIS). METHODS: We retrospectively reviewed large vessel-involved stroke patients who had baseline CTA within 24 h after symptom onset and had an MRI scan 5 days after admission from October 1, 2018, to October 31, 2021. Collaterals and SVD markers (including atrophy, leukoaraiosis, lacunes, and perivascular space) were graded on CT angiography and MR images, respectively. Modified Rankin Scale (mRS) score at 90 days was recorded, and mRS ≤ 2 was regarded as a good clinical outcome. The associations between SVD markers, collaterals, and mRS were analyzed using logistic and causal mediation regression. RESULTS: We finally enrolled 119 patients (70 ± 13 years). The multivariable regression showed atrophy (evidence: OR 0.05 [95% CI 0.01-0.31], p = 0.002; severe: OR 0.08 [95% CI 0.01-0.44], p = 0.007) and evidence of lacune (OR 0.30 [95% CI 0.08-0.96], p = 0.049) were associated with poor clinical outcomes after correcting covariables. Collaterals mediated 25.74% of the effect of atrophy on poor clinical outcomes (p < 0.001), while lacune impacted clinical outcomes without collaterals' mediation effect (p = 0.54). The classification model with atrophy and lacune had a significantly higher AUC than without markers to distinguish good and poor outcomes (p = 0.036). CONCLUSIONS: Beyond collaterals, brain frailty, specifically assessed by atrophy and lacune, was essential in evaluating stroke patients and could additionally improve the stroke outcome prediction. KEY POINTS: • Beyond collaterals, brain frailty, specifically assessed by brain atrophy and lacune, was still an independent risk factor of unfavorable clinical outcomes after AIS. • Adding brain atrophy and lacune into the model has an extra benefit in predicting stroke outcomes. • The effect of atrophy on stroke outcomes was proportionally mediated through collaterals, but about three-quarters of the effect of brain atrophy and the total effect of lacune directly impacted stroke outcomes without a mediation effect of collaterals.

Characterizing Diaschisis-Related Thalamic Perfusion and Diffusion After Middle Cerebral Artery Infarction.

Background and Purpose: Ipsilateral thalamic diaschisis (ITD) initially describes functional depression of the thalamus ipsilateral to a supratentorial lesion, but accumulating evidence has shown morphological changes also occur. Therefore, we aimed to characterize thalamic perfusion and diffusion related to ITD over time and their inter-relationships after middle cerebral artery infarction. Methods: Eighty-five patients with middle cerebral artery infarction who underwent diffusion kurtosis imaging and arterial spin labeling were retrospectively included. ITD was diagnosed as ipsilateral thalamic hypoperfusion present on ≥2 cerebral blood flow maps. The thalamic asymmetrical index was calculated as (ipsilateral value−contralateral value)/contralateral value×100%. Finally, the inter-relationships of thalamic perfusion and diffusion were analyzed. Results: ITD was present in 56/85 patients (65.9%, ITD+). In ITD+ patients, larger abnormal perfusion volume, higher perfusion-infarct mismatch and lower rates of focal hyperperfusion were observed than ITD− patients. Infarction affecting the corona radiata were more frequent among ITD+ patients. Mean kurtosis were slightly but significantly increased within the ipsilateral thalamus compared with the contralateral one in ITD+ patients of subacute and chronic groups, while fractional anisotropy was significantly increased in subacute group but decreased in chronic group for both ITD+ and ITD− patients. Mean diffusivity was significantly increased in ITD+ patients of chronic group. Furthermore, the AICBF was negatively and significantly correlated with AIMK and AIFA in ITD+ patients in subacute group, and AIMD, even after adjustment for abnormal perfusion volume and days from symptoms onset, in chronic group. ITD+ patients had significantly higher National Institutes of Health Stroke Scale and modified Rankin Scale scores at admission and discharge and also showed a trend to independent association with clinical outcome at discharge. Conclusions: The combination of arterial spin labeling and diffusion kurtosis imaging can reveal early, time-specific thalamic perfusion and diffusion changes after middle cerebral artery infarction. ITD-related hypoperfusion was significantly correlated with underlying microstructural alterations.

Identification of tumor antigens and immune subtypes of cholangiocarcinoma for mRNA vaccine development.

BACKGROUND: The mRNA-based cancer vaccine has been considered a promising strategy and the next hotspot in cancer immunotherapy. However, its application on cholangiocarcinoma remains largely uncharacterized. This study aimed to identify potential antigens of cholangiocarcinoma for development of anti-cholangiocarcinoma mRNA vaccine, and determine immune subtypes of cholangiocarcinoma for selection of suitable patients from an extremely heterogeneous population. METHODS: Gene expression profiles and corresponding clinical information were collected from GEO and TCGA, respectively. cBioPortal was used to visualize and compare genetic alterations. GEPIA2 was used to calculate the prognostic index of the selected antigens. TIMER was used to visualize the correlation between the infiltration of antigen-presenting cells and the expression of the identified antigens. Consensus clustering analysis was performed to identify the immune subtypes. Graph learning-based dimensionality reduction analysis was conducted to visualize the immune landscape of cholangiocarcinoma. RESULTS: Three tumor antigens, such as CD247, FCGR1A, and TRRAP, correlated with superior prognoses and infiltration of antigen-presenting cells were identified in cholangiocarcinoma. Cholangiocarcinoma patients were stratified into two immune subtypes characterized by differential molecular, cellular and clinical features. Patients with the IS1 tumor had immune "hot" and immunosuppressive phenotype, whereas those with the IS2 tumor had immune "cold" phenotype. Interestingly, patients with the IS2 tumor had a superior survival than those with the IS1 tumor. Furthermore, distinct expression of immune checkpoints and immunogenic cell death modulators was observed between different immune subtype tumors. Finally, the immune landscape of cholangiocarcinoma revealed immune cell components in individual patient. CONCLUSIONS: CD247, FCGR1A, and TRRAP are potential antigens for mRNA vaccine development against cholangiocarcinoma, specifically for patients with IS2 tumors. Therefore, this study provides a theoretical basis for the anti-cholangiocarcinoma mRNA vaccine and defines suitable patients for vaccination.

Identification of tumor antigens and immune subtypes of pancreatic adenocarcinoma for mRNA vaccine development.

BACKGROUND: Although mRNA vaccines have been effective against multiple cancers, their efficacy against pancreatic adenocarcinoma (PAAD) remains undefined. Accumulating evidence suggests that immunotyping can indicate the comprehensive immune status in tumors and their immune microenvironment, which is closely associated with therapeutic response and vaccination potential. The aim of this study was to identify potent antigens in PAAD for mRNA vaccine development, and further distinguish immune subtypes of PAAD to construct an immune landscape for selecting suitable patients for vaccination. METHODS: Gene expression profiles and clinical information of 239 PAAD datasets were extracted from ICGC, and RNA-Seq data of 103 samples were retrieved from TCGA. GEPIA was used to calculate differential expression levels and prognostic indices, cBioPortal program was used to compare genetic alterations, and TIMER was used to explore correlation between genes and immune infiltrating cells. Consensus cluster was used for consistency matrix construction and data clustering, DAVID was used for functional annotation, and graph learning-based dimensional reduction was used to depict immune landscape. RESULTS: Six overexpressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in PAAD, including ADAM9, EFNB2, MET, TMOD3, TPX2, and WNT7A. Furthermore, five immune subtypes (IS1-IS5) and nine immune gene modules of PAAD were identified that were consistent in both patient cohorts. The immune subtypes showed distinct molecular, cellular and clinical characteristics. IS1 and IS2 exhibited immune-activated phenotypes and correlated to better survival compared to the other subtypes. IS4 and IS5 tumors were immunologically cold and associated with higher tumor mutation burden. Immunogenic cell death modulators, immune checkpoints, and CA125 and CA199, were also differentially expressed among the five immune subtypes. Finally, the immune landscape of PAAD showed a high degree of heterogeneity between individual patients. CONCLUSIONS: ADAM9, EFNB2, MET, TMOD3, TPX2, and WNT7A are potent antigens for developing anti-PAAD mRNA vaccine, and patients with IS4 and IS5 tumors are suitable for vaccination.

Comparison of MRI and CT for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma Based on a Non-Radiomics and Radiomics Method: Which Imaging Modality Is Better?

BACKGROUND: Computed tomography (CT) and magnetic resonance imaging (MRI) are both capable of predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). However, which modality is better is unknown. PURPOSE: To intraindividually compare CT and MRI for predicting MVI in solitary HCC and investigate the added value of radiomics analyses. STUDY TYPE: Retrospective. SUBJECTS: Included were 402 consecutive patients with HCC (training set:validation set = 300:102). FIELD STRENGTH/SEQUENCE: T2-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging MRI at 3.0T and contrast-enhanced CT. ASSESSMENT: CT- and MR-based radiomics signatures (RS) were constructed using the least absolute shrinkage and selection operator regression. CT- and MR-based radiologic (R) and radiologic-radiomics (RR) models were developed by univariate and multivariate logistic regression. The performance of the RS/models was compared between two modalities. To investigate the added value of RS, the performance of the R models was compared with the RR models in HCC of all sizes and 2-5 cm in size. STATISTICAL TESTS: Model performance was quantified by the area under the receiver operating characteristic curve (AUC) and compared using the Delong test. RESULTS: Histopathologic MVI was identified in 161 patients (training set:validation set = 130:31). MRI-based RS/models tended to have a marginally higher AUC than CT-based RS/models (AUCs of CT vs. MRI, P: RS, 0.801 vs. 0.804, 0.96; R model, 0.809 vs. 0.832, 0.09; RR model, 0.835 vs. 0.872, 0.54). The improvement of RR models over R models in all sizes was not significant (P = 0.21 at CT and 0.09 at MRI), whereas the improvement in 2-5 cm was significant at MRI (P < 0.05) but not at CT (P = 0.16). DATA CONCLUSION: CT and MRI had a comparable predictive performance for MVI in solitary HCC. The RS of MRI only had significant added value for predicting MVI in HCC of 2-5 cm. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Enhancing capacity and transport kinetics of C@TiO2core-shell composite anode by phase interface engineering.

In nanocomposite electrodes, besides the synergistic effect that takes advantage of the merits of each component, phase interfaces between the components would contribute significantly to the overall electrochemical properties. However, the knowledge of such effects is far from being well developed up to now. The present work aims at a mechanistic understanding of the phase interface effect in C@TiO2core-shell nanocomposite anode which is both scientifically and industrially important. Firstly, amorphous C, anatase TiO2and C@anatse-TiO2electrodes are compared. The C@anatase-TiO2shows an obvious higher specific capacity (316.5 mAh g-1at a current density of 37 mA g-1after 100 cycles) and Li-ion diffusion coefficient (4.0 × 10-14cm2s-1) than the amorphous C (178 mAh g-1and 2.9 × 10-15cm2s-1) and anatase TiO2(120 mAh g-1and 1.6 × 10-15cm2s-1) owing to the C/TiO2phase interface effect. Then, C@anatase/rutile-TiO2is obtained by a heat treatment of the C@anatase-TiO2. Due to an anatase-to-rutile phase transformation and diffusion of C along the anatase/rutile phase interface, additional abundant C/TiO2phase interfaces are created. This endows the C@anatase/rutile-TiO2with further boosted specific capacity (409.4 mAh g-1at 37 mA g-1after 100 cycles) and Li-ion diffusion coefficient (3.2 × 10-13cm2s-1), and excellent rate capability (368.6 mAh g-1at 444 mA g-1). These greatly enhanced electrochemical properties explicitly reveal phase interface engineering as a feasible way to boost the electrochemical performance of nanocomposite anodes for Li-ion batteries.

Cold Atmospheric Pressure Plasma-Activated Medium Induces Selective Cell Death in Human Hepatocellular Carcinoma Cells Independently of Singlet Oxygen, Hydrogen Peroxide, Nitric Oxide and Nitrite/Nitrate.

Cold atmospheric pressure plasma (CAP) and plasma-activated medium (PAM) induce cell death in diverse cancer cells and may function as powerful anti-cancer agents. The main components responsible for the selective anti-cancer effects of CAP and PAM remain elusive. CAP or PAM induces selective cell death in hepatocellular carcinoma cell lines Hep3B and Huh7 containing populations with cancer stem cell markers. Here, we investigated the major component(s) of CAP and PAM for mediating the selective anti-proliferative effect on Hep3B and Huh7 cells. The anti-proliferative effect of CAP was mediated through the medium; however, the reactive oxygen species scavenger N-acetyl cysteine did not suppress PAM-induced cell death. Neither high concentrations of nitrite or nitrite/nitrate nor a low concentration of H2O2 present in the PAM containing sodium pyruvate affected the viability of Hep3B and Huh7 cells. Inhibitors of singlet oxygen, superoxide anions, and nitric oxide retained the capacity of PAM to induce anti-cancer effects. The anti-cancer effect was largely blocked in the PAM prepared by placing an aluminum metal mesh, but not a dielectric PVC mesh, between the plasma source and the medium. Hence, singlet oxygen, hydrogen peroxide, nitric oxide, and nitrite/nitrate are not the main factors responsible for PAM-mediated selective death in Hep3B and Huh7 cells. Other factors, such as charged particles including various ions in CAP and PAM, may induce selective anti-cancer effects in certain cancer cells.

Selective Anti-Cancer Effects of Plasma-Activated Medium and Its High Efficacy with Cisplatin on Hepatocellular Carcinoma with Cancer Stem Cell Characteristics.

Hepatocellular carcinoma (HCC) is a major histological subtype of primary liver cancer. Ample evidence suggests that the pathological properties of HCC originate from hepatic cancer stem cells (CSCs), which are responsible for carcinogenesis, recurrence, and drug resistance. Cold atmospheric-pressure plasma (CAP) and plasma-activated medium (PAM) induce apoptosis in cancer cells and represent novel and powerful anti-cancer agents. This study aimed to determine the anti-cancer effect of CAP and PAM in HCC cell lines with CSC characteristics. We showed that the air-based CAP and PAM selectively induced cell death in Hep3B and Huh7 cells with CSC characteristics, but not in the normal liver cell line, MIHA. We observed both caspase-dependent and -independent cell death in the PAM-treated HCC cell lines. Moreover, we determined whether combinatorial PAM therapy with various anti-cancer agents have an additive effect on cell death in Huh7. We found that PAM highly increased the efficacy of the chemotherapeutic agent, cisplatin, while enhanced the anti-cancer effect of doxorubicin and the targeted-therapy drugs, trametinib and sorafenib to a lesser extent. These findings support the application of CAP and PAM as anti-cancer agents to induce selective cell death in cancers containing CSCs, suggesting that the combinatorial use of PAM and some specific anti-cancer agents is complemented mechanistically.