Magnetic superhydrophobic cellulose nanofibril based aerogel with rope-ladder like structure incorporating both superelasticity and excellent oil absorption.

Achieving an equilibrium between exceptional oil absorption and remarkable elasticity has emerged as a formidable challenge for magnetic porous materials designed for oil absorption. Here, we propose an original, magnetic and superhydrophobic cellulose nanofibril (CNF) based aerogel system with a rope-ladder like skeleton by to greatly improve the issue. Within this system, CNF as the skeleton was combined with multiwalled carbon nanotubes (MWCNT)@Fe3O4 as the magnetic and enhanced component, both methyltrimethoxysilane (MTMS) and acetonitrile-extracted lignin (AEL) as the soft-hard associating constituents. The resultant CNF based aerogel shows a rope-ladder like pore structure to contribute to high elasticity and excellent oil absorption (28.34-61.09 g/g for various oils and organic solvents) under the synergistic effect of Fe3O4@MWCNT, AEL and MTMS, as well as good specific surface area (27.97 m2/g), low density (26.4 mg/cm3). Notably, despite the introduced considerable proportion (0.5 times of mass-CNF) of Fe3O4@MWCNT, the aerogel retained an impressive compression-decompression rate (88%) and the oil absorption efficiency of above 87% for various oils due to the soft-hard associating structure supported by both MTMS and AEL. This study provides a prospective strategy to balance between high elasticity and excellent oil absorption of CNF based aerogel doping inorganic particles.

Preparation of highly elastic superhydrophobic CNF/Fe3O4 based materials modified in aqueous phase for oil-water separation.

Magnetic superhydrophobic materials have broad application prospect in oil-water separation. In this study, a magnetic and superhydrophobic aerogel with lamellar structure was successfully prepared using cellulose nanofibrils (CNF) as the skeleton, Fe3O4 as the magnetic ion, 1H, 1H, 2H, 2H trialkylfluorooctane triethoxysilane (FS) and 3-(2-aminoethyl amino)-propyl trimethoxysilane (AS) as the combined modifier. The prepared aerogel shows lower density (38.63 mg/cm3), excellent magnetic (15.13 emu/g), high elasticity and good oil sorption properties (21 g/g). In addition, FS/AS also exhibits excellent mechanical properties and superhydrophobic ability (water contact angle (WCA) of 151.9 ± 1.4°), as it provides sufficient toughness and low surface energy for the layer-branch structure. It should be noted that the entire preparation process is carried out in the aqueous phase, without the use of any organic solvents, providing a green oil-water separation strategy.

JAK/STAT in leukemia: a clinical update.

Over the past three decades, considerable efforts have been expended on understanding the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway in leukemia, following the identification of the JAK2V617F mutation in myeloproliferative neoplasms (MPNs). The aim of this review is to summarize the latest progress in our understanding of the involvement of the JAK/STAT signaling pathway in the development of leukemia. We also attempt to provide insights into the current use of JAK/STAT inhibitors in leukemia therapy and explore pertinent clinical trials in this field.

MFG-E8 promotes M2 polarization of macrophages and is associated with poor prognosis in patients with gastric cancer.

Background: Milk Fat Globule-Epidermal Growth Factor 8 (MFG-E8) has been reported to play an oncogenic role in a variety of tumors. However, its involvement in gastric cancer (GC) development has not been described. Methods: The cancer genome atlas (TCGA) and the gene expression omnibus database (GEO) databases were used to analyze the expression of MFG-E8 in GC. These findings were further validated using immunohistochemistry (IHC) and western blotting assay (WB). Kaplan-Meier method, univariate logistic regression, and Christopher Cox regression were used to study the relationship between MFG-E8 and clinical pathology. In addition, the potential signaling pathways involved in MFG-E8 and its potential correlation with levels of immune cell infiltration were investigated. Finally, the biological function of MFG-E8 in GC cells was revealed. Results: MFG-E8 was highly expressed in GC patients and cells, and the high level of MFG-E8 was associated with poor overall survival (OS). KEGG analysis indicated that MFG-E8 may play an important role in the cAMP signaling pathway. The expression of MFG-E8 was positively correlated with the infiltration of M2 macrophages. The patients with high MFG-E8 were easy to develop chemotherapy resistance. Furthermore, the knockdown of MFG-E8 significantly inhibited the proliferation and invasion of GC cells. Conclusion: MFG-E8 in GC may serve as a prognostic marker and a potential immunotherapy target for GC.

Activating Commercial Nickel Foam to a Highly Efficient Electrocatalyst for Oxygen Evolution Reaction through a Three-Step Surface Reconstruction.

It is highly desired to directly use commercial nickel foam (CNF) as an electrocatalyst for the oxygen evolution reaction (OER) via simple surface reconstruction. In our research, a simple three-step preactivation process was proposed to reconstruct CNF as an efficient OER catalyst, including calcination, high-voltage treatment, and immersing in electrolyte. The optimal CNF after three-step activation reaches an excellent OER performance of 228 and 267 mV at η10 and η100 in alkaline media and can tolerate long-term tests under a large current density of 500 mA·cm-2. The promotion of each step was explored. The calcination step leads to a reconstructive surficial morphology with an enlarged active surface, providing a prerequisite for the following construction steps. The high-voltage treatment changes the valence of surface Ni species, generating phases with higher catalytic activity, and the immersing process introduces Fe heteroatoms into the surface of CNF, boosting the catalytic performance of CNF through Ni-Fe interactions. This research provides a simple method of making high-performance catalysts with accessible nickel foam, a potential for large-scale application in practical industry, and new thinking for the manipulation of Ni-based catalysts.

Bioinformatic Analysis of the Mechanism of Action of COL3A1 in Pan-Cancer and Design of Specific Inhibitors for Its Conformation.

In this study, we investigated the mechanism of action of COL3A1 in various types of cancers by bioinformatics analysis and designed some specific inhibitors aimed at the treatment of this gene. We found that COL3A1 was highly expressed in several cancer types and correlated with tumor progression and prognosis. Through systems biology analysis, we identified a central role for COL3A1 in cancer development, including cell proliferation, metastasis and invasion. We also used molecular dynamics simulations and drug screening techniques to design anticancer drugs with potential COL3A1 inhibitory functions. These results provide a strong rationale for the development and use of COL3A1 as a therapeutic target.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

OBJECTIVES: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. METHODS: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. RESULTS: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. CONCLUSIONS: DLCT has added value for STAS prediction in lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. KEY POINTS: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Chlorine-mediated electrodeposition of hierarchical and hydrophobic copper electrocatalysts for efficient CO2 electroreduction to ethylene.

Hierarchical Cu dendrites fabricated via Cl-mediated electrodeposition afford high C2H4 efficiency (58% faradaic efficiency at -0.9 V vs. RHE) for CO2 electroreduction thanks to not only the optimal hydrophobicity/aerophilicity, but also the dominant distribution of active (100) and (110) facets.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

CT-based decision tree model for predicting EGFR mutation status in synchronous multiple primary lung cancers.

Background: The current study aimed to construct a computed tomography (CT)-based decision tree algorithm (DTA) model to predict the epidermal growth factor receptor (EGFR) mutation status in synchronous multiple primary lung cancers (SMPLCs). Methods: The demographic and CT findings of 85 patients with molecular profiling for surgically resected SMPLCs were reviewed retrospectively. Least absolute shrinkage and selection operator (LASSO) regression was used to select the potential predictors of EGFR mutation, and a CT-DTA model was developed. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to assess the performance of this CT-DTA model. Results: The CT-DTA model was applied to predict the EGFR mutant that had ten binary split, of which eight parameters to accurately categorize the lesions as follows: the presence of bubble-like vacuole sign (19.4% importance in the development of the model), presence of air bronchogram sign (17.4% importance), smoking status (15.7% importance), types of the lesions (14.8% importance), histology (12.6% importance), presence of pleural indentation sign (7.6% importance), gender (6.9% importance), and presence of lobulation sign (5.6% importance). The ROC analysis achieved an area under the curve (AUC) of 0.854. Multivariate logistic regression analysis demonstrated that this CT-DTA model was an independent predictor of EGFR mutation (P<0.001). Conclusions: CT-DTA model is a simple tool to predict the status of EGFR mutation in SMPLC patients and could be considered for treatment decision-making.

A Novel Prognostic Score Based on Multiple Quantitative Parameters of Chest CT for Patients with Synchronous Multiple Primary Lung Cancer: Is Solid Component Size a Better Prognostic Indicator?

BACKGROUND: There is no simple and definitive way to predict the prognosis of synchronous multiple primary lung cancer (SMPLC). In this study, we developed a clinical prognostic score for predicting the survival of patients with SMPLC. PATIENTS AND METHODS: This study included 206 patients with SMPLC between 2011 and 2020 at three hospitals. Kaplan-Meier analysis was used to determine the optimal cutoff values for the quantitative chest computed tomography (CT) parameters. Multivariable Cox proportional hazards regression was carried out to identify independent prognostic factors for predicting overall survival (OS) and disease-free survival (DFS). The time-dependent receiver operating characteristic curve was analyzed to evaluate the prognostic performance. RESULTS: A CT-based prognostic score (CTPS) comprising six chest CT parameters was developed. Compared with T stage, CTPS had a higher prediction accuracy for OS and DFS. All C-indices of the model reached a satisfactory level in both the development and validation cohorts. Significant differences in the OS and DFS curves were observed when the patients were stratified into different risk groups. The high-risk group (CTPS of 5-6) had poorer survival than the low-risk group (CTPS of 0-4). CONCLUSIONS: The developed CTPS and the corresponding risk stratification system are valid for predicting the survival of patients with SMPLC.

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach.

Eco-efficiency analysis can provide useful information about sustainability in the tourism industry, which has an important role in both global economy recovery and Sustainable Development Goals (SDGs), generating considerable indirect carbon emissions with respect to the supply chain due to its significant connections to other industries. This study, from the perspective of tourism sectors, including tourism hotels, travel agencies, and scenic spots, integrated the environmentally extended input-output analysis (EEIO) and data envelopment analysis (DEA) models to develop a research framework, analyzing the indirect carbon emissions of the tourism supply chain, evaluating eco-efficiency with respect to both direct carbon emissions and total carbon emissions (including direct and indirect parts), and exploring the driving factors of eco-efficiency of tourism sectors using Tobit regression models. This study took Gansu as a case, a province in China characterized by higher carbon intensity, an underdeveloped economy, and rapid tourism growth. The results demonstrate that (1) tourism hotels contribute the most carbon emissions in tourism sectors, especially indirectly due to the supply chain, with carbon emissions mainly resulting from the manufacturing of food and tobacco; (2) the eco-efficiency of tourism sectors in Gansu presents a U-shaped curve, which is consistent with Kuznets' theory; and (3) energy technology is key to improving the eco-efficiency of tourism sectors. The research results provide a clear path for the reduction of carbon emissions and the improvement of eco-efficiency in Gansu tourism sectors. Against the backdrop of global climate change and the post-COVID-19 era, our research framework and findings provide a reference for similar regions and countries who are in urgent need of rapid tourism development to effect economic recovery.

Nickel sulfide-oxide heterostructured electrocatalysts: Bi-functionality for overall water splitting and in-situ reconstruction.

Bi-functional electrocatalysts are desired for both hydrogen and oxygen evolution reactions (HER and OER). Herein, facile O2-plasma activation is introduced to improve the bi-functionality via constructing nickel sulfide-oxide heterostructures. Ni3S2-NiOx supported by nickel foam delivers obviously elevated HER and OER activity in comparison with pristine Ni3S2 and recently reported NiSx-based electrocatalysts, featured by the low overpotentials for HER (104 mV) and OER (241 mV) at ±10 mA cm-2 in 1.0 M KOH, as well as a voltage of 1.52 V for overall water splitting. As revealed by in-situ Raman spectroscopy, on the one hand, Ni(OH)2 generated from Ni3S2 during alkaline HER accelerates water dissociation toward the gradually improved performance; on the other hand, this heterostructure undergoes extensive oxidation during OER, leading to excessive NiOOH covering on Ni3S2 and thereby declining activity. These changes are interpreted by the distinct thermodynamic relationship under specific electrochemical conditions via density functional theory calculations.

Facile Preparation of Chitosan-Based Composite Film with Good Mechanical Strength and Flame Retardancy.

To improve on the poor strength and flame retardancy of a chitosan (CS)-based functional film, cellulose nanofiber (CNF) was taken as the reinforced material and both ammonium polyphosphate (APP) and branched polyethyleneimine (BPEI) as the flame-retardant additives in the CS matrix to prepare the CS/CNF/APP/BPEI composite film by simple drying. The resulting composite film showed good mechanical strength, with a tensile strength reaching 71.84 Mpa due to the high flexibility of CNF and the combination of CS, CNF and BPEI through strong hydrogen bonding interactions. The flame retardant-performance of the composite film greatly enhanced the limit oxygen index (LOI), up to 32.7% from 27.6% for the pure film, and the PHRR intensity decreased to 28.87 W/g from 39.38% in the micro-scale combustion calorimetry (MCC) test due to the ability of BPEI to stimulate the decomposition of APP, releasing non-flammable gases such as CO2, N2, NH3, etc., and forming a protective phosphating layer to block the entry of O2. Based on the good flame retardancy, mechanical strength and transparency, the CS/CNF/APP/BPEI composite film has a great potential for future applications.

Integrin α6-Targeted Molecular Imaging of Central Nervous System Leukemia in Mice.

Central nervous system leukemia (CNS-L) is caused by leukemic cells infiltrating into the meninges or brain parenchyma and remains the main reason for disease relapse. Currently, it is hard to detect CNS-L accurately by clinically available imaging models due to the relatively low amount of tumor cells, confined blood supply, and the inferior glucose metabolism intensity. Recently, integrin α6-laminin interactions have been identified to mediate CNS-L, which suggests that integrin α6 may be a promising molecular imaging target for the detection of CNS-L. The acute lymphoblastic leukemia (ALL) cell line NALM6 stabled and transfected with luciferase was used to establish the CNS-L mouse model. CNS-L-bearing mice were monitored and confirmed by bioluminescence imaging. Three of our previously developed integrin α6-targeted peptide-based molecular imaging agents, Cy5-S5 for near-infrared fluorescence (NIRF), Gd-S5 for magnetic resonance (MR), and 18F-S5 for positron emission tomography (PET) imaging, were employed for the molecular imaging of these CNS-L-bearing mice. Bioluminescence imaging showed a local intensive signal in the heads among CNS-L-bearing mice; meanwhile, Cy5-S5/NIRF imaging produced intensive fluorescence intensity in the same head regions. Moreover, Gd-S5/MR imaging generated superior MR signal enhancement at the site of meninges, which were located between the skull bone and brain parenchyma. Comparatively, MR imaging with the clinically available MR enhancer Gd-DTPA did not produce the distinguishable MR signal in the same head regions. Additionally, 18F-S5/PET imaging also generated focal radio-concentration at the same head regions, which generated nearly 5-times tumor-to-background ratio compared to the clinically available PET radiotracer 18F-FDG. Finally, pathological examination identified layer-displayed leukemic cells in the superficial part of the brain parenchyma tissue, and immunohistochemical staining confirmed the overexpression of the integrin α6 within the lesion. These findings suggest the potential application of these integrin α6-targeted molecular imaging agents for the accurate detection of CNS-L.

Albumin-To-Alkaline Phosphatase Ratio as a Novel and Promising Prognostic Biomarker in Patients Undergoing Esophagectomy for Carcinoma: A Propensity Score Matching Study.

BACKGROUND: Albumin-to-alkaline phosphatase ratio (AAPR) has been reported as a novel prognostic predictor for numerous solid tumors. We aimed to assess the prognostic role of preoperative AAPR in surgically resectable esophageal squamous cell carcinoma (ESCC) by a propensity score matching (PSM) analysis with predictive nomograms. METHODS: Our study was conducted in a single-center prospective database between June 2009 and December 2012. Kaplan-Meier analysis was used to distinguish the difference in survival outcomes between patients stratified by an AAPR threshold. Multivariable Cox proportional hazards regression model was finally generated to specify independent prognostic markers for the entire and PSM cohorts. RESULTS: A total of 497 patients with ESCC were included in this study. An AAPR of 0.50 was determined as the optimal cutoff point for prognostic outcome stratification. Patients with AAPR<0.50 had significantly worse overall survival (OS), and progression-free survival (PFS) compared to those with AAPR≥0.50 (Log-rank P<0.001). This significant difference remained stable in the PSM analysis. Multivariable analyses based on the entire and PSM cohorts consistently showed that AAPR<0.50 might be one of the most predominant prognostic factors resulting in unfavorable OS and PFS of ESCC patients undergoing esophagectomy (P<0.001). The nomograms consisting of AAPR and other independent prognostic factors further demonstrated a plausible predictive accuracy of postoperative OS and PFS. CONCLUSION: AAPR can be considered as a simple, convenient and noninvasive biomarker with a significant prognostic effect in surgically resected ESCC.

Prognostic Significance of Regional/Systemic Metabolic Parameters on 18F-FDG PET in Pulmonary Lymphoepithelioma-Like Carcinoma.

BACKGROUND: Pulmonary lymphoepithelioma-like carcinoma (LELC) is a rare subtype of lung cancer with less than 700 cases being reported in the literature, and no specialized treatment guidelines have been established. The prognostic significance of metabolic parameters on 18F-FDG PET in pulmonary LELC still remains unknown. METHODS: From July 2011 to September 2020, 76 pulmonary LELC patients undergoing pre-treatment 18F-FDG PET imaging were enrolled, and PET parameters including maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated. In addition, whole-body tumor burdens were categorized into primary tumor lesion (PRL), thoracic lymph-node lesion (TRLN), and distant metastasis (DM) for respective metabolic parameters acquisition. ROC curves were generated to evaluate the predictive performance of the PET parameters, and correlations between tumor burdens of the different regional lesions were analyzed using linear correlation analysis. The prognostic significance for progression-free survival (PFS) and overall survival (OS) was assessed using univariate and multivariate survival analyses. RESULTS: Tumor stage, pre-/post-treatment serum EBV-DNA copies, SUVmax (cutoff 17.5), MTV, and TLG were significantly associated with PFS and OS in univariate analysis. MTV and TLG (AUC = 0.862 and 0.857, respectively) showed significantly higher predictive value than SUVmax (AUC = 0.754) and remained independent prognostic indicators for PFS in multivariate analysis (P = 0.026 and 0.019, respectively). Besides, non-colinearity was detected between metabolic burdens of the different regional lesions. MTV-PRL, MTV-DM, TLG-PRL, and TLG-DM were identified to be independent prognostic factors for PFS and OS, whereas MTV-TRLN and TLG-TRLN were not. CONCLUSION: The study demonstrated that MTV and TLG had independent prognostic significance for pulmonary LELC, which supported the incorporation of 18F-FDG PET imaging into clinical treatment protocols for pulmonary LELC and implied multi-disciplinary cooperation for primary and distant metastatic lesions to further improve prognosis.

An Optimized Integrin α6-Targeted Magnetic Resonance Probe for Molecular Imaging of Hepatocellular Carcinoma in Mice.

INTRODUCTION: Integrin α6 is an attractive diagnostic biomarker for molecular imaging of hepatocellular carcinoma (HCC) as it has an extremely high positive rate (approximately 94%) in clinical early-stage HCC. In this study, based on our previously identified integrin α6-targeted peptide, we developed an optimized integrin α6-targeted magnetic resonance (MR) probe dubbed DOTA(Gd)-ANADYWR for MR imaging of HCC in mice. MATERIALS AND METHODS: The longitudinal (R1) relaxivity of DOTA(Gd)-ANADYWR was measured on a 3.0 T MR system . The specific tumor enhancement of the agent was investigated in four distinct mouse models, including subcutaneous, orthotopic, genetically engineered and chemically induced HCC mice. RESULTS: The R1 relaxivity value of DOTA(Gd)-ANADYWR is 5.11 mM-1s-1 at 3.0 T, which is similar to that of the nonspecific clinical agent Gadoteridol. DOTA(Gd)-ANADYWR generated superior enhanced MR signal in HCC lesions and provided complementary enhancement MR signals to the clinically available hepatobiliary MR contrast agent gadoxetate disodium (Gd-EOB-DTPA). Importantly, DOTA(Gd)-ANADYWR could efficiently visualize small HCC lesion (approximately 1 mm) which was hardly detected by the clinical Gd-EOB-DTPA. CONCLUSION: These findings suggest the potential application of this integrin α6-targeted MR probe for the detection of HCC, particularly for small HCC.