Structural insights into the HBV receptor and bile acid transporter NTCP.

Around 250 million people are infected with hepatitis B virus (HBV) worldwide1, and 15 million may also carry the satellite virus hepatitis D virus (HDV), which confers even greater risk of severe liver disease2. The HBV receptor has been identified as sodium taurocholate co-transporting polypeptide (NTCP), which interacts directly with the first 48 amino acid residues of the N-myristoylated N-terminal preS1 domain of the viral large protein3. Despite the pressing need for therapeutic agents to counter HBV, the structure of NTCP remains unsolved. This 349-residue protein is closely related to human apical sodium-dependent bile acid transporter (ASBT), another member of the solute carrier family SLC10. Crystal structures have been reported of similar bile acid transporters from bacteria4,5, and these models are believed to resemble closely both NTCP and ASBT. Here we have used cryo-electron microscopy to solve the structure of NTCP bound to an antibody, clearly showing that the transporter has no equivalent of the first transmembrane helix found in other SLC10 proteins, and that the N terminus is exposed on the extracellular face. Comparison of our structure with those of related proteins indicates a common mechanism of bile acid transport, but the NTCP structure displays an additional pocket formed by residues that are known to interact with preS1, presenting new opportunities for structure-based drug design.

C-ter100 peptide derived from Vibrio vEP-45 protease acts as a pathogen-associated molecular pattern to induce inflammation and innate immunity.

The bacterium Vibrio vulnificus causes fatal septicemia in humans. Previously, we reported that an extracellular metalloprotease, vEP-45, secreted by V. vulnificus, undergoes self-proteolysis to generate a 34 kDa protease (vEP-34) by losing its C-terminal domain to produce the C-ter100 peptide. Moreover, we revealed that vEP-45 and vEP-34 proteases induce blood coagulation and activate the kallikrein/kinin system. However, the role of the C-ter100 peptide fragment released from vEP-45 in inducing inflammation is still unclear. Here, we elucidate, for the first time, the effects of C-ter100 on inducing inflammation and activating host innate immunity. Our results showed that C-ter100 could activate NF-κB by binding to the receptor TLR4, thereby promoting the secretion of inflammatory cytokines and molecules, such as TNF-α and nitric oxide (NO). Furthermore, C-ter100 could prime and activate the NLRP3 inflammasome (NLRP3, ASC, and caspase 1), causing IL-1ß secretion. In mice, C-ter100 induced the recruitment of immune cells, such as neutrophils and monocytes, along with histamine release into the plasma. Furthermore, the inflammatory response induced by C-ter100 could be effectively neutralized by an anti-C-ter100 monoclonal antibody (C-ter100Mab). These results demonstrate that C-ter100 can be a pathogen-associated molecular pattern (PAMP) that activates an innate immune response during Vibrio infection and could be a target for the development of antibiotics.

Structure of the human galanin receptor 2 bound to galanin and Gq reveals the basis of ligand specificity and how binding affects the G-protein interface.

Galanin is a neuropeptide expressed in the central and peripheral nervous systems, where it regulates various processes including neuroendocrine release, cognition, and nerve regeneration. Three G-protein coupled receptors (GPCRs) for galanin have been discovered, which is the focus of efforts to treat diseases including Alzheimer's disease, anxiety, and addiction. To understand the basis of the ligand preferences of the receptors and to assist structure-based drug design, we used cryo-electron microscopy (cryo-EM) to solve the molecular structure of GALR2 bound to galanin and a cognate heterotrimeric G-protein, providing a molecular view of the neuropeptide binding site. Mutant proteins were assayed to help reveal the basis of ligand specificity, and structural comparison between the activated GALR2 and inactive hß2AR was used to relate galanin binding to the movements of transmembrane (TM) helices and the G-protein interface.

Everolimus exerts anticancer effects through inhibiting the interaction of matrix metalloproteinase-7 with syndecan-2 in colon cancer cells.

Previous work showed that matrix metalloproteinase-7 (MMP-7) regulates colon cancer activities through an interaction with syndecan-2 (SDC-2) and SDC-2-derived peptide that disrupts this interaction and exhibits anticancer activity in colon cancer. Here, to identify potential anticancer agents, a library of 1,379 Food and Drug Administration (FDA)-approved drugs that interact with the MMP-7 prodomain were virtually screened by protein-ligand docking score analysis using the GalaxyDock3 program. Among five candidates selected based on their structures and total energy values for interacting with the MMP-7 prodomain, the known mechanistic target of rapamycin kinase (mTOR) inhibitor, everolimus, showed the highest binding affinity and the strongest ability to disrupt the interaction of the MMP-7 prodomain with the SDC-2 extracellular domain in vitro. Everolimus treatment of the HCT116 human colon cancer cell line did not affect the mRNA expression levels of MMP-7 and SDC-2 but reduced the adhesion of cells to MMP-7 prodomain-coated plates and the cell-surface localization of MMP-7. Thus, everolimus appears to inhibit the interaction between MMP-7 and SDC-2. Everolimus treatment of HCT116 cells also reduced their gelatin-degradation activity and anticancer activities, including colony formation. Interestingly, cells treated with sirolimus, another mTOR inhibitor, triggered less gelatin-degradation activity, suggesting that this inhibitory effect of everolimus was not due to inhibition of the mTOR pathway. Consistently, everolimus inhibited the colony-forming ability of mTOR-resistant HT29 cells. Together, these data suggest that, in addition to inhibiting mTOR signaling, everolimus exerts anticancer activity by interfering with the interaction of MMP-7 and SDC-2, and could be a useful therapeutic anticancer drug for colon cancer.NEW & NOTEWORTHY The utility of cancer therapeutics targeting the proteolytic activities of MMPs is limited because MMPs are widely distributed throughout the body and involved in many different aspects of cell functions. This work specifically targets the activation of MMP-7 through its interaction with syndecan-2. Notably, everolimus, a known mTOR inhibitor, blocked this interaction, demonstrating a novel role for everolimus in inhibiting mTOR signaling and impairing the interaction of MMP-7 with syndecan-2 in colon cancer.

Early-stage dynamics of chloride ion-pumping rhodopsin revealed by a femtosecond X-ray laser.

Chloride ion-pumping rhodopsin (ClR) in some marine bacteria utilizes light energy to actively transport Cl- into cells. How the ClR initiates the transport is elusive. Here, we show the dynamics of ion transport observed with time-resolved serial femtosecond (fs) crystallography using the Linac Coherent Light Source. X-ray pulses captured structural changes in ClR upon flash illumination with a 550 nm fs-pumping laser. High-resolution structures for five time points (dark to 100 ps after flashing) reveal complex and coordinated dynamics comprising retinal isomerization, water molecule rearrangement, and conformational changes of various residues. Combining data from time-resolved spectroscopy experiments and molecular dynamics simulations, this study reveals that the chloride ion close to the Schiff base undergoes a dissociation-diffusion process upon light-triggered retinal isomerization.

Validation of the conceptual framework and intervention scope of oral function rehabilitation exercise.

OBJECTIVES: This study used a Delphi survey to define the concept of oral function rehabilitation exercise (OFRE) based on the International Classification of Functioning, Disability, and Health (ICF) and to categorise intervention domains for community-dwelling older adults. BACKGROUND: While numerous studies have been conducted to improve oral function through exercise interventions, the conceptual definition of oral exercise remains unclear and there is a lack of systematic categorisation of oral exercise intervention domains. METHODS: A preliminary model was developed based on the key findings of 19 papers selected from a prior systematic review. Its validity was confirmed through a Delphi survey conducted twice with eight expert panellists. Consensus was achieved by evaluating the validity of the OFRE conceptual framework, the accuracy of OFRE conceptual definitions, and intervention domains. RESULTS: Through expert consensus, an ICF-based OFRE conceptual framework was developed that includes 21 factors that affect the oral health status of the older adults. The OFRE intervention domain for improving the health status consisted of oral function rehabilitation warm-up exercise, masticatory function exercise, swallowing function exercise, articulatory function exercise, salivary function exercise, and oral function rehabilitation cool-down exercise, and 11 specific intervention methods were derived. CONCLUSIONS: The OFRE intervention can be used for planning and applying successful interventions to improve oral function and life function of older adults.

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting.

Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive care units (ICUs). Purpose To develop and validate a deep learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change by using longitudinal follow-up data. Materials and Methods A deep learning algorithm was retrospectively developed using baseline and follow-up chest radiographs in adults from January 2011 to December 2018 at a tertiary referral hospital. Two thoracic radiologists reviewed randomly selected pairs of "change" and "no change" images to establish the ground truth, including normal or abnormal status. Algorithm performance was evaluated using area under the receiver operating characteristic curve (AUC) analysis in a validation set and temporally separated internal test sets (January 2019 to August 2021) from the emergency department (ED) and ICU. Threshold calibration for the test sets was conducted, and performance with 40% and 60% triage thresholds was assessed. Results This study included 3 304 996 chest radiographs in 329 036 patients (mean age, 59 years ± 14 [SD]; 170 433 male patients). The training set included 550 779 pairs of radiographs. The validation set included 1620 pairs (810 no change, 810 change). The test sets included 533 pairs (ED; 265 no change, 268 change) and 600 pairs (ICU; 310 no change, 290 change). The algorithm had AUCs of 0.77 (validation), 0.80 (ED), and 0.80 (ICU). With a 40% triage threshold, specificity was 88.4% (237 of 268 pairs) and 90.0% (261 of 290 pairs) in the ED and ICU, respectively. With a 60% triage threshold, specificity was 79.9% (214 of 268 pairs) and 79.3% (230 of 290 pairs) in the ED and ICU, respectively. For urgent findings (consolidation, pleural effusion, pneumothorax), specificity was 78.6%-100% (ED) and 85.5%-93.9% (ICU) with a 40% triage threshold. Conclusion The deep learning algorithm could triage pairs of chest radiographs showing no change while detecting urgent interval changes during longitudinal follow-up. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Czum in this issue.

Deep Learning-based Approach to Predict Pulmonary Function at Chest CT.

Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function with low-dose CT images in participants using health screening services. Materials and Methods In this retrospective study, participants underwent health screening with same-day low-dose CT and pulmonary function testing with spirometry at a university affiliated tertiary referral general hospital between January 2015 and December 2018. The data set was split into a development set (model training, validation, and internal test sets) and temporally independent test set according to first visit year. A convolutional neural network was trained to predict the forced expiratory volume in the first second of expiration (FEV1) and forced vital capacity (FVC) from low-dose CT. The mean absolute error and concordance correlation coefficient (CCC) were used to evaluate agreement between spirometry as the reference standard and deep-learning prediction as the index test. FVC and FEV1 percent predicted (hereafter, FVC% and FEV1%) values less than 80% and percent of FVC exhaled in first second (hereafter, FEV1/FVC) less than 70% were used to classify participants at high risk. Results A total of 16 148 participants were included (mean age, 55 years ± 10 [SD]; 10 981 men) and divided into a development set (n = 13 428) and temporally independent test set (n = 2720). In the temporally independent test set, the mean absolute error and CCC were 0.22 L and 0.94, respectively, for FVC and 0.22 L and 0.91 for FEV1. For the prediction of the respiratory high-risk group, FVC%, FEV1%, and FEV1/FVC had respective accuracies of 89.6% (2436 of 2720 participants; 95% CI: 88.4, 90.7), 85.9% (2337 of 2720 participants; 95% CI: 84.6, 87.2), and 90.2% (2453 of 2720 participants; 95% CI: 89.1, 91.3) in the same testing data set. The sensitivities were 61.6% (242 of 393 participants; 95% CI: 59.7, 63.4), 46.9% (226 of 482 participants; 95% CI: 45.0, 48.8), and 36.1% (91 of 252 participants; 95% CI: 34.3, 37.9), respectively. Conclusion A deep learning model applied to volumetric chest CT predicted pulmonary function with relatively good performance. © RSNA, 2023 Supplemental material is available for this article.

Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT.

Background Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability. Purpose To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience. Materials and Methods This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss κ. Results A total of 288 patients were included (mean age, 58 years ± 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]; P < .001). In terms of disease category, the diagnostic accuracy improved after applying CBIR in UIP (before vs after CBIR, 52.4% vs 72.8%, respectively; P < .001) and NSIP cases (before vs after CBIR, 42.9% vs 61.6%, respectively; P < .001). Interreader agreement improved after CBIR (before vs after CBIR Fleiss κ, 0.32 vs 0.47, respectively; P = .005). Conclusion The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Artificial Intelligence in Lung Imaging.

Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.

Epidermal growth factor receptor is a host-entry cofactor triggering hepatitis B virus internalization.

Sodium taurocholate cotransporting polypeptide (NTCP) is a host cell receptor required for hepatitis B virus (HBV) entry. However, the susceptibility of NTCP-expressing cells to HBV is diverse depending on the culture condition. Stimulation with epidermal growth factor (EGF) was found to potentiate cell susceptibility to HBV infection. Here, we show that EGF receptor (EGFR) plays a critical role in HBV virion internalization. In EGFR-knockdown cells, HBV or its preS1-specific fluorescence peptide attached to the cell surface, but its internalization was attenuated. PreS1 internalization and HBV infection could be rescued by complementation with functional EGFR. Interestingly, the HBV/preS1-NTCP complex at the cell surface was internalized concomitant with the endocytotic relocalization of EGFR. Molecular interaction between NTCP and EGFR was documented by immunoprecipitation assay. Upon dissociation from functional EGFR, NTCP no longer functioned to support viral infection, as demonstrated by either (i) the introduction of NTCP point mutation that disrupted its interaction with EGFR, (ii) the detrimental effect of decoy peptide interrupting the NTCP-EGFR interaction, or (iii) the pharmacological inactivation of EGFR. Together, these data support the crucial role of EGFR in mediating HBV-NTCP internalization into susceptible cells. EGFR thus provides a yet unidentified missing link from the cell-surface HBV-NTCP attachment to the viral invasion beyond the host cell membrane.

Substituted Syndecan-2-Derived Mimetic Peptides Show Improved Antitumor Activity over the Parent Syndecan-2-Derived Peptide.

We previously showed that a synthetic peptide (S2-P) corresponding to a portion of the human syndecan-2 (SDC2) sequence can bind to the pro-domain of matrix metalloproteinase-7 (MMP-7) to inhibit colon cancer activities. Since S2-P had a relatively weak binding affinity for the MMP-7 pro-domain, we herein modified the amino acid sequence of S2-P to improve the anticancer potential. On the basis of the interaction structure of S2-P and MMP-7, four peptides were generated by replacing amino acids near Tyr 51, which is critical for the interaction. The SDC2-mimetic peptides harboring an Ala-to-Asp substitution at the C-terminal side of Tyr 51 (S2-D) or with an Ala-to-Phe substitution at the N-terminal side of Tyr 51 and an Ala-to-Asp substitution at the C-terminal side of Tyr 51 (S2-FE) showed improved interaction affinities for the MMP-7 pro-domain. Compared to S2-P, S2-FE was better able to inhibit the SDC2-MMP-7 interaction, the cell surface localization of MMP-7, the gelatin degradation activity of MMP-7, and the cancer activities (cell migration, invasion, and colony-forming activity) of human HCT116 colon cancer cells in vitro. In vivo, S2-FE inhibited the primary tumor growth and lung metastasis of CT26 mouse colon cancer cells in a xenograft mouse model. Together, these data suggest that S2-FE could be useful therapeutic anticancer peptides for colon cancer.

Crystal Structures of the Plant Phospholipase A1 Proteins Reveal a Unique Dimerization Domain.

Phospholipase is an enzyme that hydrolyzes various phospholipid substrates at specific ester bonds and plays important roles such as membrane remodeling, as digestive enzymes, and the regulation of cellular mechanism. Phospholipase proteins are divided into following the four major groups according to the ester bonds they cleave off: phospholipase A1 (PLA1), phospholipase A2 (PLA2), phospholipase C (PLC), and phospholipase D (PLD). Among the four phospholipase groups, PLA1 has been less studied than the other phospholipases. Here, we report the first molecular structures of plant PLA1s: AtDSEL and CaPLA1 derived from Arabidopsis thaliana and Capsicum annuum, respectively. AtDSEL and CaPLA1 are novel PLA1s in that they form homodimers since PLAs are generally in the form of a monomer. The dimerization domain at the C-terminal of the AtDSEL and CaPLA1 makes hydrophobic interactions between each monomer, respectively. The C-terminal domain is also present in PLA1s of other plants, but not in PLAs of mammals and fungi. An activity assay of AtDSEL toward various lipid substrates demonstrates that AtDSEL is specialized for the cleavage of sn-1 acyl chains. This report reveals a new domain that exists only in plant PLA1s and suggests that the domain is essential for homodimerization.

The machinery for endocytosis of epidermal growth factor receptor coordinates the transport of incoming hepatitis B virus to the endosomal network.

Sodium taurocholate cotransporting polypeptide (NTCP) is expressed at the surface of human hepatocytes and functions as an entry receptor of hepatitis B virus (HBV). Recently, we have reported that epidermal growth factor receptor (EGFR) is involved in NTCP-mediated viral internalization during the cell entry process. Here, we analyzed which function of EGFR is essential for mediating HBV internalization. In contrast to the reported crucial function of EGFR-downstream signaling for the entry of hepatitis C virus (HCV), blockade of EGFR-downstream signaling proteins, including mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase (PI3K), and signal transducer and activator of transcription (STAT), had no or only minor effects on HBV infection. Instead, deficiency of EGFR endocytosis resulting from either a deleterious mutation in EGFR or genetic knockdown of endocytosis adaptor molecules abrogated internalization of HBV via NTCP and prevented viral infection. EGFR activation triggered a time-dependent relocalization of HBV preS1 to the early and late endosomes and to lysosomes in concert with EGFR transport. Suppression of EGFR ubiquitination by site-directed mutagenesis or by knocking down two EGFR-sorting molecules, signal-transducing adaptor molecule (STAM) and lysosomal protein transmembrane 4ß (LAPTM4B), suggested that EGFR transport to the late endosome is critical for efficient HBV infection. Cumulatively, these results support the idea that the EGFR endocytosis/sorting machinery drives the translocation of NTCP-bound HBV from the cell surface to the endosomal network, which eventually enables productive viral infection.

Solution structure of the Myb domain of Terfa derived from Zebrafish interacting with both human and plant telomeric DNA.

Telomeric repeat binding factor a (Terfa) derived from zebrafish is a homologous protein with human telomeric repeat binding factor 2 (TRF2). Terfa is known as a senescence-associated biomarker in various research through the zebrafish animal model. In addition, according to the findings so far, it has been confirmed that human or plant telomere binding proteins bind to telomeric DNA specialized for each species, but, in our result, Terfa shows it strongly binds to both human or plant type telomeric DNA. Here we characterized the DNA binding properties and demonstrate the solution structure of Terfa and identified residues participating in the interaction with both human and plant telomeric DNA. In DNA recognition of human and plant telomere binding proteins, the N-terminal loop and the α-helix 3 part of Myb domain were bound majorly, whereas, in the case of Terfa, the N-terminal loop, the α-helix 1-2 loop, and α-helix 2 of the Myb domain were dominantly bound. Therefore, when Terfa recognizes DNA, it was found that the binding module differs from previously known telomere binding proteins. The comparison of the structure of the telomere binding proteins provides an opportunity to understand more specifically how the structural properties of each telomere binding protein are associated with telomeric DNA binding from an evolutionary point of view.

NMR spectroscopy uncovers direct interaction between BAF60A and p53.

The BRG1-associated factor 60A (BAF60A), an SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1, has been known to be important for transcriptional activation and inhibition through the alteration of the DNA nucleosome. Although the association between BAF60A and p53 plays a critical role in tumor suppression, the interaction mode is still unclear. Here, we report the detailed interactions between BAF60A and p53 by both NMR spectroscopy and pull-down analysis. Both N-terminal region (BAF60ANR) and the SWIB domain (BAF60ASWIB) of BAF60A directly interact with the tetramerization domain of p53 (p53TET). NMR data show that Ile315, Met366, Ala388, and Tyr390 of BAF60ASWIB are mostly involved in p53TET binding. The calculated dissociation constant (KD) value between BAF60ASWIB and p53TET revealed relatively weak binding affinity, at approximately 0.3 ± 0.065 mM. Our data will enhance detailed interaction mechanism to elucidate the molecular basis of p53-mediated integration via BAF60A interaction.

Value of bone suppression software in chest radiographs for improving image quality and reducing radiation dose.

OBJECTIVES: To compare image quality and radiation dose between dual-energy subtraction (DES)-based bone suppression images (D-BSIs) and software-based bone suppression images (S-BSIs). METHODS: Chest radiographs (CXRs) of forty adult patients were obtained with the two X-ray devices, one with DES and one with bone suppression software. Three image quality metrics (relative mean absolute error (RMAE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM)) between original CXR and BSI for each of D-BSI and S-SBI groups were calculated for each bone and soft tissue areas. Two readers rated the visual image quality for original CXR and BSI for each of D-BSI and S-SBI groups. The dose area product (DAP) values were recorded. Paired t test was used to compare the image quality and DAP values between D-BSI and S-BSI groups. RESULTS: In bone areas, S-BSIs had better SSIM values than D-BSI (94.57 vs. 87.77) but worse RMAE and PSNR values (0.50 vs. 0.20; 20.93 vs. 34.37) (all p < 0.001). In soft tissue areas, S-BSIs had better SSIM values than D-BSI (97.56 vs. 91.42) but similar RMAE and PSNR values (0.29 vs. 0.27; 31.35 vs. 29.87) (all p < 0.001). Both readers gave S-BSIs significantly higher image quality scores than D-BSI (p < 0.001). The mean DAP in software-related images (0.98 dGy·cm2) was significantly lower than that in the DES-related images (1.48 dGy·cm2) (p < 0.001). CONCLUSION: Bone suppression software significantly improved the image quality of bone suppression images with a relatively lower radiation dose, compared with dual-energy subtraction technique. KEY POINTS: • Bone suppression software preserves structure similarity of soft tissues better than dual-energy subtraction technique in bone suppression images. • Bone suppression software achieves superior image quality for lung lesions than dual-energy subtraction technique in bone suppression images. • Bone suppression software can decrease the radiation dose over the hardware-based image processing technique.

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma.

OBJECTIVES: Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is unknown. We therefore evaluated whether DLAS can robustly extract anatomical and physiological MRI features, thereby assisting in the accurate assessment of treatment response in glioblastoma patients. METHODS: A DLAS model was trained on 238 glioblastomas and validated on an independent set of 98 pre- and 86 post-treatment glioblastomas from two tertiary hospitals. A total of 1618 radiomics features from contrast-enhanced T1-weighted images (CE-T1w) and histogram features from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) mapping were extracted. The diagnostic performance of radiomics features and ADC and CBV parameters for identifying treatment response was tested using area under the curve (AUC) from receiver operating characteristics analysis. Feature reproducibility was tested using a 0.80 cutoff for concordance correlation coefficients. RESULTS: Reproducibility was excellent for ADC and CBV features (ICC, 0.82-0.99) and first-order features (pre- and post-treatment, 100% and 94.1% remained), but lower for texture (79.0% and 69.1% remained) and wavelet-transformed (81.8% and 74.9% remained) features of CE-T1w. DLAS-based radiomics showed similar performance to human-performed segmentations in internal validation (AUC, 0.81 [95% CI, 0.64-0.99] vs. AUC, 0.81 [0.60-1.00], p = 0.80), but slightly lower performance in external validation (AUC, 0.78 [0.61-0.95] vs. AUC, 0.65 [0.46-0.84], p = 0.23). CONCLUSION: DLAS-based feature extraction showed high reproducibility for first-order features from anatomical and physiological MRI, and comparable diagnostic performance to human manual segmentations in the identification of pseudoprogression, supporting the utility of DLAS in quantitative MRI analysis. KEY POINTS: • Deep learning-based automatic segmentation (DLAS) enables fast and robust feature extraction from diffusion- and perfusion-weighted MRI. • DLAS showed high reproducibility in first-order feature extraction from anatomical, diffusion, and perfusion MRI across two centers. • DLAS-based radiomics features showed comparable diagnostic accuracy to manual segmentations in post-treatment glioblastoma.

Radiomics approach for survival prediction in chronic obstructive pulmonary disease.

OBJECTIVES: To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS). METHODS: This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. RESULTS: The five features remaining after the LASSO analysis were %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index. CONCLUSIONS: A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality. KEY POINTS: • A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA-950, AWT_Pi10_6th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

Structural and Functional Characterizations of Cancer Targeting Nanoparticles Based on Hepatitis B Virus Capsid.

Cancer targeting nanoparticles have been extensively studied, but stable and applicable agents have yet to be developed. Here, we report stable nanoparticles based on hepatitis B core antigen (HBcAg) for cancer therapy. HBcAg monomers assemble into spherical capsids of 180 or 240 subunits. HBcAg was engineered to present an affibody for binding to human epidermal growth factor receptor 1 (EGFR) and to present histidine and tyrosine tags for binding to gold ions. The HBcAg engineered to present affibody and tags (HAF) bound specifically to EGFR and exterminated the EGFR-overexpressing adenocarcinomas under alternating magnetic field (AMF) after binding with gold ions. Using cryogenic electron microscopy (cryo-EM), we obtained the molecular structures of recombinant HAF and found that the overall structure of HAF was the same as that of HBcAg, except with the affibody on the spike. Therefore, HAF is viable for cancer therapy with the advantage of maintaining a stable capsid form. If the affibody in HAF is replaced with a specific sequence to bind to another targetable disease protein, the nanoparticles can be used for drug development over a wide spectrum.