Prospective evaluation of deep learning image reconstruction for Lung-RADS and automatic nodule volumetry on ultralow-dose chest CT.

PURPOSE: To prospectively evaluate whether Lung-RADS classification and volumetric nodule assessment were feasible with ultralow-dose (ULD) chest CT scans with deep learning image reconstruction (DLIR). METHODS: The institutional review board approved this prospective study. This study included 40 patients (mean age, 66±12 years; 21 women). Participants sequentially underwent LDCT and ULDCT (CTDIvol, 0.96±0.15 mGy and 0.12±0.01 mGy) scans reconstructed with the adaptive statistical iterative reconstruction-V 50% (ASIR-V50) and DLIR. CT image quality was compared subjectively and objectively. The pulmonary nodules were assessed visually by two readers using the Lung-RADS 1.1 and automatically using a computerized assisted tool. RESULTS: DLIR provided a significantly higher signal-to-noise ratio for LDCT and ULDCT images than ASIR-V50 (all P < .001). In general, DLIR showed superior subjective image quality for ULDCT images (P < .001) and comparable quality for LDCT images compared to ASIR-V50 (P = .01-1). The per-nodule sensitivities of observers for Lung-RADS category 3-4 nodules were 70.6-88.2% and 64.7-82.4% for DLIR-LDCT and DLIR-ULDCT images (P = 1) and categories were mostly concordant within observers. The per-nodule sensitivities of the computer-assisted detection for nodules ≥4 mm were 72.1% and 67.4% on DLIR-LDCT and ULDCT images (P = .50). The 95% limits of agreement for nodule volume differences between DLIR-LDCT and ULDCT images (-85.6 to 78.7 mm3) was similar to the within-scan nodule volume differences between DLIR- and ASIR-V50-LDCT images (-63.9 to 78.5 mm3), with volume differences smaller than 25% in 88.5% and 92.3% of nodules, respectively (P = .65). CONCLUSION: DLIR enabled comparable Lung-RADS and volumetric nodule assessments on ULDCT images to LDCT images.

Sixty-four-fold data reduction of chest radiographs using a super-resolution convolutional neural network.

OBJECTIVES: To develop and validate a super-resolution (SR) algorithm generating clinically feasible chest radiographs from 64-fold reduced data. METHODS: An SR convolutional neural network was trained to produce original-resolution images (output) from 64-fold reduced images (input) using 128 × 128 patches (n = 127 030). For validation, 112 radiographs-including those with pneumothorax (n = 17), nodules (n = 20), consolidations (n = 18), and ground-glass opacity (GGO; n = 16)-were collected. Three image sets were prepared: the original images and those reconstructed using SR and conventional linear interpolation (LI) using 64-fold reduced data. The mean-squared error (MSE) was calculated to measure similarity between the reconstructed and original images, and image noise was quantified. Three thoracic radiologists evaluated the quality of each image and decided whether any abnormalities were present. RESULTS: The SR-images were more similar to the original images than the LI-reconstructed images (MSE: 9269 ± 1015 vs. 9429 ± 1057; P = .02). The SR-images showed lower measured noise and scored better noise level by three radiologists than both original and LI-reconstructed images (Ps < .01). The radiologists' pooled sensitivity with the SR-reconstructed images was not significantly different compared with the original images for detecting pneumothorax (SR vs. original, 90.2% [46/51] vs. 96.1% [49/51]; P = .19), nodule (90.0% [54/60] vs. 85.0% [51/60]; P = .26), consolidation (100% [54/54] vs. 96.3% [52/54]; P = .50), and GGO (91.7% [44/48] vs. 95.8% [46/48]; P = .69). CONCLUSIONS: SR-reconstructed chest radiographs using 64-fold reduced data showed a lower noise level than the original images, with equivalent sensitivity for detecting major abnormalities. ADVANCES IN KNOWLEDGE: This is the first study applying super-resolution in data reduction of chest radiographs.

Chronic Lung Injury after COVID-19 Pneumonia: Clinical, Radiologic, and Histopathologic Perspectives.

With the COVID-19 pandemic having lasted more than 3 years, concerns are growing about prolonged symptoms and respiratory complications in COVID-19 survivors, collectively termed post-COVID-19 condition (PCC). Up to 50% of patients have residual symptoms and physiologic impairment, particularly dyspnea and reduced diffusion capacity. Studies have also shown that 24%-54% of patients hospitalized during the 1st year of the pandemic exhibit radiologic abnormalities, such as ground-glass opacity, reticular opacity, bronchial dilatation, and air trapping, when imaged more than 1 year after infection. In patients with persistent respiratory symptoms but normal results at chest CT, dual-energy contrast-enhanced CT, xenon 129 MRI, and low-field-strength MRI were reported to show abnormal ventilation and/or perfusion, suggesting that some lung injury may not be detectable with standard CT. Histologic patterns in post-COVID-19 lung disease include fibrosis, organizing pneumonia, and vascular abnormality, indicating that different pathologic mechanisms may contribute to PCC. Therefore, a comprehensive imaging approach is necessary to evaluate and diagnose patients with persistent post-COVID-19 symptoms. This review will focus on the long-term findings of clinical and radiologic abnormalities and describe histopathologic perspectives. It also addresses advanced imaging techniques and deep learning approaches that can be applied to COVID-19 survivors. This field remains an active area of research, and further follow-up studies are warranted for a better understanding of the chronic stage of the disease and developing a multidisciplinary approach for patient management.

Effects of Fasting versus Non-Fasting on Emetic Complications in Radiological Examinations Using Intravascular Non-Ionic Iodinated Contrast Media: A Systematic Review and Meta-Analysis.

OBJECTIVE: To compare the incidence of aspiration pneumonia, nausea, and vomiting after intravascular administration of non-ionic iodinated contrast media (ICM) between patients who fasted before contrast injection and those who did not. MATERIALS AND METHODS: Ovid-MEDLINE and Embase databases were searched from their inception dates until September 2022 to identify original articles that met the following criteria: 1) randomized controlled trials or observational studies, 2) separate reports of the incidence of aspiration pneumonia, nausea, and vomiting after intravascular injection of non-ionic ICM, and 3) inclusion of patients undergoing radiological examinations without fasting. A bivariate beta-binomial model was used to compare the risk difference in adverse events between fasting and non-fasting groups. The I² statistic was used to assess heterogeneity across the studies. RESULTS: Ten studies, encompassing 308013 patients (non-fasting, 158442), were included in this meta-analysis. No cases of aspiration pneumonia were reported. The pooled incidence of nausea was 4.6% (95% confidence interval [CI]: 1.4%, 7.8%) in the fasting group and 4.6% (95% CI: 1.1%, 8.1%) in the non-fasting group. The pooled incidence of vomiting was 2.1% (95% CI: 0.0%, 4.2%) in the fasting group and 2.5% (95% CI: 0.7%, 4.2%) in the non-fasting group. The risk difference (incidence in the non-fasting group-incidence in the fasting group) in the incidence of nausea and vomiting was 0.0% (95% CI: -4.7%, 4.7%) and 0.4% (95% CI: -2.3%, 3.1%), respectively. Heterogeneity between the studies was low (I² = 0%-13.5%). CONCLUSION: Lack of fasting before intravascular administration of non-ionic ICM for radiological examinations did not increase the risk of emetic complications significantly. This finding suggests that hospitals can relax fasting policies without compromising patient safety.

Icariin Supplementation Suppresses the Markers of Ferroptosis and Attenuates the Progression of Nonalcoholic Steatohepatitis in Mice Fed a Methionine Choline-Deficient Diet.

Icariin, a flavonoid abundant in the herb Epimedium, exhibits anti-ferroptotic activity. However, its impact on nonalcoholic steatohepatitis (NASH) development remains unclear. This study aimed to investigate the potential role of icariin in mitigating methionine choline-deficient (MCD) diet-induced NASH in C57BL/6J mice. The results showed that icariin treatment significantly reduced serum alanine aminotrasferase and aspartate aminotransferase activities while improving steatosis, inflammation, ballooning, and fibrosis in the liver tissues of mice fed the MCD diet. These improvements were accompanied by a substantial reduction in the hepatic iron contents and levels of malondialdehyde and 4-hydroxynonenal, as well as an increase in the activities of catalase and superoxide dismutase. Notably, icariin treatment suppressed the hepatic protein levels of ferroptosis markers such as acyl-CoA synthetase long-chain family member 4 and arachidonate 12-lipoxygenase, which were induced by the MCD diet. Furthermore, transmission electron microscopy confirmed the restoration of morphological changes in the mitochondria, a hallmark characteristic of ferroptosis, by icariin. Additionally, icariin treatment significantly increased the protein levels of Nrf2, a cystine/glutamate transporter (xCT), and glutathione peroxidase 4 (GPX4). In conclusion, our study suggests that icariin has the potential to attenuate NASH, possibly by suppressing ferroptosis via the Nrf2-xCT/GPX4 pathway.

Correct Closure of the Left Atrial Appendage Reduces Stagnant Blood Flow and the Risk of Thrombus Formation: A Proof-of-Concept Experimental Study Using 4D Flow Magnetic Resonance Imaging.

OBJECTIVE: The study was conducted to investigate the effect of correct occlusion of the left atrial appendage (LAA) on intracardiac blood flow and thrombus formation in patients with atrial fibrillation (AF) using four-dimensional (4D) flow magnetic resonance imaging (MRI) and three-dimensional (3D)-printed phantoms. MATERIALS AND METHODS: Three life-sized 3D-printed left atrium (LA) phantoms, including a pre-occlusion (i.e., before the occlusion procedure) model and correctly and incorrectly occluded post-procedural models, were constructed based on cardiac computed tomography images from an 86-year-old male with long-standing persistent AF. A custom-made closed-loop flow circuit was set up, and pulsatile simulated pulmonary venous flow was delivered by a pump. 4D flow MRI was performed using a 3T scanner, and the images were analyzed using MATLAB-based software (R2020b; Mathworks). Flow metrics associated with blood stasis and thrombogenicity, such as the volume of stasis defined by the velocity threshold (|V̅| < 3 cm/s), surface-and-time-averaged wall shear stress (WSS), and endothelial cell activation potential (ECAP), were analyzed and compared among the three LA phantom models. RESULTS: Different spatial distributions, orientations, and magnitudes of LA flow were directly visualized within the three LA phantoms using 4D flow MRI. The time-averaged volume and its ratio to the corresponding entire volume of LA flow stasis were consistently reduced in the correctly occluded model (70.82 mL and 39.0%, respectively), followed by the incorrectly occluded (73.17 mL and 39.0%, respectively) and pre-occlusion (79.11 mL and 39.7%, respectively) models. The surface-and-time-averaged WSS and ECAP were also lowest in the correctly occluded model (0.048 Pa and 4.004 Pa-1 , respectively), followed by the incorrectly occluded (0.059 Pa and 4.792 Pa-1 , respectively) and pre-occlusion (0.072 Pa and 5.861 Pa-1 , respectively) models. CONCLUSION: These findings suggest that a correctly occluded LAA leads to the greatest reduction in LA flow stasis and thrombogenicity, presenting a tentative procedural goal to maximize clinical benefits in patients with AF.

A Deep Learning Model Using Chest Radiographs for Prediction of 30-Day Mortality in Patients With Community-Acquired Pneumonia: Development and External Validation.

BACKGROUND. Chest radiography is an essential tool for diagnosing community-acquired pneumonia (CAP), but it has an uncertain prognostic role in the care of patients with CAP. OBJECTIVE. The purpose of this study was to develop a deep learning (DL) model to predict 30-day mortality from diagnosis among patients with CAP by use of chest radiographs to validate the performance model in patients from different time periods and institutions. METHODS. In this retrospective study, a DL model was developed from data on 7105 patients from one institution from March 2013 to December 2019 (3:1:1 allocation to training, validation, and internal test sets) to predict the risk of all-cause mortality within 30 days after CAP diagnosis by use of patients' initial chest radiographs. The DL model was evaluated in a cohort of patients diagnosed with CAP during emergency department visits at the same institution from January 2020 to March 2020 (temporal test cohort [n = 947]) and in two additional cohorts from different institutions (external test cohort A [n = 467], January 2020 to December 2020; external test cohort B [n = 381], March 2019 to October 2021). AUCs were compared between the DL model and an established risk prediction tool based on the presence of confusion, blood urea nitrogen level, respiratory rate, blood pressure, and age 65 years or older (CURB-65 score). The combination of CURB-65 score and DL model was evaluated with a logistic regression model. RESULTS. The AUC for predicting 30-day mortality was significantly larger (p < .001) for the DL model than for CURB-65 score in the temporal test set (0.77 vs 0.67). The larger AUC for the DL model than for CURB-65 score was not significant (p > .05) in external test cohort A (0.80 vs 0.73) or external test cohort B (0.80 vs 0.72). In the three cohorts, the DL model, in comparison with CURB-65 score, had higher (p < .001) specificity (range, 61-69% vs 44-58%) at the sensitivity of CURB-65 score. The combination of DL model and CURB-65 score, in comparison with CURB-65 score, yielded a significant increase in AUC in the temporal test cohort (0.77, p < .001) and external test cohort B (0.80, p = .04) and a nonsignificant increase in AUC in external test cohort A (0.80, p = .16). CONCLUSION. A DL-based model consisting of initial chest radiographs was predictive of 30-day mortality among patients with CAP with improved performance over CURB-65 score. CLINICAL IMPACT. The DL-based model may guide clinical decision-making in the care of patients with CAP.

Terminal fucosylation of haptoglobin in cancer-derived exosomes during cholangiocarcinoma progression.

Background: Cholangiocarcinoma (CCA) is a silent tumor with a high mortality rate due to the difficulty of early diagnosis and prediction of recurrence even after timely surgery. Serologic cancer biomarkers have been used in clinical practice, but their low specificity and sensitivity have been problematic. In this study, we aimed to identify CCA-specific glycan epitopes that can be used for diagnosis and to elucidate the mechanisms by which glycosylation is altered with tumor progression. Methods: The serum of patients with various cancers was fractioned into membrane-bound and soluble components using serial ultracentrifugation. Lectin blotting was conducted to evaluate glycosylation. Proteins having altered glycosylation were identified using proteomic analysis and further confirmed using immunoblotting analysis. We performed HPLC, gene analysis, real-time cargo tracking, and immunohistochemistry to determine the origin of CCA glycosylation and its underlying mechanisms. Extracellular vesicles (EV) were isolated from the sera of 62 patients with CCA at different clinical stages and inflammatory conditions and used for glycan analysis to assess their clinical significance. Results: The results reveal that glycosylation patterns between soluble and membrane-bound fractions differ significantly even when obtained from the same donor. Notably, glycans with α1-3/4 fucose and ß1-6GlcNAc branched structures increase specifically in membrane-bound fractions of CCA. Mechanically, it is primarily due to ß-haptoglobin (ß-Hp) originating from CCA expressing fucosyltransferase-3/4 (FUT 3/4) and N-acetylglucosaminyltransferase-V (MGAT5). Newly synthesized ß-Hp is loaded into EVs in early endosomes via a KFERQ-like motif and then secreted from CCA cells to induce tumor progression. In contrast, ß-Hp expressed by hepatocytes is secreted in a soluble form that does not affect CCA progression. Moreover, evaluation of EV glycosylation in CCA patients shows that fucosylation level of EV-Hp gradually increases with tumor progression and decreases markedly when the tumors are eliminated by surgery. Conclusion: This study suggests that terminal fucosylation of Hp in cancer-derived exosomes can be a novel glycan marker for diagnosis and prognosis of CCA. These findings highlight the potential of glycan analysis in different fractions of serum for biomarker discover for other diseases. Further research is needed to understand the role of fucosylated EVs on CCA progression.

Performance of 1-mm non-gated low-dose chest computed tomography using deep learning-based noise reduction for coronary artery calcium scoring.

OBJECTIVE: To investigate performance of 1-mm, sharp kernel, low-dose chest computed tomography (LDCT) for coronary artery calcium scoring (CACS) using deep learning (DL)-based denoising technique. METHODS: This retrospective, intra-individual comparative study consisted of four image datasets of 131 participants who underwent LDCT and calcium CT on the same day between January and February 2020; 1-mm LDCT with DL, 1-mm LDCT with iterative reconstruction (IR), 3-mm LDCT, and calcium CT. CACS from calcium CT were considered as reference and CACS were categorized as 0, 1-10, 11-100, 101-400, and > 400. We compared CACS from LDCTs with that from calcium CT. RESULTS: Mean CACS was 104.8 ± 249.1 and proportion of positive CACS was 45% (59/131). CACS from LDCT images tended to be underestimated than those from calcium CT: 1-mm LDCT with DL (93.5 ± 249.6, p = 0.002), 1-mm LDCT with IR (94.7 ± 249.9, p < 0.001), and 3-mm LDCT (90.3 ± 245.3, p = 0.004). All LDCT datasets showed excellent agreement with calcium CT: intraclass correlation coefficient (ICC) = 0.961 (95% confidence interval (CI), 0.945-0.972) for DL, 0.969 (95% CI, 0.956-0.978) for IR, and 0.952 (95% CI, 0.932-0.966) for 3-mm LDCT; weighted kappa for CACS classification, 0.930 (95% CI, 0.893-0.966) for 1-mm LDCT with DL, 0.908 (95% CI, 0.866-0.950) for 1-mm LDCT with IR, and 0.846 (95% CI, 0.780-0.912) for 3-mm LDCT. The accuracy of CACS classification of 1-mm LDCT with DL (90%) tended to be better than 1-mm LDCT with IR (87%) and 3-mm LDCT (84.7%) (p = 0.10). CONCLUSION: DL-based noise reduction algorithm can offer reliable calcium scores in 1-mm LDCT reconstructed with sharp kernel. KEY POINTS: • Deep learning (DL)-based noise reduction enables calcium scoring at 1-mm, sharp kernel reconstructed low-dose chest CT (LDCT). • Both iterative reconstruction and DL-based noise reduction underestimated calcium score, but agreement were excellent with those from calcium CT. • Accuracy of categorical classification of calcium scoring tended to be highest in 1-mm LDCT with DL compared to 1-mm LDCT with IR and 3-mm LDCT (90%, 87%, and 84.7%, p = 0.10).

Pharyngeal Inflammation on Positron Emission Tomography/Magnetic Resonance Imaging Before and After Obstructive Sleep Apnea Treatment.

Rationale: There is upper airway inflammation in patients with obstructive sleep apnea (OSA), which reduces with continuous positive airway pressure (CPAP) therapy. Objectives: Validate the use of positron emission tomography (PET)/magnetic resonance imaging (MRI) to quantify metabolic activity within the pharyngeal mucosa of patients with OSA against nasal lavage proteomics and assess the impact of CPAP therapy. Methods: Adults with OSA underwent [18F]-Fluoro-2-deoxy-D-glucose PET/MRI of the neck before and 3 months after initiating CPAP. Nasal lavage samples were collected. Inflammatory protein expression from samples was analyzed using the Olink platform. Upper airway imaging segmentation was performed. Target-to-background ratio (TBRmax) was calculated from target pharyngeal maximum standard uptake values (SUV) and personalized background mean SUV. Most-diseased segment TBRmax was identified per participant at locations with the highest PET avidity. Correlation analysis was performed between baseline TBRmax and nasal lavage proteomics. TBRmax was compared before and after CPAP using linear mixed-effect models. Results: Among 38 participants, the baseline mean age was 46.3 years (standard deviation [SD], 12.5), 21% were female, the mean body mass index was 30.9 kg/m2 (SD, 4.6), and the mean respiratory disturbance index measured by peripheral arterial tonometry was 31 events/h (SD, 16.4). There was a significant positive correlation between pharyngeal mucosa most-diseased segment TBRmax and nasal lavage proteomic inflammation (r = 0.41 [P < 0.001, false discovery rate = 0.002]). Primary analysis revealed a reduction in the most-diseased segment TBRmax after a median of 2.91 months of CPAP therapy (-0.86 [standard error (SE) ± 0.30; P = 0.007]). Stratified analysis by smoking status revealed a significantly decreased most-diseased segment TBRmax after CPAP therapy among never-smokers but not among ever-smokers (-1.01 [SE ± 0.39; P = 0.015] vs. -0.64 [SE ± 0.49; P = 0.201]). Conclusions: CPAP therapy reduces metabolic activity measured by PET/MRI within the upper airway of adults with OSA. Furthermore, PET/MRI measures of upper airway metabolic activity correlate with a noninvasive marker of inflammation (i.e., nasal lavage inflammatory protein expression).

Studying the Role of Lipid Geometry in COPI Vesicle Formation.

The Coat Protein I (COPI) complex forms vesicles from Golgi membrane for retrograde transport among the Golgi stacks, and also from the Golgi to the endoplasmic reticulum (ER). We have been elucidating the mechanistic details of COPI vesicle formation through a reconstitution system that involves the incubation of Golgi membrane with purified components. This approach has enabled us recently to gain new insight into how certain lipids are critical for the fission stage of COPI vesicle formation. Lipid geometry has been proposed to act in the formation of transport carriers by promoting membrane curvature. However, evidence for this role has come from studies using simplified membranes, while confirmation in the more physiologic setting of native membranes has been challenging, as such membranes contain a complex composition of lipids and proteins. We have recently refined the COPI reconstitution system to overcome this experimental obstacle. This has led us to identify an unanticipated type of lipid geometry needed for COPI vesicle fission. This chapter describes the approach that we have developed to enable this discovery. The methodologies include: (i) preparation Golgi membrane from cells that are deficient in a particular lipid enzyme activity and (ii) functional rescue of this deficiency by introducing the product of the lipid enzyme, with experiments being performed at the in vitro level to gain mechanistic clarity and at the in vivo level to confirm physiologic relevance.

CD3e-immunotoxin spares CD62Llo Tregs and reshapes organ-specific T-cell composition by preferentially depleting CD3ehi T cells.

CD3-epsilon(CD3e) immunotoxins (IT), a promising precision reagent for various clinical conditions requiring effective depletion of T cells, often shows limited treatment efficacy for largely unknown reasons. Tissue-resident T cells that persist in peripheral tissues have been shown to play pivotal roles in local and systemic immunity, as well as transplant rejection, autoimmunity and cancers. The impact of CD3e-IT treatment on these local cells, however, remains poorly understood. Here, using a new murine testing model, we demonstrate a substantial enrichment of tissue-resident Foxp3+ Tregs following CD3e-IT treatment. Differential surface expression of CD3e among T-cell subsets appears to be a main driver of Treg enrichment in CD3e-IT treatment. The surviving Tregs in CD3e-IT-treated mice were mostly the CD3edimCD62Llo effector phenotype, but the levels of this phenotype markedly varied among different lymphoid and nonlymphoid organs. We also found notable variations in surface CD3e levels among tissue-resident T cells of different organs, and these variations drive CD3e-IT to uniquely reshape T-cell compositions in local organs. The functions of organs and anatomic locations (lymph nodes) also affected the efficacy of CD3e-IT. The multi-organ pharmacodynamics of CD3e-IT and potential treatment resistance mechanisms identified in this study may generate new opportunities to further improve this promising treatment.

Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer.

This study aimed to demonstrate clinical feasibility of deep learning (DL)-based fully automated coronary artery calcium (CAC) scoring software using non-electrocardiogram (ECG)-gated chest computed tomography (CT) from patients with cancer. Overall, 913 patients with colorectal or gastric cancer who underwent non-contrast-enhanced chest CT between 2013 and 2015 were included. Agatston scores obtained by manual segmentation of CAC on chest CT were used as reference. Reliability of automated CAC score acquisition was evaluated using intraclass correlation coefficients (ICCs). The agreement for cardiovascular disease (CVD) risk stratification was assessed with linearly weighted k statistics. ICCs between the manual and automated CAC scores were 0.992 (95% CI, 0.991 and 0.993, p<0.001) for total Agatston scores, 0.863 (95% CI, 0.844 and 0.880, p<0.001) for the left main, 0.964 (95% CI, 0.959 and 0.968, p<0.001) for the left anterior descending, 0.962 (95% CI, 0.956 and 0.966, p<0.001) for the left circumflex, and 0.980 (95% CI, 0.978 and 0.983, p<0.001) for the right coronary arteries. The agreement for cardiovascular risk was excellent (k=0.946, p<0.001). Current DL-based automated CAC software showed excellent reliability for Agatston score and CVD risk stratification using non-ECG gated CT scans and might allow the identification of high-risk cancer patients for CVD.

Comparison of CD3e Antibody and CD3e-sZAP Immunotoxin Treatment in Mice Identifies sZAP as the Main Driver of Vascular Leakage.

Anti-CD3-epsilon (CD3e) monoclonal antibodies (mAbs) and CD3e immunotoxins (ITs) are promising targeted therapy options for various T-cell disorders. Despite significant advances in mAb and IT engineering, vascular leakage syndrome (VLS) remains a major dose-limiting toxicity for ITs and has been poorly characterized for recent "engineered" mAbs. This study undertakes a direct comparison of non-mitogenic CD3e-mAb (145-2C11 with Fc-silentTM murine IgG1: S-CD3e-mAb) and a new murine-version CD3e-IT (saporin-streptavidin (sZAP) conjugated with S-CD3e-mAb: S-CD3e-IT) and identifies their distinct toxicity profiles in mice. As expected, the two agents showed different modes of action on T cells, with S-CD3e-mAb inducing nearly complete modulation of CD3e on the cell surface, while S-CD3e-IT depleted the cells. S-CD3e-IT significantly increased the infiltration of polymorphonuclear leukocytes (PMNs) into the tissue parenchyma of the spleen and lungs, a sign of increased vascular permeability. By contrast, S-CD3e-mAbs-treated mice showed no notable signs of vascular leakage. Treatment with control ITs (sZAP conjugated with Fc-silent isotype antibodies) induced significant vascular leakage without causing T-cell deaths. These results demonstrate that the toxin portion of S-CD3e-IT, not the CD3e-binding portion (S-CD3e-mAb), is the main driver of vascular leakage, thus clarifying the molecular target for improving safety profiles in CD3e-IT therapy.

A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography.

PURPOSE: We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition. MATERIALS AND METHODS: The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU. RESULTS: The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity. CONCLUSIONS: Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.

Relationship between SARS-CoV-2 antibody titer and the severity of COVID-19.

BACKGROUND: It remains unclear whether high titers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies aggravate clinical manifestations in patients or whether severe clinical manifestations result in high antibody titers. Thus, we investigated the cause-effect relationship between SARS-CoV-2 antibody titers and disease severity. METHODS: We prospectively enrolled patients admitted with the diagnosis of coronavirus disease-19 (COVID-19) from February 2020 to August 2020. We measured SARS-CoV-2 antibody titers, namely anti-receptor-binding domain (RBD) antibody and neutralizing antibody (NAb), from blood samples and calculated the chest radiograph (CXR) scores of the patients to evaluate the severity of COVID-19. RESULTS: Overall, 40 patients with COVID-19 were enrolled. Pneumonia was observed in more than half of the patients (25/40, 60%). SARS-CoV-2 antibody titers were higher in patients who were aged >60 years (anti-RBD antibodies, P = 0.003 and NAb, P = 0.009), presented with pneumonia (P = 0.006 and 0.007, respectively), and required oxygen therapy (P = 0.003 and 0.004, respectively) than in those who were not. CXR scores peaked (at 15-21 days after the onset of symptoms) statistically significantly earlier than SARS-CoV-2 antibody titers (at 22-30 days for NAb and at 31-70 days for anti-RBD antibody). There was a close correlation between the maximum CXR score and the maximum SAR-CoV-2 antibody titer. CONCLUSIONS: Based on the comparison of the peak time of SARS-CoV-2 antibody titers with the CXR score after symptom onset, we suggest that severe clinical manifestations result in high titers of SARS-CoV-2 antibodies.

Investigation on photopolymerization of PEGDA to fabricate high-aspect-ratio microneedles.

Microneedles (MNs) are micron-sized needles that can penetrate the stratum corneum, enabling the non-invasive and painless administration of drugs and vaccines. In this work, fabrication conditions for high-aspect-ratio MNs by the photopolymerization of polyethylene glycol diacrylate (PEGDA) were investigated. Ultraviolet (UV) light was used to crosslink photocurable prepolymers in specific areas defined by a photomask. The aspect ratio of solidified MNs is too small to penetrate the stratum corneum if the degree of polymerization is insufficient. However, if the degree of polymerization is too high, a film is formed between the MNs by solidification of an undesired area owing to the scattering effect, reducing needle height. The influence of prepolymer molecular weight and the degree of UV absorption by the photoinitiator (PI) were studied to optimize the conditions for obtaining high-aspect-ratio MNs. Additionally, the effect of spacing ratio on high-aspect-ratio MNs without film formation has been discussed. A penetration test was conducted with porcine skin to analyze the effect of mechanical properties of MN. This study could guide the fabrication of MNs by the photopolymerization of biocompatible polymers with a photomask.

Artificial intelligence system for identification of false-negative interpretations in chest radiographs.

OBJECTIVES: To investigate the efficacy of an artificial intelligence (AI) system for the identification of false negatives in chest radiographs that were interpreted as normal by radiologists. METHODS: We consecutively collected chest radiographs that were read as normal during 1 month (March 2020) in a single institution. A commercialized AI system was retrospectively applied to these radiographs. Radiographs with abnormal AI results were then re-interpreted by the radiologist who initially read the radiograph ("AI as the advisor" scenario). The reference standards for the true presence of relevant abnormalities in radiographs were defined by majority voting of three thoracic radiologists. The efficacy of the AI system was evaluated by detection yield (proportion of true-positive identification among the entire examination) and false-referral rate (FRR, proportion of false-positive identification among all examinations). Decision curve analyses were performed to evaluate the net benefits of applying the AI system. RESULTS: A total of 4208 radiographs from 3778 patients (M:F = 1542:2236; median age, 56 years) were included. The AI system identified initially overlooked relevant abnormalities with a detection yield and an FRR of 2.4% and 14.0%, respectively. In the "AI as the advisor" scenario, radiologists detected initially overlooked relevant abnormalities with a detection yield and FRR of 1.2% and 0.97%, respectively. In a decision curve analysis, AI as an advisor scenario exhibited a positive net benefit when the cost-to-benefit ratio was below 1:0.8. CONCLUSION: An AI system could identify relevant abnormalities overlooked by radiologists and could enable radiologists to correct their false-negative interpretations by providing feedback to radiologists. KEY POINTS: • In consecutive chest radiographs with normal interpretations, an artificial intelligence system could identify relevant abnormalities that were initially overlooked by radiologists. • The artificial intelligence system could enable radiologists to correct their initial false-negative interpretations by providing feedback to radiologists when overlooked abnormalities were present.

Multiple isogenic GNE-myopathy modeling with mutation specific phenotypes from human pluripotent stem cells by base editors.

Despite the great potential of disease modeling using human pluripotent stem cells (hPSCs) derived from patients with mutations, lack of an appropriate isogenic control hinders a precise phenotypic comparison due to the bias arising from the dissimilar genetic backgrounds between the control and diseased hPSCs. Herein, we took advantage of currently available base editors (BEs) to epitomize the isogenic disease model from hPSCs. Using this method, we established multiple isogenic GNE myopathy disease models that harbor point mutations on the GNE gene, including four different mutations found in GNE myopathy patients. Four different mutations in the epimerase or kinase domains of GNE revealed mutation-specific hyposialylation and hyposialylation dependent gene signature, which was closely correlated to pathological clinical phenotypes. GNE protein structure modeling based on the mutations, addressed these mutation-specific hyposialylation patterns. Furthermore, treatment with a drug candidate currently under clinical trials showed a mutation-specific drug response in GNE myopathy disease models. These data suggest that derivation of multiple isogenic disease models from hPSCs by using genome editing can enable translationally relevant studies on the pathophysiology of GNE myopathy and drug responses.

CT-defined visual emphysema in smokers with normal spirometry: association with prolonged air leak and other respiratory complications after lobectomy for lung cancer.

OBJECTIVES: To evaluate the association of visual emphysema on preoperative CT with respiratory complications and prolonged air leak (PAL) in smokers with normal spirometry who underwent lobectomy for lung cancer. METHODS: Among patients who underwent lobectomy for lung cancer between 2018 and 2019 at a single center, ever-smokers with normal spirometry were identified retrospectively. Visual emphysema was graded for centrilobular emphysema (CLE) and paraseptal emphysema (PSE), respectively, by two thoracic radiologists. The associations of visual emphysema with PAL and respiratory complications (except PAL) were investigated. RESULTS: In total, 282 patients were evaluated (257 men; mean age, 64.6 ± 9.8 years). Visual emphysema was present in 126 patients (44.7%) (CLE, 26; PSE, 40; combined CLE and PSE, 60). PAL and respiratory complications occurred in 34 (12.1%) and 26 patients (26.9%), respectively. Greater frequency of PAL and respiratory complications were observed in patients with higher grades of CLE (p = 0.002 for PAL; p = 0.039 for respiratory complications) and PSE (p < 0.001 for PAL; p < 0.001 for respiratory complications). For reader 1 evaluation, the presence of both CLE and PSE was associated with PAL (adjusted odds ratio [OR], 4.94; 95% confidence interval [CI], 1.75-13.95; p = 0.003). For reader 2 evaluation, PSE (adjusted OR, 4.26; 95% CI, 1.22-14.97; p = 0.024) and combined CLE and PSE (adjusted OR, 3.49; 95% CI, 12.1-10.06; p = 0.020) were associated with PAL. The presence of solely CLE was not associated with any adverse outcome (all p > 0.05) for both readers. CONCLUSIONS: Visual assessment of PSE in smokers with normal spirometry may help identify those who develop PAL after lobectomy. KEY POINTS: • Visual emphysema was highly prevalent (44.7%) in smokers with normal lung function who underwent lobectomy for lung cancer. • Increasing tendency of postoperative complications was observed as the grade of visual emphysema increased. • The presence of paraseptal emphysema was associated with prolonged air leak.