Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.

OBJECTIVES: To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD). MATERIALS AND METHODS: Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index. RESULTS: In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively (p <0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively (p < 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9-97.7%) and 94.3% (89.5-97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%). CONCLUSIONS: FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events. CLINICAL RELEVANCE STATEMENT: Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events. KEY POINTS: • Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. • Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. • Full automation can facilitate the application of CCTA in early detection of coronary artery disease.

Quercetin ameliorates ulcerative colitis by activating aryl hydrocarbon receptor to improve intestinal barrier integrity.

Ulcerative colitis (UC) pathogenesis is largely associated with intestinal epithelial barrier dysfunction. A therapeutic approach to UC involves the repair of damaged intestinal barrier. Our study aimed to investigate whether aryl hydrocarbon receptor (AhR) mediated the intestinal barrier repair effects of quercetin to ameliorate UC. 3% dextran sulfate sodium was used to induce colitic mice, and quercetin (25, 50, and 100 mg/kg) was administered orally for 10 days to assess the therapeutic effects. In vitro, Caco-2 cells were used to explore the effect of quercetin on tight junction protein expression and AhR activation. The results showed that quercetin alleviated colitic mice by restoring tight junctions (TJs) integrity via an AhR-dependent manner (p < 0.05). In vitro, quercetin dose-dependently elevated the expressions of TJs protein ZO-1 and Claudin1, and activated AhR by enhancing the expression of CYP1A1 and facilitating AhR nuclear translocation in Caco-2 cells (p < 0.05). While AhR antagonist CH223191 reversed the therapeutic effects of quercetin (p < 0.05) and blocked quercetin-induced AhR activation and enhancement of TJs protein (p < 0.05). In conclusion, quercetin repaired intestinal barrier dysfunction by activating AhR-mediated enhancement of TJs to alleviate UC. Our research offered new perspectives on how quercetin enhanced intestinal barrier function.

Detectability and Volumetric Accuracy of Pulmonary Nodules in Low-Dose Photon-Counting Detector Computed Tomography: An Anthropomorphic Phantom Study.

The aim of this phantom study was to assess the detectability and volumetric accuracy of pulmonary nodules on photon-counting detector CT (PCD-CT) at different low-dose levels compared to conventional energy-integrating detector CT (EID-CT). In-house fabricated artificial nodules of different shapes (spherical, lobulated, spiculated), sizes (2.5-10 mm and 5-1222 mm3), and densities (-330 HU and 100 HU) were randomly inserted into an anthropomorphic thorax phantom. The phantom was scanned with a low-dose chest protocol with PCD-CT and EID-CT, in which the dose with PCD-CT was lowered from 100% to 10% with respect to the EID-CT reference dose. Two blinded observers independently assessed the CT examinations of the nodules. A third observer measured the nodule volumes using commercial software. The influence of the scanner type, dose, observer, physical nodule volume, shape, and density on the detectability and volumetric accuracy was assessed by a multivariable regression analysis. In 120 CT examinations, 642 nodules were present. Observer 1 and 2 detected 367 (57%) and 289 nodules (45%), respectively. With PCD-CT and EID-CT, the nodule detectability was similar. The physical nodule volumes were underestimated by 20% (range 8-52%) with PCD-CT and 24% (range 9-52%) with EID-CT. With PCD-CT, no significant decrease in the detectability and volumetric accuracy was found at dose reductions down to 10% of the reference dose (p > 0.05). The detectability and volumetric accuracy were significantly influenced by the observer, nodule volume, and a spiculated nodule shape (p < 0.05), but not by dose, CT scanner type, and nodule density (p > 0.05). Low-dose PCD-CT demonstrates potential to detect and assess the volumes of pulmonary nodules, even with a radiation dose reduction of up to 90%.

Astragaloside IV inhibits AOM/DSS-induced colitis-associated tumorigenesis via activation of PPARγ signaling in mice.

BACKGROUND: Colitis-associated colorectal cancer (CAC) is a severe complication of inflammatory bowel disease (IBD), resulting from long-term inflammation in the intestines. The primary cause of CAC is the imbalance of oxidative metabolism in intestinal cells, triggered by excessive reactive oxygen (ROS) and nitrogen (NO) species production due to prolonged intestinal inflammation. This imbalance leads to genomic instability caused by DNA damage, eventually resulting in the development of intestinal cancer. Previous studies have demonstrated that astragaloside IV is effective in treating dextran sulfate sodium salt (DSS)-induced colitis, but there is currently no relevant research on its efficacy in treating CAC. METHODS: To investigate the effect of astragaloside IV against CAC and the underlying mechanism, C57 mice were treated with (20, 40, 80 mg/kg) astragaloside IV while CAC was induced by intraperitoneal injection of 10 mg/kg azoxymethane (AOM) and ad libitum consumption of 2% dextran sulfate sodium salt (DSS). We re-verified the activating effects of astragaloside IV on PPARγ signaling in IEC-6 cells, which were reversed by GW9662 (the PPARγ inhibitor). RESULTS: Our results showed that astragaloside IV significantly improved AOM/DSS-induced CAC mice by inhibiting colonic shortening, preventing intestinal mucosal damage, reducing the number of tumors and, the expression of Ki67 protein. In addition, astragaloside IV could activate PPARγ signaling, which not only promoted the expression of Nrf2 and HO-1, restored the level of SOD, CAT and GSH, but also inhibited the expression of iNOS and reduced the production of NO in the intestine and IEC-6 cells. And this effect could be reversed by GW9662 in vitro. Astragaloside IV thus decreased the level of ROS and NO in the intestinal tract of mice, as well as reduced the damage of DNA, and therefore inhibited the occurrence of CAC. CONCLUSION: Astragaloside IV can activate PPARγ signaling in intestinal epithelial cells and reduces DNA damage caused by intestinal inflammation, thereby inhibiting colon tumourigenesis. The novelty of this study is to use PPARγ as the target to inhibit DNA damage to prevent the occurrence of CAC.

Paeonol alleviates ulcerative colitis in mice by increasing short-chain fatty acids derived from Clostridium butyricum.

BACKGROUND: Increasing evidence suggests that repairing the damaged intestinal epithelial barrier and restoring its function is the key to solving the problem of prolonged ulcerative colitis. Previous studies have shown that paeonol (pae) can alleviate colitis by down-regulating inflammatory pathways. In addition, pae also has a certain effect on regulating intestinal flora. However, it remains unclear whether pae can play a role in repairing the intestinal barrier and whether there is a relationship between the therapeutic effect and the gut microbiota. PURPOSES: The aim of this study is to investigate the effect of pae on intestinal barrier repair in UC mice and how the gut microbiota plays a part in it. STUDY DESIGN AND METHODS: The therapeutic effect of pae was evaluated in a 3% DSS-induced UC mouse model. The role of pae in repairing the intestinal barrier was evaluated by detecting colonic cupped cells by Alcian blue staining, the expression of colonic epithelial tight junction protein by immunofluorescence and western blot, and the proportion of IL-22+ILC3 cells in the lamina propria lymphocytes by flow cytometry. Subsequently, 16S rRNA sequencing was used to observe the changes in intestinal flora, GC-MS was used to detect the level of SCFAs, and qPCR was used to identify the abundance of Clostridium butyricum in the intestine to evaluate the effect of pae on the gut microbiota. The antibiotic-mediated depletion of the gut flora was then used to verify that pae depends on C. butyricum to play a healing role. Finally, non-targeted metabolomics was employed to investigate the potential pathways of pae regulating C. butyricum. RESULTS: Pae could improve intestinal microecological imbalance and promote the production of short-chain fatty acids (SCFAs). Most importantly, we identified C. butyricum as a key bacterium responsible for the intestinal barrier repair effect of pae in UC mice. Eradication of intestinal flora by antibiotics abolished the repair of the intestinal barrier and the promotion of SCFAs production by pae, while C. butyricum colonization could restore the therapeutic effects of pae in UC mice, which further confirmed that C. butyricum was indeed the "driver bacterium" of pae in UC treatment. Untargeted metabolomics showed that pae regulated some amino acid metabolism and 2-Oxocarboxylic acid metabolism in C. butyricum. CONCLUSIONS: Our study showed that the restoration of the impaired intestinal barrier by pae to alleviate colitis is associated with increased C. butyricum and SCFAs production, which may be a promising strategy for the treatment of UC.

Baseline ALBI Grade Predicts Benefits After Splenectomy for Cirrhotic Patients with Hypersplenism.

PURPOSE: Splenectomy is an effective treatment for correcting cytopenia caused by hypersplenism secondary to cirrhosis. However, other potential benefits have not been well characterized. In this study, we investigated the value of splenectomy as it relates to improvement in hepatic function, liver regeneration, and health-related quality of life, and their association with baseline characteristics to clarify which patients may benefit the most from splenectomy. METHODS: Patients with hypersplenism secondary to cirrhosis treated by splenectomy were retrospectively reviewed. Hepatic function was reflected by hematologic indices and albumin-bilirubin score. Liver volume was measured by imaging software, and quality-of-life was assessed by a 36-question short-form questionnaire. The changes in these three aspects after splenectomy were evaluated in the whole cohort and compared between subgroups. RESULTS: The hepatic function of the patients significantly improved after splenectomy, and this was reflected by elevated serum albumin, shortened prothrombin time, and decreased albumin-bilirubin score. Patients with baseline albumin-bilirubin grade 2 or 3 and age < 56 years showed significantly decreased albumin-bilirubin score after splenectomy, whereas other subgroups did not. Moreover, liver volume increased remarkably after splenectomy in patients with baseline albumin-bilirubin grade 1, but not in those with grade 2 or 3. Significant improvement in quality-of-life occurred in the entire cohort after splenectomy, but more profound improvement was found in patients with albumin-bilirubin grade 2 or 3. CONCLUSIONS: Splenectomy improves hepatic function, increases liver volume, and also improves quality-of-life in different subsets of patients with cirrhosis and hypersplenism. Baseline characteristics, such as albumin-bilirubin grade and age, are helpful in estimating the potential benefits of splenectomy for patients before surgery.

Comparison of Chest Radiograph Captions Based on Natural Language Processing vs Completed by Radiologists.

Importance: Artificial intelligence (AI) can interpret abnormal signs in chest radiography (CXR) and generate captions, but a prospective study is needed to examine its practical value. Objective: To prospectively compare natural language processing (NLP)-generated CXR captions and the diagnostic findings of radiologists. Design, Setting, and Participants: A multicenter diagnostic study was conducted. The training data set included CXR images and reports retrospectively collected from February 1, 2014, to February 28, 2018. The retrospective test data set included consecutive images and reports from April 1 to July 31, 2019. The prospective test data set included consecutive images and reports from May 1 to September 30, 2021. Exposures: A bidirectional encoder representation from a transformers model was used to extract language entities and relationships from unstructured CXR reports to establish 23 labels of abnormal signs to train convolutional neural networks. The participants in the prospective test group were randomly assigned to 1 of 3 different caption generation models: a normal template, NLP-generated captions, and rule-based captions based on convolutional neural networks. For each case, a resident drafted the report based on the randomly assigned captions and an experienced radiologist finalized the report blinded to the original captions. A total of 21 residents and 19 radiologists were involved. Main Outcomes and Measures: Time to write reports based on different caption generation models. Results: The training data set consisted of 74 082 cases (39 254 [53.0%] women; mean [SD] age, 50.0 [17.1] years). In the retrospective (n = 8126; 4345 [53.5%] women; mean [SD] age, 47.9 [15.9] years) and prospective (n = 5091; 2416 [47.5%] women; mean [SD] age, 45.1 [15.6] years) test data sets, the mean (SD) area under the curve of abnormal signs was 0.87 (0.11) in the retrospective data set and 0.84 (0.09) in the prospective data set. The residents' mean (SD) reporting time using the NLP-generated model was 283 (37) seconds-significantly shorter than the normal template (347 [58] seconds; P < .001) and the rule-based model (296 [46] seconds; P < .001). The NLP-generated captions showed the highest similarity to the final reports with a mean (SD) bilingual evaluation understudy score of 0.69 (0.24)-significantly higher than the normal template (0.37 [0.09]; P < .001) and the rule-based model (0.57 [0.19]; P < .001). Conclusions and Relevance: In this diagnostic study of NLP-generated CXR captions, prior information provided by NLP was associated with greater efficiency in the reporting process, while maintaining good consistency with the findings of radiologists.

Gegen Qinlian decoction activates AhR/IL-22 to repair intestinal barrier by modulating gut microbiota-related tryptophan metabolism in ulcerative colitis mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Gegen Qinlian decoction (GQD) is a traditional Chinese medicine derived from Treatise on febrile diseases and is clinically used for the treatment of acute ulcerative colitis (UC). However, the potential mechanism of GQD treatment for UC remains elusive. AIM OF STUDY: In this study, we aimed to explore the involvement of gut microbiota-related tryptophan metabolism in mediating protective effects of GQD against intestinal barrier damage. MATERIALS AND METHODS: Mice with colitis were treated with 3% dextran sulfate sodium (DSS) for 6 days. The therapeutic effects of GQD in UC mice were examined based on body weight, disease activity index (DAI), organ index, length and pathological changes in the colon. The distribution of fluorescein isothiocyanate dextran (FITC-dextran) in the intestinal tract was observed using small animal imaging, while concentration of FITC-dextran in serum was detected using a fluorescein microplate analyser. Bacterial infiltration in colon tissues was observed by fluorescence in situ hybridisation (FISH), and the bacterial load in mesenteric lymph nodes (MLNs) was further examined through bacterial culture. Subsequently, colonic goblet cells were detected using Alcian blue staining. The tight junctions of the colonic epithelium were observed using transmission electron microscopy, and the expression of tight junction proteins was detected by immunofluorescence (IF) and western blot. In addition, flow cytometry was used to analyse the proportion of interleukin-22-positive (IL-22+) ILC3 cells in lamina propria lymphocytes, and the content of IL-22 in colon homogenates was determined using an ELISA kit. In addition, targeted tryptophan metabolomics was used to detect the concentration of indole derivatives produced by tryptophan metabolism in faeces, and 16S rDNA was used to investigate the composition and abundance of gut microbiota-related tryptophan metabolism. RESULTS: Administration of GQD significantly alleviated the pathological symptoms, including weight loss, increased DAI score, changes in organ index, colon shortening, and colon pathological injury in UC mice. In addition, GQD reduced the diffusion of FITC-dextran in the intestinal tract, the content of FITC-dextran in serum, and bacterial infiltration in MLNs and colon tissues. Additionally, GQD significantly increased the number of colonic goblet cells, repaired the structure of epithelial tight junctions and increased the expression of tight junction proteins. Furthermore, GQD significantly increased the proportion of IL-22+ ILC3 in the lamina propria, the expression of CYP1A1 protein in colon tissue, and the level of IL-22 in colon homogenates. However, the above protective effects of GQD were inhibited by co-administration of GQD and aryl hydrocarbon receptor (AhR) antagonist. Additionally, GQD restored the content of indole derivatives generated by tryptophan metabolism, regulated the diversity of the gut microbiota, and significantly increased the abundance of genes related to tryptophan metabolism. CONCLUSION: Our results confirmed that GQD repaired the damaged intestinal barrier in UC mice by regulating gut microbiota-related tryptophan metabolism and restoring the generation of indole derivatives to activate AhR-mediated IL-22 production.

Wogonin regulates colonocyte metabolism via PPARγ to inhibit Enterobacteriaceae against dextran sulfate sodium-induced colitis in mice.

To investigate the potential effects and mechanism of wogonin on dextran sulfate sodium (DSS)-induced colitis, 70 male mice were administered wogonin (12.5, 25, 50 mg·kg-1 ·d-1 , i.g.) for 10 days, meanwhile, in order to induce colitis, the mice were free to drink 3% DSS for 6 days. We found that wogonin could obviously ameliorate DSS-induced colitis, including preventing colon shortening and inhibiting pathological damage. In addition, wogonin could increase the expression of PPARγ, which not only restores intestinal epithelial hypoxia but also inhibits iNOS protein to reduce intestinal nitrite levels. All these effects facilitated a reduction in the abundance of Enterobacteriaceae in DSS-induced colitis mice. Therefore, compared with the DSS group, the number of Enterobacteriaceae in the intestinal flora was significantly reduced after administration of wogonin or rosiglitazone by 16s rDNA technology. We also verified that wogonin could promote the expression of PPARγ mRNA and protein in Caco-2 cells, and this effect disappeared when PPARγ signal was inhibited. In conclusion, our study suggested that wogonin can activate the PPARγ signal of the Intestinal epithelium to ameliorate the Intestinal inflammation caused by Enterobacteriaceae bacteria expansion.

Motion artifact removal in coronary CT angiography based on generative adversarial networks.

OBJECTIVES: Coronary motion artifacts affect the diagnostic accuracy of coronary CT angiography (CCTA), especially in the mid right coronary artery (mRCA). The purpose is to correct CCTA motion artifacts of the mRCA using a GAN (generative adversarial network). METHODS: We included 313 patients with CCTA scans, who had paired motion-affected and motion-free reference images at different R-R interval phases in the same cardiac cycle and included another 53 CCTA cases with invasive coronary angiography (ICA) comparison. Pix2pix, an image-to-image conversion GAN, was trained by the motion-affected and motion-free reference pairs to generate motion-free images from the motion-affected images. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated to evaluate the image quality of GAN-generated images. RESULTS: At the image level, the median of PSNR, SSIM, DSC, and HD of GAN-generated images were 26.1 (interquartile: 24.4-27.5), 0.860 (0.830-0.882), 0.783 (0.714-0.825), and 4.47 (3.00-4.47), respectively, significantly better than the motion-affected images (p < 0.001). At the patient level, the image quality results were similar. GAN-generated images improved the motion artifact alleviation score (4 vs. 1, p < 0.001) and overall image quality score (4 vs. 1, p < 0.001) than those of the motion-affected images. In patients with ICA comparison, GAN-generated images achieved accuracy of 81%, 85%, and 70% in identifying no, < 50%, and ≥ 50% stenosis, respectively, higher than 66%, 72%, and 68% for the motion-affected images. CONCLUSION: Generative adversarial network-generated CCTA images greatly improved the image quality and diagnostic accuracy compared to motion-affected images. KEY POINTS: • A generative adversarial network greatly reduced motion artifacts in coronary CT angiography and improved image quality. • GAN-generated images improved diagnosis accuracy of identifying no, < 50%, and ≥ 50% stenosis.

Benign incidental cardiac findings in chest and cardiac CT imaging.

With the continuous expansion of the disease scope of chest CT and cardiac CT, the number of these CT examinations has increased rapidly. In addition to their common indications, many incidental cardiac findings can be observed when carefully evaluating the coronary arteries, valves, pericardium, ventricles, and large vessels. These findings may have clinical significance or risk of complications, but they are sometimes overlooked or may not be described in the final reports. Although most of the incidental findings are benign, timely detection and treatment can improve the management of chronic diseases or reduce the possibility of severe complications. In this review, we summarized the imaging findings, incidence rate, and clinical relevance of some benign cardiac findings such as coronary artery calcification, aortic and mitral valve calcification, aortic calcification, cardiac thrombus, myocardial bridge, aortic dilation, cardiac myxoma, pericardial cyst, and coronary artery fistula. Reporting incidental cardiac findings will help reduce the risk of severe complications or disease deterioration and contribute to the recovery of patients.

Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia.

Objectives: To determine the association between radiomics signature (Rad-signature) of pericoronary tissue (PCT) in coronary computed tomography angiography (CCTA) and CT-derived fractional flow reserve (CT-FFR), and explore the influential factors of functional ischemia. Methods: We retrospectively included 350 patients who underwent CCTA from 2 centers, consisting of the training (n = 134), validation (n = 66), and testing (with CCTA and invasive coronary angiography, n = 150) groups. After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. Another 45 patients who underwent CCTA and invasive FFR were included to assure the performance of Rad-signature. Results: A total of 1046 vessels in 350 patients were analyzed, and functional ischemia was identified in 241/1046 (23.0%) vessels and 179/350 (51.1%) patients. From the 47 features highly relevant to functional ischemia, the top-8 contributive features were selected to establish Rad-signature. At the vessel level, the area under the curve (AUC) of Rad-signature to discriminate functional ischemia was 0.83, 0.82, and 0.82 in the training, validation, and testing groups, higher than 0.55, 0.55, and 0.52 of FAI (p < 0.001), respectively, and was higher than 0.72 of anatomical CT in the testing group (p = 0.017). The AUC of the combined model (Rad-signature + anatomical CT) was 0.86, 0.85, and 0.83, respectively, significantly higher than that of anatomical CT and FAI (p < 0.05). In the CCTA-invasive FFR group, using invasive FFR as the standard, the mean AUC of Rad-signature was 0.83 ± 0.02. At the patient level, multivariate logistic regression analysis showed that Rad-signature of left anterior descending (LAD) [odds ratio (OR) = 1.72; p = 0.012] and anatomical CT (OR = 3.53; p < 0.001) were independent influential factors of functional ischemia (p < 0.05). In the subgroup of nonobstructive (stenosis <50% in invasive coronary angiography) and obstructive (≥50%) cases of the testing group, the independent factor of functional ischemia was FAI of LAD (OR = 1.10; p = 0.041) and Rad-signature of LAD (OR = 2.45; p = 0.042), respectively. Conclusion: The machine-learning-derived Rad-signature of PCT in CCTA demonstrates significant association with functional ischemia.

Measurement Accuracy and Repeatability of RECIST-Defined Pulmonary Lesions and Lymph Nodes in Ultra-Low-Dose CT Based on Deep Learning Image Reconstruction.

BACKGROUND: Deep learning image reconstruction (DLIR) improves image quality. We aimed to compare the measured diameter of pulmonary lesions and lymph nodes between DLIR-based ultra-low-dose CT (ULDCT) and contrast-enhanced CT. METHODS: The consecutive adult patients with noncontrast chest ULDCT (0.07-0.14 mSv) and contrast-enhanced CT (2.38 mSv) were prospectively enrolled. Patients with poor image quality and body mass index ≥ 30 kg/m2 were excluded. The diameter of pulmonary target lesions and lymph nodes defined by Response Evaluation Criteria in Solid Tumors (RECIST) was measured. The measurement variability between ULDCT and enhanced CT was evaluated by Bland-Altman analysis. RESULTS: The 141 enrolled patients (62 ± 12 years) had 89 RECIST-defined measurable pulmonary target lesions (including 30 malignant lesions, mainly adenocarcinomas) and 45 measurable mediastinal lymph nodes (12 malignant). The measurement variation of pulmonary lesions between high-strength DLIR (DLIR-H) images of ULDCT and contrast-enhanced CT was 2.2% (95% CI: 1.7% to 2.6%) and the variation of lymph nodes was 1.4% (1.0% to 1.9%). CONCLUSIONS: The measured diameters of pulmonary lesions and lymph nodes in DLIR-H images of ULDCT are highly close to those of contrast-enhanced CT. DLIR-based ULDCT may facilitate evaluating target lesions with greatly reduced radiation exposure in tumor evaluation and lung cancer screening.

Luteolin alleviates ulcerative colitis by restoring the balance of NCR-ILC3/NCR+ILC3 to repairing impaired intestinal barrier.

Ulcerative colitis (UC) is a multifactorial, refractory chronic inflammatory disease. The primary factor leading to prolonged ulcerative colitis is the imbalance of the group 3 innate lymphoid cells (ILC3) subgroup resulting in the delayed reconstruction of damaged intestinal barrier. Previous studies show that luteolin had efficacy on UC, however, the potency of luteolin on restoring the balance of NCR-ILC3/NCR+ILC3 to repairing impaired intestinal barrier remains unclear. In this study, to investigate the potential mechanism of luteolin on ILC3 subgroup, we first evidenced that luteolin could promote transformation NCR-MNK3 to NCR+MNK3 in vitro. Then, a UC model was established in C57BL/6J mice to assess the efficacy of luteolin in restoring ILC3 subgroup balance and repairing intestinal barrier in chronic UC. Finally, the experiments in vitro validated the potential mechanism of luteolin in regulating ILC3 plasticity. The results showed that luteolin significantly alleviated the symptoms of DSS-induced UC in mice, including preventing body weight loss and decreasing the disease activity index (DAI) and intestinal damages. Additionally, luteolin increased NCR+ILC3 levels, promoted the production of IL-22 and decreased the levels of IL-17a and INF-γ in the intestine, and encourage intestinal barrier function recovery in UC mice by promoting the expression of ZO-1 and Occludin. Experiments in vitro revealed that luteolin facilitated the transformation of NCR-MNK3 to NCR+MNK3 and promoted the secretion of IL-22, which was linked to the Notch pathway. All results revealed that luteolin restored the balance of NCR-ILC3/NCR+ILC3 and contributed to repair of injured intestinal epithelium to alleviate ulcerative colitis.

Dahuang Mudan decoction repairs intestinal barrier in chronic colitic mice by regulating the function of ILC3.

ETHNOPHARMACOLOGICAL RELEVANCE: Dahuang Mudan decoction (DMD) is a classic prescription for treating intestinal carbuncle from Zhang Zhongjing's "Essentials of the Golden Chamber" in the Han Dynasty. Recent studies also prove that DMD has a therapeutic effect on ulcerative colitis (UC), but its mechanism is still unclear. AIM OF STUDY: In this study, we aim to assess the therapeutic effect of DMD on DSS-induced chronic colitis in mice and deeply expound its underlying regulative mechanism. MATERIALS AND METHODS: The efficacy of DMD on mice with 2% DSS-induced chronic colitis was examined by changes in mouse body weight, DAI score, colon length changes, peripheral blood white blood cells (WBC) and red blood cells (RBC) counts, and hemoglobin (HGB) content, using mesalazine as a positive control. A small animal imaging system observed the FITC-Dextran fluorescence distribution in mice, and the contents of IL-22 and IL-17A in colon tissue homogenate supernatant and LPS in peripheral blood were detected by ELISA. Fluorescence in situ molecular hybridization and bacterial culture were used to investigate bacterial infiltration in intestinal mucosa and bacterial translocation in mesenteric lymph nodes and spleen. Mice immune function was further evaluated by analyzing the changes in spleen index, thymus index, and the ratio of peripheral blood granulocytes, monocytes, and lymphocytes. Meanwhile, the proportion of NCR+ group 3 innate lymphoid cells (ILC3), NCR-ILC3, and IL-22+ILC3 in colonic lamina propria lymphocytes of mice was detected by flow cytometry. The contents of effectors IL-22, IL-17A, and GM-CSF were detected by RT-PCR. We use cell scratching to determine the effect of DMD conditioned medium on the migration of Caco-2 cells by establishing an in vitro model of MNK-3 conditioned medium (CM) intervening Caco-2 cells. RT-PCR and WB detect the expression of tight junction ZO-1, Occludin, and Claudin-1. RESULTS: DMD restored the body weight, colon length, peripheral blood RBC numbers, and HGB content of chronic colitis mice and reduced peripheral blood WBC and colon inflammatory cell infiltration. Moreover, DMD decreased LPS content in serum, bacterial infiltration of colonic mucosa, and bacterial translocation in spleen and mesenteric lymph nodes. Simultaneously, DMD intensified the expression of ZO-1, Occludin, and Claudin-1, the ratio of NCR+ILC3 and IL-22+ILC3, and decreased the proportion of NCR-ILC3. In vitro studies also confirmed that the conditioned medium of DMD promoted the migration of Caco-2 cells and the expression of tight junction proteins. CONCLUSION: Our results confirm that DMD improves inflammation and restores intestinal epithelial function in mice with chronic colitis, and the mechanism may be related to regulating ILC3 function.

Clinical application of deep learning and radiomics in hepatic disease imaging: a systematic scoping review.

OBJECTIVE: Artificial intelligence (AI) has begun to play a pivotal role in hepatic imaging. This systematic scoping review summarizes the latest progress of AI in evaluating hepatic diseases based on computed tomography (CT) and magnetic resonance (MR) imaging. METHODS: We searched PubMed and Web of Science for publications, using terms related to deep learning, radiomics, imaging methods (CT or MR), and the liver. Two reviewers independently selected articles and extracted data from each eligible article. The Quality Assessment of Diagnostic Accuracy Studies-AI (QUADAS-AI) tool was used to assess the risk of bias and concerns regarding applicability. RESULTS: The screening identified 45 high-quality publications from 235 candidates, including 8 on diffuse liver diseases and 37 on focal liver lesions. Nine studies used deep learning and 36 studies used radiomics. All 45 studies were rated as low risk of bias in patient selection and workflow, but 36 (80%) were rated as high risk of bias in the index test because they lacked external validation. In terms of concerns regarding applicability, all 45 studies were rated as low concerns. These studies demonstrated that deep learning and radiomics can evaluate liver fibrosis, cirrhosis, portal hypertension, and a series of complications caused by cirrhosis, predict the prognosis of malignant hepatic tumors, and differentiate focal hepatic lesions. CONCLUSIONS: The latest studies have shown that deep learning and radiomics based on hepatic CT and MR imaging have potential application value in the diagnosis, treatment evaluation, and prognosis prediction of common liver diseases. The AI methods may become useful tools to support clinical decision-making in the future. ADVANCES IN KNOWLEDGE: Deep learning and radiomics have shown their potential in the diagnosis, treatment evaluation, and prognosis prediction of a series of common diffuse liver diseases and focal liver lesions.

Machine-learning-based radiomics identifies atrial fibrillation on the epicardial fat in contrast-enhanced and non-enhanced chest CT.

OBJECTIVE: The purpose is to establish and validate a machine-learning-derived radiomics approach to determine the existence of atrial fibrillation (AF) by analyzing epicardial adipose tissue (EAT) in CT images. METHODS: Patients with AF based on electrocardiographic tracing who underwent contrast-enhanced (n = 200) or non-enhanced (n = 300) chest CT scans were analyzed retrospectively. After EAT segmentation and radiomics feature extraction, the segmented EAT yielded 1691 radiomics features. The most contributive features to AF were selected by the Boruta algorithm and machine-learning-based random forest algorithm, and combined to construct a radiomics signature (EAT-score). Multivariate logistic regression was used to build clinical factor and nested models. RESULTS: In the test cohort of contrast-enhanced scanning (n = 60/200), the AUC of EAT-score for identifying patients with AF was 0.92 (95%CI: 0.84-1.00), higher than 0.71 (0.58-0.85) of the clinical factor model (total cholesterol and body mass index) (DeLong's p = 0.01), and higher than 0.73 (0.61-0.86) of the EAT volume model (p = 0.01). In the test cohort of non-enhanced scanning (n = 100/300), the AUC of EAT-score was 0.85 (0.77-0.92), higher than that of the CT attenuation model (p < 0.001). The two nested models (EAT-score+volume and EAT-score+volume+clinical factors) for contrast-enhanced scan and one (EAT-score+CT attenuation) for non-enhanced scan showed similar AUCs with that of EAT-score (all p > 0.05). CONCLUSION: EAT-score generated by machine-learning-based radiomics achieved high performance in identifying patients with AF. ADVANCES IN KNOWLEDGE: A radiomics analysis based on machine learning allows for the identification of AF on the EAT in contrast-enhanced and non-enhanced chest CT.

COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.

OBJECTIVE: Chest CT can display the main pathogenic factors of chronic obstructive pulmonary disease (COPD), emphysema and airway wall remodeling. This study aims to establish deep convolutional neural network (CNN) models using these two imaging markers to diagnose and grade COPD. METHODS: Subjects who underwent chest CT and pulmonary function test (PFT) from one hospital (n = 373) were retrospectively included as the training cohort, and subjects from another hospital (n = 226) were used as the external test cohort. According to the PFT results, all subjects were labeled as Global Initiative for Chronic Obstructive Lung Disease (GOLD) Grade 1, 2, 3, 4 or normal. Two DenseNet-201 CNNs were trained using CT images of lung parenchyma and bronchial wall to generate two corresponding confidence levels to indicate the possibility of COPD, then combined with logistic regression analysis. Quantitative CT was used for comparison. RESULTS: In the test cohort, CNN achieved an area under the curve of 0.899 (95%CI: 0.853-0.935) to determine the existence of COPD, and an accuracy of 81.7% (76.2-86.7%), which was significantly higher than the accuracy 68.1% (61.6%-74.2%) using quantitative CT method (p < 0.05). For three-way (normal, GOLD 1-2, and GOLD 3-4) and five-way (normal, GOLD 1, 2, 3, and 4) classifications, CNN reached accuracies of 77.4 and 67.9%, respectively. CONCLUSION: CNN can identify emphysema and airway wall remodeling on CT images to infer lung function and determine the existence and severity of COPD. It provides an alternative way to detect COPD using the extensively available chest CT. ADVANCES IN KNOWLEDGE: CNN can identify the main pathological changes of COPD (emphysema and airway wall remodeling) based on CT images, to infer lung function and determine the existence and severity of COPD. CNN reached an area under the curve of 0.853 to determine the existence of COPD in the external test cohort. The CNN approach provides an alternative and effective way for early detection of COPD using extensively used chest CT, as an important alternative to pulmonary function test.

Disposable Stainless-Steel Wire-Based Electrochemical Microsensor for In Vivo Continuous Monitoring of Hydrogen Peroxide in Vein of Tomato Leaf.

As one of the pivotal signal molecules, hydrogen peroxide (H2O2) has been demonstrated to play important roles in many physiological processes of plants. Continuous monitoring of H2O2 in vivo could help understand its regulation mechanism more clearly. In this study, a disposable electrochemical microsensor for H2O2 was developed. This microsensor consists of three parts: low-cost stainless-steel wire with a diameter of 0.1 mm modified by gold nanoparticles (disposable working electrode), an untreated platinum wire with a diameter of 0.1 mm (counter electrode), and an Ag/AgCl wire with a diameter of 0.1 mm (reference electrode), respectively. The microsensor could detect H2O2 in levels from 10 to 1000 µM and exhibited excellent selectivity. On this basis, the dynamic change in H2O2 in the vein of tomato leaf under high salinity was continuously monitored in vivo. The results showed that the production of H2O2 could be induced by high salinity within two hours. This study suggests that the disposable electrochemical microsensor not only suits continuously detecting H2O2 in microscopic plant tissue in vivo but also reduces the damage to plants. Overall, our strategy will help to pave the foundation for further investigation of the generation, transportation, and elimination mechanism of H2O2 in plants.

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra-Low-Dose Chest CT.

Background Ultra-low-dose (ULD) CT could facilitate the clinical implementation of large-scale lung cancer screening while minimizing the radiation dose. However, traditional image reconstruction methods are associated with image noise in low-dose acquisitions. Purpose To compare the image quality and lung nodule detectability of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) in ULD CT. Materials and Methods Patients who underwent noncontrast ULD CT (performed at 0.07 or 0.14 mSv, similar to a single chest radiograph) and contrast-enhanced chest CT (CECT) from April to June 2020 were included in this prospective study. ULD CT images were reconstructed with filtered back projection (FBP), ASIR-V, and DLIR. Three-dimensional segmentation of lung tissue was performed to evaluate image noise. Radiologists detected and measured nodules with use of a deep learning-based nodule assessment system and recognized malignancy-related imaging features. Bland-Altman analysis and repeated-measures analysis of variance were used to evaluate the differences between ULD CT images and CECT images. Results A total of 203 participants (mean age ± standard deviation, 61 years ± 12; 129 men) with 1066 nodules were included, with 100 scans at 0.07 mSv and 103 scans at 0.14 mSv. The mean lung tissue noise ± standard deviation was 46 HU ± 4 for CECT and 59 HU ± 4, 56 HU ± 4, 53 HU ± 4, 54 HU ± 4, and 51 HU ± 4 in FBP, ASIR-V level 40%, ASIR-V level 80% (ASIR-V-80%), medium-strength DLIR, and high-strength DLIR (DLIR-H), respectively, of ULD CT scans (P < .001). The nodule detection rates of FBP reconstruction, ASIR-V-80%, and DLIR-H were 62.5% (666 of 1066 nodules), 73.3% (781 of 1066 nodules), and 75.8% (808 of 1066 nodules), respectively (P < .001). Bland-Altman analysis showed the percentage difference in long diameter from that of CECT was 9.3% (95% CI of the mean: 8.0, 10.6), 9.2% (95% CI of the mean: 8.0, 10.4), and 6.2% (95% CI of the mean: 5.0, 7.4) in FBP reconstruction, ASIR-V-80%, and DLIR-H, respectively (P < .001). Conclusion Compared with adaptive statistical iterative reconstruction-V, deep learning image reconstruction reduced image noise, increased nodule detection rate, and improved measurement accuracy on ultra-low-dose chest CT images. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee in this issue.