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Objective: To assess lung deformation in patients with idiopathic pulmonary fibrosis (IPF) using with elastic registration algorithm applied to three-dimensional ultrashort echo time (3D-UTE) MRI and analyze relationship of lung deformation with the severity of IPF. Methods: Seventy-six patients with IPF (mean age: 62 ± 6 years) and 62 age- and gender-matched healthy controls (mean age: 58 ± 4 years) were prospectively enrolled. End-inspiration and end-expiration images acquired with a single breath-hold 3D-UTE sequence were registered using elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Jac-mean (absolute value of the log means of Jacobian determinants) and the Dice similarity coefficient (Dice) were compared between different groups. Results: Compared with healthy controls, the Jac-mean of IPF patients significantly decreased (0.21 ± 0.08 vs. 0.27 ± 0. 07, p < 0.001). Furthermore, the Jac-mean and Dice correlated with the metrics of pulmonary function tests and the composite physiological index. The lung deformation in IPF patients with dyspnea Medical Research Council (MRC) ≥ 3 (Jac-mean: 0.16 ± 0.03; Dice: 0.06 ± 0.02) was significantly lower than MRC1 (Jac-mean: 0. 25 ± 0.03, p < 0.001; Dice: 0.10 ± 0.01, p < 0.001) and MRC 2 (Jac-mean: 0.22 ± 0.11, p = 0.001; Dice: 0.08 ± 0.03, p = 0.006). Meanwhile, Jac-mean and Dice correlated with health-related quality of life, 6 min-walk distance, and the extent of pulmonary fibrosis. Jac-mean correlated with pulmonary vascular-related indexes on high-resolution CT. Conclusion: The decreased lung deformation in IPF patients correlated with the clinical severity of IPF patients. Elastic registration of inspiratory-to-expiratory 3D UTE MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. Critical relevance statement: This prospective study demonstrated that lung deformation decreased in idiopathic pulmonary fibrosis (IPF) patients and correlated with the severity of IPF. Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. Key points: • Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI could evaluate lung deformation. • Lung deformation significantly decreased in idiopathic pulmonary fibrosis (IPF) patients, compared with the healthy controls. • Reduced lung deformation of IPF patients correlated with worsened pulmonary function and the composite physiological index (CPI). [ABSTRACT FROM AUTHOR]

Disproportionation of alcohols to the corresponding alkanes and aldehydes/ketones is an important reaction, especially for biomass valorisation. Previously, we reported for the first time that hydroiodic acid can be used as an efficient catalyst for the alcohol disproportionation reaction, and the interconversion between hydroiodic acid and iodine is the key step for hydrogen transfer. As a continuation of our previous work, this work mainly focused on the mechanistic study involved and studied the difference between hydroiodic acid or molecular iodine as the catalyst for the disproportionation of alcohols. Density Functional Theoretical calculation indicated the oxidation step was a synergistic reaction process through the iodine effect on oxygen-hydrogen bond, which was proved by the characteristic oxygen-iodine bond captured by in-situ Attenuated Total Reflectance Fourier Transform Infrared. A non-polar solvent was preferable for the reaction with iodine. Addition of small amount of water was shown to be beneficial for the reaction, because water promoted Brønsted acidity of hydroiodic acid and accelerated the rate-determining step. Finally, we showed that the system was also applicable to various benzyl, allyl alcohols, and some heterocyclic alcohols.Graphical Abstract: Disproportionation of alcohols to the corresponding alkanes and aldehydes/ketones is an important reaction, especially for biomass valorisation. Previously, we reported for the first time that hydroiodic acid can be used as an efficient catalyst for the alcohol disproportionation reaction, and the interconversion between hydroiodic acid and iodine is the key step for hydrogen transfer. As a continuation of our previous work, this work mainly focused on the mechanistic study involved and studied the difference between hydroiodic acid or molecular iodine as the catalyst for the disproportionation of alcohols. Density Functional Theoretical calculation indicated the oxidation step was a synergistic reaction process through the iodine effect on oxygen-hydrogen bond, which was proved by the characteristic oxygen-iodine bond captured by in-situ Attenuated Total Reflectance Fourier Transform Infrared. A non-polar solvent was preferable for the reaction with iodine. Addition of small amount of water was shown to be beneficial for the reaction, because water promoted Brønsted acidity of hydroiodic acid and accelerated the rate-determining step. Finally, we showed that the system was also applicable to various benzyl, allyl alcohols, and some heterocyclic alcohols. [ABSTRACT FROM AUTHOR]

• Firstly reported the synthesis of salicylic acid from biomass-derived 2-furoic acid. • Around 40% of 2,3-benzofuran and salicylate were respectively yielded. • The two-step processes are green and simple. Salicylic acid, generally produced by chemical synthesis based on petrochemical products, is a vital organic acid and widely used in the pharmaceutical synthesis. This work developed a new and green route for the production of salicylic acid from biomass-derived 2-furoic acid in two steps. Firstly, 2 mmol 2-furoic acid was creatively converted to 2,3-benzofuran using HZSM-5 (Si/Al=130) at 280 °C for 1.5 h under 100 psi N 2 ; and then the oxidation of 2,3-benzofuran to salicylate was conducted with tert‑ butyl hydroperoxide under basic condition without metal catalyst at 120 °C for 1 h. The stability of the catalyst for the first step and the possible reaction pathway for the second step based on the control experiments was properly envisioned. Regardless of limited salicylic acid yield (16%) from 2-furoic acid, this work paves a potentially feasible pathway for the preparation of drugs from biomass. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Green tea polyphenol (GTP) is a polyphenol source from green tea that has drawn wide attention owing to epidemiological evidence of its beneficial effects in the prevention of cardiovascular disease; the underlying molecular mechanisms of these effects are not well understood. This study aimed to investigate the effects of GTP treatment on autophagy regulation in the vessel wall and lipid metabolism of HFD-fed male ApoE-knockout mice. Methods: Adult male ApoE-knockout mice (n = 30) fed with a high-fat diet (HFD) were treated with either vehicle or GTP (3.2 or 6.4 g/L) administered via drinking water for 15 weeks, and C57BL/6J mice fed with standard chow diet (STD) were used as the control group. Metabolic parameters, expression of key mRNAs and proteins of hepatic lipid metabolism and autophagy in the vessel wall of mice were determined after the 15-week treatment. Results: A HFD induced atherosclerosis formation and lipid metabolism disorders as well as reduced autophagy expression in the vessel wall of ApoE-knockout mice, but GTP treatment alleviated the lipid metabolism disorders, decreased the oxLDL levels in serum, and increased the mRNA and protein expressions of hepatic PPARα and autophagy markers (LC3, Beclin1 and p62) in the vessel wall of ApoE-knockout mice. Conclusions: Our findings suggest that GTP supplementation showed marked suppression of atherogenesis through improved lipid metabolism as well as through a direct impact on oxLDL and autophagy flux in the vessel wall. [ABSTRACT FROM AUTHOR]

Objective: We aimed to quantitatively study the characteristic of diaphragm and chest wall motion using free-breathing dynamic magnetic resonance imaging (D-MRI) in Chinese people with normal lung function. Methods: 74 male subjects (mean age, 37 ± 11 years old) were prospectively enrolled, and they underwent high-resolution CT(HRCT), pulmonary functional tests (PFTs), and D-MRI in the same day. D-MRI was acquired with a gradient-echo sequence during the quiet and deep breathing. The motion of the diaphragm and chest wall were respectively assessed by measuring thoracic anteroposterior diameter (AP), left–right diameter (LR), cranial–caudal diameter (CC), and thoracic area ratios between end-inspiration and end-expiration. The effect of age, body mass index (BMI), and smoking on respiratory muscle function was also analyzed. Results: The mean ratio of right and left AP was greater than that of LR on three transversal planes during both quiet and deep breathing. The mean ratio at the anterior diaphragm (AND, Quiet: 1.04 ± 0.03; Deep: 1.15 ± 0.09) was weaker than that of the apex (vs. APD, Quiet: 1.08 ± 0.05, p < 0.001; Deep: 1.29 ± 0.12, p < 0.001) and posterior diaphragm (vs. POD, Quiet: 1.09 ± 0.04, p < 0.001; Deep: 1.30 ± 0.12, p < 0.001) both in quiet and deep breathing. Compared with non-smokers, the left AP and thoracic area ratios in smokers were significantly decreased (p < 0.05). However, the ratios of AP, LR, CC, and thoracic area on each plane were similar among groups in different age and BMI. Conclusions: During both quiet and deep breathing, the chest wall motion is prominent in the anteroposterior direction. The motions of diaphragm apex and posterior diaphragm were more prominent than that of the anterior diaphragm. Smoking may affect the respiratory muscle mobility. Dynamic MRI can quantitatively evaluate the motion of respiratory muscles. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a general radiomics nomogram capable of identifying EGFR mutation status in non-small cell lung cancer (NSCLC) patients, regardless of patient with either contrast-enhanced CT (CE-CT) or non-contrast-enhanced CT (NE-CT). Methods: A total of 412 NSCLC patients were retrospectively enrolled in this study. Patients' radiomics features not significantly different between NE-CT and CE-CT were defined as general features, and were further used to construct the general radiomics signature. Fivefold cross-validation was used to select the best machine learning algorithm. Finally, a general radiomics nomogram was developed using general radiomics signature, and clinical and radiological characteristics. Two groups of data collected at different time periods were used as two test sets to access the discrimination and clinical usefulness. Area under the receiver operating characteristic curve (ROC-AUC) was applied to performance evaluation. Result: The general radiomics signature yielded the highest AUC of 0.756 and 0.739 in the two test sets, respectively. When applying to same type of CT, the performance of general radiomics signature was always similar to or higher than that of models built using only NE-CT or CE-CT features. The general radiomics nomogram combining general radiomics signature, smoking history, emphysema, and ILD achieved higher performance whether applying to NE-CT or CE-CT (test set 1, AUC = 0.833 and 0.842; test set 2, AUC = 0.839 and 0.850). Conclusions: Our work demonstrated that using general features to construct radiomics signature and nomogram could help identify EGFR mutation status of NSCLC patients and expand its scope of clinical application. Key Points: • General features were proposed to construct general radiomics signature using different types of CT of different patients at the same time to identify EGFR mutation status of NSCLC patients. • The general radiomics nomogram based on general radiomics signature, and clinical and radiological characteristics could identify EGFR mutation status of patients with NSCLC and outperformed the general radiomics signature. • The general radiomics nomogram had a wider scope of clinical application; no matter which of NE-CT and CE-CT the patient has, its EGFR mutation status could be predicted. [ABSTRACT FROM AUTHOR]

Objective: To explore the application of deep learning in patients with primary osteoporosis, and to develop a fully automatic method based on deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images. Materials and methods: A total of 1449 patients were used for experiments and analysis in this retrospective study, who underwent spinal or abdominal CT scans for other indications between March 2018 and May 2020. All data was gathered from three different CT vendors. Among them, 586 cases were used for training, and other 863 cases were used for testing. A fully convolutional neural network, called U-Net, was employed for automated vertebral body segmentation. The manually sketched region of vertebral body was used as the ground truth for comparison. A convolutional neural network, called DenseNet-121, was applied for BMD calculation. The values post-processed by quantitative computed tomography (QCT) were identified as the standards for analysis. Results: Based on the diversity of CT vendors, all testing cases were split into three testing cohorts: Test set 1 (n = 463), test set 2 (n = 200), and test set 3 (n = 200). Automated segmentation correlated well with manual segmentation regarding four lumbar vertebral bodies (L1–L4): the minimum average dice coefficients for three testing sets were 0.823, 0.786, and 0.782, respectively. For testing sets from different vendors, the average BMDs calculated by automated regression showed high correlation (r > 0.98) and agreement with those derived from QCT. Conclusions: A deep learning–based method could achieve fully automatic identification of osteoporosis, osteopenia, and normal bone mineral density in CT images. Key Points: • Deep learning can perform accurate fully automated segmentation of lumbar vertebral body in CT images. • The average BMDs obtained by deep learning highly correlates with ones derived from QCT. • The deep learning–based method could be helpful for clinicians in opportunistic osteoporosis screening in spinal or abdominal CT scans. [ABSTRACT FROM AUTHOR]

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/). • Critical milestone that establishes the standard norm of the airway segmentation field in this deep learning era. • Put forward airway segmentation as a beyond pixel-wise segmentation task. • Divergent data from multi-site and multi-domain. [ABSTRACT FROM AUTHOR]

[Display omitted] • The synergistic effect occurred in co-liquefaction to produce lower N bio-oil. • Mannich and Maillard reactions occurred during the co-liquefaction process. • Nitrogen in the bio-oil and the aqueous phase was transferred to bio-char. • Co-liquefaction is beneficial to increase carbon, nitrogen and energy recovery rates. With the depletion of fossil energy, alternative biomass energy has been studied as a potential candidate. Our study focused on the co-liquefaction of livestock manure (cattle manure) and food waste (peanut residue), aiming to obtain high-quality bio-oil and bio-char to maximize resource utilization. By adjusting the ratios of the two types of biomass in a closed system, products distribution in the reaction were controlled to obtain biofuel with high carbon and hydrogen, low oxygen and nitrogen content. Under the optimal reaction conditions (270 °C, 30 min, peanut residue/cattle manure = 3/1), the overall yields of bio-oil (a mass fraction of 23.5%) and bio-char (a mass fraction of 18.5%) were obtained, and the higher heating value of the bio-oil reached the highest of 31.74 MJ/kg. It was found that Maillard and Mannich reactions occurred during the co-liquefaction process, and the experimental values of bio-char yield was higher than the calculated values with higher carbon and nitrogen content, indicating the synergistic effect during the formation of the bio-char. The migration and transformation of carbon and nitrogen were explored, and it was found that nitrogen in bio-oil and aqueous phase was transferred to bio-char. All experimental energy recoveries (bio-oils and bio-chars) of co-liquefaction were higher than the calculated ones, indicating that the synergistic effect between peanut residue and cattle manure promoted the energy recovery of the co-liquefaction products. [ABSTRACT FROM AUTHOR]

Background Hepatic fibrosis, characterized by an excessive accumulation of extracellular matrix, is associated with toxic substance exposure, chronic infections, mechanical injury, airborne fine particulate matter (PM 2.5 ) exposure and metabolic disease. This study aimed to investigate the effect and mechanism of long-term, real-world airborne particulate matter (PM) exposure on hepatic fibrosis and further explored whether combination treatment of PM exposure and high-fat diet (HFD) aggravate the adverse effects in mice. Methods and results Six-week-old male C57BL/6J mice fed with either a standard chow diet (STD) or an HFD were treated with either filtered air (FA) or PM for 18 weeks. Metabolic parameters, histological examination, gene expression analysis, and Western blot analysis were utilized to measure the effect and mechanism of PM exposure on hepatic fibrosis and to further analyze the synergistic effect of HFD. Subchronic airborne PM exposure induces hepatic fibrosis in mice, and combination treatment of PM exposure and HFD accelerate the adverse effect. Meanwhile, subchronic exposure to real-world PM increased the level of hepatic ROS, and the expression of endoplasmic reticulum (ER) stress markers (GRP78 and CHOP), p -SMAD2 and p -SMAD3, as well as up-regulated TGFβ and collagen 1 in liver tissues. Furthermore, PM exposure and HFD displayed the synergistic effects on these changes in liver. Conclusion Our findings indicate that airborne PM exposure aggravates HFD -induced hepatic fibrosis. The ROS-ER stress-TGFβ/SMADs regulatory axis mediates the effects of airborne PM exposure on accelerating hepatic fibrosis. [ABSTRACT FROM AUTHOR]