Unraveling abiotic organic synthesis pathways in the mafic crust of mid-ocean ridges.

The aqueous alteration of the oceanic lithosphere provides significant energy that impacts the synthesis and diversity of organic compounds, which are crucial for the deep carbon cycle and may have provided the first building blocks for life. Although abiotic organic synthesis has been documented in mantle-derived rocks, the formation mechanisms and complexity of organic compounds in crustal rocks remain largely unknown. Here, we show the specific association of aliphatic carbonaceous matter with Fe oxyhydroxides in mafic crustal rocks of the Southwest Indian Ridge (SWIR). We determine potential Fe-based pathways for abiotic organic synthesis from CO2 and H2 using multimodal and molecular nano-geochemical tools. Quantum mechanical modeling is further employed to constrain the catalytical activity of Fe oxyhydroxides, revealing that the catalytic cycle of hydrogen may play a key role in carbon-carbon bond formation. This approach offers the possibility of interpreting physicochemical organic formation and condensation mechanisms at an atomic scale. The findings expand our knowledge of the existence of abiotic organic carbon in the oceanic crustal rocks and emphasize the mafic oceanic crust of the SWIR as a potential site for low-temperature abiotic organic synthesis.

Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001-2018.

BACKGROUND: The prevalence of obesity-associated insulin resistance (IR) is increasing along with the increase in obesity rates. In this study, we compared the predictive utility of four alternative indexes of IR [triglyceride glucose index (TyG index), metabolic score for insulin resistance (METS-IR), the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and homeostatic model assessment of insulin resistance (HOMA-IR)] for all-cause mortality and cardiovascular mortality in the general population based on key variables screened by the Boruta algorithm. The aim was to find the best replacement index of IR. METHODS: In this study, 14,653 participants were screened from the National Health and Nutrition Examination Survey (2001-2018). And TyG index, METS-IR, TG/HDL-C and HOMA-IR were calculated separately for each participant according to the given formula. The predictive values of IR replacement indexes for all-cause mortality and cardiovascular mortality in the general population were assessed. RESULTS: Over a median follow-up period of 116 months, a total of 2085 (10.23%) all-cause deaths and 549 (2.61%) cardiovascular disease (CVD) related deaths were recorded. Multivariate Cox regression and restricted cubic splines analysis showed that among the four indexes, only METS-IR was significantly associated with both all-cause and CVD mortality, and both showed non-linear associations with an approximate "U-shape". Specifically, baseline METS-IR lower than the inflection point (41.33) was negatively associated with mortality [hazard ratio (HR) 0.972, 95% CI 0.950-0.997 for all-cause mortality]. In contrast, baseline METS-IR higher than the inflection point (41.33) was positively associated with mortality (HR 1.019, 95% CI 1.011-1.026 for all-cause mortality and HR 1.028, 95% CI 1.014-1.043 for CVD mortality). We further stratified the METS-IR and showed that significant associations between METS-IR levels and all-cause and cardiovascular mortality were predominantly present in the nonelderly population aged < 65 years. CONCLUSIONS: In conjunction with the results of the Boruta algorithm, METS-IR demonstrated a more significant association with all-cause and cardiovascular mortality in the U.S. population compared to the other three alternative IR indexes (TyG index, TG/HDL-C and HOMA-IR), particularly evident in individuals under 65 years old.

Ultrashort echo time magnetization transfer imaging of knee cartilage and meniscus after long-distance running.

OBJECTIVE: To assess the detection of changes in knee cartilage and meniscus of amateur marathon runners before and after long-distance running using a 3D ultrashort echo time MRI sequence with magnetization transfer preparation (UTE-MT). METHODS: We recruited 23 amateur marathon runners (46 knees) in this prospective cohort study. MRI scans using UTE-MT and UTE-T2* sequences were performed pre-race, 2 days post-race, and 4 weeks post-race. UTE-MT ratio (UTE-MTR) and UTE-T2* were measured for knee cartilage (eight subregions) and meniscus (four subregions). The sequence reproducibility and inter-rater reliability were also investigated. RESULTS: Both the UTE-MTR and UTE-T2* measurements showed good reproducibility and inter-rater reliability. For most subregions of cartilage and meniscus, the UTE-MTR values decreased 2 days post-race and increased after 4 weeks of rest. Conversely, the UTE-T2* values increased 2 days post-race and decreased after 4 weeks. The UTE-MTR values in lateral tibial plateau, central medial femoral condyle, and medial tibial plateau showed a significant decrease at 2 days post-race compared to the other two time points (p < 0.05). By comparison, no significant UTE-T2* changes were found for any cartilage subregions. For meniscus, the UTE-MTR values in medial posterior horn and lateral posterior horn regions at 2 days post-race were significantly lower than those at pre-race and 4 weeks post-race (p < 0.05). By comparison, only the UTE-T2* values in medial posterior horn showed a significant difference. CONCLUSIONS: UTE-MTR is a promising method for the detection of dynamic changes in knee cartilage and meniscus after long-distance running. KEY POINTS: • Long-distance running causes changes in the knee cartilage and meniscus. • UTE-MT monitors dynamic changes of knee cartilage and meniscal non-invasively. • UTE-MT is superior to UTE-T2* in monitoring dynamic changes in knee cartilage and meniscus.

Comprehensive assessment of osteoporosis in lumbar spine using compositional MR imaging of trabecular bone.

OBJECTIVES: To comprehensively assess osteoporosis in the lumbar spine, a compositional MR imaging technique is proposed to quantify proton fractions for all the water components as well as fat in lumbar vertebrae measured by a combination of a 3D short repetition time adiabatic inversion recovery prepared ultrashort echo time (STAIR-UTE) MRI and IDEAL-IQ. METHODS: A total of 182 participants underwent MRI, quantitative CT, and DXA. Lumbar collagen-bound water proton fraction (CBWPF), free water proton fraction (FWPF), total water proton fraction (TWPF), bone mineral density (BMD), and T-score were calculated in three vertebrae (L2-L4) for each subject. The correlations of the CBWPF, FWPF, and TWPF with BMD and T-score were investigated respectively. A comprehensive diagnostic model combining all the water components and clinical characteristics was established. The performances of all the water components and the comprehensive diagnostic model to discriminate between normal, osteopenia, and osteoporosis cohorts were also evaluated using receiver operator characteristic (ROC). RESULTS: The CBWPF showed strong correlations with BMD (r = 0.85, p < 0.001) and T-score (r = 0.72, p < 0.001), while the FWPF and TWPF showed moderate correlations with BMD (r = 0.65 and 0.68, p < 0.001) and T-score (r = 0.47 and 0.49, p < 0.001). The high area under the curve values obtained from ROC analysis demonstrated that CBWPF, FWPF, and TWPF have the potential to differentiate the normal, osteopenia, and osteoporosis cohorts. At the same time, the comprehensive diagnostic model shows the best performance. CONCLUSIONS: The compositional MRI technique, which quantifies CBWPF, FWPF, and TWPF in trabecular bone, is promising in the assessment of bone quality. KEY POINTS: • Compositional MR imaging technique is able to quantify proton fractions for all the water components (i.e., collagen-bound water proton fraction (CBWPF), free water proton fraction (FWPF), and total water proton fraction (TWPF)) in the human lumbar spine. • The biomarkers derived from the compositional MR imaging technique showed moderate to high correlations with bone mineral density (BMD) and T-score and showed good performance in distinguishing people with different bone mass. • The comprehensive diagnostic model incorporating CBWPF, FWPF, TWPF, and clinical characteristics showed the highest clinical diagnostic capability for the assessment of osteoporosis.

Homocysteine induces ferroptosis in endothelial cells through the systemXc-/GPX4 signaling pathway.

OBJECTIVES: To investigate whether ferroptosis is involved in HCY-induced endothelial injury and the possible mechanism of HCY-induced ferroptosis. METHODS: EA. hy926 cells were cultured in vitro. Cells were intervened using HCY and Fer-1. The cells were divided into Control groups, HCY (4 mM), HCY (8 mM), HCY + Fer-1 (4 mM HCY + 0.5/2.5/5 µM Fer-1). CCK-8 assay was used to detect cell viability; Flow Cytometry was used to detect cellular Lip-ROS, TBA and Microplate assay was used to detect MDA&GSH, Western blot was used to detect the expression of ferroptosis-related proteins GPX4 and SLC7A11. RESULTS: HCY can inhibited the proliferation of EA. hy926 cells in a time- and concentration-dependent manner; Fer-1 inhibits HCY-induced ferroptosis in EA.hy926 cells in a concentration-dependent manner; Compared with the control group, the cell viability and GSH content in the HCY group was significantly decreased (p < 0.05), and the Lip-ROS and MDA were significantly increased (p < 0.05); After co-culture of HCY and Fer-1, compared with the HCY (4 mM) group, the cell viability and GSH content in the co-culture group were significantly increased (p < 0.05), and the Lip-ROS and MDA were significantly decreased (p < 0.05) in a concentration-dependent manner; Western blotting results showed that the protein expression levels of ferroptosis-related proteins GPX4 and SLC7A11 in each experimental were significantly decreased after HCY treatment (p < 0.05), and Fer-1 could significantly reverse this effect. CONCLUSIONS: (1) HCY can induce ferroptosis in vascular endothelial cells. (2) HCY may induce vascular endothelial cell ferroptosis through the system Xc-GSH-GPX4 signaling pathway.

Sex-specific bone and muscular morphological features in ischiofemoral impingement: A three-dimensional study.

The study aimed to investigate how hip bone and muscular morphology features differ between ischiofemoral impingement (IFI) patients and healthy subjects among males and females. Three-dimensional models were reconstructed based on magnetic resonance imaging images from IFI patients and healthy subjects of different sexes. Bone morphological parameters and the cross-sectional area of the hip abductors were measured. The diameter and angle of the pelvis were compared between patients and healthy subjects. Bone parameters of the hip and cross-sectional area of the hip abductors were compared between affected and healthy hips. The comparison results of some parameters were significant for females but not males. For females, the comparison results of pelvis parameters showed that the anteroposterior diameter of the pelvic inlet (p = 0.001) and intertuberous distance (p < 0.001) were both larger in IFI patients than in healthy subjects. Additionally, the comparison results of hip parameters showed that the neck shaft angle (p < 0.001) and the cross-sectional area of the gluteus medius (p < 0.001) and gluteus minimus (p = 0.005) were smaller, while the cross-sectional area of the tensor fasciae latae (p < 0.001) was significantly larger in affected hips. Morphological changes in IFI patients demonstrated sexual dimorphism, including bone and muscular morphology. Differences in the anteroposterior diameter of the pelvic inlet, intertuberous distance, neck shaft angle, gluteus medius, and gluteus minimus may explain why females are more susceptible to IFI.

Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images.

Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images is viewer-dependent and may vary between radiologists and medical institutions. In this study, we aimed to develop a fully automatic method to segment sacroiliac joint (SIJ) and further grading diagnose sacroiliitis associated with AS on CT. We studied 435 CT examinations from patients with AS and control at two hospitals. No-new-UNet (nnU-Net) was used to segment the SIJ, and a 3D convolutional neural network (CNN) was used to grade sacroiliitis with a three-class method, using the grading results of three veteran musculoskeletal radiologists as the ground truth. We defined grades 0-I as class 0, grade II as class 1, and grades III-IV as class 2 according to modified New York criteria. nnU-Net segmentation of SIJ achieved Dice, Jaccard, and relative volume difference (RVD) coefficients of 0.915, 0.851, and 0.040 with the validation set, respectively, and 0.889, 0.812, and 0.098 with the test set, respectively. The areas under the curves (AUCs) of classes 0, 1, and 2 using the 3D CNN were 0.91, 0.80, and 0.96 with the validation set, respectively, and 0.94, 0.82, and 0.93 with the test set, respectively. 3D CNN was superior to the junior and senior radiologists in the grading of class 1 for the validation set and inferior to expert for the test set (P < 0.05). The fully automatic method constructed in this study based on a convolutional neural network could be used for SIJ segmentation and then accurately grading and diagnosis of sacroiliitis associated with AS on CT images, especially for class 0 and class 2. The method for class 1 was less effective but still more accurate than that of the senior radiologist.

Assessment of Achilles Tendon Changes After Long-Distance Running Using Ultrashort Echo Time Magnetization Transfer MR Imaging.

BACKGROUND: Long-distance running is a common cause of Achilles tendinopathy. A reliable magnetic resonance imaging (MRI) technique to track early changes in the tendon caused by running could facilitate more effective interventions to combat progression. PURPOSE: To evaluate an ultrashort echo time sequence with magnetization transfer preparation (UTE-MT) in the detection of changes in Achilles tendons of amateur marathon runners before and after long-distance running. STUDY TYPE: Prospective. POPULATION: Thirty-two runners (19 enrolled for full marathons and 13 enrolled for half-marathons) and 5 healthy non-runners. FIELD STRENGTH/SEQUENCE: 3.0 T; UTE-MT and dual-echo UTE for T2* assessment (UTE-T2*). ASSESSMENT: MRI was performed 1-week pre-race, 2-days post-race, and 4-weeks post-race. UTE-MT ratio (UTE-MTR) and UTE-T2* of tendon were measured by two independent radiologists who were blinded to the scan time point and participant data. The Achilles tendon was divided into six regions of interest (ROIs) for data analysis, namely the insertion part (INS), middle part (MID), muscle-tendon junction (MTJ), tendon-bone insertion (TBI), tendon-muscle insertion (TMI), and whole tendon (bulk). STATISTICAL TESTS: Analysis of variance and Friedman's rank tests were used to evaluate changes in UTE-MTR and UTE-T2* between time points. Tukey test and Bonferroni method were used for further comparisons. P < 0.05 was considered significant. RESULTS: The UTE-MTR values of most tendon ROIs changed significantly between the measured time points, except for the INS region (P = 0.1977). Conversely, the UTE-T2* values only showed significant changes in the MID and TBI regions. Paired comparisons showed that the UTE-MTR decreases in the MTJ, MID, TMI, and bulk regions at 2-days post-race were significant compared to measures taken pre-race and 4-weeks post-race. For UTE-T2* measurements, significant differences were observed only for the MID region between pre-race and 2-days post-race (P = 0.0408, 95% CI: 0.0061, 0.1973), and for the TBI region between pre-race and 4-weeks post-race (P = 0.0473, 95% CI: 0.0013, 0.1766). DATA CONCLUSION: The UTE-MT sequence is able to detect biochemical changes in the Achilles tendon after long-distance running. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Microbes team with nanoscale zero-valent iron: A robust route for degradation of recalcitrant pollutants.

Integrating nanoscale zero-valent iron (nZVI) with biological treatment processes holds the promise of inheriting significant advantages from both environmental nano- and bio-technologies. nZVI and microbes can perform in coalition in direct contact and act simultaneously, or be maintained in separate reactors and operated sequentially. Both modes can generate enhanced performance for wastewater treatment and environmental remediation. nZVI scavenges and eliminates toxic metals, and enhances biodegradability of some recalcitrant contaminants while bioprocesses serve to mineralize organic compounds and further remove impurities from wastewater. This has been demonstrated in a number of recent works that nZVI can substantially augment the performance of conventional biological treatment for wastewaters from textile and nonferrous metal industries. Our recent laboratory and field tests show that COD of the industrial effluents can be reduced to a record-low of 50 ppm. Recent literature on the theory and applications of the nZVI-bio system is highlighted in this mini review.

Radiomics Nomograms Based on Non-enhanced MRI and Clinical Risk Factors for the Differentiation of Chondrosarcoma from Enchondroma.

BACKGROUND: Differentiating chondrosarcoma from enchondroma using conventional MRI remains challenging. An effective method for accurate preoperative diagnosis could affect the management and prognosis of patients. PURPOSE: To validate and evaluate radiomics nomograms based on non-enhanced MRI and clinical risk factors for the differentiation of chondrosarcoma from enchondroma. STUDY TYPE: Retrospective. POPULATION: A total of 103 patients with pathologically confirmed chondrosarcoma (n = 53) and enchondroma (n = 50) were randomly divided into training (n = 68) and validation (n = 35) groups. FIELD STRENGTH/SEQUENCE: Axial non-contrast-enhanced T1-weighted images (T1WI) and fat-suppressed T2-weighted images (T2WI-FS) were acquired at 3.0 T. ASSESSMENT: Clinical risk factors (sex, age, and tumor location) and diagnosis assessment based on morphologic MRI by three radiologists were recorded. Three radiomics signatures were established based on the T1WI, T2WI-FS, and T1WI + T2WI-FS sequences. Three clinical radiomics nomograms were developed based on the clinical risk factors and three radiomics signatures. STATISTICAL TESTS: The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomics signatures and clinical radiomics nomograms. RESULTS: Tumor location was an important clinical risk factor (P < 0.05). The radiomics signature based on T1WI and T1WI + T2WI-FS features performed better than that based on T2WI-FS in the validation group (AUC in the validation group: 0.961, 0.938, and 0.833, respectively; P < 0.05). In the validation group, the three clinical radiomics nomograms (T1WI, T2WI-FS, and T1WI + T2WI-FS) achieved AUCs of 0.938, 0.935, and 0.954, respectively. In all patients, the clinical radiomics nomogram based on T2WI-FS (AUC = 0.967) performed better than that based on T2WI-FS (AUC = 0.901, P < 0.05). DATA CONCLUSION: The proposed clinical radiomics nomogram showed promising performance in differentiating chondrosarcoma from enchondroma. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Cardiac T1 and T2 Mapping Showed Myocardial Involvement in Recovered COVID-19 Patients Initially Considered Devoid of Cardiac Damage.

BACKGROUND: Myocardial injury has been found using magnetic resonance imaging in recovered coronavirus disease 2019 (COVID-19) patients unselected or with ongoing cardiac symptoms. PURPOSE: To evaluate for the presence of myocardial involvement in recovered COVID-19 patients without cardiovascular symptoms and abnormal serologic markers during hospitalization. STUDY TYPE: Prospective. POPULATION: Twenty-one recovered COVID-19 patients and 20 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3.0 T, cine, T2-weighted imaging, T1 mapping, and T2 mapping. ASSESSMENT: Cardiac ventricular function includes end-diastolic volume, end-systolic volume, stroke volume, cardiac output, left ventricle (LV) mass, and ejection fraction (EF) of LV and right ventricle (RV), and segmental myocardial T1 and T2 values were measured. STATISTICAL TESTS: Student's t-test, univariate general linear model test, and chi-square test were used for analyses between two groups. Ordinary one-way analyses of variance or Kruskal-Wallis H test were used for analyses between three groups, followed by post-hoc analyses. RESULTS: Fifteen (71.43%) COVID-19 patients had abnormal magnetic resonance findings, including raised myocardial native T1 (5, 23.81%) and T2 values (10, 47.62%), decreased LVEF (1, 4.76%), and RVEF (2, 9.52%). The segmental myocardial T2 value of COVID-19 patients (49.20 [46.1, 54.6] msec) was significantly higher than HC (48.3 [45.2, 51.7] msec) (P < 0.001), while the myocardial native T1 value showed no significant difference between COVID-19 patients and HC. The myocardial T2 value of serious COVID-19 patients (52.5 [48.1, 57.1] msec) was significantly higher than unserious COVID-19 patients (48.8 [45.9, 53.8] msec) and HC (48.3 [45.2, 51.7]) (P < 0.001). COVID-19 patients with abnormally elevated D-dimer, C-reactive protein, or lymphopenia showed higher myocardial T2 values than without (all P < 0.05). DATA CONCLUSION: Cardiac involvement was observed in recovered COVID-19 patients with no preexisting cardiovascular disease, no cardiovascular symptoms, and elevated serologic markers of myocardial injury during the whole course of COVID-19. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks.

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.

Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.

To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with deep transfer learning. In this retrospective study, the used chest CT image dataset covered 422 subjects, including 72 confirmed COVID-19 subjects (260 studies, 30,171 images), 252 other pneumonia subjects (252 studies, 26,534 images) that contained 158 viral pneumonia subjects and 94 pulmonary tuberculosis subjects, and 98 normal subjects (98 studies, 29,838 images). In the experiment, subjects were split into training (70%), validation (15%) and testing (15%) sets. We utilized the convolutional blocks of ResNets pretrained on the public social image collections and modified the top fully connected layer to suit our task (the COVID-19 recognition). In addition, we tested the proposed method on a finegrained classification task; that is, the images of COVID-19 were further split into 3 main manifestations (ground-glass opacity with 12,924 images, consolidation with 7418 images and fibrotic streaks with 7338 images). Similarly, the data partitioning strategy of 70%-15%-15% was adopted. The best performance obtained by the pretrained ResNet50 model is 94.87% sensitivity, 88.46% specificity, 91.21% accuracy for COVID-19 versus all other groups, and an overall accuracy of 89.01% for the three-category classification in the testing set. Consistent performance was observed from the COVID-19 manifestation classification task on images basis, where the best overall accuracy of 94.08% and AUC of 0.993 were obtained by the pretrained ResNet18 (P < 0.05). All the proposed models have achieved much satisfying performance and were thus very promising in both the practical application and statistics. Transfer learning is worth for exploring to be applied in recognition and classification of COVID-19 on CT images with limited training data. It not only achieved higher sensitivity (COVID-19 vs the rest) but also took far less time than radiologists, which is expected to give the auxiliary diagnosis and reduce the workload for the radiologists.

Three-Dimensional Analysis of the Pharyngeal Airway Volume and Craniofacial Morphology in Patients With Bilateral Cleft Lip and Palate.

OBJECTIVE: This study aimed to determine the correlations between the craniofacial morphology and pharyngeal airway volume in patients with complete bilateral cleft lip and palate (BCLP). DESIGN: Retrospective study. SETTING: Tertiary hospital. PARTICIPANTS: Twenty-seven patients with complete BCLP and 27 class I control patients, aged 10 to 14 years. MAIN OUTCOME MEASURE: The pharyngeal airway volume and craniofacial morphology were evaluated using cone-beam computed tomography. Measurements were compared between groups and any correlations were identified. RESULTS: A significantly smaller total pharyngeal airway volume (TPV), oropharyngeal airway volume, and upper (UOPV) and lower (LOPV) oropharyngeal airway volume were found in patients with BCLP than in class I control patients, with no difference in the nasopharyngeal volume between groups. Furthermore, the craniofacial morphology measurements of N-Me, S-Go, Or-C, Ptm-C, Me-C, Co-Go, Go-Me, Ptm-Or, N-S-Ar, and Ar-Go-Me significantly differed between the BCLP and control groups (all P < .05). Multiple regression analysis indicated that Ptm-C and Me-C; Ptm-C, Or-C, and Me-C; and Me-C explained 20.3%, 38.9%, and 17.1% of the variations in TPV (P = .025), UOPV (P = .002), and LOPV (P = .018), respectively. CONCLUSIONS: Total pharyngeal airway volume, TPV, OPV, UOPV, and LOPV were significantly smaller in patients with BCLP than in class I controls. In patients with BCLP, the maxilla showed inhibited sagittal development and a retrograde position; moreover, the pharyngeal airway volume was weakly associated with the position of the maxilla and mandible relative to the coronal plane.

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV).

In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities.

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection.

In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19).

OBJECTIVES: To explore the relationship between the imaging manifestations and clinical classification of COVID-19. METHODS: We conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), or 4 (76-100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type. RESULTS: This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severe-critical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962-0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. CONCLUSIONS: The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19. KEY POINTS: • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe-critical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality.

AMPK blocks starvation-inducible transgenerational defects in Caenorhabditis elegans.

Life history events, such as traumatic stress, illness, or starvation, can influence us through molecular changes that are recorded in a pattern of characteristic chromatin modifications. These modifications are often associated with adaptive adjustments in gene expression that can persist throughout the lifetime of the organism, or even span multiple generations. Although these adaptations may confer some selective advantage, if they are not appropriately regulated they can also be maladaptive in a context-dependent manner. We show here that during periods of acute starvation in Caenorhabditis elegans larvae, the master metabolic regulator AMP-activated protein kinase (AMPK) plays a critical role in blocking modifications to the chromatin landscape. This ensures that gene expression remains inactive in the germ-line precursors during adverse conditions. In its absence, critical chromatin modifications occur in the primordial germ cells (PGCs) of emergent starved L1 larvae that correlate with compromised reproductive fitness of the generation that experienced the stress, but also in the subsequent generations that never experienced the initial event. Our findings suggest that AMPK regulates the activity of the chromatin modifying COMPASS complex (complex proteins associated with Set1) to ensure that chromatin marks are not established until nutrient/energy contingencies are satisfied. Our study provides molecular insight that links metabolic adaptation to transgenerational epigenetic modification in response to acute periods of starvation.

Chest computed tomography in children with COVID-19 respiratory infection.

BACKGROUND: Infection with COVID-19 is currently rare in children. OBJECTIVE: To describe chest CT findings in children with COVID-19. MATERIALS AND METHODS: We studied children at a large tertiary-care hospital in China, during the period from 28 January 2019 to 8 February 2020, who had positive reverse transcriptase polymerase chain reaction (RT-PCR) for COVID-19. We recorded findings at any chest CT performed in the included children, along with core clinical observations. RESULTS: We included five children from 10 months to 6 years of age (mean 3.4 years). All had had at least one CT scan after admission. Three of these five had CT abnormality on the first CT scan (at 2 days, 4 days and 9 days, respectively, after onset of symptoms) in the form of patchy ground-glass opacities; all normalised during treatment. CONCLUSION: Compared to reports in adults, we found similar but more modest lung abnormalities at CT in our small paediatric cohort.

Prediction of Aneurysm Stability Using a Machine Learning Model Based on PyRadiomics-Derived Morphological Features.

Background and Purpose- Discrimination of the stability of intracranial aneurysms is critical for determining the treatment strategy, especially in small aneurysms. This study aims to evaluate the feasibility of applying machine learning for predicting aneurysm stability with radiomics-derived morphological features. Methods- Morphological features of 719 aneurysms were extracted from PyRadiomics, of which 420 aneurysms with Maximum3DDiameter ranging from 4 mm to 8 mm were enrolled for analysis. The stability of these aneurysms and other clinical characteristics were reviewed from the medical records. Based on the morphologies with/without clinical features, machine learning models were constructed and compared to define the morphological determinants and screen the optimal model for predicting aneurysm stability. The effect of clinical characteristics on the morphology of unstable aneurysms was analyzed. Results- Twelve morphological features were automatically extracted from PyRadiomics implemented in Python for each aneurysm. Lasso regression defined Flatness as the most important morphological feature to predict aneurysm stability, followed by SphericalDisproportion, Maximum2DDiameterSlice, and SurfaceArea. SurfaceArea (odds ratio [OR], 0.697; 95% CI, 0.476-0.998), SphericalDisproportion (OR, 1.730; 95% CI, 1.143-2.658), Flatness (OR, 0.584; 95% CI, 0.374-0.894), Hyperlipemia (OR, 2.410; 95% CI, 1.029-5.721), Multiplicity (OR, 0.182; 95% CI, 0.082-0.380), Location at middle cerebral artery (OR, 0.359; 95% CI, 0.134-0.902), and internal carotid artery (OR, 0.087; 95% CI, 0.030-0.211) were enrolled into the final prediction model. In terms of performance, the area under curve of the model reached 0.853 (95% CI, 0.767-0.940). For unstable aneurysms, Compactness1 (P=0.035), Compactness2 (P=0.036), Sphericity (P=0.035), and Flatness (P=0.010) were low, whereas SphericalDisproportion (P=0.034) was higher in patients with hypertension. Conclusions- Morphological features extracted from PyRadiomics can be used for aneurysm stratification. Flatness is the most important morphological determinant to predict aneurysm stability. Our model can be used to predict aneurysm stability. Unstable aneurysm is more irregular in patients with hypertension.