Association of Muscle Fat Content and Muscle Mass With Impaired Lung Function in Young Adults With Obesity: Evaluation With MRI.

RATIONALE AND OBJECTIVES: Although low muscle mass is associated with decreased lung function, studies exploring the relationship between muscle fat content and lung function impairment are scarce. This study aimed to evaluate the association of muscle mass and fatty infiltration with lung function in young adults with obesity. MATERIALS AND METHODS: We performed a retrospective cross-sectional study of patients aged 18-45 years with obesity who had impaired pulmonary function (case group, n = 66) and those with normal pulmonary function (control group, n = 198) by matching age, sex, body mass index (BMI), and height to assess whether muscle characteristics differed. Muscle mass and muscle fat content were assessed by MRI using a chemical shift-encoded sequence (IDEAL-IQ). RESULTS: A total of 264 patients were enrolled (124 females; mean age 32.0 years). The case group had lower muscle mass than the control group (p = 0.012), and there was an association between low muscle mass and lung function impairment (odds ratio (OR), 3.74; 95% confidence interval (CI), 1.57-8.93). Furthermore, muscle fat content was significantly higher in cases compared to controls (7.4 (2.7) % vs. 6.2 (2.5) %, p = 0.001). Multiple logistic regression analysis showed that muscle fat content was associated with a higher risk of impaired lung function (OR, 2.10; 95% CI, 1.65-2.66), regardless of adiposity and muscle mass. CONCLUSION: Both muscle fat content and muscle mass are associated with impaired lung function in young adults with obesity.

Well-controlled versus poorly controlled diabetes in patients with obesity: differences in MRI-evaluated pancreatic fat content.

Background: Patients with obesity and poorly controlled type 2 diabetes (T2D) are at high risk of diabetic complications. This study aimed to determine the associations of visceral adipose tissue (VAT), hepatic proton-density fat fraction (PDFF), and pancreatic PDFF with poor glycemic control in patients with obesity and T2D and to evaluate the metabolic effect of bariatric surgery in patients with obesity and poorly controlled diabetes. Methods: In this retrospective cross-sectional study, from July 2019 to March 2021, 151 consecutive obese patients with new-onset T2D (n=28), well-controlled T2D (n=17), poorly controlled T2D (n=32), prediabetes (n=20), or normal glucose tolerance (NGT; n=54) were included. A total of 18 patients with poorly controlled T2D were evaluated before and 12 months after bariatric surgery, and 18 non-obese healthy individuals served as controls. VAT, hepatic PDFF, and pancreatic PDFF were quantified by magnetic resonance imaging (MRI) using a chemical shift-encoded sequence [iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ)]. Univariate analysis and multivariate regression analysis were performed. Results: There were significant differences in VAT, hepatic PDFF, and all pancreatic PDFF between the new-onset T2D, prediabetes, and NGT groups (all P<0.05). Pancreatic tail PDFF was significantly higher in the poorly controlled T2D group than in the well-controlled T2D group (P=0.001). In the multivariate analysis, only pancreatic tail PDFF was significantly associated with increased odds of poor glycemic control [odds ratio (OR) =2.09; 95% confidence interval (CI): 1.11-3.94; P=0.022]. The glycated hemoglobin (HbA1c), hepatic PDFF, and pancreatic PDFF significantly decreased (all P<0.01) after bariatric surgery, and the values were statistically similar to those observed in the non-obese healthy controls. Conclusions: Increased fat in the pancreatic tail is strongly associated with poor glycemic control in patients with obesity and T2D. Bariatric surgery is an effective therapy for poorly controlled diabetes and obesity, which improves glycemic control and decreases ectopic fat deposits.

Association of body composition with postoperative complications after laparoscopic sleeve gastrectomy and Roux-en-Y gastric bypass.

OBJECTIVE: To evaluate predictive values of body composition parameters measured from preoperative CT/MRIs for postoperative complications after laparoscopic sleeve gastrectomy (LSG) and Roux-en-Y gastric bypass (LRYGB) in patients with obesity. METHODS: In this retrospective case-control study, patients performing abdominal CT/MRIs within one month before and developing 30-day complications after bariatric procedures were matched for age, sex, and type of surgery with patients without complications (1/3 ratio, respectively). Complications were determined by documentation in the medical record. Two readers blindly segmented the total abdominal muscle area (TAMA) and visceral fat area (VFA) using predetermined thresholds for the Hounsfield unit (HU) on unenhanced CT and the signal intensity (SI) on T1-weighted MRI at the L3 vertebral level. Visceral obesity (VO) was defined as VFA > 136 cm2 in males and > 95 cm2 in females. These measures, along with perioperative variables, were compared. Multivariate logistic regression analyses were performed. RESULTS: Of 145 included patients, 36 had postoperative complications. No significant differences between LSG and LRYGB were present regarding complications and VO. Hypertension (p = 0.022), impaired lung function (p = 0.018), American Society of Anesthesiologists (ASA) grade (p = 0.046), VO (p = 0.021), and VFA/TAMA ratio (p < 0.0001) were associated with postoperative complications in the univariate logistic analysis; the VFA/TAMA ratio was the only independent predictor in multivariate analyses (OR 2.01, 95% CI 1.37-2.93, p < 0.001). CONCLUSION: The VFA/TAMA ratio provides important perioperative information in predicting patients who are likely to develop postoperative complications undergoing bariatric surgery.

Electro-microenvironment modulated inhibition of endogenous biofilms by piezo implants for ultrasound-localized intestinal perforation disinfection.

Endogenous bacterial infections from damaged gastrointestinal (GI) organs have high potential to cause systemic inflammatory responses and life-threatening sepsis. Current treatments, including systemic antibiotic administration and surgical suturing, are difficult in preventing bacterial translocation and further infection. Here, we report a wireless localized stimulator composed of a piezo implant with high piezoelectric output serving as an anti-infective therapy patch, which aims at modulating the electro-microenvironment of biofilm around GI wounds for effective inhibition of bacterial infection if combined with ultrasound (US) treatment from outside the body. The pulsed charges generated by the piezo implant in response to US stimulation transfer into bacterial biofilms, effectively destroying their macromolecular components (e.g., membrane proteins), disrupting the electron transport chain of biofilms, and inhibiting bacterial proliferation, as proven by experimental studies and theoretical calculations. The piezo implant, in combination with US stimulation, also exhibits successful in vivo anti-infection efficacy in a rat cecal ligation and puncture (CLP) model. The proposed strategy, combining piezo implants with controllable US activation, creates a promising pathway for inhibiting endogenous bacterial infection caused by GI perforation.

[Measurement of Traffic Carbon Emissions and Pattern of Efficiency in the Yangtze River Economic Belt (1985-2016)].

The "top-down" method was used to measure the traffic carbon emissions from 1985 to 2016 in the Yangtze River Economic Belt and analyze its spatial pattern and temporal evolution characteristics. Considering the unexpected output, a three-stage DEA model was constructed to evaluate and compare the traffic carbon emission efficiency of the Yangtze River Economic Belt, excluding the influence of external environment variables and random errors. The study found that first, the total traffic carbon emissions in the Yangtze River Economic Belt showed a rising trend, among which the carbon emissions from petroleum energy consumption accounted for the largest proportion. Sichuan, Hubei, and Hunan and the Su-Zhe-Hu Region were the high-value areas of traffic carbon emissions in the upper, middle, and lower reaches of the Yangtze River, respectively. Second, from the east to west, the center of traffic carbon emissions generally showed a changing track of moving east first and then west; from the north to south, it highlighted the characteristics of increasing concentrated distribution along the Yangtze River in space. Third, there was an obvious spatial differentiation in the traffic carbon emission efficiency values of different provinces; from 2007 to 2016, the efficiency value of the eastern region was the highest, but the value of the central region changed from higher than that in the western region to lower than that in the western region. Finally, external environmental factors had a significant impact on the efficiency of traffic carbon emissions, in which the optimization of industrial structure was found to be conducive to the improvement of traffic carbon emission efficiency, while the influence of government intervention was changed from "innovation compensation" effect to "compliance cost" effect.

Influence of HMW tail chains on the structural evolution of HDPE induced by second melt penetration.

It is widely accepted that the role of the high molecular weight (HMW) component is cooperative in shear-induced crystallization, owing to entanglements among long chains. However, this paper demonstrates that the HMW component has a novel effect on structural evolution during the process of multi-melt multi-injection molding (M3IM), organized as follows. First, the appropriate HDPE system with an incremental concentration of HMW tails was established. Second, the crystalline morphologies and orientation behaviors of the M3IM samples were characterized using a combination of scanning electron microscopy (SEM) and two-dimensional small angle X-ray scattering (2D-SAXS), and these suggested that the amount of shish-kebabs was not monotonically promoted with an increasing content of HMW tails but tended to reduce at a certain value. Third, in order to explain this phenomenon, the special temperature and shear profiles of M3IM were depicted subsequently, and finally the mechanism of hierarchical structure formation with the influence of various amounts of HMW tail chains was discussed, based on the classical rheological viewpoint.

Computerized breast mass detection using multi-scale Hessian-based analysis for dynamic contrast-enhanced MRI.

This study aimed to investigate a computer-aided system for detecting breast masses using dynamic contrast-enhanced magnetic resonance imaging for clinical use. Detection performance of the system was analyzed on 61 biopsy-confirmed lesions (21 benign and 40 malignant lesions) in 34 women. The breast region was determined using the demons deformable algorithm. After the suspicious tissues were identified by kinetic feature (area under the curve) and the fuzzy c-means clustering method, all breast masses were detected based on the rotation-invariant and multi-scale blob characteristics. Subsequently, the masses were further distinguished from other detected non-tumor regions (false positives). Free-response operating characteristics (FROC) curve and detection rate were used to evaluate the detection performance. Using the combined features, including blob, enhancement, morphologic, and texture features with 10-fold cross validation, the mass detection rate was 100 % (61/61) with 15.15 false positives per case and 91.80 % (56/61) with 4.56 false positives per case. In conclusion, the proposed computer-aided detection system can help radiologists reduce inter-observer variability and the cost associated with detection of suspicious lesions from a large number of images. Our results illustrated that breast masses can be efficiently detected and that enhancement and morphologic characteristics were useful for reducing non-tumor regions.

Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI.

To facilitate rapid and accurate assessment, this study proposed a novel fully automatic method to detect and identify focal tumor breast lesions using both kinetic and morphologic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). After motion registration of all phases of the DCE-MRI study, three automatically generated lines were used to segment the whole breast region of each slice. The kinetic features extracted from the pixel-based time-signal intensity curve (TIC) by a two-stage detection algorithm was first used, and then three-dimensional (3-D) morphologic characteristics of the detected regions were applied to differentiate between tumor and non-tumor regions. In this study, 95 biopsy-confirmed lesions (28 benign and 67 malignant lesions) in 54 women were used to evaluate the detection efficacy of the proposed system. The detection performance was analyzed using the free-response operating characteristics (FROC) curve and detection rate. The proposed computer-aided detection (CADe) system had a detection rate of 92.63% (88/95) of all tumor lesions, with 6.15 false positives per case. Based on the results, kinetic features extracted by TIC can be used to detect tumor lesions and 3-D morphology can effectively reduce the false positives.

Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis.

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor. Modified fuzzy c-means clustering was used to identify the most representative kinetic curve of the whole segmented tumor, which was then characterized by using conventional curve analysis or pharmacokinetic model. The 3-D morphological features including shape features (compactness, margin, and ellipsoid fitting) and texture features (based on the grey level co-occurrence matrix) of the segmented tumor were obtained to characterize the lesion. One hundred and thirty-two biopsy-proven lesions (63 benign and 69 malignant) were used to evaluate the performance of the proposed computer-aided system for breast MRI. Five combined features including rate constant (kep), volume of plasma (vp), energy (G1), entropy (G2), and compactness (C1), had the best performance with an accuracy of 91.67% (121/132), sensitivity of 91.30% (63/69), specificity of 92.06% (58/63), and Az value of 0.9427. Combining the kinetic and morphological features of 3-D breast MRI is a potentially useful and robust algorithm when attempting to differentiate benign and malignant lesions.

Computer-aided diagnosis of mass-like lesion in breast MRI: differential analysis of the 3-D morphology between benign and malignant tumors.

This study aimed to evaluate the value of using 3-D breast MRI morphologic features to differentiate benign and malignant breast lesions. The 3-D morphological features extracted from breast MRI were used to analyze the malignant likelihood of tumor from ninety-five solid breast masses (44 benign and 51 malignant) of 82 patients. Each mass-like lesion was examined with regards to three categories of morphologic features, including texture-based gray-level co-occurrence matrix (GLCM) feature, shape, and ellipsoid fitting features. For obtaining a robust combination of features from different categories, the biserial correlation coefficient (|r(pb)|)≧0.4 was used as the feature selection criterion. Receiver operating characteristic (ROC) curve was used to evaluate performance and Student's t-test to verify the classification accuracy. The combination of the selected 3-D morphological features, including conventional compactness, radius, spiculation, surface ratio, volume covering ratio, number of inside angular regions, sum of number of inside and outside angular regions, showed an accuracy of 88.42% (84/95), sensitivity of 88.24% (45/51), and specificity of 88.64% (39/44), respectively. The AZ value was 0.8926 for these seven combined morphological features. In conclusion, 3-D MR morphological features specified by GLCM, tumor shape and ellipsoid fitting were useful for differentiating benign and malignant breast masses.

Diagnosis of solid breast tumors using vessel analysis in three-dimensional power Doppler ultrasound images.

This study aims to evaluate whether the distribution of vessels inside and adjacent to tumor region at three-dimensional (3-D) power Doppler ultrasonography (US) can be used for the differentiation of benign and malignant breast tumors. 3-D power Doppler US images of 113 solid breast masses (60 benign and 53 malignant) were used in this study. Blood vessels within and adjacent to tumor were estimated individually in 3-D power Doppler US images for differential diagnosis. Six features including volume of vessels, vascularity index, volume of tumor, vascularity index in tumor, vascularity index in normal tissue, and vascularity index in surrounding region of tumor within 2 cm were evaluated. Neural network was then used to classify tumors by using these vascular features. The receiver operating characteristic (ROC) curve analysis and Student's t test were used to estimate the performance. All the six proposed vascular features are statistically significant (p < 0.001) for classifying the breast tumors as benign or malignant. The A Z (area under ROC curve) values for the classification result were 0.9138. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis performance based on all six proposed features were 82.30 (93/113), 86.79 (46/53), 78.33 (47/60), 77.97 (46/59), and 87.04 % (47/54), respectively. The p value of A Z values between the proposed method and conventional vascularity index method using z test was 0.04.

Vascular morphology and tortuosity analysis of breast tumor inside and outside contour by 3-D power Doppler ultrasound.

This study aimed to evaluate morphologic and tortuous features of vessels inside and outside the tumor region on three-dimensional power Doppler ultrasonography (PDUS) in 113 breast mass lesions, including 60 benign and 53 malignant tumors. Compared with benign lesions, malignant breast lesions had significantly larger values of vascular morphologic and tortuous features and larger tumor sizes. The receiver operating characteristic curve analysis and Student's t-test were used to estimate the performance of a proposed classification system using 13 vascular features and tumor size selected by the neural network. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value and the A(Z) value of the diagnosis performance based on 14 features were 89.38% (101/113), 84.91% (45/53), 93.33% (56/60), 91.84% (45/49), 87.50% (56/64) and 0.9188, respectively. The three-dimensional PDUS morphologic and tortuous characteristics of blood vessels inside and outside breast mass lesions can be effectively used to classify benign and malignant tumors.

Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering.

The purpose of this study is to evaluate the diagnostic efficacy of the representative characteristic kinetic curve of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) extracted by fuzzy c-means (FCM) clustering for the discrimination of benign and malignant breast tumors using a novel computer-aided diagnosis (CAD) system. About the research data set, DCE-MRIs of 132 solid breast masses with definite histopathologic diagnosis (63 benign and 69 malignant) were used in this study. At first, the tumor region was automatically segmented using the region growing method based on the integrated color map formed by the combination of kinetic and area under curve color map. Then, the FCM clustering was used to identify the time-signal curve with the larger initial enhancement inside the segmented region as the representative kinetic curve, and then the parameters of the Tofts pharmacokinetic model for the representative kinetic curve were compared with conventional curve analysis (maximal enhancement, time to peak, uptake rate and washout rate) for each mass. The results were analyzed with a receiver operating characteristic curve and Student's t test to evaluate the classification performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the combined model-based parameters of the extracted kinetic curve from FCM clustering were 86.36% (114/132), 85.51% (59/69), 87.30% (55/63), 88.06% (59/67) and 84.62% (55/65), better than those from a conventional curve analysis. The A(Z) value was 0.9154 for Tofts model-based parametric features, better than that for conventional curve analysis (0.8673), for discriminating malignant and benign lesions. In conclusion, model-based analysis of the characteristic kinetic curve of breast mass derived from FCM clustering provides effective lesion classification. This approach has potential in the development of a CAD system for DCE breast MRI.