Puerarin Delays Mammary Gland Aging by Regulating Gut Microbiota and Inhibiting the p38MAPK Signaling Pathway.

Mammary gland aging is one of the most important problems faced by humans and animals. How to delay mammary gland aging is particularly important. Puerarin is a kind of isoflavone substance extracted from Pueraria lobata, which has anti-inflammatory, antioxidant, and other pharmacological effects. However, the role of puerarin in delaying lipopolysaccharide (LPS)-induced mammary gland aging and its underlying mechanism remains unclear. On the one hand, we found that puerarin could significantly downregulate the expression of senescence-associated secretory phenotype (SASP) and age-related indicators (SA-ß-gal, p53, p21, p16) in mammary glands of mice. In addition, puerarin mainly inhibited the p38MAPK signaling pathway to repair mitochondrial damage and delay mammary gland aging. On the other hand, puerarin could also delay the cellular senescence of mice mammary epithelial cells (mMECs) by targeting gut microbiota and promoting the secretion of gut microbiota metabolites. In conclusion, puerarin could not only directly act on the mMECs but also regulate the gut microbiota, thus, playing a role in delaying the aging of the mammary gland. Based on the above findings, we have discovered a new pathway for puerarin to delay mammary gland aging.

Precision of liver and pancreas apparent diffusion coefficients using motion-compensated gradient waveforms in DWI.

BACKGROUND: Diffusion weighted imaging (DWI) with optimized motion-compensated gradient waveforms reduces signal dropouts in the liver and pancreas caused by cardiovascular-associated motion, however its precision is unknown. We hypothesized that DWI with motion-compensated DW gradient waveforms would improve apparent diffusion coefficient (ADC)-repeatability and inter-reader reproducibility compared to conventional DWI in these organs. METHODS: In this IRB-approved, prospective, single center study, subjects recruited between October 2019 and March 2020 were scanned twice on a 3 T scanner, with repositioning between test and retest. Each scan included two respiratory-triggered DWI series with comparable acquisition time: 1) conventional (monopolar) 2) motion- compensated diffusion gradients. Three readers measured ADC values. One-way ANOVA, Bland-Altman analysis were used for statistical analysis. RESULTS: Eight healthy participants (4 male/4 female), with a mean age of 29 ± 4 years, underwent the liver and pancreas MRI protocol. Four patients with liver metastases (2 male/2 female) with a mean age of 58 ± 5 years underwent the liver MRI protocol. In healthy participants, motion-compensated DWI outperformed conventional DWI with mean repeatability coefficient of 0.14 × 10-3 (CI:0.12-0.17) vs. 0.31 × 10-3 (CI:0.27-0.37) mm2/s for liver, and 0.11 × 10-3 (CI:0.08-0.15) vs. 0.34 × 10-3 (CI:0.27-0.49) mm2/s for pancreas; and with mean reproducibility coefficient of 0.20 × 10-3 (CI:0.18-0.23) vs. 0.51 × 10-3 (CI:0.46-0.58) mm2/s for liver, and 0.16 × 10-3 (CI:0.13-0.20) vs. 0.42 × 10-3 (CI:0.34-0.52) mm2/s for pancreas. In patients, improved repeatability was observed for motion-compensated DWI in comparison to conventional with repeatability coefficient of 0.51 × 10- 3 mm2/s (CI:0.35-0.89) vs. 0.70 × 10-3 mm2/s (CI:0.49-1.20). CONCLUSION: Motion-compensated DWI enhances the precision of ADC measurements in the liver and pancreas compared to conventional DWI.

Tartary buckwheat flavonoids alleviates high-fat diet induced kidney fibrosis in mice by inhibiting MAPK and TGF-ß1/Smad signaling pathway.

Tartary buckwheat flavonoids (TBF) are active components extracted from Tartary buckwheat, which have abundant biological effects. According to this study, we investigated the effect of TBF on high-fat diet (HFD)-induced kidney fibrosis and its related mechanisms. In vivo, we established an HFD-induced kidney fibrosis model in mice and administered TBF. The results showed that TBF was able to alleviate kidney injury and inflammatory response. Subsequently, the mRNA levels between the HFD group and the TBF + HFD group were detected using RNA-seq assay. According to the gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) results, the differential genes were enriched in lipid metabolism and mitogen-activated protein kinases(MAPK) signaling pathways. We examined the protein expression of lipid metabolism-related pathways and the level of lipid metabolism. The results showed that TBF significantly activated the adenosine monophosphate activated protein kinase/acetyl-CoA carboxylase (AMPK/ACC) pathway and effectively reduced kidney total cholesterol (TC), triglyceride (TG) and low-density lipoproteinc cholesterol (LDL-C) levels and increased high-density lipoprotein cholesterol (HDL-C) levels in mice. TBF also inhibited transforming growth factor-ß1/Smad (TGF-ß1/Smad) and MAPK signaling pathways, thus slowing down the kidney fibrosis process. In vitro, using palmitic acid (PA) to stimulate TCMK-1 cells, the in vivo results similarly demonstrated that TBF could alleviate kidney fibrosis in HFD mice by inhibiting TGF1/Smad signaling pathway and MAPK signaling pathway.

Motion-robust, blood-suppressed, reduced-distortion diffusion MRI of the liver.

PURPOSE: To evaluate feasibility and reproducibility of liver diffusion-weighted (DW) MRI using cardiac-motion-robust, blood-suppressed, reduced-distortion techniques. METHODS: DW-MRI data were acquired at 3T in an anatomically accurate liver phantom including controlled pulsatile motion, in eight healthy volunteers and four patients with known or suspected liver metastases. Standard monopolar and motion-robust (M1-nulled, and M1-optimized) DW gradient waveforms were each acquired with single-shot echo-planar imaging (ssEPI) and multishot EPI (msEPI). In the motion phantom, apparent diffusion coefficient (ADC) was measured in the motion-affected volume. In healthy volunteers, ADC was measured in the left and right liver lobes separately to evaluate ADC reproducibility between the two lobes. Image distortions were quantified using the normalized cross-correlation coefficient, with an undistorted T2-weighted reference. RESULTS: In the motion phantom, ADC mean and SD in motion-affected volumes substantially increased with increasing motion for monopolar waveforms. ADC remained stable in the presence of increasing motion when using motion-robust waveforms. M1-optimized waveforms suppressed slow flow signal present with M1-nulled waveforms. In healthy volunteers, monopolar waveforms generated significantly different ADC measurements between left and right liver lobes ( p = 0 . 0078 $$ p=0.0078 $$ , reproducibility coefficients (RPC) =  470 × 1 0 - 6 $$ 470\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for monopolar-msEPI), while M1-optimized waveforms showed more reproducible ADC values ( p = 0 . 29 $$ p=0.29 $$ , RPC = 220 × 1 0 - 6 $$ \mathrm{RPC}=220\times 1{0}^{-6} $$ mm 2 $$ {}^2 $$ /s for M1-optimized-msEPI). In phantom and healthy volunteer studies, motion-robust acquisitions with msEPI showed significantly reduced image distortion ( p < 0 . 001 $$ p<0.001 $$ ) compared to ssEPI. Patient scans showed reduction of wormhole artifacts when combining M1-optimized waveforms with msEPI. CONCLUSION: Synergistic effects of combined M1-optimized diffusion waveforms and msEPI acquisitions enable reproducible liver DWI with motion robustness, blood signal suppression, and reduced distortion.

Automated MR Image Prescription of the Liver Using Deep Learning: Development, Evaluation, and Prospective Implementation.

BACKGROUND: There is an unmet need for fully automated image prescription of the liver to enable efficient, reproducible MRI. PURPOSE: To develop and evaluate artificial intelligence (AI)-based liver image prescription. STUDY TYPE: Prospective. POPULATION: A total of 570 female/469 male patients (age: 56 ± 17 years) with 72%/8%/20% assigned randomly for training/validation/testing; two female/four male healthy volunteers (age: 31 ± 6 years). FIELD STRENGTH/SEQUENCE: 1.5 T, 3.0 T; spin echo, gradient echo, bSSFP. ASSESSMENT: A total of 1039 three-plane localizer acquisitions (26,929 slices) from consecutive clinical liver MRI examinations were retrieved retrospectively and annotated by six radiologists. The localizer images and manual annotations were used to train an object-detection convolutional neural network (YOLOv3) to detect multiple object classes (liver, torso, and arms) across localizer image orientations and to output corresponding 2D bounding boxes. Whole-liver image prescription in standard orientations was obtained based on these bounding boxes. 2D detection performance was evaluated on test datasets by calculating intersection over union (IoU) between manual and automated labeling. 3D prescription accuracy was calculated by measuring the boundary mismatch in each dimension and percentage of manual volume covered by AI prescription. The automated prescription was implemented on a 3 T MR system and evaluated prospectively on healthy volunteers. STATISTICAL TESTS: Paired t-tests (threshold = 0.05) were conducted to evaluate significance of performance difference between trained networks. RESULTS: In 208 testing datasets, the proposed method with full network had excellent agreement with manual annotations, with median IoU > 0.91 (interquartile range < 0.09) across all seven classes. The automated 3D prescription was accurate, with shifts <2.3 cm in superior/inferior dimension for 3D axial prescription for 99.5% of test datasets, comparable to radiologists' interreader reproducibility. The full network had significantly superior performance than the tiny network for 3D axial prescription in patients. Automated prescription performed well across single-shot fast spin-echo, gradient-echo, and balanced steady-state free-precession sequences in the prospective study. DATA CONCLUSION: AI-based automated liver image prescription demonstrated promising performance across the patients, pathologies, and field strengths studied. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 1.

Astaxanthin Alleviates Nonalcoholic Fatty Liver Disease by Regulating the Intestinal Flora and Targeting the AMPK/Nrf2 Signal Axis.

Nonalcoholic fatty liver disease (NAFLD) is among the most prevalent chronic liver diseases around the globe. The accumulation of lipids in the liver and oxidative stress are important pathological mechanisms of NAFLD. Astaxanthin (AT) is a carotenoid extracted from shrimps and crabs with beneficial biological activities, including anti-oxidative and anti-inflammatory activities. 16S microflora sequencing, H&E staining, and the western blot technique were employed to investigate the impacts of AT on a high-fat diet (HFD)-induced NAFLD. Significant mitigation in lipid metabolism-related disorders and decreased oxidative stress in HFD-induced mice were observed due to AT, and significant changes in the gut flora of the model mice were also observed. The in vitro study showed that AT considerably lowered the protein expression level of fatty acid synthetase (FAS), sterol regulatory element-binding protein-1c (SREBP-1c), and acetyl-COA carboxylase (ACC) and increased the protein expression of nuclear factor-E2 associated factor 2 (Nrf2) and AMP-activated protein kinase (AMPK) in oleic acid (OA) and palmitic acid (PA)-induced HepG2 cells. Additionally, mechanistic studies revealed that compound C (AMPK inhibitor, CC) inhibited the regulatory effect of AT on the SREBP-1c and Nrf2 signaling pathways. In conclusion, AT can inhibit the SREBP-1c, FAS, and ACC signaling pathways, activate the AMPK and Nrf2 signaling pathways, and improve the structure of intestinal flora.

b value and first-order motion moment optimized data acquisition for repeatable quantitative intravoxel incoherent motion DWI.

PURPOSE: To design a b value and first-order motion moment (M1 ) optimized data acquisition for repeatable intravoxel incoherent motion (IVIM) quantification in the liver. METHODS: Cramer-Rao lower bound optimization was performed to determine optimal monopolar and optimal 2D samplings of the b-M1 space based on noise performance. Monte Carlo simulations were used to evaluate the bias and variability in estimates obtained using the proposed optimal samplings and conventional monopolar sampling. Diffusion MRI of the liver was performed in 10 volunteers using 3 IVIM acquisitions: conventional monopolar, optimized monopolar, and b-M1 -optimized gradient waveforms (designed based on the optimal 2D sampling). IVIM parameter maps of diffusion coefficient, perfusion fraction, and blood velocity SD were obtained using nonlinear least squares fitting. Noise performance (SDs), stability (outlier percentage), and test-retest or scan-rescan repeatability (intraclass correlation coefficients) were evaluated and compared across acquisitions. RESULTS: Cramer-Rao lower bound and Monte Carlo simulations demonstrated improved noise performance of the optimal 2D sampling in comparison to monopolar samplings. Evaluating the designed b-M1 -optimized waveforms in healthy volunteers, significant decreases (p < 0.05) in the SDs and outlier percentages were observed for measurements of diffusion coefficient, perfusion fraction, and blood velocity SD in comparison to measurements obtained using monopolar samplings. Good-to-excellent repeatability (intraclass correlation coefficients ≥ 0.77) was observed for all 3 parameters in both the right and left liver lobes using the b-M1 -optimized waveforms. CONCLUSIONS: 2D b-M1 -optimized data acquisition enables repeatable IVIM quantification with improved noise performance. 2D acquisitions may advance the establishment of IVIM quantitative biomarkers for liver diseases.

Characterization and correction of cardiovascular motion artifacts in diffusion-weighted imaging of the pancreas.

PURPOSE: To assess the effects of cardiovascular-induced motion on conventional DWI of the pancreas and to evaluate motion-robust DWI methods in a motion phantom and healthy volunteers. METHODS: 3T DWI was acquired using standard monopolar and motion-compensated gradient waveforms, including in an anatomically accurate pancreas phantom with controllable compressive motion and healthy volunteers (n = 8, 10). In volunteers, highly controlled single-slice DWI using breath-holding and cardiac gating and whole-pancreas respiratory-triggered DWI were acquired. For each acquisition, the ADC variability across volunteers, as well as ADC differences across parts of the pancreas were evaluated. RESULTS: In motion phantom scans, conventional DWI led to biased ADC, whereas motion-compensated waveforms produced consistent ADC. In the breath-held, cardiac-triggered study, conventional DWI led to heterogeneous DW signals and highly variable ADC across the pancreas, whereas motion-compensated DWI avoided these artifacts. In the respiratory-triggered study, conventional DWI produced heterogeneous ADC across the pancreas (head: 1756 ± 173 × 10-6 mm2 /s; body: 1530 ± 338 × 10-6 mm2 /s; tail: 1388 ± 267 × 10-6 mm2 /s), with ADCs in the head significantly higher than in the tail (P < .05). Motion-compensated ADC had lower variability across volunteers (head: 1277 ± 102 × 10-6 mm2 /s; body: 1204 ± 169 × 10-6 mm2 /s; tail: 1235 ± 178 × 10-6 mm2 /s), with no significant difference (P ≥ .19) across the pancreas. CONCLUSION: Cardiovascular motion introduces artifacts and ADC bias in pancreas DWI, which are addressed by motion-robust DWI.

Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes.

PURPOSE: to develop digital phantoms for characterizing inconsistencies among radiomics extraction methods based on three radiomics toolboxes: CERR (Computational Environment for Radiological Research), IBEX (imaging biomarker explorer), and an in-house radiomics platform. MATERIALS AND METHODS: we developed a series of digital bar phantoms for characterizing intensity and texture features and a series of heteromorphic sphere phantoms for characterizing shape features. The bar phantoms consisted of n equal-width bars (n = 2, 4, 8, or 64). The voxel values of the bars were evenly distributed between 1 and 64. Starting from a perfect sphere, the heteromorphic sphere phantoms were constructed by stochastically attaching smaller spheres to the phantom surface over 5500 iterations. We compared 61 features typically extracted from three radiomics toolboxes: (1) CERR (2) IBEX (3) in-house toolbox. The degree of inconsistency was quantified by concordance correlation coefficient (CCC) and Pearson correlation coefficient (PCC). Sources of discrepancies were characterized based on differences in mathematical definition, pre-processing, and calculation methods. RESULTS: For the intensity and texture features, only 53%, 45%, 55% features demonstrated perfect reproducibility (CCC = 1) between in-house/CERR, in-house/IBEX, and CERR/IBEX comparisons, while 71%, 61%, 61% features reached CCC > 0.8 and 25%, 39%, 39% features were with CCC < 0.5, respectively. Meanwhile, most features demonstrated PCC > 0.95. For shape features, the toolboxes produced similar (CCC > 0.98) volume yet inconsistent surface area, leading to inconsistencies in other shape features. However, all toolboxes resulted in PCC > 0.8 for all shape features except for compactness 1, where inconsistent mathematical definitions were observed. Discrepancies were characterized in pre-processing and calculation implementations from both type of phantoms. CONCLUSIONS: Inconsistencies among radiomics extraction toolboxes can be accurately identified using the developed digital phantoms. The inconsistencies demonstrate the significance of implementing quality assurance (QA) of radiomics extraction for reproducible and generalizable radiomic studies. Digital phantoms are therefore very useful tools for QA.

Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.

The purpose of this work was to investigate the potential relationship between radiomic features extracted from pre-treatment x-ray CT images and clinical outcomes following stereotactic body radiation therapy (SBRT) for non-small-cell lung cancer (NSCLC). Seventy patients who received SBRT for stage-1 NSCLC were retrospectively identified. The tumor was contoured on pre-treatment free-breathing CT images, from which 43 quantitative radiomic features were extracted to collectively capture tumor morphology, intensity, fine-texture, and coarse-texture. Treatment failure was defined based on cancer recurrence, local cancer recurrence, and non-local cancer recurrence following SBRT. The univariate association between each radiomic feature and each clinical endpoint was analyzed using Welch's t-test, and p-values were corrected for multiple hypothesis testing. Multivariate associations were based on regularized logistic regression with a singular value decomposition to reduce the dimensionality of the radiomics data. Two features demonstrated a statistically significant association with local failure: Homogeneity2 (p = 0.022) and Long-Run-High-Gray-Level-Emphasis (p = 0.048). These results indicate that relatively dense tumors with a homogenous coarse texture might be linked to higher rates of local recurrence. Multivariable logistic regression models produced maximum [Formula: see text] values of [Formula: see text], and [Formula: see text], for the recurrence, local recurrence, and non-local recurrence endpoints, respectively. The CT-based radiomic features used in this study may be more associated with local failure than non-local failure following SBRT for stage I NSCLC. This finding is supported by both univariate and multivariate analyses.