Lactococcus lactis KF140 Ameliorates Nonalcoholic Fatty Liver Disease Induced by Nε-Carboxymethyl-Lysine and High-Fat Diet.

SCOPE: Long-term consumption of excessive dietary advanced glycation end-products such as Nε-carboxymethyl-lysine (CML), which are produced by the Maillard reaction during food thermal processing, leads to nonalcoholic fatty liver disease (NAFLD) along with high fat consumption. The study previously finds that administration of Lactococcus lactis KF140 (LL-KF140) detoxifies CML by decreasing CML absorption both in a rat model and clinical trial. METHODS AND RESULTS: The present study evaluates the ameliorative effect of LL-KF140 on NAFLD and fatty liver-related biomarkers in a mouse model induced by CML and high fat. LL-KF140 is orally administered to mice at a concentration of 1 × 107 or 1 × 108 colony-forming unit (CFU) per mouse for 8 weeks. LL-KF140 administration ameliorates the NAFLD-related symptoms by reducing body weight and fat mass gain along with levels of serum aspartate transaminase, alanine transferase, and lipids as well as glucose intolerance and insulin resistance in CML-treated mice. In addition, histological analysis including staining and western blotting shows that LL-KF140 suppresses the lipogenesis pathway and CML absorption, thereby suppressing CML-induced NAFLD. CONCLUSION: These findings suggest that LL-KF140 attenuates dietary CML-induced NAFLD by suppressing the de novo lipogenesis pathway, and it may be used as a probiotic strain.

Ameliorating effects of Orostachys japonica against high-fat diet-induced obesity and gut dysbiosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Orostachys japonica (rock pine) has been used as a folk remedy to treat inflammation, hepatitis, and cancer in East Asia. AIM OF THE STUDY: The aim of this study was to investigate the effect of rock pine extract (RPE) on high-fat diet-induced obesity in mice and to examine its effects on gut dysbiosis. MATERIALS AND METHODS: The characteristic compound of RPE, kaempferol-3-O-rutinoside, was quantified using high-performance liquid chromatography. The prebiotic potential of RPE was evaluated by assessing the prebiotic activity score obtained using four prebiotic strains and high-fat (HF)-induced obesity C57BL/6 mice model. Analysis included examining the lipid metabolism and inflammatory proteins and evaluating the changes in gut permeability and metabolites to elucidate the potential signaling pathways involved. RESULTS: In vitro, RPE enhanced the proliferation of beneficial probiotic strains, including Lactiplantibacillus and Bifidobacterium. HF-induced model showed that the administration of 100 mg/kg/day of RPE for 8 weeks significantly (p < 0.05) reduced the body weight, serum lipid levels, and insulin resistance, which were associated with notable changes in lipid metabolism and inflammation-related markers. CONCLUSIONS: Our results demonstrate that rock pine consumption could mitigate obesity and metabolic endotoxemia in HF-fed mice through enhancing intestinal environment.

Resistant starch-enriched brown rice exhibits prebiotic properties and enhances gut health in obese mice.

Resistant starch serves as a prebiotic in the large intestine, aiding in the maintenance of a healthy intestinal environment and mitigating associated chronic illnesses. This study aimed to investigate the impact of resistant starch-enriched brown rice (RBR) on intestinal health and functionality. We assessed changes in resistant starch concentration, structural alterations, and branch chain length distribution throughout the digestion process using an in vitro model. The efficacy of RBR in the intestinal environment was evaluated through analyses of its prebiotic potential, effects on intestinal microbiota, and intestinal function-related proteins in obese animals fed a high-fat diet. RBR exhibited a higher yield of insoluble fraction in both the small and large intestines compared to white and brown rice. The total digestible starch content decreased, while the resistant starch content significantly increased during in vitro digestion. Furthermore, RBR notably enhanced the growth of four probiotic strains compared to white and brown rice, displaying higher proliferation activity than the positive control, FOS. Notably, consumption of RBR by high-fat diet-induced obese mice suppressed colon shortening, increased Bifidobacteria growth, and improved intestinal permeability. These findings underscore the potential prebiotic and gut health-promoting attributes of RBR, offering insights for the development of functional foods aimed at preventing gastrointestinal diseases.

Comparison of Breast Fine-Needle Aspiration Cytology and Tissue Sampling for High-Throughput Proteomic Analysis and Cancer Biomarker Detection.

INTRODUCTION: Fine-needle aspiration cytology (FNAC) specimens are widely utilized for the diagnosis and molecular testing of various cancers. We performed a comparative proteomic analysis of three different sample types, including breast FNAC, core needle biopsy (CNB), and surgical resection tissues. Our goal was to evaluate the suitability of FNAC for in-depth proteomic analysis and for identifying potential therapeutic biomarkers in breast cancer. METHODS: High-throughput proteomic analysis was conducted on matched FNAC, CNB, and surgical resection tissue samples obtained from breast cancer patients. The protein identification, including currently established or promising therapeutic targets, was compared among the three different sample types. Gene Ontology (GO) enrichment analysis was also performed on all matched samples. RESULTS: Compared to tissue samples, FNAC testing revealed a comparable number of proteins (7,179 in FNAC; 7,196 in CNB; and 7,190 in resection samples). Around 85% of proteins were mutually identified in all sample types. FNAC, along with CNB, showed a positive correlation between the number of enrolled tumor cells and identified proteins. In the GO analysis, the FNAC samples demonstrated a higher number of genes for each pathway and GO terms than tissue samples. CCND1, CDK6, HER2, and IGF1R were found in higher quantities in the FNAC compared to tissue samples, while TUBB2A was only detected in the former. CONCLUSION: FNAC is suitable for high-throughput proteomic analysis, in addition to an emerging source that could be used to identify and quantify novel cancer biomarkers.

Comparison between R2'-based and R2*-based χ-separation methods: A clinical evaluation in individuals with multiple sclerosis.

Susceptibility source separation, or χ-separation, estimates diamagnetic (χdia) and paramagnetic susceptibility (χpara) signals in the brain using local field and R2' (= R2* - R2) maps. Recently proposed R2*-based χ-separation methods allow for χ-separation using only multi-echo gradient echo (ME-GRE) data, eliminating the need for additional data acquisition for R2 mapping. Although this approach reduces scan time and enhances clinical utility, the impact of missing R2 information remains a subject of exploration. In this study, we evaluate the viability of two previously proposed R2*-based χ-separation methods as alternatives to their R2'-based counterparts: model-based R2*-χ-separation versus χ-separation and deep learning-based χ-sepnet-R2* versus χ-sepnet-R2'. Their performances are assessed in individuals with multiple sclerosis (MS), comparing them with their corresponding R2'-based counterparts (i.e., R2*-χ-separation vs. χ-separation and χ-sepnet-R2* vs. χ-sepnet-R2'). The evaluations encompass qualitative visual assessments by experienced neuroradiologists and quantitative analyses, including region of interest analyses and linear regression analyses. Qualitatively, R2*-χ-separation tends to report higher χpara and χdia values compared with χ-separation, leading to less distinct lesion contrasts, while χ-sepnet-R2* closely aligns with χ-sepnet-R2'. Quantitative analysis reveals a robust correlation between both R2*-based methods and their R2'-based counterparts (r ≥ 0.88). Specifically, in the whole-brain voxels, χ-sepnet-R2* exhibits higher correlation and better linearity than R2*-χ-separation (χdia/χpara from R2*-χ-separation: r = 0.88/0.90, slope = 0.79/0.86; χdia/χpara from χ-sepnet-R2*: r = 0.90/0.92, slope = 0.99/0.97). In MS lesions, both R2*-based methods display comparable correlation and linearity (χdia/χpara from R2*-χ-separation: r = 0.90/0.91, slope = 0.98/0.91; χdia/χpara from χ-sepnet-R2*: r = 0.88/0.88, slope = 0.91/0.95). Notably, χ-sepnet-R2* demonstrates negligible offsets, whereas R2*-χ-separation exhibits relatively large offsets (0.02 ppm in the whole brain and 0.01 ppm in the MS lesions), potentially indicating the false presence of myelin or iron in MS lesions. Overall, both R2*-based χ-separation methods demonstrated their viability as alternatives to their R2'-based counterparts. χ-sepnet-R2* showed better alignment with its R2'-based counterpart with minimal susceptibility offsets, compared with R2*-χ-separation that reported higher χpara and χdia values compared with R2'-based χ-separation.

Automated Quantification of Total Cerebral Blood Flow from Phase-Contrast MRI and Deep Learning.

Knowledge of input blood to the brain, which is represented as total cerebral blood flow (tCBF), is important in evaluating brain health. Phase-contrast (PC) magnetic resonance imaging (MRI) enables blood velocity mapping, allowing for noninvasive measurements of tCBF. In the procedure, manual selection of brain-feeding arteries is an essential step, but is time-consuming and often subjective. Thus, the purpose of this work was to develop and validate a deep learning (DL)-based technique for automated tCBF quantifications. To enhance the DL segmentation performance on arterial blood vessels, in the preprocessing step magnitude and phase images of PC MRI were multiplied several times. Thereafter, a U-Net was trained on 218 images for three-class segmentation. Network performance was evaluated in terms of the Dice coefficient and the intersection-over-union (IoU) on 40 test images, and additionally, on externally acquired 20 datasets. Finally, tCBF was calculated from the DL-predicted vessel segmentation maps, and its accuracy was statistically assessed with the correlation of determination (R2), the intraclass correlation coefficient (ICC), paired t-tests, and Bland-Altman analysis, in comparison to manually derived values. Overall, the DL segmentation network provided accurate labeling of arterial blood vessels for both internal (Dice=0.92, IoU=0.86) and external (Dice=0.90, IoU=0.82) tests. Furthermore, statistical analyses for tCBF estimates revealed good agreement between automated versus manual quantifications in both internal (R2=0.85, ICC=0.91, p=0.52) and external (R2=0.88, ICC=0.93, p=0.88) test groups. The results suggest feasibility of a simple and automated protocol for quantifying tCBF from neck PC MRI and deep learning.