Objective: To explore the characteristics of the diversity and composition of oral microbial flora with age, and to provide a reference for understanding the succession of oral microecology at different ages. Methods: Oral rinse 16S rRNA (V4 region) sequencing data from 9 021 participants 14-69 years of age in the 2009-2012 National Health and Nutrition Examination Survey (NHANES) were analyzed. Alpha diversity (Observed OTUs, Faith’s PD, Shannon Index), beta diversity (Bray-Curtis and UniFrac), and genus-level composition were examined using weighted generalized linear models (GLMs), including quadratic terms for age and adjusting for key covariates (gender, race/ethnicity, BMI, smoking status, and periodontitis severity). Results: Alpha diversity demonstrated a clear inverted U-shaped trajectory across age, peaking at 25-30 years old and declining thereafter. This trend remained consistent across sex, race, smoking, and periodontal health strata. Beta diversity analyses revealed a modest but steady age-related shift in community structure. Genus-level analyses revealed that Rothia, Prevotella_6, and Lactobacillus increased steadily with age, while Haemophilus, Porphyromonas, and Corynebacterium declined significantly. Notably, potential periodontopathogens, such as Fusobacterium and Treponema_2, peaked in early adulthood before declining with age. Conclusion Age is an important driver of oral microbial succession, and the oral microbiome exhibits dynamic changes across different life stages. Future longitudinal and multi-omic studies are warranted to elucidate the mechanisms underlying these age-related trajectories.

A variety of chromatographic techniques, including silica gel, ODS, Sephadex LH-20, and HPLC, were employed to isolate and purify the fermentation products of rice with Monascus sanguineus. A total of 38 compounds were isolated, and their structures were identified by UV, IR, NMR, MS, calculated ECD, and comparison with literature data. Compounds 1-4 were identified as new natural products, and other compounds were isolated from this fungus for the first time. A RAW264.7 macrophage model of lipopolysaccharide(LPS)-induced inflammation was used to evaluate the anti-inflammatory activities of all the compounds. The results showed that compound 6 exhibited a certain inhibitory effect on the production of nitric oxide in LPS-induced RAW264.7 cells, with an inhibition rate of 53.08%.
Monascus/chemistry* ; Mice ; Animals ; Anti-Inflammatory Agents/isolation & purification* ; RAW 264.7 Cells ; Macrophages/immunology* ; Nitric Oxide/immunology* ; Oryza/metabolism* ; Fermentation

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Background: We found microRNA (miR)-1246 to be significantly differentially expressed between severe active alopecia areata (AA) patients and healthy individuals. Objective: To explore the role and mechanism of miR-1246 in severe AA. Methods: Expression of miR-1246, dual-specific tyrosine phosphorylation-regulated kinase 1A (DYRK1A), and nuclear factor of activated T cells 1c (NFATc1) in peripheral CD4+ T cells and in scalp tissues of patients were detected using RT-qPCR, Western blot, and immunohistochemistry assays. Peripheral CD4+ T cells from the AA patients were transfected with lentiviral vectors overexpressing miR-1246. RT-qPCR and Western blot analysis were used to measure mRNA or protein expression of retinoic-acid-receptor-related orphan nuclear receptor gamma (ROR-γt), interleukin (IL)-17, DYRK1A, NFATc1, and phosphorylated NFATc1. Flow cytometry was used to assay the CD4+ IL-17+ cells proportion. ELISA was used to measure cytokine levels. Results: miR-1246 levels decreased and DYRK1A and NFATc1 mRNA levels significantly increased in the peripheral CD4+ T cells and scalp tissues of severe active AA samples.NFATc1 protein expression was also significantly increased in the peripheral CD4+ T cells but not in the scalp tissues. NFATc1 positive cells were mainly distributed among infiltrating inflammatory cells around hair follicles. In peripheral CD4+ T cells of severe active AA, overexpression of miR-1246 resulted in significant downregulation of DYRK1A, NFATc1, ROR-γt, and IL-17 mRNA and phosphorylated NFATc1 protein, as well as a decrease in the CD4+ IL-17+ cells proportion and the IL-17F level. Conclusion miR-1246 can inhibit NFAT signaling and Th17 cell activation, which may be beneficial in the severe AA treatment.

The trace chemical components in functional Monascus rice were studied to explore the potential bioactive substances. MCI column, Sephadex LH-20 gel, and preparative liquid chromatography were used to purified the ethyl acetate extract from functional Monascus rice. Two novel pyridine Monascus pigments were isolated and identified, named monascopyridine G (1) and monascopyridine H (2), respectively based on extensive mass spectrometry (MS), infrared radiation (IR), and nuclear magnetic resonance (NMR) analysis. The molecular docking experiments between compounds 1 and 2 and peroxisome proliferators-activated receptor-gamma (PPARγ) showed that they exhibited obvious binding force with the receptor protein. Besides, the biosynthetic pathways of the two compounds were proposed, which provide a valuable reference for the selective production of these potential bioactive substances.

We investigated the effects of decursin on the proliferation, apoptosis, and migration of colorectal cancer HT29 and HCT116 cells through the phosphatidylinositol 3-kinase(PI3K)/serine-threonine kinase(Akt) pathway. Decursin(10, 30, 60, and 90 μmol·L~(-1)) was used to treat HT29 and HCT116 cells. The survival, colony formation ability, proliferation, apoptosis, wound hea-ling area, and migration of the HT29 and HCT116 cells exposed to decursin were examined by cell counting kit-8(CCK8), cloning formation experiments, Ki67 immunofluorescence staining, flow cytometry, wound healing assay, and Transwell assay, respectively. Western blot was employed to determine the expression levels of epithelial cadherin(E-cadherin), neural cadherin(N-cadherin), vimentin, B-cell lymphoma/leukemia-2(Bcl-2), Bcl-2-associated X protein(Bax), tumor suppressor protein p53, PI3K, and Akt. Compared with the control group, decursin significantly inhibited the proliferation and colony number and promoted the apoptosis of HT29 and HCT116 cells, and it significantly down-regulated the expression of Bcl-2 and up-regulated the expression of Bax. Decursin inhibited the wound healing and migration of the cells, significantly down-regulated the expression of N-cadherin and vimentin, and up-regulated the expression of E-cadherin. In addition, it significantly down-regulated the expression of PI3K and Akt and up-regulated that of p53. In summary, decursin may regulate epithelial-mesenchymal transition(EMT) via the PI3K/Akt signaling pathway, thereby affecting the proliferation, apoptosis, and migration of colorectal cancer cells.
Humans ; Proto-Oncogene Proteins c-akt/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; bcl-2-Associated X Protein ; Vimentin/metabolism* ; Cell Proliferation ; Signal Transduction ; Apoptosis ; Cell Line, Tumor ; Colorectal Neoplasms/genetics* ; Cadherins/genetics* ; Cell Movement