Black tooth stain is a characteristic extrinsic discoloration commonly seen on the cervical enamel following the contour of the gingiva. To investigate the relationship between black tooth stain and the oral microbiota, we used 16S rRNA gene sequencing to compare the microbial composition of dental plaque and saliva among caries-free children with and without black stain. Dental plaque and saliva, as well as black stain, were sampled from 10 children with and 15 children without black stain. Data were analyzed using the pipeline tool MOTHUR. Student’s t-test was used to compare alpha diversities and the Mann-Whitney U test to compare the relative abundances of the microbial taxa. A total of 10 phyla, 19 classes, 32 orders, 61 families and 102 genera were detected in these samples. Shannon and Simpson diversity were found to be significantly lower in saliva samples of children with black stain. Microbial diversity was reduced in the black stain compared to the plaque samples. Actinomyces, Cardiobacterium, Haemophilus, Corynebacterium, Tannerella and Treponema were more abundant and Campylobacter less abundant in plaque samples of children with black stain. Principal component analysis demonstrated clustering among the dental plaque samples from the control group, while the plaque samples from the black stain group were not and appeared to cluster into two subgroups. Alterations in oral microbiota may be associated with the formation of black stain.

AimsGenistein, an isoflavone derivative found in soy, is known as a promising treatment for rheumatoid arthritis (RA). However, the detailed molecular mechanism of genistein in suppression of proinflammatory cytokine production remains ambiguous. The aim of this work was to evaluate the signal pathway by which genistein modulates inflammatory cytokine expression.Materials and methodsMH7A cells were stimulated with tumor necrosis factor (TNF)-α and incubated with genistein, and interleukin (IL)-1β, IL-6, and IL-8 production was measured by enzyme-linked immunosorbent assay. Nuclear translocation of nuclear factor (NF)-κB was measured by a confocal fluorescence microscopy. The intracellular accumulation of reactive oxygen species (ROS) was monitored using the fluorescent probe 5-6-chloromethyl-2′,7′-dichlorodihydrofluorescein diacetate. Signal-transduction protein expression was measured by Western blot.ResultsGenistein decreased the secretion of IL-1β, IL-6, and IL-8 from TNF-α-stimulated MH7A cells in a dose-dependent manner. Genistein prevented TNF-α-induced NF-κB translocation as well as phosphorylation of IκB kinase-α/β and IκBα, and also suppressed TNF-α-induced AMPK inhibition. The production of IL-1β, IL-6, and IL-8 induced by TNF-α was decreased by the phosphatidylinositol-3 kinase inhibitor LY294002, suggesting that inhibition of Akt activation might inhibit IL-1β, IL-6, and IL-8 production induced by TNF-α. In addition, we also found that pretreatment with the adenosine monophosphate-activated protein kinase (AMPK) agonist 5-aminoimidazole-4-carboxamide-1-β-D-ribofuranoside obviously inhibited TNF-α-induced proinflammatory cytokine production. These observations suggest that the inhibitory effect of genistein on TNF-α-induced proinflammatory cytokine production is dependent on AMPK activation.ConclusionThese findings indicate that genistein suppressed TNF-α-induced inflammation by inhibiting the ROS/Akt/NF-κB pathway and promoting AMPK activation in MH7A cells.

We explored the mechanism of early stage osteonecrotic femoral head collapse by analyzing and comparing different regions in human osteonecrotic femoral head samples. Eight osteonecrotic femoral heads (ARCO II-III) were obtained from patients undergoing total hip arthroplasty. Bone structure was observed and evaluated by micro-computed tomography (CT) scans and pathology. Osteoblast and osteoclast activities were detected by tartrate-resistant acid phosphatase, alkaline phosphatase, and immunofluorescent staining. Some trabeculae had microfractures in the subchondral bone and necrotic region, which had lower bone mineral density, as well as trabecular thickness and number, but greater osteoclast activity. A sclerotic band had already appeared in certain samples which had greater trabecular thickness and number, bone mineral density, and osteoblast activity. The appearance of the femoral head did not change significantly in the early stage of osteonecrosis of the femoral head. However, osteoblast and osteoclast activities had already changed in different regions of the osteonecrotic femoral head, which may lead to eventual collapse of the femoral head. Therefore, osteonecrosis of the femoral head must be treated during the early stage. In addition, osteoblast activity should be promoted and osteoclast activity inhibited as early as possible to prevent collapse of an osteonecrotic femoral head.

Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.