Human tenocytes are stimulated to proliferate by acetylcholine through an EGFR signalling pathway

Studies of human patellar and Achilles tendons have shown that primary tendon fibroblasts (tenocytes) not only have the capacity to produce acetylcholine (ACh) but also express muscarinic ACh receptors (mAChRs) through which ACh can exert its effects. In patients with tendinopathy (chronic tendon pain) with tendinosis, the tendon tissue is characterised by hypercellularity and angiogenesis, both of which might be influenced by ACh. In this study, we have tested the hypothesis that ACh increases the proliferation rate of tenocytes through mAChR stimulation and have examined whether this mechanism operates via the extracellular activation of the epidermal growth factor receptor (EGFR), as shown in other fibroblastic cells. By use of primary human tendon cell cultures, we identified cells expressing vimentin, tenomodulin and scleraxis and found that these cells also contained enzymes related to ACh synthesis and release (choline acetyltransferase and vesicular acetylcholine transporter). The cells furthermore expressed mAChRs of several subtypes. Exogenously administered ACh stimulated proliferation and increased the viability of tenocytes in vitro. When the cells were exposed to atropine (an mAChR antagonist) or the EGFR inhibitor AG1478, the proliferative effect of ACh decreased. Western blot revealed increased phosphorylation, after ACh stimulation, for both EGFR and the extracellular-signal-regulated kinases 1 and 2. Given that tenocytes have been shown to produce ACh and express mAChRs, this study provides evidence of a possible autocrine loop that might contribute to the hypercellularity seen in tendinosis tendon tissue

Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak

Epidemic is a rapid and wide spread of infectious disease threatening many lives and economy damages. It is important to fore-tell the epidemic lifetime so to decide on timely and remedic actions. These measures include closing borders, schools, suspending community services and commuters. Resuming such curfews depends on the momentum of the outbreak and its rate of decay. Being able to accurately forecast the fate of an epidemic is an extremely important but difficult task. Due to limited knowledge of the novel disease, the high uncertainty involved and the complex societal-political factors that influence the widespread of the new virus, any forecast is anything but reliable. Another factor is the insufficient amount of available data. Data samples are often scarce when an epidemic just started. With only few training samples on hand, finding a forecasting model which offers forecast at the best efforts is a big challenge in machine learning. In the past, three popular methods have been proposed, they include 1) augmenting the existing little data, 2) using a panel selection to pick the best forecasting model from several models, and 3) fine-tuning the parameters of an individual forecasting model for the highest possible accuracy. In this paper, a methodology that embraces these three virtues of data mining from a small dataset is proposed. An experiment that is based on the recent coronavirus outbreak originated from Wuhan is conducted by applying this methodology. It is shown that an optimized forecasting model that is constructed from a new algorithm, namely polynomial neural network with corrective feedback (PNN+cf) is able to make a forecast that has relatively the lowest prediction error. The results showcase that the newly proposed methodology and PNN+cf are useful in generating acceptable forecast upon the critical time of disease outbreak when the samples are far from abundant

Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.Comment: 19 page

Substance P Is a Mechanoresponsive, Autocrine Regulator of Human Tenocyte Proliferation

It has been hypothesised that substance P (SP) may be produced by primary fibroblastic tendon cells (tenocytes), and that this production, together with the widespread distribution of the neurokinin-1 receptor (NK-1 R) in tendon tissue, could play an important role in the development of tendinopathy, a condition of chronic tendon pain and thickening. The aim of this study was to examine the possibility of endogenous SP production and the expression of NK-1 R by human tenocytes. Because tendinopathy is related to overload, and because the predominant tissue pathology (tendinosis) underlying early tendinopathy is characterized by tenocyte hypercellularity, the production of SP in response to loading/strain and the effects of exogenously administered SP on tenocyte proliferation were also studied. A cell culture model of primary human tendon cells was used. The vast majority of tendon cells were immunopositive for the tenocyte/fibroblast markers tenomodulin and vimentin, and immunocytochemical counterstaining revealed that positive immunoreactions for SP and NK-1 R were seen in a majority of these cells. Gene expression analyses showed that mechanical loading (strain) of tendon cell cultures using the FlexCell© technique significantly increased the mRNA levels of SP, whereas the expression of NK-1 R mRNA decreased in loaded as compared to unloaded tendon cells. Reduced NK-1 R protein was also observed, using Western blot, after exogenously administered SP at a concentration of 10−7 M. SP exposure furthermore resulted in increased cell metabolism, increased cell viability, and increased cell proliferation, all of which were found to be specifically mediated via the NK-1 R; this in turn involving a common mitogenic cell signalling pathway, namely phosphorylation of ERK1/2. This study indicates that SP, produced by tenocytes in response to mechanical loading, may regulate proliferation through an autocrine loop involving the NK-1 R

Efeito dos aditivos do tabaco na iniciação e manutenção do tabagismo

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Flavan-3-ol-methylxanthine interactions: Modulation of flavan-3-ol bioavailability in volunteers with a functional colon and an ileostomy

Flavan-3-ols, including the flavan-3-ol monomer (-)-epicatechin, are dietary bioactives known to mediate beneficial cardiovascular effects in humans. Recent studies showed that flavan-3-ols could interact with methylxanthines, evidenced by an increase in flavan-3-ol bioavailability with a concomitant increase in flavan-3-ol intake-mediated vascular effects. This study aimed at elucidating flavan-3-ol-methylxanthine interactions in humans in vivo by evaluating the specific contributions of theobromine and caffeine on flavan-3-ol bioavailability. In ileostomists, the effect of methylxanthines on the efflux of flavan-3-ol metabolites in the small intestine was assessed, a parameter important to an understanding of the pharmacokinetics of flavan-3-ols in humans. In a randomized, controlled, triple cross-over study in volunteers with a functional colon (n = 10), co-ingestion of flavan-3-ols and cocoa methylxanthines, mainly represented by theobromine, increased peak circulatory levels (C ) of flavan-3-ols metabolites (+21 ± 8%; p < 0.05). Conversely, caffeine did not mediate a statistically significant effect on flavan-3-ol bioavailability (C = +10 ± 8%, p = n.s.). In a subsequent randomized, controlled, double cross-over study in ileostomists (n = 10), cocoa methylxanthines did not affect circulatory levels of flavan-3-ol metabolites, suggesting potential differences in flavan-3-ol bioavailability compared to volunteers with a functional colon. The main metabolite in ileal fluid was (-)-epicatechin-3'-sulfate, however, no differences in flavan-3-ol metabolites in ileal fluid were observed after flavan-3-ol intake with and without cocoa methylxanthines. Taken together, these results demonstrate a differential effect of caffeine and theobromine in modulating flavan-3-ol bioavailability when these bioactives are co-ingested. These findings should be considered when comparing the effects mediated by the intake of flavan-3-ol-containing foods and beverages and the amount and type of methylxanthines present in the ingested matrixes. Ultimately, these insights will be of value to further optimize current dietary recommendations for flavan-3-ol intake. CLINICAL TRIAL REGISTRATION NUMBER: This work was registered at clinicaltrials.gov as NCT03526107 (study part 1, volunteers with functional colon) and NCT03765606 (study part 2, volunteers with an ileostomy). [Abstract copyright: Copyright © 2023 Elsevier Inc. All rights reserved.

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts