Ameliorative and anti-arthritic potential of arjunolic acid against complete Freund’s adjuvant-induced arthritis in rats

Purpose: To determine the anti-arthritic effect of arjunolic acid against complete Freund’s adjuvant (CFA)-induced arthritis in rats.Methods: Arthritis was induced in male Sprague Dawley rats by intradermal injection of 0.1 mL of CFA at the right footpad. Upon induction of osteoarthritis, arjunolic acid was administered via oral gavage at doses of 40 and 80 mg/kg once daily for 25 successive days. Indomethacin was used as reference drug at a dose of 3 mg/kg via gavage twice weekly for 25 days. Changes in paw swelling, serum hematology, antioxidant enzymes, serum inflammatory mediators, and histopathology were determined using standard procedures.Results: Paw swelling and weight loss in CFA-induced arthritic rats were significantly reversed (p < 0.01) by arjunolic acid. Malondialdehyde (MDA) levels, spleen index and thymus index were significantly reduced in CFA-induced arthritic rats (p < 0.01). Moreover, arjunolic significantly increased superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) activities, while downregulating the expressions of TNF-α, IL-1β and IL-6 in serum (p < 0.01). The hematological and histopathological changes due to CFA-induced arthritis were ameliorated by arjunolic acid.Conclusion: The results obtained in this study indicate that arjunolic acid may possess therapeutic potentials for the management of arthritis. Keywords: Arjunolic acid, Triterpenoid; Oxidative stress, osteoarthritis, Inflammatio

Value of machine learning model based on MRI radiomics in predicting histological grade of cervical squamous cell carcinoma

Objective To explore the predictive value of different machine learning models based on MRI radiomics combined with clinical features for histological grade of cervical squamous cell carcinoma. Methods Clinical data of 150 patients with cervical squamous cell carcinoma confirmed by pathological biopsy were retrospectively analyzed. They were randomly divided into the training set and validation set at a ratio of 4∶1. Features were extracted from the regions of interest of T2WI fat suppression sequence （FS-T2WI） and enhanced T1WI （delayed phase）. After dimensionality reduction and feature selection， logistic regression （LR）， support vector machine （SVM）， naïve Bayes （NB）， random forest （RF）， Light Gradient Boosting Machine （LightGBM）， K-nearest neighbor （KNN） were used to construct a radiomics model for predicting the histological grade of cervical squamous cell carcinoma. The area under the receiver operating characteristic （ROC） curve （AUC） was used to evaluate the predictive performance of the six models. Univariate and multivariate logistic regression analyses were performed to predict the independent risk factors， and a combined model of clinical and radiomics was established. The differences of each model were compared by AUC， and the clinical value of the model was evaluated by decision curve （DCA）. Results In the radiomics model， the LightGBM model had the largest AUC （0.910 in the training set， and 0.839 in the validation set）. The AUC of clinical features combined with LightGBM model was the largest （0.935 in the training set， and 0.888 in the validation set）， which was higher than those of clinical model （0.762 in the training set， and 0.710 in the validation set） and LightGBM radiomics model. Conclusions The LightGBM model has a high predictive value in the radiomics model. The combined model has the optimal DCA effect and the highest clinical net benefit. The combined prediction model combining radiomics and clinical features has good predictive value for cervical squamous cell carcinoma with low differentiation， providing a non-invasive and efficient method for clinical decision-making

Decoding signaling mechanisms: unraveling the targets of guanylate cyclase agonists in cardiovascular and digestive diseases

Soluble guanylate cyclase agonists and guanylate cyclase C agonists are two popular drugs for diseases of the cardiovascular system and digestive systems. The common denominator in these conditions is the potential therapeutic target of guanylate cyclase. Thanks to in-depth explorations of their underlying signaling mechanisms, the targets of these drugs are becoming clearer. This review explains the recent research progress regarding potential drugs in this class by introducing representative drugs and current findings on them

A Single-Cell Taxonomy Predicts Inflammatory Niche Remodeling to Drive Tissue Failure and Outcome in Human AML

Cancer initiation is orchestrated by an interplay between tumor-initiating cells and their stromal/immune environment. Here, by adapted single-cell RNA sequencing, we decipher the predicted signaling between tissue-resident hematopoietic stem/progenitor cells (HSPC) and their neoplastic counterparts with their native niches in the human bone marrow. LEPR + stromal cells are identified as central regulators of hematopoiesis through predicted interactions with all cells in the marrow. Inflammatory niche remodeling and the resulting deprivation of critical HSPC regulatory factors are predicted to repress high-output hematopoietic stem cell subsets in NPM1-mutated acute myeloid leukemia (AML), with relative resistance of clonal cells. Stromal gene signatures reflective of niche remodeling are associated with reduced relapse rates and favorable outcomes after chemotherapy across all genetic risk categories. Elucidation of the intercellular signaling defining human AML, thus, predicts that inflammatory remodeling of stem cell niches drives tissue repression and clonal selection but may pose a vulnerability for relapse-initiating cells in the context of chemotherapeutic treatment.</p

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Grey Wolf Optimizer for Variable Selection in Quantification of Quaternary Edible Blend Oil by Ultraviolet-Visible Spectroscopy

A novel swarm intelligence algorithm, discretized grey wolf optimizer (GWO), was introduced as a variable selection tool in edible blend oil analysis for the first time. In the approach, positions of wolves were updated and then discretized by logical function. The performance of a wolf pack, the iteration number and the number of wolves were investigated. The partial least squares (PLS) method was used to establish and predict single oil contents in samples. To validate the method, 102 edible blend oil samples containing soybean oil, sunflower oil, peanut oil and sesame oil were measured by an ultraviolet-visible (UV-Vis) spectrophotometer. The results demonstrated that GWO-PLS models can provide best prediction accuracy with least variables compared with full-spectrum PLS, Monte Carlo uninformative variable elimination-PLS (MCUVE-PLS) and randomization test-PLS (RT-PLS). The determination coefficients (R2) of GWO-PLS were all above 0.95. Therefore, the research indicates the feasibility of using discretized GWO for variable selection in rapid determination of quaternary edible blend oil

Grain refinement and weak-textured structures based on the dynamic recrystallization of Mg–9.80Gd–3.78Y–1.12Sm–0.48Zr alloy

We utilized electron backscatter diffraction to investigate the microstructure evolutions of a newly developed magnesium-rare earth alloy (Mg–9.80Gd–3.78Y–1.12Sm–0.48Zr) during instantaneous hot indirect extrusion. An equiaxed fine-grained (average grain size of 3.4 ± 0.2 µm) microstructure with a weak texture was obtained. The grain refinement was mainly attributed to the discontinuous dynamic recrystallization (DDRX) and continuous DRX (CDRX) processes during the hot indirect extrusion process. The twin boundaries formed during the initial deformation stage effectively increased the number of high angle grain boundaries (HAGBs), which provided sites for new grain nuclei, and hence, resulted in an improved DDRX process. Along with DDRX, CDRX processes characterized by low angle grain boundary (LAGB) networks were also observed in the grain interior due to effective dynamic recovery (DRV) at a relatively high temperature of 773 K and high strain rates. Thereafter, LAGB networks were transformed into HAGB networks by the progressive rotation of subgrains during the CDRX process

Statistical Analysis of Non Linear Least Squares Estimation for Harmonic Signals in Multiplicative and Additive Noise

<div><p>In this paper we consider the problem of parameter estimation for the multicomponent harmonic signals in multiplicative and additive noise. The nonlinear least squares (NLLS) estimators, NLLS₁ and NLLS₂ proposed by Ghogho et al. (<a href="#cit0014" target="_blank">1999b</a>) to estimate the coherent model parameters for single-component harmonic signal, are generalized to the multicomponent harmonic signals for the cases of nonzero- and zero-mean multiplicative noise, respectively. By statistical analysis, some asymptotic results of the NLLS estimators are derived, including the strong consistency, the strong convergence rate and the asymptotic normality. Furthermore, the NLLS₁- and NLLS₂- based estimators are proposed to estimate the noncoherent model parameters for the cases of nonzero- and zero-mean multiplicative noise, respectively, meanwhile the strong consistency and the asymptotic normality of the NLLS-based estimators are also derived. Finally some numerical experiments are performed to see how the asymptotic results work for finite sample sizes.</p></div