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provides coniferyl ferulate(CAS#:97240-03-4) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate
The objective of this study is to investigate the synergistic effects of acid etching and metal-ion chelation in dental smear layer removal using wasted Ganoderma tsugae derived chitosans. The wasted Ganoderma tsugae fruiting body was used to prepare both acid-soluble fungal chitosan (FCS) and alkali-soluble polysaccharide (ASP). To explore the effective irrigant concentration for smear layer removal, a chelating effect on ferrous ions was conducted. Specimens of various concentrations of EDTA, citric acid, and polysaccharide solutions were reacted with FerroZine™ then the absorbance was examined at 562 nm by a UV-visible spectrophotometer to calculate their metal chelating capability. Twenty extracted premolars were instrumented and individually soaked in the solutions of 15 wt% EDTA, 10 wt% citric acid, 0.04 wt% ASP, 0.04 wt% FCS, and normal saline were randomly divided into five groups (N=4). Next, each tooth was cleaved longitudinally and examined by scanning electron microscopy (SEM) to assay the effectiveness of smear layer removal. The chelating capability for EDTA, FCS, and ASP showed no significant difference over the concentration of 0.04 wt% (p > 0.05). The SEM results showed that 0.04 wt% FCS solution was effective in smear layer removal along the canal wall. These results indicated that Ganoderma tsuage derived FCS in acid solutions could be a potential alternative as a root canal irrigant solution due to its synergistic effect.
EDTA, polysaccharide, chelating effect, smear layer removal
Wasted Ganoderma tsugae Derived Chitosans for Smear Layer Removal in Endodontic Treatment
Sheng-Tung Huang,1,2 Nai-Chia Teng,3 Hsin-Hui Wang,4 Sung-Chih Hsieh,3,4,* and Jen-Chang Yang2,5,6,7,*
2019 Nov; 1
Multimorbidity is a major challenge for healthcare systems. However, currently, its magnitude and impact in healthcare expenditures is still mostly unknown.
To present an overview of the prevalence and costs of multimorbidity by socioeconomic levels in the whole Basque population.
We develop a cross-sectional analysis that includes all the inhabitants of the Basque Country (N = 2,262,698). We utilize data from primary health care electronic medical records, hospital admissions, and outpatient care databases, corresponding to a 4 year period. Multimorbidity was defined as the presence of two or more chronic diseases out of a list of 52 of the most important and common chronic conditions given in the literature. We also use socioeconomic and demographic variables such as age, sex, individual healthcare cost, and deprivation level. Predicted adjusted costs were obtained by log-gamma regression models.
Multimorbidity of chronic diseases was found among 23.61% of the total Basque population and among 66.13% of those older than 65 years. Multimorbid patients account for 63.55% of total healthcare expenditures. Prevalence of multimorbidity is higher in the most deprived areas for all age and sex groups. The annual cost of healthcare per patient generated for any chronic disease depends on the number of coexisting comorbidities, and varies from 637 € for the first pathology in average to 1,657 € for the ninth one.
Multimorbidity is very common for the Basque population and its prevalence rises in age, and unfavourable socioeconomic environment. The costs of care for chronic patients with several conditions cannot be described as the sum of their individual pathologies in average. They usually increase dramatically according to the number of comorbidities. Given the ageing population, multimorbidity and its consequences should be taken into account in healthcare policy, the organization of care and medical research.
Prevalence and Costs of Multimorbidity by Deprivation Levels in the Basque Country: A Population Based Study Using Health Administrative Databases
Juan F. Orueta, 1 , * Arturo Garcia-alvarez, 2 Manuel Garcia-GoNi, 3 Francesco Paolucci, 4 , 5 and Roberto NuNo-Solinis 2 C. Mary Schooling, Editor
The in vitro MultiFlow DNA Damage assay multiplexes p53, γH2AX, phospho-histone H3, and polyploidization biomarkers into 1 flow cytometric analysis (Bryce, S. M., Bernacki, D. T., Bemis, J. C., and Dertinger, S. D. (2016). Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach. Environ. Mol. Mutagen. 57, 171-189). The work reported herein evaluated the generalizability of the method, as well as several data analytics strategies, to a range of chemical classes not studied previously. TK6 cells were exposed to each of 103 diverse chemicals, 86 of which were supplied by the National Toxicology Program (NTP) and selected based upon responses in genetic damage assays conducted under the Tox21 program. Exposures occurred for 24 h over a range of concentrations, and cell aliquots were removed at 4 and 24 h for analysis. Multiplexed response data were evaluated using 3 machine learning models designed to predict genotoxic mode of action based on data from a training set of 85 previously studied chemicals. Of 54 chemicals with sufficient information to make an a priori call on genotoxic potential, the prediction models’ accuracies were 79.6% (random forest), 88.9% (logistic regression), and 90.7% (artificial neural network). A majority vote ensemble of the 3 models provided 92.6% accuracy. Forty-nine NTP chemicals were not adequately tested (maximum concentration did not approach assay’s cytotoxicity limit) and/or had insufficient conventional genotoxicity data to allow their genotoxic potential to be defined. For these chemicals MultiFlow data will be useful in future research and hypothesis testing. Collectively, the results suggest the MultiFlow assay and associated data analysis strategies are broadly generalizable, demonstrating high predictability when applied to new chemicals and classes of compounds.
genotoxicity, mode of action, flow cytometry, machine learning, Tox21
Investigating the Generalizability of the MultiFlow ® DNA Damage Assay and Several Companion Machine Learning Models With a Set of 103 Diverse Test Chemicals
Steven M Bryce,1 Derek T Bernacki,1 Stephanie L Smith-Roe,2 Kristine L Witt,2 Jeffrey C Bemis,1 and Stephen D Dertinger1