RESUMEN
To investigate the quantitative relationship between the pyrolysis characteristics and chemical components of tobacco materials, various machine learning methods were used to establish a quantitative analysis model of tobacco. The model relates the thermal weight loss rate to 19 chemical components, and identifies the characteristic temperature intervals of the pyrolysis process that significantly relate to the chemical components. The results showed that: 1) Among various machine learning methods, partial least squares (PLS), support vector regression (SVR) and Gaussian process regression (GPR) demonstrated superior regression performance on thermogravimetric data and chemical components. 2) The PLS model showed the best performance on fitting and prediction effects, and has good generalization ability to predict the 19 chemical components. For most components, the determination coefficients R 2 are above 0.85. While the performance of SVR and GPR models was comparable, the R 2 for most chemical components were below 0.75. 3) The significant temperature intervals for various chemical components were different, and most of the affected temperature intervals were within 130°C-400°C. The results can provide a reference for the materials selection of cigarette and reveal the possible interactions of various chemical components of tobacco materials in the pyrolysis process.
RESUMEN
BACKGROUND: Gallic acid (GA) has an anti-inflammatory effect by regulating inflammatory molecules. This study aimed to investigate the effect of GA on atopic dermatitis (AD)-like skin inflammation. METHODS: 4-dinitrochlorobenzene (DNCB) was used to induce an AD-like skin inflammation model. The effect of GA on DNCB-induced inflammation was assessed by measuring the thickness and histopathological examination of the ear. Serum IgE and TNF-α levels were detected. The effect of GA on lymph nodes was determined by measuring the weights and mRNA/protein expression levels of TNF-α, IL-4, IFN-γ and IL-17. Ratio of Treg cells and Th17 cells was also analyzed. RESULTS: It was found that the thickness and pathology of the ear were significantly improved by GA in the DNCB-induced mice. Serum IgE and TNF-α levels were significantly reduced in GA-treated model mice compared to the model group. GA treatment lowered the weight of lymph node and the expression of mRNAs of TNF-α, IL-4, IFN-γ, and IL-17 of lymph node. In the ear, inflammatory factors (IL-4, IL-5, IL-17, or IL-23) showed a significant decrease in GA-treated model mice versus model mice, while the expression levels of IL-10 and TGF-ß showed a great increase in GA-treated model mice. ROR-γt showed a decrease in GA-treated model group, along with an increase expression of SOCS3. CONCLUSION: GA could ameliorate AD-like skin inflammation possibly through Th17 mediated immune regulation in a DNCB-induced mouse model.
RESUMEN
BACKGROUND Cutaneous squamous cell carcinoma (cSCC) is the second most widespread cancer in humans and its incidence is rising. Novel therapy with better efficacy is needed for clinical treatment of cSCC. Many studies have shown the importance of DNA repair pathways during the development of cancer. A key nucleotide excision repair (NER) protein, xeroderma pigmentosum group D (XPD), is responsible for the excision of a large variety of bulky DNA lesions. MATERIAL AND METHODS To explore the role of XPD in A431 cells, we overexpressed XPD in A431 cells and performed MTT assay, flow cytometry, and Western blot analysis to examine cell proliferation, cell apoptosis, and genes expression. RESULTS We found that the overexpression of XPD suppressed cell viability, induced cell cycle arrest at G1 phase, and promoted cell apoptosis. Additionally, XPD blocked the expression of c-myc, cdc25A, and cdk2, and improved the levels of HIPK2 and p53. CONCLUSIONS These results provide new evidence to reveal the role of XPD in cSCC A431 cells and suggest that XPD may serve as an anti-oncogene during cSCC development.
