This product is isolated and purified from the herbs of Blumea balsamifera
Ursodeoxycholyltaurine sodium/Sodium tauroursodesoxycholate/tauroursodeoxycholic acid/Tauroursodeoxycholate sodium salt/Ursodeoxycholyltaurine-Na salt/taxifolin 7-methyl ether/sodium tauroursodeoxychoate/sodium tauroursodeoxycholate/4H-1-Benzopyran-4-one, 2-(3,4-dihydroxyphenyl)-2,3-dihydro-3,5-dihydroxy-7-methoxy-, (2R,3R)-/(2R,3R)-2-(3,4-Dihydroxyphenyl)-3,5-dihydroxy-7-methoxy-2,3-dihydro-4H-chromen-4-one/Padmatin/tauroursodeoxycholic acid sodium salt
Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.
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provides coniferyl ferulate(CAS#:80453-44-7) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate
The associations between pulmonary diseases (asthma, chronic obstructive pulmonary disease [COPD], and tuberculosis [TB]) and subsequent lung cancer risk have been reported, but few studies have investigated the association with different histologic types of lung cancer.
Patients newly diagnosed with lung cancer from 2004 to 2008 were identified from the National Health Insurance Research Database in Taiwan. Histologic types of lung cancer were further confirmed using the Taiwan Cancer Registry Database. Cox proportional hazards regression was used to calculate the hazard ratio (HR) of asthma, COPD, and TB and to estimate the risk of specific types of lung cancer.
During the study period, 32,759 cases of lung cancer were identified from 15,219,024 insurants aged 20 years and older. In men and women, the adjusted HR estimates of squamous cell carcinoma were respectively 1.37 (95 % confidence interval [CI], 1.21-1.54) and 2.10 (95 % CI, 1.36-3.23) for TB, 1.52 (95 % CI, 1.42-1.64) and 1.50 (95 % CI, 1.21-1.85) for asthma, and 1.66 (95 % CI, 1.56-1.76) and 1.44 (95 % CI, 1.19-1.74) for COPD. Similarly, the adjusted HR estimates of adenocarcinoma were respectively 1.33 (95 % CI, 1.19-1.50) and 1.86 (95 % CI, 1.57-2.19) for TB, 1.13 (95 % CI, 1.05-1.21) and 1.18 (95 % CI, 1.09-1.28) for asthma, and 1.50 (95 % CI, 1.42-1.59) and 1.33 (95 % CI, 1.25-1.42) for COPD. The HRs of small cell carcinoma were respectively 1.24 (95 % CI, 1.01-1.52) and 2.23 (95 % CI, 1.17-4.25) for TB, 1.51 (95 % CI, 1.35-1.69) and 1.63 (95 % CI, 1.16-2.27) for asthma, and 1.39 (95 % CI, 1.26-1.53) and 1.78 (95 % CI, 1.33-2.39) for COPD.
Asthma, COPD, and TB were associated with an increased risk of all major subtypes of lung cancer. The risk was the highest among women with TB.
Asthma, Chronic obstructive pulmonary disease, Lung adenocarcinoma, Small cell carcinoma, Squamous cell carcinoma, Tuberculosis
The effects of pulmonary diseases on histologic types of lung cancer in both sexes: a population-based study in Taiwan
Jing-Yang Huang,1 Zhi-Hong Jian,1 Oswald Ndi Nfor,1 Wen-Yuan Ku,1 Pei-Chieh Ko,1 Chia-Chi Lung,1,2 Chien-Chang Ho,3 Hui-Hsien Pan,4,5 Chieh-Ying Huang,6 Yu-Chiu Liang,7 and Yung-Po Liawcorresponding author1,2
Significant variation in both patient case mix and the structure of care in kidney transplantation has been previously described in the United States.
The objective of our study was to characterize patient case mix, patterns of care, and inpatient outcomes across 5 kidney transplant centers in the province of Ontario, Canada.
This was a retrospective population-based cohort study using health care administrative databases.
The setting is Ontario, Canada.
We included adult (≥18 years) transplant recipients who received a primary, solitary kidney between January 1, 2000, and December 31, 2013 (N = 5037).
Using linked administrative health care databases, we characterized kidney transplant recipient and donor factors, center characteristics, provider characteristics, and inpatient outcomes across transplant centers in Ontario. To compare case mix-adjusted differences in length of stay across centers, multivariable Cox proportional hazards regression was used to obtain hazard ratios (HRs) for each center relative to the average across all centers. Center volume and provider characteristics were added to the models to examine whether these factors explain differences in length of stay across centers.
We noted significant differences across transplant centers in patient race, cause of end-stage renal disease, body mass index, comorbidities, time on dialysis, and donor type. Mean annual transplant center volumes during the study period ranged between 51.5 (9.3) and 101.7 (23.9) transplants/year across centers (P < .0001). Physician specialty most responsible for in-hospital transplant care varied significantly across centers with the most common combination being nephrologist and urologist. Less than 31 deaths occurred in hospital during the index transplant admission but mortality risk did not differ significantly between centers. Overall, 25.1% of recipients required dialysis in hospital post transplantation (range across centers 18.3%-33.5%, P < .0001) and 24.7% of recipients spent time in the intensive care unit (ICU; range across centers: 5.7%-58.0%, P < .0001). The proportion of participants requiring dialysis did not change with time (P = .12), whereas the proportion staying in the ICU increased steadily over time (P < .0001). The median length of stay in hospital after transplantation ranged from 7 to 9 days across centers (P < .0001) and decreased significantly over time. After adjusting for patient case mix as well as center and provider factors, HRs for length of stay censored at the time of death ranged between 0.75 (95% confidence interval [CI]: 0.69-0.82) and 1.29 (95% CI: 1.20-1.38) across centers. Center volume and provider experience were not independently associated with length of hospital stay.
Data were missing (0.8%-18.4%) for certain covariates of interest.
This study found significant heterogeneity across kidney transplant centers in case mix, practice patterns, and inpatient outcomes. Future studies are needed to examine the influence of length of stay and practice patterns on long-term outcomes such as patient/graft survival and quality of life.
kidney transplantation, center variation, health services delivery, in-hospital outcomes
Case Mix, Patterns of Care, and Inpatient Outcomes Among Ontario Kidney Transplant Centers: A Population-Based Study
Anne Tsampalieros,1,2 Greg A. Knoll,1,3 Stephanie Dixon,4,5 Shane English,1,6 Douglas Manuel,7 Carl Van Walraven,1,8,9 Monica Taljaard,1,10 and Dean Fergusson1
The goal of our work has been to investigate the mechanisms of gender-independent human skin ageing and examine the hypothesis of skin being an adequate model of global ageing. For this purpose, whole genome gene profiling was employed in sun-protected skin obtained from European Caucasian young and elderly females (mean age 26.7±4 years [n1 = 7] and 70.75±3.3 years [n2 = 4], respectively) and males (mean age 25.8±5.2 years [n3 = 6] and 76±3.8 years [n4 = 7], respectively) using the Illumina array platform. Confirmation of gene regulation was performed by real-time RT-PCR and immunohistochemistry. 523 genes were significantly regulated in female skin and 401 genes in male skin for the chosen criteria. Of these, 183 genes exhibited increased and 340 decreased expression in females whereas 210 genes showed increased and 191 decreased expression in males with age. In total, 39 genes were common in the target lists of significant regulated genes in males and females. 35 of these genes showed increased (16) or decreased (19) expression independent of gender. Only 4 overlapping genes (OR52N2, F6FR1OP2, TUBAL3 and STK40) showed differential regulation with age. Interestingly, Wnt signalling pathway showed to be significantly downregulated in aged skin with decreased gene and protein expression for males and females, accordingly. In addition, several genes involved in central nervous system (CNS) ageing (f.i. APP, TAU) showed to be expressed in human skin and were significanlty regulated with age. In conclusion, our study provides biomarkers of endogenous human skin ageing in both genders and highlight the role of Wnt signalling in this process. Furthermore, our data give evidence that skin could be used as a good alternative to understand ageing of different tissues such as CNS.
Identification of Biomarkers of Human Skin Ageing in Both Genders. Wnt Signalling - A Label of Skin Ageing?
Evgenia Makrantonaki,# 1 , 2 Thore C. Brink,# 3 Vasiliki Zampeli, 1 , 2 Rana Mohsen Elewa, 1 , 4 Barbara Mlody, 3 Amir M. Hossini, 1 Bjoern Hermes, 2 Ulf Krause, 5 Juergen Knolle, 5 Marwa Abdallah, 4 James Adjaye, 3 , 6 and Christos C. Zouboulis 1 , *