ABSTRACT
Objective To predict the potential suitable growth areas of Epimedium brevicornu Maxim.under current climate conditions in China;To provide basis for the sustainable utilization of Epimedium brevicornu Maxim.resources and production planning.Methods Based on 267 screened species distribution data of Epimedium brevicornu Maxim.and 8 environmental factors,the MaxEnt parameters were optimized by the R language kuenm package.The main environmental factors that affect the distribution of Epimedium brevicornu Maxim.was analyzed and its potential distribution range was predicted.Results The omission rate of the optimal model operation results was 0.044 8,AICc=6 409.884 5,AUC=0.986,indicating a high accuracy of the model.Based on the contribution rate of environmental factors and the knife cut method,it was indicated that the key environmental factors affecting the current distribution of Epimedium brevicornu Maxim.were mainly the average precipitation in July,the average highest temperature in February,the average precipitation in September,the coldest season precipitation,the coefficient of variation of precipitation,the standard deviation of seasonal temperature changes,and the warmest season precipitation.The simulation results showed that the total suitable area for Epimedium brevicornu Maxim.in China under current climate conditions was approximately 221.14×104 km2,concentrated in the central and central southern regions of China.The area of the high suitability zone was approximately 23.13×104 km2;the area of the suitable growth zone was approximately 73.78×104 km2;the area of low suitability zone was approximately 124.22×104 km2.Conclusion This study provides a basis and reference for the artificial cultivation planning and sustainable utilization of Epimedium brevicornu Maxim.resources.
ABSTRACT
Objective To investigate the establishment and optimization of guinea pigs model of cholecystitis,so as to provide the basis for further optimization of experimental design and research on the pathogenesis,treatment and mechanism of cholecystitis.Methods The guinea pigs model of cholecystitis was established by subcutaneous injection of lincomycin hydrochloride,the general condition,B-ultrasound of the gallbladder,blood routine indicators and hepatobiliary histology of the guinea pigs were observed to determine the stabili-ty and application prospect of the cholecystitis model.Results The general state observation showed that guinea pigs in the model group exhibited varying degrees of diet decrease,body weight loss,activity decrease,loose and lusterless hair,listlessness or irritability.B-ul-trasound results showed that,compared with the control group of guinea pigs,with the higher modeling dose,the thickness of the gallblad-der wall were more obvious(P＜0.05).The blood routine index detection found that,with the higher modeling dose,the blood inflamma-tory indicators such as white blood cell count(WBC),lymphocyte(LYM),neutrophil(NEUT),monocyte(MONO)were increased sig-nificantly(P＜0.05).The results of hematoxylin-eosin staining showed that,with the higher modeling dose,pathological changes such as structural changes of gallbladder and liver tissue structure,inflammatory cell infiltration,degeneration and necrosis of liver cells were more obvious.Conclusion In this study,the imaging indicators combined with blood and pathological indicators were used for multi-factor comprehensive evaluation of the modeling effect of cholecystitis and the stability of the inflammatory model,which was more intui-tive,accurate and low-cost than the conventional gallbladder pathological analysis method or combined with enzyme-linked immuno-sorptive assay,and provided a scientific and effective detection index for the evaluation of therapeutic effects of drugs.At the same time,the model with the dose of 120mg/kg lincomycin hydrochloride for guinea pigs cholecystitis was the most stable and difficult to self-heal.
ABSTRACT
Most drugs need to interact with cell membrane to reach the biological target, so that membrane affinity assay is an important early screening step in drug discovery. However, at present, the traditional oil-water distribution method is still used, a new, simple and accurate method for membrane affinity assay is urgently needed. In this study, according to the colorimetric principle, a new assay model based on polydiacetylene vesicles was optimized through a series of experiments including different concentrations of vesicle solution, temperature, or pH reaction environment. On this basis, tetracaine hydrochloride, 2-methylimidazole and histamine were used as model drugs to measure the membrane affinity constants and verify the between-batch precision of the optimized assay model (relative standard deviation less than 5%). In addition, polydiacetylene vesicles were stable for up to 180 days, demonstrating the potential application of the assay model. This strategy is simple, stable, reliable, with high reproducibility, low cost and easy to promote, which provided a new tool and a new direction for the high-throughput assay of membrane affinity.
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Near infrared spectroscopy (NIR) was used to detect trans fatty acids (TFA) in edible vegetable oils quantitatively. And prediction model of TFA was optimized through band selection, pretreatment method, variable selection and modeling method. NIR spectra of 98 edible vegetable oil samples were collected in spectral range of 4000-10000 cm-1 using an Antaris Ⅱ Fourier transform near infrared spectrometer, and the true content of TFA was measured by gas chromatography. First, optimization of waveband and pretreatment method was conducted on original spectra. On this basis, competitive adaptive reweighted sampling (CARS) was used to select important variables that related to TFA. Finally, the prediction models of TFA content in edible vegetable oils were established using principal component regression ( PCR), partial least square (PLS) and least square support vector machine (LS-SVM). The results indicated that NIR spectroscopy was feasible for detecting TFA content in edible vegetable oils, R2 of the best prediction model after optimized in calibration and prediction sets were 0. 992 and 0. 989, and root mean square error of calibration (RMSEC) and root mean square error of prediction ( RMSEP) were 0. 071% and 0. 075% , respectively. Only 26 variables were used in the best prediction model, accounting for 0. 854% of the whole waveband variables. In addition, compared with the full waveband PLS prediction model, the R2 in prediction set increased from 0. 904 to 0. 989, and RMSEP decreased from 0. 230% to 0. 075% . It shows that model optimization is very necessary, CARS method can select important variables related to TFA effectively and immensely reduce the number of modeling variables, so it can simplify the prediction model, and greatly improve the accuracy and stability of prediction model.
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This article focused on basic clinical skill training of Changhai Hospital,in such aspects as courses,teaching infrastructure,faculty competence,teaching pattern,evaluation system and incentives mechanism.These efforts aim at exploring the establishment of a new clinical skills teaching model,which prove satisfactory and improving the standardized residents basic clinical skill training system.