高效液相色谱法测定复方环丙沙星滴鼻液中3组分含量

目的　测定复方环丙沙星滴鼻液中乳酸环丙沙星、马来酸氯苯那敏、盐酸麻黄碱的含量。方法　高效液相色谱法 ,采用YWG -C18(10 μm ,4 .6mm× 15 0mm)色谱柱 ,以 0 .0 1mol·L-1NaH2 PO4-甲醇 -乙腈 (30 0∶16 0∶4 0 ,三乙胺 0 .5ml,磷酸调pH为 3.0 )为流动相 ,检测波长为 2 5 7nm ,内标法峰高定量。结果　本文可同时测定 3组分的含量。乳酸环丙沙星 0 .0 12～ 0 .0 6mg·ml-1马来酸氯苯那敏 0 .0 3～ 0 .15mg·ml-1,盐酸麻黄碱 0 .1～ 0 .5mg·ml-1范围内 ,峰高比与其浓度呈良好的线性关系 ;平均回收率分别为 10 0 .18%、99.85 %、10 1.6 3% ;RSD分别为 0 .71%、1.2 0 %、1.10 %。结论　方法可靠 ,数据准确 ,灵敏度高 ,重现性好     

双波长分光光度法测定复方甲硝唑注射液中环丙沙星、甲硝唑的含量

复方甲硝唑注射液(Ⅰ)系我院研制开发的新制剂,0.05％环丙沙星(Ⅱ)和0.13％甲硝唑(Ⅲ)等组成,用于术后需氧菌和厌氧菌的混合感染。本文试用双波长分光光度法同时测定两组分含量,方法简便,结果满意,报告如下。     

复方麻黄颗粒中麻黄碱含量测定

目的：建立复方麻黄颗粒的质量控制方法。方法：采用薄层扫描法测定麻黄碱含量。结果：样品中麻黄碱含量为11.19mg/g，标准曲线Y＝3830．48X＋6229．85（r=0.997,n=6)，平均加样回收率＝99．4％。结论：该方法稳定可靠，可作为该品的制剂质量控制的检测方法。     

HPLC法同时测定复方环丙沙星滴鼻液中三组分的含量

目的建立同时测定复方环丙沙星滴鼻液中盐酸麻黄碱、乳酸环丙沙星、马来酸氯苯那敏含量的HPLC方法。方法采用：PurospherSTAR RP-18色谱柱（4.6 mm×150 mm,5μm）,以磷酸盐缓冲液（p H 3.0±0.1）-乙腈（82∶18）为流动相,调节流速为1.0 ml·min-1,在210 nm波长处同时检测3组分。结果盐酸麻黄碱、乳酸环丙沙星和马来酸氯苯那敏分别在20.14~161.12、12.05~96.38和6.18~49.47μg·ml-1浓度范围内线性关系良好,相关系数分别为0.9996、0.9999和0.9999,平均回收率分别为101.11%、99.83%和101.07%。结论该方法快速简便、重复性、耐用性好,可用于该制剂中3组分的测定。     

多波长直线回归法测定复方阿斯匹林中3组分的含量

复方阿斯匹林片为阿斯匹林、扑热息痛、咖啡因的复方制剂,广东药品标准［１］使用容量法,需分离提取,操作较繁,测定终点较难掌握,测定结果准确度差。本文采用多波长线性回归法［２］,用ＢＡＳＩＣ语言编制程序［３］,不经分离同时测定３组分的含量。本法简便、快速、测定结果与广东药品标准基本一致。１　基本原理　　对于３组分混合物：Ａｔ＝ε１Ｃ１＋ε２Ｃ２＋ε３Ｃ３（１）若在ｎ（ｎ≥２）个波长处,存在ε１＝ε２则（１）式变为：Ａｔε１＝Ｃ１＋Ｃ２＋ε３ε１Ｃ３（２）通过Ａｔ／ε１对ε３／ε１建立直线回归方程,其…     

高效液相色谱法测定复方替硝唑栓剂中两组分的含量

目的 建立高效液相色谱法分离并测定复方替硝唑栓剂中两组分的含量,为该制剂制订质量标准提供科学依据。方法 采用Symmetry C18柱（2．5cm×4．6mm,5μm）,流动相为甲醇：水：磷酸（65：35：0．1）,pH值=3．65,检测波长为282nm,流速为1．0ml／min。结果 替硝唑、克霉唑的浓度线性范围分别为41．50～207．50μg·ml^-1;21．22—106．10μg·ml^-1。平均回收率分别为97．63％、98．02％,RSD分别为0．85％、1．09％;结论 方法简便,结果准确,重现性好,可用于该制剂的质量控制。     

HPLC法同时测定复方蜂胶喷雾剂中两组分的含量

目的 建立高效液相色谱法分离并测定复方蜂胶喷雾剂中两组分柯因和甘草酸单铵盐的含量,为该制剂质量标准提供科学依据.方法 采用Symmetry C18(4.6mm×250mm,5μm),流动相为甲醇-水-磷酸(66:34:0.4%),pH值1.7,检测波长为246nm,流速1.0ml/min.结果 柯因、甘草酸单铵盐的浓度线性范围分别为0.12～0.36、8.0～16.0mg·ml-1.平均回收率分别为99.1%、101.5%,RSD分别为1.45%、2.06%.结论 方法简便,结果准确,重现性好,可用于该制剂的质量控制.     

分光光度法测定复方氧化锌洗剂中氧化锌的含量

目的　测定复方氧化锌洗剂中氧化锌的含量。方法　采用分光光度法 ,测定波长为 6 2 0nm。结果　氧化锌在 0 .5 1～2 .5 5 μg·ml-1(r =0 .9999)范围内 ,吸收度值与其浓度呈良好的线性关系 ,方法平均回收率为 99.79% ,RSD为 0 .4 8% (n =6 )。结论　方法简便易行、快速、准确 ,可作为该制剂的质量控制方法。     

复方乳酸钠注射液中乳酸钠含量测定方法的比较研究

目的：建立一种重现性好,操作相对简单的乳酸钠含量测定方法。方法：以离子交换法（药典法）作对照,进行氧化还原法,中和法和紫外分光光度法的回收率比较研究。结果：氧化还原法和中和法与离子交换法无显著性差异（P〉0.05）,紫外分光光度法与离子交换法有显著性差异（P〈0.05）。结论：药典法结果准确、重现性好,但操作繁琐;中和法操作简单快速,但终点不易观察;紫外分光光度法简便易行,但偏差较大。改进后的氧化还原法操作简便,结果准确可靠,重现性好,适合医院制剂的分析要求,是乳酸钠含量测定比较理想的方法。     

三钾合剂中总钾含量测定的不同方法比较

目的三钾合剂中总钾含量测定的不同方法比较,寻找钾含量测定的最佳方法。方法采用中和法、四苯硼钠重量法、火焰原子吸收分光光度法（FAAS）及电感耦合等离子体发射光谱法（ICP-AES）等测定三钾合剂中总钾的含量,并对方法的精密度、线性范围及加标回收率等进行比较。结果 4种方法在测定范围内呈现良好线性关系,回收率均在95%~105%范围内,RSD均小于0.1%。结论 FAAS法为测定三钾合剂中总钾含量的较优方法。     

Multivariate linear regression modelling of lung weight in 24,056 Swedish medico-legal autopsy cases

Heavy combined lung weight at autopsy is a non-specific autopsy finding associated with certain causes of death such as intoxication. There is however no clear definition of what constitutes “heavy” lung weight. Different reference values have been suggested but previous studies have been limited by small select populations and only univariate regression has been attempted. The aim of this study was to create a model to estimate lung weight from decedent parameters. We identified all cases >18 years age autopsied at the Swedish National Board of Forensic Medicine from 2000 through 2013, excluding cases with a post-mortem interval >5 days as well as cases with extreme values, totalling 24,056 cases. We analysed body weight, body height, sex, age, BMI, BSA as well as untransformed and transformed lung weight. The analysis was stratified for sex. We evaluated the fit of the models and that the model assumptions were not violated. We set out to apply the model with the highest residual sum of squares to derive limits for heavy lungs. In univariate regression BSA and height showed best performance. The final model included height, weight and age group. After excluding large standardized residuals (>3, <−3) the final model achieved R² of 0.132 and 0.106 for women and men respectively. While we managed to create a multivariate model its performance was poor, possibly a fact reflective of the physiological nature of the lungs and in turn its variability in fluid content. Linear regression is a poor model for estimating lung weight in an unselected population.     

复杂随机抽样数据的多重线性回归分析方法及其应用

目的：探讨综合权重在复杂随机抽样数据线性回归分析中的意义和作用。方法基于蒙特卡洛随机模拟思想,采用SAS中REG和SURVEYREG两个不同的多重线性回归分析过程,分别对同一批复杂随机抽样数据（ n＝6756）在不同随机抽样率条件下进行回归建模,对所得结果进行比较。结果在未考虑和考虑观测权重与抽样权重的多重线性回归模型拟合的结果中,自变量的偏回归系数、标准误及P值的大小均有所不同。结论在对基于不同抽样率的复杂随机抽样资料,尤其是分层随机抽样调查资料的回归建模中,采用多重线性回归模型拟合资料时,将调查数据的综合权重纳入统计分析,方能更准确、灵敏地进行回归系数的参数估计和对结果变量的统计预测。     

Prediction of mild anatomical leg length discrepancy based on gait kinematics and linear regression model

BackgoundLeg length discrepancy (LLD) can be related to different pathologies, due to an inadequate distribution of mechanical loads, as well as gait kinematics asymmetries resulted from LLD.Research questionTo validate a model to predict anatomical LLD (ALLD) based on gait kinematics.MethodsGait of 39 participants with different lower limb pathologies and mild discrepancy were collected. Pelvic, hip, knee and ankle kinematics were measured with a 3D motion analysis system and ALLD, femur discrepancy (FD) and tibia discrepancy (TD) were measured by a computerized digital radiograph. Three multiple linear regression models were used to identify the ability of kinematic variables to predict ALLD (model 1), FD (model 2) and TD (model 3).ResultsDifference between peak knee and hip flexion of the long and short lower limb was selected by models 1 (p < 0.001) and 2 (p < 0.001). Hip adduction was selected as a predictor only by model 1 (p = 0.05). Peak pelvic obliquity and ankle dorsiflexion were not selected by any model and model 3 did not retain any dependent variable (p > 0.05). Regression models predicted mild ALLD with moderate accuracy based on hip and knee kinematics during gait, but not ankle strategies. Excessive hip flexion of the longer limb possibly occurs to reduce the limb to equalize the LLD, and discrepancies of the femur and tibia affects gait cycle in a different way.SignificanceThis study showed that kinematic variables during gait could be used as a screening tool to identify patients with ALLD, reducing unnecessary x-ray exposure and assisting rehabilitation programs.     

Imaging features contributing to the diagnosis of ameloblastomas and keratocystic odontogenic tumours: logistic regression analysis

Objective

The aim of this study was to clarify the characteristic imaging features that can be used to differentiate ameloblastomas from keratocystic odontogenic tumours and to examine the significant imaging features contributing to a correct diagnosis.

Methods

60 observers (39 specialists in oral and maxillofacial radiology and 21 non-specialists) examined CT and/or panoramic images of 10 ameloblastomas and 10 keratocystic odontogenic tumours shown on a webpage and made diagnoses. Their correct answer ratios were then calculated. The imaging features of the tumours were evaluated and expressed as binary numbers or quantitative values. The imaging features that contributed to a correct diagnosis were elucidated using logistic regression analysis.

Results

The mean correct answer ratio was 61.3% ± 17.2% for the diagnosis of ameloblastomas and keratocystic odontogenic tumours. CT images produced higher correct answer ratios for diagnosis of keratocystic odontogenic tumours by specialists. The significantly different imaging features between ameloblastomas and keratocystic odontogenic tumours were the degree of bone expansion and the presence of high-density areas. The significant imaging features contributing to a correct imaging diagnosis were the number of locules, the presence of high-density areas and the inclusion of impacted teeth.

Conclusion

The presence of high-density areas is the most useful feature in the differential diagnosis of ameloblastomas and keratocystic odontogenic tumours based on comparison of the imaging features of both tumours and examination of the diagnostic contributions of these features.     

Bayesian regression methodology for estimating a receiver operating characteristic curve with two radiologic applications: prostate biopsy and spiral CT of ureteral stones

RATIONALE AND OBJECTIVES: The authors evaluated two Bayesian regression models for receiver operating characteristic (ROC) curve analysis of continuous diagnostic outcome data with covariates. MATERIALS AND METHODS: Full and partial Bayesian regression models were applied to data from two studies (n = 180 and 100, respectively): (a) The diagnostic value of prostate-specific antigen (PSA) levels (outcome variable) for predicting disease after radical prostatectomy (gold standard) was evaluated for three risk groups (covariates) based on Gleason scores. (b) Spiral computed tomography was performed on patients with proved obstructing ureteral stones. The predictive value of stone size (outcome) was evaluated along with two treatment options (gold standard), as well as stone location (in or not in the ureterovesical junction [UVJ]) and patient age (covariates). Summary ROC measures were reported, and various prior distributions of the regression coefficients were investigated. RESULTS: (a) In the PSA example, the ROC areas under the full model were 0.667, 0.769, and 0.703, respectively, for the low-, intermediate-, and high-risk groups. Under the partial model, the area beneath the ROC curve was 0.706. (b) The ROC areas for patients with ureteral stones in the UVJ decreased dramatically with age but otherwise were close to that under the partial model (ie, 0.774). The prior distribution had greater influence in the second example. CONCLUSION: The diagnostic tests were accurate in both examples. PSA levels were most accurate for staging prostate cancer among intermediate-risk patients. Stone size was predictive of treatment option for all patients other than those 40 years or older and with a stone in the UVJ.