Analysis of enzymatic steroid conversions by high pressure liquid chromatography.

Enzyme catalyzed introduction of the 1–2 double bond into a steroid can be monitored through spectrophotometric changes accompanying electron acceptor reduction or through paper or thin-layer chromatographic analysis of the reaction product. The spectrophotometric method is not applicable to cases in which the oxidized form of the electron acceptor is continually regenerated. In studying such cases, we have found high pressure liquid chromatography (HPLC) to be a method of direct analysis more convenient than paper chromatography or tlc. Use of a water based eluant and a reverse phase column for the HPLC analysis allows direct injection of a sample of the aqueous reaction solution after acidification, and no extraction with an organic solvent is necessary.     

Application of the Goldman-Hodgkin-Katz current equation to membrane current-voltage data

A new approach for the analysis of current-voltage (IV) data is presented and applied to a variety of published data collected from various systems. Our analysis of published results shows that our method of analysis can account for the observed IV data. The calculated permeability coefficients are in reasonable agreement with those calculated from ion fluxes. In those cases where two ions are assumed to carry the current, the ratios of the calculated permeability coefficients are in agreement with those ratios determined from the Goldman-Hodgkin-Katz voltage equation. In most cases, the entire IV curve can be accounted for by using our method of analysis. In several examples, only a portion of the IV curve is in agreement with the predictions. We attribute the failure to account for the IV data to reflect the failure of one or more of the assumptions used by the GHK current equation. In other cases, assuming that an additional ion carries the current, the treatment can account for the IV data. However, the identity of the extra ion cannot be established from these published data without additional studies (e.g., ionic replacement studies).     

Determination of estrogen receptors in cytologic imprints of breast cancers using immunocytochemical methods.

Immunostaining of estrogen receptors (ERs) was carried out on imprints of 62 breast carcinomas using monoclonal antibodies and a sensitive immunoperoxidase technique (the Abbott ER-ICA kit). The results were compared to those obtained by the conventional biochemical analysis of cytosol proteins and to the degree of tumor differentiation. The cytologic specimens were insufficient for analysis in 6 cases; of the remaining 56 cases, 37 (66%) showed a positive ER reaction. In 51 cases with both types of ER analysis, the immunocytochemical staining of the imprints correlated strongly with the biochemical analysis in 44 cases and weakly in 3. Four cases were negative immunocytochemically and positive biochemically. Among the ductal carcinomas, well-differentiated tumors had higher percentages of ER-positive cells than did poorly differentiated tumors. These results show that the immunoperoxidase method is a highly specific and sensitive technique for the evaluation of ER content; it may be applicable to small samples of tumor tissue and may provide additional information for identifying hormonally responsive breast tumors.     

A note on an exact test and confidence interval for competition and overlap effects

A method has been proposed that gives an exact test and confidence interval for the coefficient of overlap and competition effects. The method is based on Tukey's statistic of nonadditivity. The method is very simple to adopt and can be implemented by regression analysis and analysis of variance programmes in some important cases.     

A weighted least squares method for inverse dynamic analysis

Internal forces in the human body can be estimated from measured movements and external forces using inverse dynamic analysis. Here we present a general method of analysis which makes optimal use of all available data, and allows the use of inverse dynamic analysis in cases where external force data is incomplete. The method was evaluated for the analysis of running on a partially instrumented treadmill. It was found that results correlate well with those of a conventional analysis where all external forces are known.     

An improved model for the analysis of combined antimicrobials: a replacement for the Chou–Talalay combination index method

Aims

To rationalize confusion in the literature concerning the analysis of combined antimicrobials, specifically to see if the combination index (CI) method of analysis was as rigorous as claimed.

Methods and Results

Data from previous studies of the inhibition of Staphylococcus aureus by mixed antimicrobials were re‐analysed using the CI method and a model which takes account of differences in the concentration exponents of individual antimicrobials.

Conclusions

The Chou–Talalay combination index method for the analysis of combined antimicrobials was found to be valid only in the specific cases where concentration exponents were equal. In these cases, the CI method was found to be a function of the residuals of fitting the additive model to the observed data. Where concentration exponents were not equal, the CI method was invalid, whereas the additive model took these differences into account.

Significance and Impact of Study

The CI method can be replaced wholly by the additive model described. The model allows simple regression to be used to analyse whole data sets and provides simple graphical output.     

Analysis of ligand-receptor binding by the difference method

A new method has been proposed for analysis of experimental data on ligand-receptor binding at equilibrium. This method makes it possible to detect heterogeneity of a receptor system in cases where the contribution of the high-affinity site to total binding is rather small and the problem of graphic discrimination of a model cannot be solved unambiguously by other methods. The difference method permits us to exclude experiments on measuring nonspecific binding. A computer program for analysis of ligand-receptor binding has been worked out in which the difference method and traditional methods of binding isotherm analysis are realized. Numerical modeling has shown that the best strategy in experimental data processing is the treatment of total binding isotherms by both the difference method and regression analysis, including the nonspecific binding constant as one of the regression parameters.     

A fast, convenient diagnosis of the bovine freemartin syndrome using polymerase chain reaction

To establish the polymerase chain reaction (PCR) method for detecting the XY cells in cases suspected to have the bovine freemartin syndrome, a PCR reaction test was conducted on blood from a normal bull diluted in blood from a normal cow. From the results obtained, it was shown that the Y-specific sequence was detectable down to a concentration of 0.1%. Various types of the bovine freemartin syndrome, which occurs in heterosexual twins, single-born sterile heifers, and heifers born with Acardius amorphus, were examined by the chromosome analysis and the PCR method. The Y-specific sequence was detected in all 26 cases that showed chromosome chimerism but which was absent in the 5 cases without a chimerism. The PCR method was found to be effective and convenient for quickly diagnosing the various types of bovine freemartin syndrome.     

Between-group analysis of microarray data

MOTIVATION: Most supervised classification methods are limited by the requirement for more cases than variables. In microarray data the number of variables (genes) far exceeds the number of cases (arrays), and thus filtering and pre-selection of genes is required. We describe the application of Between Group Analysis (BGA) to the analysis of microarray data. A feature of BGA is that it can be used when the number of variables (genes) exceeds the number of cases (arrays). BGA is based on carrying out an ordination of groups of samples, using a standard method such as Correspondence Analysis (COA), rather than an ordination of the individual microarray samples. As such, it can be viewed as a method of carrying out COA with grouped data. RESULTS: We illustrate the power of the method using two cancer data sets. In both cases, we can quickly and accurately classify test samples from any number of specified a priori groups and identify the genes which characterize these groups. We obtained very high rates of correct classification, as determined by jack-knife or validation experiments with training and test sets. The results are comparable to those from other methods in terms of accuracy but the power and flexibility of BGA make it an especially attractive method for the analysis of microarray cancer data.     

Exploring the Dynamics of Cell Processes through Simulations of Fluorescence Microscopy Experiments

Fluorescence correlation spectroscopy (FCS) methods are powerful tools for unveiling the dynamical organization of cells. For simple cases, such as molecules passively moving in a homogeneous media, FCS analysis yields analytical functions that can be fitted to the experimental data to recover the phenomenological rate parameters. Unfortunately, many dynamical processes in cells do not follow these simple models, and in many instances it is not possible to obtain an analytical function through a theoretical analysis of a more complex model. In such cases, experimental analysis can be combined with Monte Carlo simulations to aid in interpretation of the data. In response to this need, we developed a method called FERNET (Fluorescence Emission Recipes and Numerical routines Toolkit) based on Monte Carlo simulations and the MCell-Blender platform, which was designed to treat the reaction-diffusion problem under realistic scenarios. This method enables us to set complex geometries of the simulation space, distribute molecules among different compartments, and define interspecies reactions with selected kinetic constants, diffusion coefficients, and species brightness. We apply this method to simulate single- and multiple-point FCS, photon-counting histogram analysis, raster image correlation spectroscopy, and two-color fluorescence cross-correlation spectroscopy. We believe that this new program could be very useful for predicting and understanding the output of fluorescence microscopy experiments.     

Sensitivity analysis for matched case-control studies

A sensitivity analysis in an observational study indicates the degree to which conclusions would be altered by hidden biases of various magnitudes. A method of sensitivity analysis previously proposed for cohort studies is extended for use in matched case-control studies with multiple controls, where slightly different derivations and calculations are required. Also discussed is a sensitivity analysis for case-control studies that have two distinct types of controls, say hospital and neighborhood controls, where the two types may be affected by different biases. For illustration, the method is applied to five case-control studies, including a study of herniated lumbar disc in which there are three types of cases, and a study of breast cancer with two types of controls.     

Haplotype association analyses in resources of mixed structure using Monte Carlo testing

Background  

Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols.     

A Gaussian copula approach for the analysis of secondary phenotypes in case-control genetic association studies

In many case-control genetic association studies, a set of correlated secondary phenotypes that may share common genetic factors with disease status are collected. Examination of these secondary phenotypes can yield valuable insights about the disease etiology and supplement the main studies. However, due to unequal sampling probabilities between cases and controls, standard regression analysis that assesses the effect of SNPs (single nucleotide polymorphisms) on secondary phenotypes using cases only, controls only, or combined samples of cases and controls can yield inflated type I error rates when the test SNP is associated with the disease. To solve this issue, we propose a Gaussian copula-based approach that efficiently models the dependence between disease status and secondary phenotypes. Through simulations, we show that our method yields correct type I error rates for the analysis of secondary phenotypes under a wide range of situations. To illustrate the effectiveness of our method in the analysis of real data, we applied our method to a genome-wide association study on high-density lipoprotein cholesterol (HDL-C), where "cases" are defined as individuals with extremely high HDL-C level and "controls" are defined as those with low HDL-C level. We treated 4 quantitative traits with varying degrees of correlation with HDL-C as secondary phenotypes and tested for association with SNPs in LIPG, a gene that is well known to be associated with HDL-C. We show that when the correlation between the primary and secondary phenotypes is >0.2, the P values from case-control combined unadjusted analysis are much more significant than methods that aim to correct for ascertainment bias. Our results suggest that to avoid false-positive associations, it is important to appropriately model secondary phenotypes in case-control genetic association studies.     

P-value based visualization of codon usage data

Two important and not yet solved problems in bacterial genome research are the identification of horizontally transferred genes and the prediction of gene expression levels. Both problems can be addressed by multivariate analysis of codon usage data. In particular dimensionality reduction methods for visualization of multivariate data have shown to be effective tools for codon usage analysis. We here propose a multidimensional scaling approach using a novel similarity measure for codon usage tables. Our probabilistic similarity measure is based on P-values derived from the well-known chi-square test for comparison of two distributions. Experimental results on four microbial genomes indicate that the new method is well-suited for the analysis of horizontal gene transfer and translational selection. As compared with the widely-used correspondence analysis, our method did not suffer from outlier sensitivity and showed a better clustering of putative alien genes in most cases.     

Analysis of pooled DNA samples on high density arrays without prior knowledge of differential hybridization rates

Array based DNA pooling techniques facilitate genome-wide scale genotyping of large samples. We describe a structured analysis method for pooled data using internal replication information in large scale genotyping sets. The method takes advantage of information from single nucleotide polymorphisms (SNPs) typed in parallel on a high density array to construct a test statistic with desirable statistical properties. We utilize a general linear model to appropriately account for the structured multiple measurements available with array data. The method does not require the use of additional arrays for the estimation of unequal hybridization rates and hence scales readily to accommodate arrays with several hundred thousand SNPs. Tests for differences between cases and controls can be conducted with very few arrays. We demonstrate the method on 384 endometriosis cases and controls, typed using Affymetrix Genechip© HindIII 50 K arrays. For a subset of this data there were accurate measures of hybridization rates available. Assuming equal hybridization rates is shown to have a negligible effect upon the results. With a total of only six arrays, the method extracted one-third of the information (in terms of equivalent sample size) available with individual genotyping (requiring 768 arrays). With 20 arrays (10 for cases, 10 for controls), over half of the information could be extracted from this sample.     

Evaluation of candidate genes in case-control studies: a statistical method to account for related subjects

Traditional case-control studies provide a powerful and efficient method for evaluation of association between candidate genes and disease. The sampling of cases from multiplex pedigrees, rather than from a catchment area, can increase the likelihood that genetic cases are selected. However, use of all the related cases without accounting for their biological relationship can increase the type I error rate of the statistical test. To overcome this problem, we present an analysis method that is used to compare genotype frequencies between cases and controls, according to a trend in proportions as the dosage of the risk allele increases. This method uses the appropriate variance to account for the correlated family data, thus maintaining the correct type I error rate. The magnitude of the association is estimated by the odds ratio, with the variance of the odds ratio also accounting for the correlated data. Our method makes efficient use of data collected from multiplex families and should prove useful for the analysis of candidate genes among families sampled for linkage studies. An application of our method, to family data from a prostate cancer study, is presented to illustrate the method's utility.     

A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events

Identifying linked cases of infection is a critical component of the public health response to viral infectious diseases. In a clinical context, there is a need to make rapid assessments of whether cases of infection have arrived independently onto a ward, or are potentially linked via direct transmission. Viral genome sequence data are of great value in making these assessments, but are often not the only form of data available. Here, we describe A2B-COVID, a method for the rapid identification of potentially linked cases of COVID-19 infection designed for clinical settings. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and evolutionary analysis of genome sequences to assess whether data collected from cases of infection are consistent or inconsistent with linkage via direct transmission. A retrospective analysis of data from two wards at Cambridge University Hospitals NHS Foundation Trust during the first wave of the pandemic showed qualitatively different patterns of linkage between cases on designated COVID-19 and non-COVID-19 wards. The subsequent real-time application of our method to data from the second epidemic wave highlights its value for monitoring cases of infection in a clinical context.     

冠心病猝死心肌肌红蛋白缺失的免疫组化进一步研究

目的探讨冠脉狭窄程度与心肌肌红蛋白缺失的关系及心肌肌红蛋白缺失在冠心病猝死中的意义和诊断价值.方法综合应用常规病理学方法、免疫组化技术(SABC法)和图像分析处理系统,对本教研室26例冠心病猝死病例和17例非冠心病猝死的对照组病例的心肌肌红蛋白缺失情况进行对比研究.结果冠心病猝死者心肌内均有肌红蛋白缺失,呈多发散在灶片状分布,且冠脉狭窄最严重处相应部位心肌的肌红蛋白缺失较其他部位更明显,而对照组无或仅见轻度缺失.用SAS统计软件包对图像分析结果进行统计处理发现有显著性差异.结论心肌的肌红蛋白缺失情况可用作冠心病猝死的诊断指标之一.     

The state of water in intervertebral disks from data of relaxation analysis using an NMR-tomogram

Distribution and dynamical characteristics of water molecules in normal and degeneratively changed rabbit intervertebral discs (ID) are estimated basing on the biexponential relaxation analysis of MR-images. Degenerative-dystrophic changes in a rabbit spine were modelled via a spine segmental blood supply injury and, in some cases, removal of ID nuclei. Extreme distributions of relaxation parameters in the ID plane characteristic of intact IDs are shown to be disturbed in cases of ID degenerative changes. These results allow to link ID fibrosis to a water balance disorder and to give a method for estimating the spine destruction degree for clinical MR-imaging.     

Investigations concerning the application of the cross-correlation method in cardiac output measurements

ABSTRACT: BACKGROUND: In spite of numerous non-invasive examinations the "gold clinical standard" of cardiac output measurements is the invasive pulmonary artery catheterization by means of the Swan-Ganz catheter and the application of the thermodilution method to estimate the blood flow. The results obtained by means of thermodilution are sensitive to many physical and biological disturbances. The unreliability of this method amounts to 20-45% and depends on the given variant of the method. Therefore some other method, more accurate and resistant to disturbances, was looked for. This paper presents a new approach to cardiac output measurements, based on cross-correlation signal analysis. The goal of investigations was to verify experimentally the application of the cross-correlation method of cardiac output measurements. RESULTS: In 99.2% of the examined cases the extreme of the cross-correlation function was easy to be estimated by numerical algorithms. In 0,8% of the remaining cases (with a plateau region adjacent to the maximum point) numerical detection of the extreme was inaccurate. The typical unreliability of the investigated method amounted o 5.1% (9.8% in the worst case). Investigations performed on a physical model revealed that the unreliability of cardiac output measurements by means of the cross-correlation method is 3-5 times better than in the case of thermodilution. CONCLUSIONS: The performed investigations and theoretical analysis have shown, that the cross-correlation method may be applied in cardiac output measurements. This kind of measurements seems to be more accurate and disturbance-resistant than clinically applied thermodilution.