A novel autophagy-related lncRNA survival model for lung adenocarcinoma

Long non-coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy-related lncRNA in lung adenocarcinoma. In this study, autophagy-related mRNAs-lncRNAs were screened from lung adenocarcinoma and a co-expression network of autophagy-related mRNAs-lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy-related lncRNAs and finally obtained a survival model composed of 11 autophagy-related lncRNAs. Through Kaplan-Meier analysis, univariate and multivariate Cox regression analysis and time-dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low-risk and high-risk groups. These 11 lncRNAs were GAS6-AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA-DQB1-AS1, LINC01116, LINC01806, FAM83A-AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196-1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149-1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy-related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy-related therapeutic targets.     

Characterization of articular cartilage by combining microscopic analysis with a fibril-reinforced finite-element model

Load-bearing characteristics of articular cartilage are impaired during tissue degeneration. Quantitative microscopy enables in vitro investigation of cartilage structure but determination of tissue functional properties necessitates experimental mechanical testing. The fibril-reinforced poroviscoelastic (FRPVE) model has been used successfully for estimation of cartilage mechanical properties. The model includes realistic collagen network architecture, as shown by microscopic imaging techniques. The aim of the present study was to investigate the relationships between the cartilage proteoglycan (PG) and collagen content as assessed by quantitative microscopic findings, and model-based mechanical parameters of the tissue. Site-specific variation of the collagen network moduli, PG matrix modulus and permeability was analyzed. Cylindrical cartilage samples (n=22) were harvested from various sites of the bovine knee and shoulder joints. Collagen orientation, as quantitated by polarized light microscopy, was incorporated into the finite-element model. Stepwise stress-relaxation experiments in unconfined compression were conducted for the samples, and sample-specific models were fitted to the experimental data in order to determine values of the model parameters. For comparison, Fourier transform infrared imaging and digital densitometry were used for the determination of collagen and PG content in the same samples, respectively. The initial and strain-dependent fibril network moduli as well as the initial permeability correlated significantly with the tissue collagen content. The equilibrium Young's modulus of the nonfibrillar matrix and the strain dependency of permeability were significantly associated with the tissue PG content. The present study demonstrates that modern quantitative microscopic methods in combination with the FRPVE model are feasible methods to characterize the structure-function relationships of articular cartilage.     

Conditioning of native substrates by chondroitin sulfate proteoglycans during cardiac mesenchymal cell migration

It is generally proposed that embryonic mesenchymal cells use sulfated macromolecules during in situ migration. Attempts to resolve the molecular mechanisms for this hypothesis using planar substrates have been met with limited success. In the present study, we provide evidence that the functional significance of certain sulfated macromolecules during mesenchyme migration required the presence of the endogenous migratory template; i.e., native collagen fibrils. Using three-dimensional collagen gel lattices and whole embryo culture procedures to produce metabolically labeled sulfated macromolecules in embryonic chick cardiac tissue, we show that these molecules were primarily proteoglycan (PG) in nature and that their distribution was class specific; i.e., heparan sulfate PG, the minor labeled component (15%), remained pericellular while chondroitin sulfate (CS) PG, the predominately labeled PG (85%), was associated with collagen fibrils as "trails" of 50-60-nm particles when viewed by scanning electron microscopy. Progressive "conditioning" of collagen with CS-PG inhibited the capacity of the template to support subsequent cell migration. Lastly, metabolically labeled, PG-derived CS chains were compared with respect to degree of sulfation in either the C-6 or C-4 position by chromatographic separation of chondroitinase AC digestion products. Results from temporal and regional comparisons of in situ-labeled PGs indicated a positive correlation between the presence of mesenchyme and an enrichment of disaccharide-4S relative to that from regions lacking mesenchyme (i.e., principally myocardial tissue). The suggestion of a mesenchyme-specific CS-PG was substantiated by similarly examining the PGs synthesized solely by cardiac mesenchymal cells migrating within hydrated collagen lattice in culture. These data were incorporated into a model of "substratum conditioning" which provides a molecular mechanism by which secretion of mesenchyme-specific CS-PGs not only provides for directed and sustained cell movement, but ultimately inhibits migration of the cell population as a whole.     

Joint regression analysis of correlated data using Gaussian copulas

Summary .  This article concerns a new joint modeling approach for correlated data analysis. Utilizing Gaussian copulas, we present a unified and flexible machinery to integrate separate one-dimensional generalized linear models (GLMs) into a joint regression analysis of continuous, discrete, and mixed correlated outcomes. This essentially leads to a multivariate analogue of the univariate GLM theory and hence an efficiency gain in the estimation of regression coefficients. The availability of joint probability models enables us to develop a full maximum likelihood inference. Numerical illustrations are focused on regression models for discrete correlated data, including multidimensional logistic regression models and a joint model for mixed normal and binary outcomes. In the simulation studies, the proposed copula-based joint model is compared to the popular generalized estimating equations, which is a moment-based estimating equation method to join univariate GLMs. Two real-world data examples are used in the illustration.     

Use of predictive modeling for Propionibacterium strain classification

Computed data analysis of biochemical or molecular profiles is currently used in studies of microbial taxonomy, epidemiology, and microbial diversity. We assessed the use of Partial Least Squares (PLS) regression for multivariate data analysis in bacteriology. We identified clear relationships between RAPD profiles of propionibacteria strains and their species classification, autolytic capacities, and their origins. The PLS regression also predicted species identity of some strains with RAPD profiles partially related to those of reference strains. The PLS analysis also allowed us to identify key characteristics to use to classify strains. PLS regression is particularly well adapted to i) describing a collection of bacterial isolates, ii) justifying bacterial groupings using several sets of data, and iii) predicting phenotypic characters of strains that have been classified by routine typing methods.     

死亡相关的LncRNAs在甲状腺癌中的预后价值及预后风险模型构建

目的:探讨铁死亡相关的长链非编码RNAs(LncRNAs)在甲状腺癌中的预后价值并构建预后风险模型。方法:从癌症基因组图谱(TCGA)数据库下载甲状腺癌的转录本数据和临床数据,铁死亡相关的基因数据集是从铁死亡数据库(http://www.zhounan.org/ferrdb/)下载的259个基因集。得到铁死亡相关LncRNAs,与患者临床信息合并后,通过单因素Cox回归分析和Kaplan-Meier生存分析两种方法得到与甲状腺癌预后相关的铁死亡LncRNAs,通过R的survival包构建COX模型以此来建立最佳预后风险模型并予以验证。结果:获得30个铁死亡相关的LncRNAs,多因素cox分析得到10个与甲状腺癌预后相关的铁死亡LncRNAs,包括AL136366.1、AL162231.2、CRNDE、AC004918.3、LINC02471、AC092279.1、AC046143.1、LINC02454、DOCK9-DT、AC008063.1。Kaplan-Meier生存分析表明高风险组预后较差。单因素和多因素Cox分析表明风险评分可以作为独立预后因子。KEGG通路富集分析发现,差异基因可能与嘧啶代谢、核苷酸切除修复、NOTCH_信号通路等通路有关。结论:通过生物信息学方法筛选出10个与甲状腺癌预后的铁死亡相关LncRNAs,并成功构建预后风险模型。     

Extending induced ROC methodology to the functional context

The receiver operating characteristic (ROC) curve is the most widely used measure for evaluating the discriminatory performance of a continuous marker. Often, covariate information is also available and several regression methods have been proposed to incorporate covariate information in the ROC framework. Until now, these methods are only developed for the case where the covariate is univariate or multivariate. We extend ROC regression methodology for the case where the covariate is functional rather than univariate or multivariate. To this end, semiparametric- and nonparametric-induced ROC regression estimators are proposed. A simulation study is performed to assess the performance of the proposed estimators. The methods are applied to and motivated by a metabolic syndrome study in Galicia (NW Spain).     

Model Diagnostic Plots for Repeated Measures Data

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. Though these models have been widely used, not many studies have been performed in model diagnostic areas. In this paper, we propose simple residual plots to investigate the goodness of model fit for repeated measures data. Here, we mainly focus on the mean model diagnostics. The proposed residual plots are based on the quantile‐quantile(Q–Q) plots of a χ² distribution and a normal distribution. In particular, the proposed model is useful in comparing several models simultaneously. The proposed method is illustrated using two examples. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Inclusion of Dominance Effects in the Multivariate GBLUP Model

New proposals for models and applications of prediction processes with data on molecular markers may help reduce the financial costs of and identify superior genotypes in maize breeding programs. Studies evaluating Genomic Best Linear Unbiased Prediction (GBLUP) models including dominance effects have not been performed in the univariate and multivariate context in the data analysis of this crop. A single cross hybrid construction procedure was performed in this study using phenotypic data and actual molecular markers of 4,091 maize lines from the public database Panzea. A total of 400 simple hybrids resulting from this process were analyzed using the univariate and multivariate GBLUP model considering only additive effects additive plus dominance effects. Historic heritability scenarios of five traits and other genetic architecture settings were used to compare models, evaluating the predictive ability and estimation of variance components. Marginal differences were detected between the multivariate and univariate models. The main explanation for the small discrepancy between models is the low- to moderate-magnitude correlations between the traits studied and moderate heritabilities. These conditions do not favor the advantages of multivariate analysis. The inclusion of dominance effects in the models was an efficient strategy to improve the predictive ability and estimation quality of variance components.     

Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model

Multiple imputation (MI) is increasingly popular for handling multivariate missing data. Two general approaches are available in standard computer packages: MI based on the posterior distribution of incomplete variables under a multivariate (joint) model, and fully conditional specification (FCS), which imputes missing values using univariate conditional distributions for each incomplete variable given all the others, cycling iteratively through the univariate imputation models. In the context of longitudinal or clustered data, it is not clear whether these approaches result in consistent estimates of regression coefficient and variance component parameters when the analysis model of interest is a linear mixed effects model (LMM) that includes both random intercepts and slopes with either covariates or both covariates and outcome contain missing information. In the current paper, we compared the performance of seven different MI methods for handling missing values in longitudinal and clustered data in the context of fitting LMMs with both random intercepts and slopes. We study the theoretical compatibility between specific imputation models fitted under each of these approaches and the LMM, and also conduct simulation studies in both the longitudinal and clustered data settings. Simulations were motivated by analyses of the association between body mass index (BMI) and quality of life (QoL) in the Longitudinal Study of Australian Children (LSAC). Our findings showed that the relative performance of MI methods vary according to whether the incomplete covariate has fixed or random effects and whether there is missingnesss in the outcome variable. We showed that compatible imputation and analysis models resulted in consistent estimation of both regression parameters and variance components via simulation. We illustrate our findings with the analysis of LSAC data.     

An immunoelectron microscope study of the organization of proteoglycan monomer, link protein, and collagen in the matrix of articular cartilage

Monospecific antibodies to bovine cartilage proteoglycan monomer (PG) and link protein (LP) have been used with immunoperoxidase electron microscopy to study the distribution and organization of these molecules in bovine articular cartilage. The following observations were made: (a) The interterritorial matrix of the deep zone contained discrete interfibrillar particulate staining for PG and LP. This particulate staining, which was linked by faint bands of staining (for PG) or filaments (for LP), was spaced at 75- to 80-nm intervals. On collagen fibrils PG was also detected as particulate staining spaced at regular intervals (72 nm), corresponding to the periodicity of collagen cross-banding. The interfibrillar PG staining was often linked to the fibrillar PG staining by the same bands or filaments. The latter were cleaved by a proteinase-free Streptomyces hyaluronidase with the removal of much of the interfibrillar lattice. Since this enzyme has a specificity for hyaluronic acid, the observations indicate that the lattice contains a backbone of hyaluronic acid (which appeared as banded or filamentous staining) to which is attached LP and PG, the latter collapsing when the tissue is fixed, reacted with antibodies, and prepared for electron microscopy. Thishyaluronic acid is anchored to collagen fibrils at regular intervals where PG is detected on collagen. PG and LP detected by antibody in the interterritorial zones are essentially fully extractible with 4 M guanidine hydrochloride. These observations indicated that interfibrillar PG and LP is aggregated with HA in this zone. (b) The remainder of the cartilage matrix had a completely different organization of PG and LP. There was no evidence of a similar latticework based on hyaluronic acid. Instead, smaller more closely packed particulate staining for PG was seen everywhere irregularly distributed over and close to collagen fibrils. LP was almost undetectable in the territorial matrix of the deep zone, as observed previously. In the middle and superficial zones, stronger semiparticulate staining for LP was distributed over collagen fibrils. (c) In the superficial zone, reaction product for PG was distributed evenly on collagen fibrils as diffuse staining and also irregularly as particulate staining. LP was observed as semiparticulate staining over collagen fibrils. The diffuse staining for PG remained after extraction with 4 M guanidine hydrochloride. (d) In pericellular matrix, most clearly identified in middle and deep zones, the nature and organization of reaction product for PG and LP were similar to those observed in the territorial matrix, except that LP and PG were more strongly stained and amorphous staining for both components was also observed. (e) This study demonstrates striking regional variations of ultrastructural organization of PG and LP in articular cartilage...     

基于临床数据构建慢性乙型肝炎患者肝纤维化的新型预测评分：S-risk score

基于临床数据构建一种预测慢性乙型肝炎肝纤维化的无创诊断模型。

收集2021年1月至2023年7月宁波市医疗中心李惠利医院收治的165例CHB患者病例资料作回顾性分析,根据肝活检病理结果将患者分为无肝纤维化组（S0,n = 22）和肝纤维化组（≥S1,n = 143）。收集患者的血清学指标和临床数据,运用单因素和多因素 logitstic回归分析筛选出独立预测指标并建立模型,同时采用受试者工作特征曲线（ROC）评价模型的预测效能。

单因素分析结果显示,两组患者在白蛋白、谷草转氨酶、甘油三酯、总胆汁酸、胆碱酯酶、凝血酶原时间、 BMI、血清Ⅳ胶原和血清透明质酸等指标中存在差异（P＜0.05）。通过logistic多因素的回归分析构建肝纤维化模型S-risk score = −4.30+0.12×白蛋白+0.02×谷草转氨酶−0.05×碱性磷酸酶+0.29×甘油三酯+0.06×总胆汁酸−0.47×凝血酶原时间+0.20×BMI+0.03×血清Ⅳ胶原测定+0.02×血清透明质酸。该评分下的ROC曲线下的面积为0.866,其预测肝纤维化的准确性明显优于APRI和FIB-4两项评分模型。

我们构建的S-risk score模型对CHB患者肝纤维化有良好的预测能力,其预测准确性均高于APRI和FIB-4两项评分模型。

     

Statistical methods for temporal and space–time analysis of community composition data

This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran''s eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space–time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.     

Identification of five long noncoding RNAs signature and risk score for prognosis of bladder urothelial carcinoma

Nowadays, an increasing number of studies illustrated that bladder urothelial cancer (BLCA) may act as the most common subtype of urological malignancies with a high rate of recurrence and metastasis. In this study, we attempted to establish a prognostic model and identify the possible pathway crosstalk. Long noncoding RNAs (lncRNAs) and mRNA expression and corresponding clinical information of patients with BLCA were downloaded from The Cancer Genome Atlas (TCGA). The differentially expressed genes analysis, univariate Cox analysis, the least absolute shrinkage, and selection operator Cox (LASSO Cox) regression model were then applied to identify five crucial lncRNAs (AC092725.1, AC104071.1, AL023584.1, AL132642.1, and AL137804.1). The multivariate cox analysis was utilized to calculate the regression coefficients (β_i). The risk-score model was subsequently constructed as follows: (0.13541AC092725.1) + (0.20968AC104071.1) + (0.1525AL023584.1) − (0.14768AL132642.1) + (0.14387AL137804.1). Nomogram and assessment of overall survival (OS) prediction were verificated by the receiver operating characteristic curve in the testing group. As to 3-, 5-year OS prediction, the area under curve (AUC) for the nomogram of training data set was 0.83 and 0.86. Besides, the AUC (0.883 and 0.879) presented excellent predictive power in the testing group. In addition, the calibration plots validated the predictive performance of the nomogram. Weighted correlation network analysis (WGCNA) coupled with functional enrichment analysis contributed to explore the potential pathways, including PI3K-Akt, HIF-1, and Jak-STAT signaling pathways. Construction of the risk-score model and data analysis were both derived from multiple packages on the basis of the R platform chiefly.     

Design of Experiments-Based Monitoring of Critical Quality Attributes for the Spray-Drying Process of Insulin by NIR Spectroscopy

Moisture content and aerodynamic particle size are critical quality attributes for spray-dried protein formulations. In this study, spray-dried insulin powders intended for pulmonary delivery were produced applying design of experiments methodology. Near infrared spectroscopy (NIR) in combination with preprocessing and multivariate analysis in the form of partial least squares projections to latent structures (PLS) were used to correlate the spectral data with moisture content and aerodynamic particle size measured by a time of flight principle. PLS models predicting the moisture content were based on the chemical information of the water molecules in the NIR spectrum. Models yielded prediction errors (RMSEP) between 0.39% and 0.48% with thermal gravimetric analysis used as reference method. The PLS models predicting the aerodynamic particle size were based on baseline offset in the NIR spectra and yielded prediction errors between 0.27 and 0.48 μm. The morphology of the spray-dried particles had a significant impact on the predictive ability of the models. Good predictive models could be obtained for spherical particles with a calibration error (RMSECV) of 0.22 μm, whereas wrinkled particles resulted in much less robust models with a Q² of 0.69. Based on the results in this study, NIR is a suitable tool for process analysis of the spray-drying process and for control of moisture content and particle size, in particular for smooth and spherical particles.KEY WORDS: moisture content, multivariate analysis, NIR, particle size, spray-drying     

Dermatan sulphate proteoglycan from human articular cartilage. Variation in its content with age and its structural comparison with a small chondroitin sulphate proteoglycan from pig laryngeal cartilage.

Low molecular mass proteoglycans (PG) were isolated from human articular cartilage and from pig laryngeal cartilage, which contained protein cores of similar size (Mr 40-44 kDa). However, the PG from human articular cartilage contained dermatan sulphate (DS) chains (50% chondroitinase AC resistant), whereas chains from pig laryngeal PG were longer and contained only chondroitin sulphate (CS). Disaccharide analysis after chondroitinase ABC digestion showed that the human DS-PG contained more 6-sulphated residues (34%) than the pig CS-PG (6%) and both contained fewer 6-sulphated residues than the corresponding high Mr aggregating CS-PGs from these tissues (86% and 20% from human and pig respectively). Cross-reaction of both proteoglycans with antibodies to bovine bone and skin DS-PG-II and human fibroblasts DS-PG suggested that the isolated proteoglycans were the humans DS-PG-II and pigs CS-PG-II homologues of the cloned and sequenced bovine proteoglycan. Polyclonal antibodies raised against the pig CS-PG-II were shown to cross-react with human DS-PG-II. SDS/polyacrylamide-gel analysis and immunoblotting of pig and human cartilage extracts showed that some free core protein was present in the tissues in addition to the intact proteoglycan. The antibodies were used in a competitive radioimmunoassay to determine the content of this low Mr proteoglycan in human cartilage extracts. Analysis of samples from 5-80 year-old humans showed highest content (approximately 4 mg/g wet wt.) in those from 15-25 year-olds and lower content (approximately 1 mg/g wet wt.) in older tissue (greater than 55 years). These changes in content may be related to the deposition and maintenance of the collagen fibre network with which this class of small proteoglycan has been shown to interact.     

SEM: Structural Equation Modeling in Molecular Biology

Structural equation modeling (SEM) is a second-generation multivariate method to estimate the causal interactions in a set of variables and includes, as special cases, several statistical methods (regression analysis, path analysis, and confirmatory factor analysis). This review focuses on all of the main SEM models and various methods used to optimize the model parameters. Representative examples are discussed to illustrate SEM application in molecular biology, including modeling of biochemical processes, relationships between genetic markers and diseases, and interactions within gene networks.     

Comparison of Univariate and Multivariate Models of 13C SSNMR and XRPD Techniques for Quantification of Nimodipine Polymorphs

The focus of the present investigation was to explore the use of solid-state nuclear magnetic resonance (¹³C ssNMR) and X-ray powder diffraction (XRPD) for quantification of nimodipine polymorphs (form I and form II) crystallized in a cosolvent formulation. The cosolvent formulation composed of polyethylene glycol 400, glycerin, water, and 2.5% drug, and was stored at 5°C for the drug crystallization. The ¹³C ssNMR and XRPD data of the sample matrices containing varying percentages of nimodipine form I and form II were collected. Univariate and multivariate models were developed using the data. Least square method was used for the univariate model generation. Partial least square and principle component regressions were used for the multivariate models development. The univariate models of the ¹³C ssNMR were better than the XRPD as indicated by statistical parameters such as correlation coefficient, R², root mean square error, and standard error. On the other hand, the XRPD multivariate models were better than the ¹³C ssNMR as indicated by precision and accuracy parameters. Similar values were predicted by the univariate and multivariate models for independent samples. In conclusion, the univariate and multivariate models of ¹³C ssNMR and XRPD can be used to quantitate nimodipine polymorphs.KEY WORDS: nimodipine polymorphs, X-ray powder diffraction, solid-state nuclear magnetic resonance, univariate, multivariate