Biocompatibility and Immunotoxicology of the Preclinical Implantation of a Collagen-based Artificial Dermal Regeneration Matrix.

OBJECTIVE: Graft rejection, with the possibility of a violent immune response, may be severe and life threatening. Our aim was to thoroughly investigate the biocompatibility and immunotoxicology of collagen-based dermal matrix (DM) before assessment in clinical trials. METHODS: DM was subcutaneously implanted in BALB/c mice in two doses to induce a potential immune response. The spleen and lymph nodes were assessed for shape, cell number, cell phenotype via flow cytometry, cell activation via CCK8 kit, Annexin V kit, and Ki67 immunostaining. Serum samples were used to measure antibody concentration by enzyme-linked immunosorbent assay. Local inflammation was analyzed by histology and immunohistochemistry staining. Data analysis was performed by one-way ANOVA and non-parametric tests. RESULTS: Our data illustrate that the spleen and lymph node sizes were similar between the negative control mice and mice implanted with DM. However, in the high-dose DM (DM-H) group, the total cell populations in the spleen and lymph nodes, T cells and B cells in the spleen had slight increases in prophase, and the low-dose DM (DM-L) group did not display gross abnormities. Moreover, DM-H initiated moderate cell activation and proliferation in the early phase post-immunization, whereas DM-L did not. Neither DM-H nor DM-L implantation noticeably increased IgM and IgG serum concentrations. Examination of the local cellular response revealed only benign cell infiltration and TNF-α expression in slides of DM in the early phase. CONCLUSION: Overall, DM-H may have induced a benign temporary acute immune response post-implantation, whereas DM-L had quite low immunogenicity. Thus, this DM can be regarded as a safe product.

Tests and classification methods in adaptive designs with applications.

Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials related to genomic studies. In this article, we evaluate four test methods for biomarker identification in the first stage of an adaptive design: a model-based identification method, the popular two-sided t-test, the nonparametric Wilcoxon Rank-Sum test (two-sided), and the Regularized Generalized Linear Models. For patients grouping in the second stage, we examine classification methods such as Random Forest, Elastic-net Regularized Generalized Linear Models, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Simulation studies are carried out to assess the performance of the different methods. The best identification methods are chosen based on the well-known F 1 score, while the best classification techniques are selected based on the area under a receiver operating characteristic curve (AUC). The chosen methods are then applied to the Adaptive Signature Design (ASD) with a real data set from breast cancer patients for the purpose of evaluating the performance of ASD in different situations.

Mixed-effects state-space models for analysis of longitudinal dynamic systems.

The rapid development of new biotechnologies allows us to deeply understand biomedical dynamic systems in more detail and at a cellular level. Many of the subject-specific biomedical systems can be described by a set of differential or difference equations that are similar to engineering dynamic systems. In this article, motivated by HIV dynamic studies, we propose a class of mixed-effects state-space models based on the longitudinal feature of dynamic systems. State-space models with mixed-effects components are very flexible in modeling the serial correlation of within-subject observations and between-subject variations. The Bayesian approach and the maximum likelihood method for standard mixed-effects models and state-space models are modified and investigated for estimating unknown parameters in the proposed models. In the Bayesian approach, full conditional distributions are derived and the Gibbs sampler is constructed to explore the posterior distributions. For the maximum likelihood method, we develop a Monte Carlo EM algorithm with a Gibbs sampler step to approximate the conditional expectations in the E-step. Simulation studies are conducted to compare the two proposed methods. We apply the mixed-effects state-space model to a data set from an AIDS clinical trial to illustrate the proposed methodologies. The proposed models and methods may also have potential applications in other biomedical system analyses such as tumor dynamics in cancer research and genetic regulatory network modeling.

Human odor detectability: new methodology used to determine threshold and variation.

Current ambiguity concerning the related issues of optimal means for measurement of odor sensitivity and the functional properties of the olfactory system hinders progress in basic and applied research on the human sense of smell. To address these needs, we selected n-amyl acetate (nAA) as a test odorant and developed a methodology in which participants (Ps) receive multiple presentations each session of several concentrations. Yes-no responses as to whether odor was detected are analyzed using binomial statistics, with the probability that a given proportion of yes responses (or greater) would occur by chance alone being treated as the inverse of detectability. Over the course of multiple sessions, this information is also used to maximize the collection of data in the peri-threshold region. Surprisingly, data collected over as many as 14 sessions were fit well by a single logistic regression model relating probability and concentration. Threshold concentrations, defined as those corresponding to a probability of 0.05, varied from 7.11 to 167.53 p.p.b. (v/v) for 11 Ps. Our approach and findings, if shown to be representative of other combinations of Ps and odorants, could accelerate the pace of research in human olfaction by providing a comprehensive operational definition of the limit of the olfactory system to detect odorant molecules.