前哨淋巴结检测方法及其在肿瘤中的研究进展

前哨淋巴结(sentinel lymph node,SLN)是肿瘤淋巴结转移的第一站,SLN活检肿瘤阳性的患者需要做系统性淋巴结清扫;SLN活检阴性的患者,不需要做系统性淋巴结清扫,可以缩短手术时间,降低手术费用,减少手术并发症;目前识别SLN的方法包括生物活性染料示踪法,放射性核素示踪法,联合示踪法,纳米炭(carbon nanoparticles,CNP)标记前哨淋巴结活检技术以及吲哚菁绿(Indocyanine Green,ICG)荧光标记法。SLN活检技术在乳腺癌、甲状腺癌、胃癌、恶性黑色素瘤、宫颈癌、子宫内膜癌等肿瘤中皆有不同程度的研究。本文通过复习文献,对前哨淋巴结检测方法予以归纳及其在常见肿瘤中的研究进展予以综述,旨在为恶性肿瘤临床治疗提供参考。     

Multiple surveys employing a new sample‐processing protocol reveal the genetic diversity of placozoans in Japan

Placozoans, flat free‐living marine invertebrates, possess an extremely simple bauplan lacking neurons and muscle cells and represent one of the earliest‐branching metazoan phyla. They are widely distributed from temperate to tropical oceans. Based on mitochondrial 16S rRNA sequences, 19 haplotypes forming seven distinct clades have been reported in placozoans to date. In Japan, placozoans have been found at nine locations, but 16S genotyping has been performed at only two of these locations. Here, we propose a new processing protocol, “ethanol‐treated substrate sampling,” for collecting placozoans from natural environments. We also report the collection of placozoans from three new locations, the islands of Shikine‐jima, Chichi‐jima, and Haha‐jima, and we present the distribution of the 16S haplotypes of placozoans in Japan. Multiple surveys conducted at multiple locations yielded five haplotypes that were not reported previously, revealing high genetic diversity in Japan, especially at Shimoda and Shikine‐jima Island. The observed geographic distribution patterns were different among haplotypes; some were widely distributed, while others were sampled only from a single location. However, samplings conducted on different dates at the same sites yielded different haplotypes, suggesting that placozoans of a given haplotype do not inhabit the same site constantly throughout the year. Continued sampling efforts conducted during all seasons at multiple locations worldwide and the development of molecular markers within the haplotypes are needed to reveal the geographic distribution pattern and dispersal history of placozoans in greater detail.     

The effect of filtration method on the efficiency of environmental DNA capture and quantification via metabarcoding

Environmental DNA (eDNA) is a promising tool for rapid and noninvasive biodiversity monitoring. eDNA density is low in environmental samples, and a capture method, such as filtration, is often required to concentrate eDNA for downstream analyses. In this study, six treatments, with differing filter types and pore sizes for eDNA capture, were compared for their efficiency and accuracy to assess fish community structure with known fish abundance and biomass via eDNA metabarcoding. Our results showed that different filters (with the exception of 20‐μm large‐pore filters) were broadly consistent in their DNA capture ability. The 0.45‐μm filters performed the best in terms of total DNA yield, probability of species detection, repeatability within pond and consistency between ponds. However performance of 0.45‐μm filters was only marginally better than for 0.8‐μm filters, while filtration time was significantly longer. Given this trade‐off, the 0.8‐μm filter is the optimal pore size of membrane filter for turbid, eutrophic and high fish density ponds analysed here. The 0.45‐μm Sterivex enclosed filters performed reasonably well and are suitable in situations where on‐site filtration is required. Finally, prefilters are applied only if absolutely essential for reducing the filtration time or increasing the throughput volume of the capture filters. In summary, we found encouraging similarity in the results obtained from different filtration methods, but the optimal pore size of filter or filter type might strongly depend on the water type under study.     

Linkage disequilibrium clustering‐based approach for association mapping with tightly linked genomewide data

Genomewide association studies (GWAS) aim to identify genetic markers strongly associated with quantitative traits by utilizing linkage disequilibrium (LD) between candidate genes and markers. However, because of LD between nearby genetic markers, the standard GWAS approaches typically detect a number of correlated SNPs covering long genomic regions, making corrections for multiple testing overly conservative. Additionally, the high dimensionality of modern GWAS data poses considerable challenges for GWAS procedures such as permutation tests, which are computationally intensive. We propose a cluster‐based GWAS approach that first divides the genome into many large nonoverlapping windows and uses linkage disequilibrium network analysis in combination with principal component (PC) analysis as dimensional reduction tools to summarize the SNP data to independent PCs within clusters of loci connected by high LD. We then introduce single‐ and multilocus models that can efficiently conduct the association tests on such high‐dimensional data. The methods can be adapted to different model structures and used to analyse samples collected from the wild or from biparental F₂ populations, which are commonly used in ecological genetics mapping studies. We demonstrate the performance of our approaches with two publicly available data sets from a plant (Arabidopsis thaliana) and a fish (Pungitius pungitius), as well as with simulated data.     

STABILIZING SELECTION AND THE COMPARATIVE ANALYSIS OF ADAPTATION

Comparative studies tend to differ from optimality and functionality studies in how they treat adaptation. While the comparative approach focuses on the origin and change of traits, optimality studies assume that adaptations are maintained at an optimum by stabilizing selection. This paper presents a model of adaptive evolution on a macroevolutionary time scale that includes the maintenance of traits at adaptive optima by stabilizing selection as the dominant evolutionary force. Interspecific variation is treated as variation in the position of adaptive optima. The model illustrates how phylogenetic constraints not only lead to correlations between phylogenetically related species, but also to imperfect adaptations. From this model, a statistical comparative method is derived that can be used to estimate the effect of a selective factor on adaptive optima in a way that would be consistent with an optimality study of adaptation to this factor. The method is illustrated with an analysis of dental evolution in fossil horses. The use of comparative methods to study evolutionary trends is also discussed.     

PHYLOGENETIC ANALYSIS OF PHENOTYPIC COVARIANCE STRUCTURE. I. CONTRASTING RESULTS FROM MATRIX CORRELATION AND COMMON PRINCIPAL COMPONENT ANALYSES

Applications of quantitative techniques to understanding macroevolutionary patterns typically assume that genetic variances and covariances remain constant. That assumption is tested among 28 populations of the Phyllotis darwini species group (leaf-eared mice). Phenotypic covariances are used as a surrogate for genetic covariances to allow much greater phylogenetic sampling. Two new approaches are applied that extend the comparative method to multivariate data. The efficacy of these techniques are compared, and their sensitivity to sampling error examined. Pairwise matrix correlations of correlation matrices are consistently very high (> 0.90) and show no significant association between matrix similarity and phylogenetic relatedness. Hierarchical decomposition of common principal component (CPC) analyses applied to each clade in the phylogeny rejects the hypothesis that common principal component structure is shared in clades more inclusive than subspecies. Most subspecies also lack a common covariance structure as described by the CPC model. The hypothesis of constant covariances must be rejected, but the magnitudes of divergence in covariance structure appear to be small. Matrix correlations are very sensitive to sampling error, while CPC is not. CPC is a powerful statistical tool that allows detailed testing of underlying patterns of covariation.     

Statistical methods for estimating doubling time in in vitro cell growth

Summary Doubling time has been widely used to represent the growth pattern of cells. A traditional method for finding the doubling time is to apply gray-scaled cells, where the logarithmic transformed scale is used. As an alternative statistical method, the log-linear model was recently proposed, for which actual cell numbers are used instead of the transformed gray-scaled cells. In this paper, I extend the log-linear model and propose the extended log-linear model. This model is designed for extra-Poisson variation, where the log-linear model produces the less appropriate estimate of the doubling time. Moreover, I compare statistical properties of the gray-scaled method, the log-linear model, and the extended log-linear model. For this purpose, I perform a Monte Carlo simulation study with three data-generating models: the additive error model, the multiplicative error model, and the overdispersed Poisson model. From the simulation study, I found that the gray-scaled method highly depends on the normality assumption of the gray-scaled cells; hence, this method is appropriate when the error model is multiplicative with the log-normally distributed errors. However, it is less efficient for other types of error distributions, especially when the error model is additive or the errors follow the Poisson distribution. The estimated standard error for the doubling time is not accurate in this case. The log-linear model was found to be efficient when the errors follow the Poisson distribution or nearly Poisson distribution. The efficiency of the log-linear model was decreased accordingly as the overdispersion increased, compared to the extended log-linear model. When the error model is additive or multiplicative with Gamma-distributed errors, the log-linear model is more efficient than the gray-scaled method. The extended log-linear model performs well overall for all three data-generating models. The loss of efficiency of the extended log-linear model is observed only when the error model is multiplicative with log-normally distributed errors, where the gray-scaled method is appropriate. However, the extended log-linear model is more efficient than the log-linear model in this case.     

刚竹毒蛾幼虫空间分布型及其在实践中的应用

本应用比较频次分布法,聚集度指标法和相关系数法,测定了刚竹毒蛾(Panatana phyllostachysae Chao)幼虫的空间分布型;同时用刀切法对其种群的平均拥挤度(m^*)进行估计。研究结果表明,其分布型属于负二项分布,其分布的基本成分为个体群,中分析并计算了林间调查的最适抽样数,列出幼虫序贯抽样分析表。     

An insect cell line discrimination method by RAPD-PCR

Summary RAPD-PCR with a tenmar single primer for discrimination of insect cell lines was devised. The base sequence of the primers used were TTCGAGCCAG, CCGCATCTAC, GAACGGACTC, and TGAGTGGGTG (GC contents were 60%). Genome DNA was extracted by modified Landry et al. (1993) method. The reaction mixture consisted of 10 μl buffer, 8 μl dNTP mixture (2.5 mM each), 4 μl primer (50 μM), Taq DNA polymerase (2.5 units), 1 μl template DNA; and the reaction was run at 94° C for 2 min (denaturation), followed by 31 cycles of 94° C for 1 min, 42° C for 1 min (annealing), and 72° C for 2 min (extension) and terminated with 72° C for 7 min. By developing the reaction products with agarose gel electrophoresis, it became evident that DNA fragments were amplified with all the primers used. Among four primers, the second primer was selected as a suitable primer for distinguishing cell lines. With this method, cell lines derived from different species were clearly distinguished.