胎盘特异性基因4 mRNA基因在孕妇外周血中的检测及临床价值

目的:应用实时荧光定量PCR(RT-PCR)技术对不同孕周孕妇外周血浆胎盘特异性基因4(PLAC4)m RNA基因进行检测,寻找唐氏综合征产前诊断的可靠生物学标志物,为无创性产前诊断提供新的突破口。方法:按入组标准随机选取健康育龄未妊娠女性5例,正常健康妊娠孕妇60例(早期妊娠20例、中期妊娠20例、晚期妊娠20例),唐氏筛查高危孕妇8例,正常分娩24 h女性5例。共收集外周血浆样本78例。应用RT-PCR技术,检测样本中的PLAC4 m RNA基因含量,并进行相对定量分析。结果:健康育龄未妊娠女性及正常分娩后24 h女性外周血浆中均无游离胎儿PLAC4 m RNA基因的存在;正常健康妊娠孕妇不同孕周标本均检测到PLAC4 m RNA基因,以早期妊娠作为对照,中期妊娠是早期妊娠的1.99倍,晚期妊娠是早期妊娠的3.73倍;唐氏筛查高危孕妇均检出PLAC4 m RNA基因,含量是早期妊娠的6.36倍。结论:PLAC4 m RNA基因有望成为唐氏综合征产前诊断的可靠性生物学标志物。     

Evaluation of non‐invasive prenatal testing to detect chromosomal aberrations in a Chinese cohort

The aim of this study was to evaluate the clinical feasibility of non‐invasive prenatal testing (NIPT) to detect foetal copy number variations (CNVs). Next‐generation sequencing for detecting foetal copy number variations (CNVs) was performed on the collected samples from 161 pregnancies with ultrasound anomalies and negative NIPT results for aneuploidy. The performance of NIPT for detecting chromosome aberrations was calculated. The sensitivity and specificity of NIPT for detecting CNVs > 1 Mb were 83.33% and 99.34%; the PPV and negative predictive rate (NPV) were 90.91% and 98.68%. Non‐invasive prenatal testing can be performed to detect chromosomal aberrations in first trimester with high performance for CNVs, and occasional discordant cases are unavoidable.     

Modeling optimal responses and fitness consequences in a changing Arctic

Animals must balance a series of costs and benefits while trying to maximize their fitness. For example, an individual may need to choose how much energy to allocate to reproduction versus growth, or how much time to spend on vigilance versus foraging. Their decisions depend on complex interactions between environmental conditions, behavioral plasticity, reproductive biology, and energetic demands. As animals respond to novel environmental conditions caused by climate change, the optimal decisions may shift. Stochastic dynamic programming provides a flexible modeling framework with which to explore these trade‐offs, but this method has not yet been used to study possible changes in optimal trade‐offs caused by climate change. We created a stochastic dynamic programming model capturing trade‐off decisions required by an individual adult female polar bear (Ursus maritimus) as well as the fitness consequences of her decisions. We predicted optimal foraging decisions throughout her lifetime as well as the energetic thresholds below which it is optimal for her to abandon a reproductive attempt. To explore the effects of climate change, we shortened the spring feeding period by up to 3 weeks, which led to predictions of riskier foraging behavior and higher reproductive thresholds. The resulting changes in fitness may be interpreted as a best‐case scenario, where bears adapt instantaneously and optimally to new environmental conditions. If the spring feeding period was reduced by 1 week, her expected fitness declined by 15%, and if reduced by 3 weeks, expected fitness declined by 68%. This demonstrates an effective way to explore a species' optimal response to a changing landscape of costs and benefits and highlights the fact that small annual effects can result in large cumulative changes in expected lifetime fitness.     

Reconciling empirical interactions and species coexistence

Coexistence in ecological communities is governed largely by the nature and intensity of species interactions. Countless studies have proposed methods to infer these interactions from empirical data, yet models parameterised using such data often fail to recover observed coexistence patterns. Here, we propose a method to reconcile empirical parameterisations of community dynamics with species‐abundance data, ensuring that the predicted equilibrium is consistent with the observed abundance distribution. To illustrate the approach, we explore two case studies: an experimental freshwater algal community and a long‐term time series of displacement in an intertidal community. We demonstrate how our method helps recover observed coexistence patterns, capture the core dynamics of the system, and, in the latter case, predict the impacts of experimental extinctions. Collectively, these results demonstrate an intuitive approach for reconciling observed and empirical data, improving our ability to explore the links between species interactions and coexistence in natural systems.     

基于光谱技术的产前检测方法研究进展

优生优育是我们重要的国策。自二胎政策颁布以来,高龄产妇数量呈现明显上升的趋势,先天性畸形儿的发生几率变大。传统的产前检测方法存在较高流产风险、灵敏度低、耗时等问题,探寻一种无损快捷、经济灵敏的产前检测方法至关重要。本文首先介绍了常见的胎儿出生缺陷及当前临床常见的产前检测方法的优缺点,重点综述了光谱技术的特点及其在产前检测中的应用。其中包括光谱核型、实时荧光定量PCR、SNP等位基因位点分析等利用荧光光谱分析方法和拉曼光谱在产前检测中的应用情况。特别阐述了表面增强拉曼光谱SERS在生物检测中的优势,最后总结并展望了SERS光谱在产前检测的应用。     

Optimal thermotolerance of Bifidobacterium bifidum in gellan-alginate microparticles

The purpose of this research was to encapsulate Bifidobacterium bifidum using gellan, sodium alginate and prebiotics as coating materials, and to maximize the thermotolerance of the probiotics with an optimal combination of the coating materials. The optimal ratio of the coating materials for the microparticles under heat treatments (75 degrees C, 1 min) was obtained by using the response surface method and the sequential quadratic programming technique. Optimization results indicated that 2% sodium alginate mixed with 1% gellan gum as coating materials would produce the highest thermotolerance in terms of B. bifidum count. The verification experiment yielded a result close to the predicted values, with no significant difference (P > 0.05). The results of heat treatments also demonstrated that the addition of gellan gum in the walls of probiotic microcapsules provided improved protection for B. bifidum. These probiotic counts remained at 10(5)-10(6) CFU/g for the microcapsules stored for 2 months, then treated in heat and in simulated gastric fluid.     

In silico strategies to couple production of bioethanol with growth in cyanobacteria

Cyanobacteria have been considered as promising candidates for sustainable bioproduction from inexpensive raw materials, as they grow on light, carbon dioxide, and minimal inorganic nutrients. In this study, we present a genome-scale metabolic network model for Synechocystis sp. PCC 6803 and study the optimal design of the strain for ethanol production by using a mixed integer linear problem reformulation of a bilevel programming problem that identifies gene knockouts which lead to coupling between growth and product synthesis. Five mutants were found, where the in silico model predicts coupling between biomass growth and ethanol production in photoautotrophic conditions. The best mutant gives an in silico ethanol production of 1.054 mmol·gDW ⁻¹·h ⁻¹.     

Gestational jet lag predisposes to later-life skeletal and cardiac disease

Circadian rhythm disturbance (CRD) increases the risk of disease, e.g. metabolic syndrome, cardiovascular disease, and cancer. In the present study, we investigated later life adverse health effects triggered by repeated jet lag during gestation. Pregnant mice were subjected to a regular light-dark cycle (CTRL) or to a repeated delay (DEL) or advance (ADV) jet lag protocol. Both DEL and ADV offspring showed reduced weight gain. ADV offspring had an increased circadian period, and an altered response to a jet lag was observed in both DEL and ADV offspring. Analysis of the bones of adult male ADV offspring revealed reduced cortical bone mass and strength. Strikingly, analysis of the heart identified structural abnormalities and impaired heart function. Finally, DNA methylation analysis revealed hypermethylation of miR17-92 cluster and differential methylation within circadian clock genes, which correlated with altered gene expression. We show that developmental CRD affects the circadian system and predisposes to non-communicable disease in adult life.     

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead-end elimination (DEE). However, the mathematical assumptions of DEE-based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming-based method. PLUG's biophysical model consists only of well-known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy-based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs—the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).