肺纤维化疾病早期生物标志物研究进展

肺纤维化疾病是由多种原因引起的弥漫性间质性肺疾病的统称,是众多肺部疾病发展的晚期结局。目前针对肺纤维化尚没有特效药和措施可以逆转。肺纤维化疾病早期症状并不明显,呈进行性发展,通常被确诊时已发展为晚期肺纤维化。然而,早期采取措施可以获得很好地预防效果。因此寻找用于肺纤维化及其进程评价的早期生物效应标志物,已成为肺纤维化肺疾病研究中迫切需要解决的问题。近年来研究发现在肺纤维化早期,SP-A、SP-D、PDGF、KL-6及CC16等一种或几种因子在血液中的浓度或状态改变对早期诊断或筛查肺纤维化相关疾病具有重要意义。本文就近几年提出的早期生物标志物在肺纤维化早期诊断的作用进行综述,为肺纤维化疾病防治提供思路。     

细胞休眠在肿瘤耐药和复发中的作用

目前对于肿瘤的药物治疗已经取得了一定的进展,然而,治疗后残存肿瘤细胞的复发仍然是导致肿瘤治疗失败的主要原因．肿瘤细胞休眠在肿瘤复发及耐药中发挥着重要的作用．近年来,研究发现上皮间质转化(epithelial-mesenchymal transition,EMT)的肿瘤细胞,肿瘤干细胞(cancer stem cells,CSCs)以及循环肿瘤细胞(circulating tumor cells,CTCs)都显示出细胞周期阻滞的状态．因此,对于休眠阶段肿瘤细胞的研究将可能促进肿瘤的治疗及预防肿瘤的复发．本文总结了药物治疗诱导肿瘤细胞发生休眠的具体过程,同时认为休眠是肿瘤细胞面对药物治疗所采取的主动防御措施,而非被动逃避过程．探究药物诱导性肿瘤细胞的休眠机制对于靶向休眠肿瘤细胞治疗及提高临床治疗效果都具有非常重要的意义．     

临床蛋白质组学———蛋白质组学在临床研究中的应用

临床蛋白质组学是将蛋白质组学技术应用于临床医学研究,它主要围绕疾病的预防、早期诊断和治疗等方面开展研究,其中,恶性肿瘤是临床蛋白质组学研究的一个重点研究对象.由于肿瘤生物标志物对早期诊断具有重要价值,所以临床蛋白质组学的主要目标之一是寻找合适的肿瘤生物标志物,多分子生物标志物已成为寻找肿瘤生物标志物的一个研究趋势.简要介绍了临床蛋白质组学的基本概念,实验设计,临床样本收集与预处理以及蛋白质组学技术在临床研究中的应用与进展.     

肿瘤心脏病学的生理学基础

肿瘤心脏病学是对肿瘤病人在其恶性肿瘤的治疗期间所出现的心脏毒性的早期诊断、预防、治疗和监测。随着肿瘤学基础研究的深入及临床治疗的飞速发展,肿瘤病人的病死率逐年降低,从而引起肿瘤幸存者人数逐年升高。不幸的是,所有的抗肿瘤药物都具有不同程度的心血管毒性,所以肿瘤幸存者往往伴有心血管系统的并发症。其次,随着人口的老龄化,肿瘤与心血管疾病共患病人数逐年增加。再者,由于肿瘤与某些心血管疾病(如房颤)具有"共同发病机理",进一步增加了肿瘤和心血管疾病共患病的人数。因此,肿瘤或心血管病专家在管理越来越多的肿瘤-心脏病共患病病人时遇到了前所未有的挑战。为了全面、有效、及科学地管理肿瘤-心脏病共患病病人,预防肿瘤病人因肿瘤的治疗而诱发心血管疾病,及避免肿瘤患者因继发心脏疾病或心脏疾病患者因继发肿瘤而影响对其首发病的治疗,因而产生了肿瘤心脏病学。肿瘤心脏病学的目的在于联合心脏病和肿瘤专家、及基础、转化医学、和临床研究科学家来共同增进肿瘤幸存者心血管系统的健康。肿瘤心脏病学涉及的领域主要包括:对肿瘤患者的原发肿瘤及肿瘤患者同发或继发的心脏疾病的临床治疗;对肿瘤患者因放射及化学药物治疗而引起的心脏毒性的早期诊断;对肿瘤病人继发心脏疾病危险因素分层及预防;以及对肿瘤患者心脏病的治疗及对放化疗所引起的心脏毒性的治疗和监测。因此,肿瘤心脏病学不单是对肿瘤病人出现心脏并发症的临床治疗学,而且包括了推动肿瘤心脏病学赖以发展的基础研究,以阐明肿瘤心脏病的发病机理,从而为肿瘤心脏病的早期诊断、治疗、预防、及监测提供理论基础。     

妊娠合并恶性肿瘤疾病的诊疗探讨

目的:探讨妊娠合并恶性肿瘤在诊治过程中的临床表现,探讨其对妊娠结局的影响及预后.方法:回顾性查阅2005年至2011年9月于我院住院治疗患者病历,收集妊娠合并恶性肿瘤疾病患者10例在入院诊断、治疗经过中的相关资料.结果:10例患者中9例在终止妊娠前确诊,3例孕妇死亡,5例在随访中,2例失访;10例胎儿中有生机儿5例,其中1例放弃,在可随访的3例新生儿生存状态良好.结论:妊娠合并恶性肿瘤疾病的临床确诊及及时治疗对妊娠结局会造成影响,妊娠结局与肿瘤分期及孕周相关,应在产前进行常见恶性肿瘤疾病的筛查,早期发现,早期干预.     

SELDI-TOF-MS技术在临床肿瘤蛋白质组学研究中的应用

恶性肿瘤严重地威胁着人类的健康和生命。多年来,人类一直在为治疗肿瘤而奋斗。众所周知,恶性肿瘤的早期发现、早期诊断、早期治疗是提高大多数恶性肿瘤患者治疗效果的关键。同时,肿瘤的早期发现也能减少肿瘤转移和播散的机会。癌症发现、确诊得越早,治疗成功的可能性就越大,患者的生存率也就会越高。目前,SELDI-TOF-MS技术在临床肿瘤蛋白质组学研究中已经得到了普遍应用,研究的范围几乎覆盖所有的常见肿瘤,为临床肿瘤的蛋白质组学研究奠定了良好的基础。以下概要介绍SELDI—TOF-MS技术在部分肿瘤中的研究应用。     

表观遗传生物标志物在人类疾病早期诊治中的研究进展

表观遗传修饰异常见于人类的多种疾病(如肿瘤、老年性疾病、发育源性疾病等),影响着这些疾病的发生发展。已有的研究表明,异常表观遗传改变可以作为疾病状态和疾病预测的生物标志物。表观遗传修饰改变的可逆性和可控性也为疾病早期的预防和治疗提供了新策略。本文对DNA甲基化修饰、组蛋白共价修饰、非编码RNA等三种表观遗传方式在肿瘤、老年性疾病和发育源性疾病的研究,以及三者作为表遗传生物标志物在疾病早期诊断和治疗的应用展开介绍,以期为肿瘤、老年性和发育源性相关疾病的诊断与治疗提供借鉴和 参考。     

环状RNA的功能及其在结直肠癌中的作用

环状RNA(circular RNA, circRNA)是一类普遍存在于生物体内的闭合环状非编码RNA,能够抵抗核酸外切酶的水解作用,参与基因的表达调控过程。结直肠癌(colorectal cancer, CRC)是一类常见的恶性肿瘤,其全球发病率和死亡率居所有肿瘤前位,目前亟需一种有效的筛查手段。circRNA参与多种疾病(包括肿瘤)的发生发展,有望成为肿瘤早期筛查与诊断的生物标志物以及判断预后的指标,同时成为治疗肿瘤新的靶点。本文简要综述了环状RNA的形成和生物学功能,以及在结直肠癌中的研究进展,旨在为结直肠癌的早期诊断和治疗提供参考。     

死亡受体信号通路耐受机制及肿瘤治疗

死亡受体介导的凋亡通路是治疗肿瘤的理想方案。凋亡通路中具有复杂的调控机制,控制细胞的生死存亡。其中有多类抗凋亡因子,致使肿瘤细胞对凋亡信号耐受,使得凋亡在肿瘤治疗领域的应用受限。临床前体外细胞及裸鼠研究发现,单独靶向这些死亡受体或抗凋亡因子的药物或与传统化疗联合可以有效引起肿瘤细胞凋亡,但临床II期实验没有明显治疗效果。本综述总结分析多种抗凋亡因子产生的耐受机制以及靶向药物的研究现状,提出同时靶向死亡受体、c-FLIP及IAP是治疗肿瘤的理想方案。     

死亡受体信号通路耐受机制及肿瘤治疗CSCD

死亡受体介导的凋亡通路是治疗肿瘤的理想方案。凋亡通路中具有复杂的调控机制,控制细胞的生死存亡。其中有多类抗凋亡因子,致使肿瘤细胞对凋亡信号耐受,使得凋亡在肿瘤治疗领域的应用受限。临床前体外细胞及裸鼠研究发现,单独靶向这些死亡受体或抗凋亡因子的药物或与传统化疗联合可以有效引起肿瘤细胞凋亡,但临床II期实验没有明显治疗效果。本综述总结分析多种抗凋亡因子产生的耐受机制以及靶向药物的研究现状,提出同时靶向死亡受体、c-FLIP及IAP是治疗肿瘤的理想方案。     

A bayesian random-effects markov model for tumor progression in women with a family history of breast cancer

SUMMARY: It makes intuitive sense to model the natural history of breast cancer, tumor progression from preclinical screen-detectable phase (PCDP) to clinical disease, as a multistate process, but the multilevel structure of the available data, which generally comes from cluster (family)-based service screening programs, makes the required parameter estimation intractable because there is a correlation between screening rounds in the same individual, and between subjects within clusters (families). There is also residual heterogeneity after adjusting for covariates. We therefore proposed a Bayesian hierarchical multistate Markov model with fixed and random effects and applied it to data from a high-risk group (women with a family history of breast cancer) participating in a family-based screening program for breast cancer. A total of 4867 women attended (representing 4464 families) and by the end of 2002, a total of 130 breast cancer cases were identified. Parameter estimation was carried out using Markov chain Monte Carlo (MCMC) simulation and the Bayesian software package WinBUGS. Models with and without random effects were considered. Our preferred model included a random-effect term for the transition rate from preclinical to clinical disease (sigma(2)(2f)), which was estimated to be 0.50 (95% credible interval = 0.22-1.49). Failure to account for this random effect was shown to lead to bias. The incorporation of covariates into multistate models with random effect not only reduced residual heterogeneity but also improved the convergence of stationary distribution. Our proposed Bayesian hierarchical multistate model is a valuable tool for estimating the rate of transitions between disease states in the natural history of breast cancer (and possibly other conditions). Unlike existing models, it can cope with the correlation structure of multilevel screening data, covariates, and residual (unexplained) variation.     

An age dependent stochastic model of periodic screening: Basic properties

An age dependent stochastic model for the periodic screening of a progressive chronic disease is developed in this paper by combining results from previous modeling efforts. The basic concepts used are the random variables describing the disease free state and preclinical state sojourn times and the age at screening or observation, as well as generations of individuals defined according to time of entry into the preclinical state. At discrete time points, the model characterizes the density functions for individuals who are healthy, have preclinical disease, or have clinical disease. The joint density functions of age and sojourn times for cases detected by a periodic screening program and for cases which surface clinically between screens are derived as functions of screening interval, false negative rate, and disease natural history.     

Slip-induced fall-risk assessment based on regular gait pattern in older adults

Aging-associated fall-risk assessment is crucial for fall prevention. Thus, this study aimed to develop a prognostic model to predict fall-risk following an unexpected over-ground slip perturbation based on normal gait pattern in healthy older adults. 112 healthy older adults who experienced a novel slip in a safe laboratory environment were included. Their slip trial and natural walking trial immediately prior to it were analyzed. To identify the best fall-risk predictive model, gait related variables including step length, segment angles, center of mass state, and ground reaction force (GRF) were determined and inputted into a stepwise logistic regression. The optimal slip-induced fall prediction model was based on the right thigh angle at slipping foot touchdown (TD), the maximum GRF of the slipping limb after TD, and the momentum change from TD to recovery foot liftoff (LO), with an overall prediction accuracy of 75.9%, predicting 74.5% of falls (sensitivity) and 77.2% of recoveries (specificity). Conversely, a model based on clinical and demographic measures predicted 78.2% of falls and 47.4% of recoveries, resulting in a much lower overall accuracy of 62.5%. The fall-risk model based on normal gait pattern which was developed for slip-induced perturbations in healthy older adults was able to provide a high predictive accuracy. This information could provide insight about the ideal normal gait measures which could be used to contribute towards development of therapeutic strategies related to dynamic balance and fall prevention to enhance preventive interventions in populations with high-risk for slip-induced falls.     

Distinguishing Extinction and Natural Selection in the Anthropocene: Preventing the Panda Paradox through Practical Education Measures

In the midst of only the 6th mass extinction in the Earth's history, we must rethink how we teach evolution to prevent natural selection from being incorrectly used as a biological justification for inaction in the face of today's human-caused mass extinction crisis. Pundits, policy makers, and the general public regularly identify the extinction of endangered species as natural selection at work, rather than attributing modern-day extinction to the sudden catastrophic bad luck of human caused environmental change, a phenomenon distinct from natural selection. In this natural selection framing, the inability of species to survive in human altered environments is the normal progression of “survival of the fittest” and conservation measures designed to protect species is human interference with natural selection. Paradoxically, this erroneous framing of extinction as the normal course of natural selection ignores humanity's exceptional role in causing today's mass extinction crisis. Our examination of this issue in U.S. college students indicates that it arises from misunderstanding the role of extinction in the history of life, leading us to recommend a greater teaching emphasis on the distinction between extinction and natural selection, and on past mass extinction events. Also see the video abstract here https://youtu.be/29VRyirMdiw     

Long-lived charge-separated states in bacterial reaction centers isolated from Rhodobacter sphaeroides

We studied the accumulation of long-lived charge-separated states in reaction centers isolated from Rhodobacter sphaeroides, using continuous illumination, or trains of single-turnover flashes. We found that under both conditions a long-lived state was produced with a quantum yield of about 1%. This long-lived species resembles the normal P(+)Q(-) state in all respects, but has a lifetime of several minutes. Under continuous illumination the long-lived state can be accumulated, leading to close to full conversion of the reaction centers into this state. The lifetime of this accumulated state varies from a few minutes up to more than 20 min, and depends on the illumination history. Surprisingly, the lifetime and quantum yield do not depend on the presence of the secondary quinone, Q(B). Under oxygen-free conditions the accumulation was reversible, no changes in the normal recombination times were observed due to the intense illumination. The long-lived state is responsible for most of the dark adaptation and hysteresis effects observed in room temperature experiments. A simple method for quinone extraction and reconstitution was developed.     

Feeding activity pattern in a parasitic wasp when foraging in the field

Individual decision-making and behavioral plasticity, and hence reproductive success, depend on nutritional state. Despite the importance of food for life-history functions, when and how often parasitoids encounter and consume food in natural settings remain largely unknown. In this study, we aimed at determining the food intake history of the Venturia canescens parasitoids in field conditions. To fulfill this objective, we compared the nutritional state of lab individuals of known feeding history and of field-released individuals allowed to freely forage under natural conditions. Nutritional state and feeding history were determined by using a combination of physiological parameters based on sugar assays. Field-released wasps were caught during periods starting either 5 or 24 h after release. Our results show that in the field, the wasps search actively for carbohydrate food sources. Our work also indicates that feeding activity of wasps in the field can be better understood by using the combination of key physiological parameters. The adaptive value of the feeding pattern and the relevance of the approach used are discussed.     

Multiple imputation for discrete data: Evaluation of the joint latent normal model

Missing data are ubiquitous in clinical and social research, and multiple imputation (MI) is increasingly the methodology of choice for practitioners. Two principal strategies for imputation have been proposed in the literature: joint modelling multiple imputation (JM‐MI) and full conditional specification multiple imputation (FCS‐MI). While JM‐MI is arguably a preferable approach, because it involves specification of an explicit imputation model, FCS‐MI is pragmatically appealing, because of its flexibility in handling different types of variables. JM‐MI has developed from the multivariate normal model, and latent normal variables have been proposed as a natural way to extend this model to handle categorical variables. In this article, we evaluate the latent normal model through an extensive simulation study and an application on data from the German Breast Cancer Study Group, comparing the results with FCS‐MI. We divide our investigation in four sections, focusing on (i) binary, (ii) categorical, (iii) ordinal, and (iv) count data. Using data simulated from both the latent normal model and the general location model, we find that in all but one extreme general location model setting JM‐MI works very well, and sometimes outperforms FCS‐MI. We conclude the latent normal model, implemented in the R package jomo , can be used with confidence by researchers, both for single and multilevel multiple imputation.     

The immunological homunculus (immunculus) in normal state and pathology

The immunculus is considered as the system (general network) of constitutively expressed natural autoantibodies against different extracellular, membrane, cytoplasmic, and nuclear self-antigens (ubiquitous and organ-specific). It is specially noted that the repertoires of natural autoantibodies are surprisingly constant in healthy persons, independent of gender and age, and characterized by only minimal individual peculiarities (individual immune fingerprints). On the other hand, abnormal metabolic changes which precede the clinical manifestation of different diseases showed easily detected changes, rather quantitative than qualitative, in the systems of natural autoantibodies in patients' sera (immunculus distortions). This phenomena could supposedly be used for "mapping" the state of physiological norm in terms of the millions of natural autoantibody repertoires, and for elaboration of the methods for early (preclinical) detection of potentially pathogenic metabolic changes. Could the individual features of the general network of constitutively expressed natural autoantibodies reflect the functional state of the body and be used for "mapping" of normal and pathological functional state? Could the changes in production of some biologically active natural autoantibodies not only reflect the state of the body, but be used for partial compensation of functional deficiency of certain molecular systems? These and related questions are discussed in this article. The research project "immunculus" is proposed for international cooperative investigations.     

An early- and late-stage convolution model for disease natural history

The standard convolution model of disease natural history posits an asymptomatic (preclinical) and a symptomatic (clinical) state. An augmented model includes, in both the preclinical and clinical states, an early and late stage of disease. In the case of cancer, the early stage would generally correspond to the organ-confined stages before there is evidence of cancer spread. We compute the number of screen-detected (preclinical) and clinical cases in the early and late stages expected under a given screening program and show how the model can be fit to data from a screening trial using maximum likelihood. We also develop expressions for sojourn time, lead time, and overdiagnosis in the context of the model, where each of the above concepts incorporates disease stage. As an example, we fit the model to data from the Mayo Lung Cancer Screening trial.     

Estimating causal treatment effects from longitudinal HIV natural history studies using marginal structural models

Several recently completed and ongoing studies of the natural history of HIV infection have generated a wealth of information about its clinical progression and how this progression is altered by therepeutic interventions and environmental factors. Natural history studies typically follow prospective cohort designs, and enroll large numbers of participants for long-term prospective follow-up (up to several years). Using data from the HIV Epidemiology Research Study (HERS), a six-year natural history study that enrolled 871 HIV-infected women starting in 1993, we investigate the therapeutic effect of highly active antiretroviral therapy regimens (HAART) on CD4 cell count using the marginal structural modeling framework and associated estimation procedures based on inverse-probability weighting (developed by Robins and colleagues). To evaluate treatment effects from a natural history study, specialized methods are needed because treatments are not randomly prescribed and, in particular, the treatment-response relationship can be confounded by variables that are time-varying. Our analysis uses CD4 data on all follow-up visits over a two-year period, and includes sensitivity analyses to investigate potential biases attributable to unmeasured confounding. Strategies for selecting ranges of a sensitivity parameter are given, as are intervals for treatment effect that reflect uncertainty attributable both to sampling and to lack of knowledge about the nature and existence of unmeasured confounding. To our knowledge, this is the first use in "real data" of Robins's sensitivity analysis for unmeasured confounding (Robins, 1999a, Synthese 121, 151-179). The findings from our analysis are consistent with recent treatment guidelines set by the U.S. Panel of the International AIDS Society (Carpenter et al., 2000, Journal of the American Medical Association 280, 381-391).