点击功能化的叶酸受体靶向荧光纳米探针用于肿瘤细胞成像

采用点击化学偶联法对荧光二氧化硅纳米粒子表面进行叶酸功能化修饰,构建了一种叶酸受体靶向的荧光纳米探针,并成功用于肿瘤细胞的成像研究.首先通过St?ber法制备包裹钌联吡啶的荧光二氧化硅纳米粒子(RSiNPs),然后利用叠氮化硅烷偶联剂(Az-PTES)的水解反应在其表面引入叠氮基团,最后通过点击化学反应将炔丙基叶酸衍生物偶联到粒子表面.利用红外光谱对其偶联前后的叠氮基特征峰(2105 cm-1)进行表征,证实了叶酸功能化的荧光纳米探针(RSiNPs-Folate)已被成功制备.在生理pH条件下,以458 nm为激发波长,RSiNPs-Folate在601 nm处发射较强的红色荧光,且光稳定性较好.细胞成像结果表明,这种叶酸受体靶向的荧光纳米探针能够有效地标记叶酸受体呈阳性的人宫颈癌细胞(HeLa),而叶酸受体呈阴性的人肺癌细胞(A549)未观察到明显的荧光.叶酸竞争性结合实验证明了这种叶酸受体介导的肿瘤细胞成像机制.此探针能够实现混合细胞体系中HeLa细胞的选择性识别与荧光成像.与酰胺化反应偶联叶酸相比,这种点击功能化的纳米探针的合成方法简单、反应条件温和、产率高,可用于不同肿瘤细胞的荧光标记与成像.     

Oscillatory expression and variability in p53 regulatory network

The effect of delay, nonlinearity and noise on oscillatory motion is of permanent interest for theoretical and experimental research. Here we explore a negative feedback loop between p53 and Mdm2 with a time delay, which is a key circuit in the response of cells to damage. This circuit shows noisy sustained oscillations in individual human cells following DNA damage, and damped oscillations at the cell population level. We demonstrate the effect of delay on the oscillation, and the correlation in time course. In a multi-species system, the events at different time points which span a time delay are coupled even when the delay is large compared with the other characteristic times of the system. We also clarify that the dynamics at the single-cell level appears to be coherent resonance, and the origin of the damped oscillation at the macroscopic level out of the sustained ones at the single-cell level can be ascribed to the dephasing process which is induced by the interplay between nonlinearity and noise. The findings are consistent with experimental observations and advance our understanding of the dynamics of the p53 network.     

Using a partially observable Markov chain model to assess colonoscopy screening strategies – A cohort study

Colorectal cancer (CRC) is notoriously hard to combat for its high incidence and mortality rates. However, with improved screening technology and better understanding of disease pathways, CRC is more likely to be detected at early stage and thus more likely to be cured. Among the available screening methods, colonoscopy is most commonly used in the U.S. because of its capability of visualizing the entire colon and removing the polyps it detected. The current national guideline for colonoscopy screening recommends an observation-based screening strategy. Nevertheless, there is scant research studying the cost-effectiveness of the recommended observation-based strategy and its variants. In this paper, we describe a partially observable Markov chain (POMC) model which allows us to assess the cost-effectiveness of both fixed-interval and observation-based colonoscopy screening strategies. In our model, we consider detailed adenomatous polyp states and estimate state transition probabilities based on longitudinal clinical data from a specific population cohort. We conduct a comprehensive numerical study which investigates several key factors in screening strategy design, including screening frequency, initial screening age, screening end age, and screening compliance rate. We also conduct sensitivity analyses on the cost and quality of life parameters. Our numerical result demonstrates the usability of our model in assessing colonoscopy screening strategies with consideration of partial observation of true health states. This research facilitates future design of better colonoscopy screening strategies.     

血清一阶导数荧光光谱诊断早期恶性肿瘤

利用Wistar大鼠接种恶性肉瘤模拟人患上癌症。取Wistar大鼠眼静脉血制备血清,并用乙醇简单处理得上层清液,扫描获得其一阶导数荧光光谱,确定上层清液原卟啉发射带(630 nm附近)的峰高,观察到健康Wistar大鼠与癌变大鼠的血清样存在明显差异,10例癌变样假阳性率为0,而20例正常样中仅有1例假阳性达到了恶性肿瘤早期诊断的目的。     

具有图象处理功能的激光荧光内窥系统的研究

内窥镜荧光图象系统是体腔内早期肿瘤诊断和定位的有效手段.但是早期开发研究的LFE荧光成家系统由于存在假阳性和假阴性误诊而限制了它的广泛开展和应用.本文仔细探讨了该系统产生误诊的原因,并在此基础上提出了使用计算机图象处理技术的荧光图象系统,而且通过实验验证了这种新技术,它能有效地克服假阳性和假阴性误诊,为体腔内肿瘤的诊断提供可靠判据.     

Electrochemical Detection of a Cancer Biomarker mir‐21 in Cell Lysates Using Graphene Modified Sensors

In the present study, the voltammetric and impidimetric detection of microRNA‐21, mir‐21 from cell lysates was investigated for the first time by using graphene modified disposable pencil graphite electrodes (GME). The surface characterization of GME was performed via electrochemical impedance spectroscopy (EIS) and scanning electron microscopy (SEM). Upon passive adsorption of inosine substituted antimicroRNA‐21, antimir‐21 probe, InP, onto the surface of GME and then solid phase hybridization of InP with mir‐21, the target, the electrochemical detection was performed by using Differential Pulse Voltammetry (DPV) and EIS techniques. This developed biosensor, GME has presented a 2.77 times lower detection limit of 2.09 µg/mL (3.12 pmol) with respect to unmodified pencil graphite electrode (GE). Moreover it is capable of analyzing mir‐21 in the cell lysates of mir‐21 positive breast cancer cell line (MCF‐7) contrast to mir‐21 negative hepatoma cell line (HUH‐7). The proposed electrochemical yes‐no system does not require any purification and/or amplification step prior to fast detection of mir‐21 from real samples.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease–gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.     

Raman spectroscopy monitors adverse bone sequelae of cancer radiotherapy

Raman spectroscopy provides information on bone chemical composition and structure via widely used metrics including mineral to matrix ratio, mineral crystallinity and carbonate content, collagen crosslinking ratio and depolarization ratios. These metrics are correlated with bone material properties, such as hardness, plasticity and Young''s modulus. We review application of Raman spectroscopy to two important irradiated animalmodels: the mouse tibia, amodel for damage to cortical bone sites including the rib (breast cancer) and to healthy tissue adjacent to extremity sarcomas, and the rat mandible, a model for radiation damage in head and neck cancer radiotherapy. Longitudinal studies of irradiated mouse tibia demonstrate that radiation-induced matrix abnormalities can persist even 26 weeks postradiation. Polarized Raman spectroscopy shows formation of more ordered orientation of both mineral and collagen. At 8 weeks post-radiation, irradiated rat hemimandible exhibits transient hypermineralization, increased collagen cross-linking and decreased depolarization ratios of mineral and collagen. A standard radioprotectant, amifostine, mitigates rat mandible radiation damage, with none remaining detectable 18 weeks post-radiation. Already a powerful tool to monitor radiation damage, Raman spectroscopy may be important in development of new radiotherapy protocols and radioprotective agents. Further in vivo studies of radiation effects on the rodent models are underway, as are development of methodologies for eventual use in human subjects.