Nanotechnology has the potential to revolutionize cancer diagnosis and therapy. Advances in protein engineering and materials science have contributed to novel nanoscale targeting approaches that may bring new hope to cancer patients. Several therapeutic nanocarriers have been approved for clinical use. However, to date, there are only a few clinically approved nanocarriers that incorporate molecules to selectively bind and target cancer cells. This review examines some of the approved formulations and discusses the challenges in translating basic research to the clinic. We detail the arsenal of nanocarriers and molecules available for selective tumour targeting, and emphasize the challenges in cancer treatment. Cancer remains one of the world's most devastating diseases, with more than 10 million new cases every year 1. However, mortality has decreased in the past two years 2 owing to better understanding of tumour biology and improved diagnostic devices and treatments. Current cancer treatments include surgical intervention, radiation and chemotherapeutic drugs, which often also kill healthy cells and cause toxicity to the patient. It would therefore be desirable to develop chemotherapeutics that can either passively or actively target cancerous cells. Passive targeting exploits the characteristic features of tumour biology that allow nanocarriers to accumulate in the tumour by the enhanced permeability and retention (EPR) effect 2. Passively targeting nanocarriers first reached clinical trials in the mid-1980s, and the first products, based on liposomes and polymer–protein conjugates, were marketed in the mid-1990s. Later, therapeutic nanocarriers based on this strategy were approved for wider use (Table 1) and methods of further enhancing targeting of drugs to cancer cells were investigated. Active approaches achieve this by conjugating nanocarriers containing chemotherapeutics with molecules that bind to overexpressed antigens or receptors on the target cells. Recent reviews provide perspective on the use of nanotechnology as a fundamental tool in cancer research and nanomedicine 3,4. Here we focus on the potential of nanocarriers and molecules that can selectively target tumours, and highlight the challenges in translating some of the basic research to the clinic. PaSSive anD aCtive targeting Nanocarriers encounter numerous barriers en route to their target, such as mucosal barriers and non-specific uptake 5,6. To address the challenges of targeting tumours with nanotechnology, it is necessary to combine the rational design of nanocarriers with the fundamental understanding of tumour biology (Box 1). General features of tumours include leaky blood vessels and poor lymphatic drainage. Whereas free drugs may diffuse non-specifically, a nanocarrier can …
Studies on monocyte and macrophage biology and differentiation have revealed the pleiotropic activities of these cells. Macrophages are tissue sentinels that maintain tissue integrity by eliminating/repairing damaged cells and matrices. In this M2-like mode, they can also promote tumor growth. Conversely, M1-like macrophages are key effector cells for the elimination of pathogens, virally infected, and cancer cells. Macrophage differentiation from monocytes occurs in the tissue in concomitance with the acquisition of a functional phenotype that depends on microenvironmental signals, thereby accounting for the many and apparently opposed macrophage functions. Many questions arise. When monocytes differentiate into macrophages in a tissue (concomitantly adopting a specific functional program, M1 or M2), do they all die during the inflammatory reaction, or do some of them survive? Do those that survive become quiescent tissue macrophages, able to react as naïve cells to a new challenge? Or, do monocyte-derived tissue macrophages conserve a “memory” of their past inflammatory activation? This review will address some of these important questions under the general framework of the role of monocytes and macrophages in the initiation, development, resolution, and chronicization of inflammation.
INTRODUCTION The Need for Statistics Types of Data The Use of Computers in Statistics PROBABILITY AND DISTRIBUTIONS Probability Populations and Samples Means and Variances The Normal Distribution Sampling Distributions ESTIMATION AND HYPOTHESIS TESTING Estimation of the Population Mean Testing Hypotheses about the Population Mean Population Variance Unknown Comparison of Samples A Pooled Estimate of Variance A SIMPLE EXPERIMENT Randomization and Replication Analysis of a Completely Randomized Design with Two Treatments A Completely Randomized Design with Several Treatments Testing Overall Variation Between the Treatments CONTROL OF RANDOM VARIATION BY BLOCKING Local Control of Variation Analysis of a Randomized Block Design Meaning of the Error Mean Square Latin Square Designs Multiple Latin Squares Design The Benefit of Blocking and the Use of Natural Blocks PARTICULAR QUESTIONS ABOUT TREATMENTS Treatment Structure Treatment Contrasts Factorial Treatment Structure Main Effects and Interactions Analysis of Variance for a Two-Factor Experiment Partial Factorial Structure Comparing Treatment Means - Are Multiple Comparison Methods Helpful? MORE ON FACTORIAL TREATMENT STRUCTURE More than Two Factors Factors with Two Levels The Double Benefit of Factorial Structure Many Factors and Small Blocks The Analysis of Confounded Experiments Split Plot Experiments Analysis of a Split Plot Experiment Experiments Repeated at Different Sites THE ASSUMPTIONS BEHIND THE ANALYSIS Our Assumptions Normality Variance Homogeneity Additivity Transformations of Data for Theoretical Reasons A More General Form of Analysis Empirical Detection of the Failure of Assumptions and Selection of Appropriate Transformations Practice and Presentation STUDYING LINEAR RELATIONSHIPS Linear Regression Assessing the Regression Line Inferences about the Slope of a Line Prediction Using a Regression Line Correlation Testing Whether the Regression is Linear Regression Analysis Using Computer Packages MORE COMPLEX RELATIONSHIPS Making the Crooked Straight Two Independent Variables Testing the Components of a Multiple Relationship Multiple Regression Possible Problems in Computer Multiple Regression LINEAR MODELS The Use of Models Models for Factors and Variables Comparison of Regressions Fitting Parallel Lines Covariance Analysis Regression in the Analysis of Treatment Variation NONLINEAR MODELS Advantages of Linear and Nonlinear Models Fitting Nonlinear Models to Data Inferences about Nonlinear Parameters Exponential Models Inverse Polynomial Models Logistic Models for Growth Curves THE ANALYSIS OF PROPORTIONS Data in the Form of Frequencies The 2 ' 2 Contingency Table More than Two Situations or More than Two Outcomes General Contingency Tables Estimation of Proportions Sample Sizes for Estimating Proportions MODELS AND DISTRIBUTIONS FOR FREQUENCY DATA Models for Frequency Data Testing the Agreement of Frequency Data with Simple Models Investigating More Complex Models The Binomial Distribution The Poisson Distribution Generalized Models for Analyzing Experimental Data Log-Linear Models Logit Analysis of Response Data MAKING AND ANALYZING SEVERAL EXPERIMENTAL MEASUREMENTS Different Measurements on the Same Units Interdependence of Different Variables Repeated Measurements Joint (Bivariate) Analysis Indices of Combined Yield Investigating Relationships with Experimental Data ANALYZING AND SUMMARIZING MANY MEASUREMENTS Introduction to Multivariate Data Principal Component Analysis Covariance or Correlation Matrix Cluster Analysis Similarity and Dissimilarity Measures Hierarchical Clustering Comparison of PCA and Cluster Analysis CHOOSING THE MOST APPROPRIATE EXPERIMENTAL DESIGN The Components of Design Units and Treatments Replication and Precision Different Levels of Variation and Within-Unit Replication Variance Components and Split Plot Designs Randomization Managing with Limited Resources Factors with Quantitative Levels Screening and Selection On-Farm Experiments SAMPLING FINITE POPULATIONS Experiments and Sample Surveys Simple Random Sampling Stratified Random Sampling Cluster Sampling, Multistage Sampling and Sampling Proportional to Size Ratio and Regression Estimates REFERENCES APPENDIX INDEX
We review the recent rapid progress in the statistical physics of evolving networks. Interest has focused mainly on the structural properties of complex networks in communications, biology, social sciences and economics. A number of giant artificial networks of this kind have recently been created, which opens a wide field for the study of their topology, evolution, and the complex processes which occur in them. Such networks possess a rich set of scaling properties. A number of them are scale-free and show striking resilience against random breakdowns. In spite of the large sizes of these networks, the distances between most of their vertices are short - a feature known as the 'small-world' effect. We discuss how growing networks self-organize into scale-free structures, and investigate the role of the mechanism of preferential linking. We consider the topological and structural properties of evolving networks, and percolation and disease spread on these networks. We present a number of models demonstrating the main features of evolving networks and discuss current approaches for their simulation and analytical study. Applications of the general results to particular networks in nature are discussed. We demonstrate the generic connections of the network growth processes with the general problems of non-equilibrium physics, econophysics, evolutionary biology, and so on.
The single-function agents with wide-spectrum activity which tend to disturb the ecological balance of oral cavity cannot satisfy dental treatment need. A multi-functional agent with specifically targeted killing property and in situ remineralization is warranted for caries management. A novel multi-functional agent (8DSS-C8-P-113) consisting of three domains, i.e., a non-specific antimicrobial peptide (AMP) (P-113), a competence stimulating peptide (C8), and an enhancing remineralization domain (8DSS), is fabricated and evaluated in this study. The findings demonstrates that 2 μM mL−1 of 8DSS-C8-P-113 eliminates planktonic Streptococcus mutans (S. mutans) without disrupting the oral normal flora. At a concentration of 8 μM mL−1, it exhibits the ability to prevent S. mutans' adhesion. Furthermore, 8DSS-C8-P-113 self-assembles a hydrogel state at the higher concentration of 16 μM mL−1. This hydrogel self-adheres on the tooth surface, resisting acid attack, eradicating S. mutans’ biofilm, and inducing mineralization in order to facilitate the repair of demineralized dental hard tissue. Its significant effectiveness in reducing the severity of dental caries is also demonstrated in vivo in a rat model. This study suggests that the multi-functional bioactive AMP 8DSS-C8-P-113 is a promising agent to specifically target pathogen, prevent tooth demineralization, and effectively induce in situ bio-mimic remineralization for the management of dental caries.
Materials of engineering and construction. Mechanics of materials, Biology (General)
True limpets in the gastropod subclass Patellogastropoda are familiar members of shallow-water rocky environments but are much rarer in the deep, with just three families adapted to bathyal depths or more. Of these, Lepetidae is the only one found on ambient seafloor habitats, and Bathylepeta is a very deep genus known from two species off Chile and Antarctica. Here, we report a giant Bathylepeta up to a shell length of 40.5 mm from 5922 m deep in the northwestern Pacific and name it Bathylepeta wadatsumi sp. nov. Phylogenetic reconstruction using the mitochondrial cytochrome c oxidase subunit I (COI) gene supports the placement of this new species in Bathylepeta. Our new species is most similar to B. linseae from the Weddell Sea but can be distinguished by its much more developed second lateral and marginal teeth, as well as a larger size. Bathylepeta wadatsumi sp. nov. also has slightly imbricating radular basal plates, a feature previously unknown from this genus; we therefore emend the genus diagnosis. Our finding not only extends the distribution of this enigmatic limpet genus to Japan but also marks the deepest bathymetric record for the entire Patellogastropoda.
The family of forkhead box O (FoxO) transcription factors regulate cellular processes involved in glucose metabolism, stress resistance, DNA damage repair, and tumor suppression. FoxO transactivation activity is tightly regulated by a complex network of signaling pathways and post-translational modifications. While it has been well established that phosphorylation promotes FoxO cytoplasmic retention and inactivation, the mechanism underlying dephosphorylation and nuclear translocation is less clear. Here, we investigate the role of protein phosphatase 2A (PP2A) in regulating this process. We demonstrate that PP2A and AMP-activated protein kinase (AMPK) combine to regulate nuclear translocation of multiple FoxO family members following inhibition of metabolic signaling or induction of oxidative stress. Moreover, chemical inhibitor studies indicate that nuclear accumulation of FoxO proteins occurs through inhibition of nuclear export as opposed to promoting nuclear import as previously speculated. Functional, genetic, and biochemical studies combine to identify the PP2A complexes that regulate FoxO nuclear translocation, and the binding motif required. Mutating the FoxO-PP2A interface to enhance or diminish PP2A binding alters nuclear translocation kinetics accordingly. Together, these studies shed light on the molecular mechanisms regulating FoxO nuclear translocation and provide insights into how FoxO regulation is integrated with metabolic and stress-related stimuli.