A systematic review of the frequency of neurocyticercosis with a focus on people with epilepsy

Background

The objective of this study is to conduct a systematic review of studies reporting the frequency of neurocysticercosis (NCC) worldwide.

Methods/Principal Findings

PubMed, Commonwealth Agricultural Bureau (CAB) abstracts and 23 international databases were systematically searched for articles published from January 1, 1990 to June 1, 2008. Articles were evaluated for inclusion by at least two researchers focusing on study design and methods. Data were extracted independently using standardized forms. A random-effects binomial model was used to estimate the proportion of NCC among people with epilepsy (PWE). Overall, 565 articles were retrieved and 290 (51%) selected for further analysis. After a second analytic phase, only 4.5% of articles, all of which used neuroimaging for the diagnosis of NCC, were reviewed. Only two studies, both from the US, estimated an incidence rate of NCC using hospital discharge data. The prevalence of NCC in a random sample of village residents was reported from one study where 9.1% of the population harboured brain lesions of NCC. The proportion of NCC among different study populations varied widely. However, the proportion of NCC in PWE was a lot more consistent. The pooled estimate for this population was 29.0% (95%CI: 22.9%–35.5%). These results were not sensitive to the inclusion or exclusion of any particular study.

Conclusion/Significance

Only one study has estimated the prevalence of NCC in a random sample of all residents. Hence, the prevalence of NCC worldwide remains unknown. However, the pooled estimate for the proportion of NCC among PWE was very robust and could be used, in conjunction with estimates of the prevalence and incidence of epilepsy, to estimate this component of the burden of NCC in endemic areas. The previously recommended guidelines for the diagnostic process and for declaring NCC an international reportable disease would improve the knowledge on the global frequency of NCC.     

Statistical data processing in clinical proteomics

This review discusses data analysis strategies for the discovery of biomarkers in clinical proteomics. Proteomics studies produce large amounts of data, characterized by few samples of which many variables are measured. A wealth of classification methods exists for extracting information from the data. Feature selection plays an important role in reducing the dimensionality of the data prior to classification and in discovering biomarker leads. The question which classification strategy works best is yet unanswered. Validation is a crucial step for biomarker leads towards clinical use. Here we only discuss statistical validation, recognizing that biological and clinical validation is of utmost importance. First, there is the need for validated model selection to develop a generalized classifier that predicts new samples correctly. A cross-validation loop that is wrapped around the model development procedure assesses the performance using unseen data. The significance of the model should be tested; we use permutations of the data for comparison with uninformative data. This procedure also tests the correctness of the performance validation. Preferably, a new set of samples is measured to test the classifier and rule out results specific for a machine, analyst, laboratory or the first set of samples. This is not yet standard practice. We present a modular framework that combines feature selection, classification, biomarker discovery and statistical validation; these data analysis aspects are all discussed in this review. The feature selection, classification and biomarker discovery modules can be incorporated or omitted to the preference of the researcher. The validation modules, however, should not be optional. In each module, the researcher can select from a wide range of methods, since there is not one unique way that leads to the correct model and proper validation. We discuss many possibilities for feature selection, classification and biomarker discovery. For validation we advice a combination of cross-validation and permutation testing, a validation strategy supported in the literature.     

Robust Estimation of Area Under ROC Curve Using Auxiliary Variables in the Presence of Missing Biomarker Values

Summary In medical research, the receiver operating characteristic (ROC) curves can be used to evaluate the performance of biomarkers for diagnosing diseases or predicting the risk of developing a disease in the future. The area under the ROC curve (ROC AUC), as a summary measure of ROC curves, is widely utilized, especially when comparing multiple ROC curves. In observational studies, the estimation of the AUC is often complicated by the presence of missing biomarker values, which means that the existing estimators of the AUC are potentially biased. In this article, we develop robust statistical methods for estimating the ROC AUC and the proposed methods use information from auxiliary variables that are potentially predictive of the missingness of the biomarkers or the missing biomarker values. We are particularly interested in auxiliary variables that are predictive of the missing biomarker values. In the case of missing at random (MAR), that is, missingness of biomarker values only depends on the observed data, our estimators have the attractive feature of being consistent if one correctly specifies, conditional on auxiliary variables and disease status, either the model for the probabilities of being missing or the model for the biomarker values. In the case of missing not at random (MNAR), that is, missingness may depend on the unobserved biomarker values, we propose a sensitivity analysis to assess the impact of MNAR on the estimation of the ROC AUC. The asymptotic properties of the proposed estimators are studied and their finite‐sample behaviors are evaluated in simulation studies. The methods are further illustrated using data from a study of maternal depression during pregnancy.     

Nonspecific cytotoxic cells as effectors of immunity in fish

Nonspecific cytotoxic cells (NCC) may be the teleost fish equivalent of mammalian natural killer (NK) cells. Although significant differences exist between species regarding many characteristics of these cells, both NCC and NK cells share similarities: in the types of target cells sensitive to lysis; in mechanisms of target cell recognition; in the requirements for a competent lytic cycle; and both types of effectors participate in mediating the lysis of infectious microorganisms. A putative antigen binding receptor obtained from catfish NCC has now been characterized using monoclonal antibodies (mabs). This receptor is a vimentin-like protein. Preliminary studies indicate that NCC recognize a 40 kD protein on the membranes of susceptible target cells. Solubilized target cell protein can specifically bind to NCC and inhibit killing.Similar to NK cells, NCC require cell contact with the target cell to deliver the lethal cytotoxic hit. NCC appear to be the more potent cytotoxic cells because fewer are required to kill an individual target cell and less time is required for this action to occur than for NK cells. Unlike NK cells, NCC do not recycle under experimental conditions. Preliminary studies were also reviewed to characterize signal transduction responses. Monoclonal antibody against the vimentin-like protein receptor activates NCC cytotoxicity, initiates the production of significant increased levels of free cytoplasmic calcium, and causes the production of inositol lipid intermediates (specifically phosphotidylinositol 1, 4–5 trisphosphate). NCC may be important effectors of anti-parasite immunity. Although these cells probably do not elicit memory responses, data suggest that they do recognize antigen and can be activated and recruited into peripheral tissue where they mediate cytolytic responses.     

The receptor tyrosine kinase RET regulates hindgut colonization by sacral neural crest cells

The enteric nervous system (ENS) is formed from vagal and sacral neural crest cells (NCC). Vagal NCC give rise to most of the ENS along the entire gut, whereas the contribution of sacral NCC is mainly limited to the hindgut. This, and data from heterotopic quail-chick grafting studies, suggests that vagal and sacral NCC have intrinsic differences in their ability to colonize the gut, and/or to respond to signalling cues within the gut environment. To better understand the molecular basis of these differences, we studied the expression of genes known to be essential for ENS formation, in sacral NCC within the chick hindgut. Our results demonstrate that, as in vagal NCC, Sox10, EdnrB, and Ret are expressed in sacral NCC within the gut. Since we did not detect a qualitative difference in expression of these ENS genes we performed DNA microarray analysis of vagal and sacral NCC. Of 11 key ENS genes examined from the total data set, Ret was the only gene identified as being highly differentially expressed, with a fourfold increase in expression in vagal versus sacral NCC. We also found that over-expression of RET in sacral NCC increased their ENS developmental potential such that larger numbers of cells entered the gut earlier in development, thus promoting the fate of sacral NCC towards that of vagal NCC.     

Modeling intrinsic kinetics of enzymatic cellulose hydrolysis

A multistep approach was taken to investigate the intrinsic kinetics of the cellulase enzyme complex as observed with hydrolysis of noncrystalline cellulose (NCC). In the first stage, published initial rate mechanistic models were built and critically evaluated for their performance in predicting time-course kinetics, using the data obtained from enzymatic hydrolysis experiments performed on two substrates: NCC and alpha-cellulose. In the second stage, assessment of the effect of reaction intermediates and products on intrinsic kinetics of enzymatic hydrolysis was performed using NCC hydrolysis experiments, isolating external factors such as mass transfer effects, physical properties of substrate, etc. In the final stage, a comprehensive intrinsic kinetics mechanism was proposed. From batch experiments using NCC, the time-course data on cellulose, cello-oligosaccharides (COS), cellobiose, and glucose were taken and used to estimate the parameters in the kinetic model. The model predictions of NCC, COS, cellobiose, and glucose profiles show a good agreement with experimental data generated from hydrolysis of different initial compositions of substrate (NCC supplemented with COS, cellobiose, and glucose). Finally, sensitivity analysis was performed on each model parameter; this analysis provides some insights into the yield of glucose in the enzymatic hydrolysis. The proposed intrinsic kinetic model parametrized for dilute cellulose systems forms a basis for modeling the complex enzymatic kinetics of cellulose hydrolysis in the presence of limiting factors offered by substrate and enzyme characteristics.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

Role of nonspecific cytotoxic cells in the induction of programmed cell death of pathogenic protozoans: participation of the Fas ligand-Fas receptor system

Numerous different species of parasites and pathogenic microorganisms produce programmed cell death (PCD) and apoptosis in eukaryotic targets. How ever, only a few studies have demonstrated that effector cells, cytokines, growth factors, or soluble apoptosis-inducing factors are capable of initiating apoptosis in protozoan parasites. Certain Tetrahymena spp. in teleosts are opportunistic pathogens. In the present study these pathogenic protozoans were developed as a model system to describe the potential role of the Fas ligand (FasL)-Fas receptor (FasR) system as a means of innate immunity in teleosts. Nonspecific cytotoxic cells (NCC) constitutively express soluble FasL (sFasL). Binding of the antigen receptor (i.e., NCCRP-1) on NCC to target cells caused the release of sFasL into the milieu. The presence of functional sFasL in these supernatants was determined by Western blot analysis and by demonstrating the lysis of FasR(+) HL-60 but not IM-9 (FasR(-)) targets. Soluble FasL containing supernatants generated by tumor cell-activated NCC also produced a reduction in 2 N DNA (i.e., DNA hypoploidy) of T. furgasoni. The induction of DNA hypoploidy by NCC supernatants could be neutralized by adsorption of the supernatants with anti-FasL antibody (but not with an isotype control). Experiments were next done to determine the expression of FasR on Tetrahymena and study the effects of anti-FasR monoclonal crosslinkage and treatment with soluble human recombinant FasL (huFasL) on initiation of PCD in Tetrahymena. Cell cycle analysis revealed that both crosslinkage and soluble huFasL binding to Tetrahymena produced DNA hypoploidy. The reduction in diploid DNA was confirmed by observing oligonucleosome fragmentation (DNA laddering) following anti-FasR treatment. Additional evidence for FasR expression on Tetrahymena was obtained using fluorescence microscopy and flow cytometry. Both methods showed that all Tetrahymena examined (three species consisting of four isolates) expressed membrane FasR. These studies demonstrated the potential of the FasL-FasR system in teleosts for initiation of antiparasite innate immunity. Effector NCC may initiate PCD of Tetrahymena that express a FasR-like protein. Induction of apoptosis may be a major mechanism of homeostatic control of protozoan parasite infestations/infections.     

Incorporating monotonicity into the evaluation of a biomarker

In the assessment of clinical utility of biomarkers, case-control studies are often undertaken based on existing serum samples. A common assumption made in these studies is that higher levels of the biomarker are associated with increased disease risk. In this article, we consider methods of analysis in which monotonicity is incorporated in associating the biomarker and the clinical outcome. We consider the roles of discrimination versus association and assess methods for both goals. In addition, we propose a semiparametric isotonic regression model for binary data and describe a simple estimation procedure as well as attendant inferential procedures. We apply the various methodologies to data from a prostate cancer study involving a serum biomarker.     

Biomarker evaluation and comparison using the controls as a reference population

The classification accuracy of a continuous marker is typically evaluated with the receiver operating characteristic (ROC) curve. In this paper, we study an alternative conceptual framework, the "percentile value." In this framework, the controls only provide a reference distribution to standardize the marker. The analysis proceeds by analyzing the standardized marker in cases. The approach is shown to be equivalent to ROC analysis. Advantages are that it provides a framework familiar to a broad spectrum of biostatisticians and it opens up avenues for new statistical techniques in biomarker evaluation. We develop several new procedures based on this framework for comparing biomarkers and biomarker performance in different populations. We develop methods that adjust such comparisons for covariates. The methods are illustrated on data from 2 cancer biomarker studies.     

Auto-adaptive Alpha Allocation: A Strategy to Mitigate Risk on Study Assumptions

In some clinical development programs, there are potential biomarkers with promising but uncertain predictive effect, while the probability of success in the overall population cannot be readily dismissed. It is risky to focus only on the overall population, or just the biomarker subpopulation. In 2009, Chen and Beckman proposed a Bayesian decision framework to optimize the type I error rate (alpha) allocation in a Phase III clinical study with possible predictive subset effect. The utilization of internal data in this framework is of particular interest because it provides an opportunity to mitigate the potential risk of misspecified study assumptions using an auto-adaptive strategy. In this paper, we examine this auto-adaptive strategy in detail through extensive numerical case studies and provide guidance on the appropriate use of partial current trial (internal) data in this data-driven optimization framework. We show that internal data can be used to inform the alpha allocation to hypothesis testing in the overall population and the subgroup. The resulting adaptive testing strategy is robust with respect to the uncertainty in the predictive subgroup effect and biomarker prevalence.     

Identification of a scavenger receptor homologue on nonspecific cytotoxic cells and evidence for binding to oligodeoxyguanosine

In mammals scavenger receptors (SR) are expressed by monocytic-macrophage lineage cells and B-cells. Studies of various teleost species have indirectly demonstrated the presence of SR receptors on phagocytic or endothelial cells by showing the uptake of SR ligands (i.e. derivatised (acetylated) lipoproteins) by these cells. In the present study, nonspecific cytotoxic cells (NCC) were examined for membrane expression of an SR-like protein. Approximately 15-25% of purified NCC expressed scavenger receptor class A (SR-A) demonstrated by binding by a monoclonal (2F8) specific for mouse SR-A (types I, II). Flow cytometric analysis determined that SR binding cells had the same size and 'side scatter' characteristics as NCC. Two colour flow analysis of NCC demonstrated that only a subset of NCC expressed the SR-A-like protein and non-NCC were SR-A negative. Membrane expression of SR on NCC was confirmed by fluorescence microscopy. Analysis of the tissue distribution of SR bearing cells demonstrated that in both catfish and tilapia, SR-A was expressed by NCC in the peripheral blood, spleen and anterior kidney. Experiments were also done to determine if the ligands known to bind mammalian SR-A had a similar specificity for the teleost receptor. Cold competition binding experiments determined that anti-SR-A antibody competed with and reduced biotinylated polyguanosine 20-mer binding to NCC by approximately 40%. Two other types of ligands known to bind (mammalian) SR-A (i.e. polyvinyl sulphate and dextran sulphate) likewise decreased anti-SR-A antibody binding to NCC by 40%. These studies for the first time demonstrated that NCC express the teleost orthologue of mammalian SR-A, suggesting that NCC may participate in physiologic regulation of lipid metabolism in addition to functions of innate immunity.     

A tool for biomarker discovery in the urinary proteome: a manually curated human and animal urine protein biomarker database

Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics studies. Therefore, it is not clear how well the animal models mimic human diseases.     

Evaluating the Predictive Value of Biomarkers with Stratified Case‐Cohort Design

Summary Identification of novel biomarkers for risk assessment is important for both effective disease prevention and optimal treatment recommendation. Discovery relies on the precious yet limited resource of stored biological samples from large prospective cohort studies. Case‐cohort sampling design provides a cost‐effective tool in the context of biomarker evaluation, especially when the clinical condition of interest is rare. Existing statistical methods focus on making efficient inference on relative hazard parameters from the Cox regression model. Drawing on recent theoretical development on the weighted likelihood for semiparametric models under two‐phase studies ( Breslow and Wellner, 2007 ), we propose statistical methods to evaluate accuracy and predictiveness of a risk prediction biomarker, with censored time‐to‐event outcome under stratified case‐cohort sampling. We consider nonparametric methods and a semiparametric method. We derive large sample properties of proposed estimators and evaluate their finite sample performance using numerical studies. We illustrate new procedures using data from Framingham Offspring Study to evaluate the accuracy of a recently developed risk score incorporating biomarker information for predicting cardiovascular disease.     

Characterization of the human gastric fluid proteome reveals distinct pH-dependent protein profiles: implications for biomarker studies

Gastric fluid is a source of gastric cancer biomarkers. However, very little is known about the normal gastric fluid proteome and its biological variations. In this study, we performed a comprehensive analysis of the human gastric fluid proteome using samples obtained from individuals with benign gastric conditions. Gastric fluid proteins were prefractionated using ultracentrifuge filters (3 kDa cutoff) and analyzed by two-dimensional gel electrophoresis (2-DE) and multidimensional LC-MS/MS. Our 2-DE analysis of 170 gastric fluid samples revealed distinct protein profiles for acidic and neutral samples, highlighting pH effects on protein composition. By 2D LC-MS/MS analysis of pooled samples, we identified 284 and 347 proteins in acidic and neutral samples respectively (FDR ≤1%), of which 265 proteins (72.4%) overlapped. However, unlike neutral samples, most proteins in acidic samples were identified from peptides in the filtrate (i.e., <3 kDa). Consistent with this finding, immunoblot analysis of six potential gastric cancer biomarkers rarely detected full-length proteins in acidic samples. These findings have important implications for biomarker studies because a majority of gastric cancer patients have neutral gastric fluid compared to noncancer controls. Consequently, sample stratification, choice of proteomic approaches, and validation strategy can profoundly affect the interpretation of biomarker findings. These observations should help to refine gastric fluid biomarker studies.     

Lack of organ specific commitment of vagal neural crest cell derivatives as shown by back-transplantation of GFP chicken tissues

Neural crest cells (NCC) are multipotent progenitors that migrate extensively throughout the developing embryo and generate a diverse range of cell types. Vagal NCC migrate from the hindbrain into the foregut and from there along the gastrointestinal tract to form the enteric nervous system (ENS), the intrinsic innervation of the gut, and into the developing lung buds to form the intrinsic innervation of the lungs. The aim of this study was to determine the developmental potential of vagal NCC that had already colonised the gut or the lungs. We used transgenic chicken embryos that ubiquitously express green fluorescent protein (GFP) to permanently mark and fate-map vagal NCC using intraspecies grafting. This was combined with back-transplantation of gut and lung segments, containing GFP-positive NCC, into the vagal region of a second recipient embryo to determine, using immunohistochemical staining, whether gut or lung NCC are competent of re-colonising both these organs, or whether their fate is restricted. Chick(GFP)-chick intraspecies grafting efficiently labelled NCC within the gut and lung of chick embryos. When segments of embryonic day (E)5.5 pre-umbilical midgut containing GFP-positive NCC were back-transplanted into the vagal region of E1.5 host embryos, the GFP-positive NCC remigrated to colonise both the gut and lungs and differentiated into neurons in stereotypical locations. However, GFP-positive lung NCC did not remigrate when back-transplanted. Our studies suggest that gut NCC are not restricted to colonising only this organ, since upon back-transplantation GFP-positive gut NCC colonised both the gut and the lung.     

Comparison of cohort and nested case-control designs for estimating the effect of time-varying drug exposure on the risk of adverse event in the presence of ties

Cohort and nested case-control (NCC) designs are frequently used in pharmacoepidemiology to assess the associations of drug exposure that can vary over time with the risk of an adverse event. Although it is typically expected that estimates from NCC analyses are similar to those from the full cohort analysis, with moderate loss of precision, only few studies have actually compared their respective performance for estimating the effects of time-varying exposures (TVE). We used simulations to compare the properties of the resulting estimators of these designs for both time-invariant exposure and TVE. We varied exposure prevalence, proportion of subjects experiencing the event, hazard ratio, and control-to-case ratio and considered matching on confounders. Using both designs, we also estimated the real-world associations of time-invariant ever use of menopausal hormone therapy (MHT) at baseline and updated, time-varying MHT use with breast cancer incidence. In all simulated scenarios, the cohort-based estimates had small relative bias and greater precision than the NCC design. NCC estimates displayed bias to the null that decreased with a greater number of controls per case. This bias markedly increased with higher proportion of events. Bias was seen with Breslow's and Efron's approximations for handling tied event times but was greatly reduced with the exact method or when NCC analyses were matched on confounders. When analyzing the MHT-breast cancer association, differences between the two designs were consistent with simulated data. Once ties were taken correctly into account, NCC estimates were very similar to those of the full cohort analysis.     

Translational biomarker discovery in clinical metabolomics: an introductory tutorial

Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start “speaking the same language” in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multi-metabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.     

Mechanical and barrier properties of nanocrystalline cellulose reinforced chitosan based nanocomposite films

Nanocrystalline cellulose (NCC) reinforced chitosan-based biodegradable films were prepared by solution casting. The NCC content in the films was varied from 1 to 10% (dry wt. basis). It was found that the tensile strength (TS) of the nanocomposite films with 5% (w/w) NCC content was optimum with an improvement of 26% compared to the control chitosan films. Incorporation of NCC also significantly improved barrier properties. Water vapor permeability (WVP) of the chitosan/NCC films was decreased by 27% for the optimum 5% (w/w) NCC content. Swelling studies revealed a decrease in water uptake of the NCC-reinforced chitosan films. Analyses of thermal properties showed no significant effect of NCC whereas X-ray diffraction studies confirmed the appearance of crystalline peaks in the nanocomposite films. Surface morphology of the films was investigated by scanning electron microscopy and it was found that NCC was dispersed homogenously into chitosan matrix.