The prediction quality was tested with samples from independent evaluation datasets, and the implemented method received noteworthy Receiver Operating Characteristic-Area Under Curve (ROC-AUC) values, ranging from 0.69–0.96 depending on the molecule cluster.In-silico methods for the prediction of epitopes can support and improve workflows for vaccine design, antibody production, and disease therapy. So far, the scope of B cell and T cell epitope prediction has been directed exclusively towards peptidic antigens. Nevertheless, various non-peptidic molecular classes can be recognized by immune cells. These compounds have not been systematically studied yet, and prediction approaches are lacking. The ability to predict the epitope activity of non-peptidic compounds could have vast implications; for example, for immunogenic risk assessment of the vast number of drugs and other xenobiotics. Here we present the first general attempt to predict the epitope activity of non-peptidic compounds using the Immune Epitope Database (IEDB) as a source for positive samples. The molecules stored in the Chemical Entities of Biological Interest (ChEBI) database were chosen as background samples. The molecules were clustered into eight homogeneous molecular groups, and classifiers were built for each cluster with the aim of separating the epitopes from the background. Different molecular feature encoding schemes and machine learning models were compared against each other. For those models where a high performance could be achieved based on simple decision rules, the molecular features were then further investigated. Additionally, the findings were used to build a web server that allows for the immunogenic investigation of non-peptidic molecules  were classified by molecular structure and studied with the aim of identifying specific parts of the molecules responsible for such immune responses. Using a machine-learning approach , we identified some substructures that appear strikingly often in non-peptidic epitopes and which may be responsible for the hazardous immune response. Such knowledge may help to explain allergic reactions to chemicals and also to minimize the health risks of new chemicals in industrial production. To support this endeavor, we have implemented the method in a publicly available web application. This can be used for the prediction and identification of non-peptidic epitopes and their underlying substructures. Defense against pathogens via the adaptive immune system depends on the distinction between endogenous and exogenous molecules produced by the host and pathogen, respectively. This distinction is made by receptors located on the surface of T and B lymphocytes. The specific part of an antigen that interacts with the T cell receptor (TCR) or B cell receptor (BCR) is known as the epitope.T cells recognize antigens bound to the major histocompatibility complex (MHC) presented on the surface of cells. All nucleated cells present endogenous antigens via MHC class I molecules as a self/non-self distinction feature. Professional antigen-presenting cells, such as macrophages and B cells, present antigens primarily derived from the extracellular space via MHC class II molecules. B cell recognition is mediated by receptors located on the cell membrane. Activated B cells differentiate into plasma cells, which can secrete a soluble form of their receptors as antibodies. Antibodies can impede the function of pathogens or tag the pathogen for elimination by macrophages. Specific antibodies with targeted recognition are widely used as therapeutic antibodies , immunodThe vast majority of known epitopes are derived from proteins. However, peptides are not the only entities that can be detected by the immune system. In fact, there are other molecular classes that elicit an immune response, such as lipids, carbohydrates, drugs, and metals . Small mAlthough the exact molecular activation mechanism of haptens can be complex and is often not completely understood, hapten-carrier conjugates are frequently recognized by antibodies produced by B-cells . HoweverRecognition of epitopes associated with pathogens  leads to the protection of the host from further exposure. However, unwanted immunogenicity can lead to serious health problems for the host. When natural or synthetic compounds, derived from food, cosmetics, or plants, are recognized by the immune system, an allergy may occur, which can lead to symptoms such as skin inflammation and asthma. The immune response against therapeutics, mediated by so-called anti-drug antibodies, can decrease or even reverse the effects of the drug . FurtherVarious approaches for the prediction of peptidic epitopes have been described ,21. MostTo the best of our knowledge, the immunogenic recognition of non-peptidic compounds has not yet been studied systematically and, thus far, no method has been described to predict non-peptidic epitopes. The prediction is a crucial step towards the prevention of allergic reactions and for the development of non-harzadous materials, cosmetics, and drugs. Furthermore, it would allow for risk assessment prior to labor-intensive experimental assays.The largest collection of curated non-peptidic epitopes exists in the IEDB, where more than 2700 non-peptidic structures with reported positive B cell and/or T cell assays are described. Detailed information about the molecules, the selection process, and applied assays for epitope detection are provided in the IEDB and in the related manuscripts ,23. Theshttps://www.iedb.org/; downloaded: May 11, 2020). All structures were parsed using the cheminformatics python package RDKit [The entirety of molecules in the ChEBI database  was assige RDKit , and thoge RDKit  were remMolecules that were added to the IEDB or ChEBI databases after May 11, 2020 were used as an independent test dataset to benchmark the developed prediction tool using samples that were not used in the cross-validation. The test dataset included 2,190 ChEBI background molecules; 71 molecules tested positive in T cell assays and 47 molecules tested positive in B cell assays.The molecules were encoded into vectors by applying the Morgan fingerprint algorithm, also referred to as Extended-connectivity fingerprint (ECFPs) , using RThe RDKit implementation of the Morgan fingerprint allows for the creation of different features considering molecular chirality, which were then also benchmarked.Molecular subgroups, such as fatty acids, carbohydrates, and small molecules might activate B cells and T cells with different mechanisms. To examine such a dependency, we clustered all molecules into structural classes.The ChEBI dataset was converted into folded Morgan bit fingerprints . The molecules were clustered using the k-means clustering algorithm implemented in the machine learning python package Scikit-learn . The optTo visualize the clusters in a 2D representation, the vectors were transformed into 2 orthogonal components that explain the maximum amount of variance using Principal Component Analysis (PCA)  as impleUnfolded Morgan count fingerprints  were used to train the classification models. We considered count features as advantageous to bit features since there are various examples where repetitive molecular structures  play an important role in immune cell recognition . These mThe fingerprints were computed for each cluster separately. Molecules with identical fingerprints were removed from the dataset. The fingerprints were trimmed to include only those features which occurred in at least 10 molecules. Specific count features in a Morgan fingerprint can highly correlate. To derive clear decision rules, correlating fingerprint features, which exceeded a Pearson Correlation Coefficient (PCC) of more than 0.8 to any other feature, were removed.For each cluster, two machine learning models were compiled that predict the probability of a molecule to act as a B cell or T cell activating epitope. Different models were created and compared against each other. RF, k-NN, and NN algorithms with default parameters as implemented in Scikit-learn were used. For the RF models, 100 iterations were selected. Furthermore, dummy RF models were designed to validate the experimental set-up. For the dummy models, the positive samples were assigned from the background by random shuffling. The percentage of positive samples was identical to the real epitope percentage in each cluster. The dummy classifier should demonstrate that no learning process can be achieved from arbitrary samples. All classifiers were benchmarked using a repeated (3 times) 5-fold stratified cross-validation. The classifier performance was compared using the ROC-AUC metric.The RF classifiers were compared against a classifier that uses a Tanimoto similarity-based prediction approach. The similarity classifiers were designed as follows: for each molecule, the Tanimoto similarity to all known epitopes in a cluster was calculated using unfolded Morgan count fingerprints . The highest similarity was then assigned as a score of this structure to be an epitope.All similarity-based classifiers were benchmarked using repeated (3 times) 5-fold stratified cross-validation. The classifier performance was estimated by computing the ROC-AUC metric.To extract fingerprint features that are important to distinguish epitopes from the background, a statistical investigation of the feature importance was carried out for each cluster. The investigation was focused on those features for which classification models with high performance (> 0.8 ROC-AUC) could be built using a set of not more than 8 features. To analyze the feature importance, the chi-squared feature selection approach implemented in the Scikit-learn  “SelectKThe following statistical parameters were calculated for important features:1) fraction of epitopes that contain the feature (epitope coverage); 2) epitope coverage divided by the background coverage (fold-enrichment), and 3) the mean count difference between epitopes and the background (only molecules that have this feature at least once were included). 2D Depictions of the substructures corresponding to the fingerprint features were computed using the custom RDKit function.The molecules stored in the IEDB  were merged with the ChEBI molecules  and converted into bit features using the Morgan fingerprints algorithm , and cluEven though an unambiguous kink is not observed, the cluster inertia decreases only marginally for more than 8 clusters. This cluster number was chosen and the individual clusters were further described. The principal component visualization of the clusters  shows thEpitopes that tested positive in B and T cell assays are present in all clusters except for Cluster 3, which contains only non-epitopes. Cluster 3 comprises exclusively Coenzyme A (CoA)-derived molecules . The cluMost clusters can be distinguished, since their ontology terms are highly enriched—except for clusters 6 and 7, which do not allow a clear cluster description .The diversity of the clusters is represented by the number of automatically generated Morgan fingerprint features . ClusterThe radius and chirality options for the Morgan fingerprint generation were analyzed with regard to the epitope prediction performance of random forest (RF) classifiers. The performance was evaluated using the Receiver Operating Characteristic Area Under Curve (ROC-AUC) metric. The classification performance was slightly better using chiral fingerprints as compared to non-chiral fingerprints (ROC-AUC difference of 0.01–0.02) for all radii parameters . All cluChiral fingerprints with a radius of 3 were chosen for the following model benchmark and comparison with Tanimoto similarity-based reference classifiers.The epitope prediction performance of different machine learning models were compared. The RF and neural network (NN) models performed similarly for most molecular clusters and immunogenic pathway Both models outperformed the k-nearest neighbor (k-NN) models. RF models, trained on randomly assigned positive samples (referred to as dummy models), yielded an ROC-AUC close to 0.5 for all feature sets Figs .The RF classifiers were compared to Tanimoto similarity-based classifiers . It can Most remarkable are those RF models that yielded high ROC-AUC (> 0.8) values even with low feature sets (clusters 4 and 5 and the T cell epitopes of cluster 2). The related substructures were investigated in detail in the following section.The best RF models were used to predict the samples from the independent test dataset of molecules that were not used for the initial training of the classifiers. The performance on the test dataset is shown in For those models where a small feature set was sufficient to reach ROC-AUC values above 0.8, the specific features were further analyzed. Each feature is described by an enrichment analysis (epitope coverage and fold-enrichment). The feature interpretation comprises the models for the T cell epitopes of the fatty acid derivatives (cluster 2), the T and B cell epitopes of the glucoside / oligosaccharide derivatives (cluster 4) and the T and B cell epitopes of the nucleobase-containing molecular entities (cluster 5).The significant fingerprint features for T cell classification of fatty acid derivatives are listed in The most important fingerprint feature (ID: 161963127) represents a carbon chain substructure. This substructure can be found in almost all molecules (epitopes and background) in this cluster (Fold-enrichment: 1.32). The significant difference is given by the mean substructure count of 14.21. Most epitopes possess much longer fatty acid chains than the ChEBI background molecules in the cluster.Most of the other substructures can be associated with the attachment of a single sugar moiety to the fatty acid molecules. The most significant of those features (ID: 408739733) can be found in 27% of the epitopes, but not at all in the ChEBI background dataset.The T cell epitopes of the glucoside/oligosaccharide derivatives can be classified with high accuracy based on only one substructure see . SurprisA histogram of the substructure distribution in the epitopes and the ChEBI dataset is shown in The model for the B cell epitopes of glucoside/oligosaccharide derivatives requires 8 features to reach a ROC-AUC of 0.8 see . The staThe most important substructure is again represented by the fingerprint feature with the ID 161963127. Nevertheless, the fatty acid attachment is much less common for the B cell epitopes (18%) as compared to the T cell epitopes (85%). Surprisingly, the B cell epitopes that possess this fatty acid attachment tend to have shorter chains as compared to the background molecules of the cluster (mean count difference: -8.43).The other fingerprint features correspond either to specific sugar moieties  or aromatic substructures . While the specific substructures of sugar moieties can be found predominantly in the epitopes, features involving aromatic entities are more often found in the background molecules. Another substructure that is enriched in the epitope dataset is given by the feature with the ID 411967733, a secondary amide.Most features of the B cell epitopes of nucleobase-containing molecules can be associated with common nucleobases see . The claThe T cell epitopes can be classified solely on the presence of a single carbon chain moiety at the phosphorus backbone of the nucleobase see . This suhttp://tools-staging.iedb.org/np_epitope_predictor). The tool takes a simplified molecular-input line-entry system (SMILES) representation of a molecule as input and performs a two-step analysis. First, the molecular class of the compound is predicted. In addition to the class membership, BiNChE statistics of the given class are shown  between the query molecule and the target epitopes, is computed. A link to the ChEBI entry of the query molecule is provided, which allows further investigation of similar epitopes. It is planned to update the tool regularly to allow for an analysis of non-peptidic epitopes based on the current state of the IEDB.https://github.com/IEDB/NP_epitope_predictor) under the NPOSL-3.0 license.The tool was built as a small python application, that is controlled via a web interface based on the Django  web framA benefit of the application of unfolded Morgan fingerprints is that the features can be easily interpreted; an advantage that has also been exploited before, e.g., for predictive (Q)SAR models . The preMost of the computed clusters contained uniform molecule sets, which could be described with BiNCHE ontology analysis. Nevertheless, the entire ChEBI database contains a diverse range of small biologically relevant molecules and, subsequently, some molecules are difficult to aggregate. This could be observed especially for cluster 6, which is a collection of diverse small molecules, simply because molecules with few substructure features are aggregated by k-means clustering logic. Interestingly, the most distinct cluster, cluster 3, which comprises exclusively CoA derivatives, does not contain any known epitopes. It can be hypothesized that the lack of CoA-related epitopes is due to the involvement of CoA in various crucial biological functions, such as fatty acid synthesis and the citric acid cycle. An immune response against such a biologically vital molecule might be generally suppressed due to negative selection during immune cell maturation. The putative suppression of immune cell responses against vital self-molecules should be further investigated, for example based on co-factor-related compounds.The radius parameter of the Morgan fingerprint algorithm led to different classification performances concerning the observed clusters. We settled for a radius of 3 as a compromise for the subsequent analysis, since most models showed good performance with this option. We decided that the additional inclusion of different radius options for the model comparison would have made the subsequent investigation too complex. Nevertheless, the strong performance fluctuation of clusters 1 and 2 concerning the radius option may be related to fatty acid and glycolipid molecules, which possess repetitive molecular characteristics.Different machine learning models were compared for the ability to predict B cell- or T cell-related epitope activity. The RF and NN models could outperform the k-NN models in all categories. The hyperparameters of the models were not optimized, because this could have led to overfitting of the models to the training samples. The overall experimental set-up was tested using the dummy RF classifiers (trained on randomly assigned positive samples). These classifiers showed the expected ROC-AUC of 0.5 for all instances, confirming that no information leak or stratification bias occurred with the given dataset.The performance of all models increased with the number of features used to train the models. In most cases, the ROC-AUC approached a plateau, indicating that more features did not lead to further information gain. The increase of features for a fixed number of training samples can often lead to a performance drop, due to the addition of potentially uninformative features—referred to as the Hughes phenomenon . This drThe RF classifiers were compared against Tanimoto-similarity based classifiers. The Tanimoto-similarity based classifiers can be regarded as reference models to estimate the performance of memorizing the overall molecular structure as opposed to generalization. Indeed, it can be observed that for most clusters the similarity models and the RF models perform comparably, given enough features. Only the RF models built for the B cell epitopes achieved significantly better ROC-AUC scores in some cases. This could be explained by the higher number of positive training samples for the B cell epitopes.Because the epitopes were manually collected, it is conceivable that a certain amount of sampling bias can be attributed to the dataset. Overfitting to similar molecules is a frequently encountered problem in molecular encoding based machine learning tasks . A straiA common approach to estimate the performance of a classifier on novel samples is the usage of an independent test dataset, such as one that is not used in the model building process. Therefore, the updated samples from the IEDB and ChEBI, which were collected after the initial training of the classification tool, were chosen. Although some clusters had no representative positive samples in the test dataset, the overall performance allowed for the classification of B and T cell epitopes with high ROC-AUC scores.et al. [The most important factor to classify fatty acid T cell epitopes (cluster 2) can be associated with the length of the fatty acid. Another important feature is given by the attachment of a sugar to the fatty acid chain. This finding is consistent with the features observed for the glucoside/oligosaccharide derivatives (cluster 4). The attachment of long carbon chains to the glycoside is highly correlated to T cell activation. In summary, it can be concluded that long fatty acid chains, especially with specific saccharide moieties, are the most significant indicator for T cell recognition. The T cell recognition of glycolipids by CD1 proteins has been described by Young et al. . It was The most important feature for glucoside/oligosaccharide derivative (cluster 4) B cell epitope classification is also given by the chain length of fatty acid attachments. Surprisingly, the chain length is negatively correlated with B cell activation (observed in 18% of the epitopes). This means that glycoside molecules, which do have a short fatty acid, tend to be recognized by B cells, while longer fatty acid attachments are not. In general, the B cell epitope classification of cluster 4 is rather difficult to interpret, because various sugar and aromatic substructures are involved. The most intuitive finding is given by the feature with the ID 411967733 see . The corThe B cell epitopes of the nucleobase-containing molecules (cluster 5) can be classified based on the number of nucleobases. This finding may be attributed to the data collection process of the ChEBI and IEDB databases. ChEBI does not curate nucleobases derived from normal metabolism , while the IEDB includes any nucleobase-containing entity with a positive immune cell assay. This could have led to an accumulation of molecules with longer nucleobase chains in the IEDB dataset.et al. [et al [The T cell epitopes of the nucleobase-containing molecules (cluster 5) could be classified based on only one substructure feature. This substructure, an ethyl ester attached to the phosphor part (ID: 399408862), could be found in 60% of the epitopes and none of the 187 background molecules. An investigation of the samples revealed that all the epitopes were collected from the same study by Tanaka et al. . On the . [et al  showed tIn the presented work, the first general attempt was made to predict the recognition of non-peptidic molecules by B cell and/or T cell receptors. The generated models, as implemented in the web server, allow for a comprehensive analysis of non-peptidic molecules regarding epitope activity—despite the limitations of the available training dataset. The implemented prediction, as well as the shown similarity to known epitopes, allows users to judge whether the prediction is based on specific molecular features or on overall molecular similarity. The noteworthy ROC-AUC scores for the independent test dataset demonstrate the general usability of the software to investigate the epitope activity of novel non-peptidic molecules. The provided framework allows for a continuous update of the generated models and calculated decision rules with each major update of the IEDB. Thus, our framework provides a solid basis for the community to further explore non-peptidic epitopes.S1 AppendixFig A. Distribution of the non-peptidic epitopes within each cluster. Table A. Ontology enrichment analysis of all ChEBI clusters.(DOCX)Click here for additional data file.


Despite advances in post-transplant management, the long-term survival rate of kidney grafts and patients has not improved as approximately forty percent of transplants fails within ten years after transplantation. Both immunologic and non-immunologic factors contribute to late allograft loss. Chronic kidney transplant rejection (CKTR) is often clinically silent yet progressive allogeneic immune process that leads to cumulative graft injury, deterioration of graft function. Chronic active T cell mediated rejection (TCMR) and chronic active antibody-mediated rejection (ABMR) are classified as two principal subtypes of CKTR. While significant improvements have been made towards a better understanding of cellular and molecular mechanisms and diagnostic classifications of CKTR, lack of early detection, differential diagnosis and effective therapies continue to pose major challenges for long-term management. Recent development of high throughput cellular and molecular biotechnologies has allowed rapid development of new biomarkers associated with chronic renal injury, which not only provide insight into pathogenesis of chronic rejection but also allow for early detection. In parallel, several novel therapeutic strategies have emerged which may hold great promise for improvement of long-term graft and patient survival. With a brief overview of current understanding of pathogenesis, standard diagnosis and challenges in the context of CKTR, this mini-review aims to provide updates and insights into the latest development of promising novel biomarkers for diagnosis and novel therapeutic interventions to prevent and treat CKTR. Chronic kidney transplant rejection (CKTR) is characterized by progressive decrease of renal graft function that starts to manifest at one-year after the transplantation and usually accompanied by hypertension and proteinuria . CKTR usEffective treatment and prognosis of CKTR are largely dependent upon the severity and reversibility of rejection at the time of diagnosis. However, it remains a major challenge to identify early changes before irreversible damage to the graft occurs. Currently, no immunotherapies are clinically proven to be effective in prevention and treatment of CKTR, particularly ABMR. Recent advances in high-throughput cellular and molecular biotechnologies have allowed for in-depth analyses of cellular and molecular processes and deconvolutions of mechanisms underlying CKTR and have led to identification and validation of new molecular and cellular biomarkers through non-invasive or minimal-invasive approaches. The discovery of these biomarkers holds tremendous promise for early detection and development of promising novel therapies for improvement of kidney transplant outcomes. This review will first provide a brief summary on current understanding of pathogenesis and standard mothed challenges for the diagnosis of CKTR, and then, focus on more in-depth discussions to the area of biomarker discovery and novel therapeutic interventions to improve long term transplant outcome.Chronic active ABMR represents most cases of CKTR , featurivia CD16a Fc receptors  against donor HLA antigens, especially HLA class II antigens expressed by endothelial cells of the microvascular circulation, initiates ABMR . DSAs bieceptors , 20. Depeceptors . NK celleceptors  and NK ceceptors .Persistent T cell–mediated injuries can lead to chronic active TCMR . Alloreade novo DSAs is associated with increased risk for graft failure as a result of chronic active ABMR , blood urea nitrogen (BUN) and cystatin C are commonly used to evaluate graft function. The estimated glomerular filtration rate (eGFR), calculated based on sCr level, age, weight and gender, is considered as an accurate indicator and predictor for graft function and long-term graft survival . Proteinive ABMR , 32. Proive ABMR , 34, howive ABMR  and DSA ive ABMR . Imagingive ABMR , 37, graive ABMR , and anaive ABMR . HoweverCurrently, graft biopsies still remain the gold standard for diagnosing graft rejection. Graft histology provides visual evidence of the underlying pathology and pathogenesis of graft dysfunction. More recently, genetic analysis of biopsy tissue has been used to assist in the differential diagnosis of allograft rejection in conjunction with histology and immunohistochemistry. The Banff classification, founded in 1991, has established specific criteria for the diagnosis of kidney allograft rejection. It has been updated multiple times in the past two decades . C4d comRecent development of high-throughput cellular and molecular biotechnologies has led to tremendous advances in biomarker discoveries in the field of transplantation, with great promise for better understanding and management of CKTR. Contributions of biomarker studies are multifold, including 1) generating new insight into molecular mechanisms of CKTR, 2) allowing for early and differential diagnosis, 3) providing evaluation of therapeutic intervention, and 4) predicting prognosis. Principal characteristics of the biomarkers have been thoroughly reviewed elsewhere –44. AlthCD6, INPP5D, ISG20, NKG7, PSMB9, RUNX3, and TAP1) were identified as predictors for the development of progressive i-IFTA at 24 months posttransplant  in urine samples has been identified as reliable non-invasive biomarkers for i-IFTA  cells within graft infiltrates showed significantly better 5-year graft function evolution than patients without Foxp3+ T(reg) cell infiltration , isobaric tag for relative and absolute quantitation (iTRAQ), protein microarray, and bead-based immunoassay. Studies investigating non-invasive proteomic biomarkers in urine and blood , have diMetabolomics is a rapidly emerging research field that involves comprehensive analysis of all metabolites in a single biological sample  and has de novo DSA  is a critical cytokine that enhances B cell and plasma cell survival . TargetiDiscovery of novel earlier diagnostic biomarkers will not only allows designing individualized therapy for timely therapeutic intervention, but also further advance understanding of pathogenesis of CKTR. Although many biomarkers listed in XL and XZ: participated in manuscript preparation and writing. JM, LG, and JL: provided suggestion and edits. ZZ conceptualized, wrote, and revised manuscript All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Packages from renewable sources have been the focus of many studies, due to the consumer needs for high-quality food, environmental concern related to the inadequate discard of packaging, low percentage of packaging recycling, and starch application by a viable method. Thus, this work aimed to develop bioactive packages based on white, red, and black rice starch and analyze the influence of macromolecule and plasticizer type, even its blends, on the characteristics of films. Films were characterized by color, opacity, thickness, water solubility, water vapor permeability, and bioactive properties. The use of rice starch in the development of edible and/or biodegradable films was feasible, with all the formulations tested presenting a homogeneous matrix and the films obtained varying in hue, to the naked eye, as a function of the starch used. Variation of the type of starch and plasticizer, as well as the concentrations of the same, resulted in films with differences in all studied properties. Films prepared with 5% of starch and 30% of sorbitol showed phenolic compounds and antioxidant capacity, using the DPPH and ABTS methods, indicating that these can be considered bioactive packages and also suitable for food application. Oryza sativa) is considered a source of income and nutrients to many people around the world, especially in Asian countries  , and Folin–Ciocalteau reagent  were used for analyses.g) for 15 min using a Simplex II centrifuge . Starch from white, red, and black rice presented an amylose content of 18.61, 25.75, and 20.02%, respectively, and pasting temperature ranged from 79.0 to 89.0 °C [Starches were extracted from white, red, and black rice, by wet extraction method. The varieties of rice were submitted to wet extraction in order to obtain the starches. Firstly, the raw material was washed and weighed. The rice was submitted to disintegration in water (1:2 rice: water) in an industrial blender  for 5 min. The disintegrated material was filtered in order to separate the residues, which were again disintegrated and filtered. The filtered liquid was purified with a strainer (mesh 100/0.149 mm) and placed on a bench for 24 h to decant the starch. The starch was dried in an oven with air circulation  at 45 °C for 10 h, and stored in plastic bags, in the dark. To extract starch from red rice, the filter needed to be centrifuged at 1100 rpm (G force = 318× Rice starch films were carried out by casting technique. Twelve formulations of film (for each starch) were developed, varying starch quantity and plasticizer type and quantity , resultiL* value (luminosity parameter) ranging from black (L* = 0) to white (L* = 100); a* value, which range from green (−a*) to red (+a*); and b* value, which range from blue (−b*) to yellow (+b*). Chromaticity (C*) is the relation between a* and b* values, resulting in the real color of the analyzed object (Equation (1)). The readings were realized in five different points in both backgrounds. Using L* values on white (wL*) and black (L*b) backgrounds, the opacity of films was calculated (Equation (2)).Instrumental color of films was carried out by direct reading by digital colorimeter CR 400 (Konica Minolta), on white and black backgrounds. The following were measured: The film thickness was carried out by a micrometer , ± 0.001 mm of accuracy, in fifteen different points of each sample.iM is the initial dry mass of the films (g), and fM is the final dry mass of nonsolubilized films (g).Water solubility of films was carried out according to Gontard, Guilbert, and Cuq . Film saWater vapor permeability rate of the films was determined gravimetrically based on ASTM E96 method , using aWVP is water vapor permeability (g mm/m2 dkPa), e is mean film thickness (mm), A is permeation area (m2), ∆p is partial vapor pressure difference between two sides of film , and M is absorbed moisture rate, calculated by linear regression of weight gain and time, in steady state (g/day).Water vapor transferred through the film was determined by mass gain of calcium chloride in 24 h. The film thickness consisted of the average of five random measurements made on different parts of the film. The effect of air, as described by McHugh and Krochta  and Genng) for 30 min using a Simplex II centrifuge . The supernatants were placed in volumetric flasks of 100 mL, and the residues were submitted to another extraction with 40 mL of acetone 70%, homogenized, and kept resting for 1 h at room temperature (25 °C). They were centrifuged at 1100 rpm (G force = 318× g) for 30 min using a Simplex II centrifuge , the supernatants were transferred to the volumetric flask, and the volume was completed to 100 mL with distillated water.For phenolic compounds and antioxidant activity, film extracts  were prepared according to Rufino et al. . An aliqThe determination of total phenolic compounds follows the Folin and Ciocalteu  method, Antioxidant activity was evaluated by DPPH method , in a spData were submitted to analysis of variance (ANOVA) and compared by the Tukey test, at 5% significance level, with STATISTICA 8.0 software .The developed films demonstrated great film formation, homogeneity, and no rupture, fissure zone, or insoluble particles . Films oL*. Starch concentration on films does not affect the luminosity of them. Otherwise, plasticizer significantly influenced formulations with 3% of starch and 25% of plasticizer, and sorbitol contributed to increase this parameter on the packaging. In general, L* value decreased in higher quantities of starch for all studied plasticizers. For black rice starch films, sorbitol produced packaging with higher L* values, unlike Maniglia et al. [L* values to those made with sorbitol, compared to glycerol. This demonstrates that the plasticizers have different behaviors in distinct starches.The color difference between films based on rice starch can be seen in a et al.  in babasa* chroma in all film formulations. This indicates, in films based on red rice starch, that higher concentrations of starch can result in more reddish packaging, as can be seen by the naked eye  for the packaging with 3 g of starch and 25% glycerol (W2), while the highest value was seen for the sample with 5 g of starch and 25% sorbitol + glycerol (R9 = 11.15). Among the studied films, those made by red rice starch presented higher values of b* chroma, followed by films based on black and white rice starch.The increase in the starch quantity promoted positive changes of aked eye . Homez-JThe highest values of chromaticity were observed in films based on black rice starch, ranging from 12.74 (B1) to 18.82 (B10). A low variation occurred for this parameter when related to starch mass, type, and content of plasticizer. However, a high color saturation was seen on the samples with higher starch content.Films made of black rice starch demonstrated higher values of opacity when compared to those elaborated with white and red rice starch. In films made of white rice starch, the starch mass significantly influenced the opacity of them. Maniglia et al. , studyinThe starch mass difference significantly influenced the thickness of all films formulations, where those made with higher concentrations of starch presented higher thickness . LaohakuThe films elaborated with white, red, and black rice starch presented higher water solubility in formulations with sorbitol as plasticizer, and lower in those with glycerol . FormulaChanges in macromolecule and plasticizer concentration on film formulations and, consequently, their thickness significantly influenced the water solubility of our films. Higher values were observed with the increase in concentrations for all starch studied. The highest water solubilities were observed on formulations W10 (20.09%), R10 (20.66%), and B7 (28.14%). These results are important for determining the film applications. Maniglia et al.  observedOur results corroborate other studies ,54, and 2 dkPa (2 dkPa and 0.60 to 15.75 g mm/m2 dkPa, respectively. Films produced by different sources tend to present different characteristics [2 dkPa, respectively [White rice starch films presented water vapor permeability between 0.32 and 7.35 g mm/m2 dkPa , whereaseristics ,56. Flexectively .For bioactive compounds and antioxidant activity, we selected films with the highest water solubility and lowest water vapor permeability; therefore, formulations with 5% of starch and 30% of sorbitol were chosen. Films based on studied rice starches are significantly different in phenolic compounds content, being 0.58 ± 0.02, 25.26 ± 0.87 and 164.96 ± 0.25 mg EAG/100 g, for films of white, red, and black rice starch, respectively . The difAntioxidant capacity of films, by DPPH and ABTS, are shown at The starches were submitted to many processes, from the extraction to the film obtaining, and still resulted in packaging with antioxidant activity. Thus, black rice starch films presented the highest antioxidant capacity when compared to others. Many works have developed bioactive films based on the incorporation of some additives, such as extracts , oils 1, or pulpBioactive packaging with antioxidant activities can be applied in many products, protecting them against free radicals. In addition, as an edible packaging, it can contribute to consumer health. Nogueira et al. , studyinThe developed bioactive films present some advantages, such as the elaboration of them without the need for incorporation of any other source of bioactive compounds, which contributes to reduction in production costs; the use of starch from an abundant raw material that could be obtained from its byproducts, which can contribute to the environmental issue by reducing waste; and the application of this packaging in food products, which could extend its shelf life and reduce food loss.The use of starch from studied varieties of rice in the development of edible and/or biodegradable films is viable, as all tested formulations presented film formation, homogeneity, and no ruptures or insoluble particles.Changes in type of starch and plasticizer, as well as their concentrations, resulted in films with differences in opacity, thickness, water solubility, and water vapor permeability.Films made of 5% of starch and 30% of sorbitol presented phenolic compounds and antioxidant capacity using DPPH and ABTS methods; thus, they can be considered bioactive packaging, and could be used by the food industry. However, the interaction of bioactive compounds and food matrix should be tested to prove the efficiency of them.


Coxiella burnetii is the Gram-negative bacterium responsible for Q fever in humans and coxiellosis in domesticated agricultural animals. Previous vaccination efforts with whole cell inactivated bacteria or surface isolated proteins confer protection but can produce a reactogenic immune responses. Thereby a protective vaccine that does not cause aberrant immune reactions is required. The critical role of T-cell immunity in control of C. burnetii has been made clear, since either CD8+ or CD4+ T cells can empower clearance. The purpose of this study was to identify C. burnetii proteins bearing epitopes that interact with major histocompatibility complexes (MHC) from multiple host species .Of the annotated 1815 proteins from the Nine Mile Phase I (RSA 493) assembly, 402 proteins were removed from analysis due to a lack of inter-isolate conservation. An additional 391 proteins were eliminated from assessment to avoid potential autoimmune responses due to the presence of host homology. We analyzed the remaining 1022 proteins for their ability to produce peptides that bind MHCI or MHCII. MHCI and MHCII predicted epitopes were filtered and compared between species yielding 777 MHCI epitopes and 453 MHCII epitopes. These epitopes were further examined for presentation by both MHCI and MHCII, and for proteins that contained multiple epitopes. There were 31 epitopes that overlapped positionally between MHCI and MHCII across host species. Of these, there were 9 epitopes represented within proteins containing ≥ 5 total epitopes, where an additional 24 proteins were also epitope dense. In all, 55 proteins were found to contain high scoring T-cell epitopes. Besides the well-studied protein Com1, most identified proteins were novel when compared to previously studied vaccine candidates.C. burnetii peptide epitopes. Furthermore, the inclusion of human, mouse, and bovine data capture a range of hosts for this zoonotic pathogen plus an important model organism. This work provides new vaccine targets for future vaccination efforts and enhances opportunities for selecting multiple T-cell epitope types to include within a vaccine.These data represent the first proteome-wide evaluation of The online version contains supplementary material available at 10.1186/s12859-021-04181-w. Coxiella burnetii is the causative agent of Q fever in humans [C. burnetii may present with a variety of different symptoms, ranging from asymptomatic to acute and further to chronic disease [C. burnetii is endemic worldwide, except for New Zealand, and most human outbreaks are blamed on domestic agricultural animals acting as reservoirs of the bacterium [C. burnetii [C. burnetii is found in large numbers within the placenta of aborted neonates but detection of the bacterium in the urine, milk, uterine fluid, vaginal mucus, and feces of parenteral animals has also occurred [The obligate intracellular bacterium n humans –3. Centen humans , 4, 5. H disease . Acute d disease . Individacterium , 7. Cowsburnetii , 5, 6, 8burnetii . While coccurred , 11.C. burnetii fixed with formalin [The most widely accepted vaccines against Q fever, or coxiellosis, are known as Q-vax and Coxevac, where the vaccine contains either the Henzerling or Nine Mile Phase I (RSA 493) isolate of formalin , 12–14. formalin . Q-vax iformalin . Contrasformalin , 10. Eitformalin , 16. WhiC. burnetii vaccines has begun based on specific epitope definition. Multiple studies have identified small numbers of epitopes used in human or mouse immune responses, and a few studies have produced subunit vaccines [A clear need exists for low cost, broadly applicable vaccines and especially those that can be produced in safer biosafety level 2 conditions. Subunit vaccines can meet this need, and a new generation of work on vaccines , 17–19. vaccines , 19. TheBioinformatic tools have been developed to more quickly and cost effectively assess proteins as host antigens –23. ThisBrucella mellintensis protein Omp31 has been of major study during multi-subunit vaccine development against this bacterial agent [Success in the use of T-cell epitope predictors has been seen in rapidly mutating viruses, like HIV and influenza, and in fastidious bacteria , 20. Moral agent –30. Reseal agent , 29. AddC. burnetii, addition of either CD4+ or CD8+ T lymphocytes alone to infected SCID mice was sufficient to achieve immune control of C. burnetii [C. burnetii clearance by macrophages has been shown to rely on IFN-γ production by T-cells during the adaptive immune response, which requires accurate loading of antigenic peptides into MHCII molecules for T-cell presentation [+ T-cells in controlling bacterial replication and host tissue pathology, suggesting that MHCI peptide loading also plays an important role during C. burnetii infection [C. burnetii infection, the inability to link humoral immune responses to restricted bacterial replication suggests that B-cells are not a major player in the control of disease [C. burnetii epitopes have focused on known type IV secretion system (T4SS) effectors or proteins eliciting antibody response [C. burnetii T-cell epitopes on a proteome-wide scale. This will also be one of the few applications to investigate a bacterial proteome, since most prior work has focused on smaller viral proteomes [C. burnetii isolates to identify conserved epitopes with broad utility and leverage predictions from human, mouse, and ruminant hosts to facilitate development of optimally useful vaccines for this zoonotic pathogen.For burnetii . C. burnentation , 21, 32.nfection , 31. Furnfection . While B disease . Thus, tresponse , 17, 19.roteomes . FurtherC. burnetii isolates are genetically diverse, wherein they secrete different type four secretion system effectors, contain antigenic variation, and form a plethora of genomic groups based on multiple loci variable number of tandem repeats analysis (MVLA) [Coxiella isolates was completed to ensure pursuit of epitopes within conserved proteins. Nine Coxiella burnetii isolates were referenced against Nine Mile Phase I (RSA 493) during proteome-wide comparison. Each strain, with its genomic grouping, tissue of isolation, characteristic of interest, and human virulence, if known, are listed in Table s (MVLA) , 35–37. C. burnetii isolates (Table The tested isolate with the highest percent identity to Nine Mile Phase I (RSA 493) is Ohio 314 RSA 270)  resulted in the identification of 1,413 conserved proteins.A total of 352 proteins were removed upon the basis that the Nine Mile Phase I (RSA 493) proteome lacked a homolog in one of the nine isolates aligned. These predominantly consisted of hypothetical proteins and transposases as opposed to better studied proteins. Overall, proteome-wide comparison between C. burnetii. BlastGrabber analysis determined that 391 of 1,413 C. burnetii conserved proteins shared homology with species of interest [C. burnetii proteins for further analysis consisted of 1022 proteins and an overview of the protein selection process can be seen in Fig. During epitope identification, and future vaccine generation, it is necessary to avoid sensitizing the host’s immune system against itself. Therefore, the resultant protein list was queried using Blastp analysis against the host species of interest  and the murine disease model for interest . Thus, tC. burnetii proteins, which lacked host homology, it was possible to exploit NetMHCIIpan 4.0 to define MHCII epitopes. While every murine allele was tested, there were an abundance of human alleles known. To mitigate the number of human alleles, allelic frequency, geographical abundance, and phylogenetic distance were considered , did not bind all eight murine alleles analyzed.Overall, there were 453 peptides, corresponding to 338 total proteins, determined to bind a high number of human and murine alleles or interact with many of the tested alleles strongly. Peptides within this data set that bound to 100% of the tested alleles or proteins that contained greater than or equal to 3 epitopes were isolated to further consolidate the data. Ten peptides bound all 206 human alleles Table . This isftsA), AAO90965.2, and AAO91357.1 (parC). Furthermore, AAO90965.2, along with AAO90357.1 (parC), encompassed the highest number of epitopes per protein with 5 total epitopes present in either protein.Evaluation for epitope dense proteins consisted of data consolidation through isolation of proteins containing a high number of epitopes , 46. Ana+ T-cells play just as important of a role during resolution of C. burnetii infection as CD4+ T-cells [+ T-cells, there are also MHCI epitope prediction programs available that can help identify antigenic peptides specific for CD8+ T-cell recognition [C. burnetii proteins without host-similarity was tested against human, mouse, and bovine MHCI alleles. Similar to NetMHCIIpan 4.0, NetMHCpan 4.1 has a large number of human alleles available for testing. Therefore, phylogenetic trees and geographical frequency of alleles were exploited to alleviate the total number of human alleles run , CBU_1242, CBU_1489 (lpxH), CBU_1928, and CBU_1978 (ostA).In studying MHCI epitopes for epitope dense proteins, we found a higher number of epitopes per protein (7 in AAO91182.1) was achieved as compared to a maximum of 5 MHCII epitopes Table . There wC. burnetii proteome for both MHCI and MHCII epitopes enables identification of multi-use epitopes and proteins. There were 31 epitopes that had overlapping use by MHCI and MHCII (Table Assessment of the II Table . Of thesthiDE), AAO90155.1 (yaeT), AAO90323.2, AAO90990.2, AAO91128.1 (icmO), AAO91393.1, and AAO91455.1 (hemA)). Additionally, there were 19 proteins absent from the combined epitopes dense protein list that were previously encompassed in either the MHCI or MHCII data. Many of the proteins which were lost in the combined epitope dense protein table represent proteins containing the number of epitopes near the bottom of the previous cut-off values. None of the previously studied proteins in Additional file GenBank IDs from MHCI and MHCII output summary tables, Additional files C. burnetii and host genomic diversity to predict widely useful T-cell epitopes across a range of hosts for this zoonotic pathogen. Epitopes were identified by leveraging an array of MHCII and MHCI alleles for antigen presentation, thereby capturing epitopes incorporated in both MHC systems across multiple host species. The results highlight broadly useful epitopes, including many with minimal prior study, that can be used for future work and vaccine development.We sought to leverage both C. burnetii and focus on proteins that would avoid self-reactive antigens. In particular, we selected at least one sequence from each genomic group  genome lack discovery work and that many underlying functions are suggested by homology to alternate bacterial proteins. This promotes analyzing the bacterial proteome in its entirety, as the importance of many C. burnetii proteins has yet to be determined.Foundational data aimed to capture broad representation of up Table , includiC. burnetii proteins for host cell epitopes, wherein previous work has focused on proteins injected into the host cytoplasm by the type four secretion system (T4SS) or proteins which elicit an antibody response [C. burnetii infection is known to rely on the production of a Th1 type immune response that results in the production of IFN-γ [C. burnetii contained epitopes began with identifying MHC class II interacting peptides (See Repository). The MHC class II analysis herein identified numerous epitopes with relatively high allelic interactions , AAO89682.2 (ftsI), and AAO90095.2 (rodA) [Coxiella burnetii infection in these species causes clear economic losses and requires intervention.Examination of either the MHC class I or II datasets demonstrates the return of proteins which have not previously been studied for T-cell epitopes. As mentioned before, much of the earlier work identifying T-cell epitopes has focused on certain protein subsets , 16, 19.2 (rodA) . The MHC2 (rodA) , 6, 7. FC. burnetii defined T-cell epitopes have used methodologies that measure the ability to achieve host T-cell activation in response to epitopes of interest; including EliSpot, ELISA, flow cytometry, and peptide loading into MHCs [A potential pitfall of bioinformatic analysis of T-cell epitopes is the possibility of false positives , 21, 52.nto MHCs , 18, 19.C. burnetii protein of interest, was represented within this output. Importantly, former analysis of Com1 as a vaccine candidate against C. burnetii has demonstrated a decent amount of promise [Once data had been acquired for both MHC class I and II alleles, it became possible to cross-analyze outputs. Investigation into overlapping MHC class II and I epitopes defined 31 peptides of interest Table . Com1, a promise , 18, 19. promise , 18, 36. promise , 53. WhithiDE), AAO90155.1 (yaeT), AAO90323.2, AAO90990.2, AAO91128.1 (icmO), AAO91393.1, and AAO91455.1 (hemA), which represent epitope rich proteins that have a balanced MHC class I and II coverage. Three of the previously mentioned proteins are designated as secreted or membrane exposed proteins by Inmembrane, AAO90155.1 (yaeT), AAO91128.1 (icmO), and AAO91393.1. Therefore, these proteins are suggested to more readily interact with the immune system upon arrival of the bacterium within host tissues. IcmO and YaeT are significant proteins in regards to host:pathogen interaction as IcmO is part of the multi-subunit T4SS and YaeT is responsible for assembly of beta-barrel surface proteins [Com1 did not remain in the MHC class I and II cross-analysis when assessing for epitope dense proteins Table . Likewisproteins –56.Coxiella burnetii to move forward without restrictions on the approach to be used.Cross-analysis between MHC class I and II data allows for future vaccination efforts to cover both classes of T-cell epitopes. Furthermore, the investigation herein also aids in epitope decision with regards to alternate vaccine types. For instance, identified epitope dense proteins provide a source of epitopes which can partake in a vectored vaccine , 34. On C. burnetii. The use of multiple divergent C. burnetii isolates enabled the identification of widely conserved proteins and epitopes to empower future work. Furthermore, the use of multiple host species for antigen presentation analyses supports the existence of widely conserved epitopes that can be broadly useful across many host species for this zoonotic pathogen. The specific results highlight many proteins and epitopes not previously described in regards to host immune recognition, and in so doing provide useful direction for future work in developing epitope-rich vaccines.These data represent the first comprehensive, proteome-wide examination of T-cell epitopes for C. burnetii isolates (https://www.patricbrc.org/) [−8 was used, where proteins were considered homologs if the percent identity was 90% or above [The PATRIC database (Pathosystems Resource Integration Center) was exploited to run proteome-wide comparisons between rc.org/) , 58. Bacrc.org/) , 59–62. rc.org/) , 39. An or above , 63.C. burnetii isolates were entered as a multi-FASTA file onto the Blastp server and analyzed for homologs present in host species. The host species tested and their taxonomic Id’s are as follows human (txid 9606), mouse , cow (txid 9913), goat (txid 9925), and sheep (txid 9940). BlastGrabber was exploited to analyze results obtained from NCBI’s basic local alignment search tool (BLASTp) [−2) and a percent identity greater than 35% was set based on previous experimental methods used to remove host homologs from analysis [Nine Mile Phase I (RSA 493) proteins found to be conserved between (BLASTp) . An E-vaanalysis , 63, 64.http://www.allelefrequencies.net/default.asp) [https://www.ebi.ac.uk/ipd/imgt/hla/) [The top ten most common MHCI alleles for eleven global regions were determined using the Allele Frequency Net Database (AFND) (ult.asp) , 66. Dupgt/hla/) . Of the gt/hla/) . Afterwagt/hla/) . Proteingt/hla/) . The remhttps://services.healthtech.dtu.dk/service.php?NetMHCpan-4.1) and (http://www.cbs.dtu.dk/services/NetMHCpan/) [Conserved Nine Mile Phase I (RSA 493) proteins lacking homology to host species were loaded onto the NetMHCpan 4.1 database for analysis across multiple host species (MHCpan/) , 47, 69 https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-4.0) [NetMHCIIpan 4.0 was exploited to study peptides that can bind human or murine MHCII alleles (pan-4.0) , 23, 70.The multi-FASTA file that contained conserved bacterial and nonhomologous host proteins was run through Inmembrane to determine each protein’s localization within the bacterium . The proAdditional file 1. C. burnetiiproteins lacking host homologs and containing inter-isolate conservation. A FASTA format list of the C. burnetii proteins studied for T-cell epitopes. Additional file 2. Allelic phylogenetic analysis. Phylogenetic trees containing MHCII (A and B) or MHCI (C and D) alleles from human species. MHCII alleles were included based on geographical representation for the DRB1 locus or an allelic frequency of 0.05 or greater for the remaining loci. MHCI alleles were included based on geographical representation as denoted by AFND. 999 bootstraps were run during neighbor-joining tree generation for MHCII alleles (A), while 1,000 bootstraps were completed when producing the maximum likelihood tree for MHCII alleles (B). MHCI allelic comparison using either the neighbor-joining (C) or the maximum likelihood method (D) using 1,000 bootstraps. Trees were condensed to only show branching when bootstrap values were 80 or above.Additional file 3. Isolation of quality controlled MHCII and MHCI epitopes. Contains the methodology used to select output epitopes of interest. Additional file 4. Previously studiedCoxiella burnetii epitopes. Locus tag and gene name based upon genomic assembly annotations for Nine Mile Phase I (RSA 493) on National Center for Biotechnology and Information (NCBI). Species indicates which host or model organism the epitope was analyzed for. The epitope type column describes if the peptide studied was in regards to a B-cell or T-cell (MHCI or MHCII) epitope. If more than one epitope was isolated, then the epitope types are separated by backslashes to indicate the order of MHC epitopes or an ampersand to indicate B-cell production of antibodies. Locus tag superscripts denote protein subcellular location and if the protein was disqualified from NetMHCpan due to previous analysis. 1 is for membrane associated, 2 is for cytoplasmic location, 3 is for unknown location, and an asterisk indicates removal. Epitope amino acid positions are annotated to represent the pre-processed forms of the proteinsAdditional file 5. MHCII epitopes with high scoring allelic interactions. MHCII epitopes found to bind either 186  human alleles or 8  murine alleles. Data also includes MHCII epitopes found to interact strongly with 90  human alleles or 5  murine alleles. NCBI retrieved information includes the GenBank ID, gene name, and locus tag. Location of given proteins was determined through use of the program Inmembrane, where PSE represents a potential surface exposed protein. The peptide column contains the 15mer peptides generated by NetMHCIIpan 4.0 for MHCII binding assessment, position of peptide start within a protein is indicated within the pos (position) column. NB, WB, and SB represent peptide and MHCII allele interactions, where NB is the total number of alleles bound, WB signifies weak binders, and SB represents strong binders. Species dictates the animal in which the alleles tested originated from.Additional file 6. Condensed MHCII epitopes. Pos indicates the position within the protein in which the NetMHCIIpan 4.0 15mer generated peptide begins. GenBank ID, gene name, and locus tag are protein specific information originating from NCBI. Location of proteins was annotated using the Inmembrane program, where PSE stands for potentially surface exposed protein.Additional file 7. Murine MHCII epitopes with exceptional allelic coverage. Rows with asterisks present next to the position number indicate a peptide shift from epitopes defined in Additional Table 4, where peptides bound more murine alleles when the 15mer was shifted one to two amino acids over. Pos indicates the starting position of the peptide of interest. Protein identification is determined by NCBI annotated information given by the GenBank ID, gene name, or locus tag. NB, WB, and SB described the character of peptide:MHCII allele interaction, where total alleles bound, weak binding, and strong binding are respectively defined. Inmembrane was used to define protein location within the bacteriumAdditional file 8. MHCI epitopes with high allelic interactions. NetMHCpan 4.1 designated MHCI epitopes within human, murine, and bovine species, wherein epitopes bound 60% of alleles tested or interacted strongly with 45% of alleles tested for each species. Position of peptide start is defined in the pos column. GenBank ID, gene name, and locus tag were defined by Nine Mile Phase I (RSA 493) assembly on NCBI. NB, WB, and SB represent number of alleles bound, weak binders, and strong binders respectively. The species defines what animal the alleles were being tested for and location was designated by Inmembrane.Additional file 9. Condensed MHCI epitopes. Manually annotated high binding MHCI epitopes that are present in all three species, human, murine, and bovine. Pos indicates the peptide of interest start site within the NCBI cited protein . Location was derived through use of the program Inmembrane, wherein PSE defines a potentially surface exposed protein.Additional file 10. Murine MHCI epitopes with exceptional allelic binding. MHCI epitopes that bound 8  alleles in the murine species. Rows with positions labeled by asterisks and bolded text represent epitopes that are different in position as compared to human MHCI epitopes that bind 74 to 76 alleles; while rows with asterisks and underlined text represent peptides that vary in position between murine and bovine epitopes that bind 98% of tested alleles. Pos describes the starting position of the peptide within the protein. Identification of the protein is given through the NCBI obtained GenBank ID, gene name, and locus tag. Location was defined by the Inmembrane program, where PSE is an acronym for potentially surface exposed. The number of alleles bound by a peptide are indicated in the NB column. If the peptide:MHCI interaction was weak it was quantified as WB and if the peptide:MHCI interaction was strong it was quantified as SB.Additional file 11. MHCI and MHCII tested alleles. (A) MHCI alleles tested during the use of NetMHCpan 4.1. Human, murine, and bovine alleles are notated HLA, H-2, and BoLA respectively. (B) MHCII alleles tested during exploitation of NetMHCIIpan 4.0. Murine and human alleles are designated by H-2 or HLA respectively. Notably, the human DRA1 locus is not highly variable, therefore only the DRB1 allele for this pairing changed. Otherwise, each of the DPA1 and DQA1 loci were paired and tested with each of their respective DPB1 and DQB1 loci.Additional file 12. Exclusion of Dugway 5J108-111. (A) Protein GenBank IDs returned to analysis when Dugway 5J108-111 was removed from inter-isolate comparison. Homology to host species is noted in the second column, where a yes indicates removal of the protein before T-cell epitope analysis. (B) C. burnetii defined MHCI T-cell epitopes represented within human, murine, and bovine species during Dugway 5J108-111 exclusion. Pos indicates the position at which the peptide begins within the protein of interest. GenBank ID, gene name, and locus tag provide protein identification parameters present in assembly ASM776v2. Protein localization was defined through the use of Inmembrane. Program updates labeled the location of AAO91013.1 as IM+peri (inner membrane plus the periplasmic space), this was altered to Membrane (non-PSE) to keep with location labels in the remainder of the manuscript.


In the AAA cohort, subjects with T2DM showed higher levels of IL-10 . We observed a positive correlation between PWVcf and CRP in the control group (r = 0.332) but not the AAA group. PWVcf and CRP were negatively correlated (r = 0.571) in the T2DM subjects treated with metformin in the AAA group. Conclusion: Arterial stiffness is related to the degree of inflammation reflected by CRP and IL-10 levels in males with an AAA. IL-10 is negatively correlated with arterial stiffness in these subjects. This finding suggests that IL-10 may decrease arterial stiffness in males with AAA. The negative correlation between CRP and PWVcf in males with T2DM treated with metformin may indicate that metformin influences the arterial wall to decrease stiffness in subjects with AAA.Background: Markers of inflammation and arterial stiffness are predictors of cardiovascular morbidity and events, but their roles in the mechanisms and progression of abdominal aortic aneurysm (AAA) in males have not been fully investigated. This study explored possible associations between inflammatory marker levels and arterial stiffness in males with AAA. Methods: A total of 270 males (191 AAA and 79 controls) were included in the study. Arterial stiffness was assessed using non-invasive applanation tonometry to measure the regional pulse wave velocity between the carotid and femoral arteries and the carotid and radial arteries. Blood samples were obtained, and interleukin-10 (IL-10) and CRP levels were analysed. Results: Subjects with an AAA had higher levels of IL-10 (21.5 ± 14.0 ng/mL versus 16.6 ± 9.3 ng/mL) compared to controls ( Stiffening in large elastic arteries significantly contributes to cardiovascular disease (CVD) and is a well-known independent predictor of cardiovascular morbidity and mortality ,2. InflaImmune system activation is related to diabetes incidence and prevalence, and a well-known clinical relationship exists between diabetes, atherosclerosis, and CVD . HoweverInflammatory markers and arterial stiffness are predictors of cardiovascular events; however, the roles of these markers in the development and progression of arterial stiffness have not been fully investigated. The aim of the present study was to explore possible associations between inflammatory marker levels and metformin use and arterial stiffness in males with AAA.Participants were recruited from an ongoing ultrasound AAA screening program and a regional ultrasound surveillance program of known AAA in two neighbouring regions in southern Sweden. From 2011–2016, a total of 270 males (mean age 70 ± 4 years) were included in the study: 191 patients with AAA and 79 controls . SubjectThe regional ethical review board in Linköping, Sweden, approved the study (Dnr 2016/143-32), and all patients accepting gave written consent to participate in the study.The participants were instructed to abstain from alcohol for 12 h and from caffeinated beverages and tobacco for 4 h prior to their examination. Examinations were performed at the Department of Clinical Physiology at Linköping University Hospital and Ryhov County Hospital. At the time of the examination, a questionnaire regarding smoking status, CVD, and current medications was completed by the examiner based on participant responses. Arterial stiffness was measured with a SphygmoCor system . Simultaneous electrocardiogram recording allowed pulse wave velocity (PWV) to be calculated. The pulse pressure waveforms were non-invasively recorded with a Millar pressure tonometer  at the carotid to the femoral PWV  and the carotid to the radial artery PWV  .g for 10 min at 4 °C. All samples were stored at −70 °C until analysis in the chemistry laboratories at Linköping University Hospital and Ryhov County Hospital, Jönköping. Both laboratories are ISO/IEC 17025-accredited by the Swedish Board for Accreditation and Conformity Assessment. CRP was analysed by an immunoturbidimetric method according to the manufacturer’s recommendations with ADVIA 1800 . An inflammatory biomarker panel was analysed with commercial Bio-Plex Pro™ Human Chemokine Panel, 40-Plex kits  according to the manufacturer’s recommendations. The panel measures a variety of cytokines, chemokines , interleukins , interferon gamma, and tumour necrosis factor alpha. An automatic magnetic washer  was used during assay implementation. The 96-well microtiter plates were measured with the Luminex 200 system . A five-parameter logistic curve was generated for each analyte using the Masterplex computer software.After an overnight fast, morning blood samples were collected in prechilled plastic Vacutainer tubes . Plasma was prepared by centrifugation at 3000× t-tests, Fisher’s exact tests, or non-parametric means tests (Mann–Whitney U tests). Pearson’s bivariate correlation coefficients were calculated used to investigate the relationships between PWV and IL-10. Multivariate analysis was made in order to adjust for confounders. Differences were considered significant at p ≤ 0.05.Data were analysed using SPSS 27.0 for Windows . Continuous variables are expressed as mean ± standard deviation (SD), and categorical variables are expressed as number of participants and per cent. Comparisons between the AAA and control groups were performed using unpaired Student’s p < 0.01) compared to the control group. The AAA group also included more current and former smokers (p < 0.01). The prevalence rates of T2DM and the reported occurrence of symptomatic cerebrovascular disease were similar between the two groups, while hyperlipidaemia, hypertension, and heart diseases were more frequent in the AAA cohort. A greater proportion of subjects in the AAA population were treated with b-blockers and aspirin (p < 0.001). No differences were found in the reported use of other anti-hypertension drugs, including diuretics, calcium channel blockers and statins.Demographic data are presented in p = 0.007). When the AAA cohort was divided into no T2DM (n = 156) and T2DM (n = 35), the latter subgroup showed a significantly higher level of IL-10 (p = 0.036) (The AAA cohort had a significantly higher mean level of IL-10 (21.5 ± 14.0 ng/mL) compared to controls (16.6 ± 9.3 ng/mL) (= 0.036) . Among tp = 0.018, p < 0.01) but not in the AAA group. This correlation remained in controls without T2DM  but disappeared in controls with T2DM. In the AAA group, we found a negative correlation between PWVcf and CRP , but only in the metformin-treated T2DM subgroup, not in subjects with T2DM without metformin. PWVcr was not correlated with CRP in the control or AAA group. A significant negative correlation was found between PWVcf and IL-10 in subjects with T2DM in the AAA cohort (r = 0.424). However, the significant correlation disappeared after adjusting age, smoking, and blood pressure. The correlation data are summarized in CRP levels were higher in subjects with AAA compared to controls, 4.5 ± 6.8 mg/L versus 3.0 ± 4.0 mg/L (The main novel finding of our study is the negative correlation between aortic PWV and anti-inflammatory cytokine IL-10 levels in males with AAA. In addition, males with AAA and T2DM had further IL-10 increases compared to subjects with AAA but without T2DM. Interestingly, we found no correlation between aortic and brachial PWV and CRP in the AAA cohort, but these factors were associated with the control group. These data suggest that arterial stiffness is related to inflammation as measured as CRP and IL-10, but this relationship varies in males with and without AAA. This indicates that these inflammatory markers do not affect PWV in males with an AAA, but the results do not exclude the possibility that the vascular inflammation is affected. Further investigations are needed to assess local inflammation and vessel wall morphology.PWVcf is the gold-standard measurement for arterial stiffness through its proven association with cardiovascular morbidity and mortality . AAA leaAAA pathogenesis is characterized by chronic inflammation and MMP-mediated aortic wall destruction . PreviouWe found higher levels of IL-10 in the AAA cohort compared to controls, and AAA subjects with T2DM had higher levels compared to those without T2DM. IL-10 is an anti-inflammatory cytokine involved in the atherosclerotic process. AAA is characterized by the destruction of elastin, increased collagen levels, and smooth muscle cell apoptosis . Since lThe diabetic drug metformin has anti-inflammatory effects , but we The AAA group was not only affected by AAA, but they were also more affected by other CVD. This may not be surprising; AAA shares a number of risk factors in common with other CVD. It was more current and former smokers in the AAA and one of the most well-known risk factors for AAA as well as for CVD is smoking .Our results should be considered in the context of several potential limitations. This was a cross-sectional study, so direct cause-and-effect associations can be derived. The size of the T2DM cohort was small, and even fewer patients were treated with metformin. Moreover, the durations of T2DM and metformin treatment were not assessed. Finally, only males with small AAAs were included in the study. However, the prevalence of AAA is higher in males than females, females are less than one fourth as likely as males to have an AAA.Arterial stiffness in males with AAA is related to the level of inflammation as measured by CRP and IL-10 in a different way than in male subjects without AAA. Males with AAA had higher IL-10 levels, which were negatively correlated with arterial stiffness, suggesting that IL-10 may decrease arterial stiffness in males with AAA. We found no relationship between CRP and arterial stiffness in males with AAA, even though they had higher CRP levels and increased arterial stiffness compared to controls. The negative correlation between CRP and PWVcf in males with T2DM who were treated with metformin could indicate that metformin influences the arterial wall and decreases arterial stiffness in subjects with AAA.


For this purpose, we divided the N-terminal region of Arabidopsis SS3 in three domains: D1, D2, and D3 (each of which contains an SBD and a coiled-coil site). Our analyses indicate that the N-terminal region is sufficient to determine the same localization pattern observed with the full-length protein. D2 binds tightly the polypeptide to the polymer and it is necessary the contribution of D1 and D3 to avoid the polypeptide to be trapped in the growing polymer. The localization pattern of Arabidopsis SS3 appears to be the result of the counterbalanced action of the different domains present in its N-terminal region.The elongation of the linear chains of starch is undertaken by starch synthases. class 3 of starch synthase (SS3) has a specific feature: a long N-terminal region containing starch binding domains (SBDs). In this work, we analyze  Arabidopsis contains six classes of SSs, GBSS (granule-bound starch synthase), SS1, SS2, SS3, SS4, and SS5. In some cases, such as cereals, maize or rice, there are more than one isoform for each class  activity showed significant increases in the frequency of linear chains of amylopectin with a degree of polymerization >60. Furthermore, the total SS activity is increased in Atss3 mutants, with plants showing greater starch content in the leaves during the light period, suggesting that SS3 has a negative regulatory function on the biosynthesis of transitory starch in Arabidopsis . These SBDs have been comprehensively analyzed in vitro both in Arabidopsis  in a greenhouse under a 16-h light/8-h dark cycle (light intensity 250 μmoles m−2s−1) at 22°C.N. benthamiana were cloned using the Gateway system (ThermoFisher Scientific). The different fragments were amplified using the oligonucleotides shown in via BP clonase reaction and subsequently transferred to pEarleyGate103  with the sequence of interest.cDNAs required for the transient expression of truncated versions of AtSS3 in Agrobacterium tumefaciens strain C58  equipped with a ×63 oil immersion objective and a ×20 objective was used to detect the localization of the GFP fusion proteins. GFP and chlorophyll autofluorescence imaging was performed by exciting the cells with an argon laser at 488 nm and detecting fluorescence emissions at 500–525 nm and 630–690 nm, respectively.N. benthamiana leaves by agroinfiltration as described above. Leaf material (500 mg) was harvested 2 days after infiltration at the same time of the day/night cycle to avoid differences of starch accumulation between samples analyzed. Samples were disrupted in liquid nitrogen with a mortar and pestle. Extracts were resuspended in 500 μl PD buffer  supplemented with 2 μl protease inhibitor cocktail (SIGMA) and filtered through Miracloth (Merck). The extract was centrifuged at 12,000 × g at 4°C for 20 min over 1 ml Percoll 90% diluted in PD buffer. The pellet (starch granules) was washed with 500 μl PD buffer supplemented with 0.2% Triton X-100 and pelleted again by centrifugation at 12,000 × g at 4°C for 20 min. The pellet was resuspended in 20 μl preloading buffer  and boiled for 10 min. Samples were then centrifuged a 12,000 × g at 4°C for 10 min to eliminate debris. Protein in the supernatant fractions was determined according to the Lowry method  plus CC domains of imprecise localization. The DeepCoil tool of the web service for protein bioinformatic analysis MPI Bioinformatics Toolkit (https://toolkit.tuebingen.mpg.de) . This confirmed D2-GFP and D23-GFP to be tightly bound to the starch  whereas others, such as GBSS, require interaction with other polypeptides that contain a CBM. Indeed, the localization of GBSS in the starch granule requires an interaction with Protein Targeting To Starch 1, a polypeptide with a CC domain through which interacts with GBSS and a SBD domain belonging to the CBM48 family required for binding the protein to starch  and the European Fund for Regional Development.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Post-stroke muscle stiffness is a major challenge in the rehabilitation of stroke survivors, with no gold standard in clinical assessment. Muscle stiffness is typically evaluated by the Modified Ashworth Scale or the Tardieu Scale; however, these can have low reliability and sensitivity. Ultrasound elastography is an advanced imaging technology that can quantitatively measure the stiffness of a tissue and has been shown to have good construct validity when compared to clinically assessed muscle stiffness and functional motor recovery.The purpose of this article is to systematically review the literature regarding the change in muscle stiffness as measured by ultrasound elastography in stroke survivors.Scopus, PubMed, Embase, CINAHL, MEDLINE and Cochrane Library were searched for relevant studies that assessed the change in stiffness of post-stroke muscle stiffness measured by ultrasound elastography following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.In total, 29 articles were identified, using either strain elastography and shear wave elastography to measure the stiffness of muscles in stroke survivors, most frequently in the biceps and medial gastrocnemius muscles. The stiffness was typically higher in the paretic compared to the non-paretic or healthy control. Other variations that increased the stiffness include increasing the joint angle and introducing a passive stretch or muscle activation. The paretic muscle has also been assessed pre- and post-treatment demonstrating a decrease in stiffness.Ultrasound elastography is a promising imaging technology for determining the muscle stiffness in stroke survivors with need for a standardized imaging protocol. Ultrasound elastography can assess the change in muscle stiffness of stroke survivors.Paretic limbs display higher stiffness than non-paretic limbs.Altering the joint angle will alter the muscle stiffness.Ultrasound elastography stiffness correlated with clinical assessments most of the time.Muscle stiffness decreased after rehabilitation or pharmacological treatment.Advancements in the treatment of stroke have resulted in an increased survival rate; however, the likelihood of developing spasticity remains unchanged , 2. SpasFollowing a stroke, the upper motor neuron lesion may result in alterations of the neural input to muscles and then non-neural mechanical alterations to muscle as a secondary consequence. The response of a muscle to a stretch is influenced by the speed of the stretch, whereby when stretched at low velocities the muscle resistance is generated by the passive elastic properties . After aThere are no clear indicators as to the degree of spasticity that may occur; however, severe paresis in the subacute stage post-stroke does increase the likelihood –9. The dCurrent clinical assessments of spasticity typically include the Modified Ashworth Scale (MAS) or the Tardieu Scale (TS), both of which are ordinal scales. These assessments measure the resistance to a passive movement and/or the angle of the catch where there is a distinct brief resistance, perceived subjectively by the physician , 10, 11.The clinical assessments do not directly measure spasticity, as they cannot differentiate the neural hypertonicity from the non-neural mechanical characteristics of the limb , 11. TheUltrasound elastography is an advanced imaging technique that assesses the stiffness of tissue that has been used clinically in the evaluation of liver, breast, thyroid and prostate mechanical properties , 18. BotThe increasing understanding of ultrasound elastography in healthy muscles has allowed research to progress to the assessment of muscle stiffness in pathological conditions. Benefits of using elastography in post-stroke muscles include detecting changes, quantifying the severity of disease and commenting on the change in stiffness after treatments. Zúñiga et al.  recentlyThe databases Scopus, PubMed, Embase, CINAHL, MEDLINE and Cochrane Library were searched between October 2020 and October 2021. The following search terms: (‘stroke’ or ‘poststroke’ or ‘post-stroke’ or ‘cerebrovascular AND accident’) AND (‘spastic*’ or ‘hypertonia’ or ‘stiffness’) AND (‘ultrasound’ or ‘elastography’ or ‘shear wave elastography’ or ‘shearwave’ or ‘sonoelastography’ or ‘strain imaging’ or ‘supersonic’) were used with Boolean operators. Abstracts were screened for inclusion and duplicates were removed. Selection of studies was based on: (1) articles published in English; (2) human populations in vivo; (3) use of ultrasound elastography on muscles for stiffness; (4) participants were stroke survivors. Conference papers and proceedings have been included in this review. Exclusion criteria included: (1) cadaveric studies; (2) published in languages other than English. Backward searching through reference lists of the selected articles and forward searching through cite searches were performed to identify other publications. This review was conducted by a single reviewer (J.R.) following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines  and prosInitial searches returned 476 results which were assessed for eligibility. After removing duplicates and screening for relevance, 29 articles were included in this review Fig. . There hIt is important to note the methodological differences in the studies mentioned as this can have an impact on the results. Table While Hong et al.  and Yoldn = 14), with other upper limb muscles investigated including the brachialis [n = 8), though the lateral gastrocnemius, tibialis anterior [The biceps brachii was assessed most frequently ; however, the GE Logiq S7, Logiq S9, Logiq E9, Toshiba Aplio 500, Verasonics, Hitachi Hi Vision Ascendus, Siemens Acuson S2000 and Acuson S3000 were also used. The machine used was not stated in two studies [The SuperSonic Imagine Aixplorer machine was used in the majority of studies  can be used to monitor electrical activity of the muscles during assessment. Nine studies used sEMG to confirm that muscles were assessed at rest or to visually control the level of voluntary contraction. Other passive muscle assessments have relied on subjective clinical judgment from the ultrasound machine operator.Gao, Chen et al.  used pasThe comparison of the paretic muscle to the non-paretic muscle was the most common method to determine whether there was a change in muscle stiffness after a stroke. The muscle stiffness was typically increased when compared to the non-paretic side , 42–48. There were three instances in the literature that reported a small decrease in the stiffness of the paretic muscle. The shear modulus of the paretic muscle was decreased in the study by Mathevon et al.  and in tThe stiffness of paretic muscles was compared to muscles of healthy controls, with no history of stroke in 6 studies. All six studies found an increased stiffness in the paretic muscle compared to healthy controls , 44, 47.The angle of the joint can influence the stiffness of the muscles. The passive paretic biceps were assessed in different elbow positions in 4 studies, each finding an increased muscle stiffness with increased elbow extension with Wu et al.  and Gao,Eby et al.  assessedTo assess the paretic muscle in an active state, Saadat et al.  assessedThe correlation between ultrasound elastography stiffness and clinical assessments was not a primary aim of any of the studies; however, some authors attempted to analyze the relationship. The MAS and TS have been frequently used as a scale to grade the spasticity. The Fugl-Meyer Assessment, used for physical independence and capability , and theSWE provides a measurement of the elasticity of the tissue and makes the assumption that the viscosity is negligible. The SWV averaged over all velocities, has been coined as group velocities, and the frequency-dependent SWV is termed phase velocity. The use of SWE by most authors has been of group velocities; however, this has been investigated. Rasool et al.  assessedThere were no studies that specifically assessed for changes over time; however, a few authors attempted to correlate the time since stroke with the muscle stiffness. Wu et al.  found a The objectives of therapy and antispasticity medications include reducing the degree of spasticity, improving joint function and decreasing pain. Rehabilitation therapy is highly patient-specific and requires daily commitment. Botulinum Toxin-A (BoNT-A) injections are administered into the muscle by the physician as a relaxant treatment to reduce muscle activation . The then = 7) were evaluated again between 17 and 30 days after injection, and there was a decrease in SWV from 1.90 to 1.63 m/s at 90° elbow flexion suggesting reduced muscle stiffness. More recently, Furukawa and Masakado [The effect of BoNT-A injections into the biceps muscle was evaluated by Aşkın et al.  by straiMasakado  investigLiu et al.  measuredHuang et al.  exploredYoldaş Aslan et al.  assessedThe passive state of muscles was confirmed by sEMG in nine studies which allows for a greater confidence that there was no contraction. Studies that did not use sEMG cannot rule out the possibility that there was underlying muscle contraction, which would increase the stiffness of the muscle.The ultrasound elastography systems have a maximum measurable velocity and any velocities higher than this are only recorded as being at the limit. Therefore, if the true velocity was higher than the machine maximum, it would be underreported. This is not a concern for the studies that assess the muscle in a passive state; however, this is a limitation in the active studies. Lee et al.  were limThe transducer pressure applied by the operator during both shear wave and strain elastography can deform and affect the deeper structures. An increased amount of transducer pressure would result in altered values for strain elastography and higher velocities in SWV. Similarly, an unequal pressure along the length of the transducer will create disproportionate measurements within the elastogram.The experience level of the elastography performer was not mentioned in every study. Ultrasound and, by extension, elastography are highly operator dependant, and the level of experience of the operator can have an impact on the results.n = 29/31). Liu et al. [One major consideration is the time since stroke of the participants in the studies. The earliest assessment of spasticity post-stroke was performed by Wu et al.  where thu et al.  set an iThis systematic review has appraised the literature regarding ultrasound elastography in the assessment of muscle stiffness after a stroke. The studies of SWE in post-stroke muscle stiffness have substantial methodological differences such as machine, body/joint position and active/passive state which should be highlighted. The image acquisition protocol should be described and maintained throughout the assessment with the intent that future researchers can replicate the protocol. There were no overarching guidelines or protocols followed, nor found within the literature. All these factors contribute to the variation in results and must be considered when attempting to generalize findings.The inclusion and exclusion criteria are the leading varying factors with some studies limiting the severity of spasticity to those with an MAS score equal or greater than 1. The studies by Yaşar et al. , Galvão The stiffness of the muscles was increased compared to the paretic side in most of the studies which is suggestive of an alteration in the elastic properties. However, Mathevon et al.  and the Similar to assessments made in healthy muscles the stiffness of paretic muscles is affected by the joint angle and the level of activation. The increased elbow extension increased the stiffness of the biceps , 29, 44.Clinical implications of detecting changes in muscle stiffness include potentially establishing a more accurate assessment for spasticity; however, research correlating clinical assessments of muscle stiffness with ultrasound elastography measurements is still in infancy. Most studies have commented on the correlation with clinical assessments, but the generally low sample sizes do limit evaluations. Despite no studies having a primary aim of correlating clinical assessments with muscle stiffness measured by ultrasound elastography, there were multiple attempts made in most of which the correlation proved true. Congruently, the recent meta-analysis by Miller et al.  found thThe assessment of muscle mechanical properties by strain elastography and SWE can be used to comment on the effect of the pharmacological or physical therapies. The literature supports findings of decreased stiffness which is in line with the aims of treatments such as BoNT-A injections and shock wave therapy. This demonstrates the potential for ultrasound elastography to be used not only in categorizing the changes in muscle stiffness in post-stroke, but also for quantifying the effect of treatment and potentially guiding the treatment to increase the efficacy.An early assessment of the muscle mechanical properties before the non-neural components begin to take effect would be, in our opinion, the most appropriate ‘base-line’ value. Wu et al.  had the In particular, a longitudinal assessment of post-stroke muscle stiffness would help diagnose spasticity development or predict the benefit of therapy. A longitudinal assessment would provide a patient-specific, directly comparable result. The many variables for SWE and the dynamic nature of muscle may be, at least in part, overcome by comparing a patient’s SWV with their SWV at a different point in time, rather than comparing them with a clinical standard. This is especially logical since the literature review has shown that there is still a lot of discrepancy in what can be considered an acceptable clinical standard.Ultrasound elastography to assess muscle stiffness in the post-stroke population has overt clinical applications. This systematic review found that the paretic limb typically had an increased stiffness compared to the non-paretic as well as to control participants. The muscle stiffness increased under a variety of conditions such as altering the joint angle, the level of rest and contraction, and the speed of dynamic movement. The correlation between the current clinical assessments and ultrasound elastography is promising, and these advanced technologies could provide an excellent quantitative and reliable alternative.Despite this, there are many facets of research surrounding ultrasound elastography in post-stroke muscles which remain untouched. There appears to be gaps in the literature regarding the assessment of muscle stiffness in the acute stage after a stroke, which would give an understanding of the development of spasticity from a baseline assessment. Another gap includes a longitudinal assessment, which would be integral in determining the patient-specific development of stiffness. A longitudinal assessment of the change in muscle stiffness in the acute stage post-stroke would address the gap in the research and provide therapists with valuable information about the development of non-neural components of spasticity.