Journal of Functional Morphology and Kinesiology , which was firstly released in March 2016 [JFMK meets the growing demand for high-quality, peer-reviewed international journals, supplying easy access, high publicity of Open Access, a digital object identifier (DOI), an ORCID, and CrossRef to all researchers. JFMK is indexed in the databases Scopus (Elsevier’s abstract and citation database), Directory of Open Access Journals (DOAJ), Scilit , as well as in Google Scholar’s, World Health Organization’s Hinari, and Food Science and Technology Abstracts (FSTA)’s (produced by IFIS) databases and in the Norwegian Register for Scientific Journals, Series, and Publishers (NSD). Our full texts are archived in CLOCKSS , e-Helvetica , and J-Gate (Informatics India). We hope we can be included in Web of Science and PubMed in the near future, considering that six JFMK papers are already indexed in PubMed (https://www.ncbi.nlm.nih.gov/pubmed/?term=jfmk). JFMK is a member of the Committee on Publication Ethics (COPE). To verify the originality of content submitted to our journal, we use iThenticate and compare submissions with previous publications. MDPI works with Publons to provide reviewers with credit for their work and MDPI Scitations Alert to provide our authors information on new publications in their research field. The JFMK publishes articles focusing on molecular, cellular, tissue, system, and whole-body responses to broadly defined physical activity. Furthermore, it provides an advanced forum for the analysis of the structure, function, development, and evolution of cells and tissues of the musculoskeletal system and associated disorders.JFMK has continued to grow in the fields of morphology, kinesiology, movement, biomechanics, sport medicine, and musculoskeletal disorders. It is my pleasure to confirm the progress recorded in the last three years [https://www.mdpi.com/journal/jfmk/stats. We are proud to let you know that, thanks to your continuous support, ee years ,3,4 as sJFMK receives more manuscripts than it is able to publish, and the decision as to which papers are accepted or rejected is a difficult one. This decision is based on several factors, including originality, experimental design, scientific quality, data interpretation, clarity, and English quality, to maintain the high standards of our journal.Indeed, the number of published manuscripts has jumped from 55 in the 2018 volume to 63 in the 2019 volume, and we rejected in total 51 contributions to maintain the high standards of our journal. In 2019, various Special Issues were published thanks to the huge support of our editors. They include the following: “Psychology of Development and Education Applied to Movement”, edited by Prof. Dr. Marianna Alesi and Dr. Sebastiano Costa ; “SelectIn 2019, three distinguished scientists joined the Editorial Board: Prof. Dr. Angela Lucariello , Dr. Grazia Maugeri , and Prof. Michele Vecchio , for a total of 77 editorial board members and 7 advisory board members, besides the editor-in-chief.JFMK are published in open access; in order to provide free access to readers and to cover the costs of peer review, copyediting, typesetting, long-term archiving, and journal management, an article processing charge (APC) of 1000 CHF (Swiss Francs) is applied to papers accepted after peer review. Since 2019, all articles appearing in In 2020, we shall continue our efforts to improve the journal through further growth and increased visibility. Make sure that planned papers are followed up by editorial board members;JFMK increasingly well-known among scholars;Contact international conferences recommended by the editor-in-chief or by editorial board members and try to establish media partnerships with them to make Communicate with editorial board members regularly and ask for their help and suggestions for the journal’s development;Post high-quality papers through social media  and increase online readership;Reduce the processing time of each manuscript;Try to have publications indexed by the Emerging Sources Citation Index (Web of Science), by EMBASE (Elsevier) and by PubMed;Try to be included in the SCImago Journal Rank in the kinesiology-related section;JFMK paper” award and the “JFMK travel grant” award;Launch, for our authors, the “best Garner, for the sake of journal promotion, support from sponsors for our editors to participate in and disseminate our journal to international conferences.In order to achieve this target and lay a strong foundation for high-quality publications and application for indexing in 2020, we have made the plans reported below. We will:JFMK a leader in its field. Your contributions are vital for the success of this new journal. We look forward to receiving your contributions  as well as proposals for Special Issues.We hope that you share our enthusiasm for this new journal and look forward to working with you to make It is my pleasure to end this editorial by wishing you a healthy and prosperous new year. This is also the opportunity for me to warmly thank, for their confidence, the following: our authors, readers, and reviewers, as well as our editorial advisors, eminent scientists in this field, who with their experience and important suggestions, are guiding us in this great enterprise; our excellent editorial board members, whose depth of experience covers a very broad spectrum in different disciplines related to morphology and kinesiology; the managing editor Olivia Yu for her huge support, the publishing manager Peter Roth, the assistant managing editor Molly Lu, and the assistant editor Sydney Tang, who day after day, with their valuable contributions, ensure the growth of this journal; finally, all members of our teams in Basel, Barcelona, Beijing, Belgrade, Romania, Tokyo, and Wuhan, as well as our sponsors.


Dear Editor,Malaysian Journal of Medical Sciences (MJMS)’ performance status in 2018 , followed by Turkey (five manuscripts), Saudi Arabia (five manuscripts) and Iraq (one manuscript) . As the As elegantly stated on the journal’s introductory web page, MJMS accepts high-quality papers especially from low- and middle-income countries . Iran (u


For decades, the evaluation of rye milling products have been aimed at detecting raw material defects that are linked to excessive enzyme activity. Those defects were indirectly characterized by the rheological methods of the dough or the final products. However, such methods do not sufficiently reflect the baking properties of all rye flours present on the market. A further problem is the continuing climate change, which affects compound composition in rye. So far, these bread defects can only be corrected by process engineering . Therefore, it is necessary to characterize the main determinants of the quality defects prior to the baking process in order to predict baking quality and not waste raw material, energy, and time. In this study, it was found that the water accessibility of starch for gelatinization and its partial inhibition by certain components play a major role in baking quality. Specifically, high amounts of insoluble nonstarch-polysaccharides (NSPSs) and a hindered denaturation of proteins seem to be an indication and reason for poor baking quality. However, traditional quantitative analysis of the ingredients and properties of the rye milling products  does not allow any reliable conclusions about rye flour suitability for use as bread rye. It can be concluded that more complex compositional aspects  need to be characterized for future quality control of rye. Leitsätze des Deutschen Lebensmittelbuchs für Brot und Kleingebäck”), consist of more than 50% of rye flour fractions and are usually produced with sourdough [Traditional rye bread is the term used for rye and rye-containing mixed bread, which, according to the official German food guidelines  in a viscous liquid phase, consisting mainly of nonstarch-polysaccharides (NSPSs). Hemicelluloses like arabinoxylan and beta-glucan are colloidally dissolved in water ,12. ThusIn the context of water-binding capability, hemicelluloses, in particular pentosans and glucans, play a major role as they are able to absorb water up to 100 times their own mass ,15,16,17With regard to sensory characteristics, the proportions of water -soluble and insoluble pentosans are also of importance. Insoluble pentosans possess a higher swelling capacity. However, with a higher content of soluble pentosans, more elastic and loose crumb characteristics can be achieved . StępnieBuksa et al. (2010) found that in bread made from wholemeal rye flour with low enzymatic activity, a higher bread volume is achieved when the pentosan fraction contains a large proportion of polymers that are extractable with water .Besides NSPSs, rye also contains another group of polymers that are able to form functional network-selected proteins. The role of the proteins in rye dough formation has, so far, received little attention in terms of baking quality. Although the proteins principally form a wheat-gluten-like network, the interactions with hemicelluloses prevent the formation of typical gluten structures ,23. WhenIn an attempt to investigate the functionality of the rye proteins in relation to bread quality, Beck et al. (2011) were able to create a rye dough structure that is similar to wheat dough in rye doughs by crosslinking the rye proteins with transglutaminase . As a reWith regard to dough formation and bread quality, it becomes apparent that overall composition is becoming more and more important for predicting the baking quality of rye. Torbica et al. (2019) showed that it is possible to predict specific bread volume, an important parameter for the evaluation of bread quality, by torque analysis using the Mixolab system . This teThe problem behind this study has an intense history. In the last 10 years, rye harvests have been evaluated using several hundred rye samples and the use of a (modified) standard baking test . Howeverα-Amylase , L-(+)-arabinose, i-erythritol ≥ 99.0%, N,O-bis-(trimethylsilyl)-acetamide ≥ 99.0%, phloroglucinol ≥ 99,0%, sulfosalicylic acid ≥ 99.0%, and sorbitol ≥ 99.0% were all purchased from Sigma-Aldrich . Ethanol , 1,1,2-trichlorotrifluoroethane, Rhodamine R ~95%, and D-(+)-xylose were purchased from Merck KGaA . Ulmer Boerol was purchased from CSM Deutschland GmbH . β-Glucan was purchased from Glucagel® . Dichloromethane ≥ 99.8%, sodium chloride ≥ 99.5%, methanol, pyridine ≥ 99.5%, potassium iodide ≥ 99.5%, iodine- ≥ 99.8%, and acetone were purchased from Carl Roth GmbH + Co. KG . Acetic acid 100% was purchased from AppliChem GmbH ; amyloglucodidase  was purchased from Megazyme Ltd. ; hydrochloric acid 37% was purchased from Honeywell . Rye Samples.Overall, 154 different samples from three different harvest years (2011–2013) were the basis for this approach. From harvest year 2012, 31 rye samples cultivated in Germany were taken into account for the present study. Of these 31 samples, 21 samples came from organic cultivation, eight samples from integrated cultivation, and eight samples were type flours. Out of these 31 samples, 13 samples were population rye (P), and 18 samples were hybrid rye (H). Eight different cultivars were present in this sample set. The six exemplarily selected rye samples presented in the figures were No. 1. cv. Baro (P); No. 2. cv. Brasetto (H); No. 3. cv. Minello (H); Nos. 4–6. cv. Palazzo (H).All 31 rye samples were gently dried by simple ventilation in order to reach a final moisture content of ≤14%. A hammer mill (Laboratory Mill 3100) and a sieve insert  were used for yielding wholemeal flours from the cleaned rye grains.International Association for Cereal Science and Technology (ICC method No. 107) [Falling number (FN) was determined according to the Hagberg-Perten principle and the method collection of the No. 107) . This meThe gelatinization properties of starch during heating were determined using an amylograph  according to ICC method No. 126/1 : A flourA modified standard baking test was applied, which allows the interpretation of the rheological data using the baking result . In thisThe standard baking test for rye flour was used to assess the baking and sensory properties of rye type flour. Processing was carried out as a sourdough baking test based on the Berlin short sour test (variant III of the standard baking test) . The bakThe technical execution of the baking test is based on mechanical kneading with a small planetary mixer and a flat dough stirrer, manual preparation, and baking in a deck oven . The use of a constant quantity of flour (14% moisture) requires a loss-free preparation of the dough, finally enabling the calculation of the volume yield related to flour. Baking in a box, as opposed to a released bread, increases the sensitivity of the statements with regard to loosening and poring. In addition to the basic evaluation, further tests were carried out according to the sensory characteristic groups . The chaFor dough preparation, a planetary flat dough stirrer (Hobart N50) was used. The kneading time amounted to 10 min at 30 °C. The dough resting time was 45 min. For dough processing, two pieces per 50% of dough were matured at 32 °C (78% rel. humidity). Fermentation time amounted to 45 ± 5 min. The dough was baked at 210 °C for 55 min with two steam applications, the first for 1 s and the second for 3 s (vapor separation after 1 min).The evaluation based on the physical data of the baked goods plus texture profile analysis was performed with an in-house trained sensory panel. The inclusion of aged bread (120 h) allowed the consideration of subsequently occurring crumb cracks and defects in the tactile and chewing impressions.The maximum possible number of points for structure-relevant quality characteristics was 19 for breads using a dough acidifier and 20 for breads produced with sourdough. In this study, all breads were produced with a dough acidifier. A score of 16 points and above was considered good crumb quality, 12–15 points was considered medium quality, and 12 points or lower was considered poor baking quality. If crumb cracks were visible after 120 h, a devaluation by four points was made. Increased strength and reduced elasticity led to lower devaluations. According to a previously developed score system , specifiIn order to determine the water absorption of rye-type flours in the standard baking test, the equivalent of 300 g flour with 14% moisture was mixed in the farinograph while the kneader was running with the amount of water absorption to be expected. Within 10 min., the final viscosity of the kneading curve must be 300 ± 10 FU (farinogram units). The amount of water added must be adjusted within the first 5 min. When necessary, kneading has to be done again and again until the correct water addition is determined [Rye flours were subsequently fractionated/separated regarding their density in order to analyze the pure starch granules or, more specifically, proteins and hemicelluloses adhering to the granules. Consequently, the resulting change in starch gelatinization behavior in different quality classes can be studied. Furthermore, this technique allows us to analyze and quantify insoluble components, e.g., proteins and hemicelluloses, separatedly from starch. It can be assumed that these substances have, in particular, a great influence on the water balance in rye dough and on the transition to crumb. Therefore, it is necessary to study changes in the compositions of these components in different quality classes.As rye-based doughs usually have a pH value <5, the separation of the ingredients was also carried out at a lower pH value. The sample processing procedure is shown schematically in g; 30 min), leading to the three follow-up phases Gel I, Gel II, and Starch II . The middle phase was Gel I, which included limited swellable proteins and hemicelluloses and the fraction of small starch granules. This fraction of starch could be largely separated by repeated washing and centrifugation (leading to phases Starch II and Gel II). Starch II contained the smaller starch grains, and Gel II was a starch-free phase, mainly consisting of hemicelluloses and proteins. The supernatant(s) contained all soluble ingredients, especially soluble proteins and dietary fibers.German Food and Feed Code , and crude protein was determined by the Kjeldahl method according to Method No. L06-00-7 (§ 64 LFGB) [For the analysis of the main components, dry matter was determined at 130 °C according to a method described in the method collection of the 64 LFGB) . Starch 64 LFGB) .For the determination of the soluble fraction of glucans and pentosans, a modified method according to § 64 LFGB (Method No. L 00.00-13) was applied.−1) was prepared using 1, 0.5, 0.25, 0.1, or 0.05 mL of the solution. For the monomers of the pentosans, arabinose and xylose (50.0 mg each) were dissolved in water (50 mL) and consecutive dilution series were prepared.For the calibration curves, commercially available β-glucan (200.0 mg) was dissolved in boiling water (80 mL). After cooling to room temperature, the solution was filled up to 100 mL. A dilution series  was added and heated to 40 °C for 10 min. Centrifugation (1878× g) yielded a residue that was extracted a second time. The remaining residue was washed twice with ethanol  and centrifuged (1878× g) repeatedly. The remaining residue was dissolved in water  and filled up to 100 mL after cooling to room temperature.For fat separation, the sample (2.00 g) was dissolved in water (23 mL). To remove grease, a mixture of acetone and dichloromethane , and amyloglucosidase solution  and kept in a water bath at 60 °C for 90 min.To degrade the starch enzymatically, 30 mL of the fat free solution was mixed with sodium chloride solution , α-amylase solution  for protein precipitation. After 10 min., the precipitated components were separated by centrifugation (1878× g) and the supernatant was used for analysis.After starch degradation, the solution was mixed with saturated sodium chloride solution (2 mL) and sulfosalicylic acid , washed three times with ethanol and once with methanol, and dried for 30 min at 105 °C.The target polysaccharides  were precipitated with ethanol . After 14 h, the gelatinous precipitate was separated by centrifugation (1878× −1) and incubated in a carefully closed screw vessel at 100 °C for 4 h. After cooling, a solution of i-erythritol (0.33 mg) and sorbitol (0.500 mg) was added as internal standard (dissolved in 1 mL methanol), as well as pyridine (0.05 mL). After removing the solvent in a vacuum, the oily residue was used for derivatization.The residue was dissolved in methanolic hydrochloric acid  mixed with N,O-bis-(trimethylsilyl)-acetamide , trimethylchlorosilane (0.05 mL), and 1,1,2-trichlorotrifluoroethane (0.25 mL) and incubated in a carefully closed screw-cap vessel for 1 h at 60 °C. In case of turbidity, further BSA was added until it was dissolved. After membrane filtration and, when necessary, dilution with 1,1,2-trichlorotrifluoroethane, this solution was used for measurements by gaschromatography coupled with a flame-ionization detector (GC-FID).−1 and an injection volume of 1 µL.The GC-FID measurements were performed on a 5890 Series II instrument  using a DB-5 column , split 1:15, with nitrogen as the carrier gas. The following conditions were applied: temperature program from 120 to 220 °C at 4 °C mink was calculated and the total amount of sugar was quantified.For evaluation, the sugar-specific area correction factor FFor determining the total content of pentosans, the phloroglucinol method, according to Douglas (1981), followed by an optimized version, according to Kiszonas et al. (2012), was applied .At least 10 mg of the sample was weighed into heat-resistant sealable glass reaction tubes. The weight of the sample was chosen so that the difference in absorption measured after the reaction was at least 0.1 and not more than 0.7. The absorbance at 558 nm must not exceed 1.0. To each sample, 2 mL of distilled water was added.Calibration standards  were also prepared in sealable reaction tubes. The reagent solution was prepared by dissolving 1 g phloroglucinol in 10 mL ethanol and subsequently adding 1.515 mL water, 167 mL of acetic acid, and 4.545 mL concentrated hydrochloric acid (37%).Every 15 s, 10 mL of the reagent solution was pipetted to the samples or 5 mL of the reagent solution was pipetted to each calibration standard. All preparations were then well vortexed at equal intervals (15 s) in the same order and incubated in a boiling water bath for 25 min. After the reaction, all samples were removed from the water bath and cooled in an ice bath for 10 min. The absorbance at the wavelengths 558 and 505 nm was determined by means of a photometer  at the corresponding 15 s intervals. The absorption difference of these two values was used for quantification. The content of xylose or pentosan in the sample was determined using the calibration curve .A commercial enzyme kit  was used to determine the ratio of amylose to amylopectin in starch. Procedures were done in accordance with the manufacturer’s protocol.® 2920, TA Instruments Inc., New Castle, DE, USA). For this purpose, starch samples (Starch I and Starch II) were weighed into a pan, and an excess of water  was added. The crucible was hermetically sealed.Enthalpy at gelatinization of starch or denaturation of proteins is a measure of how much energy is required to cause the phase transition. The more compact the starch grains, the higher the enthalpy of gelatinization. The higher the proportion of undenatured proteins, the higher the denaturation enthalpy. The conversion of enthalpy was determined by thermal analysis  and incubated for at least 2 h. Heating was carried out in a water bath at 95 °C for 30 min. After cooling to room temperature, the vials were opened, and the partially gelatinized starch was homogenized with a mortar. Analytical tests on partially gelatinized starch were carried out not earlier than 3 h and not later than 24 h after the heat treatment.The state of gelatinization can be visualized microscopically after staining with iodine–potassium iodide. For the analysis, a Leitz Aristoplan light microscope  was used. The lower the degree of gelatinization of the starch, the more intensive the dark coloring. Well-gelatinized starch appears light-brownish and transparent.Confocal laser scanning microscopy (CLSM) is able to distinguish groups of substances  in a matrix. Laser light is focused on a small volume element in the sample, and a confocal point detector was used to record the resulting signal. In this way, only an image of the focused plane was generated. The lens functioned as both a condenser and a collector. To be able to examine a sufficiently large area of a sample, the examination plane was scanned. In order to make the substances in the observation plane visible, it was necessary that they were doped with a fluorescent substance that can be excited by laser light. Light of a complementary wavelength was then emitted. To illustrate the coating of the starch surface, the proteins were doped with Rhodamine R . Protein structures appeared red. CLSM images were generated with an Eclipse E600 microscope .All analytical measurements were carried out in duplicates. The presented data show the mean values. In addition, measurement uncertainties of the individual methods are indicated as error bars. In the last ten years, rye harvest has been evaluated using several hundred rye samples and a (modified) standard baking test . HoweverThe differentiation of rye bread in different quality classes is based on the point loss of the sensory properties of the bread according to a (modified) standard baking test, as described in To investigate the coherence between rye flour ingredients and the quality of baked goods, the main components  of selected rye flours were quantified. In order to draw conclusions from the results of these quantifications on bread quality, a standardized rye flour baking test was performed to categorize the different rye flour samples into the quality classes of good, medium, and poor quality rye bread . ConsequTo assign which rye flour sample should be subjected to more precise characterization, only the baking volume and structure-relevant sensory properties  were included. These characteristics were determined 24 and 120 h after baking. Three quality classes were formed  according to the cumulative overall result. As the aim is to find flour characteristics that influence baking quality independently of FN and amylographic data, the samples of the quality classes were selected in clearly different ranges of FN. Out of 31 flour samples, two were assigned to good baking quality, two to poor baking quality, and 27 were assigned to medium baking quality. From the 27 samples with a medium baking quality, two representatives that differed significantly in their cereal analytical characteristics  were selected. Six samples, selected as exemplary representatives, are described in The results presented in Initially, the main compounds were quantified directly from rye wholemeal. The results showed that protein and total starch content  were larHowever, the total starch content should not be the only starch parameter analyzed as starch is a polysaccharide consisting of amylose and amylopectin units. These two components, composed of glucose units, differ in their linkage and, thus, in their physicochemical properties as well. The role of amylose and amylopectin with regard to the staling of bread has already been studied, revealing a loss of crystallinity for amylopectin during baking. In fresh bread, amylopectin is in an amorphous state. During the aging process, the bread becomes more rigid due to the reorganization of the crystalline amylopectin structure . The proThe characterized classes of baking quality showed a tendency to have higher amounts of amylose for “good” quality rye flours, whereas “medium” and “poor” quality flours provided higher amounts of amylopectin. During the gelatinization process, amorphous regions absorb water, destabilizing the crystallites, leading to increased swelling of the granules and the loss of the crystalline structure by melting. The swelling power of starch granules is mainly attributed to amylopectin . MeanwhiHowever, the percentage of soluble proteins in the selected rye flours, as shown in The contents of soluble and insoluble pentosans and glucans, as well as the sum of NSPS, measured directly from rye flour, showed no correlation with baking quality. However, the ratio of insoluble-to-soluble NSPS was significantly higher for the poor baking quality class . HoweverWith regard to cultivars of the rye samples, Samples 5 and 6  with poor baking quality and the cv. Palazzo sample with medium baking quality seem to have similar ratios of amylose/amylopectin, as well as soluble protein content, but differ in the amount of NSPS. Sample 5 displays approx. 14% higher content of NSPS in comparison to Sample 6 and an approx. 24% higher ratio in comparison to Sample 5. Although, at first, it seems likely that baking quality could depend on cultivar, these observations suggest opposite and more complex dependencies. This is also obvious from the following analyses of the chemical composition of those six exemplarily selected rye samples.As mentioned before, this topic has an intense history. In the last ten years, rye harvests have been evaluated using several hundred rye samples and a (modified) standard baking test . As thisSupernatant (water-soluble components)Starch I (fraction of larger starch granules or granules of higher density)Starch II Gel (insoluble proteins and hemicelluloses)The following fractions/phases were obtained:By separating the samples into different phases, their individual contribution, as well as their limitations regarding the formation of dough and the production of a good quality rye bread, could be characterized.Starch is the main component of rye flour; it has a decisive influence on baking quality as starch gelatinization is the most important outcome during dough preparation. Starch granules consist of amorphous and crystalline regions of amylose and amylopectin. They are surrounded by a protein layer, through which all interactions of native starch granules with each other or with other compounds  take place. Overall, prerequisites for starch gelatinization are the availability of water and the accessibility of starch granules. However, as already described above, proteins in rye do not form a gluten-like network that allows an adequate formation of pores. In rye bread, NSPS, mainly hemicelluloses, contribute a large part to baking properties. Insoluble pentosans contribute to water retention in the dough, whereas soluble pentosans have a positive effect on dough loosening, crumb, and bread volume. It becomes obvious that starch gelatinization is largely dependent on the proteins and hemicelluloses surrounding the granules, as well as the granule structure itself.As already mentioned, the interactions between native starch granules and between starch granules and other components are restricted by the protein layer on the granules’ surface. For gelatinization and other interactions with other components, whether the starch granules are completely coated with proteins, thereby sealing the granules, is crucial. Therefore, the amount of proteins adhering to the surface of the starch was determined quantitatively  and charIn general, there was a large variance in the protein content of the two starch phases. As phase Starch II was obtained from the gel phase by repeated washing steps, it can be concluded that the larger protein deposits  were alsProteins were not homogeneously distributed on the starch grain surface, as the CLSM images . The concentration of glucans was also rather low (0.01–0.03 g/g dm). This is in accordance with the CLSM images , which sThe Starch II phase contained considerably more NSPSs , allowinSo far, the results suggeste that an insufficient starch gelatinization leads to an unsatisfying bread quality, probably caused by interactions between proteins and hemicelluloses on the surfaces of the starch grains. To further understand and evaluate the gelatinization process of rye flours resulting in different quality baked goods, gelatinization enthalpy was measured by differential scanning calorimetry. No differences between the classes of baking quality or dependencies on FN could be identified. This means that the native starch is easily accessible to water in the case of a surplus of water. However, this result does not necessarily apply to gelatinization with a limited water supply. Here, denaturation of the proteins on the starch grain’s surface in interaction with the hemicelluloses may well result in a reduction in the accessibility of starch grains, which could not be detected in the case of excess water.Therefore, the starch phases were subjected to partial gelatinization with a water content of 45%, which approximately corresponds to real conditions in rye dough. The degree of starch gelatinization and the ability to form structure-stabilizing agglomerates were qualitatively illustrated using light microscopy by staining with iodine–potassium iodide .Well gelatinized starch is characterized by low coloration. The darker the color, the more compact the starch granules after partial gelatinization. The light microscopy images of the good and medium quality rye flours indicated more compact starch granules for the higher FN. The images for poor quality rye flour showed a dense distribution of compact starch granules after partial gelatinization, especially in the Starch I phase, illustrating poor gelatinization.Whether a lack of water or a thermal modification of the surface properties of starch granules inhibits gelatinization can be analyzed by determining the enthalpy of gelatinization in the case of excess water .Low enthalpies occur either when starch is already well gelatinized or has no access to water. Even in a partially gelatinized state, high enthalpies occur when a lack of water (caused by the competition with other components such as hemicelluloses) hinders starch gelatinization, although the starch is, in principle, accessible to water.There were two obvious examples (Starch II–Sample 4 and Starch I–Sample 5) in which the optically visible gelatinization state did not correspond to the measured residual enthalpy. However, further analysis results were only conditionally suitable to explain this phenomenon. It can therefore be assumed that quality-relevant modifications of starch granule surfaces also occur during partial gelatinization under the condition of reduced water supply.The components of the gel phases probably have a much greater significance within the processes of structural change in the production of rye products than previously assumed. The essential components of the gel phase are insoluble proteins and hemicelluloses, which have high molecular masses and a complex structure and form a soft viscoelastic network. Sárossy et al. (2011) found very similar molecular masses for water-extractable and nonextractable rye hemicelluloses of about 700 kDa. Their molecular weight can range from 200 to 900 kDa, depending on branching as well as the analytical method  systems), the eluent, and, especially, the method used for calculation . The mosThe results of the quantification of proteins and NSPSs are illustrated in With regard to glucan content and total NSPS content , the “goAlthough the gel phase of the “poor” quality class had significantly higher protein contents, the lowest conversion enthalpy  was founBuksa et al. (2016) found possible evidence for the formation of high molecular weight complexes consisting of proteins and arabinoxylans, the most abundant pentosan in rye . These sAn overview of the results from the quantitative analysis is shown in In order to analyze the relationship between the composition of the main components and the resulting quality of rye breads, 31 rye flour samples from a 2012 harvest were categorized into three different quality classes  according to a traditional standard baking test. From a selection of prominent samples of these quality classes, starch (fractions), proteins, and NSPSs were quantified.The quantitative measurements directly from the rye flours did not lead to a clear prediction of the quality of baked goods. Although the samples with poor baking quality belonged to the same cultivar, the analysis of the chemical compositions showed that cultivars do not necessarily correlate with chemical composition as they are significantly influenced by ecophysiological conditions. However, rye flours of poor baking quality showed a higher ratio of insoluble-to-soluble NSPSs than those of medium or good quality, confirming that NSPSs, especially hemicelluloses, play an important role in final bread quality. This result is in accordance with the studies performed by Stępniewska et al. 2018), who showed a possible prediction of dough and bread quality using a curve swelling test. The results of the curve swelling test are decisively influenced by the presence and composition of hemicelluloses [18, who sArabinoxylans are the most abundant hemicelluloses in rye . Their sBy quantification of NSPSs in the starch phase, a coherence between baking quality and pentosan and glucan contents could be determined, linking a high amount of pentosans and glucans adhering to starch granules with poor baking quality. This suggests that hemicelluloses interact with starch granules in a way that negatively affects dough formation and stability. It is known that proteins can form layers around starch granules, and it is also known that proteins and hemicelluloses  can form complexes . WhetherAlthough protein content could not be correlated with baking quality directly, a low conversion enthalpy in the gel phase, despite higher protein content, illustrated an inadequate denaturation process; this caused a lack of free water necessary for starch gelatinization. This might be evoked by the interactions of the proteins with hemicelluloses, hindering the denaturation process.Additionally, the high residual enthalpy of partially gelatinized starch with excess water, measured for the samples of poor baking quality, indicate a lack of water for the process (caused by competition with other components such as hemicelluloses), hindering starch gelatinization due to the limited water supply, although the starch is, in principle, accessible to water.It can be assumed that a complex formation between hemicelluloses and proteins interferes with starch gelatinization during dough formation and, thus, leads to quality defects in bread rye. The results suggest two possible scenarios regarding poor baking quality: (1) The starch is, in principle, accessible to water, but the free water needed for gelatinization is permanently absorbed by hemicelluloses and/or hemicellulose–protein complexes. These complexes might also inhibit protein denaturation and, thereby, inhibit the release of free water. (2) The starch is not accessible to water due to the accumulation of proteins, hemicelluloses, and/or hemicellulose–protein complexes on the surface of the starch granules. When comparing protein crosslinks that are artificially formed with transglutaminase and the (plant) complexes endogenously formed with ferulic acid, there might be a different outcome. While the enzymatically induced complexes of molecular weight seem to lead to improved baking quality , the fer


Correction to: International Journal of Obesity10.1038/ijo.2017.239This Article was originally published under a CC BY NC-ND 4.0 license, but has now been made available under a CC BY 4.0 license.


These data provide strong evidence that the ARS-protective agent has a great potential for protecting against chemotherapy-induced toxicity. Thus, XH-003 can be considered in antitumor therapy.Acute renal injury has an incidence of 25%–30% in patients with tumors who are treated with cisplatin and in patients for whom no specific drugs are available for treatment. Amifostine is the only FDA-approved chemoprotective drug; however, its clinical application is limited because of side effects. The small-molecule antioxidant XH-003, an acute radiation syndrome- (ARS-) protective drug independently developed in our laboratory, with 100% intellectual property rights, overcomes the side effects of amifostine but retains its high efficacy. In this study, XH-003 showed a chemoprotective effect similar to that of amifostine. A mechanistic study showed that XH-003 could significantly reduce cisplatin-induced increases in serum creatinine and urea nitrogen, increase the activity of antioxidant enzymes , reduce oxidative stress and tissue inflammation, and alleviate renal tissue damage by blocking the activity of the mitochondrial apoptosis pathway. Most importantly, XH-003 could reduce the accumulation of cisplatin in renal tissue by regulating the expression of proteins involved in cisplatin uptake and excretion, such as organic cation transporter 2 and MRP2. Moreover, in an  Cisplatin (DDP), a potent chemotherapeutic agent, is widely used to treat various types of solid tumors, such as bladder, cervical, head and neck, esophageal, triple-negative breast, and small-cell lung cancers –3; howevThe proposed pathophysiological mechanisms of DDP-induced nephrotoxicity primarily involve DNA damage, the mitochondrial apoptosis pathway, inflammation, and oxidative stress –8. The uO-gallate [α-lipoic acid [Amifostine   were used in all acute nephrotoxicity experiments. The animals were purchased from SPF Biotechnology Co. Ltd. (Beijing) and were bred in a certified animal facility at the Institute of Radiation Medicine (IRM) of the Chinese Academy of Medical Sciences (CAMS).All experimental procedures were carried out in accordance with the NIH Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee of the IRM, CAMS (permit number: 2017053). The animals were cared for in accordance with the guidelines of the National Animal Welfare Law of China.n = 3 each), including a control group and three DDP treatment groups . DDP was administered as a single intraperitoneal (i.p.) injection. The rats were monitored twice daily and weighted once a day, with their survival and behavior recorded. The experiment was terminated after 3 days, and all the rats were anesthetized and sacrificed. Blood samples were obtained, and sera were separated. Serum creatinine and urea were calculated using the SMT-100V portable automatic animal biochemical analyzer.A total of 12 female SD rats (230–260 g) were divided into four groups (n = 3 each), including a control group, DDP (5 mg/kg) group, XH-003 (800 mg/kg) group, and DDP (5 mg/kg)+XH-003 (800 mg/kg) groups A and B. Group A received a single PO dose of XH-003 (800 mg/kg) 30 min before DDP administration, and group B received a single PO dose of XH-003 (800 mg/kg) 4 h before DDP administration. On day 3, all the animals were anesthetized and sacrificed; blood samples were obtained, and sera were separated. Serum creatinine and urea were measured according to the abovementioned method.A total of 15 female SD rats (230–260 g) were divided into five groups (n = 3 each), including a control group, DDP (5 mg/kg) group, XH-003 (800 mg/kg) group, amifostine (200 mg/kg) group, DDP (5 mg/kg)+XH-003 (800 mg/kg) group, and DDP (5 mg/kg)+amifostine (200 mg/kg) group. The DDP+XH-003 group received a single PO dose of XH-003 (800 mg/kg) 30 min before DDP administration, and the DDP+amifostine group received a single 200 mg/kg i.p. dose of amifostine 30 min before DDP administration. The rats were monitored twice daily and weighted once a day. The experiment was terminated after 3 days, and all the rats were anesthetized and sacrificed. Blood samples were obtained. The right kidney was fixed in 4% neutral formaldehyde for histological examination, and the left kidney was frozen in liquid nitrogen and stored at −80°C until analysis.A total of 18 female SD rats (230–260 g) were divided into six groups , red blood cells, platelets (PLTs), hemoglobin, monocytes (MOs), and basophils (BAs). Cells were counted using a Celltac E hemocytometer .Blood was obtained from the rats via the orbital sinus and was collected in a micropipette coated with KThe MDA content was measured in kidney tissue using an MDA detection kit , according to the manufacturer's instructions.The activities of T-SOD, GSH-Px, and CAT in rat kidney tissues were tested using commercial kits , as specified by the manufacturer (Nanjing Jiancheng Bioengineering Institute).μm sections using a paraffin section machine. Subsequently, the sections were dewaxed with xylene and absolute ethanol to water, then stained with HE  and analyzed under a microscope.The samples of renal tissue fixed in 4% paraformaldehyde were embedded in paraffin and cut into 5 μM tissue sections were treated according to the protocol for a TUNEL kit . Analysis was performed under a microscope.The 5 β-actin  antibodies overnight at 4°C. Then, the membranes were incubated with a suitable horseradish peroxidase-conjugated secondary antibody for 60 min at room temperature. Finally, an ECL western blotting substrate was used to visualize the immunoblots.Proteins were extracted from renal tissue using an ice-cold lysis buffer, and the protein concentrations were quantified using a BCA assay kit . SDS-PAGE was used to separate equal amounts of protein. The blocked membrane was incubated using anti-Bax , anti-P-p53 , anti-PUMA , anti-caspase-3 , anti-Nrf2 , anti-HO-1 , anti-OCT2 , anti-MRP2 , and anti-μg/g tissue) at each time point.To evaluate the effect of XH-003 on the uptake of DDP by renal tissue, we examined the platinum (Pt) content in renal tissue using inductively coupled plasma mass spectrometry. In brief, tissue samples were lyophilized using a freeze dryer system. The lyophilized samples were digested with 0.2 mL of high-purity nitric acid at room temperature for 4 h, then diluted with 2 mL of deionized water and centrifuged at 6,000 rpm for 15 min. Finally, a 0.2 mL aliquot was transferred to an inductively coupled plasma mass spectrometry autosampler tube to determine the Pt content : control group, XH-003 (500 mg/kg) single-dose group, DDP (10 mg/kg) single-dose group, and 500 mg/kg XH-003+10 mg/kg DDP group. XH-003 was administered orally once a week for 2 weeks, the first administration of XH-003 was 30 min before DDP administration, and the second administration of XH-003 was 8 h after the first administration. DDP was administered by tail vein injection once a week for 2 weeks. The therapeutic effect was evaluated based on the relative tumor inhibition rate. The results of tumor volume were expressed by mean ± standard error of mean. All data were analyzed with Prism 6.0. p < 0.05 was considered statistically significant.H385 cells were cultured in Dulbecco's modified Eagle medium containing 10% fetal bovine serum. After reaching the exponential growth stage, cells were resuspended in phosphate-buffered saline and mixed (1 : 1) with Matrigel for subcutaneous inoculation into BALB/c nude mice. Fifty female mice were inoculated with 5 × 10μmol/L and 5.86 ± 0.53 mmol/L in the control group and increased to 173.53 ± 49.53 μmol/L and 37.45 ± 8.85 mmol/L in the 5 mg/kg DDP group (p < 0.05), 328 ± 104.5 μmol/L and 41.8 ± 1.91 mmol/L in the 7.5 mg/kg DDP group (p < 0.05), and 370.8 ± 69.3 μmol/L and 46.69 ± 2.69 mmol/L in the 15 mg/kg DDP group (p < 0.05), respectively. Notably, one rat died in each 7.5 and 15 mg/kg DDP group, which was attributed to DDP intolerance. Considering these factors, the optimal single dose of DDP of 5 mg/kg was applied to study the protective effect of XH-003 against DDP-induced nephrotoxicity.A single high dose of DDP, ranging from 5 to 15 mg/kg, is widely used to build renal injury models in rats . Howeverμmol/L; p < 0.01) and 4.7  times higher, respectively. Interestingly, compared with those in the DDP group, the levels of creatinine  and urea nitrogen  slightly improved upon administration of XH-003 4 h prior to DDP administration, with no statistically significant differences. However, the administration of XH-003 30 min prior to DDP administration significantly decreased the levels of creatinine  and urea nitrogen . In addition, compared with the control group, the XH-003-alone treatment group did not show any differences in the levels of creatinine  and urea nitrogen . Therefore, administration of XH-003 30 min prior to DDP administration could significantly improve DDP-induced nephrotoxicity and play a chemoprotective role.The most optimum administration time for XH-003 was 4 h before irradiation, and its half-life was 6.65 h. However, it remains unknown whether XH-003 would show a chemoprotective effect at this time of administration. Therefore, two times of administration were tested, 30 min and 4 h prior to DDP administration. The results are shown in μmol/L; p < 0.001) and urea nitrogen , caused by DDP, were prevented in the XH-003+DDP group. Although amifostine significantly decreased creatinine  and urea nitrogen , the decrease in the latter was not statistically significant. In addition, DDP-induced severe weight loss was alleviated by XH-003 and amifostine  of XH-003 and amifostine against DDP-induced nephrotoxicity. Thus, XH-003 retained the advantages of amifostine in protection against nephrotoxicity.The primary purpose of designing XH-003 was to retain the efficacy of amifostine and overcome its drawbacks, such as nonoral administration, a short half-life, and strong side effects. As an ARS-protective agent, XH-003 has achieved this goal. However, it remains unknown whether the efficacy of XH-003, as a chemoprotective agent, is similar to that of amifostine. Therefore, we compared the protective effects of XH-003 and amifostine under the same experimental conditions. The results are shown in ifostine . It shoup < 0.01), BA% , PLTs , and WBCs  significantly increased compared with their levels in the control group, suggesting that DDP induced inflammatory responses. However, XH-003 significantly improved DDP-induced levels of MO% , BA% , PLTs , and WBCs . There were no changes in other peripheral blood indexes (data not shown).The evaluation of peripheral blood  showed tGiven the results of the peripheral blood evaluation, HE staining was used to observe whether DDP caused the inflammatory response in renal tissue and whether XH-003 could alleviate this effect . In the p < 0.01) and decreased the levels of the antioxidant enzymes SOD , GSH-Px , and CAT  in renal tissue compared with those in the control, respectively. Meanwhile, compared with DDP group, XH-003 significantly reduced the MDA level  and increased the antioxidant enzymes SOD , GSH-Px , and CAT . These results indicated that XH-003 effectively alleviated the DDP-induced oxidative stress.Oxidative stress influences DDP-induced renal injury. The increase of MDA level and the decrease of antioxidant enzyme level are observed in DDP-induced renal injury. Next, we examined whether XH-003 could attenuate oxidative stress in the kidneys of DDP-treated rats. The results are shown in in vivo  was half of that in the DDP group , indicating that XH-003 significantly reduced the level of Pt in renal tissue.To examine whether the renal protection effects of XH-003 are because of a decrease of DDP in kidney tissue, Pt levels were determined 3 days after DDP administration. The results are shown in We speculated that XH-003 might affect the uptake or excretion of DDP from renal tissue. OCT2 and MRP2 influence DDP uptake and excretion. Western blotting analysis was used to test whether XH-003 affected the expression of the OCT2 and MRP2 proteins . Compare3) and XH-003 (460.4 ± 80.2 mm3) groups, tumor volumes significantly declined in the DDP (247.3 ± 129.1 mm3) and DDP+XH-003 (184.4 ± 106.5 mm3) groups. Interestingly, the antitumor effect was significantly better (p < 0.001) in the XH-003+DDP group than in the DDP group, suggesting that XH-003 did not affect the antitumor activity of DDP but the combination of the two drugs might have had a synergistic effect.In addition to its ability to reduce DDP-induced nephrotoxicity, it was very important to determine whether XH-003 attenuates the antitumor effect of DDP. The results of the H358 xenotransplantation experiment  showed tDDP, used as an antitumor agent since 50 years, has significantly contributed to the enormous advances in the field of oncology; however, there is no effective solution to the problem of DDP-induced nephrotoxicity. Oxidative stress and DNA damage play key roles in DDP-induced nephrotoxicity. Therefore, the inhibition of ROS by antioxidants is a potential approach to the treatment of DDP-induced nephrotoxicity.In this study, we found that XH-003 also showed a significant protective effect against DDP-induced nephrotoxicity. XH-003 significantly reduced DDP-increased levels of creatinine and urea nitrogen in the plasma, which are well-known biomarkers of kidney function. Furthermore, XH-003 alleviated the DDP-induced abnormal changes in BA%, MO%, PLTs, and WBCs in the peripheral blood, suggesting that XH-003 could effectively improve DDP-induced inflammation. HE staining confirmed that XH-003 relieved DDP-induced renal tissue inflammation. Together with the results of TUNEL staining for apoptosis, the data confirmed that XH-003 had a good protective effect against DDP-induced nephrotoxicity.Evidence obtained in recent years has indicated that DDP not only directly damages DNA through chelation but also induces renal damage by producing large amounts of ROS and indirectly damaging DNA. When DNA is damaged, it immediately activates the ataxia telangiectasia mutant protein, which can cause phosphorylation of p53. The role of p53, which is a well-recognized tumor suppressor protein, was elucidated in renal injury by Cummings and Schnellmann. Activated p53 can promote the expression of PUMA; disturb the interaction between the BCl-XL and Bax proteins; lead to the formation of the mitochondrial pore and the release of apoptotic proteins, such as cytochrome c; activate the caspase cascade; and induce apoptosis. Moreover, the increase in ROS can activate the endoplasmic reticulum pathway, followed by activation of the caspase-9/3 cascade and induction of apoptosis –43. Our Oxidative stress in a physiological state can promote the dissociation of Nrf2 and keap-1 and Nrf2 translocation into the nucleus. Nrf2 is combined with ARE through MAF and activates the expression of downstream antioxidant genes, promoting the expression of antioxidant proteins, such as HO-1, SOD, and CAT , 45. WheDDP is easily accumulated, excreted, and metabolized in the kidney. Therefore, reducing the concentration of DDP in the kidney is an important strategy for kidney protection. Interestingly, in this study, XH-003 significantly reduced the DDP concentration in renal tissue, resulting in decreased nephrotoxicity. A previous study has shown that the uptake of DDP by renal tubular cells involves OCT2 and CTR1 and the efflux involves the MRP2 and MRP4 transporters . Our resXH-003 reduced the concentration of DDP in renal tissue, downregulated the expression of apoptosis-related proteins in the mitochondrial pathway, and protected normal renal tissue; however, it is unknown whether it also protected tumor tissue. The XH-003 dose used in this study was not effective in significantly reducing the antitumor effect of DDP and may have had a synergistic effect with DDP. Some antitumor drugs, such as metal drugs and DNA-damaging drugs, whose primary mechanism involves the activation of the apoptosis pathway and induction of cell death through excessive ROS production, can kill tumor cells by increasing ROS levels. However, there are some drugs that cause antitumor effects by removing ROS from tumor cells; these include vitamins C and E, whose primary mechanism targets the ability of ROS to promote tumor development via the oxidation of specific chemical groups in cells. This oxidation reaction can induce gene mutations and activate related biochemical pathways, thus promoting cell proliferation and tumorigenic transformation. Therefore, as a powerful antioxidant, XH-003 may also have potential application in the field of antitumor therapy.Although current studies have shown that XH-003 has a protective effect on DDP-induced nephrotoxicity, there are some limitations. For example, in the experimental condition section, the selection of animal species and feeding conditions in each laboratory have an influence on the dosage of cisplatin, and there is no standardized modeling method, which can lead to variations in results. Although the experiment has been repeated many times, the number of rats in each group is small, which may affect the accuracy. In addition, the renal function index of this experiment evaluated creatinine and urea nitrogen, but did not evaluate other important indexes such as glomerular filtration rate and serum uric acid. Most importantly, the existing animal models can not completely simulate the situation of real clinical patients, so we need to explore new animal models to meet the clinical needs. Therefore, in the follow-up research, it is necessary to improve the experimental conditions and animal model, so that the preclinical research results can better reflect the situation of clinical patients.XH-003 significantly reduced the accumulation of DDP in renal tissue, downregulated the expression of mitochondrial apoptosis pathway-related proteins, and alleviated oxidative stress and inflammation, thus providing renal protection without affecting the antitumor activity of DDP. Based on these results, chemoprotection can be considered an extended indication of XH-003.


Our results highlight the general importance of the SARS-CoV-2 structural proteins in immunological control in early infection spread looking at a global census in various countries and taking case fatality rate into account. Other factors such as health system and control measures become more important after the early spread. Our study should encourage further studies on MHCII alleles as potential risk factors in COVID-19 including assessment of local populations and specific allele distributions.We observed substantial differences in predicted Major Histocompatibility Complex II (MHCII) epitope presentation of SARS-CoV-2 proteins for different populations but only minor differences in predicted MHCI epitope presentation. A comparison of this predicted epitope MHC-coverage revealed for the early phase of infection spread (till day 15 after reaching 128 observed infection cases) highly significant negative correlations with the case fatality rate. Specifically, this was observed in different populations for MHC class II presentation of the viral spike protein ( Since early 2020, SARS-CoV-2, which causes COVID-19, developed into a global pandemic. Many potential approaches to treatment and prophylaxis have been developed over a relatively short period. Currently, 238 vaccine candidates are in pre-clinical and clinical development  and six are at the same time already used worldwide . However, most of the treatment agents show disputable efficacy or side effects ,5. HowevThe spread of viral diseases is influenced by several factors, such as the movements of the human population, social behavior, virus mutations and population immunity. Therefore, to understand the country-specific responses elicited by the MHCI and MHCII repertoires, population data on the spread of COVID-19 infection and the calculated lethality should be correlated with predictions of the immunological repertoire in a particular population.To address this issue, our analysis considers: (i) infection spread and pattern of COVID-19-patient growth curves in different countries; (ii) focus on naïve population and natural immunity, defined for the time period from disease-free population till day 15 after reaching 128 infected cases in the population and (iii) country-specific human host immune response according to epitope response reaction frequencies separated for T- and B-cells (major eliciting MHCII epitopes mapped on SARS-CoV-2 surface epitopes).In this paper, we establish and present several correlations between pandemic spread in different countries and the immune response to epitope-representation in immunogenic SARS-CoV-2 proteins. Full data of a broad analysis including different parameter choices are given see also , includiProtein annotation and protein–protein interactions with the human host did provide some hints on SARS-CoV-2 infection biology and treatment strategies. However, there are some lacunas in the methods and in the analyses provided to date. Therefore, we decided to take the analysis one decisive step further to in silico predictions of potential linear B-cell epitopes. These analyses were performed with several online tools to rule out bias from individual prediction algorithms. The number of predicted epitopes significantly varied between the analyzed proteins.For the analysis of B-cell epitopes, three distinct methods were used. The methods include ABCpre, BepiPred and IEDB. These methods work on discrete principles which help to explore various aspects of epitopes. Therefore the results are in varied combinations which can be further processed and interpreted. For example, the ABCpred method predicted a high number of epitopes with the default parameters. However, compared to BepiPred, IEDB analysis revealed unique epitopes which are neither predicted by BepiPred nor ABCpred. The BepiPred is one of the most popular tools for linear B-cell analysis. We compared the epitopes predicted in this study with experimentally verified epitopes, and most of them also matched . All theThe non-structural protein ORF1ab was evaluated as the most antigenic protein. However, due to its nature and length, it is controversial to consider ORF1ab as a vaccine candidate. On the other hand, the ORF1ab encodes viral enzymes crucial for viral replication, such as viral protease . The predictions by IEDB are overlapping 7-mers starting at 782 AA.To compare our findings with already experimentally validated epitopes, we selected epitope patients . Interes280NENGTITDA288) and IEDB (1229MVTIMLCCMTS1239).Several studies have recently been published on the bioinformatics prediction of B-cell epitope prediction in SARS-CoV-2 proteins. A summary of these articles is summarized in Noorimotlagh et al. 2020 . In the Also, we found several similarities between already validated epitopes of previous SARS-CoV and novel SARS-CoV-2 epitopes, however, the amino acid sequences of these viruses are not identical, and thus several differences in predicted epitopes were observed . A similIdentification of T-cell epitopes was performed using the analysis resource TepiTool (IEDB). We included the 27 most frequent alleles for the MHCI and the 26 most frequent alleles of MHC class II. Interestingly, multiple alleles of each MHCI and MHCII bind the same epitopes or overlap. For example, we found 16 epitopes in OFR10, of which only two (YINVFAFPF and MGYINVFAF) bind exclusively MHCI. The number of epitopes predicted in silico for each structural protein is given in Each of the viral proteins was further analyzed by the VaxiJen server  alleles is crucial for understanding the spread of disease, and the pathogenicity of viruses, bacteria and parasites. MHC molecules are highly polymorphic and provide information on how the patients will respond to an antigen . AllelicThe population coverage analysis was performed by testing in silico predicted epitopes and their recognizing HLA alleles using the IEDB Population coverage analysis tool . For anaWe observed significant differences in the coverage of MHC alleles by SARS-CoV-2 proteins for different populations. Similar results were observed for MHCII alleles . The higAn important factor in understanding the prevalence of SARS-CoV-2 is the analysis of different ethnicities. MHC molecules are highly polymorphic and their frequencies vary in different ethnical groups . These d7) COVID-19 patients were observed in each country. An exception to this common pattern can be seen in South Korea with a much lower slope of growth of identified cases. We speculate that this is due to better protection from the beginning of the pandemic because wearing a protective mask is widespread in the Korean population even before the pandemic. Countries such as Colombia, South Africa, Italy, but also Belgium and Mexico, have much higher disease burden  over time due to an underestimation of cases at the beginning of an epidemic. To analyze whether we see the same trend with COVID-19, we plotted the CFR against time . Surprishttps://nextstrain.org/ncov/global; 4992 genomes sampled December 2019 till the end of September 2020 according to Nextstrain database [p-value: 0.00053) and the envelope protein , but not for other proteins or MHCI coverage.We correlated the overall predicted MHC-presentation of the four structural SARS-CoV-2-proteins for different populations with the case fatality rate (CFR) observed in different countries at day 15 since 128 patients . We chosdatabase —accessedp-value: 0.1 level , since it is probably the important target for neutralizing antibodies and it is, therefore, a remarkable observation that predicted MHCII-coverage of epitopes of this protein reveals the strongest correlation with the observed CFR in different countries.The quality of MHCII presentation by the T-cell is an important prerequisite for T-cell-dependent antibody production. Besides the envelope protein and membrane protein, the SARS-CoV-2 surface (spike) protein also shows a clear correlation but only below the The correlations in this study investigate differences in the adaptive immune response to SARS-CoV-2 envelope proteins in different populations. Several observed strong correlations suggest that these may play a role in the observed clear variation in fatality rates in different countries. According to these observations, strong MHC-restricted responses directed at the membrane protein and the envelope protein would be important to achieve better control over SARS-CoV-2-infection and are implied to lower CFR. The correlation of MHCII-presentation of the predicted epitopes of membrane, envelope and spike protein with a low CFR observed in different populations supports also the importance of these proteins as vaccine and potential therapeutic candidates.As a major conclusion of our study, we see that overall the strong epitope binders in T-cells are underrepresented in the Italian population as compared to the German population. However, the current data are not strong enough to identify particular alleles that definitely drive the differences. What we did study and deliver in our work are the differences in adaptive immune response regarding T-cell and B-cell epitope presentation averaging on the global population, not looking at individual populations but covering most of the prevalent alleles in most of the world’s major geographic regions. We primarily investigated MHC-restricted responses, which comprise both, humoral and cell-mediated reactions.Doubtless, many other factors may also contribute to country-specific differences: for instance in some Asian countries such as in Japan may be connected to cultural habits such as many people wearing a face mask even before the pandemic. Nevertheless, for viral diseases such as H7N9 influenza A virus, analysis of T-cell immunity in human populations applying refined immune-informatics provided also here valuable insights such as clear ethnic differences ,25 and aRegarding responses in specific subpopulations, the panel and the predictive power are not strong enough. For example, alleles associated with the DR14 and DR16 serological families, or various DR4 splits such as DR0404 or DR0407 (which will have different binding patterns than DR0401) that may be prevalent in the “negative” populations with reasonably high frequency, but not in the “positive” populations, are not represented in our prediction analysis. Associations of infectious diseases with the HLA DR4 type of the host have been found among distinct populations, racial or ethnic groups. In Mexico, a greater frequency of HLA-DR4 antigens was found in patients with Chagas’ disease with an increased frequency of HLA-DR16 antigens compared to asymptomatic patients or healthy controls . ParticuAnother important fact is the impact of COVID-19 on different races and ethnicities, which was noticed in several studies ,29,30. Ap-value: 0.00053) and for the envelope protein , and still clear for the viral spike protein . However, we cannot, of course, rule out that a completely different factor such as health system and non-HLA immunological factors could bear out an even higher correlation. However, for the latter, we did all reasonable efforts to account for this by (i) purposely averaging on the world population so that local trends do not lead to biases , (ii) monitoring overall immune response by looking at case fatality rate and (iii) by avoiding multiple other comparisons and hypothesis as then the statistical correlation has to be corrected and is weakened by multiple testing. In addition, the obviously higher correlation for MHCII compared to MHCI may indicate that CD4 cells play a more crucial role in fighting against the SARS-CoV-2 infection for the human immune system, in particular for severe COVID-19 patients. This may lead us to reconsider the strategy and development of novel therapeutics. An exception is a nucleocapsid, though highly expressed, this is unlikely able to offer sufficient neutralizing antibody response on its own.There is also another important point to discuss: could it be that other factors  have an equally high correlation or even higher? Well, firstly we only observe a correlation, not a molecular proof. To claim molecular proof, we would need to consider various additional factors: rare antigens, highly represented antigens in local populations, and importantly, direct molecular assays of the immune response. Instead, we focused here only on the correlation of MHC epitopes. However, this correlation is surprisingly strong for the membrane protein . Accession numbers, annotations, PDB identifiers and the length of analyzed proteins are given in Protein sequences of SARS-CoV-2 were screened for linear continuous B-cell epitopes using three tools:http://www.cbs.dtu.dk/services/BepiPred, accessed date: 5 February 2020 [BepiPred Linear Epitope Prediction tool (ver. 2) (ary 2020 ) was usehttps://www.iedb.org, accessed date: 5 February 2020).IEDB Database  used thehttp://crdd.osdd.net/raghava/abcpred, accessed date: 5 February 2020) [The parameters for the ABCpred Tool (ry 2020) ) were ushttp://tools.iedb.org/tepitool; accessed date: 10 February 2020 [T-cell epitopes prediction was performed using TepiTool (ary 2020 ). To preary 2020 .The distribution of COVID-19 recognizing MHCI and MHCII alleles was assessed using the IEDB population coverage tool . Here, wThe analysis was implemented in R by acquiring data from the nCov2019 library  and the During the outbreak, the proportion of death from COVID-19 (CFR) is observed for different countries at 15 days since 128 cases were reported. Accordingly, antigen presentation is collected as a percentage of MHCI and MHCII epitopes. The linear correlation is calculated using the “ggpubr” package in R, and a Pearson method is applied to run the test. We hence assume that the population data are normally distributed.We hypothesize that countries with poor case fatality rates are associated with poor immune response. We predict immunity by monitoring well-represented epitopes, whether these are already sufficient to derive a good correlation with the observed immunity, which is given inversely correlating parameter case fatality rate. We observe that this is really the case in the initial phase of infection. Our analysis combines multiple approaches to investigate the pandemic of SARS-CoV-2 in different countries. There are striking differences predicted for natural population immunity in different populations. Differences in the severity of COVID-19 disease observed in different appear to be related to differences in the potential of different populations to present SARS-CoV-2 epitopes on MHCII in a naïve population . Geographical areas with a high incidence of virus and disease burden such as Italy and Iran have a low epitope-binding MHCII-repertoire compared to countries with less severe disease progression such as Germany, Austria, Sweden, Norway and South Korea. We emphasize here that these are only correlations but these were confirmed using well-established epitope prediction tools that include experimental validation data and predict successfully also established SARS-CoV-2 and other viral epitopes. Moreover, the case fatality rate is a direct indicator of whether the immune response in the population is sufficient, including other epitopes or other immune factors. Our results highlight the importance of the SARS-CoV-2 structural proteins as a target to gain MHCII restricted control over the infection, e.g., by vaccine strategies and should encourage further studies probing the molecular immunology of SARS-CoV-2 further and examining MHCII alleles as potential individual risk factors in COVID-19. Future studies should focus on specific geographic regions as well as other factors taking over after the initial phase (later than day 15 after reaching 128 observed cases) such as health system, potential strain variation effects and, most importantly, different control measures. The evidence presented here suggests that epitopes predicted to provide broad coverage worldwide are also likely to provide broad coverage in specific populations and correlated strongly and inversely with a case fatality rate in the early phase of infection spread.


CCI1) and CCI2). CCI1 measures antagonist muscle activation relative to not only total activation of agonist plus antagonist muscles but also agonist muscle activation, while CCI2 measures antagonist muscle activation relative to only total muscle activation. We computed the correlation between these two CCIs and model-based estimates of sagittal plane joint stiffness for the hip, knee, and ankle of both legs. Although we observed moderate to strong correlations between some CCI formulations and corresponding joint stiffness, these associations were highly dependent on the methodological choices made for CCI computation. Specifically, we found that: (1) CCI1 was generally more correlated with joint stiffness than was CCI2, (2) CCI calculation using EMG signals with calibrated electromechanical delay generally yielded the best correlations with joint stiffness, and (3) choice of antagonist muscle pairs significantly influenced CCI correlation with joint stiffness. By providing guidance on how methodological choices influence CCI correlation with joint stiffness trends, this study may facilitate a simpler alternate approach for studying joint stiffness during human movement.Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. However, quantification of joint stiffness is difficult using either experimental or computational means. In contrast, quantification of muscle co-contraction using an EMG-based Co-Contraction Index (CCI) is easier and may offer an alternative for estimating joint stiffness. This study investigated the feasibility of using two common CCIs to approximate lower limb joint stiffness trends during gait. Calibrated EMG-driven lower extremity musculoskeletal models constructed for two individuals post-stroke were used to generate the quantities required for CCI calculations and model-based estimation of joint stiffness. CCIs were calculated for various combinations of antagonist muscle pairs based on two common CCI formulations:  Muscle co-contraction refers to the simultaneous activation of muscles on opposite sides of a joint. It is an important mechanism used by the central nervous system to regulate joint stability  and provStroke is a common clinical condition that often impairs movement through an increase in joint stiffness  and spasQuantification of muscle co-contraction may offer an alternative for estimating joint stiffness. Although previous studies have reported that muscle co-contraction and joint stiffness are related , the relThe Co-Contraction Index (CCI) is a commonly used method for quantifying muscle co-contraction during human movement. Computation of a CCI involves choosing from a wide selection of methods, and previous studies have examined how differences in method affect CCI results . Two comThis study provides a quantitative evaluation of the relationship between lower extremity muscle co-contraction indices and corresponding joint stiffnesses during gait. We calculated CCIs and lower body joint stiffnesses using EMG data collected from the lower extremity muscles of two individuals post-stroke walking at their self-selected speed. CCIs were calculated for the two common CCI formulations noted above using four different methods for EMG data post-processing. Lower body joint stiffnesses were calculated using EMG-driven musculoskeletal models calibrated using the EMG, motion capture, and ground reaction data collected from each subject. Correlations between CCIs and joint stiffnesses for each subject were calculated, and CCI calculation methods that helped improve the correlations were identified. These findings could help clinicians formulate CCIs that yield results more strongly aligned with joint stiffness trends.Walking data collected from two hemiparetic male subjects post-stroke were used for this study. The first subject , herein referred to as subject S1, walked at a self-selected speed of 0.5 m/s. The second subject , herein referred to as subject S2, walked at a self-selected speed of 0.45 m/s. Both subjects walked for multiple cycles on a split-belt instrumented treadmill  while motion capture , ground reaction , and EMG  data were collected. EMG signals were measured at 1,000 Hz from 16 muscles in each leg  using a EMGbasic.Raw EMG data were processed using a standard methodology. The data were high-pass filtered at 40 Hz, demeaned, rectified, and low-pass filtered at a variable cutoff frequency of 3.5/period of the gait cycle  while usEMGbasic. Electromechanical delay is defined as the duration from the instant an electrical signal is received to the instant a force response is generated by the muscle. Electromechanical delay was assumed to be the same for all muscles in each leg  delayed EMG signals EMGdelayed, which are electromechanically delayed EMG signals that are not normalized to the optimized maximum value. These signals were obtained as shown below:To isolate the underlying effect of the two EMG parameters on quantification of co-contraction, we introduced two additional types of EMG signals: (1) scaled EMG signals CCI1 was based on the formulation reported by CCI values were computed from processed EMG data using the two most common formulations. CCI2 was based on the formulation reported by while InputL and InputH represent EMG signals from an antagonist muscle pair, where both signals were resampled to 101 normalized time points (0 – 100% of gait cycle at 1% increment). InputL is the EMG signal with the lower absolute magnitude at time t while InputH is the EMG signal with the higher absolute magnitude. For both CCI formulations, the input quantities include the four types of EMG signals  described above. Each CCI calculation method used in this study is described by a combination of the selected CCI formulation and the selected EMG type. For example, EMGdelayedCCI1 means the CCI values are calculated using delayed EMG signals based on the For both formulations, In addition to varying the types of EMG signals used to compute CCI, this study also investigated how the difference in constituent muscles for an antagonist muscle pair could affect the relationship between CCI and joint stiffness. CCI was computed for three lower limb degrees of freedom (DOFs) in the sagittal plane: hip, knee, and ankle flexion and extension for both non-paretic side and paretic side. Lower extremity muscles were classified by their functional roles during gait , and oneMj with respect to generalized coordinate jθ corresponding to degree of freedom (DOF) j:Sagittal plane stiffness of the lower extremity joints  in each leg was estimated using a model-based formulation . The derMj can be expressed as the sum of the product of muscle moment arm and tendon force for each muscle spanning the joint:The net joint moment rij represents the moment arm of the ith muscle about DOF j, ith muscle, and n is the total number of muscles. By substituting the expression of joint moment Mj into Eq. (5) and performing partial differentiation via product rule, one obtainswhere MTil represents the muscle-tendon length of the ith muscle, Kjoint can be expressed as the sum of the stiffness contributed by each individual muscle Kmus as shown below:Re-expressing This model-based stiffness formulation assumes that the muscle model possesses a rigid tendon. As moment arms and muscle-tendon lengths of the musculoskeletal model  were repCCI1 or CCI2) and joint stiffness Kjoint was quantified by the Pearson correlation coefficient using the corrcoef function in MATLAB . Correlation was calculated between the two time series for each of the 10 gait cycles analyzed:The strength of association between CCI (ranksum function to compare the mean correlation coefficient between the two classes of data (10 pairs of correlation coefficients for 10 gait cycles). The analysis tested the null hypothesis that the two classes of data came from samples with continuous distributions possessing equal medians. The level of statistical significance was set at p = 0.05.The Wilcoxon rank sum test was performed in MATLAB using the Joint stiffness trends were mostly symmetrical between the non-paretic and paretic side for subject S1 , 1st rowIn contrast, joint stiffness trends were asymmetrical between the non-paretic and paretic side for subject S2 , 1st rowCCI1 and joint stiffness  worked better than did EMGbasic or EMGscaled when calculating CCI1, (3) Some antagonist muscle pairs worked better than did other antagonist muscle pairs when calculating CCI1. These findings could be used as a preliminary foundation for predicting joint stiffness trends from EMG-based measurement of muscle co-contraction.This study evaluated how well different CCI formulations approximate lower extremity joint stiffness trends during gait for individuals post-stroke. Joint stiffness trends may help reveal gait pathologies in these individuals as demonstrated in this study. In addition, joint stiffness may potentially be used to improve the design of rehabilitation treatments and assistive devices for individuals post-stroke. However, the difficulty of measuring or computing joint stiffness is well documented. It would therefore be beneficial to the clinical community if commonly used co-contraction indices correlated well with joint stiffness, thereby providing easy-to-calculate surrogate measures of joint stiffness. Although moderate to strong correlation was observed between some CCI formulations and corresponding joint stiffness, this correlation was highly dependent on the methodological choices made for CCI computation. The conditions under which we observed the highest CCI correlations with joint stiffness were obtained can be summarized as follows: (1) mulation  was bettmulation ; (2) EMGJoint stiffness trends can help reveal gait pathologies as demonstrated in this study. On the surface, the joint stiffness trends confirmed clinical observations about the post-stroke subjects studied. Subject S1 has relatively high motor functioning post-stroke (Fugl-Meyer Motor Assessment score: 32 points), and joint stiffness trends between both non-paretic and paretic sides were symmetrical to a certain extent just as the gait patterns were. Subject S2 has relatively low motor functioning (Fugl-Meyer Motor Assessment score: 25 points) and gait asymmetry is a direct consequence. This was observed as longer than normal stance phase on the non-paretic side and shorter than normal stance phase on the paretic side, indicating possibly a compensation from the non-paretic side for weakness on the paretic side. The gait asymmetry observed was well supported by the trends of joint stiffness we estimated. Delving deeper into the gait pathologies, both subjects experienced a sudden spike in joint stiffness at joints on the paretic side: S1 (ankle) and S2 (hip). The former incidence was due to the abnormally high activation of the soleus muscle, which was not observed on the non-paretic side. The latter incidence was due to the abnormal activation of hip flexors in iliacus and psoas, compounded by the unexpectedly large stiffness generation by adductors, which are not conventionally considered as major hip flexors. The model-based estimation of joint stiffness, and calculated CCIs to some extent, may offer a way to probe into the root cause of these pathologies, providing valuable knowledge to both diagnostics and treatment of gait pathologies for stroke survivors.Co-Contraction Index would be a suitable candidate to consider for addressing the aforementioned clinical needs because of its common adoption in the clinical community and simplicity in usage. Various methodological choices in CCI calculation were explored in this study. One key decision was to not include moment-based CCI and instead focus on EMG-based CCI. Even though moment-based CCI explored in previous studies  may achiCCI1 and joint stiffness Kjoint (r1) was generally higher than for CCI2 (r2) in more cases if each comparison at a joint on one of the subjects was considered one case , S2 Hip (P), and Ankle (P). Kjoint is a sum of the stiffness generated by all the individual muscles Kmus. The sum term in the CCI1 formulation, InuptL+InputH, was more effective at characterizing this summation than any term in the CCI2 formulation. This effectiveness became more pronounced when the quantities used for CCI computation from each muscle were accurate proxies for the corresponding Kmus. On the other hand, the CCI2 formulation was more suitable for quantifying the ratio of antagonist muscle activities. This is demonstrated by the observation that the correlation between CCI2 and Kjoint was comparable to the correlation between the InputL / InputH term of CCI1 and Kjoint. Close examination also showed that CCI2 values for subject S1 reached a peak in magnitude in the swing phase (∼60 to 100% gait cycle) comparable to that during the stance phase (0 to ∼60% gait cycle) at several joints  at the hip joint on the non-paretic side of subject S1; (2) at the hip joint on the paretic side of subject S2; and (3) at the ankle joint on the paretic side of subject S2, where CCI2 was better correlated with Kjoint. These discrepancies are possibly because this study presented CCI data of only the muscles from which EMG data could be obtained by surface measurement. Although fine-wire EMG data for some muscles were also collected from the subjects studied to be used for EMG-driven model calibration and estimation of joint stiffness, these muscles were excluded from the CCI analysis, since EMG data would not be available from them in a clinical setting. Among the muscles omitted were iliacus and psoas, two primary hip flexors; and extensor digitorum longus and tibialis posterior, one primary dorsiflexor, and one primary plantarflexor, respectively. These discrepancies could be rectified if the EMG data from the omitted muscles were made available for CCI calculation. Even though invasive, the fine-wire EMG measurement technique could still provide valuable information for the estimation of muscle co-contraction and joint stiffness when such measurements were allowed.Contrary to the general trend noted above that the correlation between This study also found that the correlation between CCIs and joint stiffness is generally lower at the ankle joints than at other lower body joints for both subjects. It is possibly because a complex joint in the likes of ankle is actuated by a relatively small number of muscles in the musculoskeletal models . These muscles actuate motion in the subtalar inversion-eversion DOF in addition to the dorsi or plantar flexion. It is difficult to allocate the precise amount of muscle activation to the actuation of ankle joint in the sagittal plane. Therefore, the comparison between muscle co-contraction and joint stiffness is skewed. Moreover, we chose to omit muscle activities from extensor digitorum longus and tibilais posterior because they were obtained through fine-wire EMG and thus were not suitable for the goals of this study. This decision limited the number of muscles available for representing co-contraction around ankle joint, limiting our options to identifying muscle activities in synchronization with the generation of joint stiffness, hence resulting in a relatively lower correlation between CCIs and joint stiffness at the ankle joints than at the other joints.One limitation of this study was that the joint stiffness used for comparison with different CCI methods was obtained from a neuromusculoskeletal model instead of experimentally using a joint perturbation technique. A model-based approach was used since the perturbation approach is difficult to implement experimentally, especially for dynamic tasks such as gait . ConsequAnother limitation of this study was that it analyzed gait data collected from only two hemiparetic subjects. Although the collection of sixteen channels (including six fine-wire) of EMG from each leg of the subject during walking was time-consuming and not a common practice, it facilitated the calibration of our musculoskeletal model. Although analyzing two subjects limits our ability to draw more general conclusions that could be applied to the stroke population, at the same time, this dataset provided a unique opportunity to build a musculoskeletal model of the subjects and calibrate the model parameters using an EMG-driven framework that did not require prediction of any missing EMG signals as in CCI1 formulation and adding calibrated electromechanical delay to the EMG signals for computing EMG-based CCI1. Antagonistic muscle pairings that yielded the highest correlations between CCI and joint stiffness were also identified. These findings provide the preliminary knowledge to help clinicians formulate CCI that may yield results more aligned with joint stiffness trends during gait for individuals post-stroke. By using CCI to approximate joint stiffness trends, this study may open an alternative approach to estimate joint stiffness, which is difficult to obtain through either computational modeling or experimental measurement.In conclusion, this study demonstrated the feasibility of using EMG-based CCIs to approximate lower limb joint stiffness trends during gait for individuals post-stroke. A number of methodological choices for CCI computation were examined. Key methodological choices to achieve the highest possible correlation between CCI and joint stiffness should include the use of https://simtk.org/projects/ccivsjointstiff.The experimental data, OpenSim models, and Matlab code used to perform this study are available at All experimental procedures were approved by the University of Florida Health Science Center Institutional Review Board (IRB-01) and the subjects provided written informed consent prior to participation.GL, DA, MS, and BF: conceptualization, investigation, methodology, and formal analysis. BF: funding acquisition. BF and CP: data collection. MS and BF: supervision. GL, MS, BF, and CP: drafting the manuscript. GL, DA, MS, BF, and CP: revising the 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.