Staphylococcus aureus (MRSA), influenza virus and filamentous fungi were performed together with qualitative chemical analyses for bioactive fungal metabolites and mycotoxins. Swab samples were analyzed for total viable counts.The quality of mink feed and raw ingredients affect health and growth. The objectives of this study were to examine the microbiological quality of ready-to-eat mink feed and its raw ingredients, screen the plant part of the feed for mycotoxins, and determine the hygiene of the production environment in the feed processing facilities. The results of the study are important for identification of critical steps in the feed production and for formulation of recommendations for improvements of production processes to obtain better quality feed. Feed and swab samples were taken at three Danish mink feed producers October 2016 and May 2017, respectively. Viable counts, detection of methicillin-resistant 2 and 9.3 × 107 cfu/g in raw ingredients and between 107 and 109 cfu/cm2 on different surfaces at the feed production facilities. A pork meat product, pork haemoglobin, pork liver and a poultry mix was found positive for MRSA, while monophasic Salmonella  was detected in a pork meat product. Neither MRSA nor Salmonella was detected in any ready-to-eat feed. Influenza A virus was not detected in any sample. Filamentous fungi were detected in all analysed samples of ready-to-eat feed while dihydro-demethyl-sterigmatocystin was found in almost 50% of all ready-to-eat feed samples and in 80% of the sugar beet pulp. Fumonisins and other Fusarium toxins were found especially in corn gluten meal and extruded barley and wheat.Viable counts varied between 7.2 × 10Mink feed contained a cocktail of mycotoxins and bacteria, which may not per se cause clinical disease, but may affect organ function and animal performance and well-being. Neovison vison) is used for livestock production with 1248 commercial mink farms registered in 2018, which produced approximately 17.2 million mink skins (https://statistikbanken.dk). Feed factories supply the mink farms with pumpable wet feed consisting of a mix of animal by-products and slaughter offal (e.g. by-product from the fishing and meat industries) and plant origin .Denmark is the world’s largest producer of mink skins. The American mink  in 20 out of 58 (34%) of clinical mink samples and in 20/108 (19%) of feed samples. Studies have shown mink to be sensitive to a number of mycotoxins, including aflatoxin B1, deoxynivalenol, zearalenone, fumonisins, moniliformin and ergot alkaloids  was detected in a pork meat product from feed producer A. Neither MRSA nor Salmonella were detected in the ready-to-eat feed from any of the three feed producers. All samples of raw ingredients and ready-to-eat feed were influenza-negative.In 2016, pork meat product from feed producer A and in 2017, both haemoglobin and pork liver from feed producer A and poultry mix from feed producer B were positive for MRSA. In 2017, Absidia corymbifera, Mucor racemosus, Rhizopus oryzae, R. stolonifer and/or Syncephalastrum racemosum), while three of the samples also contained Aspergillus niger, A. flavus and/or A. glaucus. In nine samples Penicillium discolor was detected whereas P. polonicum was detected in five and P. albocoremium in one sample + 313.0707 Dalton] a precursor to aflatoxins, fits the data. Furthermore, enniatins were found in six samples and one sample also contained deoxynivalenol (DON) , heat treatment up to 90 °C (poultry) or acid treatment (silage of fish or spent laying hens) are used. However, high total viable counts (TVC) of bacteria up to 10okslahti . Elevateokslahti .6 cfu/g in ready-to-eat products. During this study, TVC between 104 and 107 cfu/g were found in the ready-to-eat feed. When we simulated time/temperature procedures  of the ready-to-eat feed by storing sampling containers with lid open at room temperature overnight, TVC increased up to 1.4 × 109 cfu/g in ready-to-eat feed II. This result more or less corresponds to the bacterial concentration in a fully outgrown broth culture, and clearly shows the importance of maintaining the correct temperature of the ready-to-eat feed product during storage in the feed production facilities. Furthermore, bacteria can spread to the raw ingredients and ready-to-eat feed product during contact with contaminated feed handling equipment and storage containers. This condition applies not only to the feed producer, but also to mink farms, where cleaning and disinfection of feed silos and feeders should be included in daily routines. High bacterial counts of the environmental samples swapped from different surfaces at the feed production facilities with levels of 107 to 109 cfu/cm2 for TVC indicate low hygiene, poor cleaning and manufacturing practices. Thus, there is a risk of recontamination of raw ingredients and final ready-to-eat product by feed- and processing handling equipment and during storage. Several mink diseases caused by e.g. Enterobacteriaceae, E. coli, staphylococci and streptococci are caused by contaminated or spoiled feed [To the authors’ knowledge, few studies have been published on the bacterial counts of freshly produced ready-to-eat mink feed products. When studying the effect of mink feed supplementation with sodium bentonite, Wlazlo et al.  reportedled feed .Influenza A virus was not detected in the feed studied, but outbreaks of influenza in mink have previously been linked to feed as a potential source of infection . Gagnon Salmonella Typhimurium was detected in a pork meat product, but not in the final products. Dietz et al. [Salmonella Dublin-contaminated feed to be associated with an abortion storm in Danish mink and fox farms. Salmonella infections are zoonotic, which can lead to infections in both humans and animals. Our findings of pathogenic bacteria show that proper treatment and processing of raw ingredients are essential to reduce the risk of both the contamination of the ready-to-eat feed and a possible exposure to humans during handling the feed.In the present study, LA-MRSA was only found in raw ingredients: pork meat product, pork haemoglobin, pork lever and poultry mix. All samples of ready-to-eat feed were LA-MRSA negative. Hansen et al.  reportedz et al.  concludePenicillium and Aspergillus spp. Our results correspond well with a study on poultry feed, where Penicillium was the most frequent genus, followed by Aspergillus and Mucor [The results of the mycological analyses show that the ready-to-eat feed products were heavily contaminated with different fungal species, especially Zygomycetes, nd Mucor .Penicillium discolor was the most dominant species in the ready-to-eat feed products followed by P. polonicum. They both tolerate low temperatures, low pH values and low water activities [Aspergillus flavus together with A. niger and A. glaucus were found in samples collected in August and November 2016. These fungi thrive at higher temperatures and lower water activities than the Penicillium species and are common on stored cereals, and on nuts, beans and cereals grown in the tropics or subtropics [Aspergillus flavus and A. niger are known mycotoxin producers [Fusarium mycotoxins were detected in this study. Their presence suggests contamination in the field and/or during storage of cereals (e.g. maize). A number of Fusarium toxins were also detected in the barley, wheat and maize raw ingredients. These metabolites are usually produced in the field during growth and remain after the field fungi have been replaced with storage fungi like Aspergillus and Penicillium. Sugar beet pulp, together with the extruded cereals and corn gluten seem to be the raw ingredients that are most susceptible to fungal contamination and thereby contribute the most to the overall mycotoxin content in the ready-to-eat feed.Zygomycetes are fast growing, soil fungi  that protivities  and are tivities , 32. Thebtropics , 32. Asproducers , but onlFusarium species in cereals [The results of the chemical analyses show that 12 out of 25 ready-to-eat feed samples contained dihydro-demethyl-sterigmatocystin, a precursor to aflatoxin , possibl cereals , 36 and  cereals  while ot cereals . Cats fe cereals . HoweverMycotoxins make up only a small part of the many biologically active metabolites molds can secrete into mink feed. Different metabolites have different biological activity depending on dose: few are acute toxic, some have an estrogen-like effect, others cause liver and kidney damage and others again are carcinogenic. A group of fungal metabolites are also able to suppress the immune system , which lFrequent microbiological control is important as many raw ingredients of mink feed have a very high number of bacteria and fungi and thus a short shelf life. The analytical methods have a natural limitation, as the sample volume is very small compared to the total production volume and microbial or chemical hotspots can easily be missed. As the raw material composition is constantly changing, potential cocktail effects should be considered when introducing new types of raw materials. Bacteria and fungi can spread to feed through contact with contaminated feed-handling equipment and grow in storage containers. Proper cleaning of equipment and proper timing of handling, storage, processing of ingredients and of the final products are essential to guarantee a high quality feed and to prevent or control infections in mink as well as the risk of exposure to humans from handling of mink feed. This study has shown that ready-to-eat mink feed can contain a cocktail of mycotoxins and bacteria, which may not in themselves cause clinical disease in the animals, but which may affect the overall health and well-being of the mink. These synergies may play an important role for the appearance of new disease complexes with unknown etiology.Additional file 1. Microbiological quality/bacterial counts in raw ingredients of animal origin and ready-to-eat feed at producer A in 2016.Additional file 2. Microbiological quality/bacterial counts in raw ingredients of animal origin and ready-to-eat feed at producer B in 2016.Additional file 3. Microbiological quality/bacterial counts in raw ingredients of animal origin and ready-to-eat feed at producer C in 2016.Additional file 4. Microbiological quality/bacterial counts in raw ingredients of animal origin and ready-to-eat feed at producer A in 2017.Additional file 5. Microbiological quality/bacterial counts in raw ingredients of animal origin and ready-to-eat feed at producer B in 2017.Additional file 6. Microbiological quality/bacterial counts in raw ingredients of animal origin and ready-to-eat feed at feed producer C in 2017.Additional file 7. Qualitative determination of mycotoxins and other biologically active metabolites present in samples of ready-to-eat feed from the three feed producers (A to C).


Chitosan macro- and micro/nano-gels have gained increasing attention in recent years, especially in the biomedical field, given the well-documented low toxicity, degradability, and non-immunogenicity of this unique biopolymer. In this review we aim at recapitulating the recent gelling concepts for developing chitosan-based physical gels. Specifically, we describe how nowadays it is relatively simple to prepare networks endowed with different sizes and shapes simply by exploiting physical interactions, namely (i) hydrophobic effects and hydrogen bonds—mostly governed by chitosan chemical composition—and (ii) electrostatic interactions, mainly ensured by physical/chemical chitosan features, such as the degree of acetylation and molecular weight, and external parameters, such as pH and ionic strength. Particular emphasis is dedicated to potential applications of this set of materials, especially in tissue engineering and drug delivery sectors. Lastly, we report on chitosan derivatives and their ability to form gels. Additionally, we discuss the recent findings on a lactose-modified chitosan named Chitlac, which has proved to form attractive gels both at the macro- and at the nano-scale. The term “gel” is usually associated with highly hydrated networks in which two components are present in different proportion, i.e., the solvent—the one prevailing in mass—and the polymeric solute; typically, the latter ones are either natural or synthetic macromolecules able to properly retain a large amount of the former component. Although this simple description is reasonably valid, it is complicated to define gels in detail. One of the safest ways to categorize pure viscous liquids from strong elastic gels is to assess their mechanical behavior, specifically undergoing the system to strain under oscillatory conditions for a large range of frequencies using rheometry as the analytical tool . For limThe use of gels is widespread and growing, especially in the biomedical sector ,3,4. ForThe present review article summarizes the basilar gelling concepts for developing chitosan and chitosan-derivatives gels. Although the fabrication of chitosan-based gels may be achieved through different strategies, herein we focus the attention exclusively on those materials synthesized by exploiting physical interactions. The potential applications of such chitosan gels in the biomedical field are discussed as well. In the last section, we report on a lactose-modified chitosan produced by our research group, named Chitlac, or CTL, which recently proved to form both macro- and nano-gels, with very interesting physical and biological features.d-glucopyranose  and 2-amino-2-deoxy-β-d-glucopyranose  residues, present in different relative proportion and sequence along the chain (Chitosans (CH) are considered as a family of binary heteropolysaccharides composed of β-1 → 4 linked 2-acetamido-2-deoxy-β-he chain . GeneralA), ) ,41 an orSacco et al. reported the fabrication of cylindrical chitosan gels through a controlled external gelation using tripolyphosphate (TPP) as crosslinker. TPP is a multivalent anion, which shows from one to five negative charges depending on pH. A typical gelation requires that chitosan is acid solubilized in order to protonate all D sugars. The resulting chitosan solution was casted into a mold, and subsequently placed in a gelling bath containing TPP  42]..42].A ..14].A = 0.40) is mixed with mannuronan oligomers at a pH around 7.5. Chitosans with FA ≥ 0.40 are well known to be soluble at approximately neutral pH and above, [This gelling system is based on two main steps: the first one requires that a medium acetylated chitosan  and D residues on chitosan. Resulting gels showed lower mechanical properties if compared with Ca2+-alginate gels produced via internal gelation using similar experimental conditions, due to the different binding mode of Mo7O246− to chitosan with respect to Ca2+ in alginate. Finally, the authors demonstrated that Mo containing chitosan gels swelled/deswelled depending on the ionic strength of the incubation medium.Ionic chitosan gels could be obtained also using metallic anion based on Mo(VI) . In the Chitosan forms polyelectrolyte complexes with negatively charged small molecules or macromolecules if the concentration of polymer is below or close to C*. Different polyanions have been extensively investigated to form micro- and nano-gels with chitosan, including inorganic ions such as TPP, polysaccharides as hyaluronan or alginate and synthetic polymers ,49. The The first evidence of the possibility to form nano-gels based on chitosan was reported by Calvo and co-workers in 1997 , with thA and molecular weight of chitosan, pH, ionic strength and concentrations of polymer and crosslinker [A and particle concentration at physiological pH and ionic strength [A.Lapitsky and co-workers extensively investigated the formation mechanism of chitosan/TPP micro-gels and their properties by tuning different parameters such as Fsslinker ,54,55,56sslinker . Micro- Chitosan-based nano-gels usually tend to aggregate after lyophilization and spray-drying . RampinoA of chitosan, the encapsulation efficiency was enhanced and the release rate was slowed down [Chitosan/TPP micro- and nano-gels were widely investigated as systems for drug, protein, and gene delivery ,64,65,66wed down .Due to the muco-adhesive properties of chitosan , nano-geComplex coacervation of chitosan can be achieved through electrostatic interactions with negatively charged polysaccharides mixed in the right proportions. The most widely used anionic polysaccharides are hyaluronan, alginate, dextran sulfate, and carrageenan . Such poOne of the most interesting polyelectrolyte chitosan-based complex is that formed with hyaluronic acid  . ChitosaA on the stability of resulting nano-gels and its biological implications, since most authors used commercial chitosans with very similar physical-chemical properties, typically FA < 0.2 and medium/high molecular weight. Delair and co-authors reported limited stability of polyelectrolyte complexes based on chitosan and hyaluronan [2+).Very little is known about the influence of Faluronan . The autA in the stability and biological activity of chitosan-hyaluronan nanogels [Some of the authors of the present review evaluated the role played by Fnanogels . Systemsnanogels . Specifinanogels . They wenanogels . Nano-genanogels , reachinnanogels .A) in CD44-expressing cells [A showed limited stability, were poorly internalized and, therefore, were not effective in RNA delivery. The best transfecting particles were those with lower FA. Tirelli and co-workers deeply investigated the possibility to use a hyaluronan coating on chitosan-TPP nano-gels [Hyaluronic acid is of particular interest due to its ability to interact with membrane receptor CD44,  known beng cells . Chitosaano-gels ,87,88,89ano-gels ,90. The ano-gels . Moleculano-gels .Alginate is another widely used polysaccharide to form chitosan-based nano-gels . Stokke A in order to retain the colloidal properties of the carrier in physiological conditions. After subcutaneous injection in mice, a specific immune response was observed and both the cellular and the humoral responses were stimulated.Costalat et al. developed reversible nano-gels based on chitosan and dextran sulfate . Nano-geRodrigues et al. studied the possibility to form nano-gels based on chitosan and carrageenan . The furThermo-sensitive gelling systems consist of stable, low viscosity, aqueous solutions that turn into a gel state upon heating. The sol-gel transition usually occurs at neutral pH and takes a few minutes ,99. The A and with increasing salt content. Supper et al. further investigated the mechanism of the gelation process, comparing different systems based on chitosan and various polyol-phosphates, such as β-glycerophosphate, glucose-1-phosphate and glucose-6-phosphate, varying in the chemical structure of the polyol parts [The gelling mechanism of chitosan in the presence of β-GP was elucidated by Lavertu and co-workers. They proposed that precipitation and gelation of chitosan derived from heat-induced transfer of protons from chitosan to β-GP . This phol parts . MacroscInjectable thermo-sensitive gels have shown great potential in biomedical applications, including local drug delivery and tissue engineering ,104. NevGiving the peculiar properties of this set of materials, Chenite and co-workers undertook biological tests: specifically, they injected such formulations, which gelled upon implantation in vivo; all implants were intimately integrated within the subdermal fibrous membranes. Formulations were used also as an encapsulating matrix of chondrocytes for tissue engineering applications .These thermogelling solutions were widely studied for the development of parenteral drug delivery systems, which enabled a sustained release of potentially encapsulated active substances . In ordeThermosensitive gels based on chitosan and β-GP were studied as materials for cartilage and bone regeneration. Hoemann and co-workers described the utility of such materials as arthroscopically-injectable vehicles for cell-assisted cartilage repair . They deSome authors reported issues about the inadequate mechanical strength of thermoresponsive gels based on chitosan for osteochondral repair ,114. To The addiction of zinc (Zn) to thermosensitive gels allowed for enhancing antibacterial activity and promoted osteoblast differentiation, therefore having a potential as injectable in situ forming matrix for bone tissue engineering applications . The antDifferent approaches for obtaining thermosensitive gels based on chitosan were undertaken using inorganic phosphate hydrogen phosphate salts. Nair and co-workers reported that by adding different concentrations of ammonium hydrogen phosphate to chitosan solution it was possible to tune the gelling time from 5 min to 30 h at 37 °C . These gAmong physical gels, an interesting category is represented by the interpenetrating polymer networks (IPNs). According to the definition given by the IUPAC Gold Book, IPNs are networks comprising two or more polymers that are at least partially interlaced on a molecular scale, but not covalently bonded to each other and cannot be separated unless chemical bonds are broken. Unlike blends of heterogeneous polymers, IPNs show a network of chains held together by physical entanglements or weak interactions and, thus, are intimately mixed and mechanically interlocked . In lighFIPNs are defined as 3D systems of two (or more) polymers each of which is crosslinked separately , while SThe second classification criterion refers to the number of steps required for the synthesis and distinguishes between IPNs produced by sequential and in situ synthesis . The mosThe overall tendency is to design medical devices based on IPNs that can mimic the ECM, thus allowing and enhancing the adhesion and growth of cells on the substrate. Chitosan represents an ideal polymer for such a purpose. Chitosans can behave as “smart” stimuli-responsive polymers when are part of gel systems, they can respond to external factors such as temperature, electric current, solvent composition, pH, and ionic concentration. Chitosan-based IPNs can be prepared for different applications in the biomedical field, such as drug delivery, tissue engineering, and scaffolding .N-isopropylacrylamide (PNIPAM) coupled with chitosan and synthesized according to the in situ technique [N-N′-methylenbisacrylamide (BIS) were added. Finally, tetramethylethylenediamine (TEMED) and ammonium persulfate (APS) were mixed and let the reaction occurring. Finally, gels were dialyzed and freeze-dried. The gel showed a high percentage of swelling at low pH because of the presence of chitosan, while the response to temperature was principally due to PNIPAM, which had a lower critical solution temperature (LCST) of 32 °C. Under these experimental conditions amide groups in hydrophilic segments of the polymer and water molecules showed strong interactions because of hydrogen bonds. These characteristics made the gel as an ideal system for the setting up of controlled drug delivery devices. Another example of how to exploit the pH sensitivity of this kind of systems comes from [N-Succinyl chitosan, NSC) crosslinked with glutaraldehyde and non-crosslinked poly-acrylamide-co-acrylic acid (poly(AAm-co-AA)) embedded into the chitosan network. The gel was synthesized by the in situ approach and showed a porous structure thanks to the high hydrophilicity of NSC, while the embedded Poly(AAm-co-AA) created more homogeneous pores. The system greatly swelled at pH 7.4 because of the presence of the carboxylic groups that deprotonated into carboxylate ions, thus causing repulsion of the chains. It has been tested for the controlled release of 5-fluorouracil, an anti-tumor and anti-metabolite drug in order to increase the leakage of the drug in the colon rather than in the stomach after oral administration. Furthermore, the semi-IPN could be also considered for tissue engineering purposes thanks to the excellent interconnected porous network, which favors an efficient exchange of nutrient and gas. The idea of controlling the gel swelling by multi-stimuli response systems has been deeply described in [2 and a polymer of hydroxyethylacryl chitosan (HC) in which the crosslinker was added after mixing of alginate and HC: in this way HC was homogeneously dispersed into the matrix of alginate. The system was stable at pH 1.2, and degraded at pH 7.4, making the network useful for a controlled drug release. The tensile strength and elongation of the gel films were 12.1 MPa and 162%, respectively, granting the system with the mechanical properties adequate for the development of wound dressings. A different way to interlace polymers is represented by UV treatment in the presence of a photoinitiator. As reported in [N-carboxylethyl chitosan (CECS), and 2-hydroxyethyl methacrylate (HEMA). Both polymers were solubilized in water with a photoinitiator, then poured into a mold and finally irradiated by a UV light source. The system is suitable for transdermal drug delivery and wound dressing. Similar chitosan derivatives and partner polymers were used in applications somehow resembling the ones described above.Hernández et al. proposed a full-IPN as micro-gel for drug delivery based on poly-echnique . Such symes from . In thisribed in . In thisribed in  with a sorted in , a SIPN N,N-dimethylacrylamide (DMAAm) crosslinked with gamma-irradiation. In situ synthesis was performed for both SIPNs (only DMAAm crosslinked) and FIPNs. SIPNs showed a higher percentage of swelling compared to FIPN due to the higher crosslinking of DMAAm, while the degradation is higher in the case of FIPN as temperature increased. Moreover, the pore size was smaller and less dispersed in SIPN than in FIPN.An interesting application of IPNs is the design of 3D scaffolds for the growth of various cells, since they can mimic the cellular environment and improve cell viability and proliferation. Some authors worked on a SIPN composed of a chitosan-derivative  crosslinked in situ by UV light in the presence of an initiator to form the first network and then loaded with gelatin . The gelThe capability of chitosan to respond to electric current variations has led to the development of biosensors and energy transducers that can find large applications in the field of electro-sensing and robotics. Kim et al.  reportedHuang et al. developed a semi-IPN based on two types of crosslinking, both covalent and ionic . The firA < 0.4 and lack of specific biological signals are severe limitations to its large-scale use in the biotechnological field where direct contact with cells and/or tissues is foreseen. The derivatization of chitosan can overcome some of aforementioned flaws. The modification can be specific, involving the amino group (–NH2) at C2 position, or nonspecific, involving hydroxyl groups (–OH) at C3 and C6 positions [Chitosan displays several interesting features, which have been discussed in the previous sections. However, poor solubility at neutral pH, especially for medium/high molecular weight chitosans with Fositions . The hydositions . Anotherositions .In a paper by Diolosà et al. chitosan was modified with methacrylic acid (Chit-MA70) on 16% of the amino groups aiming at investigating its effect on the durability of adhesive interfaces to improve the clinical performance of dental restorations . Chit-MAAn interesting approach for the modification of chitosan backbone is based on the introduction of sugars as flanking groups. As an example, mannosylated chitosan was used for the specific recognition to antigen presenting cells such as B-cells, dendritic cells, and macrophages . Other sN-isopropylacrylamide), PNIPAm, and poly (N-vinylcaprolactam), PVCL [N-isopropylacrylamide) to obtain temperature-sensitive gels able to release in a controlled way both hydrophilic and lipophilic drugs in an in vitro drug release experiment [The use of chemically-modified chitosans was widely exploited to enhance pH sensitivity or biodegradability of thermo-sensitive gels based on chitosan . Interesm), PVCL . Fang anperiment . In vitrperiment .2O2 [An alternative approach for fabricating thermosensitive gels based on chitosan derivatives was proposed by Tang and co-workers . Carboxy2O2 . Short g2O2 . ChitosaN-alkylation reaction, which involves the amino groups of chitosan , have recently emerged as intriguing examples of engineered biopolymers for potential applications in the field of drug delivery and tissue engineering . From thchitosan .2 helical geometry, but the preference shifted to the 21 geometry with limited electrostatic interactions [The introduction of lactitol side chains on the chitosan backbone markedly modifies the physical-chemical properties of the polysaccharide rendering it soluble at neutral pH values and surprisingly miscible with polyanions, such as hyaluronan and alginate . In the ractions .Leaving aside the physical-chemical properties, Chitlac has raised interest for its biological activity towards different primary cultures and cell lines. In particular, Chitlac promoted the aggregation of articular chondrocytes through the involvement of Galectin-1, ,162 stimRecently, a peculiar response of Chitlac-based macro-gels was noticed in the presence of a transient crosslinker, such as boric acid. In particular, shear-thickening and strain hardening effects of the chitlac-boric acid system grant it a mechanical response which is very similar to the one found for natural components of the extracellular matrix, such as collagen and neurofilaments ,168. ThiMoreover, Chitlac was found to form nano-gels in the presence of TPP as a crosslinker . ResultiN-acetyl-glucosamine—and molecular weight are pivotal for the setting up of ordered networks. In addition, polymer concentration and external variables, such as temperature, pH, ionic strength, and crosslinker to chitosan monomer ratio, are in the same way fundamental for achieving a homogeneous gelation. It is possible nowadays to switch from a stable ensemble of sphere-like micro/nano-colloids to macrostructures by varying the aforementioned parameters. Resulting chitosan-based systems find a wide range of applications in the biomedical sector, spanning from the encapsulation and ensuing delivery of molecule therapeutics to their use as suitable scaffolds for cellular colonization.In this review we have summarized the main gelling concepts for synthesizing physical chitosan gels with different sizes and geometries. Key polymer features, such as the frequency of two building sugars—i.e., glucosamine and In view of properly optimizing and customizing chitosan gels, chemical modifications of the parent biopolymer are often required. A lactose-modified chitosan named Chitlac is currently under investigation in our research group, since it showed intriguing biological features. Recent research records have highlighted the feasibility of forming Chitlac-based networks characterized by intriguing physical properties. One of the striking findings addressed the need to create a mechano-responsive material akin to what known for proteins composing the extracellular matrix (ECM). These results pave the way for a potential usage of Chitlac as a biomimetic ECM-substitute.


Although some machine learning-based and convolutional neural network (CNN)-based methods have been proposed, prediction accuracy remains to be improved. Here, firstly we improved architectures of current CNNs for predicting gRNA on-target efficacy. Secondly, we proposed a novel hybrid system which combines our improved CNN with support vector regression (SVR). This CNN-SVR system is composed of two major components: a merged CNN as the front-end for extracting gRNA feature and an SVR as the back-end for regression and predicting gRNA cleavage efficiency. We demonstrate that CNN-SVR can effectively exploit features interactions from feed-forward directions to learn deeper features of gRNAs and their corresponding epigenetic features. Experiments on commonly used datasets show that our CNN-SVR system outperforms available state-of-the-art methods in terms of prediction accuracy, generalization, and robustness. Source codes are available at  The CRISPR/Cas9 system, adapted from a bacterial defense mechanism, is a promising genomic editing tool that has recently revolutionized the field of biology, biotechnology, and medicine . This syPrevious efforts have been made to assist gRNA on-target identification and efficacy prediction based on different design rules. The alignment-based methods align the gRNAs from the given genome purely by locating the PAM [e.g. CCTop ]. HypothDeep learning allows computational models that consist of multiple processing layers to learn representations of features with multiple levels of abstraction . The layThe above two CNN-based models showed good performance in CRISPR gRNA efficacy prediction compared with machine learning-based methods. CNNs are multi-layer architectures where the successive layers are designed to learn progressively higher-level features, until the last layer which produces the classifiers . The lasThe prior success of CNN-SVM in computer vision inspired us to extend CNN-SVM application to CRISPR/Cas9 gRNA efficacy prediction. Until now, to the best of our knowledge, there is no such application. Previous studies have suggested that CRISPR gRNA efficacy prediction using linear regression achieved better performance than classification . TherefoPrevious studies have shown that PAM-distal region has a high tolerance for sequence mismatches . To be sIn order to evaluate the performance of our method, we used four public experimental validated gRNA on-target cleavage efficacy independent human datasets, which were integrated and processed by Chuai et al . These eEach entry in the datasets contained the 23-nt gRNA sequence, four kinds of corresponding symbolic epigenetic features, as well as numerical and binary cleavage efficacy. The epigenetic features information was obtained from ENCODE , includimny represented the n-th gRNA on-target cleavage efficacy in the m-th experiment. Normalized numerical gRNA on-target efficiency value was defined asNumerical cleavage efficiency of candidate gRNA was calculated using a collaborative filtering-based data normalization technique . In partrowm denoted the mean value for each row, colm represented the mean value for each column, and allm denoted the mean value of Y. Next, a rank-based normalization method  × 23 (sequence length) binary matrix. The first layer of the sub-network is a 1D convolution layer (conv_1), which is designed for extracting the important local features between neighboring element values of gRNA sequence information using 256 convolution kernels of size 5. Rectified linear unit (ReLU)  is used The second layer is a local average pooling layer (pool_1) with window size of 2 connected with the outputs of previous layer for down-sampling. Each of the average-pooling windows only outputs the average value of its respective convolution layer outputs.The structures of the following convolution layer (conv_2) and average pooling layer (pool_2) are identical with the first (conv_1) and second (pool_1) layers mentioned above. After being flatten, the features are followed by four fully connected layers  with the sizes of 256, 128, 64, and 40, respectively. We used dropout for model regularization to avoid overfitting.The feature maps of the fourth fully connected layer from both gRNA and epigenetic branches are concatenated by the “concatenate” operator. Subsequently, the outputs of the concatenation layer are input to the last fully connected layer of the merged CNN network. The final output layer consists of one neuron corresponding to a regression score that highly correlates with gRNA activity. The loss function for our CNN is mean squared error (MSE) which was adapted in a previous study . We chosWe next proposed a network combining CNN and SVR called CNN-SVR to provide a data-driven and deep learning method for CRISPR/Cas9 gRNA activity prediction. For cell line-specific prediction, CNN-SVR receives a 23-nt gRNA sequence and four “A-N” symbolic epigenetic sequences with length of 23 as inputs, and it produces a regression score of gRNA on-target cleavage efficacy. Compared with machine learning-based methods that rely heavily on hand-crafted features, CNN-SVR can get rid of the dependence on manual feature engineering. The basic flowchart of CNN-SVR consists of two major stages, namely model selection and pre-training stage as well as fine-tuning and testing stage (Feature Representation Optimization). The extracted low-dimensional representative feature data and their corresponding gRNA cleavage efficacy values are fed into SVR classifier for model training.In the model selection and pre-training stage, there are mainly three steps: first, the encoded benchmark dataset is fed into the proposed CNN model for pre-training by the back-propagation algorithm. Randomized five-fold cross-validation tests are conducted to determine hyperparameters of the merged CNN model. Model with the minimum average validation loss is regarded as the base model. Second, the initial CNN extracted features are input to SVR classifier for training and evaluating. SVR  is optimized using a grid search approach to achieve the optimal performance. Third, a two-step strategy is employed to remove the redundancy of CNN features  with Tensorflow (1.4.0) as the backend, running on Intel Core i7 CPU at 3.6 GHz with 16 GB RAM and NVIDIA 8 GB GTX 1080 GPU. The optimized parameters were tuned automatically under the Adam optimizer .In the proposed architecture, the distribution of each network parameter was determined empirically. The main purpose of hyperparameter optimization was to choose a set of hyperparameters for a deep architecture, usually with the goal of optimizing performance of the architecture on an independent dataset. Grid search from the Scikit-learn Python library was adopted to tune the hyperparameters of the proposed architectures. Hyperparameter optimization experiments were performed sequentially as follows: the network weight initialization over the choice , dropout regularization over the choice , batch size over the choice , and number of epochs over the choice .All the constructed neural network models were trained and validated on the benchmark dataset (180512 samples). We randomly assigned the samples of the no-redundant dataset with 80% of samples for training and 20% of samples for testing with five-fold cross-validation in the training phase. Cross-validation contributed to avoiding overfitting and guaranteeing the accuracy of our model in which the datasets were divided into five equal parts randomly. In each training, one part was regarded as the testing dataset, while the remaining four parts were taken as the training dataset. Thus, we obtained 115528 training samples, 28881 validation samples, and 36103 testing samples, respectively. Separate training and validation data were applied to train the model, while the test data was used to evaluate the performance of the trained model. We chose the model that showed the minimum average validation loss as the final CNN model. After optimization, the hyperparameters were as follows: kernel_initializer: glorot_uniform; batch size: 256; epoch: 200; dropout: 0.3 (keeping 70% of the connections).Next, CNN extracted features were fed into the SVR classifier. We implemented the SVR algorithm in Scikit-learn library. Grid search procedure was performed to find the optimal penalty parameter C, kernel parameter gamma, and epsilon. For training SVR with Gaussian radial basis kernel (RBF) classifier, grid search range of each parameter was as follows: cost C from the choice , kernel coefficient gamma over the choice , epsilon from the choice . We selected the parameters that maximized the maximum average area under ROC curve (AUROC) value as the final parameters of SVR classifier. The optimized parameters of the SVR were as follows: C was 1.7, gamma was 0.12, epsilon was 0.11. These parameters were then used to train the CNN-SVR model.CNN Model Selection and Training section. The well-trained CNN-SVR reached an overall Spearman correlation of 0.952, AUROC value of 0.977.Considering that CNN extracted features might introduce redundancy which can undermine model performance, we employed a two-step feature optimization strategy to identify important feature subsets from the initial CNN features. To be specific, we first applied random forest to the learnt feature representation from well-trained CNN model and obtained the ranked feature list based on information gain . We traiCNN Model Selection and Training section. Randomized five-fold cross-validation was implemented in the training phase.In this section, we proposed a fine-tune strategy by borrowing information from the benchmark data, aiming at boosting the prediction performance on small sample size cell line-specific data. To this end, four above cell-line datasets were combined together for model training and testing. We constructed the training, validation, and test data from total four datasets based on gRNA sequence composition and epigenetic feature information. The training data (13401 samples) and test data (3748 samples) were also generated in the same way in the Pre-train a CNN model with the benchmark data for 200 epochs.Freeze the convolution layers, average pooling layers, the first three fully connected layers (for both the gRNA stream and epigenetic stream).Train the fourth fully connected layer of the above two streams and the top fully connected layer of the merged CNN model with training data from the cell line of interest for another 200 epochs.Evaluate the model on the test data.Considering training a full CNN network with small number of cell line data may result in overfitting, which may lead to poor performance. Transfer learning  is effecFor the L1-regularized linear regression (L1), we applied LassoCV from Scikit-learn Python library to find out the optimal parameters of alpha by cross-validation. Grid searching range of regularization parameter alpha was . Other parameters were set with default values. We achieved an optimal value of 0.01. Similarly, we applied RidgeCV and ElasticNetCV with the same grid searching range of L1 to set parameter alpha for L2-regularized linear regression (L2) and L1L2-regularized linear regression (L1L2), respectively. After optimization, the best alpha values of L2 and L1L2 were 0.04 and 0.01, respectively. These parameters were then used to train the CNN-L1, CNN-L2, and CNN-L1L2 models. Other parameters of L2 and L1L2 were set with default values.https://github.com/MyungjaeSong/Paired-Library). Note that, the input of Seq_deepCpf1 was a 4-by-34 dimensional binary matrix. Here, we changed the input shape of Seq_deepCpf1 model into 4-by-23 to match the size of the data in this study. Besides, we used the benchmark dataset to pre-train the Seq_deepCpf1 model. To make a fair comparison, we only fine-tuned the weights parameters in the last two layers (1681 free parameters) for cell line-specific prediction. The numerical experimental condition was set consistent with DeepCRISPR. The source codes of DeepCRISPR were downloaded from https://github.com/bm2-lab/DeepCRISPR. SSC, sgRNA Designer and WU-CRISPR provided available web based applications. More details can be found in We ran the code of Seq_deepCpf1 using the same data and basic training process (downloaded from GitHub at http://scipy.org). In addition, AUROC was employed to comprehensively quantify the overall predictive model performance. The value of AUROC ranges from 0.5 to 1. A larger AUROC value represents that model achieves better and more robust performance. Note that, we used 0.5 AUROC as the baseline. Statistical test was performed using SciPy library for comparing the differences between GC content distributions of different datasets. Two-sample Kolmogorov–Smirnov test was used for testing the distance between two distributions under the null hypothesis that samples from the same continuous distribution. P < 0.05 was considered to indicate statistically significant difference.To quantitatively evaluate the performance of our CNN-SVR, Spearman correlation coefficient between predicted and measured on-target activity was calculated. We chose Spearman correlation is due to it is more robust to outliers than Pearson’s correlation coefficient . BesidesTo verify the feasibility of our approach, we compared our CNN-SVR with CNN model on the above four cell-line datasets. The current practice of training a model was to use cell-line specific data for prediction. Each data set was randomly split into a training set and an independent testing set with 80% and 20% classes. Comparison CNN-SVR With CNN Model. Ten-time 10-fold cross-validation tests were randomly performed and the average of the individual performance were summarized in We then attempted to access the regression performance of CNN-SVR. To this end, we compared CNN-SVR with three CNNs plus regression approaches, including CNN plus L1 (CNN-L1), CNN plus L2 (CNN-L2), and CNN plus L1L2 (CNN-L1L2) on the above four cell lines datasets. Note that for each cell line, the training data and test data were generated in the same way as described in the section Comparison CNN-SVR With CNN Model. For any given cell line of interest, the training data was built by integrating all the training data from four cell lines. The performance was evaluated on each cell line-specific testing set, respectively.To validate the performance of proposed CNN-SVR, we compared it with one deep learning-based method (DeepCRISPR) and three machine learning methods including sgRNA Designer, SSC, and WU-CRISPR . In the testing stage, we evaluated the performance on the test data of the given cell-line of interest. Taking leave-HCT116-out procedure as an example, we trained the model by combining training data of HEK293T, HELA and HL60 cell lines (without HCT116 cell line training data), and evaluated the model on HCT116 cell line testing set. For fair comparison, we tested the proposed CNN-SVR under the same condition with DeepCRISPR, sgRNA Designer, SSC, and WU-CRISPR on the four cell-line datasets.Next, we investigated the generalizability ability of CNN-SVR in new cell types. For this purpose, we took turns to test the model on the above four cell-line datasets using a leave-one-cell-out approach. The training data and test data for each cell line were built in advance. Note that, the partitioning method for each cell line data followed the way illustrated in the section As can be seen from In this section, we aimed to compare the robustness of the above methods. To this end, we examined the changes between Spearman correlation and AUROC values obtained by training with four cell datasets . We trained the prediction models without epigenetic information (sequence only) for each cell line with a leave-one-cell-out procedure. Note that, we trained the model just considering the gRNA stream. Other numerical experimental conditions were in accord with the section Assessment of Generalization Performance With a Leave-One-Cell-Out Procedure. For fair comparison, we compared our methods with two deep learning-based methods  only considering sequence composition.In this section, we determined whether cell line-specific epigenetic features really boost the predictive performance. We examined the performance of deep learning-based methods on the four cell-line datasets only considering gRNA sequence composition and compared them with those considering both gRNA sequence and epigenetic information  at every position k and repeated the aforementioned process for all subsequences. The scores of all the constructed subsequences for all the positions were plotted as a heatmap in Accurate prediction of gRNA cleavage efficacy is pivotal to understanding the mechanisms of CRISPR/Cas9 system. Although computational prediction of gRNA cleavage efficiency has made much progress recently, the accuracy remains to be improved. In this study, we introduced a novel and interpretable deep learning framework named CNN-SVR for CRISPR/Cas9 gRNA on-target activity prediction. Specifically, CNN works as a trainable feature extractor and SVR performs as a gRNA cleavage efficacy predictor. Compared with CNN and three CNNs combined regression-based algorithms, CNN-SVR achieved the best performance. CNN-SVR could not only automatically extract gRNA sequence and the corresponding epigenetic features using the CNN, but also improve the generalization ability of CNN and regression accuracy.Previous studies suggested that ensemble learning  by incorBesides, we found our CNN-SVR system has good generalizability in new cell types by using leave-one-cell-out approach on the four testing datasets. By analyzing the changes of prediction results with and without considering epigenetic information, we observed that considering of epigenetic features slightly improves the accuracy of CRISPR/Cas9 gRNA activity prediction. This result was consistent with previous studies . Chuai eComparison CNN-SVR With CNN Model. Overall, addition of GC content to CNN-SVR increased the predictive ability, with Spearman correlation coefficients of 0.645, 0.656 and 0.608 on datasets HCT116, HELA and HELA, respectively. Detailed results can be found in Our model focused on gRNA sequence and four kinds of epigenetic features for CRISPR/Cas9 on-target prediction. A recent study on protein-related prediction has shown that integration of other manual extracted features, such as molecular weight and hydrophobicity into the deep learning model could improve the predictive power . It has Visualization method was applied to our model. Note that the PAM and the core region (1-5 nt adjacent to the PAM) are very important for gRNA target efficacy. However, we observed that the most top features were generated by convolving the middle region of the input matrix. Therefore, we believe expanding the upstream and downstream of the target sequence in a proper length can enhance the generalization performance of the model. For example, Kim et al. found 34 bp (4 bp + PAM + 23bp protospacer + 3bp) was adequate as the input sequence of their models in CRISPR/Cpf1 system .Several future improvements are expected. First, in the present study, taking advantage of CNN and SVR, we designed the relative concise hybrid CNN-SVR architecture. Research on the deep learning-based model for CRISPR/Cas9 system gRNA cleavage efficiency prediction is still at an early stage. Numerous complex and modern deep learning models await exploration. Second, as pre-training technique has great influence on the final predictive performance, therefore critical to know on what a model was trained before use. In general, sequencing-based models are more general applicable, but are only capable of predicting the genotype changes rather than functional result. On the contrary, phenotypic trained models are fit for recognizing target sites that cause functional changes but limited to numerical experiments with the same condition as the training set. However, the amount of available gRNA knockout data is relatively small, which provides a big challenge for training the deep learning model. Consequently, appropriate data augmentation techniques are needed to increase the training sample size. Third, reasonable encoding schemes, which provide maximum biological characteristics information as well as reducing the compute costs, will boost the CRISPR/Cas9 gRNA activity prediction accuracy. Finally, it is possible that integration of manual extracted features associated with gRNA activity can also improve predictive power of deep learning models.In this study, we present CNN-SVR, an efficient and extendable method to automatically learn the sequence features for CRISPR/Cas9 gRNA activity prediction. We adopt a merged CNN architecture for gRNA and its corresponding epigenetic features extraction, and subsequently incorporate SVR classifier to predict gRNA cleavage efficiency. Compared with CNN, two state-of-the-art deep neural network based models (e.g. DeepCRISPR and Seq-deepCpf1) and three machine learning tools , CNN-SVR can effectively exploit features interactions from feed-forward directions to learn deeper features of gRNAs and their corresponding epigenetic features. Experimental results on the published datasets demonstrate the superiority of our CNN-SVR for CRISPR/Cas9 gRNAs on-target activity prediction.https://github.com/Peppags/CNN-SVR.Publicly available datasets were analyzed in this study. This data can be found here: All authors contributed to the project design. GZ wrote the analysis source code, analyzed the data, and drafted the full manuscript. ZD and XD critically revised the final manuscript. All authors read and approved the final manuscript.This research was funded by the National Natural Science Foundation of China (NSFC) , and also by Pearl River Nova Program of Guangzhou (201710010044).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.


Arterial stiffness is an age-related disorder. In the medial layer of arteries, mechanical fracture due to fatigue failure for the pulsatile wall strain causes medial degeneration vascular remodeling. The alteration of extracellular matrix composition and arterial geometry result in structural arterial stiffness. Calcium deposition and other factors such as advanced glycation end product-mediated collagen cross-linking aggravate the structural arterial stiffness. On the other hand, endothelial dysfunction is a cause of arterial stiffness. The biological molecular mechanisms relating to aging are known to involve the progression of arterial stiffness. Arterial stiffness further applies stress on large arteries and also microcirculation. Therefore, it is closely related to adverse outcomes in cardiovascular and cerebrovascular system. Cardio-ankle vascular index (CAVI) is a promising diagnostic tool for evaluating arterial stiffness. The principle is based on stiffness parameter β, which is an index intended to assess the distensibility of carotid artery. Stiffness parameter β is a two-dimensional technique obtained from changes of arterial diameter by pulse in one section. CAVI applied the stiffness parameter β to all of the arterial segments between heart and ankle using pulse wave velocity. CAVI has been commercially available for a decade and the clinical data of its effectiveness has accumulated. The characteristics of CAVI differ from other physiological tests of arterial stiffness due to the independency from blood pressure at the time of examination. This review describes the pathophysiology of arterial stiffness and CAVI. Molecular mechanisms will also be covered. Arterial stiffness increases with age . HoweverFor this purpose, appropriate evaluation for arterial stiffness is necessary. Cardio-ankle vascular index (CAVI) as an index of arterial stiffness was first proposed in 2006 ; therefoArterial stiffness is characterized by impaired distensibility of large arteries. The structure of the arterial wall consists of three layers: intima, media, and adventitia. The possible mechanisms of arterial stiffness in each layer of arterial wall are shown in 2+ concentration leading to vascular relaxation. Therefore, deficiency of NO production caused by endothelial dysfunction increase peripheral arterial resistance, which promotes pulse wave reflection in the central arteries and causes additional stress on the vessel wall leading to intima–media thickening [Endothelial dysfunction is considered to be a functional cause of arterial stiffness in addition to the medial degeneration. Vascular tone is determined by the balance of endothelium-derived vasoactive substances that include vasodilators such as nitric oxide (NO), prostaglandins, endothelium-derived hyperpolarization factor, epoxyeicosatrienoic acids, and vasoconstrictors such as angiotensin II, endothelin-1, prostanoids, and isoprostanes . In themickening .l-Arginine serves as the sole source of nitrogen for NO. Endothelial NO synthase (eNOS) converts l-arginine to l-citrulline with producing molecules of NO. The restoration of l-arginine from l-citrulline is catalyzed by the enzymes argininosuccinate synthase and argininosuccinate lyase in urea cycle [l-asymmetric dimethylarginine (l-ADMA), a l-arginine derivative, increases with cardiovascular risks. The methylation of l-arginine is catalyzed by S-adenosylmethionine in methionine cycle. l-ADMA acts as a competitive NO synthase inhibitor and the relative increase of l-ADMA to l-arginine is associated with arterial stiffness [l-arginine bioavailability defined as the ratio of serum l-arginine to the sum of l-citrulline and l-ornithine [l-arginine, especially eNOS, and arginase. Arginase is a rate-limiting enzyme in the urea cycle converting l-arginine to l-ornithine and urea. The enhanced activity of arginase induces chronic l-arginine deficiency and endothelial dysfunction. Arginase expression or activity is upregulated in hypertension, diabetes, and oxidative stress, as well as inflammation in human and animal models of various vascular diseases [l-ornithine is the precursor of polyamines that induce proliferation of vascular smooth muscle cells. Proline and hydroxyproline, which are the main precursors of collagens, are also synthesized from l-ornithine. Vascular endothelium-specific arginiase-1 knockout decreases arterial stiffening and fibrosis, elevated BP induced by high fat-high sucrose diet in mice [l-arginine flux into the arginase pathway is involved in aortic stiffness via metabolic mechanisms.ea cycle . l-asymmtiffness . Furtherrnithine . The ratdiseases . Moreove in mice . Thus, a−, one of the reactive oxygen species, is produced instead of NO [Although the receptor of shear forces is not identified , increasad of NO . The defad of NO ,28.Sirtuin-1 (SIRT-1) is a nuclear nicotinamide adenine dinucleotide-dependent nuclear deacetylase  that actThere are other underlying mechanisms of arterial stiffening which may occur independently but relates to each other. Arterial stiffness has been considered to be related with lipids, calcification, fibrosis, chronic inflammation, advanced glycation end product-mediated collagen cross-linking, oxidative stress, and sympathetic nervous activity ,39. The There have been many non-invasive methods for validating arterial stiffness. The approaches of the methods can be mainly classified into the three groups; two-dimensional (2D) imaging technique, pulse wave analysis technique (PWA), and PWV. The approach of the 2D imaging technique is to measure the change of diameter, or sectional area under distensional pressures . A reprePWV is the velocity at which the pulse wave propagates through the circulatory system . The parIn addition, endothelial function tests are also important for patients suspected vascular disease because endothelial dysfunction causes functional arterial stiffening as described above. The endothelial function tests fundamentally assess the reactivity to hyperaemia after releasing the cuff at an upper arm or a forearm. This type of measurements includes venous occlusion digital plethysmography, flow-mediated dilation (FMD) of the brachial artery, and reactive hyperemia index (RHI) using fingertip peripheral arterial tonometry. However, the difference in the results of FMD and RHI was reported. This is supposed to be due to the difference of the targeted arteries ,58. CAVI is based on the principle of stiffness parameter β, an index of the 2D imaging technique, but requires PWV for the calculation. Therefore, reevaluation from clinical data is needed to know whether CAVI is superior to PWV for prediction of further cardiovascular events. The detail of the principle of CAVI is described in the following section.CAVI was developed as a clinical test of arterial stiffness in Japan after the development of PWV. Previously, Hayashi et al. developed stiffness parameter β to stand for arterial stiffness in the 1980s . The stuCAVI is calculated using BP and heart-ankle PWV (haPWV), monitoring of heart sounds and electrocardiogram. haPWV is calculated by dividing the distance from the aortic valve to the ankle artery by the sum of the time intervals between aortic valve closure sound and notch of the brachial pulse wave and between the rise of the brachial pulse wave and the ankle pulse wave. CAVI is derived from the stiffness parameter β and Bramwell-Hill formula  and deteThe pressure-diameter curve of an artery is linearized by log-transformation of BP and a linear function is established between ln(Ps/Pd) and ΔD/D. The inclination of the straight line is defined as stiffness parameter β (ln(Ps/Pd)/(ΔD/D)).2 is small and negligible):On the other hand, the Bramwell-Hill formula was proven and derived in experiments. It is determined by the following formula: follows :CAVI = as method ,63,64.This equation reflects the global stiffness of the aorta, femoral arteries, and tibial artery; CAVI represents the stiffness parameter β of a whole arterial segment . CAVI coThe most important characteristic of CAVI is the reproducibility due to the independence from BP at testing ,70. The The difference of drug-response between CAVI and baPWV was shown in a previous paper . MetoproCBP is a non-invasive test estimating ascending aortic BP and reflects direct stress on the heart. Therefore, CBP is thought to be a better indicator than brachial BP. CBP can also distinguish the different effects of anti-hypertensive drugs . A previThe limitations of CAVI are mainly attributed to the placement of cuffs on ankles, which are equivalent to baPWV. For the CAVI evaluation, patients with severe aortic insufficiency, bilateral ankle-brachial index <0.9, or persistent, atrial fibrillation should be excluded because it is difficult to obtain accurate measurements in such patients . Thus, pClinically, increased arterial stiffness contributes to elevating left ventricular afterload and developing LV diastolic dysfunction. The Windkessel effect is when intermittent blood flow injected from the heart becomes a smoother and steadier stream after passing through the elastic aorta . This is2 consumption for maintaining adequate flow is increased [However, cardiac function can be deteriorated when the Windkessel effect has declined. The return of the reflection pulse wave occurs during late systole when PWV is fast due to arterial stiffness. The change in loading sequence amplifies systolic BP and increases pulse pressure ,80,81 anncreased . This moMoreover, progression of large artery stiffness induces excessive penetration of pulsatility . Aortic Approximately half of the patients with heart failure have preserved LV ejection fraction, called HFpEF ,92,93. T2), E’ , E/E’ (>14) and tricuspid regurgitation velocity (>2.8 m/s) [The diagnosis of diastolic dysfunction is achieved by abnormal left ventricular relaxation and increased left ventricular chamber stiffness. Cardiac catheterization is the gold standard for assessment of diastolic dysfunction . Cathete2.8 m/s) .The close relationships between CAVI, diastolic dysfunction classified by E/A, and DT in Doppler echocardiography were described previously . The reln = 100, mean age: 70 ± 8 years old) and a subgroup of patients with HFpEF  [Several studies demonstrated that CAVI positively correlates with the index of LV filling pressure measured by tissue Doppler echocardiography (E/E’) ,108. Thears old) . CAVI alars old) .Additionally, there are previous studies evaluating diastolic dysfunction with ultrasonic strain imaging, although it is not currently included in the criteria of diastolic dysfunction ,113,114.In previous cross-sectional studies, CAVI is associated with various cardiovascular risk factors. CAVI correlates with high-sensitivity cardiac troponin T , renal fCAVI is significantly lower in physically active women doing aerobic exercise in comparison with controls with similar ages . HoweverTo date, there are increasing numbers of studies suggesting the prognostic value of CAVI for cardiovascular events. But most of them are single-centered and the sample size are small. The studies targeted mainly thromboembolic events for the endpoints such as traditional major adverse cardiac events (MACE) including cardiac death myocardial infarction and stroke ,138,139.Hemodialysis (HD) patients are characterized as severe arterial stiffness by various causes including hypertension, fluid overload, and arterial calcification due to secondary hyperparathyroidism. In patients with end-stage chronic kidney disease who underwent surgical construction of arteriovenous shunt for HD, CAVI reflects the histological arterial fibrosis in the arterial fragments . HoweverThere are more obvious causal relationships between arterial stiffness and heart failure because decrease of arterial wall compliance increases cardiac afterload and exacerbate the condition of heart failure. There are the evidences that arterial stiffness assessed by baPWV ,145 or cCAVI is independently associated with brain natriuretic peptide levels in patients with hypertension . HoweverThe prospective multicenter cohort study to evaluate the usefulness of the CAVI to predict cardiovascular events in Japan (CAVI-J) is ongoing. The primary endpoints are cardiovascular death, nonfatal myocardial infarction, and stroke, and the secondary endpoints are composite cardiovascular events including all cause death, heart failure requiring hospitalization, angina pectoris with revascularization, new incidence of peripheral artery disease, abdominal aortic aneurysm, aortic dissection, and deterioration in renal function  The largFurthermore, we recently published a paper of a study investigating the association of CAVI with endothelial insulin resistance. Freshly isolated arterial endothelial cells (FIECs) were harvested from radial catheter sheath, a disposable device for coronary angiography. Endothelial insulin resistance was defined as a decrease of insulin-stimulated phosphorylation of eNOS at serine 1177 (p-eNOS Ser1177). CAVI negatively correlated with percent increase of insulin-mediated p-eNOS Ser1177 in the FIECs. The result suggests that high CAVI is associated with deteriorated signal transduction of IRS1-PI3K-Akt-eNOS pathway indicating endothelial dysfunction . Thus, wArterial stiffness is a factor of arteriosclerosis and heart failure, especially in HFpEF. Endothelial dysfunction involves the progression of arterial stiffness for which various clinical and molecular biological research has been conducted. CAVI is a convenient yet effective marker of this disorder. The efficacy will be clarified to a greater and necessary extent in future clinical research.


This Special Issue contains thirteen articles that provide a vivid snapshot of the state-of-the-art of molecular modeling in drug design, illustrating recent advances and critically discussing important challenges. The eight Original Research Articles, three Reviews, one Opinion, and one Perspective explore the application of computational methods, ranging from virtual screening and pharmacophore modelling through artificial intelligence and machine learning to molecular dynamics simulation and enhanced sampling to drug design against diverse targets, including protein-protein interfaces and membrane protein receptors. The challenges for predictive methods addressed include molecular flexibility, solvation properties, hydrogen-bonding, and ligand polarization.2AR). This G-protein coupled receptor (GPCR), a promising drug target for neurogenerative diseases, was embedded in a solvated neuronal-like membrane and its interaction with a high-affinity antagonist was studied by well-tempered metadynamics. These calculations were confirmed by experimental binding affinity studies and they suggest the importance of interactions between membrane lipids and the protein extracellular loops in the ligand recognition process. The results give valuable insight for the design of hA2AR ligands, as well as other GPCR targeting ligands. Kouza et al. [Three of the Original Research Articles describe the application of enhanced molecular dynamics (MD) simulation methods to drug design problems. Cao et al.  investiga et al. , who prea et al.  discuss ecto-5-nucleotidase inhibitors were actually inhibiting the enzyme due to aggregate formation. Their study underlines the importance of not only filtering the virtual hits by predicting their aggregate forming potential computationally, but also of experimental assays for aggregation. The study by Vincenzi, Bednarska and Leśnikowski [in silico predictions.Applications of virtual screening and molecular docking are described in three of the Original Research Articles. Chen et al.  carried nikowski  highlighTwo of the Reviews discuss the application of a combination of molecular docking and MD simulation-based approaches for target-based drug design. Krammer and co-workers  review tab initio-quantum mechanics-based scoring model to rank the affinities of a set of lithocholic acid derivatives at the ligand-binding domain of the erythropoietin-producing hepatocellular carcinoma subtype 2 (EphA2) receptor. These inhibitors prevent the physiological ligand ephrin-A1 from binding to EphA2, thus showing potential for becoming leads for future anti-cancer agents. This simple scoring model, comprising long-range multipole electrostatic and approximate dispersion interactions, yielded comparable or better binding affinity predictions than any of the tested empirical scoring functions. On the other hand, Mortier, Dhakal, and Volkamer [2F) pharmacophore modelling, to identify pharmacophoric features at protein surfaces. These features represent the key favourable interaction possibilities of ligands binding to the particular site. Such a target-based pharmacophore model can be valuable in drug design cases where the target protein structure is available, but there is limited information about possible ligands binding to the target. In addition, the tool can be used for exploring allosteric pockets and protein-protein interactions for possible ligand sites. Lastly, in their Review, Hessler and Baringhaus [de novo ligand design and retrosynthetic approaches. While machine learning has long been used for drug design, new methods and applications are currently appearing at a rapid pace and, together with contemporary molecular modelling and simulation approaches, can be expected to improve the quality and value of computational approaches to drug design. Two Original Research Articles address methodological advances. Jedwabny, Lodola, and Dyguda-Kazimierowicz  test an Volkamer  have devringhaus  give an https://www.mdpi.com/journal/molecules/special_issues/MMDD.This special issue is accessible through the following link:


Additionally, we provide an exploratory finding of gyrification, a surface-based morphometric marker, correlating with a right postcentral gyrus cluster . Our findings provide first preliminary evidence of an inverse relationship for subjects in the absence of clinical depression and might thus point to processes related to compensation. Similar effects have been observed in remission from major depression and thus deserve further study to evaluate hippocampal volume not only as a state-dependent marker of disease but also of resilience.Case-control studies in major depression have established patterns of regional gray matter loss, including the hippocampus, which might show state-related effects dependent on disease stage. However, there is still limited knowledge on compensation effects that might occur in people resilient to depression showing only subclinical symptoms. We used voxel-based morphometry on a multicenter data set of 409 healthy nonclinical subjects to test the hypothesis that local hippocampal volume would be inversely correlated with subclinical depressive symptoms [Symptom Checklist 90-Revised (SCL-90-R) depression scores]. Our region-of-interest results show a significant ( Major depression is associated with brain structural changes documented in several imaging studies. A recent mega-analysis has demonstrated brain matter reductions in multiple cortical areas, including the orbital frontal cortex, cingulate and insular cortex, and temporal lobes . Many ofResilience to psychopathology and compensation are underestimated factors in these anatomical models of depressive symptoms and depressive disorder. These might not be captured in typical approaches to imaging studies of depression, which rely on case–control designs. Although these studies have largely increased our understanding of the anatomy of major depression, they still lack good mechanistic models for compensation that leads to either remission of existing symptoms or individual resilience preventing individuals from developing manifest depressive psychopathology. Also, they do not account very well for dimensional approaches to depression. Not only do depressive symptoms occur in a number of psychiatric conditions other than major depression, they might also arise (at least transiently) in a large proportion of nonclinical subjects, for example, after stressful life events or at times of higher stress. Many individuals suffering such stress, often related to environmental impact, however do not develop manifest depression. Also, depressive symptoms are very common in a number of other psychiatric disorders, such as anxiety disorders, somatization, eating disorders, psychosis, and so on. Therefore, our understanding of hippocampal structure and a dimensional depressive phenotype will have to depend on studying subjects across a wider spectrum of both clinical pathologies and the subclinical spectrum .Indeed, in recent studies, healthy subjects with a family history of major depression who show resilience to developing a clinical phenotype have been shown to have relatively greater or intact hippocampal volume . If relaIn the present study, we sought to use a large data set from a multicenter study , 13 to tFor this study, we relied on a multicenter sample of 409 clinically healthy young adults from Jena, Germany, and Verona, Italy, for which we have already published analyses on aggressive traits  and subcAll study participants provided written informed consent to protocols approved by the local Ethics Committee of Jena University Medical School and the Ethics Committee of the Azienda Ospedaliera Universitaria of Verona.Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) axis I disorders, we used careful screening via phone in the Jena samples and a modified Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-IV) nonpatient version (SCID-NP) interview derived in the Verona/Milano sample. IQ was estimated using the MWT-B (Mehrfach-Wortschatz-Intelligenztest), a German-language inventory , the SCL-90-R is a self-rating instrument for a broad range of psychopathological symptoms  scale, as in previous studies including other symptom dimensions. As outlined previously , standard quadrature head coil and a T1-weighted axial 3-dimensional magnetization prepared rapid gradient echo (MP-RAGE) sequence .Jena-2 sample: 3 Tesla Siemens Prisma fit system , that is, the scanner used for the Jena-1 sample after hardware and software changes to upgrade this to a Prisma system; standard quadrature head coil; MP-RAGE sequence with parameters similar to Jena-1 sample .Verona/Milano sample: 3 Tesla Magnetom Allegra Syngo MR 2004A ; standard head coil for radio frequency transmission and reception of the MRI signal; T1-weighted MP-RAGE sequence .Quality assurance for the MRI scans included visual inspection for gross artifacts (after scanning) and the automated quality check implemented in CAT12.a posteriori estimations  of the Structural Brain Mapping group , a toolbox implemented in SPM12  for VBM analyses. This included correction for bias-field inhomogeneities; segmentation into gray matter, white matter, and cerebrospinal fluid ; and spaimations . We usedWe also used the CAT12 toolbox  to analyze local gyrification. This analysis is based on an absolute mean curvature approach . The corWe used the general linear model implemented in SPM12 for statistical analyses. The analyses included a factor scanner (to correct for the different acquisition sites and scanner settings) and age.p < 0.05. In a second step, we provide a whole-brain analysis, first at p < 0.05 FWE (cluster-level) corrected levels and then at p < 0.001 (uncorrected) for an exploratory view and further hypothesis-generating findings. For all VBM analyses, we included total intracranial volume as a nuisance variable.For VBM analyses, we first performed a hippocampal region-of-interest (ROI) analysis  using the AAL toolbox spatial definition of the left and right hippocampi. Given our anatomical hypothesis, we first focused on this area and, using the ROI approach, we restricted the search space (and thus the number of multiple voxel-wise comparisons). This analysis was corrected for multiple comparisons using the cluster-level FWE corrected at p < 0.05.For gyrification, we also applied a general linear model and again applied cluster-level FWE corrections at For all analyses, we computed both positive correlations (anticipating such a relation for compensation/markers of resilience) and negative correlations .Z score 3.59; p = 0.010, FWE-corrected at peak level, p = 0.042, FWE-corrected at cluster level). A similar right hippocampus ROI cluster showed a positive correlation only at uncorrected levels. An overview of the exploratory analysis at p < 0.001 peak levels is shown in For the hippocampus ROI-based/small-volume correction VBM analysis, we found a significant positive correlation of voxels in a left hippocampus cluster within the ROI search volume  only revealed a small 5-voxel cluster in the right middle frontal cortex.We did not find negative correlations of hippocampal ROI/small-volume-based analyses, and exploratory analysis  and no negative correlations  showed only one positive correlation in a right postcentral cortical cluster , however, did find some association of (negative) symptoms with hippocampal texture and volume  did not show an association with minor depressive features other than a right postcentral cortical cluster. Although this is consistent with the notion that this marker of early neurodevelopment is not related to temporary fluctuations, it does not argue for early neocortical abnormalities related to subclinical symptom expression.Our study needs to consider some methodological limitations. Although larger than most other studies on brain structure in subclinical symptoms and including multicenter data, further replication of our positive association seems warranted. Also, larger samples across the life span might be used to identify changes in associations in particular (adult) age ranges or linked to other demographic factors. The cross-sectional nature of our data (and thus lack of follow-up) precluded the use of prediction models to be tested.Minor or subclinical depressive symptoms are increasingly recognized to be of relevance for our understanding of major depression and early intervention, even if those symptoms are only transient in nature and often unspecific reactions to stress or negative life events. There is increasing evidence from multiple studies suggesting that psychotherapy for subclinical depression not only is effective but also might reduce the incidence of major depression , 35. HipThe authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All study participants provided written informed consent to protocols approved by the local Ethics Committee of Jena University Medical School, and the Ethics Committee of the Azienda Ospedaliera Universitaria of Verona, respectively.BB, PB, and IN conceived of the study. BB, RS, PB, MB, and IN contributed to recruitment. BB, LS, CG, and IN contributed to data analysis. BB and IN wrote the first draft of the manuscript. All authors contributed to manuscript revision and approval.The study was in part supported by grants of IZKF Jena to BB (Jena-2 sample) as well as of the Italian Ministry of Health to MB (GR-2010-2319022), and to PB and LS (RF-2016-02364582).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.


Background: Age-related changes in the sensorimotor system and cognition affect gait adaptation, especially when locomotion is combined with a cognitive task. Performing a dual-task can shift the focus of attention and thus require task prioritization, especially in older adults. To gain a better understanding of the age-related changes in the sensorimotor system, we examined how age and dual-tasking affect adaptive gait and task prioritization while walking on a split-belt treadmill.Methods: Young  and older adults  walked on a split-belt treadmill with a 2:1 belt speed ratio, with and without a cognitive Auditory Stroop task. Symmetry in step length, limb excursion, and double support time, and strategy variables swing time and swing speed were compared between the tied-belt baseline (BL), early (EA) and late split-belt adaptation (LA), and early tied-belt post-adaptation (EP).Results: Both age groups adapted to split-belt walking by re-establishing symmetry in step length and double support time. However, young and older adults differed on adaptation strategy. Older vs. young adults increased swing speed of the fast leg more during EA and LA (0.10–0.13 m/s), while young vs. older adults increased swing time of the fast leg more (2%). Dual-tasking affected limb excursion symmetry during EP. Cognitive task performance was 5–6% lower during EA compared to BL and LA in both age groups. Older vs. young adults had a lower cognitive task performance (max. 11% during EA).Conclusion: Healthy older adults retain the ability to adapt to split-belt perturbations, but interestingly age affects adaptation strategy during split-belt walking. This age-related change in adaptation strategy possibly reflects a need to increase gait stability to prevent falling. The decline in cognitive task performance during early adaptation suggests task prioritization, especially in older adults. Thus, a challenging motor task, like split-belt adaptation, requires prioritization between the motor and cognitive task to prevent adverse outcomes. This suggests that task prioritization and adaptation strategy should be a focus in fall prevention interventions. Humans adapt their gait to environmental challenges, which allows walking on uneven surfaces, avoiding obstacles, and maintaining balance when slipping or tripping. Advancing age modifies the locomotor system, which reduces the ability of older adults to adapt to environmental perturbations while walking  The effect of split-belt adaptation and age on gait strategy and symmetry; (2) The effect of motor-cognitive dual-tasking on gait adaptation, and (3) Age-related differences in task prioritization during split-belt adaptation.We hypothesized that adaptations to split-belt walking occur independent of age, but young vs. older adults are “timing” and “speed” adaptors, respectively. We also expected that motor-cognitive dual-tasking would affect the ability to adapt to split-belt walking in the spatial or temporal gait outcomes. Furthermore, we hypothesized older vs. young adults will prioritize the motor adaptation task over the cognitive task in the most challenging early period of adaptation to split-belt walking.n = 10) and older adults  who could walk without aids and follow verbal instructions were included in the study. Criteria for exclusion were orthopedic, neurological, and/or psychiatric disorders that might affect gait, and prior experience with split-belt walking. The Ethical Committee of the Center of Human Movement Sciences at the University Medical Center Groningen approved the study. All participants signed a written informed consent before the measurements.Healthy young , with two belts that can be controlled separately. With the embedded force plates in the treadmill, ground reaction forces were sampled at 1,000 Hz. Infrared emitting diodes were placed on the feet  and ankle  and recorded at 100 Hz .Participants walked on the split-belt treadmill, starting with 3 min of tied-belt walking at both 1.0 m/s and 0.5 m/s (baseline), then split-belt walking with one belt moving at 1.0 m/s and the other belt at 0.5 m/s for 6 min (adaptation) and finally with tied-belts at 0.5 m/s for 6 min , participants walked on the split-belt treadmill for a total of 18 min without a cognitive task. In the second condition , participants walked while performing a cognitive task. This task order was chosen in order to minimize the learning effect of re-exposure to split-belt walking, which could potentially affect single-task adaptation and task prioritization during the second condition. Even though we cannot exclude that there were effects of motor learning in the second condition, due to the first condition, earlier studies have revealed that participants still show adaptation during re-exposure to the split-belt . Vertical ground reaction forces were filtered with a second-order low-pass Butterworth filter (15 Hz cut-off). Data from the Optotrak markers were filtered with a second-order high-pass Butterworth filter (0.5 Hz cut-off).Heel-strike and toe-off were determined at the moment the vertical forces crossed the threshold of 50 N. The foot contact moments were then used to calculate the outcome variables for the first and last five strides of the BL, EA, LA, and EP phases, allowing the following three comparisons: EA vs. BL (effect of the perturbation), LA vs. EA (adaptive change), EP vs. BL (aftereffects). For both spatial variables, limb excursion and step length, the foot contact moments were resampled to match the Optotrak sample frequency.x as the position of the lateral malleolus marker  at the ith sample.Symmetry variables were calculated for spatial variables step length (SL) and limb excursion (LE) and the temporal variable double support time (DS). SL was calculated as the anterior posterior distance between the ankle markers of both legs at heel-strike of the leading leg  both feet were in contact with the ground , LE (LES), and DS (DSS) was calculated as follows  increasing the time spent in swing, or 2) increasing swing speed. Therefore, the strategy variables percentage swing time (SwT) and swing speed (SwS) were calculated as  was determined from the single and dual-task condition values, with DTC values above zero indicating a larger value in the single-task condition and values below zero indicating that there was an effect of the dual-task  was calculated with the following formula, with n as the number of stimuli. A values of one indicates a perfect performance.post-hoc dependent samples t-test with Holm-Bonferroni correction was applied to the following phase comparisons: EA vs. BL (effect of the perturbation), LA vs. EA (adaptive change), EP vs. BL (aftereffects). For significant interaction effects, the difference between age groups was tested with an independent samples t-test for each phase separately.To examine the effect of split-belt walking and if young and older adults differ on split-belt adaptation (aim 1), a repeated measures ANOVA was conducted with within factor Phase (BL-EA-LA-EP) and between factor Group (young vs. older adults) during single-task split-belt walking for the dependent gait variables. When a main effect of phase was found, a t-tests to determine if DTC was different from zero for each of the four phases (BL-EA-LA-EP).To address the second aim, the differences between single and dual-task split-belt walking were assessed using planned comparison Post-hoc testing was done similarly as for the first aim. The cognitive task performance was tested with a similar repeated measures ANOVA with the CTP as dependent variable. Differences between the age groups during the control task (sitting) were tested with an independent samples t-test to test if there were differences between the age groups during the single task. Statistical analysis was performed using SPSS . Level of significance was set at p < 0.05.For the third aim, the differences in prioritization during dual-task split-belt adaptation between young and older adults, adaptation effects were first tested with a repeated measures ANOVA for the dual-task condition with within factor Phase (BL-EA-LA-EP) and between factor Group (young vs. older adults) for all gait variables. F = 28.3; p < 0.001], limb excursion symmetry  and double support symmetry , as well as for the strategy variables, swing time of the fast  and slow leg  and swing speed of the fast  and slow leg . Post-hoc testing revealed that for the symmetry variables SLS and DSS, an asymmetry occurred in early adaptation (EA) due to the perturbation of the changing belt speeds, while symmetry was re-established in late adaptation (LA). The early post-adaptation phase (EP) showed aftereffects of SLS and DSS asymmetry in the opposite direction from early adaptation. During the entire adaptation phase, both early and late adaptation, there is an asymmetry in LES due to the longer stride on the fast belt  = 4.4; p = 0.024], SwTfast , SwSfast , and SwSslow . Post-hoc testing revealed that during baseline (BL) there was a slight asymmetry of LES in opposite directions in young and older adults . SwTfast showed that young adults had a trend of ±2% higher swing time of the fast leg compared to older adults during early  and late adaptation . During both early and late adaptation older vs. young adults had, respectively a 0.13 and 0.10 m/s higher swing speed of the fast leg .There were no main effects of group for the gait variables, but Phase by Group interactions were significant for LES . During motor-cognitive dual-tasking, the aftereffects of LES were lower than during the single-task.No significant effects for dual task cost were found between the single and dual-task condition, except for LES during early post-adaptation , LES  and DSS , and strategy gait variables, SwTfast , SwTslow , SwSfast , and SwSslow , similar to the single-task condition. Post-hoc testing revealed that the symmetry variables SLS and DSS showed the similar pattern of asymmetry during early adaptation and re-established symmetry during late adaptation, with opposite aftereffects during early post-adaptation . Thus, there was still adaptation during this second exposure of split-belt walking necessary for testing task prioritization  = 6.5; p = 0.002]. Post-hoc testing showed that all adults made on average 5–6% more errors on the Auditory Stroop task during early adaptation compared to their performance during baseline  and late adaptation . There was a significant main effect of group on CTP over all the split-belt phases (BL-EA-LA-EP). Young vs. old adults performed better on the cognitive task [F = 11.0; p = 0.003; see During motor-cognitive dual-tasking, there was a significant main effect of phase for CTP [The overall aim of the present study was to gain insight into the effects of age and dual-tasking on adaptation to a perturbation induced by a split-belt treadmill and task prioritization. More specifically, we examined the effects of age and dual-tasking on gait symmetry and strategy.Both young and older adults adapted to split-belt walking and re-established gait symmetry, but the two age groups achieved this using a different strategy. Older adults increased swing speed of the fast leg, whereas young adults showed a trend of increased swing time of the fast leg. Dual-tasking compared with single-task split-belt walking did not affect gait adaptation strategies, but only affected limb excursion symmetry during the early post-adaptation phase, as indicated by smaller aftereffects. The lack of dual-task effects on all other gait variables and phases is likely due to task prioritization. Task prioritization is clearly present within the dual-task condition in the early adaptation phase, as revealed by a worse cognitive task performance, even more so for older compared to young adults. We discuss these results with a perspective on how healthy older adults retain the ability to adapt to the split-belt perturbation.In line with results of previous research (Reisman et al., 2, while in the previous study both belts accelerated with max 0.3 m/s2 (Bruijn et al., While both age groups adapt to split-belt walking, our results confirm previous data showing that the adaptation strategies were age-dependent (Bruijn et al., The age-related change in adaptation strategy might be beneficial for older adults. Switching from a time to a speed strategy in response to a split-belt perturbation decreases the time spent in swing and thus decreases single leg stance, which increases gait stability during split-belt walking, especially immediately after the perturbation (Bruijn et al., Beyond gait stability, age-related decreases in neuromuscular function could contribute to the age-related differences in timing and speed strategies. The age-related reductions in leg muscle strength (Nigg et al., Dual-tasking did not affect gait adaptation as there were no significant effects on dual-task cost, except for the aftereffects of limb excursion symmetry. The smaller aftereffects could be due to the fact that the dual-task caused participants to retain less of the split-belt adaptation. Since there was no randomization between the two conditions, we cannot exclude that fatigue might have had effects on the dual-task results, which is a limitation of the study. The lack of further dual-task effects is in contrast to the previously discussed motor-cognitive dual-tasking studies during split-belt walking (McFadyen et al., At the onset of the speed differences between the belts, the early adaptation phase, both young and older adults performed worse on the cognitive task, as indicated by fewer responses to the Auditory Stroop stimuli compared to baseline and late adaptation. This result suggests that immediately after being exposed to split-belt walking there is a need to prioritize gait over performing the cognitive task for the sake of safety. Our findings support the theory of task prioritization (Yogev-Seligmann et al., Furthermore, during dual-task split-belt walking older vs. young adults had 3–11% fewer correct responses on the cognitive task, with the largest differences also during the early adaptation phase. This implies that performing an additional task while reacting to the split-belt perturbation is harder for older adults. Therefore, it seems that older adults even more than young adults need to prioritize gait. The increased need to prioritize gait may be due to age-related changes. Interference from dual-tasking on walking ability starts at a lower level for individuals with less task-relevant resources, like older adults (Huxhold et al., Age did not affect gait symmetry after a split-belt perturbation, but did affect adaptation strategy, with young and older adults adapting through “timing” and “speed,” respectively. The role of this change in adaptation strategy is likely to increase gait stability in older adults to prevent falling. Task prioritization of the motor over the cognitive task may underlie the lack of dual-tasking effects on gait adaptation. This is supported by a decline in cognitive task performance during early adaptation, more so in older compared with young adults. We conclude that healthy older adults retain the ability to adapt to split-belt perturbations, but interestingly they adapt through a different adaptation strategy. Moreover, in challenging motor tasks, like split-belt adaptation, this requires task prioritization to prevent adverse outcomes. This suggests that task prioritization and adaptation strategy should be a focus in fall prevention interventions. Furthermore, future research should determine the relationship between gait adaptation strategies, gait stability, and neuromuscular function to understand the underlying mechanisms of age-related differences in split-belt adaptation.The data set used and analyzed in the current study is available from the corresponding author on reasonable request.DV, CL, TH, and NV designed the study protocol. DV collected and analyzed the data with supervision of CL. Results were interpreted by DV, AdO, NV, TB, TH, and CL. DV wrote the first draft under supervision of CL. DV, CL, AdO, NV, TB, and TH contributed significantly to revising the manuscript. All authors read and approved the final manuscript.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.