The antimicrobial efficiency of OFX formulated in cross-linked and non-cross-linked nanofibers was affirmed by observing the inhibition zones of Staphylococcus aureus and Escherichia coli. In vivo studies using the OFX nanofibrous inserts on a rabbit eye confirmed a sustained release pattern for up to 96 h. It was found that the cross-linking of the nanofibers by GA vapor could reduce the burst release of OFX from OFX-loaded CS/PVA in one layer and multi-layered nanofibers. In vivo results showed that the AUC0–96 for the nanofibers was 9–20-folds higher compared to the OFX solution. This study thus demonstrates the potential of the nanofiber technology is being utilized to sustained drug release in ocular drug delivery systems.A novel nanofiber insert was prepared with a modified electrospinning method to enhance the ocular residence time of ofloxacin (OFX) and to provide a sustained release pattern by covering hydrophilic polymers, chitosan/polyvinyl alcohol (CS/PVA) nanofibers, with a hydrophobic polymer, Eudragit RL100 in layers, and by glutaraldehyde (GA) cross-linking of CS-PVA nanofibers for the treatment of infectious conjunctivitis. The morphology of the prepared nanofibers was studied using scanning electron microscopy (SEM). The average fiber diameter was found to be 123 ± 23 nm for the single electrospun nanofiber with no cross-linking (OFX-O). The single nanofibers, cross-linked for 10 h with GA (OFX-OG), had an average fiber diameter of 159 ± 30 nm. The amount of OFX released from the nanofibers was measured The online version contains supplementary material available at 10.1208/s12249-021-02051-5. Bacterial conjunctivitis is a common infection, which occurs in patients of all ages and requires emergency treatment. Ofloxacin (OFX) is a fluoroquinolone antibacterial agent, which is extremely effective against a wide variety of bacteria including Gram-positive and Gram-negative microorganisms by inhibiting DNA gyrase , 2. NanoChitosan (CS) is a broadly used polymer to design ocular drug delivery systems because of its unique biological properties including antimicrobial activity, biodegradability, and biocompatibility . Polyvinin situ gel  was obtained from Acros Organics . Eudragit RL100 and polyvinyl alcohol (PVA)  were purchased from Merck . Ofloxacin was purchased from Sina Daru (Iran). Acetic acid, glutaraldehyde, and methanol were purchased from Merck  and were of analytical grade.CS/OFX/PVA solution was prepared by mixing 10 mL of chitosan solution (4% w/v) in acetic acid (1% v/v) with 10 mL of PVA solution (8% w/v) in distilled water and dissolving OFX at 25°C under continuous stirring condition (300 rpm) to obtain a final solution containing 2%, 4%, and 0.6% (w/v) of CS, PVA, and OFX, respectively.Eudragit RL100 solution (10% w/v) in methanol was obtained using magnetic stirring at 300 rpm at 25°C. The one-layer nanofiber (denoted as the OFX-O formulation) was prepared using an electrospinning device  by loading the CS/OFX/PVA solution in an injector and injecting the solution with a flow rate of 0.5 mL/h under high voltage application (28 kV) toward a cylindrical collector (10 cm diameter). The distance between the injector and the collector was fixed at 15 cm, and the whole process was performed at 25°C and 25% of relative humidity.For the preparation of multi-layered nanofibers (denoted as OFX-M), a specified amount of Eudragit RL100 solution (10% w/v in methanol) was electrospun at a flow rate of 2 mL/h toward a cylindrical collector (10 cm diameter) covered by aluminum foil; then, the core layer was prepared by the spinning of CS/OFX/PVA solution and, lastly, the core layer was covered with another Eudragit RL100 layer. The electrospinning condition maintained the same as the one-layer nanofiber. The experimental flow chart is described in Fig. The cross-linking procedure was performed using the method as explained by Zhou  with somTo investigate the morphology of the nanofibers, the samples were dried at 25°C for 3 h and put on a metal stub using adhesive tape, sputter-coated with gold, and then observed under a scanning electron microscope .FTIR spectra were observed by using the FTIR- spectrometer . A vacuum desiccator was used to dry the nanofibers at room temperature (25°C for 5 h). The samples were thereafter mixed with micronized KBr powder and finally compressed into discs using a manual tablet press.To investigate the thermal properties of the prepared nanofibers and evaluate the solid-state of the drug in the nanofiber formulations before and after the cross-linking procedure, the DSC analysis was performed. The specimens (around 5 mg) were placed in an aluminum pan. The samples were run under nitrogen gas flow, and the samples were heated from 30 to 500°C with a scanning rate of 5°C/min .2 value of 0.9994 at a range of OFX concentrations from 0.781 to 25 μg/mL.To evaluate the drug content uniformity, 10-mg samples were dissolved in 5 mL of acetic acid (1% v/v) for single-layered nanofibers and a mixture of acetic acid (1% v/v) and methanol in a 1:1 (v/v) ratio for the multi-layered nanofibers at 100 rpm for 5–6 h to extract OFX from the nanofibers. The drug content was evaluated at a wavelength of 284 nm by the use of a UVmini-1240 spectrophotometer  . A calibThe thickness of the nanofibers (n = 3) was evaluated by a screw gauge with an accuracy of 0.01 mm at various spots of samples.To evaluate the moisture uptake, the nanofibers were weighed after the electrospinning process and kept in a desiccator with 79.5% relative humidity (RH) . Nanofibers were removed and reweighed after 3 days, and the percentage moisture uptake was calculated . To meas2 pieces of nanofibers at the same place manually, till it breaks. The folding endurance is the average number (n = 3) of folds in which the nanofibers could be folded at the same place without breaking.Folding endurance was evaluated by frequently folding the 3 × 3-cmFor the determination of tensile strength, one end of the prepared insert (n = 3) was fixed to the movable clip and the other end was fixed to the base plate . Using the movable clip which constantly moves upward, a force was gradually added, and the nanofiber was pulled until it was broken  as a Gram-positive organism and Escherichia coli (ATCC 35218) as a Gram-negative organism. The same swaps were fully soaked with an aliquot  of the McFarland standard suspension of microorganism , followed by spreading onto an agar plate uniformly . Approximate bacterial suspension per milliliter was 1.5 × 108 CFU/mL. Then, the formulated nanofibers of OFX-O, OFX-M, OFX-OG, and OFX-MG were cut into a diameter of 6 mm were placed on the plates and incubation performed at 35°C for 24 h  were immersed in the culture media and incubated at 35°C (25°C for fungi) for 7, 14, and 28 days to investigate any microorganism growth in case of contaminations.The sterility test was carried out under aseptic conditions for the fungi, aerobic bacteria, and anaerobic bacteria utilizing soyabean casein digest medium and thioglycolate broth. Same blank pieces of nanofibers (1 × 1 cmStaphylococcus aureus (ATCC 6538) using the standard disc diffusion method. Tryptic soy agar (TSA) plates were used to cultivate the bacteria. The spread-plate method was utilized  were placed inside a donor compartment containing 25 mL of pH 7.4 phosphate-buffered saline (PBS). A dialysis membrane  was tied at one end of the donor compartment. Then, it was placed from the tied end in the receptor compartment contained 25 mL of the same buffer (to preserve the sink condition). A volume of 2 mL of the OFX content diffused from the dialysis membrane from the tested formulation sample was taken at different time intervals from the receptor compartment for analysis. This was replaced immediately with a volume of 2 mL fresh PBS to maintain the sink condition. The released drug (OFX) was measured using a UV-Vis spectrophotometer  at a wavelength of 284 nm.A simple in-house laboratory 5 cells/mL in 200 μL medium per well for 72 h. Dulbecco’s modified Eagle’s medium (DMEM/F12), medium , supplemented with 10% fetal bovine serum , 100 U/mL penicillin, and 100 μg/mL streptomycin in a humidified incubator at 37°C with 5% CO2 were used.Cytotoxicity of the nanofibrous inserts was determined against L929 (mouse fibroblast). Firstly, the cells were added to each well of a 96-well tissue culture plate at a density of 4 × 10A certain row of 24-well plates without installation of nanofibers was considered as the control. OFX nanofibers with various concentrations of OFX in the medium  were placed in the other wells. Thirty microliters of the medium and 3--2,5-diphenyl-2H-tetrazolium bromide (MTT) assay was placed into all of the wells and incubated for 4 h. Of DMSO solution, 150 μL was placed into the wells and the plate observed by the microplate reader. The absorbance ratio of sample cells to control cells measured at the wavelength of 560 nm was then calculated. All experiments were carried out in triplicate .To evaluate the irritancy of nanofibers, the Draize test was adopted. One of the rabbits’ eyes received sterile inserts (inserts were prepared in aseptic conditions followed by 40 min exposure to UV light) formulations , while the other eye was treated with sterile PBS as a control for. The eyes were observed for 7 days for any sign of irritancy including abnormal discharge, congestion, and redness of the conjunctiva. Corneal opacity and irritation were graded using the scoring system considered in guidelines for ocular irritation study between 0 and 2 showing the intensity of irritation .Permission for the use of animals was obtained from the animals’ ethics committee of Kermanshah University of Medical Sciences . Twelve New Zealand white rabbits weighing 3.8–4.1 kg were used in the experimental and control groups. A punch of nanofibrous inserts  and OFX solution with a drug content of 3% w/v for multi-layer and 9% w/v for one-layer nanofibers used as the standard was introduced into the rabbit’s conjunctival sac. At the time of sampling, 50 μL of sterile PBS was poured into the eyes of the rabbit and the tears collected by the sterile paper discs. These discs were transferred directly onto the culture medium, and the amount of drug remaining in the paper disks was measured by the microbial assay method as explained earlier.CS/PVA nanofibers were successfully manufactured by electrospinning. The process of manufacturing the ocular inserts with single-layered and multi-layered nanofibrous structures is demonstrated in Fig. It was interesting to note that the entrapment efficiency was found to be > 95% for all the 4 formulations  Table . This hiThe physicochemical parameters related to the prepared OFX inserts like thickness, swelling, moisture uptake, moisture loss, folding endurance, and tensile strength were also evaluated and displayed in Table The degree of the swelling has a significant effect on the drug-releasing behavior of the nanofibers. The swelling degree was lower in the cross-linked nanofibers (OFXOG and OFX-MG) compared to the non-cross-linked counterparts (OFX-O and OFX-M). This pattern could be due to the cross-linking of chitosan, PVA, and CS/PVA and the enhancement of intermolecular bonding which result in the reduction of the degree of swelling, and the rate of drug release  Furtherm−1 for C-F, 1712 cm−1 for C=O, and 3400 cm−1 for OH. The appearance of OFX characteristic peaks in the nanofibers confirmed the presence of ofloxacin inside in nanofibers structure. Some characteristic bands for PVA and chitosan in the prepared OFX nanofibers were consistent with the literature data  occurred with the cross-linking reaction which could be due to the reduction of amino and hydroxyl groups. The peaks at 1598 cm−1 related to NH2 were mostly eliminated for the cross-linked OFX nanofibers which are indications of engaging the amino group in chitosan following GA cross-linking. These results indicated the successful occurrence of cross-linking reaction between PVA/CS nanofibrous structure and GA  groups present in the Eudragit. In the multi-layered nanofibers (OFX-M and OFX-MG), the ester vibration group of Eudragit RL100 was observed at 1157 and 1261 cm−1.The spectra produced by FTIR for the Eudragit RL100 are also presented in Fig. The DSC analysis was carried out to observe the phase transition and thermal properties of drug and polymers in the nanofibrous structures. The DSC traces of all excipients and materials used in the preparation of nanofibers are shown in Fig. SEM images of CS/PVA nanofibers before (OFX-O) and after GA cross-linking (OFX-OG) are shown in Fig. The multi-layered nanofibers before the cross-link (OFX-M) and after cross-link (OFX-MG) are shown in Fig. The results indicated that the viability of L929 cells (mouse fibroblasts) could decrease with increasing OFX concentration in the OFX nanofibers formulations  Fig. . As a reBased on the previous studies, the cell viability > 70% could be an indication of non-irritant formulations . OFX-OM S. aureus and 36 ± 1, 41 ± 1, 35 ± 1, and 41 ± 1 for E. coli, respectively.The inhibitory zones for the nanofibers are shown in Fig. S. aureus and E. coli. There were bigger inhibitory zones on E. coli cultures compared to S. aureus, however, the inhibitory efficacy of OFX against S. aureus was still retained  compared to single-layered nanofibers . These smaller inhibitory zones may be due to the covering of the CS/PVA layer containing antibiotics by Eudragit RL100 layers and the reduction of OFX release rate as a result. The OFX nanofibers were sterilized by UV radiation, and sterility testing was performed under aseptic conditions; as such, there was no evidence of the growth of fungi and bacteria in the culture media.A previous study reported that cross-linked CS/PVA with GA had suitable antibacterial efficacy for applications in drug delivery . SmallerThe results indicated that OFX nanofibers can control drug release up to about 103 h Fig. . The OFXTwo important factors affected the prolongation of the OFX release from inserts: (1) covering the CS/PVA layer containing the OFX drug by the Eudragit RL100 layers and (2) GA cross-linking the CS/PVA nanofibers. In the case of CS/PVA nanofibers, the immersion in tear fluid causes the instant dissolution of the nanofiber into a substance similar to that of gelatinous material. This can therefore limit the usage of a CS/PVA layer in the eye tissues. To improve its mechanical properties, chemical cross-linking with GA vapor was utilized due to the bonds formed between the reactive side groups of CS (amino groups) and PVA (hydroxyl groups) , 51. ThiEudragit RL100 polymer can thus effectively provide prolonged and sustained release of OFX from multi-layered nanofibers . Indeed, combinations of hydrophilic and hydrophobic nanofiber layers can control the drug release and therefore reduce the toxicity as a result of the fast release of drugs (13). These nanofibrous structures can be useful for the management of ocular bacterial infections when the prolonged release of an antibiotic is essential. Although the identical formulations had not been prepared in any previous studies, a similar study has designed and prepared OFX-loaded chitosan/PVA nanofibers for hernia repair . They shDuring the Draize irritancy test, no symptoms of ocular irritation such as inflammation, the opacity of the cornea, conjunctival redness, and discharge were observed. Redness of conjunctivae was however observed after administration of the cross-linked nanofibers with GA, which was reduced and vanished over time. There is either negligible or no irritation and inflammation of the eye for all the tested eyes with all the formulations compared to control eyes which suggested that the OFX nanofibers were well-tolerated.Staphylococcus aureus) using the standard curve. This method was reproducible, linear, precise, and simple. Moreover, this method does not require toxic solvents and specialized equipment. Drug released from nanofibers (96 h reading) and pure commercial OFX standard demonstrated substantially similar clear inhibitory zones.The OFX nanofibers were inserted into the rabbits' conjunctival sac without the application of any invasive method. The concentration of released OFX from nanofibers in the tear fluid was determined by microbiological assay (max) of 60 ± 4 μg/mL and 37 ± 6 μg/mL were measured at 5 h after OFX-OG and OFX-MG administration, respectively, followed by a steady release in tear fluid for 96 h. The non-cross-linked OFX nanofibers indicated a maximum concentration (Cmax = 202 ± 15 μg/mL (OFX-O) and 90 ± 5 μg/mL (OFX-M)) followed by a steady release in tear fluid for 96 h  after 10 h. Maximum concentrations  of OFX-MG was enhanced significantly compared to the other formulations and the OFX solution. This was attributed to the covering of the CS/PVA layer containing the drug by the Eudragit RL100 layers and GA cross-linking.AUCThe minimum inhibitory concentration (MIC90%) of OFX against microorganisms which is infective to the eye including Gram-negative and Gram-positive was reported to be 2 μg/mL . While tIn the present study, electrospinning technology was successfully used for the preparation of single-layered and multi-layered nanofibers with OFX incorporated into the CS/PVA layers. Morphological characterization of the ofloxacin nanofibers revealed highly homogeneous nanofibrous structures with a tight connection between the individual nanofiber layers. Glutaraldehyde cross-linked and non-cross-linked nanofibers for all formulations had an acceptable level of toxicity on cultured L929 (mouse fibroblast) cells. The OFX nanofibers could be placed in the cul-de-sac of the rabbit eyes as an insert without any invasive methods or surgery. There was not any sign of significant inflammation or redness in the rabbits’ eyes. The drug release profile was evaluated by a microbiological assay method using sterile paper discs which are a non-invasive, cost-effective, and a simple method. The developed OFX nanofibers formulations effectively retained the drug concentration in the tear fluid of rabbits above the MIC90% for up to 95 h. This eliminates the need for frequent installation of the drug and enhances patient compliance.ESM 1(DOCX 774 kb)


This work presents a method to move beyond the recently introduced atomic fragment approximation. Like the bare atomic fragment approach, the new method is an ab initio, parameter-free, orbital-free implementation of density functional theory based on the bifunctional formalism that treats the potential and the electron density as two separate variables, and provides access to the Kohn–Sham Pauli kinetic energy for an appropriately chosen Pauli potential. In the present ansatz, the molecular Pauli potential is approximated by the sum of the bare atomic fragment approach, and a so-called deformation potential that takes the interaction between the atoms into account. It is shown that this model can reproduce the bond-length contraction due to multiple bonding within the list of second-row homonuclear dimers. The present model only relies on the electron densities of the participating atoms, which themselves are represented by a simple monopole expansion. Thus, the bond-length contraction can be rationalized without referring to the angular quantum numbers of the participating atoms. Given the enormous success of the orbital-based Kohn–Sham (KS) method =Ts=alues ϵi (8)vPΦA,,c and d The point, here, is to investigate which parts of the molecular KS eigenfunctions are the necessary ingredients for an approximate PP in order to properly model chemical bonding. As will be shown in the following section, in case of chemical bonding, such a mandatory ingredient seems to be the proper mixture (according to the MO concept) of constructive and destructive terms given by Equation , as the envalues .Compare the molecular PP (second column) with its components, Equation , while tEquation . In the Equation , since tIn line with the ideas mentioned above, various deformation potentials have been created in order to test their performance with respect to chemical bonding. Here, adding constructive terms to the deformation potential should favor chemical bonding and, consequently, yield shorter equilibrium bond lengths, while adding destructive combinations should weaken the chemical bond and yield larger equilibrium bond distances or no bond at all. Exemplarily, the data are compiled for Nc and d, thus yielding bound atoms with reasonable bond distances, it also yields reasonable results among different ansatzes (using different numbers for c and d). Changes in the bond length (the minima of the respective bonding curves) follow the reasoning of the traditional electronic structure theory. As can be seen from However, notice the systematic change in the equilibrium bond lengths for the proposed model. As mentioned in Equilibrium bond lengths from OF-DFT using the deformation potential, in accordance with the MO scheme, are compiled in B with the Pauli potential at atom A into account. However, the influence on the molecular Pauli potential due to the interacting atoms is not captured by this ansatz, since the bare atomic fragment potential is not optimized during the molecular formation. The present work proposes an ansatz to relax the Pauli potential due to the influence of the other atoms, by the introduction of a deformation potential. The deformation potential is build from the electron density of the interacting atoms and can be of a constructive as well as of destructive nature. This model allows to obey traditional chemical-counting rules, and, based on them, bond-length contraction due to multiple bonding can be appropriately modeled. Bear in mind that the present ansatz does not contain any parameterization with respect to experimental values or data fitting. Since the Pauli kinetic energy is evaluated via the bifunctional formalism, it can, in principle, yield exactly the KS Pauli kinetic energy for an appropriately chosen deformation potential.Finally, a comparison is made to bond distances from the orbital-based KS scheme, as well as to experimental bond lengths. Similar to previously published methods ,54, energiven by ,71(21)ϕZ is the nuclear charge, s is the so-called shielding constant [constant . Shell cconstant , meaningThe OF-DFT methods presented in this paper allow for the relaxation of the density as well as for a relaxation of the deformation potential, due to the interaction between the atoms. The inner part of the Pauli potential, however, obtained by the superposition of closed-shell atomic Pauli potentials, is kept fixed (at a given internuclear distance) during the optimization process. Those atomic Pauli potentials were taken from a LDA (Xonly) KS calculation performed with ADF  using atKS calculations for molecular PP were equally obtained at the LDA (Xonly) level using the QZ4P basis sets.This study presents an ab initio, parameter-free, orbital-free implementation of density functional theory. Here, the functional value of the Pauli kinetic energy is evaluated from the density-potential pair using the bifunctional formalism. The recently introduced bifunctional formalism allows for the treatment of the functional derivative, the potential, and the electron density as two separate variables, while yielding exactly the Kohn–Sham (KS) Pauli kinetic energy when the corresponding electron density and the orbital-based KS Pauli potential are inserted. However, the bifunctional approach allows for further meaningful approximations, and thus provides a new and strategic means for the design of functionals.This work extends the bifunctional approach beyond the recently introduced atomic fragment approximation through the help of a deformation potential that accounts for the constructive and destructive effects when two atoms approach each other. The interaction is mimicked with the help of the electron density of the participating atoms and, similar to molecular orbital (MO) theory, the interaction is taken into account with the help of a constructive or destructive combination of those densities. This approach allows study of the impact of electronic counting rules, with the help of the electron density only, and it was found that the influence of multiple bonding within the homonuclear second-row dimers is sufficiently well modeled, in the sense that the proposed model yields the shortest bond length for N


Electrospun polymer nanofibers have gained much attention in blood vessel tissue engineering. However, conventional nanofiber materials with the deficiencies of slow endothelialization and thrombosis are not effective in promoting blood vessel tissue repair and regeneration. Herein, biomimetic gelatin (Gt)/polycaprolactone (PCL) composite nanofibers incorporating a different amount of chondroitin sulfate (CS) were developed via electrospinning technology to investigate their effects on antithrombogenicity and endothelial cell affinity. Varying CS concentrations in PG nanofibers affects fiber morphology and diameter. The CS/Gt/PCL nanofibers have suitable porosity (~ 80%) and PBS solution absorption (up to 650%). The introduction of CS in Gt/PCL nanofibers greatly enhances their anticoagulant properties, prolongs their coagulation time, and facilitates cell responses. Particularly, 10%CS/Gt/PCL nanofibers display favorable cell attachment, elongation, and proliferation. Thus, the Gt/PCL nanofibers containing a certain amount of CS could be excellent candidates as a promising tissue-engineering scaffold in blood vessel repair and regeneration. The development of nanofibrous materials have elicited much attention as biomimetic scaffolds in tissue repair and regeneration owing to their unique (bio)physicochemical features, including their extracellular matrix (ECM)-like ultrafine fiber structure, excellent mechanical properties, large specific surface area, and high porosity with interconnectivity , 2. It hIn addition to (bio)physical signals, the selection of suitable biomaterials can significantly affect cell activities as well as tissue repair and regeneration . Some keIn the present work, biomimetic PG composite nanofibers containing different CS ratios were developed via one-step electrospinning. The morphology, chemical feature porosity, and degradation and of CS/PG composite nanofibers were detected by different characterization techniques. The anticoagulant of PG/CS composite nanofibers was evaluated. Further, these composite nanofibers with different CS ratios were seeded with human aortic endothelial cells (HAECs) to investigate their effects on cellular responses.CS  was supplied by Shanghai Macklin Biochemical Technology Co., Ltd. (China). PCL was obtained from Aladdin Biochemical Polytron Technologies Inc. (China). Gt  were obtained from Sigma-Aldrich Biochemical Technology Co., Ltd., (China). The activated partial thromboplastin time (APTT) kit was purchased from Leigen Biotechnology Co., Ltd (China). Acetic acid (purity: 99.5%) was purchased from Sinopharm Chemical Reagent Co., Ltd. (China). The human aortic endothelial cells (HAECs) were obtained from the affiliated hospital of Qingdao University (China). Culture medium Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12 (DMEM/F-12), fetal bovine serum (FBS), 0.25% trypsin–EDTA were purchased from Biological Industries (Israel). All reagents were purchased from Sigma Aldrich (China) unless otherwise noted. The water used in all experiments was deionized.PCL (10% w/v) was dissolved in acetic acid under mechanic stirring at room temperature for 4 h. Gt (10% w/v) was dissolved in 90% acetic acid with constant stirring for 2 h. CS in different concentrations was added into the Gt solution and stirred gently at room temperature for 1 h to obtain the homogenous solutions containing 5, 10, and 15 wt.% CS relative to the total polymer concentration. Then, PG solution was prepared by mixing two above solutions in a weight ratio of 50/50 (w/w) under stirring for 2 h, named as 5%CS@PG, 10%CS@PG, and 15%CS@PG.The prepared homogenous solutions were subjected to the electrospinning process, equipped with a 1 mL syringe with 21 G needle and a collector covered by aluminum foil. In this study, the distance between the collector plate and the needle tip was fixed at around 18 cm, the voltage was set at 23 kV, and the polymeric solutions were pumped out at a rate of 1 mL/h. All solutions were electrospun in an electrospinning instrument  at room temperature and carefully controlled humidity (< 40%). Prior to any further experiments, the samples were put in a vacuum drying oven for 72 h at least to remove any remaining solvent.n = 100) was further measured from SEM images using image analysis software (Image J).The morphology of CS@PG nanofibrous scaffolds was investigated by an SEM  at an acceleration voltage of 20 kV at room temperature. The diameter of nanofibers (The TEM observations and energy-dispersive X-ray spectroscopy (EDS) analyses were carried out using a JEOL JEM-2100 plus (Japan).−1 with a resolution of 2 cm−1.FTIR was carried out by a Nicolet iN10 FTIR spectrometer  to evaluate the characteristic functional groups of CS, PG nanofiber, and CS@PG nanofibers. The spectra of samples were recorded with the transmission mode over a wavelength range of 4000–500 cm1. Then, four groups of nanofibers were immersed in ethanol for 2 h at 25 °C and weighted as W2. The ethanol on the sample surface was removed by filter paper, and the weights of samples were then noted as W3. The porosity of the nanofibers was calculated by the following formula:The porosity of nanofibers was carried out using the liquid displacement method. Firstly, the dry weight of nanofibers was weighed as WFor the PBS solution absorption test, the obtained nanofibers were weighed in a dry state and recorded as Wd. Then, the nanofibers were soaked in PBS for 24 h at 25 °C and the weight at wet state was recorded as Ww after removing the excess liquid on the sample surface. The swelling ratio can be measured by the following equation:n = 3).All the values in the above experiments are expressed as the mean ± SD  containing lysozyme (500 μg/mL) and incubated in vitro in 37 °C. At the predetermined degradation intervals, each group of samples was removed and washed by deionized water, and freeze-dried to obtain the final weight (Wf). The lysozyme solution was changed three times a week. The mass remaining (%) of the nanofibrous scaffolds was estimated as defined in the following formula:To determine the obtained nanofibers’ resistance to lysozyme, the degradability of the samples was measured at a scheduled time . The initial weight of the nanofibers was recorded as WTo evaluate the degradation behavior of the sample after 7 days, the morphology of nanofibers was observed by SEM.n = 3).Activated partial thromboplastin time (APTT) was used to assess the activity of intrinsic and common pathways of coagulation through testing the clotting times of platelet-poor plasma (PPP) after incubation with electrospun nanofibers on coverslips. For this, anticoagulated blood (approximately 10 mL) was collected and centrifuged at 3000 rpm for 20 min to obtain platelet-poor plasma (PPP). Each sample was incubated with 200 μL of PPP for 10 min at 37 °C and analyzed using APTT kit following the manufacture’s instruction (n = 3 for each testing group). Then, 40 μL of aforementioned anticoagulated fresh whole blood was added into each well and incubated for 60 min at 37 °C, after which the suspensions were removed into centrifuge tubes and centrifuged at 100× g for 5 min. The supernatants were subjected to measure the absorbance at 570 nm using a microplate reader .The hemolysis rate was evaluated by measuring the concentration of hemoglobin released into the solution phase from erythrocytes in diluted whole blood exposed to the electrospun nanofibers. The samples on coverslips were individually placed into 24-well plates and immersed in 2 mL PBS at 37 °C for 30 min. The negative control groups contained only 2 mL of PBS, while positive control groups were comprised of 2 mL deionized water for the aim of inducing maximal lysis of erythrocytes . PRP was prepared by centrifugation  and the upper one-half of plasma was discarded. Then, the nanofibers on coverslips were incubated in 100 μL of PRP at 37 °C for 2 h and gently rinsed with PBS three times for subsequent experiments. To evaluate the platelet activity after being incubated with PRP, electrospun nanofibers were kept in the DMEM supplemented with 10% FBS for 2 h and 24 h, and then a CCK-8 assay kit was utilized to measure thrombocyte viability on the nanofibers. CCK-8 (20 μL) was added to 200 μL of serum-free DMEM medium in 24-well plates and incubated for 1 h. Finally, suspensions were measured by a microplate reader at a wavelength of 450 nm.2. The culture medium was replaced three times a week. The cells were generally isolated by 0.05% trypsin/EDTA for 3 min, centrifuged at 1000 rpm, and suspended in the fresh medium to carry out cell passage or cell seeding.Human aortic endothelial cells (HAECs) were cultured in DMEM supplemented with FBS , and streptomycin/penicillin  in an atmosphere of 37 °C and 5% CO2 incubator. The cells were seeded at a density of 1 × 104 cells per well on nanofibers and the medium was replaced every day. After co-cultured 24 h, cells were washed by PBS solution three times and then stained with a Calcein-AM/PI Double stain Kit . PI was used to stain dead cells and Calcein-AM stained for living cells.Prior to cell seeding, all the nanofibrous materials in a 24-well plate were sterilized under UV irradiation for 1 h and immersed in 75% ethanol  for 1 h. Then, the nanofibers were rinsed four times with PBS solution and soaked in DMEM for 12 h in COTo observe the cell adhesion on the nanofibers, the morphology of the cells was observed after co-cultured 24 h by Fluorescence Microscope . Firstly, the cells were fixed with 4% paraformaldehyde at room temperature for 30 min. Then, 0.5% Triton X-100 solution was used for 5 min to permeabilize the cell membrane. Finally, 4′6-diamidino-2-phenylindole (DAPI) was used to stained cell nuclei and rhodamine-phalloidin was used to stain the F-actin. Quantitative analysis  was further performed with Image J.3 cells/well. The culture medium was changed every two days. After 1, 4, 7 days of co-culturing, the cells were washed by PBS solution to remove non-adherent cells. Then, 20 μL CCK-8 and the complete medium at a volume ratio of 1: 10 was added into the 24-well plate for 1 h at the incubator. The absorbance was tested by Elisa Reader at 450 nm.Cell proliferation activity on the composite nanofibers was carried out with a CCK-8 Assay Kit. The HAECs were seeded on nanofibers at a density of 8 × 10Cell staining with DAPI and rhodamine-phalloidin was performed with Fluorescence Microscope to observed directly the changes of cell counts after co-cultured with different nanofibers for two days and six days.p < 0.05.All samples in the experiments were processed in triplicate. All data are represented as means ± standard deviations (SD). Statistical analysis of samples was determined by one-way analysis variance (ANOVA) to compare differences with significance assigned at Figure SEM was performed to observe the structure of CS@PG electrospun nanofibers. As shown in Fig. As depicted in Fig. −1 and 2935 cm−1 can be attributed to the –NH2 and –OH stretching vibration, and CH2 asymmetrical stretching, respectively. Several characteristic absorption peaks were appeared at 1652 cm−1 (amide I) for C=O stretching vibration, 1542 cm−1 (amide II) for N–H bending vibration, 1441 cm−1 for CH2 stretching, and 1267 cm−1 (amide III) for C–N stretching vibration [−1, representing the S=O stretching vibration in negatively charged SO42− [FTIR spectra analysis was carried out to determine the characteristic absorption peaks of the chemical group of prepared nanofibrous scaffolds. The FTIR spectrum of PG and CS were considered as the control group to compare with CS@PG nanofiber groups. In PG spectrum, strong absorption bands that appeared at 3300 cmibration , 22. Theed SO42− , 24.−1, and the intensity of CS band increased by the presence of CS in nanofibers. This finding confirmed the presence of varying contents of CS in the CS@PG electrospun nanofibers.As shown in Fig. The high porosity of the fiber indicates that the material has excellent interconnectivity, which is conducive to the diffusion of oxygen and nutrients into the pores and promote cell infiltration , 26. FigThe PBS absorption ratio of the CS@PG nanofibrous scaffolds was compared to evaluate the effect of CS on the PBS uptake of the films. As indicated in Fig. 42− groups [The data of in vitro degradability experiment was depicted in Fig. − groups . As demoAPTT is a simple and reliable method for detecting the blood or plasma via the intrinsic coagulation mechanism. APTT was tested to measure the influence of the electrospun nanofibers on the possible delay of blood coagulation. Clotting time detected by APTT displayed a statistically significant decrease of coagulation times between control PG and 5%CS@PG and 10%CS@PG nanofibers. In contrast, prolonged APTT was found after incubation of 15%CS@PG nanofibers with PPP, indicating its improved anticoagulant feature. As presented in Fig. sam − ODneg)/(ODpos − ODneg) × 100%. As indicated in Fig. According to the ASTM F756-00 (2000), the hemolysis rate of the biomaterials is required to be < 5% . The degThe data of platelet viability experiment are shown in Fig. The influence of CS on the cytocompatibility of the nanofibers was explored by cell viability, adhesive morphology, and proliferation of HAECs evaluation onto nanofibers for 6 days. The attached cells were assessed using the Calcein-AM/PI double stain kit, DAPI staining, and CCK-8 assay kit.Figure Nanofibrous scaffolds with good biocompatibility are considered to be the primary requirement for the generation of blood vessels. PCL with good biodegradability and natural Gt with excellent biocompatibility were widely used in tissue engineering with different structures, including hydrogels, electrospun nanofibers, and three-dimensional scaffolds , 33, 34.Cell growth and viability on CS@PG nanofibers were investigated in vitro by fluorescence microscope and CCK-8 test Fig. a–c. The In summary, biomimetic CS@PG composite nanofibers were successfully fabricated using electrospinning technology. Changed fiber morphology and diameter were obtained by varying CS concentrations in PG nanofibers. The CS@PG nanofibers possessed appropriate porosity (~ 80%) and PBS solution absorption (up to 650%). The incorporation of CS in PG nanofibers significantly improved their anticoagulant properties, prolonged their coagulation time, and enhanced cellular responses. Particularly, 10%CS@PG nanofibers exhibited favorable cell adhesion, elongation, and proliferation. Therefore, the PG composite nanofibers incorporated with a certain amount of CS inhibited antithrombogenicity and enhanced endothelial cell responses, which could be developed as a promising tissue-engineering scaffold in blood vessel repair and regeneration.


It has been observed in vitro that complete clot lysis is generally preceded by a slow phase of lysis during which the degradation seems to be inefficient. However, this slow regime was merely noticed, but not yet quantitatively discussed. In our experiments, we observed that the lysis ubiquitously occurred in two distinct regimes, a slow and a fast lysis regime. We quantified extensively the duration of these regimes for a wide spectrum of experimental conditions and found that on average, the slow regime lasts longer than the fast one, meaning that during most of the process, the lysis is ineffective. We proposed a computational model in which the properties of the binding of the proteins change during the lysis: first, the biochemical reactions take place at the surface of the fibrin fibers, then in the bulk, resulting in the observed fast lysis regime. This simple hypothesis appeared to be sufficient to reproduce with a great accuracy the lysis profiles obtained experimentally. Although the interplay between the main components of the fibrinolytic system is well understood, some dynamical aspects of the fibrinolysis remain unclear. Notably, we observe that in vitro, fibrin-rich clots undergo a slow and inefficient phase of degradation when subject to endogenous fibrinolysis. In fact, it turns out that a large part of the lysis process operates in this slow regime. To explain this observation, we proposed a computational model in which the properties of the binding of the proteins change during the lysis. First, plasminogen and tissue plasminogen activator bind at the surface of the fibers, resulting in a slow lysis, then in the bulk of the fibers, thus speeding up the degradation of the clot.α2-antiplasmin. The endothelial cells are responsible for the production and blood release of tPA and of PAI-1. Multiple factors such as lipoproteins, cytokines, and inflammatory markers modulate endothelial cells to produce tPA and PAI-1 , a plasma protein that circulates as a zymogen to the serine protease, plasmin. Plasmin is a trypsin-like enzyme that cleaves a wide variety of plasma proteins. However, many of these reactions require extremely high levels of plasmin. The physiological function of plasmin is limited primarily to the degradation of the fibrin clot and extracellular matrix molecules. The conversion of Pg to plasmin is achieved by a variety of plasminogen activators that exist in tissues throughout the body . The pland PAI-1 . In 2016nd PAI-1 . Clinicand PAI-1 ,9. Howevnd PAI-1 , but trend PAI-1 . Meanwhind PAI-1 . A bettend PAI-1 ,14, as wnd PAI-1 ,16.In this work, we combine in vitro experiments and computational modeling analysis of fibrin-rich clot formation and lysis. The dynamics of the processes were monitored from the time of clot formation to the time of complete lysis. A computerized semiautomatic device based on turbidity measurements was used to determine the amount of fibrin present in the system. We investigated the effect of changing the concentration of the main components of the fibrinolytic system  on the clot formation and lysis times. Analysis of turbidity time curves reveals that for all experimental conditions, the lysis of fibrin-rich clots is characterized by two regimes: a slow regime in which the change in turbidity is linear, followed by a regime with faster turbidity change. The presence of two regimes of lysis was already visible in several experimental studies such as plasma clot lysis with addition of vessel wall components , euglobuA. The x axis and y axis, respectively, represent time and evolution of the signal sensor. A block is made of pure aluminum (360 × 30 × 100 mm) and is warmed by two resistances of 20 W. It is designed to insert spectrophotometric microcuvettes  into eight wells. Each well includes one emitter (diode: SFH 409) and one receptor (phototransistor: SFH 309FA), both operating at 890 nm. When the clot is formed, the phototransistor signal decreases, reflecting a Tyndall effect. For clot formation and lysis analysis, we used the purified proteins tPA , PAI-1 , Pg , and fibrinogen . Experimental conditions are presented in Our experiment consisted of mixing fibrinogen, Pg, tPA, and PAI-1 in various concentrations in a test tube. Then, an excess of thrombin was added, and the attenuation of a light beam through the test tube was measured as a function of time, first indicating the clot formation and then the clot lysis. For this purpose, we designed a completely computerized semiautomated eight-channel measurement and determination of fibrin clot lysis  . Brieflyμg ⋅ mL−1. This range covered the physiological value of 0.005 μg ⋅ mL−1. In our experiments, the total lysis time decreased rapidly with the concentration of tPA. To not overwhelm the effect of the other molecules, we used a default value of tPA, 0.001 μg ⋅ mL−1, lower than the physiological concentration. Pg values were lower than physiological conditions. However, in vivo Pg must be high enough to compensate the presence of α2-antiplasmin. In our experiments, we observed that the effect saturated for concentrations of Pg greater than 13 μg ⋅ mL−1, suggesting that we covered a sufficiently large range of values for Pg. The default concentrations of PAI-1 and fibrinogen have been chosen near their physiological concentrations Original data were sliced, and we retained one time step over three to discard the fluctuations of the signal between two consecutive time points.2)Sliced data were then smoothed using the Savitzky-Golay filter  with a window of five points before and after the point of interest. Following the standard good practice with the Savitzky-Golay filter for signals with change in concavity, we used a third-order polynomial fit.3)Smoothed data were then normalized by the maximal value of the turbidity after subtracting the minimal value.To compare lysis curves from different experimental conditions, we transformed the original turbidity curve into normalized turbidity (NT) as follows:This procedure resulted in values of NT between 0 and 1 and allowed us to compare different experimental conditions. This step of normalization was necessary as we found that the maximal to minimal variation of turbidity (Δ turbidity) varied with the experimental conditions. In particular, an increase in the concentration of fibrinogen was strongly associated with an increase of Δ turbidity .We determined the slow lysis regime by fitting the NT curves between two time points corresponding to NT values of 0.01 and 0.075. Note that those two values did not define the slow regime but were only used as boundaries for the linear fit of the NT curve and chosen so that they can span a sufficiently large time window while remaining in the slow lysis regime. Linear fitting was done using the optimization and root finding from scipy.optimize. Then, to determine the ending point of the linear regime, we browsed backward along the NT curve and identified the time point on the NT curve that was distant from the linear fit by an arbitrary threshold value Δ = 0.005 .γ = 0.1) and is described by a Michaelis-Menten equation. For simplicity, we separated the formation of the clot from its lysis and assumed that the lysis started once the clot was formed. Nonetheless the two models work together as the model of fibrin polymerization provided inputs  to the model of fibrinolysis.The two models of clot formation and clot lysis are described in detail in the main text see . Briefly1)For each experimental condition given in 2)For each simulated experiment, we stored the maximal error between experimental and simulated lysis profiles up to the total lysis time. We have chosen the maximal error to be more conservative compared to the mean error. Note that in the context of lysis, it is not possible that the simulated lysis curve deviates at a single point and then returns afterward close to the experimental lysis curve. Indeed, a deviation of a point  means that, at this point, a lot of fibrin had been lysed, but to return near the experimental curve, it would require new fibrin to be created. For similar reasons, a point cannot experience a sudden drop below the experimental curve.3)We sorted by increasing values the stored error (each error corresponded to a set of parameters) and then scored each set of parameters by summing its rank over all experiments.4)We then selected the set of parameters that had the smallest score. We have chosen a rank-based score to more strongly penalize the simulations with the highest error, no matter the value of the errors.From the values given in A). The parameters we retained for the clot formation are given in Considering the 92 sets of parameters and the 74 experiments, the worst possible score turned out to be 6808, assuming that always the same set of parameters was ranked last. We normalized the rank-based score by this value and found that our best solution had a score of 0.22. It means that, on average, this set of parameters was in the best quarter. We also found other possible candidates; in particular, there were in total 19 set of parameters that were on average in a third of the best solutions A. The pFibrinogen is the key component involved in the blood clotting system. In the presence of thrombin, fibrinogen is converted to fibrin monomers after successive cleavages of the fibrinopeptides A and B. Fibrin monomers then self-assemble and polymerize into oligomers that lengthen to form protofibrils and eventually associate to build fibers. Ultimately, these fibers branch to form a three-dimensional network, which constitutes the clot.α2-antiplasmin. We analyzed clotting and fibrinolysis using the experimental procedure previously developed in  to the dissolution of the fibers (increase of the turbidity). Each step could be identified from the turbidity curve; the clot formation time (CFT) corresponded to the time needed to reach the minimum of turbidity, and the total clot lysis time (TLT) was defined as the time interval from the end of the clot formation to the point at which the derivative of the turbidity curve reaches its maximal value (fastest lysis rate). It is worth mentioning that the TLT and the end of the lysis  are different. It is common not to consider as lysis time the time for complete lysis, but rather the time corresponding to the midpoint of the reduction of 50% decrease of turbidity , and second, the inhibition of plasmin by the loped in  , thus indicating that the experimental conditions affect the formation time of the clot. We measured a mean TLT of 140 min also with a high dispersion (from 12 to 472 min). An important observation was that the lysis part of the curve appeared to be composed of two regimes. The first one was characterized by a slow increase of turbidity, a slow lysis regime. The second regime corresponded to a fast increase of turbidity (fast lysis regime). Independently of the experimental conditions, the slow lysis regime appeared to be a linear function of time, whereas in the fast lysis regime, the turbidity increases much faster (A). It is worth mentioning that a priori, the slope of the linear regime and its duration have no reason to be correlated. However, because the data were normalized, this correlation implied that the slow lysis regime might end for a specific value of the NT, as shown by the peak around 0.1 in the distribution of the values NT(t = SLT). This suggests that the duration of the slow lysis regime may be an intrinsic property (B). The fast lysis regime was on average of 67 min (from 19 to 280 min), and for the definition of total lysis used here, the slow lysis regime accounted for a significantly longer duration than the fast lysis regime (C). A shows that if the slow lysis regime is long, it will also be the case for the fast lysis regime. From a practical point of view, we show that by measuring the slope of the turbidity curve in the slow lysis regime, it becomes possible to provide an estimation of the TLT from early measurements  without waiting until the actual end of the lysis.Across all our experimental conditions, we measured a mean CFT of 19 min, but the values are broadly distributed . As expected, we found that the initial concentration of tPA has a strong effect on the CFT, which decreases as the tPA concentration increases. An explanation is that an excess of tPA rapidly increases plasmin production in the presence of a small amount of fibrin, and it is known that plasmin can induce the production of a clottable form of fibrinogen , indicating that PAI-1 inhibited tPA before lysis. Considering the time required to form the clot and the fast kinetics of inhibition of tPA by PAI-1 (second-order rate constant 7 M−1 s−1 (T between the beginning of the experiment and the end of the clotting (D). To a lesser extent, ΔT decreased (resp. increased) with tPA (resp. PAI-1) jointly with the time needed to form the clot.We then investigated how each event depends on the initial experimental conditions. We compared the CFT for different concentrations of tPA, PAI-1, Pg, and fibrinogen , A–D. Asbrinogen , thus re M−1 s−1 ), tPA miclotting D. To a , F and H), and it decreased with the concentration of tPA and Pg. However, the effect of Pg was mainly significant for concentrations lower than 13 μg ⋅ mL−1. Above this value, the TLT saturated, suggesting that we covered a sufficiently large range of values for Pg, even with concentrations lower than physiological ones. Comparing the slow and the fast lysis regimes side by side, we could observe how the initial conditions affect the relative contribution of each regime on the TLT. For each experiment, we measured the ratios RSL = RFL = Then, we investigated the dynamics of the fibrinolysis. As expected from the respective roles of the different components of the fibrinolytic system, the TLT increased with the concentrations of PAI-1 and fibrinogen , F and H0. Both RSL and RFL were fluctuating around 0.5 (I), thus suggesting that the relative durations of the slow and fast lysis regimes were similar for the range of tPA concentrations we used in our experiments. More precisely, we observed that the fast lysis times rapidly decreased with the concentration of tPA and eventually plateaued for [tPA]0 ≥ 0.002. On the other hand, the slow lysis time decreased less rapidly and became similar to the fast lysis time for [tPA]0 ≥ 0.004 (A). Regarding the effect of PAI-1, both slow and fast lysis times increased with the values of [PAI-1]0 (B), and for any values of [PAI-1]0, the slow lysis regime dominated the fibrinolysis time (J). For higher concentration of tPA, we observed a similar behavior for the slow lysis time but a saturation of the fast lysis times (C), thus indicating that at a physiological concentration of tPA, the total lysis time was modulated by the slow lysis regime rather than the fast lysis regime. Pg and fibrinogen displayed opposite trends . Increasing [Pg]0 corresponded to a decrease of RFL, with a crossing point around [Pg]0 = 13 μg ⋅ mL-1. This crossover was due to a saturation of the slow lysis times, whereas the fast lysis times kept decreasing with an increase of Pg (D). An increase of [Fg]0 corresponded to an increase of RFL, with the two curves collapsing around [Fg]0 = 4 mg ⋅ mL-1. The relative contribution may depend on the definition of the lysis time. Nonetheless, regardless of the definition considered, our results suggest that the lysis is in the slow regime over a significant amount of time m(Rf)κ = 0, reactions occur at the very surface of the fibers , whereas for κ = q is the number of binding sites per monomer of fibrin for tPA (qtPA = 1.5) and Pg (qPg = 2.4) (Pnb) by the activation of Pg by tPAb. The time evolution of these proteins in their free and bound forms  were determined with the following set of equations, using the parameters given in For g = 2.4) . We willb and Pgb . Forb and Pgb, and the initial fiber radius Rf0 for the lysis model. As already discussed, without binding occurring during the formation of the clot, no lysis would be possible  because of the fast inhibition of tPA by PAI-1.Because the experiments were conducted without flow and the different species were homogenized at the beginning of the experiments, we did not incorporate advection or diffusion in the model. The role of the diffusion is discussed elsewhere ,38 in thb and Rf0. We computed the first-order, second-order, and total-order Sobol indices with parameters sampled with the Saltelli scheme  from the values given in b, we found that the most influential parameters were first kFG (representing the addition of protofibrils to growing fibers) and kFA (describing the aggregation of fibers), then kPG (growth in length of protofibrils) and kA (cleavage of fibrinopeptides A). On the other hand, Rf0 was influenced by kFG, kA, kFA, and kPG (D). We then ran the model of clot formation for all possible sets of parameters . In parallel, we measured fiber diameters from electron microscopy images of clot made in similar experimental conditions (F), and we retained the kinetics parameters that provide similar fiber diameters (G), assuming that at least kA = 0.02 s−1, kPI = 10−17kPG = 1015kFI = 10−18kFG = 10−16kFA = 10−18Rf0, it is interesting to note that they have distinct concentrations of tPAb . The best and worst sets of parameters had identical parameters except for kFI and kA, whose values were, respectively, 10−19−1. From the best-ranked set of parameters, we found that the model of clot formation resulted in a concentration of tPAb around 2% of the initial concentration of tPA.The experimental values of the kinetic parameters of the polymerization remain largely unknown despite recent experimental progress in live observations of fibrin polymerization . We perfi scheme   by integrating over time the instantaneous rate of lysis kinetically described by a Michaelis-Menten equation:γ = 0.1 is the solubilization factor, i.e., the quantity of fibrin that returns to the plasma phase per number of plasmin cleavage. The percentage of lysis was then obtained by dividing the historical amount of lysis L(t) by the initial amount of fibrin. Because the lysis proceeds from the outside shell, Rf is directly proportional to the amount of lysis L(t) , Nav is the Avogadro number, and λf is the linear density of fibrin monomers along the fibers. Simultaneously with the decrease of Rf, the clot porosity ϵ also varies asTo model the plasmin-mediated lysis of the fibrin clot, we adapted the framework proposed in  and moresis L(t) :(8)Rf2. Then, we ran the model of clot formation to obtain the initial conditions for the lysis . During the simulation of the lysis, we had to estimate at each time t the number of shells that are used for the reactions. At each time step, we determined this number by solving independently all the equations of fibrinolysis for all the values of the number of available shells. The optimal number of shells was defined as the value that, at a time t, minimized the squared error between Lsim(t + Δt), the simulated amount of lysis at the next iteration, and the experimental one, given by NT(t + Δt). Once this value was obtained, the equations for the fibrinolysis were actually computed for the time t + Δt and the simulation continued by repeating the optimization procedure until all fibrin was consumed. It is worth mentioning that the optimal number of shells could not be freely chosen because it was constrained by the instantaneous number of available shells; hence, the difference between Lsim(t + Δt) and NT(t + Δt) could not be made arbitrarily small. Considering that the state of the system at time t depended on all the history of the lysis, errors could potentially accumulate during the simulations despite the optimization procedure. Overall, we found that the mean-square error between the simulated and experimental lysis curves stayed quite small for most of the experimental conditions. Importantly, our model appeared to be able to better perform compared to the standard approaches with kinetic reactions only at the surface or in the volume of the entire fiber (A). Both have higher errors compared to the model with the time-changing number of shells. This supports the idea that a transition between surface and bulk reactions may be a relevant mechanism regarding the transition between slow and fast lysis, as a surface-only or volume-only reaction model could not capture it.We did not model how the number of shells varied during the lysis; rather, we estimated at each time step the optimal value that best fit our experimental turbidity curves, based on the fact that NT is a good proxy for the concentration of fibrin in the fibers. Yet, we will see that this approach provides relevant information for the elaboration of a surrogate model that does not require dynamical optimization. In practice, for each experimental condition, we first interpolated linearly the turbidity curves between the experimental time points to achieve a similar resolution in time as in simulations . Note, however, that our model poorly captured the experimental fast lysis regime for the highest concentration of PAI-1 (C). Numerical simulations predicted a long TLT, as one would expect considering that PAI-1 inhibits tPA. Yet, the TLT was experimentally very short (F). We could not exclude an experimental issue in this case; two out of the four replicates with the highest concentration of PAI-1 had short TLTs, whereas the two other replicates did not have any lysis at all, thus indicating that experiments with too high a concentration of PAI-1 were hard to conduct and that the results are not totally reliable.As a further investigation of the lysis model, we directly compared the simulated lysis profiles with the corresponding experimental one. Our results are summarized in Fig. 4ry short F. We coD). Another discrepancy between simulation and experiment was observed for concentrations of fibrinogen greater than 5 mg ⋅ mL−1 (E). Between the end of the slow lysis regime and the start of the fast lysis regime, the model failed to reproduce the observations. The slow lysis was found in the simulation to be faster than in the experiments, yet the fast lysis regime was recovered at high fibrinogen concentration. It is likely that this difference between the simulations and the experiments was due to an earlier start of the lysis that we did not consider in the clot formation. If, at some point, clotting and lysis balance, our approach would detect the end of the clotting too early and initiate the fibrinolysis too early, too, hence shortening the slow lysis regime in the simulations.The slow lysis regime was difficult to recapitulate for the lowest concentration of Pg, but overall, we also found a good agreement with the experimental curves D. Anothg ⋅ mL−1 E. BetweRf , and the second one, in which Rf dropped sharply to a value close to 0. More interestingly, the distributions of the normalized fiber radius B), the distribution of We then investigated the change in time of Rf . As expes regime B, the dF) and eventually reached the maximal number of shells available in the fibers (inset in F).We have not yet discussed the time evolution of the number of shells during the lysis. According to our hypothesis, if there is a dynamic transition between surface and bulk reactions, this should be reflected by an increase of the number of shells as the lysis advances. To aggregate and make the different experimental conditions comparable, we first had to normalize the time so that the slow and fast lysis regimes had the same duration. For each simulation, the corresponding experimental slow lysis and fast lysis times were known, and we could also measure the end point of the lysis . Then, each simulation was divided into three distinct parts , and the time was normalized by the length of each respective regime. Put another way, the slow lysis time, fast lysis time, and end of lysis time became, respectively, 1, 2, and 3. Finally, for each part with normalized time, we aggregated the fraction of shells  in bins of length 0.1. Using this reconstructed time evolution, we found that the fraction of shells invariably increased with time F and evinset in F.F. For each simulation, we computed the mean fraction of shells in the slow and fast regimes and built the two corresponding distributions . Next, we found the transition without relying on the experiments. As discussed before, it is possible to identify in which state the system is by measuring A; otherwise, it was drawn from the distribution shown on the lower panel. Note that this surrogate model bypasses the fitting step but still requires the equations of the lysis to be solved.Finally, we combined our results to propose a surrogate model that could reasonably predict slow and fast lysis times without relying on empirical curve fitting. The underlying idea of this surrogate model is to use the distribution of the fraction of shells in the different regimes instead of searching for the optimal fraction of shells at each time step. To do so, first we need the distribution of the fraction of shells in both regimes, and second, we need to identify, without using experimental curves, the time points of the transition between the slow and fast lysis regimes. The distribution of the fraction of shells can be calculated from , B and C). For these comparisons, the slow and fast lysis times for the surrogate model were determined according to the value of , B and C). The surface model better reproduced the slow lysis times compared to the volume model, in agreement with the hypothesis of surface reactions during the slow lysis regime, and the distribution of the relative error of slow lysis times for the surrogate model was very similar to the distribution of the relative error of the surface model . The estimated fast lysis times for the surface (resp. volume) models were longer (resp. shorter) compared to the experiments, whereas the times predicted by the surrogate model were between them and closer to the optimal model. The results for the surrogate model were more dispersed than for the optimal one because we lost the smooth temporal evolution of the fraction of shells by using random distributions. Nonetheless, this surrogate model predicts more reliably both the slow and fast lysis times than the standard approaches based on either surface or bulk reactions. Our model hypothesized a uniform distribution of protofibrils across the fiber. However, it has been shown that the distribution of protofibrils in the cross section of a fiber is not homogeneous. Instead, protofibrils are more densely packed in the core of the fiber than on the outer part, with a fractal lateral structure , but the median value for the fast regime was shifted toward negative values , hence indicating that the heterogeneous cross-sectional distribution of protofibrils could not explain alone the transition between the slow and fast regimes of lysis.We compared the slow and fast lysis times of this surrogate model with the results of the models with optimal number of shells, surface reactions only, and bulk reactions only , B and Ceactions , B and Ctructure , 52, 53.tructure . We fountructure . The medtructure , A and Be values , C and DAlthough the interplay between the main components of the fibrinolytic system is well understood, some dynamical aspects of the fibrinolysis remain unclear. For all experimental conditions, we observe that in vitro fibrin-rich clots undergo a slow and inefficient phase of degradation, characterized by a well-defined linear increase of turbidity, when subjected to endogenous fibrinolysis. This slow regime is always followed by a fast lysis regime, marked by a fast increase of turbidity. Although the durations of the slow and fast lysis regimes are in general correlated, their relative contributions depend on the experimental conditions and display opposite trends depending on the varying experimental parameter. Overall, we found that a large part of the lysis, if not most of it, operates in this slow regime regardless of the experimental conditions. To explain this observation, we proposed a computational model in which the properties of the binding of the proteins change during the lysis. First, Pg and tPA bind at the surface of the fibers, resulting in a slow lysis, then in the bulk of the fibers, hence speeding up the degradation of the clot. The model of fibrinolysis is complemented by a model of clot formation that aims to estimate the initial conditions for the concentrations of bound proteins and the radius of the fibers for the fibrinolysis model. Overall, the model of clot formation reasonably matches the experimentally measured radius of the fibers but seems to be less accurate than the model of fibrinolysis. This was somehow anticipated, first because of the simplifications we made in this model and second because of the lack of in-depth knowledge of the kinetic parameters. Having a model for clot formation able to reproduce the microstructure of a centimetric clot (or more) over a period of about 20 min would be of great interest but is a hard challenge that remains out of the scope of this work.αC domains are cleaved by plasmin during the first stage of the lysis. Because these domains are responsible for interprotofibril interactions, the fibers start loosening, expose more binding sites, and facilitate the diffusion of plasmin through the fibers and, eventually, the binding of plasmin on the protofibrils. Then, a rapid phase of lysis follows, due to axial fragmentation of the protofibrils. In their work, the authors directly used plasmin, whereas in our case, we used tPA and Pg. However, the diffusion constants of plasmin, Pg, and tPA are quite similar (Our model of fibrinolysis differs from the seminal work of Diamond and Anand , as well similar ; thus, i similar  observedPrevious works such as ours suffer from the assumption of a homogeneous shrinkage of the fibers, whereas the lysis of fiber instead occurs with transverse cutting across the surface of the fiber. Unfortunately, such an assumption has to be made to describe macroscopic lysis of centimetric clots with a duration of hundreds of minutes. Our description in term of number of shells involved in the kinetics of the lysis is strongly associated with the idea of uniform degradation. However, considering that lysis is facilitated because of easier access to binding and cleavage sites is not in contradiction with the formulation in terms of transverse cuts. A better understanding of dynamical change during lysis of protofibrils packing would certainly be of great interest for further experimental and theoretical works.F.R., K.Z.B., and B.C. designed the study and wrote the manuscript. F.R. developed the numerical models and performed data analysis. K.Z.B. and A.R. performed experiments. D.M. and D.P.-M. provided scanning electron microscopy images and performed scanning electron microscopy image analysis. K.Z.B. and B.C. supervised the project.


In this study, the viscosity behavior of two mixtures of Ethaline (1 ChCl:2 ethylene glycol) with either methanol or ethanol were investigated over the temperature range of 283.15–333.15 K at atmospheric pressure. The measured viscosities of neat Ethaline, methanol, and ethanol showed reliable agreement with the corresponding reported literature values. The mixture viscosities were modeled by an Arrhenius-like model to determine the behavior of viscosity with respect to temperature. The data were also modeled by the four well-known mixture viscosity models of Grunberg–Nissan, Jouyban–Acree, McAllister, and Preferential Solvation. All of the model results were reliable, with the Jouyban–Acree and Preferential Solvation models showing the most accurate agreement with the experimental measurements. The Jones–Dole viscosity model was also investigated for the measured viscosities, and by analyzing the results of this model, strong interactions among Ethaline and the alcohol molecules were proposed for both systems. As a final analysis, viscosity deviations of the investigated systems were calculated to study the deviations of the viscosity behaviors with respect to ideal behavior. Both systems showed negative viscosity deviations at all of the investigated temperatures, with the negative values tending towards zero, and hence more ideal behavior, with increasing temperatures. Moreover, in order to correlate the calculated viscosity deviations, the Redlich–Kister model was successfully used for both systems and at each investigated temperature. The concept of Deep Eutectic Solvents (DESs) was introduced by Abbott et al.  in 2003 The hydrogen bond interactions, which are dominant in DES solutions, are very complicated . This caThe complexity of hydrogen bond interactions is amplified when the DES is mixed with yet another associating component, such as an alcohol which itself accommodates hydrogen bonds. How the hydrogen bond networks of the DES and alcohol are changed, and whether new hydrogen bonds are established between the DES and alcohol, are the main questions arising for such solutions. One of the methods to provide initial answers to these questions is to investigate and compare the physical properties of the DES and alcohol in their pure state and also in solution. Density and viscosity are the most important, yet easy to measure physical properties, which can provide clues to answer these questions.Furthermore, viscosity in itself is a fundamental physical property whose values must be available for any fluid before modeling, simulation, and design can be carried out. In this respect, the generally high values of viscosities for most DESs are a major drawback for their widespread use . The addIn 2016, Sas et al. were the first group who investigated the viscosity behavior of DES mixtures with alcohols. They chose the choline chloride: levulinic acid (1:2) DES in mixture with either of the normal alcohols of ethanol, 1-propanol, 1-butanol, or 1-pentanol at the three temperatures of 298.15, 308.15, and 318.15 K. They reported negative viscosity deviations for all of the investigated systems with respect to ideal mixture viscosities . GajardoThe limited number of studies on the viscosities of DES mixtures with alcohols, in comparison to the large number of introduced DESs, and the importance of alcohols in the chemical industries show the significant gap in the literature on this topic. Therefore, in this study, the viscosity behavior of Ethaline (1 ChCl:2 ethylene glycol), as one of the less viscous and most commonly used DESs, is determined experimentally in mixtures with the two common alcohols of methanol and ethanol over the temperature range of 283.15–333.15 K and at atmospheric pressure. Then, the four well-established models of Grunberg–Nissan , JouybanThe detailed information of the compounds used in this study including vendor, purity, and purification methods are presented in Ethaline, which is the combination of one mole of choline chloride and two moles of ethylene glycol, was prepared by precise weighing of the dried choline chloride and ethylene glycol using a digital balance by Shimadzu UW1020H, with an uncertainty of 0.001 g. After mixing the weighed amounts of dried choline chloride and ethylene glycol into a sealed vial, the mixture was kept in a shaker-incubator for 24 h at a temperature of 60 °C. Then, the mixture was placed in a vacuum oven for 24 h at the temperature of 60 °C to remove the possibly absorbed moisture during preparation. In this way, Ethaline, which is a homogeneous transparent liquid, was prepared. In order to determine the water content of the prepared Ethaline, tests were carried out using a Metrohm 787 KF Titrino Karl-Fischer titrator. The amount of water in Ethaline was recorded as 0.00421 in mass fraction, which is acceptable for a DES.Nine pseudo-binary mixture samples over the whole composition range of Ethaline + methanol and Ethaline + ethanol were prepared at step sizes of about 0.1 in molar composition. The required amounts of Ethaline and methanol/ethanol were weighed with an uncertainty of 0.001 g by the digital balance mentioned above, and collected in closed-top vials.TM 3000 viscometer. Before the measurements, the viscometer was calibrated by the Anton Paar set of standard oils. The relative uncertainty of viscosity measurements was 0.35%. The viscosities of the Ethaline + methanol and Ethaline + ethanol samples were measured at temperatures ranging from 283.15 to 323.15 and 293.15 to 333.15 K, respectively. Five temperature steps, with intervals of 10 K, were considered for each temperature range. In addition to the mixture samples, the viscosities of pure Ethaline, methanol, and ethanol were measured at the corresponding temperatures. The uncertainty of temperature measurement was 0.02 K. All of the measurements were carried out at the atmospheric pressure of 100 kPa, with an uncertainty of 5 kPa.Viscosities of the prepared samples of Ethaline + methanol and Ethaline + ethanol were measured by an Anton Paar SVMThe modeling of the investigated Ethaline + methanol/ethanol systems could be divided into two parts. First, viscosity estimations of the mixtures at different compositions and temperatures were investigated, and then, the viscosity deviations of the mixtures were treated.The Arrhenius-type model is the simplest and most commonly used model for correlating the viscosities of liquids, whether in the pure state or as mixtures. Equation (1) presents this model .(1)η=η0η0 and aE, which are the reference viscosity and activation energy parameter, respectively, are optimized to the experimental viscosity data. R is the universal gas constant. Although this equation is limited to a specific composition, it can be used as a very simple viscosity model for thermodynamic derivations.This model does not directly consider the effect of concentration of the mixture on the liquid viscosity, and only has a functionality of temperature. For each composition, the parameters of η0 and aE, Equation (2) is considered as the objective function,To optimize the parameters of i and j. The mutual complex molecule, or better called associated molecules, in DESs (ij or ii or jj) is formed by the HBA (iS) and HBD (jS) because of the prevailing strong hydrogen bonds. Equations (3)–(5) formulate the solvation equations of some possible interactions [L symbolizes local molecules, The preferential solvation theory proposes the definition of mutual complex molecules or associated molecules in a mixture of molecules ractions ,(3)(SiLi = 1) and a DES (j = 2), the self-association interactions of the DES (22) are dominant in this theory, thus Equation (6) is simplified to Equation (7) [In this equation, tion (7) .(7)ηmixIn this equation,  follows .(8)x1L=By inserting Equations (8)–(10) into Equation (7), the final viscosity model of the preferential solvation theory for the binary mixture of a molecular solvent and a DES is derived.The parameter m is considered as 2.5 , and thex1 and x2 are the molar compositions of components 1 and 2, respectively, and A correlation for the viscosities of binary mixtures was proposed by Grunberg–Nissan as :(15)lnlA0, A1 and A2 are the interactions parameters of the model which are fit based on the experimental data, with Equation (2) as the objective function.The Jouyban–Acree viscosity model is a simple viscosity model for mixtures. For binary liquid mixtures, it takes the following form :(21)lnηectively .The Falkenhagen coefficient denotes the solute–solute interactions, and is calculated theoretically. However, it is usually considered to be negligible for nonelectrolyte solutions ,28. ThenB-coefficient is calculated as the slope of relative viscosity (C). By analyzing the values of this coefficient at different temperatures, some information about the DES–molecular solvent interactions is obtained [According to this equation, the viscosity obtained .In addition to having the tools to estimate the mixture viscosity of the investigated systems at any temperature and composition, as given above, it is also valuable to look into the behavior of mixture viscosity for insight on the potential interactions within the mixture. To do this, one can investigate the viscosity deviation of a mixture, x1 and x2 are the molar compositions of the molecular solvent and the DES, respectively. In order to model the viscosity deviation, the Redlich–Kister expansion, being one of the best-established models for excess properties, is used here. For a binary mixture, the Redlich–Kister model is [iD’s are the Redlich–Kister coefficients, and their number is dependent on the desired value of k. At any temperature, these coefficients are optimized to the calculated values of viscosity deviations from the experimental data, using Equation (26) as the objective function. Depending on the number of experimental data available at any temperature, this series can be expanded to the desired number of sentences by choosing the parameter k. In this study, the value of k was considered as three, in order to produce four sentences for the Redlich–Kister series.This property is an indicator to investigate how the mixture viscosity changes with respect to the molar-based arithmetic average of its individual constituent viscosities. In this equation, model is :(25)ln), which does not consider any concentration changes; therefore, this model must be fit for each concentration separately. The second category consists of the models which consider both the temperature and composition of the mixture for viscosity estimations. The Preferential Solvation (Equation (11)), Grunberg–Nissan (Equation (15)), Jouyban–Acree (Equation (21)), and McAllister (Equation (17)) viscosity models belong to this category. Finally, as expected, the Grunberg–Nissan model, because of its very simple mathematical expression and the least number of adjustable parameters, showed the least reliable results. However, it should be considered that all of the investigated models, according to As explained, the relative viscosity of the mixture  is represented by the well-known Jones–Dole model (Equation (23)). For clues on molecular interactions in a mixture, one important method is to study the excess properties. The values of viscosity deviations from ideality of both the systems of Ethaline + methanol/ethanol were calculated based on the measured experimental viscosities using Equation (24), and presented in The interstitial accommodation can either increase or decrease the viscosity according to the nature of components. For example, if the small molecules completely fill the void spaces of the larger molecules, being placed within the structure of larger molecules (for example between the branches of a branched molecule), the molecular size discrepancy of the mixture is decreased, possibly leading to more facile flow, and hence, decreasing viscosity. However, if the smaller molecules do not make a nearly perfect fit with the void spaces of the large molecules, with parts of the smaller molecule sticking out, this creates some “branches” protruding out of the large molecules, resulting in resistance to flow and increased viscosities.According to our measurements and results for both systems, the values of viscosity deviations from ideality plummeted to even further negativity by decreasing the temperature, which shows that the rate of viscosity increase by decreasing temperature is higher for pure Ethaline and alcohols with respect to the mixture. If the temperature is lowered, both systems tend to more non-ideal states, likely with stronger hydrogen bond interactions between the unlike molecules. A similar behavior was observed by other researchers for different DES mixtures ,19.A comparison of excess volume between the two systems at the same temperature shows that the values are slightly more negative for Ethaline + methanol than for Ethaline + ethanol, hinting at the possibly stronger interactions and more non-ideal behavior for the Ethaline + methanol system. This is consistent with the proposed discussion on the viscosity B-coefficients of the Jones–Dole model, where we suggested stronger interactions for the Ethaline + methanol system. However, methanol, because it is of smaller size than ethanol, may possibly result in better-packed interstitial accommodations, leading to greater viscosity reduction for its mixtures. Both systems, however, show quite similar trends of viscosity deviation with respect to concentration, with the minimum viscosity deviations in the Ethaline-rich region, occurring at an alcohol molar composition of about 0.3 to 0.4.The Redlich–Kister model (Equation (25)) was used to correlate the viscosity deviations with respect to concentration at each investigated temperature. In order to have a quantitative index for the accuracies of the fitted Redlich–Kister models, the corresponding values of AARD%s are also presented in Among the important gaps of literature on the physical properties of DESs is the scarce number of studies on viscosity investigations of DES mixtures with alcohols, especially by taking into account the importance of alcohols in the chemical industries. This gap is even more highlighted when considering the insignificant number of studies as compared to the large number of DESs already introduced. This study investigated the viscosity behavior of the two systems of Ethaline (1 ChCl:2 ethylene glycol) + methanol or ethanol. Before the mixture viscosity measurements, validations with literature values were carried out at all the desired temperatures using pure Ethaline, as well as pure methanol and ethanol. Nine mixtures of different compositions were prepared for each system over the entire concentration range and their viscosities were measured, in addition to those of neat Ethaline, methanol, and ethanol. The data covered the temperature range of 283.15–333.15 K at atmospheric pressure. The behavior of viscosity with respect to temperature was modeled by an Arrhenius-like model at each investigated composition. Furthermore, the behavior of viscosity as functions of both composition and temperature was modeled by the four models of Grunberg–Nissan , Jouyban