{"paper_id":"720c6e82-254a-46ba-8af9-f5dce64fb459","body_text":"MATERIALE  PLASTICE                                                                                                                                                                \nhttps://revmaterialeplastice.ro \nhttps://doi.org/10.37358/Mat.Plast.1964 \nMater. Plast., 57 (4), 2020, 155-165                                                            155                                 https://doi.org/10.37358/MP.20.4.5415                                                         \n    \n \n \nA Drug Release Mechanism Controlled by Hydrophobic/ \nHydrophilic Balance of the Matrix.  \nTheoretical and Experimental Perspectives \n \nLOREDANA HIMINIUC1, MARICEL AGOP2, 3, VLAD GHIZDOVAT4,  \nMARIA-ALEXANDRA PAUN5, VLADIMIR-ALEXANDRU PAUN6,  \nCONSTANTIN BACIU7, VIOREL-PUIU PAUN8, 3*, BOGDAN TOMA1 \n1“Grigore T. Popa” University of Medicine and Pharmacy Iasi, Department of Obstetrics and Gynecology, 16 Universitatii \nStr., 700115, Iasi, Romania  \n2 Gheorghe Asachi” Technical University, Physics Department, 59A Prof. dr. docent Dimitrie Mangeron Blvd., 700050, Iasi, \nRomania \n3Academy of Romanian Scientists, 54 Splaiul Independentei, 050094 , Bucharest, Romania \n4“Grigore T. Popa” University of Medicine and Pharmacy, Faculty of Medicine, Biophysics and Medical Physics Department, \n16 University Str., 700115, Iasi, Romania \n5School of Engineering, Swiss Federal Institute of Technology (EPFL),  Route Cantonale, 1015 Lausanne, Switzerland \n6Five Rescue Research Laboratory, 35 Quai d’Anjou, 75004, Paris, France  \n7“Gheorghe Asachi” Technical University, Faculty of Science and Material Engineering, 59A, Prof. dr. docent Dimitrie    \nMangeron Blvd., 700050, Iasi, Romania \n8Physics Department, Faculty of Applied Sciences, University Politehnica of Bucharest, 313 Splaiul Independentei, 060042, \nBucharest, Romania \n \nAbstract: Controlled drug release is a promising pathway of biomedicine, meant to suppress side effects \nwith the aim of increasing patient`s  comfort. A route to achieve this goal represents the encapsulation \nof drugs into matrixes, capable to develop physical forces, which further can control the drugs release. \nTo this purpose, mathematical modeling is an important tool, which offers the possibility to understand \nthe drug release mechanisms and to further design new performant systems. In this paper, a theoretical \nmodel for drug release from an amphiphilic matrix is presented. This is achieved using a conservation \nmultifractal law of probabilit y density followed by validation of the model. Moreover, because non -\nsteroidal anti-inflammatory drugs (NSAIDs), such as diclofenac, are widely used in endometriosis as \npainkillers for dysmenorrhea management or Asherman syndrome for reducing the endometri al \ninflammation, some implications of our model for drug delivery systems applied in the field of \ngynecology have been discussed.  \n \nKeywords: controlled drug release, drug delivery systems, amphiphilic matrix, nonsteroidal anti -\ninflammatory drugs, diclofenac \n \n \n1.Introduction \nChitosan is a biopolymer used in a large area of applications due to its beneficial properties such as \nbiocompatibility, biodegradability and antimicrobial activity [1-5]. Among these applications, its use as \na matrix for drug delivery holds the promise to overcome the side effects of the systemic administration, \ni.e. nausea, vomiting, diarrhea, or even hepatotoxicity [6 -12]. This is due to the polycationic nature of \nchitosan which favors a strong anchoring of the drug molecules by Coulomb forces, and also by \nformation of H -bonds with the hydroxyl groups [7 -10]. The development of such interfacial forces \ncompetes with the hydrogen bonds developed between the drug molecules and water solvent, assuring a \nslow release. The main drawback of the use of chitosan for drug delivery systems is its low solubility at \nthe physiological pH [13]. A route to overcome this disadvantage is the grafting of water soluble \npoly(ethylene glycol) (PEG) chains on the chitosan backbones, by obtaining PEGylated chitosan [9].  \n \n \n*email:viorel.paun@physics.pub.ro, viorel_paun2006@yahoo.com \n\nMATERIALE  PLASTICE                                                                                                                                                                \nhttps://revmaterialeplastice.ro \nhttps://doi.org/10.37358/Mat.Plast.1964 \nMater. Plast., 57 (4), 2020, 155-165                                                            156                                 https://doi.org/10.37358/MP.20.4.5415                                                         \n    \n \n \nReplacing chitosan with PEGylated chitosan in view of the development of matrixes for controlled \ndrug release, not only suppresses the disadvantage of the chitosan hydrophobicity in neutral or basic pH \nmedia, but also creates an important tool towards the control of the drug release rate by simple control \nof the hydrophobic/hydrophilic balance [9, 14]. In this line of thoughts, an amphiphilic matrix based on \nchitosan was prepared and also its ability to release a model drug in controlled manner was investigated \n[9]. It was demonstrated that the degree of substitution of the chitosan backbones with PEG chains tune \nthe drug release rate by the dissolution rate. These systems demonstrated lack of any in vivo toxicity, \nencouraging further investigation of the laws which govern their ability to function as an efficient matrix.  \nA large study found a statistically significant increased risk of various adverse cardio-vascular effects \nsuch as atrial fibrillation, ischemic events and vascular deaths after NSAIDs use but mainly after \ndiclofenac intake. The authors indicated that the effects of various treatments in specific patients can be \nforecasted, helping in guiding decisions in inflammatory pathologies [15 -17]. Another large study \nshowed 50% rate of side effects among diclofenac initiators compared with 20% among paracetamol \ngroup or 30% among naproxen initiators compared with non -initiators [18]. However, the therapeutic \napproach via nanomedicine may reduce these side effect and increase the efficacy of the drugs.  \nIn the present paper a new theoretical model for drug release from an amphiphilic matrix was \nproposed. The model is based on a set of multifractal conservation laws for probability density. This \napproach becomes operational if we take into account the fact that the most usual procedure of \nmultifractal presentation is stochasticity. Finally the model will be validated by empirical data.  \n \n2. Materials and methods \n2.1 Materials  \nDiclofenac sodium salt, citral, chitosan (low molecular weight), phosphate buffer saline ( pH = 7.4) \nand ethanol were provided by Sigma Aldrich and used without further purification.  \n \n2.2 Synthesis of the drug delivery systems \nFormulations were prepared by in situ hydrogelation of PEGylated chitosan with citral in the \npresence of diclofenac sodium salt, following a receipt already published [9]. Shortly, two hydrophilic \nPEGylated chitosan derivatives with different content of PEG were reacted with hydrophobic citral  in \nthe presence of model drug, at 55 oC, under vigorous magnetic stirring. The drug amount was kept \nconstant, while the amounts of PEGylated chitosan and citral varied in order to assure a different ratio \nof the hydrophilic/hydrophobic components (Scheme 1). \n \n \nScheme 1. Graphical representation of the components \n of the formulations \n \n\n\nMATERIALE  PLASTICE                                                                                                                                                                \nhttps://revmaterialeplastice.ro \nhttps://doi.org/10.37358/Mat.Plast.1964 \nMater. Plast., 57 (4), 2020, 155-165                                                            157                                 https://doi.org/10.37358/MP.20.4.5415                                                         \n    \n \n \n 2.3 Methods \nThe investigation of the supramolecular architecture of the systems was realized by polarized optical \nmicroscopy (POM), using a Leica DM 2500 microscope. \nThe morphology of the samples was evaluated with a field emission scanning electron microscope \n(Scanning Electron Microscope SEM EDAX – Quanta 200) at accelerated electron energy of 10 eV.  \nThe in vitro release kinetic was monitored by batch experiment performed in phosphate buffer saline \n(PBS) ( pH=7.4), at the human body temperature (37 oC), following a previously used experimental \nprocedure, which mainly consisted in the determination of the percent of released drug at different \nmoments [9, 17]. The experiments were done in triplicate and the values were given as the mean value \nof three independent measurements. The kinetic data was fitted on several mathematic models, as \nfollows:  \ni.Zero order model: Qt = K0 ∙ t, where Qt is the amount of drug dissolved in the time t and K0 is the \nzero-order release constant. \nii.Higuchi model: Qt = KH ∙ t\n1\n2, where Qt is the amount of drug released in the time t and K H is the \nHiguchi dissolution constant.  \niii.Hixson-Crowell mode l: W0\n1/3 − Wt\n1/3 = K ∙ t, where W0 is the initial amount of drug in the \nformulation, Wt is the remaining amount of drug in the formulation at time t and K is a constant. \niv.Korsmeyer-Peppas model: \nMt\nM∞\n= K ∙ tn, where Mt/M∞ is the fraction of drug released at the time \nt, K is the release rate constant and n is the release exponent. \nv.First order model: 𝑙𝑜𝑔𝑄𝑡 =  𝑙𝑜𝑔 𝑄0 + 𝐾 ∙ 𝑡/2.303, where Qt is the amount of drug released in the \ntime t, Q0 is the initial amount of drug and K is the first order release constant. \n \n2.4 Theoretical model \nIn the past years a wide range of theoretical models aiming at describing drug release mechanisms \nhave been developed. The first type of models are empirical and semi-empirical models. The most used \nones are the zero-order model, Higuchi model, Hixon - Crowell model, Korsmeyer-Peppas model, first \norder model etc. [18 -21]. There are also kinetic models developed on spaces with integer dimensions, \ni.e. those based on the usual conservation laws for mass, momentum or velocity, or kinetic models \ndeveloped on spaces with a non-integer dimension, i.e. based on the conservation laws explicitly written \nthrough fractional derivatives [22]. Recently, models have been develo ped based on operational \nprocedures, i.e. on the group invariance of the conservation laws (groups’ automorphism and iso -\nmorphism, dimensions compactizations, integral invariant functions, embeddings of spaces etc.) [23]. \nLastly, a new generation of theore tical models has arisen, based on Scale Relativity, either in the \nmonofractal dynamics as in the case of Nottale [24], or in the multifractal dynamics as is the case for \nThe Multifractal Theory of Motion [25, 26].  \nIn the following we will build a mathematical model based on the paradigm of a multifractal theory \nof motion for the analysis of a complex polymer -drug dynamics. Therefore, admitting that from both \nstructural and functional perspectives the polymer-drug complex system is assimilated to a multifractal \nsystem [26-29] the wide ranges of drug release dynamics can be described through the movement of the \nso-called polymer-drug complex system entities (or structural units) on trajectories that contain non -\ndifferentiability (multifractal curves). Accepti ng multifractality as a fundamental property in drug \nrelease dynamics (and since multifractality is induces through stochasticity [30, 31]), the drug release \ndynamics can be associated to various flow regimes of a stochastic fluid at various scale resolutions. For \na large temporal scale resolution, with respect to the inverse of the highest Lyapunov exponent, the \ndeterministic trajectories of the polymer-drug system structural units can be replaced by a collection of \npotential trajectories (virtual trajecto ries), while the concept of definite trajectories can be replaced by \nthat of probability density. In such a context a multifractal probability density conservation law will \nbecome functional [30 -32] for the drug release dynamics. If we assume that multifra ctalizaion is \n\nMATERIALE  PLASTICE                                                                                                                                                                \nhttps://revmaterialeplastice.ro \nhttps://doi.org/10.37358/Mat.Plast.1964 \nMater. Plast., 57 (4), 2020, 155-165                                                            158                                 https://doi.org/10.37358/MP.20.4.5415                                                         \n    \n \n \nachieved through stochasticization, then the one-dimensional density probability multifractal law takes \nthe form:  \n \n 𝜕𝑡𝑃 + 𝜕𝑥𝑗𝑥 = 0      (1) \nwith  \n𝜕𝑡 =\n𝜕\n𝜕𝑡 , 𝜕𝑥 =\n𝜕\n𝜕𝑥      (2) \n \nIn the above relations P(x,t,dt) is the probability density, jx(x,t,dt) is the probability current density, \nx (t,dt)  is the spatial coordinate, t is the temporal coordinate and dt is the scale resolution. From a \nmathematical perspective P, j x and x are multifractal variables, while t is a non -multifractal variable \nhaving the affine parameter role for the movement curves [23, 25, 26]. The probability current density \nmust contain both the drift multifractal component: \n \n𝑗𝑥\n𝑑𝑟𝑖𝑓𝑡(𝑥, 𝑡, 𝑑𝑡) = 𝜇(𝑥, 𝑡, 𝑑𝑡)𝑃(𝑥, 𝑡, 𝑑𝑡)    (3) \n \nand the Fickian multifractal component (the diffusion one): \n \n  𝑗𝑥\n𝑑𝑖𝑓𝑓𝑢𝑠𝑖𝑜𝑛(𝑥, 𝑡, 𝑑𝑡) = −\n1\n2 𝜕𝑥[𝜎2(𝑥, 𝑡, 𝑑𝑡)𝑃(𝑥, 𝑡, 𝑑𝑡)]   (4) \n \nwhere 𝜇(𝑥, 𝑡, 𝑑𝑡) and 𝜎2(𝑥, 𝑡, 𝑑𝑡) are multifractal variables. The probability density dynam ics are \ndetermined only through 𝜇 and 𝜎2; this results from the explicit form of (1), taking into account relations \n(2) and (3), i.e: \n \n      𝜕𝑡𝑃(𝑥, 𝑡, 𝑑𝑡) + 𝜕𝑥 {𝜇(𝑥, 𝑡, 𝑑𝑡)𝑃(𝑥, 𝑡, 𝑑𝑡) − −\n1\n2 𝜕𝑥[𝜎2(𝑥, 𝑑𝑡)𝑃(𝑥, 𝑑𝑡)]}  (5) \n \nEquation (5) can be analytically solved only for particular cases. By choosing: \n \n𝜇 = −𝜂𝑥, 𝜂 =  𝜂̅(𝑑𝑡)[ 2\n𝑓(𝛼)]−1\n     (6) \n              𝜎2 = −𝜆, 𝜆 =  𝜆̅(𝑑𝑡)[ 2\n𝑓(𝛼)]−1\n     (7) \n \nand through integration we obtain the following solution: \n \n                                            𝑃(𝑥, 𝑡, 𝑑𝑡) =\n1\n{2𝜋𝜆\n𝜂[1−exp(−2𝜂𝑡)]}\n1 2⁄ exp{−\n[𝑥−𝑥0exp(−𝜂𝑡)]2\n2𝜆\n𝜂[1−exp(−2𝜂𝑡)]\n}              (8) \n \nIn relations (6) (7) and (8) 𝑓(𝛼) is the singularity spectrum of order 𝛼 and 𝛼 is the singularity index \nthrough which the fractal dimension DF is specified (for DF we can use any definitions – Kolmogorov \nfractal dimension, Hausdorff-Besikovich fractal dimension etc. [34]; it is regularly found that  DF < 2 \nfor correlative processes and DF > 2 for non -correlative processes). From such a perspective, through \n𝑓(𝛼) it is possible to identify not only the areas of drug release that are characterized by a certain fractal \ndimension (i.e. the case of mono-fractal drug release dynamics), but also the number of areas for which \nthe fractal dimensions are situated in an interval  of values (i.e. the case of multifractal drug release \ndynamics). More than that, though the same 𝑓(𝛼), it is possible to identify classes of universality in the \ndrug release dynamics laws, even when regular or strange attractors have various aspects [30, 31]. \nTherefore, equation (8) is a multifractal Gaussian (dependent on the scale resolution) for which the \ncentral values, still dependent on scale resolution, decrease exponentially towards zero (η >0) and its \nvariation asymptotically goes through ( 𝜆/𝜂). In particular, in the case of mono -fractal drug release \n\nMATERIALE  PLASTICE                                                                                                                                                                \nhttps://revmaterialeplastice.ro \nhttps://doi.org/10.37358/Mat.Plast.1964 \nMater. Plast., 57 (4), 2020, 155-165                                                            159                                 https://doi.org/10.37358/MP.20.4.5415                                                         \n    \n \n \ndynamics, described through Peano type curves, i.e. 𝑓(𝛼) → 2, the solution (8) can be reduced to the \nstandard form [30, 31]: \n        𝑃(𝑥, 𝑡) =\n1\n{2𝜋𝜆̅\n𝜂̅[1−exp(−2𝜂̅𝑡)]}\n1 2⁄ exp{−\n[𝑥−𝑥0exp(−𝜂̅𝑡)]2\n2𝜆̅\n𝜂̅[1−exp(−2𝜂̅𝑡)]\n}    (9) \n \nRelation (8) can be written in a more simplified way if we introduce the non-dimensional variable: \n𝜉 =\n𝑥\n𝑥0\n, 𝜏 = 𝜂𝑡       (10) \nand the non-dimensional parameter \n     𝜈 =\n𝑥\n𝜂𝑥0\n       (11) \nIt results  \n𝜌(𝜉, 𝜏, 𝜈) =\n1\n{𝜈[1−exp(−2𝜏)]}1 2⁄ exp{−\n[𝜉−𝜉0exp(−𝜏)]2\n2𝜈[1−exp(−2𝜏)]}    (12) \n \nFrom here, considering the physical significance of 𝜌 [30, 32], by multiplying (12) with a non -\ndimensional constant playing the role of rest mass of the complex polymer-drug systems entities, we can \nwrite the multifractal law that can generate the release dynamics as: \n \n𝑀(𝜏)\n𝑀∞\n= 𝐴𝜌(𝜉, 𝜏, 𝜈), 𝐴 = 𝑐𝑜𝑛𝑠𝑡.     (13) \n \n3. Results and discussions \nA series of four amphiphilic formulations with different mass ratios of the hydrophilic/hydrophobic \nstructural blocks and the same amount of model drug were prepared by condensation reaction of amines \nwith aldehydes, and their drug release behavior was tes ted into an in vitro physiologic environment [9, \n33].  \nBy spectroscopic and microscopic measurements it was established that the drug has been finely \ndispersed into the chitosan based matrix due to the strong electrostatic and H -bond forces which \nhampered the natural tendency of crystallization of the drug. This can be easily seen in the polarizing \nmicroscopy (POM) images in Figure 1 which showed a continuous fine birefringent texture and no \nobvious drug crystals, which clearly indicate no phase separation of the drug in the matrix [34].    \n \n   \n \n \nFigure 1. POM images of \nthe amphiphilic formulations \n \n\nMATERIALE  PLASTICE                                                                                                                                                                \nhttps://revmaterialeplastice.ro \nhttps://doi.org/10.37358/Mat.Plast.1964 \nMater. Plast., 57 (4), 2020, 155-165                                                            160                                 https://doi.org/10.37358/MP.20.4.5415                                                         \n    \n \n \nThe formulations displayed a porous morphology, with interconnected pores which assure favorable \nsink conditions of the drug (Figure 2). Moreover, the scanning electron microscopy (SEM) images \nconfirmed the POM observations, indicating no sub -micrometric crystals into the pores or on the pore \nwalls, suggesting a good dispersion of the drug into the matrix [35, 36].   \n \n \nFigure 2. SEM images of the drug release formulations \n \nThe in vitro investigation of the drug release indicated the influence of the hydrophobic/hydrophilic \nbalance on the release rate (Figure 3). It can be observed that the progressive increase of the hydrophilic \ncomponent into formulation lead to the fastening of the drug release. Thus, the simple manipulation of \nthe hydrophobic/hydrophilic  balance can tune the drug release in agreement with the addressed \nrequirement [9, 35 -37]. Quantitatively speaking, in 7 days the CP4B2.5D sample released more than \n99% from the entire amount of the encapsulated DCF, while the sample CP5B1.5D released only 91% \n(Figure 3).  \n \nFigure 3. The in vitro drug release in a medium mimicking \nthe physiologic medium \n\n\nMATERIALE  PLASTICE                                                                                                                                                                \nhttps://revmaterialeplastice.ro \nhttps://doi.org/10.37358/Mat.Plast.1964 \nMater. Plast., 57 (4), 2020, 155-165                                                            161                                 https://doi.org/10.37358/MP.20.4.5415                                                         \n    \n \n \nThe fitting of the in vitro release data on the five traditional mathematical equations didn’t conclude \non the mechanism of the drug release, as all the equations fitted very well, indicating that many factors \nare influencing the delivery process. Concerning the theoretic model developed in section 3, this can be \nvalidated through an adequate calibration of the empirical data, by choosing the constants accor ding to \nthe particularities of our polymer-drug system followed by a normalization of the data. The calibration \nprocess is not a trivial one as it strictly depends on the nature of the investigated phenomena. This method \nwas previously tested for other physical phenomena with promising results [38-40]. We can observe that \nthe model fits well the CPD2.5, where the saturation region is reached earlier. This is also due to the \nmorphology of the formulation which has a more organized structure enhancing the rel ease. This, \ntranslated into the fractal paradigm used for the theoretical model, means that a non-fractal morphology \nwill lead to a higher fractality in the geodesics of the release drugs as it enhances the interactions between \nthe drug and the release media. As the morphology of the polymer formulations becomes fractalized the \nrelease is reduced and the overall fractalization degree of the drug-release is reduced (Figure 4).  \n \n \nFigure 4. Multifractal theoretical fir of drug release in \nphysiological like medium \n \nOther authors reached similar results with respect to the presented theoretical model of drug delivery \nsystems and controlled drug release by using similar mathematical procedures, but further studies are \nneeded in order to increase the application of this methods [41-46]. \nIn the following we would like to present about the perspectives of NSAIDSs controlled release in \nthe field of gynecology. NSAIDs (such as diclofenac) are widely employed first choice drugs for \ntreatment of different medical afflictions in the field obstetrics and gynecology domain. The traditional \nNSAIDs action mechanism is the inhibition of the prostaglandin G/H synthase enzymes, also known as \nthe cyclooxygenases. These enzymes convert arachidonic acid to the unstable  intermediates \nprostaglandin G2 and prostaglandin H2, leading to the production of thromboxane A2 and a variety of \nother prostaglandins which contribute to pain. In higher concentrations, NSAIDs are also known to \nreduce the production of superoxide radicals, induce apo ptosis, inhibit the expression of adhesion \nmolecules, decrease nitric oxide synthase, decrease proinflammatory cytokines, modify lymphocyte \nactivity and alter cellular membrane functions. In obstetrics and gynecology, NSAID’s have long been \nused to control acute and chronic postoperative pain, menstrual pain, pain related to medical abortions, \nmenorrhagia, intrauterine device, assist in fertility treatment, and administered as tocolytics in preterm \nlabor [47]. \n\n\nMATERIALE  PLASTICE                                                                                                                                                                \nhttps://revmaterialeplastice.ro \nhttps://doi.org/10.37358/Mat.Plast.1964 \nMater. Plast., 57 (4), 2020, 155-165                                                            162                                 https://doi.org/10.37358/MP.20.4.5415                                                         \n    \n \n \nWhen using nonsteroidal anti-inflammatory drugs (NSAIDs) for analgesic purposes, drugs represent \nthe main treatment choice in gynecology inflammatory diseases and acute or chronic pelvic pain. \nEndometriosis or intrauterine adhesions are two frequent inflam matory diseases in which the common \nsymptom is represented by cyclic pelvic pain that often requires the use of NSAIDs. Pharmacotherapy \nplays an important role in the management of these two pathologies with long -term treatment \nadministration compared with clinical efficacy as pain control and recurrence prevention after surgical \ntreatment [48]. Non -steroidal anti -inflammatory drugs poses anti -inflammatory, antipyretic and \nanalgesic features. The action of NSAIDs is to inhibit the cyclooxygenase (COX), an enzyme that have \ntwo isoforms - COX-1 and COX -2, which are responsible for creating prostaglandins. Under \nphysiological circumstances, the expression of COX-1 forms prostaglandins whilst COX-2 is expressed \nfollowing pathophysiological conditions in injured tissues in order to form prostaglandins. The \nmechanism of action is to block these isoforms but their selectivity varies between. However, the \nefficacy and related side effects are mostly due to their common action pathways [49].  \nDrug delivery systems are becoming more achievable in the gynecological field due to their capacity \nto decrease various side effects of the drugs. In our opinion, after further studies, our model could be \nemployed for developing new controlled drug release mechanism for the therap eutic use of NSAIDs in \ngynecology. \n \n4. Conclusions  \nAn amphiphilic matrix based on chitosan was developed and its ability for controlled drug release \napplications was investigated. The investigated systems demonstrated a lack of any in vivo toxicity \nencouraging further investigations of the laws which gover ns their ability to function as an efficient \nmatrix. However future studies are need it in order to establish its applications in endometriosis or \nintrauterine adhesions management. A theoretical model was built on a set of multifractal conservations \nlaws for density probability considering a manifestation of the multifractality through stochasticity. In \nthis complex paradigm the multifractal -continuous functions correspondence is ruled by the release \nmodes.  \n \nReferences \n1.JOMMANEE N., CHANTHA C., MANOKRUAN G K., Preparation of injectable hydrogels from \ntemperature and pH responsive grafted chitosan with tuned gelation temperature suitable for tumor \nacidic environment. 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