Purpose: 5-Fluorouracil (5FU) and Fe3O4 nanoparticles were encapsulated in core-shell polycaprolactone (PCL)/chitosan (CS) nanofibers as a multi-mode anticancer system to study drug release sustainability. The structure of the core-shell drug delivery system was also optimized according to drug release behavior by artificial intelligence.Methods: The core-shell nanofibers were electrospun by a coaxial syringe. Artificial neural network (ANN) was used for function approximation to estimate release parameters. A genetic algorithm was then used for optimizing the structure. Chemical assay of the optimized sample was performed by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and energy-dispersive X-ray spectroscopy (EDX). vibration sample magnetometer (VSM) test was conducted to measure the real amount of loaded magnetic nanoparticles. HepG2 cell cytotoxicity was studied and the results for the optimized samples with and without Fe3O4 after 72 hours were reported.Results: Feeding ratio of sheath to core and the amount of CS, Fe3O4, and 5FU had a statistical effect on nanofibers diameters, which were 300-450 nm. The drug loading efficiency of these nanofibers was 65-86%. ANN estimated the release parameters with an error of 10%. The temperature increased about 5.6°C in the alternative magnetic field (AMF) of 216 kA.m-1~300 kHz and 4.8°C in the AMF of 154 kA.m-1~400 kHz after 20 minutes. HepG2 cell cytotoxicity for the optimized samples with and without Fe3O4 after 72 hours were 39.7% and 38.8%, respectively.Conclusion: Since this core-shell drug release system was more sustainable compared to the blend structure despite the low half-life of 5FU, it is suggested to utilize it as post-surgical implants for various cancer treatments such as liver or colorectal cancer in the future. This system is capable of providing chemotherapy and hyperthermia simultaneously. Targeted drug delivery systems can release pharmaceutical agents on the desired spot which can reduce the systemic effect. Moreover, controlled delivery of drug can increase concentration of drug around the tumor while no degradation of pharmaceutics occurs in intact tissues. Hyperthermia can be considered as a supplementary treatment in addition to common therapies such as chemotherapy and radiotherapy. In addition, it increases the sensitivity of the cancer cells to anticancer drugs which leads to a decrease in the size of tumor according to related studies.Despite current cancer biological treatments progress, yet it takes millions of lives around the world every year. These structures are capable of being utilized in drug releasing systems which can be encapsulated by different therapeutic agents such as chemotherapy drugs. Applying electrospun nanofibers, as anticancer drug carriers, has advantages such as increasing drug concentration around tumor with less amount of drug, prolonging the effect of drug on the target area, and decreasing toxicity on the non-tumorous organs. Other benefits include; more efficient drug loading in comparison with other drug carriers, broad special surface offering a readier release for hydrophobic drugs, flexible geometry, low cost, biodegradability, and high water absorption.Polymeric nanofibers have gained great attention for various biomedical applications. and hydroxycamptothecin (HCPT) were studied for cancerous cells treatment. The results showed an increased effect of drug loaded nanofibers in comparison with pure drug. Also nanofibrous structures of chitosan (CS)/polycaprolactone (PCL) including 5FU or cisplatin were designed for anti-cancer drug delivery systems. In another work, polyethylene oxide (PEO)/PLA including cisplatin was used for cervical cancer. The results showed a significant decrease in drug concentration in blood circulation system and other organs in contrast with targeted tissue which led to less side effects of the drugs. Core-shell gelatin/polyvinyl alcohol (PVA) nanofibers carrying DOX were used for breast cancer treatment. The results showed lower concentration of drug around heart and kidney after implantation of nanofibers compared to direct injection of the drug which caused less side effects.Nanofibers loaded by anti-cancer drugs for different cancer treatments have been utilized in a number of studies. A wide range of biodegradable and biocompatible polymers have been applied for such drug delivery systems. Polylactic acid (PLA)/polyethylene glycol nanofibers including doxorubicin (DOX)3O4 nanoparticles was characterized for skin cancer apoptosis. Thermo-responsive structure of the nanofibers was sensitive to temperature increase and the drug was released in an alternative magnetic field (AMF) after thermal response of Fe3O4 nanoparticles. Having considered AMF as a heat source, Fe3O4 nanoparticles were encapsulated in PCL/CS nanofibers merely for hyperthermia as a supplementary treatment. Nonetheless, one of the major challenges of using Fe3O4 nanoparticles for hyperthermia is locating them on the targeted area.A triggered drug release system from nanofibers made of n-isopropyl acrylamide/n-hydroxymethyl acrylamide copolymer having DOX and Fe Although 5FU is among the superior chemotherapeutic agents for various cancers, there are several disadvantages, such as rapid metabolism, short half-life, low bioavailability, high cell toxicity and inadequate selectivity for cancerous cells, all of which limit the effectiveness of 5FU in cancer chemotherapy. It is crucial to develop drug delivery systems for 5FU to achieve a better therapeutic effect with fewer side effects and a good targeting effect to overcome the disadvantages of 5FU like its short half-life and poor efficacy.5-Fluorouracil (5FU), which is one of the most widely used antimetabolite chemotherapeutic agents in recent decades, has been employed as an antineoplastic agent in the treatment of several cancers, such as colorectal, breast, head and neck, pancreas and stomach cancers. Then, 5FU is converted to fluorodeoxyuridine monophosphate (FDUMP), fluorodeoxyuridine triphosphate (FDUTP) and fluorouridine triphosphate (FUTP), which are the active metabolites of 5FU. RNA synthesis and the operation of thymidylate synthase are interrupted by these metabolites. By this action, 5FU can fight cancerous cells. Nonetheless, most of the provided 5FU is catabolized by dihydropyrimidine dehydrogenase (DPD) to dihydrofluorouracil, which is an inactive metabolite and a retarding enzyme for 5FU catabolism and mainly found in liver and cancer cells. Since upregulation of DPD gene expression in cancer cells is associated with 5FU resistance, higher doses of 5FU are needed in cancer cells with acquired drug resistance.5FU can enter cells via the same mechanism of facilitated transport as uracil.A polyacrylic acid (PAA) grafted-chitosan (CS-g)/polyurethane (PU) core-shell nanofiber was loaded with magnetic nanoparticles in shell and temozolomide (TMZ) and paclitaxel (PTX) in core. The nanofibers were then characterized for controlled release system against glioblastoma cancer cells. These nanofibers could provide a combination of chemotherapy and hyperthermia methods for glioblastoma cancer treatment. The cell cytotoxicity indicated that 31.3 and 49.6% of apoptosis cell was occurred for U-87 MG glioblastoma cells treated with CS-g-PAA–TMZ-PTX/PU/magnetic MIL-53 in the absence and presence of AMF, respectively.3O4 nanoparticles in core-shell PCL/CS nanofibers as a multi-mode anticancer system which could provide chemotherapy and hyperthermia simultaneously. Despite 5FU has a low half-life of 8 to 20 minutes, core-shell structure of the nanofiber contributes to a more sustainable release system of the drug in comparison with blend structure, which was studied in previous work. PCL/CS nanofibers can increase the presence time and concentration of it in the release area. Moreover, Fe3O4 nanoparticles in nanofibers provide the possibility of hyperthermia in the area where fibers present. Fe3O4 nanoparticles generate heat more evenly under AMF which can overcome hyperthermia challenges. Therefore, these nanofibers can be utilized as post-surgical implants for various cancer treatments such as liver or colorectal cancer in future.The aim of this study was to encapsulate 5FU and Fe3O4 nanoparticles coated by acetic acid were obtained from Nanosany Corporation, Iran. Phosphate-buffered saline (PBS) solution was supplied from Cyto Matin Gene (CMG), Iran. Hepatoma cell line (HepG2) was obtained from Pastor Institute, Iran.PCL with molecular weight of 70-80 kDa, CS with medium molecular weight of 190-310 kDa, 12 kDa dialysis tube cut-off, and MTT tetrazolium were all purchased from Sigma, the US. 5FU, 98% formic acid, and 99% acetic acid were prepared by Merck, Germany. 20 nm Fe3O4 nanoparticles were applied to the solution and mixed mechanically for 5 minutes for obtaining even dispersion. At the end, the solution was placed in an ultrasonic bath  for an additional 15 minutes. The design of experiments (DOE) was conducted by Taguchi method using Minitab. The variables were PCL:CS ratio, nanoparticle and drug percentage, feeding ratio of sheath to core, and magnetic field frequency. Core and sheath solutions were prepared separately. Pure 12%wt. PCL with 1, 3, and 5% 5FU concentrations were dissolved in 70:30 formic acid/acetic acid and stirred for 15 minutes for core solution. In order for preparing sheath solution, pure 12%wt. PCL with 2%wt. CS concentrations in different polymer ratios were dissolved in 70:30 formic acid/acetic acid and stirred for 15 minutes. After adding 5FU and stirring for another 15 minutes, various amounts of FeNanofibers were electrospun through a coaxial syringe with a core needle gauge of 22 and sheath needle gauge of 16. The distance from the needle tip to a 200-rpm rotating drum was 14 cm in a 15 kV applied voltage. The syringe pump and power supply were provided by Pars Nanoris, Iran. The feeding ratio of sheath was 0.08-0.1 mL/h while for core, it was considered according to sheath feeding ratio and DOE. All nanofibers were also produced without drug as for blind samples.To investigate nanofibers morphology, scanning electronic microscopy (SEM),  and transmittance electron microscopy (TEM),  images were analyzed. In order to measure fibers diameter, Digimizer software was applied in which 100 fibers from each SEM image were opted randomly.10 ml of sheath solution was added to the cup of a Brookfield LVDV-II+Pro Rotational Viscometer, Canada in 25°C with a cone spindle speed of 3rpm in order to measure the viscosity. The conductivity was measured by a Jenway 4510 Bench conductivity meter, China.2 optimized nanofibrous layer sample in a Jikan CAG-10 contact angle goniometer, Iran. A photograph was then taken from the droplet on the surface after 20 seconds. This procedure was repeated 3 times and the contact angle was measured by Digimizer software.A 1 µL droplet of water was dripped on the surface of a 1×1 cm-1 after calibrating the apparatus with a potassium bromide (KBr) compressed film to assay chemical structure of optimized nanofibers. Grazing X-ray diffraction (XRD) analysis was conducted by a PANalytical X-ray diffractometer, the Netherlands at a voltage of 40kV with Ni-filtered Cu-Kα radiation from 2θ 10° to 80° to visualize iron oxide structure. Differential scanning calorimetry (DSC) was applied for the optimized nanofibrous layer by a BÄHR Thermoanalyse DSC 302, Germany to assess the capability of the layers for hyperthermia. For DSC, 5 mg of nanofibrous layer with and without 5FU was placed in the sample holder and the temperature was increased with a gradient of 10°C.min-1 from 25 to 400°C. Energy-dispersive X-ray spectroscopy (EDX) was done by an EDAX EDS Silicon Drift 2017, the US to demonstrate elemental analysis of the optimized sample.Fourier transform infrared spectroscopy (FTIR) was performed in a Bomem-MB100 spectrophotometer, Canada over the range of 400 to 4000 cm2 and placed between the grippers on a paper frame. The gauge length and test speed were 20mm and 10 mm/min, respectively. The thickness of each specimen was measured for 10 times using a digital micrometer .A tensile tester of Zwick 1446-60, Germany was used for measuring tensile strength, strain, and modulus of the nanofibrous layers. Three samples for each nanofibrous layer were cut into 30×5 mm-1, 400 kHz and 216kA.m-1, 300 kHz. For this, 200 mg of the optimized sample was soaked in 2 mL PBS for 30 minutes and then exposed to the AMFs for a 20-minute period.Magnetic properties of the optimized sample were measured by a vibration sample magnetometer . The applied magnetic fields ranged in an interval of ±15kOe at 300°K. In order to investigate nanofibrous layers capability for hyperthermia, thermal behavior of the optimized sample was studied under two AMFs of 154 kA.m2 were prepared and soaked in PBS solution with a pH of 7.4 at 37°C for 24, 48, 96, 240, 504, and 1000 hours. The samples were dried in a sealed and vacuumed desiccator containing silica gel for 24 hours after each time period and then weighed.Degradation of the optimized sample was assessed according to ASTM F1635-04A. Three samples all with a dimension of 4×4 cm The Beer-Lambert’s law was verified for the solvent solution (formic acid/acetic acid 70:30 v/v) and the maximum absorption wavelength for 5FU was 266 nm using a UV-mini-1240, Shimadzu, Japan spectrophotometer.Since proportion of drug was the same in core and sheath, loading efficiency could be considered as for blend nanofibers and was determined according to the previous work.max = 266 nm. The samples were placed in dialysis tubes with a cut-off of 12 kDa which were prepared in advanced. Three samples with dimensions of 4×4 cm2 for each composition were placed in wrapped tubes containing 10 mL PBS. The tubes were then immersed in beakers containing 30 mL PBS and the whole systems were sealed and placed in a shaking incubator with a rotation speed of 110 rpm in 37°C. The release profiles were investigated within a week after exposing the beakers for 10 minutes to various AMFs according to 5FU loaded nanofibers was immersed in PBS with a pH of 7.4 and the calibration curve was verified with Beer-Lambert’s law in PBS at λThe release behavior of nanofibers was studied through zero order, first order, Hixson-Crowell, Higuchi, Korsmeyer-Peppas, and Weibull models. On the other hand, since there was not a precise perception about the way how structural parameters , nanoparticle and drug percentage) were related to release parameters , ANN was used for function approximation in the definite intervals of structural parameters. A multilayer perceptron ANN was applied since it is a common method in nonlinear regression problems. The number of hidden layers and their neurons could be determined through trial and error. However, based on Kolmogorov’s theorem, an ANN with one hidden layer was applied.Artificial neural network (ANN) is a more efficient and suitable method in comparison with standard modelling methods such as response surface methodology. and k-fold cross validation method were applied. Finally, Gaussian noise method was selected according to the training error. Another way to prevent overfitting is adopting suitable ANN training method. Early stopping and Bayesian regularization (BR) are the most commonly used methods for ANN training. In comparison with early stopping method, BR needs no validation data  and has higher coefficient of determination (R2). Therefore, BR was applied for ANN training. The obtained ANN with the above-mentioned architecture was used for deriving the genetic algorithm fitness function.In order to prevent over fitting, sufficient number of training data are essential. Since training data were limited due to experimental restrictions, Gaussian noise exertion on real dataOptimizing is defined as finding the best answer regarding the requirements and restrictions of the problems which can be solved through analytical and numerical methods. Classical mathematical methods have some disadvantages as opposed to smart methods such as genetic algorithm. The drawbacks include considering local optimized points instead of the real ones and the necessity for initial answer guess which affects the final results. On the other hand, genetic algorithm is inspired by creatures evolution in which optimizing problems can be solved sufficiently based on the ability to compete, survive, and reproduce. The whole data pool is searched to select initial random population, and eventually by crossover and mutation the algorithm will evade to be trapped in local optimized points. The next generation is then created from the present generation based on fitness value which must be better or, in other words more optimized. This process will be stopped under certain conditions which are defined in genetic algorithm structure parameters.In genetic algorithm each individual or chromosome is a proposed solution for a problem, and a set of chromosomes is called a population or generation.2 were reported for training and testing data. The ANN with the least MSE and R2 for testing data was selected as the best ANN for modelling whose outputs were used in genetic algorithm for optimizing the structure of core-shell nanofibers. There were constraints on defining the chromosome of the algorithm regarding production restrictions. Chromosomes include feeding ratio of sheath to core with a constraint of [1-2], CS percent in sheath with a constraint of [7-23], nanoparticle percentage with a constraint of [1-7], and drug percentage with a constraint of [1-5].A perceptron ANN with one hidden layer was applied for estimating and approximating release parameters according to structural parameters. The number of input and output neurons was the same as the number of input  and output (release) parameters, respectively. For determining the number of hidden layer neurons, various ANNs with 2-7 neurons in hidden layer were designed. Each ANN was run 10 times and the average of the mean square error (MSE), and RFitness function was defined in a way that drug loading efficiency, maximum release time and its relevant release amount were maximum and BR was minimum. Ideally, when fitness value is zero the optimized structure is obtained. TRmax was maximum release amount in maximum release time, BR was burst release which was the release amount in the first 30 minutes, TRmax was the maximum release time, and Le was drug loading efficiency.Wherein R-6), and maximum stall generation (5). Stopping conditions consisted of either reaching to 100 generations, or achieving zero for fitness value, or having the best fitness value difference less than the function tolerance for 5 consecutive generations. The algorithm was run 20 times and the best values for gens were reported as the optimized structure.A MATLAB code was used for genetic algorithm whose parameters are population size (20), the number of generations (100), creation function (Roulette wheel), the number of elites (1), cross over fraction (0.8), migration fraction (0.2), mutation fraction (0.2), migration direction (forward), fitness limit (0), function tolerance  was added. Eventually the cells were detached and pipetted into a sterile 15 mL centrifuge tube. Prepared cell culture media were spun at 130 g for 5 minutes. The cells were then counted after aspirating the supernatant and adding DMEM containing 10% FBS to the pellet.HepG2, a perpetual cell line consisting human liver carcinoma cells, was derived from the liver tissue of a 15-year-old Caucasian male patient who had a well-differentiated hepatocellular carcinoma (HCC). HepG2 cells were cultured in Dulbecco’s modiﬁed Eagle’s medium (DMEM) supplemented with 10% PBS and 1% penicillin-streptomycin at 37°C in an incubator with 5% CO4 cells were seeded in a 96-well plate within 100 µL culture medium for 24 hours. The wells were divided into 4 groups of negative control, positive control, extraction of optimized nanofibers without Fe3O4, and extraction of optimized nanofibers with Fe3O4. The cells in positive control group were exposed to 20, 60, 100, 175, and 250 µg/ml of 5FU after aspirating initial culture medium. In the third and fourth groups, nanofibers without and with Fe3O4 were extracted separately after 24, 48, and 72 hours and the extracted drug was added to the seeded cells after the initial culture medium was pipetted out. Having sterilized by UV, extraction was done by adding 1 mL free-FBS DMEM to a 2×3 cm2 nanofibrous mat with an approximate thickness of 10 µm, in which the concentration of 5FU was close to its half maximal inhibitory concentration (IC50).Cytotoxicity was conducted according to ISO 10993-5. 2×10 by a fully automatic reading unit of Awareness Technology Stat Fax 2100 Microplate Reader, the US. Cell viability was calculated according to Culture media for all groups were aspirated after 24 hours. In order to assess drug toxicity, 100 µL 3--2, 5-diphenyltetrazolium bromide (MTT) with a concentration of 5 mg/ml was then added to each well and incubated for 4 hours at 37°C. After incubation, MTT was pipetted out and 100 µL isopropanol was added and incubated for another 20 minutes until formazan crystals were solved. Absorbance of the solution was measured at 570 nm as a criterion of alive cellss and ODc were the mean of optic density for the samples and negative control, respectively.Wherein ODThe results were statistically analyzed in 95% level of significance using one-way ANOVA and ANCOVA by SPSS 23 software.P = 0.039), percentages of Fe3O4 and 5FU (P < 0.001) had a significant effect on viscosity. In other words, the viscosity was increased by increasing CS, 5FU, and decreasing magnetic nanoparticles. Also, increasing CS percentage in sheath polymer ratio remarkably increased conductivity (P < 0.001). The number of magnetic nanoparticles (P = 0.865) and 5FU (P = 0.994) had no effect on conductivity.SEM images were used to investigate morphology of various core-shell nanofibers which were electrospun according to Taguchi DEO conditions. 3O4, and 5FU had a statistical effect on nanofibers diameters (P < 0.001). Increasing feeding ratio caused an increase in diameter. Increasing CS in sheath led to a decrease in diameter due to increasing sheath conductivity in spite of an increase in viscosity. Decreasing Fe3O4 and increasing 5FU led to an increase in diameter due to an increase in viscosity. Since the solvent of core and sheath was the same and thus had the same conductivity, nanofibers were thick and diametrically uneven.The results also indicated that feeding ratio of sheath to core and the amount of CS, FeP = 0.061) and strain (P = 0.159) while it affected modulus (P = 0.033). Increasing feeding ratio decreased modulus as a result of increasing nanofibers diameter.P < 0.001). Increasing CS percentage caused an increase in modulus and a decrease in strain due to presence of hydroxyl groups in CS polymer chain and thus inter-chain hydrogen bonds. Percentage of Fe3O4 had no effect on strain and stress (P > 0.05) unlike modulus (P = 0.002). Increasing Fe3O4 caused viscosity to decrease which led to a decrease in diameter and eventually an increase in modulus. The amount of drug had no effect on tensile properties (P > 0.05). Although there are no predefined intervals for tensile properties of nanofibrous drug delivery systems, the results showed that the produced mats were capable of being used as implants.The results also showed that the amount of CS had no effect on strength (p = 0.316). Nevertheless, changing CS content affected strain and modulus (P < 0.001), feeding ratio (P = 0.764), Fe3O4 (P = 0.183) and drug (P = 0.971) percentage were not statistically effective on biodegradability of nanofibrous layers after 42 days. Increasing CS, as a hydrophilic component, led to an increase in hydrophilicity of the layer which caused more biodegradability.The results for weight loss of the mats are shown in P < 0.001), feeding ratio, drug and magnetic nanoparticle amount (P > 0.05) were not statistically effective on drug loading efficiency. Increasing CS amount was directly related to drug loading efficiency. Hydrophilic nature of the CS  on one hand and presence of amine groups in CS, which can charge them positively, on the other hand can cause enhanced interaction of CS with negatively charged C = O and C-F bands of 5FU which are hydrophilic, too.The ratio of actual amount of drug trapped in the nanofibrous layer to theoretical amount is considered as drug loading efficiency. The results are shown in max) and its relevant release amount (RTRmax) (P < 0.001). Increasing CS content increased BR, TRmax, and RTRmax due to improved interaction of CS and 5FU and therefore an increase in drug trapping. AMF and 5FU amount had no effect on BR, TRmax, and RTRmax (P > 0.05). Feeding ratio had a direct effect on BR, TRmax, and RTRmax (P < 0.05). Increased feeding ratio led to an increase in CS content which ended in better interaction with 5FU. More trapped 5FU led to increased BR, TRmax, and RTRmax. Moreover, increasing feeding ratio caused an increase in nanofiber diameter and hence a decrease in special surface. Magnetic nanoparticles affected TRmax (P = 0.016), however it had no effect on BR (P = 0.117) and RTRmax (P = 0.574). Increasing Fe3O4 decreased viscosity and thus the diameter of nanofibers which led to an increase in special surface. Consequently, larger special surface reduced TRmax despite it was not desirable for drug release system. However, increasing Fe3O4 caused an improvement in hyperthermia functionality. In order to compensate lower TRmax and increase release sustainability, core-shell structure was utilized in which a hydrophobic polymer (PCL) was loaded with the drug in the core.It was concluded from the results that Korsmeyer-Peppas, and Weibull models fitted well for modelling release behavior of nanofibers. 2 for the various nodes respectively. By increasing the number of nodes up to 6, MSE decreased and R2 increased for testing and training data. However, for 7 nodes MSE increased and R2 decreased. Therefore, 6 nodes in hidden layer were selected as the optimum number of nodes.At first, tangent sigmoid activation functions for all networks with various nodes of 2-7 in hidden layers were defined. 2 were reported. According to Taguchi analysis, tangent sigmoid and logarithm sigmoid functions were considered as input layer and hidden layer output activation functions, respectively. Next, in order to determine the best activation functions, Taguchi DOE was performed with 9 permutations. Each permutation was run for 10 times and the average of the MSE and R2 for cross validation and Gaussian noise methods. Since the latter had smaller MSE and larger R2, it was selected as a method to compensate the lack of data. Consequently, 540 data were derived from 54 real data. The derived data along with 18 real data were introduced as training data and 36 remaining real data were considered as testing data. All the data were normalized between 1 and -1 and introduced to ANN in a bipolar form.In order to compare Gaussian noise method with cross validation, proper Gaussian coefficients had to be chosen. Thus, the standard deviation with minimum testing error in the range of 0.01 to 0.1 was determined.e and RTRmax were considered 100% and zero for BR. According to the drug release curves, maximum amount of TRmax was 144 hours. Thus, the optimum amount was considered 240 hours in fitness function. To determine suitable crossover and mutation fractions, the genetic algorithm was run 10 times for various fractions and the average of the best fitness value was reported. Crossover fraction of 0.8 and mutation fraction of 0.2 were then selected as the best ones according to minimum best fitness value. The fitness function was defined as 3O4 percentage of 5.9%, and loaded 5FU percentage of 4.6% were considered as the optimum amounts of structural parameters and the fitness value for this chromosome structure was 0.2065. Finally, the optimum core-shell nanofiber was produced and investigated. The release behavior of optimum nanofibrous layer was studied in absence of AMF since it had no effect on release behavior.Afterward, genetic algorithm was run for 20 times. The chromosomes values related to the minimum fitness value were then determined as the optimum number of structural parameters. Feeding ratio of 2, the CS amount of 22% in sheath polymer composition, loaded Fe-1 was related to Fe-O, which was referred to intrinsic stretching vibration of Fe in tetrahedral site. The peak at 441 cm-1 was referred to octahedral-metal stretching of Fe-O. There was no distinguished peak for maghemite phase due to an overlap of maghemite with magnetite phase peaks  oxide-hydroxide. Fe3O4 was changed to other iron oxide compositions slightly because of formic acid and Fe3O4 surface interaction in preparation of polymer solution. However, there was no interaction between the other components in chemical structure of nanofibers.The chemical structure of optimum nanofibers was confirmed by FTIR. e peaks . The spebration . A mediuctively . The brostretch . Figure 3O4 surface interaction which is in agreement with FTIR results. Regarding the similarity of iron oxides XRD patterns, the structure of them could not be ascertained due to peaks overlap. However, the diffraction peaks of iron oxides can be seen at 36.7°, 40.4°, 42.7°, 46.9°, 60.1°, 66.4° and 71.8°. The peaks at 36.7°, 42.7°, 46.9°, 60.1°, and 71.8° refer to Fe3O4 assigned to (311), (400), (110), (440), and (620) crystal planes, respectively. The peaks at 40.4° and 42.7° refer to other iron oxides and the peaks between 20° to 26° determine polymeric structure of CS and PCL.The XRD pattern of optimum nanofibers is shown in  Nevertheless, the effect of hydrophilicity in drug release was negligible since drug release followed Fick’s law of diffusion.3O4 were suitable for hyperthermia functional temperature of approximately 45°C.3O4 and optimized nanofibers. 3O4 and optimized nanofibers, respectively. Saturation magnetization for pure Fe3O4 and optimized nanofibers was calculated by the use of these curves. The actual weighted fraction of magnetic nanoparticles was calculated by VSM test was conducted for pure Fesn (52 emu.g-1) and Msf (2.9 emu.g-1) were saturation magnetization for pure Fe3O4 and optimized nanofibers, respectively. The actual magnetic nanoparticles per mass unit of nanofibers was 5.6% while nominal amount of loaded Fe3O4 was 5.9% in optimized nanofibers. This revealed that nanoparticles were not completely loaded and/or they were slightly transformed to other nonmagnetic iron oxides due to interactions between formic acid and Fe3O4.Wherein Ø was the weighted actual magnetic nanoparticles in optimized nanofibers, M-1, 300 kHz and 4.8°C in the AMF of 154 kA.m-1, 400 kHz. Therefore, it can be concluded that the time of AMF exposure can be reduced by increasing the AMF power to reach hyperthermia effect.As it is shown, the temperature increased more noticeably after 20 minutes in exposure to the stronger AMF despite lower frequency. The temperature increased about 5.6°C in the AMF of 216 kA.mmax was 144 hours in pH of 7.4 while it was 72 hours in pH of 4.4 due to CS sensitivity to acidic ambience.Since AMF had no effect on release parameters, the release behavior of optimized nanofibers was studied in the absence of AMF in pHs of 4.4 and 7.4 . TRmax wThe results for release parameters of optimized nanofibers, the relevant ANN estimated values, ANN model errors for neutral pH, and the coefficients for Korsmeyer-Peppas and Weibull mathematical models are shown in  it was concluded that 5FU and iron oxide nanoparticles were distributed evenly in the optimized nanofibers.3O4, the concentrations of 5FU were calculated as 235 and 220 µg/mL, respectively. Cell cytotoxicity for the optimized samples with and without Fe3O4 after 24, 48, and 72 hours of extraction are shown in 3O4 after 72 hours were 39.7% and 38.8%, respectively.3O4 (P = 0.001) and without Fe3O4 (P < 0.001). The results showed that the cytotoxicity increased by increasing extraction time. ANCOVA test was applied to show the effect of the presence of Fe3O4 in optimized samples on cell cytotoxicity during extraction time. The results indicated that cytotoxicity of the samples with and without Fe3O4 were not statistically different (P = 0.097).ANOVA test results showed that the extraction time had significant effect on cell cytotoxicity of the optimized samples with Femax, RTRmax, and Le increased 4%, 28 minutes, 10%, and 17%, respectively. Increasing Fe3O4 from 1% to 5% increased TRmax for 16min. Changing the amount of Fe3O4 had no significant effect on BR, RTRmax, and Le. Apparently, increasing the amount of magnetic nanoparticles enhances hyperthermia functionality. ANN was then run to estimate release parameters from the structural parameters. Afterward, genetic algorithm fitness function was defined based on ANN. Finally, structural parameters for producing the optimized sample were derived. The release parameters of the produced optimized sample were compared with those of ANN outputs and the errors for BR, TRmax, RTRmax, and Le were approximately 5%, 2%, 10%, and 6% which showed an acceptable compliance. In order to chemically investigate the optimized sample, FTIR, XRD, and EDX were performed. FTIR results showed that there was no interaction between components. However, it was shown that Fe3O4 was slightly transformed to other iron oxide compositions because of formic acid and Fe3O4 surface interaction in preparation of polymer solution. XRD results confirmed other forms of iron oxides. EDX results verified even distribution of Fe and F in the optimized sample. VSM test was conducted to measure the real number of magnetic nanoparticles. The results indicated that the actual magnetic nanoparticles amount was 0.3% less than nominal amount due to Fe3O4 transformation to other nonmagnetic iron oxides. To investigate hyperthermia effect, optimized sample was exposed to 2 different AMFs. The results suggested that increasing magnetic field intensity by 40% increased the temperature by 16% in a constant AMF exposing time of 20 minutes. Therefore, it was concluded that more powerful AMFs can reach to the hyperthermia temperature in a shorter time. HepG2 cell cytotoxicity was studied for four groups of negative control, positive control, extraction of optimized sample with Fe3O4, and without Fe3O4. IC50 was calculated 226 µg/mL. The results showed that cell cytotoxicity of the optimized samples with and without Fe3O4 after 72 hours were 39.7% and 38.8%, respectively.Different structures of core-shell nanofibers based on Taguchi DOE were produced. Morphology, tensile properties, biodegradability, and release behavior of the produced samples were investigated. The results showed that by increasing the amount of CS from 7% to 23% BR, TRmax and decrease BR which can be done in future works. Moreover, different drugs can be used in core and shell, separately and the release profiles can be studied as well. Another work which can be done in the future is to utilize more Fe3O4 in more powerful AMFs to achieve more rapid hyperthermia. Finally it seems these nanofibers can be used as post-surgical implants for various cancer treatments such as liver or colorectal cancer, after more comprehensive in vivo studies.Crosslinking CS can help increase TRThe submission does not report on or involve the use of any animals or human data or tissues, so ethical approval does not applicable.There are no conflicts of interest to declare.


Nanofibers have a wide range of applications in many fields such as energy generation and storage, environmental sensing and treatment, biomedical and health, thanks to their large specific surface area, excellent flexibility, and superior mechanical properties. With the expansion of application fields and the upgrade of application requirements, there is an inevitable trend of improving the performance and functions of nanofibers. Over the past few decades, numerous studies have demonstrated how nanofibers can be adapted to more complex needs through modifications of their structures, materials, and assembly. Thus, it is necessary to systematically review the field of nanofibers in which new ideas and technologies are emerging. Here we summarize the recent advanced strategies to improve the performances and expand the functions of nanofibers. We first introduce the common methods of preparing nanofibers, then summarize the advances in the field of nanofibers, especially up-to-date strategies for further enhancing their functionalities. We classify these strategies into three categories: design of nanofiber structures, tuning of nanofiber materials, and improvement of nanofibers assemblies. Finally, the optimization methods, materials, application areas, and fabrication methods are summarized, and existing challenges and future research directions are discussed. We hope this review can provide useful guidance for subsequent related work. Nanofiber, in a broad sense, refers to ultrafine fibers with a diameter of less than 1000 nm. Benefitting from the nano-size, nanofiber has the characteristics of a large specific area, large aspect ratio, small size effect, superior mechanical properties, and is widely used in many fields, including air filter, drug delivery, and battery . For oneResearch on nanofibers has boomed in recent decades and a large variety of novel fiber synthesis approaches have been developed ] (PE-co-GMA), and the sacrificial polymer is cellulose acetate butyrate (CAB) //[Bi2WO6/C] carbon-based Janus nanofiber. In this nanofiber, TiO2 and Bi2WO6 form heterojunction that can absorb both ultraviolet light and visible light for hydrogen production and photodegradation. In 2018, Wang et al. [In Janus nanofibers, the coordination of two fiber materials enables complex functionalities. In 2020, Cao et al.  electrosg et al.  mixed pog et al.  preparedHollow nanofibers have a hollow tubular structure, as shown in Fig. 0.6Mn0.3Fe0.1O2 layered porous hollow nanofibers as efficient anodes for mixed direct carbon fuel cells. The hollow structure provides more opportunity for CO permeability and effectively improves catalytic activity during CO oxidation. In 2019, Hwang et al. [Hollow nanofibers with inner and outer surfaces provide larger specific surface area and can provide more active sites for chemical reactions, which has advantages in sensors , 77, eleg et al.  used holg et al.  preparedBesides, benefiting from the structural features including plentiful active sites, continuous conducting pathways, and benign mass transfer channels, hollow nanofibers are suitable for electrocatalysis . In 2019Apart from the structures discussed above, there are other nanofiber structures, including multifaceted, necklace-like, and multi-walled nanofibers . In 2019Tg (glass transition temperature) of PLA, improve the mobility of PLA molecular chains, reduce the complex viscosity of PLA/PEG blends, and play a role in plasticization. This improves the tensile strength of the fiber.Polymer-blended nanofibers belong to multi-component systems in which two or more materials are fused to form new nanofibers , 79. PolCatalysts play an important role in the oxygen reduction reaction , polluta3 nanofibers by high-temperature heat treatment, as shown in Fig. 3 nanofibers exhibit excellent sensing performance for H2S and acetone, with potential applications for portable diagnosis of halitosis and diabetes. In 2018, Wang et al. [3O4 nanoparticles with specific (112) crystal facet in α-Fe2O3 nanofibers, and the so-formed heterostructured nanofibers can be used as high-performance flexible sensing devices of ammonia gas. In this structure, the (112) crystal facet in the Co3O4 nanoparticles produces a large number of highly active sites, which are favorable for the selective adsorption of ammonia molecules, thereby improving the sensitivity, as shown in Fig. In some cases, nanofibers not only provide support for catalysts but also have other functions, such as providing conducting pathways, and direct participation in chemical reactions. In 2016, Panomsuwan et al.  preparedg et al.  preparedChemical modification improves the performance and function of nanofibers by reactions, such as polymerization, grafting, oxidation, and hydrolysis, or other methods to obtain specific functional groups on the surface of the nanofibers. Compared with ordinary physical adhesion and doping, the connection between substances through chemical bonds is more reliable, and the composite material prepared therefrom is more durable.In the chemical modification of nanofibers, a significant requirement is to change the hydrophilic or hydrophobic properties of their surfaces to realize their applications in water treatment , medicin3O4 nanoparticles modified by 3-mercaptopropionic acid (Fe3O4-MPA) through chemical cross-linking. The modified APAN/Fe3O4-MPA nanofibers resulted in a maximum loading capacity of 154.5 mg/g for the indigo carmine (IC) dye. The adsorption efficiency of indigo carmine was kept almost constant when the nanofibers were reused several times, as shown in Fig. 2+, Pb2+, Cd2+, Ag+, and other metal ions in an aqueous solution.Based on the high porosity and high specific surface area, chemically modified nanofiber can be used to purify water and adsorb harmful substances by changing the functional groups on the surface. It has been previously shown that the adsorption of metals ions and organic dyes can be achieved by using compounds containing functional groups such as amidoxime, carboxyl, amino, phosphoric, and imidazoline, which have a complexing effect on metal ions and dyes . In 20182 into polyvinylidene fluoride (PVDF) nanofibers, and the doping of TiO2 particles promoted ordered arrangement of moleculars and increased β polymorphism, which significantly improved piezoelectric performance of the nanofibers in geophone applications. In 2018, Cao et al. [2 nanofibers as gas sensors, and when the Sn/La molar ratio was 100:7, the sensitivity was increased by nearly 10 times. On this basis, they further improved the ordering of the nanofibers, thereby enhancing the sensing performance, as shown in Fig. co-ethylene) (PVA-co-PE) nanofibers/PA6 composite fibers as twisted and coiled fiber-based actuators, as shown in Fig. In addition to the polymer blends and catalyst-doped nanofibers described above, many other materials have been incorporated into nanofibers, thereby optical , electrio et al.  synthesiMany methods have been developed to achieve ordered nanofibers, some use a drum yarn collector , 115, asSome process the nanofiber after the electrospinning using the stretching method to get orderly nanofiber. In 2004, Li et al. , 108 genMechanical properties are key parameters that should be considered for practical application of nanofibers. Previous research revealed that structural characteristics  largely determine the mechanical strength of nanofibers. Generally speaking, orderly arranged nanofibers exhibit significantly different mechanical properties compared to randomly arranged ones. In 2011, Arshad et al.  reportedNanofibers can generate and detect polarized light when their size is comparable to or smaller than the wavelength of light. In 2001, Wang et al.  found thBenefiting from high orderliness, nanofibers have special applications in oriented growth of cells and tissues, and conductive pathways as well. Biocompatible nanofibers with aligned structural characteristics show advantages in assisting the oriented growth of cells and tissues. In 2015, Wang et al.  presente2 (NTO) nanofiber array for NO2 detection, as shown in Fig. 2 and gold nanoparticle (GNP)-TiO2 for CO detection, and the CO concentration thresholds for the pure TiO2 and GNP-TiO2 nanofiber were about 5 ppb and 700 ppt, respectively.The ordered nanofiber array is beneficial for electron transport, which offers improved device performance over random nanofibers in terms of sensing response and time for recovery. In 2017, Li et al.  used a m+. This combination achieved a high initial capacity (1393 mAh/g at 0.1 A/g) and a coulombic efficiency of 99.6%. In 2016, Oh et al. [2 microsphere cushion wrapped by polyvinylpyrrolidone/polyacrylonitrile nanofibers served as a support layer to capture heavy metal ions dissolved in liquid electrolyte. A thin mat of polyetherimide nanofibers wrapped by nanotubes served as the top layer to improve the kinetics of redox reactions.Mixing different nanofibers or combining different nanofiber layers can achieve more complex functions, often achieving the effect of 1 + 1 > 2, making them widely used in energy, environmental purification, and biomedical applications. In 2019, Kong et al.  used a ch et al.  preparedη = 5.12), IR absorbance/emittance (εAW = 87.31%) and sunlight reflectance (ρ = 93.28%), as shown in Fig. 3O4 nanoparticles (NPs)-polyacrylonitrile (PAN) electrical-magnetic bifunctional nanofibrous layer and Eu(TTA)3(TPPO)2-polyvinylpyrrolidone (PVP) photoluminescent layer, as shown in Fig. The combination of different functional fibers provides the possibility for smart fabrics. In 2020, Song et al.  preparedNanofiber plays an increasingly important role in modern society , 123. ThFor large-scale fiber preparation, current technologies are not mature enough and their robustness needs to be improved. For example, in the field of energy generation and storage, although nanofibers with high efficiency and high-density energy storage have been achieved in the laboratory stage, there are still many problems to be solved for mass production.It is desirable to achieve more complex and diverse functions. For example, in the field of environmental sensing and governance, how to realize the combination of filtration, sensing, catalysis, reusability and other functions deserves further exploration.At present, smart wearable devices are the future trend. Realizing the combination of nanofibers and electronic devices for detection, early warning, diagnosis and treatment of human health is one of the promising development directions.Realizing the compatibility of nanofiber production methods with traditional industries is one of the major challenges. For example, in the textile industry, nanofibers are one to two orders of magnitude smaller than conventional textile fibers in diameter and are easy to break and difficult to handle. Current methods for fusion of different nanofibers are mainly mixing and multi-layer stacking. These two methods cannot fully utilize the advantages of different nanofibers. If the regular arrangement or orderly weaving of multiple functional nanofibers can be effectively realized, it will be a huge breakthrough in the application of nanofibers.It is important to identify “killer” applications of nanofibers, which proves the core value of this technology. The emerging applications of nanofibers have been summarized in many fields, but most of them are at the early proof-of-concept stage, and there is still a long way to go before commercialization.So far, many materials have been integrated into nanofibers, and the applications of nanofibers have covered many aspects of life and production. Although a lot of original studies have been carried out, there are still issues that call for better solutions.Nanofibers are the frontier of nanotechnology. As technology advances, all of these challenges will be overcome, and we will likely see stronger, more functional nanofibers used more widely in human life.


The equation y = axn well reproduced the behaviour of the molecular weights of the BTES series, in which a and n were obtained using the calculated molecular parameters for monomers as the explanatory parameters. Detailed understanding and discussion were theoretically possible on the basis of the mathematical model. We predicted the molecular weights of polymers that would be obtained from monomers BTES-P and BTES-Ph with C3H6 and C6H4 as the spacer, respectively, using the mathematical model. Experimental validation of these polymers clearly showed the possibility of qualitative categorization. Our proposed concept, MBR, is a powerful tool to analyse materials science toward innovative materials design.Toward the design and manipulation of innovative materials, we propose a new concept called “model-based research (MBR)”. In MBR, measurable physical and chemical properties of materials are mathematically modelled by explanatory parameters obtained by computer simulation from an atomistic point of view. To demonstrate the potential of MBR, we modelled the molecular weights of a series of polysilsesquioxanes with respect to the H Modeling and prediction of molecular weight by MBR. This also applies to the monomer–polymer relationship as the properties and functionalities of polymers do not always directly reflect those of monomers. For example, network or highly branched polymers have molecular weights that are difficult to control, in marked contrast to that linear polymers have molecular weights that well depend on monomer reactivity and can be much more easily controlled by, for example, the loading ratio of the monomer and the terminating and/or initiating reagent.6–8Materials development based on simple molecular design is generally difficult as the atomic-level molecular properties do not always relate directly to the functionalities of materials that are composed of a huge number of molecules in the condensed states. Even though the molecular properties can be predicted with high reliability by molecular simulation at a high level of theory,9–13 They can be prepared by the hydrolysis/condensation polymerization of trifunctionalized silanes and consist of a three-dimensional siloxane network that gives rise to thermal and mechanical stability of the resulting PSQ materials. For the preparation of PSQ films, liquid PSQs prepared by controlled polymerization are coated on a substrate and then calcined at a high temperature to further facilitate the condensation, as shown in 14,15Polysilsesquioxanes (PSQs) have received much attention as typical organic–inorganic hybrid materials.i.e., molecular weight, as possible. However, an exceedingly high polymerization degree should be avoided as it would result in the formation of non-processible solid PSQs. We were able to prepare three types of bridged PSQs from the corresponding monomers, as shown in 14,15 However, to our surprise, their polymerization behaviours were markedly different in spite of the fact that the monomer structures were nearly the same except for the bond orders of the bridging C2 units. Based on both experimental and theoretical considerations, we concluded that the different polymerization behaviours arise from the different rigidities of the C2 bridges. However, the molecular weights of such network polymers as PSQs are still difficult to predict precisely.In this process, the control of the molecular weights of the liquid PSQs is of utmost importance. To avoid significant shrinking of the film due to condensation, which may produce cracks and/or pinholes on the film, the liquid PSQs should have as high a polymerization degree, 16,17 to understand the driving force for the formation of macroscopic properties from an atomistic point of view and to predict and design new materials. The use of machine learning techniques in materials science is still in its infancy and the applications are limited to simple surface adsorption, stability of homogeneous materials, derivatives of molecules, and so on.18–20 To apply machine learning for macroscopic properties, such as thermal conductivity, hardness, density, and so on in complex systems, it is necessary to propose a new strategy beyond the present machine learning approaches.To approach this challenging problem, the key feature of our work is the application of machine learningIn this study, we propose a new concept on the basis of machine learning toward materials design, using the control of the molecular weights of the bridged PSQs as an example. We call this “model-based research (MBR)”, which is clearly different from the conventional machine learning approach. In MBR, material properties that are usually difficult to predict are analysed by using a uniform mathematical model equation with rather accurate parameters that are readily obtained by theoretical simulation at the atomic level. Even though the relationship between the material properties and the parameters at the atomic level is not precisely explained, they should have a strong correlation that may be linked by an equation. This concept is expected to provide a new strategy for the design of materials at the atomic level.21 The basic idea of MBD is to divide a complex whole-engine system into simple parts that can be simulated by a computer. In fact, MBD achieved a new high-performance automobile engine by the so-called “V-type development process”. Although MBD is very useful for efficient automobile engine manufacture as it reduces both cost and time, the direct application of MBD to materials development is difficult because material properties are based on a high complexity of multi-scale and multi-physics phenomena. To overcome such difficulties in materials development, we proposed the new concept, MBR, for efficient research in materials science, as mentioned above.MBR is based on the efficient automobile engine manufacturing process called “model-based development (MBD)”.i.e., measurement and analysis by experimental techniques, obtaining geometry and electronic structure by computer simulation, and modelling by machine learning, are necessary in MBR. Based on a fundamental understanding of physical and chemical properties and models, it will be possible to design and develop innovative materials. Furthermore, the constructed models may be used for the design of related materials. Here, we modelled the measured molecular weights of the bridged PSQs by using explanatory parameters based on geometry and electronic structure obtained by computer simulation as an example of MBR.We believe that acquiring a fundamental understanding at the atomic level is one of the key factors for modelling material properties. To construct a model for material properties, the machine learning technique is a very powerful tool. The collaboration of three techniques, −1 hydrochloric acid used as the reaction solvent and catalyst, respectively, were purchased from Wako Pure Chemical Industries. These reagents were used as received without purification. Allyltriethoxysilane and triethoxysilane were purchased from Tokyo Chemical Industry Co., Ltd. Toluene was purchased from Nacalai Tesque, Inc., distilled from calcium hydride, and dried over 4 Å molecular sieves. The structures are shown in −1. A 5–10 wt% solution of sample was introduced after filtration through a 0.20 μm diameter membrane filter . Polystyrene samples were used as standards. NMR spectra were measured on a Varian System500 spectrometer.Bis(triethoxysilyl)methane (BTES-M) was purchased from Gelest, Inc. 1,4-Bis(triethoxysilyl)benzene (BTES-Ph) and chloroplatinic acid hexahydrate were purchased from Sigma-Aldrich Co. LLC. Ethanol (super dehydrated (99.5)) and 6 mol L22 The reaction was carried out under dry air. A toluene solution (50 mL) of 3-(triethoxysilyl)propene , triethoxysilane , and chloroplatinic acid hexahydrate  was stirred for 22 hours at room temperature. Chloroplatinic acid hexahydrate  was added and the reaction mixture was stirred for another 16 hours. The reaction mixture was evaporated and distilled under reduced pressure to yield BTES-P as a clear colorless liquid . Bp 101–103 °C (0.21 torr). 1H NMR  δ 0.73 , 1.22 , 1.54–1.58 , 3.81 . 13C{1H} NMR  δ 14.46, 16.63, 18.45, 58.42. 29Si{1H} NMR  δ −45.3.BTES-P was synthesized with a slight modification of a previously reported method.14,15 Into a four-necked flask equipped with nitrogen inlet and outlet tubes and a mechanical stirrer, BTES-R (5.00 mmol) and EtOH (50.00 mmol) were charged and the reaction mixture was cooled on an ice bath for 10 minutes with stirring at 150 rpm under nitrogen flow (360 mL min−1). 6 mol L−1 Hydrochloric acid (0.525 mmol) and distilled water were added in molar ratios of 0.9–3.2 for H2O/BTES-monomer. The reaction mixture was stirred for an additional 10 minutes at 0 °C and then warmed to room temperature and further stirred for 10 minutes. The reaction mixture was stirred for 3 hours at 80 °C to generate the polymer as a viscous liquid. Molecular weights were determined by GPC analysis.The reactions were carried out in a fashion similar to that for the polymerization of BTES-E1, -E2, and -E3.23 All ethoxy groups in the monomers were substituted by hydroxyl groups in this study. As an explanatory variable for reactivity, the dimerization energy of two monomers was calculated. The proton association energy to a hydroxyl group, which is one of the most important reaction steps for dimerization, was also calculated. The flexibilities between Si and Si stretching and Si–X–Si  bending in the monomer structure were considered by ab initio molecular dynamics (MD) calculation under B3LYP/6-31G(d). The time step of 1.0 fs was used in the Verlet method.24 Iterative calculations were done until 10 000 steps. All electronic structure calculations were performed by using the general atomic and molecular electronic structure system (GAMESS).25 We also modified GAMESS program for ab initio MD calculations.To obtain the explanatory parameters for the molecular weights of the four types of alkoxysilanes, geometry optimization calculations for the monomers were done by using B3LYP/6-31G(d) level of theory. Solvent effects from ethanol were considered by the polarized continuum model (PCM).26 method was used for logistic regression analysis because this method provides a prediction model even when the number of data is smaller than that of parameters. This method can perform both variable selection and regularization to optimize the regression model while avoiding overfitting. The LASSO has been extensively used for material science fields.27–32 The big data analysis is generally popular approach for materials science.27,31 However, it is sometimes difficult to prepare many experimental or calculation data directly. Recently, the LASSO was applied to small datasets for materials science.28 The preparation of enough datasets for molecular weight of our target polymer is also quite difficult due to the difficulty of experiments and need long time for experimental cost. It is necessary to analyse molecular weight trend from small datasets. Therefore, the LASSO method was adopted to optimize the prediction model fitting of the experimental values with the molecular parameters. We first determined the fitting parameters of experimental results (molecular weight). The fitting parameters and calculated values from electronic structure calculations were prepared as objective and explanatory variables, respectively. By using LASSO analysis, we obtained the explanatory parameters based on electronic structure calculation for fitting parameters of experimental data of molecular weight. Detailed procedure of model equation construction in this study was shown in ESI.†The least absolute shrinkage and selection operator (LASSO)versus amount of water for the polymerization of BTES-E1, -E2, and -E3, as reported previously,16 together with those for BTES-M obtained in the present study (nt study .29Si nuclear magnetic resonance (NMR) for BTES-M as shown in The behaviour of the molecular weight changes of BTES-M is similar to that of BTES-E1. To check the detailed structure of BTES-M, we analysed the gel permeation chromatography (GPC) and 2 and T3 mean two and three dimensional bridged structures, respectively. The T2 and T3 structures are mixed in BTES-M. The GPC chart and NMR spectrum of BTES-M were also similar to the BTES-E1.7 This behaviour markedly differed from our previous consideration that higher molecular weights would be obtained as molecular rigidity was enhanced. We had assumed that more flexible monomers should provide cyclic oligomers more easily, thereby suppressing the molecular weight increase . Generally, with increasing the number of parameters, the R2 is improved. While the equation y = axn has only two parameters, this equation well reproduced the experimental molecular weights for all the BTES series, i.e., the R2 values were higher than 0.8, as shown in a was the smallest for BTES-M. For the C2-linked BTES compounds, parameter a increased with increasing bond order of the bridges. On the other hand, parameter n was the smallest for BTES-E1.To find a mathematical fit of the plots in a and n, regression analysis was carried out by using the least absolute shrinkage and selection operator (LASSO) method. As the first step, we selected atomic-scale parameters that had been obtained by computer simulation of the compounds in ethanol as the potential controlling factors. Meso-scale information, such as the branching degree of the polymer structures and the reactivity of the polymers/oligomers, seems to be also important factors that control the polymerization reactions. It is also likely that larger scale information, including viscosity of the polymer solution and polymer shape , exerts an influence on GPC analysis, thereby also affecting the molecular weights. However, these meso-scale information may be under the control of monomer molecular factors directly or indirectly. It should be worthwhile if we could find some monomer-scale parameters that strongly affect the meso-scale properties, such as the molecular weights of the polymers in the present work, as this would prove the possibility of materials design by atomic-scale tuning of geometry and electronic structure. As the explanatory parameters for objective variables a and n, geometry, electronic structure, and flexibility parameters of the monomer structures of BTES-M, -E1, -E2, and -E3 were selected. The potential explanatory parameters examined in this study are listed in To find the controlling factors for Eassociation) was calculated by the following 2, C2H4, C2H2, and C2).The numbers of carbon and hydrogen atoms in the spacers and their ratio (H/C), the surface area, and the volume of monomers are the geometry parameters of the monomers. The energy levels of the highest-occupied molecular orbital (HOMO) and the lowest-unoccupied molecular orbital (LUMO) and the atomic charges of silicon and oxygen are the electronic structure parameters. The solvation energy was also calculated and the proton association energy (ΔEdimerization) was also calculated on the basis of the following equation,Proton association is considered an important step in acid-catalyzed reactions. The dimerization energy of the unit structure (Δab initio molecular dynamics (MD) calculations were performed for BTES-M, -E1, -E2, and -E3. We focused on the averages of the Si⋯Si distance and the Si⋯C⋯Si angle during the MD simulations. Differences between the maximum and the minimum of those motions were also analysed.To take the structural flexibility of the monomers into account, a and n, we analysed the relationship between these explanatory parameters and the objective parameters. By applying the LASSO method, which is one of the most common techniques for machine learning, three explanatory parameters were found as the important controlling factors for each of a and n in y = axn. Based on these results, the model equations for a and n are shown in To obtain the mathematical model equation for parameters a. Parameter n includes a large contribution from the LUMO of the unit structure. We clearly found different controlling factors for parameters a and n. As shown in The atomic charges of oxygen and silicon strongly affect parameter 16 we demonstrated that all the water used in the reactions was consumed to convert ethoxy groups into silanol or disiloxane bonds. We also found that the formation of cyclic units was mainly responsible for suppressing the molecular weight increase in BTES-E1. We, therefore, consider that the reactivity of silanol towards condensation to form disiloxane bonds and the reaction selectivity, i.e., linear chain growth or cyclization, significantly affect the molecular weights of the polymers.In our previous work on the reaction mechanism in the polymerization of BTES-E1, -E2, and -E3,a. The atomic charges of silicon are decreased in the order of BTES-M > BTES-E1 > BTES-E2 > BTES-E3. Silicon is a π-acceptor as well as a σ-donor.34 As π-acceptors, the silicon atoms in BTES-E2 and BTES-E3 accept a negative charge from ethylene and acetylene units. In fact, the σ*(Si)–π(C) interaction is clearly seen in the HOMO of these molecules in addition to the n(O)–σ*(Si) interaction –π*(C) interaction. Molecular volume is related to molecular diffusion, and thus to reactivity. Increasing the number of hydrogen atoms seems to suppress reactivity due to steric hindrance of the spacer.On the other hand, the LUMO energy level, the molecular volume, and the number of hydrogen atoms on the spacer contribute to parameter a and n have different controlling factors. Although it is not easy to explain why these explanatory parameters are selected as the controlling factors for parameters a and n, it is true that the molecular weights can be reproduced on the basis of these molecular factors of the monomers.From the results described above, we can definitely conclude that parameters 3H6 and C6H4 spacers, respectively  scale" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z M0 280 l0 -40 320 0 320 0 0 40 0 40 -320 0 -320 0 0 -40z"/></g></svg>C/C PBM data was replaced with SVG by xgml2pxml:<glyph-data id="z.tbd" format="PBM" resolution="300" x-size="17" y-size="11" xml:space="preserve">0000000000000000000000000000000000000000000000000000111111111111111000000000000000000011111111111111100000000000000000001111111111111110000000000000000000000000000000000000000000000000000</glyph-data><svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="23.636364pt" height="16.000000pt" viewBox="0 0 23.636364 16.000000" preserveAspectRatio="xMidYMid meet"><metadata>Created by potrace 1.16, written by Peter Selinger 2001-2019</metadata><g transform="translate scale" fill="currentColor" stroke="none"><path d="M80 600 l0 -40 600 0 600 0 0 40 0 40 -600 0 -600 0 0 -40z M80 440 l0 -40 600 0 600 0 0 40 0 40 -600 0 -600 0 0 -40z M80 280 l0 -40 600 0 600 0 0 40 0 40 -600 0 -600 0 0 -40z"/></g></svg>C bonds of monomers, was succeeded. The gap between predicted curve and experimental data is improved by adding other data, although the Reflecting the similarity of parameter  BTES-Ph  and the 35,36 On the other hand, the unsaturated phenylene bridge in BTES-Ph led to the generation of high molecular weight polymers and gelation even with low water content, similar to BTES-E3. The GPC charts of BTES-P and -Ph synthesized under the H2O/silane ratio = 3.0 and 1.0, respectively, are shown in Compared to BTES-E1 and -M, BTES-P showed even lower molecular weights in both prediction and experiments. BTES-P is known to easily undergo intramolecular cyclization to form stable six-membered cyclic disilsesquioxanes in acid-catalysed hydrolytic polycondensation reactions.As well as the BTES-E1 (and -M), a single sharp peak in the low molecular weight region was observed in the case of BTES-P including single C–C single bonds. Total shape of GPC chart for BTES-Ph having linear main structure was similar to the BTES-E3 , although some small peaks are observed in the low molecular weight region. One of the reasons of shape difference of GPC charts is that the cyclic compounds of BTES-Ph was obtained due to the rigidity of main structure. However, overall shape of GPC chart and molecular weight curve of BTES-P and -Ph were similar to the BTES-E1, -M and BTES-E2, -E3, respectively. In this sense, we clearly demonstrated the reliable categorization of PSQ derivatives with respect to the molecular weight based on the concept of MBR.29Si NMR spectra of BTES-P and -Ph are presented in 16,35,36 The spectrum of BTES-P reveals sharp T1 and T2 signals ascribed to cyclic dimers and/or cyclic units involved in the polymer structure. Relatively large and broad signals likely due to the polymer T2 and T3 structures are also seen in the spectrum, similarly to that of BTES-E1.16 In contrast, the spectrum of BTES-Ph shows only tiny T3 signals, suggesting that BTES-Ph should have more linear backbone than BTES-P, like BTES-E2 and -E3.16 These agree well with the categorization based on their GPC analysis mentioned above.The y = axn well reproduced the behaviour of the molecular weights of BTES-M, -E1, -E2, and -E3. Parameters a and n were modelled by using the explanatory parameters of geometry, electronic structure, reactivity, and dynamics of monomer structures. The explanatory parameters were essential to understanding the controlling factors for the polymerization of PSQs. Based on the model for molecular weight, we predicted the molecular weight theoretically. Our experimental results for the two types of spacers (C3H6 and C6H4) could be categorized on the basis of the predicted curves. This indicates that the model developed in this study includes the fundamental characteristics of molecular weight change with respect to the H2O/silane molar ratio, although further improvement of the model is necessary for accurate analysis. We have clearly shown the usefulness of concept of MBR for molecular weight prediction and finding of controlling factors. The MBR approach is a powerful tool in materials science with an eye to realizing innovative materials design.We have proposed a new concept called “MBR”, which is based on the machine learning technique, with an eye to enhancing materials design. In MBR, macroscopic properties are mathematically modelled by explanatory parameters obtained by computer simulation from an atomistic point of view. As an example, we modelled the molecular weights of bridged PSQs by using MBR. The equation The authors declare no competing interests.RA-010-D0RA02909B-s001


Escherichia coli, regarded as a sentinel organism.Livestock systems have been proposed as a reservoir for antimicrobial-resistant (AMR) bacteria and AMR genetic determinants that may infect or colonise humans, yet quantitative evidence regarding their epidemiological role remains lacking. Here, we used a combination of genomics, epidemiology and ecology to investigate patterns of AMR gene carriage in E. coli isolates from 311 human, 606 livestock and 399 wildlife faecal samples. We used statistical models to investigate the prevalence of AMR carriage and characterise AMR gene diversity and structure of AMR genes in different host populations across the city. We also investigated household-level risk factors for the exchange of AMR genes between sympatric humans and livestock.We conducted a structured epidemiological survey of 99 households across Nairobi, Kenya, and whole genome sequenced We detected 56 unique acquired genes along with 13 point mutations present in variable proportions in human and animal isolates, known to confer resistance to nine antibiotic classes. We find that AMR gene community composition is not associated with host species, but AMR genes were frequently co-located, potentially enabling the acquisition and dispersal of multi-drug resistance in a single step. We find that whilst keeping livestock had no influence on human AMR gene carriage, the potential for AMR transmission across human-livestock interfaces is greatest when manure is poorly disposed of and in larger households.Findings of widespread carriage of AMR bacteria in human and animal populations, including in long-distance wildlife species, in community settings highlight the value of evidence-based surveillance to address antimicrobial resistance on a global scale. Our genomic analysis provided an in-depth understanding of AMR determinants at the interfaces of One Health sectors that will inform AMR prevention and control.The online version contains supplementary material available at 10.1186/s12916-022-02677-7. Rising levels of bacterial infections resistant to last-line antimicrobial agents represent a global health crisis , and theThe links between human and livestock populations provide an opportunity for either population to act as a reservoir from which antimicrobial-resistant bacteria or their antimicrobial resistance (AMR) determinants could be transmitted in either direction . WildlifEscherichia coli, a common commensal and pathogenic bacterium [Cities in low-income countries have been posited as ‘melting-pots’ of both infectious disease and AMR, due to poor hygiene and sanitation, densely populated human settlements found alongside livestock and a rich assortment of wildlife, and largely unregulated antimicrobial usage. Here, we focused on the role of livestock keeping within households across Nairobi, Kenya, as a high-risk urban interface for the emergence and transmission of AMR bacteria between humans and animals. We use acterium  in verteE. coli isolates obtained from cohabiting human and animal populations, we determined the prevalence and mechanisms of resistance and characterised AMR gene diversity and structure of AMR genes in different host populations across the city. At a finer scale of individual households, we use ecological models to investigate risk factors for the exchange of AMR genes between sympatric humans and livestock, thus shedding light on pathways of AMR transfer at household interfaces.Current surveillance of AMR tends to focus on tracking specific resistance phenotypes and genotypes separately in human and animal populations, without making epidemiological comparison of resistances between the two . The appA cross-sectional study targeting synanthropic wildlife and sympatric human and livestock populations in Nairobi, Kenya, was carried out from August 2015 to October 2016 as part of the Urban Zoo Project . Briefly2) was measured using ArcGIS and livestock and human abundance data derived from each household. Details of taxon-specific methods used to trap wildlife and to collect samples from humans and animals are described in the supplementary methods and elsewhere [E. coli by biochemical testing, using triple sugar iron agar, Simmons citrate agar and motility-indole-lysine media.In each household, a questionnaire was used to collect data on (i) household composition, food consumption, medical history and socio-economic variables and (ii) livestock ownership and management practices. The household area including outdoor spaces  at the International Livestock Research Institute, Nairobi, Kenya, and transported under licence to The Wellcome Trust Centre for Human Genetics, Oxford, UK. Whole-genome sequencing was carried out at the Wellcome Trust Centre for Human Genetics on the Illumina HiSeq 2500 platform.Sequenced reads were filtered for quality and trimmed for adaptors with BBDuk (v38.46), k = 19 mink = 11 hdist = 1 ktrim = r minoverlap = 12 qtrim = rl trimq = 15. The following sequencing quality thresholds were used based on Quast: (i) at least 3 Mb aligned to EC958, (ii) a maximum assembly length of 6.5 Mb, (iii) GC content of between 50 and 51% and (iv) assembly N50 of > 30 kb or a maximum of 100 cgMLST missing loci. A total of 1316 genomes passed this quality threshold.gyrA and parC alleles.Acquired antibiotic resistance genes were identified from the assemblies using starAMR , with pep values of < 0.05 were considered significant.Hosts were categorised into five broad types: (i) humans; (ii) livestock birds, poultry dominated by chickens; (iii) livestock mammals consisting of ruminants and monogastric livestock; (iv) wild birds, predominantly seed-eating birds such as house sparrows; and (v) Wild mammals, predominantly rodents and bats. Differences in the distribution of AMR genes between hosts were calculated using the chi-squared tests and one-way ANOVA using R package stats. Tukey’s multiple-comparison test was performed post hoc for pairwise comparisons between the groups, and Comparisons of alpha diversity between host groups were conducted using the Richness index (observed richness) (defined as the number of unique AMR genes in an isolate), Simpson diversity index and Shannon index using the diversity function in the vegan package . The KruTo estimate whether or not differential sampling bias could be, in part, responsible for the observed diversity of resistance determinants in the isolates, we performed a sample-based rarefaction analysis and rank abundance analysis  using thCo-occurrence patterns among pairs of acquired AMR genes based on presence/absence data were assessed using Veech’s pairwise co-occurrence approach  via the To test for differences in AMR gene assemblage between host populations, permutational multivariate analysis of variance (PERMANOVA) on the Jaccard Distance Matrix of ARG composition was performed using the adonis function in the vegan package with 999 permutations. In the adonis analysis, ‘host group’ was used as a fixed factor and a strata argument set to ‘household ID’ to constrain randomizations within each household. Principal coordinate analysis (PCoA) was used to ordinate the Jaccard Distance Matrix in the vegan package and visualised using ggplot2 . ConfideTo investigate the potential drivers of AMR gene carriage in humans, we used a zero-inflated Poisson general linear mixed effects model (GLMM) with counts of the individual AMR genes  in each of the isolate aggregated at the antibiotic class level as the dependent variable. Risk factors analysed included the following: household size (persons in a household as a function of household area), disposal practices (manure disposed in the household compound or outside), toilet sharing, kinds of livestock kept in the household . Moreover, for households that kept livestock, a separate zero-inflated Poisson GLMM was fitted to investigate the influence of household size and manure disposal practices (manure disposed in the household compound or outside) on AMR gene length.χ2 tests via package car [n = 99) was included as a random effect. We plotted the diagnostic plots of the zero-inflated Poisson model, including random effects, to check that the model assumptions were not violated using the package DHARMa [Analyses were performed using the glmmTMB package  and signkage car . The topkage car . To accoe DHARMa . All anan = 324), goats and sheep (n = 134), cattle (n = 61), pigs (n = 49) and rabbits (n = 38). In addition, 399 (30.3%) of the genomes were obtained from 63 wildlife species, primarily comprised of wild birds (n = 245), rodents (n = 130), bats (n = 20) and primates (n = 4)  from humans and 606 (46.1%) from 13 different species of livestock primarily composed of poultry . In total, these genes and mutations confer resistance to a total of nine antibiotic classes  than humans  and livestock  . Sixty percent (n = 100), 45.5% (n = 282) and 36.6% (n = 225) of human, livestock and wildlife isolates respectively carried AMR mechanisms known to confer resistance to three or more antibiotic classes (or multi-drug resistance carriage), representing an overall carriage of 46.3% (n = 609) , strA (36.8%), strB (37.2%), tetA (35.6%) and blaTEM-1B (23%) conferring resistance to sulphonamides, aminoglycosides, tetracyclines and beta-lactams respectively  were significantly more common in human isolates than in livestock and wildlife, whilst strA, strB and sul2 genes were detected at higher rates in livestock isolates than in wildlife .The distributions of most  of the AMR genes and mutations did not significantly differ between the host groups. Nevertheless, eight ARGs (n = 378) pairs of AMR genes co-occurring significantly more frequently than expected, 22 (5.8%) pairs of AMR genes co-occurring significantly less frequently than expected and n = 198 (52.3%) random AMR genes associations (observed frequency of co-occurrence does not significantly depart from expected)  either singly or in combination with other ARGs and was significantly more common in humans than animal hosts . Further, the strA-strB-sul2 gene combination significantly co-occurred with tetA in 334 (25.1%) isolates and with both tetA and blaTEM-1B in 190 (14.4%) isolates   . Poultry isolates had higher AMR carriage than ‘livestock mammals’, ‘wildlife avian’ and ‘wildlife mammals’ isolates   Fig. a. Similaed) Fig. b. This ped) Fig. . Rarefaced) Fig. . Additioed) Fig. b.Fig. 2ap > 0.05).However, these differences in the relative abundance and alpha diversity of AMR genes were not reflected in the overall AMR gene community composition when compared between host groups via beta diversity, which did not significantly differ between human and animal host groups .Next, we assessed whether livestock keeping, and livestock keeping practices, influenced AMR gene carriage in humans. First, we analysed whether AMR gene composition in isolates from humans varied with respect to their livestock-keeping status. Permutational multivariate analysis of variance (PERMANOVA) analysis showed that the AMR gene composition of humans keeping livestock did not differ from that of humans not keeping livestock (adonis p > 0.05)    influences the risk of human AMR gene carriage 5) model , Table 1 model , E. coli isolates obtained from sympatric human and animal populations in a rapidly developing urban landscape. We showed that whilst patterns of carriage, diversity and assemblages of resistance mechanisms in E. coli are generally similar across human, livestock and wildlife hosts, some differences exist across the three groups, attributable to host-specific drivers. Our analyses indicate that differences in carriage of AMR mechanisms in humans were not associated with livestock keeping per se but instead associated with livestock-keeping practices that influence environmental contamination such as manure disposal practices, and household size.This study combines epidemiological approaches and genomic analyses to understand AMR on systematically collected blaCTX-M-15 gene. Several studies conducted mostly in E. coli have identified AMR genes including ESBL-carrying bacteria in numerous species of wildlife dominated by wild birds [Nearly half of all isolates (46.3%) carried AMR gene mechanisms known to confer resistance to three or more antibiotic classes. The most common resistance mechanisms encoded in the mapped genomes were associated with resistance to sulphonamides, aminoglycosides, tetracyclines and beta-lactams, in agreement with our earlier studies exploring phenotypic patterns expressed by bacteria , 27 and ld birds –37. CommWe found that overall AMR community composition overlapped among host groups and that AMR gene communities of humans keeping livestock were not different from those of people who did not keep livestock. In complex urban interfaces such as Nairobi, humans, livestock and wildlife are linked in many ways, including direct contact, consumption of livestock products by humans and shared contaminated environments for example through human sewage and manure from livestock . These rOur analysis suggests that the potential for AMR transmission across human-livestock interfaces is greatest when manure is poorly disposed of, and when household size is larger. Environmental exposure to manure—a common soil fertiliser in crop farming—provides an ideal environment for the amplification and persistence of AMR determinants , 41 and E. coli isolate from each host, the within-host genetic diversity of E. coli was not considered in this study. Recent studies have revealed that in some bacteria there is considerable within-host diversity and AMR gene diversity [E. coli genetic diversity within each individual host and the number of unique individuals from which samples could be included. Future research could benefit from the recent development and use of metagenomics to characterise the abundance, diversity and structure of acquired resistomes [By only sequencing a single iversity . In thissistomes .Findings from this study fill important data gaps concerning the frequency of AMR genes in human and animal reservoirs in LMICs and provide further support for sustainable application of WGS and genomic epidemiology in contributing to surveillance efforts of AMR mechanisms circulating in different ecosystems , 45. FurE. coli in sympatric human and animal populations in a rapidly developing urban setting. By stepping outside of the ‘blame game’ of livestock, and human health, our study, applying a ‘One Health’ approach, demonstrates that AMR genes conferring resistance to critically important antimicrobials for both human and veterinary medicine are widespread in humans, livestock and wildlife hosts. We found that human and animal AMR gene profiles were similar but did not detect an association between livestock keeping and AMR gene numbers or composition in humans. Instead, the impact of keeping livestock on human AMR carriage was mediated by practices associated with livestock keeping, namely the presence or absence of animal manure in the household and with household size. Taken together, our study serves as a model for the targeted sampling to harness the power of whole genome sequencing for understanding the epidemiology of AMR across developing urban landscapes, a key in developing effective strategies to reduce the development and spread of such resistance in the future.In conclusion, we present a genomic study of Additional file 1.