TextNetTopics Pro, which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short-text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers.With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles’ content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called  Numerous scientific research papers are being published daily with the increasing advancement of computer and information technologies and digital platforms. Publication output is reported to reach 2.6 million worldwide in 2018 . 36% of In fact, the number of features in a short text is a small portion of the set of all features presented in all of the training records. This situation creates feature sparseness. Although this is problematic for regular-sized text, it is more critical for short text. In particular, short texts tend to have diversification in content; the same topic can be expressed in multiple ways, which increases the feature space and reduces the frequency of a feature occurring in a given record, as well as the number of records in which a particular feature occurs, leading to the scarcity of feature overlap. This situation makes it challenging to accurately determine a feature’s salience in a specific class in supervised machine-learning tasks .As mentioned above, the severe data sparsity, high dimensionality, lack of word co-occurrence, and insufficient shared context are other distinctive characteristics that distinguish short texts from general ones. Inevitably this situation negatively affects the classification performance when conventional machine-learning methods are used. In other words, traditional classification methods are not optimized for use in such a context .Topic Modeling (TM) is an unsupervised learning task aiming to discover latent thematic contents in a collection of text documents. TM provides a simple way to analyze, understand, summarize, and categorize large volumes of unlabeled text (document-topic distribution matrix containing the distribution of topics over documents (reflecting a high-level representation of the document’s semantics). 2) a topic-word distribution matrix containing the distribution of words across topics . Each topic comprises a group of words that co-occur in documents according to specific patterns. In other words, a topic model can differentiate words with distinct semantics and group them into topics based on their co-occurrence  as the main source for performing topic modeling to enrich the short text, which only works efficiently with long text. Nevertheless, many other topic modeling algorithms exist, with some of them customized for short texts and well-tuned for sparse documents, and picking a good one is not straightforward. This study proposes a novel short text classification approach called Topic modeling refers to a set of algorithms that aim to discover the underlying structures and hidden topics in an unlabeled text corpus. In contrast to regular-sized documents, inferring the latent topics and discovering the hidden semantic structure in a collection of short documents is challenging.Insufficient word co-occurrence information within individual short texts significantly contributes to a notable decline in performance  when utilizing traditional topic models across short texts, which implicitly captures the word co-occurrence patterns at the document level to discover topics . This si- Dirichlet multinomial mixture (DMM): Unlike the LDA, which adopts a complex assumption that each text is sampled over a set of topics, DMM follows a simple assumption that each text is modeled from only one latent topic.- Global word co-occurrences: It infers latent topics leveraging the global word co-occurrence patterns obtained from the whole corpus.- Self-aggregation: It aggregates short texts into lengthy pseudo-document before conducting topic inference (training a topic model) to improve word co-occurrence information.Recently, researchers have proposed topic models specially designed to handle short texts to overcome the problem of shortness, severe data sparseness, high dimensionality, and the minimal availability of word co-occurrence information in each of them . These sSome relevant algorithms related to short-text topic modeling can be summarized as follows:Gibbs Sampling algorithm for Dirichlet Multinomial Mixture. GSDMM is inherently a mixture of a unigrams model, with the generative assumption that the document is sampled from one topic instead of multiple topics like in the LDA, and the words depend on that topic. GSDMM samples a latent topic for a document based on collapsed Gibbs sampling  in the whole corpus to infer topics over short text. It considers that the corpus composes a mixture of topics, and each biterm is drawn from a specific topic only . It can discover rare topics contained in fewer documents. WNTM learns the latent word groups (topic components) by applying the standard Gibbs sampling for LDA in a word co-occurrence network rather than the document collection. Moreover, unlike other approaches, WNTM models the distribution of latent topic components for each word instead of the distribution of topics for each document (WNTM: document .Self-Aggregation-based Topic Model integrates topic modeling and text self-aggregation (clustering) simultaneously during topic inference. It assumes that each piece of short text is sampled from a long pseudo-document hidden in the current text corpus, following the multinomial distribution. It uses the standard topic modeling  to infer latent topics from pseudo-documents without relying on metadata or auxiliary contextual information, which is too costly for deployment and not always available. However, SATM is prone to overfitting since the number of SATM parameters grows linearly with the size of the corpus, and its time complexity is very high  through the generalized Polya urn (GPU) model. After sampling a topic, words highly relevant to the topic are selected and linked with their semantically related words together under the same topic, even though they share limited or no co-occurrences in the modeled short-text corpus . Regardif topics .Latent feature model with DMM incorporates word embeddings into Dirichlet Multinomial Mixture by replacing the topic-word Dirichlet multinomial component with a two components mixture of a Dirichlet multinomial component and a latent feature (a continuous word embedding) component. It uses a binary switch variable (sampled from a Bernoulli distribution) to choose which component generates a word. The model projects the topics into the same continuous latent space as the word embeddings to estimate the word embedding component of each word. This is achieved by optimizing a regularized log-linear model , which form an aggregated subset of words that effectively discriminate the two classes of documents . These selected topics are then used to train the classifier. Scoring topics is computed by using a machine learning model   . As showSimilar bioinformatics tools were developed based on the G-S-M that also perform grouping based on prior biological knowledge. These are some of those tools: maTE  which usr topics, each defined as sets of semantically related words, that align most effectively with the topic distribution for providing the best classification performance.TextNetTopics Pro is an enhancement over TextNetTopics. It is a topic model-based approach to short-text classification, which integrates lexical information (topic words) and document-topic distribution information. In other words, it strives to find the top-ranked The main aim of TextNetTopics Pro is to reduce the original text’s dimensionality and make the short text less sparse and more topic-oriented for classification purposes.By using semantically richer document representation, TextNetTopics Pro can distinguish alternative forms expressing the same notion or concept. Therefore, it reduces the noise caused by synonymy and polysemy found in textual data.1, d2, … , dn} be the collection of n short-text documents  to detect latent topics from the preprocessed collection of documents. The number of topics (k) and the number of terms per topic (m) are user-defined parameters. The main output of this component is two byproducts: a document-topic distribution matrix (TD) reflecting the topics’ proportions over documents and a topic-word distribution matrix (TW) where each word is assigned to each topic with probability.- T component incorporates a short-text topic model  that represents the detected topics. For each topic that consists of m terms/words, the G component generates a representative two-class m-dimensional sub-dataset with its associated class labels from the training Bag-of-Words (BOW) table. In other words, each of these sub-datasets corresponds to a specific topic, containing only the terms that constitute that topic.topic score indicates the ability of words belonging to a topic to classify class labels.- S component utilizes an internal machine learning cross-validation applied to each representative two-class sub-dataset to assign a score or weight as the mean accuracy of the cross-validation. The score also might be another performance measurement, such as the area under the curve, and rank them accordingly. A i, where i = 1 … r. Each C_TWDi will serve as input to the M component.- C component considers the top-ranked topics (topic = set of terms) in an accumulated fashion (referred to in - M component performs the training of the machine learning model (we use Random Forest) and the testing to create the performance table. From all the candidate subsets of features, we choose the optimal feature subset containing topics’ terms and distributions that provide the best performance  with a reduced number of features for training the final classifier.TextNetTopics Pro algorithm consists of the following five main components, as shown in CAMDA dataset: As part of a contest, the CAMDA panel has collected titles and abstracts of a large set of PubMed papers relevant to Drug-Induced Liver Injury  and a challenging set of unrelated papers . The DILI-related papers are validated by a committee of DILI specialists and referenced in the NIH LiverTox database. Additionally, in CAMDA the following datasets are provided:1) - Testing dataset (T1-T3): CAMDA has provided three unbalanced test sets at different difficulty levels. In other words, these datasets include an increasing amount of true negatives (where the majority of manuscripts are non-DILI related) to reflect the difficulty of the real-world task.- Validation (V1-V4): Four validation sets are provided at the end of the competition. In addition to hidden parts of T1-T3 datasets, CAMDA has prepared V4 - a domain transfer challenge to enable participants to assess the generalizability of the “DILI relevance detection” models.In order to evaluate TextNetTopics and its enhanced version on short text documents, we conducted experiments on two publicly available datasets, the CAMDA dataset  and the CAMDA has provided Test Leaderboards that allow the user to upload a list of predictions of each test (T1-T3) and other Validation Leaderboards for validation datasets (V1-V4).arXiv paper abstract dataset: This dataset is found in Kaggle, and it is used for building multi-label text classifiers. In this study, we have converted this dataset into two balanced class dataset to evaluate our approach. We chose the Computer Vision and Pattern Recognition papers as a positive class  and merged the remaining fields to create a negative class . We performed stratified sampling for the non-relevant fields to retain their distribution in the final corpus.2) Although both datasets consist of titles and abstracts of scientific publications, this study utilizes the titles’ part for experimental evaluation, except for the final section, which also uses the abstract part.We plot the title length histogram to gain more insight into both datasets refer to  and S2. This step is crucial to refine the text data to remove irrelevant, redundant, noninformative, and noisy data. Otherwise, their presence misleads the classifier, degrades performance, and substantially increases the computational time of machine learning. For the preprocessing task, we utilized the Knime workflows found in To extract the topic-word matrix and the latent semantic representation (document-topic matrix) from the above-mentioned state-of-the-art algorithms , we used STTM (Version = 1.8) , an openFor all algorithms, we use fixed hyperparameters of α = 0.1 and β = 0.01. We run Gibbs sampling inference for 1,000 iterations in all methods to guarantee convergence, and the final samples are utilized to estimate model parameters. We set the number of topics and the number of words per topic to twenty since it yielded the best performance among variants.Concerning TextNetTopics and TextNetTopics Pro, we utilized their KNIME workflow implementations, which can be found in .Finally, we employed a stratified Monte Carlo Cross-Validation (MCCV) to evaluate the performance of our approach and measure its statistical significance, repeated ten times. Each time, we divide the dataset into two parts: ninety percent for training and ten percent for testing. Using MCCV enables every observation in the dataset to have an opportunity of appearing in the training and testing set. We utilized stratified splitting, keeping the proportions of instances in each class equal.We adopt the standard performance measures as the evaluation criteria for our proposed short-text classification framework, such as accuracy, recall, specificity, precession, the area under the curve, and the F1-score. However, in the experimental discussion section, we focused on F1-score as the primary metric for evaluation.Given that the utilized short-text topic models yielded approximately similar performance patterns across the two datasets, we report only the results obtained for the CAMDA dataset in this section. However, we included the results attained by the arXiv dataset as To this end, we attribute the minor differences in the performance of all the short-text topic models to the number of overlapped terms. We have comparatively evaluated TextNetTopic utilizing various short-text topic models with three different feature selection methods, namely, selectKBest (SKB), Fast Correlation Based Filter (FCBF), and Extreme Gradient Boosting (XGBoost), using four different classifiers: Adaboost, Decision Tree, Random Forest, and LogitBoost. We present the results in In the CAMDA dataset, TextNetTopics with the PTM topic model achieves the highest accuracy, specificity, and precision. Although XGBOOST with Random Forest got the highest F1-score and AUC, TextNetTopics has comparable results. FCBF got the lowest performance scores , which reflects its inability to handle short text.In the arXiv dataset, XGBOOST with Random Forest and TextNetTopics with the PTM topic model achieves comparable accuracy, recall, and F1-score performance. Regarding specificity, AUC, and precision, SKB got the highest results. Concerning FCBF, it got the lowest performance scores , reflecting its inability to handle short text.The results obtained for TextNetTopics are reasonable since each short text contains a limited number of words after preprocessing, e.g., the omission of stop words, which leads to the scarcity of discriminative features. In addition, the available information, such as word frequency, is insufficient for classification, and these problems inevitably compromise the quality of a classifier. Thus, using only BOW representation with top-weighted topics is insufficient to boost the short-text classification performance.In contrast to TextNetTopics, TextNetTopics Pro performs semantic extension, combining top topics’ words with the topic distribution generated by the topic model. Such features captured at the corpus level preserve the relationship information of words with similar meanings and alleviate data sparsity problems which in turn improves the classification efficiency.In the CAMDA dataset, according to Moreover, we compared the performance results obtained when taking all terms in the preprocessed dataset combined with the semantic features (topic distribution extracted by the topic model) with our enhanced tool refer to . TextNetIn the arXiv dataset, TextNetTopics Pro enhances the F1-score performance of TextNetTopics by 4.93%, 4.84%, 3.90%, 3.76%, 3.05%, 2.77%, 1.01%, and 0.69% when utilizing GPU-DMM, BTM, GPM, GSDMM, GPU_PDMM, LF-DMM, PTM, and WNTM topic models, respectively  with our enhanced tool shown in . We get Although the performance of each topic modeling method is dataset dependent, in both datasets, we observe that DMM-based methods (GSDMM and GPU-DMM) and BTM outperform others. Moreover, the SATM and WNTM methods are unable to achieve a high F1 score.In this section, we have comparatively evaluated the performance of TextNetTopics with TextNetTopics Pro on regular-sized text  for the CAMDA dataset when short-text topic modeling is employed in the T component. Compared with the use of titles only, In our further experiments, we want to test the robustness and effectiveness of our models in handling imbalanced data, particularly in the classification of DILI (Drug-Induced Liver Injury) articles as part of the CAMDA challenge . After cThis study evaluated the performance of TextNetTopics on short text using various short-text topic models. TextNetTopics utilizing the PTM topic model reported the highest performance results along both datasets. Moreover, TextNetTopics achieved competing performance metrics with other feature selection algorithms, such as the XGBOOST.Additionally, in this study, we proposed TextNetTopics Pro, a novel approach that performs feature selection oriented towards the short text classification domain, and presented its application on article title categorization. Our findings show that TextNetTopics Pro improved the overall classification performance of short text by incorporating the two types of representative features learned from the same corpus for semantically richer text representation, topical words, and topic distributions. Among various short-text topic models, TextNetTopics Pro utilizing BTM and DMM-based methods (GSDMM and GPU-DMM), reported the highest performance results along both datasets.Moreover, our study has demonstrated the robustness and effectiveness of TextNetTopics Pro in handling imbalanced data, particularly in the classification of DILI (Drug-Induced Liver Injury) articles as part of the CAMDA challenge.The significance of our approach is that it provides dimensionality reduction of the Vector Space Model (VSM) while preserving semantic structures in the text and making sparse short texts more related and topic-oriented. Unlike the BOW model, our approach considers the relationship between words, maintaining semantic and syntactic information in document representation, such as word meanings and context association, and thus alleviating short text sparseness and the low number of feature problems. Such semantically enhanced feature representation enables machine learning algorithms to discover deeper patterns in data and assure better generalization. In addition, it greatly affects the performance of text classifiers in terms of computational time and learning accuracy over the traditional one.One main limitation of our approach is that the number of topics to detect from the unstructured textual documents is user-defined. However, determining the optimal number of relevant latent topics is not straightforward, as this quantity is dataset dependent and not known in advance. An inadequate or exorbitant number of topics can degrade the predictive performance of the classification algorithms built upon topic modeling. In addition, each topic model requires several other parameters to calibrate, which are critical to their performance, and their configuration is not a tedious task.


Composite nanofibers with excellent physical and chemical properties are widely used in new energy, biomedical, environmental, electronic, and other fields. Their preparation methods have been investigated extensively by many experts. High-speed centrifugal spinning is a novel method used to fabricate composite nanofibers. The slip mechanism of polymer solution flows is an important factor affecting the morphology and quality of composite nanofibers prepared by high-speed centrifugal spinning. As the polymer solution flows, the liquid wall slip occurs inside the nozzle, followed by liquid–liquid interface slip and gas–liquid interface slip. The factors affecting polymer slip were investigated by developing a mathematical model in the nozzle. This suggests that the magnitude of the velocity is an important factor that affects polymer slip and determines fiber quality and morphology. Under the same rotational speed, the smaller the nozzle diameter, the greater the concentration of velocity distribution and the smaller the diameter of the produced composite nanofibers. Finally, PEO/PVA composite nanofibers were prepared using high-speed centrifugal spinning equipment at 900–5000 rpm and nozzle diameters of 0.2 mm, 0.4 mm, 0.6 mm, and 0.8 mm. The morphology and quality of the collected PEO/PVA composite nanofibers were analyzed using scanning electron microscopy (SEM) and TG experiments. Then, the optimal parameters for the preparation of PEO/PVA composite nanofibers by high-speed centrifugal spinning were obtained by combining the external environmental factors in the preparation process. Theoretical evaluation and experimental data were provided for the centrifugal composite spinning slip mechanism and for the preparation of composite nanofibers. Nanofibers exhibit excellent physical and chemical properties and are widely considered to be nanomaterials with great application potential . The mosCompared with several traditional methods of preparation methods, high-speed centrifugal spinning is simple and easy to use to produce nanofibers from various materials efficiently and cost-effectively . A serieA schematic illustration of the planar structure of the high-speed centrifugal spinning device is presented in In the early stages of hydrodynamic development, a few simple experimental observations were consistent with the assumption of no slip, so the slip phenomenon received little further attention. In recent years, with the rapid development of nanofabrication technologies, slip has played an important role in many industrial fields, such as electrochemical devices, polymer processing, and microfluidics ,29. The The slip problem was first introduced by Mooney , who expHatzikiriakos  investigYou et al.  analyzedRecent studies have shown that spinning parameters such as motor speed, nozzle length, nozzle diameter, nozzle structure, and solution concentration directly affect the morphological distribution of nanofibers during high-speed centrifugal spinning . Our stuIn previous studies, the no-slip boundary condition was one of the core concepts of fluid mechanics. This theory is reflected in a vast number of experimental results and utilized in many studies, which led to the neglect of the slip theory in related studies. With the significant improvement in experimental techniques, the slip phenomenon has been clearly demonstrated. The no-slip boundary condition is usually not maintained on the microscopic scale at sufficiently high shear rates. Many complex fluids exhibit inconsistencies with the no-slip boundary conditions, as most spinning solutions are non-Newtonian fluids characterized by a certain degree of viscoelasticity. Therefore, it is important to accurately determine the fluid properties of the polymer. In the case of high-speed rotational motion, the viscosity of the polymer solution of the wall-adherent layer varies with the shear rate, so both polymer solutions slip with the nozzle wall, the solution contact surface, and the air. The liquid–wall slip of polymer solutions during centrifugal composite spinning improves the extrusion and expansion of composite nanofibers and thins the average diameter of composite nanofibers. The liquid–liquid slip changes the internal structure of the composite nanofibers, resulting in composite nanofibers with different properties. Therefore, the polymer slip mechanism has a remarkably important effect on both polymer flow and deformation.b is the slip length . If b = 0, then there is no-slip boundary condition. However, if b ≠ 0, there is a slip boundary condition. su indicates the liquid wall slip velocity; The “slip length” is the most common concept used currently to quantify non-liquid slip on solid surfaces. This theoretical model was introduced into the nozzle to describe the slip on liquid and solid surfaces using the slip length:A parametric model was built in the nozzle, as shown in k and n are the rheological parameters. As the polymer solution is a non-Newtonian fluid, it does not conform to Newton’s law of internal friction. Considering it as a power-law fluid, its flow velocity distribution is determined:In Equation (2) above, r is the length of the distance between the nozzle wall and the middle partition; The shear stress also increases as the nozzle radius increases. The polymer flow rate distribution starts to decrease. Substituting Equation (3) into Equations (4) and (5), the following can be obtained:Q in the nozzle is:The flow rate Substituting Equations (2) and (3) into Equation (6) gives:The size of the nozzle diameter affects the velocity distribution and flow rate of the polymer solution in the nozzle as well as polymer jet formation. It also indicates differences in the quality of the composite nanofibers obtained via nozzles of different diameter.High-speed centrifugal spinning technology uses the centrifugal force generated by high-speed rotation as the driving force to produce nanofibers, primarily via high-speed rotation of the polymer in solution as the spinning material. At this time, the nozzle is in a high-speed rotational motion. The nozzle compound spinning solution in the multi-field exhibits a coupling effect along the nozzle outward movement. As the two different polymer solutions are continuous media, they permeate and couple with each other. However, the different kinematic properties lead to sliding of the two polymer solutions against each other, creating a two-phase flow. The flow of spinning solutions with different characteristics and concentrations generates a contact surface between the two solutions. The solution molecules continuously diffuse through the contact surface, forming a mixed layer with limited miscibility between the two phases, as shown in q phase is determined by:p phase; p phase; p phase; p to phase q. Using the mass equation, Equation (9) can be derived:The continuity equation of the The momentum conservation equation is as follows:In Equation (10) above, In the initial stage of jet injection, the power wave is generated by fluctuations between the liquid–liquid two-phase interface in the mixed layer of the polymer solutions due to flow instability, as the polymer solution has yet to be solidified. The liquid–liquid interface with fiber length dx was selected for analysis, as shown in The static force per unit width acting on the micro-element body of solution 2 is:The total force per unit volume of solution 1 minus the total force per unit volume of fluid 2 can be expressed as:The flow tends to stabilize when the slip velocity of the two solutions has the following relationship:Therefore, the slip velocity of the liquid–liquid two-phase is an important factor determining the fluctuation across the liquid–liquid two-phase interface, and the fluctuation occurring at the two-phase interface will lead to the instability of the flow of the two polymer solutions, which in turn will affect the preparation of the composite nanofibers and the quality of the fibers after forming.Centrifugal forces are generated when the nozzle is in high-speed rotational motion, which causes the polymer solution to be subjected to a high-speed airflow from the time it forms a jet until after it leaves the nozzle. When the polymer solution forms a jet leaving the nozzle, it is stretched and evaporated to form composite nanofibers that are eventually collected. A gas–liquid layer is generated between the jet formed by the polymer and the air, resulting in gas–liquid slip. The micro-element dx at the fiber–gas–liquid interface in the nozzle was selected for analysis. Gas–liquid slip has a phase interface, with interactions at the interface as well as between the gas–liquid phases. Energy transfer also occurs across the gas–liquid phase, but both the gas-phase medium and the liquid-phase medium have their own separate physical characteristics, so the basic equations of each phase should be established to investigate the gas–liquid slip. The gas–liquid interface with fiber length dx was selected for analysis, as shown in q is the heat flow entering from the wall; Because the polymer solution undergoes stretching and evaporation, the conversion of liquid to gas under thermodynamic equilibrium is expressed as:A is the flow cross-sectional area and In Equation (17) above, Also from the mass flow rate calculation equation:x:The continuity equation for the gas–liquid two-phase mixture at the time of gas–liquid slip is obtained by combining Equations (17)–(19):So, the slip model for polymers is summarized as follows: the liquid–wall slip of polymers is related to the size of the nozzle diameter; the liquid–liquid slip velocity affects the stability of the polymer flow; the gas–liquid slip velocity influences the motion of the polymer.There is a wide range of materials that can be applied to the preparation of composite nanofibers by high-speed centrifugal spinning, and polyethylene oxide (PEO) and polyvinyl alcohol (PVA) were selected as the centrifugal spinning composite fibers in this experiment. The PEO exhibits excellent properties such as low solution rheology, the combined action with organic solvents, low ash content, and thermo-plasticity. It can be used as a detergent, flocculant, thickener, lubricant, dispersant, aqueous phase reducer, textile slurry, and water-soluble film. PVA is a non-toxic, tasteless, harmless water-soluble polymer, characterized by good density, high crystallinity, and strong adhesion. It is made of flexible and smooth film. It exhibits resistance to oil and abrasion, good gas permeability, as well as water resistance. It has a wide range of uses. It is a natural non-polluting resource. However, at low concentrations, the concentration of the prepared PVA solution viscosity is not adequate to meet the requirements of centrifugal spinning out of the fiber. It exhibits worse performance. However, when the concentration of the prepared PVA solution is large, the solution viscosity is increased. Due to its strong hydrophilicity, the solvent is not easy to volatilize, seriously affecting the structure and performance of centrifugal spinning PVA fiber. The prepared PEO solution exhibits large viscosity at small concentrations. Together with PVA, it can effectively improve the material spinnability for the preparation of composite nanofibers by centrifugal spinning.In this experiment, specific amounts of PEO and PVA powder were added to water as solutes. PVA was fully dissolved at 90 °C to 100 °C with a magnetic stirrer at 200 rpm for about 5 h. PEO was fully dissolved at room temperature with a magnetic stirrer at 260 rpm for about 10 h. PVA solution with a mass fraction of 9% and PEO solution with a mass fraction of 4% were prepared.The high-speed centrifugal spinning equipment used in the experiments of this paper is shown in The centrifugal force is generated by high-speed rotation. The difference in the cooling rate of the spinning solution, and the change in concentration, viscous force, and surface tension during the solidification of the jet affect the steady-state motion of the composite jet, which eventually has an important effect on the final morphology of the composite nanofibers. Therefore, the experimental setup parameters of high-speed centrifugal spinning, the selected polymer solution parameters, and the experimental environment are important factors for the preparation and collection of composite nanofibers. In this paper, comparative experiments were performed at different nozzle diameters, different concentrations, and different motor speeds. Finally, the diameter and morphology of the collected composite nanofibers were characterized using scanning electron microscopy (SEM) and TG experiments.In the tank, 9 wt.% PVA solution and 4 wt.% PEO solution were added. The motor was connected and started after adjusting the device.Spinning experiments indicated that when the rotational speed was below 900 rpm, significant droplets were ejected and no fibers were collected. Because the rotational speed was too small, the polymer solution was not stretched enough and the jet was less stable when rotating in air. Continuing to increase the speed, the PEO/PVA composite nanofibers were prepared and successfully collected in the range of 1200~2100 rpm. Based on the morphology of PEO/PVA composite nanofibers at 1200 rpm and 1500 rpm by SEM, as shown in When the motor speed was in the range of 1200–3000 rpm, the SEM images at 1500 rpm, 2100 rpm, and 2700 rpm were selected, as shown in The initial diameter of the rotating jet is limited by the nozzle diameter as it leaves the nozzle, which in turn affects the diameter of the fibers. In order to investigate the morphological effect of nozzle diameter, PEO/PVA nanofibers were prepared using nozzles of different diameters. The SEM images of PEO/PVA nanofibers prepared with different nozzle diameters are shown in The thermal decomposition of PEO/PVA films was analyzed at a temperature increase rate of 10 °C/min and a range of 30–800 °C. As shown in According to the TG curves, the initial decomposition temperature of PEO/PVA composite film was 38.9 °C at 1500 rpm. The initial decomposition temperature of PEO/PVA composite film was 78.9 °C when the speed was increased to 2100 rpm. The thermal decomposition rate of PEO/PVA composite film fabricated at 2100 rpm was lower at 250~440 °C. Thus, the increase in the rotational speed enhanced the mixing of PEO and PVA and improved the thermal stability of the composite nanofibers of PEO/PVA. In addition, the improved spinning condition led to a gradual increase in the decomposition temperature, which enhanced the thermal stability.The external environment in centrifugal spinning experiments is also one of the most important factors affecting the final morphology and quality of composite nanofibers. In this regard, temperature and humidity are important factors of the external environment affecting the preparation of composite nanofibers. Temperature affects the solidification rate of the spinning jet, and the ability of the spinning jet to solidify quickly and be sufficiently stretched is the key to the preparation and collection of nanofibers. If the spinning jet is incompletely solidified by the time it reaches the collection device, a large number of droplets will appear on the surface of the collected composite nanofibers and stick together, which will lead to a poor morphology and quality of the prepared composite nanofibers. The effect of humidity on the water-soluble spinning solution is attributed to the solidification of PEO upon exposure to water. Therefore, excessive humidity during spinning can lead to solidification of the solution, resulting in nozzle clogging. Therefore, the temperature and humidity of spinning indirectly affected the experimental success of preparing PEO/PVA composite nanofibers. Appropriate adjustment of the environment temperature and humidity based on the physical properties of the spinning material and the device parameters is necessary to ensure a suitable experimental environment for the preparation of composite nanofibers.In this paper, the slip mechanism of the polymer in centrifugal composite spinning is analyzed and three slip models are developed. The slip distribution of the polymer is influenced by the rotational speed. As the rotational speed increases, slip occurs first between the nozzle wall and the polymer solution, then between the interfaces of the two different polymer solutions, and finally at the gas–liquid interface. At the same polymer solution concentration, fluctuations at the interface of the two phases can lead to an unstable flow of the two polymer solutions, and the slip velocity is an important factor affecting the fluctuations between the liquid–liquid interface. After the concentration of the solution is determined, a slow motor speed prevents fiber formation. As the the motor speed gradually increases, the polymer solution flow is gradually stabilized and the collected fiber morphology and quality are significantly improved; when the speed exceeds a certain range, the composite nanofibers break on the collection device. Therefore, it is necessary to control the speed within a certain range to make the polymer solution flow more stable, and the morphology and quality of the prepared composite fibers can improve. Moreover, when the speed is too large, the airflow near the nozzle is enhanced, which affects the process of gas–liquid slip and the fiber is easily broken. Meanwhile, when the velocity is the same, the smaller the nozzle diameter, the more concentrated the polymer flow velocity distribution, and the fiber diameter also decreases with decreasing nozzle diameter. Therefore, repeated controlled experiments are needed to find the optimal parameters for the preparation of composite nanofibers. The morphology, diameter distribution, and thermal decomposition process of the PEO/PVA composite nanofibers are analyzed and compared by SEM experiments and TG experiments. The effects of motor speed and nozzle diameter on the surface morphology and internal structure of the composite nanofibers during fiber preparation are obtained. It is confirmed that the motor speed and nozzle diameter are important factors affecting the polymer slip and the morphology of the composite nanofibers. However, the influence of the slip mechanism of polymer solutions on the structure and performance of composite nanofibers still needs further investigation and refinement. For example, the roughness of the wall surface during the liquid–wall interface slip and the high-speed airflow around the gas–liquid slip affect the preparation and collection of composite nanofibers significantly. Therefore, the selection of suitable experimental parameters is important to ensure composite nanofibers with good morphology for widespread application. Centrifugal spinning for the preparation of composite nanofibers has great potential for improvement. As further research and improvements are made to centrifugal spinning technology, the technology will continue to develop and be more extensively used in various fields.


Whether on a global scale or within China, the issue of bacterial antimicrobial resistance (AMR) has become a pressing concern. This study aimed to comprehensively investigate the disease burden of infectious diseases and AMR in China.Data on infectious diseases and AMR were collected by the Global Antimicrobial Resistance Burden study 2019 database. Our analysis steps consisted of 5 broad components: estimating the disease burdens of the total and 12 infectious syndromes by age and sex; describing the burden caused by 43 pathogens; calculating the burden caused by bacterial AMR in two scenarios , respectively; estimating the AMR burden of 22 bacteria; calculating the burden of AMR by 22 bacteria and 19 antibiotics combinations.S aureus, A baumannii, E coli, S pneumoniae, and E spp., which were associated with 41.2% (502658/1218693) of all infection-related deaths. The pathogens of different infectious syndromes exhibit significant heterogeneity. In 2019, 602561 deaths were associated with bacterial AMR, including 145465 deaths attributable to bacterial AMR. Six leading pathogen-drug combinations were responsible for more than 0.5 million deaths in 2019: macrolides resistance S aureus, fourth-generation cephalosporins resistance A baumannii, carbapenems resistance A baumannii, third-generation cephalosporins resistance A baumannii, aminopenicillin resistance E coli, macrolides resistance S pneumoniae.In 2019, there were an estimated 1.3 million (95% UI 0.8-1.9) infection-related deaths in China, accounting for 12.1% of the total deaths for that year. Males were 1.5 times more affected than females. Bloodstream infections was the largest fatal burden syndrome, associating with 521392 deaths (286307-870583), followed by lower respiratory infections (373175 deaths), and peritoneal and intra-abdominal infections (152087 deaths). These five leading pathogens were This study provides the comprehensive assessment of the burden of infectious diseases and bacterial AMR in China. The annual death toll of 1.3 million infection-related deaths, including 600000 deaths related to antibiotic resistance, requires sufficient attention.All Authors: No reported disclosures


Livestock farming provides a major source of animal protein and occupation opportunities for a large proportion of the world’s population, and its profitability could be effectively increased by improvement of either feed efficiency  or reproThis Research Topic aimed to collect new research in the field of “Genetics of reproduction in livestock species”. After announcing the Research Topic, a total of 20 original articles were received. However, after intensive review processes, only five articles were accepted and published in this Research Topic. A summary of the results of the articles in this Research Topic is as follows.Yang et al.).1. Identification of genes related to sexual differentiation and sterility in embryonic gonads of mule ducks by transcriptome analysis 2. Genetic parameters estimation and genome molecular marker identification for gestation length in pigs  as the molecular breeding markers 3. Genome-wide analysis for the melatonin trait associated genes and SNPs in dairy goat  4. Comparative gonad transcriptome analysis in cobia 5. The alternative transcription and expression characterization of Cobia is known as a fast-growing fish with a high commercial value, and many fish producers favor a monosex culture because of the higher body size of female fish. However, the effects of genetic factors associated with the sexual development and sexual phenotype in cobia are largely unknown. This study is possibly the first attempt to detect sex-biased differentially expressed genes (DEGs) in cobia fish gonads, using comparative gonad transcriptome sequencing analysis. A total of 76 candidate genes, including 42 known testis-biased and 34 known ovary-biased DEGs were identified as the genes involved in sex determination cascades or sex differentiation pathways. A total of 11 pathways, including Wnt signaling, oocyte meiosis, TGF-β signaling, and MAPK signaling pathways, were identified as functionally related to sex determination and differentiation. The results of this study provide a molecular basis for further studies on cobia’s sex determination mechanism and facilitation of monosex culturing.Dmc1 gene (DNA meiotic recombinase 1) in autotetraploid goldfish. This study showed different expressions of Dmc1 in diploid and tetraploid goldfish, and specific expression of the duplicated Dmc1 in the autotetraploid individuals is probably involved in meiosis progression of diploid-like chromosome pairing.The different fertilities and meiosis stabilities of established and neo-autotetraploids are known . This stIn addition to the subjects published in this Research Topic, there are several other interesting areas, including high-throughput methods, that should be subjected to future studies on the genetics of reproduction.Genome-wide association studies (GWAS) are widely used for detection of genomic regions associated with productive and reproductive traits. Previous GWASs resulted in detection of many quantitative trait loci and candidate genes associated with reproductive traits in different livestock species, such as cattle , sheep , goats , PoultryBMPR1B (BMP15 knockout in goats (GDF9 (OCT4) gene in cattle  in a single generation by using the CRISPR/Cas9 approach. This technology has been used for the identification of three well-known major fecundity genes in farm animals, including the Booroola mutation in ovine BMPR1B , the BMPin goats , and thets (GDF9 , as welln cattle . HoweverGene drive comprises the introduction of synthetic fragments involving a driving element, the CRISPR/Cas9 system, into the target genome, which can increase the spreading chance of specific alleles through the population compared to the standard Mendelian inheritance . A main Understanding major genes/mutations associated with the reproductive performance of farm animals could have a great impact on the economic efficiency of animal production enterprises. The articles published in this Research Topic improved our understanding of the genetics of reproduction in livestock species. However, there are huge numbers of questions about genetic factors associated with reproduction performance in farm animals, which should be considered in future studies.


Inappropriate use of antimicrobials exacerbates antimicrobial resistance (AMR) in the poultry sector. Information on factors driving AMR in the layer poultry sector is scarce in Zambia. This study examined the drivers of AMR in the layer poultry sector in the Lusaka and Copperbelt Provinces of Zambia.This cross-sectional study employed a structured questionnaire in 77 layer poultry farms in the provinces of Lusaka and Copperbelt, Zambia, from September 2020 to April 2021. Data analysis was conducted using Stata version 16.1. Antimicrobial resistance was defined as the presence of multidrug resistance (MDR) isolates. Multivariable regression analysis was used to identify drivers of AMR.Escherichia coli and 308 (84.4%) Enterococcus spp. were isolated. Multidrug resistance was identified in 39% of the E. coli and 86% of the Enterococcus spp. The overall prevalence of AMR in layer poultry farms was 51.7% . Large-scale farmers  than small-scale and farmers who were aware of AMR than those who were unaware  were less likely to experience AMR problems.In total, 365 samples were collected, from which 339 (92.9%) This study found a high prevalence of AMR in layer poultry farming linked to the type of farm management practices and lack of AMR awareness. Evidence of high MDR in our study is of public health concern and requires urgent attention. Educational interventions must increase AMR awareness, especially among small- and medium-scale poultry farmers. Antimicrobial resistance (AMR) is a public health problem that has been linked to the inappropriate use of antibiotics –4 and inEscherichia coli and enterococci ) of the respondents were large-scale farmers, and 39 (50.7%) reported never using prescriptions when accessing antimicrobials. A larger proportion (66 [85.7%]) used antibiotics for poultry, 66 (85.7%) never consulted a veterinarian, 45 (58.4%) used antibiotics for infection prophylaxis, and 40 (52.0%) used antimicrobials to improve production. Nearly all (90.9%) who owned a farm were male, 75 (97.4%) had a concrete type of floor in the poultry house, and 70 (90.9%) had biosecurity measures in place. Furthermore, 61 (79.2%) reported using farm workers to administer antimicrobials, 48 (62.3%) knew about the withdrawal period, and 41 (53.3%) were aware of AMR. The overall median age of chickens at the time of assessment was 53 weeks (IQR: 38–68). In addition, there was evidence of an association between AMR and the person who administered antibiotics (p = 0.038) and awareness of AMR (p = 0.015) .E. coli isolates were highly resistant to tetracycline (54.6%), ampicillin (54.0%), and cefotaxime (30.4%) and highly susceptible to meropenem (94.7%), chloramphenicol (85.8%), and ceftazidime (85.3%) (The findings demonstrated that  (85.3%) .Enterococci isolates were highly resistant to tetracycline (80.5%), erythromycin (53.6%), and quinupristin-dalfopristin (53.2%), but highly susceptible to nitrofurantoin (77.6%) and chloramphenicol (71.1%) .E. coli isolates was 96.5% (95% CI: 93.73%–98.07%), of which 64.6% (95% CI: 59.22%–69.64%) were MDR. The prevalence of AMR in Enterococcus spp. was 99.4%, of which 86.0% (95% CI: 81.7%–89.5%) were MDR (The prevalence of AMR in were MDR .The prevalence of AMR at the farm level is shown in Results from multivariable logistic regression analysis are shown in E. coli and enterococci as indicator organisms. The study found that the prevalence of AMR in layer poultry farms was 51.9%. The prevalence of AMR for the isolated organism was 92.6% for E. coli and 84.4% for Enterococcus spp. The prevalence of MDR for E. coli was 39% and that of Enterococcus spp. was 86%. Large-scale poultry farmers were less likely to have AMR in their poultry than small-scale farmers, because commercial farmers employed professionals trained in managing an enterprise. In addition, farmers who were aware of AMR were less likely to have AMR in their poultries than those who were unaware.This study identified the drivers of AMR in layer poultry farms in Lusaka and Copperbelt provinces using E. coli and enterococci. Consistently, other studies have reported high AMR rates of E. coli and enterococci [E. coli and enterococci develop natural resistance to antibiotics [E. coli and enterococci were too high to be solely accounted for by the evolution of natural resistance.In our study, the prevalence of MDR was high, translating into a serious AMR problem in layer poultry farms. This was evidenced by the high AMR and MDR rates of erococci , 98–100.erococci . In addiibiotics . TherefoThis study found that large-scale layer poultry farmers were less likely to encounter AMR problems in their flocks. These findings are similar to those reported in China, where small- and medium-scale poultry farmers were more likely to misuse antibiotics than commercial farmers . A studyOur findings showed that layer poultry farmers who were aware of AMR were less likely to have AMR problems on their farms than those who were unaware. These findings agree with those of other studies that reported low awareness of AMR among poultry farmers –61, 64. Our study revealed that most farmers used antibiotics that were accessed without prescriptions. Some farmers used antibiotics for prophylaxis and growth promotion. These findings are similar to those reported in other studies , 112. ThOur study highlights the potential drivers of AMR in poultry in Zambia. However, the results might have been better with a qualitative study because we collected data using a cross-sectional study. Consequently, we could not use focus group discussions due to the emphasis on adhering to the COVID-19 prevention measures. Despite these limitations, our findings concerning AMR and intervention measures in poultry are important for health authorities and decision-makers.This study found a high prevalence of AMR in layer poultry farming, especially among small-scale layer poultry farmers who were mostly unaware of AMR. The drivers of AMR identified in this study demonstrate the need to provide educational interventions to poultry farmers for disease prevention practices. Finally, there is a need to increase awareness of AMR and the contributing factors, especially among small- and medium-scale farmers. This can be done during outreach services in poultry farms and through activities, such as educational workshops and conferences, with the farmers.StM and JBM: Conceptualized the study. StM, KY, MAH, GM, and GS: Collected the data and conducted laboratory work. StM, MoM, and JBM: Performed data analysis. StM, FNB, KY, MuM, SyM, MoM, MAH, VD, SKM, GS, GM, MeM, BMH, and JBM interpreted the data. StM, KY, MuM, SyM, MoM, VD, SKM, FNB, GS, MeM, BMH, and JBM: Wrote the original draft preparation. StM, MuM, SyM, GM, MoM, KY, MAH, VD, SKM, FNB, GS, MeM, BMH, and JBM: Reviewed and edited the manuscript. StM, FNB, KY, MuM, SyM, MoM, MAH, VD, SKM, GM, MeM, BMH, and JBM revised the manuscript for important intellectual content. MuM, SyM, BMH, and JBM: Supervised the project. All authors have read, reviewed, and approved the final. manuscript.


In the last decade, significant progress in tissue engineering, repairing, and replacing organs has been achieved. The design and production of scaffolds for tissue engineering are one of the main areas which have attracted the researcher’s interest. In this regard, electrospinning is one of the most popular methods of nanoscale scaffold similar to extracellular matrix production. This paper reports the fabrication of scaffolds consisting of radially aligned PCL nanofibers by utilizing a collector composed of a central point electrode and a peripheral ring electrode. The chemical and physical properties were compared using SEM, FTIR, XRD, and DSC experiments, as well as biological performance using the MTT method and cell morphology with nanofibers with random and unidirectionally morphology. Results of this study showed greater physical and biological properties for radially aligned nanofibers which make them an excellent candidate for wound healing applications due to the guided cell growth on this type of nanofiber. Wound dressings are tissue engineering scaffolds that assist the healing of wounds caused by burns, cuts, trauma, and diabetes . These dThe nanofibrous structure can be tailored for the final application by altering the biopolymers and production process. The first thoroughly studied strategy is utilizing a biopolymeric combination for scaffold fabrication ,6. NaturNumerous techniques have been reported so far for producing nanofibers, the majority of which have produced nanofibers with random morphology. The nanofiber scaffold surface morphology can also be modified to adjust its final properties. Recently, novel techniques have been developed to give nanofibers a specific alignment. This alignment can be in one direction or radially aligned. Recent publications claim that physical characteristics like morphology and surface roughness impact biological responses and cell growth ,13. For Recently developed nanofibers with radially aligned morphology are used in biomedical applications. With the assistance of a collector made up of a central point and periphery ring electrode, Xie et al. developed radially aligned PCL nanofibers for the first time. They reported that the cells grew from the outermost part of the nanofibrous scaffold to the center of this construct . Shim etAccording to the literature review, various studies have been conducted on nanofiber fabrication with unidirectionally and radially aligned nanofibers. However, no comparative study has been reported on fibroblast cell biocompatibility in any of these studies. Therefore, the current study’s main objective was to investigate previously unreported effects of different morphology on the growth of fibroblast cells in the same experimental environment. The current manuscript involves the fabrication of nanofibers with a random, unidirectional, and radially aligned morphology. Then their physical characteristics are investigated using scanning electron microscopy (SEM), fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). Moreover, their biological properties, such as biocompatibility behavior, are studied. We hypothesize that radially aligned nanofibers will have the same chemical composition but different physical properties. In addition, we believe that the nanofiber alignment will influence faster fibroblast cell growth and a shorter healing process.All chemicals in this study were analytical grade. Polycaprolactone (PCL) (MW= 80000 Da), penicillin-streptomycin solution, trypsin, and methylthiazolyldiphenyl-tetrazolium bromide (MTT) were purchased from Aldrich. Phosphate-buffered saline (PBS) tablet, acetic acid, dimethyl sulfoxide (DMSO), and potassium bromide (KBr) were provided by Merck. The fetal bovine serum (FBS) was bought from Bioidea.In this research, nanofibers with random, unidirectionally, and radially aligned morphology are produced. The electrospinning process for the mentioned nanofibers used the same polymer solution. PCL granules were dissolved in 90% acetic acid at a concentration of 15% (w/v) and mixed for 6 h to get a homogeneous polymer solution . For ranFirst, a small piece of each sample was mounted using carbon glue on an SEM sample holder. Then, Bal-tec equipment was used to sputter a thin gold layer onto nanofibrous samples. The morphology of the electrospun nanofibers was analyzed using a Seron AIS2100 SEM at 5 kV accelerating voltage and ≈12 cm working distance. The average diameter of nanofibers was determined using Image J image analysis software.−1 wavenumber, with 40 scans conducted for each sample.The samples were ground into small pieces using a ceramic porcelain pestle and mortar, then mixed with KBr salt and pressed into a tablet. These tablets were then mounted in a NEXUS 670 (Thermo Nicolet Co. USA) FT-IR spectrophotometer. The FTIR spectrum was then recorded in transmittance mode from 400–4000 cmAbout 5 mg of each sample was placed in a sealed DSC pan and transferred into a DSC 2010 TA instrument. The samples were heated at a rate of 10 °C/min from 0 to 90 °C. They were then kept at 90 °C for 10 min to remove the polymer’s thermal history. Finally, they were cooled to 0 °C at a rate of 10 °C/min.α beam (λ of 1.54 Å) at a step width of 0.25° min−1.X-ray diffraction measurement was used to investigate the crystalline structure of electrospun nanofibers with different morphologies. First electrospun meshes were detached from the collector and placed in an X-ray sample holder, and the spectrum was recorded using Inel Equinox 3000 XRD equipment. The X-ray diffraction pattern was recorded from 5° to 80° in reflectance mode using a Cu K3 cells maintained in cell culture media  were seeded in each well. The samples were incubated at 37 °C, 5% CO2, and 95% relative humidity, and the culture medium was changed every 48 h after incubation for 1, 3, and 7 days, the culture medium was removed, and 100 μL MTT solution (0.5 mg/mL) was added to each well and incubated at 37 °C for 4 h. The MTT dye reacts with cells and produces formazan precipitates. Then 100 μL DMSO was added to each well after removing the MTT solution. Note that the control sample consists of wells without nanofibers. Optical density was measured using a spectrophotometer. The cell viability was then calculated using the following equation.L929 cell line was used for the MTT assay. After being sterilized with UV light for 2 h and submerged for 30 min in 70% (v/v) ethanol, nanofiber mats were placed in 96 well plates. Then 100 μL of 3 × 10s and ODc are the respectively average optical density for the examined substrate and control sample. This experiment was replicated three times.Where OD4 fibroblast cells were seeded on each mat. Afterward, Plate was incubated at 37° C, with 95% relative humidity and 5% CO2 for 24 h. Then culture medium was removed, and cells were fixed using 2.5% (v/v) glutaraldehyde aqueous solution at 4 °C overnight. Finally, glutaraldehyde was discarded, and samples were washed using distilled water to remove unreacted chemicals or salt precipitates. Then samples were dried in a freeze drier at −80 °C overnight.The morphology of the cultured cells was examined using scanning electron microscopy (SEM). Electrospun mats were placed in 24 well cell culture plate after sterilization. Then 1 × 10Radially aligned nanofibers at various sites were recorded in low magnification SEM pictures . These r−1 (C-H stretch sp3 asymmetric), 2866 cm−1 (C-H stretch sp3 symmetric), 1730 cm−1 (C=O stretch ester), 1468 cm−1 (CH2 bending oop), 1365 cm−1 (CH3 bending oop), 1294 cm−1 , 1240 cm−1 (C-O-C stretch asymmetric) and 1174 cm−1 (C-O-C stretch symmetric) are confirming the molecular structure of the PCL polymer.The functional groups of produced samples were analyzed using FTIR spectroscopy, shown in Analyzing the crystalline structure of the produced nanofibers with different morphologies was examined by the X-ray diffractometer . There wThe peak ratios for random, unidirectional, and radially aligned nanofibers were 4.29, 5.78, and 7.29, respectively. As a result, random nanofibers exhibit the lowest crystallinity, while the radially aligned samples show the highest crystalline phase. During the electrospinning process, the fibers are drawn due to the electrostatic forces. In the unidirectionally aligned sample, extra mechanical tension is applied to nanofibers due to collector rotational speed, resulting in a higher drawing rate and further polymer alignment, and a higher crystalline phase. In the radially aligned sample, the electrostatic force is applied to fibers in two stages, first from the nozzle to the collector and second from the central point to the outer circular electrode. Therefore they are drawn twice during the fiber fabrication. This has resulted in a higher possibility of crystalline phase formation. When the solvent evaporates for electrospinning, molecular chains fold locally along the fiber axis, forming a crystallite. Wang et al. reported that PCL chain folding occurred along the [110] and [010] growth planes in a PCL lamellar single crystal, as it does in other aliphatic polyesters, and the polymer chains were mostly aligned perpendicular to the crystal’s base plane . In partm) at 56.8 °C with melting enthalpy (Hm) of 49.7 J/g. The crystalline volume can be calculated according to mo is the melting enthalpy of perfectly crystalline PCL, and Xc (%) is the volume percentage of the crystalline phase. Sheikholeslami Kandelousi et al. have reported that Hmo for PCL is 136 (J/g) [mo) and final (Tmf) melting points were respectively 50.8 °C and 59.2 °C. As can be seen from this observation, the crystal sizes in this sample are more uniform than those at random. During nanofiber fabrication, mechanical tension could have caused this phenomenon. Compared to unidirectionally aligned samples and random samples, radially aligned samples displayed different thermal behavior. Based on melting enthalpy, 50% of the sample was shown to be composed of a crystalline phase. Due to the double electrostatic tension mentioned earlier, this has occurred. Moreover, we observed two melting points in this sample: the first at 56.8 °C and the second at 58.5 °C. We believe polymers are located on the center and outer circular electrodes that cause the first melting point. While the second melting point, stronger than the first, could be attributed to nanofibers between electrodes. Therefore, we can conclude that there are two crystal sizes in this sample: small and large.Where H36 (J/g) . The cryThe biocompatibility of the nanofibers at different time intervals is tested using the MTT assay, and its results are reported in 2 for random, unidirectionally, and radially aligned nanofibers. These findings were comparable to the MTT assay, which shows improved cell adhesion and growth on aligned nanofibers. Thus, the alignment of nanofibers can significantly affect the cell growth behavior.The effect of nanofiber scaffold morphology on cell attachment was studied using SEM images of cultured cells on nanofiber samples . The resIn summary, we successfully produced PCL nanofibers with random, unidirectionally, and radially aligned morphologies. The chemical characterization showed that there is no significant difference in their chemical structure. However, their physical properties, such as crystallinity and thermal behavior, were influenced by the electrospinning technique. Results from DSC and XRD studies indicated that radially aligned nanofiber exhibits higher crystalline portion and thermal behavior, which is essential for biomedical applications. Moreover, biological studies also showed better performance of aligned nanofibers that can promote cell attachment and growth. Therefore results of this study suggest aligned nanofibers for wound healing applications.