However, pathophysiological conditions (insulin resistance) are characterized by a sustained DNL in the liver and aimed at preventing the excess accumulation of glucose, which result in increased tissue content of PA and disrupted homeostatic control of its tissue concentration. This leads to an overaccumulation of tissue PA, which results in dyslipidemia, increased ectopic fat accumulation, and inflammatory tone via toll-like receptor 4. Any change in dietary saturated FAs (SFAs) usually reflects a complementary change in polyunsaturated FA (PUFA) intake. Since PUFA particularly n-3 highly PUFA, suppress lipogenic gene expression, their reduction in intake rather than excess of dietary SFA may promote endogenous PA production via DNL. Thereby, the increase in tissue PA and its deleterious consequences from dysregulated DNL can be mistakenly attributed to dietary intake of PA.Palmitic acid (PA) is ubiquitously present in dietary fat guaranteeing an average intake of about 20 g/d. The relative high requirement and relative content in the human body, which accounts for 20–30% of total fatty acids (FAs), is justified by its relevant nutritional role. In particular physiological conditions, such as in the fetal stage or in the developing brain, the respectively inefficient placental and brain blood–barrier transfer of PA strongly induces its endogenous biosynthesis from glucose  Palmitic acid (PA) is one of the most abundant saturated fatty acids (SFAs) in nature, which is present in animal and human tissues, plants, algae, fungus, yeast, and bacteria. Its distribution varies both within species and among species, and its content can be influenced by several environmental factors as the variation of soil pH, nutrient–ion interaction, age, water, and climate , 2.The average dietary intake of PA is around 20–30 g/d representing about 8–10%en  and can In evaluating the effects of food on health, the overall macronutrient composition and structure need to be considered, i.e. the “food matrix” , meaningDietary PL represents 1–10% of total daily fat intake . PL is mOver 90% of dietary FAs are esterified to TAG preferentially hydrolyzed by digestive lipases  on sn-1,PA peculiar tissue distribution results in its better incorporation in several tissues, for example, adipose tissues, with a lower deposition of fat in the visceral depots and higher in the subcutaneous fat . InteresMost of the studies aimed at evaluating dietary FA intake rely on food frequency questionnaires (FFQs), and food diaries where even repeated measurements do not necessarily provide valid measures of individual intake. Extreme intakes may reflect under- and overreporting rather than true low or high intakes, and subjects most prone to reporting bias may be repeatedly misclassified in quantiles of the distribution . In addir = −0.02 to 0.09)  –60.Factors other than dietary intake have been suggested to influence FA composition in tissues, first FA metabolism efficiency, genetic variations, and even intrauterine and perinatal program. In fact, considering the relationship between the tissue FA composition and dietary fat, among plasma lipid fractions, only TAGs appear to reflect dietary polyunsaturated FA (PUFA) and SFA, but not monounsaturated FA (MUFA)  within tde novo lipogenesis (DNL) with the accumulation of VLDL-TAG PA that led to linoleic acid (LA) reduction probably due to dilution effect, whereas with high-fat  DNL is neglectable  and further desaturation via SCD1 to form oleic acid (OA). A possible protective capacity of OA to drive PA to be deposited in the neutral form of TAG , by the insertion of one double-bond through stearoyl-CoA desaturase-1 (SCD1) , which rm of TAG , 65 and m of TAG .When the energetic sources are in excess, the non-fat surplus, mainly carbohydrates, is converted to FA by DNL, a pathway that begins with the conversion of acetyl-CoA into malonyl-CoA by acetyl-CoA carboxylase (ACC). During fed and insulin-stimulated conditions, ACC increases malonyl-CoA levels whereas AMP-activated protein kinase (AMPK) stops the synthesis, probably by inhibiting sterol regulatory element-binding protein (SREBP) .Further evidence indicates that adipose tissue DNL supports metabolic homoeostasis of distant organs, as in liver and muscle, by producing cytokine-like lipids, lipokines, with antidiabetogenic and antiinflammatory activities, such as POA and branched FA esters of hydroxy FA (FAHFA) , 68.In normal conditions, adipose tissue is the major site for DNL, which significantly contributes to body lipid reserves, energy storage, and to the maintenance of serum TAG homeostasis that derived instead from dietary sources –75. FurtOn the other hand, under specific conditions in the liver, such as insulin resistance, the impaired glycogen biosynthesis and consequent accumulation of glucose induce DNL that may contribute up to 26% to ectopically intrahepatocellular lipids in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). In fact, hepatic DNL is positively correlated with insulin resistance and fatty liver, whereas the correlation with adipose tissue DNL is the opposite , 79–81.In addition, a high-carbohydrate diet, particularly rich in simple sugars as fructose –84, actiRegulation of DNL occurs through the regulation of transcriptional factors as SREBP-1c and carbohydrate-responsive element-binding protein (ChREBP), activated by increased insulin signaling and increased glucose concentrations, respectively, and both induced by feeding , 92–95.via decreased expression of SREBP-1c  is quite controversial . The CocIn vitro cell culture studies showed that PA in the free form in the medium elicits, insulin resistance  and FADS2 (rs3834458) were associated with unfavorable FA profile in red blood cells . It has via DNL. Thus, the increase in tissue PA from dysregulated DNL and its deleterious consequences can be mistakenly attributed to dietary intake of PA .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We present an approach to discriminate SARS-CoV-2 virus types based on their RNA sequence descriptions avoiding a sequence alignment. For that purpose, sequences are preprocessed by feature extraction and the resulting feature vectors are analyzed by prototype-based classification to remain interpretable. In particular, we propose to use variants of learning vector quantization (LVQ) based on dissimilarity measures for RNA sequence data. The respective matrix LVQ provides additional knowledge about the classification decisions like discriminant feature correlations and, additionally, can be equipped with easy to realize reject options for uncertain data. Those options provide self-controlled evidence, i.e., the model refuses to make a classification decision if the model evidence for the presented data is not sufficient. This model is first trained using a GISAID dataset with given virus types detected according to the molecular differences in coronavirus populations by phylogenetic tree clustering. In a second step, we apply the trained model to another but unlabeled SARS-CoV-2 virus dataset. For these data, we can either assign a virus type to the sequences or reject atypical samples. Those rejected sequences allow to speculate about new virus types with respect to nucleotide base mutations in the viral sequences. Moreover, this rejection analysis improves model robustness. Last but not least, the presented approach has lower computational complexity compared to methods based on (multiple) sequence alignment.The online version contains supplementary material available at 10.1007/s00521-021-06018-2. The coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 viruses, whose origin lies probably in Wuhan (China), is a severe respiratory disease . Yet theTherefore, an analysis of virus sequences is essential to understand the spreading and the behavior of the virus population. One aspect is to distinguish several types of the virus, which may force different symptoms and medical conditions. Thus, sequences have to be compared regarding their genomic structure. This can be done by alignment methods or by alignment-free approaches, both coming with pros and cons. Further, the sequences have to be distinguished or classified with respect to their virus types. For this purpose, interpretable models are favored in comparison with black-box approaches like deep networks, because a medical interpretation of the classification decision process is highly desirable. In fact, this could help to detect new virus variants.The analysis of the genomic structure by sequencing is currently topic of ongoing research to better understand the molecular dynamics . ObviousCoronaviridae possess a single-stranded, positive-sense RNA genome ranging from 26 to 32 kilobases in length and frequently are extremely similar [Viruses of the family  similar . Therefo similar . Further similar . The mol similar . Yet, SN similar . General similar , 86. The similar , 72.Frequent mutations in SARS-CoV-2 genomes are in the genes encoding the S-protein and RNA polymerase, RNA primase, and nucleoprotein. Applying a sequence alignment and similarity comparison using the Jaccard index, a method for monitoring and tracing SARS-CoV-2 mutations was established in . HoweverAlignment-based methods usually rely on variants of the Levenshtein distance , which, lgorithm  and for lgorithm , 68, wheN being the number of sequences and l is the uniform sequence length. Other alignment-based methods for SARS-CoV-2 data consider (multiple) longest common subsequences with similar complexity [In case of multiple sequence alignments (MSAs), the dissimilarity problem is NP-hard . Currentmplexity .Bag-of-Words , informmplexity  and the Distance , 40. Rectraction , 42, 92.traction , 95. Theinterpretable machine learning methods for clustering and classification [In the present publication, we investigate whether alignment-free dissimilarities are suitable for the identification of SARS-CoV-2 clusters/classes in combination with fication , 5. Thisfication , whereasAlthough deep neural network approaches provide impressive results in sequence classification , 69, 72,Therefore, we focus on applying prototype-based methods using alignment-free dissimilarity measures for sequence comparison. In fact, prototype-based machine learning models for data classification and representation are known to be interpretable and robust , 82, 93.In order to investigate SARS-CoV-2 viruses in terms of sub-type spreading, two virus sequence datasets were considered.Rhinolophus affinis. After preprocessing, 160 complete human sequences are obtained as described in [B and C are sequences obtained from A by two mutations: the synonymous mutation T8782C and the non-synonymous mutation C28144T changing a leucine to a serine. A further non-synonymous mutation G26144T changing a glycine to a valine lead from B to type C. In this sense, the classes (virus types) code implicitly the evolution in time of the virus.The first one, abbreviated by ribed in , where tIn our data analysis, we removed two sequences, whose accession numbers occur twice in the data record, and another two, which we identified as not human resulting in 156 final sequences. Additionally, we take the type/class information as label for the virus genome sequences and, hence, as reference. A detailed data description as well as complete list of sequences can be found in . The virThe complete data information is found in supplementary files S12 Data.The second dataset including 892 complete genomes has been selected from the National Center for Biotechnology Information (NCBI) Viral Genome database  and GenBRemark, although the SARS-CoV-2 virus is an RNA virus, the sequences provided by databases are given using the DNA coding. In the following, we take over this convention and do not explicitly refer to that later.Again, the complete data information is found in supplementary files S12 Data.Natural Vectors and Bag-of-Words.Several approaches were published to represent sequences adequately for alignment-free comparison. These methods range from chaos game representation to standard unary coding or matrix representations. An overview is given in , 94, 95.Natural Vectors (NV) for nucleotide sequence comparison are based on a statistical sequence description for the distribution of nucleotide positions within a sequence relative position of the kth nucleotide L in the sequence. Let r. With this convention, we get L. Further, we denote by L in the sequence. The kth centralized moment K for a sequence K and =A,C,G,T , 42. LetNatural vectors are usually compared in terms of the  used in .L. These weights have to be taken into account during the expectation value calculations [The NV description of sequences can also be applied to nucleotide sequences containing ambiguous characters (degenerate bases) collected in the extension set cument}E , 92. ThiBag-of-words (BoW) based on 3-mers, where the set S of words contains all possible 64 triplets defined by the nucleotide alphabet probability densities. If the latter constraint is dropped, we have discrete representations of positive functions. The assignments of the triplets to the vector components Another popular method to compare RNA/DNA sequences is the method =A,C,G,T , 21, 84.cross-entropy. Yet, other divergences like Rényi divergences could be used [Obviously, comparison of those histogram vectors can be done using the usual Euclidean distance. However, motivated by the already mentioned density property, an alternative choice is to compare them by means of divergence measures . In the  be used . We refe be used  for a geThe assignment of the nucleotide triplets to the histogram dimension is found in supplementary material S13 Histogram Coding of Nucleotide Triplets.Median Neural Gas algorithm (MNG) is a neural data quantization algorithm for data compression based on (dis-)similarities [k-median centroid method improved by neighborhood cooperativeness enhanced learning, where k is the predefined number of representatives [k data points to serve as representatives of the data. Thereby, the data space is implicitly sampled according to the underlying data density in consequence of the so-called magnification property of neural gas quantizers [The larities , 16. It ntatives , 49. In antizers , 78.It should be emphasized that despite the weak assumption of a given similarity matrix, MNG always delivers exact data objects as representatives. Hence, any averaging for prototype generation like in standard vector quantizers is avoided here. This is essential, if averaged data objects are meaningless like for texts, music data, or RNA/DNA sequences, for example.Affinity propagation (AP) introduced by Frey and Dueck in [c-means or neural maps, where the number c of prototypes has to be chosen beforehand, AP starts assuming that all N data points are potential exemplars and reduces the number of valid prototypes (cluster centroids) iteratively. More precisely, AP realizes an exemplar-dependent probability model where the given similarities Dueck in  is an itk serves as prototype for data point i. The availabilities k is seen as a potential prototype for the points i.The cost function During the optimization, both kinds of messages are iteratively exchanged between the data by means of the alternating calculations according tording to .Hence, y ratios . The iter graphs , which cr graphs .The number of resulting clusters is implicitly determined by the self-similarities ustering . Common Learning Vector Quantization (LVQ) is an adaptive prototype-based classifier introduced by T. Kohonen [generalized LVQ [ Kohonen . A cost-ized LVQ . This coized LVQ . In partf and the classifier functionf is chosen as sigmoid: W to obtain an optimum prototype configuration in the data space.During the learning, the cost-based LVQ minimizes the expected classification error g (SGDL) , 57 of \standard generalized LVQ (GLVQ). If both The dissimilarity x,w from  come intrding to  is not rnetworks .divergence-based GLVQ (GDLVQ). We refer to [The resulting LVQ variant is denoted as refer to  for furtm being the projection dimension usually chosen Generalized Matrix LVQ (GMLVQ) [Another popular choice is the squared Euclidean mapping distanceposed in  with thementclasspt{minima (GMLVQ) . Note thechnique .classification correlation matrix, i.e., the matrix entries i and j, which contribute to the class discrimination [After training, the adapted projection matrix mination , 77. Thuained in  are not Instead of the linear ested in  or impliested in , 80.self-controlled evidence (SCE) [A trained LVQ model can be applied to newly incoming data of unknown distribution. However, care must be taken to ensure that the model remains applicable and that there is no inconsistency with the new data. Therefore, each LVQ can be equipped with a reject option for the application phase , 29. If ce (SCE) . The thrIn fact, this reject option improves the robustness of the model .The method of stochastic neighbor embedding (SNE) was developed to visualize high-dimensional data in a typically two-dimensional visualization space . For thit-distributed SNE (t-SNE) was developed [Yet, SNE suffers from the fact that the distance densities in the original data space are frequently heavy-tailed , which leveloped .Alphabet A natural vector representation mentclass2pt{minimA BoW-representation for 3-mers is generated for each sequence Coding of all sequences of A, B, or C taken as class labels.Training data are all samples of For all LVQ variants, we take only one prototype per class.For GMLVQ, the projection matrix is chosen as W using the GLVQ for NV representation.Training of W and GDLVQ is not applicable for this sequence representation due to mathematical reasons.Training of W using the GLVQ for BoW representation.Training of W and Training of SGDL training as tenfold cross-validation to determine the best LVQ architecture for the given problem.W.W and If GMLVQ architecture is selected for final training: training of both Determination of the reject thresholds for each prototype for self-controlled evidence use based on the Final training of the best LVQ architecture with optimum training schedule to achieve best prototype configuration Training of LVQ-classifiers for lts from  obtainedCompression of the subset of 706 US sequences of March by MNG to achieve 50 representatives by MNG using 50 prototypes.Generating a balanced subset consisting of all China samples (63), all Europe samples (33), and USA samples (114) for cluster analysis. The US samples comprise the 50 representatives from MNG and all US samples from January and February. The samples from other regions are not considered for cluster analysis. We denote this balanced dataset extracted from Clustering and identification of stable cluster solutions using affinity propagation by means of the control parameter Clustering Classification of the Evaluation of the data rejected by the SCE rule.Classification of the In the following, we describe and motivate the steps of data processing and analysis. W containing one prototype per class and the mapping matrix According to the processing workflow, we trained several LVQ classifier variants for the entclass1pt{minimaThe list of rejected sequences is provided in supplementary material t-SNE as depicted in S5 Fig. The respective cluster centroids are visualized in S6 Fig.The clustering of the A, 95 data points to class B, and 2 data points to class C. According to the SCE decision, 59 data points were rejected from classification by the learned GMLVQ classifier. The result is given in S7 Fig using the t-SNE as visualization scheme. The visualization of the classification result by means of the Applying the trained GMLVQ classifier to the The distribution of the sequence data from the A, 293 data points to class B, and 20 data points to class C, whereas 495 data points are rejected according to the SCE rule. The class assignments are visualized in S11 Fig.The classification of the full The predicted virus type or the rejection decision for each sequence from The classification analysis of the ested in . Only seentclass1pt{minimaested in . Thus, wested in .This agreement, however, is obtained by alignment-free sequence comparisons. More precisely, the nucleotide-based BoW sequence coding delivers a perfect separation of the given classes for the learned mapping distance N of data, we have an overall complexity of l, we finally obtain an overall complexity of Yet, the computational complexity of a single dissimilarity calculation for the encoded sequences is only  methods , becauseFurther, because GMLVQ is an interpretable classifier, we can draw further conclusions from the trained model: The resulted classification correlation matrix utations , 28. TheFurther, the GMLVQ prototypes serve as class “detectors.” If the encoded sequences are most similar to them with respect to the mapping distance, the sequences are assigned to the respective classes according to the WTA rule . Howevert-SNE visualization S7 Fig of t-SNE and AP implicitly reflect data densities in the data space. Class densities, however, do not have to coincide with the overall data density. Thus, the Application of the GMLVQ to the C for the C samples in the Comparing the class distributions of the sequences with respect to origins (regions) and collection dates for dings in . Thus, tWe observe from the visualization S11 Fig of the classification for the generalized matrix learning vector quantizer classifier model (GMLVQ) for a dataset with given virus type information, which was obtained by phylogenetic tree analysis [In this contribution, we investigate the application of interpretable machine learning methods to identify types of SARS-CoV-2 virus sequences based on alignment-free methods for RNA sequence comparison. In particular, we trained a analysis . GMLVQ sBy means of the trained GMLVQ, we first verified the SARS-CoV-2 virus types determined in this first dataset. Further, considering a classification correlation matrix delivered by GMLVQ optimization, we are able to identify features which contribute decisively to a type separation.Second, we applied the trained GMLVQ to another dataset obtained from the NCBI database without virus type information. Using the self-controlled evidence property of the GMLVQ, we are able to classify these sequences to the previously identified types, avoiding the application of the model to inconsistent data compared to the training data. Further, the rejected data allow speculations about new virus types with respect to nucleotide base mutations in the viral sequences.Yet, an appropriate training and data coding for successful GMLVQ application require a careful and precise data handling as well as model training regime, i.e., respective expert knowledge.Future work will consider the replacement of the WTA rule  by a fuzSupplementary material 1 (pdf 601 KB)Supplementary material 2 (XLSX 45KB)Supplementary material 3 (XLSX 10 KB)Supplementary material 4 (XLSX 25KB)Below is the link to the electronic supplementary material.


The purpose of this study was to evaluate annual incidence and burden of revision THAs and to project the future burden in South Korea.The annual number of hip arthroplasties is increasing combined with the aging population worldwide. In accordance with the increasing number of primary hip arthroplasties, the number of revision total hip arthroplasties (THAs) is expected to increase. The incidence and burden of revision THAs in the United States and have been reported by registry studies. To identify potential differences according to ethnics and regional practice, it is important to obtain data from East Asia. Nevertheless, there has been a lack of studies on the burden and future projection of revision THA based on a large-scale database in East AsiaWe identified primary THAs, primary hemiarthroplasties (HAs) and revision THAs, which were performed from 2010 to 2018, using database of Health Insurance and Review and Assessment (HIRA); nation-wide medical claim system of South Korea. The annual incidence rates  of primary THA, primary HA and revision THA, and the annual burden of revision THA; the number of revision THAs divided by the sum of primary hip arthroplasties and revision THAs, were calculated. The future burden of revision THAs were projected through 2030 using generalized linear model with Quasi-poisson regression.During the 9-year period, the annual incidences of primary THA, primary HA and revision THA increased by 47, 29 and 3%, respectively, while the revision burden decreased from 0.13 to 0.10. Compared to 2018, the annual incidences of primary THA, HA, and revision THA were projected to increase by 7.2, 2.3 and 1.1% per year, respectively, whereas the burden of revision THA was projected to decrease to 0.07 in 2030.Trends of revision THA in South Korea were similar with those of national registry studies from the United States. The annual incidence of revision THA has steadily increased, whereas its burden has decreased. Findings of our study could be used for epidemiological comparison between Western countries and East Asia as well as for the establishment of medical policies of revision THA in East Asian countries.The online version contains supplementary material available at 10.1186/s12891-021-04235-3. Total hip arthroplasty (THA) is one of the most effective orthopedic procedures and has been utilized as the treatment of choice for the patients with advanced osteoarthritis of the hip –4. HowevIn the United States, the annual number of revision THA has steadily increased, , 9 but tThere might be regional differences in the burden of revision THA according to ethnicity, implant design, fixation method; cemented versus cementless, and bearing surface .Number of studies outside the United States reported the annual incidence of revision THA –16. EpidNevertheless, there is lack of epidemiological study of revision THA in East Asia, which is necessary for regional comparison of the revision THA burden between Western countries and East Asia and for awareness of worldwide tendency.The purpose of this study was 1) to analyze the burden of hip arthroplasty including primary THA, HA and revision THA from 2010 to 2018 and 2) to provide a future projection of these procedures to 2030 in South Korea.This is a retrospective study using secondary register-based data analysis. The Korean Health Insurance and Review and Assessment (HIRA) database includes medical claims from entire South Korean institutions. Ninety-seven percent of South Korean population is obliged to register into the Korea National Health Insurance Program (KNHIP).The remaining 3% of the population is covered by the Medical Aid program, which is paid by the government. Data of patients under the.Medical Aid program are submitted to HIRA in the same manner as KNHIP. Therefore, the HIRA database included the entire South Korean population.The HIRA database contains demographics, diagnoses, procedures and prescriptions of all THA patients in South Korea.We identified primary THAs, primary HAs and revision THAs, which had been done from January 2010 to May 2014 in South Korea, using Electronic Data Interchange (EDI) codes of THA (N0711) and hemiarthroplasty (HA) (N0715).Since June 2014, the Korean National Health Insurance Service (NHIS) added complex surgical procedure codes  (N2070) and hemiarthroplasty-hip (complex) (N2710)) for the specific 21 complex conditions for reimbursement of an additional cost to the medical institute  and complex revision THA .The numbers and crude incidence rates  of THA, HA, and revision THA procedures were calculated according to age and gender. Annual incidence rate of each arthroplasty was calculated by dividing the number of each procedure at each year with corresponding year-specific South Korean population. The annual population data were acquired from database of the Korean Statistical Information Service. Then, the incidence rate per 100,000 person-year was calculated. The annual rates were stratified according to age and gender.The burden of revision THA was defined as the proportion of revision THAs to overall sum of hip arthroplasties; primary THAs, primary HAs and revision THAs . The futFrom 2010 to 2018, 87,213 primary THAs, 116,079 primary HAs, and 25,232 revision THAs were done in South Korea. The number of each surgery at each year is summarized in Table During the 9-year period, the annual crude incidence of primary THA and that of primary HA increased by 47%  and 29% , respectively. Nevertheless, the annual incidence rate of revision THA remained stable at an average of 5.5/100,000   16,792 – 27,451) and 18,384 , respectively, while the number of revision THA was predicted to be 3241 (95% CI 3042 – 3454) in 2030  to national registry data of Australia, New Zealand, England, Wales, Sweden and Norway . In the As opposed to the study of Heckmann et al., Patel et al. reported an increase of revision THA burden in England and Wales from 0.09 in 2008 to 0.11 in 2012 .The annual incidence rate  of primary THA and revision THA increased from 101 to 134 and from 19.2 to 21.1 per 100,000 Danish inhabitants from 1996 to 2002, respectively. The incidence rates of primary THA and revision THA increased by 30 and 10% during the same period . In lineIn a previous study of THA epidemiology from South Korea, both the number and incidence rate of primary hip arthroplasty increased steadily from 2007 to 2011, but there was no significant change in the number of revision THA during the period .The worldwide trend of decrease in the burden of revision THA Table  means reThere might be some explanations for this decrease of the revision burden. First, surgical technique for primary THA has been improved with time . Second,The strength of our study was that the study is based on national registry data. The results cover nearly 100% of all hip arthroplasties in South Korea. Such data are only available in few countries. Our study was based on national registry data and has inherent limitation. We could not identify the reason of arthroplasty, type of original prosthesis, type of fixation; cementless versus cemented, bearings surfaces and the reason of revision surgery in patients undergoing revision THA, because of de- identification of the HIRA database.In this study, we confirmed an increase in number and incidence of revision THA as well as primary THA and HA from 2010 to 2018 in South Korea. The burden of revision THA in that period gradually decreased from 0.13 to 0.10, which was similar to reports from Western countries.Additional file 1. Complex conditions for reimbursement in arthroplasty in South Korea.


However, precise measures of symmetry are often difficult to formulate and apply in a meaningful way to biological systems, where symmetries and asymmetries can be dynamic and transient, or be visually apparent but not reliably quantifiable using standard measures from mathematics and physics. Here, we present and illustrate a novel measure that draws on concepts from information theory to quantify the degree of symmetry, enabling the identification of approximate symmetries that may be present in a pattern or a biological image. We apply the measure to rotation, reflection and translation symmetries in patterns produced by a Turing model, as well as natural objects . This method of symmetry quantification is unbiased and rigorous, and requires minimal manual processing compared to alternative measures. The proposed method is therefore a useful tool for comparison and identification of symmetries in biological systems, with potential future applications to symmetries that arise during development, as observed This article is part of the theme issue ‘Recent progress and open frontiers in Turing’s theory of morphogenesis’. We see symmetry in virtually all forms of life, from single-cell eukaryotes like ydamonas  to complydamonas . Symmetrydamonas , the dynydamonas ,5 and thydamonas –8. Symmeydamonas ,10. Grouydamonas ,12.not possessed by the patterned state.A general mathematical theory for the role of symmetry in the formation of spatio-temporal patterns through nonlinear dynamical processes was developed, in part, through interactions between mathematicians and experimental fluid dynamicists . The speSymmetries in models consisting of coupled differential equations typically have one or more of the following consequences, as discussed in : multiplSymmetries are often approximate in biological settings and the quantification of asymmetry can, in conjunction with mathematical models, potentially provide useful insights into underlying regulatory mechanisms. Many biological studies do not explicitly provide a definition of symmetry, but instead assume that readers understand what is meant by, for instance, bilateral symmetry in flowers . Indeed,minima in the resulting TI function are then identified as approximate symmetries of the object. Moreover, the relative values of the TI measures at these minima provide a way to quantify deviation from perfect symmetry. While this approach can be automated, optimization of the images to enhance contrast and crop out neighbouring objects can improve identification of the approximate symmetries. We apply this measure to a number of test cases, and show that it correctly identifies fivefold symmetry in patterns produced by a Turing model, and approximate rotation and translation symmetries in organic objects including flowers and leaves. By quantifying the asymmetry of approximate symmetries in a system, this measure allows for comparison across diverse samples, and a method for identifying and quantifying changes in symmetries due to perturbation in an unbiased way.Here, we propose a measure of asymmetry that is grounded in concepts of entropy and information theory , and app. 2et al. [Methods for the quantification of asymmetry have appeared in a variety of contexts, ranging from automatic facial recognition  to quantet al. ,37 to quet al. ,39. A diet al.  or rotatet al. . The metTransformation Information has been proposed as a quantitative measure of symmetry breaking in the context of condensed matter systems . It linkIn order to define the transformation information-based measure of asymmetry, we first construct a positive-valued function, (pi/qi) ,41. One In practice, since we apply this measure to images, we use pixel intensities as a choice for the function equation  over the (a)In appendix A, we provide an algorithm for the application of TI to identify approximate symmetry in biological images. We summarize these steps in We consider a reaction–diffusion system, numerically studied in –23, thatvided in .We use the numerical scheme proposed in  to solvea shows a fivefold regular pattern similar to the ones obtained in [figure 1a is already stored as an array of intensity values given that we numerically solve equations  of the method for identifying the coordinates of the image centre is not necessary and does not impact the results of the symmetry analysis.For the pattern shown in figure 1equation  and Stepμ=u+v in  as the sc provides an alternate representation of TI in the form of a polar plot (as in Step (4) of the algorithm). In both representations, we mark a peak of d, where blue indicates positive values and red indicates negative values. Such a minimum can be considered an approximate symmetry, with the deviation from perfect symmetry measured by the difference of TI from zero. The local maximum of TI, with d, corresponds to rotations by angles which can be considered least symmetric. The difference in intensities are large and the value of TI associated with these approximate antisymmetries can be useful in providing context for interpreting values of the measure associated with symmetries.Figure 1. 3The proposed method of identifying approximate symmetries can be applied to biological objects which can exhibit a variety of symmetry properties, for instance, objects which possess a left/right or anterior/posterior symmetry. This type of symmetry is called bilateral symmetry, where an object produces two mirror image halves when divided along an axis . For thiMeasures have been proposed to quantify the deviation between two halves in a bilaterally symmetric object such as a leaf ,30. In tbay leaf , we compa, which can be classified as having a strong degree of bilateral symmetry about the central vein. Although this central vein is not straight and the leaf may not be exactly divided along the horizontal in the image, we can attempt to detect the symmetry by considering a reflection about a horizontal line through the centre of the image. The deviation from perfect symmetry becomes more apparent in figure 2b, which shows the reflected image superimposed on the original image. Panel (c) quantifies the asymmetry using the difference in total pixel intensity between the image and its reflection, with red (blue) indicating higher values for the original (transformed) image.Consider the image of the leaf shown in figure 2c indicates that considering reflections about a slightly tilted axis may provide a better approach for bilateral symmetry. We therefore find a centre point using the algorithm described in appendix A and compute the TI for reflections about an axis with angle d shows the axis that minimizes TI (solid green) for axes within the dashed lines. The minimum TI is associated to an angle of approximately e.The difference in pixel intensity shown in figure 2f by thresholding on the greyscale image. We find a central point by equalizing the total area of the leaf above/below and left/right, and consider the SI for axes shown between the dashed orange lines in panel (f). The same angle of approximately h), although the centre points found by the two methods are not exactly in the same location.In order to compare the TI approach to the area-based SI measure of asymmetry proposed in , we alsofigure 2Micrasteria) shown in figure 3b. This cell consists of two semi-cells and is regenerating its smaller bottom portion after having previously divided. As with the bay leaf, the bilateral symmetry can be associated with a reflection, this time about a vertical line through the centre of the cell. Although the upper and lower sections of the cell have a similar structure, there is a clear asymmetry between the top and bottom with the top having longer cellular extensions and the bottom still regenerating. As a result, the line dividing the upper structure of the desmid from the lower structure does not fall in the centre of the organism. Thus, the upper and lower halves cannot be easily detected by considering reflection symmetries. The difference between the image of the desmid and its reflection about the central line is shown in panel (a). The difference for reflection about the line dividing the upper and lower halves is shown in panel (c). Since the differences are larger in panel (c), transformation-based methods have difficulty detecting the upper and lower structure in the image.Another example of bilateral symmetry can be seen in the microscope image of the freshwater unicellular desmid , there are a number of possibilities that would be interesting to explore as a way of extending this measure in a similar way. Spatially dependent transformations may also provide a path towards identification of the upper and lower portions of the desmid, e.g. one could consider transformations consisting of a reflection along with an appropriate rescaling of the portion of the image on one side of the reflection axis. See the discussion of . 4Pachypodium flower in figure 4a is used here to illustrate the identification of approximate rotation symmetries. The specimen has not been flattened before being photographed, so the non-planar curvature of petals adds to the asymmetry. Figure 4b shows the excess of red pixel intensity over the average pixel intensity across the three colour bands. The edge of the flower along with the centre have been manually identified with appropriately tuned thresholding on various combinations of the colour intensities in figure 4c. The longest distance from the centre to the petal edge is also marked in panel (c).The The flower has five petals that are approximately evenly spaced and similar in size. There is a clear reflection asymmetry to the petal morphology, and thus one might expect rotational symmetries but not reflection symmetries of an idealized model of the flower.c. We compute TI associated to rotations at 12 evenly spaced angles about centre points within a 500 by 500 pixel window near the centre of the flower, and downsample the image by a factor of 16. Figure 4e shows the search window within the image, with the middle of the d), provides a good measure for the quality of the symmetries for rotations. The motivation to consider this measure is that, for perfect symmetries, TI has very large amplitude of variation as a function of rotation angle (see §2(a) and e. We also compare this to finding the centre by minimizing TI associated to reflections about horizontal and vertical lines passing through the search window, indicated by the green-circled e shows that the horizontal coordinate of the centre is well-approximated, presumably because the flower is aligned nearly optimally for vertical reflections to minimize TI. The vertical coordinate is further off , since there is no such alignment for vertical reflections. However, we do note a small local minimum in the TI associated to vertical reflections that is near the manually identified centre, indicated with the green dotted line in figure 4f. Lastly, we attempted the method of matching areas left/right and up/down, and indicate the result with a circled triangle in the search window. This provides a closer match than the reflection TI, but not as good as the rotation TI measure.We explore transformation-based automated methods for identifying the centre of the flower for rotations and reflections as compared to the manual identification shown in figure 4et al. [b). The symmetries detected as minima of this measure (which we denote by ZI) are the rotations associated to exact fivefold symmetry: We compute TI associated to rotations about the identified centre point by an angle re point . We compy et al.  (figure entified . By conta). The difference in pixel intensity between the transformed and original image for these two rotations are shown in figure 5d,e. Red in these images indicates higher pixel intensity for the original image, and the dashed lines indicate the location of the rightmost petal in the original and transformed images. Although the other rotation angles (c). The difference in pixel intensity between the reflection about f, with the dashed line indicating the axis of reflection.According to the local minima of the rotations (and −73∘ a. The di. 5a) of b), has a minimum at the centre which corresponds to the identity transformation (c) shows the absolute value of the Fourier spectrum of the deviation of We explore TI in the context of translation symmetry using the Turing model  and 2.32.3 on a a) of b)) and the Fourier spectrum (column panel (c)) show an associated increase in signal along the preferred We can induce a preferred direction by introducing a symmetry-breaking advection term of the form equation . For lar. 6minima in TI over the set of all transformations associated with potential symmetries. The minima in the TI function correspond to transformations exhibiting the highest degree of symmetry and are, therefore, considered the approximate symmetries with a lower TI value indicating that the symmetry is closer to exact. In addition, TI can be used to identify an optimal axis of symmetry for translation transformations, and optimal coordinates of the centre of the image for rotation transformations. These two features provide an advantage over current symmetry measures. In particular, current measures may introduce user error through identification of symmetry axes and landmark points on the boundary of the object of interest, both of which are determined algorithmically in the approach developed in this work. Importantly, the symmetry properties identified by the new measure were verified and shown to be reasonable for representative biological images and Turing patterns.In this study, we have developed an asymmetry measure based on transformation information (TI) and implemented it as a tool for identifying approximate symmetries in uni- and multicellular biological systems. The TI measure, originally proposed in the context of condensed matter systems, quantifies the difference between an object and its transformation associated with a symmetry of interest. In order to identify a set of approximate symmetries, we search for local b. There is a stem at the centre, with a series of opposing pairs of leaves moving outward in the image. The structure exhibits a fractal symmetry in leaf arrangement associated with rotation and rescaling of the image. Figure 7a shows TI for transformations that are a composition of rotation by an angle b), remains fixed under the identity transformation associated to c), (d). Panel (c) corresponds to the transformation with d) is associated to the transformation by We applied the new measure to a range of objects with distinct symmetry properties, including fivefold patterns produced by a Turing model, a bilaterally symmetric algal cell and leaf, and a pentamerous flower with rotational symmetry, thereby demonstrating the flexibility of the measure as well as its ability to reliably identify different types of symmetries. As an illustration that the TI approach applies beyond the symmetries commonly considered by other measures in the literature, we consider the cross-section through a catnip stem shown in figure 7The potential applications of this measure are broad, and especially suitable for analysis of objects under different conditions, e.g. TI can be used to quantify (i) the extent to which organic structures (such as leaves and flowers) manifest phenotypic plasticity in response to different environmental conditions, (ii) morphometric changes during ontogeny and growth, (iii) phenotypic transformations attending evolutionary transitions, (iv) pheno-type genetic variants within populations and (v) assessing pathological conditions.Symmetry is a ubiquitous feature of organic systems, and is often correlated with fecundity, survival and evolvability. The measure proposed here provides a robust and rigorous method for identifying approximate symmetries in any object, providing insights into fundamental structures and their properties. In addition, it can provide insights into the establishment of polarity through symmetry breaking in biological structures. It is worth noting that this measure provides an accurate description of symmetry phenomena in biological objects, but does not currently identify the corresponding symmetry-breaking mechanisms. While we primarily use the Turing reaction–diffusion process as an example of a pattern formation mechanism here, many other mechanisms have been identified and studied in the context of biological development, i.e. mechanochemical , local a


Aim: The aim of this study was to determine the health problems experienced by young adults after the COVID-19 vaccine. Method: This study is a quantitative and descriptive study and was completed with 590 undergraduate students studying at a state university in Central Anatolia in the spring semester of the 2021–2022 academic year. The data were collected by the researcher through a one-to-one interview with the students and a questionnaire prepared in line with the literature. Number, percentage and chi-square tests were used in the analysis of the data. Results: A total of 81.4% of the students participating in the study had the BioNTech–Pfizer vaccine. A total of 67.3% of them had two doses of COVID-19 vaccine, 35.9% of the vaccinated students experienced some health problems in the days following the vaccination, and the most common health problems were fatigue, a cough, sleep disturbance, psychological discomfort, a heart ache feeling and sweating. Most of the post-vaccine health problems lasted for 2 days, 3.7% of the participants were diagnosed with hypertension, 2.7% were diagnosed with diabetes mellitus and 10.52% of the female participants went to the doctor due to menstrual irregularity and received treatment. It was determined that 12.2% of the vaccinated students gained weight after vaccination and 63.89% of those who gained weight attributed this to increased appetite, 9.2% continued to have a cough and 9.2% used herbal products. Conclusion: It was determined that one out of every three young adults experienced a health problem after the COVID-19 vaccine. It is recommended that studies be conducted in different sample groups. COVID-19 is a contagious disease caused by coronavirus 2 (SARS-CoV-2); it manifests itself ,4,5.The disease first occurs when the virus binds to the cell receptor into which it will enter. Then, the RNA of the virus enters the cell. COVID-19 has two stages that it must perform in the cell. The first is binding to the receptor, and the other is membrane fusion. These stages are assisted by the Coronavirus Spike Protein (CSP). The CSP is found both in the receptor of the target cell and in the envelope membrane of the virus. The disease is mainly airborne. Spike protein is found in all COVID disease species that can be transmitted to humans. The spike protein is a Type 1 transmembrane protein. The N-terminal side faces the extracellular space and the C-terminal side faces the intracellular space. As the infection begins, the spike protein is cleaved by furin to form Spike 1 and Spike 2. Spike 1 contains a receptor-binding domain. The receptor-binding domain allows COVID-19 to bind to the complementary peptide domain on ACE2. Spike 2 facilitates the fusion of viral and host cell membranes. Interventions made during all these events will cause the failure of the stages. The N-terminal or C-terminal domains of Spike 1 can serve as a receptor-binding area. COVID-19 uses its C-terminal domain to bind to ACE2. It has been stated that the dimerization of the ACE2 peptide domain allows two viruses to bind simultaneously and increases the viral load. Proteases, ions and pH have direct and indirect roles for membrane fusion. While calcium stabilizes the peptide structure fusion, zinc and magnesium do the opposite ,7,8,9,10The best way to prevent the disease is to prevent transmission. COVID-19 PCR tests are one of the most important tools in diagnosing the disease and preventing its transmission. However, poor sampling techniques, sample deterioration and contamination of samples cause false negative results. According to the results of seroconversion, disease states can be determined. This may be an indicator of false negative results ,12.Many different vaccine studies are still being carried out in the scope of combating COVID-19 around the world. All developed vaccines are reviewed by the WHO and those which meet the requirements are approved . AlthougHowever, there are not enough studies to determine the long-term effects of vaccines approved for emergency use, the level of vaccination and opinions about vaccines.However, it is a fact that vaccines protect against deaths due to communicable diseases and save the lives of at least 2.5 million people every year . DespiteHerd immunity occurs when a sufficiently large portion of the population becomes immune to a contagious disease . VaccineRecent vaccine studies are using different novel technologies, including the use of lipid nanoparticle mRNA, inactivated virus particles, DNA, nonreplicating viral vectors such as spike, receptor-binding domains of spike and viral nucleocapsids .The purpose of starting vaccination studies quickly during pandemics is to protect healthy people from disease, to prevent epidemics and to prevent deaths. However, until effective vaccine studies are completed, epidemics become pandemics and hospitalization and death rates increase exponentially. For this reason, some of the vaccines developed are approved for immediate use by the WHO and vaccinations are started. Some of the vaccines that have been approved for emergency use by the World Health Organization in the COVID-19 pandemic are used in Turkey. BNT162b2 (Pfizer–BioNTech), Sinovac: CoronaVac and Turkovac vaccines are still used in Turkey .The Pfizer–BioNTech vaccine has been recognized as the first mRNA-based vaccine authorized for human use for infectious diseases. As with all vaccines, various side effects may occur after vaccination. These side effects were determined as muscle discomfort, fatigue, headaches, fever, swelling, joint pain, tingling, itching and chills ,30.Synthetic mRNA-based vaccines provide adaptive immunity by essentially hijacking the cell’s replication mechanism. For this, the vaccine genome needs to be delivered to ACE2 target cells with an excellent delivery vehicle. Recalibrated host cells produce viral antigens that trigger and stimulate an adaptive immune response through antibody production and T-cell response in exocytosis . HoweverTurkovac, on the other hand, was developed by the Presidency of Turkish Health Institutes (TÜSEB) by starting phase I trials in 2020, and it has been included in the vaccine list after receiving approval for emergency use in Turkey at the end of 2021, and it is still being applied .The aim of this study was to determine the health problems experienced by young adult university students studying in the Central Anatolian region of Turkey after having the COVID-19 vaccine.This descriptive study was planned quantitatively with the participation of undergraduate students studying at a state university in Central Anatolia in the spring semester of the 2021–2022 academic year.p = 0.5, 1 − p or q = 0.5) of at least 384 students. An example of stratification was used, taking into account the number of students enrolled in the faculties. For the determined total number of 384 students, there were at least 54 from the Faculty of Medicine, 11 from the Faculty of Dentistry, 33 from the Faculty of Education, 27 from the Faculty of Engineering and Architecture, 54 from the Faculty of Health Sciences, 82 from the Faculty of Sport Sciences, 34 from the Faculty of Communication, 15 from the Faculty of Economics and Administrative Sciences. There must have been at least 21 students from the Faculty of Agriculture and Natural Sciences and at least 53 students from the Faculty of Theology.The population of the research consists of 6254 undergraduate students studying at a state university located in Central Anatolia. The sample of the study was determined by using a ±5% margin of error, a 95% confidence interval and sample rate size formula .COVID-19 Post-Vaccine Identification Form: It consisted of 29 questions to identify the health problems, if any, after COVID-19 vaccination and was prepared by the researchers in line with the literature.p values of ≤0.05 were considered statistically significant.The Statistical Package for the Social Sciences 21.0  program was used for statistical analysis. Frequency, mean, standard deviation and minimum–maximum values were examined for descriptive analysis. A chi-square test was used to determine the differences between groups. The results were evaluated within the 95% confidence interval and Institutional permission from the relevant university and ethics committee approval from the Ethics Committee were obtained before starting the research (number: E-39243114-770-62444). The purpose of the study was explained to the individuals participating in the study and their written and verbal consent were obtained.Some demographic data of the students participating in the study are given in In p < 0.05). There was no statistically significant difference between income status, chronic disease status and health problems of the type of vaccine administered (p > 0.05) ( > 0.05) .COVID-19 is still in effect and continues to affect people’s lifestyles. However, there are serious decreases in mortality and hospitalization rates due to herd immunity, which is caused by the availability of many different vaccines, vaccinating people and having the disease .Finding different vaccines with different technological methods has become easier with the analysis of the mechanism of action. However, the induction of proteases such as furin, the modification of subunits such as S1 and S2, and methods of adapting host cells to these vaccine technologies are considered to increase the possibility of the immune system encountering new unknown situations and health problems.There are three types of COVID-19 vaccines, one mRNA and two inactive, applied in Turkey. Vaccination rates have increased, and hospitalization and death rates due to COVID-19 have decreased despite the fact that the vaccination application is challenging and some precautions have been undertaken . COVID-19 measures have begun to be relaxed, and it can be said that it is almost back to the pre-pandemic period. In cases where the vaccine is so important, the talk of health problems with human interaction creates a negative effect, causing the vaccination rates to not increase sufficiently and rapidly.In this study, we aimed to determine the post-vaccine health problems in young university students who were administered one of the three types of COVID-19 vaccines administered in Turkey. A total of 54.90% of the participants in the study were men and the mean age was 20.74 ± 2.32 years. The mean age of the participants in the study by Truong et al., (2022) was <21, and our study is consistent with studies conducted with young adults in the literature .In our study, 42.7% of the participants received expert information about the vaccine, and 73.01% of the expert information received was from the doctor. Mohamed et al., (2021) stated in their study that 38% of the participants received expert information. Our study is compatible with the literature on obtaining expert information about COVID-19 vaccines.Currently, three types of COVID-19 vaccines are used in Turkey. It was determined that the vast majority of the participants, 84.4%, had received the vaccine developed by Pfizer–BioNTech. In addition, 67.7% received two doses and 16.6% received three doses. The World Health Organization recommended at least two doses of the COVID-19 vaccine. In their study, Okamoto et al., (2022) stated that 76.5% of the participants had two doses of COVID-19 vaccine. This result is compatible with the literature; it can be said that the recommendations of official authorities such as the World Health Organization and the Turkish Ministry of Health are taken into account among young people .A total of 35.9% of those who had been vaccinated reported health problems after vaccination. In studies in the literature, the incidence of health problems in vaccinated patients is between 30.6% and 59.2%. The literature supports our study findings. Mannan et al., (2020) reported in their study that the most common health problems were fever, a cough, sore throat, nausea and vomiting, shortness of breath, and diarrhea. Marshall et al., (2021) reported that acute myocarditis occurred in seven patients after receiving the BioNTech vaccine in their study. Moeller et al., (2021) reported in their study that psychological disorders were observed among children and young people who received the BioNTech vaccine. In our study, the participants who reported that there was a feeling of heart pain, but did not receive a diagnosis related to it, were 1% of the people who stated that they had health problems. In addition, it was determined that 0.7% of them had psychological disorders. These results revealed that the studies conducted after the vaccine showed similar health problems at almost similar rates, confirming each other ,37.It was determined that 19.05% of the post-vaccine health problems lasted for 1 day, 40% for 2 days and 19.05% for 3 days. Riad et al. (2021) reported that most of the local (94.2%) and systemic (93.3%) health problems improved within three days after vaccination, and these results are consistent with our study .In our study, participants stated that high blood pressure disease (3.7%), diabetes mellitus (2.7%) and menstrual cycle irregularity (4.7%) occurred after vaccination. Meylan et al., (2021) reported in their study that individuals who received BioNTech and Moderna vaccines experienced hypertensive events. Samuel et al., (2022) stated that hyperglycemia has been reported in some individuals receiving the BioNTech, Moderna, AstraZeneca and Janssen vaccines. In his study, Male (2021) reported that changes in the menstrual cycles of individuals with the COVID-19 vaccine were reported; however, how long they lasted and what may have caused them should be investigated in detail ,40,41. In our study, it was stated that 12.2% of the participants gained weight after vaccination and 63.89% reported the reason for the weight gain was due to increased appetite. Vaccine-induced weight gain and increased appetite have not been reported in the literature. It is thought that the results of the study will guide future studies.In addition, people have started to use various support products to strengthen the immune system during the COVID-19 pandemic. These include black seed and vitamins. A total of 70.37% of the participants in the study reported that they used vitamins. Saeed et al., (2022) reported in their study that cholecalciferol levels change inversely with the severity of the disease. Calder et al., (2020) stated in their study that long-term daily doses of vitamin D protect against acute respiratory tract infections. For this purpose, the reason for the use of supplemental vitamins can be explained ,43.In our study, it was determined that working status affected thinking about the vaccine. In order to enter the workplace in Turkey, the PCR test result has to be negative. Therefore, it can be evaluated that the working situation (a kind of necessity) may have influenced thoughts about getting vaccinated.Health problems were observed in 35.9% of the participants. At the same time, 67.2% of the participants had two doses of vaccine. It is considered that people who are hesitant about the vaccine due to health problems may take the next doses more easily because they do not have any health problems after the first dose of the vaccine or because their health problems last a short time. Thus, it has been determined that having at least two doses of vaccine affects the state of having health problems after vaccination. Income status and chronic disease status did not affect participants’ thoughts about vaccination.A limitation of the study was that since the study was conducted in one region and with young adults only, it cannot be generalized to the whole population. It is recommended that larger studies are conducted with different age groups and different regions. All the data obtained from the study include the self-reports of the participants participating in the study.The COVID-19 pandemic, which continues rapidly all over the world, continues to affect vaccination studies, various restrictions and social life. The aim of this study was to determine the health problems experienced by young people, if any, after they have had the COVID-19 vaccines applied in Turkey.While some health problems are experienced with similar incidence in different countries where the same vaccine is administered, some health problems were reported for the first time in this study.The true long-term health problems related to vaccines will only be fully determined years after the date of administration. The limitation of our study is that it was only applied to young people. Studies with more participants will also help to determine if there are different health problems not mentioned in this study.


These new compounds formed two series according to the substitution of position 2 on the quinoxaline core, with chlorine or phenylacetylene respectively. Each of these isomers was evaluated for antiproliferative activity against neuroblastoma cell lines SK-N-SH and IMR-32 by MTT assay. All cell viability assay results were analyzed using R programming, as well as a statistical comparison between groups of compounds. Our evaluation showed no difference in drug sensitivity between the two neuroblastoma cell lines. Moreover, trans derivatives were observed to display better activities than cis derivatives, leading us to conclude that stereochemistry plays an important role in the antiproliferative activity of these compounds. Further support for this hypothesis is provided by the lack of improvement in antineoplastic activity following the addition of the phenylacetylene moiety, probably due to steric hindrance. As a result, compounds with nitrofuran substituents from the TDAE series demonstrated the highest antiproliferative activity with IC50 = 2.49 ± 1.33 μM and IC50 = 3.96 ± 2.03 μM for compound 11a and IC50 = 5.3 ± 2.12 μM and IC50 = 7.12 ± 1.59 μM for compound 11b against SK-N-SH and IMR-32, respectively. Furthermore, an in silico study was carried out to evaluate the mechanism of action of our lead compounds and predict their pharmacokinetic properties.The quinoxaline core is a promising scaffold in medicinal chemistry. Multiple quinoxaline derivatives, such as the topoisomerase IIβ inhibitor XK-469 and the tissue transglutaminase 2 inhibitor GK-13, have been evaluated for their antiproliferative activity. Previous work reported that quinoxaline derivatives bearing an oxirane ring present antiproliferative properties against neuroblastoma cell lines SK-N-SH and IMR-32. Likewise, quinoxalines with an arylethynyl group displayed promising antineoplastic properties against glioblastoma and lung cancer cell lines, U87-MG and A549 respectively. Here, 40 new quinoxaline derivatives bearing an oxirane ring were synthesized using a tetrakis(dimethylamino)ethylene (TDAE) strategy and a Sonogashira cross-coupling reaction. Each reaction with TDAE furnished a pair of diastereoisomers  Neuroblastoma is a neuroendocrine tumor of the sympathetic nervous system that develops from immature nerve tissue cells called neuroblasts. With 90% of cases diagnosed under 5 years old, it is the most common extra-cranial solid tumor, and the 4th cause of cancer in children. Treatment options for this pediatric cancer rely on risk classification, depending on age at diagnosis and staging, among other factors, with surgery remaining the only effective treatment. The most threatening risk group, called “High-risk Neuroblastoma“, is a therapeutic challenge because of its frequent metastases at the time of diagnosis . This grQuinoxaline derivatives show a wide range of therapeutic properties such as anti-infectious ,13, antiAnother derivative of interest is the arylethynylquinoxaline GK-13, which demonstrated antiproliferative activity by inhibiting tissue transglutaminase 2 (TG2) , a ubiqu50 = 3.9 ± 0.2 μM and IC50 = 5.0 ± 0.9 μM for the most active compound, similar to the reference XK-469 (IC50 = 4.6 ± 1.0 μM and IC50 = 13.0 ± 2.9 μM) against neuroblastoma cell lines SK-N-SH and IMR-32, respectively +: 305.0452; Found: 305.0450.cis isomer 2b: yield: 42%, yellow solid, mp = 103 °C. 1H-NMR : δ (ppm) = 4.62 , 4.73 , 7.06 , 7.29 , 7.68–7.76 , 7.88 , 8.19 . 13C-NMR : δ (ppm) = 58.87, 59.31, 126.61 (2C), 127.84 (2C), 128.14 (2C), 129.21, 130.46, 130.98, 132.86, 140.34, 141.12, 145.68, 147.67. HRMS-ESI: m/z calcd for C16H11ClN2O [M+Na]+: 305.0452; Found: 305.0449.p-tolyl)oxiran-2-yl]quinoxaline (3)2-chloro-3-+: 319.0609; Found: 319.0606.4)2-chloro-3-{3-[4-(trifluoromethyl)phenyl]oxiran-2-yl}quinoxaline : δ (ppm) = 4.49 , 4.56 , 7.58 , 7.68 , 7.79–7.82 , 8.01–8.04 , 8.14–8.18 . 13C-NMR : δ (ppm) = 59.06, 60.29, 123.97 , 125.74 , 126.25, 128.30, 129.29, 130.82, 131.00 , 131.53, 140.01, 140.93, 141.67, 146.30, 148.14. HRMS-ESI: m/z calcd for C17H10ClF3N2O [M+H]+: 351.0507; Found: 351.0504.cis isomer 4b: yield: 48%, yellow solid, mp = 109 °C. 1H-NMR : δ (ppm) = 4.66 , 4.78 , 7.35 , 7.45 , 7.71–7.79 , 7.89–7.92 , 8.15–8.18 . 13C-NMR : δ (ppm) = 58.73, 58.83, 123.79 , 124.86 , 127.06, 128.21, 129.15, 130.30 , 130.70, 131.29, 137.02, 140.27, 141.24, 145.49, 146.91. HRMS-ESI: m/z calcd for C17H10ClF3N2O [M+H]+: 351.0507; Found: 351.0503.5)2-chloro-3-[3-(4-chlorophenyl)oxiran-2-yl]quinoxaline : δ (ppm) = 4.39 , 4.55 , 7.40 , 7.81 , 8.02–8.05 , 8.15–8.19 . 13C-NMR : δ (ppm) = 58.93, 60.55, 127.29 (2C), 128.27, 128.97 (2C), 129.27, 130.76, 131.41, 134.50, 134.74, 140.92, 141.60, 146.31, 148.41. HRMS-ESI: m/z calcd for C16H10Cl2N2O [M+Na]+: 339.0062; Found: 339.0059.cis isomer 5b: yield: 31%, yellow solid, mp = 113 °C. 1H-NMR : δ (ppm) = 4.58 , 4.73 , 7.05 , 7.24 , 7.73–7.78 , 7.90–7.92 , 8.16–8.18 . 13C-NMR : δ (ppm) = 58.72, 58.81, 127.99 (2C), 128.13 (2C), 128.22, 129.16, 130.63, 131.18, 131.45, 134.07, 140.29, 141.20, 145.55, 147.24. HRMS-ESI: m/z calcd for C16H10Cl2N2O [M+Na]+: 339.0062; Found: 339.0062.6)2-chloro-3-[3-(2-chlorophenyl)oxiran-2-yl]quinoxaline : δ (ppm) = 4.47 , 4.70 , 7.27–7.35 , 7.39 , 7.47 , 7.79 , 8.00–8.03 , 8.16–8.19 . 13C-NMR : δ (ppm) = 58.28, 58.95, 126.25, 127.21, 128.26, 129.32, 129.40, 129.61, 130.68, 131.37, 133.47, 134.11, 141.00, 141.67, 146.32, 148.37. HRMS-ESI: m/z calcd for C16H10Cl2N2O [M+Na]+: 339.0062; Found: 339.0062.7)2-chloro-3-[3-(4-fluorophenyl)oxiran-2-yl]quinoxaline : δ (ppm) = 4.40 , 4.56 , 7.12 , 7.44 , 7.79–7.84 , 8.02–0.06 , 8.17–9.21 . 13C-NMR : δ (ppm) = 59.00, 60.81, 115.93 , 127.85 , 128.42, 129.43, 130.88, 131.52, 131.85 , 141.11, 141.75, 146.46, 148.68, 163.25 . HRMS-ESI: m/z calcd for C16H10ClFN2O [M+Na]+: 323.0358; Found: 323.0351.cis isomer 7b: yield: 36%, yellow solid, mp = 98 °C. 1H-NMR : δ (ppm) = 4.60 , 4.72 , 6.77 , 7.27– 7.29 , 7.71–7.78 , 7.90–7.92 , 8.16–8.18 . 13C-NMR : δ (ppm) = 58.83, 58.87, 115.03 , 128.28, 128.45 , 128.73 , 129.25, 130.68, 131.21, 140.40, 141.25, 145.69, 147.51, 162.59 . HRMS-ESI: m/z calcd for C16H10ClFN2O [M+Na]+: 323.0358; Found: 323.0358.8)2-chloro-3-[3-(3-fluorophenyl)oxiran-2-yl]quinoxaline : δ (ppm) = 4.40 , 4.56 , 7.12 , 7.42– 7.46 , 7.80–7.83 , 8.02–8.06 , 8.16–8.20 . 13C-NMR : δ (ppm) = 58.94, 60.44 , 112.75 , 115.86 , 121.76 , 128.28, 129.30, 130.39 , 130.77, 131.44, 138.64 , 140.94, 141.64, 146.33, 148.34, 163.15 . HRMS-ESI: m/z calcd for C16H10ClFN2O [M+Na]+: 323.0358; Found: 323.0361.cis isomer 8b: yield: 38%, brown solid, mp = 128 °C. 1H-NMR : δ (ppm) = 4.61 , 4.75 , 6.72–6.78 , 7.02–7.10 , 7.71–7.78 , 7.89–7.92 , 8.17–8.20 . 13C-NMR : δ (ppm) = 58.71 , 58.74, 113.83 , 115.19 , 122.38 , 128.15, 129.22, 129.51 , 130.62, 131.17, 135.54 , 140.30, 141.18, 145.57, 147.16, 162.31 . HRMS-ESI: m/z calcd for C16H10ClFN2O [M+Na]+: 323.0358; Found: 323.0357.9)4-[3-oxiran-2-yl]benzonitrile : δ (ppm) = 4.51 , 4.55 , 7.58 , 7.73 , 7.82–7.85 , 8.05–8.07 , 8.17–8.19 . 13C-NMR : δ (ppm) = 59.22, 60.04, 112.70, 118.47, 126.59 (3C), 128.35, 129.31, 130.92, 131.67, 132.61 (2C), 140.94, 141.30, 141.76, 147.82. HRMS-ESI: m/z calcd for C17H10ClN3O [M+Na]+: 330.0405; Found: 330.0403.cis isomer 9b: yield: 38%, yellow solid, mp = 103 °C. 1H-NMR : δ (ppm) = 4.66 , 4.81 , 7.39–7.47 , 7.77–7.80 , 7.92–7.95 , 8.14–8.17 . 13C-NMR : δ (ppm) = 58.56, 58.76, 112.12, 118.30, 127.44 (2C), 128.28, 129.12, 130.85, 131.47, 131.68 (2C), 138.35, 140.22, 141.28, 145.42, 146.60. HRMS-ESI: m/z calcd for C17H10ClN3O [M+Na]+: 330.0405; Found: 330.0404.10)2-chloro-3-[3-(4-nitrophenyl)oxiran-2-yl]quinoxaline : δ (ppm) = 4.58 , 7.65 , 7.83–7.85 , 8.05–8.08 , 8.18–8.20 , 8.30 . 13C-NMR : δ (ppm) = 59.30, 59.82, 124.08 (2C), 126.75 (2C), 128.36, 129.32, 130.94, 131.72, 140.94, 141.79, 143.21, 146.29, 147.71, 148.27. HRMS-ESI: m/z calcd for C16H10ClN3O3 [M+Na]+: 350.0303; Found: 350.0298.cis isomer 10b: yield: 21%, yellow solid, mp = 220 °C. 1H-NMR : δ (ppm) = 4.71 , 4.84 , 7.53 , 7.76–7.81 , 7.91–7.94 , 7.97 , 8.15–8.18 . 13C-NMR : δ (ppm) = 58.44, 58.80, 123.13 (2C), 127.66 (2C), 128.28, 129.14, 130.90, 131.51, 140.21, 140.30, 141.30, 145.38, 146.48, 147.75. HRMS-ESI: m/z calcd for C16H10ClN3O3 [M+Na]+: 350.0303; Found: 350.0298.11)2-chloro-3-[3-(5-nitrofuran-2-yl)oxiran-2-yl]quinoxaline : δ (ppm) = 4.64 , 5.04 , 6.80 , 7.36 , 7.83–7.86 , 8.05–8.07 , 8.13–8.16 . 13C-NMR : δ (ppm) = 52.82, 58.30, 111.83, 112.68, 128.37, 129.21, 130.99, 131.77 (2C), 140.30, 141.62, 145.67, 146.03, 150.84. HRMS-ESI: m/z calcd for C14H8ClN3O4 [M+Na]+: 340.0096; Found: 340.0087.cis isomer 11b: yield: 27%, brown solid, mp = 157 °C. 1H-NMR : δ (ppm) = 4.62 , 4.91 , 6.38 , 7.01 , 7.82–7.86 , 8.03 , 8.20 . 13C-NMR : δ (ppm) = 53.34, 56.92, 112.35, 112.71, 128.36, 129.26, 131.01 (2C), 131.95, 140.81, 141.89, 146.40, 146.79, 152.46. HRMS-ESI: m/z calcd for C14H8ClN3O4 [M+Na]+: 340.0096; Found: 340.0092.12)ethyl 3-oxirane-2-carboxylate : δ (ppm) = 1.35 , 4.13 , 4.30–4.37 , 4.78 , 7.77–7.81 , 7.99–0.02 , 8.07–8.10 . 13C-NMR : δ (ppm) = 14.14, 54.42, 54.71, 62.18, 128.27, 129.30, 130.86, 131.81, 140.76, 141.80, 146.47, 146.86, 167.57. HRMS-ESI: m/z calcd for C13H11ClN2O3 [M+Na]+: 301.0350; Found: 301.0352.13)diethyl 3-oxirane-2,2-dicarboxylate (3): δ (ppm) = 1.03 , 1.36 , 4.11 , 4.35–4.41 , 5.04 , 7.75–7.83 , 8.00–8.02 , 8.07 . 13C-NMR : δ (ppm) = 13.79, 13.99, 58.83, 62.02, 62.16, 63.30, 128.33, 129.25, 130.90, 131.90, 140.26, 141.70, 145.38, 146.19, 163.04, 164.77. HRMS-ESI: m/z calcd for C16H15ClN2O5 [M+Na]+: 373.0562; Found: 373.0552.yield: 67%, yellow solid, mp = 64 °C. 1H-NMR  before being dried with sodium sulfate. Each compound was then purified by flash chromatography puriFlash® using an IR-50SI-F0040 regular silica column and a dichloromethane/cyclohexane gradient (40:60 to 60:40).To each quinoxaline of the previous series , dichlorobis(triphenylphosphine)palladium(II) (0.05 eq.) and cuprous iodide (0.1 eq.) dissolved in acetonitrile in a two-necked flask, were added triethylamine (10 eq.) and phenylacetylene (1.5 eq.). The reaction mixture was stirred for 2h at room temperature. Then, the mixture was extracted in dichloromethane (3 × 40 mL) and washed with H14)2-(phenylethynyl)-3-(3-phenyloxiran-2-yl)quinoxaline : δ (ppm) = 4.43 , 4.77 , 7.28–7.52 , 7.80 , 8.10–8.13 , 8.16–8.20 . 13C-NMR : δ (ppm) = 60.37, 61.07, 85.53, 97.04, 121.03, 125.94 (2C), 128.52 (2C), 128.78 (3C), 128.93, 129.32, 129.91, 130.72, 130.91, 132.21 (2C), 136.46, 138.63, 140.60, 141.68, 151.54. HRMS-ESI: m/z calcd for C24H16N2O [M+Na]+: 371.1155; Found: 371.1147.cis isomer 14b: yield: 80%, orange solid, mp = 125 °C. 1H-NMR : δ (ppm) = 4.65 , 4.93 , 7.07 , 7.29–7.31 , 7.47–7.50 , 7.70–7.75 , 7.98–8.00 , 8.15–8.18 . 13C-NMR : δ (ppm) = 59.24, 59.38, 85.37, 96.80, 121.35, 126.66 (2C), 127.80 (2C), 128.02, 128.77 (2C), 128.84, 129.22, 130.05, 130.45, 130.69, 132.23 (2C), 133.24, 138.08, 140.02, 141.17, 150.28. HRMS-ESI: m/z calcd for C24H16N2O [M+Na]+: 371.1155; Found: 371.1147.p-tolyl)oxiran-2-yl]quinoxaline (15)2-(phenylethynyl)-3-+: 363.1492; Found: 363.1488.16)2-(phenylethynyl)-3-{3-[4-(trifluoromethyl)phenyl]oxiran-2-yl}quinoxaline : δ (ppm) = 4.50 , 4.72 , 7.31 , 7.38–7.42 , 7.61 , 7.70 , 7.80–7.82 , 8.11–8.14 , 8.15–8.19 . 13C-NMR : δ (ppm) = 60.08, 60.62, 85.33, 97.14, 120.91, 124.02 , 125.79 , 126.18 (2C), 128.57 (2C), 129.00, 129.36, 130.09, 130.95 , 130.95, 131.06, 132.11 (2C), 138.61, 140.60, 141.82, 150.87. HRMS-ESI: m/z calcd for C25H15F3N2O [M+Na]+: 439.1029; Found: 439.1023.cis isomer 16b: yield: 83%, white solid, mp = 159 °C. 1H-NMR : δ (ppm) = 4.67 , 4.98 , 7.35 , 7.43– 7.50 , 7.71–7.77 , 8.00–8.02 , 8.13–8.15 . 13C-NMR : δ (ppm) = 58.82, 59.19, 85.17, 97.01, 121.20, 123.84 , 124.81 , 127.09, 128.84 (2C), 128.92, 129.19, 130.20, 130.22 , 130.77, 130.95, 132.23 (2C), 137.37, 137.96, 139.97, 141.30, 149.53. HRMS-ESI: m/z calcd for C25H15F3N2O [M+Na]+: 439.1029; Found: 439.1024.17)2-[3-(4-chlorophenyl)oxiran-2-yl]-3-(phenylethynyl)quinoxaline : δ (ppm) = 4.41 , 4.70 , 7.30–7-40 , 7.77–7.79 , 8.09–8.12 , 8.13–8.16 . 13C-NMR : δ (ppm) = 60.35, 60.41, 85.44, 97.04, 120.98, 127.25 (2C), 128.57 (2C), 128.98, 129.01 (2C), 129.34, 130.03, 130.83, 130.98, 132.16 (2C), 134.63, 135.07, 138.61, 140.61, 141.75, 151.14. HRMS-ESI: m/z calcd for C24H15ClN2O [M+Na]+: 405.0765; Found: 405.0762.cis isomer 17b: yield: 58%, red solid, mp = 160 °C. 1H-NMR : δ (ppm) = 4.60 , 4.92 , 7.04 , 7.24 , 7.46 , 7.68–7.77 , 7.98–8.01 , 8.13–8.15 . 13C-NMR : δ (ppm) = 58.80, 59.17, 85.26, 96.92, 121.24, 128.03 (2C), 128.07 (2C), 128.81 (2C), 128.91, 129.20, 130.14, 130.65, 130.86, 131.82, 132.22 (2C), 133.95, 137.99, 139.98, 141.25, 149.86. HRMS-ESI: m/z calcd for C24H15ClN2O [M+Na]+: 405.0765; Found: 405.0764.18)2-[3-(2-chlorophenyl)oxiran-2-yl]-3-(phenylethynyl)quinoxaline : δ (ppm) = 4.65 , 4.77 , 7.28–7.44 , 7.53 , 7.79 , 8.10–8.13 , 8.16–8.20 . 13C-NMR : δ (ppm) = 58.77, 59.56, 85.45, 96.94, 121.18, 126.19, 127.26, 128.51 (2C), 128.97, 129.32, 129.46, 129.48, 129.88, 130.81, 130.93, 132.19 (2C), 133.55, 134.56, 138.64, 140.73, 141.83, 151.05. HRMS-ESI: m/z calcd for C24H15ClN2O [M+Na]+: 405.0765; Found: 405.0759.19)2-[3-(4-fluorophenyl)oxiran-2-yl]-3-(phenylethynyl)quinoxaline : δ (ppm) = 4.42 , 4.72 , 7.09–7.14 , 7.30–7.40 , 7.44–7.48 , 7.79 , 8.09–8.13 , 8.14–8.17 . 13C-NMR : δ (ppm) = 60.28, 60.46, 85.48, 96.95, 115.80 , 121.02, 127.68 , 128.57 (2C), 128.97, 129.34, 130.00, 130.80, 130.97, 132.16 (2C), 132.27 , 138.61, 140.63, 141.74, 151.27, 163.09 . HRMS-ESI: m/z calcd for C24H15FN2O [M+H]+: 367.1241; Found: 367.1234.cis isomer 19b: yield: 90%, brown solid, mp = 156 °C. 1H-NMR : δ (ppm) = 4.62 , 4.91 , 6.74–6.78 , 7.27–7.29 , 7.45–7.48 , 7.69–7.75 , 7.98–8.01 , 8.13–8.15 . 13C-NMR : δ (ppm) = 58.83, 59.12, 85.29, 96.86, 114.88 , 121.28, 128.39 , 128.80 (2C), 128.89, 128.99 , 129.19, 130.12, 130.60, 130.82, 132.22 (2C), 138.04, 139.99, 141.22, 150.03, 162.46 . HRMS-ESI: m/z calcd for C24H15FN2O [M+H]+: 367.1241; Found: 367.1236.20)2-[3-(3-fluorophenyl)oxiran-2-yl]-3-(phenylethynyl)quinoxaline : δ (ppm) = 4.62 , 4.94 , 6.72–6.77 , 7.03–7.10 , 7.46–7.49 , 7.69–7.75 , 7.98–8.01 , 8.14–8.16 . 13C-NMR : δ (ppm) = 58.80 , 59.10, 85.22, 96.91, 113.91 , 115.08 , 121.24, 122.40 , 128.80 (2C), 128.85, 129.25, 129.43 , 130.12, 130.64, 130.86, 132.23 (2C), 135.90 , 138.01, 139.98, 141.24, 149.78, 162.31 . HRMS-ESI: m/z calcd for C24H15FN2O [M+H]+: 367.1241; Found: 367.1233.cis isomer 20b: yield: 90%, brown solid, mp = 124 °C. 1H-NMR : δ (ppm) = 4.44 , 4.72 , 7.06–7.11 , 7.17–7.20 , 7.29–7.41 , 7.77–7.80 , 8.09–8.11 , 8.14–8.16 . 13C-NMR : δ (ppm) = 60.25 , 60.30, 85.44, 97.00, 112.68 , 115.71 , 121.00, 121.78 , 128.58 (2C), 128.97, 129.34, 130.00, 130.42 , 130.85, 130.98, 132.18 (2C), 138.63, 139.20 , 140.61, 141.77, 151.04, 163.22 . HRMS-ESI: m/z calcd for C24H15FN2O [M+H]+: 367.1241; Found: 367.1233.21)4-{3-[3-(phenylethynyl)quinoxalin-2-yl]oxiran-2-yl}benzonitrile : δ (ppm) = 4.57 , 4.90 , 7.34–7.42 , 7.49–7.53 , 7.74 , 7.92–7.95 , 8.12–8.16 . 13C-NMR : δ (ppm) = 59.37, 61.01, 86.04, 96.56, 111.78, 119.11, 120.69, 127.60 (2C), 129.12, 129.33 (3C), 130.89, 131.66, 131.99, 132.31 (2C), 133.11 (2C), 138.49, 140.22, 141.47, 142.61, 151.84. HRMS-ESI: m/z calcd for C25H15N3O [M+Na]+: 396.1107; Found: 396.1106.cis isomer 21b: yield: 86%, yellow solid, mp = 190 °C. 1H-NMR : δ (ppm) = 4.99 , 5.15 , 7.40 , 7.57–7.60 , 7.84–7.91 , 8.00 , 8.12–8.15 . 13C-NMR : δ (ppm) = 57.89, 59.72, 85.58, 96.83, 111.10, 118.81, 120.87, 127.76 (2C), 128.98, 129.30, 129.59 (2C), 131.04, 131.57, 131.87, 132.14 (2C), 132.78 (2C), 137.95, 139.67, 140.21, 140.96, 150.54. HRMS-ESI: m/z calcd for C25H15N3O [M+Na]+: 396.1107; Found: 396.1104.22)2-[3-(4-nitrophenyl)oxiran-2-yl]-3-(phenylethynyl)quinoxaline : δ (ppm) = 4.61 , 4.74 , 7.33 , 7.42 , 7.66 , 7.81–7.83 , 8.12–8.18 , 8.29 . 13C-NMR : δ (ppm) = 58.52, 59.16, 85.08, 97.14, 121.08, 123.06 (2C), 127.68 (2C), 128.89 (2C), 128.95, 129.15, 130.30, 130.96, 131.13, 132.24 (2C), 137.90, 139.87, 140.70, 141.34, 147.68, 149.09. HRMS-ESI: m/z calcd for C24H15N3O3 [M+Na]+: 416.1006; Found: 416.0997.cis isomer 22b: yield: 75%, yellow solid, mp = 210 °C. 1H-NMR : δ (ppm) = 4.71 , 5.02 , 7.50 , 7.70–7.78 , 7.96 , 7.99–8.01 , 8.10–8.13 . 13C-NMR : δ (ppm) = 59.71, 60.55, 85.27, 96.97, 120.89, 124.07 (2C), 126.69 (2C), 128.64 (2C), 129.02, 129.35, 130.17, 131.14, 131.18, 132.10 (2C), 138.62, 140.64, 141.89, 143.79, 148.19, 150.34. HRMS-ESI: m/z calcd for C24H15N3O3 [M+Na]+: 416.1006; Found: 416.0996.23)2-[3-(5-nitrofuran-2-yl)oxiran-2-yl]-3-(phenylethynyl)quinoxaline : δ (ppm) = 4.65 , 5.39 , 6.83 , 7.35 , 7.43–7.47 , 7.68–7.71 , 7.81–7.74 , 8.11–8.15 . 13C-NMR : δ (ppm) = 53.33, 57.35, 85.24, 97.49, 112.34, 113.32, 120.89, 128.69, 128.79 (2C), 129.06, 129.29, 130.29, 131.18, 131.30, 132.35 (2C), 138.90, 140.54, 142.02, 149.48, 152.62. HRMS-ESI: m/z calcd for C22H13N3O4 [M+Na]+: 406.0798; Found: 406.0794.cis isomer 23b: yield: 24%, red solid, mp = 150 °C. 1H-NMR : δ (ppm) = 5.55 , 6.05 , 6.75 , 7.24 , 7.47–7.52 , 7.79–7.86 , 8.02–8.05 , 8.16–8.19 . 13C-NMR : δ (ppm) = 36.92, 69.25, 84.82, 98.34, 112.04, 112.96, 120.69, 128.46, 128.89 (3C), 129.11, 130.46, 130.92, 131.23, 132.61 (2C), 137.22, 139.12, 142.10, 155.42, 156.33. HRMS-ESI: m/z calcd for C22H13N3O4 [M+Na]+: 406.0798; Found: 406.0792.24)ethyl 3-[3-(phenylethynyl)quinoxalin-2-yl]oxirane-2-carboxylate : δ (ppm) = 1.34 , 4.19 , 4.28–4.39 , 5.00 , 7.40–7.46 , 7.66–7.68 , 7.78–7.82 , 8.08–8.12 . 13C-NMR : δ (ppm) = 14.14, 54.84, 55.32, 62.10, 85.18, 96.99, 121.06, 128.66 (2C), 128.98, 129.36, 130.13, 131.10, 131.23, 132.34 (2C), 138.84, 140.55, 141.97, 149.48, 167.94. HRMS-ESI: m/z calcd for C21H16N2O3 [M+Na]+: 367.1053; Found: 367.1051.25)diethyl 3-[3-(phenylethynyl)quinoxalin-2-yl]oxirane-2,2-dicarboxylate (3): δ (ppm) = 1.05 , 1.31 , 4.15 , 4.30–4.39 , 5.32 , 7.38–7.44 , 7.66 , 7.73–7.80 , 8.04 , 8.08 . 13C-NMR : δ (ppm) = 13.83, 13.95, 59.56, 62.07, 62.22, 63.21, 85.00, 97.58, 121.03, 128.64 (2C), 128.99, 129.28, 130.16, 131.10, 131.33, 132.35 (2C), 138.85, 139.95, 141.80, 147.94, 163.24, 165.18. HRMS-ESI: m/z calcd for C24H20N2O5 [M+H]+: 417.1445; Found: 417.1445.yield: 84%, yellow solid, mp = 113 °C. 1H-NMR - 2,5-diphenyltetra-zolium bromide  assay according to our previous work .™ RPMI-1640 Glutamax™) supplemented with 10% fetal bovine serum (Lonza) and 1% penicillin-streptomycin 5000 U/mL (Fisher Gibco™ Pen Strep) at 37 °C and 5% CO2. Cell cultures between 3 and 17 passages from defrosting were used for MTT assays.Neuroblastoma cancer cells, namely SK-N-SH  and IMR-32  cells, were purchased from the American Type Culture Collection and routinely maintained in standard RPMI 1640 (L-glutamine +) culture medium . The purity of all compounds was determined over 95%, before testing, by integrations on 1H-NMR spectra, and confirmed by UHPLC. Stock solutions were aliquoted and stored at −20 °C. For culture and experiments in living cells, the drugs were freshly diluted at an appropriate concentration in a culture medium.2 for SK-N-SH and 31,250 cells/cm2 for IMR-32 respectively) were detached with 20% trypsin Fisher Gibco™  and seeded by 150 μL/well of a 96-well plate  for 24h for SK-N-SH, and 72h for IMR-32. The culture medium was then replaced by the same volume of freshly diluted drugs at an appropriate concentration , or fresh culture medium for control wells. These concentrations were diluted 2-fold for drugs with IC50 < 10 μM for more accurate IC50 determination. Each of the 6 concentration points of the dilution range was iterated 4 times. To avoid more than 4% DSMO at the highest concentration, which could have an impact on cell viability, the maximal tested concentration was 100 μM. After 72h of drug treatment, the medium of each well was replaced by 150 μL of fresh medium containing MTT at 0.5 mg/mL, and cells were incubated at 37 °C for 4h. Then, the MTT solution was removed and 150 μL/well of DMSO was used to dilute the formazan crystals formed by the mitochondrial reductase of surviving cells. Finally, absorbance was measured at both 550 and 600 nm with a POLARstar Omega BMG LABTECH  plate reader. At least three independent experiments (in quadruplicate) were performed, and data were expressed as mean ± SD.Exponentially growing cells ) and median (Equation (3)) allowing us to remove outliers with a 1.96 cut-off for 95% data accuracy [Outliers were identified by univariate analysis estimating location and scale. Thus, two accuracy .(2)zi=x50 was determined a by 4-parameter logistic regression fitting model using R package ’drm’ [Y is the response, a is the lower asymptote, d is the upper asymptote, X is the concentration, c is the EC50, b is the slope factor of the curve.ICge ’drm’ , as showge ’drm’ , where Yp-values were calculated to quantify statistical significance, with the criterion set at p < 0.05. The IC50 obtained for each experiment was statistically analyzed first by the nonparametric Wilcoxon Mann–Whitney test to compare the IC50 distribution between the cell lines, between cis isomers and trans isomers, and between TDAE and Sonogashira series. Secondly, we performed a nonparametric Kruskal–Wallis one-way analysis of variance and finally a nonparametric post-hoc Dunn’s test for pairwise comparison to assess whether the IC50 of each isomer from each series differs significantly with varying epoxide substituents. All these tests were performed in RStudio using the native “R Stat Package” and “rstatix” package [Statistical analyses were performed using R programming.  package  for Dunn3 cube, and exhaustiveness to 64. The other parameters were adopted as the program’s default values. Analysis of the results was performed by ranking the different ligand poses accordingly to their binding energy. We considered the molecule adopting the lowest energetic conformation as a promising compound. Visual analysis of the lowest energy solutions for each compound allowed us to identify the protein binding site. All the figures were drawn using the program ChimeraX.Crystal structures of human Topoisomerase IIβ in complex with DNA (PDB code: 3QX3)  and huma® 10.3. Pharmacokinetic parameters were determined with GastroPlus® 9.8.2. From GastroPlus®, a compartmental model was repeated for each drug as an administration to a 70 kg fasted human with normal gut physiology in a 100 mg immediate-release tablet dosage form.The drug database for pharmacokinetic modeling was set up with MedChem Studio™ 4.0 from the Simulation-Plus software suite. Drug likeliness parameters were determined with ADMET Predictortrans derivatives were significantly more active than cis ones from both TDAE and Sonogashira series. Secondly, we evaluated the influence of the substitution of position 2 on the quinoxaline core. Combining the epoxide and the arylethynyl group within the same structure in the Sonogashira series improved the antiproliferative activity of 6 out of the 20 compounds synthesized in the TDAE series. Since this induced a loss of activity for the other quinoxaline derivatives, it seems to demonstrate that the activity of most compounds is negatively influenced by the steric hindrance from the 2-arylethynyl substituent. Thirdly, we evaluated the influence of a variety of substituents on the oxirane ring in both the TDAE and Sonogashira series. As in the previously described TDAE series [50 was observed for the derivatives on which the epoxide is substituted by 5-nitrofuran . Our analysis also revealed that the nature of the epoxide’s substituent and the substitution pattern of the benzene ring can have a considerable impact on the antiproliferative activity of the synthesized compounds. Indeed, halogenated phenyl  and 5-nitrofuran 11 seem to be the most appropriate options from the TDAE series. Likewise, unsubstituted benzene 14, fluorinated phenyl 20, 5-nitrofuran 23, and carboxylate 25 are the most active compounds in the Sonogashira series.In this work, three chemical aspects of our synthesized compounds were evaluated. Firstly, we demonstrated the influence of stereochemistry on the antiproliferative activity of our compounds. The E series , the low50 against SK-N-SH and IMR-32, we could think that our compounds are not substrates of the P-gp which is an encouraging feature. Furthermore, most compounds are active against aggressive MYCN amplified cell line IMR-32, which is also an encouraging feature for further evaluations. In conclusion, we presented in this work multiple quinoxaline derivatives that display antiproliferative activity against resistant cell lines and aggressive ones. Moreover, we evaluated each compound against two neuroblastoma cell lines that were different in many aspects, more specifically by their expression of the efflux pump P-gp and the MYCN gene amplification. Since no significant difference could be demonstrated between ICFurther work will allow us to dig into the mechanism of action of these molecules. From our work, several hypotheses are made. From the similarity of structure with compounds XK-469 and CQS, which are both topoisomerase IIβ inhibitors, we could think that our products have the same target. Based on the results of our molecular docking study and the structural similarity with compounds XK-469 and CQS, which are both topoisomerase IIβ inhibitors, we were able to suggest that our products have the same target. Similarly, it allowed us to identify another potential target: the tissue transglutaminase responsible for tumor resistance. According to other oxirane ring carrier molecules described in the literature , other m