Pancreatic Ductal Adenocarcinoma (PDAC) is the most lethal type of pancreatic cancer, late detection leading to its therapeutic failure. This study aims to determine the key regulatory genes and their impacts on the disease’s progression, helping the disease’s etiology, which is still mostly unknown. We leverage the landmark advantages of time-series gene expression data of this disease and thereby identified the key regulators that capture the characteristics of gene activity patterns in the cancer progression. We have identified the key gene modules and predicted the functions of top genes from a reconstructed gene association network (GAN). A variation of the partial correlation method is utilized to analyze the GAN, followed by a gene function prediction task. Moreover, we have identified regulators for each target gene by gene regulatory network inference using the dynamical GENIE3 (dynGENIE3) algorithm. The Dirichlet process Gaussian process mixture model and cubic spline regression model (splineTimeR) are employed to identify the key gene modules and differentially expressed genes, respectively. Our analysis demonstrates a panel of key regulators and gene modules that are crucial for PDAC disease progression. Biological mechanisms are dynamic in nature; therefore, their activities must be supervised at multiple time points. Time-series gene expression experiments are widely used to monitor biological processes in a time-series paradigm8. Analyzing these time-series gene expression data helps identify transient transcriptional changes, temporal patterns, and causal effects of the genes. Time-series gene expression studies can be utilized to predict phenotypic outcomes over a period of time9.In genetics, gene expression is one of the elementary constitutional blocks which gives rise to a phenotype from a genotype, i.e., a trait which is observable in all living cells, including prokaryotes and eukaryotes. Multiple techniques are available to quantify gene expression and regulation, like DNA microarray, RNASeq, etc. The area of gene expression analysis undergone several significant advancements in biomedical research. With increased efficiency and quality, these measurements led to improvements in disease sub-classification, gene identification problems, and studying progression characteristics of diseases10. A gene is considered differentially expressed (DE) if a statistically significant difference in gene expression levels is observed between a pair of experimental conditions. Various statistical distribution models like the Poisson and the Negative Binomial (NB) distribution estimate the differential gene expression patterns. Gene selection refers to detecting the most significant DE genes under various conditions11. Selection is made based on a combination of score cutoff, and expression change threshold, commonly generated by the statistical design itself12. Popular time-course DE analysis tools include edgeR, DESeq2, TimeSeq, and Next maSigPro based on the NB distribution model. Some DE tools, like ImpulseDE2 and splineTimeR, based on impulse and spline regression models between two groups, respectively, are used on short time-series data13.DNA microarrays and RNASeq data have been accepted as gold standards for analyzing and measuring gene expressions across different biological circumstances15. Several statistical clustering techniques have been widely used like k-means, hierarchical clustering17 and self-organizing maps18 to produce modules from time-series gene expression profiles. Gradually, various techniques have been developed, especially for clustering time-series data. In19, Short Time-series Expression Miner (STEM) has been used as a clustering technique that maps genes to their representative expression profile. Cluster Analysis of Gene Expression Dynamics (CAGED), a clustering technique proposed by Ramoni et al.20, uses the Bayesian method to model gene-expression dynamics using auto-regressive equations. TimeClust21 uses temporal gene expression profiles to produce clusters. TMixClust22, Dirichlet process Gaussian process mixture model14 are some of the significant non-parametric model-based clustering methods.Gene expression is a strongly regulated spatio-temporal process. Genes having identical expression patterns are associated with the same biological function. Clustering genes with similar expression pattern reduces the transcriptional response complexities by grouping genes responsible for a distinct cellular process23, biomarker discovery24, identification of hub genes25, cell cycle progression26, cancer classification27 and several other bio-medically important processes. Moreover, the advancements in information system infrastructure facilitated utilizing time-series models more feasible for studying complex psychological phenomena. Numerous tools are now available for enriched network and pathway analysis of the gene modules, enabling further analysis and a deeper understanding of the biological mechanisms.The analysis of time-series gene expression modules helped us unravel major biological complications. It provides deep insights into the disease progression28. The top 100 DE genes at each time point were analyzed using a R/Bioconductor package Linear Models for Microarray Data (limma)29. Dirichlet process Gaussian process mixture model, a non-parametric model-based clustering method, was applied on the DE gene expression profiles to discover gene modules based on the similar responses across the time points14. REGulator-Gene Association Enrichment (REGGAE)30 was used to identify key transcriptional regulators and the number of targets for each of them from the list of DE genes.This article proposed a framework to discover key transcriptomic regulators and key modules from time-series microarray gene expression data in pancreatic ductal adenocarcinoma (PDAC). Initially, differentially expressed (DE) genes were identified by analyzing the empirical Bayes statistics on multivariate time-course gene expression data of PDAC using the splineTimeR31 from the time-course gene expression data of PDAC. Prediction of gene functions of the top genes in the interaction network also been carried out using GeneMANIA prediction server32. We have identified the key gene modules from the set of all modules obtained from our cluster analysis. Subsequently, transcriptomic regulatory genes were also detected against a curated database of DNA-binding RNA polymerase II TF (DbTF) using TFcheckpoint33. Furthermore, biological significance like the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway34, Gene Ontology (GO) and gene-disease associations of the gene modules were also observed using Enrichr35.Most experimental gene expression analyses only focus on determining the DE genes by considering them independent events and not investigating the identified genes’ interaction. Reconstruction of the possible gene association network (GAN) among DE genes helps us find genes in the studied phenotype interaction network. Therefore, GAN reconstruction, followed by identifying the top genes in the network, was also performed. Moreover, we have identified regulators for each target gene by gene regulatory network inference for the whole set of genes using the dynamical GENIE3 (dynGENIE3) algorithmThis section provides insight into the detailed findings of our present work.28 described in section "p-value 36 correction method and optimum degree of freedom We have processed normalized gene expression values of 42412 genes described in section "37. Limma utilizes empirical Bayes smoothing on the estimated fold-changes and standard errors from a linear model fitting. Table Additionally, to identify the DE genes at each time point, we have employed the limma packageAdopting the methodologies discussed in section "31 as described in section "We have also inferred a gene regulatory network (GRN) using the time-course gene expression data of control and treated samples in PDAC and identified the regulators for each target gene using the dynamical GENIE3 (dynGENIE3) algorithm32. GeneMANIA algorithm creates a weighted connected network of the query genes, including several suggested genes. The resultant network is demonstrated in Fig. We have also extracted the top 150 genes from the reconstructed GAN with 0.9 as the probability cutoff for predicting their gene function, chosen based on the aggregation of three centrality measures: degree, betweenness, and closeness centrality. The top 15 genes among them include ‘CUEDC1’, ‘ABCA6’, ‘MRPL50’, ‘LYPD3’, ‘KRT19’, ‘OLFML3’, ‘LGALS3’, ‘TBPL1’, ‘LOC285074’, ‘ANKRD26’, ‘FDXR’, ‘Hs.540914’, ‘CCL2’, ‘CENPJ’, and ‘CXCL2’. We utilized this list of 150 genes to obtain their gene function using GeneMANIA38 web server.In Table 30, with the time-course genes expression profiles of the control and the treated samples. We obtained a ranked list of 66 key TR for the PDAC dataset from a Regulator Target Interactions (RTI) collection, which contains a list of regulators for each deregulated gene that influence gene expression. Regulatory genes were ranked according to the score provided by the REGGAE algorithm. The top 5 key TR’s are ‘GATA6’, ‘NFYB’, ‘IRF1’, ‘TRIM22’, ‘SREBF1’.We have identified the key transcriptional regulators (TR) for the DE genes using REGGAE33 from the list of key TRs. We retrieved 47 DNA-binding RNA polymerase II TFs (DbTFs) among the 66 key TR’s. Among these DbTFs, we have discovered 10 proteins, viz., ‘FOXO1’, ‘SOX9’, ‘GATA6’, ‘SMAD3’, ‘NFKB1’, ‘KLF6’, ‘TBX3’, ‘SREBF1’, ‘NR4A2’, ‘TCF3’ that are directly associated with PDAC using DisGeNET. We have also observed that among the top 50 ranked genes obtained from the GeneMANIA, there are 8 DNA-binding TF’s: ‘SMAD3’, ‘STAT1’, ‘ZNF34’, ‘ATF3’, ‘ZNF395’, ‘JARID2’, ‘E2F4’, ‘SOX9’.Additionally, we have detected the proteins playing central role in DNA transcription using a curated database of specific DbTFs: TFcheckpoint40, with an open source manually curated database of eukaryotic transcription factors called TRANSFAC41. We observed that 44 transcription factors of our potential set discovered from REGGAE (containing 66 transcription factors) are common with the combined set  kernel with MAP clustering and Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) hyperprior optimization technique, concentration parameter  KEGG pathways (Panel A), biological processes (Panel B) and disease-genes association (Panel C) enrichment of genes of key cluster 1. Outcomes of our biological significance analysis for the other 5 key modules have been attached in Supplementary Fig. We have performed several analysis on the key modules to gain more insights into the pathways and biological processes (BP) of the involved genes inside the key modules. Additionally, we have identified key TRs in each of the key modules. We have identified KEGG pathways, associated biological processes (BP), and disease-gene associations using the DisGeNET database via Enrichr44. ‘CXCL6’, ‘CSF2’, ‘CCL20’, ‘TNFAIP3’, ‘CXCL1’, ‘PTGS2’ inside key module 2 takes part in IL-17 signaling pathway that promotes the transition to pancreatic cancer from chronic pancreatitis45. ‘TNF-46. ‘CSF2’, ‘CCL20’, ‘LIF’, ‘TNFAIP3’, ‘CXCL1’, ‘PTGS2’ in key module 2 influences the TNF signalling pathway. Moreover, ‘IL11’, ‘CSF2’, ‘LIF’, ‘SOCS5’ participates in JAK-STAT signaling pathway which directly involves in pancreatic tumorigenesis47 and ‘CSF2’, ‘NOTCH1’, ‘CLCF1’, ‘LIF’, ‘CRLF1’ genes are responsible for positive regulation of JAK-STAT cascade which have been detected in the key module 2. ‘LFNG’, ‘NOTCH1’, ‘NUMB’ in key module 2 are actively involved in Notch signaling pathway which is reactivated in PDAC initiation and development48. Focal Adhesion Kinase (FAK) play a crucial role and is highly activated and over-expressed in PDAC49. In key module 3, we discovered ‘MYL7’, ‘SHC3’, ‘ITGA4’, ‘COL4A4’, ‘ITGA1’, ‘FN1’, ‘FLNA’, ‘MYL9’ that take part in the Focal adhesion pathway. Key module 3 also contains ‘SFRP4’, ‘FOXC1’, ‘NCOA3’ which give rise to protein N-linked glycosylation via asparagine process. Unusual glycosylation has been recognized as a molecular feature of malignant transformation in PDAC50. Disease progression in PDAC is impacted by mutant ‘p53’ tumor suppressor51. Presence of ‘GADD45B’, ‘SESN1’, ‘TNFRSF10B’, ‘PPM1D’ genes in the key module 4 indicates their involvement with ‘p53’ signaling pathway. ‘GPT2’, ‘GLS’ genes in key module 4 play roles in Arginine biosynthesis. Arginine metabolism is spiked in PDAC cells and it’s deprivation may be considered as a potential strategy for PDAC therapy52. Key module 4 contains ‘DDIT3’, ‘TNFRSF10B’, ‘TRIB3’, ‘CHAC1’ genes which are active in intrinsic apoptotic signaling pathway in response to endoplasmic reticulum (ER) stress. ER stress contributes to pancrititis, a risk factor for PDAC53. Pancreatic cancer can be recognized by integral activation of the mitogen-activated protein kinase (MAPK) pathway54. A large number of genes, ‘CSF1R’, ‘IL1A’, ‘TGFB3’, ‘PDGFC’, ‘PLA2G4C’, ‘HSPB1’, ‘FGF2’, ‘RASGRP1’, ‘NFKB1’, ‘HSPA1B’, ‘RELB’ of the key module 5 are involved in the MAPK signaling pathway. Moreover, ‘NFKBIA’, ‘NFKB1’, ‘BIRC3’, ‘RELB’ inside the key module 5 are involved in the Nuclear Factor-kappaB 57. In key module 6, ‘PTPRB’, ‘SMAD3’, ‘RAC2’ genes are associated with the Adherens junction pathway. We found that ‘TGFB2’, ‘SMAD3’, ‘RAC2’ inside key module 6 are directly associated with Pancreatic cancer pathway. Additionally, ‘TGFB2’, ‘SMAD3’, ‘INHBE’ are responsible for TGF-beta signaling pathway in the key module 6. Activation of the TGF-58.Moreover, we have identified the top 10 KEGG pathways and GO terms (BP) for each of the key modules to understand their relevance and significance in PDAC. We observed that key module 1 contains ‘FGF7’, ‘IL6’, ‘FGF9’, ‘GSTA4’, ‘GADD45A’, ‘CDK4’, ‘STAT1’, ‘TXNRD1’, ‘WNT2’ genes which contributes to pathways in cancer and ‘STAT1’, ‘GADD45A’, ‘CDK4’ are directly associated to pancreatic cancer pathway. ‘IL6’, ‘STAT1’, ‘ST3GAL6’ inside the key module 1 is responsible for cellular response to Interleukin 6 (IL -6) that governs the pancreatic cancer progressionp-values. This analysis for the other 5 key modules has been attached in the Supplementary Fig. Figure The present section provides an overview of our systematic approach to data collection, data preprocessing, and the overall framework for the different methodologies used in our present analysis Fig. .Figure 959. The dataset has 48701 genes and 30 samples of each gene, along with their periodic gene expression changes. Samples include cubic spline-normalized intensity values of two conditions, viz., pancreatic stellate cell line before and after being treated with a 1-molecular concentration of all-trans retinoic acid (ATRA) on plastic. The gene expressions were recorded at 5-time points , having 3 replicates at each time point60. Gene expression dataset was org.Hs.eg.db61. Later, we have used the gene expression profiles of the annotated 42412 official genes for our differential gene expression (DE) analysis.We have collected the PDAC microarray gene expression profiles of pancreatic stellate cell line samples from the Gene Expression Omnibus (GEO) via the accession number GSE14426splineTimeR package28 to discover significant DE genes. SplineTimeR operates on the values obtained from the parameters of a fitted natural cubic spline regression model, which is utilized in our time-course gene expression profiles for control and treated samples28 (for a detailed description see Supplementary text). The differential expression of a gene has been discovered by applying empirical Bayes moderated F-statistics on the spline regression model’s coefficient differences. The detection of DE genes using splineTimeR28 has been carried out by setting the Benjamini-Hochberg36 adjusted p-value threshold to 0.05 and with a degree of freedom of 4 for all genes. Top regulated genes have been identified as differentially expressed and used for our subsequent analysis in module discovery. Supplementary Fig. Analysis and detection of differential expression of genes have been carried out using the R/Bioconductor 37. Limma utilizes empirical Bayes smoothing on the estimated fold-changes and standard errors from a linear model fitting to assesses the differential gene expression across a pair conditions. Table Additionally, to identify the DE genes at each time point, we have employed the limma package62. GeneNet62 has been used to analyze covariance matrices with a dynamic shrinkage method. Analyses have been performed with a posterior probability cutoff of 0.8, and 0.9. GAN reconstruction has been implemented using splineNetRecon function from the splineTimeR package28 to identify regulatory association between genes under a specific condition (treatment)28. Top significant edges have been identified based on their cutoff posterior probability. We have identified the top genes by analyzing the overall graph topology in the resultant GAN based on a higher partial correlation score and widely used centrality measures: degree, betweenness and closeness centrality. These centrality measures were aggregated for selecting the top 150 ranked genes by taking the average of the quantile normalized values of these measures to perform gene function prediction.In this work, we have performed a gene association network (GAN) reconstruction from the identified DE genes’ time-course data using a regularized dynamic partial correlation method32 infers possible connections between the query genes by searching many large, publicly available biological databases to find related genes. These include gene and protein expression data, protein-protein, protein-DNA and genetic interactions, pathways, reactions, protein domains and phenotypic screening profiles. GeneMANIA operates on a ridge regression-based fast heuristic algorithm to integrate multiple functional gene association networks using a label propagation algorithm. GeneMANIA weights data sources based on all genes’ connectivity strength with each other in the query list and suggests relatively similar non-queried genes and their connection types. It returns an interactive functional gene association network of all resultant genes and their relationship. It also performs gene functions prediction of queried genes based on non-negative weights of gene sources (i.e. association of two genes) as a binary classification problem. GeneMANIA further returns a ranked gene list presumably sharing phenotypes with queried genes based on it’s large and diverse databases. We have analyzed the top 150 ranked genes obtained from GAN for gene function prediction using GeneMANIA32.GeneMANIA31 is an adaptation of the GENIE3 method for gene regulatory network (GRN) inference that handles time series and steady-state data jointly. It is based on a semi-parametric model that models gene expression’s temporal evolution using a non-linear ordinary differential equation (ODE). An ensemble of non-parametric regression tree (Random Forest) model is used to learn transcription function in each ODE. It assesses each input feature’s importance to measure the variable importance scores (VIS) by considering the Mean Decrease Impurity measure. Finally, it uses the normalized sum of VIS to assign ranks for each learning sample from which the tree was built to identify regulators of each target gene. In our work, we have identified regulators for each target gene by GRN inference for the whole set of genes using the dynGENIE3 algorithm from the time-course gene expression data of PDAC.Dynamical GENIE3 (dynGENIE3)TSrepr R package63 for dimensionality reduction of multivariate time-series data. The DEGs obtained from the dataset contained 3 replicate values for each time point, which have been converted into univariate expression values using the mean function of PAA. These expression values were clustered to form the gene modules.Piece-wise Aggregate Approximation (PAA) has been used from the et al.14 to obtain gene modules from the univariate time-course expression data. It can simultaneously model cluster number with a Dirichlet process (DP) and temporal dependencies with Gaussian Processes (GP). DPGP uses a Bayesian non-parametric model for time course paths N is the number of genes and T is the number of time points (see the Supplementary text for a detailed description of DPGP method). For Markov chain Monte Carlo (MCMC), we have estimated the probability of the trajectory of gene j inside the cluster i according to the DP prior with the likelihood that gene j belongs to class i according to the cluster-specific GP distribution. Neal’s Gibbs Sampling “Algorithm 8” has been used to compute the posterior distribution of the trajectory class assignments64.We have utilized a non-parametric model-based approach, the Dirichlet Process Gaussian Process mixture model (DPGP), presented by McDowell 65. Here, the cluster assignment vector is sampled at every 14. MCMC generates a sequence of states produced by a Gibbs sampler, where each state delineates a group of genes into disjoint modules. Results generated from Gibbs samples were compiled into a posterior similarity matrix (PSM). The outcomes from the Markov chain was summarized with three different clustering criteria, viz., the maximum a posteriori (MAP), the maximization of posterior expected adjusted rand (MPEAR), and the least-squares66 to discover the best cluster assignment. DPGP conservatively declares convergence ensuring convergence plateau for at least 10 samples based on the iterative changes in posterior log-likelihood or the squared distance between sampled partitions and the posterior similarity matrix. We have also performed hyperparameter tuning for finding the concentration parameter , the function minimization using gradient information in a truncated Newton (fmin_tnc), and stochastic conjugate gradient (SCG)This subsection provides information about approaches that have been used to find the biological significance of our obtained results.30. It operates by using Kolmogorov-Smirnov-like test statistics and an implicit combination of regulator target interactions (RTI’s) for the prioritizing influence of TR’s. REGGAE returns a ranked list of key TRs with p-value aggregations. It uses an extensive collection of RTIs that relies on diverse external databases: ChEA, ChipBase, ChIP-Atlas, ENCODE, Signalink, Jaspar and TRANSFAC. Some of these databases provide manually curated RTIs extracted from the primary literature, while others provide binding information extracted from ChIP-Seq experiments. Additionally, we have also identified regulators for each target genes through the gene regulatory network inference analysis. We have also identified the DNA binding transcription factors (DbTF) through a cumulative and high-quality knowledge source of genome-scale information, TFcheckpoint33, from the detected transcriptional regulators obtained using REGGAE. The TFs in TFcheckpoint are investigated for experimental evidence supporting their role in specific DNA binding activity and RNA polymerase II regulation.We have utilized time-course expression profiles of the DE genes across the control and treated samples to identify the key transcriptional regulators (TR) using REGGAE35. Additionally, we have studied the genes inside the key modules for their direct associations with PDAC.Identification and analysis of key gene modules which contain the highest number of transcriptional regulators have been performed. The top 6 key modules have been analyzed to discover biological processes, pathway analysis and disease-gene associations for the involved genes inside each module using EnrichrThis article developed a framework to discover the key regulatory genes and the key gene modules from multivariate time-series Pancreatic Ductal Adenocarcinoma (PDAC) microarray data. Here, we have identified the top differentially expressed (DE) genes with a cubic spline regression model. We have performed the Gene Association Network (GAN) reconstruction to discover the regulatory association between genes in PDAC. Moreover, identifying key regulatory genes for each target gene has been carried out through a GRN inference analysis. Additionally, we have detected transcriptional regulators and DNA binding Transcription Factors (DbTFs) using REGulator-Gene Association Enrichment (REGGAE) and TFcheckpoint databases. Gene modules have been identified through a Dirichlet Process Gaussian Process (DPGP) mixture model. We have identified and analyzed the top 6 key gene modules that contain a significant number of regulatory genes. Biological significance analysis reveals that the genes inside the key gene modules are highly associated with PDAC.in vitro experiments to reveal some important findings in PDAC and its pathogenesis. One may also verify the role of key regulators in the modules as potential biomarkers. Survival analysis of the key transcriptional regulators may enlighten us with more significant insights about this disease.Our analysis can be further extended by analyzing integrated multi-omics data of PDAC patients. Common symptoms of PDAC include weight loss, indigestion, abdominal and back pain. Thus, studying pathway networks of the key gene modules may unravel deep insights into this disease. Moreover, the genes inside the key modules may be further validated using Supplementary Information (pdf 6408 KB)


Scientific Reports 10.1038/s41598-021-91917-6, published online 17 June 2021Correction to: The original version of this Article contained a repeated error in the Abstract, Introduction, the legends of Figure 4 and 5, the legend of Table 2, and in the Supplementary Information file, whereLactocaseibacillus paracasei”.“now reads:Lacticaseibacillus paracasei”.“The original Article and accompanying Supplementary Information file have been corrected.


Isognomon alatus through dietary exposure. As such, marine microalgae Tisochrysis lutea were exposed to environmentally weathered mixed NPs from Guadeloupe (NPG), crushed pristine polystyrene nanoparticles (PSC), and carboxylated polystyrene nanoparticles of latex (PSL). Oysters were fed with NP-T. lutea at 10 and 100 µg L−1, concentrations considered environmentally relevant, combined or not with 1 mg L−1 pentoxide arsenic (As) in water. We investigated key gene expression in I. alatus’ gills and visceral mass. NP treatments revealed significant induction of cat and sod1 in gills and gapdh and sod1 in visceral mass. As treatment significantly induced sod1 expression in gills, but once combined with any of the NPs at both concentrations, basal mRNA levels were observed. Similarly, PSL treatment at 100 µg L−1 that significantly induced cat expression in gills or sod1 in visceral mass showed repressed mRNA levels when combined with As . This study suggested a protective effect of the interaction between NPs and As, possibly by decreasing both contaminants’ surface reactivity.Nanoplastics (NPs) are anthropogenic contaminants that raise concern, as they cross biological barriers. Metals’ adsorption on NPs’ surface also carries ecotoxicological risks to aquatic organisms. This study focuses on the impacts of three distinct NPs on the Caribbean oyster  Plastic contamination is a worldwide environmental issue, as recognized by the United Nations Environment Program . Daily uNanoplastics (NPs) are commonly referred to as plastic nanoparticles smaller than 1000 nm in one of the three dimensions of space ,11. IndeIsognomon alatus wild oysters native to Guadeloupean mangrove swamps, since oysters are relevant organisms for ecotoxicological studies. Bivalves have indeed been commonly used as bioindicators for decades [I. alatus oysters. Indeed, filter-feeding bivalves rely on ciliated structures to collect phytoplankton but also organic and inorganic particles , the crushed pristine polystyrene nanoparticles , in the presence or absence of As in the water. These NP concentrations are environmentally representative [I. alatus gills and visceral mass. Overall, the main novelty of our study lies in comparing the effects of three relevant NPs, combined or not with arsenic, on I. alatus key genes.To tackle the impacts of ingested NPs, we studied NPs’ effects alone and combined with arsenic (As) on Caribbean oysters. We conducted our experiment on  decades ,36,37, p decades ,38 and n decades . Additio decades ,40,41. T decades ,43,44 th 500 nm; ,46). Thuary NPs; ), and thary NPs; ). Most Nary NPs; ,49. Therentative  and beloentative . Conside®, Pessac, France). The intensity fluctuations as a function of the time were processed as an autocorrelation function. The cumulants algorithm was used to fit this function in order to obtain a size distribution (z-average). The surface charges of particles  were assessed using a Wallis zetameter  (Plastics used to generate the mixed nanoplastics from Guadeloupe (NPG) were collected in 2016 on the Guadeloupean beaches , naturally aged in situ by environmental factors . Polystyrene pristine pellets used to produce crushed nanoparticles (PSC) were commercially purchased from Goodfellow . Both nanoplastic dispersions were prepared according to an optimized protocol . Plastic France) . Details−1, Millipore water purification system, Merck, Darmstadt, Germany) in cleaned glass vials. The mass concentration of each nanoplastic dispersion was measured by total organic carbon (TOC) analysis on a Shimadzu ® instrument .Noteworthily, polystyrene was chosen for both PSL and PSC, as it is one of five main plastics produced, representing 90% of global demand . All thrTisochrysis lutea [Thalassiosira weissflogii [T. weissflogii was only used during oyster acclimation for nutritional purposes [The microalgae species used to feed the oysters during the acclimation period were is lutea ,53 and Tssflogii  obtainedpurposes . Both alpurposes , the micI. alatus were collected from their natural habitat in Grand Cul-de-sac Marin , a natural reserve to the north of Pointe-à-Pitre in Guadeloupe. Once brought to the laboratory, they were individually brushed to remove external parasites and stored in 30 L tanks (100 oysters per tank) lined with tiles to fulfill I. alatus’ need for a hanging substrate. The tanks were filled with reconstituted seawater (Instant Ocean ®) at 32‰ salinity, oxygenated, and filtered by an aquarium filter pump. Additionally, 2/3 of the water tanks were renewed every two days during the first week of oysters’ acclimation given their high organic matter release. Similar to I. alatus native environmental conditions, they were acclimatized at 26 °C with aquarium heaters and under 12:12  for 15 days according to A. Arini . Oysters were fed twice a week with 100 mL of mixed T. lutea and T. weissiflogii  per tank.This study used native tropical flat oysters, as they are representative of mangrove swamps ,58. IndiT. lutea optimal exposure to NP solutions  as the acclimation phase previously mentioned. To avoid NP accumulation onto the air pump tubing, the NP-T. lutea solutions were oxygenated through an agitation table [A preliminary experiment was conducted to determine olutions . Tisochrntration . Prior t®, Darmstadt, Germany). Among inorganic forms of arsenic, pentoxide arsenic has been chosen, as it is the most abundant form found in oxygenated marine waters [−1 As was conducted through batch injection at the beginning of the exposure (day 0). This As nominal concentration was based on oyster bioaccumulation tolerance and As levels in seawater [−1 throughout the exposure, water samples were collected in the morning and As levels were quantified. Thus, As concentration was adjusted one day out of two if needed.A single solution of dissolved pentoxide arsenic (hereafter referred to As) was used  at 10 and 100 µg L−1, and a mix of As (1 mg L−1) with each of the NP treatments (As + 10 µg L−1 NP and As + 100 µg L−1 NP). All treatments were run in quadruplicate with two oysters per 500 mL glass jar.The experimental design encompassed  a contro3 cells oyster−1 L−1 microalgae considered as a relevant environmental concentration [Each jar was parafilm-covered to minimize evaporative loss. Water oxygenation was ensured by an air distribution pump. The oysters were under the same abiotic conditions  as the above-mentioned acclimation period. Oysters of each condition were fed every two days with 35 × 10ntration . After ontration  was calc−1 was prepared from a 1 g L−1 As solution. The 1 mg L−1 solution was then acidified with 3% nitric acid to measure the final concentration on an inductively coupled plasma optical emission spectrometry . To monitor and compensate for the As concentration throughout the exposure week, water samples of 0.5 mL were collected per jar and acidified at 3% nitric acid for ICP-OES analysis. To evaluate the efficiency of the As dissolution and dilution, a nominal solution of 1 mg LFor tissue analyses, the pooled gills and visceral mass were dried (48 h at 50 °C) and weighted prior to digestion (dry weight). Tissue samples were then acidified with 70% nitric acid (3 mL per sample) and heated at 100 °C for 3 h on a hot plate . After dilution of the digestates with 18 mL of pure water, total As bioaccumulation was measured using an ICP-OES . Certifi® (40 s at 6 movements/s) in 500 µL of RNA lysis buffer, using ceramic pellets . Deproteinization was done by adding 500 µL of phenol-chloroform isoamyl alcohol, as this organic solvent is very suitable for oyster tissues rich in proteins and fat [g to separate the organic and the aqueous phases. From there, total RNA was extracted using the Promega kit “SV Total RNA Isolation System” according to the manufacturer. Extracted RNA concentration was measured by using a microplate spectrophotometer at 260 nm, and RNA quality was estimated with a Take3 plate (nucleic acids purity ensured by 260/280 ratio ≥ 2), both from BioTek EPOCH ®. All samples were diluted to obtain the same RNA concentration (1000 ng in 10 µL) before performing reverse transcription to synthesize complementary DNA (cDNA) with the Promega kit “GoScript Reverse Transcription System” according to the supplier’s instructions.Gill and visceral mass tissues were preserved in RNA later at −20 °C right after dissections. For minimal weight purposes, tissues from two oysters were pooled together. Tissues were ground with a Biorad Fastprep  and fat . Then, sI. alatus, genes of interest were selected from the reconstituted I. alatus transcriptome sequenced by Lemer (2019) [cav (cell membrane invagination for endocytosis) and cltc , cat , normalized by 12S being indicative of the ribosomic RNA level [mdr  . For eacansport; ,72). Genpressor; ), gadd45 repair; ), and bativator; ). Oxidatand H2O; ), and gaivities; ,77). MitNA level ,79, and pulsion; ).® qPCR Master Mix” containing the 5× buffer, the Taq polymerase, MgCl2, dNTP, and SybrGreen dye. Validation of the primer’s efficiency and specificity was conducted so that 1 µL of the forward and reverse primers (100 µM each) was optimal for conducting the reactions. Together with the qPCR mix, primers were added to 1/10 diluted cDNA. The resulting final volume was poured into white 96-well qPCR plates, including two no-template control wells without cDNA, and replaced by RNAse free-water. Plates were sealed and quickly centrifuged, then analyzed by the LightCycler 480 Roche ®  starting with one cycle at 95 °C for 2 min, then 30 amplification cycles at 95 °C for 30 s and 60 °C for 30 s.A real-time quantitative polymerase chain reaction (qPCR) was performed with the Promega kit “GoTaq −ΔCt method described by Livak and Schmittgen (2001) [β-actin and rpl7 [The amplification efficiency of qPCR primers was assessed by 10-fold sample serial dilutions. Specificity was determined for each reaction from the dissociation curve of the qPCR products, obtained by following the SyberGreen fluorescent level during gradual heating from 60 to 95 °C. Based on these indicators, non-conforming data were not analyzed ND in . Followin (2001) , genes oand rpl7 . Gills aTo satisfy the conditions for parametric analyses, raw data were transformed by commonly used functions . Normality was then confirmed using the Shapiro–Wilk test, and homoscedasticity was confirmed by the Student test (α = 0.05). Transformed data were then compared between each treatment and concentration exposure using a two-way analysis of variance (ANOVA). The significant differences were identified by a post-hoc Tukey HSD test on Prism 8.0 for gene expression and the Bonferroni test on SigmaPlot 12.0 for arsenic dosage.−1 As in gills and 133 µg g−1 As in visceral mass . No significant differences were observed for the total fresh tissue weights and the CI among treatments , cell cycle regulation , oxidative stress , mitochondrial metabolism (cox1 and 12S), and detoxification (mdr) were assessed in gills and visceral mass , which supports p53 inhibition for As treatment.In the visceral mass, interestingly, the expression of sod1 and cox1 previously observed with As alone, whatever the level of exposure or the type of NPs. The presence of As also canceled the induction of cat observed with NPG at 100 µg L−1  or even repressed its expression with PSL at 100 µg L−1 (reduction of 2222% compared to control).In gills, the presence of NPs totally inhibited the induction of cav observed with As alone, except for PSC+As at 100 µg L−1. In the same way, the induction of cltc observed for PSL at 100 µg L−1 was canceled in the presence of As. The expression of bax showed a significant downregulation for NPG+As at both 10 and 100 µg L−1, and only at 10 µg L−1 for PSC+As. Since PSL+As could not be detected, it is worth noting bax downregulation for PSL also at 10 µg L−1. The same downregulation pattern was shared in gadd45 for PSC+As and PSL+As treatments at both concentrations. For gapdh expression, there was a significant downregulation for NPG+As at 10 µg L−1. The sod1 expression response for PSL+As at 100 µg L−1 was significantly inhibited compared to the PSL treatment, and interestingly, aljso significantly lower than for the arsenic treatment. Expression of mdr was not modulated except for the NPG+As treatment, significantly inhibited at 10 and 100 µg L−1.In the visceral mass, the presence of NPs canceled the induction of The statistical analyses conducted on biometric parameters do not show any significant differences for the measured shell length and fresh tissue mass, nor for the calculated CI . PotentiI. alatus in their native habitat. Despite these rather high As values, they are comparable with a reviewed range of bioaccumulated As occurring naturally in bivalves [−1 As, which allowed an increase in As bioaccumulation for both tissues after a short-term exposure (one week). No speciation analysis has been conducted, as we did not aim to address the As behavior in this study. Given chemical reactions in seawater, the initial As form (arsenate) underwent speciation changes [I. alatus is most likely responsible for the tissue bioaccumulation found in controls. Under environmental conditions, oyster exposure to As most likely came from sediment through the waterborne route [I. alatus tissues. Yet, there were no statistical differences in As bioaccumulation between the As and the NPG+As treatments, at 10 and 100 µg L−1 NPG concentrations for both tissues. Similarly, Freitas et al. (2018) did not observe an increased As bioaccumulation in clams in the presence of As (0.1 mg L−1) combined with multi-walled carbon nanotubes (0.1 mg L−1) [I. alatus.The total As tissue bioaccumulation measured in controls demonstrated the biogeochemical background of Guadeloupean mangroves. These levels provide us with the environmental As baseline of bivalves ,83. For bivalves ,85) compbivalves ,86). Thi changes ,88,89. Tne route ,64,83 anne route ,89,90. C mg L−1) . Our resT. lutea growth, a 96 h exposure was conducted with NPG, PSC, and PSL solutions at 10 and 100 µg L−1  [T. lutea surface for a concentration grade including 10 and 100 µg L−1 by scanning electron microscopy (SEM).No significant changes were observed under any NP treatments. Noteworthily, higher optical density dispersions happened at 72 h for both NP concentrations. Thus, we decided to expose ion rate ,93. Ther., 2021) , we attegadd45 mRNA level for PSC and PSL treatments at both concentrations, except for PSL 100 µg L−1 in visceral mass . Yet, we observed lower toxicity in NPs+As treatments compared to single As treatments for endocytosis (cav), oxidative stress (sod1), and mitochondrial metabolism (cox1). Antagonist effects have been recently documented for MP-NP exposures combined with metals [Expression of tressors . NP toxitressors . Similarsix days . Due to six days ,108,109,six days ,9,28. Thsix days ,111. Thih metals ,113 inclh metals . Antagonh metals . HoweverBased on the genes mRNA levels, −1 by cav induction. Since a fraction of PSC might be aggregated, resulting in bigger particles similar to MPs, a higher PSC concentration might be required to witness their internalization. Polystyrene nanoplastics (PS NPs) were consistent in DNA damage, showing no change of toxicity for PSC+As and PSL+As treatments on gadd45 repression. Additionally, the change of bax expression for NPG+As treatment at 10 µg L−1 was most likely caused by As. Contrary to what was expected, no synergetic effects were observed between As and NPs in combined treatments. At a subcellular level, the As treatment alone affected mitochondria homeostasis by cox1 upregulation, but once in presence of any NPs, cox1 upregulation was canceled. Thus, we hypothesize that NPs’ presence could decrease the As availability, most likely through As adsorption on NPs, but no affirmation can be done, as there were no significant differences. In the presence of As, there was an inhibition of cat expression for PSL +As treatments at 10 and 100 µg L−1, contrary to NP single treatments. Moreover, As alone induced oxidative stress revealed by sod1 expression and so did the PSL treatment at 100 µg L−1, but once As was in presence of PSL (PSL+As treatment), a putative decrease of oxidative stress was observed. Therefore, data highlight a potential protective role of PSL against the oxidative stress caused by As, but also a protective role of As toward the oxidative stress generated by NPG and PSL at 100 µg L−1 revealed by the cat upregulation.In gills A, PSC in−1 by cav induction, leading to membrane invagination. PS NPs triggered DNA damage given the gadd45 repression. Based on their physico-chemical characteristics, PSC and PSL yielded a low and very low endogenous ROS burden, respectively. Thus, DNA damage most likely came from the physical nanoparticle toxicity, excluding additional toxicity from As in PSC+As and PSL+As treatments. However, an opposite pattern was observed for NPG+As, where the bax mRNA level decreased once NPG were in presence of As. Therefore, As interaction showed different effects among NPs toward cell cycle regulation and apoptosis. Moreover, PSL and NPG expressed an opposite mdr response at 100 µg L−1. Indeed, as PSL were very stable, an increased exposure seemed to lead to a greater induction response from the cell to expel them. Oxidative stress was triggered by PSL at 100 µg L−1 inducing sod1 regulation, but was canceled in presence of As (PSL+As treatment), while gapdh expression was inhibited by NPG+As treatment at 10 µg L−1. Thus, As presence seemed to cancel the oxidative stress production seen for NP single treatments. It has to be kept in mind that NPG were the most weathered NPs, with high porosity and high surface oxidative degree that potentially increased its interaction with As. The same logic applies for PSL by As adsorption on its carboxylated functions. Noteworthily, As treatment induced endocytosis activation by cav expression. Again, we may have observed As interaction with NPs through adsorption that led to canceling cav induction inNPG+As treatment at 10 µg L−1.In visceral mass B, data sWe demonstrated that sublethal exposures of NPs impaired cellular functions at the molecular level on native Guadeloupean oysters. Among our three NP solutions, PSL treatments showed the most consistent toxic effects by triggering oxidative stress and cell cycle regulation in gills and visceral mass tissues. These results shed light on the interest to use PSL additives-free nanoparticles with surface functionalized groups  rather than conventional commercial PSL. Similarly, but with fewer effects observed, PSC toxicity came most likely from their irregular surface, with a highly specific surface that potentially enabled more adsorption on the microalgae surface. Interestingly, NPG treatments also showed toxic effects, but fewer than PSL by only triggering oxidative stress in gills. In light of these results, we suggest that studies might include more complex environmental NPs in their study design. Moreover, to our knowledge, we present the first results showing a specific response of NPs+As exposure with a protective effect on oxidative stress and on endocytosis. All these results underline the threat of plastics for marine wildlife and the need to study crossed effects between NPs and other contaminants.


With the rapid development of wearable electronics, looking for flexible and wearable generators as their self-power systems has proved an extensive task. Fiber-based thermoelectric generators (FTEGs) are promising candidates for these self-powered systems that collect energy from the surrounding environment or human body to sustain wearable electronics. In this work, we overview performances and device structures of state-of-the-art fiber-based thermoelectric materials, including inorganic fibers , organic fibers, and hybrid fibers. Moreover, potential applications for related thermoelectric devices are discussed, and future developments in fiber-based thermoelectric materials are also briefly expected. With the development of science and technology and the progress of industrialization, the increasing demand for electricity, the excessive consumption of fossil energy, and serious environmental problems have attracted more attention in the world in recent years . Therefo2T/κ is determined by the performance of the thermoelectric material itself, where T is the average temperature, κ is the thermal conductivity, and σS2 is described as the power factor. In this formula, S is the Seebeck coefficient, which can be described as S = ΔV/ΔT. σ is the electrical conductivity, which can be expressed as σ = neμ, where μ is electron mobility, and n is carrier concentration. Thermal conductivity κ = κe + κl is composed of the electronic thermal conductivity and the lattice thermal conductivity κ [The principle of fiber-based TEGs (FTEGs) is the thermoelectric effect (or Seebeck effect), which means temperature difference can generate thermoelectric voltage when it exists between both sides of FTEGs . The maxtivity κ ,6,7,8,9.tivity κ . Over thtivity κ ,6,9,11. tivity κ . In geneGenerally, most conventional flexible thermoelectric (TE) devices are constructed based on bulk rigid TE elements, films with flexible, elastomer substrates, and flexible fabrics ,13,14,15Thus, fiber-based TEGs (FTEGs) will be promising candidates for self-powered systems that collect energy from the temperature difference between natural sources and the human body to sustain wearable electronics . In factConsidering that the review on enhancing the performance of FTEGs is outdated, in this work, we overviewed the performances and device structures of state-of-the-art fiber-based thermoelectric materials. We exemplified the particulars of different materials and structures, such as preparation method, characteristics, power performance, evaluation, analysis, and potential applications. Finally, the current challenges and outlook for the future development of fiber-based thermoelectric materials were briefly discussed.2Te fiber exhibits better TE performance due to its narrow band gap, low thermal conductivity, and unique structure. Thus, semiconductor materials based on nanowires [Compared with carbon fiber, semiconductor fiber including Bi-Sb-Te/Se, SnSe, PbTe, and Aganowires ,22,23,242Te3-, Bi2Se3-, and Sb2Te3-based alloys, are widely used for TE devices with high ZT value within the temperature range of 200 to 400 K [2Te3, Bi2Se3, Sb2Te3, and their alloys crystallize in a rhombohedral structure belonging to the tetradymite space group 2Se3) nanowires with uniform single-crystal structure successfully synthesized by stress-induced growth method [2Se3 nanowire (NW) at room temperature was 39.2 × 10−5 Wm−1K−2, and the thermal conductivity κ, measured by 3ω technique, was 2.05 Wm−1K−1 [2Se3 NW rose to ZT = 0.06 [2Te3 was put inside a glass tube and fabricated into Bi2Te3 core fibers via a modified thermal drawing process [2Te3 unit cell had a hexagonal structure, and the angle between the hexagonal symmetry axis of the Bi2Te3 unit cell and the fiber axis was close to 90 degrees, which was beneficial to increase electrical conductivity [2Se3 core thermoelectric fibers using a thermal drawing method, and the 50 μm diameter Bi2Se3 core fibers exhibited an ultrahigh Seebeck coefficient of −150.85 μVK−1 and a figure of merit of 0.18 at a temperature of 300 K [0.5Sb1.5Te3 core fibers and n-type Bi2Te3 TE core fibers into a two-dimensional fabric, as shown in −2 and 1.46 mWcm−2, respectively, as demonstrated in The semiconductors of group V–VI, exhibiting narrow band gaps, such as Bito 400 K ,28,29,30h method . The pow Wm−1K−1 . At roomT = 0.06 . To impr process . It was uctivity . There wuctivity . The expuctivity . SimilarIt is well known that SnSe has ultra-low intrinsic thermal conductivity and has an anisotropic thermoelectric property along a, b, and c axes due to its layered structure ,35.2, SnSe2, or Sn and Se by-products [−1 at 300 K, the absolute value of which is a little less than that of a BiCl3 doping SnSe crystal (−176 μVK−1 at 300 K) due to the poly-crystal state core and the secondary phase SnSe2 [2 laser was used to precisely control crystallization along the length of fibers, as shown in In 2014, Zhao et al. indicated that the ZT for a single crystalline SnSe at 923 K reached 2.6 along axis b . Moreoveproducts . Thermoeproducts . Furtherproducts . This meproducts . Neverthse SnSe2 . Zhang ese SnSe2 . In orde¯m phase . After s¯m phase .The phonon dispersion of rock-salt PbTe is similar to that of rock-salt SnSe ,39. PbTe2Te nanofibers), and cationic exchange reaction (from Ag+ cations to Pb+ cations) [−1 (at 300 K), when the content of remaining Ag was 30%. Additionally, the pure PbTe nanotubes realized the power factor as high as 0.567 μWm−1K−2 [1−xSnxTe nanowires were prepared with catalysts of Au nanoparticles via the vapor-transport approach [1−xSnxTe nanowires reaches ~0.035 for a width of 459 nm and thickness of 170 nm, and 0.018 for a width of 45 nm and thickness of 17 nm [−1 [−1K−2 and 0.73 at 400 K, respectively [Moreover, PbTe hollow nanofibers with a wall thickness of about 20 nanometers were synthesized through a sequential process in three stages of electrospinning (synthesized silver nanofibers), electrodeposition (transformed into Agcations) . The synμWm−1K−2 . In addi72 μVK−1 . Besides72 μVK−1 ,41, PbTe72 μVK−1  and hydr72 μVK−1 . Furtherectively .Figure 2Te forms the monoclinic phase (β) with a narrow bandgap of 0.06 eV at low temperature and transforms into the face-centered-cubic phase (α) with the carrier ions being Ag ions of more than 423 K [2Te nanowires have been reported, including electrospinning [Agan 423 K ,46,47. Cspinning , chemicaspinning ,49, micrspinning , and solspinning ,52.xTey hollow nanofibers were synthesized by combining electrospinning with a galvanic displacement reaction (GDR) to convert the electro-spun materials to the desired hollow chalcogenide nanofibers [xTey nanofibers (<67%) were p-type, whereas high Ag-content AgxTey nanofibers (>67%) were n-type [−1 in Ag5Te95 [2Te nanowires were synthesized by a simple method of chemical vapor transport [2Te NWs can also be converted to films with excellent thermoelectric performance at room temperature using facile vacuum filtration and drop-coating methods [2Te NWs film was made by transferring the film from the glass-fiber sheet to the copy paper via a new cold press method. The optimal power factor of paper-based Ag2Te NWs film was prepared at 30 MPa and displayed 192 μWm−1K−2 at a temperature of 468 K [Ultralong Agnofibers . Low Ag-e n-type . The abs Ag5Te95 . Additioransport . Ag2Te N methods . Additioof 468 K .−1K−1) of carbon materials seriously limits its potential to be an excellent thermoelectric material, and various approaches of nanostructured defects  [−1K−1 to 500 Wm−1K−1, when the outer diameter increased from 10 nm to 28 nm [Due to the special atomic arrangement of carbon materials, many allotropes of carbon exhibit high electrical and thermal conductivity properties ,55. Howeomeshes) ,57,58 anomeshes)  decreaseomeshes) . In prevto 28 nm ,60,61. TCNTs are cylinders derived from rolling up a single layer of the graphite honeycomb lattice , which lead to a special electronic band structure  . In addi−1K−2, more than six times that of the original CNTY, as illustrated in Generally, fiber-based CNTs materials can be divided into p-type and n-type thermoelectric materials, depending on different categories of dopants, which determine the type of main charge carriers . It was −1 per unit [−1, a power factor of 1.65 × 10−3 μWm−1K−2, and a figure of merit of 1.14 × 10−6 [It is worth noting that N-type CNTs are more challenging to obtain than P-type CNTs, because N-type CNTs should consider their doping quality and their stability in the air ,69. Figuper unit . In addiper unit . Additioper unit . Unlike per unit . Paleo eper unit . Figure 4 × 10−6 . In this−1K−2 at a temperature range of 25–100 °C by making a composite with P3HT, followed by the F4TCNQ doping [−1K−2 at room temperature.Furthermore, the thermoelectric properties can be improved by forming CNTs/organic conductive polymers ,75 or CNQ doping . Wang etAs a monolayer carbon atom in a honeycomb lattice, graphene exhibits a number of advantages over other materials, including high specific surface area, thermal conductivity, electrical conductivity, charge density, carrier mobility, and strength . In prev−6 with increasing temperature from 100 to 290 K [−1K−2 and 0.23 μWm−1K−2, and the thermal conductivity was measured as 0.26 Wm−1K−1 and 0.24 Wm−1K−2, from which the ZT was calculated as 2.04 × 10−6 and 0.96 × 10−6 [The graphene fibers had maximal a ZT value of 3.7 × 10to 290 K . In ordeto 290 K , polyethto 290 K , embeddito 290 K . For exato 290 K . Additiosolution . The fab6 × 10−6 . Moreove6 × 10−6 . Compare6 × 10−6 .3Co4O9, NaxCoO2) [3 [3, and ZnO. High electrical conductivity and low thermal conductivity are required to maximize and optimize ZT value in oxide TE materials. For oxide TE material, phonon transmission in the lattice contributes the predominant part of the total thermal conductivity [2O4 crystal was measured to be 19 Wm−1K−1, in which phonons contributed 16.5 Wm−1K−1, and charge carriers contributed 2.5 Wm−1K−1 [Compared with the elements in conventional intermetallic alloys, oxide TE materials are of low toxicity, cost-effective, and suitable for high-temperature applications due to their particular structure and excellent chemical stability ,81,82,83NaxCoO2) ,85, SrTiCoO2) [3 , CaMnO3,uctivity . For exa Wm−1K−1 .2O4) is a transition metal, owing to a hexagonal layered structure in which CoO2 sheets are attached with the edge-sharing Na layers, CoO2 sheets tend to transport electrons, while Na layers decrease thermal conductivity [3COO·3H2O/Co(CH3COO)2 nanofibers via an electrospinning technique and deposited the composite directly onto the silicon, as shown in 2O4 nanofibers with an average diameter of ~130 nm were obtained due to the decomposition of PVP and formation of γ-phase crystalline NaCo2O4 nanofibers  i50 μVK−1 . Compare50 μVK−1 . It is red fiber .In the past few decades, commonly used inorganic TE materials have been widely studied due to their high TE performance, while the disadvantages, such as high cost, rigidness, toxicity, and poor processability, limit their integration into wearable electronic devices ,91. In c−1 in 2002 [PEDOT:PSS consists of positively charged conductive conjugated PEDOT and negatively charged insulating PSS and was first reported with a Seebeck coefficient of 12 μVK in 2002 ,93. Figu in 2002 . As an o in 2002 ,94. Over−1 and 3.2 × 10−3 μWm−1K−2, respectively [−1 and a power factor of 147.8 μWm−1K−2 [−2 and an output voltage of 4.87 mV [−1, Young’s modulus of 22 GPa, and break stress of about 550 MPa [−1K−2 and 3.79 μWm−1K−2, respectively [−1K−2 at a temperature difference of 60 K, as shown in Recently, PEDOT:PSS cotton fabric was fabricated by impregnating cotton fabric with a polymer solution, achieving a Seebeck coefficient and power factor of 10.3 μVKectively . Similarectively . The fleectively . The genectively . The PEDectively . Under oμWm−1K−2 . Five pa 4.87 mV . In the  550 MPa . Kim et  550 MPa . Moreove 550 MPa . Based oectively . A TEG wIt is well known that organic TE materials, such as PANI, PEDOT:PSS, and poly(N-vinylpyrrolidone) (PVP), are proved highly attractive due to low thermal conductivity, easy processing, and excellent flexibility, while their low electrical conductivity and Seebeck coefficient limit improve TE properties . Recentl−1K−2, due to the separation of the insulating PSS chains from the highly conductive PEDOT chains [−1K−2 [−1 [−1K−2 and 113 μWm−1K−2, respectively. For other conductive polymers, they can also be used to form hybrid materials with CNT. Ryan et al. reported air-stable and flexible n-type yarns by coating MWCNTs and poly(N-vinylpyrrolidone) (PVP) nanocomposites on commercial sewing threads [−1K−2 and 289 ± 98 μWm−1K−2, respectively.PEDOT: PSS/single-walled carbon nanotubes (SWCNTs) hybrid fibers were prepared by a gelation method . With thT chains . To furts [−1K−2 . A therms [−1K−2 . In orde1K−2 [−1 . Additio threads . In orde threads . In addi threads . In orde threads . As a re−1 to 96.3 Scm−1 due to an increase in σ, which might be ascribed to the π−π stacking of graphene and PEDOT chains, and the power factor was about 2.9 μWm−1K−2, as shown in −2 [−1 and low thermal conductivity of 0.249 Wm−1K−1 [In addition to CNT, graphene is a highly conductive filler based on theoretical calculations and previous experimental results. The hybrid fibers graphene and PEDOT: PSS were prepared by using a hydrothermal process with ascorbic acid (AA) in a tube mold, and n-type fibers were achieved by treating hybrid fibers with polyethyleneimine ethoxylated (PEIE), as shown in wn in −2 . In addiwn in −2 . After c Wm−1K−1 . Further Wm−1K−1 ,106.−1, and the electrical conductivity reached a maximum of 102.2 Scm−1 due to the more ordered PEDOT layer, which was led by the interaction between Te and PEDOT:PSS [−2, respectively [−1K−2; this holds the potential to scale up [Non-carbon inorganic materials usually exhibit excellent TE performance. However, their toxicity, high cost, poor processing, and low flexibility also need to be solved . PEDOT: EDOT:PSS . A simplectively . In Figuscale up . Figure scale up .−1, as shown in −1 and excellent robustness after washing, which was attributed to a strong electrostatic interaction between the PEDOT: PSS coating and the silk [2O4 can be fabricated into composite nanofiber webs by dual electrospinning [−1 with the increase in relative content of nanofibers [2Te3/PAN yarn [Moreover, a fiber-based TE device was designed by weaving TE fibers into a fabric, which achieved a mass specific power of 9.48 μWgthe silk . In addispinning . At a fanofibers . Furthernofibers , Sb2Te3/PAN yarn , and othThe temperature sensor is an important device applied to industrial production, medical diagnosis, and military defense and provides essential information about the dynamics of many physical, chemical, and biological phenomena ,113,114.−2 per area at a temperature difference of 55 °C [−2 and 0.21 μW, respectively. Moreover, the tiger-yarn textile structure, a plain weave of identical segmented TE yarns with alternating segments of n- and p-type TE fibers, is shown in −2, which is higher than that of zigzag switch textile and garter-stitch textile. Improving on the previous study, Sun et al. designed a 3D thermoelectric generator without any supported substrates [Generally, traditional film TE generators merely harvest the thermal energy in the in-plane direction without effectively utilizing the perpendicular temperature gradient between the human body and the environment ,116. Comof 55 °C . Additioof 55 °C . The gar0.5Sb1.5Te3 fiber and n-type Bi2Se3 fiber, as shown in As the same application of thermoelectric materials, fiber-based TE materials can also be applied in solid-state cooling due to the Peltier cooling effect ,8. With In recent years, developing a high TE performance and flexible thermoelectric fiber has attracted much attention. In this paper, we overview performances, device structures, and applications of state-of-the-art fiber-based thermoelectric materials. TE fibers can be divided into organic fibers, inorganic fibers , and hybrid fibers . As a material with excellent TE properties, inorganic TE fibers have limited applications due to their fragility, high cost, and complex preparation process. On the contrary, organic polymers have been studied extensively due to their high flexibility, low cost, and light weight. By combining inorganic materials and organic polymers, hybrid fibers have excellent TE properties and increased flexibility and have become promising material candidates. However, balancing flexibility and high TE performance to avoid the combination of defects of the two materials also faces some challenges. The reported preparations of TE fibers mainly include wet-spinning, electrospinning, thermal drawing, coating, etc. Furthermore, we summarize the temperature, Seebeck coefficient, electrical conductivity, thermal conductivity, power factor, ZT, and method of thermoelectric fibers according to the classification mentioned above in this paper, as shown in In short, fiber-based TE materials have had promising applications, but research is still in the early stages of development. Looking forward, we summarize the potential development of TE fibers and devices in the future; the following direction should be paid more attention.A flexible fiber thermoelectric device is an intelligent fabric that realizes a sizeable and flexible area by weaving with the industrial loom or cotton yarn, demonstrating certain cooling functions. This large-area thermoelectric fabric, which has flexible 3D structures, can be deformed, bent, and twisted and can cover curved surfaces with arbitrary geometry and an extensive temperature range. The refrigeration device based on thermoelectric fabric makes it possible to regulate human body temperature automatically and expands prospects for infrared stealth  in future military applications. Human body temperature regulation achieves fast temperature modulation by touching human skin with cooling elements, and thermal camouflage is accomplished by exposing thermoelectric materials to the outside and changing the surface temperature near the target. Both of these functions need efficient thermal management. In future research work, exploring a single high thermoelectric performance fiber with high mechanical properties for different temperature ranges is interesting and significant. Furthermore, the stability of thermoelectric and mechanical properties, as well as the comfort and practicality of wearing are also worth considering.With the development of science and technology, people pay more and more attention to medical health. The advent of fabric electronics, including detectors composed of thermoelectric fibers, promises to break the time and space constraints of the traditional medical industry. At present, in medical treatment, the application of textile electronics is mainly in flexible measurement technology. Combining thermoelectric fiber with other sensors, such as strain, pressure, optical, gas, etc., and by monitoring some main physiological parameters of the human body, including body temperature, pulse, ECG, movement characteristics, and external environment, we can judge the current physiological state of the human body and provide early warning signals for sudden diseases or other high-risk conditions.In addition, thermoelectric fiber can realize the mutual conversion of heat energy and electric energy, which has important applications in wearable power supplies for the human body. By developing thermoelectric fibers suitable for the range of human body temperature, a portable power supply can be realized, and the performance of thermoelectric fiber devices can be adjusted by optimizing device size, geometry, and weaving mode. In addition to being limited by its technical level, there are many challenges in applying fabric electronics. One challenge lies in reconciling the brittleness of thermoelectric materials with the flexibility of fibers: excellent thermoelectric properties means that the proportion of thermoelectric fibers is high, but the brittleness of thermoelectric materials is not conducive to flexibility. Another challenge is the need to make traditional hard materials flexible, washable, wearable, etc. In recent years, microelectronics and micro-electromechanical have developed rapidly, and the equipment is becoming more integrated and smaller. The integration of thermoelectric fiber power supplies, integrated circuits, and thin film electronic devices through material integration technology is also a consequent development direction of fabric electronics.


The transient conductivity ultimately vanishes as ions encounter blockages. The initial and final values of the transient conductivity correspond to the high-frequency and low-frequency limits of the frequency-dependent conductivity, respectively. Carrier dynamics determines the conductivity between these limits. Typically, the conductivity rises monotonically with increasing frequency. By contrast, when pathology examinations found exceptionally disorganized excised tissue, the conductivity falls with increasing frequency as it approaches its high-frequency limit. To analyze these measurements, excised tissues are modeled as mixtures of “normal” tissue within which sodium cations can diffuse and “abnormal” tissue within which sodium cations are trapped. The decrease in the conductivity with increasing frequency indicates the predominance of trapping. The high-frequency conductivity decreases as the rate with which carriers are liberated from traps decreases. A relatively low conductivity results when most sodium cations remain trapped in “abnormal” brain tissue, while few move within “normal” brain tissue. Thus, the high densities of sodium nuclei observed by  I.σ(ω). Qualitative changes in σ(ω) occur for excised samples whose pathology examination finds to be exceptionally disorganized. The following five paragraphs summarize the previously published experimental findings. The remainder of this paper develops a theoretical framework with which to understand these experimental results.Brain tissue of 67 epilepsy patients at the UCLA medical center was excised from a variety of lesion locations. Each freshly excised sample was subjected to (1) pathology examination, (2) measurement of the diffusion MRI [spin-echo nuclear magnetic resonance (NMR)] of its hydrogen nuclei, and (3) measurement of its frequency-dependent conductivity 25 ions/m3 moving with a diffusion constant of 10−9 m2/s generates a room-temperature conductivity of about 0.16 S/m.Transient currents induced by application of dc electric fields to cm-sized samples decayed in about 100 s as charge transport stopped at cellular blockages separated by about 0.5 mm.−9 m2/s.Solvation occurs as oxygen atoms at the apex of the surrounding water molecules orient themselves toward cations.σ(ω)/∂ω > 0. At high enough frequencies , the frequency dependence of the conductivity weakens as most diffusing cations avoid blockages. The conductivity then simply becomes proportional to the product of the density of diffusing ions and their diffusion constant. Distinctively, the relative variations in the measured conductivities were an order of magnitude greater than those of diffusion constants obtained from diffusion-MRI measurements.In most instances, the conductivity rises as the applied frequency was increased, ∂decrease with increasing frequency, ∂σ(ω)/∂ω < 0, at high frequencies  measured on excised epileptic human brain tissue and sodium concentrations in epileptic lesions as measured by 23Na-MRI.Thus, measurements of the frequency-dependent conductivity imply that the densities of conducting sodium cations are especially low for especially disorganized brain tissue excised from epilepsy patients. By contrast, unusually, high concentrations of sodium atoms measured Ra = 0 in the formulas of Sec. Rn is the rate at which sodium cations in the normal material migrate to blockages at which they are stopped. Second, Rl is the rate at which ions are liberated from traps in the abnormal material to then move within the normal material. We find that the sign of the slope of the conductivity with respect to the applied frequency, ∂σ(ω)/∂ω, in the high-frequency limit just depends on the ratio Rl/Rn and on f, the fraction of ions initially trapped in the abnormal material. For inefficient transfer of ions from abnormal tissue into normal tissue, Rl/Rn ≪ 1, the anomalous frequency-dependence of the high-frequency conductivity, ∂σ(ω)/∂ω < 0, only occurs when most ions are initially trapped in abnormal tissue, f → 1. Concomitantly, relatively few sodium cations initially move through normal tissue.To analyze published measurements, we model excised samples as mixtures of “normal” tissue and “abnormal” (severely disorganized) tissue. Normal tissue generally exists at the margins of excised tissue and also may be included within its lesion. At its most extreme  and “abnormal” , based on the severity of their structural disruptions. Most generally, ions can move within each category until being blocked by their respective cell structures. Thus, each category is treated as composed of polarization centers defined by their respective blockages. Ions’ motion within normal and abnormal polarization centers in addition to transfer from abnormal to normal tissue generates their frequency-dependent conductivities.t = 0 initiates processes that alter the probabilities of cations (1) moving in the normal material (Pn), (2) moving in the abnormal material (Pa), and (3) being stopped and bound at blockages (Pb). In particular, three master equations govern the temporal evolution of the occupation probabilities for cations moving in normal tissue, in abnormal tissue, and being stopped at cellular blockages,Rl represents the rate characterizing the electric-field-induced liberation of cations from being trapped within abnormal structures. Liberated cations then transfer to normal matter within which their motion is relatively rapid. The rates with which cations move to blockages within normal and abnormal structures are denoted by Rn and Ra, respectively.The application of an electric field at A and B are determined from the initial conditions that Pa = f and Pn = 1 − f at t = 0, where f equals the fraction of cations initially occupying abnormal tissue and 1 − f equals the fraction of cations initially occupying normal tissue. Then, after some straightforward algebra, Eqs. Ra = 0.The first-order linear differential equations Eqs.  have thep−Rl+Rat.The coeffp−Rl+Rat.The coeffσn and σa denote the initial ionic conductivities of normal tissue and abnormal tissue, respectively, where they are occupied by the totality of mobile ions, nc. Specifically, σn = nc(q2/kT)Dn and σa = nc(q2/kT)Da, where q represents the ion’s charge; k denotes the Boltzmann constant; T signifies the temperature; and Dn and Da denote the ionic diffusion constants in normal and abnormal tissues, respectively. Employing Eqs. The transient ionic conductivity most generally is the sum of (1) that from mobile cations drifting through regions of normal tissue until they are stopped at blockages and (2) that from mobile cations drifting through damaged regions until being stopped at their blockages,f = 0, the transient conductivity, σt(t) ∝ σn exp(−Rnt), falls monotonically with time as solvated cations move through the normal material until they are stopped at a blockage. Second, if all the tissue is damaged with no transfer of trapped cations in abnormal tissue to normal tissue being possible, f = 1 and Rl = 0, the transient conductivity becomes simply σt(t) ∝ σa exp(−Rat). That is, the transient current density falls monotonically with time as solvated cations move relatively slowly, Ra ≪ Rn, through the damaged material until they are stopped at blockages.Consider two limiting situations. First, if there are no damaged regions, σa = 0 and Ra = 0, Eq. rises with time for sufficiently large f with fRl > Rn(1 − f),In between these two extreme limits, the transfer of sodium cations from abnormal tissue to normal tissue can produce a qualitatively different behavior, trap-limited motion through normal tissue. For example, in the absence of transport in abnormal tissue, These qualitatively different time dependences of transient conductivity manifest themselves in qualitatively different frequency dependences of the frequency-dependent conductivity. Indeed, the frequency-dependent current density is obtained from transforming the transient current density as follows:ω → 0, the ac conductivity approaches the final value, t = ∞, of the transient conductivity, where all carriers are stopped by blockages: σ(0) = 0. In the high-frequency limit, ω → ∞, the ac conductivity approaches the initial, t = 0, value of the transient conductivity,Rl → 0, the frequency-dependent conductivity becomes simply the sum of that from polarization centers in the two types of tissues as follows:σa ≪ σn, the ionic conductivity of Eq. f → 1. By contrast, as Rl → ∞, all trapped carriers are readily liberated from abnormal tissue and are thereafter free to move through normal tissue. The effects of trapping then disappear from Eq. Some limiting values of Eq. Rl/Rn = 0. By contrast, the solid curves depict the liberation of carriers from traps generating a substantial range of frequencies over which the conductivity falls with increasing frequency. As evidenced in this figure, the relatively mild frequency dependence of the conductivity observed experimentally occurs when ω/Rn ≫ 1.∂ [σ(ω)/σn]/∂[ω/Rn] is straightforwardly obtained from Eq. ω/Rn → ∞, with vanishing transport through abnormal brain tissue, Ra → 0,Rl = 0. By contrast, a negative slope is obtained when the second term within the square brackets of Eq. A relatively simple expression for the slope of the relative conductivity Rl/Rn ≫ 1, then, as illustrated in f ≪ 1. Second, if Rl/Rn ≪ 1, then, as illustrated in f → 1 − (Rl/Rn). The high-frequency conductivity is then relatively small since it involves only a small fraction of tissue’s ions: σ(100 Hz) ≈ σn(1 − f) ≈ σn(Rl/Rn) ≪ σn. Rather, most ions are initially trapped within abnormal tissue.Consider the two limiting situations that generate a negative slope. First, if The second scenario appears most relevant to the observations reported in Refs. III.Biological processes depend on the movement of ions. Ionic transport generally depends on the quality of the biological structures within which ions exist. The quality of these structures is evaluated by pathologists who relate it to the presence of disease. Charge transport studies can be combined with pathology investigations. For example, anomalous frequency dependences of the ionic conductivities are observed in brain tissues excised from pediatric epilepsy patients whose pathology examination reveals to be exceptionally disorganized.increases slowly with the frequency of the applied electric field that drives cation transport. This behavior is understood as arising from ions moving until they encounter intrinsic blockages associated with cellular structures.decreases with the frequency of the applied electric field.Here, we employ a simple model to describe changes in the ionic transport through brain tissue as a function of the extent of its structural abnormality. In particular, brain tissue is regarded as the mixture of normal tissue, which supports ionic transport, and abnormal tissue within which ions are trapped. As is widely observed, the ionic conductivity of typical freshly excised tissue 23Na-MRI in epilepsy patients’ lesions is taken as indicative of the severity of this affliction.23Na-MRI measurements indicate that the density of trapped sodium cations rises with the severity of the structural disruptions associated with epilepsy. In other words, epilepsy’s etiology in these samples is consistent with (1) disorganized brain tissue, (2) high densities of trapped sodium atoms, and (3) low densities of mobile solvated sodium cations.Solvated sodium cations are the predominant charge carriers in tissues excised from epilepsy patients.The qualitative features of these results are robust. However, we cannot estimate the fractions of excised brain tissue that should be regarded as normal and abnormal. Normal and abnormal tissues may be mixed within lesions. Furthermore, surgeons generally include some surrounding normal tissue when they excise a lesion. This effect will tend to exaggerate the fraction of normal tissue in an excised sample and its conductivity. As such, the prevalence of the distinctive features associated with abnormal tissue may be underestimated.Our measurements do not allow us to reliably designate sodium cations as either intercellular or intracellular sodium cations. However, we note that the intercellular sodium concentration and volume fraction of the healthy rat brain (140 mM and 0.2) are quite different from the intracellular concentration and volume fraction (10 mM and 0.8).


In the fully adjusted models, the OR for the non-asthma group with vigorous PA versus non-vigorous PA was 0.89 (95% CI: 0.84–0.94). However, the OR for the asthma group with vigorous PA versus non-vigorous PA was 0.70 (95% CI: 0.57–0.86), indicating that adolescents who engage in vigorous PA had lower stress in the asthma group (p < 0.05). This study demonstrated that adolescents with asthma had higher stress levels than those without asthma; however, vigorous PA was associated with lower stress. These effects were more pronounced in adolescents with asthma.Asthma is a very common condition that affects 5–10% of the global population, and its prevalence is increasing. Vigorous physical activity (PA) is effective in improving cardiorespiratory fitness and managing stress. This study aimed to investigate the association between vigorous PA and stress among Korean adolescents with asthma using large-scale survey data. The questionnaire data of 57,303 adolescents were analyzed using raw data from the 2019 Korea Youth Risk Behavior Web-Based Survey. We performed logistic regression analysis to calculate the stress odds ratios (ORs) and 95% confidence intervals (CIs) for asthma and non-asthma groups using models 1 and 2. We also performed logistic regression analysis to calculate the stress OR for the asthma group with vigorous PA and non-vigorous PA using models 1, 2, and 3. Model 1 was adjusted for age, sex, obesity, smoking, and alcohol status; model 2 was further adjusted for household income, academic achievement, and comorbidities. Model 3 was further adjusted for moderate activity and resistance exercise. The OR of stress was 20% higher in the asthma group than in the non-asthma group ( Asthma is a very common condition that affects 5–10% of the global population . The preA stress management model is needed for asthma management . Asthma 1), and 6-min walk distance [There are several stress management programs; however, physical activity (PA) provides specific psychological treatment that may be particularly effective for patients for whom more conventional psychological interventions are less appropriate . Furtherdistance . Previoudistance .However, previous studies on the management of asthma used small sample sizes, and the sample population was usually limited to adult  or pediath, which was a self-administered, online, statistical survey that was approved by the Korean government to understand the health behavior of Korean middle and high school students (7th–12th grade). Of the total 60,100 survey participants, 57,303 (95.3%) were analyzed in the current study. Since private or identifiable information such as telephone numbers, social security numbers, and home addresses was not collected by the KYRBWS-XV, ethical approval was not required for this study. Furthermore, all research procedures were controlled and approved by the Korea Centers for Disease Control and Prevention (KCDCP) and conducted in accordance with the principles outlined in the Declaration of Helsinki. All the details pertaining to data collection procedures have been reported by the KCDCP [This cross-sectional study used raw data from the 2019 Korea Youth Risk Behavior Web-Based Survey (KYRBWS)-15he KCDCP , and thehe KCDCP ,26.If the participant responded “yes” to the question “Did a doctor ever say that you had asthma?”, this participant was allocated to the asthma group. If the participants responded “no” or “don’t know”, they were allocated to the non-asthma group. Participants were assigned to the non-vigorous PA group if they responded “no exercise”, “1 day”, or “2 days” to the question “In the last 7 days, how many days did you do vigorous PA that caused you to be out of breath or sweat for more than 20 min?” Participants were allocated to the vigorous PA group if they answered “3 days”, “4 days”, or “more than 5 days” to this question .Participants who responded “very often,” “often,” or “seldom” to the question “How often do you feel stress?” were assigned to the stress category, and those who answered “very seldom” or “never” were assigned to the non-stress category.Sociodemographic characteristics included age, sex, BMI, and grade (middle or high school). Smoking status was classified using the responses “no” and “yes” to the question “Have you ever smoked (even once) in the past?” Alcohol consumption was classified using the responses “no” and “yes” to the question “Have you ever drunk more than one glass of alcohol?”Economic status included household income and was classified as “high,” “medium high,” “medium,” “medium low,” or “low” based on the response to the question “How would you classify your family’s economic status?” Academic achievement was classified as “high,” “medium high,” “medium,” “medium low,” or “low” based on the response to the question “How was your academic achievement over the past 12 months?”Health-related PA included moderate activity and resistance exercise. Moderate activity was classified using the responses “no exercise,” “1 day,” “2 days,” “3 days,” “4 days,” or “more than 5 days” to the question “In the last 7 days, on how many days did you do PA (regardless of type) for more than 60 min that resulted in an accelerated heart rate or feeling out of breath?” Resistance exercise was classified using the responses “no resistance exercise,” “1 day,” “2 days,” “3 days,” “4 days,” or “more than 5 days” to the question “In the last 7 days, on how many days did you do resistance exercises such as push-ups, sit-ups, lifting dumbbells and barbells, chin-ups, or use of parallel bars?”t-test and a Chi-square test. These tests were conducted to analyze different physical characteristics, smoking status, alcohol consumption, household income, academic achievement, moderate activity, vigorous PA, and resistance exercise in relation to asthma. The stress odds ratios (ORs) ) according to the frequency of vigorous PA was calculated using logistic regression. The stress OR according to the asthma group and the vigorous PA group was calculated using multiple logistic regression with models 1 and 2. The OR (95% CI) of stress according to vigorous PA versus non-vigorous PA in the asthma and non-asthma groups was calculated using multiple logistic regression with models 1, 2, and 3. Model 1 was adjusted for age, sex, and obesity ; model 2 was further adjusted for household income (<medium-high and ≥medium-high), academic achievement (<medium-high and ≥medium-high), and comorbidities ; and model 3 was further adjusted for resistance exercise (<3 days per week or ≥3 days per week) and moderate activity (<5 days per week or ≥5 days per week). All statistical significance was set at p < 0.05.All analyses were conducted using the STATA software, version 15.0 . All variables were expressed as mean, standard deviation, and percentages (%). The characteristics of the groups (non-asthma and asthma) were compared using an independent 2 higher BMI than the non-asthma group (p < 0.001). Academic achievement was better in the asthma group than in the non-asthma group (p < 0.001). Moderate activity and resistance exercise were significantly higher in the asthma group (p < 0.05). No significant differences in vigorous PA were found between the groups (p > 0.05) (The characteristics of the asthma and non-asthma groups are shown in  > 0.05) .p < 0.05) ; more than four days a week, 0.79 (0.72–0.87); and more than five days a week, 0.76 (0.71–0.81) ( < 0.05) .2), smoking status, and alcohol status. This figure shows the OR (95% CI) of stress in relation to vigorous physical activity, adjusted for age, sex, obesity . Conversely, the vigorous PA group had an OR of 0.82 (0.78–0.86) in model 1 and an OR of 0.84 (0.80–0.88) in model 2. The OR of this group had a lower OR than that of the non-vigorous PA group (p < 0.05)  in model 1 and an OR of 1.14 (1.04–1.25) in model 2. This group had a higher OR than the non-asthma group ( < 0.05) .p < 0.05) (The non-asthma group that engaged in vigorous PA displayed an OR of 0.82 (0.79–0.86) in model 1, an OR of 0.84 (0.80–0.88) in model 2, and an OR of 0.89 (0.84–0.94) in model 3. This group had a lower OR than the non-vigorous PA group. The asthma group with vigorous PA had an OR of 0.77 (0.64–0.92) in model 1, an OR of 0.78 (0.65–0.93) in model 2, and an OR of 0.70 (0.57–0.86) in model 3. This group had a lower OR than the non-vigorous PA group ( < 0.05) .This study used data from a large-scale survey of Korean adolescents and revealed that adolescents with asthma had higher stress levels than those without asthma. However, more than 20 min of vigorous PA for more than three days a week resulted in lower stress among these participants. Vigorous PA was related to low levels of stress, but adolescents with asthma experienced lower stress than those without asthma.In this study, adolescents with asthma displayed 1.24 and 1.14-fold higher stress levels than adolescents without asthma following adjustments in models 1 and 2, respectively. These results were consistent with the results of previous studies ,29. A prIn this study, the OR of stress in relation to the frequency of vigorous PA was identified in order to examine the association between vigorous PA and lower levels of stress among adolescents. Several studies have demonstrated a strong correlation between stress and PA among healthy adolescents ,32,33. HFinally, our study detected that regular vigorous PA, despite higher stress levels in the asthma group, had a lower impact on adolescents with asthma than on those without asthma. These results are consistent with the results of previous studies. Adolescent PA guidelines recommend more than 60 min of daily moderate-to-vigorous PA with additional vigorous PA more than three times per week . Our resThis study had several limitations. First, this study was based on a questionnaire survey; therefore, a history of asthma treatment and severity could not be determined. Additionally, some participants may not have been clinically diagnosed but still may have experienced some symptoms of asthma. However, the incidence rate of asthma among Korean adolescents is approximately 7%, and this sample demonstrated a prevalence of 7.05%, which was similar to the domestic criteria. Second, this study was conducted online using multiple-choice questions; therefore, it is possible that the exact amount of vigorous PA and self-reported PA may have varied. However, our data identified moderate and resistance strengthening exercises, thus enabling the classification of the intensity and type of exercise. Third, racial and socioeconomic differences may have existed, and these factors were not considered in this study. Fourth, this study was a cross-sectional study with a retrospective cohort design. Therefore, we did not examine cause and effect—only interrelationships. Nevertheless, this study had the advantage of being a government-oriented large survey with 57,303 participants.In this study, stress was higher among adolescents with asthma than adolescents without asthma. However, more than 20 min of vigorous PA more than three times a week was demonstrated to lower stress, and the association between stress and vigorous PA was more pronounced for adolescents with asthma than those without asthma. It is therefore necessary to emphasize the importance of vigorous PA for adolescents with elevated stress along with respiratory diseases such as asthma.