The CSCO and ASCO guidelines in 2021 recommend chemotherapy for stage III–IVA (8th edition of AJCC staging) nasopharyngeal carcinoma (NPC). Actually, patients with stage T3–4N0M0 are often excluded from various clinical trials of the locoregionally advanced NPC, and the survival benefit of chemotherapy in such patients has always been controversial. This study aims to explore the benefit of chemotherapy in patients with locoregionally advanced NPC, especially those with negative lymph nodes.A total of 2741 patients were extracted from the SEER database. After a 1:1 PSM analysis, 272 patients were obtained to further explore whether the addition of chemotherapy would achieve survival benefits.p = 0.031) was higher than those receiving radiotherapy alone. Similar results were observed for cancer‐specific survival (CSS). We further stratified the patients according to lymph node status and found that the addition of chemotherapy in patients with positive lymph nodes could significantly improve 5‐year OS rates  and 5‐year CSS rates  compared with radiotherapy alone, but there was no additional benefit of chemotherapy in patients with negative lymph nodes. For all 449 cases of T3–4N0M0 NPC, radiotherapy improved the OS rates  and the CSS rates  compared with no radiotherapy, while chemotherapy did not show significant survival benefit compared with no chemotherapy.After PSM, Kaplan–Meier curves showed that the overall survival (OS) of patients receiving chemoradiotherapy (Our results reveal that stage T3–4N0M0 NPC may be exempted from chemotherapy, and use radiotherapy alone to reduce toxic and side effects. These results still need to be verified by future prospective trials. It has a low incidence in most of the world, but it is more common in southern China and Southeast Asia.For stage I NPC, radiotherapy alone is currently the main treatment option.However, we have noticed that some clinical studies for locoregionally advanced NPC have excluded patients with negative lymph nodes, such as the researches of some large‐scale phase 3 clinical trials of Sun Yat‐sen University cancer center.In conclusion, for patients with locoregionally advanced and low‐risk NPC, the value of combined chemotherapy is challenged. Therefore, this study intends to explore the therapeutic efficacy and prognostic factors of locoregionally advanced NPC based on the Surveillance, Epidemiology, and End Results (SEER) database, especially pay attention to the survival benefits of chemotherapy based on radiotherapy for patients with negative lymph nodes, hoping to provide a certain basis for clinical practice and clinical trials in the future.22.1https://seer.cancer.gov/data/). The seer database surveys approximately 27.8% of the United States population, which represent the overall level of the United States in terms of demographics, cancer incidence, and mortality.We performed a retrospective study of locoregionally advanced NPC patients by using data from the National Cancer Institute SEER program (The inclusion criteria included: (1) International Classification of Disease‐O‐3 (ICD‐O‐3) site codes C11.0‐C11.3, C11.8, and C11.9 and histologic codes 8010, 8020, 8021, 8070–8073, 8082, and 8083; (2) NPC diagnosed as stage III–IVB according to the 6th and 7th editions of the American Joint Commission on Cancer (AJCC) staging system, where stage IVB refers to any T, N3, M0; (3) patients had positive histological findings; (4) NPC was the first and only primary malignancy. We also excluded the following patients: (1) survival data were missing or unknown; (2) reporting source was autopsy or death certificate only. Finally, a total of 2741 eligible patients from 2004 to 2016 were enrolled in our study.2.2The demographic variables of the patients were obtained from the database, including age at diagnosis, sex, race, and marital status. The tumor characteristics were extracted from the database, including grade, histology, stage, T stage, and N stage. Also the treatment methods, such as surgery to the primary site, radiotherapy, and chemotherapy, were also collected. According to the World Health Organization (WHO) classification scheme, we divided the histology of NPC into four categories, of which ICD‐O‐3 codes 8070 and 8071 were keratinizing squamous cell carcinoma (KSCC), ICD‐O‐3 codes 8072 and 8073 were differentiated non‐keratinizing squamous cell carcinoma (DNKSCC), ICD‐O‐3 codes 8020, 8021, 8082, and 8083 were undifferentiated non‐keratinizing squamous cell carcinoma (UNKSCC), and ICD‐O‐3 code 8010 were classified as other unspecified Group. OS status and cancer‐specific survival (CSS) status were also collected as the outcomes of our study.2.3t‐test or independent‐samples Mann–Whitney U‐test was applied to the continuous variable. Pearson's chi‐squared test or Fisher's exact test of categorical variables was used to compare the differences in covariate distributions between patients who received radiotherapy only and those who received chemoradiotherapy. The 1:1 paired propensity score matching (PSM) was performed to balance the observed bias in baseline characteristics between the two treatment groups. The standard deviation of each covariate was applied to compare the similarity between the two groups, and the absolute value less than 0.1 represented a good balance. Independent prognostic indicators were evaluated by univariate and multivariate Cox analysis. Kaplan–Meier curves and log‐rank (Mantel–Cox) tests were applied to describe the survival rates of patients. We use SPSS 23.0 , GraphPad Prism 8 (GraphPad Software), and R software (version 4.2.1) for all statistical analysis, and two‐tailed tests and p‐values <0.05 is considered statistically significant in all statistical analyses.Descriptive analysis was used for each demographic and clinicopathological variable. Student's 33.1We included a total of 2741 patients with stage III–IVB NPC from the SEER database  and CSS (p = 0.032) than those who received radiotherapy alone. Furthermore, Kaplan–Meier analyses were performed on the matched population after PSM  and CSS (p = 0.010).Multivariate Cox regression analysis was applied to all patients receiving radiotherapy before PSM Table . We founM Figure . Compare3.4p = 0.025; Figure p = 0.015; Figure In matched population, 226 patients were diagnosed with stage III–IVA and 46 patients were stage IVB (T1–4N3M0). Among the stage III–IVA patients, 72 patients were diagnosed with stage N0 (T3–4N0M0), 56 patients were stage N1 (T1–4N1M0), and 98 patients were stage N2 (T1–4N2M0). We further explored the survival benefit of two treatment methods in stage III–IVB patients stratified by lymph node status. Multivariate cox analysis was shown in Table 3.5p = 0.326) or CSS (p = 0.155) benefit compared with radiotherapy alone. Kaplan–Meier curve illustrated there was no difference in 5‐year OS rates between the chemoradiotherapy group and radiotherapy alone group  NPC patients from the 2741 total study population to explore their prognostic factors. The results of the univariate analysis were elaborated in Table 4Our research revealed that based on radiotherapy alone, the addition of chemotherapy significantly improved the OS and CSS rates of patients with advanced NPC, especially for those with positive lymph nodes, while those with negative lymph nodes may not benefit from additional chemotherapy.CCRT is considered to be the main treatment for locoregionally advanced NPC. The evidence sources mainly include several randomized clinical studies such as intergroup 0099Our results conflict with the latest CSCO and ASCO guidelines in 2021. Theoretically, radiotherapy combined with chemotherapy has many advantages: (a) Chemotherapy enables to eliminate or inhibit the possible micrometastasis and reduce the rates of distant metastasis; (b) Chemotherapy can also reduce the tumor burden, improve the oxygenation state in the center of the tumor and improve the sensitivity of radiotherapy.Based on the above, patients who may be exempted from concurrent chemotherapy should be screened out, and patients with negative lymph nodes in the locoregionally advanced low‐risk group should be explored from the perspective of clinical staging. Our study stratified the benefits of chemotherapy according to the lymph node status of NPC. Our study still has inevitable defects: First, it is a pity that the SEER database does not collect specific information of the treatment, such as the strategy and sequence of chemotherapy, the technology, and dose of radiotherapy, as well as the status of Epstein–Barr virus DNATo our knowledge, this is the first study to use a large sample obtained from the SEER database to expound the efficacy of chemotherapy for patients with stage III–IVA NPC based on lymph node status. Our results showed that for patients with negative lymph nodes, radiotherapy combined chemotherapy did not show a significant advantage over radiotherapy alone. Therefore, in the future, it is necessary to combine clinical staging, prognostic risk stratification, and prognostic‐related biomarkers to stratify patients and implement precise policies to further improve the efficacy and reduce treatment‐related toxic and side effects. The results of this study urgently require prospective clinical trials to clarify appropriate treatments for this subgroup.Weiling Qu: Conceptualization (lead); data curation (lead); formal analysis ; funding acquisition ; investigation (lead); methodology (lead); project administration ; resources ; software ; supervision ; validation ; visualization ; writing – original draft (lead); writing – review and editing (lead). Xuan Wang: Data curation (supporting); formal analysis ; investigation ; methodology (supporting); resources (supporting); software (supporting); supervision ; validation ; visualization . Qiao Qiao: Conceptualization (supporting); data curation ; formal analysis ; funding acquisition (lead); investigation ; methodology ; project administration (supporting); resources (supporting); software (supporting); supervision ; validation (supporting); visualization (supporting); writing – original draft (supporting); writing – review and editing (supporting). Yanli Wang: Conceptualization (lead); data curation ; formal analysis ; funding acquisition (lead); investigation ; methodology ; project administration ; resources ; software (supporting); supervision ; validation ; visualization (supporting); writing – original draft ; writing – review and editing .This research was funded by the Beijing Xisike Clinical Oncology Research Foundation (grant no. Y‐HR2019‐0326) and the Shenyang Science and Technology Bureau (grant no. RC210153 and 21‐172‐9‐14).The authors have no conflict of interest to declare.Not applicable. This is a retrospective study. The data of this paper was downloaded and obtained from the National Cancer Institute SEER program. We consulted extensively with the relevant responsible persons who determined that our study did not need ethical approval, as anonymous data were used.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.Table S6Click here for additional data file.Table S7Click here for additional data file.Table S8Click here for additional data file.


JUN, NPY, SST, FLT4, FGF13, HBEGF, NR0B2, AREG, NR1I2, SEMA5B). Moreover, we substantiated the elevated affinity of tobacco reproductive toxicants, specifically nicotine and nitrosamine, with hub genes through molecular docking . This suggested that these genes could potentially serve as crucial loci for tobacco's influence on the placental immune microenvironment. To further elucidate the immune microenvironment landscape, consistent clustering analysis was conducted, yielding three subtypes, where the abundance of follicular helper T cells (p < 0.05) in subtype A, M2 macrophages (p < 0.01), neutrophils (p < 0.05) in subtype B and CD8+ T cells (p < 0.05), resting NK cells (p < 0.05), M2 macrophages (p < 0.05) in subtype C were significantly different from the control group. Additionally, three pivotal modules, designated as red, blue and green, were identified, each bearing a close association with differentially infiltrated immunocytes, as discerned by the weighted gene co‐expression network analysis (WGCNA). Functional enrichment analysis was subsequently conducted on these modules. To further probe into the mechanisms by which immune‐associated DEGs are implicated in intercellular communication, 20 genes serving as ligands or receptors and connected to differentially infiltrating immunocytes were isolated. Employing a variety of machine learning techniques, including one‐way logistic regression, LASSO regression, random forest and artificial neural networks, we screened 11 signature genes from the intersection of immune‐associated DEGs and secretory protein‐encoding genes derived from the Human Protein Atlas. Notably, CCL18 and IFNA4 emerged as prospective peripheral blood markers capable of identifying PTE‐induced immune disorders. These markers demonstrated impressive predictive power, as indicated by the area under the curve (AUC) of 0.713 (0.548–0.857) and 0.780 (0.618–0.914), respectively. Furthermore, we predicted 34 potential compounds, including cyclosporine, oestrogen and so on, which may engage with hub genes and attenuate immune disorders instigated by PTE. The diagnostic performance of these biomarkers, alongside the interventional effect of cyclosporine, was further corroborated in animal studies via ELISA, Western blot and immunofluorescence assays. In summary, this study identifies a disturbance in the placental immune landscape, a secondary effect of PTE, which may underlie multiple pregnancy complications. Importantly, our research contributes to the noninvasive and timely detection of PTE‐induced placental immune disorders, while also offering innovative therapeutic strategies for their treatment.Prenatal tobacco exposure (PTE) correlates significantly with a surge in adverse pregnancy outcomes, yet its pathological mechanisms remain partially unexplored. This study aims to meticulously examine the repercussions of PTE on placental immune landscapes, employing a coordinated research methodology encompassing bioinformatics, machine learning and animal studies. Concurrently, it aims to screen biomarkers and potential compounds that could sensitively indicate and mitigate placental immune disorders. In the course of this research, two gene expression omnibus (GEO) microarrays, namely GSE27272 and GSE7434, were included. Gene set enrichment analysis (GSEA) and immune enrichment investigations on differentially expressed genes (DEGs) indicated that PTE might perturb numerous innate or adaptive immune‐related biological processes. A cohort of 52 immune‐associated DEGs was acquired by cross‐referencing the DEGs with gene sets derived from the ImmPort database. A protein–protein interaction (PPI) network was subsequently established, from which 10 hub genes were extracted using the maximal clique centrality (MCC) algorithm ( Besides fulfilling several roles such as serving as a barrier, facilitating substance exchange and secreting hormones, the placenta acts as a critical immune regulator.Recent advances in microarray technology and bioinformatics have substantially enriched our understanding of pathological mechanisms and facilitated the precise identification of candidate targets for drug design.In this study, microarrays concerning PTE from placenta and peripheral blood were sourced from the GEO database. This allowed us to examine the impact of PTE on the placental immune microenvironment, identify sensitive biomarkers within peripheral blood that could noninvasively indicate placental immune damage, and pinpoint potential therapeutic compounds. Initially, a GSEA and immune enrichment scrutiny were conducted on DEGs to elucidate the intimate link between PTE and altered immunological attributes in the placenta. Subsequently, the focus shifted to immune‐related DEGs, from which a PPI network was constructed, and hub genes were identified. This led to the exploration of tissue distribution, physicochemical properties and the expression regulation network of the aforementioned hub genes. Additionally, molecular docking was undertaken to assess the affinity between hub genes and representative reproductive toxicants found in tobacco. Moreover, a consensus clustering analysis was performed predicated on hub genes to ascertain the immune infiltration attributes of distinct subtypes. Following this, a WGCNA was employed to identify modules intimately associated with differentially infiltrated immunocytes, with functional enrichment analysis carried out on genes within key modules. Furthermore, visualization of the intercellular communication network among differentially infiltrating immunocytes was accomplished. Regarding the screening of signature biomarkers, machine learning was deployed to identify secretory sensitive markers from immune‐related genes. Notably, these signature genes were validated in gene sets containing peripheral blood samples from PTE or non‐PTE women. Also, we uncovered 34 active compounds either backed by empirical evidence or approved by the Food and Drug Administration (FDA) that could interact with representative hub genes, potentially beneficial during pregnancy. Lastly, animal experiments were conducted to corroborate changes in signature genes attributed to PTE and to determine the intervention effect of potential compounds on these genes. The comprehensive workflow diagram is depicted in Figure 22.1https://www.ncbi.nlm.nih.gov/geo/),Microarray data were acquired from the National Center for Biotechnology Information (NCBI) GEO database (2.2https://www.r‐project.org/) and Bioconductor analysis tools (http://www.bioconductor.org/). The ‘affy’ R language package was applied to conduct robust multi‐array averaging (RMA) background correction, complete log2 transformation.The raw data were preprocessed and normalized by R statistical software . Permutations were set to 10,000 to obtain normalized enrichment score (NES) in GSEA. Gene sets with p < 0.05 were considered significantly enriched.GSEA is a statistical method for analysing genome‐wide expression profile microarray data that allows the ranking of predefined genes according to the degree of differential expression in two types of samples to determine whether there are statistically significant differences in the expression of predefined gene sets.2.4p value <0.05 and |log2FC| > 0.1. Besides, the volcano map and heat map of DEGs were, respectively, drawn by the ‘ggplot2’ package and the ‘pheatmap’ package. Then, the ClueGO plug‐in in Cytoscape After that, DEGs were screened out by using the ‘limma’ package, with the filtering condition of 2.5https://www.immport.org/),http://www.bioinformatics.com.cn/),To further explore the expression profiling change of immune‐related DEGs caused by PTE, we first accessed immPort database (2.6https://www.string‐db.org/)To explore the physical and functional relationship between proteins encoded by immune‐related DEGs, the search tool for the retrieval of interacting genes (STRING 10.5) (2.7https://pubchem.ncbi.nlm.nih.gov/),http://www.rcsb.org/).For docking studies, the two‐dimensional structures of representative reproductive tobacco toxicants were downloaded from Pubchem database (2.8http://biogps.org)https://web.expasy.org/protparam/),https://www.genecards.org/)https://www.uniprot.org/).First, the tissue localization of proteins encoded by hub genes was obtained through the bioGPS (building your own mash‐up of gene annotations and expression profiles) website  clustering algorithm was selected to perform the clustering analysis with a euclidean distance function. Besides, cumulative distribution function (CDF) curve was drawn, where the number of possible clusters in the dataset was obtained using the consensus clustering method through the Consensus Cluster Plus package in R language. The smooth curve represents a reasonable clustering result. After the clustering analysis, PCA was performed using the ‘ggplot2’ package, and the classification points were plotted to verify the clustering results.2.10P < 0.05 was considered statistically significant.In this study, CIBERSORT algorithm was utilized to evaluate 22 immunocyte profiling in distinct subtypes obtained by consensus clustering analysis with ‘CIBERSORT’ package.2.11To explore how immune‐related DEGs are involved in the communication between differentially immunoinfiltrated cells, we constructed an immunocyte communication network. The cells in the immunocyte communication network were composed of differentially infiltrated immunocytes in different subgroups obtained by immune infiltration landscape analysis. The ligands and receptors that connect cells are obtained as follows. First, we acquired ligand–receptor pairing information from curated ligand–receptor pairs database and obtained average cap analysis of gene expression (CAGE) data for all ligands and receptors in the 144 human primary cells from the previously published study.2.12k and p (k). Subsequently, the correlation matrix was converted into an adjacency matrix, which was further processed into a topological overlap matrix (TOM). The dynamic tree cutting approach was performed to identify various modules. Finally, modules with the greatest spearman correlation coefficient were picked for further investigation.The WGCNA is a widely used method to uncover critical interacted genetic modules and key genes by linking gene networks to clinical traits.2.13http://www.webgestalt.org/)p value <0.05 were displayed in a circle plot by the ‘GOplot’ package in R software.The genes in the key modules derived from WGCNA that were associated with PTE were imported into the Webgestalt website (2.14https://www.proteinatlas.org/) to download the dataset of secreted protein‐encoding genes. In addition, then, the overlapped genes of secreted protein‐encoding genes and immune‐related DEGs were obtained by using the Venn diagram tool, which were taken as potential biomarkers of immune disorders due to PTE, namely secretory protein‐encoding DEGs. Subsequently, machine learning methods were utilized to screen signature genes base on them.Secreted proteins are potential biomarkers for pathologic diagnosis. In this part, we retrieved the Human Protein Atlas website  algorithm was employed to further screen signature genes based on the result of one‐way logistic regression, bounded by MeanDecreaseGini greater than 0.8 for variable filtering. Similarly, the least absolute shrinkage and selection operator (LASSO) regression was also utilized based on critical genes recognized from the one‐way logistic regression analysis. After that, Venn diagrams were constructed to obtain the overlapped signature genes of RF and LASSO. In addition, we constructed an artificial neural network (ANN) model for the signature genes obtained from the above methods according to the gene score by using package of ‘neuralnet’ and ‘neuralnettools’ in R software. In addition, ROC was utilized to evaluate the accuracy of the ANN model in the training set (GSE7434 and placenta samples of GSE27272) and the testing set . Most importantly, the expression profiling of the overlapped signature genes were further validated in the peripheral blood samples included in the GSE27272, and the receiver operating characteristic (ROC) curve was constructed by ‘pROC’ package in R software to evaluate the prediction accuracy of these signature genes. Finally, ‘rms’ package in R software was also utilized to establish the nomogram prediction model. Each signature gene was converted into an assessment point system in the model. The total score determined the final risk assessment value. Besides, the performance of the nomogram was assessed by calibration and discrimination,2.15http://ctdbase.org/),https://go.drugbank.com/)www.dgidb.org/).Potential compounds that were able to target hub genes and ameliorate immune disorders due to PTE were predicted via the following database, including Comparative Toxicogenomics Database (CTD)  2017‐0005. The animals were raised in the Laboratory Animal Center (LARC) of Zhejiang Chinese Medical University, and the licence number of the Laboratory animal is SYXK (Zhejiang) 2021‐0012. 6–8 weeks of age, the body weight of mice was 20 ± 2 g, respectively. The ambient temperature must fluctuate within 22 ± 1°C, and the humidity must fluctuate between 50% and 60%. Light (12 h day/12 h night). Female mice were randomly divided into control group, nicotine exposure group and nicotine exposure+CsA group. Female and male rats closed their cages at 2:1 and began to be administered on the first day of discovery of the vaginal suppository. The control group was injected with sterile 0.9% physiological saline. Nicotine hydrogen tartrate salt was purchased from Aladdin  dissolved in sterile 0.9% physiological saline and adjusted to pH 7.2–7.4 with sodium hydroxide.2.17The serum samples were taken from the whole blood of mice without anticoagulant and centrifuged at 4°C and 3000 rpm for 15 min, and the supernatant was taken. Use ELISA kit to determine serum CCL18  and IFNA4  levels according to the corresponding instructions.2.18The RIPA buffer , protease inhibitors , phosphatase inhibitors  and pierceTM universal nuclease for placenta sample lysis . First, gel electrophoresis was performed, and the concentrated glue voltage was adjusted to 80v and the separated glue voltage to 100v. The protein was then transferred to the Nitrocellulose (NC) film using a Mini TransBLot® electrophoresis transfer tank (Bio Rad Laboratories GmbH). The condition was set to 300 mA or 70 min. After transmembrane, the film was closed for 1 h, and primary antibodies  were incubated overnight at 4°C. The next day, goat anti‐rabbit IgG (H&L) was added . After the film was scanned in the Odyssey fluorescence imaging system, quantitative analysis was performed by Image J software .2.19The immunofluorescence method was used to detect the content of CCL18 and IFNA4 in placenta. Frozen sections of the placenta tissue of mice were taken, washed and blocked, and then, CCL18 and IFNA4 antibodies (1:200) were added and incubated overnight at 4°C. Then, the samples were incubated with goat anti‐rabbit IgG H&L (Cy3®) in the dark (1:200) for 2 h. After that, the antifade mounting medium with DAPI was added. The CCL18 and IFNA4 were discovered by digital pathological section (fluorescence) scanning analyser  and were analysed by Image J software. Besides, three sections and three different fields of each section were randomly selected for semi‐quantitative evaluation.2.20The embryo absorption rate is calculated by the following formula: Embryo absorption rate = number of absorbed foetuses/(number of absorbed foetuses + number of surviving foetuses) × 100%.2.21anova was used to analyse ELISA, Western blotting and immunofluorescence, followed by Dunnett's test for the post hoc test. In all calculations, a difference of p < 0.05 was considered significant. All data were plotted in GraphPad Prism 8 .All results were expressed as Mean ± SEM. One‐way 33.1p < 0.05), and the results of the top 10 pathways sorted according to the normalized enrichment scale were shown in Figure GSEA method was applied to appraise the differences in activations of KEGG pathway between the PTE placenta and health controls. Our results revealed that a total of 31 pathways were obtained (3.2p < 0.05)  Figure . It sugg3.3Given that DEGs were involved in a variety of immune regulation processes, we first obtained 1794 immune‐related gene from the ImmPort database, and then intersected them with the DEGs in PTE placenta. Finally, we obtained a total of 52 overlapping genes, which were taken as a new gene set for subsequent analysis, namely immune‐related DEGs Figure .3.4JUN, NPY, SST, FLT4, FGF13, HBEGF, NR0B2, AREG, NR1I2, SEMA5B, among which 3 hub gene, JUN, FGF13 and NR1I2, were ranked in the top 10 in all the seven algorithms in Cytohubba , as shown in Figure Here, we selected two representative reproductive toxicants in cigarette, nicotine and nitrosamine, for molecular docking with proteins encoded by representative hub genes, 3.6JUN was expressed in CD19+ B cells, BDCA4+ dendritic cells, CD33+ myeloid cells and CD14+ monocytes. FLT4 was expressed in CD8+ T cells. HBEGF was expressed in CD19+ B cells, CD4+ T cells, CD56+ NK cells and CD14+ monocytes. Localization on immunocytes is an essential prerequisite for the above genes to be able to regulate placental immune function , subtype B (n = 9) and subtype C (n = 5) based on the immune‐related DEGs in PTE placenta , M2 macrophages were elevated (p < 0.01) and neutrophils were significantly decreased (p < 0.05) in subtype B and CD8+ T cells, resting NK cells were significantly elevated (p < 0.05) and M2 macrophages were significantly decreased (p < 0.05) in subtype C . In subtype B, ANGPT1  and TEK were positively associated with M2 macrophages , and CXCR4 was positively associated with neutrophils . In subtype C, IFNAR2 exhibited a significant negative correlation with M2 macrophages , and IL1R1 showed a significant positive correlation with resting NK cells   Figure .3.8In this part, 28 genes as ligands or receptors among the 52 immune‐related DEGs were obtained. Among them, 14 genes acted as ligands corresponding to 62 receptors, and 14 genes acted as receptors, corresponding to 50 receptors. In addition, 20 of the 28 genes that acted as ligands (or receptors) corresponded to CD8+ T cells, neutrophils, macrophage and NK cells derived from immune infiltration analysis in subtypes. Finally, Cytoscape was used to visualize the immunocyte communication network interfaced by the 20 ligands or receptors, as shown in Figure 3.9β = 16  was determined as soft‐thresholding to acquire co‐expressed gene modules , M2 macrophages  and CD8+ T cells ; the blue module was significantly correlated with M2 macrophages ; the green module was significantly correlated with follicular helper T cells , resting NK cells , CD8+ T cells . As the genes in red, blue and green modules were positively or negatively relevant to the most immunocytes, they were considered as important transcriptionally dysregulated gene modules with regard to the altered immune microenvironment.First, we examined the distribution of expression profiles in 22 PTE placental samples, all of which were included for WGCNA to identify gene co‐expression modules associated with immune cell infiltration. The soft power of s Figure . In addis Figure . Ultimats Figure . Among t3.10In addition, to further understand the biological pathways by which these genes impinge on the characteristics of immune infiltration, we performed GO and KEGG enrichment analysis on the gene in these three modules. The GO‐BP analysis revealed that genes in the red module were enriched in regulation of T‐cell activation, cellular response to cytokine stimulus and B cell adhesion Figure . Besides3.11p = 0.015) and IFNA4 was significantly decreased (p = 0.0011) in peripheral blood in the PTE group , and IFNA4 was remarkably decreased (p < 0.05), both in peripheral blood   and reduced the level of embryo absorption rate (p < 0.05)  Figure . This in) Figure , which c4JUN, FGF13 and NR1I2, were discerned via the MCC algorithm within the PPI network. These genes played a paramount role in protein interaction, mediating intricate signal intercommunication under PTE circumstances. To further comprehend the immune disturbance instigated by these hub genes in the presence of PTE, we meticulously explored the immune localization, physicochemical properties and molecular functions of the proteins encoded by these genes. It was found that the hub genes were primarily expressed in immunocytes and implicated in the regulation of immune function. As an exemplar, NR1I2 encodes the Pregnane X Receptor (PXR), a crucial modulator of xenobiotic and detoxification responses. Prior research confirmed that PXR could modulate Nuclear Factor κ‐Light‐Chain‐ Enhancer of B cells (NFκB) activation and the expression of numerous inflammatory mediators, thus precipitating aberrant tissue functionality.This current study initially disclosed the intimate association between PTE and immunological dysfunctions in the placenta, applying GSEA and immune enrichment examination. This led to the identification of 52 immune‐related DEGs, from which 12 pivotal genes, such as Then, to delve further into the repercussions of PTE on the immune landscape within the placenta, an immune infiltration analysis was conducted on subtypes derived from consensus clustering of PTE samples. These subtypes were based on the 52 immune‐related genes previously identified. The ensuing findings disclosed notable shifts in both intrinsic immunocytes  and adaptive immunocytes  when juxtaposed with control samples. Among these, NK cells and macrophages—comprising approximately 70% and 20%–30% of decidual leukocytes in the first trimester, respectively—predominate at the maternal‐foetal Interface.FGR, ANGPT1, TEK, IFNAR2, CXCR4 and IL1R1. Among them, IL1R1 demonstrated a significant correlation with natural killer (NK) cells in subtype C. Existing research substantiates IL1R as a critical mediator facilitating IL‐1‐dependent activation through its binding to IL‐1α and IL‐1β,To further explore the molecular mechanisms underlying immunocyte disruption in PTE, a Spearman correlation analysis was employed, leading to the identification of six genes, namely Molecular contact on the membrane surface represents the primary mode of communication between immunocytes. Given the enrichment of DEGs related to immune function, chiefly in the cytokine‐cytokine receptor interaction, we scrutinized the ligand–receptor attributes of these 52 DEGs. This exploration facilitated the construction of a network that incorporated variably infiltrated immune cells, thereby elucidating potential intercellular communication mechanisms attributable to PTE. Our investigation suggested that a considerable portion (20 out of 52) of the immune‐related DEGs served as ligands or receptors, facilitating cross‐talk with variably infiltrated immune cells such as CD8+ T cells, neutrophils, macrophages and NK cells. For instance, platelet factor 4 (PF4) and protein C receptor (PROCR) constituted significant nodal points of interaction among these cells. Previous research has verified the role of PF4, secreted by the alpha granules of activated platelets, in exerting chemotactic influences on several cell types and acting as an inhibitor of haematopoiesis, angiogenesis and T‐cell function.Concerning diagnosis, the significant alterations in gene expression profiles due to PTE present considerable obstacles for conventional research methodologies to discern sensitive biomarkers capable of promptly identifying placental immune abnormalities. This difficulty precipitates delays in preemptive measures. The integration of transcriptome microarray‐based bioinformatics and machine learning offers an optimal solution to navigate this impasse, markedly enhancing diagnostic model precision. In the present investigation, univariate logistic regression, LASSO, RF and ANN algorithms identified 11 signature genes. Subsequent testing sets, incorporating peripheral blood samples with and without PTE as well as animal experiments, provided further validation for CCL18 and IFNA4. Of these, CCL18 has been identified as a macrophage differentiation factor promoting an M2 spectrum macrophage phenotype.5Despite a substantial number of women experiencing PTE, our grasp of how such exposure precipitates pathologic pregnancy outcomes remains relatively nascent. Given the intimate relationship between the placental immune microenvironment and foetal development, an exhaustive exploration of how PTE modulates placental immune regulation holds transformative potential for timely intervention and treatment. Our study, the first to delve into PTE's impact on the placental immune landscape at molecular and systemic levels, employs an integrated approach of bioinformatics, machine learning and animal experimentation. We have established that one salient pathological characteristic under PTE is immune disruption, evidenced by significant alterations in numerous immune‐related genes, molecular signalling pathways, and the prevalence and function of placental immunocytes. Moreover, our research not only elucidates these pathological modifications but also examines the interconnections between these variations, offering novel perspectives to interpret the pathogenesis linked with adverse pregnancy outcomes due to PTE, and charting a pioneering trajectory for studying PTE‐induced placental immune disorders. Furthermore, we are the first to pinpoint sensitive peripheral blood biomarkers capable of reflecting placental immune disorders, achieved through machine learning and animal testing, while also investigating potential compounds capable of intervening in these immune disorders. In essence, our work proposes an effective strategy to preempt and counteract pathological pregnancy outcomes due to tobacco exposure, significantly bridging the current research chasm in this field. We anticipate our research will inspire additional studies in this domain. Nonetheless, our study is not without limitations. First, our results primarily hinge on bioinformatics analysis, and although validated with an external test dataset and animals, these results still warrant further in vitro and clinical experiments for accuracy confirmation. Second, the immune infiltration analysis employed the widely used CIBERSORT algorithm for immunocyte quantification, but single‐cell sequencing remains necessary for the most accurate data. Finally, while we identify a correlation between DEGs and abnormal immune microenvironment, the cause–effect relationship between the two necessitates further exploration. In conclusion, our results affirm the intimate association of PTE with placental immune microenvironment disorders, contributing new insights into understanding the pathological mechanisms, diagnosis and treatment of adverse pregnancy outcomes consequent to PTE.Xiaoxuan Zhao: Conceptualization ; data curation ; formal analysis ; software ; writing – original draft . Yuepeng Jiang: Data curation ; formal analysis ; methodology ; project administration . Xiao Ma: Investigation ; methodology ; validation . Qujia Yang: Funding acquisition ; methodology ; software . Xinyi Ding: Data curation ; formal analysis ; resources . Hanzhi Wang: Formal analysis ; investigation . Xintong Yao: Data curation ; formal analysis ; validation . Linxi Jin: Data curation ; methodology ; software . Qin Zhang: Data curation ; formal analysis ; supervision ; writing – review and editing .This research was funded by National Natural Science Foundation of China (82305294); TCM Science and Technology Project of Zhejiang Province (2023ZR038); 2022 Research Project of the Affiliated Hospital of Zhejiang Chinese Medical University (2022FSYYZQ16); Hangzhou Health Science and Technology Project (A20230675) to X.X.Z.; China Postdoctoral Science Foundation (2023M733193) to Y.P.J.; Hangzhou Health Science and Technology major project (Z20230103) to Q.Z.The authors declare no conflict of interest.Not applicable.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Figure S3.Click here for additional data file.Figure S4.Click here for additional data file.Figure S5.Click here for additional data file.Table S1.Click here for additional data file.


Having been reported to be a crucial prognostic factor in solid tumours, the role of high endothelial venule (HEV) in intrahepatic cholangiocarcinoma (ICC) remains unclear, however. The data of ICC and healthy individuals were downloaded from the Gene Expression Omnibus (GEO), and The Cancer Genome Atlas (TCGA) databases. Meanwhile, a cutting‐edge ICC high‐resolution spatial transcriptome was also acquired before these data were comprehensively analysed using bioinformatics approaches. Moreover, 95 individuals with ICC who had undergone resection surgery were enrolled in this study to investigate the relationship between HEV and tumour microenvironment (TME) applying immunohistochemistry and multiple immunofluorescence techniques. The high‐HEV subtype contains rich immune infiltrates including tertiary lymphoid structure (TLS), CD8+ T cells, and CD20+ B cells. Furthermore, HEV and TLS exhibited a strong relationship of spatial colocalization. Correlated with improved prognostic outcomes in ICC, the high‐HEV subtype could be an independent prognostic indicator for individuals with ICC. This study revealed the association of HEV with immune function and observed a strong spatial colocalization correlation between HEV and TLS. Moreover, correlated with immunotherapeutic response, HEV could improve prognostic outcomes, which may be a potential indicator of immunotherapy pathology in ICC. This study revealed the association of high endothelial venule (HEV) with immune function and observed a strong spatial colocalization correlation between HEV and tertiary lymphoid structure. Moreover, HEV was correlated with immunotherapeutic response and improved prognostic outcomes and may be a potential indicator of immunotherapy pathology in ICC. This study also reveals the association of HEV and TILs as well as the effect of the former one on prognosis in resectable ICC patients.22.1A total of 95 individuals with ICC who had undergone radical resection at Nanjing Drum Tower Hospital from 2017 to 2022 were enrolled in this study, in which those who had received neoadjuvant chemotherapy prior to surgery or had diagnosed with distant metastases were not included. Clinicopathological information of individuals at the time of diagnosis, including their age, gender, pathological stage, microvascular invasion, maximum tumour diameter, tissue type, tumour recurrence, and survival status, was retrieved from a prospectively maintained database with a pre‐designed format for data gathering.2.2https://portal.gdc.cancer.gov/). Moreover, the high‐resolution spatial transcriptomic data of ICC was retrieved from a study by Rui Wu et al. (http://lifeome.net/supp/livercancer-st/data.htm).Transcriptomic data and related clinicopathological data of 83 individuals with ICC were retrieved from the Gene Expression Omnibus (GEO) database (accession number: GSE89749). The same information for 30 individuals with ICC as well as 8 healthy samples were acquired from the Cancer Genomic Atlas  | > 0.5.The differential expression of mRNAs was assessed by means of the Wilcoxon rank sum test method. The screening criteria for selecting significant differentially expressed mRNAs were set as 2.4The single‐sample gene set enrichment analysis (ssGSEA) algorithm supported by the GSVA (v1.38.2) package in R was applied to calculate enrichment scores for individual samples. The differentially expressed genes (DEGs) obtained above were utilized as the gene set for ssGSEA.2.5k = 2 and 9 were analysed, which was repeated 1000 times to ensure stable results. Then, the pheatmap (v1.0.12) package in R was employed to generate a cluster map.The ConcensusClusterPlus (v1.60.0) package in R was applied to perform consensus clustering to establish the HEV‐associated molecular subtypes. First, the ideal cluster numbers between 2.6p‐value threshold was set as 0.05. Besides, Radar plot was used to demonstrate the outcomes of KEGG and GO analyses.Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed to examine the differential physiological processes and signal pathways between the low‐HEV and high‐HEV cohorts. The mentioned analyses were conducted utilizing the clusterProfiler (v4.4.4) package. The 2.7To explore the immune features of ICC samples, TIMER v2.0 was applied to determine the relative fraction of 22 immune cell types, which then was compared between the low‐HEV and high‐HEV cohorts. The findings were illustrated in a landscape map.2.8http://tide.dfci.harvard.edu/) was also applied to predict immunotherapy response based on two major tumour immune evasion mechanisms: T‐cell dysfunction and reduced T‐cell infiltration.Tumour immune dysfunction and exclusion (TIDE) analysis was utilized to explore immunotherapy response in cancer treatment. And it  in R, which is usually applied to visualize and summarize the mutated genes.2.10The fixation of ICC samples was first performed using 4% formaldehyde solution before they were embedded in paraffin. The formalin‐fixed paraffin‐embedded tissue specimens were cut into sections of 4 μm and incubated with MECA‐79 , CD8 , and CD20  primary antibodies at 4°C (incubation time: 12 h). Subsequently, the sections were subjected to incubation at ambient temperature for 1 hour with the HPR‐labelled secondary antibodies. After that, the samples were subjected to 3,3'‐diaminobenzidine and haematoxylin staining. The intensity of staining was independently assessed by two experienced pathologists. TME patterns were also independently evaluated based on 20 randomly selected regions of each slide by two experienced pathologists. The Ethics Committee of Nanjing Drum Tower Hospital (2016‐057‐01) approved this research, and all procedures were carried out following the Declaration of Helsinki and government policies. All subjects consented to participate in this study.2.11The Kaplan–Meier (KM) curve was utilized to compare the OS across the low‐HEV and high‐HEV cohorts using the survminer and survival package in R. The prognostic indicators were detected by means of the univariate and multivariate Cox analyses.2.12Multiple immunofluorescence (mIF) was performed on a series of 5.0 μm histological tumour sections from a representative high‐HEV sample. These sections were treated using an opal fIHC kit . Across all sections, the procedure of antibody staining was consistent. The sections were subjected to back staining with DAPI . Multiple immunofluorescent plates include CD8, CD20, and MECA‐79. An antibody thinner  was utilized to dilute all antibodies. The secondary antibody was processed with the ImmPRESS™ HRP (peroxidase) polymer Cancer Detection kit . A 1 × Plus amplification thinner  was utilized to dilute the TSA reagent.2.13p‐value, in which p < 0.05 indicated a significance level.Statistical analyses were conducted in R software (v4.2.0). Spearman correlation was utilized to determine the correlations across immune cell subsets. Survival analysis was measured using the log‐rank test. The Benjamini–Hochberg method was utilized to adjust the 33.1In order to investigate the role of HEV in ICC, the ICC based on HEV‐associated genes was reclassified, and the potential distinction among different subtypes was explored. As illustrated in the flowchart Figure , 83 indip < 0.001, Figure Numerous research reports have demonstrated that many microenvironmental factors orchestrate tumour progression. In order to identify the potential impact of HEVs on the TME, the correlation between HEV ssGSEA scores and multiple microenvironment‐related pathways was further tested, which include hypoxia, immune responses, and the regulation of cellular pH. Amid them, the immune response showed a considerably positive link to HEV ssGSEA scores  in immune infiltration of 22 kinds of immune cells in the TME between the high‐HEV subtype (C1) and the low‐HEV subtype (C2) were assessed based on the ICC cohort from GSE89749. The result showed that the high‐HEV subtype exhibited remarkably elevated percentages of naïve B cells, plasma cells, activated DCs, and CD8 T cells as well as substantially reduced proportions of memory B cells, M0 macrophages, activated mast cells, and Treg cells Figure . Naïve Bp < 0.001; Figure Tertiary lymphoid structures (TLSs), ectopic lymphoid structures composed predominantly of B cells, T cells, and DCs, have been strongly associated with improved survival and clinical outcome upon cancer immunotherapies. Consistent with the immune infiltration results mentioned above, the HEV ssGSEA scores also showed a high positive correlation with the TLS ssGSEA scores  projection space as well as physical tissue space Figure . Having Moreover, the expression of CD79A (B‐cell marker), CD3D (T‐cell marker), and MZB1 (Plasma cells marker) showed regional consistency with essential molecules for TLS formation Figure . As each3.4In order to examine the relationship between HEV and immunotherapeutic effects, the expression profiles of immune checkpoints were assessed between the low‐HEV and high‐HEV subtypes. The results showed that several immune checkpoints were up‐regulated in the high‐HEV subtype in comparison with the low‐HEV one Figure . Subsequ3.5p < 0.05; Figure In order to determine the genomic landscape related to the HEV subtype, variations in the tumour‐intrinsic mutations between the low‐HEV and high‐HEV groups were compared in the TCGA‐CHOL ICC cohort. The results show that the group with high HEV had higher mutation rates of MLL3, RYR3, EP400, ARFGAP2, and GLG1 than that with low HEV Figure . In addiThe co‐occurrence and mutual exclusion between high‐frequency mutant genes were explored in this research, and it was revealed that RYR3 mutation was concurrent with MLL3 mutation in the high‐HEV subtype. Moreover, the AFF4 mutation was concurrent with MUC5B or N4BP2 mutation Figure . However3.6n = 60) and the high‐HEV groups (n = 30). It was observed that MECA79, TLS, CD8, and CD20 were substantially up‐regulated in the group with high HEV compared to the low‐HEV group , the tumour sections were classified into the low‐HEV  than those with low HEV. In addition, high HEV tumour is closely associated with prolonged OS . Concerning the tissue type, more HEVs were observed in the small bile duct type of ICC (p = 0.024). Based on immune cell composition and spatial structure in TME, immune cell infiltration was classified into ‘Desertic’ (a lack of immune infiltration of T and B cells), ‘Excluded’ (immune cells present at the tumour margins without the ability to penetrate the tumour bed), ‘Dispersed’  and ‘Structured’ , TLS (p = 0.012), and CD20+ (p = 0.004) immune cells or structures infiltrated in the group with high HEV, which was considerably correlated with better DFS (p = 0.048) and OS (p = 0.003).In a further clinicopathological analysis Table , the higp = 0.009) and tumour grade  were significantly correlated with OS  and tumour grade  were determined as independent predictors of improved OS (Table p = 0.007), tumour grade , and small bile duct typing  were considerably associated with RFS. In the multivariate Cox regression model, high HEV  and tumour grade  were deemed as independent predictors of RFS . In a prognostic model, premising immune cell infiltration score was created, and HEV activation was considerably linked to OS in individuals with ICC, implying that HEV can serve as an independent factor to predict the prognosis of individuals with ICC. Collectively, these findings indicate that HEV represents an immunocompetent TME in which the cancer immune cycle is functionally activated.In contrast, the activation degrees of memory B cells, M0 macrophages, mast cells, and Treg cells decreased dramatically in individuals of the high‐HEV subtype. In addition, Naive B cells, plasma cells, DC activation, and CD8 T cells were significantly positively correlated with HEV ssGSEA scores. By comparison, memory B cells, M0 macrophages, mast cell activation, and Treg cells showed clear opposite trends in the correlation analysis. Nevertheless, the low‐HEV subtype showed the opposite trend. In recent years, tumour‐associated TLSs have attracted much attention.Due to recent technological advances in next‐generation sequencing and imaging approaches, spatial transcriptomic RNA sequencing has emerged as a cutting‐edge technology to systematically measure gene expression levels at distinct spatial locations in a tissue.By fitting the change curves of the six cell types, HEV, and TLS scores, it was discovered that the cluster structure which is linked to the matrix region shaped the change patterns of EC, fibroblasts, T cells, B cells, and even HEV and TLS. A research has shown that ICI efficacy is considerably correlated with intra‐tumoral HEV.Subsequently, immunohistochemical (IHC) staining of specimens from 95 individuals with ICC was performed, and it was noted that individuals with ICC in the high‐HEV group had more infiltration of CD8+, TLS, and CD20+ immune cells than those in the low‐HEV group. Representative samples with high‐HEV expression were selected for mIF staining in order to find the correlation between HEV and immune cells. The results showed an obvious colocalization relationship between HEV and immune cells. Meanwhile, individuals with ICC in the high‐HEV group had smaller tumour diameters, along with better disease‐free survival and OS. Traditionally, ICC can result from small intrahepatic bile ducts in the liver  as well as large intrahepatic bile ducts in the liver .For the very first time, this study demonstrated that high HEV expression was usually accompanied by more immune cell infiltration in individuals with ICC, and HEV was identified as an important immunological prognostic factor for RFS and OS in individuals with ICC who could undergo resection surgery. However, this research also has a few limitations. The cohort in this study features a limited sample size, consisting of only 95 individuals with surgically resected ICC. By using an external validation cohort, this research could be validated and strengthened in a larger sample size. Moreover, given the limited time and funding, merely the relationship between HEV and CD8+ TLS, CD20+ immune cells or structures was observed, whilst the specific mechanism of HEV effect on the immune microenvironment of ICC has not identified. These issues will be included in the next research.5This study for the first time demonstrated that high‐HEV expression was usually accompanied by more immune cell infiltration in individuals with ICC, and identified HEV as an important immunological prognostic factor for RFS and OS in individuals with ICC who could undergo resection surgery.Jun Chen, Chunping Jiang, Xiaohong Pu, and Yan Zhou conceived the project and designed the study. Yan Zhou wrote the article. Yan Zhou, Linxi Zhu, Shuo Zhang, and Xiaohong Pu performed the experiments. Qian Gu and Hongyan Wu analysed the clinical data. Jun Chen, Chunping Jiang and Xiaohong Pu obtained funding.This research was supported by funding for Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University (2022‐LCYJ‐MS‐27); Major Project of Nanjing Health Science and Technology Development (ZDX22001); the Chinese National Science Foundation (82172631), and The Nanjing Municipal Administration of Health and Human Services (YKK21074); Research Project of Jinan Microecological Biomedicine Shandong Laboratory .The authors declare that they have no conflict of interest.FIGURE S1. Tumour microenvironments patterns of different HEV expression. (A) Representative images of different tumour microenvironments. (B) HEV expression in different tumour microenvironment (proportion).Click here for additional data file.


In recent decades, heavy metals (HMs) have emerged as a global health concern. Unfortunately, in Pakistan, there is a general lack of awareness regarding the potential health risks associated with HMs pollution among automobile workers. Herein, we investigated the concentration of heavy metals such as lead (Pb), cadmium (Cd), and chromium (Cr) among automobile workers who were occupationally exposed in Mingora City, Khyber Pakhtunkhwa, Pakistan. Three different automobile groups, i.e., battery recyclers, spray painters, and mechanics were studied in detail. A total of 40 blood samples were collected from automobile workers groups while 10 blood samples were collected as control individuals from different locations in the study area. We investigated heavy metals concentration with a standard method using an atomic absorption spectrometer AAS . Based on our findings, the battery recycling group displayed the most elevated Pb levels (5.45 ± 2.11 μg/dL), exceeding those of both the spray painters’ group (5.12 ± 1.98 μg/dL) and the mechanics’ group (3.79 ± 2.21 μg/dL). This can be attributed to their higher exposure to Pb pollution resulting from the deterioration, dismantling, grinding, or crushing of old batteries. In the context of chromium (Cr) exposure, a similar trend was observed among the battery recycling group, as well as the spray painters and mechanics groups. However, in the case of cadmium (Cd), the mechanics’ group exhibited the highest level of exposure (4.45 ± 0.65 μg/dL), surpassing the battery recycling group (1.17 ± 0.45 μg/dL) and the spray painters’ group (1.35 ± 0.69 μg/dL), which was attributed to their greater exposure to welding fumes and other activities in their workplace. We believe that our findings will encourage regulatory measures to improve the health of automobile workers. However, further work is needed to determine various health-related issues associated with heavy metal exposure among automobile workers.  They are present everywhere throughout the earth and may also accumulate in living organisms as well. Among 35 natural obtainable metals, 23 metals  have high obvious density with an atomic weight greater than 40.04 are known as heavy metals , 2. ThesAll workplaces have some specific levels of work-related dangers, and each workplace condition is remarkable in nature. Synthetic mixtures are discharged at a workplace from various operations such as residue or splash. It may get into the body either through dermal contact, inhalation, or ingestion . WorkersGenerally, automobile workshops consist of different groups such as mechanics, spray painters, battery recyclers, and radiator workers for specific tasks. However, these certain activities are the major sources of toxic pollution due to the irresponsible behavior of the occupational workforce. Several studies have suggested that autoworkers are commonly exposed group to toxic metal pollution , 10. WhiThe present study was conducted in Mingora city district Swat where automobile workshops are situated in various locations and the reason that these sites are most familiar for different vehicular activities. This study reveals that some automobile workshops were small having 4–5 workers, while others were large having 8–10 working groups. Unfortunately, these automobile workers have no knowledge and awareness about the toxic effects of HMs, nor do they use any protective measures, as a result, they pay slight consideration to protect themselves from the possible impacts due to ingestion and inhalation of these toxic substances. Herein, the evaluations of selected HMs concentrations of Pb, Cd, and Cr were done in exposed autoworkers and a comparison was done with a control group (non-workers).2.2.1.The study area is located in the district Swat of Khyber Pakhtunkhwa (KPK) province, Pakistan, and lies between 34° 46′ 25.1292″ north and 72° 21′ 35.6436″ east. Mingora is the third-largest city in KPK with a high population ratio. This study focused on occupationally exposed autoworkers of Mingora city, district Swat. A total number of 40 autoworkers including mechanics, spray painters, and battery repairing, and 10 non-workers (control group) have participated in this study from different locations including Takhtaband Bypass Rd., Shahdara, and Watkay site, respectively, as shown in All these workers had been working in workshops for 4 to 5 years or more than 10 years, while the workers who had joined workshops for less than 1 year were excluded from this survey. Furthermore, all the workers were interviewed regarding workplace safety precautions. However, it was observed that all the exposed autoworkers had a lack of awareness regarding workplace safety measures.2.2.Several workshops were visited, and the purpose of the study was discussed with the workers and non-worker groups. The samples were collected from different automobile workshops including mechanics, battery recyclers, and spray painters. Each 3 mL blood sample was collected in heparinized tubes using the standard method. Blood was taken with the help of a trained nurse from both autoworkers and non-worker groups. After collection, blood samples were directly subjected to the laboratory for further experimental work.2.3.The sample preparation and analysis were carried out according to the previous method of Ishola et al.  with a s2.4.p < 0.05). Furthermore, graphs were made by using the graph pad prism version (5.01).Data were analyzed through the Statistic 9 software. One-way ANOVA was performed considering the , surpassing both the spray painters’ group (5.12 ± 1.98 μg/dL) and mechanics group (3.79 ± 2.21 μg/dL). The mean values of Pb, Cd, and Cr were recorded higher in the mechanic, battery recyclers, and spray painters’ group whereas their mean values were recorded lower in the control group as presented in A high concentration of Pb was observed in battery workers as compared to automobile workers in Pakistan Ahmad et al. . MeanwhiMoreover, in In the present study, a significant variation was observed in the level of Cr among different groups. The battery recyclers group exhibited a higher mean value of Cr (5.13 ± 2.37 μg/dL), whereas the mechanic’s group had a lower mean value (4.38 ± 2.76 μg/dL), however, the spray painters group showed an intermediate level of Cr (4.72 ± 2.27 μg/dL) when compared to a control group (0.87 ± 0.41 μg/dL). However, the concentration of these HMs in the spray painters group follows the order Pb > Cr > Cd as shown in 4.In the present work, it is concluded that the levels of lead and chromium in the battery recycling group were significantly higher when compared to both the mechanics and spray printer groups due to their higher exposure to different activities in their work. Moreover, in the case of Cd, the Mechanics groups have the maximum concentrations compared to other groups. Nonetheless, the concentration of all HMs among the autoworker’s groups was greater in comparison to control individuals. In the present study, we noticed that workers did not utilize any personal protective equipment and had limited awareness regarding the toxic effects of heavy metals released in their workplaces. However, here are some measures including regular cleanup, safe handling and storage, proper education and awareness, training, regular assessment and improvement, regular health checking, and compliance with regulations might be helpful for workers to reduce potential exposure to heavy metals and other toxic chemicals in workshops.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.The studies involving humans were approved by the Ethics Committee of the International Islamic University Islamabad (IIUI). Consent was obtained from all the individual participants included in the study. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.KK: Formal analysis, Investigation, Writing – original draft. SR: Writing – review & editing. A-U-RB: Supervision, Writing – original draft, Writing – review & editing. IN: Writing – review & editing. SA: Writing – review & editing. MY: Writing – review & editing. ZU: Supervision, Writing – review & editing. AI: Writing – review & editing. AA: Funding acquisition, Writing – review & editing. MA: Funding acquisition, Writing – review & editing. J-WC: Writing – review & editing. KC: Writing – review & editing.


Rapid eye movement (REM) sleep behavior disorder (RBD) is widely considered a prodromal synucleinopathy, as most with RBD develop overt synucleinopathy within ~10 years. Accordingly, RBD offers an opportunity to test potential treatments at the earliest stages of synucleinopathy. The North American Prodromal Synucleinopathy (NAPS) Consortium has created a multisite RBD participant, primarily clinic‐based cohort to better understand characteristics at diagnosis, and in future work, identify predictors of phenoconversion, develop synucleinopathy biomarkers, and enable early stage clinical trial enrollment.Participants ≥18 years of age with overnight polysomnogram‐confirmed RBD without Parkinson's disease, dementia, multiple system atrophy, or narcolepsy were enrolled from nine sites across North America (8/2018 to 4/2021). Data collection included family/personal history of RBD and standardized assessments of cognitive, motor, sensory, and autonomic function.n = 295 male, n = 66 female), and secondarily based on history of antidepressant use  and based on extent of synucleinopathy burden . Overall, these participants commonly demonstrated abnormalities in global cognition , motor function , sensory , autonomic function , and anxiety/depression .Outcomes are primarily reported based on sex (361 total: These RBD participants, assessed with extensive history, demographic, cognitive, motor, sensory, and autonomic function demonstrated a lack of sex differences and high frequency of concomitant neurological abnormalities. These participants will be valuable for future longitudinal study and neuroprotective clinical trials. The North American Prodromal Synucleinopathy (NAPS) Consortium for Rapid Eye Movement (REM) sleep behavior disorder (RBD) was established to facilitate neuroprotective clinical trials for neurodegenerative diseases characterized by synuclein pathology. Such “synucleinopathies,” including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), affect ~2‐million people in the US, have a staggering economic footprint,RBD is a disorder characterized by a lack of muscle atonia during REM sleep and dream enactment behaviors,https://www.naps‐rbd.org) comprises a coordinated effort across nine sites in North America to establish an RBD cohort prospectively assessed by standardized assessments and biomarker collection. Herein we describe the baseline characteristics of the NAPS Consortium, a primarily but not exclusively clinic‐based cohort, which at present, reflecting a single cross‐sectional timepoint . Data are presented describing the whole cohort, with special attention placed on sex, whether prodromal synucleinopathy features are present, antidepressant use, and family history, given that each of these traits have proved meaningful in past studies.Building upon existing RBD research groups ,n = 361 participants were enrolled at Washington University School of Medicine (n = 26), Mayo Clinic Rochester (n = 50), University of Minnesota (n = 30), Center of Advanced Research in Sleep Medicine at the Hôpital du Sacré‐Coeur de Montréal (n = 94), Harvard/Massachusetts General Hospital (n = 26), Emory University (n = 32), University of California Los Angeles (n = 32), Stanford University (n = 20), and the VA Portland Health Care System (n = 51). Participants from a tenth site, at Banner Sun Health, are not included in this analysis. Referrals were made primarily from clinics offered on a consecutive basis and supplemented by community referrals through the www.naps‐rbd.org website. There were no a priori demographic for goals recruitment, and information regarding those who chose not to participate was not retained. This study was performed according to the Declaration of Helsinki and approved by the Institutional Review Boards at each enrollment site. All participants provided written informed consent prior to participation.A complete methodological description of the NAPS Consortium protocol is published in a separate overview paper . Briefly, participants >18 years of age with polysomnogram‐confirmed RBD by ICSD‐3 criteriaData collection procedures and practices were rigorously standardized across sites, which included structured interviews and questionnaires on health history, structured neurological, and physical examinations, and an objective test battery of cognitive, motor, autonomic, and sensory functions. If available, a co‐participant  provided information via structured interviews and written questionnaires. Variables presented in this analysis are detailed below; according to the NAPS1 REDCap database export dated May 26, 2022 prior to complete import into the NAPS2 RAVE database. Biofluid assay data, polysomnograms, data from participants enrolling after April 2021, and data from longitudinal study visits are not presented in this analysis.n = 305); or as “isolated RBD” indicating no abnormalities were detected during the clinical evaluation and test battery (n = 56). Participants with diagnosis of any overt neurodegenerative disease  are excluded from this analysis.Based on the clinical evaluation, questionnaires, exam, and objective tests, the clinician at each site (a board‐certified neurologist) completed a standardized, structured assessment to render clinical diagnoses. Determinations were supported by a quarterly, panel‐based adjudication process with three or more NAPS clinicians. Participants were categorized as “RBD+” indicating one or more abnormalities  in any of the cognitive, motor, autonomic, or sensory domains , restless legs syndrome, and periodic limb movement were determined during the clinician's structured interview. The severity and frequency of RBD symptoms was assessed by the RBD Severity Scale (RBDSS),https://naccdata.org/data‐collection/forms‐documentation/uds‐3) and custom NAPS‐specific forms. Questionnaires assessing neuropsychiatric function included the Beck Anxiety Inventory (BAI), Patient Health Questionnaire‐9 (PHQ‐9) for depression, the Post‐Traumatic Stress Disorder (PTSD) checklist (PCL‐5),Demographic information and health history (including comprehensive family history) were obtained via structured interview and forms, including those from the Uniform Data Set version 3 (UDS3), from the National Alzheimer Coordinating Center  function. Cognitive assessments included the psychometric battery from the UDS3 standard and LBD modulesMotor function was assessed via the Timed Up and Go (TUG),Autonomic function was measured with orthostatic blood pressure, in which blood pressure was measured after lying supine for 5 min, then 3 min after standing. Impaired orthostatic tolerance was defined as ≥20 mm Hg decrease in systolic blood pressure and/or a ≥ 10 mm Hg decrease in diastolic blood pressure. Severe orthostatic intolerance was defined as ≥30 mm Hg decrease in systolic blood pressure and/or a ≥ 15 mm Hg decrease in diastolic blood pressure.The Brief Smell Identification Test t test, Mann–Whitney U test, or χ2 test, as appropriate. Group differences between sex within RBD and sleep‐related outcomes , while a greater proportion of women were divorced/separated  or were widowed .The participant cohort n = 36; Table 1The overall cohort reported mean RBD symptom onset at age 51.1 ± 16.3 Table . There wp = 0.244). Women reported fewer RBD‐related injuries, including to their bed partner . Despite the greater proportion of women reporting a history of antidepressant usage , women less frequently associated RBD behavior with medication use . None of these RBD‐related differences in women were associated with history of antidepressant usage. With respect to other sleep disorders, women reported higher rates of insomnia , and lower rates of obstructive sleep apnea , of which only the latter was statistically significant after adjustment for antidepressant usage. The only difference in self‐reported sleep scores was seen in the co‐participant's report of the SCOPA‐Sleep total score  and daytime sub‐score .With regard to sex differences, RBD symptom onset occurred ~3 years earlier in women than men, a difference that was not statistically significant . Restless legs syndrome , and insomnia  were more frequent in participants with antidepressant usage. Medications reported to worsen RBD symptoms primarily included antidepressants including selective serotonin reuptake inhibitors , selective norepinephrine reuptake inhibitors , norepinephrine dopamine reuptake inhibitors , tri/tetracyclic antidepressants , and monoamine oxidase inhibitors , anxiolytics (buspirone), dopamine agonists (pramipexole), and to a lesser extent, sedative‐hypnotics, and antihistamines .Antidepressant usage within the overall population was examined based on participants reporting any history of antidepressant usage  compared to participants with no history of antidepressant usage Table . Despitep < 0.001), reported an earlier age of RBD symptom onset , and were more likely to be female (p < 0.001). Isolated RBD participants less frequently associated RBD behavior with medication usage , and reported lower rates of RBD behavior being associated with self‐injury . Differences may be explained by the higher proportion of women (and greater rate of antidepressant use) in the isolated RBD group, as there was no difference in the duration between symptom onset/diagnosis and NAPS referral with respect to isolated RBD versus RBD+, men versus women.RBD and sleep‐related outcomes were compared between participants determined to have isolated RBD  and those with possible neurodegenerative signs  and hypertension , and ~50% higher rate of arthritis  and thyroid disease  than men.The most commonly reported health problems in the cohort Table  were hypAutonomic function, as assessed by SCOPA‐AUT, averaged 13.5 ± 7.8 in the overall cohort, not significantly different between men and women. Urinary and bowel incontinence, as well as orthostatic hypotension were not different between men and women. Men reported a nearly two‐fold higher rate of sexual dysfunction compared to women (68% vs. 37%).p = 0.056). Similarly, TBI‐related metrics  were also potentially higher in men than women.Approximately 27% of the cohort reported a history of traumatic brain injury (TBI) occurring on average 31 years ago, which was more common in men  and depression . Individuals self‐reporting anxiety and depression averaged 14.4 ± 11.4 and 9.1 ± 6.1 for BAI and PHQ‐9 scores, respectively. However, self‐reported BAI and PHQ‐9 scores in the overall group averaged 8.1 ± 8.8 and 5.2 ± 5.4, respectively, which are scores below clinical thresholds for anxiety or depression . Considering previously reported associations between PTSD and RBD,p < 0.001) domain, no sex‐related differences were found in the other three features.The Neuropsychiatric Inventory assessed domains related to delusions, hallucinations, anxiety, and apathy/indifference. This cohort had low rates of current delusions or hallucinations (~4%–5%), whereas anxiety was reported at 22.2% and apathy/indifference at 19.7%. Although men had higher rates in the “apathy/indifference” , worse scores on the MINT , and improved phonemic/categorical verbal fluency (Table n = 116) with RBD+ had MoCA scores ≤25  met criteria for mild cognitive impairment.The cognitive battery revealed relatively normal mean scores in the overall cohort, with women having better scores in the MoCA , with 13.3% having values in the severe range ; there were no differences between men and women.p = 0.024) (Table p = 0.26). Olfactory impairment was self‐reported in 21.9% of the overall population, however, 57% showed abnormal BSIT scores. Similarly, women had better olfaction than men on the BSIT . Among RBD+ participants, ~50% were abnormal in both color vision and olfactory function.Average FM‐100 scores in non‐color‐blind participants were 143.6 ± 97.6, indicating 74% of the cohort was abnormal, with women showing slightly better mean color vision ,Even across 9 sites, there was a high male:female ratio in our RBD cohort, yet a striking lack of differences between men and women except for younger age of onset and higher antidepressant usage in women. Previous work has described an earlier age of RBD onset in women that can be attributed to previous or current antidepressant usage.Antidepressants are known to cause changes in sleep architecture and polysomnography findings, predominantly during REM sleep .n = 305 or 84%). We confirm that individuals with such findings are appropriate for inclusion in clinical trials since such features are common among RBD patients. We compared RBD+ participants with isolated RBD, who had no neurological abnormalities identified during a very detailed and comprehensive evaluation. Except for higher rate of restless legs syndrome and periodic limb movement disorders in RBD+, there were no other differences between the groups except older age in RBD+ and higher rate of antidepressant usage in the isolated RBD group. These data suggest that isolated RBD participants may be at an earlier stage of prodromal synucleinopathy rather than having a separate underlying etiology, although this will require longitudinal follow‐up for confirmation. Prospective comprehensive evaluations in both groups will provide insights on if, when, and to what phenotype participants with RBD +/− other neurodegenerative signs/symptoms will evolve. The NAPS Consortium is developing a clinical rating scale that encompasses the broad range of neurological dysfunction that is detectable during the prodromal phase of synucleinopathies. We anticipate that this Prodromal Synucleinopathy Rating Scale (PSRS) will serve as a clinical measure of synucleinopathy burden that can be used in neuroprotective clinical trials in RBD. Additionally, all willing current NAPS participants, and future enrollees, will undergo an expanded clinical and biomarker assessment as part of the NAPS Stage 2 (NAPS2) protocol (U19 AG071754) to develop additional biomarkers and achieve readiness for neuroprotective clinical trials.“Soft” neurological abnormalities were very common in our cohort . JEE, MML, ATK, RMP, BFB, YSJ.Dr. Elliott has received support from the Department of Veteran Affairs, NIH , Oregon Medical Research Foundation, Portland VA Research Foundation, Eugene & Clarissa Evonuk Foundation in Environmental Physiology, and American Heart Association.Dr. Lim has received support from Department of Veteran Affairs, Department of Defense, NIH , NSF, Center for Neuroscience & Regenerative Medicine (Henry Jackson Foundation), Oregon Medical Research Foundation, Collins Medical Trust, Brain & Behavior Foundation (NARSAD), American Sleep Medicine Foundation, Hartford Center of Gerontological Excellence, Pacific Northwest National Laboratory, and Portland VA Research Foundation.Allison Keil, Dr. Pelletier, Dr. Forsberg, Jennifer McLeland has no disclosures.Dr. Postuma has received support from the Fonds de Recherche du Quebec – Santé, the Canadian Institutes of Health Research, the Parkinson Society of Canada, the Weston‐Garfield Foundation, the Michael J. Fox Foundation, the Webster Foundation; and personal fees from Takeda, Roche/Prothena, Teva Neurosciences, Novartis Canada, Biogen, Boehringer Ingelheim, Ther‐ anexus, GE HealthCare, Jazz Pharmaceuticals, Abbvie, Jannsen, Otsuko, Phytopharmics, Inception Sciences, and Curasen.Fonds de Recherche du Québec – Santé.Dr. Gagnon has received support from the NIH, the Canadian Institutes of Health Research and the Dr. St. Louis has received support from NIH , the Michael J. Fox Foundation, Harmony, Inc., and Sunovion, Inc.Dr. Fields has received support from the NIH.Dr. Bliwise has received support from the NIH and has been a Consultant to CliniLabs, Eisai, Ferring, Huxley, Idorsia, and Merck.Dr. Huddleston has received support from NIH .Dr. Avidan has received consultant fees from Avadel, Merck, Takeda, Eisai, Idorsia, and Harmony, and speaker honoraria from Merck, Eisai, Harmony, and Idorsia.Dr. Howell has received research support from the NIH.Dr. Schenck has received a one‐time speaker honorarium from Eisai, Inc.Dr. Criswell has received support from the NIH and consulting fees from Abbvie and Sio Gene Therapies.Dr. Videnovic has received research support from the NIH and the Michael J Fox Foundation; consultancy fees from Alexion Pharmaceuticals, Biogen, XW Pharma, Jazz.Dr. During has received support from Jazz Pharmaceuticals, Sanofi, Takeda, Rythm Inc., and the Feldman Foundation CA.Dr. Miglis has received support from Jazz Pharmaceuticals, Embr Wave, and Biohaven Pharmaceuticals; Consulting fees from 2nd MD, Infinite MD, and Guidepoint LLC; Payments for CME lectures from MED‐IQ; Royalties from Elsevier Inc.Dr. Shprecher has received support from the Arizona Alzheimer's Consortium, Abbvie, Acadia, Aptinyx, Axovant, Biogen, Eisai, Eli Lilly, Enterin, Neurocrine, Michael J Fox Foundation, NIH, Nuvelution, Theravance, and Teva; consultant fees from Amneal, Forensis, and Neurocrine; speaker honoraria from Acorda, Amneal, Neurocrine, Sunovion, Teva, and US World Meds/Supernus.Dr. Lee‐Iannotti has received support from NIH, Liva Nova, Respicardia, and the Arizona Alzheimer's Consortium. She serves on the Scientific Advisory Board for Jazz Pharmaceuticals, INSPIRE, and Avadel. She is a consultant and speaker for Jazz Pharmaceuticals.Dr. Boeve has served as an investigator for clinical trials sponsored by Alector and Biogen. He serves on the Scientific Advisory Board of the Tau Consortium. He receives support from NIH, the Mayo Clinic Dorothy, and Harry T. Mangurian Jr. Lewy Body Dementia Program, the Little Family Foundation, and the Ted Turner and Family Foundation.Dr. Ju has received support from the NIH and the Centene Corporation contract (P19‐00559) for the Washington University‐Centene ARCH Personalized Medicine Initiative; and compensation for consultant activities for Applied Cognition.


The faces of our friends and loved ones are among the most pervasive and important social stimuli we encounter in our everyday lives. We employed electroencephalography to investigate the time line of personally relevant face processing and potential interactions with emotional facial expressions by presenting female participants with photographs of their romantic partner, a close friend and a stranger, displaying fearful, happy and neutral facial expressions. Our results revealed elevated activity to the partner’s face from 100 ms after stimulus onset as evident in increased amplitudes of P1, early posterior negativity, P3 and late positive component, while there were no effects of emotional expressions and no interactions. Our findings indicate the prominent role of personal relevance in face processing; the time course of effects further suggests that it might not rely solely on the core face processing network but might start even before the stage of structural face encoding. Our results suggest a new direction of research in which face processing models should be expanded to adequately capture the dynamics of the processing of real-life, personally relevant faces. Face familiarity has a large impact on face processing: familiar faces can be detected and recognized in a highly robust, quick and effortless way . In contEven though the most important familiar faces in our lives are those of our relevant others, family and friends, relatively few studies have investigated the impact of genuine personal relevance on face processing. Instead, research on familiar faces has mostly used portraits of famous persons like actors or politicians since the use of one single stimulus set for all participants is convenient and affords a level of experimental control . In contElectroencephalography (EEG) and ERP studies provide information about the time course of personal relevance processing, which is informative for theoretical models of face processing. Some studies have reported effects of personal relevance to occur from 170 ms after stimulus onset, increasing the amplitude of the N170/M170 component in response to family members . At a laSo far, no ERP study has reported earlier effects of personal relevance, i.e. before the stage of structural face encoding as reflected by the N170 component. These early, perceptual stages of processing are reflected in the P1 component, an occipital positivity generated in the extrastriate visual cortex around 100 ms after stimulus onset . Most ofBesides the personal relevance of faces, emotional facial expressions have been shown to have a large impact on face processing. Previous research has demonstrated increased activity for emotional as compared to neutral facial expressions, starting at the stage of perceptual encoding . The occvs disgusted faces specifically for familiar faces  coregistration study. fMRI results and cross-modal representational similarity analyses (RSAs) as well as EEG regression analyses have been previously published  had normal or corrected-to-normal vision. All participants were in a heterosexual relationship at the time of data collection . Participants received a mean score of 7.66 points (s.d. = 0.61 points) on the Passionate Love Scale . In addip2 = 0.236, with at α = 0.05 and a power of 0.80. Similar or higher effect sizes have been reported in the previous literature for the effects of personal relevance  < 1, P = 0.747, ηp2 = 0.017. For stimulus ratings, see Participants were presented with portraits of two personally relevant faces  and a male stranger , showing fearful, happy and neutral facial expressions in a 3 (Identity) x 3 (Emotion) design. Stimuli were acquired as screen shots during Skype sessions; depicted individuals were directed to look straight at the camera and received guidance by the experimenter in order to display the correct emotional expressions. After acquiring the neutral expression, we used short verbal instructions in order to elicit happy and fearful expressions, e.g. by imagining a frightening or happy situation, and gave feedback on the facial expression when necessary. We took care to provided sufficient time for our participants’ relevant others to feel comfortable with the photo session and to imagine a respective emotional situation. We aimed to collect authentic rather than extreme variants of fearful and happy expressions in order to ensure a naturalistic stimulus set. Pictures were edited to show the complete face (including hair) in color on a gray background. After the experiment, participants rated their individual stimulus set using 7-point Likert scales regarding the valence and arousal of the depicted facial expression, as well as the attractiveness of the depicted person . In order to control for lower-level stimulus features, we conducted control analyses by applying a hierarchical computational model of cortical responses of neurons in the primary visual cortex  with a mean ITI of 3 s and a minimum ITI of 2.5 s, sampled using an exponential function using the program fMRI Simulator . During EEG data were acquired using an MRI-compatible system (Brain Products) with 64 Ag–AgCl electrodes, and the electrocardiogram (ECG) was recorded from an electrode placed left of the spinal column. Electrode impedances were kept <20 kΩ. Data were sampled with 5000 Hz with electrodes FCz as reference and AFz as ground and high-pass filtered at 0.1 Hz. EEG and fMRI clocks were synchronized using a sync box (Brain Products) in order to align EEG recordings and MRI slice acquisition.Fs < 2.11, all Ps > 0.136; descriptive statistics on trial numbers per condition are listed in Supplementary Table S2. Data were averaged per participant and experimental condition.Offline, MR gradient artifacts were identified using synchronized scanner markers and removed from continuous EEG data with a template subtraction algorithm using a sliding window of 21 artifacts on baseline-corrected data (whole artifacts used for baseline correction) . Data weAnalyses were performed on ERP components P1, N170, P3 and LPC. P1 amplitudes were quantified at the average of occipital electrodes PO8, PO4, POz, PO3, PO7, O1, Oz and O2 using a semi-automatic peak detection algorithm in the time window from 90 to 130 ms after stimulus onset (mean peak latency = 107 ms). N170 amplitudes were detected at averaged electrodes TP9, TP7, TP8, TP10, P7 and P8 in the time window from 150 to 220 ms after stimulus onset (mean peak latency = 172 ms).vs neutral facial expressions  and 400–800 ms (LPC). In addition, we performed exploratory analyses of the early posterior negativity (EPN), a negativity at temporo-occipital electrode sites frequently reported for emotional ressions , which wpost hoc tests were Bonferroni-corrected for multiple comparisons.Analyses were performed in JASP  with rep01, i.e. the Bayes factor that quantifies evidence for the null hypothesis, with values >1 indicating evidence for H0 over H1  including the factors Identity (3) and Emotion (3) in order to quantify the level of support for the null hypothesis, i.e. for a null model that assumes no differences between conditions, compared to a model that includes such differences . We report BF over H1 .post hoc tests for ratings of valence and arousal were conducted as analyses of Identity effects within each emotion category, followed up by pairwise comparisons in case of significant effects.Ratings of stimulus valence, arousal and attractiveness were analyzed with repeated-measures ANOVAs in parallel to ERP analyses . Planned F = 3.80, P = 0.031, and ηp2 = 0.183, based on larger amplitudes for Partner compared to Stranger, t = 2.71, P = 0.031, and Cohen’s d = 0.640; there were no significant effects for the comparisons of Partner vs Friend and Friend vs Stranger, all Ps > 0.22. Furthermore, there was no significant effect of Emotion, F < 1, and no significant interaction of Emotion and Identity, F = 1.21 and P = 0.315. See Analyses of P1 peak amplitudes revealed a main effect of Identity, Fs < 1.53 and all Ps > 0.21.For N170 amplitudes, no effects of Identity, Emotion or any interactions were observed, all F = 12.61, P < 0.001, and ηp2 = 0.426. The effect was based on larger amplitudes for Partner compared to Friend, t = 3.452, P = 0.005, and Cohen’s d = 0.814, and to Stranger, t = 4.885, P < 0.001, and Cohen’s d = 1.151; there was no significant difference between Friend and Stranger. No significant effects emerged for Emotion or the interaction of Emotion and Identity, all Fs < 1.48 and all Ps > 0.244.Analyses of P3 amplitudes showed a significant effect of Identity, F = 18.76, P < 0.001, and ηp2 = 0.525. Again, amplitudes were increased for Partner compared to Friend, t = 3.32, P = 0.007, and Cohen’s d = 0.781, and to Stranger, t = 6.12, P < 0.001, and Cohen’s d = 1.442. Finally, LPC amplitudes were also increased for Friend compared to Stranger, t = 2.803, P = 0.025, and Cohen’s d = 0.661. No significant effects were observed for Emotion or the interaction of Emotion and Identity, Fs < 2.02 and Ps > 0.148.As for the P3, analyses of the LPC revealed an effect of Identity, F = 5.38, P = 0.021, and ηp2 = 0.24, with increased, i.e. more negative-going, amplitudes for Partner than Stranger, t = −3.21, P = 0.009, and Cohen’s d = −0.758, whereas amplitudes did not differ for Partner vs Friend and Friend vs Stranger. The main effect of Emotion was not significant, F = 2.98 and P = 0.057, and there was no significant interaction between Identity and Emotion, F = 1.52 and P = 0.222.Analyses of EPN amplitudes revealed a main effect of Identity, 01 = 6.67 for the factor emotion, indicating that the data are 6.67 times more likely under a null model than under a model that assumes differences between emotion categories. For the N170, analyses revealed a BF01 = 15.67 for the factor Emotion and 1.81 for Identity. Likewise, Bayes factors for the other components showed evidence for the null model over models including the factor Emotion, with BF01 = 10.17 (EPN), BF01 = 5.31 (P3) and BF01 = 4.79 (LPC). In summary, while our data revealed significant main effects of Identity, Bayesian analyses showed strong and consistent evidence for models that assume no effect of the factor Emotion.For P1 amplitudes, Bayesian repeated-measures ANOVA revealed a BFF = 30.58, P < 0.001, and ηp2 = 0.643, based on higher ratings for Partner compared to Friend and Stranger  = 14.45, P < 0.001, and ηp2 = 0.462, with more positive ratings for Partner compared to Friend and Stranger, as well as a main effect of Emotion, F = 59.26, P < 0.001, and ηp2 = 0.777: as expected, valence ratings were highest for happy facial expressions, intermediate for neutral expressions and lowest for fearful expressions. Finally, there was an interaction of Identity and Emotion, F = 5.43, P < 0.001, and ηp2 = 0.242. Within fearful faces, the Partner was rated as more positive than Stranger; within happy faces, the Partner received more positive ratings than Friend and Stranger, and Friend was rated more positive than Stranger. Within neutral faces, there was no effect of Identity.For a complete list of statistical details, please see Supplementary Table S1. Here, we focus on main effects and the description of  significant interactions. For attractiveness ratings, there was a main effect of Identity, nger see . For valF = 24.39, P < 0.001, and ηp2 = 0.589, based on higher arousal ratings for Partner compared to Friend and Stranger. A main effect of Emotion, F = 13.25, P < 0.001, and ηp2 = 0.438, was based on higher arousal ratings for happy faces compared to neutral and fearful faces. Finally, there was an interaction of Identity and Emotion F = 3.33, P < 0.029, and ηp2 = 0.164; however, planned post hoc tests revealed higher arousal ratings for Partner compared to Friend and Stranger in all emotional facial expressions.For arousal ratings, we also found a main effect of Identity, We investigated the impact of personal relevance and emotional facial expressions on the time course of face processing using ERPs. Seeing the face of one’s partner compared to a stranger increased the amplitudes of the P1 component at 100 ms after stimulus onset, even before the stage of structural face encoding. While no effects of Identity were observed during structural encoding as indicated by the N170, later, evaluative stages showed pronounced effects of Identity, reflecting the degree of personal relevance. In contrast, we observed no effects of emotional facial expressions, suggesting the paramount impact of personal relevance in everyday face processing.The finding of increased P1 amplitudes in response to the Partner’s face is especially noteworthy since it is related to perceptual processing at around 100 ms after stimulus onset in the extrastriate cortex and thus precedes structural face encoding as indexed by the N170 component , as wellThe stage of structural encoding, as captured in the N170 component, did not show modulations by Identity or Emotion, or their interaction. For both Identity and Emotion effects, previous research is characterized by pronounced heterogeneity concerning modulations of the N170. In the case of emotional expressions, it has previously been suggested that the N170 might be affected by overlapping but independent of emotion-related processing , while oAt the stage of higher-order processing after 300 ms from stimulus onset, reflected in the amplitudes of the P3 and LPC, personally relevant faces of the Partner elicited considerably increased amplitudes compared to both Friend and Stranger, as well as for Friend compared to Stranger. These findings are in line with previous reports .vs Stranger were only visible during the LPC time window. This is in contrast to some previous studies that reported earlier effects in response to friends’ faces (vs Stranger in the observed time intervals (Finally, it is noteworthy that in the present study, we report no ERP effects of emotional facial expressions, while personal relevance was strongly reflected in our ERP data (however, note that the effects of emotional facial expressions were evident in unimodal fMRI analyses, see ntervals , 2020. Antervals . A possintervals . Taken tvs neutral vs fearful faces. However, there was also a main effect of Identity, based on higher valence ratings for Partner compared to both Friend and Stranger. Similarly, arousal ratings were increased for Partner compared to both Friend and Stranger. These findings are in line with previously reported findings of increased valence and arousal for relevant others (Finally, it is noteworthy that even though ERP data did not reveal effects of emotion, stimulus ratings of valence and arousal did show effects of both emotion and personal relevance, as well as interactions of the two factors. As expected, valence ratings showed higher ratings for happy t others , which eThis point is also relevant for interactions between Identity and Emotion. In our data, valence ratings were increased for Partner compared to other identities only in fearful and happy expressions, whereas there was no effect of Identity within neutral expressions. These findings are in line with our hypothesis, showing increased effects of emotional facial expressions for relevant others compared to a stranger’s face. Even though interactions between Identity and Emotion have been previously reported for rating data , it woulTaken together, our results demonstrate the strong impact of personal relevance on face processing, most likely based on the involvement of brain regions outside of the core face processing network. These effects occur as early as 100 ms after stimulus onset in the extrastriate visual cortex, as well as in higher processing stages. Our study also shows the importance to include individualized stimulus sets in order to capture our real-life social interactions.nsad021_SuppClick here for additional data file.