MT3, which is involved in growth, nervous, and immune processes, is identified. We demonstrate the genetic differences between direct genetic effects and social genetic effects and provide an anchor for investigating the genetic architecture underlying direct genetic effects and social genetic effects on socially affected traits in pigs.Socially affected traits in pigs are controlled by direct genetic effects and social genetic effects, which can make elucidation of their genetic architecture challenging. We evaluated the genetic basis of direct genetic effects and social genetic effects by combining single-locus and haplotype-based GWAS on imputed whole-genome sequences. Nineteen SNPs and 25 haplotype loci are identified for direct genetic effects on four traits: average daily feed intake, average daily gain, days to 100 kg and time in feeder per day. Nineteen SNPs and 11 haplotype loci are identified for social genetic effects on average daily feed intake, average daily gain, days to 100 kg and feeding speed. Two significant SNPs from single-locus GWAS  are shared by a significant haplotype locus with haplotype alleles ‘GGG’ for both direct genetic effects and social genetic effects in average daily feed intake. A candidate gene,  Pingxian Wu and Kai Wang et al. perform single-locus and haplotype-based GWAS in Yorkshire pigs to evaluate genetic factors involved in socially affected traits, like feed intake and weight gain. These results indicate several genetic factors that may ultimately be targeted to improve livestock breeding. In current pig production systems, pigs are penned together into contemporary groups, social interactions, and observed phenotypes genetically contribute to socially affected traits of individuals and their group mates2. Socially affected traits are influenced both by the genes of the individual itself  and by the genotype of other individuals in the same group 5. Previous research has proven that many phenotypes are affected by both DGE and SGE and the contribution of SGE to total genetic variance is large in pigs, such as growth rate, body weight (BW)7, feed intake, backfat thickness (BFT), muscle depth2, behavior9, and average daily gain (ADG)11. The existence of SGE affect the estimation of DGE and therefore result in unfavorable consequence on genetic improvement of complex traits. Until recently, the genetic architecture of socially affected traits that involve both DGE and SGE is still poorly understood in pigs.Social interactions inevitably occur among groups of individuals and these interactions influence the genetic variation of a trait in livestock2. Based on the social genetic model, the classical (direct) genetic model is expanded with SGE12. This model simultaneously contains both DGE and SGE that enables researchers to simultaneously estimate DGE and SGE values.With the availability of genomic information, GWAS have increased the understanding of the contributions of genetic variants toward various complex traits. However, previous studies have mainly focused on DGE and have not considered the contributions of SGE. For many phenotypes, DGE and SGE can be directly quantified using a social genetic model13. In pigs, five SNPs on chromosome 6 associated with social ADG were detected with an Illumina panel14. Additionally, in our previous study, a total of 27 SNPs associated with six growth traits were identified using whole-genome sequences (WGS) of 40 Yorkshire pigs 15. However, because of the limited sample size, the study was not efficient in detecting credible associations with DGE and SGE.Detecting the genetic architecture of DGE and SGE may provide deeper insight into a better understanding of complex traits. To date, only a few studies have identified some SNPs associated with SGE. Using the Illumina 60 K SNP BeadChip, an important SNP was identified to be associated with DGE and SGE in chickens16. Therefore, to better investigate the genetic architecture of socially affected traits in pigs, the present study used both GWAS approaches to estimate the DGE and SGE on each trait by employing a social genetic model, WGS of 60 pigs, and genotyping of 1204 Yorkshire pigs with Illumina 50 K SNP chips. Therefore, the main objectives of this study were (i) to construct haplotype loci based on imputed WGS data and identify associations between haplotype-DGE and haplotype-SGE by GWAS; (ii) to perform single-locus analysis to explore SNPs associated with socially affected traits by considering both DGE and SGE in Yorkshire pigs; (iii) to compare the results of GWAS based on haplotypes and SNPs for DGE and SGE; and (iv) to combine single-locus and haplotype-based GWAS to reveal important SNPs and genes for DGE and SGE in pigs.Assessing the impact of SGE on socially affected traits is challenging. Haplotype-based GWAS can capture those genetic variants that are not detected by single-locus GWASSummary statistics for all phenotypic data from 1204 Yorkshire pigs were shown in Table A total of 60 pigs were sequenced and the statistic results were list in Supplementary Data This study conducted genotype imputation from a 50 K SNP chip to WGS data in Yorkshire pigs using a two-breed reference population (20 Landrace and 40 Yorkshire pigs). In total, 36,969 and 14,539,997 SNPs were identified in the target and reference populations, respectively. Beagle 5.1 software was used to impute the WGS reference panel across the 1204 genotyped pigs. After genotype imputation, a total of 14,539,997 SNPs with an average accuracy of 0.63 were available for this study. For each chromosome, the average imputation accuracy was in the range of 0.59–0.67  were calculated and are shown in Fig. 17. The λ calculated for the DGE on ADFI was 1.01; implying no population stratification , days to 100 kg (D100), feed conversion ratio (FCR), residual feed intake (RFI), time in feeder per day (TPD), and feeding speed (FS) traits  for both DGE and SGE on ADFI. A candidate gene, 18. Using the social genetic model12, we estimated the DGE and SGE of each socially affected trait in Yorkshire pigs. Then the single-locus and haplotype-based GWAS were performed to investigate associations for DGE and SGE using imputed data.The socially affected traits are influenced by multiple genes. This study is designed to investigate DGE and SGE for eight socially affected traits using imputed WGS, as there is strong evidence that these traits are socially affected in pigs19. The use of multi-breed reference populations is an effective way to improve the accuracy of imputation20. However, a large genetic distance between the reference and target populations can result in extremely low accuracies (less than 0.49)21. Landrace and Yorkshire pigs are bred according to the same breeding goals and are therefore likely genetically similar22. Additionally, because of the small effective population size for pigs23, the imputation parameter of effective population size was set at 100 in the Beagle 5.1 software. After imputation, the average accuracy at the whole-genome level was 0.63, and 3,072,572 SNPs had a Beagle R2 higher than 0.80 in the imputed WGS dataset. The imputation accuracy observed in the current study was higher than that reported in other studies21, for example, a study using a ten-breed reference population of 168 pigs and using imputation from low- or high-density SNP data to WGS resulted in relatively low accuracy (0.39–0.49)21. Our results were more similar to the accuracy obtained from a multi-breed reference population of cattle (>0.70)20. Thus, the present study indicates that using mixed reference populations with similar genetic backgrounds can result in reasonable imputation accuracy in pigs. The imputation accuracy increased with reference population size24. In this study, a total of 40 sequenced pigs was used. Although the reference population size is smaller than that used in previous studies (>100) for pigs25, this study also obtained a high imputation accuracy, and therefore, the imputed data can provide important information regarding socially affected traits in pigs.Different imputation strategies can result in different levels of accuracy26. Rare variants with a low MAF result in poor imputation accuracy and therefore decrease the average imputation accuracy. In our study, the accuracy decreased sharply when the MAF was lower than 0.10. Filtering out the SNPs with MAF < 0.10 from the imputed data increased the average imputation accuracy from 0.63 to 0.71. In our study, the imputation accuracy increased with the MAF, which is in agreement with previous studies in livestock28. Rare variants are expected to have larger effects on complex traits than common variants29. It is also harder to construct the haplotype background using rare SNPs and they decrease the set of template haplotypes for genotype imputation because they are observed only a few times in reference genotype datasets26. Thus, genotype imputation of rare SNPs does not provide high accuracy levels. It is important to investigate imputation methods for rare variants in further studies.Imputation accuracy is heavily affected by the MAF13, but this method has only identified a small fraction of genetic variants in pigs. Haplotype-based GWAS is a complementary method that intensifies the benefits from linkage disequilibrium (LD) and enables the evaluation of the genetic determinants of complex traits. The use of haplotype information in GWAS would likely be beneficial for detecting further genetic variants30. However, haplotype-based GWAS using imputed WGS has not yet been reported in pigs. The rare variants may contribute large effects to complex traits29. Our analysis were done on imputed sequence data, which may contain imputation errors31. To alleviate this issue, further quality control was also conducted on the imputed sequence genotypes, rare variants with a MAF lower than 0.01 were moved. After quality control, the average MAF is 0.30 for chip-based data, 0.27 for imputed data, and 0.09 for haplotype alleles. Our results show that haplotype-based GWAS can be effective and can provide more genetic information than single-locus GWAS. These results are in agreement with previous studies; for example, haplotype-based GWAS in plants32 and cattle16 captured genetic variants that were not identified by single-locus analysis. Importantly, by combining single-locus and haplotype-based GWAS, we confirmed the presence of two important SNPs  for DGE and SGE in ADFI. Therefore, our results highlight the advantages of haplotype-based GWAS for detecting genetic variants that influence complex traits in pigs. The power of GWAS is limited by sample size and SNP density. Using the imputed WGS instead of the 50 K SNP chip is powerful to investigate socially affected traits in pigs. Although only 1204 imputed WGS were used in this study, our results also provided important genetic variation for socially affected traits. Further studies are recommended to validate these results using a larger sample size.In the processes of natural selection and the artificial selection of pigs, complex traits have developed high levels of genetic variation. The single-locus analysis focuses on identifying SNPs and mining functional genes for complex traits33. However, these studies have mainly focused on investigations of DGE, despite the majority of complex traits in pigs being affected by social interactions among individuals2. Pigs are highly social animals, so ignoring SGE could incorrectly estimate genetic variance and restrict genetic improvement.Socially affected traits are known to be affected by SGE, but the mechanisms of SGE are not fully known. Many studies have been conducted to understand the genetic architecture of complex traits and they have identified many important SNPs and genes associated with these traits10. However, few studies have investigated the genetic architecture of DGE and SGE in pigs34. On the basis of our GWAS results, we found that the significant loci and candidate genes of DGE are different from SGE for each trait in Yorkshire pigs. These results are in agreement with previous studies, for example, a distinct difference was found between DGE and SGE for socially affected traits in laying hens13, and a study based on single-step GWAS found only one QTL (SSC6: 19.9–20.9 Mb) for DGE and SGE on ADG in pigs34. The population composition may affect the estimation of DGE and SGE. An experimental design using groups comprising two families is optimal for estimating SGE35. Our group’s composed of numerous families because all pigs in our study were from a commercial pig performance testing station. The group design in this study may have affected the estimation of DGE and SGE; however, it is difficult to conduct an experiment containing only two families from a commercial breeding program. Moreover, in our study, all pigs were derived from the same herd and there were 20 pigs in each group, which made it easier to estimate DGE and SGE5.Recently, numerous studies have shown that using social genetic models during genetic evaluation can enhance the genetic improvement of socially affected traits in pigsOur results provide further evidence for the genetic differences in DGE and SGE with regard to socially affected traits. Economically important traits are selected by artificial selection when based on the classical model that only considers DGE. SGE is, therefore, less frequently selected when compared with DGE. The complex traits in pigs are highly polygenic, and more research is required to understand the genetic architecture of the SGE on the complex traits of pigs.12. Thus, the investigation of the genetic architecture of SGE has important implications for pig breeding programs.To assess whether the identified SNPs associated with socially affected traits in our study replicate previously identified QTL, we compared our results with the PigQTL database based on the genome position of each SNP and QTL. A total of 78 important QTL were found to overlap with previously identified QTL for DGE of complex traits in pigs  between group pigs would influence their health. Interestingly, the significant SNPs within the region SSC6: 45.95–46.95 Mb were located within 20 QTL known to be associated with health traits50 in pigs .In the region of 45.95–46.95 Mb on SSC6, numerous zinc-finger protein family genes were found to be associated with both DGE and SGE. The zinc-finger protein is one of the most abundant classes of transcription factors and regulates cell growth and differentiation. According to our gene ontology (GO) analysis, these genes regulate transcription, nucleic acid binding, and metal ion binding. Furthermore, this chromosome region overlapped with four reported QTL affecting ADGMT3 gene was found in both single-locus and haplotype-based GWAS for socially affected traits. This gene encodes a growth inhibitory factor that regulates many biological processes, particularly within the nervous and immune systems, and thereby influences health. MT3 plays an important role in zinc homeostasis and cell death51, and inhibits cell growth under zinc-deficient conditions52. Interestingly, our study also identified numerous zinc-finger protein family genes to be associated with DGE and SGE in SSC6: 45.95–46.95 Mb.In the region of SSC6: 18.14–19.14 Mb, 34. The QTL (SSC6: 18.14–19.14 Mb) in the current study was close to the reported QTL (SSC6: 19.9–20.9 Mb) for both DGE and SGE on ADG in pigs34. Furthermore, there were eight reported QTL associated with growth traits in the region of SSC6: 18.14–19.14 Mb. Among them, five QTL were related to ADG53 and feeding intake54. The significant SNPs associated with DGE and SGE are located in nine reported QTL for health traits46. And, it was reported that three QTL located on SSC6: 18.14–19.14 Mb in pigs were associated with social interaction traits, and health traits55.The previous studies identified several QTL on SSC6 for DGE and SGE in pigsIn summary, we report the study employing combined single-locus and haplotype-based GWAS to identify the genetic architecture of socially affected traits that are influenced by both DGE and SGE in pigs. Our study shows the feasibility of mapping genomic variants that underlie SGE and provides genomic information for socially affected traits in pigs. These results provide evidence that the genomic architecture of SGE and DGE is different for socially affected traits. Hence, we recommend the use of both DGE and SGE to evaluate genetic architecture for socially affected traits in pigs.All experimental procedures were performed in accordance with the Institutional Review Board (IRB14044) and the Institutional Animal Care and Use Committee of the Sichuan Agricultural University under permit number DKY-B20140302.During the period of 2017–2019, phenotypic data were collected for Yorkshire pigs from the national nucleus pig breeding farm of New Hope Group, Co., Ltd.  using the Osborne FIRE Pig Performance Testing System . As target population, a total of 1204 Yorkshire pigs were placed in a temperature-controlled room at 25 ± 2 °C and relative humidity of 65–80% during the period of performance test from 30 to 110 kg. As reference population, a total of 60 pigs (20 Landrace and 40 Yorkshire pigs) were randomly selected from core populations. These pigs were group-housed in cement-floor pens (20 pigs in each pen) and the nutrient requirements were met as recommended by the National Research Council (NRC 2012).57. FCR, D100, B100, and RFI were calculated as follows58:59. The true days and B100 were firstly obtained based on two data that were the closest to 100 kg using linear interpolation. Then the nonlinear models of D100 and B100 were constructed based on linear interpolation as models (2) and (3). Finally, the A and B were calculated based on models (2) and (3) using the NLIN procedure in SAS software, respectively. BFT is the tested backfat thickness at the end of a performance test. AMW is an average metabolic BW.A total of 1112 pigs’ phenotypic data were collected, including ADG (kg/d), D100, B100, ADFI (kg/d), RFI, FCR, TPD (min/d), and FS (g/min). In this study, each pig was labeled with a unique electric identification tag on ear that was detected by the Osborne FIRE Pig Performance Testing System. The feed time, feed consumption, and BW were recorded at each visit to the feeder for each pig. At the end of the performance test, BFT between the third and fourth last ribs of each pig was calculated by PIGLOG 105B ultrasound machine . ADG was calculated as linear regressions of BW from 30 to 110 kg on the performance test days. ADFI was calculated based on the total amount of recorded total feed intake (TFI) divided by the number of corresponding feed days at the feeder. TPD was calculated based on the total amount of recorded total time divided by the number of corresponding feed days (min/d), and FS = ADFI/TPD (g/min)12, as follows:60. The estimated accuracies of DGE and SGE were calculated based on the formula 61. Then, the deregressed EBVs were used to implement for association analysis.The DGE and SGE were estimated for eight socially affected traits using a social genetic effect modelThe ear tissue samples were collected and stored in 75% alcohol. Genomic DNA from 1264 ear tissues was extracted using the Tissues Genomic DNA  kit according to the manufacturer’s instructions, and then the quality and quantity were measured using a Nanodrop-2000 spectrophotometer. The genomic DNA with the ratio of light absorption (A260/280) between 1.8 and 2.0, concentration ≥50 ng/µL, and total volume ≤50 µL were eligible.62. The SNPs with call rate < 0.90, MAF < 0.05, and Hardy–Weinberg equilibrium test (HWE) <10−6, were excluded from the dataset. After quality control, a total of 1854 SNPs were removed. Finally, a total of 36,969 SNPs were used as target genotype data.A total of 1204 Yorkshire pigs, 442 boars and 762 gilts, were genotyped by the Illumina Porcine 50K SNP Chip , which contained 50,697 SNPs. Firstly, the SNPs with no position information and located on sex chromosomes were removed from the genotype data, which contains 11,874 SNPs. Then, quality control of the genotype data was performed using PLINK softwareN reads (where N > 10% of one read) to produce clean reads by FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/). Then the clean reads were mapped to the pig reference genome (Sscrofa11.1) using BWA (version 0.7.15) software with the parameters mem -t 10 -k 32 -M63. The SAM files produced from BWA were converted to BAM files using SAMtools (version 1.19)64. The potential PCR duplicates were removed by MarkerDuplicates utility in Picard release 1.119 (https://sourceforge.net/projects/picard/files/picardtools/1.119/). After that, BAM files were used to call SNPs using GATK (version 3.5) software65 with multi-sample approaches. The raw SNPs generated from GATK were filtered with QualByDepth (QD) < 2.0, FisherStrand (FS) < 60.0, RMSMappingQuality (MQ) < 40.0, MappingQualityRankSumTest < –12.5, and ReadPosRankSumTest < –8.0. After the initial filtering, a total of 21,104,245 SNPs remained. For further analyses, the SNPs with MAF >0.05, missing rate <0.1, HWE <10−6, read depth (dp) >6, and the SNPs located on autosomes were considered as reference genotype data.The WGS reference data were obtained for 60 pigs (20 Landrace and 40 Yorkshire pigs), with 150 bp paired-end reads on the Illumina HiSeq PE150 platform. The sequencing was performed by BGI Co., Ltd. . After sequencing, the quality of raw reads was checked with a Phred score of 20 as the minimum to filter the adapter polluted reads and multiple 66 with default parameter settings, except for setting the effective population size to 10022. The imputation accuracy of each SNP was assessed using the Beagle R2, which is the estimated squared correlation between the estimated allele dosage and the true allele dosage67. A two-breed reference population (20 Landrace and 40 Yorkshire pigs) with a small genetic distance22 was used to perform genotype imputation from 50 K SNPs to WGS. After imputation, to maintain a balance between the average accuracy and the number of SNPs, SNPs with a Beagle R2 < 0.8 were excluded. Furthermore, SNPs with MAF less than 0.01 and HWE less than 10−6 were removed from imputed data. Finally, a total of 3,072,572 SNPs were retained for further analyses.Genotype imputation between target and reference genotype data were performed by Beagle (version 5.1)68 using PLINK v1.90 software62. The parameters were set to “-blocks no-pheno-req -blocks-max-kb 1000 -blocks-strong-lowci 0.8 -geno 0.1”. A haplotype block containing two or more SNPs with high LD was defined, and loci with a confidence interval of r2 higher than 0.8 were considered into one block. Then, the haplotype calling and identification of haplotype alleles were performed by GHap package69. Furthermore, haplotype loci were transformed into multi-allelic markers, and the haplotype genotype matrix was used to perform GWAS. Using the imputed data with 3,072,572 SNPs, a total of 33,708 haplotype loci were constructed and each haplotype loci contained 34.8 alleles. Finally, 274,741 haplotype alleles with MAF >0.01 were retained.Haplotype loci were identified from the imputed WGS data in 1204 pigs. Haplotype loci were detected for each chromosome as proposed by Gabriel et al.65. Before association analysis, the centered genotypes were used to estimate the n × n genomic relationship matrix between the individuals. The genomic relatedness matrix was calculated as follows:n × p matrix of genotypes; n × 1 vector of 1’s.GWAS were performed independently for eight socially affected traits by considering both DGE and SGE using GEMMA softwareG is a genomic relationship matrix, The deregressed EBVs were used to perform single-locus GWAS (including chip-based GWAS and imputed-GWAS) and haplotype-based GWAS, the following unified mixed linear model was used:70. For chip-based GWAS, the genome-wide significant and suggestive levels were set as P = 0.05/N1 = 1.35 × 10−6 and P = 1/N1 = 2.70 × 10−5, respectively, where N1 is the number of analyzed SNPs. For imputed-GWAS, the genome-wide significant and suggestive levels were set as P = 0.05/N2 = 1.63 × 10−8 and P = 1/N2 = 3.25 × 10−7, respectively, where N2 is the number of analyzed SNPs. For haplotype-based GWAS, the genome-wide significant and suggestive levels were calculated as P = 0.05/N3 = 1.82 × 10−7 and P = 1/N3 = 3.64 × 10−6, respectively, where N3 is the number of studied haplotype alleles.The genome-wide significant threshold value was determined using the Bonferroni correction method71. Genomic inflation factor (λ) was calculated to judge the extent of false-positive signals with estlambda function in GenABEL package (72.The Manhattan plots were drawn using qqman packagehttps://asia.ensembl.org/Sus_scrofa/Info/Index). Then, the biological functions of these candidate genes were investigated on PubMed (https://www.ncbi.nlm.nih.gov/pubmed) and the reported literature. For functional annotation, GO analysis was performed on DAVID Bioinformatics Resources (https://david.ncifcrf.gov). The Fisher’s test was used to assess the significance of the determined enriched terms74. Enriched GO terms (P < 0.05) were selected to investigate the genes involved in biological processes75.The region within a 1 Mb region centering each significant SNP was defined as the QTL region. The candidate functional genes were searched within the identified QTL regions on the pig genome assembly 11.1 (Further information on research design is available in the Supplementary Data 1-2COMMSBIO-21-0181B-nr-editorial-policy-checklistSupplementary InformationDescription of Additional Supplementary FilesReporting Summary


The proposed DLR model based on conventional CT images showed the outperforming performance over the HCR signature model in noninvasively individualized prediction of the 3-year survival rate in esophageal cancer patients. The proposed model can potentially provide prognostic information that guides and helps the clinical decisions between observation and treatment.The purpose of this study was to explore the deep learning radiomics (DLR) nomogram to predict the overall 3-year survival after chemoradiotherapy in patients with esophageal cancer. The 154 patients' data were used in this study, which was randomly split into training (116) and validation (38) data. Deep learning and handcrafted features were obtained via the preprocessing diagnostic computed tomography images. The selected features were used to construct radiomics signatures through the least absolute shrinkage and selection operator (LASSO) regression, maximizing relevance while minimizing redundancy. The DLR signature, handcrafted features' radiomics (HCR) signature, and clinical factors were incorporated to develop a DLR nomogram. The DLR nomogram was evaluated in terms of discrimination and calibration with comparison to the HCR signature-based radiomics model. The experimental results showed the outperforming discrimination ability of the proposed DLR over the HCR model in terms of Harrel's concordance index, 0.76 and 0.784, for training and validation sets, respectively. Also, the proposed DLR nomogram calibrates and classifies better than the HCR model in terms of AUC, 0.984 (vs. 0.797) and 0.942 (vs. 0.665) for training and validation sets, respectively. Furthermore, the nomogram-predicted Kaplan–Meier survival (KMS) curves differed significantly from the nonsurvival groups in the log-rank test ( Esophageal cancer (EC) is the eighth most common malignancy and the sixth most common disease-related cause of death worldwide , 2. The Radiomics features , 12 haveDeep learning, especially CNN, has recently achieved promising results in medical image analysis –18. DeepHowever, unlike handcrafted radiomics studied widely for radiological diagnosis and prediction , the appIt is worthwhile to highlight three aspects of the contributions here. (1) This study investigated DLR features in the survival prediction of patients with esophageal cancer. Unlike the traditional handcrafted features, clinical target-oriented DLR features can be automatically learned from data. (2) Both HCR features and DLR features are considered in the prediction model to characterize the esophageal lesions thoroughly. The main reason is that the DLR and HCR can describe imaging heterogeneity of esophageal lesions in different levels. In particular, HCR comprise shape features, first-order statistics, and texture features; DLR contains the “real-world” textures, which are extracted from the pretrained DenseNet-169 network via transfer learning strategy. (3) This study develops a noninvasive predictive model that combines deep learning-based radiomics features, handcrafted features, and clinical factors to predict survival rates within three years at diagnosis of esophageal cancer. The DLR nomogram survival prediction of esophageal cancer patients can allow more proper treatment. The experimental results showed that the DLR nomogram outperforms the HCR model and the clinical model.Esophageal cancer patients at Shanxi Cancer Hospital were the subject of our retrospective study. The patients were included according to the inclusion criteria: (a) patients who had pathologically confirmed esophageal cancer, (b) a standard CT scan performed before any treatment, and (c) clinical characteristics available. The patients were excluded with the following criteria: (a) too poor CT image quality, which may affect the diagnosis of the patient, (b) patients who had chemotherapy treatment at another institution, and (c) patients who are also suffering from other cancers.The survival group includes patients who survived more than three years since the treatment, whereas the nonsurvival group includes patients who died within three years. A total of 154 esophageal cancer patients diagnosed from November 2012 to February 2015 participated in our retrospective study. Those data were grouped into two sets: training (116) and validation (38) data at a ratio of 3 : 1.Baseline clinical data were collected via the electronic medical record system (EMRS) , includiGeneral Electric Light Speed RT16 was used for scanning, with a CT thickness of 5 mm. The primary tumor volumes for radiotherapy planning were set as the ROI to quantitatively analyze the images. Two skilled radiologists manually selected the three-dimensional tumor ROI using the software package 3D Slicer .2 pixel size, and the resampling with cubic interpolation to 1 × 1 × 1 mm3 pixels was conducted, minimizing CT images variabilities [Training CT images were preprocessed to avoid accuracy degradation of DL models caused by noises introduced with the interval change, which include resampling, rescaling, and voxel normalization. Those CT images were reconstructed with a matrix of 512  × 512 and 0.5 × 0.5 mmbilities .The tumor area was located with a rectangle bounding box that covers the primary tumor area. The ROI for each patient was obtained with three cropped consecutive slices to avoid the bias of manual segmentation that affects the location of a bounding box. Lastly, the tumor image was resized to 224 × 224 × 3 voxels.Phenotypic differences between tumors can be captured by a large number of quantitative radiomics features. In this study, deep learning features and handcrafted features were extracted to quantify tumor phenotype to enhance the learning efficiency of the radiomics model. Those two feature sets have complementary advantages that can be combined to improve the model. Also, expert knowledge on the esophageal cancer lesion can be reflected with shape and texture features. On the contrary, the high-level DLR features can significantly represent complex spatial features in both global and local perspectives.The handcrafted feature extraction algorithm was standardized by referring to the Image Biomarker Standardization Initiative (IBSI) –34 and Rhttps://keras.io/) with the TensorFlow library as the backend (https://www.tensorflow.org/). As depicted in The DenseNet-169, designed for the image classification task, was adopted to extract DLR features. In the training cohort, data augmentation approaches including random rotation, random shear, and random zoom were employed before the training procedure. The deep learning model was pretrained on the ImageNet dataset, one of the largest image datasets, and then fine-tuned in a transfer learning strategy to avoid the overfitting problem . The netp < 0.05) were obtained through the Mann–Whitney U (MWU) test. Then, the features were sorted based on the mutual information (MI) between features and the survival status using the minimum redundancy maximum correlation (mRMR) scheme [In order to obtain the most effective feature, three stages of feature selection were carried out. First, features  scheme . It shou) scheme . The surA DLR nomogram was built by integrating DLR signature, HCR signature, and clinical features with a multivariable logistic regression model. Backward stepping selection was used with information criterion of Akaike as the stopping rule . The varHarrel's C -index was employed to evaluate the discrimination ability of the DLR nomogram in both training and testing datasets. The bootstrap method was used to resampling 1,000 times, and the C index in both cohorts was calculated with 95% confidence intervals. The AUC, accuracy, specificity, and sensitivity were calculated on the plotted ROC curves. The calibration ability of the DLR nomogram was evaluated using the calibration curve that depicts the consistency between predicted and actual survival probabilities. Hosmer–Lemeshow (HL) test  and decihttp://www.Rproject.org). MWU and Chi-square tests were adopted for univariate analysis, and Spearman's correlation rank was employed for correlation. The penalty parameter (λ) was tuned by LASSO logistic regression model. This study used the following packages for each analysis. “glomnet” package: LASSO logistic regression, “rms” package: nomograms and calibration plots, “ResourceSelection” package: HL test, “car” package: VIFs calculation, “survivalROC” package: AUC analysis, “survminer” package: KMS analysis, and “dca.R” function: DCA performance. This study utilized a bilateral statistical significance level p value <0.05.All the statistical analyses were conducted with R software  for two data shows no significant observable difference in the survival rate, 30.2% and 36.8%, for training and validation, respectively.λ) selection and coefficients of LASSO are given in Supplementary Material .The results show that scores of the survival group were higher than the nonsurvival group with a significant difference in terms of DLR signatures (1.10 ± 0.75 vs. −2.22 ± 0.75) in the training cohort and (0.15 ± 1.11 vs. −1.90 ± 1.12) in the validation cohort. MWU test was used with a p value <.001, MWU test, and in the validation data, 0.80 ± 1.26 vs. −0.23 ± 1.48, p value = 0.035, MWU test. HCR feature selection by LASSO regression is described in detail in Supplementary Material  showed no deviation from the ideal ﬁt. In the validation cohort, the 3-year survival rate was also well-calibrated .p value <.05, log-rank test) between prediction survival and nonsurvival groups was found in KMS curves . The proposed model can present the basis for clinicians to make better treatment decisions and personalized diagnoses. Future works will include the model improvement based on larger data and complementary clinical factors.This study details the development of a noninvasive predictive model that combines deep learning-based radiomics features, handcrafted features, and clinical factors to predict survival rates within three years at diagnosis of esophageal cancer. The performance of the proposed DLR nomogram is superior to the traditional radiomics model in terms of Harrel's concordance index and AUC. The calibration curves show the good prediction performance of the nomogram. The nomogram-predicted Kaplan–Meier survival (KMS) curves differed significantly from the nonsurvival groups in the log-rank test (


Idiopathic olfactory loss (IOL) is thought as an early marker for neurodegenerative disease. This study investigated the effect of olfactory training (OT) on regional gray matter volume (GMV) among patients with IOL. A total of 24 patients  with IOL and 30 control participants with normal olfaction  were included in the study. Voxel-based morphometry was performed to compare the GMV between patient and control groups. Only the patients received OT (averaged duration 7 months), and a longitudinal approach was used to examine the GMV change from pre- to post-OT. Moreover, the effect of OT on GMV change was explored for patients with different severity of olfactory loss (anosmia vs. hyposmia). Olfactory performance was measured alongside using the “Sniffin’ Sticks.” Patients had improved odor identification and larger GMV in the bilateral cerebellum, bilateral thalamus, left precentral gyrus, right gyrus rectus, and medial orbitofrontal cortex after OT. However, no correlation was found between changes of odor identification and increased regional GMV. Besides, patients with anosmia, compared with patient with hyposmia, demonstrated increased GMV in the left precuneus, left superior frontal medial cortex, and left midcingulate cortex after OT. The study showed improved odor identification ability among patients with IOL after OT, which is unlikely related to spontaneous recovery. In this specific patient group, the GMV alterations may be associated with factors not directly predicted by the currently performed measurements, but possibly higher order olfactory-related functional changes.  Olfactory dysfunction is an increasingly recognized form of sensory impairment. It is estimated that one fifth of the general population has a decreased sense of smell including approximately 5% with little or no olfactory function . Apart fAcquired olfactory loss is related to alterations of the central olfactory system, including but not limited to attenuated brain responses to odor stimuli, reduction of gray matter volume (GMV) and white matter volume, or decreased olfactory bulb (OB) size . Cross-sAmong available treatments, the olfactory training (OT) has been suggested to be an effective way to improve olfactory performance . The humThe current study was aimed to investigate the effect of OT on olfactory performances and brain regional GMV of patients with IOL. Moreover, the study also explored whether the effect of OT will differ as a function of the olfactory loss severity .SD = 10.2, range from 44 to 79 years; 11 males) and 30 control participants  were included in the final study. Olfactory loss was due to an unidentifiable cause after an extensive clinical examination, including detailed and structured history, nasal endoscopy, documentation of missing responsiveness to topical or systemic steroids (indicating absence of inflammatory causes of olfactory loss), and detailed evaluation of smell and taste function. The patients had an average olfactory loss duration of 4.9 years (range from 1 to 17 years). Olfactory function was tested using the “Sniffin’ Sticks” test battery  and 12 had idiopathic anosmia . One patient was diagnosed with phantosmia, one patient with parosmia, and one with both parosmia and phantosmia. The control and the patient groups matched in terms of age, t(52) = 0.54, p = .59, and sex distribution, χ2 = 0.034, p = .54. The hyposmia and anosmia patient subgroups matched in terms of age, t(22) = 0.59, p = .50, and sex distribution, χ2 = 0.17, p = .68. This study was approved by the Ethics Committee at the Medical Faculty of the TU Dresden. All participants gave informed, written consent.Twenty-six patients with IOL and 32 control participants were initially included in the study. The following participants were excluded: one patient with normal olfaction (Sniffin’ Sticks TDI score = 30.75); one patient with blurred raw magnetic resonance imaging (MRI) image; one control participant with abnormal olfactory function (TDI = 21.5); and one control participant with corrupted MRI images. Therefore, 24 patients with IOL  with a 12-channel phase-array head coil. Structural T1-weighted images of the participants were acquired using a 3D magnetization prepared gradient rapid acquisition gradient echo (MPRAGE) sequence, with the following parameters: TR 2530 ms; TE 2.34 ms; TI 1100 ms; field of view 256 mm; voxel size 1 × 1 × 1 mm; flip angle 7°, and 192 contiguous slices of 1 mm thickness. Images were acquired in the axial plane oriented parallel to the planum sphenoidale to minimize artifacts.http://dbm.neuro.uni-jena.de/vbm/) implemented in SPM12  and MATLAB . T1 images were first segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid. A two-step segmentation approach was applied: First, the normal segmentation in CAT12 was used for T1 images from the control group and the patient group before OT; second, the T1 images from the patient group (pre- and post-OT) was segmented using the method for longitudinal data in CAT12.Voxel-based morphometry of T1-weighted images was performed using the CAT12 software  space using the high-dimensional Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra DARTEL; . The quat-test model in SPM12, including TIV as covariate. To compare the GMV of the patient groups between the pre-OT and post-OT sessions, a flexible factorial design was specified, including the subject factor  and the training factor . Besides, to investigate the varied effect of OT on GMV change in patients with different severity of olfactory loss, a flexible factorial design was specified, including the subject factor , the severity factor , and the training factor . For longitudinal models, age, TIV, and the training time (in month) were included as covariates. As covariate inclusion during voxel-based morphometry analyses has strong impact on the results  was considered as statistically significant  empirically determined by Monte Carlo simulations , by means of AlphaSim procedure . TDI scores for patients and controls in the pre-OT session were compared using one-way analysis of variance (ANOVA). The odor threshold, odor discrimination, and odor identification scores between the pre- and post-OT sessions in the patient group were analyzed using three by two repeated-measure ANOVA. A Bonferroni correction was used for post hoc tests to control for Type-1 errors due to multiple comparisons. The alpha level was set at F = 44.91, p < .001; odor discrimination, F = 37.29, p < .001; and odor identification, F = 121.74, p < .001  = 4.46, p = .02. The post hoc analysis showed that the odor identification score was significantly improved after OT among the patient group  = 12.30, p = .002), but not for odor threshold or odor discrimination scores . However, no significantly larger GMV in control participants when compared with patient participants was observed.After OT, increased GMV was found in the bilateral cerebellum, the bilateral thalamus, the left precentral gyrus and thalamus, also in the right superior frontal cortex, right supplementary motor area, right medial OFC, and the right gyrus rectus . The oppA significant Severity × Training interaction effect was found, showing that patients with anosmia compared with patient with hyposmia had increased GMV in the left precuneus, left superior frontal medial cortex, and the left midcingulate cortex after OT . Howeverp > .1 for all Pearson’s correlation tests) between the increased GMV in the identified clusters and the change of odor identification scores among patients.There was no significant correlation  are comparable to previous findings among olfactory-loss patients with mixed causes who received OT .The patients showed significantly improved odor identification ability after OT, which is congruent with the literature on the effectiveness of OT for patients with olfactory dysfunction: OT mainly improves performance in higher order tasks such as odor identification or discrimination rather than odor detection thresholds . BecauseSeveral other brain regions that showed increased GMV after OT are implicated in higher order olfactory-related processes. For example, the thalamus is associated with olfactory attention . The fusMoreover, compared with patients with idiopathic hyposmia, patients with idiopathic anosmia showed significantly increased GMV in the precuneus, the superior frontal medial cortex, and the midcingulate cortex after OT, all in the left hemisphere. Although not characterized as the main olfactory areas, these regions were suggested in relation to olfactory memory . This fiSeveral study limitations need to be noted. First, the study lacks an OT control group; therefore, it is difficult to determine the causes for the post-OT GMV changes (including reduction or increase) observed in the patient group. A preferable design is to have a separate group of patients being scanned twice at the same time points but without having conducted OT. A further limitation is related to the compliance to OT. For example, the long duration of training increases the chances of losing participants with the attrition rate as high as 45% in some 6-month studies , althougIn conclusion, the current study showed that in a group of patients with IOL, OT is associated with improved odor identification ability and regional GMV in several brain areas. However, in this specific group of patients, the alteration of GMV may be associated with factors not predicted by the currently performed measurements but likely to be mediated by higher order brain functional changes related to odor-related memory, or attention.


In total, 411 patients who underwent preoperative FDG PET/computed tomography (CT) within 1 month of surgery were enrolled. The mean standardized uptake values of the bone marrow and spleen were normalized to the value of the liver, thereby generating bone marrow-to-liver uptake ratio (BLR) and spleen-to-liver uptake ratio (SLR) estimates. The value of BLR and SLR in predicting overall survival (OS) was assessed using the Cox proportional hazards model. The correlation between BLR or SLR and neutrophil-to-lymphocyte ratio (NLR) was evaluated. The predictive accuracy of BLR alone and in combination with SLR was compared using the integrated area under the receiver operating characteristic curves (iAUC). In the univariate analysis, BLR (> 1.06) and SLR (> 0.93) were significant predictors of OS. In the multivariate analysis, BLR was an independent predictor of OS . Both BLR and SLR were correlated with NLR (p < 0.001). A combination of BLR and SLR was better than BLR alone at CRC prognostication . FDG uptake estimates in the bone marrow and spleen may be useful imaging-derived biomarkers of systemic inflammation, supporting CRC prognostication.Serum inflammatory markers are used in the prognostication of colorectal cancer (CRC); however, the corresponding role of positron emission tomography (PET)-derived inflammatory markers remains unclear. This study aimed to investigate the prognostic value of  Although incidence and mortality rates remain high, survival outcomes of patients with CRC have improved owing to advances in surgical techniques, advent of improved chemotherapeutic drugs, and refinements in radiation therapy2. Reliable prognostic biomarkers and accurate pretreatment evaluation tools are also paramount to the management of CRC.Colorectal cancer (CRC) is among the most common causes of death worldwide18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used in CRC staging, treatment response assessment, recurrence diagnosis, and prognostication3. PET/CT is mostly used for primary lesion assessments; however, it can also be used to investigate non-oncologic pathologies such as infection and inflammation4. Recent studies have implicated inflammation as a critical part of tumor microenvironment in various types of cancer, where it affects tumor development, survival, and response to treatment5. Consequently, systemic inflammatory surrogate markers such as C-reactive protein levels, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio have been investigated as candidate predictors of CRC prognosis8. Meanwhile, clinical studies have reported a positive correlation between FDG uptake in the bone marrow (BM) and spleen on PET/CT and the levels of serum inflammatory and immune markers11, suggesting these parameters may be useful in evaluating inflammation and CRC prognostication.13. Thus, this study aimed to investigate the prognostic value of FDG uptake in the BM and spleen in patients with CRC, along with other clinical and pathologic factors, and evaluate the relationship between FDG uptake estimates in these organs and serum inflammatory markers.However, the relevance of PET-derived inflammatory markers  remains unclear in patients with CRC; to date, only two studies have reported regarding the prognostic value of FDG uptake in the BM and spleenPatients who underwent preoperative PET/CT examinations, followed by curative surgical resection of stage I–IV CRC between November 2007 and November 2013 were enrolled in this study. Patients were eligible for the present study if they met the following criteria: (1) pathologically confirmed stage I–IV CRC; (2) availability of preoperative PET/CT scans; and (3) surgery with curative intent, including resection of distant metastasis. Patients were excluded from the present study if they met the following criteria: (1) baseline PET/CT examinations performed more than 1 month before the surgery; (2) diagnosis of hereditary nonpolyposis CRC, ulcerative colitis, or Crohn’s disease; (3) diagnosis of another acute inflammatory disease, or (4) unmeasurable target organ or tumor uptake  levels were assessed; colonoscopy, chest and abdominopelvic CT, and PET/CT were performed for all enrolled patients. The American Society of Anesthesiology (ASA) grade and body mass index (BMI) were recorded. NLR was based on the white blood cell count. The median interval between baseline PET/CT and surgical resection was 6 days (interquartile range [IQR] 3–10 days). After a preoperative staging workup, all patients underwent curative surgery, including resection of distant metastasis. The T and N histopathologic stages were determined according to the 7th American Joint Committee on Cancer (AJCC) staging guidelines. The presence of lymphovascular invasion (LVI), microsatellite instability (MSI), and KRAS mutations was evaluated using surgical specimens. Methodological details of MSIThe study protocol adhered to the ethical standards of the institutional and/or national research committees and 1964 Helsinki Declaration and its later amendments. The institutional review board of the Gangnam Severance Hospital, Yonsei University College of Medicine, approved this study and waived the requirement for written informed consent owing to the retrospective study design.All patients were asked to fast for at least 6 h before undergoing PET/CT, which was performed when blood glucose levels were < 150 mg/dL. Sixty minutes after intravenously administering FDG (5.5 MBq/kg of body weight), PET/CT was performed using a hybrid PET/CT scanner . Low-dose CT data were first obtained for attenuation correction using the automatic dose modulation with a reference of 40 mA and 120 kV without contrast enhancement. The PET data were then acquired from the skull base to the proximal thigh for 3 min per bed position in a three-dimensional mode. The PET images were reconstructed using the ordered subset expectation maximization with attenuation that involved two iterations and 21 subsets. The matrix size and thickness of the reconstructed PET images were 128 × 128 mm and 5 mm, respectively.max) of the primary tumor was calculated as follows: (decay-corrected activity per milliliter of tissue volume)/(injected FDG activity per gram of body mass). Subsequently, FDG uptake values in the BM, spleen, and liver were measured. For the BM, the spheroid VOIs were drawn over at least five vertebral bodies of the lumbar and lower thoracic spines. Vertebrae with severe compression, osteoporosis, or postoperative change were excluded from the measurements. For each VOI, an isocontour set at 75% of SUVmax was automatically generated and the mean value of the voxels within the isocontour was measured and defined as the BM SUV. For the spleen SUV, one spherical VOI of size 1.5 cm in diameter was placed at the center of the spleen, and the mean SUV was measured. Finally, for liver measurements, a spherical VOI of 2 cm was placed in the right lobe of the normal liver, and the mean SUV was then measured. The tumor-to-liver uptake ratio (TLR), BM-to-liver uptake ratio (BLR), and spleen-to-liver uptake ratio (SLR) were then calculated by dividing the SUVmax of the primary tumor, BM SUV, and spleen SUV each by the mean SUV of the liver.PET/CT images were retrospectively assessed by two board-certified nuclear medicine physicians who were blinded to patient outcomes. Discrepancies in assessments between the physicians were resolved by consensus. First, a volume of interest (VOI) was manually drawn around the primary tumor, taking care to avoid physiologic FDG uptake, especially in the urinary bladder and both urinary tracts. The maximum standardized uptake value , defined as the time between the day of imaging acquisition and death owing to any cause or the last follow-up date. The Cox proportional hazards regression model was used to assess the association between variables of interest and OS in the univariate and multivariate analyses. Variables with To explore the clinical utility of BLR and SLR as a combined variable, patients were divided into four groups: low BLR and low SLR (group 1), low BLR and high SLR (group 2), high BLR and low SLR (group 3), and high BLR and high SLR (group 4). Differences in the levels of serum inflammatory markers and OS estimates were evaluated among these four groups. Moreover, the accuracy of PET-derived biomarkers in predicting OS was compared between BLR alone and in combination with SLR using an integrated time-dependent area under the curve (iAUC). Differences in iAUC estimates between the PET-derived parameters were measured using bootstrapping with a resampling number of 1,000.p values < 0.05.Continuous variables are reported as medians with an IQR, unless otherwise specified. Spearman’s correlation coefficients were calculated to assess the correlation between two variables. The Mann–Whitney U test and Kruskal–Wallis test were used to compare median values among groups. All statistical analyses were conducted using GraphPad Prism 8 . Statistical significance was set at A total of 411 (247 men) patients with CRC were included in this study , AJCC stage, MSI, KRAS mutation status, NLR, TLR, BLR, and SNR was assessed in the survival analysis. The optimal cut-off values for BLR and SLR were determined based on the X-tile program, which yielded 1.06 for BLR and 0.93 for SLR , ASA grade (p = 0.002), BMI (p = 0.001), CEA (p = 0.001), tumor size (p = 0.023), LVI , number of retrieved LNs (p = 0.031), AJCC stage , postoperative chemotherapy (p < 0.001), BLR (p < 0.001), and SLR (p = 0.030) were significantly associated with OS (Table p = 0.965). The Kaplan–Meier method revealed that patients with higher BLR (> 1.06) had shorter OS than those with lower BLR , ASA grade , BMI (p = 0.002), LVI , number of retrieved LNs (p < 0.001), AJCC stage , chemotherapy (p = 0.002), and BLR (p < 0.001) were independent predictors of OS . The high BLR group demonstrated higher NLR than the low BLR group; the median LNRs were 3.16  and 2.39 (2.22) for the high BLR and low BLR groups, respectively (p < 0.001)   Fig. A. The hi01) Fig. B.p < 0.001). Post-hoc analyses revealed that patients with BLR of > 1.06 and SLR of > 0.93 (group 4) had poorer prognosis than those with BLR of ≤ 1.06 and SLR of ≤ 0.93 (group 1) (p < 0.001) (Supplementary Table p = 0.001)  , low BLR and high SLR group 2, n = 50), high BLR and low SLR , and high BLR and high SLR group 4, n = 34) groups. The univariate analysis demonstrated a significant difference in OS among the four groups , suggesting that it may help identify patients at high risk of death and those requiring close monitoring or anti-inflammatory therapy. These findings may be associated with the distinct roles of these two organs in hematopoiesis, suggesting that the assessment of FDG uptake in the BM and spleen may help predict patient outcomes. However, in the present sub-group analysis, group 3 (n = 18) and group 4 (n = 34) were both smaller than group 1 (n = 309) and group 2 (n = 50), which may have skewed the presented estimates. Further large studies are required to validate the present findings.p = 0.965). Some previous studies have demonstrated that SUVmax of the primary tumor may affect CRC prognosis27; other studies have reported that PET/CT parameters are not associated with either disease-free survival or OS30. These discrepancies in findings may be because of the variability in tumor SUV measurements, confounded by tumor-associated inflammation. A previous study suggested that higher tumor uptake may reflect tumor cell viability and proliferation, contributing to secondary inflammatory reaction31. However, the relationship between tumor and inflammation may differ among patients and tumor types.In the present study, FDG uptake by the primary CRC was not associated with OS in the univariate analysis , whereas the latter study examined only FDG uptake in the spleen of patients with rectal cancer. The present study involved a larger population of patients with CRC, demonstrating the potential usefulness of both BM and spleen FDG uptake values for reflecting systemic inflammation levels, which may support CRC prognostication.To date, only one study with 226 patients with CRC investigated the prognostic value of FDG uptake in the BM in predicting recurrence-free survival after curative resectionThis study had several limitations. This was a single-center retrospective study that only included patients whose post-surgical pathological assessment findings were available and therefore may involve some selection bias. Thus, the present findings need to be validated in large multicenter studies. Second, different PET/CT scanners were used in this study, which may have resulted in variations in SUV measurements. However, SUV was normalized to that of the liver, instead of using absolute values, to reduce the impact of inter-instrumental variability. Moreover, relative quantification is practical and convenient for researchers and clinicians alike as one can visually evaluate FDG uptake in the tumor and BM relative to that in the liver without using any specialized software or equipment. Therefore, FDG uptake values, presented as the SUV ratio relative to that in the liver, is a reproducible and generalizable parameter. Nevertheless, further studies are required to elucidate the relationship between the increased SUV of the BM and spleen and systemic inflammation related to tumor cells; such studies may involve the measurement of serum cytokine levels and histopathologic analysis of BM aspirates as the tumor-infiltrating immune cells.In conclusion, BLR and SLR were significantly associated with CRC prognosis in the univariate analysis; meanwhile, BLR was an independent predictor of OS in the multivariate analysis. Both BLR and SLR demonstrated a significant positive correlation with NLR. Moreover, a combination of BLR and SLR was better in predicting OS than BLR alone. Hence, both BLR and SLR examined on preoperative PET/CT can be useful image-derived biomarkers of systemic inflammation, thus supporting CRC prognostication.Supplementary Information.


The biomarker serum S100 calcium-binding protein B (S100B) is used in in-hospital triage of adults with mild traumatic brain injury to rule out intracranial lesions. The biomarker glial fibrillary acidic protein (GFAP) is suggested as a potential diagnostic biomarker for traumatic brain injury. The aim of this study was to investigate the diagnostic accuracy of early prehospital S100B and GFAP measurements to rule out intracranial lesions in adult patients with mild traumatic brain injury.Prehospital and in-hospital blood samples were drawn from 566 adult patients with mild traumatic brain injury . The index test was S100B and GFAP concentrations. The reference standard was endpoint adjudication of the traumatic intracranial lesion based on medical records. The primary outcome was prehospital sensitivity of S100B in relation to the traumatic intracranial lesion.Traumatic intracranial lesions were found in 32/566 (5.6%) patients. The sensitivity of S100B > 0.10 μg/L was 100%  in prehospital samples and 100%  in in-hospital samples. The specificity was 15.4%  in prehospital samples and 31.5%  in in-hospital samples. GFAP was only detected in less than 2% of cases with the assay used.Early prehospital and in-hospital S100B levels < 0.10 μg/L safely rules out traumatic intracranial lesions in adult patients with mild traumatic brain injury, but specificity is lower with early prehospital sampling than with in-hospital sampling. The very limited cases with values detectable with our assay do not allow conclusions to be draw regarding the diagnostic accuracy of GFAP.NCT02867137.ClinicalTrials.gov identifier: The online version contains supplementary material available at 10.1186/s13049-021-00891-5. Trauma to the head potentially results in traumatic brain injury (TBI), but it more often causes a concussion or no brain injury at all –3. This In ED’s the biomarker serum S100 calcium-binding protein B (S100B) is used as a supportive tool for initial in-hospital triage of adult patients with mild TBI , 8. S100Implementing a prehospital point-of-care measurement of S100B and/or GFAP may facilitate treating and leaving patients on scene, who have no need for hospitalization. However, no studies have investigated the use of samples drawn that close to the trauma, why release kinetics are unclear regarding safe ruleout of traumatic intracranial lesions from very early blood samples.The aim of this study was to investigate the diagnostic accuracy of prehospital S100B and GFAP concentrations for ruling out traumatic intracranial lesions in unselected adult patients with mild TBI. We hypothesized that 1) S100B concentrations < 0.10 μg/L in prehospital blood samples from adult patients with mild TBI rules out traumatic intracranial lesions with a sensitivity of > 97%, 2) GFAP from prehospital blood samples rules out traumatic intracranial lesions with a sensitivity of > 97%, but with a lower false positive rate than S100B and, 3) Prehospital S100B and GFAP values yield lower false positive rates than in-hospital values.The study  was an investigator driven, outcome assessor-blinded, prospective, observational, multi-center, diagnostic accuracy, cohort study conducted in the Central Denmark Region from 15 February 2017 to 01 February 2019. The study is reported according to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) guidelines .Eligible patients were adults ≥18 years of age who had suffered head trauma and was transferred by an ambulance by the Prehospital Emergency Medical Services, Central Denmark Region within 6 h of trauma. The patient inclusion and exclusion criteria are listed in Fig. The Central Denmark Region is one of five Danish regions. The region is inhabited by 1.3 million people, accounting for 23% of the total Danish population. The Danish National Health Service is a tax-supported system, ambulance services are subsidized, and all ambulance dispatch in the region is coordinated through one Emergency Medical Coordination Center (EMCC). Ambulance personnel have access to consultation with critical care physicians staffing the EMCC and rapid-response vehicles. Ambulance personnel are obligated to consult with a critical care physician if they wish to leave a patient on scene. Five centers with emergency facilities participated in the study: four regional hospitals  and one university hospital (Aarhus).Ambulance personnel selected patients for enrolment in the study at the scene of the accident. Ambulance personnel were trained to suspect TBI based on standard operating procedures. Oral and written informed consent was obtained from all participants prior to inclusion. A tablet device with a direct online connection to the PreTBI Database, provided by TrialPartner® , allowed for electronic participant signature of informed consent forms and collection of prehospital clinical data Fig. .Following patient inclusion, prehospital blood samples were collected from the routinely inserted peripheral venous catheter either on scene or en route to hospital. Patients were subsequently transferred to one of five emergency hospitals, where in-hospital blood samples were collected, alongside routine diagnostic workup and treatment Fig. .The index tests were S100B and GFAP concentrations in prehospital blood samples. The prehospital blood sample was drawn from a peripheral venous catheter into a 5.5 mL adaptable Sarstedt Monovette®  for serum.The prehospital blood sample was transported at ambient temperature by ambulance and passed to the laboratory technician at the hospital during collection of the in-hospital sample Fig. . The in-All samples were centrifuged at 2200 G for 10 min within four hours of collection, and serum was aliquoted into four 1 mL CryoPure  vials containing 0.5–0.6 mL serum and stored at − 80 °C.All biochemical analysis was performed at Department of Clinical Biochemistry, Regional Hospital West Jutland, Herning. Samples were analyzed in blinded batches within 6 months of collection.Serum S100B concentrations were measured with a routine Cobas S100 chemiluminescence immunoassay using a Cobas® e602 analyzer  after one freeze-thaw cycle.Serum GFAP concentration was measured after one freeze-thaw cycle using a commercial Human GFAP enzyme-linked immunosorbent assay (ELISA), according to the manufacturer’s protocol  for research only. Standard curves and results were generated using a Multiskan FC Microplate Photometer, software version 1.00.96 . All samples were assayed in duplicate, and average results were used for analysis. According to the manufacturer, the lower limit of detection for this assay was 0.045 ng/mL, and the lower limit for quantification was set to 0.50 ng/mL. All concentrations lower than the limit of detection were reported as zero, and all concentrations between the limit of detection and 0.50 ng/mL were reported as 0.50 ng/mL.A precondition for S100B concentrations to be diagnostically useful is a hemolysis index < 10 . InterfeClinical information and reference standard results were not available to performers of the index tests.The reference standard was endpoint adjudication of the target-condition traumatic intracranial lesion, based on medical records. An outcome assessor committee, consisting of two senior researchers , evaluated all patient courses at the end of the study period. The target-condition traumatic intracranial lesion comprised an abnormal cerebral CT examination and/or neurosurgical observation/intervention and/or death caused by TBI within 7 days of trauma. Cerebral CT examinations were categorized as normal or abnormal with respect to the type of traumatic intracranial hemorrhage , cerebral edema, pneumocephalus, cerebral contusion, and/or skull cap/base fractures. Neurosurgical observation/intervention was registered as a dichotomized yes/no-option based on clinical information on admission to a neurointensive care unit and/or neurosurgery performed due to TBI. Death within 7 days due to TBI was assessed by identification of all patients dying within seven days and reviewing causes of death registered in the Cause of Death Register, in addition to medical records.The outcome assessors were blinded to all S100B and GFAP values. Cases of discrepancy were resolved by discussion and consensus. Cases of inconclusive cerebral CT examinations were consulted with a specialist in neuroradiology.The predefined test positivity cut-off was a S100B level ≥ 0.10 μg/L, yielding two result categories. Cut-off was determined from the 2014 Scandinavian Neurotrauma Committee guideline . The preAll patients underwent prehospital clinical assessment, and ambulance personnel registered initial prehospital data in the PreTBI Database. The personnel were guided through the inclusion and consent procedures by a simple tablet algorithm Fig. . Prior tAmbulance personnel registered initial GCS, time of trauma, isolated head trauma or multi trauma, suspected alcohol and/or narcotic intake, known antithrombotic treatment, vomiting, loss of consciousness, neurological deficits, seizures, and clinical signs of cranial fractures. The first set of prehospital vital parameters was extracted from the electronical real-time prehospital medical record. Relevant medical history, prescription medication, trauma mechanism, alcohol consumption, cerebral CT examinations, neurosurgical interventions, intensive care unit admission, and final diagnosis were extracted from the electronical in-hospital medical records.Demographic data, vital status, comorbidities for Charlson Comorbidity Index score calculation, and cause of death was extracted from the Danish National Patient Registry, the Danish Causes of Death Register and the Central Person Registry administered by the Danish Health Data Authority.We predefined that we wanted the capability to demonstrate that a traumatic intracranial lesion can be ruled out with a negative predictive value of 99%  with an S100B < 0.10 μg/L. The sample size calculation was conducted by Biostatistical Advisory Service, Faculty of Health, Aarhus University, based on the following preconditions: 30% of TBI patients have S100B below guideline cut-off < 0.10 μg/L and 7% have a traumatic intracranial lesion. Our study required 500 patients with two-tailed alfa = 0.05 and 1-beta = 0.90. Inclusion was performed as consecutive sampling and ended at sample size saturation.All statistical analysis was done using STATA© intercooled, version 15, . Missing data were not imputed. Categorical data are presented as numbers and proportions. Continuous data are presented as means with 95% confidence interval (CI) or medians and interquartile ranges (IQR) according to distribution. Normality was assessed by visual inspection of histograms and QQ-plots. The S100B and GFAP concentrations were log-transformed to meet linearity prior to analysis. Diagnostic accuracy was estimated in two-by-two tables and are presented as percentages with 95% CI.p-values < 0.05 were considered statistically significant.For comparison between groups, a parametric paired t-test of log-transformed variables and non-parametric Wilcoxon signed rank test of ordinary variables were conducted or confidence intervals were evaluated. All calculations were two-sided and Approvals were obtained from the Danish Data Protection Agency , the Regional Scientific Ethical Committee System  and the Danish Patient Safety Authority  prior to conduction of the study. The study was reported to ClinicalTrials.gov before inclusion of the first patient (identifier: NCT02867137). The study was performed in accordance with the Declaration of Helsinki. Participants were not exposed to additional invasive procedures or alteration of treatment.The biomarker values in study samples were not disclosed to the treating medical staff and thus not used for clinical decision-making. Participants did not experience any physical or physiological advantage or disadvantage by participating in the study. Participation in the study did not cause patient or system delays.During the two-year study period, 1389 patients were examined for eligibility in the study. Of 1067 eligible patients, 566 (53.0%) adult patients with mild TBI were included Fig. .Fig. 2PTable p-value < 0.001)  for prehospital samples and 108 min  for in-hospital samples  and the mean in-hospital concentration was 0.17 μg/L  . Concentrations were higher in prehospital samples than in in-hospital samples in 512/566 (90.5%) patients.GFAP concentrations above the detection limit ≥0.045 ng/mL were detected in 6/566 (1.0%) of prehospital and 9/566 (1.6%) of in-hospital samples in a total of 11/566 patients (1.9%). The highest prehospital concentration of GFAP was 1.78 ng/mL and the highest in-hospital concentration was 2.34 ng/mL. GFAP concentrations were higher in prehospital than in-hospital samples in 4/11 (36.4%) patients.Initial agreement of the outcome assessors on the target-condition traumatic intracranial lesion was achieved in 535/566 (94.5%) of the cases  patients. In these 32 patients, neurosurgical observation and/or intervention was necessary in 4/32 (12.5%) patients, and a total of two patients (6.25%) died within 7 days of their trauma due to TBI. Details on the cases where the cause of death was evaluated are presented in Additional file The sensitivity of S100B ≥0.10 μg/L for an intracranial lesion in prehospital samples was 100% , with a negative predictive value of 100% for a concentration < 0.10 μg/L  Table . The speThe sensitivity of GFAP above the detection limit (0.045 ng/mL) for an intracranial lesion in prehospital samples was 6.2% , with a negative predictive value of 94.6 .  [Prior to initiating the study, we expected S100B to reach a peak blood concentration within 1–3 h after the trauma  and thusThe GFAP results of this study was hampered by the detection limit of the chosen GFAP assay at 0.045 ng/mL. When initiating the study, the GFAP assay was chosen to the best of our knowledge at the time. In the meantime, Bazarian et al. suggested a GFAP cut-off at 0.022 ng/ml, why an assay with a lower detection limit should have been used for analysis . On thatTo address pitfalls prior to initiation of prehospital sampling, we included unselected patients with mild TBI in this study. Despite 2014 guidelines limitations of age and antiplatelet/anticoagulant treatment, we aimed to test the absolute safety of prehospital S100B measurements as a diagnostic tool, even in cases of uncertainty about age and antiplatelet/anticoagulant treatment. The results of the present study indicate that S100B-based rule out seems safe, even with median age of 62 years and with 25.4% of the cohort receiving antiplatelet/anticoagulant treatment. This conclusion is supported by findings of Thaler et al. , who conBased on the findings of this study, pushing forward the in-hospital guideline approach with prehospital point-of-care testing using the same cut-off of 0.10 μg/L will miss no intracranial lesions. Pushing forward the guideline using prehospital point-of-care technology may safely reduce the number of precautionary ED visits by 15%, as long as the patient in question are in no need of other treatment . The 15% reduction must be compared to a current 100% transferal to ED rate to perform in-hospital diagnostics. Ananthaharan et al.  and MikkA strength to this study was its multicenter design with ambulance personnel from the entire Prehospital Emergency Medical Services, Central Denmark Region including consecutive unselected patients suffering head trauma. The setup was pragmatic and resembles everyday work of ambulance personnel, which strengthens the external validity of the findings. Blinding of outcome assessors is also a strength, as it reduces the risk of detection bias. Lastly, our approach to initial timestamps being the estimated time of trauma rather than time of admission is a strength. This close-to-trauma sampling yields unprecedented insight to applicability of the biomarkers and early diagnostic accuracy.The relatively high exclusion rate caused by insufficient blood sampling is a limitation of this study. This is explained mostly by lack of timely handover of the prehospital sample and/or forgotten in-hospital sampling and/or invalid marking of samples. These problems were most likely due to a high workload in the EDs, caused by a large hospital fusion process concurrent with the inclusion for this study in 2017–2019. We do not expect this to bias the results of this study.Using two different types of tubes for blood sampling (Becton-Dickinson and Sarstedt Monovettes) could be considered a limitation. However, we have previously demonstrated no significant difference between S100B concentrations in blood samples drawn in the two types of tubes .A major limitation is the detection limit of the GFAP assay. We only detected GFAP concentrations above the assay detection limit of 0.045 ng/mL in 6 prehospital and 9 in-hospital samples. Patients with mild TBI may have GFAP concentrations below this detection limit and it could be useful for triage.Early prehospital and in-hospital S100B levels < 0.10 μg/L can rule out traumatic intracranial lesion following mild TBI with 100% sensitivity in adult patients. The specificity is lower with pre-hospital sampling (15.4%) than in-hospital sampling (31.5%). Early S100B sampling seems safe even when applied to elderly patients and to patients receiving antiplatelet/anticoagulant treatment. Due to a high cut-point of the assay used, we cannot validly conclude on the diagnostic accuracy of GFAP.Additional file 1:. Diagnostic accuracy tables of GFAP concentrations in prehospital blood samples and of GFAP concentrations in in-hospital blood samples for ruling out of traumatic intracranial lesions in mild TBI patients.Additional file 2:. Post-hoc sensitivity analysis of the data in the current study to investigate diagnostic accuracy of prehospital S100B concentrations adjusted for the maximum effect of prehospital sample transport.Additional file 3:. Supplementary clinical patient outcome evaluated by outcome assessors. Cases of cerebral CT examinations marked by one or both assessors as needing evaluation by specialist in neuroradiology, non-traumatic incidental findings on cerebral CT examinations and cause of death within 7 days of the trauma extracted from the Cause of Death Register.


To explore the characteristics of unintentional childhood-injury during the COVID-19 pandemic and assess the association of unintentional-injury with maternal emotional status.A cross-sectional survey was conducted with a convenience sample of 1300 children under 12-years-old from 21 schools (including nurseries/ kindergartens/ primary schools) in Wuhan and Shanghai during March to April 2020, and the mothers completed questionnaires online. Self-rating Depression/Anxiety Scales were used to evaluate maternal emotional status, questions on child unintentional-injury were based on the International-Statistical-Classification-of-Diseases-and-Related-Health-Problems-version-10 (ICD-10), and a total of 11 kinds of unintentional injuries were inquired. Information on socio-demographic and family-background factors was also collected.P < 0.001), and boys had higher rates than girls. The three leading causes included “being struck by/against”, falls and animal bites . Lower maternal educational, living in suburban/rural (vs. urban) areas, grandparents (vs. mothers) being main caregivers, more child exposure to secondhand smoke, close relatives being suspected/ confirmed COVID-19 cases were associated with a higher risk of child unintentional-injury. After adjusting for related confounders, higher maternal depression levels were associated with a higher risk of unintentional injury.The children of 0–4, 5–9, and 10–12 years accounted for 29.2, 55.2 and 15.6%, respectively, the unintentional-injury rates were 10.29, 4.18 and 3.45%, respectively (The characteristics of unintentional childhood injury were different from those in non-pandemic periods. The main causes, risk factors and the association of unintentional injury with maternal depression deserve attention for development of effective measures for preventing children from unintentional injury during COVID-19 pandemic.The online version contains supplementary material available at 10.1186/s12887-021-02846-2. As a public health disaster, the coronavirus disease 2019 (COVID-19) swept across the globe. So far, the situation of the epidemic prevention and control is still not optimistic. Until August 4th, 2021, worldwide COVID-19 cases have exceeded 199 million and deaths were over 4 million , people , childhood unintentional injuries include falls, being struck by/against, animal bites (including insect stings), transport accidents, drowning, burns or fires, cuts, poisonings, electrical shocks, suffocation and others [As defined by the World Health Organization, unintentional injuries occur without any plan or intent . The unid others . More thd others . Becaused others , 10, theFamily functioning has significant influencers on the well-beings of children and family members. Although there is no definite definition, it is usually considered that family functioning is about how family members interact, keep relationships, and solve problems. Previous studies reported that the occurrence of unintentional childhood injury was mainly determined by child behavioral attributes and caregiver supervisory patterns –13. DuriCOVID-19 is a source of unexpected stress and adversity for most of people. Home isolation can prevent the spread of the epidemic, but a large number of studies reported negative psychological effects of isolation , 17. PeoTherefore, this study aims to explore the characteristics of child unintentional injury during the COVID-19, and the association of maternal emotional status in the pattern of family functioning with unintentional childhood injury, so as to prevent children from unintentional injury during the COVID-19 and develop effective injury prevention strategies.This cross-sectional study was conducted online during the COVID-19 pandemic , and involved a convenience sample of 6 schools (1 nurseries/ 4 kindergartens/ 1 primary school) in Wuhan and 15 schools (1 nurseries/ 4 kindergartens/ 10 primary schools) in Shanghai. These schools were regionally and socio-economically diverse in the city and responded positively to the invitation to participate in this study. The online questionnaire was distributed to the mothers of the participating children by the teachers in charge of the classes via WeChat class groups. All the children in these schools were under 12 years old, and all the mothers have electronic devices  to complete the questionnaire. Mothers having any diagnosis of mental illness/ disorder were not invited to participate in this study. Mothers were administered the validated Chinese-version Self-rating Depression/Anxiety Scales (SDS/SAS) and the Family Environment Scale (FES-CV). Mothers were asked to recall whether their children were suffered from unintentional injury since the beginning of the pandemic (in recent 2–3 months). The information on socio-demographic and family background factors was also collected. Before distributing the questionnaire, an introduction about the study and the guidance on how to complete the questionnaire was provided to the mothers of the study children via WeChat class groups. Finally, a total of 1315 questionnaires were collected, and 15 questionnaires were not included because of double submission or logic errors detected during data cleaning. Therefore, 1300 questionnaires were included in the final statistical analysis.The study was approved by the Research Ethics Committee Board of the International Peace Maternity and Child Health Hospital. The informed consents were obtained from all the study mothers. Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.Maternal depression and anxiety levels were assessed through SDS  and SAS FES-CV is a self-reported measure of family functioning, and is widely used to evaluate the relationship and interaction among family members . The FESIn this study, questions on child unintentional injury since the beginning of the COVID-19 pandemic  were based on the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) . A totalCovariates on child factors included child age, gender, primary caregivers , daily time the child was exposed to secondhand smoke, daily time child playing with peers, and daily time child playing outside.Covariates on maternal factors included maternal age, education , and knowledge on healthy parenting during COVID-19 pandemic. Other family factors investigated included family structure , home residency , and whether their close relatives were infected with COVID-19 .2) test was used to compare the rates of child unintentional injuries by age  and gender. One-way ANOVA  were used to compare maternal emotional status/ family cohesion/ contradiction levels between children with and without overall injuries/ main injury causes. The Chi-square (χ2) test and multi-variate analyses were both used to analyze the risk factors of childhood unintentional injuries, and multi-variate logistic regression analyses were used to explore the adjusted associations between maternal emotional status, family cohesion/ contradiction and unintentional childhood injuries.The demographic characteristics of the subjects were presented as means and standard deviations (Mean ± SD) or percentages (%). The Chi-square (χP < 0.05 (two-tailed).Statistics analyses were performed using SAS 9.4 software , and Origin Pro 2020b . Differences were considered significant at The socio-demographic characteristics of the study children and their families were shown in Table A total of 15.7 and 4.1% of the study mothers had positive depression and anxiety scores, while the average SDS and SAS scores were 39.25 ± 9.43 and 34.66 ± 7.48, respectively. Up to 79.1% of mothers and 15.6% of grandparents of the study children were primary caregivers for their children, respectively. The percentages of the mothers with educational levels of junior high school, senior high school , college/ university, and postgraduate were 3.2, 7.1, 58.1 and 31.6%, respectively. 29.5% of mothers had little knowledge on how to take care of children during the pandemic.As shown in Fig. P = 0.078). For most causes, boys had higher or similar unintentional-injury rates compared with girls. Among the children suffered from the fall injury, the ratio of boy to girl was 72%: 28% (P = 0.038). However, for the causes of drowning and poisoning, unintentional-injury rates were a little higher in girls than in boys (P > 0.050).As illustrated in Fig. P < 0.001). Among the children less than 4 years old, unintentional injuries were mainly due to being struck by or against an object (6.07%), followed by the causes of falls (4.22%) and cuts (1.58%). In the children aged 5–9 and 10–12 years old, the top cause was still being struck by or against an object , followed by animal bites  and falls . As shown in Fig. P = 0.010) and falls (P = 0.011) among the three age groups and both rates were highest in the group of 0–4 years. The rate of cuts was highest in the group of 0–4 years (1.58%), followed by the rate in the groups of 5–9 years (0.70%) and 10–12 years (0.49%). Rates of other injuries including animal bites, poisoning, burns or fires, drowning, suffocation, electrical shocks, and transport accidents were not statistically significantly different among the three age groups (P > 0.05). Detailed data on the rates of childhood unintentional injuries were presented in Supplemental Material Table Among the study children, the overall unintentional-injury rates decreased with age. The rate was 10.29% among children aged 0–4 years, 4.18 and 3.45% among children aged 5–9 years and 10–12 years, respectively, and the rate differences reached statistical significance (P = 0.041), residency in the suburban/ rural areas , lower family cohesion levels (P = 0.005), younger children (P < 0.001), child having more outdoor time daily (P = 0.005), child being exposed to secondhand smoke for more time (P < 0.001), more time child playing with peers (P < 0.001), close relatives being suspected COVID-19 cases , younger mothers (P = 0.003), lower maternal educational levels (P = 0.026) and higher maternal depression levels (P = 0.017). Mothers having less knowledge on how to care for children during the pandemic had a marginal association with the occurrence of unintentional injuries (P = 0.050).As shown in Fig. P = 0.027), younger child (P < 0.001), child being exposed to secondhand smoke for more time (P = 0.029) and more time child playing with peers (P = 0.030), younger mothers (P = 0.013), lower maternal educational levels (P = 0.009) were associated with a higher risk , younger children (P = 0.008), boys , child being exposed to secondhand smoke for more time (P = 0.045), close relatives were suspected COVID-19 cases , younger mothers (P = 0.028), lower maternal educational levels (P = 0.047), higher levels of maternal depression (P = 0.005) and anxiety (P = 0.041) were associated with a higher risk . Detailed data on risk factors for childhood unintentional injuries were presented in Supplemental Material Table The risk factors for each cause of unintentional injuries were also analyzed. For child being struck by or against an object, close relatives were confirmed COVID-19 cases . Higher levels of family cohesion were significantly associated with a lower risk of the overall unintentional injury among the study children (P = 0.020). However, adjusted models didn’t show significant relationships between maternal emotional status/ family functioning and the unintentional injury due to being struck by or against an object (P > 0.05). For the fall injury, higher maternal depression (P = 0.008) levels were associated with a higher risk. However, adjusted models didn’t show significant relationships between maternal anxiety and the unintentional injuries (P > 0.050). After adjusting for confounders, lower family cohesion levels were significantly associated with higher levels of maternal depression .This study reported that the prevalence rate of maternal depression reached 15.7%. Compared with other studies conducted in China during the COVID-19 pandemic, the depression level in this study was slightly higher than the level reported by Lei (12.93%) , and mucThis study showed that the ranking of the causes of unintentional childhood injury was different from the ranking before the pandemic. The traffic injury usually remained one of the three leading causes of unintentional childhood injuries in non-pandemic period in most countries. However, in this study, we found that the traffic injury only accounted for 0.31%, less than the rates of poisoning (0.77%), burns or fires (0.69%), drowning (0.46%), suffocation (0.38%), and electrical shock (0.31%). Traffic volumes had experienced a sharp decrease on roads, highways, or airways due to the home quarantine policy during the pandemic , perhapsInconsistent with the studies before the pandemic , we founThis study found that unintentional injury rates differed by child age, and children aged 0–4 years had the highest rate of unintentional injury, followed by children aged 5–9 years and 10–12 years. Consistent with this study, American and Chinese Centers for Disease Control and Prevention both reported that children aged 0–4 years accounted for the highest proportion of unintentional injury compared with children aged 5–9 years and 10–12 years , 35. In This study explored risk factors of unintentional childhood injury during the COVID-19 pandemic. The close relatives being suspected or confirmed COVID-19 cases were found to be associated with a higher risk of injury, which may be because that family members of the COVID-19 cases were quarantined due to close contacts and had psychological distress or panic, and parenting styles were affected, thereby increase the risk of child unintentional injury. This study was consistent with previous studies in the association between more child exposure to secondhand smoke and a higher risk of child behavioral problems. The tobacco smoke exposure was reported to be positively correlated with hyperactivity-impulsivity and conduct problems among children aged 0–4 years , hereby All the characteristics of unintentional childhood injury during the pandemic remind us of the necessity of focusing on the high-risk children, and strategies are required to improve the risk management. Better education on the prevention of unintentional injury during the pandemic for parents and administrators may bring about improvements in rule modification and facility development.Consistent with the studies before the pandemic –45, thisConsistent with previous study showing the association between low family cohesion and maternal mental problems  and showThe study had limitations. First, the schools were not selected at random, which implied that the mother-child pairs might be not representative of the general population, and selection bias might be induced. Second, the sample size of this study was relatively small, and larger sample size may better describe the characteristics of unintentional childhood injury and define the association of injuries with maternal emotional status. Third, the potential cause-effect relationship between maternal depression and unintentional childhood injury could not be determined due to the cross-sectional design of this study, and maternal depression and unintentional childhood injury may contribute to each other. Fourth, maternal emotional status, family functioning (cohesion/ conflict) and unintentional childhood injury were assessed by the mothers’ reports, and reporter bias may have influenced the results of this study. Previous studies investigating unintentional childhood injury showed that the occurrence of unintentional childhood injury within half-to-1 year could be clearly reported by parents. Although the possibility was relatively low, maternal recall bias on unintentional childhood injury could not be completely excluded , 52–54. This study reported the characteristics of the unintentional injuries among children aged 0–12 during the COVID-19 pandemic, the potential association between maternal depression and child unintentional injury, and highlighted the importance of the development of a comprehensive planning for preventing children from unintentional injury in exceptional circumstances, such as the quarantine period. Education and prevention on unintentional childhood injury during the pandemic require a comprehensive approach that works at all levels of society—from the mother, family, and community levels to the broader social environment. Our study may help governments or administrators and families develop comprehensive planning for prevention from childhood unintentional injury.Additional file 1 : Supplemental Material Table 1. Number and rates of unintentional injury cases by child gender and age (N = 1300). Supplemental Material Table 2. Risk factors of childhood unintentional injuries during the COVID-19 pandemic. Supplemental Material Table 3. Unadjusted and adjusted relationship between family cohesion and maternal depression/anxiety levels. Supplemental Material Table 4. Unadjusted and adjusted relationship between family contradiction and maternal depression/anxiety levels.