The prediction model constructed had good predictive accuracy (C‐index = 0.886). Elevated levels of donor kidney PRKDC are associated with renal dysfunction after transplantation. The prediction model of renal function status for post‐transplant recipients based on PRKDC has good predictive accuracy and clinical application.Renal transplantation is the only efficacious treatment for end‐stage kidney disease. However, some people have developed renal insufficiency after transplantation, the mechanisms of which have not been well clarified. Previous studies have focused on patient factors, while the effect of gene expression in the donor kidney on post‐transplant renal function has been less studied. Donor kidney clinical data and mRNA expression status were extracted from the GEO database (GSE147451). Weight gene co‐expression network analysis (WGCNA) and differential gene enrichment analysis were performed. For external validation, we collected data from 122 patients who accepted renal transplantation at several hospitals and measured the level of target genes by qPCR. This study included 192 patients from the GEO data set, and 13 co‐expressed genes were confirmed by WGCNA and differential gene enrichment analysis. Then, the PPI network contained 17 edges as well as 12 nodes, and four central genes  were identified. We found by collecting data from 122 patients who underwent renal transplantation in several hospitals and by multivariate logistic regression that acute graft‐versus‐host disease postoperative infection, PRKDC ;  However, in the early stages of transplantation, delayed graft function (DGF), or malfunction of a transplanted kidney, is a severe complication. DGF is a severe dysfunction that can give rise to a prolonged hospital stay, a high risk of acute and chronic acute graft‐versus‐host disease (GVHD), and even graft failure.The PRKDC gene is located on chromosome 8q11. The PRKDC gene encodes the DNA‐dependent protein kinase catalytic subunit (DNA‐PKcs), a member of the phosphatidylinositol 3‐kinase‐related kinase family, a serine/threonine protein phosphorylation kinase.With the technological advances in gene high‐throughput technology and proteomics, combined with bioinformatics approaches, access to validated biomarkers for diagnosis and patient survival has been facilitated. Bioinformatics‐based multi‐gene co‐expression analysis is critical. WGCNA is a multigene analysis method first proposed in 2008.In this project, we obtained high‐throughput data from the GEO database for 192 kidney transplant donor kidneys. We used differential gene expression analysis and WGCNA to search for potential genes that affect post‐transplant kidney function. In addition, we used differential gene enrichment analysis and PPI networks to screen out central genes. We proceeded to examine the potential role of PRKDC in influencing post‐transplant renal function through comprehensive bioinformatics analysis and validated the results with 122 patients in our medical centre. We performed a logistic regression analysis based on the screened target genes to screen for high‐risk factors and constructed a predictive model for post‐transplant renal functional status based on these genes for clinical application.22.1https://www.ncbi.nlm.nih.gov/gds). Data filtering was performed using a function ‘rpmk’ in the edgeR package.Clinical data and gene data related to kidney transplant donor kidneys were obtained from the GEO database  and Gene Ontology (GO) pathway analysis were performed by the cluster profile R package.2.5https://string‐db.org/). The network was analysed and established with Cytoscape software (v 3.8.1) using ≥0.4 points as the extraction value. In the network, edges represent protein interactions, and nodes represent proteins. To specify the hub genes in the network, we used the maximum cluster centrality algorithm calculated by CytoHubba's plug‐in. The top four genes were set as hub genes.We have constructed the PPI network through the STRING database .To further verify the accuracy of the screened genes, we analysed the expression of these critical genes in different renal functional states of patients after renal transplantation. A bar graph represented the expression of each gene in other renal functional forms. The statistical significance between the two groups was determined by Student's 2.7−ΔΔCt) method. The sequences used were as follows: RPL35A, forward, 5′‐TTGAAGGTGTTTACGCCCGAG‐3′ and reverse, 5′‐TGCTTCGGAATTTGGCACGA‐3′; UBQLN4, forward, 5′‐ATTCGGGTCACCGTCAAGAC‐3′ and reverse, 5′‐GCCTTAAACCTCCGGGAGATTT‐3′; GAPDH, forward, 5′‐ GAGAAGGCTGGGGCTCATTT‐3′ and reverse, 5′‐ ATGACGAACATGGGGGCATC‐3′.RT‐qPCR was performed using QuantStudio 3 platform with SYBR Green. qPCR was started at 50°C for 2 min and 95°C for 10 min, followed by 40 cycles of amplification at 95°C for 15 s and 60°C for the 60 s. Reverse transcription was performed at 25°C for 10 min, 42°C for 15 min, and 85°C for 5 min. mRNA levels were standardized with GAPDH as an internal control and determined according to the 2, and the project included 74 (61.0%) men. Patient data, including gender, age, body mass index, primary disease, chronic disease history, dialysis method, immunosuppressant use, aGVHD, infection, preoperative and postoperative creatinine, Ccr and PRKDC expression levels were selected for analysis. Glomerular filtration rate ≤ 50 mL/min was regarded as poor renal function group. Informed consent was obtained from all patients' immediate family members or patients' own. All research projects conformed to the guidelines of the Soochow University Ethics Committee and followed the Declaration of Helsinki (ID202214523). The subjects included in the external validation portion of this study were subjected to the same conditions as the cases in the GSE147451 data set.2.92 tests. Skewed variables were expressed as medians with interquartile ranges. Kruskal‐Wallis tests or one‐way anova were used for comparisons between groups. COX regression models were employed for both univariate and multivariate post‐transplant renal functional status analyses, which were adjusted accordingly. COX regression we utilized is consistent with the pH hypothesis. A multifactorial regression model was utilized to confirm risk factors and predict renal functional status after kidney transplantation. To ensure the validity of our analysis, we included significant variables from the multifactorial regression in the nomogram. The weights of each variant were confirmed and nomograms were generated and internally validated via a 1001‐bootstrap method18. The C statistic was calculated using the ‘rms’ package in R software. Statistical analyses were performed using several R packages, namely ‘PredictABEL’, ‘rms’, ‘risk regression’, ‘survminer’, and ‘ggplot2’19. A significance level of p < 0.05 was used throughout the analysis.Missing values, accounting for ≤5.0% of the data, were estimated using the ‘mice’ package2.10The study was a comprehensive study based on a public database and local hospital cases, and the project evaluated renal reconstruction and function after kidney transplantation. The public—that is a statutory health insurer, physicians, local medical managers and patient representatives—was involved in the design and implementation of the entire project. In addition, practitioners  and scientific experts were involved in the discussions of this study. The results of the study and the project as a whole will be disseminated to participants through practise‐oriented publications and newsletters.3p = 0.011, lightcyan module: cor = 0.17, p = 0.069). Other modules are displayed in the The flow chart for this project is shown in Figure p < 0.05 and |logFC| ≥ 1.0 as the cut‐off criteria. We decided on the top 50 genes based on expression differences and statistical significance to plot the heat map  in the multifactorial analysis. It showed that aGVHD ; p < 0.001], infection ; p = 0.003] and PRKDC ; p < 0.006] were all independent risk factors for post‐transplant renal insufficiency . GO and KEGG analysis were performed using the function “ClusterProfiler” of R software. Immediately after, the four genes with the highest MCC scores in PPI were screened, including PRKDC, RFC5, RFC3 and RBM14. These essential genes impact the renal functional status of patients after kidney transplantation. PRKDC, whose high expression was associated with post‐transplant renal insufficiency. In the validation phase, we collected clinical data and gene expression assays from 122 kidney transplant patients in our hospital. Four independent risk factors, aGVHD, infection and PRKDC, were screened by multifactorial analysis, and a prediction model was established focusing on these genes, which showed good prediction accuracy.Recently, gene networks have been increasingly used for bioinformatics analysis. WGCNA is a efficacy method to analyse the expected effects of multiple genes among revealed genomic data.In the project, we focused on the hub gene PRKDC and combined it with the external validation of 122 patients in our medical centre. Both PRKDC and DNA‐PKcs affect tumorigenesis, progression, invasion and metastasis through different pathophysiological processes and further prognostic analyses have shown that DNA‐PKcs expression can help predict patient prognosis.Lu et al.The present study has its limitations. The effect of PRKDC should be verified in vitro experiments. Then, studies on the mechanisms affecting renal insufficiency in recipients after renal transplantation should also be continued. Prediction maps also need to be confirmed using larger sample sizes. Moreover, a prospective investigation should be initiated before the clinical application of the prediction model. We are preparing to conduct further prospective clinical trials in our follow‐up studies to validate the findings of this study.In conclusion, we identified four essential genes affecting post‐transplant renal function through comprehensive bioinformatics analysis. By differential gene expression analysis and WGCNA, PRKDC was closely associated with post‐transplant renal function. External validation demonstrated that PRKDC expression levels are an independent risk factor for predicting renal functional status in patients after renal transplantation. Furthermore, nomograms based on aGVHD, infection and PRKDC proved more effective in assessing kidney function after transplantation.This study found that infection and aGVHD are related to renal dysfunction after transplantation. Elevated levels of donor kidney PRKDC are associated with renal dysfunction after transplantation. The prediction model of renal function status for post‐transplant recipients based on PRKDC has good predictive accuracy and clinical application. The present study has its limitations. The effect of PRKDC should be verified in vitro experiments. Then, studies on the mechanisms affecting renal insufficiency in recipients after renal transplantation should also be continued. Prediction maps also need to be confirmed using larger sample sizes. Moreover, a prospective investigation should be initiated before the clinical application of the prediction model.Zhijun Cao: Formal analysis ; investigation ; writing – original draft ; writing – review and editing . Hao Jiang: Data curation ; investigation ; methodology ; visualization . Chunchun Zhao: Resources ; software . Huifeng Zhou: Data curation ; formal analysis . Zheng Ma: Visualization ; writing – original draft . Chen Xu: Formal analysis ; investigation . Jianglei Zhang: Project administration ; supervision . Minjun Jiang: Conceptualization (lead); data curation (lead); project administration (lead); writing – original draft ; writing – review and editing . Zhenfan Wang: Data curation ; formal analysis ; methodology .This work was supported by the Natural Science Foundation of Jiangsu Province (Grants Number BK20210094), the Suzhou Gusu Medical Youth Talent (GSWS2021012), Suzhou Youth Science and Technology Program KJXW2022076 and the Jiangsu Innovative and Entrepreneurial Talent Programme(JSSCBS20211560).The authors declare no competing financial interests.Appendix S1: Supporting InformationClick here for additional data file.


Coronary artery disease (CAD) is one of the major cardiovascular diseases and represents the leading causes of global mortality. Developing new diagnostic and therapeutic approaches for CAD treatment are critically needed, especially for an early accurate CAD detection and further timely intervention. In this study, we successfully isolated human plasma small extracellular vesicles (sEVs) from four stages of CAD patients, that is, healthy control, stable plaque, non‐ST‐elevation myocardial infarction, and ST‐elevation myocardial infarction. Surface‐enhanced Raman scattering (SERS) measurement in conjunction with five machine learning approaches, including Quadratic Discriminant Analysis, Support Vector Machine (SVM), K‐Nearest Neighbor, Artificial Neural network, were then applied for the classification and prediction of the sEV samples. Among these five approaches, the overall accuracy of SVM shows the best predication results on both early CAD detection (86.4%) and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for CAD early detection. Many advanced techniques have been developed, reported, and clinically applied to the diagnostic and prognostic workup of CAD, such as electrocardiogram,Small Extracellular vesicles (sEVs) are small lipid‐bilayer enveloped assemblies with sizes ranging from 20 nm to several micrometers.Surface‐enhanced Raman scattering (SERS) is a commonly used sensing technique in which inelastic light scattering by molecules is greatly enhanced when the molecules are absorbed onto corrugated metal surfaces .In this study, we demonstrated a noninvasive, label‐free SERS technique to diagnose CAD by assessing and monitor progression of the disease. To the best of our knowledge, direct, label‐free in vitro characterization of sEVs for the early CAD stage diagnosis through SERS at a biomolecular level has not previously been demonstrated. A Raman spectral library of plasma‐derived sEVs from patients with various degrees of CAD, including SP, NSTEMI, and STEMI CAD stages, was developed. Plasma from patients without CAD was used as healthy control (HC) group. We hypothesized that SERS data combining with ML algorithms can accurately classify the sEVs from different CAD stages and be used to predict potential risks in CAD patients. Firstly, we isolated and characterized sEVs from human plasma samples with various degrees of CAD and collected their SERS signals Figure . Then, t22.1Plasma‐derived sEVs were isolated from patient plasma using a standard procedure of ultracentrifugation as shown in Figure 2.2−1. A peak at 830 cm−1 was observed corresponding to C—O—O vibration typical of phospholipids. Other peaks such as 856 cm−1 (glycogen), 879 cm−1 (C—C stretch proline ring), 1005 cm−1 , 1126 cm−1 (C—C vibrations in lipid), 1244 cm−1 (amide III), 1450 cm−1 (CH2 bending vibration of proteins/lipids), and 1668 cm−1 (amide I) were also observed.To provide an overview of the Raman spectra of four CAD stages, all spectra after standard normal variate processing were simply averaged and shown in Figure When we closely look at each individual spectrum as shown in Figure acc and Specificityacc. As an example, in the ROC curve of HC, the Sensitivityacc and Specificityacc are calculated as results of HC response against other three stages  responses. Area under the curve (AUC) of ROC is the indicator of the goodness of fit for the model, and a value of 1 indicates a perfect fit and a value near 0.5 indicates that the model cannot discriminate among the stages. The AUC results by QDA for each stage range from 0.9366 to 0.9824, indicating the QDA method shows good fitting results  and overall prediction (92.3%). SVM also possesses the highest sensitivity (97.69%) and specificity (95.7%). Thus, our study demonstrates a promising strategy for noninvasive, safe, and high accurate diagnosis for early CAD detection.44.1g for 5 min, 1000 × g for 20 min and then at 10,000 × g for 30 min sequentially. The supernatant was then filtered with a 0.22 μm filer, and ultracentrifuged  at 100,000 g for 70 min. Subsequently, the sEV pellet was washed with PBS and ultracentrifuged at 100,000 × g for 70 min. The collected sEVs were reconstituted in PBS buffer and then preserved at −80°C. For SERS analysis, parts of exosomes were resuspended in phosphate buffer (PB) . The size and concentration of the final sEVs were examined by NTA using a NanoSight (NS300) measurement. The exosome morphology was evaluated as previously described by using atomic force microscopy .Deidentified human plasma samples were donated by the Nebraska Cardiovascular Biobank and Registry (IRB approved protocol 133‐14‐EP). The human plasma was obtained from patients at the time of cardiac catheterization and immediately stored at −80°C for further use. Samples were obtained from patients who presented with chest pain and a positive stress test, and those who presented with a NSTEMI or a STEMI. Patients who had nonobstructive CAD  at catheterization were defined as stable CAD patients (SP). The bank ID, Gender, Age, Race of each patient is recorded in Table 4.2The markers (CD63 and Alix) of the isolated sEVs were correspondingly detected by Western blotting as reported previously.4.3The sEV samples were immediately measured within 12 hours after taking from −80°C. Gold‐coated glass slides  were used for better suppression of fluorescence background and surface plasmonic enhancement for SERS. Five microliters of each sEV samples was dropped onto the gold slide and then measured immediately before drying at room temperature. Raman spectra were recorded using a commercial micro‐Raman microscope (Renishaw InVia Reflection) with 633‐nm diode laser excitation. The laser power was set to 10 mW. The laser beam was focused on the 5 μl droplet by using a 50× microscope objective with a numerical aperture of 0.75 (Leica n PLAN EPI 50×/0.75). The laser spot is estimated to be 1 μm in diameter. In the experiment, due to the sample availability, the sample size was experimentally boosted by automated measuring at different spots of the sEV droplet in 4 × 5 grid. Each Raman spectrum obtained for following analyzes was recorded with an exposure time of 1 s and accumulated by 10 times. The Raman data set is shown in Figure 4.4Data preprocessing of the raw Raman spectra included baseline correction and normalization. Baseline correction was performed by Vancouver Raman algorithm with five‐point boxcar smoothing and a five‐order polynomial fit. After baseline correction, the spectra were normalized using the standard normal variate technique, which can remove multiplicative error and preserve each preprocessed spectrum having same contribution to the following classification analysis.4.5Dimensionality reduction. Since the preprocessed spectra were of high dimensionality, classification analysis on these data directly can be computationally expensive. Principal component analysis (PCA),features, from the spectra data. For a n×p data matrix,X belongs to Rp. PCA uses singular value decomposition (SVD)l principal components (PCs) from X, with l<p. Each PC consists of elementwk is the weights extracted by SVD that map each row of with X to a PC. The extracted PCs are ranked based on the percentage of data variance they explain. For example, the 1st PC explains the highest percentage of data variance among all the PCs; the 2nd PC explains the 2nd largest percentage of data variance, so on and so forth. These PCs are then the features to be used in subsequent classification analysis. In this study, R software was used for PCA implementation.Feature selection. The PCA reduces the high dimensionality of the Raman spectra (1008 independent variables from 648 cm−1 to 1747 cm−1) to a few PCs. To determine the optimal number of PCs for following machine learning models, Quadratic Discriminant Analysis (QDA) was performed with leave‐one‐out cross‐validation method.F0=Nμ0∑0 and F1=Nμ1∑1. For a feature vector tj=tj1tj2…tjl, QDA predicts the likelihood ratio of the two classes,Classification analysis was adopted to identify the CAD stage based on SERS measurements. Raman spectra were processed to reduce dimensionality, and then input to classifiers for CAD stage prediction. Cross‐validation (CV) was adopted for robust model training and validation. The diagnostic capability of the classification models, characterized by sensitivity and specificity, was analyzed with Receiver Operating Characteristics (ROC).T, if Lj<T, the tj is assigned class 0. Otherwise, it is assigned class 1. As shown in Figure Classification analysis. Four other supervised learning classification methods, SVM, KNN, ANN and XGB were also performed by R software on these 15 PCs which served as input variables. The 90% SERS data set of sEVs were used to train the models. The remaining 10% spectral data were used to predict the CAD stages through the models.Analysis of diagnostic capability. To gain insights into the diagnostic capability of the proposed method, we adopted receiver operating characteristic (ROC) curves to analyze the classifiers' sensitivity and specificity. The conventional ROC is a graphic representation of the diagnostic capability for a binary classification model. It visualizes the model's true positive rate (TPR) against the false positive rate (FPR) as the discriminant threshold varies.TPR: rate of correctly predicting class 1 (TPR = sensitivity);FPR: rate of falsely predicting class 1 (FPR = 1 − specificity);Given a threshold of discrimination, acc and Specificityacc in ROC curves. However, in other conditions, Sensitivity and Specificity for the five ML models were calculated by counting HC group as negative and other three CAD stages as positive.For a complete ROC curve, the area under curve (AUC) represents the capability of accurately predicting the positive cases, which is the larger the better. In our study, there are four classes. To enable the analysis with ROC curves, we counted the representation stage as positive response and other three stages as the negative response for calculation of Sensitivity4.6p values are shown in the figures as *p < 0.05, which are considered to be statistically significant.The mean size of small extracellular vesicles is expressed as means ± SD. The statistical differences among multiple groups were analyzed by ANOVA. The Xi Huang: Data curation ; formal analysis ; investigation ; methodology ; validation ; writing – original draft . Bo Liu: Data curation ; formal analysis ; investigation ; methodology ; validation ; writing – original draft . Shenghan Guo: Data curation (supporting); formal analysis ; software . Weihong Guo: Conceptualization ; data curation (supporting); formal analysis ; software . Ke Liao: Data curation (supporting). Guoku Hu: Data curation (supporting). Wen Shi: Data curation (supporting); methodology (supporting). Mitchell Kuss: Data curation (supporting); methodology (supporting). Michael Duryee: Resources ; writing – original draft (supporting). Daniel Anderson: Conceptualization ; funding acquisition ; supervision ; writing – review and editing . Yongfeng Lu: Conceptualization ; funding acquisition ; supervision ; writing – review and editing . Bin Duan: Conceptualization ; funding acquisition ; project administration (lead); supervision ; writing – review and editing .https://publons.com/publon/10.1002/btm2.10420.The peer review history for this article is available at Figure S1 All processed Raman spectra of health control (HC) group and three CAD stages, SP, NSTEMI, and STEMI. The variations in spectra were significantly notable even within the same CAD stage.Figure S2 The plot of overall accuracy vs PC numbers to determine the optimized number of PCs used for all machine learning models.Figure S3 Raman data set of health control (HC) group and three CAD stages, SP, NSTEMI, and STEMI.Figure S4 Typical raw Raman data obtained from plasma samples of patient #8 (before data preprocessing). Black: spectrum without sEVs (only with PBS buffer). Blue: spectrum with high PBS peak and high background level (Removed). Red: spectrum with low PBS peak and low background level. (Kept for following machine learning models)Table S1 Averaged confusion matrix from the 50 round testsTable S2 Final coding and demographics of samplesClick here for additional data file.


Although early diagnosis and surgical resection of the tumor have been shown to be the most important predictors of lung cancer survival, long‐term survival for surgically‐resected early‐stage lung cancer remains poor.In this prospective study we aimed to investigate the survival and prognostic factors of surgically‐resected early‐stage non‐small cell lung cancer (NSCLC) in Central and Eastern Europe.We recruited 2052 patients with stage I‐IIIA NSCLC from 9 centers in Russia, Poland, Serbia, Czech Republic, and Romania, between 2007–2016 and followed them annually through 2020.p‐trend = 0.04), quantity of smoking (p‐trend = 0.008), years of smoking (p‐trend = 0.010), gram‐days of alcohol drank (p‐trend = 0.002), frequency of drinking (p‐trend = 0.006), and years of drinking (p‐trend = 0.016).During follow‐up, there were 1121 deaths (including 730 cancer‐specific deaths). Median survival time was 4.9 years, and the 5‐year overall survival was 49.5%. In the multivariable model, mortality was increased among older individuals (HR for each 10‐year increase: 1.31 [95% CI: 1.21–1.42]), males (HR:1.24 [1.04–1.49]), participants with significant weight loss (HR:1.25 [1.03–1.52]), current smokers (HR:1.30 [1.04–1.62]), alcohol drinkers (HR:1.22 [1.03–1.44]), and those with higher stage tumors (HR stage IIIA vs. I: 5.54 [4.10 – 7.48]). However, education, chronic obstructive pulmonary diseases (COPD), and tumor histology were not associated with risk of death. All baseline indicators of smoking and alcohol drinking showed a dose‐dependent association with the risk of cancer‐specific mortality. This included pack‐years of cigarettes smoked (This study shows that the 5‐year survival rate of surgically‐resected stage I‐IIIA NSCLC is still around 50% in Central and Eastern Europe. In addition to non‐modifiable prognostic factors, lifetime patterns of smoking and alcohol drinking affected the risk of death and disease progression in a dose‐dependent manner in this population. Upon enrolment, informed written consent was obtained from all participants. This study was approved by the local ethical committees of each study center and also the ethical committee of the International Agency for Research on Cancer.2.2Upon enrolment all participants underwent an interview using a structured questionnaire to gather information on demographics, family and medical histories, and exposures and lifestyle habits. Participants were asked if they had been diagnosed with chronic health conditions including diabetes mellitus and hypertension, as well as any chronic obstructive pulmonary disorders (COPD) including chronic bronchitis and emphysema. Participants were also asked about their current weight and height, and their weight 2 years before diagnosis. We observed significant correlations between pre‐diagnostic weight loss and body mass index (BMI) at diagnosis Table . Based oThe questionnaire included questions about lifetime history of cigarette smoking, the starting and stopping ages to smoke cigarettes, as well as the frequency and amount of smoking cigarettes. Cigarette smokers were defined as participants who reported to have smoked at least 100 cigarettes through their lifetime and were categorized as “current smokers” or “former smokers.” Former smokers were defined as cigarette smokers who reported to have quit smoking at least 1 year before diagnosis. We calculated the cumulative cigarettes smoked in pack‐years (a pack includes 20 cigarettes) by multiplying the number of cigarette packs smoked per day by the number of years the participant had smoked.The participants were asked about their lifetime history of drinking different types of alcoholic beverages. For each type of alcoholic beverage, the participants were further asked about the starting and stopping ages, as well as the frequency and amount of drinking the specified alcoholic beverage. Regular alcohol drinkers were defined as participants who reported to have drunk alcoholic beverages at least once per week for 1 year, who were further categorized as “current drinkers” and “former drinkers.” Former drinkers were defined as regular alcohol drinkers who reported to have quit drinking at least 1 year before diagnosis. We calculated the average daily consumption of alcoholic beverages using the reported frequency and amount of each alcoholic beverage. Then, we converted the average daily consumption of alcoholic beverages into milliliters of ethanol based on the ethanol content of alcoholic beverages in eastern and central European countries Table  and multThe relevant medical, imaging, and pathology documents were reviewed by an expert local team to complete a separate questionnaire that included necessary information on the histopathological features of the tumor. The completed questionnaires and data underwent a quality control and quality assurance process by a central team. We categorized the participants based on their tumor histology as having “squamous cell carcinoma,” “adenocarcinoma,” and “neuroendocrine tumors.” The classification of tumor stage was performed upon diagnosis and before receiving any treatment for the current tumor, based on the 7th edition of the TNM classification system that is proposed by the American Joint Commission on Cancer (AJCC).2.3Patients were followed twice per year. Active and passive follow‐up processes were used to determine vital status, tumor progression (recurrence and metastasis), treatments, and causes of death. Active follow‐up was performed by contacting the patients, their families, their responsible physicians when applicable, and medical record review. Passive follow‐up was performed by linkage to the national cancer and death registries in each country. The primary and secondary causes of death were recorded based on the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems.2.4We used the Kaplan–Meier method and log‐rank tests to calculate and compare unadjusted survival probabilities. We used Cox proportional hazards regression models to estimate the probabilities of overall survival and progression‐free survival and identify their determinants. To assess the probability of lung‐cancer specific survival and its determinants, we used Fine‐Gray competing‐risks regression models, accounting for death from other causes as the competing event.In all models the entry time was defined as the date at which the participant was diagnosed with NSCLC, and the exit time was defined as the end date of follow‐up time. For assessing the probability of overall survival and hazards of all‐cause mortality, date of death from any cause was set as the end of follow‐up time, while date of the last contact was set as the censoring date for patients who were alive at the last contact . For assessing the probability of progression‐free survival, date of death from any cause or date of tumor progression, whichever occurred first was set as the end of follow‐up time, while date of the last contact was set as the censoring date for patients who were alive and did not have any documented tumor progression at the last contact. For assessing the probability of lung cancer‐specific mortality and its hazards, date of death from lung cancer was set as the end of follow‐up time for the event of interest, date of death from any other cause was set as the end of follow‐up time for the competing event, and date of the last contact was set as the censoring date for patients who were alive at the last contact.The multivariable models were adjusted for study location, age at diagnosis, sex, education, amount of weight loss in the 2 years before diagnosis, history of COPD, history of chronic general health conditions, smoking status at diagnosis, regular‐alcohol drinking status at diagnosis, tumor histology, and tumor stage at diagnosis. All models were also stratified by year of diagnosis, chemotherapy, and radiotherapy after diagnosis. The proportional hazards assumption was tested using Schoenfeld's global test, which was met for all variables in the multivariable models, except for tumor stage which showed time‐varying effects and was therefore treated as a time‐varying covariate.We also evaluated the effects of different baseline indicators of cigarette smoking/drinking alcohol on NSCLC survival by replacing the smoking/drinking variables in the main models with variables that indicate cumulative cigarettes smoked/alcohol drank, intensity of smoking/drinking, and duration of smoking/drinking.p value for trend by treating these variables as continues variables in the adjusted models.We also assessed the dose–response associations of different smoking and alcohol drinking indicators with risk of the study outcomes by assigning consecutive integers to the categories of each exposure indicator and calculating the All statistical analyses were two‐sided and performed using Stata statistical software version 14 (Stata Corporation).33.1Of the initial 2291 patients recruited into this study, 239 were excluded because of having incomplete data for variables used for downstream analysis, leaving 2052 patients eligible for analysis. Most patients were recruited in Russia (55%), a quarter were recruited in Poland (25%), and the remainder were recruited in Serbia, Czech Republic, and Romania (20%). The mean age at diagnosis was 63.5 years (SD 8.7 years), and most participants were male (71%). At the time of diagnosis, 41% had a diagnosis of hypertension and/or type 2 diabetes and 21% had COPD. Significant weight loss (5% or greater) in the 2 years prior to diagnosis was reported in 19% of participants; 54% of participants were current smokers, 32% were current alcohol drinkers, and presented with squamous cell carcinoma (46%) or adenocarcinoma (44%). Approximately half of the patients presented at stage I (51%) (Table 3.2p < 0.001), those who did not smoke (p < 0.001), did not drink alcohol (p < 0.001), did not lose weight in the last 2 years (p < 0.001), were diagnosed with adenocarcinoma (p = 0.006), and diagnosed at the earliest stages (p < 0.001) , males (HR: 1.24 [1.04–1.49]), those who exhibited weight loss ≥5% (HR: 1.25 [1.03–1.52]), current smokers (HR: 1.30 [1.04–1.62]), and regular alcohol drinkers  , current smokers (HR: 1.11 [1.03–1.72]), current drinkers (HR: 1.25 [1.03–1.52]), and those with higher stage tumors (HR for stage IIIA: 5.31 [3.68–7.65]) (Table 2), curreFrom the multivariable disease progression model, the hazard of disease progression was increased among older individuals (HR for each 10‐year increase in age: 1.25 [1.16–1.35]), regular alcohol drinkers , and neuroendocrine tumors (HR: 1.22 [0.99–1.49]). In contrast, having a chronic illness was associated with a lower hazard of disease progression (HR: 0.87 [0.77–0.99]). There was increasing hazards of progression with increasing tumor stage, reaching almost a fourfold increase in the hazards of disease progression among individuals diagnosed at stage IIIA (HR: 3.85 [2.94–5.03]) (Table 3.4>40 pack/years = 1.26 [1.00–1.59]), quantity of smoking (HR>1 pack/day = 1.31 [1.03–1.68]), and smoking duration (HR>50 years = 1.41 [1.07–1.85]) were associated with increased hazards of overall death (Table ptrend = 0.039) and smoking duration (ptrend = 0.044). Similarly, the hazard of cancer‐specific mortality showed a dose‐dependent increase with increasing amounts of cumulative cigarettes smoked (ptrend = 0.040), quantity of smoking (ptrend = 0.008), and smoking duration (ptrend = 0.010) (Table ptrend = 0.050) , drinking frequency , and years of drinking  were associated with increased hazard of overall mortality, cancer‐specific mortality, and disease progression, respectively, in a dose‐dependent manner  NSCLC remains below 50% in Central and Eastern Europe. Poor survival is a shared feature of lung cancer across Europe, as shown in an analysis of the EUROCARE project that included data from 87 European cancer registries in 28 countries.In our study, patients with NSCLC who were older, male, and current smokers had lower survival. The duration and intensity of smoking increased the hazards of all‐cause and cancer‐specific death, in a dose‐dependent manner. The prognostic value of age and sex for lung cancer survival has been previously demonstrated.There is also conflicting evidence on the relationship between alcohol consumption and lung cancer survival, ranging from studies that showed beneficial effects for consuming low amounts of alcoholMany studies have previously shown the inverse relationship between BMI at diagnosis and lung cancer survival.The strengths of our study are its multicentric prospective design, large sample size, high data quality, and long‐term follow‐up which allowed assessment of multiple outcomes among early‐stage surgically resected lung cancer patients across several countries. One limitation is that the exposures were based on self‐reported data. However, due to the prospective nature of this study, any measurement errors in exposures are likely to be non‐differential by the studied outcomes. Due to the observational nature of the study, we did not intervene in patients' management process and ascertained the cause of death and disease progression using the available death certificates and patients' medical records. Consequently, some misclassifications in the cancer‐specific mortality and disease progression are inevitable due to the variation in physicians' therapeutic and diagnostic approaches and patient's adherence to the follow‐up visits. However, death status is not likely to be affected by these measurements error.In conclusion, this study shows that the survival of early‐stage surgically resected NSCLC is still poor in Central and Eastern Europe. In addition to non‐modifiable prognostic factors , lifetime patterns of smoking and alcohol drinking affected the risk of death and disease progression in a dose‐dependent manner. Therefore, to decrease the burden of lung cancer, implementation of early detection and treatment strategies should be accompanied by supporting behavioral changes.Mahdi Sheikh: Conceptualization (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration ; supervision (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Shama Virani: Formal analysis ; investigation ; methodology ; visualization ; writing – original draft ; writing – review and editing . Hilary A Robbins: Methodology ; visualization ; writing – review and editing . Lenka Foretova: Project administration ; resources ; writing – review and editing . Ivana Holcatova: Project administration ; resources ; writing – review and editing . Vladimir Janout: Project administration ; resources ; writing – review and editing . Jolanta Lissowska: Project administration ; resources ; writing – review and editing . Marie Navratilova: Project administration ; resources ; writing – review and editing . Anush Mukeriya: Project administration ; resources ; writing – review and editing . Miodrag Ognjanovic: Project administration ; resources ; writing – review and editing . Beata Swiatkowska: Project administration ; resources ; writing – review and editing . David Zaridze: Project administration ; resources ; writing – review and editing . Paul Brennan: Conceptualization ; data curation ; investigation ; methodology ; supervision ; writing – review and editing .This work was funded by the International Agency for Research on Cancer (IARC).No potential conflict of interest was reported by the authors.This study was performed in line with the principles of the Declaration of Helsinki. Upon enrolment, informed written consent was obtained from all participants. This study was approved by the local ethical committees of each study center and also the ethical committee of the International Agency for Research on Cancer (IARC).Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.Supporting information S1. Supplementary materialClick here for additional data file.


Currently, immune checkpoint blockers (ICB) and radiotherapy (RT) combination therapy is broadly applied in non‐small cell lung cancer (NSCLC) patients. However, meta‐analysis about safety and efficacy of RT + ICB versus ICB has not yet been reported. To evaluate safety and efficacy of the combination therapy of ICB and RT in patients with recurrent or metastatic NSCLC and explore factors related to higher response rates, longer lifetime, and lower toxicity, meta‐analysis of previous clinical data will be presented in this article.A literature search on patients with recurrent or metastatic NSCLC treated with RT + ICB versus ICB was performed using the Cochrane Library, Embase and PubMed up to December 10, 2022. Suitable quality assessment checklists were selected corresponding to various types of research studies. Comparative and single‐arm studies were analyzed using Stata 14.0.I2) = 0.00%, odds ratio (OR) 1.28, 95% confidence interval (CI) 1.09–1.49, I2 = 0.00%, OR 1.12, 95% CI 1.00–1.25, I2 = 42.1%, OR 0.81, 95% CI 0.72–0.92, I2 = 34.5%, OR 0.80, and 95% CI 0.71–0.89, respectively). Toxicity between combination therapy and ICB monotherapy did not significantly differ in any grade or in ≥3 grade of tr‐AEs . Subgroup analyses based on single‐arm studies showed that applications of SRS/SBRT, PD‐1 inhibitor, and administration of ICB after RT were conducive to a better DCR, longer OS and mild adverse events (heterogeneity between groups (HBG) all p < 0.05).10 comparative studies and 15 arms of combination therapy were included for this meta‐analysis. RT significantly improved objective response rate (ORR), disease control rate (DCR), and overall survival (OS) and progression‐free survival (PFS) of ICB  and radiotherapy (RT) in patients with advanced or recurrent non‐small cell lung cancer and explore factors related to higher response rate, longer lifetime, and lower toxicity, this article was written. The results showed that RT can significantly improve efficacy and survival of ICB and factors of the Asian population, application of traditional RT, Durvalumab as ICB combined with RT, ICB, and RT used in sequence, may enhance clinical benefits without increased toxicity. For example, the sequence and interval between RT and ICB are always in dispute. Some studiesIn view of the controversy about the efficacy and safety of adding RT on the basis of ICB, we conducted the systematic review and meta‐analysis aiming to evaluate safety and efficacy of ICB + RT vs ICB in patients with recurrent or metastatic NSCLC. Potential factors synergizing the curative effects as well as mitigating toxic side effects are further explored.22.1Eligible studies were searched using the Cochrane Library, Embase, and PubMed from inception until December 10, 2022, and no language restrictions were placed. Search terms were included “NSCLC”, “RT”, “CRT”, “ICB”, “PD‐1 inhibitor”, “PD‐L1 inhibitor”, and “CTLA‐4 inhibitor”, specific drug brand/generic names (search strategies of the three database are presented in Supplement 2.2The inclusion criteria are outlined below: (a) metastatic or recurrent NSCLC patients; (b) combination therapy using ICB and RT; (c) studies reporting efficacy and/or safety endpoints, including overall response rate (ORR), disease control rate (DCR), PFS relevant data, OS relevant data, and treatment related adverse events (tr‐AEs). No limitations were applied in publication date, region, language, study design, or sample size.The exclusion criteria are outlined below: (a) study population concomitant with other primary malignancies, (b) studies from which relevant data was duplicated and non‐extractable, (c) studies that reported only protocols or abstracts, (d) studies that reported in special population, and (e) second analysis of previous clinical trials.2.3For assessing the risk of bias, we utilized Cochrane Collaboration Risk of Bias Tool 2.4Two investigators independently performed data extraction from the qualified studies and the third author would discuss together any differences between two investigators. Clinical features of authors, publication date of records, article type, region, study design, recruitment/case review period, follow‐up period, disease status, invention, radiation strategy, type of ICB, sample size, reported endpoints, standard of response assessment, and adverse event evaluation was recorded. Available efficacy outcomes  and safety outcomes (any grade tr‐AEs and ≥3 grade tr‐AEs) were collected. All evaluation indicators are based on the RECIST version 1.1 standard. While only survival curves were published, we extracted data from the survival curves using the Engauge Digitizer software version 10.8 and estimated the hazard ratio (HR).2.5p < 0.05 was statistically significant. Fifty percent of I‐square value (I2) was set as the cut‐off value to stratify heterogeneity into high and low levels. If the pooled effect sizes had low heterogeneity (I2 < 50%), a fixed‐effect model was employed. If the pooled effect sizes had high heterogeneity (I2 ≥ 50%), we used a random‐effects model and further analyzed potential factors using subgroup analysis, meta‐regression, and sensitivity analysis.All the data were conducted with Stata 14.0, using the metan, metaprop, metaninf, and metabias (Begg's and Egger's tests) commands. The pooled effect size was measured by HR for OS and PFS, with their corresponding 95% confidence interval (CI). 33.1The flowchart of the literature search is presented in Figure 3.2In general, the quality items of 3 RCTs, 2 NRCTs, 12 case control studies, 1 single‐arm studies, and 7 case series were well reported. For three RCTs, we used Cochrane Collaboration's tool to assess risk of bias and evaluated the overall risk of bias as low risk Table . For two3.3I2 = 0.00%, OR 1.31, 95% CI 1.13–1.52, I2 = 0.00%, OR 1.13, 95% CI 1.02–1.26, I2 = 42.1%, OR 0.81, 95% CI 0.72–0.92, I2 = 34.5%, OR 0.80, and 95% CI 0.71–0.89, respectively). In studies available for, results of the meta‐analysis showed toxicity between combination therapy and ICB monotherapy did not significantly differ in any grade or in ≥3 grade of tr‐AEs . All the literature was examined using the Begg's and Egger's tests, and there was no potential publication bias.Results of the meta‐analysis in 10 studies3.4p < 0.005). Different from meta‐analysis in comparative studies, which can directly compare PFS and OS of patients, we can only analyze binary variables in single‐arm studies, that is, time survival rate. Considering follow‐up periods of trials, we selected the 2‐years as the time point for OS subgroup analysis. The analysis of a 2‐year OS rate could be concluded that SRS/SBRT, PD‐L1 inhibitor, combined with concurrent CT, and sequential treatment between ICB and RT were related factors affecting the survival period . Grade 3 or greater tr‐AEs associated with patients' quality of life and adherence to medication were selected as a representative indicator of therapeutic safety to explore the possible related factors. According to the results in Asian populations, PD‐1 inhibitor and sequential treatment were safety factors. On the contrary in North American populations, ICB + ICB and concurrent ICB + RT were related factors to obtain severe adverse events. All the literature were examined using the Begg's and Egger's tests, and there was no potential publication bias.We defined the arm of ICB combined with RT as a single‐arm test for analysis, for articles without our comparison content in their control group. Since most studies had no control group, we conducted a single‐arm meta‐analysis of available outcomes in included studies. The number of single‐arm studies and heterogeneity was relatively large, and heterogeneous. Subgroup analysis was used to further explore efficacy and safety. Considering representativeness and comprehensiveness of results, we selected DCR, 2‐year OS rate and ≥3 grade tr‐AEs as evaluation factors of efficacy, survival time and therapeutic safety. Subgroup analyses based on region, type of RT, and type of ICB, whether combined with concurrent CT, disease status, sequence between radiation, and ICB indicated in Table Analyzing short‐term efficacy, long‐term efficacy, and safety together in the asian population it was observed that ICB and RT used in sequence had favorable efficacy and lower toxicity. Application of SRS/SBRT could improve OS and did not show serious toxicity compared to RT techniques. As for ICB selection, our research results showed that, although the patients receiving PD‐1/PD‐L1 inhibitor + CTLA‐4 inhibitor could increase the 2‐year OS rate and may withstand more serious toxicity. Therefore, it was determined which ICB was more suitable for combined RT and ICB therapy from a horizontal comparison. Regarding CT, current data is difficult to determine its use value.4It is known that ICB + RT is one of the most effective therapy strategies among the different therapeutic modalities. Preclinical models revealed unconventional fractionation RT techniques especially SBRT combined with ICB was the most effective combination.Efficacy outcomes of our meta‐analysis proved that ICB combined with RT was superior to single therapy of ICB in ORR, DCR, OS, and PFS, which was not proved conclusively in a previous study. From meta‐analyses of Geng Y et al.To select the most suitable treatment population, our research focused on identifying the factors influencing the efficacy and safety of the combination therapy. Based on the outcomes from subgroup analyses of combined effect in single‐arm studies, we concluded that in the Asian population, application of PD‐1 inhibitors and administration of ICB after RT are favorable for efficacy and lower toxicity. Since treatment strategy combined with concurrent CT and disease status of stage III improved DCR and OS but aggravated toxicity, it should be applied cautiously. However, from comparative studies included in this review, we observed that most of their experimental design was composed of clinical features without concurrent CT, ICB using anti‐PD‐1 inhibitor Nivolumab/Pembrolizumab, and treatment in stage IV/PD NSCLC patients, which were certified as potential disadvantages. In turn, if favorable factors are met in the experimental design, the efficacy would be further improved. Considering the complexity of efficacy and safety in different disease statuses, more comparative studies are needed.RT‐related factors, including dosage, fractionation scheme, site, and novel techniques, are the focus of our study potentially affecting the efficacy and toxicity of the combined therapy. In terms of metastases, the most frequent locations are lung, mediastinum, liver, and brain. Among patients with stage IV lung cancer, brain metastasis are the most studied. The effect of RT based on ICB is confirmed, which can control local lesions and prolong the survival of patients. SRS technology is popular among patients with brain metastasis, although it will increase adverse events of nervous systems. Liver metastasis is one of the most common metastatic sites, with the worst prognosis, the shortest survival period, and poor efficacy of PD‐1 inhibitors. The existing studies have not yet taken the population with lung cancer and liver metastasis as the research object alone, so it is impossible to know whether ICB + RT is effective for the population with liver metastasis. In terms of dosage and fractionation scheme, most of the studies included in this paper used unconventional fractionation, and the fractionation dose exceeds 5gy. Based on preclinical studies, RT and ICB have a synergistic effect. Some expertsBesides RT related factors, we also analyzed the impact of other factors on treatment strategies. One is the sequence and interval time between ICB and RT. Updated exploratory analyses from the PACIFIC TrialThe present review does have several limitations. First, most of the included studies were retrospective with lack of a control group. Besides, in comparative studies, some of them designed RT or CRT as control group were not in line with the purpose of our research. Only 2 RCTs reported results for comparison. Second, the single‐arm analysis only can combine effects from the data of binary variables not for measurement data and contributed to significant heterogeneity. Therefore, the reference value of the subgroup results analysis is limited. Third, although we had carried out subgroup analyses for baseline prognostic characteristics and various therapy regimens. Current data cannot satisfy the needs of such analyses of other underlying factors, such as age, ECOG performance, histological types, tumor PD‐L1 expression, NSCLC‐associated genes driver mutation status, the dose of RT, different irradiated sites, etc. However, we still found meaningful results which may provide reference to clinicians. In the near future, we are looking forward to analyze more large‐scale multi‐center RCTs to determine increase curative effects from this combination, which can help to select a suitable ICB and sequence between RT and ICB and to screen out suitable patients.5From analyses of completed comparative clinical studies, ICB combined with RT showed superior ORR, DCR, OS, and PFS than ICB without RT, without an increase in any grade or ≥3 grade tr‐AEs. According to subgroup analysis, comprehensively, PD‐1 inhibitor following SRS/SBRT could be the best option to maximally benefit patients. Moreover, on‐going large‐scale RCTs are expected to further confirm this notion.Yichun Zeng: Conceptualization (lead); data curation (lead); formal analysis (lead); methodology (lead); writing – original draft (lead). Liying Zhang: Conceptualization (lead); data curation (lead); methodology (lead); resources (lead). Yichen Liang: Supervision ; writing – review and editing . Xian Zhang: Supervision ; validation ; writing – review and editing . Lei Li: Supervision ; writing – review and editing . Maoqi Wang: Supervision ; validation ; writing – review and editing . Jingliang Guo: Supervision ; validation ; writing – review and editing . Qiuxian Li: Supervision ; writing – review and editing . Jing Cao: Supervision ; writing – review and editing . Juan J Gu: Supervision (lead); writing – review and editing (lead). Buhai Wang: Funding acquisition (lead); project administration (lead); supervision (lead); writing – review and editing (lead).This study was supported by the science and technology bureau fund of Yangzhou City (No. YZ2019057).The authors declared that they had no conflict of interests.Supplementary Information S1.Click here for additional data file.Supplementary Information S2.Table S1.Table S2.Table S3.Table S4.Click here for additional data file.Supplementary information S3.Click here for additional data file.


To investigate cortical organization in brain magnetic resonance imaging (MRI) of preterm‐born adults using percent contrast of gray‐to‐white matter signal intensities (GWPC), which is an in vivo proxy measure for cortical microstructure.Using structural MRI, we analyzed GWPC at different percentile fractions across the cortex  in a large and prospectively collected cohort of 86 very preterm‐born  adults and 103 full‐term controls at 26 years of age. Cognitive performance was assessed by full‐scale intelligence quotient (IQ) using the Wechsler Adult Intelligence Scale.p < 0.05). Furthermore, GWPC in right paracentral lobule was negatively correlated with IQ (p < 0.05).GWPC was significantly decreased in VP/VLBW adults in frontal, parietal, and temporal associative cortices, predominantly in the right hemisphere. Differences were pronounced at 20%, 30%, and 40%, hence, in middle cortical layers. GWPC was significantly increased in right paracentral lobule in VP/VLBW adults. GWPC in frontal and temporal cortices was positively correlated with birth weight, and negatively with duration of ventilation (Widespread aberrant gray‐to‐white matter contrast suggests lastingly altered cortical microstructure after preterm birth, mainly in middle cortical layers, with differential effects on associative and primary cortices. Using structural magnetic resonance imaging, this study shows widespread lower gray‐to‐white matter tissue contrast in associative cortices, mainly in middle cortical layers, and higher contrast in primary cortex. Results suggest lastingly altered cortical microstructure after preterm birth with differential effects on associative and primary cortices. The study was carried out in accordance with the Declaration of Helsinki and was approved by the local ethics committee of the Klinikum rechts der Isar, Technische Universität München and the University Hospital Bonn. All study participants gave written informed consent. They received travel expenses and a small payment for participation.Our study sample, the Bavarian Longitudinal Study, has been previously described2.2Gestational age (GA) was estimated from maternal reports on the first day of the last menstrual period and serial ultrasounds during pregnancy. In cases in which the two measures differed by more than 2 weeks, clinical assessment at birth with the Dubowitz method was applied.2.3To assess global cognitive performance at the age of 26, prior to and independent of the MRI examination, study participants were asked to complete a short version of the “Wechsler Intelligenztest für Erwachsene” (WIE), the German adaptation of the Wechsler Adult Intelligence Scale, third edition (WAIS‐III).2.4F = 1.84, p = 0.11). A high‐resolution T1w 3D‐MPRAGE sequence  was acquired. All images were visually inspected for artifacts.MRI data acquisition has been described previously2.5http://surfer.nmr.mgh.harvard.edu/) was used to process the T1w images. These automated methods have been described in detail elsewhere and have been validated against histological data.Images saved as DICOMs were converted to Nifti‐format using dcm2nii.https://surfer.nmr.mgh.harvard.edu/fswiki/QATools), resulting in the exclusion of additional 13 subjects. GWPC values were available for 91 VP/VLBW subjects and 105 FT subjects.Hence, lower GWPC indicates lower contrast between GMI and WMI. This formula differs from other previously reported measures of tissue contrast that have used a ratio calculation (GMI/WMI) with higher values indicating lower contrast.2.6p = 0.274). To control for possible scanner effects, we applied ComBat.All statistical analyses were performed using IBM SPSS Version 26 (IBM Corp). Age was not included as a covariate in our analyses, as VP/VLBW subjects and FT controls had the same mean age of 26 years  were entered as dependent variables. Group (VP/VLBW vs. FT) was entered as a fixed factor, and sex as a factor of no interest. The analyses at different projection fractions were false discovery rate (FDR) corrected for multiple comparisons across all 68 ROIs, respectively, using the Benjamini‐Hochberg procedure.As a control analysis for possible scanner effects, we repeated general linear model analyses for ROIs with significant differences between VP/VLBW individuals and FT controls using only data from one scanner, Munich Achieva 3 T  in SPSS.To investigate whether group differences in GWPC were specifically related to birth variables, we used two‐tailed partial correlation analyses within the VP/VLBW group. If group differences were identified, GWPC at the respective projection fractions in the respective ROIs was correlated with GA, BW, and duration of ventilation. Sex was entered as a covariate of no interest. Results at different projection fractions are reported at 2.6.2p < 0.05, uncorrected, and at p < 0.05, FDR‐corrected for multiple comparisons across all seven correlations regarding cognitive performance, respectively. And 95% confidence intervals for partial correlation analyses were obtained using a bootstrap approach (with 5000 repetitions) in SPSS.To explore the relationship between altered GWPC after preterm birth and cognitive performance as measured by full‐scale IQ, we used two‐tailed partial correlation analyses within the VP/VLBW group. If group differences were found, GWPC at the respective projection fractions in the respective ROIs was correlated with full‐scale IQ. Sex was entered as a covariate of no interest. Results at different projection fractions are reported at 33.1p = 0.736) and age at scanning (p = 0.247). By design of the study, VP/VLBW subjects had significantly lower GA (p < 0.001) and lower BW (p < 0.001). Furthermore, VP/VLBW subjects had significantly lower full‐scale IQ scores compared to FT controls (p < 0.001).Table 3.2p < 0.05, FDR‐corrected) lower in associative cortices in the left inferior frontal and left superior temporal lobes, and in right frontal, parietal, and temporal lobes at 10%, 20%, 30%, 40%, and 50% projection fraction. Differences were more pronounced in the right hemisphere and at 20%, 30%, and 40% projection fraction. ROIs in which GWPC of VP/VLBW individuals was significantly lower compared to FT individuals with respective p‐values are listed in Table p < 0.05, FDR‐corrected) higher in the right paracentral lobule . Except for two results in right inferior frontal gyrus, pars orbitalis, group differences in GWPC remained significant  using only data from one scanner  when additionally controlling for CTh . However, only the associations between the duration of ventilation and left and right superior temporal gyrus at 30% and 40% projection fraction and between the duration of ventilation and right banks of the superior temporal sulcus at 40% projection fraction remained significant after FDR‐correction. Significant (p < 0.05) results are listed in Table To test whether group differences in GWPC are specifically related to preterm birth, we performed partial correlation analyses between GWPC in ROIs with significant differences at 10%, 20%, 30%, 40%, and 50% projection fraction and GA, BW, and duration of ventilation. GWPC in the left inferior frontal gyrus, pars orbitalis, and right superior temporal gyrus was positively correlated with BW, and GWPC in the left inferior frontal gyrus, pars orbitalis, left superior temporal gyrus, right banks of the superior temporal sulcus, right middle temporal gyrus, and right superior temporal gyrus was negatively correlated with duration of ventilation (3.3p < 0.05). However, results did not remain significant after FDR correction. All significant (p < 0.05) results are listed in Table To explore the functional relevance of lower GWPC, we performed partial correlation analyses between GWPC in ROIs with significant differences at 10%, 20%, 30%, 40%, and 50% projection fraction and full‐scale IQ. GWPC in the right paracentral lobule was negatively correlated with full‐scale IQ at 10%, 20%, 30%, and 40% projection fraction  to 60%.Another possible explanation for differences in GWPC could also be abnormal pre‐OL development. Besides SPNs, pre‐OLs are particularly vulnerable to hypoxia‐ischemia.Importantly, our results suggest differential effects of prematurity on the microstructural organization of associative and primary cortices. Diverse neurobiological properties reveal a sensorimotor‐to‐association axis of cortical organization, such as cortical myelination and thickness.Furthermore, we previously investigated cortical architecture after preterm birth using CTh.4.2p < 0.05), indicating that altered GWPC might contribute to lasting cognitive deficits after preterm birth.GWPC in the right paracentral lobule was negatively correlated with full‐scale IQ . At this level of resolution that neuroimaging techniques currently offer, it is not possible to distinguish between different aspects of cortical cytoarchitecture or accurately delineate particular layers of the cortical sheet as defined by histological staining.Furthermore, the current sample is biased toward VP/VLBW adults with less severe neonatal complications, less functional impairments, and higher IQ. Individuals with more birth complications in the initial BLS sample were more likely to be excluded due to exclusion criteria for MRI. Thus, the reported differences in GWPC between VP/VLBW and FT controls are conservative estimates of true differences. However, in terms of GA and BW, our final sample was still representative of the full cohort since these values were not significantly different in VP/VLBW subjects with MRI data compared to subjects without MRI data .5Tissue contrast between gray matter within the cortex and white matter is lower in preterm‐born adults compared to controls. Our results indicate lastingly altered cortical microstructure after preterm birth with differential effects on associative and primary cortices.This work was supported by the Deutsche Forschungsgemeinschaft , German Federal Ministry of Education and Science , the RECAP preterm project, an EU Horizon 2020 study , the UKRI Frontier Research Grant EP/X023206/1  and the Kommission für Klinische Forschung, Technische Universität München .No potential conflicts of interest.Appendix S1Click here for additional data file.


Rhynchophorus ferrugineus , to disperse. However, the question of how the patterns of attachment sites are formed remains intriguing. Here, we examined RPW‐associated phoretic mites in four districts in Northern Portugal (macrohabitat), and investigated the patterns of mite distribution on six body parts of RPW (microhabitat). At the macrohabitat level, we detected seven phoretic mite taxa using the RPW host in each of the four studied districts, all documented for the first time in association with this invasive exotic species in Portugal. However, their relative abundance (species evenness) varied between districts, as did species diversity. All examined weevils carried mites, and the prevalence of the different taxa did not differ between districts or sex of weevils. Measured by mean abundance and degree of aggregation, Centrouropoda sp. proved to be the dominant taxon, while Acarus sp. and Curculanoetus rhynchophorus were considered common subordinate taxa and Uroovobella sp., Mesostigmata, Nenteria extremica and Dendrolaelaps sp. sparse taxa. At the microhabitat level, all taxa were present on all body parts of the RPW; the highest abundance was in a region encompassing the inner surface of the elytra and the membranous hind wings . Analysis of niche overlap revealed that the distribution patterns of phoretic mite taxa on the RPW were not randomly structured. In the subelytral space, interspecific coexistence of mites increased as a function of body size difference with the dominant Centrouropoda sp. We found that in the subelytral space the large dominant species Centrouropoda sp. displaced the larger species Uroobovella sp. and the similarly sized species Nenteria extremica, but coexisted with smaller taxa.Phoretic mites attach to different body parts of the red palm weevil (RPW),  We report a high species richness of up to seven phoretic mite taxa, including five unspecified taxa, actively using the red palm weevil as a host in the first study of these organisms in Portugal. We also found body size‐dependent effects on the coexistence or exclusion of phoretic mite taxa that ultimately determine their distribution patterns on the weevil body. Since the 1980s, RPW has become an invasive exotic insect in the Euro‐Mediterranean region, the Near East, parts of North Africa and the Caribbean , one of its preferred hosts, which is widely used as an ornamental plant in Portuguese coastal cities , an EPPO, . In Portan EPPO, , and hasan EPPO, . In the Numerous groups of mites form phoretic associations with RPW, either as specialised deutonymphs , unspecialised deutonymphs  or as adults  have been found to parasitise their dispersal host Chilocorus cacti  by using its body fluids  is the cause of interspecific interactions that shape species distribution patterns and lead to species coexistence  containing RPW‐specific synthetic aggregation pheromones (Ao Midori Biocontrol S.L.) were placed at eight different sites in the Northern Portugal districts of Viana do Castelo, Braga, Porto and Aveiro near visibly infected Canary Island date palms. The traps were placed at ground level to attract more weevils  and subelytral space (membranous hind wings + inner elytra surface), then removed with a camel hair brush and preserved in 70% ethanol.For identification, selected specimens of each observed mite morphotype were mounted on microscopic slides in lactic acid  at p < .05 as significance level were performed to detect differences between districts in the mean intensity of each mite species   of the three q orders, that is, q = 0 (species richness), q = 1 (Shannon diversity or effective number of frequent species in the assemblage) and q = 2 (Simpson diversity or effective number of highly frequent species in the assemblage) were performed using the R package iNEXT  with expected values from simulated 7 × 6 resource utilisation matrices generated using the Monte Carlo randomisation algorithms RA2 and RA3 for null model analysis  with p < .05 was used for all correlation analyses.All analyses were conducted using the open‐source R environment  and Curculanoetus rhynchophorus Fain, Centrouropoda sp. Berlese 1916 (Mesostigmata: Uropodidae), Uroobovella sp. Berlese 1905 (Mesostigmata: Urodinychidae), Acarus sp. Linnaeus 1758 (Acariformes: Acaridae), Dendrolaelaps sp. Halbert 1915 (Mesostigmata: Digamasellidae), and one from the order Mesostigmata. The order of size of the identified mites by length of idiosoma ± standard error was Uroobovella sp. (886.3 ± 30.9 μm) > Centrouropoda sp. (614.2 ± 29.2 μm) > N. extremica (394.8 ± 16.4 μm) > Mesostigmata (340.3 ± 8.3 μm) > Dendrolaelaps sp. (296 ± 15.7 μm) > C. rhynchophorus (239.1 ± 16.3 μm) > Acarus sp.  was compared using rarefaction/extrapolation curves of Hill numbers y Figure . We founy Figure . In comby Figure , confirmCentrouropoda sp. (p = .877), 66.7% to 44.1% for N. extremica (p = .067), 72.7% to 56.6 for Acarus sp. (p = .214), 37.7% to 25% for Mesostigmata (p = .561), 50% to 32.4% for Dendrolaelaps sp. (p = .277), 30.6% to 15.9% for Uroobovella sp. (p = .234) and 48.6% to 36.7% for C. rhynchophorus (p = .644). The prevalence of each mite taxa also showed no differences between male and female weevils . The mean intensity (the mean number of mites per infected weevil) of each mite taxa differed significantly between districts only for Mesostigmata  of the seven mite taxa in the four mite assemblages showed a highly significant, strong negative correlation   with medium to high MA and D values, which were considered common subordinate taxa. The remaining four taxa, that is, Uroobovella sp., N. extremica, Mesostigmata and Dendrolaelaps sp., were each found in very low numbers and highly aggregated in the four RPW‐associated mite assemblages and were then considered sparse taxa.The ecology of the four assemblages—in terms of descriptive statistics, such as prevalence, mean intensity, mean abundance and Poulin's index of discrepancy for each RPW‐associated mite taxa—is summarised in Table 3.2Centrouropoda sp., C. rhynchophorus and Acarus sp., in that order  and quantified Figure . A two‐sr Figure . A hierap Figure .p < .05), regardless of whether the RA2 (relaxed niche breadth) or RA3 (retaining niche breadth) random algorithm was used for their constructions  = −.13, p = .046], Centrouropoda sp.‐C. rhynchphorus , Centrouropoda sp.‐N. extremica , Centrouropoda sp.‐Acarus sp.  and Acarus sp.‐Dendrolaelaps sp.   and the difference in relative abundance in the subelytra (Δ relative abundance) for pairs of taxa formed between the dominant taxon Centrouropoda sp. and each of the other six taxa. The results showed a strong negative correlation between the two variables, r(4) = −.85, p = .033  could be identified to the species level based on the original description of the life form of deutonymphs  (Coleoptera: Curculionidae)  Fain,  and was e) Fain, . The othe) Fain, . HoweverAn interesting observation of this study was that no member of the mites that are phoretic as adults  was found. Other phoretic groups, such as Parasitidae and the rare phoretics Cheyletidae and Oribatida, were also not found. Although we cannot say for sure, some mites, especially external mites and those that do not have strong means of attachment, may have been lost in the traps, during transport of the weevils to the laboratory or during storage in the refrigerator until dissection.C. rhynchophorus and Acarus sp.) or occurred in low numbers  and may have been overlooked in the other studies.The seven species were present at each of the four sites. However, their relative abundance (species evenness) varied between the districts, as did their diversity. The high number of mite taxa associated with RPW in Northern Portugal contrasts with the few species reported from other European countries where the invasive exotic RPW is also present, such as Italy ; investigation ; methodology ; writing – review and editing . Diogo Silva: Formal analysis ; investigation ; methodology ; writing – review and editing . João Oliveira: Investigation . Claúdia Gonçalves: Investigation . Rita Alves: Investigation . Nuno Pereira: Investigation . Francisco Catarino: Investigation . Olga M. C. C. Ameixa: Methodology ; supervision ; writing – review and editing . José Américo Sousa: Conceptualization ; funding acquisition ; methodology ; writing – review and editing . Luis Filipe Rangel: Conceptualization ; formal analysis ; funding acquisition ; methodology ; project administration ; writing – review and editing . Maria João Santos: Conceptualization ; funding acquisition ; methodology ; project administration ; supervision ; writing – review and editing . Camilo Ayra‐Pardo: Conceptualization ; formal analysis ; funding acquisition ; methodology ; project administration ; supervision ; writing – original draft (lead); writing – review and editing (lead).The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.


Dermatomyositis (DM) is a poorly prognostic autoimmune disease the pathogenesis of which is multifactorial and not clearly defined. DM may be influenced by genes, environment, and immunity. The typical manifestations of DM are Gottron rash, heliotrope rash, rash on the shoulders and buttocks, erythema around fingernails, excessive keratosis of the epidermis, mechanic's hands, and interstitial lung disease (ILD), among others. Anti‐melanoma differentiation‐associated 5 gene (MDA5) antibody has been strongly associated with DM. Furthermore, anti‐SSA/Ro52 antibody has been reportedly associated with DM. A 49‐year‐old woman presented with cough, expectoration, and dyspnea. Relevant examinations revealed elevated levels of muscle enzyme, double‐positive anti‐MDA5 and anti‐SSA/Ro52 antibodies, positive rheumatoid factor, and a high titer of anti‐citrullinated protein antibody. DM overlapping rheumatoid arthritis with ILD was confirmed. We suggest the use of glucocorticoids combined with immunosuppressant therapy, supplemented with gastric and liver protection, and recommend the use of intravenous immunoglobulins and rituximab. Clinical manifestations include symmetrical proximal muscle weakness and typical skin lesions.2A 49‐year‐old woman presented with a 5‐year history of pain in both shoulders, knees, ankles, and especially in both palmar knuckles, with positive rheumatoid factor and high titer of anti‐citrullinated protein antibody, consistent with a diagnosis of RA. The patient presented with cough and expectoration for half a month, with shortness of breath and dyspnea for 4 days. Physical examination revealed purple erythema around both orbits with edema Figure , large dTreatments included methylprednisolone sodium succinate pulse therapy and oral hydroxychloroquine sulfate tablets to improve the symptoms of DM, oral pirfenidone to improve ILD, intravenous injection of pantoprazole sodium to inhibit acid and protect the stomach, and glycyrrhizin to protect the liver. After treatment with this regimen, the muscle enzyme index of the patient was still high. However, clinical symptoms significantly improved, no new rash developed, and joint pain, weakness, and other symptoms were significantly reduced. It was recommended that the patient receive intravenous immunoglobulins (IVIG) and rituximab (RTX), and that she and her family be discharged due to financial problems. One month later, the patient's family reported the patient's death.3DM is a rare autoimmune disease that mainly affects the skin, muscles, and lung.A literature search was performed in PubMed for the last 5 years, using the terms “anti‐MDA5 antibody,” “anti‐SSA/RO52 antibody,” “DM,” “RA,” “ILD,” and “case report”. The search revealed 7 cases of anti‐MDA5 antibody‐positive DM with ILD, 1 case of RA with anti‐MDA5 antibody‐positive ILD, and 2 cases of anti‐MDA5 antibody‐positive DM with systemic lupus erythematosus. Anti‐MDA5 antibody‐positive DM overlapping with other rheumatic diseases is relatively rare, and there are currently no guidelines for the treatment of such diseases. The ideal drug for the treatment of DM is not clear. The treatment plan is mainly based on the results of clinical observation. The current cornerstone of DM therapy is glucocorticoids alone or in combination with an immunosuppressant,4In this case, 2 autoimmune diseases overlapped, and both led to ILD. The clinical characteristics, laboratory examination data, and immunological characteristics of the patient met both the European Neuro Muscular Centre 2018 DM diagnostic criteria and the 2010 American College of Rheumatology / European Alliance of Associations for Rheumatology RA classification criteria.


Myocarditis caused by the mRNA‐1273 coronavirus disease 2019 vaccine must be considered for patients complaining of acute constant general fatigue postvaccination. A 43‐year‐old previously healthy man presented with acute diarrhea and constant general fatigue for 11 days. In addition, he complained of a cough and arthralgia that persisted for 2 days. Twenty‐two days earlier, he received his third dose of the mRNA‐1273 (Moderna) coronavirus disease 2019 (COVID‐19) vaccine. Prior to this illness, he had no contact with any individuals with fever, including those positive for severe acute respiratory syndrome coronavirus 2. After the first two vaccine doses, he developed a moderate fever that lasted for 1 day on both occasions. Upon arrival at our clinic, he appeared unwell, and his vital signs were as follows: blood pressure of 122/90 mmHg, heart rate of 125 beats/min, body temperature of 36.3°C, respiratory rate of 26 breaths/minute, and transcutaneous oxygen saturation of 93% in room air. His COVID‐19 antigen test result was negative. An electrocardiogram (Figure The author declare no conflict of interest.Ethical approval was not required for this study.Written informed consent was obtained from the patient to publish this report as per the journal's patient consent policy.