bluuebunny/crossref_metadata_2025_split huggingface dataset:
Induced expression of CCL19 promotes the anti-tumor ability of CAR-T cells by increasing their infiltration ability
Authors: Hu Jian-fei , Wang Zu-wei , Liao Cheng-yu , Chen Zhi-wen , Kang Feng-ping , Lin Cai-feng , Lin Tian-sheng , Huang Long , Tian Yi-feng , Chen Shi 
Published: August 2022

BackgroundChimeric antigen receptor-engineered T cell (CAR-T) therapy has shown promising potential for anti-cancer treatment. However, for pancreatic ductal adenocarcinoma (PDAC), the lack of infiltrative ability of these CAR-T cells leads to sub-optimal treatment outcome.MethodsChemokine (C-C motif) ligand 19 (CCL19), the expression of which is regulated by the nuclear factor of activated T cell pathway, was transfected into targeting mesothelin CAR-T cells (mesoCAR-N19) using NFAT regulating element. It was expressed in activated CAR-T cells by OKT3 or mesothelin+ tumor cells but not in inactive cells. The migratory ability of these CAR-T cells was then measured. Subsequently, functional identification of these CAR-T cells was performed in vivo. In addition, the tumor lytic activity and proliferation of the CAR-T cells were measured in vitro. The degree of CAR-T cell infiltration and distribution into the PDAC tumors was examined using the immunohistochemical staining of hCD3 and the detection of CAR gene copy number by quantitative PCR. Finally, the functional assessment of chemokine (C-C motif) receptor 7 knock-out was performed in the CAR-T cells.ResultsThrough in vitro Transwell assays, it was demonstrated that mesoCAR-N19 can be specifically expressed in CAR-T cells activated by tumor cells compared with conventional mesothelin CAR-T (mesoCAR) cells. We also observed that upregulating the expression of CCL19 can increase the recruitment of additional T cells. In vivo studies subsequently revealed that this highly specific recruitment of T cell infiltration is associated with enhanced tumor-suppressive activities downstream.ConclusionInduced expression of CCL19 can promote the anti-tumor ability of CAR-T cells by increasing their infiltrative ability. This study potentially uncovered novel method of activating CAR-T cells to enhance their infiltrative capacities, which offers a novel direction for PDAC treatment.
Application of metagenomic next-generation sequencing in the detection of pathogens in bronchoalveolar lavage fluid of infants with severe pneumonia after congenital heart surgery
Authors: Zheng Yi-Rong , Lin Shi-Hao , Chen Yu-Kun , Cao Hua , Chen Qiang 
Published: August 2022

BackgroundMetagenomic next-generation sequencing (mNGS) has become a valuable diagnostic tool in clinical etiology detection due to its rapidity, accuracy, and high throughput. However, the role of this technology in the diagnosis and treatment of infants with severe pneumonia after congenital heart surgery is still unclear.MethodsWe conducted a retrospective cohort study of infants with severe pneumonia after congenital heart surgery. Samples were collected from infants in the hospital’s cardiac intensive care unit between January 2010 and January 2022. The conventional microbiological test (CMT) group consisted of patients who underwent routine microbiological examination, and the infants’ bronchoalveolar lavage fluid was examined. The mNGS group consisted of patients who underwent mNGS and routine microbiological examinations.ResultsThe overall positive rate of mNGS was significantly higher than that of CMT (88.4 vs. 62.5%, P = 0.009). After receipt of the microbiological results, 30/43 (70%) patients in the mNGS group had a change in antibiotic use compared with 14/40 (35%) in the CMT group (P = 0.002). Subsequently, after adjusting the treatment plan according to the microbiological test results, the number of people with improved pulmonary infection in the mNGS group was significantly higher than that in the CMT group (63 vs. 28%, P &lt; 0.05). In addition, the duration of invasive ventilation, length of CICU stay and total hospital length of stay in the mNGS group were significantly lower than those in the CMT group (P &lt; 0.05).ConclusionmNGS is a valuable tool to determine the etiology of infants with severe pneumonia after congenital heart disease surgery. It can significantly improve the sensitivity of pathogen detection, which can help determine appropriate antimicrobial drugs, improve the diagnostic accuracy of the disease, and improve outcomes.
Identification of Potential Ferroptosis Key Genes in the Pathogenesis of Lumbosacral Spinal Root Avulsion by RNA Sequencing and Bioinformatics Analysis
Authors: Zhou Zhibin , Lu Jiajia , Ma Jun , Zhu Lei 
Published: August 2022

Objective: Ferroptosis is a type of cell death involved in various human diseases, including nerve injury. However, the role of ferroptosis in lumbosacral spinal root avulsion (LSRA) remains unknown. This study aims to investigate whether ferroptosis is induced after LSRA and the key ferroptosis-related genes and their potential function in LSRA.Methods: The biochemical and morphological changes of ferroptosis were determined by detection of iron accumulation and by transmission electron microscopy in a rat LSRA model. The transcriptional expression profile following LSRA was investigated by RNA sequencing and ferroptosis-related genes were downloaded from FerrDb and used to identify ferroptosis differentially expressed genes (DEGs). The differential expressions of ferroptosis DEGs were confirmed by qRT-PCR analysis. The potential functions of ferroptosis DEGs were revealed by DAVID 6.8 and WebGestalt. A protein–protein interaction (PPI) network and gene–miRNA interaction network were further constructed to identify key modules in ferroptosis DEGs, and the results were verified by qRT-PCR and western blot analysis.Results: LSRA was followed by ferroptosis-specific changes, such as shrunken mitochondria and increased iron accumulation, that can be alleviated by ferroptosis inhibitor deferoxamine (DFO). A total of 2,446 DEGs and 46 ferroptosis DEGs were identified after LSRA, and over 90% of the ferroptosis DEGs were confirmed to be differentially expressed following LSRA, which can also be eliminated by DFO treatment. Functional analysis demonstrated significant enrichment of the ferroptosis DEGs in pathways related to the oxidative stress response, the HIF-1 signaling pathway, and the tumor necrosis factor signaling pathway. PPI network analysis demonstrated that a set of key modules in ferroptosis DEGs were related to the HIF-1 signaling pathway: Il6, Nos2, Stat3, Hif1a, Vegfa, Cdkn1a, and Rela. Construction of a gene–miRNA network predicted miRNAs targeting four key ferroptosis DEGs—Stat3, Hif1a, Vegfa, and Rela, and further western blot analysis confirmed their upregulation after LSRA, which can be alleviated by DFO pretreatment.Conclusion: The data revealed the induction of ferroptosis in a rat LSRA model and identified possible regulatory roles for ferroptosis-related genes in the molecular mechanisms of LSRA, which provides new insights into the pathogenesis and helps to find new molecular targets for the treatment of LSRA.
Amino acid metabolism genes associated with immunotherapy responses and clinical prognosis of colorectal cancer
Authors: Peng Xinyi , Zheng Ting , Guo Yong , Zhu Ying 
Published: August 2022

Based on amino acid metabolism-related genes (AAMRGs), this study aimed at screening out key prognosis-related genes and finding the underlying correlation between the amino acid metabolism and tumor immune microenvironment of colorectal cancer. A total of 448 amino acid metabolism-related genes were obtained from MsigDB. The risk signature was built based on differential expression genes, univariate Cox, and LASSO analyses with 403 patients’ data downloaded from the TCGA database. Survival analysis and independence tests were performed to confirm the validity of the risk signature. Single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), the score of tumor immune dysfunction and exclusion (TIDE), the immunophenoscore obtained from The Cancer Immunome Atlas database, and the IC50 of drugs were used to find the relationship among the risk signature, immune status, immunotherapy response, and drug sensitivity of colorectal cancer. We identified five amino acid metabolism-related genes for the construction of the risk signature, including ENOPH1, ACAT1, ALDH4A1, FAS, and ASPG. The low-risk group was significantly associated with a better prognosis (p &lt; 0.0001). In the entire set, the area under the curve (AUC) for 1, 3, and 5 years was 0.717, 0.734, and 0.764, respectively. We also discovered that the low-risk subgroup was related to more activity of immune cells, had higher expression of some immune checkpoints, and was more likely to benefit from immunotherapy. ssGSEA revealed that except the processes of glutamine histidine, lysine, tyrosine, and L-phenylalanine metabolism, the other amino acid metabolism pathways were more active in the samples with the low risk scores, whereas the activities of synthesis and transportation of most amino acids were similar. Hedgehog signaling, WNT/β-catenin signaling, mitotic, notch signaling, and TGF-β signaling were the top five pathways positively associated with the risk score. To sum up, AAMRGs were associated with the immune microenvironment of CRC patients and could be applied as biomarkers to predict the prognosis and immunotherapy response of patients.
Green tea improves cognitive function through reducing AD-pathology and improving anti-oxidative stress capacity in Chinese middle-aged and elderly people
Authors: Zhang Ran , Zhang Lei , Li Zeng , Zhang Ping , Song Hao , Yao Dong-ai , Cao Jing , Zhang Jun-jian 
Published: August 2022

Background: Numerous benefits of green tea have been reported. However, the effects of green tea on cognitive function remain disputable and the mechanism is still unclear.Objective: To investigate the relationship of green tea consumption with cognitive function and related blood biomarkers among Chinese middle-aged and elderly people.Methods: A total of 264 participants aged 50–70 years old were enrolled from Zhongnan Hospital of Wuhan University. They were interviewed about green tea consumption patterns and underwent neuropsychological tests covering five main cognitive domains to assess cognition including Montreal Cognitive Assessment (MoCA) and the other 10 scales. Then we detected serum oxidative stress biomarkers including Superoxide Dismutase (SOD), Malondialdehyde (MDA), Glutathione Peroxidase (GPx), Glutathione Reductase (GR), and Alzheimer’s disease (AD) markers including β-amyloid (Aβ)40, Aβ42, and phosphorylated tau-181 (pTau181).Results: In the tea-consuming group, the MoCA scores (P = 0.000), Hopkins Verbal Learning Test (HVLT) immediate recall (P = 0.012) and delayed recall (P = 0.013) were significantly higher while Trail Making Test-B (P = 0.005) and Victoria Stroop test interference (P = 0.000) were lower. In terms of oxidative stress markers, the tea-consuming group had lower serum MDA levels (P = 0.002) and higher serum SOD (P = 0.005) and GPx (P = 0.007) levels. In terms of AD markers, serum pTau181 (P &lt; 0.000), Aβ42 (P = 0.019) and total Aβ levels (P = 0.034) but not serum Aβ40 levels, were lower in the tea-consuming group. In the logistic regression analysis, there was a significant negative correlation between green tea consumption and cognitive impairment (OR = 0.26, 95 % CI 0.13 0.52 for high group).Conclusion: Regular green tea consumption is associated with better cognitive function among Chinese middle-aged and elderly people, mainly reflected in memory and executive function. It may achieve protective effects by reducing AD-related pathology and improving anti-oxidative stress capacity and higher levels of tea consumption have a stronger protective effect.
m6A regulator–mediated RNA methylation modification patterns and immune microenvironment infiltration characterization in patients with intracranial aneurysms
Authors: Maimaiti Aierpati , Turhon Mirzat , Cheng Xiaojiang , Su Riqing , Kadeer Kaheerman , Axier Aximujiang , Ailaiti Dilimulati , Aili Yirizhati , Abudusalamu Rena , Kuerban Ajimu , Wang Zengliang , Aisha Maimaitili 
Published: August 2022

BackgroundThe role of epigenetic modulation in immunity is receiving increased recognition—particularly in the context of RNA N6-methyladenosine (m6A) modifications. Nevertheless, it is still uncertain whether m6A methylation plays a role in the onset and progression of intracranial aneurysms (IAs). This study aimed to establish the function of m6A RNA methylation in IA, as well as its correlation with the immunological microenvironment.MethodsOur study included a total of 97 samples (64 IA, 33 normal) in the training set and 60 samples (44 IA, 16 normal) in the validation set to systematically assess the pattern of RNA modifications mediated by 22 m6A regulators. The effects of m6A modifications on immune microenvironment features, i.e., immune response gene sets, human leukocyte antigen (HLA) genes, and infiltrating immune cells were explored. We employed Lasso, machine learning, and logistic regression for the purpose of identifying an m6A regulator gene signature of IA with external data validation. For the unsupervised clustering analysis of m6A modification patterns in IA, consensus clustering methods were employed. Enrichment analysis was used to assess immune response activity along with other functional pathways. The identification of m6A methylation markers was identified based on a protein–protein interaction network and weighted gene co-expression network analysis.ResultsWe identified an m6A regulator signature of IGFBP2, IGFBP1, IGF2BP2, YTHDF3, ALKBH5, RBM15B, LRPPRC, and ELAVL1, which could easily distinguish individuals with IA from healthy individuals. Unsupervised clustering revealed three m6A modification patterns. Gene enrichment analysis illustrated that the tight junction, p53 pathway, and NOTCH signaling pathway varied significantly in m6A modifier patterns. In addition, the three m6A modification patterns showed significant differences in m6A regulator expression, immune microenvironment, and bio-functional pathways. Furthermore, macrophages, activated T cells, and other immune cells were strongly correlated with m6A regulators. Eight m6A indicators were discovered—each with a statistically significant correlation with IA—suggesting their potential as prognostic biological markers.ConclusionOur study demonstrates that m6A RNA methylation and the immunological microenvironment are both intricately correlated with the onset and progression of IA. The novel insight into patterns of m6A modification offers a foundation for the development of innovative treatment approaches for IA.
Adjuvant medical therapy in cervical dystonia after deep brain stimulation: A retrospective analysis
Authors: Martinez-Nunez Alfonso E. , Sidiropoulos Christos , Wall Julia , Schwalb Jason , Air Ellen , LeWitt Peter , Bulica Bisena , Kaminski Patricia , Patel Neepa 
Published: August 2022

BackgroundThere is limited information on optimization of symptomatic management of cervical dystonia (CD) after implantation of pallidal deep brain stimulation (DBS).ObjectivesTo describe the long-term, “real-world” management of CD patients after DBS implantation and the role of reintroduction of pharmacologic and botulinum toxin (BoNT) therapy.MethodsA retrospective analysis of patients with focal cervical or segmental craniocervical dystonia implanted with DBS was conducted.ResultsNine patients were identified with a mean follow-up of 41.7 ± 15.7 months. All patients continued adjuvant oral medication(s) to optimize symptom control post-operatively. Three stopped BoNT and four reduced BoNT dose by an average of 22%. All patients remained on at least one medication used to treat dystonia post-operatively.ConclusionOptimal symptom control was achieved with DBS combined with either BoNT and/or medication. We suggest utilization of adjuvant therapies such as BoNT and/or medications if DBS monotherapy does not achieve optimal symptom control.
Clinical scoring model based on age, NIHSS, and stroke-history predicts outcome 3 months after acute ischemic stroke
Authors: Ding Gang-yu , Xu Jian-hua , He Ji-hong , Nie Zhi-yu 
Published: August 2022

BackgroundThe clinical nomogram is a popular decision-making tool that can be used to predict patient outcomes, bringing benefits to clinicians and patients in clinical decision-making. This study established a simple and effective clinical prediction model to predict the 3-month prognosis of acute ischemic stroke (AIS), and based on the predicted results, improved clinical decision-making and improved patient outcomes.MethodsFrom 18 December 2021 to 8 January 2022, a total of 146 hospitalized patients with AIS confirmed by brain MR were collected, of which 132 eligible participants constituted a prospective study cohort. The least absolute shrinkage and selection operator (LASSO) regression was applied to a nomogram model development dataset to select features associated with poor prognosis in AIS for inclusion in the logistic regression of our risk scoring system. On this basis, the nomogram was drawn, evaluated for discriminative power, calibration, and clinical benefit, and validated internally by bootstrap. Finally, the optimal cutoff point for each independent risk factor and nomogram was calculated using the Youden index.ResultsA total of 132 patients were included in this study, including 85 men and 47 women. Good outcome was found in 94 (71.212%) patients and bad outcome in 38 (28.788%) patients during the follow-up period. A total of eight (6.061%) deaths were reported over this period, of whom five (3.788%) died during hospitalization. Five factors affecting the 3-month prognosis of AIS were screened by LASSO regression, namely, age, hospital stay, previous stroke, atrial fibrillation, and NIHSS. Further multivariate logistic regression revealed three independent risk factors affecting patient outcomes, namely, age, previous stroke, and NIHSS. The area under the curve of the nomogram was 0.880, and the 95% confidence interval was 0.818–0.943, suggesting that the nomogram model has good discriminative power. The p-value for the calibration curve is 0.925, indicating that the nomogram model is well-calibrated. According to the decision curve analysis results, when the threshold probability is &gt;0.01, the net benefit obtained by the nomogram is the largest. The concordance index for 1,000 bootstrapping calculations is 0.869. The age cutoff for predicting poor patient outcomes using the Youden index was 76.5 years (specificity 0.777 and sensitivity 0.684), the cutoff for the NIHSS was 7.5 (specificity 0.936, sensitivity 0.421), and the cutoff for total nomogram score was 68.8 (sensitivity 81.6% and specificity 79.8%).ConclusionThe nomogram model established in this study had good discrimination, calibration, and clinical benefits. A nomogram composed of age, previous stroke, and NIHSS might predict the prognosis of stroke after AIS. It might intuitively and individually predict the risk of poor prognosis in 3 months of AIS and provide a reference basis for screening the treatment plan of patients.
Observations from a prospective small cohort study suggest that CGRP genes contribute to acute posttraumatic headache burden after concussion
Authors: La Fountaine Michael F. , Hohn Asante N. , Leahy Caroline L. , Weir Joseph P. , Testa Anthony J. 
Published: August 2022

IntroductionPost-traumatic headache (PTH) is commonly reported after concussion. Calcitonin gene-related peptide (CGRP) is implicated in the pathogenesis of migraine. We explored how single nucleotide polymorphisms (SNPs) from CGRP-alpha (CALCA) and the receptor activity modifying protein-1 (RAMP1) related to headache burden during the first week after concussion.MethodsA prospective study was performed in 34 collegiate athletes who sustained a concussion. Participants completed the symptom evaluation checklist from the SCAT3 within 48 h of injury (V1), and again 4 (V2) and 7 (V3) days after injury. For each visit, the self-reported score (0–6) for headache, pressure in head, blurred vision, and sensitivity to light/noise were reported and summed to calculate the headache burden. A saliva sample was obtained and genotyped for CALCA (rs3781719) and RAMP1 (rs10185142). RAMP1 (TT, TC, CC) and CALCA (AA, AG, GG) were dichotomized (A+, A- and T+, T-, respectively), and concatenated (T+A+, T+A-, T-A+, T-A-) for analyses.ResultsHeadache Burden at Visit 1 was greatest in T+A+ compared to T-A+, and trended toward a significant difference with T+A-. Repeated-measures ANOVA revealed the presence of significant visit main effects (p&lt; 0.001, η2= 0.404), but the group (p= 0.055) and interaction effects only trended (p= 0.094). Pearson's χ2-tests revealed that 88% of those with return-to play (RTP) exclusions ≥15 days had PTH with multi-sensory symptoms (PTH+SENS) as compared to 35% in those with RTP &lt; 14 day.ConclusionKnowledge of RAMP1 and CALCA genotypes appear to improve an understanding the presenting features and magnitude of headache burden after concussion injury.
Deep learning-based relapse prediction of neuromyelitis optica spectrum disorder with anti-aquaporin-4 antibody
Authors: Wang Liang , Du Lei , Li Qinying , Li Fang , Wang Bei , Zhao Yuanqi , Meng Qiang , Li Wenyu , Pan Juyuan , Xia Junhui , Wu Shitao , Yang Jie , Li Heng , Ma Jianhua , ZhangBao Jingzi , Huang Wenjuan , Chang Xuechun , Tan Hongmei , Yu Jian , Zhou Lei , Lu Chuanzhen , Wang Min , Dong Qiang , Lu Jiahong , Zhao Chongbo , Quan Chao 
Published: August 2022

ObjectiveWe previously identified the independent predictors of recurrent relapse in neuromyelitis optica spectrum disorder (NMOSD) with anti-aquaporin-4 antibody (AQP4-ab) and designed a nomogram to estimate the 1- and 2-year relapse-free probability, using the Cox proportional hazard (Cox-PH) model, assuming that the risk of relapse had a linear correlation with clinical variables. However, whether the linear assumption fits real disease tragedy is unknown. We aimed to employ deep learning and machine learning to develop a novel prediction model of relapse in patients with NMOSD and compare the performance with the conventional Cox-PH model.MethodsThis retrospective cohort study included patients with NMOSD with AQP4-ab in 10 study centers. In this study, 1,135 treatment episodes from 358 patients in Huashan Hospital were employed as the training set while 213 treatment episodes from 92 patients in nine other research centers as the validation set. We compared five models with added variables of gender, AQP4-ab titer, previous attack under the same therapy, EDSS score at treatment initiation, maintenance therapy, age at treatment initiation, disease duration, the phenotype of the most recent attack, and annualized relapse rate (ARR) of the most recent year by concordance index (C-index): conventional Cox-PH, random survival forest (RSF), LogisticHazard, DeepHit, and DeepSurv.ResultsWhen including all variables, RSF outperformed the C-index in the training set (0.739), followed by DeepHit (0.737), LogisticHazard (0.722), DeepSurv (0.698), and Cox-PH (0.679) models. As for the validation set, the C-index of LogisticHazard outperformed the other models (0.718), followed by DeepHit (0.704), DeepSurv (0.698), RSF (0.685), and Cox-PH (0.651) models. Maintenance therapy was calculated to be the most important variable for relapse prediction.ConclusionThis study confirmed the superiority of deep learning to design a prediction model of relapse in patients with AQP4-ab-positive NMOSD, with the LogisticHazard model showing the best predictive power in validation.