bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gut Microbial Characteristics of Adult Patients With Epilepsy
Authors: Dong Lian , Zheng Qian , Cheng Yongran , Zhou Mengyun , Wang Mingwei , Xu Jianwei , Xu Zucai , Wu Guofeng , Yu Yunli , Ye Lan , Feng Zhanhui 
Published: February 2022

ObjectiveTo characterize the intestinal flora of patients with epilepsy and its correlation with epilepsy.MethodsPatients with ages &gt; 18 years were consecutively enrolled from the outpatient department, Affiliated Hospital of Guizhou Medical University from January 2018 to December 2019. A total of 71 subjects were recruited, including epilepsy patients (n = 41) as an observation group and patient family members (n = 30) as a control group. Fresh stool specimens of all the subjects were collected. The 16S ribosomal RNA sequencing was analyzed to determine changes in intestinal flora composition and its correlation with epilepsy. Subgroup analysis was then conducted. All patients with epilepsy were divided into an urban group (n = 21) and a rural group (n = 20) according to the region, and bioinformatics analyses were repeated between subgroups.ResultsLEfSe analysis showed that Fusobacterium, Megasphaera, Alloprevotella, and Sutterella had relatively increased abundance in the epilepsy group at the genus level. Correlation analysis suggested that Fusobacterium sp. (r = 0.584, P &lt; 0.01), Fusobacterium mortiferum (r = 0.560, P &lt; 0.01), Ruminococcus gnavus (r = 0.541, P &lt; 0.01), and Bacteroides fragilis (r = 0.506, P &lt; 0.01) were significantly positively correlated with the occurrence of epilepsy (r ≥ 0.5, P &lt; 0.05). PICRUSt function prediction analysis showed that there were significant differences in 16 pathways between the groups at level 3. Comparing the rural group with the urban group, Proteobacteria increased at the phylum level and Escherichia coli, Fusobacterium varium, Prevotella stercorea, and Prevotellaceae bacterium DJF VR15 increased at the species level in the rural group.ConclusionThere were significant differences in the composition and functional pathways of gut flora between epilepsy patients and patient family members. The Fusobacterium may become a potential biomarker for the diagnosis of epilepsy.
Quantitative MR-Neurography at 3.0T: Inter-Scanner Reproducibility
Authors: Preisner Fabian , Behnisch Rouven , Schwehr Véronique , Godel Tim , Schwarz Daniel , Foesleitner Olivia , Bäumer Philipp , Heiland Sabine , Bendszus Martin , Kronlage Moritz 
Published: February 2022

BackgroundQuantitative MR-neurography (MRN) is increasingly applied, however, the impact of the MR-scanner on the derived parameters is unknown. Here, we used different 3.0T MR scanners and applied comparable MR-sequences in order to quantify the inter-scanner reproducibility of various MRN parameters of the sciatic nerve.MethodsTen healthy volunteers were prospectively examined at three different 3.0T MR scanners and underwent MRN of their sciatic nerve using comparable imaging protocols including diffusion tensor imaging (DTI) and T2 relaxometry. Subsequently, inter-scanner agreement was assessed for seven different parameters by calculating the intraclass correlation coefficients (ICCs) and the standard error of measurement (SEM).ResultsAssessment of inter-scanner reliability revealed good to excellent agreement for T2 (ICC: 0.846) and the quantitative DTI parameters, such as fractional anisotropy (FA) (ICC: 0.876), whereas moderate agreement was observed for proton spin density (PD) (ICC: 0.51). Analysis of variance identified significant inter-scanner differences for several parameters, such as FA (p &lt; 0.001; p = 0.02), T2 (p &lt; 0.01) and PD (p = 0.02; p &lt; 0.01; p = 0.02). Calculated SEM values were mostly within the range of one standard deviation of the absolute mean values, for example 0.033 for FA, 4.12 ms for T2 and 27.8 for PD.ConclusionThis study quantifies the measurement imprecision for peripheral nerve DTI and T2 relaxometry, which is associated with the use of different MR scanners. The here presented values may serve as an orientation of the possible scanner-associated fluctuations of MRN biomarkers, which can occur under similar conditions.
Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer
Authors: Abousamra Shahira , Gupta Rajarsi , Hou Le , Batiste Rebecca , Zhao Tianhao , Shankar Anand , Rao Arvind , Chen Chao , Samaras Dimitris , Kurc Tahsin , Saltz Joel 
Published: February 2022

The role of tumor infiltrating lymphocytes (TILs) as a biomarker to predict disease progression and clinical outcomes has generated tremendous interest in translational cancer research. We present an updated and enhanced deep learning workflow to classify 50x50 um tiled image patches (100x100 pixels at 20x magnification) as TIL positive or negative based on the presence of 2 or more TILs in gigapixel whole slide images (WSIs) from the Cancer Genome Atlas (TCGA). This workflow generates TIL maps to study the abundance and spatial distribution of TILs in 23 different types of cancer. We trained three state-of-the-art, popular convolutional neural network (CNN) architectures (namely VGG16, Inception-V4, and ResNet-34) with a large volume of training data, which combined manual annotations from pathologists (strong annotations) and computer-generated labels from our previously reported first-generation TIL model for 13 cancer types (model-generated annotations). Specifically, this training dataset contains TIL positive and negative patches from cancers in additional organ sites and curated data to help improve algorithmic performance by decreasing known false positives and false negatives. Our new TIL workflow also incorporates automated thresholding to convert model predictions into binary classifications to generate TIL maps. The new TIL models all achieve better performance with improvements of up to 13% in accuracy and 15% in F-score. We report these new TIL models and a curated dataset of TIL maps, referred to as TIL-Maps-23, for 7983 WSIs spanning 23 types of cancer with complex and diverse visual appearances, which will be publicly available along with the code to evaluate performance.Code Available at:https://github.com/ShahiraAbousamra/til_classification.
Radiomic and Dosiomic Features for the Prediction of Radiation Pneumonitis Across Esophageal Cancer and Lung Cancer
Authors: Puttanawarut Chanon , Sirirutbunkajorn Nat , Tawong Narisara , Jiarpinitnun Chuleeporn , Khachonkham Suphalak , Pattaranutaporn Poompis , Wongsawat Yodchanan 
Published: February 2022

PurposeThe aim was to investigate the advantages of dosiomic and radiomic features over traditional dose-volume histogram (DVH) features for predicting the development of radiation pneumonitis (RP), to validate the generalizability of dosiomic and radiomic features by using features selected from an esophageal cancer dataset and to use these features with a lung cancer dataset.Materials and MethodsA dataset containing 101 patients with esophageal cancer and 93 patients with lung cancer was included in this study. DVH and dosiomic features were extracted from 3D dose distributions. Radiomic features were extracted from pretreatment CT images. Feature selection was performed using only the esophageal cancer dataset. Four predictive models for RP (DVH, dosiomic, radiomic and dosiomic + radiomic models) were compared on the esophageal cancer dataset. We further used a lung cancer dataset for the external validation of the selected dosiomic and radiomic features from the esophageal cancer dataset. The performance of the predictive models was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROCAUC) and the AUC of the precision recall curve (PRAUC) metrics.ResultThe ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on esophageal cancer dataset were 0.67 ± 0.11 and 0.75 ± 0.10, 0.71 ± 0.10 and 0.77 ± 0.09, 0.71 ± 0.11 and 0.79 ± 0.09, and 0.75 ± 0.10 and 0.81 ± 0.09, respectively. The predictive performance of the dosiomic- and radiomic-based models was significantly higher than that of the DVH-based model with respect to esophageal cancer. The ROCAUCs and PRAUCs of the DVH, dosiomic, radiomic and dosiomic + radiomic models on the lung cancer dataset were 0.64 ± 0.18 and 0.37 ± 0.20, 0.67 ± 0.17 and 0.37 ± 0.20, 0.67 ± 0.16 and 0.45 ± 0.23, and 0.68 ± 0.16 and 0.44 ± 0.22, respectively. On the lung cancer dataset, the predictive performance of the radiomic and dosiomic + radiomic models was significantly higher than that of the DVH-based model. However, the PRAUC of the dosiomic-based model showed no significant difference relative to the corresponding RP prediction performance on the lung cancer dataset.ConclusionThe results suggested that dosiomic and CT radiomic features could improve RP prediction in thoracic radiotherapy. Dosiomic and radiomic feature knowledge might be transferrable from esophageal cancer to lung cancer.
Screening and Identification of Human Endogenous Retrovirus-K mRNAs for Breast Cancer Through Integrative Analysis of Multiple Datasets
Authors: Wei Yongzhong , Wei Huilin , Wei Yinfeng , Tan Aihua , Chen Xiuyong , Liao Xiuquan , Xie Bo , Wei Xihua , Li Lanxiang , Liu Zengjing , Dai Shengkang , Khan Adil , Pang Xianwu , Hassan Nada M. A. , Xiong Kai , Zhang Kai , Leng Jing , Lv Jiannan , Hu Yanling 
Published: February 2022

ObjectiveHuman endogenous retroviruses (HERVs) make up 8% of the human genome. HERVs are biologically active elements related to multiple diseases. HERV-K, a subfamily of HERVs, has been associated with certain types of cancer and suggested as an immunologic target in some tumors. The expression levels of HERV-K in breast cancer (BCa) have been studied as biomarkers and immunologic therapeutic targets. However, HERV-K has multiple copies in the human genome, and few studies determined the transcriptional profile of HERV-K copies across the human genome for BCa.MethodsNinety-one HERV-K indexes with entire proviral sequences were used as the reference database. Nine raw sequencing datasets with 243 BCa and 137 control samples were mapped to this database by Salmon software. The differential proviral expression across several groups was analyzed by DESeq2 software.ResultsFirst, the clustering of each dataset demonstrated that these 91 HERV-K proviruses could well cluster the BCa and control samples when the normal controls were normal cells or healthy donor tissues. Second, several common HERV-K proviruses that are closely related with BCa risk were significantly differentially expressed (padj &lt; 0.05 and absolute log2FC &gt; 1.5) in the tissues and cell lines. Additionally, almost all the HERV-K proviruses had higher expression in BCa tissue than in healthy donor tissue. Notably, we first found the expression of 17p13.1 provirus that located with TP53 should regulate TP53 expression in ER+ and HER2+ BCa.ConclusionThe expression profiling of these 91 HERV-K proviruses can be used as biomarkers to distinguish individuals with BCa and healthy controls. Some proviruses, especially 17p13.1, were strongly associated with BCa risk. The results suggest that HERV-K expression profiles may be appropriate biomarkers and targets for BCa.
Predicting HER2 Status in Breast Cancer on Ultrasound Images Using Deep Learning Method
Authors: Xu Zilong , Yang Qiwei , Li Minghao , Gu Jiabing , Du Changping , Chen Yang , Li Baosheng 
Published: February 2022

PurposeThe expression of human epidermal growth factor receptor 2 (HER2) in breast cancer is critical in the treatment with targeted therapy. A 3-block-DenseNet-based deep learning model was developed to predict the expression of HER2 in breast cancer by ultrasound images.MethodsThe data from 144 breast cancer patients with preoperative ultrasound images and clinical information were retrospectively collected from the Shandong Province Tumor Hospital. An end-to-end 3-block-DenseNet deep learning classifier was built to predict the expression of human epidermal growth factor receptor 2 by ultrasound images. The patients were randomly divided into a training (n = 108) and a validation set (n = 36).ResultsOur proposed deep learning model achieved an encouraging predictive performance in the training set (accuracy = 85.79%, AUC = 0.87) and the validation set (accuracy = 80.56%, AUC = 0.84). The effectiveness of our model significantly exceeded the clinical model and the radiomics model. The score of the proposed model showed significant differences between HER2-positive and -negative expression (p &lt; 0.001).ConclusionsThese results demonstrate that ultrasound images are predictive of HER2 expression through a deep learning classifier. Our method provides a non-invasive, simple, and feasible method for the prediction of HER2 expression without the manual delineation of the regions of interest (ROI). The performance of our deep learning model significantly exceeded the traditional texture analysis based on the radiomics model.
An Integrated Approach Based on Network Analysis Combined With Experimental Verification Reveals PI3K/Akt/Nrf2 Signaling Is an Important Way for the Anti-Myocardial Ischemia Activity of Yi-Qi-Tong-Luo Capsule
Authors: Duan Huxinyue , Li Meiyan , Liu Jia , Sun Jiayi , Wu Chunjie , Chen Yu , Guo Xiaohui , Liu Xinglong 
Published: February 2022

Background:Yiqi-Tongluo Capsule (YTC) is a Chinese traditional patent medicine that has been used in the treatment of myocardial ischemia (MI). However, its molecular mechanisms against MI have not been clear.Methods: Network analysis and experimental verification were used to explore the potential molecular mechanisms of YTC for MI treatment. Firstly, the main components in the capsules and the potential targets of these components were predicted by online databases. The MI related genes were collected from Genecards and Online Mendelian Inheritance in Man (OMIM) databases. The drug targets and disease targets were intersected, and then the protein-protein interaction (PPI) and Drug-Molecular-Target-Disease Network (DMTD) were constructed, and GO enrichment analysis and KEGG pathway enrichment analysis were performed. Based on the H2O2-stimulated H9c2 cells, flow cytometry, western blot (WB) and immunofluorescence experiments were performed to verify the network analysis prediction.Results: A total of 100 active components and 165 targets of YTC were predicted, in which there were 109 targets intersected with the targets of MI. GO and KEGG analysis showed that these potential targets were related to a variety of biological processes and molecular mechanisms, including oxidative stress and PI3K/AKT pathway. Astragaloside IV (AS IV) and paeoniflorin (PAE) might be the main active components in YTC. The results of cell counting kit-8 (CCK-8) showed that YTC alleviated the damage of H2O2 to H9c2 cells. The results of flow cytometry, DAPI staining and JC-1 probe showed that YTC alleviated H2O2 induced apoptosis in H9c2 cells. In addition, YTC reduced the level of intracellular superoxide anion, increased the activities of superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GSH-Px), and reduced the content of malondialdehyde (MDA) in H2O2-induced H9c2 cells. The results of immunofluorescence and WB showed that the phosphorylation of PI3K and Akt were increased, the expression of Bcl-2 was up-regulated and the expression of cleaved caspase-3 and Bax were down-regulated. Besides, the nuclear translocation of Nrf2 were increased.Conclusion: In conclusion, the results of this study showed that YTC might alleviate MI by suppressing apoptosis induced by oxidative stress via the PI3K/Akt/Nrf2 signal pathway.
Optical Feedback Linear Cavity Ringdown Spectroscopy
Authors: Wang Xingping , Zhao Gang , Jiao Kang , Chen Bing , Kan Ruifeng , Cong Zhenhua , Liu Jianguo , Ma Weiguang 
Published: February 2022

Optical feedback cavity ringdown spectroscopy is presented with a linear Fabry–Pérot cavity and a cost-effective DFB laser. To circumvent the low coupling efficiency caused by the broad laser linewidth, an optical feedback technique is used, and an enhanced coupling efficiency of 31%, mainly limited by impedance mismatch and mode mismatch, is obtained. The trigger of the ringdown event is realized by the shutoff of the laser driving current, and a novel method with the aid of one electronic switch is applied to avoid the ringdown events excited by the unexpected cavity modes during the process of laser current recovery. As a result, the ringdown signal with a signal-to-noise ratio of 2500 is achieved. Through continuous monitoring, the fractional uncertainty of the empty cavity ringdown times is assessed to be 0.04%. An Allan variance analysis indicates a detection sensitivity of 4.3 × 10−10 cm−1 is resulted at an integration time of 120 s, even with a moderate finesse cavity. To further improve the long-term stability, we regularly rectify the empty cavity ringdown time, and an improvement factor of 2.5 is demonstrated.
The Integration of Metabolomic and Proteomic Analyses Revealed Alterations in Inflammatory-Related Protein Metabolites in Endothelial Progenitor Cells Subjected to Oscillatory Shear Stress
Authors: Yu Jie , Fu Jie , Zhang Xiaoyun , Cui Xiaodong , Cheng Min 
Published: February 2022

BackgroundEndothelial progenitor cells (EPCs) play essential roles in vascular repair. Our previous study suggests OSS would lead EPCs transdifferention into the mesenchymal cell that aggravates pathological vascular remodeling. The primary purpose of this study was to apply OSS in vitro in EPCs and then explore proteins, metabolites, and the protein-metabolite network of EPCs.MethodsEndothelial progenitor cells were kept in static or treated with OSS. For OSS treatment, the Flexcell STR-4000 parallel plate flow system was used to simulate OSS for 12 h. Subsequently, an untargeted metabolomic LC/MS analysis and a TMT-labeled quantitative proteomic analysis were performed.ResultsA total of 4,699 differentially expressed proteins (DEPs) were identified, among which 73 differentially expressed proteins were potentially meaningful (P &lt; 0.05), with 66 upregulated and 7 downregulated expressions. There were 5,664 differential metabolites (DEMs), of which 401 DEMs with biologically potential marker significance (VIP &gt; 1, P &lt; 0.05), of which 137 were upregulated and 264 were downregulated. The Prison correlation analysis of DEPs and DEMs was performed, and the combined DEPs–DEMs pathway analyses of the KGLM database show 39 pathways. Among the DEPs, including the Phosphoserine phosphatase (PSPH), Prostaglandin E synthase 3 (PTGES3), Glutamate–cysteine ligase regulatory subunit (GCLM), Transaldolase (TALDO1), Isocitrate dehydrogenase 1 (IDH1) and Glutathione S-transferase omega-1 (GSTO1), which are significantly enriched in the citric acid cycle (TCA cycle) and fatty acid metabolic pathways, promoting glycolysis and upregulation of fatty acid synthesis. Moreover, we screened the 6 DEPs with the highest correlation with DEMs for predicting the onset of early AS and performed qPCR to validate them.ConclusionThe comprehensive analysis reveals the following main changes in EPCs after the OSS treatment: dysregulation of glutamate and glycine metabolism and their transport/catabolic related proteins. Disorders of fatty acid and glycerophospholipid metabolism accompanied by alterations in the corresponding metabolic enzymes. Elevated expression of glucose metabolism.
Effectiveness of Recombinant Human Growth Hormone Therapy for Children With Phelan-McDermid Syndrome: An Open-Label, Cross-Over, Preliminary Study
Authors: Li TianXiao , Xie Ruijin , Zhao Jinling , Xu Hua , Cui Ying , Sun Chenyu , Wang Chunhong , Liu Yueying 
Published: February 2022

BackgroundPhelan-McDermid syndrome (PMS), also known as the 22q13. 3 deletion syndrome, is a rare neurodevelopmental syndrome with approximately 2,800 patients reported worldwide. Previous pilot study demonstrated that IGF-1 could significantly improve in both social impairment and restrictive behaviors of the patients. However, most of the patients in the developing countries like China cannot afford the high cost of using IGF-1. Our research team speculated that rhGH might serve as a low-cost and more accessible treatment for PMS. Therefore, the purpose of this open-label, cross-over, pilot study was to further investigate the safety and efficiency of rhGH in patients with PMS.MethodsA total of six children with PMS were enrolled in in this open-label, cross-over, pilot study. The children were randomly divided into two different groups. Group A received placebo followed by rhGH, while group B was treated with rhGH first. Neuropsychological and behavior assessments of the patients were performed before the stage I of study and 3 months after the intervention of stage I. After a 4-week period of washout, these assessments were conducted again before the stage II of study and 3 months after the intervention of stage II. Serum insulin-like growth factor-1 (IGF-1) and insulin-like growth factor binding-protein (IGFBP)-3 were also evaluated monthly during the intervention phases of the pilot studyResultsCompared with the placebo, rhGH treatment significantly decreased subscale scores of GDS (P &lt; 0.0085) and trended to improve the total scores of GDS (P &lt; 0.05), while the total scores and subscale scores of SC-ABC significantly decreased (P &lt; 0.0085) following 3-months rhGH treatment. The similar results were also observed in comparison with baseline. Compared with the baseline, the level of serum IGF-1 and IGFBP-3 increased significantly (P &lt; 0.05) following 3-months rhGH treatment, while the placebo group had no significant impact on serum IGF-1 and IGFBP-3 (P &gt; 0.05). One child developed skin allergy the day after the first rhGH treatment, which were resolved later.ConclusionsIn summary, this pilot study involving six PMS children patients reveals that rhGH has a positive treatment effect on PMS. These results encourage the undertaking of a large, randomized placebo-controlled trial to conclusively prove rhGH efficacy and tolerability in PMS, thereby promoting it as a low-cost, more accessible treatment for PMS, as compared to IGF-1.