bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm
Authors: Maharjan Jenish , Ektefaie Yasha , Ryan Logan , Mataraso Samson , Barnes Gina , Shokouhi Sepideh , Green-Saxena Abigail , Calvert Jacob , Mao Qingqing , Das Ritankar 
Published: January 2022

BackgroundStrokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.MethodsA retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.ResultsAfter training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.ConclusionMLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.
In silico Single-Cell Analysis of Steroid-Responsive Gene Targets in the Mammalian Cochlea
Authors: Nelson Lacey , Lovett Braeden , Johns J. Dixon , Gu Shoujun , Choi Dongseok , Trune Dennis , Hoa Michael 
Published: January 2022

BackgroundTreatment of many types of hearing instability in humans, including sudden sensorineural hearing loss, Meniere's disease, and autoimmune inner ear disease, rely heavily on the utilization of corticosteroids delivered both by oral and transtympanic routes. Despite this use, there is heterogeneity in the response to treatment with corticosteroids in humans with these diseases. The mechanisms by which corticosteroids exert their effect and the cell types in which they exert their effects in the inner ear remain poorly characterized. In this study, we localize steroid-responsive genes to cochlear cell types using previously published transcriptome datasets from the mammalian cochlea.MethodsSteroid-responsive genes were localized to specific cochlear cell types using existing transcriptome datasets from wild-type mammalian cochlea exposed to systemic and transtympanic steroids, as well as previously published single-cell and single-nucleus RNA-sequencing datasets from the mammalian cochlea. Gene ontology (GO) analysis of differentially expressed genes (DEGs) was performed using PANTHER to investigate cellular processes implicated in transtympanic vs. systemic steroid action in the cochlea.ResultsSteroid-responsive genes were localized to specific cell types and regions in the cochlea including the stria vascularis, organ of Corti, and spiral ganglion neurons (SGN). Analyses demonstrate differential prevalence of steroid-responsive genes. GO analysis demonstrated steroid-responsive DEGs in the SGN to be associated with angiogenesis, apoptosis, and cytokine-mediated anti-inflammatory pathways.ConclusionsSingle-cell and single-nucleus transcriptome datasets localize steroid-responsive genes to specific regions in the cochlea. Further study of these regionally-specific steroid-responsive genes may provide insight into the mechanisms of and clinical response to corticosteroids in diseases of hearing instability.
Case Report: Triphasic Waves in a 9-Year-Old Girl With Anti-NMDAR Encephalitis
Authors: Zhang Ke , Xu Shuang , Zhou Yalan , Su Tangfeng 
Published: January 2022

BackgroundTriphasic waves (TWs) are mainly described in association with metabolic encephalopathy, especially hepatic encephalopathy. Now, as different conditions including non-metabolic and structural abnormalities have been reported to be associated with TWs, the presence of TWs becomes a non-specific finding for metabolic encephalopathy.Case PresentationWe report the first case of anti-NMDAR encephalitis in a 9-year-old girl presenting with TWs on EEG. The TWs background EEG lasted for about 12 h on the 40th day of the disease course. No epileptic wave was found during a series of EEG examinations. The child was discharged from the hospital and no neurological sequelae remained after a six-month follow-up.ConclusionsTWs are not specific to metabolic encephalopathy, but can also occur in children with autoimmune encephalitis. This case achieved a good prognosis after the early initiation of immunotherapy.
Development and Validation of a Prognostic Index Based on Genes Participating in Autophagy in Patients With Lung Adenocarcinoma
Authors: Wu Zi-Xuan , Huang Xuyan , Cai Min-Jie , Huang Pei-Dong , Guan Zunhui 
Published: January 2022

BackgroundLung adenocarcinoma (LUAD) is a deadly respiratory system malignancy with poor prognosis. Autophagy is essential for the beginning, development, and therapy resistance of cancer. However, the expression of genes participating in autophagy in LUAD and their associations with prognosis remain unclear.MethodsPredictive genes participating in autophagy in LUAD samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were investigated. TCGA and GEO cohorts were divided into two risk groups, while the low-risk group having a longer overall survival (OS) time. This article aims to point out the interaction between genes participating in autophagy and immune function, immune checkpoints, and m6a in LUAD. The prediction model was designed for exploring least absolute shrinkage and selection operator (LASSO) regression. It has been revealed that gene expression and autophagy are inextricably connected.ResultsGenes participating in autophagy were shown to be somewhat overexpressed in the high-risk group even though no different clinical symptoms were present, indicating that they might be used in a model to predict LUAD prognosis. The majority of genes participating in autophagy prognostic signatures controlled immunological and tumor-related pathways, according to gene set enrichment analysis (GSEA). KRT6A, KYNU, IGFBP1, DKK1, PKP2, PLEK2, GAPDH, FLNC, and NTSR1 might be related to the oncology process for LUAD patients. CERS4, CMAHP, and PLEKHB1 have been identified as being associated with low risk in patients with LUAD. Furthermore, the immune function and m6a gene expression differed significantly between the two groups.ConclusionsGenes participating in autophagy are connected to the development and progression of LUAD. LUAD patients’ prognoses are often foreseen utilizing matched prognostic models. Genes participating in autophagy in LUAD may be therapeutic targets that ought to be investigated more.
First-Line Tyrosine Kinase Inhibitors Combined With Local Consolidative Radiation Therapy for Elderly Patients With Oligometastatic Non-Small Cell Lung Cancer Harboring EGFR Activating Mutations
Authors: Hu Xiaolong , Li Hongqi , Kang Xiaoli , Wang Xuan , Pang Haifeng , Liu Chen , Zhang Jianchun , Wang Yingjie 
Published: January 2022

ObjectiveThe aim of this study was to investigate the efficacy and safety of combined applications of local consolidative radiation therapy (LCRT) and first-line tyrosine kinase inhibitors (TKIs) for the treatment of primary tumors and oligometastatic sites in oligometastatic NSCLC harboring Epidermal Growth Factor Receptor (EGFR) activating mutations.Patients and MethodsElderly patients with oligometastatic NSCLC (≤5 metastases) harboring EGFR activating mutations at the time of diagnosis were identified. They were treated with first-line TKIs alone or in combination with LCRT. Progression‐free survival (PFS) and overall survival (OS) were estimated through the Kaplan–Meier method.ResultsA total of 122 elderly patients were enrolled between February 2010 and January 2018. Among them, 41.0% (n = 50) received TKIs combined with LCRT (TKIs + LCRT group), whereas 59.0% (n = 72) received TKIs monotherapy (TKIs alone group). Patients were followed up for a median length of 34 months (ranging from 7.0 to 64 months). The median PFS in TKIs + LCRT group was 17 months (95%CI: 15.37–18.63), which was significantly longer than that of the TKIs-alone group (12 months; 95%CI: 11.05–12.95) (p &lt;0.001). Median OS in TKIs + LCRT group was 38 months (95%CI: 35.61–40.39), while that of the TKIs-alone group was 29 months (95%CI: 26.86–31.14) (p &lt;0.001). Multivariate analyses revealed that LCRT, one to two metastases, and good ECOG PS were independent predictors for better PFS (p &lt;0.001, p = 0.004, and p = 0.027). Moreover, LCRT, good ECOG PS, and T1-2 stage were independent predictors for better OS (p &lt;0.001, p = 0.007 and p = 0.007). Most of the patients suffered from grade 1 to 2 toxicities, and treatment-related deaths were not recorded.ConclusionFirst-line TKIs combined with LCRT may improve survival outcomes for elderly patients with oligometastatic NSCLC harboring EGFR activating mutations. This approach was not associated with much toxicity, therefore, it can be used for the treatment of elderly patients with oligometastatic disease.
Construction and Multicenter Diagnostic Verification of Intelligent Recognition System for Endoscopic Images From Early Gastric Cancer Based on YOLO-V3 Algorithm
Authors: Yao Zhendong , Jin Tao , Mao Boneng , Lu Bo , Zhang Yefei , Li Sisi , Chen Weichang 
Published: January 2022

IntroductionEndoscopy is an important tool for the diagnosis of early gastric cancer. Therefore, a combination of artificial intelligence and endoscopy has the ability to increase the speed and efficiency of early gastric cancer diagnosis. YOU ONLY LOOK ONCE (YOLO) is an advanced object detection depth neural network algorithm that has not been widely used in gastrointestinal image recognition.ObjectiveWe developed an artificial intelligence system herein referred to as “EGC-YOLO” for the rapid and accurate diagnosis of endoscopic images from early gastric cancer.MethodsMore than 40000 gastroscopic images from 1653 patients in Yixing people’s Hospital were used as the training set for the system, while endoscopic images from the other two hospitals were used as external validation test sets. The sensitivity, specificity, positive predictive value, Youden index and ROC curve were analyzed to evaluate detection efficiencies for EGC-YOLO.ResultsEGC-YOLO was able to diagnose early gastric cancer in the two test sets with a high superiority and efficiency. The accuracy, sensitivity, specificity and positive predictive value for Test Sets 1 and 2 were 85.15% and 86.02%, 85.36% and 83.02%, 84.41% and 92.21%, and 95.22% and 95.65%, respectively. In Test Sets 1 and 2, the corresponding Threshold-values were 0.02, 0.16 and 0.17 at the maximum of the Youden index. An increase in Threshold-values was associated with a downward trend in sensitivity and accuracy, while specificity remained relatively stable at more than 80%.ConclusionsThe EGC-YOLO system is superior for the efficient, accurate and rapid detection of early gastric cancer lesions. For different data sets, it is important to select the appropriate threshold-value in advance to achieve the best performance of the EGC-YOLO system.
Caffeine Therapy for Apnea of Prematurity: Role of the Circadian CLOCK Gene Polymorphism
Authors: Guo Hong-Li , Long Jia-Yi , Hu Ya-Hui , Liu Yun , He Xin , Li Ling , Xia Ying , Ding Xuan-Sheng , Chen Feng , Xu Jing , Cheng Rui 
Published: January 2022

Standard-dose caffeine citrate has been routinely prescribed for apnea of prematurity (AOP) management; however, some preterm infants respond well to the therapy while others do not. The AOP phenotype has been attributed solely to the immature control of the respiratory system consequent to preterm birth, but there are also important genetic influences. Based on our previous report, we tested the hypothesis that the human circadian locomotor output cycles kaput (CLOCK) gene polymorphisms play a role in the response to caffeine citrate therapy in preterm infants. We also studied the interactions of the circadian clock with aryl hydrocarbon receptor (AHR) signaling pathways in preterm babies who received caffeine citrate. This single-center study collected data from 112 preterm infants (&lt;35 weeks gestational age) between July 2017 and July 2018, including apnea-free (n = 48) and apneic (n = 64) groups. Eighty-eight candidate single nucleotide polymorphisms (SNPs) were tested using the MassARRAY system. Association analysis was performed using the PLINK Whole Genome Data Analysis Toolset and SNPStats software. Linkage disequilibrium (LD) and haplotype analyses were performed using Hapview software. No significant intergroup differences in allele distributions or genotype frequencies of CYP1A2, CYP3A4, CYP3A5, and CYP3A7 were detected in our study on preterm babies. Two more SNPs in AHR were found to be associated with determining the response to caffeine citrate therapy in our pediatric patients. Of the 46 candidate SNPs in the CLOCK gene, 26 were found to be associated with determining the response to caffeine treatment in these babies. Interestingly, a significant association was retained for 18 SNPs in the CLOCK gene after false discovery rate correction. Moreover, strong LD formed in those variants in AHR, ADORA2A, and CLOCK genes was confirmed to be significantly associated with a better response to standard-dose caffeine therapy. In summary, CLOCK gene polymorphisms play a role in determining the response to caffeine therapy in premature neonates with AOP. However, whether the AHR and CLOCK signaling pathways crosstalk with each other during caffeine treatment remains largely unclear. Future clinical studies including more immature babies and basic research are needed to explore the mechanism by which circadian rhythms affect the response to caffeine therapy.
Metabolomic Fingerprint of Behavioral Changes in Response to Full-Spectrum Cannabis Extracts
Authors: Maayah Zaid H. , Raposo Pamela J. F. , Silver Heidi , Mandal Rupasri , Ellis Lee , Alam Abrar S. , Takahara Shingo , Ferdaoussi Mourad , Mathewson Kyle E. , Eurich Dean T. , Fouad Karim , Wishart David S. , Dyck Jason R. B. 
Published: January 2022

Numerous existing full-spectrum cannabis extract products have been used in clinical trials for the treatment of various diseases. Despite their efficacy, the clinical use of some of these full-spectrum cannabis extracts is limited by behavioral side effects such as cognitive dysfunction and impaired motor skills. To better understand what constitutes cannabis-induced behavioral effects, our objective was to identify a novel panel of blood-based metabolites that are predictive, diagnostic, and/or prognostic of behavioral effects.At 8 weeks of age, male rats were randomly assigned to groups and were gavage fed with full-spectrum cannabis extract (tetrahydrocannabinol/cannabidiol (THC/CBD) along with all other cannabis compounds, 15 mg/kg), broad-spectrum cannabis extract (CBD along with all other cannabis compounds, 15 mg/kg), or vehicle oil. Four hours after being gavage fed, behavioral assessments were determined using the open field test and the elevated plus maze. Following these assessments, serum was collected from all rats and the serum metabolites were identified and quantified by LC–MS/MS and 1H NMR spectroscopy.We found that only rats treated with full-spectrum cannabis extract exhibited behavioral changes. Compared to vehicle-treated and broad-spectrum extract–treated rats, full-spectrum extract–treated rats demonstrated higher serum concentrations of the amino acid phenylalanine and long-chain acylcarnitines, as well as lower serum concentrations of butyric acid and lysophosphatidylcholines. This unique metabolomic fingerprint in response to cannabis extract administration is linked to behavioral effects and may represent a biomarker profile of cannabis-induced behavioral changes. If validated, this work may allow a metabolomics-based decision tree that would aid in the rapid diagnosis of cannabis-induced behavioral changes including cognitive impairment.
CNN-Based Cell Analysis: From Image to Quantitative Representation
Authors: Allier Cédric , Hervé Lionel , Paviolo Chiara , Mandula Ondrej , Cioni Olivier , Pierré William , Andriani Francesca , Padmanabhan Kiran , Morales Sophie 
Published: January 2022

We present a novel deep learning-based quantification pipeline for the analysis of cell culture images acquired by lens-free microscopy. The image reconstruction part of the pipeline features a convolutional neural network performing phase unwrapping and accelerating the inverse problem optimization. It allows phase retrieval at the 4K level (3,840 × 2,748 pixels) in 3 s. The analysis part of the pipeline features a suite of convolutional neural networks estimating different cell metrics from the reconstructed image, that is, cell surface area, cell dry mass, cell length, and cell thickness. The networks have been trained to predict quantitative representation of the cell measurements that can be next translated into measurement lists with a local maxima algorithm. In this article, we discuss the performance and limitations of this novel deep learning-based quantification pipeline in comparison with a standard image processing solution. The main advantage brought by this method is the fast processing time, that is, the analysis rate of ∼25.000 cells measurements per second. Although our proof of principle has been established with lens-free microscopy, the approach of using quantitative cell representation in a deep learning framework can be similarly applied to other microscopy techniques.
Effects of Intradialytic Cognitive and Physical Exercise Training on Cognitive and Physical Abilities in Hemodialysis Patients: Study Protocol for a Randomized Controlled Trial
Authors: Bogataj Špela , Trajković Nebojša , Pajek Maja , Pajek Jernej 
Published: January 2022

The prevalence of cognitive impairment in hemodialysis (HD) patients is extremely high. Despite the well-documented benefits of interventions on cognitive function, there is a widespread call for effective strategies that will show the long-term consequences in patients undergoing dialysis. The aim of this research protocol was to investigate the effect of cognitive training combined with physical exercise on cognitive function, physical performance, and frailty indicators in the HD population. We will conduct a randomized controlled intervention trial to examine the effects of a combined non-pharmacological intervention in the form of intradialytic physical exercise and intradialytic cognitive training on cognitive function, indicators of frailty, and physical performance measures in HD patients. The group of patients receiving the study intervention will be compared to the control group receiving standard HD care. The duration of the intervention will be 12 weeks. We will use sensitive instruments (cognitive domain tests) to assess cognitive functions. The primary outcome of the study at 12 weeks will be performance on the Alertness subtest of the computerized Test of Attentional Performance. Secondary study outcomes are: Performance in other domains of cognitive function (executive function, psychomotor speed, information processing efficiency, working memory, and attention), physical fitness (10 repetition sit-to-stand test, timed up and go test, handgrip strength test, spontaneous gait speed, and stork balance test), and assessment of frailty (Edmonton Frail Scale). Study outcomes will be assessed at baseline, immediately after the 12-week intervention, and 6 months after the end of the study without specific further intervention (retention effect assessment). This study will be among the first to test the synergistic effects of a uniquely designed physical exercise and cognitive training intervention on functional status in HD patients. We believe our results will contribute to dementia prevention research by demonstrating the long-term efficacy of our combined intervention.Clinical Trial Registration:ClinicalTrials.Gov, NCT05150444.
Perceived Academic Stress and Depression: The Mediation Role of Mobile Phone Addiction and Sleep Quality
Authors: Zhang Xin , Gao Fei , Kang Zheng , Zhou Hongguo , Zhang Jianfeng , Li Jingjing , Yan Jun , Wang Jiahui , Liu Huan , Wu Qunhong , Liu Baohua 
Published: January 2022

BackgroundAlthough academic stress is a well-known risk factor for students' depression, little is known about the possible psychological mechanisms underlying this association. In this study, we investigated the prevalence of depression and sleep disturbance among Chinese students, examined the relationship between perceived academic stress and depression, considered if mobile phone addiction and sleep quality is a mediator of this relationship, and tested if mobile phone addiction and sleep quality together play a serial mediating role in the influence of perceived academic stress on depression.MethodA cross-sectional survey was conducted among students from September to December 2018 in Heilongjiang Province, China. The final analysis included 5,109 students. Mobile phone addiction, sleep quality, and depressive symptoms were assessed using the Mobile Phone Addiction Index, Pittsburgh Sleep Quality Index, and Center for Epidemiologic Studies-Depression scales, respectively. The serial mediation model was used to analyse the relationship between perceived academic stress, mobile phone addiction, sleep quality, and depression.ResultsAmong all participants, the prevalence of depressive symptoms and sleep disturbance was 28.69 and 27.95%, respectively. High school students showed the highest scores of perceived academic stress (2.68 ± 1.06), and the highest prevalence of depressive symptoms (33.14%) and sleep disturbance (36.47%). The serial mediation model indicated that perceived academic stress was a significant predictor of depression (B = 0.10, SE = 0.02, 95% CI = 0.06 – 0.13). Additionally, mobile phone addiction (B = 0.08, 95% boot CI = 0.06–0.11) and sleep quality (B = 0.27, 95% boot CI = 0.22–0.33) played a mediating role between perceived academic stress and depression. Mobile phone addiction and sleep quality together played a serial mediating role in the influence of perceived academic stress on depression (B = 0.11, 95% boot CI = 0.08–0.14). Furthermore, the indirect effect (i.e., the mediating effect of mobile phone addiction and sleep quality) was significant and accounted for 64.01% of the total effect.ConclusionsOur research results underscore the need for stakeholders—including family members, educators, and policy makers—to take preventative intervention measures to address depression among Chinese students, especially high school students.