bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adsorption performance of coated bentonite via graphene oxide
Authors: Shaarawy H H , Hussein H S , Kader E Abdel , Hussien Nabila H , Hawash S I 
Published: April 2020

Abstract
Background
The nano-adsorbents present considerable decontamination potential due to their unique characteristics. This study aimed to investigate the adsorption efficiency and capacity of synthesized nano graphene oxide coated layer over Egyptian clays (bentonite, kaolinite and feldspar) as substrate. The experimental work involves nano graphene oxide coating using acid dehydration via sulfuric acid in presence of different percentage of sugar (fine powder from 1 to 7%), followed by thermal activation to improve the adsorption capacity of natural clay that is locally available in Egypt. Several operating parameters were studied to prepare the superior adsorbent (type of substrate, sulfuric acid concentration, sugar concentration and contact time).

Results
The optimum preparation conditions of nano graphene oxide coated bentonite (NGOCB) adsorbent were bentonite substrate 1 kg, sulfuric acid concentration 1.5 kg/kg clay, sugar fine powder 50 g/kg clay, water 3000 ml/kg clay, and stirring time of 30 min at 100 °C. The morphology characterization showed that the deposited graphene oxide layer is in the nano form (6 to 33 nm). Sets of experiments were conducted to evaluate the adsorption performance of synthesized NGOCB for removal of methylene blue (MB) dye from aqueous solutions. Effect of initial dye concentration, contact time, pH and temperature on the adsorption capacity of the modified adsorbent were studied. The capacity of the prepared nano graphene oxide coated bentonite adsorbent was 1000 mg/g.

Conclusions
The removal efficiency of nano graphene coated clay reached to 99.9% that indicates the Egyptian bentonite could be employed as a low-cost absorbent for dye removal.
Engaging Patients in Precision Oncology: Development and Usability of a Web-Based Patient-Facing Genomic Sequencing Report
Authors: Solomon Ilana B. , McGraw Sarah , Shen Jenny , Albayrak Adem , Alterovitz Gil , Davies Melanie , Del Vecchio Fitz Catherine , Freedman Rachel A. , Lopez Lisa N. , Sholl Lynette M. , Van Allen Eliezer , Mortimer Joanne , Fakih Marwan , Pal Sumanta , Reckamp Karen L. , Yuan Yuan , Gray Stacy W. 
Published: April 2020

PURPOSE Evidence-based somatic and germline sequencing has transformed cancer care and improves patient outcomes. However, patients’ low genetic literacy and misunderstanding of their own genomic results poses a threat to the realization of precision oncology. To optimize patient genomic comprehension, we developed a Web-based, patient-directed, genomic sequencing education and return-of-results tool, HOPE-Genomics. METHODS The HOPE-Genomics prototype included somatic and germline sequencing results, embedded multimedia genomic education, and interactive features (eg, request for genetic counseling). Between January and April 2018, we elicited feedback on tool usability and comprehensiveness through participant surveys, 4 focus groups of patients with cancer and their family members, and 3 provider focus groups (comprising 8 patients, 5 family members, and 19 providers). RESULTS We identified themes in patient/family tool-related responses, including the desire to view a patient-friendly report, a desire to receive multiple types of genomic information (eg, prognostic and uncertain), high acceptability of report content, and interest in tool-enabled access to genetic counseling. Major themes from the clinician focus groups included believing the tool could help patients formulate questions and facilitate patients’ communication of results to family members. However, there were diverse responses from all participants in terms of tool implementation (ie, timing and nature of report release). Some participants preferred report release before meeting with the provider, and others preferred it during the appointment. Additionally, some clinicians were concerned about providing prognostic and treatment information through the tool. CONCLUSION There was high acceptability and interest from patients, family members, and providers in a patient-directed genomics report. Future work will determine whether direct-to-patient reporting of genomic results improves patient knowledge, care engagement, and compliance with genomically guided interventions.
Hypermethylated RASSF1A as Circulating Tumor DNA Marker for Disease Monitoring in Neuroblastoma
Authors: van Zogchel Lieke M. J. , van Wezel Esther M. , van Wijk Jalenka , Stutterheim Janine , Bruins Wassilis S. C. , Zappeij-Kannegieter Lily , Slager Tirza J. E. , Schumacher-Kuckelkorn Roswitha , Verly Iedan R. N. , van der Schoot C. Ellen , Tytgat Godelieve A. M. 
Published: April 2020

PURPOSE Circulating tumor DNA (ctDNA) has been used for disease monitoring in several types of cancer. The aim of our study was to investigate whether ctDNA can be used for response monitoring in neuroblastoma. METHODS One hundred forty-nine plasma samples from 56 patients were analyzed by quantitative polymerase chain reaction (qPCR) for total cell free DNA (cfDNA; albumin and β-actin) and ctDNA (hypermethylated RASSF1A). ctDNA results were compared with mRNA-based minimal residual disease (qPCR) in bone marrow (BM) and blood and clinical patient characteristics. RESULTS ctDNA was detected at diagnosis in all patients with high-risk and stage M neuroblastoma and in 3 of 7 patients with localized disease. The levels of ctDNA were highest at diagnosis, decreased during induction therapy, and not detected before or after autologous stem-cell transplantation. At relapse, the amount of ctDNA was comparable to levels at diagnosis. There was an association between ctDNA and blood or BM mRNA, with concordant results when tumor burden was high or no tumor was detected. The discrepancies indicated either low-level BM infiltration (ctDNA negative/mRNA positive) or primary tumor/soft tissue lesions with no BM involvement (ctDNA positive/mRNA negative). CONCLUSION ctDNA can be used for monitoring disease in patients with neuroblastoma. In high-risk patients and all patients with stage M at diagnosis, ctDNA is present. Our data indicate that at low tumor load, testing of both ctDNA and mRNA increases the sensitivity of molecular disease monitoring. It is likely that ctDNA can originate from both primary tumor and metastases and may be of special interest for disease monitoring in patients who experience relapse in other organs than BM.
Tumor-Infiltrating Neutrophils Predict Poor Survival of Non-Functional Pancreatic Neuroendocrine Tumor
Authors: Zhang Wu-Hu , Wang Wen-Quan , Gao He-Li , Xu Shuai-Shuai , Li Shuo , Li Tian-Jiao , Han Xuan , Xu Hua-Xiang , Li Hao , Jiang Wang , Ye Long-Yun , Lin Xuan , Wu Chun-Tao , Yu Xian-Jun , Liu Liang 
Published: April 2020

AbstractObjectiveThis study retrospectively characterized the immune infiltrating profile in nonfunctional pancreatic neuroendocrine tumors (NF-PanNETs).MethodsTumor tissues from the 109-patient Fudan cohort and a 73-patient external validation set were evaluated by immunohistochemistry for 9 immune cell types: tumor-infiltrating neutrophils (TINs), tumor-associated macrophages (TAMs), CD11c+ dendritic cells, anti-NCR1+ natural killer (NK) cells, CD4+ and CD8+ T cells, CD45RO+ memory T cells, FOXP3+ regulatory T cells (Tregs), and CD20+ B cells.ResultsTINs were primarily distributed in the intratumoral area, dendritic cells and NK cells were scattered evenly in intratumoral and stromal areas, and Tregs were rarely detected. The remaining 5 cell types were primarily present in peritumoral stroma. Total TINs (P &lt; .001) and TAMs (P = .002) increased as NF-PanNET grade rose. Kaplan-Meier analyses showed that high intratumoral TINs, total TAMs, and stromal CD4+ T-cell infiltration correlated with shorter recurrence-free survival (RFS, P = .010, P = .027, and P = .035, respectively) and overall survival (OS, P = .017, P = .029, and P = .045, respectively). Additionally, high intratumoral CD8+ T cell infiltration correlated with prolonged RFS (P = .039). Multivariate Cox regression demonstrated that intratumoral TINs, World Health Organization (WHO) classification, and eighth edition of the American Joint Committee on Cancer tumor-node-metastasis staging system (AJCC8th TNM) were independent factors for RFS (P = .043, P = .023, and P = .029, respectively), whereas intratumoral TINs and WHO classification were independent factors for OS (P = .010 and P = .007, respectively). Furthermore, the combination of TINs, WHO classification, and AJCC8th TNM remarkably improved prognostic accuracy for RFS. These results have been verified in the external validation set.ConclusionIntratumoral TINs are an independent and unfavorable predictor of postoperative NF-PanNETs. A combination of TINs, WHO classification, and AJCC8th TNM could improve prognostic accuracy for RFS.
Prediction of an Acute Hypotensive Episode During an ICU Hospitalization With a Super Learner Machine-Learning Algorithm
Authors: Cherifa Ményssa , Blet Alice , Chambaz Antoine , Gayat Etienne , Resche-Rigon Matthieu , Pirracchio Romain 
Published: April 2020

BACKGROUND:
Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated with adverse outcome in critically ill patients. AHE prediction is of prime interest because it could allow for treatment adjustment to predict or shorten AHE.

METHODS:
The Super Learner (SL) algorithm is an ensemble machine-learning algorithm that we specifically trained to predict an AHE 10 minutes in advance. Potential predictors included age, sex, type of care unit, severity scores, and time-evolving characteristics such as mechanical ventilation, vasopressors, or sedation medication as well as features extracted from physiological signals: heart rate, pulse oximetry, and arterial blood pressure. The algorithm was trained on the Medical Information Mart for Intensive Care dataset (MIMIC II) database. Internal validation was based on the area under the receiver operating characteristic curve (AUROC) and the Brier score (BS). External validation was performed using an external dataset from Lariboisière hospital, Paris, France.

RESULTS:
Among 1151 patients included, 826 (72%) patients had at least 1 AHE during their ICU stay. Using 1 single random period per patient, the SL algorithm with Haar wavelets transform preprocessing was associated with an AUROC of 0.929 (95% confidence interval [CI], 0.899–0.958) and a BS of 0.08. Using all available periods for each patient, SL with Haar wavelets transform preprocessing was associated with an AUROC of 0.890 (95% CI, 0.886–0.895) and a BS of 0.11. In the external validation cohort, the AUROC reached 0.884 (95% CI, 0.775–0.993) with 1 random period per patient and 0.889 (0.768–1) with all available periods and BSs <0.1.

CONCLUSIONS:
The SL algorithm exhibits good performance for the prediction of an AHE 10 minutes ahead of time. It allows an efficient, robust, and rapid evaluation of the risk of hypotension that opens the way to routine use.
Intraoperative Electronic Alerts Improve Compliance With National Quality Program Measure for Perioperative Temperature Management
Authors: Lakha Sameer , Levin Matthew A. , Leibowitz Andrew B. , Lin Hung-Mo , Gal Jonathan S. 
Published: April 2020

BACKGROUND:
Reimbursement for anesthesia services has been shifting from a fee-for-service model to a value-based model that ties payment to quality metrics. The Centers for Medicare & Medicaid Service’s (CMS) value-based payment program includes a quality measure for perioperative temperature management (Measure #424, Perioperative Temperature Management). Compliance may impose new challenges in clinical practice, data collection, and reporting. We investigated the impact of an electronic decision-support tool on adherence to this emerging standard.

METHODS:
In this retrospective observational study, perioperative temperature data were collected from cases eligible for reporting this measure to CMS from a single academic medical center before and after the implementation of an electronic decision-support tool that prompted temperature measurement and maintenance of normothermia. Proportions of measure compliance were assessed using segmented regression analysis. Proportions of intraoperative temperature measurement were also assessed, and multivariable logistic regression was performed to assess the association between patient and surgical factors and measure compliance.

RESULTS:
A total of 24,755 cases eligible for reporting in 2017 were assessed, and 25,274 cases from 2016 were included as an extended baseline. Segmented time-series regression did not show a significant baseline trend in measure compliance. Introduction of the alerts was associated with an increase in overall compliance from 84.4% (95% confidence interval [CI], 83.6%–85.2%) to 92.4% (91.4%–93.4%), and an increase in intraoperative compliance from 26.8% (25.8%–27.8%) to 71.0% (69.6%–72.4%). The association between the alerts and overall compliance was also present on multivariable analysis.

CONCLUSIONS:
Implementation of an intraoperative decision-support tool was associated with statistically significant improvement in the maintenance of normothermia in cases eligible for reporting to CMS. This led to improved compliance with Measure #424 and suggests that electronic alerts can help practices improve their performance and payment bonus eligibility.
Forecasting a Crisis: Machine-Learning Models Predict Occurrence of Intraoperative Bradycardia Associated With Hypotension
Authors: Solomon Stuart C. , Saxena Rajeev C. , Neradilek Moni B. , Hau Vickie , Fong Christine T. , Lang John D. , Posner Karen L. , Nair Bala G. 
Published: April 2020

BACKGROUND:
Predictive analytics systems may improve perioperative care by enhancing preparation for, recognition of, and response to high-risk clinical events. Bradycardia is a fairly common and unpredictable clinical event with many causes; it may be benign or become associated with hypotension requiring aggressive treatment. Our aim was to build models to predict the occurrence of clinically significant intraoperative bradycardia at 3 time points during an operative course by utilizing available preoperative electronic medical record and intraoperative anesthesia information management system data.

METHODS:
The analyzed data include 62,182 scheduled noncardiac procedures performed at the University of Washington Medical Center between 2012 and 2017. The clinical event was defined as severe bradycardia (heart rate <50 beats per minute) followed by hypotension (mean arterial pressure <55 mm Hg) within a 10-minute window. We developed models to predict the presence of at least 1 event following 3 time points: induction of anesthesia (TP1), start of the procedure (TP2), and 30 minutes after the start of the procedure (TP3). Predictor variables were based on data available before each time point and included preoperative patient and procedure data (TP1), followed by intraoperative minute-to-minute patient monitor, ventilator, intravenous fluid, infusion, and bolus medication data (TP2 and TP3). Machine-learning and logistic regression models were developed, and their predictive abilities were evaluated using the area under the ROC curve (AUC). The contribution of the input variables to the models were evaluated.

RESULTS:
The number of events was 3498 (5.6%) after TP1, 2404 (3.9%) after TP2, and 1066 (1.7%) after TP3. Heart rate was the strongest predictor for events after TP1. Occurrence of a previous event, mean heart rate, and mean pulse rates before TP2 were the strongest predictor for events after TP2. Occurrence of a previous event, mean heart rate, mean pulse rates before TP2 (and their interaction), and 15-minute slopes in heart rate and blood pressure before TP2 were the strongest predictors for events after TP3. The best performing machine-learning models including all cases produced an AUC of 0.81 (TP1), 0.87 (TP2), and 0.89 (TP3) with positive predictive values of 0.30, 0.29, and 0.15 at 95% specificity, respectively.

CONCLUSIONS:
We developed models to predict unstable bradycardia leveraging preoperative and real-time intraoperative data. Our study demonstrates how predictive models may be utilized to predict clinical events across multiple time intervals, with a future goal of developing real-time, intraoperative, decision support.
Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach
Authors: Belur Nagaraj Sunil , Ramaswamy Sowmya M. , Weerink Maud A. S. , Struys Michel M. R. F. 
Published: April 2020

BACKGROUND:
Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method.

METHODS:
Using deep learning algorithms, we developed a novel data-repurposing framework to predict hypnotic levels from sleep brain rhythms. We used an online large sleep data set (5723 clinical EEGs) for training the deep learning algorithm and a clinical trial hypnotic data set (30 EEGs) for testing during dexmedetomidine infusion. Model performance was evaluated using accuracy and the area under the receiver operator characteristic curve (AUC).

RESULTS:
The deep learning model (a combination of a convolutional neural network and long short-term memory units) trained on sleep EEG predicted deep hypnotic level with an accuracy (95% confidence interval [CI]) = 81 (79.2–88.3)%, AUC (95% CI) = 0.89 (0.82–0.94) using dexmedetomidine as a prototype drug. We also demonstrate that EEG patterns during dexmedetomidine-induced deep hypnotic level are homologous to nonrapid eye movement stage 3 EEG sleep.

CONCLUSIONS:
We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors.
Sports Participation Fee Policies in US High Schools: A National Survey of Athletic Directors
Authors: Eyler Amy A. , Valko Cheryl A. , Curoe Katherine , Ramadas Ramya 
Published: April 2020

ABSTRACT

Purpose
Because of budget constraints, many school districts are reducing or eliminating sports programs. An alternative trend is to maintain programs but charge fees for student participation in sports. Fees may discourage participation and unfairly burden students unable to pay. The purpose of this study was to explore sports participation fee policies in US high schools among a national sample of athletic directors.

Methods
We developed a survey and sent it online or through postal service to a national sample of athletic directors (N = 3342) from a randomized list of US school districts. Descriptive statistics and multilevel regression models were computed comparing respondents from districts with and without policies, and those with and without fee waivers.

Results
We received 991 responses (response rate of 30%). Almost 40% (39.9%) reported participation fees for interscholastic sports, and 26% reported the likelihood of a policy within 5 yr. These policies varied; 32% charged fees per season, 26% per year, and 42% by sport. The average amount was $120. Most (78%) had waiver provisions. Lower-income districts had significantly lower odds of charging fees than higher-income districts, and when they did, they were less likely to have waivers.

Conclusion
Overall, respondents from districts with fee polices had significantly different perceptions of policies compared with no policy respondents. High schools are implementing a wide variety of sports participation fee policies, and majority of those with policies provide waivers for students unable to pay. There were significant differences by district income level. Further exploration on the extent to which these policies are influencing sports participation, especially among lower-income students, is needed.
The clinical utility of imaging methods used to measure hypoxia in cervical cancer
Authors: Waller Joseph , Onderdonk Benjamin , Flood Ann , Swartz Harold , Shah Jaffer , Shah Asghar , Aydogan Bulent , Halpern Howard , Hasan Yasmin 
Published: April 2020

While it is well-established that hypoxia is a major factor that affects clinical outcomes in cervical cancer, widespread usage of clinically available methods to detect and evaluate hypoxia during the course of treatment have not been established. This review compares these methods, summarizes their strengths and weaknesses, and assesses the pathways for their useful employment to alter clinical practice. We conducted a search on PubMed for literature pertaining to imaging hypoxic cervical cancer, and implemented keywords related to oxygen measurement tools to improve the relevance of the search results.
Oxygenation level-dependent applications of MRI have demonstrated hypoxia-induced radioresistance, and changes in cervix tumor oxygenation from hyperoxic therapy.
The hypoxic areas within tumors can be indirectly identified in dynamic contrast-enhanced images, where they generally display low signal enhancement, and diffusion-weighted images, which demonstrates areas of restricted diffusion (which correlates with hypoxia). Positron emmision tomography, used independently and with other imaging modalities, has demonstrated utility in imaging hypoxia through tracers specific for low oxygen levels, like Cu-ATSM tracers and nitroimidazoles. Detecting hypoxia in the tumors of patients diagnosed with cervical cancer via medical imaging and non-imaging tools like electron paramagnetic resonance oximetry can be utilized clinically, such as for guiding radiation and post-treatment surveillance, for a more personalized approach to treatment. The merits of these methods warrant further investigation via comparative effectiveness research and large clinical trials into their clinical applications.