bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and pilot testing of PROACTIVE: A pediatric onco‐critical care capacity and quality assessment tool for resource‐limited settings
Authors: Arias Anita V. , Sakaan Firas M. , Puerto‐Torres Maria , Al Zebin Zebin , Bhattacharyya Parthasarathi , Cardenas Adolfo , Gunasekera Sanjeeva , Kambugu Joyce , Kirgizov Kirill , Libes Jaime , Martinez Angelica , Matinyan Nune V. , Mendez Alejandra , Middlekauff Janet , Nielsen Katie R. , Pappas Andrew , Ren Hong , Sharara‐Chami Rana , Torres Silvio F. , McArthur Jennifer , Agulnik Asya 
Published: November 2022

AbstractBackgroundNearly 90% children with cancer reside in low‐ and middle‐income countries, which face multiple challenges delivering high‐quality pediatric onco‐critical care (POCC). We recently identified POCC quality and capacity indicators for PROACTIVE (PediatRic Oncology cApaCity assessment Tool for IntensiVe carE), a tool that evaluates strengths and limitations in POCC services. This study describes pilot testing of PROACTIVE, development of center‐specific reports, and identification of common POCC challenges.MethodsThe original 119 consensus‐derived PROACTIVE indicators were converted into 182 questions divided between 2 electronic surveys for intensivists and oncologists managing critically ill pediatric cancer patients. Alpha‐testing was conducted to confirm face‐validity with four pediatric intensivists. Eleven centers representing diverse geographic regions, income levels, and POCC services conducted beta‐testing to evaluate usability, feasibility, and applicability of PROACTIVE. Centers' responses were scored and indicators with mean scores ≤75% in availability/performance were classified as common POCC challenges.ResultsAlpha‐testing ensured face‐validity and beta‐testing demonstrated feasibility and usability of PROACTIVE (October 2020–June 2021). Twenty‐two surveys (response rate 99.4%) were used to develop center‐specific reports. Adjustments to PROACTIVE were made based on focus group feedback and surveys, resulting in 200 questions. Aggregated data across centers identified common POCC challenges: (1) lack of pediatric intensivists, (2) absence of abstinence and withdrawal symptoms monitoring, (3) shortage of supportive care resources, and (4) limited POCC training for physicians and nurses.ConclusionsPROACTIVE is a feasible and contextually appropriate tool to help clinicians and organizations identify challenges in POCC services across a wide range of resource‐levels. Widespread use of PROACTIVE can help prioritize and develop tailored interventions to strengthen POCC services and outcomes globally.
Novel combination approaches with targeted agents in frontline chronic lymphocytic leukemia
Authors: Lipsky Andrew H. , Lamanna Nicole 
Published: November 2022

AbstractTargeted therapies have revolutionized the frontline treatment landscape for patients with chronic lymphocytic leukemia (CLL) and have largely displaced a reliance on chemoimmunotherapy when treating this disease. Multiple randomized trials have documented the efficacy of oral therapy with the Bruton tyrosine kinase inhibitors ibrutinib and acalabrutinib (and zanubrutinib, pending a supplemental new drug application in CLL), as well as BCL2 inhibition using venetoclax. In this review, the authors highlight novel therapeutic strategies for using these agents in combination, either as doublet therapy or as triplet therapy, with anti‐CD20 antibodies. First, the current treatment landscape is outlined, and the data are reviewed for continuous and time‐limited therapeutic approaches, which constitute the current standard of care. Then, more recent reports are described from phase 2 and 3 studies exploring different combination strategies of Bruton tyrosine kinase and BCL2 inhibition for treatment‐naive patients. In addition, relevant differences are emphasized between patient characteristics (e.g., patient fitness and the presence of high‐risk disease features) and study methodology (e.g., dosing schedule, randomization, and assessment of measurable residual disease) across trials. Finally, the authors revisit the currently available data for these approaches in the context of ongoing studies and future planned trials, evaluating their potential impact on the frontline treatment landscape for CLL in the years to come.
Performance analysis of regression‐based machine learning models towards intelligent selection of MIMO configurations
Authors: Beeharry Yogesh , Calchand Dujaya R. 
Published: November 2022

AbstractFifth Generation (5G) fosters the integration of several technologies for achieving the requirements of rapid and reliable communications. Multiple Input Multiple Output (MIMO) is one such technology implemented in communication systems with the aim of delivering better services to users. Machine Learning (ML) technologies are also being used alongside MIMO since they can aid with the selection of the most appropriate MIMO configuration based on antenna, data, and channel parameters. These parameters can be further used as input features to the models for obtaining the best data rate and throughput. In this paper, three different MIMO configurations, 2 × 2, 3 × 3, and 4 × 4 MIMO using directional and omnidirectional antennas have been simulated. Power propagation from each antenna, MIMO power, channel capacity, data rate, and throughput have been used as response variables. The data obtained from these simulations are used to train regression models for prediction of new data. Frankfurt city is used as scenario in this simulation and the models are analyzed based on the root mean squared error (RMSE) values obtained. The results demonstrate that the interactions linear regression, fine tree and medium tree models provide the lowest RMSE values when predicting the variables for different MIMO configurations using directional and omnidirectional antenna.
A remote speech‐based AI system to screen for early Alzheimer's disease via smartphones
Authors: Fristed Emil , Skirrow Caroline , Meszaros Marton , Lenain Raphael , Meepegama Udeepa , Cappa Stefano , Aarsland Dag , Weston Jack 
Published: November 2022

AbstractIntroductionArtificial intelligence (AI) systems leveraging speech and language changes could support timely detection of Alzheimer's disease (AD).MethodsThe AMYPRED study (NCT04828122) recruited 133 subjects with an established amyloid beta (Aβ) biomarker (66 Aβ+, 67 Aβ–) and clinical status (71 cognitively unimpaired [CU], 62 mild cognitive impairment [MCI] or mild AD). Daily story recall tasks were administered via smartphones and analyzed with an AI system to predict MCI/mild AD and Aβ positivity.ResultsEighty‐six percent of participants (115/133) completed remote assessments. The AI system predicted MCI/mild AD (area under the curve [AUC] = 0.85, ±0.07) but not Aβ (AUC = 0.62 ±0.11) in the full sample, and predicted Aβ in clinical subsamples (MCI/mild AD: AUC = 0.78 ±0.14; CU: AUC = 0.74 ±0.13) on short story variants (immediate recall). Long stories and delayed retellings delivered broadly similar results.DiscussionSpeech‐based testing offers simple and accessible screening for early‐stage AD.
Evaluation of dose‐dependent treatment effects after mid‐trial dose escalation in biomarker, clinical, and cognitive outcomes for gantenerumab or solanezumab in dominantly inherited Alzheimer's disease
Authors: Wang Guoqiao , Li Yan , Xiong Chengjie , McDade Eric , Clifford David B. , Mills Susan L. , Santacruz Anna M. , Aschenbrenner Andrew J. , Hassenstab Jason , Benzinger Tammie L.S. , Gordon Brian A. , Fagan Anne M. , Coalier Kelley A. , Libre‐Guerra Jorge J. , McCullough Austin , Joseph‐Mathurin Nelly , Chen Charles D. , Mummery Catherine , Wendelberger Barbara A. , Gauthier Serge , Masellis Mario , Holdridge Karen C. , Yaari Roy , Chatterjee Saptarshi , Sims John , Delmar Paul , Kerchner Geoffrey A. , Bittner Tobias , Hofmann Carsten , Bateman Randall J. ,   for the DIAN‐TU Study Team
Published: November 2022

AbstractIntroductionWhile the Dominantly Inherited Alzheimer Network Trials Unit (DIAN‐TU) was ongoing, external data suggested higher doses were needed to achieve targeted effects; therefore, doses of gantenerumab were increased 5‐fold, and solanezumab was increased 4‐fold. We evaluated to what extent mid‐trial dose increases produced a dose‐dependent treatment effect.MethodsUsing generalized linear mixed effects (LME) models, we estimated the annual low‐ and high‐dose treatment effects in clinical, cognitive, and biomarker outcomes.ResultsBoth gantenerumab and solanezumab demonstrated dose‐dependent treatment effects (significant for gantenerumab, non‐significant for solanezumab) in their respective target amyloid biomarkers (Pittsburgh compound B positron emission tomography standardized uptake value ratio and cerebrospinal fluid amyloid beta 42), with gantenerumab demonstrating additional treatment effects in some downstream biomarkers. No dose‐dependent treatment effects were observed in clinical or cognitive outcomes.ConclusionsMid‐trial dose escalation can be implemented as a remedy for an insufficient initial dose and can be more cost effective and less burdensome to participants than starting a new trial with higher doses, especially in rare diseases.Highlights
We evaluated the dose‐dependent treatment effect of two different amyloid‐specific immunotherapies.
Dose‐dependent treatment effects were observed in some biomarkers.
No dose‐dependent treatment effects were observed in clinical/cognitive outcomes, potentially due to the fact that the modified study may not have been powered to detect such treatment effects in symptomatic subjects at a mild stage of disease exposed to high (or maximal) doses of medication for prolonged durations.
Development and pilot testing of an early childhood somatosensory assessment: Somatosensory test of reaching
Authors: Chu Virginia Way Tong , Dusing Stacey C. 
Published: November 2022

AbstractThirty‐two children (50% female, 59.3% White, 7–60 months), from middle to high socioeconomic status families, participated in pilot feasibility and validity testing of the somatosensory test of reaching (STOR). STOR tested the child's accuracy of reach to visual and somatosensory targets. All children were able to complete the assessment. Statistically significant differences were found between age groups (p = .0001), showing developmental trends, and between test conditions (p < .001), showing that the ability to reach to visible targets develops before somatosensory targets. STOR also showed a moderate correlation with the Developmental Assessment of Young Children 2nd edition. STOR appears to be a promising tool for assessing somatosensory processing in very young children, and it warrants additional testing in larger participant samples.
Performance evaluation of artificial neural networks for a fish oil biodiesel fueled diesel engine: Paying a pathway to sustainable energy in environmental progress
Authors: Lakshmipathi Jakkamputi , Senthilkumar Marikannan , Jegadeeshwaran Rakkiyanan , Sakthivel Gnanasekaran , Gangadhar Kotha , Kannan Thangavelu 
Published: November 2022

AbstractThe purpose of this study is to develop a neural networks model to predict the performance monitoring of the compression ignition engine at various injection timings (21°, 24°, 27°bTDC) using fish oil biodiesel thereby increasing the sustainability of the biodiesel and leading to greener environment. The influence of the injection timing on the engine performance, emission, and combustion are observed in a four‐stroke single cylinder, constant speed, direct injection with a rated output of 4.4 kW using fish oil biodiesel blended with diesel. Feed forward back propagation, Elman feed forward, and Cascade back propagation neural networks‐based models are created to predict the parameters like ignition delay, maximum rate of pressure rise, combustion duration, smoke, hydrocarbon, oxides of nitrogen, carbon monoxide, and carbon dioxide, brake thermal efficiency, brake specific fuel consumption, and exhaust gas temperature. Scaled conjugate gradient and Levenberg–Marquardt have been used as training functions. The predictive capability of the models is compared with each other. It is observed that Elman feed forward with trainlm is the best model with the correlation coefficient is 0.9–1 and low root mean square error. The developed artificial neural network (ANN) predicts the engine parameters with the acceptable correlation limits and proves the efficiency and precision of the model which significantly reduces the time consumption and cost incurred in real time application. Hence by using limited experimental investigations the more accurate engine performance and emissions can be determined for wider engine operating conditions using Elman feed forward ANN.
Development and validation of blood tumor mutational burden reference standards
Authors: Raiber‐Moreau Eun‐Ang , Portella Guillem , Butler Matthew G. , Clement Omoshile , Konigshofer Yves , Hadfield James 
Published: November 2022

AbstractTumor mutational burden (TMB), measured by exome or panel sequencing of tumor tissue or blood (bTMB), is a potential predictive biomarker for treatment benefit in patients with various cancer types receiving immunotherapy targeting checkpoint pathways. However, significant variability in TMB measurement has been observed. We developed contrived bTMB reference materials using DNA from tumor cell lines and donor‐matched lymphoblastoid cell lines to support calibration and alignment across laboratories and platforms. Contrived bTMB reference materials were developed using genomic DNA from lung tumor cell lines blended into donor‐matched lymphoblastoid cell lines at 0.5% and 2% tumor content, fragmented and size‐selected to mirror the size profile of circulating cell‐free tumor DNA with TMB scores of 7, 9, 20, and 26 mut/Mb. Variant allele frequency (VAF) and bTMB scores were assessed using PredicineATLAS and GuardantOMNI next‐generation sequencing assays. DNA fragment sizes in the contrived reference samples were similar to those found within patient plasma‐derived cell‐free DNA, and mutational patterns aligned with those in the parental tumor lines. For the 7, 20, and 26 mut/Mb contrived reference samples with 2% tumor content, bTMB scores estimated using either assay aligned with expected scores from the parental tumor cell lines and showed good reproducibility. A bioinformatic filtration step was required to account for low‐VAF artifact variants. We demonstrate the feasibility and challenges of producing and using bTMB reference standards across a range of bTMB levels, and how such standards could support the calibration and validation of bTMB platforms and help harmonization between panels and laboratories.
Key challenges and developments in wildlife ecological risk assessment: Problem formulation
Authors: Sample Bradley E. , Johnson Mark S. , Hull Ruth N. , Kapustka Lawrence , Landis Wayne G. , Murphy Cheryl A. , Sorensen Mary , Mann Gary , Gust Kurt A. , Mayfield David B. , Ludwigs Jan-Dieter , Munns Wayne R. 
Published: November 2022

Abstract
Problem formulation (PF) is a critical initial step in planning risk assessments for chemical exposures to wildlife, used either explicitly or implicitly in various jurisdictions to include registration of new pesticides, evaluation of new and existing chemicals released to the environment, and characterization of impact when chemical releases have occurred. Despite improvements in our understanding of the environment, ecology, and biological sciences, few risk assessments have used this information to enhance their value and predictive capabilities. In addition to advances in organism-level mechanisms and methods, there have been substantive developments that focus on population- and systems-level processes. Although most of the advances have been recognized as being state-of-the-science for two decades or more, there is scant evidence that they have been incorporated into wildlife risk assessment or risk assessment in general. In this article, we identify opportunities to consider elevating the relevance of wildlife risk assessments by focusing on elements of the PF stage of risk assessment, especially in the construction of conceptual models and selection of assessment endpoints that target population- and system-level endpoints. Doing so will remain consistent with four established steps of existing guidance: (1) establish clear protection goals early in the process; (2) consider how data collection using new methods will affect decisions, given all possibilities, and develop a decision plan a priori; (3) engage all relevant stakeholders in creating a robust, holistic conceptual model that incorporates plausible stressors that could affect the targets defined in the protection goals; and (4) embrace the need for iteration throughout the PF steps (recognizing that multiple passes may be required before agreeing on a feasible plan for the rest of the risk assessment). Integr Environ Assess Manag 2024;20:658–673. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
No cost but high performance–An alternative open source solution for 3D‐visualizations in morphology
Authors: Kalke Paul , Helm Conrad 
Published: November 2022

Abstract3D‐visualization has become common courtesy in science and also found its way into teaching in schools and universities. Nevertheless, the way to high performance 3D‐visualization and analyses remains difficult and is often also a matter of budget. Due to the obvious advantages of presenting morphological and anatomical datasets with the help of 3D‐figures, all in one software solutions usually come along with high rental and maintenance fees. For that reason, it is more than overdue to establish and use open source software solutions with all their obvious advantages ‐ as other disciplines already do. Here we provide a modular, highly adaptive and freely available software pipeline for high performance 3D‐visualizations of (not only) morphological datasets by combining features of ImageJ, MeshLab and Blender, without any additional costs. Exemplarily using serial‐block face SEM data as well as serial AZAN‐stained histological sections, the herein presented step‐by‐step protocol allows for a fast and efficient analysis, visualization and animation of large, anatomical datasets. Regardless which type of serial, morphological datasets needs to be analyzed, our open source guide provides an easy to handle and promptly adaptable solution. Therefore, our pipeline for 3D‐visualization represents a valuable alternative to conventional, commercial packages.Research HighlightsWe provide a highly modular and easy to learn open source solution for multiple 3D‐visualizations. The step‐by‐step‐guide make it easy to start, and advanced users can replace software, add others and such build your own individual software pipeline.
Adverse events reported after first dose of SARS‐CoV‐2 vaccine in the Northern Region of Ghana
Authors: Dzantor Edem Kojo , Asumah Mubarick Nungbaso , Inusah Abdul‐Wahab , Nukpezah Nimota Ruth , Agyeman Yaa Nyarko , Kukeba Margaret Wekem , Braimah Baba Abubakari , Adjeso Theophilus , Tahiru Mohammed Mutaru 
Published: November 2022

AbstractAimThe study examined the associated adverse events following SARS‐CoV‐2 vaccination among healthcare workers during the first dose of the vaccine in the Northern Region of Ghana.DesignThe study was a cross‐sectional survey involving 463 healthcare workers.MethodThe data were collected using a structured questionnaire. The data were analysed descriptively, and binary logistics was performed using SPSS version 25.ResultsThe mean age was 33.4 ± 9.7 years, the majority (43.6%) being ≤30 years and males (57.2%). The self‐reported prevalence of SARS‐CoV‐2 vaccine adverse events was 75.5%. Common systemic adverse events comprised headache (47.5%), dizziness (18.4%) and local adverse events included generalized body pains (44.0%) and abscess around the injection sites (11.2%). The study found a high prevalence of self‐reported SARS‐CoV‐2 vaccine adverse events involving both systemic and local adverse events. Our study gives useful information that can be used for public health‐targeted interventions to boost public confidence in SARS‐CoV‐2 vaccines.
Core competencies of healthcare professionals in Oman: Research and evidence‐based practice needs attention
Authors: Al Jabri Fatma , Kvist Tarja , Turunen Hannele 
Published: November 2022

AbstractAimThe aim of the study was to examine (1) the perceptions on core competencies of healthcare professionals working at clinical settings in Oman and (2) which demographic characteristics explain the overall core competency.DesignA cross‐sectional design.MethodsHealthcare Professional Core Competency Instrument, consisting of 11 sub‐scales with 81 items, was distributed to healthcare professionals (n = 1,543; 826 nurses and 717 physicians) who worked at primary, secondary and tertiary healthcare institutions. Descriptive statistics, t‐test, ANOVA and linear regression were used for data analysis.ResultsAltogether 1,078 healthcare professionals (628 nurses and 450 physicians) responded representing 70% overall response rate. Healthcare professionals perceived their overall core competence as excellent, safety being the highest, and research and evidence‐based practice was the lowest. The multiple linear regression analysis revealed that ethnicity, gender and years of working experience were the characters that explained the overall core competence, where expatriate senior professionals reported higher competency levels compared with counterparts.
Clinician perceptions of Passport for Care, a web‐based clinical decision support tool for survivorship care plan delivery
Authors: King Jason E. , O'Connor Michael C. , Shohet Ellen , Krause Susan M. , Scheurer Michael E. , Horowitz Marc E. , Poplack David G. , Fordis C. Michael , Gramatges Maria M. 
Published: November 2022

AbstractBackgroundThe Children's Oncology Group Long‐Term Follow‐Up Guidelines provide exposure‐based risks and recommendations for late effects screening of survivors of childhood cancer. Passport for Care (PFC) is a web‐based clinical decision support tool for generating a personalized survivorship care plan (SCP) derived from the Guidelines and user‐entered exposures. We assessed PFC clinician user practices and perceptions of PFC impact on clinic workflow, guidelines application, and survivor shared decision‐making.ProcedureA 35‐item REDCap survey was emailed to all PFC users (n = 936) in 146 current and former PFC user clinics. Anonymous responses were permitted. Results were summarized and compared with a 2012 survey.ResultsData were available from 148 respondents representing 64 out of 146 PFC user clinics (minimum clinic response rate 44%, excluding 49 anonymous responses). Generation of a personalized SCP was the most common application of PFC, followed by determination of surveillance recommendations and use as a survivor database. Twenty‐five respondents (17%) felt data entry was a significant or insurmountable barrier to PFC application. Sixty‐nine percent of respondents attributed PFC with a very high/high impact on guidelines adherence in their clinical practice, compared with 40% who attributed PFC with having a significant impact on adherence in 2012 (p < .001).ConclusionThe survey results provide valuable insights on patterns of SCP delivery and Survivor Clinic workflow. User‐perceived benefits to PFC included facilitating clinician ability to follow guidelines recommendations in clinical practice. Importantly, some barriers to resource utilization were also identified, suggesting a need for user‐informed adaptations to further improve uptake.