bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine-learning approach for Type 2 Diabetes diagnosis and prognosis models over heterogeneous feature spaces
Authors: Navarro-Cerdán J.Ramón , Pons-Suñer Pedro , Arnal Laura , Arlandis Joaquim , Llobet Rafael , Pérez-Cortés Juan-Carlos , Lara-Hernández Francisco , Alvarez Luis , García-García Ana-Bárbara , Chaves Felipe Javier 
Published: July 2023

AbstractThis research aims to evaluate the Type 2 Diabetes (T2D) diagnosis and prognosis power from heterogeneous environmental, lifestyle and biochemistry data. Model estimation has previously addressed three main actions as: 1) Missingvalue imputation using specific univariant and multivariant imputers accommodated to each particular feature; 2) Quasi-constancy detection in variables; 3) Constructing geographical pollution and rent data from municipality information. Next, different T2D diagnosis and prognosis models are fitted and evaluated, showing increasing performance as more specific features become available while the prediction cost rises as a consequence of requiring more specific data. Finally, four models are obtained: two of them for T2D diagnosis and the other two for T2D prognosis respectively, with performances ranging from 73.3 to 95.41 AUC-ROC. One pair of diagnosis and prognosis models were thought for a global testing that can be done in general locations by only asking general lifestyle-related questions. On the other hand, the other pair, which achieves higher performances, is thought to be applied in a clinical environment where it is easy to obtain more specific biochemistry measures.
Routine surveillance of kidney allograft rejection using a fully automated urinary CXCL9 and CXCL10 immunoassay
Authors: Tinel Claire , Sauvaget Virginia , Aouni Laïla , Lamarthée Baptiste , Leclaire Charlotte , Legendre Christophe , Marquet Pierre , Normand Corinne , Rabant Marion , Terzi Fabiola , Anglicheau Dany 
Published: July 2023

ABSTRACTBackgroundFor kidney transplant recipients, assessing non-invasively the individualized risk of acute rejection is one of the most unmet need. Urinary chemokines are one of the short-term most promising biomarkers, because of their simple and low-cost analytical method in easily accessible samples, and their high diagnostic performance consistently assessed over the last decade. In this study, we aimed at confronting all practical issues of routine implementation of kidney allograft rejection monitoring using urine CXCL9 and CXCL10.MethodsThe next-generation immunoassay Ella® was investigated as feasible quantification platform, from sample collection to render of the results. Urine CXCL9/10 levels were measured using Ella® microfluidic cartridges, across preanalytical (sample preparation, storage conditions, freeze-thaw cycles) and analytical (linearity, ranges, intra/inter-assay variability) performances studies, and diagnostic accuracy was assessed in comparison to the ELISA reference method, in urine samples from previously published cohorts.ResultsUpon assay preparation, Ella® appeared very efficient with a minimal workflow (urine sample thaw> centrifugation>1:2 dilution>loading) and a time to result of 90 minutes. Preanalytical studies showed high stability of urine CXCL9/10 levels across various temperatures (4° vs 25°C) and time (24/48/72H) before storage and over 5 freeze-thaw cycles. How complex urine matrix, analytical studies confirmed excellent linearity of dosage, as well as intra-assay (≤5%) and inter-assay precision. Across 1024 samples, Ella® results were highly correlated to those quantified by ELISA (P<0.0001), and further entered into our previously validated urine chemokine model. 268 out of the 1024 samples were collected at time of acute rejection (26.2%). In this cohort, accuracy was 0.85 for acute rejection diagnosis.ConclusionElla® fulfils all prerequisites for clinical implementation of urinary chemokines monitoring. It has proven a robust, easy-to-use platform with unprecedented validation to quantify urine chemokines.
Human and computer attention in assessing genetic conditions
Authors: Duong Dat , Johny Anna Rose , Hanchard Suzanna Ledgister , Fortney Chris , Hellmann Fabio , Hu Ping , Javanmardi Behnam , Moosa Shahida , Patel Tanviben , Persky Susan , Sümer Ömer , Tekendo-Ngongang Cedrik , Hsieh Tzung-Chien , Waikel Rebekah L. , André Elisabeth , Krawitz Peter , Solomon Benjamin D. 
Published: July 2023

AbstractDeep learning (DL) and other types of artificial intelligence (AI) are increasingly used in many biomedical areas, including genetics. One frequent use in medical genetics involves evaluating images of people with potential genetic conditions to help with diagnosis. A central question involves better understanding how AI classifiers assess images compared to humans. To explore this, we performed eye-tracking analyses of geneticist clinicians and non-clinicians. We compared results to DL-based saliency maps. We found that human visual attention when assessing images differs greatly from the parts of images weighted by the DL model. Further, individuals tend to have a specific pattern of image inspection, and clinicians demonstrate different visual attention patterns than non-clinicians.
Impact of Community Masking on SARS-CoV-2 Transmission in Ontario after Adjustment for Differential Testing by Age and Sex
Authors: Peng Amy , Bosco Savana , Simmons Alison , Tuite Ashleigh R. , Fisman David N. 
Published: July 2023

AbstractBackgroundUse of masks and respirators for prevention of respiratory infectious disease transmission is not new, but has proven controversial, and even politically polarizing during the SARS-CoV-2 pandemic. In the Canadian province of Ontario, mask mandates were introduced by the 34 regional health authorities in an irregular fashion from June to September 2020, creating a quasi-experiment that can be used to evaluate impact of community mask mandates. Ontario SARS-CoV-2 case counts were strongly biased by testing focussed on long-term care facilities and healthcare workers. We developed a simple regression-based test-adjustment method that allowed us to adjust cases for undertesting by age and gender. We used this test- adjusted time series to evaluate mask mandate effectiveness.MethodsWe evaluated the effect of masking using count-based regression models that allowed adjustment for age, sex, public health region and time trends with either reported (unadjusted) cases, or testing-adjusted case counts, as dependent variables. Mask mandates were assumed to take effect in the week after their introduction. Model based estimates of effectiveness were used to estimate the fraction of SARS- CoV-2 cases, severe outcomes, and costs, averted by mask mandates.ResultsModels that used unadjusted cases as dependent variable identified protective effects of masking (effectiveness 15-42%), though effectiveness was variably statistically significant, depending on model choice. Mask effectiveness in models predicting test-adjusted case counts was substantially higher, ranging from 49% (44- 53%) to 73% (48-86%) depending on model choice. Effectiveness was greater in women than men (P = 0.016), and in urban health units as compared to rural units (P < 0.001). The prevented fraction associated with mask mandates was 46% (41-51%), averting approximately 290,000 clinical cases, averting 3008 deaths and loss of 29,038 QALY. Costs averted represented $CDN 610 million in economic wealth.ConclusionsLack of adjustment for SARS-CoV-2 undertesting in younger individuals and males generated biased estimates of infection risk and obscures the impact of public health preventive measures. After adjustment for under-testing, the effectiveness of mask mandates emerges as substantial, and robust regardless of model choice. Mask mandates saved substantial numbers of lives, and prevented economic costs, during the SARS-CoV-2 pandemic in Ontario, Canada.
Drivers of bias in diagnostic test accuracy estimates when using expert panels as a reference standard
Authors: Kellerhuis BE , Jenniskens K , Schuit E , Hooft L , Moons KGM , Reitsma JB 
Published: July 2023

AbstractObjectivesTo assess the impact of study and expert panel characteristics on index test diagnostic accuracy estimates.Study Design and SettingSimulations were performed in which an expert panel was used as reference standard to estimate the sensitivity and specificity of an index diagnostic test. Diagnostic accuracy was determined by combining probability estimates of target condition presence, as provided by experts using four component reference tests, through a predefined threshold. Study and panel characteristics were varied in several scenarios: target condition prevalence (20%, 40%, 50%), accuracy of component reference tests (70%, 80%, mixed), expert panel size (2, 3, 10), study population size (360, 1000), and random or systematic differences between expert’s probability estimates. Bias in accuracy estimates across all possible true index test values was quantified for all scenarios. The total bias in each scenario was quantified using the mean squared error (MSE).ResultsWhen estimating an index test with 80% sensitivity and 70% specificity, bias in these estimates was hardly affected by the study population size or the number of experts. When one expert was systematically biased, bias in sensitivity and specificity estimates increased, but this effect lessened when the number of experts in the panel was higher. Prevalence had a large effect on bias, scenarios with a prevalence of 0.5 estimated sensitivity between 63.3% and 76.7% and specificity between 56.1% and 68.7%, whereas scenarios with a prevalence of 0.2 estimated sensitivity between 48.5% and 73.3% and specificity between 65.5% and 68.7%. Random and systematic differences between experts also increased bias, with estimated sensitivity between 48.6% and 77.4% and specificity between 59.1% and 69.1% as opposed to scenarios without random or systematic differences, which estimated sensitivity between 58.0% and 77.4% and specificity between 56.1% and 69.1%. More accurate component reference tests also reduced bias. Scenarios with four component tests of 80% sensitivity and specificity estimated index test sensitivity between 60.1% and 77.4% and specificity between 62.9% and 69.1%, whereas scenarios with four component tests of 70% sensitivity and specificity estimated index test sensitivity between 48.5% and 73.4% and specificity between 56.1% and 67.0%.ConclusionBias in accuracy estimates when using an expert panel will increase if the component reference tests (combined) are less accurate. Prevalence, the true value of the index test accuracy, and random or systematic differences between experts can also impact the amount of bias, but the amount and even direction will vary between scenarios.
Functionalizing face masks with natural clays: preliminary results
Authors: Hernández D. , Rodríguez-de-Torner L. A. , Altshuler E. , Rivera A. 
Published: July 2023

AbstractThe materials used in the fabrication of standard, three-layered surgical masks are functionalized by the incorporation of Cuban natural clay with potential microbicidal properties. Different treatments involving aqueous solutions of bentonite (Bent) clay and small amounts of a cationic surfactant are studied. Optical microscopy indicates that the clay particles are effectively adhered to the fibers forming the mask layers. The capacity of the materials to block ballistic droplets showed to be very high for the external and medium layers of a surgical mask both before and after functionalization. However, functionalization demonstrated to increase the blocking capacity of the material in the inner layer, i.e., that closer to the user’s face.
Unveiling the Genetic Landscape of Basal Ganglia: Implications for Common Brain Disorders
Authors: Bahrami Shahram , Nordengen Kaja , Rokicki Jaroslav , Shadrin Alexey A. , Rahman Zillur , Smeland Olav B. , Jaholkowski Piotr Pawel , Parker Nadine , Parekh Pravesh , O’Connell Kevin S. , Elvsåshagen Torbjørn , Toft Mathias , Djurovic Srdjan , Dale Anders M. , Westlye Lars T. , Kaufmann Tobias , Andreassen Ole A. 
Published: July 2023

AbstractThe basal ganglia are subcortical brain structures involved in motor control, cognition, and emotion regulation. We conducted a multivariate genome-wide association analysis (GWAS) to explore the genetic architecture of basal ganglia volumes using brain scans obtained from 34,794 European individuals with replication in 5,236 non-Europeans. We identified 72 genetic loci associated with basal ganglia volumes with a replication rate of 87.5%, revealing a distributed genetic architecture across basal ganglia structures. Of the 72 loci, 51 are novel. Of these,APOE,NBR1andHLAA, are all exonic and among the novel loci. Furthermore, we examined the genetic overlap between basal ganglia volumes and several neurological and psychiatric disorders. The most prominent overlap was seen with Parkinson’s disease, schizophrenia and migraine.HPandTMEM161Bshowed overlap between basal ganglia and Parkinson’s disease, but also three different psyciatric or nevrodevelopmental disorder(s), demonstrating important shared biology between brain disorders. Functional analyses implicated neurogenesis, neuron differentiation and development in basal ganglia volumes. These results enhance our understanding of the genetic architecture and molecular associations of basal ganglia structure and their role in brain disorders.
Attitudes and Perceptions of Medical Researchers Towards the Use of Artificial Intelligence Chatbots in the Scientific Process: A Protocol for a Cross-Sectional Survey
Authors: Ng Jeremy Y. , Maduranayagam Sharleen G. , Lokker Cynthia , Iorio Alfonso , Haynes R. Brian , Moher David 
Published: July 2023

AbstractArtificial intelligence (AI) refers to computer systems or robots that can perform tasks associated with human intelligence, such as reasoning, problem-solving, and learning. While AI programs have not matched human versatility, they are increasingly used in various domains like self-driving cars, speech transcription, medical diagnosis, and smart assistants. AI has benefited fields like medicine, healthcare, and scientific research by improving productivity, reducing errors, and lowering costs. AI chatbots are conversational programs used for customer service, mental health support, and education. In scientific research, chatbots have the potential to automate tasks like literature searches, data analysis, and manuscript writing, improving efficiency and addressing the reproducibility crisis. However, there are challenges to overcome, including accuracy, reliability, ethical concerns, and limitations of current chatbot models. Scholarly publishing faces debates about authorship and guidelines have been established by journals and publishing organizations regarding the use of AI chatbots. To understand researchers’ attitudes towards AI chatbots, an international survey is proposed to explore their familiarity, perceived benefits, limitations, and factors influencing adoption. Findings can guide policy development and implementation of AI chatbots in scientific research.
Interaction of Post-Traumatic Stress Disorder and Race on Readmissions after Stroke
Authors: Lin Chen , King Peter H. , Richman Joshua , Davis Lori L. 
Published: July 2023

AbstractBackgroundThere is limited research on outcomes of patients with post-traumatic stress disorder (PTSD) who also develop stroke, particularly regarding racial disparities. Our goal was to determine whether PTSD is associated with the risk of hospital readmission after stroke and if racial disparities existed.MethodsThe analytical sample consisted of all veterans receiving care in the Veterans Health Administration (VHA) who were identified as having a new stroke requiring inpatient treatment based on International Classification of Diseases codes. The retrospective cohort data was obtained from the VA Corporate Data Warehouse. The main outcome was any readmission to VHA. The hypothesis that PTSD is associated with readmission after stroke was tested using Cox regression adjusted for patient characteristics with PTSD as a time-varying covariate.ResultsOur final cohort consisted of 93,652 patients with inpatient stroke diagnosis and no prior VHA codes for stroke starting from 1999 with follow-up through 6-August-2022. Of these patients, 12,916 (13.8%) had comorbid PTSD. Of the final cohort, 16,896 patients (18.0%) with stroke were readmitted. Our fully-adjusted model for readmission found an interaction between African Americans (AA) and PTSD with a hazard ratio of 1.09 (95% CI 1.00-1.20; p<0.05). In stratified models, PTSD has a significant HR of 1.10 (1.02-1.18, p=0.01) for AA but not White veterans 1.05 (0.99-1.11, p=0.10).ConclusionAmong AA Veterans who suffered stroke, pre-existing PTSD was associated with increased risk of readmission, which was not significant among White veterans. This study highlights the need to focus on high-risk groups to reduce readmissions after stroke.
Interventions to reduce peripheral blood culture contamination in acute care settings: A systematic review and meta-analysis
Authors: Hughes James A. , Cabilan C.J. , Williams Julian , Ray Mercedes , Coyer Fiona 
Published: July 2023

AbstractBackgroundBlood culture contamination is a significant problem in acute care settings. Contamination of a blood sample with pathogens not present in the patient’s blood leads to increases in length of stay, overuse of antimicrobials, and increases in healthcare cost. Several interventions have been reported in different settings within the literature to decrease the contamination. However, their overall effectiveness is currently unknown.ObjectiveThis systematic review aimed to identify interventions to reduce contamination from peripherally collected blood cultures and to evaluate the effectiveness of these interventions.DesignSystematic review and meta-analysisMethodsIn March 2019 we performed a systematic search of English language literature from academic databases, registers of clinical trials and grey literature for interventions aimed at reducing blood culture contamination in adult acute care settings. Studies meeting inclusion criteria were reviewed and data were extracted by two independent reviewers.ResultsA total of 6,302 articles were retrieved from searches. After removal of duplicates and screening against inclusion criteria 57 studies were included. The majority of the 57 studies had a medium to high risk of bias. These studies identified eight specific interventions (collection packs, dedicated collection teams, education, staff feedback, intervention bundle, sterile procedure, Initial Specimen Diversion Devices, or change of asepsis solution) used in acute care. Thirty-four studies were included in the meta-analysis. There was a wide variation in the definition of contamination which precluded many studies from being included in the meta-analysis. Dedicated collection teams (RR 0.40, 95%CI 0.21 – 0.76, I287%, p<0.001) and initial specimen diversion devices (RR 0.43, 95%CI0.31 – 0.58, I284%, p<0.001) were the most successful at reducing blood culture contamination. Heterogeneity was high across all studies and interventions.ConclusionsThe use of dedicated collection teams or initial specimen diversion devices showed the most significant reduction in blood culture contamination; however, other interventions such as intervention bundles, education or feedback, may have benefits in terms of ease of implementation, and have still been shown to lower blood culture contamination.
Multi-region spatial transcriptome analysis reveals cellular networks and pathways associated with hepatocellular carcinoma recurrence
Authors: Aiderus Aziz , Seshachalam Pratap Veerabrahma , Idzham Khaireen , Caldez Matias , Shanmugam Raghuvaran , Sari Ita Novita , Ying Dorcas Hei Hui , Chong Shay Lee , Sekar Karthik , Chew Sin Chi , Chen Gao Bin , Chung Alexander Yaw-Fui , Cheow Peng Chung , Kam Juinn Huar , Kow Alfred Wei-Chieh , Ganpathi Iyer Shridhar , Loong Shihleone , Leow Wei-Qiang , Chen Kaina , Chanwat Rawisak , de Villa Vanessa H. , Koh Peng Soon , Bonney Glenn K , Goh Brian K. P. , Tam Wai Leong , Tergaonkar Vinay , Chow Pierce Kah Hoe 
Published: July 2023

AbstractHepatocellular carcinomas (HCC) are driven by various etiologies and molecular diversity at presentation. Patient prognosis post-surgery is generally dismal, and the majority respond poorly to adjuvant targeted and/or immuno-therapies. Tumours are an ecosystem comprised of organization and interaction between different cell types that may contribute to clinically significant outcomes, such as disease recurrence. To better understand this phenomenon, we leveraged on a local cohort of patients with or without recurrence to generate spatial transcriptome profiles from multiple sectors from each tumour. We identified widespread gene expression intra- and inter tumour heterogeneity observed across the tumour sectors. Our analysis also revealed the cell type enrichment and localization, and ligand-receptor interactions identify a specific subset of endothelial cell enriched in primary tumours of patients with recurrence. Altogether, this study describes the spatial gene expression landscape in HCC patients associated with disease recurrence.
Exploring the Relation Between Stiffness Perception and Action Using Models and Artificial Neural Networks
Authors: Lev Hanna Kossowsky , Nisky Ilana 
Published: July 2023

AbstractApplying artificial skin stretch with force feedback increases perceived stiffness and affects grip force. We explored if participants’ perceptual responses in a stiffness discrimination task could be predicted solely from their action signals using models and artificial neural networks. Successful prediction could indicate a relation between participants’ perception and action. We found that the skin stretch perceptual augmentation could be predicted to an extent from action signals alone. We predicted the general trend of increased predicted augmentation for increased real augmentation, and average augmentation effect across participants, but not the precise effect sizes of individual participants. This indicates some relation between participants’ perceptual reports and action signals, enabling the partial prediction. Furthermore, of the action signals examined, grip force was necessary for predicting the augmentation effect, and a motion signal (e.g., position) was needed for predicting human-like perception, shedding light on what information may be present in the different signals.
Survey of commercial antibodies targeting Y chromosome-encoded genes
Authors: Gelfand Bradley D. , Argyle Dionne A. , Olivieri Joseph J. , Ambati Jayakrishna 
Published: July 2023

AbstractCommercial antibodies are both a mainstay of biomedical research and subject to increasingly widespread scrutiny for their specificity. We evaluated the specificity of commercially available research grade antibodies by investigating those whose antigen targets are encoded by the Y chromosome. We assessed vendor-provided validation data such as immunoblotting and identified 65 commercial research grade antibodies that exhibited positive reactivity in female-derived tissues and/or cell lines. A thorough analysis of 30 commercial antibodies marketed as targeting the Y chromosome-encoded protein DDX3Y found that 16 (53%) provided no marketing data on specificity, 9 (30%) provided positive data in female or likely female materials, 4 (13%) provided only positive data in male materials, and 1 (3%) provided positive data in male materials and negative data in female materials. Together, these findings suggest that many commercial antibodies may be unsuitable to distinguish between related X and Y chromosome-encoded gene pairs and should instill caution in researchers using these tools to investigate sex chromosome-encoded proteins.