bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inter-Rater Agreement for the Annotation of Neurologic Concepts in Electronic Health Records
Authors: Oommen Chelsea , Howlett-Prieto Quentin , Carrithers Michael D. , Hier Daniel B. 
Published: November 2022

ABSTRACTThe extraction of patient signs and symptoms recorded as free text in electronic health records is critical for precision medicine. Once extracted, signs and symptoms can be made computable by mapping to clinical concepts in an ontology. Extracting clinical concepts from free text is tedious and time-consuming. Prior studies have suggested that inter-rater agreement for clinical concept extraction is low. We have examined inter-rater agreement for annotating neurologic concepts in clinical notes from electronic health records. After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. Inter-rater agreement between the three annotators was high for text span and category label. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators, but one that was lower than human inter-rater agreement. We conclude that high levels of agreement between human annotators are possible with appropriate training and annotation tools. Furthermore, more training examples combined with improvements in neural networks and natural language processing should make machine annotators capable of high throughput automated clinical concept extraction with high levels of agreement with human annotators.
Effect of energy shortages on institutional delivery in India
Authors: Amporfu Eugenia , Irwin Bridget R. , Trakinsky Benjamin Sas , Grépin Karen A. 
Published: November 2022

AbstractIntroductionEnergy shortages are a common challenge in many low- and middle-income countries and can disrupt the operation of healthcare facilities, which can compromise patient outcomes and affect health service utilization. Maternal healthcare use, in particular, has been found to be negatively correlated with power outages in other contexts. The following study investigates the association between state-level energy shortages and institutional delivery rates in India and how the association varies according to women’s socio-economic status.MethodsUsing data from the 1998-99 and 2005-06 India Demographic and Health Surveys, along with information on power outages from India’s Central Electricity Authority, we estimate the association between energy shortages and institutional delivery rates using both logistic and multinomial regressions.ResultsEnergy shortages were associated with reduced rates of institutional delivery: a 10% increase in the shortage level corresponded to a 1.1% decline in the percentage of women giving birth in a healthcare facility. Deliveries in public health facilities were more likely to be disrupted by energy shortages than deliveries in private facilities.ConclusionEnergy shortages are an important determinant of institutional delivery in India. Given that increasing institutional delivery rates is likely important to reduce maternal mortality, policymakers should work to mitigate the impact of energy disruptions on healthcare seeking behaviours.
The Clinical Utility of the BMD-related comprehensive Genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures
Authors: Xiao Xiangxue , Wu Qing 
Published: November 2022

AbstractPurposeThis study sought to construct genome-wide polygenic scores for femoral neck and total body BMD and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures.MethodsGenome-wide polygenic scores were developed and validated for femoral neck and total body BMD. We externally tested the PGSs, both by themselves and in combination with available clinical risk factors, in 455,663 European ancestry individuals from the UK Biobank. The predictive accuracy of the developed genome-wide PGS was also compared with previously published restricted PGS employed in fracture risk assessment.ResultsFor each unit decrease in PGSs, the genome-wide PGSs were associated with up to a 1.17-fold increased fracture risk. Out of four studied PGSs,PGS_TBBMD81(HR: 1.03; 95%CI 1.01-1.05, p=0.001) had the weakest and thePGS_TBBMDldpred(HR: 1.17; 95%CI 1.15-1.19, p<0.0001) had the strongest association with an incident fracture. In the reclassification analysis, compared to the FRAX base model, the models with,PGS_FNBMD63,PGS_TBBMD81,PGS_FNBMDldpred, andPGS_TBBMDldpredimproved the reclassification of fracture by 2% (95% CI, 1.5% to 2.4%), 0.2% (95% CI, 0.1% to 0.3%), 1.4% (95% CI, 1.3% to 1.5%), and 2.2% (95% CI, 2.1% to 2.4%), respectively.ConclusionsOur findings suggested that an efficient PGS estimate enables the identification of strata with up to 1.5-fold difference in fracture incidence. Incorporating PGS information into clinical diagnosis is anticipated to increase the benefits of screening programs in the population level.
Intrathecal oxytocin for neuropathic pain: A randomized, controlled, cross-over trial
Authors: Eisenach James C. , Curry Regina S. , Houle Timothy T. 
Published: November 2022

AbstractObjectiveTo investigate the effect of intrathecal oxytocin compared to placebo on pain and hypersensitivity in individuals with chronic neuropathic pain.Study designRandomized, controlled, double-blind cross-over studySettingOutpatient clinical research unit.SubjectsIndividuals between ages of 18 and 70 years with neuropathic pain caudal to the umbilicus for at least 6 months.MethodsIndividuals received two blinded intrathecal injections of either oxytocin or saline, separated by at least 7 days, and ongoing neuropathic pain (VAS: visual analog scale) and areas of hypersensitivity were measured at intervals for 4 hours. The primary outcome was VAS pain, analyzed by linear mixed effects model. Secondary outcomes were verbal pain intensity scores at intervals for 7 days and areas of hypersensitivity and elicited pain for 4 hr after injections.ResultsThe study was stopped early after completion of 5 of 40 subjects planned due to slow recruitment and funding limitations. Pain intensity prior to injection was 4.75 ± 0.99 and modeled pain intensity decreased more after oxytocin than placebo to 1.61 ± 0.87.and 2.49 ± 0.87, respectively (p=0.003). Daily pain scores were lower in the week following injection of oxytocin than saline (2.53 ± 0.89 vs 3.66 ±0.89; p=0.001). Hypersensitivity differed between oxytocin and placebo by small amounts in opposite directions depending on modality tested. There were no study drug related adverse effects.DiscussionAlthough limited by the small number of subjects studied, oxytocin reduced pain more than placebo in all subjects. Further study of spinal oxytocin in this population is warranted.
The Impact of State-Level Prenatal Substance Use Policies on Rates of Maternal and Infant Mortality in the United States: A Legal Epidemiology Study
Authors: Thomas Kathryn A. , Struble Cara A. , Stenersen Madeline R. , Moore Kelly 
Published: November 2022

AbstractRates of maternal and infant mortality remain significantly higher in the United States than other similarly developed countries. Prior research has shown that substance use during pregnancy leads to increased rates of maternal and infant mortality, yet no studies have examined the impact of state-level policies regarding substance use during pregnancy on rates of maternal and infant mortality across all 50 states. The current study utilized publicly available data to examine state-level impact of punitive prenatal substance use laws on maternal and infant mortality. Results revealed that mandated testing laws significantly predicted maternal mortality after controlling for race, poverty, neonatal abstinence syndrome, prenatal care, and substance use in pregnant women, with the entire model accounting for 59.6% of the variance in maternal mortality. Child abuse laws significantly predicted rates of infant mortality, when controlling for race, health insurance, neonatal abstinence syndrome, prenatal care, and substance use in pregnant women, with the entire model accounting for 70.8% of variance in infant mortality. In both regression models, lack of prenatal care increased rates of maternal and infant mortality. Results provide support for the position that laws punishing prenatal substance use may lead to higher rates of maternal and infant mortality. Clinical and policy implications are discussed.
GMRC: Gene Median Ratio Clustering Algorithm to Rank Clusters and Identify Driver Genes in Intrahepatic Cholangiocarcinoma
Authors: Puri Nandita , Arora Ridhi , Tanwar Vishesh Kumar , Das Sajal K. 
Published: November 2022

AbstractIntrahepatic Cholangiocarcinoma (iCCA) is an asymptomatic malignancy of the bile ducts in the liver. The research is predicated on the assumption that a disease is a cluster of genes interacting in a regulatory network and a few driver genes regulating the network. Keeping this in mind, the paper proposes a Gene Median Ratio Clustering (GMRC) algorithm to independently rank the clusters and identify driver genes by hybridizinggene-median ratio(GMR) andgene cluster network(GCN). GMR then employs the Poisson distribution to translate right-skewed data into the Normal distribution and the Anscombe transformation to eliminate data noise and determine the median difference in gene concentration between distinct disease stages. Additionally, hierarchical clustering is separately applied to raw data for gene clustering based on the RNA sequence count. In the process, GMRC is evaluated over the GSE32225 dataset of 155 patients to extract 12 clusters network for proliferation patients.
Coalescent RNA-localizing and transcriptional activities of SAM68 modulate adhesion and subendothelial basement membrane assembly
Authors: Rekad Zeinab , Ruff Michaël , Radwanska Agata , Grall Dominique , Ciais Delphine , Van Obberghen-Schilling Ellen 
Published: November 2022

AbstractEndothelial cell interactions with their extracellular matrix are essential for vascular homeostasis and expansion. Large-scale proteomic analyses aimed at identifying components of integrin adhesion complexes have revealed the presence of several RNA Binding Proteins (RBPs) of which the functions at these sites remain poorly understood. Here, we explored the role of the RBP SAM68 (Src associated in mitosis, of 68 kDa) in endothelial cells. We found that SAM68 is transiently localized at the edge of spreading cells where it participates in membrane protrusive activity and the conversion of nascent adhesions to mechanically-loaded focal adhesions by modulation of integrin signaling and local delivery of β-actin mRNA. Furthermore, SAM68 depletion impacts cell-matrix interactions and motility through induction of key matrix genes involved in vascular matrix assembly. In a 3D environment SAM68-dependent functions in both tip and stalk cells contribute to the process of sprouting angiogenesis. Altogether, our results identify the RBP SAM68 as a novel actor in the dynamic regulation of blood vessel networks.
Arabidopsis pollen Prolyl-hydroxylases P4H4/6 are required for correct hydroxylation and secretion of LRX11 in pollen tubes
Authors: Sede Ana R. , Wengier Diego L. , Borassi Cecilia , Ricardi Martiniano , Somoza Sofía C. , Aguiló Rafael , Estevez José M. , Muschietti Jorge P. 
Published: November 2022

AbstractMajor constituents of the plant cell walls are structural proteins that belong to the Hydroxyproline-rich glycoprotein family. Leucine-rich repeat extensis are contain a leucine-rich domain and a C-terminal domain with repetitive Ser-Pro(3-5) motifs plausible to be glycosylated. We have demonstrated that pollen-specific LRX8-11 fromArabidopsis thalianaare necessary to maintain the integrity of the pollen tube cell wall during polarized growth. In classical EXTs and likely in LRXs, proline residues are converted to hydroxyproline by Prolyl-4-hydroxylases, thus defining novelO-glycosylation sites. In this context, we aimed to determine whether hydroxylation and subsequentO-glycosylation of Arabidopsis pollen LRXs are necessary for their proper function and cell wall localization in pollen tubes. We hypothesized that pollen-expressed P4H4 and P4H6 catalyze the hydroxylation of the proline units present in Ser-Pro(3-5) motifs of LRX8-LRX11. Here, we show thep4h4-1 p4h6-1double mutant exhibits a significant reduction in pollen germination rates and a slight reduction in pollen tube length. Pollen germination is also inhibited by specific P4Hs inhibitors, suggesting that prolyl hydroxylation is required for pollen tube development. Plants expressingpLRX11::LRX11-GFPin thep4h4-1 p4h6-1background show partial relocalization of LRX11-GFP from the pollen tube tip apoplast to the cytoplasm. Finally, IP-MS- MS analysis revealed a decrease in oxidized prolines in LRX11-GFP in thep4h4-1 p4h6-1background when compared tolrx11plants expressingpLRX11::LRX11-GFP. Together, these results suggest that P4H4 and P4H6 are required for pollen germination and are also involved in LRX11 hydroxylation necessary for its localization at the cell wall of pollen tubes.One Sentence SummaryPollen-expressed P4H4 and P4H6 are required for pollen germination and for proper hydroxylation and secretion of LRX11 in pollen tubes.
CT perfusion stroke lesion threshold calibration between deconvolution algorithms
Authors: Chung Kevin J. , De Sarno Danny , Lee Ting-Yim 
Published: November 2022

Structured AbstractObjectiveCTP is an important diagnostic tool in managing patients with acute ischemic stroke, but challenges persist in the reliability of stroke lesion volumes determined with different software. We investigated a systematic method to calibrate CTP lesion thresholds between deconvolution algorithms using a digital perfusion phantom.ApproachThe accuracy of one model-independent and two model-based deconvolution algorithms in estimating ground truth cerebral blood flow (CBF) and Tmax in the phantom was quantified. Reference thresholds for ischemic core and penumbra were model-independent CBF<30% and Tmax>6 s, respectively, which is the current clinical standard. The equivalent model-based CBF and Tmax thresholds were determined by comparing linear regressions of phantom ground truth and deconvolution-estimated perfusion between algorithms. Calibrated thresholds were then validated in 63 patients with large vessel stroke by comparing admission CTP ischemic core and <3-hour diffusion-weighted imaging (DWI) lesion volume by Bland-Altman analysis. Agreement in target mismatch (core < 70 ml, penumbra ≥ 15 ml, mismatch ratio ≥ 1.8) determined by the three methods was assessed by Cohen’s kappa (κ) and concordance.Main ResultsThe calibrated thresholds were CBF<15% and Tmax>6 s for both model-based methods. DWI minus CTP lesion mean volume differences (95% limits of agreement) were +16.2 (−30.9 to 63.3) ml, +10.9 (−32.9 to 54.7) ml, and +13.8 (−48.1 to 75.7) ml for model-independent and the two calibrated model-based approaches, respectively. Agreement in mismatch profiles with the two model-based deconvolution methods versus model-independent assessment was κ = 0.87 (95% confidence interval [CI]: 0.72 to 1.00) and κ = 0.86 (95% CI: 0.70 to 1.00), and both achieved 95% concordance.SignificanceWe reported a systematic method of calibrating perfusion thresholds between deconvolution algorithms based on their quantitative accuracy. This may harmonize ischemic lesion volumes determined by different CTP software.
Real-world usage diminishes validity of Artificial Intelligence tools
Authors: Vaid Akhil , Sawant Ashwin , Suarez-Farinas Mayte , Lee Juhee , Kaul Sanjeev , Kovatch Patricia , Freeman Robert , Jiang Joy , Jayaraman Pushkala , Fayad Zahi , Argulian Edgar , Lerakis Stamatios , Charney Alexander , Wang Fei , Levin Matthew , Glicksberg Benjamin , Narula Jagat , Hofer Ira , Singh Karandeep , Nadkarni Girish N 
Published: November 2022

AbstractBackgroundSubstantial effort has been directed towards demonstrating use cases of Artificial Intelligence in healthcare, yet limited evidence exists about the long-term viability and consequences of machine learning model deployment.MethodsWe use data from 130,000 patients spread across two large hospital systems to create a simulation framework for emulating real-world deployment of machine learning models. We consider interactions resulting from models being re-trained to improve performance or correct degradation, model deployment with respect to future model development, and simultaneous deployment of multiple models. We simulate possible combinations of deployment conditions, degree of physician adherence to model predictions, and the effectiveness of these predictions.ResultsModel performance shows a severe decline following re-training even when overall model use and effectiveness is relatively low. Further, the deployment of any model erodes the validity of labels for outcomes linked on a pathophysiological basis, thereby resulting in loss of performance for future models. In either case, mitigations applied to offset loss of performance are not fully corrective. Finally, the randomness inherent to a system with multiple deployed models increases exponentially with adherence to model predictions.ConclusionsOur results indicate that model use precipitates interactions that damage the validity of deployed models, and of models developed in the future. Without mechanisms which track the implementation of model predictions, the true effect of model deployment on clinical care may be unmeasurable, and lead to patient data tainted by model use being permanently archived within the Electronic Healthcare Record.
Blind validation of MSIntuit, an AI-based pre-screening tool for MSI detection from histology slides of colorectal cancer
Authors: Saillard Charlie , Dubois Rémy , Tchita Oussama , Loiseau Nicolas , Garcia Thierry , Adriansen Aurélie , Carpentier Séverine , Reyre Joelle , Enea Diana , Kamoun Aurélie , Rossat Stéphane , Sefta Meriem , Auffret Michael , Guillou Lionel , Fouillet Arnaud , Kather Jakob Nikolas , Svrcek Magali 
Published: November 2022

ABSTRACTObjectiveMismatch Repair Deficiency (dMMR) / Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for dMMR/MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted.DesignWe developed an MSI pre-screening tool, MSIntuit, that uses deep learning to identify MSI status from H&E slides. For training, we used 859 slides from the TCGA database. A blind validation was subsequently performed on an independent dataset of 600 consecutive CRC patients. Each slide was digitised using Phillips-UFS and Ventana-DP200 scanners. Thirty dMMR/MSI slides were used for calibration on each scanner. Prediction was then performed on the remaining 570 patients following an automated quality check step. The inter and intra-scanner reliability was studied to assess MSIntuit’s robustness.ResultsMSIntuit reached a sensitivity and specificity of 97% (95% CI: 93-100%) / 46% (42-50%) on DP200 and of 95% (90-98%) / 47% (43-51%) on UFS scanner. MSIntuit reached excellent agreement on the two scanners (Cohen’s κ: 0.82) and was repeatable across multiple rescanning of the same slide (Fleiss’ κ: 0.82).ConclusionWe performed a successful blind validation of the first clinically approved AI-based tool for MSI detection from H&E slides. MSIntuit reaches sensitivity comparable to gold standard methods (92-95%) while ruling out almost half of the non-MSI population, paving the way for its use in clinical practice.
Hospital length of stay throughout bed pathways and factors affecting this time: a non-concurrent cohort study of Colombia COVID-19 patients and an unCoVer network project
Authors: Ruiz Galvis Lina Marcela , Perez Aguirre Carlos Andres , Pérez Bedoya Juan Pablo , Mendoza Cardozo Oscar Ignacio , Christopher Barengo Noël , Sanchez Escudero Juan Pablo , Jimenez Jonathan Cardona , Diaz Valencia Paula Andrea 
Published: November 2022

AbstractPredictions of hospital beds occupancy depends on hospital admission rates and the length of stay (LoS) according to bed type (hospital and intensive care unit beds). The objective of this study was to describe the LoS of COVID-19 hospital patients in Colombia during 2020-2021. Accelerated failure time models were used to estimate the LoS distribution according to each bed type and throughout each bed pathway. Acceleration factors and 95% confidence intervals were calculated to measure the effect on LoS of the outcome, sex, age, admission period during the epidemic (i.e., epidemic waves, peaks or valleys, and before/after vaccination period), and patients geographic origin. Most of the admitted COVID-19 patients occupied just hospital bed. Recovered patients spent more time in the hospital and intensive care unit than deceased patients. Men had longer LoS than women. In general, the LoS increased with age. Finally, the LoS varied along epidemic waves. It was lower in epidemic valleys than peaks, and became shorter after vaccinations began in Colombia than before. Our study highlights the necessity of analyzing local data on hospital admission rates and LoS to design strategies to prioritize hospital beds resources during the current and future pandemics.
Evolutionarily conserved waves of tooth replacement in the gecko are dependent on local signaling
Authors: Brink Kirstin S. , Cytrynbaum Eric , Grieco Theresa M. , Henriquez Joaquin I. , Zhitnitsky Anna , Richman Joy M. 
Published: November 2022

AbstractThe fossil record contains dinosaur jaws with rows of unerupted successional teeth that are arranged in a variety of elegant patterns. The remnants of these patterns are visible in modern dentate reptiles but the mechanism for generating and maintaining these rows of teeth is unknown. The biology underlying the tooth replacement pattern was hypothesized to either be stimuli transmitted across tooth families in the jaws (Edmund) or secretion of local inhibitory molecules that would stagger development of adjacent tooth families (Osborn). To test these hypotheses and generate new ones, we completed a study on 6 treated adult geckos in which one side of the jaw had teeth removed. Wax bites were used to record the maxillary teeth 2 times a week. Tooth presence or absence was recorded and transformed mathematically. The time between eruption at each tooth position was measured as was the relative phase compared to the immediate adjacent teeth over successive bites. The period between eruption events at each tooth position was approximately 30 days with some lengthening over time. The average relative phase showed there was a tilt in the data that fit the observation that alternating teeth were being shed. This tilt was opposite on the left and right sides of the jaw. The asymmetry of the right and left sides was consistent across the dentition. After plucking, the pattern recovers after 3 periods fitting with the consistent finding that there are 3 teeth in each tooth family. Ablated areas did not recover tooth formation even after 14 months. The plucked animals showed evidence of fixed, local signaling that restores the pattern. Two models based on Osborn’s concept of a “zone of inhibition” deviate from the observed data. The ablated animals show no change in patterns of tooth eruption anterior and posterior to the gap. Thus there is no support for the Wave stimulus theory of Edmund. Finally, we propose a new Phase Inhibition Model. This model assumes fixed initiation sites at which teeth are initiated at some phase within a month-long cycle and that, as a tooth is initiated, the cycles of nearby initiation sites are inhibited in their progress. This inhibition causes nearest neighbours to erupt in anti-synchrony. This model best maintained the tilt, spacing timing of the real biological data. Mathematical modeling was sensitive enough to measure the normal developmental instability and the resilience of the gecko to restore homeostasis after tooth removal.