bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk Reduction Strategy to Decrease Incidence of Retained Surgical Items
Authors: Kaplan Harrison J , Spiera Zachary C , Feldman David L , Shamamian Peter , Portnoy Bonnie , Ioannides Paula , Leitman I Michael 
Published: August 2022

BACKGROUND:
Retained surgical items (RSIs) are rare but serious events associated with significant morbidity and costs. We assessed the effectiveness of radiofrequency (RF) detection technology and Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) in reducing the incidence of RSIs.

STUDY DESIGN:
All RSIs reported to the New York Patient Occurrence Reporting and Tracking System at five large urban teaching hospitals from 2007 to 2017 were analyzed. In 2012, TeamSTEPPS training was provided to all perioperative staff at each site, and use of RF detection became required in all procedures. The incidence of events before and after the interventions were compared using odds ratios.

RESULTS:
A total of 997,237 operative procedures were analyzed. After the interventions, the incidence of RSIs decreased from 11.66 to 5.80 events per 100,000 operations (odds ratio [OR] [95% CI] = 0.50 [0.32 to 0.78]). The frequency of RSIs involving RF-detectable items decreased from 5.21 to 1.35 events per 100,000 operations (OR [95% CI] = 0.26 [0.11 to 0.60]). The difference in RSIs involving non-RF-detectable surgical items was not statistically significant.

CONCLUSIONS:
The incidence of RSIs was significantly lower during the time period after implementing RF detection technology and after TeamSTEPPS training, primarily driven by a decrease in retained RF-detectable items. RF detection technology may be worth pursuing for hospitals looking to decrease RSI frequency. The benefit of TeamSTEPPS training alone may not result in a reduction of RSIs.
Association Between Statewide Medicaid Opioid Policy and Postoperative Opioid Prescribing among Surgeons at a Large Safety-Net Hospital
Authors: Zhang Irene Y , Wong Edwin S , Rosen Joshua E , Gordon Debra B , Flum David R , Liao Joshua M 
Published: August 2022

BACKGROUND:
Restrictive state and payer policies may be effective in reducing opioid prescribing by surgeons, but their impact has not been well studied. In 2017, Washington Medicaid implemented an opioid prescribing limit of 42 pills, prompting a large regional safety-net hospital to implement a decision support intervention in response. We aimed to evaluate the effects on surgeons’ prescribing.

STUDY DESIGN:
We retrospectively studied postoperative opioid prescribing (quantity of pills prescribed at discharge) to opioid-naïve surgical patients at a regional safety-net hospital from 2016 to 2020. We investigated associations between the policy and opioid prescribing by using interrupted time series analysis, adjusting for clinical and sociodemographic factors.

RESULTS:
A total of 12,799 surgical encounters involving opioid-naïve patients (59% male, mean age 52) were analyzed. Opioids were prescribed for 75%. From 2016 to 2020, the mean prescribed opioid quantity decreased from 36 pills to 17 pills. In interrupted time series analysis, the Medicaid policy implementation was associated with an immediate change of –8.4 pills (95% CI –12 to –4.7; p < 0.001) per prescription and a subsequent rate of decrease similar to that prepolicy. In a comparison of changes between patients insured through Medicaid vs Medicare, Medicaid patients had an immediate change of –9.8 pills (95% CI –19 to –0.76; p = 0.03) after policy implementation and continued decreases similar to those prepolicy. No immediate or subsequent policy-related changes were observed among Medicare patients.

CONCLUSION:
In a large regional safety-net institution, postoperative opioid prescriptions decreased in size over time, with immediate changes associated with a state Medicaid policy and corresponding decision support intervention. These findings pose implications for surgeons, hospital leaders, and payers seeking to address opioid use via judicious prescribing.
Deep Neural Network for the Prediction of KRAS Genotype in Rectal Cancer
Authors: Ghareeb Waleed M , Draz Eman , Madbouly Khaled , Hussein Ahmed H , Faisal Mohammed , Elkashef Wagdi , Emile Mona Hany , Edelhamre Marcus , Kim Seon Hahn , Emile Sameh Hany ,   for the Anam Hospital KRAS Research Group
Published: August 2022

Background:

KRAS mutation can alter the treatment plan after resection of colorectal cancer. Despite its importance, the KRAS status of several patients remains unchecked because of the high cost and limited resources. This study developed a deep neural network (DNN) to predict the KRAS genotype using hematoxylin and eosin (H&E)–stained histopathological images.

Study design:
Three DNNs were created (KRAS_Mob, KRAS_Shuff, and KRAS_Ince) using the structural backbone of the MobileNet, ShuffleNet, and Inception networks, respectively. The Cancer Genome Atlas was screened to extract 49,684 image tiles that were used for deep learning and internal validation. An independent cohort of 43,032 image tiles was used for external validation. The performance was compared with humans, and a virtual cost–saving analysis was done.

Results:
The KRAS_Mob network (area under the receiver operating curve [AUC] 0.8, 95% CI 0.71 to 0.89) was the best-performing model for predicting the KRAS genotype, followed by the KRAS_Shuff (AUC 0.73, 95% CI 0.62 to 0.84) and KRAS_Ince (AUC 0.71, 95% CI 0.6 to 0.82) networks. Combing the KRAS_Mob and KRAS_Shuff networks as a double prediction approach showed improved performance. KRAS_Mob network accuracy surpassed that of two independent pathologists (AUC 0.79 [95% CI 0.64 to 0.93], 0.51 [95% CI 0.34 to 0.69], and 0.51 (95% CI 0.34 to 0.69]; p < 0.001 for all comparisons).

Conclusion:
The DNN has the potential to predict the KRAS genotype directly from H&E-stained histopathological slide images. As an algorithmic screening method to prioritize patients for laboratory confirmation, such a model might possibly reduce the number of patients screened, resulting in significant test-related time and economic savings.
A psycho-cognitive model for exertional heatstroke: theory and preliminary testing using self-report measures in a case-control study
Authors: Verdonk Charles , Mellier Camille , Charlot Keyne , Jouvion Arnaud , Trousselard Marion , Sagui Emmanuel , Tardo-Dino Pierre-Emmanuel , Malgoyre Alexandra 
Published: August 2022

ABSTRACTBackgroundExertional heatstroke (EHS) stands as the most severe manifestation within the spectrum of heat-related illnesses arising during sport competition or physical activity within specific contexts such as military training. Despite substantial progress in comprehending the physiological aspects of EHS, current evidence underscores the necessity for a more comprehensive perspective that incorporates psycho-cognitive factors.MethodsThe present study introduces a psycho-cognitive model for EHS and initiates its empirical validation through self-report measures in a case-control study. Our central hypothesis suggests that EHS may emanate from a disruption in the cost-benefit trade-off associated with prolonged physical activity. Specifically, we propose that EHS could be a consequence ofovervaluingthe benefits linked to physical activity due to excessive motivation to succeed andundervaluingeffort exertion costs resulting from low interoceptive awareness, characterized by disrupted processing of signals related to the body’s internal state.ResultsAnalysis of our psychometric data revealed that individuals with a history of EHS (cases, N=51) self-report diminished interoceptive awareness compared to their healthy counterparts (controls, n=43). There was no significant difference in the global motivation trait between cases and controls. Furthermore, cases exhibited a less developed trait mindfulness compared to controls.ConclusionOur study establishes theoretical foundations and offers preliminary support for a psycho-cognitive model of EHS. The results suggest that relatively straightforward measures, such as self-report questionnaires, can be instrumental in identifying intrinsic (i.e., individual-specific) risk factors for EHS. The psycho-cognitive approach to understanding EHS holds promise for informing innovative prevention strategies aimed at mitigating its risk in sports competitions and military settings.
Explainable-AI to Discover Associated Genes for Classifying Hepato-cellular Carcinoma from High-dimensional Data
Authors: Hasan Easin , Mostafa Fahad , Hossain Md S , Loftin Jonathon 
Published: August 2022

AbstractKnowledge-based interpretations are essential for understanding the omic data set because of its nature, such as high dimension and hidden biological information in genes. When analyzing gene expression data with many genes and few samples, the main problem is to separate disease-related information from a vast quantity of redundant data and noise. This paper uses a reliable framework to determine important genes for discovering Hepato-cellular Carcinoma (HCC) from micro-array analysis and eliminating redundant and unnecessary genes through gene selection. Several machine learning models were applied to find significant predictors responsible for HCC. As we know, classification algorithms such as Random Forest, Naive Bayes classifier, or a k-Nearest Neighbor classifier can help us to classify HCC from responsible genes. Random Forests shows 96.53% accuracy with p < 0.00001, which is better than other discussed Machine Learning(ML) approaches. Each gene is not responsible for a particular patient. Since ML approaches are like black boxes and people/practitioners do not rely on them sometimes. Artificial Intelligence(AI) technologies with high optimization interoperability shed light on what is happening inside these systems and aid in the detection of potential problems; including causality, information leakage, model bias, and robustness when determining responsible genes for a specific patient with a high probability score of almost 0.99, from one of the samples mentioned in this study.
Incidence and management of inflammatory arthritis in England before and during the COVID-19 pandemic: a population-level cohort study using OpenSAFELY
Authors: Russell Mark D , Galloway James B , Andrews Colm D , MacKenna Brian , Goldacre Ben , Mehrkar Amir , Curtis Helen J , Butler-Cole Ben , O’Dwyer Thomas , Qureshi Sumera , Ledingham Joanna M , Mahto Arti , Rutherford Andrew I , Adas Maryam A , Alveyn Edward , Norton Sam , Cope Andrew P , Bechman Katie ,   OpenSAFELY Collaborative
Published: August 2022

AbstractObjectiveTo use the OpenSAFELY platform to replicate key metrics from a national clinical audit, and assess the impact of COVID-19 on disease incidence and care delivery for inflammatory arthritis (IA) in England.DesignPopulation-based cohort study, with the approval of NHS England.SettingPrimary care and linked hospital outpatient data for more than 17 million people registered with general practices in England that use TPP electronic health record software.ParticipantsAdults (18-110 years) with new diagnoses of IA (rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, undifferentiated IA) between 1 April 2019 and 31 March 2022.Main outcome measuresThe following outcomes were explored before and after April 2020: 1) incidence of IA diagnoses; 2) time from primary care referral to first rheumatology assessment; 3) time to first prescription of a disease-modifying anti-rheumatic drug (DMARD) in primary care.ResultsFrom a reference population of 17,683,500 adults, there were 31,280 incident IA diagnoses between April 2019 and March 2022. The incidence of IA decreased by 20.3% in the year commencing April 2020, relative to the preceding year (5.1 vs. 6.4 diagnoses per 10,000 adults, respectively). For those who presented with IA, the time to first rheumatology assessment was shorter during the pandemic (median 18 days; interquartile range 8 to 35 days) than before (21 days; 9 to 41 days). Overall, the proportion of patients prescribed DMARDs in primary care was comparable during the pandemic to before; however, the choice of medication changed, with fewer people prescribed methotrexate or leflunomide during the pandemic, and more people prescribed sulfasalazine or hydroxychloroquine.ConclusionsThe incidence of IA diagnoses in England decreased markedly during the early COVID-19 pandemic. However, for people who sought medical attention, the impact of the pandemic on service delivery was less marked than might have been anticipated. This study demonstrates that it is feasible to use routinely captured, near real-time data in the secure OpenSAFELY platform to benchmark care quality for long-term conditions on a national scale, without the need for manual data collection.
Structure of the catalytically active APOBEC3G bound to a DNA oligonucleotide inhibitor reveals tetrahedral geometry of the transition state
Authors: Maiti Atanu , Hedger Adam K. , Myint Wazo , Balachandran Vanivilasini , Watts Jonathan K. , Schiffer Celia A. , Matsuo Hiroshi 
Published: August 2022

AbstractAPOBEC3 proteins (A3s) are enzymes that catalyze deamination of cytidine to uridine in single-stranded DNA (ssDNA) substrates, thus playing a key role in innate antiviral immunity. However, APOBEC3 family has also been linked to many mutational signatures in cancer cells, which has led to intense interest to develop inhibitors of A3’s catalytic activity as therapeutics as well as tools to study A3’s biochemistry, structure and cellular function. Recent studies have shown that ssDNA containing 2′-deoxy-zebularine (dZ-ssDNA) is an inhibitor of A3s such as A3A, A3B and A3G, although atomic determinants of this activity remained unknown. To fill this knowledge gap, we determined a 1.5 Å resolution structure of a dZ-ssDNA inhibitor bound to active A3G. The crystal structure revealed that the activated dZ/H2O mimics the transition state by coordinating the active site Zn2+ and engaging in additional stabilizing interactions, such as the one with the catalytic residues E259. Therefore, this structure allowed us to capture the first snapshot of the A3’s transition state, and suggests that developing transition-state mimicking inhibitors may provide a new opportunity to design more targeted molecules for A3s in the future.
Hedgehog signaling via its ligand DHH acts as cell fate determinant during skeletal muscle regeneration
Authors: Norris Alessandra M. , Appu Ambili Bai , Johnson Connor D. , Zhou Lylybell Y. , McKellar David W. , Renault Marie-Ange , Hammers David , Cosgrove Benjamin D. , Kopinke Daniel 
Published: August 2022

AbstractSuccessful muscle regeneration relies on the interplay of multiple cell populations. However, the signals required for this coordinated intercellular crosstalk remain largely unknown. Here, we describe how the Hedgehog (Hh) signaling pathway controls the fate of fibro/adipogenic progenitors (FAPs), the cellular origin of intramuscular fat (IMAT) and fibrotic scar tissue. Using conditional mutagenesis and pharmacological Hh modulatorsin vivoandin vitro, we identify DHH as the key ligand that acts as a potent adipogenic brake by preventing the adipogenic differentiation of FAPs. Hh signaling also impacts muscle regeneration, albeit indirectly through induction of myogenic factors in FAPs. Our results also indicate that ectopic and sustained Hh activation forces FAPs to adopt a fibrogenic fate resulting in widespread fibrosis. In this work, we reveal novel post-developmental functions of Hh signaling in balancing tissue regeneration and fatty fibrosis. Moreover, they provide the exciting possibility that mis-regulation of the Hh pathway with age and disease could be a major driver of pathological IMAT formation.
Nutrients and flow shape the cyclic dominance games betweenEscherichia colistrains
Authors: Kuhn Thierry , Pilar Junier , Bshary Redouan , Terrettaz Céline , Gonzalez Diego , Richter Xiang-Yi Li 
Published: August 2022

AbstractEvolutionary game theory has provided various models to explain the coexistence of competing strategies, one of which is the rock-paper-scissors (RPS) game. A system of threeEscherichia colistrains—a toxin-producer, a resistant, and a sensitive—has become a classic experimental model for studying RPS games. Previous experimental and theoretical studies, however, often ignored the influence of ecological factors such as nutrients and toxin dynamics on the evolutionary game dynamics. In this work, we combine experiments and modeling to study how these factors affect competition dynamics. Using 3D-printed mini-bioreactors, we tracked the frequency of the three strains in different culturing media and under different flow regimes. Although our experimental system fulfilled the requirements of cyclic dominance, we did not observe clear cycles or long-term coexistence between strains. We found that both nutrients and flow rates strongly impacted population dynamics. In our simulations, we explicitly modeled the release, removal and diffusion of toxin. We showed that the amount of toxin that is retained in the system is a simple indicator that can predict competition outcomes across broad parameter space. Moreover, our simulation results suggest that high rates of toxin diffusion might have prevented cyclic patterns from emerging in our experimental system.
Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE
Authors: You Yupei , Prawer Yair D.J. , De Paoli-Iseppi Ricardo , Hunt Cameron P.J. , Parish Clare L. , Shim Heejung , Clark Michael B. 
Published: August 2022

AbstractSingle-cell RNA sequencing (scRNA-seq) has revolutionised our ability to profile gene expression. However, short-read (SR) scRNAseq methodologies such as 10x are restricted to sequencing the 3’ or 5’ ends of transcripts, providing accurate gene expression but little information on the RNA isoforms expressed in each cell. Newly developed long-read (LR) scRNA-seq enables the quantification of RNA isoforms in individual cells but LR scRNA-seq using the Oxford Nanopore platform has largely relied upon matched short-read data to identify cell barcodes and allow single cell analysis. Here we introduce BLAZE (Barcode identification from long-reads for AnalyZing single-cell gene Expression), which accurately and efficiently identifies 10x cell barcodes using only nanopore LR scRNA-seq data. We compared BLAZE to existing tools, including cell barcodes identified from matched SR scRNA-seq, on differentiating stem cells and 5 cancer cell lines. BLAZE outperforms existing tools and provides a more accurate representation of the cells present in LR scRNA-seq than using matched short-reads. BLAZE provides accurate cell barcodes over a wide range of experimental read depths and sequencing accuracies, while other methodologies commonly identify false-positive barcodes and cell clusters, disrupting biological interpretation of LR scRNA-seq results. In conclusion, BLAZE eliminates the requirement for matched SR scRNA-seq to interpret LR scRNA-seq, simplifying procedures and decreasing costs while also improving LR scRNA-seq results. BLAZE is compatible with downstream tools accepting a cell barcode whitelist file and is available at https://github.com/shimlab/BLAZE.
Extracellular vesicles induce aggressive lung cancer via non-canonical integrin-EGFR-KRAS signaling
Authors: Rubio Karla , Romero-Olmedo Addi J. , Sarvari Pouya , Günther Stefan , Mehta Aditi , Bassaly Birgit , Braubach Peter , Dobreva Gergana , Wygrecka Malgorzata , Gattenlöhner Stefan , Braun Thomas , Tresch Achim , Graumann Johannes , Barreto Guillermo 
Published: August 2022

AbstractSmall cell lung cancer (SCLC) is an extremely aggressive lung cancer type with a patient median survival of 6-12 months. Epidermal growth factor (EGF) signaling plays an important role in triggering SCLC. In addition, growth factor-dependent signals and alpha-, beta-integrin (ITGA, ITGB) heterodimer receptors functionally cooperate and integrate their signaling pathways. However, the precise role of integrins in EGF receptor (EGFR) activation in SCLC has remained elusive. We analyzed RNA-sequencing data, human precision-cut lung slices (hPCLS), retrospectively collected human lung tissue samples and cell lines to demonstrate that non-canonical ITGB2 signaling activates EGFR and RAS/MAPK/ERK signaling in SCLC. Further, we identified a novel SCLC gene expression signature consisting of 93 transcripts that were induced by ITGB2, which might be used for stratification of SCLC patients, prognosis prediction of LC patients and development of patient-tailored therapies. We also found by proteomic analysis a cell-cell communication mechanism based on extracellular vesicles (EVs) containing ITGB2, which were secreted by SCLC cells and induced in control human lung tissue RAS/MAPK/ERK signaling and SCLC markers. We uncovered a mechanism of ITGB2-mediated EGFR activation in SCLC that explains EGFR-inhibitor resistance independently of EGFR mutations, suggesting the development of therapies targeting ITGB2 for patients with this extremely aggressive lung cancer type.
Synchronicity of viral shedding in molossid bat maternity colonies
Authors: Hoarau Axel O. G. , Köster Marie , Dietrich Muriel , Minter Gildas Le , Joffrin Léa , Ramanantsalama Riana V. , Mavingui Patrick , Lebarbenchon Camille 
Published: August 2022

ABSTRACTInfection dynamics in vertebrates are driven by biological and ecological processes. For bats, population structure and reproductive cycles have major effects on RNA virus transmission. On Reunion Island, previous studies have shown that parturition of pregnant females and aggregation of juvenile Reunion free-tailed bats (Mormopterus francoismoutoui) are associated to major increase in the prevalence of bats shedding viruses. The synchronicity of such shedding pulses, however, is yet to be assessed, between viruses but also maternity colonies. Based on 3422 fresh faeces collected every two to five weeks during four consecutive birthing seasons, we report the prevalence of bats shedding astroviruses (AstVs), coronaviruses (CoVs), and paramyxoviruses (PMVs) in two maternity colonies on Reunion Island. We found that the proportion of bats shedding viruses is highly influenced by sampling collection dates, and therefore by the seasonality of parturition. We highlight that virus shedding patterns are reproducible among years and colonies for CoVs and at a lesser extent for PMVs, but not for AstVs. We also report 1% of bats harbouring double infections, mostly CoVs and PMVs, but none shedding simultaneously AstVs, CoVs and PMVs.
Kinase Suppressor of RAS 1 (KSR1) maintains the transformed phenotype of BRAFV600E mutant human melanoma cells
Authors: Liu Zhi , Krstic Aleksandar , Neve Ashish , Rauch Nora , Wynne Kieran , Cassidy Hilary , McCann Amanda , Kavanagh Emma , McCann Brendan , Blanco Alfonso , Rauch Jens , Kolch Walter 
Published: August 2022

AbstractKSR1 is a scaffolding protein for the RAS-RAF-MEK-ERK pathway, which is one of the most frequently altered pathways in human cancers. Previous results have shown that KSR1 has a critical role in mutant RAS mediated transformation. Here, we examined the role of KSR1 in mutant BRAF transformation. We used CRISPR/Cas9 to knock out KSR1 in a BRAFV600E transformed melanoma cell line. KSR1 loss produced a complex phenotype characterized by impaired proliferation, cell cycle defects, decreased transformation, decreased invasive migration, increased cellular senescence, and increased apoptosis. To decipher this phenotype, we used a combination of proteomic ERK substrate profiling, global protein expression profiling, and biochemical validation assays. The results suggest that KSR1 directs ERK to phosphorylate substrates that have a critical role in ensuring cell survival. The results further indicate that KSR1 loss induces the activation of p38 Mitogen-Activated Protein Kinase (MAPK) and subsequent cell cycle aberrations and senescence. In summary, KSR1 function plays a key role in oncogenic BRAF transformation.