bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of WHO-UMC and Naranjo Scales for Causality Assessment of Reported Adverse Drug Reactions
Authors: Pandit Sukant , Soni Dhruve , Krishnamurthy Bhaskar , Belhekar Mahesh N. 
Published: February 2024

Objectives
The 2 most commonly used scales worldwide are the World Health Organization–Uppsala Monitoring Centre (WHO-UMC) and the Naranjo scales. The present study was planned to assess the degree of agreement between the 2 scales when the same adverse drug reactions (ADR) were assessed by 5 raters independently.

Methods
One hundred individual case safety reports were selected randomly from the ADR database of our institute and the details were emailed to 5 different experts (raters), who were DM Clinical Pharmacology residents from different institutes in India. An independent causality assessment of these ADRs was performed independently by these raters using both the WHO-UMC and Naranjo causality assessment scales. The agreement between the 2 scales was assessed for each rater using Cohen κ, and the overall interrater agreement was assessed using Fleiss κ.

Results
The Cohen κ level of agreement between the 2 scales for the 5 raters were substantial, fair, substantial, moderate, and substantial, respectively. The most common causality assessment category as per WHO-UMC scale was “possible” but varied among the raters on the Naranjo scale. No ADR was categorized as “certain” by any rater on the Naranjo scale. The Fleiss κ value for agreement among the 5 raters was found to be 0.2 (slight) for the WHO-UMC scale and 0.297 (fair) for the Naranjo scale.

Conclusions
A moderate level of agreement was observed in this study between the WHO-UMC and Naranjo scales. The level of agreement among these 5 raters was found to be similar for the WHO-UMC and the Naranjo scales, indicating a similar degree of subjectivity for the 2 scales. Hence, more robust and less subjective scales are required for causality assessment.
Should opioids be used for breathlessness and in whom? A PRO and CON debate of the evidence
Authors: Ekström Magnus , Janssen Daisy J.A. 
Published: February 2024

Purpose of review
The net clinical benefit of opioids for chronic breathlessness has been challenged by recent randomized clinical trials. The purpose was to review and weigh the evidence for and against opioid treatment for chronic breathlessness in people with serious disease.

Recent findings
Evidence to date on the efficacy and safety of opioids for chronic breathlessness was reviewed. Findings supporting a benefit from opioids in meta-analyses of earlier, mostly smaller trials were not confirmed by recent larger trials. Evidence pertains mostly to people with chronic obstructive pulmonary disease but also to people with pulmonary fibrosis, heart failure, and advanced cancer. Taken together, there is no consistent evidence to generally recommend opioids for severe breathlessness or to identify people who are more likely to benefit. Opioid treatment may be tested in patients with intractable breathlessness and limited other treatment options, such as in end-of-life care. Knowledge gaps were identified and recommendations were made for future research.

Summary
Opioids cannot be generally recommended for treating breathlessness based on insufficient evidence for net clinical benefit.
Quantitative measurement of geodiversity uniqueness: research implications and conservation applications
Authors: Alahuhta Janne , García-Girón Jorge , Hjort Jan , Salminen Henriikka , Tukiainen Helena , Heino Jani 
Published: February 2024

Quantitative approaches are needed to complement qualitative explorations to identify sites with unique geodiversity and thereby guide geoconservation and geoheritage programmes. Here, we introduce the concept and associated index of ‘geodiversity uniqueness’. This index is based on a numerical analysis of geofeatures and allows the identification of sites with unique geodiversity in a study area. We applied this approach to geofeature data from three areas in Finland. Our results showed that patterns of geodiversity uniqueness varied profoundly among the three study areas and across sites within each area. This was due to different sets of geofeatures and distinct characteristics of each study area. More importantly, the approach presented here was robust across the datasets and selection criteria for sets of sites, showing potential for geoconservation in each study area. The geodiversity uniqueness approach is a promising starting point to identify and map sites with unique geodiversity that can be further verified using field observations. To improve our knowledge of geodiversity variation, complementary approaches providing objective information on contributions to total beta geodiversity are needed to advance geoconservation programmes across areas and different spatial scales.
This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.
Geodiversity data for Europe
Authors: Toivanen M. , Maliniemi T. , Hjort J. , Salminen H. , Ala-Hulkko T. , Kemppinen J. , Karjalainen O. , Poturalska A. , Kiilunen P. , Snåre H. , Leppiniemi O. , Makopoulou E. , Alahuhta J. , Tukiainen H. 
Published: February 2024

Geodiversity is an essential part of nature's diversity. However, geodiversity is insufficiently understood in terms of its spatial distribution and its relationship to biodiversity over large spatial extents. Here, we present European geodiversity data at resolutions of 1 km and 10 km. We assess terrestrial geodiversity quantitatively as a richness variable (georichness) using a commonly employed grid-based approach. The data incorporate aspects of geological, pedological, geomorphological and hydrological diversity, which are also available as separate richness variables. To evaluate the data, we correlated European georichness with empirically tested national georichness data from Finland, revealing a positive correlation at both 1 km (

r
p

= 0.37,
p
< 0.001) and 10 km (

r
p

= 0.59,
p
< 0.001) resolutions. We also demonstrate potential uses of the European data by correlating georichness with vascular plant species richness in two contrasting example areas: Finland and Switzerland. The positive correlations between georichness and species richness in Finland (

r
p

= 0.34,
p
< 0.001) and Switzerland (

r
p

= 0.26,
p
< 0.001) further support the use of our data in geodiversity–biodiversity research. Moreover, there is great potential beyond geodiversity–biodiversity questions, as the data can be exploited across different regions, ecosystems and scales. These geodiversity data provide an insight on abiotic diversity in Europe and establish a quantitative large-scale geodiversity assessment method applicable worldwide.

This article is part of the Theo Murphy meeting issue ‘Geodiversity for science and society’.
Age, not body size, influences flight distance in the bumble beeBombus terrestrisL. (Hymenoptera: Apidae)
Authors: Gilgenreiner Milena , Kurze Christoph 
Published: February 2024

AbstractBumble bees and honey bees provide crucial pollination service and have become important insect model system. Despite their close relation, they differ in their morphology, physiology, and social organisation. Notably, honey bees workers exhibit age-based polyethism, while bumble bees display considerable body size variation. Though body size is known to affect foraging range, behaviour, and flight ability, the influence of age remains less explored. Here we studied the flight performance (distance and speed) in aging bumble bee workers using tethered flight mills. Additionally, we measured their intertegular distance (ITD), dry mass, and fat content. Flight distance was predominantly influenced by age, challenging assumptions that age does not play a role in foraging and task allocation. Between 7 to 14 days, flight distance increased six-fold before a slight decline at the age of 21 days, aligning with age-dependent flight physiology in honey bees. Larger workers had decreasing mass-specific fat reserves, challenging the notion about their energy preservation for oocyte development. Our findings indicate that age substantially influences flight distance, impacting foraging performance and potentially altering task allocation strategies. This underscores the need to consider individual age and physiological changes alongside body size and mass in experiments involving bumble bee workers.
Genome-wide association study reveals different T cell distributions in peripheral blood of healthy individuals at high genetic risk of type 1 diabetes and long COVID
Authors: Deecke Laura , Homann Jan , Goldeck David , Luessi Felix , Vandebergh Marijne , Ohlei Olena , Toepfer Sarah , Zipp Frauke , Demuth Ilja , Morgan Sarah L. , Bertram Lars , Pawelec Graham , Lill Christina M. 
Published: February 2024

AbstractThe immune system plays a crucial role in many human diseases. In this context, genome-wide association studies (GWAS) offer valuable insights to elucidate the role of immunity in health and disease. The present multi-omics study aimed to identify genetic determinants of immune cell type distributions in the blood of healthy individuals and to assess whether the distributions of these cells may play a role for autoimmune and COVID-19 disease risk.To this end, the frequencies of different immune cells in 483 healthy individuals from the Berlin Aging Study II were quantified using flow cytometry, and GWAS was performed for 92 immune cell phenotypes. Additionally, we performed linear regression analyses of immune cell distributions using polygenic risk scores (PRS) based on prior GWAS for five autoimmune diseases as well as for COVID-19 infection and post-COVID syndrome (“long COVID”).We validated seven previously described immune loci and identified 13 novel loci showing genome-wide significant (α=5.00E-8) association with different immune cell phenotypes. The most significant novel signal was conferred by theSLC52A3locus, encoding for a riboflavin transporter protein, which was associated with naïve CD57+ CD8+ T cells (p=4.13E-17) and colocalized withSLC52A3expression. Several novel loci contained immunologically plausible candidate genes, e.g., variants nearTBATAandB3GAT1representing genes associated with T cell phenotypes. The PRS of type 1 diabetes were significantly associated with CD8+ T cells at different differentiation states (p≤7.02E-4), and PRS of long COVID were associated with early-differentiated CD4+ T cells (p≤1.54E-4).In conclusion, our extensive immune cell GWAS analyses highlight several novel genetic loci of likely relevance for immune system function. Furthermore, our PRS analyses point to a shared genetic basis between immune cell distributions in healthy adults and T1D (CD8+ T cells) as well as long COVID (CD4+ T cells).
High Throughput Deep Learning Detection of Mitral Regurgitation
Authors: Vrudhula Amey , Duffy Grant , Vukadinovic Milos , Liang David , Cheng Susan , Ouyang David 
Published: February 2024

AbstractBackgroundDiagnosis of mitral regurgitation (MR) requires careful evaluation of echocardiography with Doppler imaging. This study presents the development and validation of a fully automated deep learning pipeline for identifying apical-4-chamber view videos with color Doppler and detection of clinically significant (moderate or severe) mitral regurgitation from transthoracic echocardiography studies.MethodsA total of 58,614 studies (2,587,538 videos) from Cedars-Sinai Medical Center (CSMC) were used to develop and test an automated pipeline to identify apical-4-chamber view videos with color Doppler across the mitral valve and then assess mitral valve regurgitation severity. The model was tested on an internal test set of 1,800 studies (80,833 videos) from CSMC and externally evaluated in a geographically distinct cohort of 915 studies (46,890 videos) from Stanford Healthcare (SHC).ResultsIn the held-out CSMC test set, the view classifier demonstrated an AUC of 0.998 (0.998 - 0.999) and correctly identified 3,452 of 3,539 MR color Doppler videos (sensitivity of 0.975 (0.968-0.982) and specificity of 0.999 (0.999-0.999) compared with manually curated videos). In the external test cohort from SHC, the view classifier correctly identified 1,051 of 1,055 MR color Doppler videos (sensitivity of 0.996 (0.990 – 1.000) and specificity of 0.999 (0.999 – 0.999) compared with manually curated videos). For evaluating clinically significant MR, in the CSMC test cohort, moderate-or-severe MR was detected with AUC of 0.916 (0.899 - 0.932) and severe MR was detected with an AUC of 0.934 (0.913 - 0.953). In the SHC test cohort, the model detected moderate-or-severe MR with an AUC of 0.951 (0.924 - 0.973) and severe MR with an AUC of 0.969 (0.946 - 0.987).ConclusionsIn this study, we developed and validated an automated pipeline for identifying clinically significant MR from transthoracic echocardiography studies. Such an approach has potential for automated screening of MR and precision evaluation for surveillance.
MUC17 is an essential small intestinal glycocalyx component that is disrupted in Crohn’s disease
Authors: Layunta Elena , Jäverfelt Sofia , van de Koolwijk Fleur C. , Sivertsson Molly , Dolan Brendan , Arike Liisa , Thulin Sara , Vallance Bruce A. , Pelaseyed Thaher 
Published: February 2024

AbstractCrohn’s disease (CD) is the chronic inflammation of the ileum and colon triggered by bacteria, but insights into molecular perturbations at the bacteria-epithelium interface are limited. We report that membrane mucin MUC17 protects small intestinal enterocytes against commensal and pathogenic bacteria. In non-inflamed CD ileum, reduced MUC17 levels correlated with a compromised glycocalyx, allowing bacterial contact with enterocytes.Muc17deletion in mice rendered the small intestine prone to atypical infection while maintaining resistance to colitis. The loss of Muc17 resulted in spontaneous deterioration of epithelial homeostasis and extra-intestinal translocation of bacteria. Finally, Muc17-deficient mice harbored specific small intestinal bacterial taxa observed in CD. Our findings highlight MUC17 as an essential line of defense in the small intestine with relevance for early epithelial defects in CD.One-sentence summaryMembrane mucin MUC17 protects enterocytes against bacterial attachment and constitutes an early defect in Crohn’s disease.
UXS1 regulates UDP-GlcA levels to support growth of UGDH-high cancer cells
Authors: Wang Yunyi , Zhou Chen , Bou-Tayeh Berna , Lisle Richard , Kriaucionis Skirmantas , Shi Yang 
Published: February 2024

AbstractIdentifying genes that are crucial for cancer cell survival but dispensable in normal cells holds immense therapeutic potential. The DepMap Consortium’s extensive datasets have paved the way for uncovering such selectively essential genes in cancer. However, it remained challenging to efficiently prioritize understudied, selectively essential genes for validation and characterization. To this end, our lab has previously ranked and prioritized potentially understudied, selectively essential genes based on their PubMed publication numbers. This approach led to successful identification and detailed characterization of two top understudied genes. Building on this methodology, our current research identified UXS1, an enzyme responsible for catalyzing the conversion from UDP-glucuronic acid (UDP-GlcA) to UDP-xylose, as a selectively essential gene in cancer cells expressing elevated levels of UGDH, an enzyme responsible for producing UDP-GlcA. Through an integrated approach combining genetic and biochemical assays, we discovered that UXS1 plays a critical role in these UGDH-overexpressing cancer cells by preventing the harmful buildup of UDP-GlcA, which otherwise would lead to cellular toxicity. Our findings not only validate our strategy for prioritizing underexplored but potentially pivotal selectively essential genes but also highlight UXS1 as a potential vulnerability and therapeutic target in cancers characterized by high UGDH expression.
Investigating EMT-mediated resistance to EGFR tyrosine kinase inhibitors in NSCLC using innovative organoid models
Authors: Kobayashi Nobuaki , Katakura Seigo , Fukuda Nobuhiko , Teranishi Shuhei , Kubo Sousuke , Kamimaki Chisato , Matsumoto Hiromi , Somekawa Kohei , Kaneko Ayami , Ishikawa Yoshihiro , Okudela Koji , Sekine Keisuke , Kaneko Takeshi 
Published: February 2024

AbstractBackgroundEpithelial-mesenchymal transition (EMT) has emerged as a key mechanism underlying resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in EGFR-mutant non-small cell lung cancer (NSCLC). However, the intricacies of EMT-mediated resistance, driven by tumor microenvironment (TME) interactions, remain enigmatic. This study aimed to probe EMT-induced resistance in NSCLC using innovative in vitro organoid models.MethodsWe generated organoids by co-culturing EGFR-mutant NSCLC cells (HCC827, H1975), mesenchymal stem cells and endothelial cells. Drug susceptibility was compared between organoids and spheroids (cancer cells only) using EGFR TKIs - Gefitinib, Afatinib, Osimertinib. EMT marker (E-cadherin, ZEB1) expression was analyzed via immunofluorescence and western blotting. The effects of Bevacizumab and miR200c on overcoming resistance were also investigated.ResultsThe study identified a significant link between EMT and EGFR-TKI resistance. Notable findings included the decrease of E-cadherin and an increase in ZEB1, both of which influenced EMT and resistance to treatment. Bevacizumab showed promise in improving drug resistance and mitigating EMT, suggesting an involvement of the VEGF cascade. Transfection with miR200c was associated with improved EMT and drug resistance, further highlighting the role of EMT in TKI resistance.ConclusionsThis study offered vital insights into EMT-driven EGFR TKI resistance, highlighting the utility of organoid models in evaluating resistance modulated by TME interactions. Our findings reveal promising directions for overcoming EMT-mediated resistance involving Bevacizumab and miR200c, warranting further in vivo validation.
Polygenic architecture of adaptation to a high-altitude environment forDrosophila melanogasterwing shape and size
Authors: Pelletier Katie , Bilodeau Megan , Pellizzari-Delano Isabella , Siemon M. Daniel , Huang Yuheng , Pool John E. , Dworkin Ian 
Published: February 2024

AbstractAs is typical of small insects, populations ofDrosophila melanogasteradapted to high altitude environments evolved increased body size, disproportionality large wings, and differing wing shape compared to low-altitude ancestors. In one instance the colonization of high-altitude environments in Ethiopia is recent (2000-3000 years ago), and is a useful system to study alleles contributing to adaptive divergence. Unlike predictions derived from formulations Fisher-Kimura-Orr geometric model based onde novomutations concurrent with selection, recent models predict segregating alleles in a population are more likely to contribute to adaptation on short time scales, particularly when populations are large and genetically diverse, likeD. melanogaster. Strains derived from lowland (∼500m above sea level – ASL) and highland (∼3000m ASL) populations were used to generate F20 advanced-intercrosses. From each cross, phenotypically extreme individuals for size and shape were pool-sequenced, and genetic differentiation among pools of individuals demonstrated a polygenic architecture of divergence for size and shape. We identified one QTL of large effect, contributing to adaptive divergence in shape. This QTL is not observed in all crosses, pointing to the importance of examining independent genetic backgrounds when mapping alleles contributing to adaptation. Despite the intrinsic links between shape and size, we find a unique genetic basis of adaptation for these traits. This work demonstrates that many alleles, throughout the genome, rather than single, large effect alleles, contribute to adaption forDrosophilawing shape and size, adding to the growing body of evidence for polygenic adaptation.
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
Authors: Fernandes Igor K. , Vieira Caio C. , Dias Kaio O. G. , Fernandes Samuel B. 
Published: February 2024

AbstractComplementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common practice in breeding programs. This study explored new ways to integrate non-genetic information in genomic prediction models using machine learning (ML). Using the multi-environment trial data from the Genomes To Fields initiative, different models to predict maize grain yield were adjusted using various inputs: genetic, environmental, or a combination of both, either in an additive (genetic-and-environmental; G+E) or a multiplicative (genotype-by-environment interaction; GEI) manner. When including environmental data, the mean predictive ability of machine learning genomic prediction models increased from 7-9% over the well-established Factor Analytic Multiplicative Mixed Model (FA) among the three cross-validation scenarios evaluated. Moreover, using the G+E model was more advantageous than the GEI model given the superior, or at least comparable, predictive ability, the lower usage of computational memory and time, and the flexibility of accounting for interactions by construction. Our results illustrate the flexibility provided by the ML framework, particularly with feature engineering. We show that the featured engineering stage offers a viable option for envirotyping and generates valuable information for machine learning-based genomic prediction models. Furthermore, we verified that the genotype-by-environment interactions may be considered using tree-based approaches without explicitly including interactions in the model. These findings support the growing interest in merging high-dimensional genotypic and environmental data into predictive modeling.Key messageIncorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to model genotype-by-environment interactions.
TopoQual polishes circular consensus sequencing data and accurately predicts quality scores
Authors: Weerakoon Minindu , Lee Sangjin , Mitchell Emily , Heaton Haynes 
Published: February 2024

AbstractSummaryPacific Biosciences (PacBio) circular consensus sequencing (CCS) aka high fidelity (HiFi) technology has revolutionized modern genomics by producing long (10+kb) and highly accurate reads by sequencing circularized DNA molecules multiple times and combining them into a consensus sequence. Currently the accuracy and quality value estimation is more than sufficient for genome assembly and germline variant calling, but the estimated quality scores are not accurate enough for confident somatic variant calling on single reads. Here we introduce TopoQual, a tool utilizing partial order alignments (POA), topologically parallel bases, and deep learning to polish consensus sequences and more accurately predict base qualities. We correct ~31.9% of errors in PacBio consensus sequences and validate base qualities up to q59 which is one error in 0.9 million bases enabling accurate somatic variant calling with HiFi data.Availability and implementationThe source code and installation instructions as well as validation dataset used are freely available athttps://github.com/lorewar2/TopoQual