bluuebunny/crossref_metadata_2025_split huggingface dataset:
Minimal protein list for chromatin replication-coupled symmetric histone recycling revealed throughin vitroreconstitution
Authors: Nagae Fritz , Endo Shizuko , Murayama Yasuto , Terakawa Tsuyoshi 
Published: November 2024

ABSTRACTUpon eukaryotic DNA replication, symmetric histone recycling from parental to daughter strands is vital for transmitting epigenetic information to the next generations. Recent genome-wide sequencing studies have identified several protein regions contributing to symmetric histone recycling in a cell. However, a minimal list of proteins for symmetric histone recycling remains unknown. Here, we successfully reconstituted histone recycling with ∼30 purified proteins and analyzed the products digested by Micrococcal nuclease with the newly developed pipeline called a Repli-pore-seq in which nanopore sequencing and deep-learning-based classification were combined. As a result, we confirmed that the histones were recycled symmetrically to the lagging and leading strands. The recycled histones form tetrasomes or hexasomes and are deposited on the DNA sequences energetically favorable for forming nucleosomes. We also observed the discordance of the recycled position between lagging and leading strands on the GC-rich DNA sequences. Among proteins dispensable for chromatin replication, the lagging-strand maturation factors, Fen1/Cdc9, were also dispensable for symmetric recycling. The removal of Pol δ disrupted the recycling symmetry, and that of Ctf4 or Csm3/Tof1 altered the recycling position. These findings provide critical insights into the molecular players and mechanisms underlying symmetric histone recycling.
A histomorphological atlas of resected mesothelioma discovered by self-supervised learning from 3446 whole-slide images
Authors: Seyedshahi Farzaneh , Rakovic Kai , Poulain Nicolas , Quiros Adalberto Claudio , Powley Ian R. , Richards Cathy , Uraiby Hussein , Klebe Sonja , Nakas Apostolos , Wilson Claire , Sereno Marco , Officer-Jones Leah , Ficken Catherine , Teodosio Ana , Ballantyne Fiona , Murphy Daniel , Yuan Ke , Le Quesne John 
Published: November 2024

1AbstractMesothelioma is a highly lethal and poorly biologically understood disease which presents diagnostic challenges due to its morphological complexity. This study uses self-supervised AI (Artificial Intelligence) to map the histomorphological landscape of the disease. The resulting atlas consists of recurrent patterns identified from 3446 Hematoxylin and Eosin (H&E) stained images scanned from resected tumour slides. These patterns generate highly interpretable predictions, achieving state-of-the-art performance with 0.65 concordance index (c-index) for outcomes and 85% AUC in subtyping. Their clinical relevance is endorsed by comprehensive human pathological assessment. Furthermore, we characterise the molecular underpinnings of these diverse, meaningful, predictive patterns. Our approach both improves diagnosis and deepens our understanding of mesothelioma biology, highlighting the power of this self-learning method in clinical applications and scientific discovery.
MRI R2* captures inflammation in disconnected brain structures after stroke: a translational study
Authors: Koubiyr Ismail , Yamamoto Takayuki , Petit Laurent , Dubourdieu Nadège , Avignone Elena , Cozensa Elise , Galmiche Chloé , Fukutomi Hikaru , Sibon Igor , Dousset Vincent , de Schotten Michel Thiebaut , Panatier Aude , Tible Marion , Tourdias Thomas 
Published: November 2024

AbstractIschemic strokes disrupt brain networks, leading to remote effects in key regions like the thalamus, a critical hub for brain functions. However, non-invasive methods to quantify these remote consequences still need to be explored. This study aimed to demonstrate that MRI-derived R2* changes can capture iron accumulation linked with inflammation secondary to stroke-induced disconnection.In order to link remote R2* changes to stroke-induced disconnection, we first conducted a secondary analysis of 156 prospectively included stroke patients who underwent MRI at baseline and 1-year follow-up. We mapped fibers disconnected by baseline infarcts to compare the R2* changes over 1 year according to the disconnectivity status in specific thalamic nuclei groups. We also identified the predictors of elevated R2* at 1 year in a multivariate context through linear regressions. In parallel, to understand the biological underpinning of the remote R2* changes, we set up a translational mouse model through photothrombotic induction of focal cortical infarcts or sham procedures in 110 C57BL/6J mice. We explored the mice through combinations ofin vivoMRI at 72h, 2-, 4-, and 8-weeks, histology, qPCR for gene expression, mass spectrometry for iron concentration quantification, and additionalex vivohigh-resolution diffusion tensor imaging.In stroke patients, we found a significant increase of R2* within severely disconnected medial and lateral thalamic nuclei groups from baseline to 1 year. At the same time, no change occurred if these structures were not disconnected. We also showed that the disconnectivity status at baseline was a significant predictor of R2* at follow-up, independently from confounders, establishing a direct and independent relationship between baseline disconnection and the subsequent R2* increase within the associated locations. In mice, we recapitulated the patients’ conditions by observing increased R2* in the stroke groups, specifically within the disconnected thalamic nuclei. Such remote and focal R2* changes peaked at 2 weeks, preceding and correlating with longer-term atrophy at 8 weeks. We established that the remote R2* increase was spatially and temporally correlated with a significant increase of chemically determined iron load bound to ferritin within activated microglial cells.This study provides critical evidence that R2* is a sensitive marker of inflammation secondary to network disconnection, potentially informing future neuroprotective strategies targeting remote brain regions after stroke.
Detecting interspecific positive selection using convolutional neural networks
Authors: West Charlotte , Walker Conor R. , Arasti Shayesteh , Vasilev Viacheslav , Xu Xingze , De Maio Nicola , Goldman Nick 
Published: November 2024

AbstractTraditional statistical methods using maximum likelihood and Bayesian inference can detect positive selection from an interspecific phylogeny and a codon sequence alignment based on model assumptions, but they are prone to false positives due to alignment errors and can lack power. These problems are particularly pronounced when faced with high levels of indels and divergence. Leveraging the feature-detection capabilities of convolutional neural networks (CNNs), we achieve higher accuracy in detecting selection across a specific range of phylogenetic scenarios and evolutionary modes. This advantage is particularly evident with noisy data prone to misalignments. Our method shows some ability to account for these errors, where most statistical frameworks fail to do so in a tractable manner. We explore generalisability and identify future avenues to achieve broader utility. Once trained, our CNN model is faster at test time, making it a scalable alternative to traditional statistical methods for large-scale, multi-gene analyses. In addition to binary classification (inference of the presence or absence of positive selection during the evolution of the sequences), we use saliency maps to understand what the model learns and observe how this could be leveraged for sitewise inference of positive selection.
Detritus identification in FlowCAM using a simple binary classifier
Authors: García-Oliva O. 
Published: November 2024

AbstractPhytoplankton and detritus particles may be co-captured in Flow-CAM systems, leading to misrepresentation of phytoplankton abundance. In this study, I use logistic regression as a binary classifier to identify detritus particles in FlowCAM data from the Southern North Sea. Standard particle features from the manufacturer’s software were used as inputs, with surface texture (intensity variance) and compactness (derived from particle perimeter and area) proving most relevant to detritus classification. This classifier achieved 81% accuracy using a training dataset of approximately 7300 observations, reducing the workload compared to other classification methods. The reconstructed particle size spectra closely matched the observed spectra for detritus and phytoplankton. Binary classifiers like this offer a fast, effective alternative for detritus screening, aiding the pre-processing and re-analysis of FlowCAM datasets.
Ice gliding diatoms establish record-low temperature limit for motility in a eukaryotic cell
Authors: Zhang Qing , Leng Hope T. , Li Hongquan , Arrigo Kevin R. , Prakash Manu 
Published: November 2024

AbstractDespite periods of permanent darkness and extensive ice coverage in polar environments, photosynthetic ice diatoms display a remarkable capability of living inside the ice matrix. How these organisms navigate such hostile conditions with limited light and extreme cold remains unknown. Using a custom sub-zero temperature microscope during an Arctic expedition, we present the discovery of motility at record-low temperatures in a Eukaryotic cell. By characterizing the gliding motility of several ice diatom species, collected from ice cores in the Chukchi Sea, we record that they retain motility at temperatures as low as –15 °C. Remarkably, ice diatoms can glide on ice substrates, a capability absent in temperate diatoms of the same genus. This unique ability arises from adaptations in extracellular mucilage that allow ice diatoms to adhere to ice, essential for gliding. Even on glass substrates where both cell types retain motility at freezing temperatures, ice diatoms move an order of magnitude faster, with their optimal motility shifting toward colder temperatures. Combining field and laboratory experiments with thermo-hydrodynamic modeling, we reveal adaptive strategies that enable gliding motility at extreme cold temperatures. These strategies involve increasing internal energy efficiency with minimal changes in heat capacity and activation enthalpy, and reducing external dissipation by minimizing the temperature sensitivity of mucilage viscosity. The discovery of diatoms’ ice gliding motility opens new routes for understanding their survival within a harsh ecological niche and their migratory responses to rapid environmental changes. Our work highlights the robust adaptability of ice diatoms in one of Earth’s most extreme settings.
The impact of vaccination on preventing long COVID in the Omicron era: a systematic review and meta-analysis
Authors: Green Rhiannon , Marjenberg Zoe , Lip Gregory Y. H. , Banerjee Amitava , Wisnivesky Juan , Delaney Brendan C. , Peluso Michael J. , Wynberg Elke , Abduljawad Sultan 
Published: November 2024

AbstractMillions worldwide are living with long COVID. Since therapeutic research is ongoing, long COVID prevention is a pragmatic public health strategy. While prior analyses have shown the benefit of primary vaccination, the effect of booster vaccination on preventing long COVID caused by an Omicron infection has not been fully investigated. This systematic review identified 31 observational studies, among which 11 were deemed suitable for pairwise meta-analyses. Herein, the pooled risk of long COVID was 22–29% (P<0.0001 andP<0.0001, respectively) lower for vaccinated (any vaccination) populations versus unvaccinated (10 studies); 19% (P<0.0001) lower for primary course vaccination versus no vaccination (3 studies); 26% (P<0.0001) lower for booster vaccination versus no vaccination (4 studies) and 23% (P=0.0044) lower for booster vaccination versus primary course vaccination (3 studies). These findings indicate that booster vaccination can provide additional protection against long COVID; thereby, highlighting the importance of seasonal vaccination against new SARS-CoV-2 variants.
ANTIMICROBIAL SUSCEPTIBILITY PATTERNS OF BACTERIAL PATHOGENS IN ACUTE EXACERBATIONS OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN PATIENTS AT A TERTIARY CARE FACILITY
Authors: Dahal Susma , Thapa Renuka , Gurung Puja , Chaudhary Abinas , Dhakal Dinesh , Shrestha Upendra Thapa , Chaudhary Raina , Acharya Amrit 
Published: November 2024

ObjectivesThe study was aimed to determine the predominant bacteria causing Acute Exacerbations of Chronic Obstructive Pulmonary Disease (AECOPD) infection in patients and their antibiotics sensitivity pattern in tertiary care setup.MethodsThis descriptive Cross-sectional study was conducted from September 27th2023 to December 26th2023 at Shree Birendra Hospital, chhauni, Kathmandu. Sputum samples that were received in the Microbiology laboratory from in patients for routine diagnosis were included in the study. The samples that were received was subjected to gram staining to assess the quality of the sputum sample, and those samples with good quality (mucoid and muco-purulent) was inoculated onto Blood agar, Chocolate agar and Mac Conkey agar for the isolation of the pathogens, and the media was incubated at 37 degrees Celsius overnight. Culture isolates were identified by standard technique(Cowan and Steel’s Manual for the Identification of Medical Bacteria, 1993) and Kirby-Bauer method was used to test antibiotic sensitivity of the pathogenic organisms following protocol of (Clinical and Laboratory Standards Institute, 2018) guidelines.ResultsOut of 273 sputum samples, 42/273 (15.4%) showed growth. Five different bacterial species were identified. Among the isolates,Acinetobacterspp. was the most common pathogen 21(44.7%) followed byPseudomonas aeruginosa11(23.4%),Klebsiella pneumoniae10(21.3%),Escherichia coli4(8.5%) andCitrobacter freundii 1(2.1%). Highest number of AECOPD cases were observed in female 157(57.9%) with 25(15.9%) positivity and highest number of organisms were isolated in age group 56-70(17) and least in age group 40-55(3).All theAcinetobacterspp. (n=21) isolates were resistant to all tested medicine. Almost 90.90%Pseudomonas aeruginosa(n=11) were sensitive to Gentamycin and 81.82% to Meropenem, 70%Klebsiella pneumoniae(n=10) were sensitive to Gentamycin, 75%Escherichia coli(n=4) and 100 %Citrobacter freundii(n=1) to Amikacin. 100% (n=47) isolates were resistant to antibiotic penicillin and 100%Escherichia coli and Citrobacter freundii were resistance toCeftriazone and Cefepime while 100% (n=47) were found to be multi-drug resistant.ConclusionAcinetobactersppP. aeruginosa, Klebsiella pneumoniae, andEscherichia coliwere the most common bacterial isolates in the current investigation, which revealed a 15.4 % of culture positive. There is a high rate of MDR pattern in all isolated isolates. To further enhance treatment quality and prevent antibiotic resistance, regular surveillance of the etiologies of AECOPD and their pattern of antimicrobial susceptibility is crucial.
Detection of hypertrophic cardiomyopathy on electrocardiogram using artificial intelligence
Authors: Hillis James M , Bizzo Bernardo C , Mercaldo Sarah F , Ghatak Ankita , MacDonald Ashley L , Halle Madeleine A , Schultz Alexander S , L’Italien Eric , Tam Victor , Bart Nicole K , Moura Filipe A , Awad Amine M , Bargiela David , Dagen Sarajune , Toland Danielle , Blood Alexander J , Gross David A , Jering Karola S , Lopes Mathew S , Marston Nicholas A , Nauffal Victor D , Dreyer Keith J , Scirica Benjamin M , Ho Carolyn Y 
Published: November 2024

AbstractBackgroundHypertrophic cardiomyopathy (HCM) is associated with significant morbidity and mortality including sudden cardiac death in the young. Its prevalence is estimated to be 1 in 500, although many people are undiagnosed. The ability to screen electrocardiograms (ECGs) for its presence could improve detection and enable earlier diagnosis.ObjectivesThis study evaluated the accuracy of an artificial intelligence device (Viz HCM) in detecting HCM based on 12-lead ECG.MethodsThe device was previously trained using deep learning and provides a binary outcome (HCM suspected or not suspected). This study included 293 HCM-Positive and 2912 HCM-Negative cases, which were selected from three hospitals based on chart review incorporating billing diagnostic codes, cardiac imaging, and ECG features. The device produced an output for 291 (99.3%) HCM-Positive and 2905 (99.8%) HCM-Negative cases.ResultsThe device identified HCM with sensitivity 68.4% (95% CI: 62.8-73.5%), specificity 99.1% (95% CI: 98.7-99.4%) and area under the curve 0.975 (95% CI: 0.965-0.982). With assumed population prevalence of 0.002 (1 in 500), the positive predictive value was 13.7% (95% CI: 10.1-19.9%) and the negative predictive value was 99.9% (95% CI: 99.9-99.9%). The device demonstrated consistent performance across demographic and technical subgroups.ConclusionsThe device identified HCM based on 12-lead ECG with good performance. Coupled with clinical expertise, it has the potential to augment HCM detection and diagnosis.
Multi-ancestry GWAS of severe pregnancy nausea and vomiting identifies risk loci associated with appetite, insulin signaling, and brain plasticity
Authors: Fejzo Marlena , Wang Xinran , Zöllner Julia , Pujol-Gualdo Natàlia , Laisk Triin ,   Estonian Biobank Research Team, Finer Sarah , van Heel David A. ,   Genes & Health Research Team, Brumpton Ben , Bhatta Laxmi , Hveem Kristian , Jasper Elizabeth A. , Velez Edwards Digna R. , Hellwege Jacklyn N. , Edwards Todd , Jarvik Gail P. , Luo Yuan , Khan Atlas , MacGibbon Kimber , Gao Yuan , Ge Gaoxiang , Averbukh Inna , Soon Erin , Angelo Michael , Magnus Per , Johansson Stefan , Njølstad Pål R. , Vaudel Marc , Shu Chang , Mancuso Nicholas 
Published: November 2024

AbstractWhile most pregnancies are affected by nausea and vomiting, hyperemesis gravidarum (HG) is at the severe end of the clinical spectrum and is associated with dehydration, undernutrition, and adverse maternal, fetal, and child outcomes. Herein we performed a multi-ancestry genome-wide association study (GWAS) of severe nausea and vomiting of pregnancy of 10,974 cases and 461,461 controls across European, Asian, African, and Latino ancestries. We identified ten significantly associated loci, of which six were novel (SLITRK1,SYN3,IGSF11,FSHB,TCF7L2, andCDH9),and confirmed previous genome-wide significant associations with risk genesGDF15,IGFBP7,PGR, andGFRAL. In a spatiotemporal analysis of placental development,GDF15andTCF7L2were expressed primarily in extra villous trophoblast, and using a weighted linear model of maternal, paternal, and fetal effects, we confirmed opposing effects forGDF15between maternal and fetal genotype. Conversely,IGFBP7andPGRwere primarily expressed in developing maternal spiral arteries during placentation, with effects limited to the maternal genome. Risk loci were found to be under significant evolutionary selection, with the strongest effects on nausea and vomiting mid-pregnancy. Selected loci were associated with abnormal pregnancy weight gain, pregnancy duration, birth weight, head circumference, and pre-eclampsia. Potential roles for candidate genes in appetite, insulin signaling, and brain plasticity provide new pathways to explore etiological mechanisms and novel therapeutic avenues.
First Detection of Xylazine in Texas Wastewater and Its Association with Fentanyl Use
Authors: Joseph Katherine M. , Parikh Dhvani , Xuan Qin , Li Feng , Balliew John , Mena Kristina D. , Wu Fuqing 
Published: November 2024

AbstractThe United States is dealing with the drug overdose crisis that has intensified over the past decade and compounded by the emergence of new threats particularly xylazine, a veterinary sedative increasingly found in illicit drug supplies. This study investigates the prevalence of xylazine in El Paso, Texas, a U.S.-Mexico border city where its impact remains poorly understood. We employed wastewater analysis to detect xylazine and examine its potential correlation with fentanyl use over a 14-month period (June 2023 to July 2024). Our results show that xylazine was detected in wastewater samples from three of the four treatment plants serving the city. The prevalence of xylazine was heterogeneous, with the highest detection rate of 29% observed in one sewershed. All samples on xylazine-positive days also tested positive for norfentanyl, a fentanyl metabolite, demonstrating the widespread fentanyl consumption. Notably, sewersheds with higher xylazine detection exhibited significantly higher fentanyl loads, suggesting a community-level association between the two substances use. This study provides the first evidence of xylazine in Texas wastewater and highlights the urgent need for enhanced monitoring and targeted public health interventions to mitigate the growing threat of xylazine, particularly in border communities affected by the opioid crisis.
The osteoarthritis associated sphingolipid sphingomyelin 34:1 causes inflammatory pain in mice
Authors: Rickman Rebecca H. , Pattison Luke A. , Qiu Lanhui , Dannawi Maya , Welsh Fraser , Smith Ewan St. John 
Published: November 2024

AbstractOsteoarthritis (OA) is a condition affecting synovial joints that has a multifactorial pathogenesis and where pain is the main symptom driving clinical decision making. During OA, a plethora of mediators are released by infiltrating immune cells and resident cells, such fibroblast-like synoviocytes. Although the roles of certain OA-associated disease mediators are well-understood, there are a number of molecules that are dysregulated in OA for which no role has been identified. For example, in dogs and humans with OA, dysregulation of the synovial fluid lipidome occurs and some findings have been replicated by studying the plasma lipidome in a mouse model of osteoarthritis. One upregulated lipid is the sphingomyelin N-palmitoyl-D-erythro-sphingosylphosphorylcholine (d18:1/16:0), also known as SM(d34:1), referred to here as SM. This study investigated the ability of SM to cause joint pain and neuronal hyperexcitability in mice. Overnight incubation of sensory neurons with either SM or a structurally related ceramide produced a decrease in rheobase, i.e. hyperexcitability. By contrast, when injected into the knee joint of mice, SM, but not the related ceramide, evoked joint swelling, mechanical hyperalgesia and decreased digging behaviour. Moreover, when studying the excitability of retrograde traced, knee-innervating sensory neurons, only those isolated from SM-injected mice exhibited hyperexcitability. The results generated demonstrate that a dysregulated lipidome can contribute to inflammatory OA pain, further work being necessary to determine the mechanism by which SM exerts its activity.