bluuebunny/crossref_metadata_2025_split huggingface dataset:
Validity of European-centric cardiometabolic polygenic scores in multi-ancestry populations
Authors: Topriceanu Constantin-Cristian , Chaturvedi Nish , Mathur Rohini , Garfield Victoria 
Published: January 2024

AbstractPolygenic scores (PGSs) provide an individual level estimate of genetic risk for any given disease. Since most PGSs have been derived from genome wide association studies (GWASs) conducted in populations of White European ancestry, their validity in other ancestry groups remains unconfirmed. This is especially relevant for cardiometabolic diseases which are known to disproportionately affect people of non-European ancestry. Thus, we aimed to evaluate the performance of PGSs for glycaemic traits (glycated haemoglobin, and type 1 and type 2 diabetes mellitus), cardiometabolic risk factors (body mass index, hypertension, high- and low-density lipoproteins, and total cholesterol and triglycerides) and cardiovascular diseases (including stroke and coronary artery disease) in people of White European, South Asian, and African Caribbean ethnicity in the UK Biobank. Whilst PGSs incorporated some GWAS data from multi-ethnic populations, the vast majority originated from White Europeans. For most outcomes, PGSs derived mostly from European populations had an overall better performance in White Europeans compared to South Asians and African Caribbeans. Thus, multi-ancestry GWAS data are needed to derive ancestry stratified PGSs to tackle health inequalities.
Decoding complex inherited phenotypes in rare disorders: the DECIPHERD initiative for rare undiagnosed diseases in Chile
Authors: Poli M. Cecilia , Rebolledo-Jaramillo Boris , Lagos Catalina , Orellana Joan , Moreno Gabriela , Martín Luz M. , Encina Gonzalo , Böhme Daniela , Faundes Víctor , Zavala M. Jesús , Hasbún Trinidad , Fischer Sara , Brito Florencia , Araya Diego , Lira Manuel , de la Cruz Javiera , Astudillo Camila , Lay-Son Guillermo , Cares Carolina , Aracena Mariana , Martin Esteban San , Coban-Akdemir Zeynep , Posey Jennifer E. , Lupski James R. , Repetto Gabriela M. 
Published: January 2024

AbstractRare diseases affect millions of people worldwide, and most have a genetic etiology. The incorporation of next-generation sequencing into clinical settings, particularly exome and genome sequencing, has resulted in an unprecedented improvement in diagnosis and discovery in the past decade. Nevertheless, these tools are unavailable in many countries, increasing health care gaps between high- and low-and-middle-income countries and prolonging the “diagnostic odyssey” for patients. To advance genomic diagnoses in a setting of limited genomic resources, we developed DECIPHERD, an undiagnosed diseases program in Chile. DECIPHERD was implemented in two phases: training and local development. The training phase relied on international collaboration with Baylor College of Medicine, and the local development was structured as a hybrid model, where clinical and bioinformatics analysis were performed in-house and sequencing outsourced abroad, due to lack of high-throughput equipment in Chile. We describe the implementation process and findings of the first 103 patients. They had heterogeneous phenotypes, including congenital anomalies, intellectual disabilities and/or immune system dysfunction. Patients underwent clinical exome or research exome sequencing, as solo cases or with parents using a trio design. We identified pathogenic, likely pathogenic or variants of unknown significance in genes related to the patients´ phenotypes in 47 (45.6%) of them. Half were de novo informative variants, and half of the identified variants have not been previously reported in public databases. DECIPHERD ended the diagnostic odyssey for many participants. This hybrid strategy may be useful for settings of similarly limited genomic resources and lead to discoveries in understudied populations.
Ghrelin mediated cardioprotection using in vitro models of oxidative stress
Authors: Kok Cindy Y. , Ghossein George , Igoor Sindhu , Rao Renuka , Titus Tracy , Tsurusaki Shinya , Chong James JH. , Kizana Eddy 
Published: January 2024

AbstractGhrelin is commonly known as the ‘hunger hormone’ due to its role in stimulating food intake in humans. However, the roles of ghrelin extend beyond regulating hunger. Our aim was to investigate the ability of ghrelin to protect against hydrogen peroxide (H2O2), a reactive oxygen species commonly associated with cardiac injury. An in vitro model of oxidative stress was developed using H2O2 injured H9c2 cells. Despite lentiviral ghrelin overexpression, H9c2 cell viability and mitochondrial function were not protected following H2O2 injury. We found that H9c2 cells lack expression of the preproghrelin cleavage enzyme prohormone convertase 1 (encoded by PCSK1), required to convert ghrelin to its active form. In contrast, we found that primary rat cardiomyocytes do express PCSK1 and were protected from H2O2 injury by lentiviral ghrelin overexpression. In conclusion, we have shown that ghrelin expression can protect primary rat cardiomyocytes against H2O2, though this effect was not observed in other cell types tested.
Convergence of resistance and evolutionary responses in Escherichia coli and Salmonella enterica co-inhabiting chicken farms in China
Authors: Baker Michelle , Zhang Xibin , Maciel-Guerra Alexandre , Babaarslan Kubra , Dong Yinping , Wang Wei , Hu Yujie , Renney David , Liu Longhai , Li Hui , Hossain Maqsud , Heeb Stephan , Tong Zhiqin , Pearcy Nicole , Zhang Meimei , Geng Yingzhi , Zhao Li , Hao Zhihui , Senin Nicola , Chen Junshi , Peng Zixin , Li Fengqin , Dottorini Tania 
Published: January 2024

AbstractSharing of genetic elements among different pathogens and commensals inhabiting same hosts and environments has significant implications for antimicrobial resistance (AMR), especially in settings with high antimicrobial exposure. We analysed 661 Escherichia coli and Salmonella enterica isolates collected within and across hosts and environments, in 10 Chinese chicken farms over 2.5 years using data-mining methods. Most isolates within same hosts possessed the same clinically relevant AMR-carrying mobile genetic elements (plasmids: 70.6%, transposons: 78%), which also showed recent common evolution. Supervised machine learning classifiers revealed known and novel AMR-associated mutations and genes underlying resistance to 28 antimicrobials, primarily associated with resistance in E. coli and susceptibility in S. enterica. Many were essential and affected same metabolic processes in both species, albeit with varying degrees of phylogenetic penetration. Multi-modal strategies are crucial to investigate the interplay of mobilome, resistance and metabolism in cohabiting bacteria, especially in ecological settings where community-driven resistance selection occurs.
MENDER: fast and scalable tissue structure identification in spatial omics data
Authors: Yuan Zhiyuan 
Published: January 2024

AbstractTissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER’s identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER’s scalability makes it freely appliable on a million-level brain spatial atlas. MENDER’s discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.
Direct translation of incoming retroviral genomes
Authors: Köppke Julia , Keller Luise-Elektra , Stuck Michelle , Arnow Nicolas D. , Bannert Norbert , Doellinger Joerg , Cingöz Oya 
Published: January 2024

AbstractViruses that carry a positive-sense, single-stranded (+ssRNA) RNA translate their genomes soon after entering the host cell to produce viral proteins, with the exception of retroviruses. A distinguishing feature of retroviruses is reverse transcription, where the +ssRNA genome serves as a template to synthesize a double-stranded DNA copy that subsequently integrates into the host genome. As retroviral RNAs are produced by the host cell transcriptional machinery and are largely indistinguishable from cellular mRNAs, we investigated the potential of incoming retroviral genomes to directly express proteins. Here we show through multiple, complementary methods that retroviral genomes are translated after entry. Our findings challenge the notion that retroviruses require reverse transcription to produce viral proteins. Synthesis of retroviral proteins in the absence of productive infection has significant implications for basic retrovirology, immune responses and gene therapy applications.
The virulence regulator VirB from Shigella flexneri uses a CTP-dependent switch mechanism to activate gene expression
Authors: Jakob Sara , Steinchen Wieland , Hanßmann Juri , Rosum Julia , Langenfeld Katja , Osorio-Valeriano Manuel , Steube Niklas , Giammarinaro Pietro I. , Hochberg Georg K. A. , Glatter Timo , Bange Gert , Diepold Andreas , Thanbichler Martin 
Published: January 2024

AbstractThe transcriptional antisilencer VirB acts as a master regulator of virulence gene expression in the human pathogen Shigella flexneri. It binds DNA sequences (virS) upstream of VirB-dependent promoters and counteracts their silencing by the nucleoid-organizing protein H-NS. However, its precise mode of action remains unclear. Notably, VirB is not a classical transcription factor but related to ParB-type DNA-partitioning proteins, which have recently been recognized as DNA-sliding clamps using CTP binding and hydrolysis to control their DNA entry gate. Here, we show that VirB binds CTP, embraces DNA in a clamp-like fashion upon its CTP-dependent loading at virS sites and slides laterally on DNA after clamp closure. Mutations that prevent CTP-binding block VirB loading in vitro and abolish the formation of VirB nucleoprotein complexes as well as virulence gene expression in vivo. Thus, VirB represents a CTP-dependent molecular switch that uses a loading-and-sliding mechanism to control transcription during bacterial pathogenesis.
Proxy-analysis of the genetics of cognitive decline in Parkinson’s disease through polygenic scores
Authors: Faouzi Johann , Tan Manuela , Casse Fanny , Lesage Suzanne , Tesson Christelle , Brice Alexis , Mangone Graziella , Mariani Louise-Laure , Iwaki Hirotaka , Colliot Olivier , Pihlstrøm Lasse , Corvol Jean-Christophe 
Published: January 2024

AbstractCognitive decline is common in Parkinson’s disease (PD) and its genetic risk factors are not well known to date, besides variants in the GBA and APOE genes. However, variation in complex traits is caused by numerous variants and is usually studied with genome-wide association studies (GWAS), requiring a large sample size, which is difficult to achieve for outcome measures in PD. Taking an alternative approach, we computed 100 polygenic scores (PGS) related to cognitive, dementia, stroke, and brain anatomical phenotypes and investigated their association with cognitive decline in six longitudinal cohorts. The analysis was adjusted for age, sex, genetic ancestry, follow-up duration, GBA and APOE status. Then, we meta-analyzed five of these cohorts, comprising a total of 1702 PD participants with 6156 visits, using the Montreal Cognitive Assessment as a cognitive outcome measure. After correction for multiple comparisons, we found four PGS significantly associated with cognitive decline: intelligence (p = 5.26e–13), cognitive performance (p = 1.46e–12), educational attainment (p = 8.52e–10), and reasoning (p = 3.58e–5). Survival analyses highlighted an offset of several years between the first and last quartiles of PGS, with significant differences for the PGS of cognitive performance (5 years) and educational attainment (7 years). In conclusion, we found four PGS associated with cognitive decline in PD, all associated with general cognitive phenotypes. This study highlights the common genetic factors between cognitive decline in PD and the general population, and the importance of the participant’s cognitive reserve for cognitive outcome in PD.
Belantamab mafodotin, pomalidomide and dexamethasone in refractory multiple myeloma: a phase 1/2 trial
Authors: Trudel Suzanne , McCurdy Arleigh , Louzada Martha L. , Parkin Stephen , White Darrell , Chu Michael P. , Kotb Rami , Mian Hira , Othman Ibraheem , Su Jiandong , Khan Aniba , Gul Engin , Reece Donna 
Published: January 2024

AbstractDue to evolving treatment standards for newly diagnosed multiple myeloma, many patients will be triple-class exposed after initial relapses and have poor survival. Novel therapies and combinations are therefore required to improve outcomes. B cell maturation antigen (BCMA)-targeted biologics have emerged as an important new area of therapeutics for relapsed multiple myeloma. The two-part ALGONQUIN trial evaluated various doses and schedules of the anti-BCMA antibody–drug conjugate belantamab mafodotin plus pomalidomide and dexamethasone for patients who are lenalidomide refractory and proteosome inhibitor exposed. The primary endpoints, including evaluating dose-limiting toxicities, establishing the recommended Part 2 dose (RP2D) and overall response rate for patients treated at the RP2D, were met. Secondary efficacy endpoints included progression-free survival and overall survival. Patients treated on study (N = 87) had a median of three previous regimens and 55.2% were triple-class refractory. At the RP2D the most common adverse events were decrease in best-corrected visual acuity (71.1%), keratopathy (65.8%), fatigue (57.9%), infection (47.4%; 7.9% grade ≥3), neutropenia (39.5%) and thrombocytopenia (39.5%). For RP2D patients (n = 38), the overall response rate was 85.3%, ≥very good partial response 75.7% and estimated two-year progression-free survival 52.8% (95% confidence interval, 33.9% to 82.4%), at a median follow-up of 13.9 months. The RP2D schedule was associated with manageable antibody–drug conjugate-associated corneal adverse events and improved tolerability without compromising efficacy. Belantamab mafodotin plus pomalidomide and dexamethasone induced durable responses with promising overall survival in relapsed multiple myeloma, the results of which are yet to be confirmed in the phase 3 DREAMM-8 study. ClinicalTrials.gov Identifier: NCT03715478.
RNA structure profiling at single-cell resolution reveals new determinants of cell identity
Authors: Wang Jiaxu , Zhang Yu , Zhang Tong , Tan Wen Ting , Lambert Finnlay , Darmawan Jefferson , Huber Roland , Wan Yue 
Published: January 2024

AbstractRNA structure is critical for multiple steps in gene regulation. However, how the structures of transcripts differ both within and between individual cells is unknown. Here we develop a SHAPE-inspired method called single-cell structure probing of RNA transcripts that enables simultaneous determination of transcript secondary structure and abundance at single-cell resolution. We apply single-cell structure probing of RNA transcripts to human embryonic stem cells and differentiating neurons. Remarkably, RNA structure is more homogeneous in human embryonic stem cells compared with neurons, with the greatest homogeneity found in coding regions. More extensive heterogeneity is found within 3′ untranslated regions and is determined by specific RNA-binding proteins. Overall RNA structure profiles better discriminate cell type identity and differentiation stage than gene expression profiles alone. We further discover a cell-type variable region of 18S ribosomal RNA that is associated with cell cycle and translation control. Our method opens the door to the systematic characterization of RNA structure–function relationships at single-cell resolution.
Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening
Authors: Oh Sangseok , Kang Woo Young , Park Heejun , Yang Zepa , Lee Jemyoung , Kim Changwon , Woo Ok Hee , Hong Suk-Joo 
Published: January 2024

AbstractTo evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson’s correlation and intraclass correlation. Receiver operating characteristic curve (ROC) analysis identified the diagnostic ability of DL-BMD for low BMD and osteoporosis, determined by dual-energy X-ray absorptiometry (DXA) and m-BMD. Excellent concordance were seen between m-BMD and DL-BMD in total CT scans (r = 0.961/0.979). The ROC-derived AUC of DL-BMD compared to that of central DXA for the low-BMD and osteoporosis patients was 0.847 and 0.770 respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to central DXA for low BMD were 75.0%, 75.0%, and 75.0%, respectively, and those for osteoporosis were 68.0%, 80.5%, and 77.7%. The AUC of DL-BMD compared to the m-BMD for low BMD and osteoporosis diagnosis were 0.990 and 0.943, respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to m-BMD for low BMD were 95.5%, 93.5%, and 94.6%, and those for osteoporosis were 88.2%, 94.5%, and 92.9%, respectively. DL-BMD exhibited excellent agreement with m-BMD on L1 and L2 vertebrae in the various routine clinical CT scans and had comparable diagnostic performance for detecting the low-BMD and osteoporosis on conventional QCT.
Demonstration of enhanced long-range cosmic time synchronization for wireless and secure dissemination of standard time
Authors: Tanaka Hiroyuki K. M. 
Published: January 2024

AbstractAccurate traceability of time is prerequisite to the proper functioning of many necessary aspects of our modern life including making financial transactions, managing automated technology and navigating the transport of goods and human beings. One of the most reliable international time references is the Coordinated Universal Time (UTC) that can be distributed wirelessly in principle. However, this wireless option is currently limited to GPS and other global navigation satellite systems. GPS signals are weak and easily affected by environmental conditions. Moreover, since GPS signals are unencrypted, the possibility of a signal spoofing attack remains a continuous threat. Prior works showed the potential of the alternative wireless time synchronization technique called Cosmic Time Synchronization (CTS), in which, clocks are located 50 m apart were wirelessly synchronized with a sub-microsecond level accuracy, and its operation time was limited to 20 min. However, for the actual implementation of CTS to real-life situations, these distance and stability values are not sufficient. In this study, we constructed a dedicated CTS facility and conducted a long-haul (180 m) CTS demonstration. As a result, it was verified that this long-range CTS is capable of maintaining stable sub-microsecond time synchronization for 3 days with a granularity of 148.8 ns (SD) and an offset of 22.97 ns. Since the current version of CTS can now operate over an area that has been enlarged by more than one order of magnitude, it is possible to utilize for more diverse applications, and the application to a banking synchronization system is proposed. As a case study, it is shown that CTS now has the capability to offer wireless time synchronization service to large clusters of financial firms in large cities. With its accurate time dissemination (the metrological traceability to UTC), its reasonable cost, and its hack-proof, stable design, this latest CTS model has the capacity to improve the accuracy of timing for a wide variety of sectors.
Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma
Authors: Schön Felix , Kieslich Aaron , Nebelung Heiner , Riediger Carina , Hoffmann Ralf-Thorsten , Zwanenburg Alex , Löck Steffen , Kühn Jens-Peter 
Published: January 2024

AbstractTo examine the comparative robustness of computed tomography (CT)-based conventional radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival (OS) in HCC patients. Retrospectively, 114 HCC patients with pretherapeutic CT of the liver were randomized into a development (n = 85) and a validation (n = 29) cohort, including patients of all tumor stages and several applied therapies. In addition to clinical parameters, image annotations of the liver parenchyma and of tumor findings on CT were available. Cox-regression based on radiomics features and CNN models were established and combined with clinical parameters to predict OS. Model performance was assessed using the concordance index (C-index). Log-rank tests were used to test model-based patient stratification into high/low-risk groups. The clinical Cox-regression model achieved the best validation performance for OS (C-index [95% confidence interval (CI)] 0.74 [0.57–0.86]) with a significant difference between the risk groups (p = 0.03). In image analysis, the CNN models (lowest C-index [CI] 0.63 [0.39–0.83]; highest C-index [CI] 0.71 [0.49–0.88]) were superior to the corresponding radiomics models (lowest C-index [CI] 0.51 [0.30–0.73]; highest C-index [CI] 0.66 [0.48–0.79]). A significant risk stratification was not possible (p > 0.05). Under clinical conditions, CNN-algorithms demonstrate superior prognostic potential to predict OS in HCC patients compared to conventional radiomics approaches and could therefore provide important information in the clinical setting, especially when clinical data is limited.
YOLO_Bolt: a lightweight network model for bolt detection
Authors: Liu Zhenyu , Lv Haoyuan 
Published: January 2024

AbstractAccurate, fast, and intelligent workpiece identification is of great significance to industrial production. To cope with the limited hardware resources of factory equipment, we have made lightweight improvements based on You Only Look Once v5 (YOLOv5) and proposed a lightweight YOLO named YOLO_Bolt. First, ghost bottleneck lightweight deep convolution is added to the backbone module and neck module of the YOLOv5 detection algorithm to reduce the model volume. Second, the asymptotic feature pyramid network is added to enhance the feature utilization ability, suppress interference information, and improve detection accuracy. Finally, the relationship between the loss function and the decoupling head structure was focused on, and the number of decoupling head layers was redesigned according to different tasks to further improve the detection accuracy of the workpiece detection model. We conducted experimental verification on the MSCOCO 2017 dataset and the homemade bolt dataset. The experimental results show that compared with YOLOv5s, the number of model parameters is only 6.8 M, which is half that of the original model. On the MSCOCO 2017 dataset, the mAP increased by 2.4%. FPS increased by 104 frames/s. On the homemade dataset, the mAP 0.5 increased by 4.2%, and our proposed method is 1.2% higher than the latest YOLOv8s. The improved network can provide effective auxiliary technical support for workpiece detection.