bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fibroblast-derived Osteoglycin Promotes Epithelial Cell Repair
Authors: van der Koog Luke , Woest Manon E. , Gorter Iris C. , Verschut Vicky , Elferink Robin A.B. , Zuidhof Annet B. , Nugraha Dyan F. , Koloko Ngassie Maunick L. , Bos Sophie I.T. , Dhakad Deepesh , Wolters Justina C. , Horvatovich Peter L. , Prakash Y.S. , Timens Wim , Yildirim Önder A. , Brandsma Corry-Anke , Frijlink Henderik W. , Nagelkerke Anika , Gosens Reinoud 
Published: November 2024

ABSTRACTThere is an urgent need for innovative pharmacological treatments targeting defective epithelial repair in chronic diseases, such as chronic obstructive pulmonary disease. The mesenchymal niche is a critical regulator in epithelial stem cell activation during repair. We hypothesized that secreted factors in this interaction are potent drug targets. Utilizing a cutting-edge proteomics-guided drug discovery strategy, we explored the lung fibroblast secretome to uncover impactful drug targets. Our lung organoid assays identified several regenerative ligands, with the secreted matrix protein osteoglycin (OGN) surprisingly showing the most profound effects. Transcriptomic analyses revealed that OGN enhances alveolar progenitor cell differentiation, boosts reactive oxygen species detoxification, reduces cellular senescence, and strengthens fibroblast-epithelial crosstalk. Critically, OGN expression was diminished in COPD patients and smoke-exposed mice. An active fragment of OGN, encompassing leucine-rich repeat regions 4-7, demonstrated regenerative potential akin to full-length OGN. This fragment significantly ameliorated elastase-induced lung injury precision-cut lung slices and improved lung functionin vivo. These findings highlight lung fibroblast-derived OGN as a pivotal secreted protein for alveolar epithelial growth, positioning its active fragment as a promising therapeutic for epithelial repair in individuals with accelerated lung tissue damage.
Multi-layered ecological interactions determine growth of clinical antibiotic-resistant strains within human microbiomes
Authors: Leon-Sampedro Ricardo , Boumasmoud Mathilde , Reichlin Markus , Pfrunder-Cardozo Katia R. , Noll Nicholas , Egli Adrian , Hall Alex R. 
Published: November 2024

AbstractUnderstanding the spread of antibiotic-resistant bacteria in human-associated microbial communities, particularly in the gut microbiome, is critical for combating the global resistance crisis. However, the role of various ecological factors, such as intraspecies or interspecies interactions, in modulating this process is poorly understood. We hypothesized that different strains within the same species would exhibit distinct interactions with the resident microbiota, leading to variable invasion outcomes. To test this, we examined the population growth of multiple clinically relevant antibiotic-resistantE. colistrains carrying ESBL and carbapenemase resistance plasmids. We introduced these strains into human gut microbiome samples from healthy individuals, using replicated anaerobic microcosms treated or untreated with antibiotics. We found that while antibiotic exposure significantly influenced the growth of these incoming strains, some were successful even in the absence of antibiotics. The variation in ecological success across strain-microbiome sample combinations was positively associated with the intrinsic growth capacities of the strains in local abiotic conditions and their competitive interactions with residentE. coli. Furthermore, the incoming resistant strains had strain- specific effects on the taxonomic composition of the resident microbiota; different strains not only varied in their growth performance in human microbiomes, in some cases they pushed taxonomic composition of the resident community in different directions. In contrast with phenotypes measured in gut microcosms, metabolic profiles measured across various single-carbon-source environments were not reliable predictors of population growth in human microbiomes. Overall, this controlled design reveals the multi-layered ecological dynamics and strain-specific mechanisms that govern the spread of antibiotic-resistant bacteria in individual human-associated microbiomes.
Tumor Spheroid Uptake of Fluorescent Nanodiamonds is limited by Mass Density: a 4D Light-Sheet Assay
Authors: Niora Maria , Dufva Martin , Jauffred Liselotte , Berg-Sørensen Kirstine 
Published: November 2024

AbstractFluorescent nanodiamonds (FNDs) with nitrogen-vacancy centers are promising candidates for long-term biolabeling and biosensing applications due to their biocompatibility and unique optomagnetic properties. The employment of nanomaterials in cancer therapy and diagnostics, requires a deep understanding of how nanoparticles (NPs) interact with the three-dimensional (3D) tumor environment. We developed a novel approach, the “Tumor-in-a-Tube” platform, using 4D light-sheet microscopy to explore the spatio-temporal dynamics of FNDs with 3D tumor spheroids. By monitoring the real-time NP sedimentation, spheroid penetration, and cellular uptake of FNDs and polystyrene nanoparticles (PNPs), we marked the impact of NP mass density on their spheroid interaction. Unlike PNPs, higher-density FNDs underwent rapid sedimentation, which minimized their effective concentration and hindered the FND – spheroid interactions. This results in constrained intratumoral accumulation, and size-independent uptake and penetration. Longer FND effective-exposure-time promotes size-dependent cell uptake, verified by FND treatment on 2D monolayers. Nonetheless, FNDs exhibited good biocompatibility and long-term spheroid labeling, allowing for cell isolation from different spheroid layers. Our results suggest the need for NP effective-exposure-time calibration in comparative NP assays, in 3D static models. Overall, our platform provides a valuable tool for bridging the gap between 2D and 3D static models in NP assessment, drug delivery, toxicology profiling and translational research.
Development and use of integrated wetland condition index for lacustrine fringe wetlands of Lake Tana, Ethiopia
Authors: Wondim Yirga Kebede , Melese Ayalew Wondie 
Published: November 2024

AbstractThe primary objective of this research was to develop an integrated wetland condition index (IWCI) for lacustrine fringe wetlands (LFWs) in Lake Tana, Ethiopia, which has been highly impacted by recessionary agriculture, water hyacinth infestation, and both short- and long-term water level fluctuations (WLFs), as well as a heavy sediment load. The index of lacustrine wetland condition was developed based on four wetland characteristics that define wetlands: hydrology, water quality, sediment quality, and wetland biota. The value obtained from field and laboratory measurements of each wetland hydrology, water chemistry, and sediment characteristics indicator was normalized by allocating scores based on literature information and the results of this research project from three different disturbance-level lacustrine wetlands in Lake Tana. Macrophyte, diatom, and zooplankton indicator species tests (IndVal% and p (raw)) were calculated using PAST (PAleontological STatistics). Scores were allocated on a scale from 1 to 10, in such a way that the highest scores reflected the best conditions and the lowest scores the most disturbed and unhealthy conditions. Within four sub-indexes, there is more than one measure, with each measure given equal weighting, whereas the four wetland components (hydrology, water chemistry, sediment, and biota) are weighted as per their contribution to the overall wetland condition. Weights were assigned using the Decision Support System Software of DEFINITE. Finally, the total IWCI was classified into five wetland condition categories, namely: very poor or very far from the reference (0–3), poor (3–5), moderate (5–7), good (7-9), and excellent wetland condition or reference condition (9–10). According to the IWCI of the low water level period, eight wetland sampling sites were found within a moderately impacted condition (44.44%); similarly, eight wetland sampling sites were found within a good or mildly impacted condition; and only two wetland sampling sites (11.11%) were found in an excellent or reference condition. Thus, this IWCI developed and tested in this study could be a powerful tool to inform decision-makers on lacustrine wetland conservation and restoration priorities throughout the Lake Tana Ecosystem.
Thermal Plasticity of Stage Specific Development Time inDrosophila melanogasterunder Shifting Temperatures
Authors: Chattopadhyay Aradhya , Roy Rishav , Biswas Payel , Ghosh Shampa M. 
Published: November 2024

AbstractThermal shifts and fluctuations are inherent aspects of the natural environment. How different developmental stages of the fly respond to thermal fluctuations and shifts are not properly understood. In view of this, we studied how durations of different developmental stages are affected by current and previous stage developmental temperatures when they experience upshifts and downshifts of temperature during developmental transitions. The study revealed a complex interaction of current and prior stage temperatures in shaping development time. The egg-stage temperature is found to exert its effect on both larval and pupal stage. However, the impacts of larva-pupa thermal shifts on pupal and overall development time are greater than that of egg-larva thermal shifts. As one would predict, a prior cold temperature extends subsequent pupal duration, while prior warm condition reduces it. Surprisingly, for larval duration, both prior warm and cold condition had the same effect-both led to significant reductions in subsequent larval stage duration. The warm egg temperature had an accelerating effect on larval development as expected. The other shift from cold egg to warm larval condition that also made development faster than constant warm rearing, was possibly caused by a shift from prior sub-optimal to optimal thermal condition used in the experiment. We also found warm larval stage followed by cold pupal stage makes development time shorter than cold larval followed by warm pupal rearing, suggesting a dominating role of the early-stage temperature in determining overall course of development. And lastly, we also observed two subsequent thermal shifts leave a decelerating effect on pupal development, possibly revealing a difficulty in developmental adjustments in the face of frequent thermal changes.
Engineered paediatric tumours retain maintains tumour genotype and phenotype for precision medicine
Authors: Jung MoonSun , Poltavets Valentina , Skhinas Joanna N. , Tax Gabor , Kamili Alvin , Xie Angela (Jinhan) , Ghamrawi Sarah , Graber Philipp , Mao Jie , Wong-Erasmus Marie , Cui Louise , Kimpton Kathleen , Venkat Pooja , Mayoh Chelsea , Fleuren Emmy D. G. , Fordham Ashleigh M. , Barger Zara , Grady John , Thomas David M. , Du Eric Yiwei , Cowley Mark J. , Gifford Andrew J. , Fletcher Jamie I. , Lau Loretta M. S. , Dolman M. Emmy M. , Gooding J. Justin , Kavallaris Maria 
Published: November 2024

AbstractPrecision medicine for paediatric and adult cancers that includes drug sensitivity profiling, can identify effective therapies for individual patients. However, obtaining adequate biopsy samples for high-throughput (HTP) screening remains challenging, with tumours needing to be expanded in culture or patient-derived xenografts – this is time-consuming and often unsuccessful. Herein, we have developed paediatric patient-derived tumour models using an engineered extracellular matrix (ECM) tissue mimic hydrogel system and HTP 3D bioprinting. Gene expression analysis from neuroblastoma and sarcoma patients identified key components of the ECM in these tumour types. Engineered hydrogels with ECM-mimic peptides were used to create patient-specific tumour organoids, modelling tumour growth conditions. Expanded tumour organoids recapitulated the genetic and phenotypic characteristics of the original tumours and retained tumourgenicity. Screening of these models identified individualised drug sensitivities. Our approach offers a timely and clinically relevant technology platform for precision medicine in paediatric cancers, potentially transforming preclinical testing across cancer types.
Generative Approaches for Nucleotide Sequences to Enhance Non-coding RNA Classification
Authors: Chiquitto Alisson Gaspar , Oliveira Liliane Santana , Bugatti Pedro Henrique , Saito Priscila Tiemi Maeda , Basham Mark , Raittz Roberto Tadeu , Paschoal Alexandre Rossi 
Published: November 2024

AbstractClassifying non-coding RNA (ncRNA) sequences, particularly mirtrons, is essential for elucidating gene regulation mechanisms. However, the prevalent class imbalance in ncRNA datasets presents significant challenges, often resulting in overfitting and diminished generalization in machine learning models. In this study,GENerative Approaches forNUcleotideSequences (GENNUS) is proposed, introducing novel data augmentation strategies using Generative Adversarial Networks (GANs) and Synthetic Minority Over-sampling Technique (SMOTE) to enhance ncRNA classification performance. Our GAN-based methods effectively generate high-quality synthetic data that capture the intricate patterns and diversity of real mirtron sequences, eliminating the need for extensive feature engineering. Through four experiments, it is demonstrated that models trained on a combination of real and GAN-generated data improve classification accuracy compared to traditional SMOTE techniques or only with real data. Our findings reveal that GANs enhance model performance and provide a richer representation of minority classes, thus improving generalization capabilities across various machine learning frameworks. This work highlights the transformative potential of synthetic data generation in addressing data limitations in genomics, offering a pathway for more effective and scalable ncRNA classification methodologies.
Pharming: Joint Clonal Tree Reconstruction of SNV and CNA Evolution from Single-cell DNA Sequencing of Tumors
Authors: Weber Leah L. , Hart Anna , Ochoa Idoia , El-Kebir Mohammed 
Published: November 2024

AbstractCancer arises through an evolutionary process in which somatic mutations, including single nucleotide variants (SNVs) and copy number aberrations (CNAs), drive the development of a malignant, heterogeneous tumor. Reconstructing this evolutionary history from sequencing data is critical for understanding the order in which mutations are acquired and the dynamic interplay between different types of alterations. Advances in modern whole genome single-cell sequencing now enable the accurate inference of copy number profiles in individual cells. However, the low sequencing coverage of these low pass sequencing technologies poses a challenge for reliably inferring the presence or absence of SNVs within tumor cells, limiting the ability to simultaneously study the evolutionary relationships between SNVs and CNAs.In this work, we introduce a novel tumor phylogeny inference method, Pharming, that jointly infers the evolutionary histories of SNVs and CNAs. Our key insight is to leverage the high accuracy of copy number inference methods and the fact that SNVs co-occur in regions with CNAs in order to enable more precise tumor phylogeny reconstruction for both alteration types. We demonstrate via simulations that Pharmingoutperforms state-of-the-art single-modality tumor phylogeny inference methods. Additionally, we apply Pharmingto a triple-negative breast cancer case, achieving high-resolution, joint reconstruction of CNA and SNV evolution, including thede novodetection of a clonal whole-genome duplication event. Thus, Pharmingoffers the potential for more comprehensive and detailed tumor phylogeny inference for high-throughput, low-coverage single-cell DNA sequencing technologies compared to existing approaches.Availabilityhttps://github.com/elkebir-group/Pharming
HG-DCM: History Guided Deep Compartmental Model for Early Stage Pandemic Forecasting
Authors: Wei Ziming , Li Michael Lingzhi 
Published: November 2024

AbstractWe introduce the History-Guided Deep Compartmental Model (HG-DCM), a novel framework for early-stage pandemic forecasting that synergizes deep learning with compartmental modeling to harness the strengths of both interpretability and predictive capacity. HG-DCM employs a Residual Convolutional Neural Network (RCNN) to learn temporal patterns from historical and current pandemic data while incorporating epidemiological and demographic metadata to infer interpretable parameters for a compartmental model to forecast future pandemic growth. Experimental results on early-stage COVID-19 and Monkeypox forecasting tasks demonstrate that HG-DCM outperforms both standard compartmental models (e.g., DELPHI) and standalone deep neural networks (e.g., GRU) in predictive accuracy and stability, particularly with limited data. By effectively integrating historical pandemic insights, HG-DCM offers a scalable approach for interpretable and accurate forecasting, laying the groundwork for future real-time pandemic modeling applications.
Effectiveness and safety of psychosocial interventions for the treatment of cannabis use disorder: a systematic review and meta-analysis
Authors: Halicka Monika , Parkhouse Thomas L , Webster Katie , Spiga Francesca , Hines Lindsey A , Freeman Tom P , Sanghera Sabina , Dawson Sarah , Paterson Craig , Savović Jelena , Higgins Julian PT , Caldwell Deborah M 
Published: November 2024

ABSTRACTAimTo evaluate the effectiveness, safety, and cost-effectiveness of psychosocial interventions for cannabis use disorder (CUD).DesignA systematic review of randomized controlled trials (RCTs; PROSPERO protocol CRD42024553382). We searched databases (MEDLINE/PsycInfo/Cochrane CENTRAL) to 12-Jun-2024. We assessed results using Risk of Bias 2 and conducted meta-analyses where possible.SettingInpatient/outpatient/community-based.ParticipantsIndividuals with CUD aged ≥16 years.InterventionsPsychosocial interventions lasting >4 sessions, delivered in real time.MeasurementsPrimary outcomes were continuous- and point-abstinence, withdrawal intensity, treatment completion and adverse events.FindingsWe included 22 RCTs (3,304 participants). At the end of treatment, cognitive-behavioural therapy (CBT) increased point abstinence (odds ratio [OR]=18.27, 95% confidence interval [9.00; 37.07]), and continuous abstinence (OR=2.72 [1.20; 6.19]), but reduced treatment completion (OR=0.53 [0.35; 0.85]) versus inactive/nonspecific comparators. Dialectical behavioural/acceptance and commitment therapy increased point abstinence versus inactive/nonspecific comparators (OR=4.34 [1.74; 10.80]). The effect of CBT plus affect management versus CBT on point abstinence was OR=7.85 [0.38; 163.52]. The effect of CBT plus abstinence-based contingency management versus CBT on point abstinence was OR=3.78 [0.83; 17.25], and on continuous abstinence OR=1.81 [0.61; 5.41]. For CBT plus abstinence-contingency management versus CBT plus attendance-contingency management, the effect on point abstinence was OR=1.61 [0.72; 3.60], and on continuous abstinence OR=2.04 [0.75; 5.58]. The effect of community reinforcement on point abstinence was OR=0.29 [0.04; 1.90] versus CBT, and on continuous abstinence OR=47.36 [16.00; 140.21] versus nonspecific comparator. Interventions other than CBT may not affect treatment completion. No adverse events were reported. No study reported withdrawal intensity. Two economic evaluations reported higher costs for more complex psychosocial interventions and contingency management.ConclusionsCognitive-behavioural and dialectical behavioural/acceptance and commitment therapies may increase abstinence relative to inactive/nonspecific comparators. The conclusions remain tentative due to low to very low certainty of evidence and small number of studies.
Integrating spatial transcriptomics and snRNA-seq data enhances differential gene expression analysis results of AD-related phenotypes
Authors: Tang Shizhen , Liu Shihan , Buchman Aron S. , Bennett David A. , De Jager Philip L. , Hu Jian , Yang Jingjing 
Published: November 2024

AbstractBackgroundSpatial transcriptomics (ST) data provide spatially-informed gene expression for studying complex diseases such as Alzheimer’s disease (AD). Existing studies using ST data to identify genes with spatially-informed differential gene expression (DGE) of complex diseases have limited power due to small sample sizes. Conversely, single-nucleus RNA sequencing (snRNA-seq) data offer larger sample sizes for studying cell-type specific (CTS) DGE but lack spatial information. In this study, we integrated ST and snRNA-seq data to enhance the power of spatially-informed CTS DGE analysis of AD-related phenotypes.MethodFirst, we utilized the recently developed deep learning toolCelEryto infer the spatial location of ∼1.5M cells from snRNA-seq data profiled from dorsolateral prefrontal cortex (DLPFC) tissue of436postmortem brains in the ROS/MAP cohorts. Spatial locations of six cortical layers that have distinct anatomical structures and biological functions were inferred. Second, we conducted cortical-layer specific (CLS) and CTS DGE analyses for three quantitative AD-related phenotypes –– β-amyloid, tangle density, and cognitive decline. CLS-CTS DGE analyses were conducted based on linear mixed regression models with pseudo-bulk scRNA-seq data and inferred cortical layer locations.ResultsWe identified 450 potential CLS-CTS significant genes with nominal p-values<10-4, including 258 for β-amyloid, 122 for tangle density, and 127 for cognitive decline. Majority of these identified genes, including the ones having known associations with AD (e.g.,APOE,KCNIP3, andCTSD), cannot be detected by traditional CTS DGE analyses without considering spatial information. We also identified 8 genes shared across all three phenotypes, 21 between β-amyloid and tangle density, 10 between cognitive decline and tangle density, and 10 between β-amyloid and cognitive density. Particularly, Gene Set Enrichment Analyses with the CLS-CTS DGE results of microglia in cortical layer-6 of β-amyloid identified 12 significant AD-related pathways.ConclusionIncorporating spatial information with snRNA-seq data detected significant genes and pathways for AD-related phenotypes that would not be identified by traditional CTS DGE analyses. These identified CLS-CTS significant genes not only help illustrate the pathogenesis of AD, but also provide potential CLS-CTS targets for developing therapeutics of AD.