bluuebunny/crossref_metadata_2025_split huggingface dataset:
Composite interventions on outcomes of severely and critically ill patients with COVID-19 in Shanghai, China
Authors: Shao Jiasheng , Fan Rong , Guo Chengnan , Huang Xuyuan , Guo Runsheng , Zhang Fengdi , Hu Jianrong , Huang Gang , Cao Liou 
Published: May 2023

AbstractBackgroundThe sixty-day effects of initial composite interventions for the treatment of severely and critically ill patients with COVID-19 are not fully assessed.MethodsUsing a bayesian piecewise exponential model, we analyzed the 60-day mortality, health-related quality of life (HRQoL) and disability in 1082 severely and critically patients with COVID-19 between December 8, 2022 and February 9, 2023 in Shanghai, China. The final 60-day follow-up was completed on April 10, 2023.ResultsAmong 1082 patients (mean age, 78.0 years), 421 [38.9%] women), 139 patients (12.9%) died within 60 days. Azvudine had a 99.8% probability of improving 2-month survival (adjusted HR, 0.44 [95% credible interval, 0.24-0.79]) and Paxlovid had a 91.9% probability of improving 2-month survival (adjusted HR, 0.71 [95% credible interval, 0.44-1.14]) compared with the control. IL-6 receptor antagonist, Baricitinib, and a-thymosin each had a high probability of benefit (99.5%, 99.4%, and 97.5%, respectively) compared to their controls, while the probability of trail-defined statistical futility (HR >0.83) was high for therapeutic anticoagulation (99.8%; HR, 1.64 [95% CrI, 1.06-2.50]), and glucocorticoid (91.4%; HR, 1.20 [95% CrI, 0.71-2.16]). Paxlovid, Azvudine and therapeutic anticoagulation showed significant reduction in disability (p<0.05)ConclusionsAmong severely and critically ill patients with COVID-19 who received 1 or more therapeutic interventions, treatment with Azvudine had a high probability of improved 60-day mortality compared with the control, indicating its potential in resource-limited scenario. Treatment with IL-6 receptor antagonist, Baricitinib, and a-thymosin also had high probabilities of benefit of improving 2-month survival, among which a-thymosin could improve HRQoL. Treatment with Paxlovid, Azvudine and therapeutic anticoagulation could significantly reduce disability at day 60.
The effect of genetic predisposition to Alzheimer’s disease and related traits on recruitment bias in a study of cognitive ageing
Authors: Gomez Lina M. , Mitchell Brittany L. , McAloney Kerrie , Adsett Jessica , Garden Natalie , Wood Madeline , Diaz-Torres Santiago , Garcia-Marin Luis M. , Breakspear Michael , Martin Nicholas G. , Lupton Michelle K. 
Published: May 2023

AbstractThe recruitment of participants for research studies may be subject to bias due to an overrepresentation of those more willing to participate voluntarily. No study has analysed the effect of genetic predisposition to Alzheimer’s disease (AD) on study participation. The Prospective Imaging Study of Ageing (PISA), aims to characterise the phenotype and natural history of healthy adult Australians at high future risk of AD. Participants approached to take part in PISA were selected from existing cohort studies with available genome-wide genetic data for both successfully and unsuccessfully recruited participants, allowing us to investigate the genetic contribution to voluntary recruitment. From a recruitment pool of 13,432 individuals (age 40-80), 64% of participants were successfully recruited into the study. Polygenic risk scores (PRS) were computed in order to test to what extent the genetic risk for AD, and related risk factors (including educational attainment, household income and IQ), predicted participation in PISA. We examined the associations between PRS andAPOEε4 with recruitment using logistic regression models. We found significant associations of age and sex with study participation, where older and female participants were more likely to complete the core module. We did not identify a significant association of genetic risk for AD with study participation. Nonetheless, we identified significant associations with genetic scores for key causal risk factors for AD, such as IQ, household income and years of education. Our findings highlight the importance of considering bias in key risk factors for AD in the recruitment of individuals for cohort studies.
OPTIMIZING THERAPEUTIC TARGETS FOR BREAST CANCER USING BOOLEAN NETWORK MODELS
Authors: Sgariglia Domenico , Gonçalves Carneiro Flavia Requel , Vidal de Carvalho Luis Alfredo , Pedreira Carlos Eduardo , Carels Nicolas , Barbosa da Silva Fabricio Alves 
Published: May 2023

ABSTRACTStudying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data. A key therapeutic objective is to guide cells, through targeted interventions, to transition from the current cancer attractor to a physiologically distinct attractor unrelated to cancer. To achieve this, we developed a computational method that identifies network nodes whose inhibition can facilitate the desired transition from one tumor attractor to another associated with apoptosis, leveraging transcriptomic data from cell lines. To validate the model, we utilized previously published in vitro experiments where the downregulation of specific proteins resulted in cell growth arrest and death of a breast cancer cell line. The method proposed in this manuscript combines diverse data sources, conducts structural network analysis, and incorporates relevant biological knowledge on apoptosis in cancer cells. This comprehensive approach aims to identify potential targets of significance for personalized medicine.
Optical nanosensor passivation enables highly sensitive detection of the inflammatory cytokine IL-6
Authors: Gaikwad Pooja , Rahman Nazifa , Parikh Rooshi , Crespo Jalen , Cohen Zachary , Williams Ryan 
Published: May 2023

AbstractInterleukin-6 (IL-6) is known to a play critical role in the progression of inflammatory diseases such as cardiovascular disease, cancer, sepsis, viral infection, neurological disease, and autoimmune diseases. Emerging diagnostic and prognostic tools, such as optical nanosensors, experience challenges in successful clinical application in part due to protein corona formation dampening their selectivity and sensitivity. To address this problem, we explored the rational screening of several classes of biomolecules to be employed as agents in non-covalent surface passivation as a strategy to screen interference from non-specific proteins. Findings from this screening were applied to the detection of IL-6 by a fluorescent antibody-conjugated single-walled carbon nanotube (SWCNT)-based nanosensor. The IL-6 nanosensor exhibited highly sensitive and specific detection after passivation with a polymer, poly-L-lysine, as demonstrated by IL-6 detection in human serum within a clinically relevant range of 25 pg/mL to 25,000 pg/mL, exhibiting a limit of detection over three orders of magnitude lower than prior antibody-conjugated SWCNT sensors. This work holds the potential for rapid and highly sensitive detection of IL-6 in clinical settings with future application to other cytokines or disease-specific biomarkers.
Quantify nanoscopic alterations associated with mitochondrial dysfunctions using three-dimensional single-molecule localization microscopy
Authors: Brenner Benjamin , Zhang Yang , Kweon Junghun , Fang Raymond , Sheibani Nader , Zhang Sarah X. , Sun Cheng , Zhang Hao F. 
Published: May 2023

ABSTRACTThe morphology of mitochondria provides insights into their functions. Three-dimensional (3D) single-molecule localization microscopy (SMLM) uniquely enables the analysis of mitochondrial morphological features individually, thanks to its high resolution. However, nearly all reported studies of mitochondrial morphology have been qualitative and without statistical analysis. We report a quantitative method to extract mitochondrial morphological metrics, including volume, aspect ratio, and local protein density, from 3D SMLM images, with single-mitochondrion sensitivity. We validated our approach using simulated ground-truth SMLM images of mitochondria. We further tested our morphological analysis on mitochondria that have been altered functionally and morphologically in controlled manners. This work sets the stage to analyze mitochondrial morphological alterations associated with disease progression quantitatively.
The kinetics and basal levels of the transcriptome response during Effector-Triggered Immunity in Arabidopsis are mainly controlled by four immune signaling sectors
Authors: Hillmer Rachel A. , Igarashi Daisuke , Stoddard Thomas , Lu You , Liu Xiaotong , Tsuda Kenichi , Katagiri Fumiaki 
Published: May 2023

SUMMARYTo observe the transcriptome response during Effector-Triggered Immunity (ETI) without complications from any other pathogen factors or heterogeneously responding cell populations, we transgenically and conditionally expressed thePseudomonas syringaeeffector AvrRpt2 in Arabidopsis leaves. We studied this ETI-specific, cell-autonomous transcriptome response in 16 exhaustively combinatorial genetic backgrounds for the jasmonate (JA), ethylene (ET), PAD4, and salicylate (SA) immune signaling sectors. Removal of some or all four sectors had relatively small impacts on the intensity of the overall ETI transcriptome response (1972 upregulated and 1290 downregulated genes). Yet, we found that the four signaling sectors strongly affect the kinetics of the ETI transcriptome response based on analysis of individual genes via time-course modeling and of the collective behaviors of the genes via a PCA-based method: the PAD4 sector alone and the JA;SA sector interaction (defined by the averaging model) accelerated the response, while the ET;SA sector interaction delayed it. The response acceleration by the PAD4 sector or the JA;SA sector interaction was consistent with their positive contributions to ETI measured by pathogen growth inhibition. The responsive genes overlapping between ETI and Pattern-Triggered Immunity (PTI) had distinct regulatory trends regarding the four sectors, indicating different regulatory circuits in upstream parts of ETI and PTI signaling. The basal mRNA levels of most ETI-upregulated genes, but not downregulated genes, were predominantly positively regulated by the PAD4;SA sector interaction. This detailed mechanistic decomposition of the roles of four signaling sectors allowed us to propose a potential regulatory network involved in ETI signaling.
Pregnancy and postpartum dynamics revealed by millions of lab tests
Authors: Bar Alon , Moran Ron , Mendelsohn-Cohen Netta , Kohanim Yael Korem , Mayo Avi , Toledano Yoel , Alon Uri 
Published: May 2023

AbstractPregnancy and delivery involve dynamic alterations in many physiological systems. However, the physiological dynamics during pregnancy and after delivery have not been systematically analyzed at high temporal resolution in a large human population. Here we present the dynamics of 76 lab tests based on a cross-sectional analysis of roughly 41 million measurements from over 300,000 pregnancies. We analyzed each test at weekly intervals from 20 weeks preconception to 80 weeks postpartum, providing detailed temporal profiles. About half of the tests take three months to a year to return to baseline during postpartum, highlighting the physiological load of childbirth. The precision of the data revealed effects of preconception supplements, overshoots after delivery and intricate temporal responses to changes in blood volume and renal filtration rate. Pregnancy complications – gestational diabetes, pre-eclampsia and postpartum hemorrhage – showed distinct dynamical changes. These results provide a comprehensive dynamic portrait of the systems physiology of pregnancy.
A phylogenomic perspective on interspecific competition
Authors: Louw Nicolas , Wolfe Benjamin E. , Uricchio Lawrence H. 
Published: May 2023

AbstractEvolutionary processes may have substantial impacts on community assembly, but evidence for phylogenetic relatedness as a determinant of interspecific interaction strength remains mixed. In this perspective, we consider a possible role for discordance between gene trees and species trees in the interpretation of phylogenetic signal in studies of community ecology. Modern genomic data show that the evolutionary histories of many taxa are better described by a patchwork of histories that vary along the genome rather than a single species tree. If a subset of genomic loci harbor trait-related genetic variation, then the phylogeny at these loci may be more informative of interspecific trait differences than the genome background. We develop a simple method to detect loci harboring phylogenetic signal and demonstrate its application through a proof of principle analysis ofPenicilliumgenomes and pairwise interaction strength. Our results show that phylogenetic signal that may be masked genome-wide could be detectable using phylogenomic techniques and may provide a window into the genetic basis for interspecific interactions.Data & code accessibilityData and code for this project are freely available in the repository linked below and will be permanently archived upon publication.Statement of authorshipNL, BEW, and LHU designed the research; NL performed experiments; NL and LHU performed computational analyses; NL, BEW, and LHU wrote the manuscript.Code repositoryhttps://github.com/uricchio/ILSComp
Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize
Authors: Ferebee Taylor H. , Buckler Edward S. 
Published: May 2023

AbstractGenomic selection and gene editing in crops could be enhanced by multi-species, mechanistic models predicting effects of changes in gene regulation. Current expression abundance prediction models require extensive computational resources, hard-to-measure species-specific training data, and often fail to incorporate data from multiple species. We hypothesize that gene expression prediction models that harness the regulatory network structure ofArabidopsis thalianatranscription factor-target gene interactions will improve on the present maize models. To this end, we collect 147Oryza sativaand 99Sorghum bicolorgene expression assays and assign them to maize family-based orthologous groups. Using three popular graph-based machine learning frameworks, including a shallow graph convolutional autoencoder, a deep graph convolutional autoencoder, and the inductive GraphSage strategy, we encode anArabidopsis thalianaintegrated gene regulatory network (iGRN) structure and TF gene expression values to predict gene expression both within and between species. We then evaluate the network methods against a partial least-squares baseline. We find that the baseline gives the best predictions within species, with Spearman correlations averaging between 0.74 and 0.78. The graph autoencoder methods were more variable with correlations between -0.1 and 0.65. In particular, the GraphSage and deep autoencoders performed the worst, and the shallow autoencoders performed the best. In the most challenging prediction context, where predictions were in new species and on genes that were not seen, we found that the shallow graph autoencoder framework averaged around 0.65. Unlike initial thoughts about preserved network structure improving gene expression predictions, this study shows that within-species predictions only need simple models, such as partial least squares, to capture expression variations. In cross-species predictions, the best model is often a more complex strategy utilizing regulatory network structure and other studies’ expressions.
Anex vivomodel of interactions between extracellular vesicles and peripheral mononuclear blood cells in whole blood
Authors: Rodriguez Blanca V. , Wen Yi , Shirk Erin N. , Vazquez Samuel , Gololobova Olesia , Maxwell Amanda , Plunkard Jessica , Castell Natalie , Carlson Bess , Queen Suzanne E. , Izzi Jessica M. , Driedonks Tom A.P. , Witwer Kenneth W. 
Published: May 2023

AbstractExtracellular vesicles (EVs) can be loaded with therapeutic cargo and engineered for retention by specific body sites; therefore, they have great potential for targeted delivery of biomolecules to treat diseases. However, the pharmacokinetics and biodistribution of EVs in large animals remain relatively unknown, especially in primates. We recently reported that when cell culture-derived EVs are administered intravenously toMacaca nemestrina(pig-tailed macaques), they differentially associate with specific subsets of peripheral blood mononuclear cells (PBMCs). More than 60% of CD20+B cells were observed to associate with EVs for up to 1 hr post-intravenous administration. To investigate these associations further, we developed anex vivomodel of whole blood collected from healthy pig-tailed macaques. Using thisex vivosystem, we found that labeled EVs preferentially associate with B cells in whole blood at levels similar to those detectedin vivo. This study demonstrates thatex vivoblood can be used to study EV-blood cell interactions.
Co-engagement of TIGIT+immune cells to PD-L1+tumours by a bispecific antibody potentiates T-cell response and tumour control
Authors: Cui Xiaopei , Zhu Xi , Yu Haijia , Xu Jingen , Wei Xiaoyue , Chen Shi , Wang Yangtin , Chen Xiaofang , Feng Yujie , Ren Xiaochen , Fei Liyang , Xie Bin , Li Mingwei , Li Xue , Jia Huifeng , Xie Simin , Chen Li , Cheng Yong , Zhang Lei , Li Haidong , Zhu Xiangyang , Zhan Yifan 
Published: May 2023

AbstractCo-targeting PD-1/PD-L1 and TIGIT/CD226 is being pursued to broaden the efficacy of current immunotherapy. Here we demonstrate that a bispecific antibody (BsAb) targeting PD-L1 and TIGIT, HB0036, shows major advantages over the combination of the two parental monoclonal Abs (mAbs). We demonstrated that HB0036 co-engages PD-L1+tumour cells and TIGIT+T cells, and thereby upregulates CD226 on T cells and induces a greater T-cell proliferative response compared to the combination of the parental antibodiesin vitro.In vivo, HB0036 recruits greater amounts of TIGIT antibody in PD-L1+tumours but not in PD-L1-tumours, compared with therapy using the two parental antibodies. We also observed improved tumour control and favourable immunological signatures with HB0036 in syngeneic and xenograft tumour models. Collectively, these findings demonstrate that bispecific antibodies targeting PD-L1 and TIGIT offer superior benefits in cancer immunotherapy compared with therapy using the two parental antibodies. Based on these studies, a phase I clinical trial with HB0036 has been initiated in patients with solid tumours (NCT05417321).
Advancing the communication of genetic risk for cardiometabolic diseases: A critical interpretive synthesis
Authors: Law Jing Hui , Sultan Najia , Finer Sarah , Fudge Nina 
Published: May 2023

AbstractBackgroundGenetics play an important role in risk for cardiometabolic diseases—including type 2 diabetes, cardiovascular disease and obesity. Existing research has explored the clinical utility of genetic risk tools such as polygenic risk scores—and whether interventions communicating genetic risk information using these tools can impact on individuals’ cognitive appraisals of disease risk and/or preventative health behaviours. Previous systematic reviews exploring the evidence base suggest mixed results. To expand current understanding and address knowledge gaps, we undertook a reflexive method of evidence synthesis to the literature—questioning the theoretical basis behind current interventions that communicate genetic risk information and exploring how the effects of genetic risk tools can be fully harnessed for cardiometabolic diseases.MethodsWe obtained 188 records from a combination of database, website and grey literature searches—supplemented with reference chaining and expert subject knowledge within the review team. Using pre-defined critical interpretive synthesis methods, quantitative and qualitative evidence was synthesised and critiqued alongside theoretical understanding from surrounding fields of behavioural and social sciences.FindingsExisting interventions communicating genetic risk information focus predominantly on the “self”, targeting individual-level cognitive appraisals, such as perceived risk and perceived behavioural control. This approach risks neglecting the role of contextual factors and upstream determinants that can reinforce individuals’ interpretations of risk. It also assumes target populations to embody an “ascetic subject of compliance”—the idea of a patient who strives to comply diligently with professional medical advice, logically and rationally adopting any recommended lifestyle changes. We developed a synthesising argument—beyond the “ascetic subject of compliance”—grounded in three major limitations of this perspective: (1) Difficulty applying existing theories/models to diverse populations; (2) The role of familial variables and (3) The need for a life course perspective.ConclusionsInterventions communicating genetic risk information should account for wider influences that can affect individuals’ responses to risk at different levels—including through interactions with their family systems, socio-cultural environments and wider health provision.Protocol registrationPROSPERO CRD42021289269
Deep learning for rapid and reproducible histology scoring of lung injury in a porcine model
Authors: Silva Iran A. N. , Rashed Salma Kazemi , Hedlund Ludwig , Lidfeldt August , Gvazava Nika , Stegmayr John , Skoryk Valeriia , Aits Sonja , Wagner Darcy E 
Published: May 2023

AbstractAcute respiratory distress syndrome (ARDS) is a life-threatening condition with mortality rates between 30-50%. Althoughin vitromodels replicate some aspects of ARDS, small and large animal models remain the primary research tools due to the multifactorial nature of the disease. When using these animal models, histology serves as the gold standard method to confirm lung injury and exclude other diagnoses as high-resolution chest images are often not feasible. Semi-quantitative scoring performed by independent observers is the most common form of histologic analysis in pre-clinical animal models of ARDS. Despite progress in standardizing analysis procedures, objectively comparing histological injuries remains challenging, even for highly-trained pathologists. Standardized scoring simplifies the task and allows better comparisons between research groups and across different injury models, but it is time-consuming, and interobserver variability remains a significant concern. Convolutional neural networks (CNNs), which have emerged as a key tool in image analysis, could automate this process, potentially enabling faster and more reproducible analysis. Here we explored the reproducibility of human standardized scoring for an animal model of ARDS and its suitability for training CNNs for automated scoring at the whole slide level. We found large variations between human scorers, even for pre-clinical experts and board-certified pathologies in evaluating ARDS animal models. We demonstrate that CNNs (VGG16, EfficientNetB4) are suitable for automated scoring and achieve up to 83% F1-score and 78% accuracy. Thus, CNNs for histopathological classification of acute lung injury could help reduce human variability and eliminate a time-consuming manual research task with acceptable performance.