bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of antibiotics and hospitalization on the nasopharyngeal microbiome in very preterm infants
Authors: Rajar Polona , Dhariwal Achal , Salvadori Gabriela , Åmdal Heidi Aarø , Berild Dag , Dahle Ulf R. , Fugelseth Drude , Greisen Gorm , Lausten-Thomsen Ulrik , Saugstad Ola Didrik , Petersen Fernanda Cristina , Haaland Kirsti 
Published: September 2023

AbstractRationalePreterm infants are often exposed to antibiotics early in life. They are at increased risk for inflammation and infection due to immature immune system, weak mucosal barriers of the intestinal and respiratory tract and ineffective defense from non-pathogens of normal flora. Longitudinal research of respiratory microbiota of preterm infants is limited.ObjectivesInvestigate the development of nasopharyngeal microbiota in preterm infants from birth until six months corrected age.MethodsIn our observational cohort study, we obtained 369 nasopharyngeal aspirates from 66 very preterm infants sampled at six time points spanning from birth until six months corrected age. Deep shotgun metagenomic sequencing was used to determine the microbiome composition.Measurements and Main ResultsPreterm infants were grouped according to postnatal antibiotic exposure (naïve; n=21, “Only Early antibiotics”; n=24 and “Other antibiotics”; n=21). Inter-individual variability, followed by postmenstrual age had the largest significant effect on the overall microbiome composition. We identified six microbial community types, determined by various abundances ofCutibacterium,Gemella, Serratia, Streptococcus and Staphylococcus. Antibiotic exposure resulted in significant, apparently transient diversity changes. The abundance ofSerratiaindicates that hospitalization can lead to microbiome scars lasting for at least six months after discharge.ConclusionsGenera associated with respiratory health in non-premature populations were sparse. The respiratory microbiome of preterm infants is transiently affected by antibiotic exposure, while signatures of hospitalization may persist for at least six months after hospital discharge.
Most Hospitalized Patients with Significant Tricuspid Regurgitation Have Advanced Disease Preventing Transcatheter Tricuspid Valve Intervention
Authors: Dumitriu Carcoana Allison O. , Scoma Christopher B. , Maletz Sebastian N. , Malavet Jose A. , Crousillat Daniela R. , Matar Fadi A. 
Published: September 2023

ABSTRACTBackgroundMore than moderate tricuspid regurgitation (TR) is associated with high mortality. Surgical tricuspid valve repair and replacements are rarely performed due to high operative mortality risk, mainly attributed to late presentation. Novel transcatheter tricuspid valve intervention (TTVI) devices are being developed as an alternative to surgery. The population of patients presenting to tertiary care centers who can benefit from TTVI has not been well defined. Our objective was to study the characteristics of these patients and to define the sub-population who may be candidates for TTVI.MethodsWe analyzed 12,677 consecutive 2D echocardiograms completed at our tertiary care center between March 2021 and March 2022 and identified 581 inpatients with more than moderate TR. Clinical and echocardiographic data were collected by individual chart review. The 2021 European Society of Cardiology (ESC) guidelines on the management of valvular disease are currently the only guidelines that include TTVI. We used these guidelines to retrospectively assign the 581 patients to medical therapy, surgery, or TTVI treatment.Results470 patients (80.9%) were assigned to medical therapy, 57 (9.8%) were assigned to TTVI, and 54 (9.3%) were assigned to tricuspid valve surgery. Of note, 76.2% (443/581) of patients were precluded from any intervention due to advanced disease or anatomic ineligibility, and only 4.6% (27/581) presented too early for intervention, being both asymptomatic and without RV dilatation.ConclusionOnly 9.8 percent of patients presenting to a tertiary care center with significant TR would be hypothetical candidates for TTVI when these technologies are approved in the United States. Earlier identification and treatment of TR could increase the number of patients who may benefit from interventions including TTVI.CLINICAL PERSPECTIVEWhat is New?Most patients presenting to a tertiary care center with significant TR are too advanced for any intervention and have high in-hospital and late mortality rates.Tertiary care centers planning to offer TTVI should expect that, based on the current ESC guidelines and late presentation of TR, approximately 10% of patients with more than moderate TR will be TTVI candidates.What are the clinical implications?There is a need to improve care for patients with TR through earlier detection and referral for intervention.Future clinical trials with longer follow up will be needed to assess TTVI devices in patients at earlier or even asymptomatic stages of TR when there may be a greater likelihood of improving survival.
Perioperative risk of non-cardiac surgery in patients with asymptomatic significant aortic stenosis: a 10-year retrospective study
Authors: Bak Minjung , Lee Seung-Hwa , Park Sung-Ji , Park Jungchan , Kim Jihoon , Kim Darae , Kim Eun Kyoung , Chang Sung-A , Lee Sang-Chol , Park Seung Woo 
Published: September 2023

ABSTRACTBACKGROUNDAortic valve stenosis (AS) is a representative geriatric disease, and there is an anticipated rise in the number of patients requiring non-cardiac surgeries in patients with AS. However, there is still a lack of research on the primary predictors of non-cardiac perioperative complications in patients with asymptomatic significant AS.METHOD AND RESULTAmong the cohort of non-cardiac surgeries under general anesthesia, with an intermediate-to-high risk of surgery from 2011 to 2019, at Samsung Medical Center, 221 patients were identified to have asymptomatic significant AS. First, to examine the impact of significant AS on perioperative adverse events, the occurrences of Major Adverse Cardiovascular Events (MACE) and Perioperative Adverse Cardiovascular Events (PACE) were compared between patients with asymptomatic significant AS and the control group. Second, to identify the factors influencing the perioperative adverse events in patients with asymptomatic significant AS, multivariable logistic regression model was used.There was no significant difference between the three groups consisting of control group, moderate asymptomatic AS group, and severe asymptomatic AS group in the event rate of MACE (p value 0.971) and PACE (p value 0.185). Cardiac damage stage was significant risk factor of MACE (OR 9.241, 95% CI: 1.314 - 64.976, p-value = 0.025) and PACE (OR 2.199, 95% CI: 1.055 - 4.584, p-value = 0.036).CONCLUSIONThere was no significant difference in major post-operative cardiovascular events between patients with asymptomatic significant AS and the control group. Advanced cardiac damage stage in significant AS is a key factor in perioperative risk of non-cardiac surgery.CLINICAL PERSPECTIVEWhat Is New?Advanced cardiac damage is a major risk factor for perioperative complications in non-cardiac surgeries of patients with asymptomatic significant AS.What Are the Clinical Implications?Patients with asymptomatic significant AS showed no significant difference in perioperative complications compared to the control group, but caution is needed in those with advanced cardiac damage, as they are more likely to experience adverse events.
Fully addressable, designer superstructures assembled from a single modular DNA origami
Authors: Weck Johann M. , Heuer-Jungemann Amelie 
Published: September 2023

ABSTRACTIntricate self-organization is essential in many biological processes, underpinning vital functions and interactions. In an effort to mimic such processes, synthetic biology aims to engineer dynamic structures with controllable functions using nanotechnological tools. A key requirement of engineered building blocks is the ability to assemble and disassemble hierarchically with precision. Using the DNA origami technique, we here present the moDON, a modular DNA origami nanostructure, which is capable of assembling into almost 20 000 diverse monomers, forming complex and controlled superstructures in three dimensions. While shape and addressability of DNA origami are nearly arbitrary, its overall size is limited by the scaffold size. Previous methods of extending the size of DNA origami (e.g. hierarchical assembly, modified scaffolds, etc.), either led to loss over control of shape and addressability beyond monomers or to proportionally increased cost and design effort. With the moDON we were able to overcome both issues. The modular design combines xy- and z-plane assembly methods, enabling the construction of finite and periodic structures beyond 1 GDa. We demonstrate xy-z orthogonality, by enabling controlled selective or parallel assembly and disassembly via distinct orthogonal triggers. The kinetic profile of assembly and disassembly aligns with biological time scales, paving the way for applications in dynamic nanomachinery and advanced biomaterials. Finally, we showcase the conjugation of gold nanoparticles to specific positions within superstructures, underscoring the efficacy of this approach for creating intricate and orthogonal nanoscale architectures with preserved site-specific addressability. The moDON thus offers an efficient, cost-effective solution for constructing large, precisely organized, and fully addressable structures with vast potential in synthetic cellular systems design.
Artificial neural networks for model identification and parameter estimation in computational cognitive models
Authors: Rmus Milena , Pan Ti-Fen , Xia Liyu , Collins Anne G. E. 
Published: September 2023

1AbstractComputational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best accounts of the data. Cognitive modeling uses statistical tools to quantitatively relate models to data that often rely on computing/estimating the likelihood of the data under the model. However, this likelihood is computationally intractable for a substantial number of models. These relevant models may embody reasonable theories of cognition, but are often under-explored due to the limited range of tools available to relate them to data. We contribute to filling this gap in a simple way using artificial neural networks (ANNs) to map data directly onto model identity and parameters, bypassing the likelihood estimation. We test our instantiation of an ANN as a cognitive model fitting tool on classes of cognitive models with strong inter-trial dependencies (such as reinforcement learning models), which offer unique challenges to most methods. We show that we can adequately perform both parameter estimation and model identification using our ANN approach, including for models that cannot be fit using traditional likelihood-based methods. We further discuss our work in the context of the ongoing research leveraging simulation-based approaches to parameter estimation and model identification, and how these approaches broaden the class of cognitive models researchers can quantitatively investigate.2Author SummaryComputational cognitive models occupy an important position in cognitive science research, as they offer a simple way of quantifying cognitive processes (such as how fast someone learns, or how noisy they are in choice selection), and testing which cognitive theories offer a better explanation of the behavior. To relate cognitive models to the behavioral data, researchers rely on statistical tools that require estimating the likelihood of observed data under the assumptions of the cognitive model. This is, however, not possible to do for all models as some models present significant challenges to likelihood computation. In this work, we use artificial neural networks (ANNs) to bypass likelihood computation and approximation altogether, and demonstrate the success of this approach applied to model parameter estimation and model comparison. The proposed method is a contribution to ongoing development of modeling tools which will enable cognitive researchers to test a broader range of theories of cognition.
Ellagic acid ameliorates atherosclerosis by targeting the epidermal growth factor receptor
Authors: Huang Ye-Wei , Wang Li-Tian , Yin Huai-Liu , Hu Dan-Dan , Sheng Jun , Wang Xuan-Jun 
Published: September 2023

BACKGROUNDHigh levels of plasma low-density lipoprotein (LDL) are a key risk factor for atherosclerosis. Low-density lipoprotein receptor (LDLR) mediates the degradation of plasma LDL. Therefore, it may be possible to prevent and treat atherosclerosis by increasing the levels of LDLR. The natural polyphenolic compound ellagic acid (EA) has various biological activities. In mice, EA alleviated the progression of atherosclerosis; however, the underlying mechanism remains unclear.METHODSMolecular interaction, cell, and animal experiments were used to explore the role and mechanism of EA in improving atherosclerosis.RESULTSEA binds to the extracellular domain of the epidermal growth factor receptor (EGFR), thus activating the EGFR-extracellular signal-regulated kinase (EGFR-ERK) signaling pathway, stabilizing LDLR mRNA, and promoting the expression of LDLR protein. The development of EA-loaded human serum albumin nanoparticles enabled intravenous administration in animal experiments.CONCLUSIONSThe research verified the in vivo effects of EA on the EGFR-ERK signaling pathway, LDLR levels, and atherosclerosis. EA may assist in the prevention and treatment of atherosclerosis.
CRISPR-Cas9 Engineered Extracellular Vesicles for the Treatment of Dominant Progressive Hearing Loss
Authors: Pan Xiaoshu , Huang Peixin , Ali Samantha S. , Renslo Bryan , Hutchinson Tarun E , Erwin Nina , Greenberg Zachary , Ding Zuo , Li Yanjun , Warnecke Athanasia , Fernandez Natalia E. , Staecker Hinrich , He Mei 
Published: September 2023

AbstractClinical translation of gene therapy has been challenging, due to limitations in current delivery vehicles such as traditional viral vectors. Herein, we report the use of gRNA:Cas9 ribonucleoprotein (RNP) complexes engineered extracellular vesicles (EVs) forin vivogene therapy. By leveraging a novel high-throughput microfluidic droplet-based electroporation system (μDES), we achieved 10-fold enhancement of loading efficiency and more than 1000-fold increase in processing throughput on loading RNP complexes into EVs (RNP-EVs), compared with conventional bulk electroporation. The flow-through droplets serve as enormous bioreactors for offering millisecond pulsed, low-voltage electroporation in a continuous-flow and scalable manner, which minimizes the Joule heating influence and surface alteration to retain natural EV stability and integrity. In the Shaker-1 mouse model of dominant progressive hearing loss, we demonstrated the effective delivery of RNP-EVs into inner ear hair cells, with a clear reduction ofMyo7ash1mRNA expression compared to RNP-loaded lipid-like nanoparticles (RNP-LNPs), leading to significant hearing recovery measured by auditory brainstem responses (ABR).One sentence summaryThe scalable microfluidic electroporation system enables the loading of gRNA:Cas9 ribonucleoprotein (RNP) complexes into extracellular vesicles, which leads to clinical translation potential employed in hearing disease gene therapy.
Low-latency extracellular spike assignment for high-density electrodes at single-neuron resolution
Authors: Lai Chongxi , Kim Dohoung , Lustig Brian , Tanaka Shinsuke , Barbarits Brian , Narayan Lakshmi , Colonell Jennifer , Paulsen Ole , Lee Albert K. , Harris Timothy D. 
Published: September 2023

AbstractReal-time neural signal processing is essential for brain-machine interfaces and closed-loop neuronal perturbations. However, most existing applications sacrifice cell-specific identity and temporal spiking information for speed. We developed a hybrid hardware-software system that utilizes a Field Programmable Gate Array (FPGA) chip to acquire and process data in parallel, enabling individual spikes from many simultaneously recorded neurons to be assigned single-neuron identities with 1-millisecond latency. The FPGA assigns labels, validated with ground-truth data, by comparing multichannel spike waveforms from tetrode or silicon probe recordings to a spike-sorted model generated offline in software. This platform allowed us to rapidly inactivate a region in vivo based on spikes from an upstream neuron before these spikes could excite the downstream region. Furthermore, we could decode animal location within 3 ms using data from a population of individual hippocampal neurons. These results demonstrate our system’s suitability for a broad spectrum of research and clinical applications.
Neurodegeneration is strongly linked to heart failure severity and outcomes: framing the cardiocerebral syndrome
Authors: Wurm Raphael , Prausmüller Suriya , Ponleitner Markus , Spinka Georg , Weidenhammer Annika , Arfsten Henrike , Heitzinger Gregor , Panagiotides Noel Gilian , Bartko Philipp , Goliasch Georg , Stögmann Elisabeth , Hengstenberg Christian , Hülsmann Martin , Pavo Noemi 
Published: September 2023

ABSTRACTBackground and ObjectivesCognitive impairment is prevalent in patients with heart failure with reduced ejection fraction (HFrEF), affecting self-care and outcomes. Novel blood-based biomarkers have emerged as potential diagnostic tools for neurodegeneration. This study aimed to assess neurodegeneration in HFrEF by measuring neurofilament light chain (NfL), total tau (t-tau), amyloid-beta 42 (Aβ42), and 40 (Aβ40) in a large, well-characterised cohort.MethodsThe study included 470 HFrEF patients from a biobank-linked prospective registry at the Medical University of Vienna. High-sensitivity single-molecule assays were used for measurement. Unplanned hospitalisations and all-cause death were recorded as outcome parameters.ResultsAll markers, but not the Aβ42/Aβ40 ratio, correlated with heart failure (HF) severity, i.e. NTproBNP and NYHA class, comorbidity burden and were significantly associated with all-cause death and HF-hospitalisations [crude HR for 1-log unit increase (95%CI): 4.44 (3.02-6.53), 5.04 (2.97-8-58), 3.90 (2.27-6.72) and 5.14 (2.84-9.32) for all-cause death and 2.48 (1.60-3.85), 3.44 (1.95-6.04), 3.13 (1.84-5.34) and 3.48 (1.93-6.27) for HHF, p<0.001 for all]. These markers remained significant after adjustment in multivariate models including NT-proBNP. NfL and t-tau showed the highest prognostic ability in the receiver operating characteristic analysis [AUC: 0.72, 0.68, 0.66, 0.67 for NfL, t-tau, Aβ40 and Aβ42, respectively]. The performance of NfL was comparable to that of NT-proBNP [C-index: 0.70 vs 0.72, p=0.225].ConclusionsNeurodegeneration is directly interwoven with the progression of HF. Biomarkers, particularly NfL, may help identify patients profiting from detailed neurological workups. Further research is necessary to test if early diagnosis or optimised HFrEF treatment can preserve cognitive function.
Tobacco Dependency in the United Kingdom: Prevalence, Risk Factors, Interventions, Disparities, and Policy Implications
Authors: Weerakoon Tharumini 
Published: September 2023

AbstractTobacco dependency remains a persistent global public health challenge, with significant implications for morbidity, mortality, and healthcare costs. This systematic review presents a comprehensive analysis of tobacco dependency studies conducted in the United Kingdom (UK). The objective is to distill existing research to inform evidence-based interventions and policies targeting this pressing issue.The primary purpose of this systematic review is to provide an up-to-date synthesis of the current state of tobacco dependency in the UK. By examining a wide range of studies, we aim to identify trends, risk factors, and effective interventions, ultimately contributing to the development of targeted strategies for reducing tobacco use in the UK population.A rigorous systematic search across various academic databases yielded a substantial body of evidence encompassing diverse study designs, including epidemiological surveys, qualitative investigations, and intervention trials. Studies meeting predefined inclusion criteria were systematically reviewed and assessed for quality. Key themes explored include the prevalence of tobacco dependency, associated risk factors, intervention effectiveness, and disparities across demographic groups.Our review highlights several critical findings:The prevalence of tobacco dependency in the UK has declined over the past decade, but challenges persist, particularly among vulnerable populations.Multiple risk factors, including socioeconomic status, mental health conditions, and peer influences, contribute to tobacco dependency in the UK.Evidence supports the effectiveness of various interventions, including smoking cessation programs, taxation policies, and public awareness campaigns.Disparities in tobacco dependency exist along demographic lines, necessitating targeted strategies to address these inequalities.Understanding the complex landscape of tobacco dependency in the UK is imperative for informing public health policies and interventions. This systematic review underscores the need for continued efforts to reduce tobacco use, particularly among disadvantaged groups. The identified risk factors and effective interventions provide valuable insights for designing evidence-based strategies to combat tobacco dependency and its associated health and economic burdens. Addressing these challenges is essential for improving the overall well-being and health equity of the UK population.
Transcriptomic signatures of Aβ- and tau-induced neuronal dysfunction reveal inflammatory processes at the core of Alzheimer’s disease pathophysiology
Authors: Sanchez-Rodriguez Lazaro M. , Khan Ahmed F. , Adewale Quadri , Bezgin Gleb , Therriault Joseph , Fernandez-Arias Jaime , Servaes Stijn , Rahmouni Nesrine , Tissot Cécile , Stevenson Jenna , Jiang Hongxiu , Chai Xiaoqian , Carbonell Felix , Rosa-Neto Pedro , Iturria-Medina Yasser 
Published: September 2023

AbstractMolecular mechanisms enabling pathology-induced neuronal dysfunction in Alzheimer’s disease (AD) remain elusive. Here, we use mechanistic computational models to infer the combined influence of PET-measured Aβ and tau burdens on fMRI-derived neuronal activity and to subsequently identify the transcriptomic spatial correlates of AD pathophysiology. Our results reveal overrepresented genes and biological processes that participate in synaptic degeneration and interact with Aβ and tau deposits. Furthermore, we confirmed the central role of the immune system and neuroinflammatory pathways within AD pathogenesis; microglia were significantly enriched in the gene set associated with Aβ and tau synergistic influences on neuronal activity. Lastly, our computational approach unveiled drug candidates with the potential to halt or reduce the observed pathological effects on neuronal activity, including existing medication for cancer, immune disorders, and cardiovascular diseases, many currently under clinical evaluation in AD. Overall, these findings support the notion that the AD brain experiences functional changes intricately associated with a diverse spectrum of molecular processes.