bluuebunny/crossref_metadata_2025 huggingface dataset:
Examining the effect of information channel on COVID-19 vaccine acceptance
Authors: Piltch-Loeb R. , Savoia E. , Goldberg B. , Hughes B. , Verhey T. , Kayyem J. , Miller-Idriss C. , Testa MA.
Published: January 2021

Hesitancy towards the COVID-19 vaccine remains high among the US population. Now that the vaccine is available to priority populations, it is critical to convince those that are hesitant to take the vaccine. Public health communication about the vaccine as well as misinformation on the vaccine occurs through a variety of different information channels. Some channels of information are more commonly found to spread misinformation. Given the expansive information environment, we sought to characterize the use of different media channels for COVID-19 vaccine information and determine the relationship between information channel and vaccine acceptance. We conducted a convenience sample of vaccine priority groups (N=2,650) between December 13 and 23, 2020 and conducted bivariate chi-squared tests and multivariable multinomial logistic regression analyses to determine the relative impact of channels of information on vaccine acceptance. We found traditional channels of information, especially National TV, National newspapers, and local newspapers increased the relative risk of vaccine acceptance. Individuals who received information from traditional media compared to social media or both traditional and social media were most likely to accept the vaccine. The implications of this study suggest social media channels have a role to play in educating the hesitant to accept the vaccine, while traditional media channels should continue to promote data-driven and informed vaccine content to their viewers.
Analysis of communities of countries with similar dynamics of the COVID-19 pandemic evolution
Authors: Alvarez Emiliano , Brida Juan Gabriel , Limas Erick , Rosich Lucia
Published: January 2021

This work addresses the spread of the coronavirus through a non-parametric approach, with the aim of identifying communities of countries based on how similar their evolution of the disease is. The analysis focuses on the number of daily new COVID-19 cases per ten thousand people during a period covering at least 250 days after the confirmation of the tenth case. Dynamic analysis is performed by constructing Minimal Spanning Trees (MST) and identifying groups of similarity in contagions evolution in 95 time windows of a 150-day amplitude that moves one day at a time. The number of times countries belonged to a similar performance group in constructed time windows was the intensity measure considered. Groups’ composition is not stable, indicating that the COVID-19 evolution needs to be treated as a dynamic problem in the context of complex systems. Three communities were identified by applying the Louvain algorithm. Identified communities analysis according to each country’s socioeconomic characteristics and variables related to the disease sheds light on whether there is any suggested course of action. Even when strong testing and tracing cases policies may be related with a more stable dynamic of the disease, results indicate that communities are conformed by countries with diverse characteristics. The best option to counteract the harmful effects of a pandemic may be having strong health systems in place,with contingent capacity to deal with unforeseen events and available resources capable of a rapid expansion of its capacity.
Enhancing Cognitive Restructuring with Concurrent Repetitive Transcranial Magnetic Stimulation: A Transdiagnostic Randomized Controlled Trial
Authors: Neacsiu Andrada D. , Beynel Lysianne , Powers John P. , Szabo Steven T. , Appelbaum Lawrence G. , Lisanby Sarah H. , LaBar Kevin S.
Published: January 2021

Introduction

Emotional dysregulation constitutes a serious public health problem in need of novel transdiagnostic treatments.

Objective

To this aim, we developed and tested a one-time intervention that integrates behavioral skills training with concurrent repetitive transcranial magnetic stimulation (rTMS).

Methods

Forty-six adults who met criteria for at least one DSM-5 disorder and self-reported low use of cognitive restructuring (CR) were enrolled in a randomized, double-blind, sham-controlled trial that used a between-subjects design. Participants were taught CR and underwent active rTMS applied at 10 Hz over the right (n= 17) or left (n= 14) dorsolateral prefrontal cortex (dlPFC) or sham rTMS (n= 15) while practicing reframing and emotional distancing in response to autobiographical stressors.

Results

Those who received active left or active right as opposed to sham rTMS exhibited enhanced regulation (ds = 0.21 - 0.62) as measured by psychophysiological indices during the intervention (higher high-frequency heart rate variability, lower regulation duration). Those who received active rTMS over the left DLPFC also self-reported reduced distress througout the intervention (d= 0.30), higher likelihood to use CR, and lower daily distress during the week following the intervention. The procedures were acceptable and feasible with few side effects.

Conclusions

These findings show that engaging frontal circuits simultaneously with cognitive skills training and rTMS may be clinically feasible, well-tolerated and may show promise for the treatment of transdiagnostic emotional dysregulation. Larger follow up studies are needed to confirm the efficacy of this novel therapeutic approach.
Evaluation of crowdsourced mortality prediction models as a framework for assessing AI in medicine
Authors: Bergquist Timothy , Schaffter Thomas , Yan Yao , Yu Thomas , Prosser Justin , Gao Jifan , Chen Guanhua , Charzewski Łukasz , Nawalany Zofia , Brugere Ivan , Retkute Renata , Prusokas Alidivinas , Prusokas Augustinas , Choi Yonghwa , Lee Sanghoon , Choe Junseok , Lee Inggeol , Kim Sunkyu , Kang Jaewoo ,   Patient Mortality Prediction DREAM Challenge Consortium, Mooney Sean D. , Guinney Justin
Published: January 2021

Applications of machine learning in healthcare are of high interest and have the potential to significantly improve patient care. Yet, the real-world accuracy and performance of these models on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate different methods that predict healthcare outcomes. To overcome patient privacy concerns, we employed a Model-to-Data approach, allowing citizen scientists and researchers to train and evaluate machine learning models on private health data without direct access to that data. We focused on the prediction of all-cause mortality as the community challenge question. In total, we had 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries. The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI 0.942, 0.951) and an area under the precision-recall curve of 0.487 (95% CI 0.458, 0.499) on patients prospectively collected over a one year observation of a large health system. Post-hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data and have similar accuracy on the population. This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.
Model design for non-parametric phylodynamic inference and applications to pathogen surveillance
Authors: Didelot Xavier , Geidelberg Lily ,   The COVID-19 Genomics UK (COG-UK) consortium, Volz Erik M
Published: January 2021

Inference of effective population size from genomic data can provide unique information about demographic history, and when applied to pathogen genetic data can also provide insights into epidemiological dynamics. The combination of non-parametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for non-parametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on non-parametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. We demonstrate the flexibility and speed of this approach in a series of simulation experiments, and apply the methodology to reconstruct the previously described waves in the seventh pandemic of cholera. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.
Neuroprotective derivatives of tacrine that target NMDA receptor and acetyl cholinesterase - Design, synthesis and biological evaluation
Authors: Remya Chandran , Dileep K.V. , Reddy Eeda Koti , Mantosh Kumar , Lakshmi Kesavan , Jacob Reena Sarah , Sajith Ayyiliyath M , Variyar E.Jayadevi , Anwar Shaik , Zhang Kam Y. J. , Sadasivan C. , Omkumar R. V.
Published: January 2021

The complex and multifactorial nature of neuropsychiatric diseases demands multi-target drugs that can intervene with various sub-pathologies underlying disease progression. Targeting the impairments in cholinergic and glutamatergic neurotransmissions with small molecules has been suggested as one of the potential disease-modifying approaches for Alzheimer’s disease (AD). Tacrine, apotent inhibitor of acetylcholinesterase (AChE) is the first FDA approved drug for the treatment of AD. Tacrine is also a low affinity antagonist of N-methyl-D-aspartate receptor (NMDAR). However, tacrine was withdrawn from its clinical use later due to its hepato-toxicity. With an aim to develop novel high affinity multi-target directed ligands (MTDLs) against AChE and NMDAR, with reduced hepatotoxicity, we performedin silicostructure-based modifications on tacrine, chemical synthesis of the derivatives andin vitrovalidation of their activities. Nineteen such derivatives showed inhibition with IC50values in the range of 18.53±2.09 to 184.09±19.23 nM against AChE and 0.27±0.05 to 38.84±9.64 μM against NMDAR. Some of the selected compounds also protected rat primary cortical neurons from glutamate induced excitotoxicity. Two of the tacrine derived MTDLs, 201 and 208 exhibitedin vivoefficacy in rats by protecting against behavioral impairment induced by administration of the excitotoxic agent, monosodium glutamate. Additionally, several of these synthesized compounds also exhibited promising inhibitory activities against butyrylcholinesterase and β-secretase. Given the therapeutic potential of MTDLs in disease-modifying therapy, our studies revealed several promising MTDLs of which 201 appears to be a potential candidate for immediate preclinical and clinical evaluations.
Diel transcriptional pattern contributes to functional and taxonomic diversity in supraglacial microbial communities
Authors: Pittino Francesca , Zordan Simone , Azzoni Roberto S. , Diolaiuti Guglielmina , Ambrosini Roberto , Franzetti Andrea
Published: January 2021

Despite the harsh environmental conditions, glacier surfaces host metabolically active bacterial communities, especially in cryoconite holes, small ponds filled with melting water and with a fine-grained sediment at the bottom. We investigated the daily changes in transcript profiles of the microbial community of a cryoconite hole on an Alpine glacier. Using a metatranscriptomic shotgun sequencing, we observed different level of expression of the main carbon and energy metabolisms along the day. Oxygenic and anoxygenic photosynthesis peaked their activity at the sunrise and sunset, respectively, and showed an inhibition at midday, in response to high solar radiation. Carbon fixation genes were expressed all day long with the lowest coverage at night. Different microbial populations were responsible for this metabolic function along the day. Cyanobacteria and Algae were the most active primary producers at the sunrise and the sunset, whereas at night and at noon chemosynthetic proteobacteria, likely hydrogen oxidisers, were most active. Furthermore, the observed temporal cascade of transcript peaks of photosynthesis and respiration recalls those occurring in both coastal and open waters in ocean, thus supporting the hypothesis that conserved temporally phased biotic interactions are ubiquitous among aquatic communities worldwide.
Learning to see colours: generating biologically relevant fluorescent labels from bright-field images
Authors: Wieslander Håkan , Gupta Ankit , Bergman Ebba , Hallström Erik , Harrison Philip J
Published: January 2021

Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images would get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images to enable virtual staining for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key features required for downstream analysis of these channels.
miRge3.0: a comprehensive microRNA and tRF sequencing analysis pipeline
Authors: Patil Arun H. , Halushka Marc K.
Published: January 2021

MicroRNAs and tRFs are classes of small non-coding RNAs, known for their roles in translational regulation of genes. Advances in next-generation sequencing (NGS) have enabled high-throughput small RNA-seq studies, which require robust alignment pipelines. Our laboratory previously developed miRge and miRge2.0, as flexible tools to process sequencing data for annotation of miRNAs and other small-RNA species and further predict novel miRNAs using a support vector machine approach. Although, miRge2.0 is a leading analysis tool in terms of speed with unique quantifying and annotation features, it has a few limitations. We present miRge3.0 which provides additional features along with compatibility to newer versions of Cutadapt and Python. The revisions of the tool include the ability to process Unique Molecular Identifiers (UMIs) to account for PCR duplicates while quantifying miRNAs in the datasets and an accurate GFF3 formatted isomiR tool. miRge3.0 also has speed improvements benchmarked to miRge2.0, Chimira and sRNAbench. Finally, miRge3.0 output integrates into other packages for a streamlined analysis process and provides a cross-platform Graphical User Interface (GUI). In conclusion miRge3.0 is our 3rdgeneration small RNA-seq aligner with improvements in speed, versatility, and functionality over earlier iterations.
Untargeted metabolomic analyses reveal the diversity and plasticity of the specialized metabolome in seeds of differentCamelina sativagenotypes
Authors: Boutet Stéphanie , Barreda Léa , Perreau François , Totozafy Jean-Chrisologue , Mauve Caroline , Gakière Bertrand , Delannoy Etienne , Martin-Magniette Marie-Laure , Monti Andrea , Lepiniec Loïc , Zanetti Federica , Corso Massimiliano
Published: January 2021

Despite the essential role of Specialized Metabolites (SMs) in the interaction of plants with the environment, studying the ability of crop seeds to produce these protective compounds has been neglected. Furthermore, seeds produce a myriad of SMs providing an interesting model to investigate their diversity and plasticity.Camelina sativagained a lot of interest in the past few years as rustic oil seed crop. A characterization of seed SM landscapes in six camelina genotypes grown in the field and harvested during five growing seasons has been undertaken in this work. This allowed a comprehensive annotation of seed SMs combining analyses that cluster SMs based on their chemical structures and co-accumulation patterns. These data showed broad effects of the environment on the stimulation of the seed-specialized metabolome. Among well annotated compounds, flavonols were identified as the metabolic class characterised by high plasticity, revealing significant variable accumulation according to the year and/or the genotype. Lastly, a deeper characterisation of primary metabolites and lipids in two selected genotypes has been performed. We showed that, in addition to flavonols, alkaloids and glucosinolates displayed a higher phenotypic plasticity with respect to most of the primary metabolites, including some sugars and major storage compounds such as fatty acids, proteins and most lipid classes (e.g. DAG, TAG), but similar plasticity compared to free aminoacids and carboxylic acids. This work highlighted major and unexplored effects of the environment on the seed specialized metabolome demonstrating that seeds exhibit a dynamic and plastic metabolism, with an impact on seed quality.

Significance statement

Seeds produce a myriad of Specialized Metabolites (SMs) with an essential role in the interaction of plants with the environment. We characterized SM landscapes, primary metabolites and lipid composition in the seeds of camelina genotypes grown in the open field in five consecutive growing seasons. Our results showed the predominant effect of the environment on the regulation of the seed - specialized metabolome, with a potential impact on seed quality of camelina that may also occur in other oilseed crops.
Predicting genotype-specific gene regulatory networks
Authors: Weighill Deborah , Guebila Marouen Ben , Glass Kimberly , Quackenbush John , Platig John
Published: January 2021

Understanding how each person’s unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, eQTL data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses message passing to integrate this prior network with gene expression and TF protein-protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer GRNs for two blood-derived cell lines and identified genotype-associated, cell-line specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET GRNs for three cell types from each of 119 individuals and identified cell type-specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks that reflect individual genetic variation in a way that provides insight into genetic regulatory associations that drive complex phenotypes.

EGRET is available through the Network Zoo R package (netZooR v0.9;netzoo.github.io).
Highly accurate barcode and UMI error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing
Authors: Philpott Martin , Watson Jonathan , Thakurta Anjan , Brown Tom , Brown Tom , Oppermann Udo , Cribbs Adam P
Published: January 2021

Droplet-based single-cell sequencing techniques have provided unprecedented insight into cellular heterogeneities within tissues. However, these approaches only allow for the measurement of the distal parts of a transcript following short-read sequencing. Therefore, splicing and sequence diversity information is lost for the majority of the transcript. The application of long-read Nanopore sequencing to droplet-based methods is challenging because of the low base-calling accuracy currently associated with Nanopore sequencing. Although several approaches that use additional short-read sequencing to error-correct the barcode and UMI sequences have been developed, these techniques are limited by the requirement to sequence a library using both short- and long-read sequencing. Here we introduce a novel approach termed single-cell Barcode UMI Correction sequencing (scBUC-seq) to efficiently error-correct barcode and UMI oligonucleotide sequences synthesized by using blocks of dimeric nucleotides. The method can be applied to correct either short-read or long-read sequencing, thereby allowing users to recover more reads per cell and permits direct single-cell Nanopore sequencing for the first time. We illustrate our method by using species-mixing experiments to evaluate barcode assignment accuracy and evaluate differential isoform usage and fusion transcripts using myeloma and sarcoma cell line models.
Dermatan Sulfate Is a Potential Master Regulator of IgH via Interactions with Pre-BCR, GTF2I, and BiP ER Complex in Pre-B Lymphoblasts
Authors: Lee Jongmin , Rho Jung-hyun , Roehrl Michael H. , Wang Julia Y.
Published: January 2021

Dermatan sulfate (DS) and autoantigen (autoAg) complexes are capable of stimulating autoreactive CD5+ B-1 cells. We examined the activity of DS on CD5+ pre-B lymphoblast NFS-25 cells. CD19, CD5, CD72, PI3K, and Fas possess varying degrees of DS affinity. The three pre-BCR components, Ig heavy chain mu (IgH), VpreB, and lambda 5, display differential DS affinities, with IgH having the strongest affinity. DS attaches to NFS-25 cells, gradually accumulates in the ER, and eventually localizes to the nucleus. DS and IgH co-localize on the cell surface and in the ER. DS associates strongly with 17 ER proteins (e.g., BiP/Grp78, Grp94, Hsp90ab1, Ganab, Vcp, Canx, Kpnb1, Prkcsh, Pdia3), which points to an IgH-associated multiprotein complex in the ER. In addition, DS interacts with nuclear proteins (Ncl, Xrcc6, Prmt5, Eftud2, Supt16h) and Lck. We also discovered that DS binds GTF2I, a required gene transcription factor at theIgHlocus. These findings support DS as a potential master regulator of IgH in pre-B cells at protein and gene levels. We propose a (DS•autoAg)-autoBCR dual signal model in which an autoBCR is engaged by both autoAg and DS, and, once internalized, DS recruits a cascade of molecules that may help avert apoptosis and steer autoreactive B cell fate. Through its affinity with autoAgs and its control of IgH, DS emerges as a potential key player in the development of autoreactive B cells and autoimmunity.
Characterization of the nanomechanical properties of the fission yeast (Schizosaccharomyces pombe) cell surface by atomic force microscopy
Authors: Gibbs Ellie , Hsu Justine , Barth Kathryn , Goss John W.
Published: January 2021

Variations in cell wall composition and biomechanical properties can contribute to the cellular plasticity required during complex processes such as polarized growth and elongation in microbial cells. This study utilizes atomic force microscopy (AFM) to map the cell surface topography of fission yeast,Schizosaccharomyces pombe, at regions of active polarized growth and to characterize the biophysical properties within these regions under physiological, hydrated conditions. High-resolution images acquired from AFM topographic scanning reveal decreased surface roughness at actively growing cell poles. Force extension curves acquired by nanoindentation probing with AFM cantilever tips under low applied force revealed increased cell wall elasticity and decreased cellular stiffness (cellular spring constant) at cell poles (17 ± 4 mN/m) relative to the main body of the cell that is not undergoing growth and expansion (44 ± 10 mN/m). These findings suggest that the increased elasticity and decreased stiffness at regions undergoing polarized growth at fission yeast cell poles provide the plasticity necessary for cellular extension. This is the first direct biophysical characterization of theS. pombecell surface by AFM, and it provides a foundation for future investigation of how the surface topography and local nanomechanical properties vary during different cellular processes.
SARS-CoV-2 501Y.V2 escapes neutralization by South African COVID-19 donor plasma
Authors: Wibmer Constantinos Kurt , Ayres Frances , Hermanus Tandile , Madzivhandila Mashudu , Kgagudi Prudence , Oosthuysen Brent , Lambson Bronwen E. , de Oliveira Tulio , Vermeulen Marion , van der Berg Karin , Rossouw Theresa , Boswell Michael , Ueckermann Veronica , Meiring Susan , von Gottberg Anne , Cohen Cheryl , Morris Lynn , Bhiman Jinal N. , Moore Penny L.
Published: January 2021

SARS-CoV-2 501Y.V2 (B.1.351), a novel lineage of coronavirus causing COVID-19, contains substitutions in two immunodominant domains of the spike protein. Here, we show that pseudovirus expressing 501Y.V2 spike protein completely escapes three classes of therapeutically relevant antibodies. This pseudovirus also exhibits substantial to complete escape from neutralization, but not binding, by convalescent plasma. These data highlight the prospect of reinfection with antigenically distinct variants and foreshadows reduced efficacy of spike-based vaccines.
Assessment of Flourishing Levels of Individuals by Using Resting State fNIRS with Different Functional Connectivity Measures
Authors: Eken Aykut
Published: January 2021

Flourishing is an important criterion to assess wellbeing, however, controversies remain, particularly around assessing it with self-report measures. Due to this reason, to be able to understand the underlying neural mechanisms of well-being, researchers often utilize neuroimaging techniques. However, rather than individual answers, previous neuroimaging studies using statistical approaches provided an answer in average sense. To overcome these problems, we applied machine learning techniques to discriminate 43 highly flourishing from regular flourishing individuals by using a publicly available resting state functional near infrared spectroscopy (rs-fNIRS) dataset to get an answer in individual level. We utilized both Pearson’s correlation (CC) and Dynamic Time Warping (DTW) algorithm to estimate functional connectivity from rs-fNIRS data on temporo-parieto-occipital region as input to nine different machine learning algorithms. Our results revealed that by utilizing oxyhemoglobin concentration change with Pearson’s correlation (CC – ΔHbO) and deoxy hemoglobin concentration change with dynamic time warping (DTW – ΔHb), we could be able to classify flourishing individuals with 90 % accuracy with AUC 0.90 and 0.93 using nearest neighbor and Radial Basis Kernel Support Vector Machine. This finding suggests that temporo-parieto-occipital regional based resting state connectivity might be a potential biomarker to identify the levels of flourishing and using both connectivity measures might allow us to find different potential biomarkers.
Simultaneous Control of Infection and Inflammation by Keratin-Derived Antibacterial Peptides (KAMPs) Targeting TLRs and Co-Receptors
Authors: Sun Yan , Chan Jonathan , Bose Karthikeyan , Tam Connie
Published: January 2021

The use and the timing of initiation of steroids for controlling unwanted infectious inflammation are major clinical dilemmas due to their possible adverse effects including delayed microbial clearance and wound healing. Compounding this difficulty is the continued emergence of drug-resistant bacteria; yet anti-infective strategies aiming at augmenting inflammatory responses to facilitate bacterial killing cannot be used to treat infections involving vulnerable tissues. As is the case with bacterial keratitis, excessive inflammation jeopardizes corneal transparency leading to devastating vision loss. Hence, a two-pronged remedy possessing both anti-infective and anti-inflammatory properties would be helpful for tackling antibiotic resistance and enabling prompt inflammation control at once. Using murine primary neutrophils, macrophages and sterile corneal inflammation models, we found that non-toxic and pro-healing human keratin 6a-derived antimicrobial peptides (KAMPs) with a native 10-or 18-amino-acid sequence suppress LTA- and LPS-induced NF-кB and IRF3 activation, proinflammatory cytokine production, as well as phagocyte recruitment, independently of their bactericidal function. Mechanistically, direct binding of KAMPs to cell surface TLR2 and TLR co-receptors CD14 and MD-2 not only blocks their bacterial ligand docking sites, but also reduces cell surface availability of TLR2 and TLR4 through promotion of receptor endocytosis. Benefitting from the dual functions of topical KAMPs, experimental bacterial keratitis caused was effectively prevented or controlled, as evidenced by significant reductions of corneal opacification and inflammatory cell infiltration in addition to enhanced bacterial clearance. These findings reveal multiple TLR-targeting activities of KAMPs and demonstrate their therapeutic potential as a multifunctional drug for managing sterile and infectious inflammatory diseases.

One Sentence Summary

Bifunctional native keratin peptides allow concurrent alleviation of inflammation and infection to avoid functional damages in vulnerable tissues.
Inflammatory Cytokines Regulate T-Cell Development from Blood Progenitor Cells in a Stage and Dose-Specifc Manner
Authors: Edgar John M. , Zandstra Peter W.
Published: January 2021

T-cell development from hematopoietic stem and progenitor cells (HSPCs) is tightly regulated through Notch pathway activation by the Notch ligands Delta-like (DL) 1 and 4 and Jagged-2. Other molecules, such as stem cell factor (SCF), FMS-like tyrosine kinase 3 ligand (Flt3L) and interleukin (IL)-7, play a supportive role in regulating the survival, differentiation, and proliferation of developing progenitor (pro)T-cells. Numerous other signaling molecules are known to instruct T-lineage developmentin vivo, but little work has been done to optimize their use for T-cell productionin vitro. Using a defined T-lineage differentiation assay consisting of plates coated with the Notch ligand DL4 and adhesion molecule VCAM-1, we performed a cytokine screen that identified IL-3 and tumor necrosis factor α (TNFα) as enhancers of proT-cell differentiation and expansion. Mechanistically, we found that TNFα induced T-lineage differentiation through the positive regulation of T-lineage genesGATA3, TCF7, andBCL11b. TNFα also synergized with IL-3 to induce proliferation by upregulating the expression of the IL-3 receptor on CD34+HSPCs, yielding 753.2 (532.4-1026.9; 5-95 percentile)-fold expansion of total cells after 14 days compared to 8.9 (4.3-21.5)-fold expansion in conditions without IL-3 and TNFα. We then optimized cytokine concentrations for T-cell maturation. Focusing on T-cell maturation, we used quantitative models to optimize dynamically changing cytokine requirements and used these to construct a three-stage assay for generating CD3+CD4+CD8+and CD3+CD4−CD8+T-cells. Our work provides new insight into T-cell development and a robustin vitroassay for generating T-cells to enable clinical therapies for treating cancer and immune disorders.
Sodium channel Nav1.2-L1342P variant displaying complex biophysical properties renders hyperexcitability of cortical neurons derived from human iPSCs
Authors: Que Zhefu , Olivero-Acosta Maria I. , Zhang Jingliang , Eaton Muriel , Skarnes William C. , Yang Yang
Published: January 2021

With the wide adoption of whole-exome sequencing in children having seizures, an increasing number ofSCN2Avariants has been revealed as possible genetic causes of epilepsy. Voltage-gated sodium channel Nav1.2, encoded by geneSCN2A, is strongly expressed in the pyramidal excitatory neurons and supports action potential firing. One recurrentSCN2Avariant is L1342P, which was identified in multiple patients with early-onset encephalopathy and intractable seizures. Our biophysical analysis and computational modeling predicted gain-of-function features of this epilepsy-associated Nav1.2 variant. However, the mechanism underlying L1342P mediated seizures and the pharmacogenetics of this variant in human neurons remain unknown. To understand the core phenotypes of the L1342P variant in human neurons, we took advantage of a reference human induced pluripotent stem cell (hiPSC) line, in which L1342P was engineered by CRISPR/Cas9 mediated genome-editing. Using patch-clamping and micro-electrode array (MEA) recording, we found that the cortical neurons derived from hiPSCs carrying heterozygous L1342P variant presented significantly increased intrinsic excitability, higher sodium current density, and enhanced bursting and synchronous network firing, showing clear hyperexcitability phenotypes. Interestingly, the L1342P neuronal culture displayed a degree of resistance to the anti-seizure medication (phenytoin), which likely recapitulated aspects of clinical observation of patients carrying the L1342P variant. In contrast, phrixotoxin-3 (PTx3), a Nav1.2 isoform-specific blocker, was able to potently alleviate spontaneous and chemical-induced hyperexcitability of neurons carrying the L1342P variant. Our results reveal a possible pathogenic underpinning of Nav1.2-L1342P mediated epileptic seizures, and demonstrate the utility of genome-edited hiPSCs as anin vitroplatform to advance personalized phenotyping and drug discovery.
Factors associated with COVID-19 related hospitalisation, critical care admission and mortality using linked primary and secondary care data
Authors: Cummins Lisa , Ebyarimpa Irene , Cheetham Nathan , Brown Victoria Tzortziou , Brennan Katie , Panovska-Griffiths Jasmina
Published: January 2021

Background

To identify risk factors associated with increased risk of hospitalisation, intensive care unit (ICU) admission and mortality in inner North East London (NEL) during the first UK COVID-19 wave.

Methods

Multivariate logistic regression analysis on linked primary and secondary care data from people aged 16 or older with confirmed COVID-19 infection between 01/02/2020-30/06/2020 determined odds ratios (OR), 95% confidence intervals (CI) and p-values for the association between demographic, deprivation and clinical factors with COVID-19 hospitalisation, ICU admission and mortality.

Results

Over the study period 1,781 people were diagnosed with COVID-19, of whom 1,195 (67%) were hospitalised, 152 (9%) admitted to ICU and 400 (23%) died. Results confirm previously identified risk factors: being male, or of Black or Asian ethnicity, or aged over 50. Obesity, type 2 diabetes and chronic kidney disease (CKD) increased the risk of hospitalisation. Obesity increased the risk of being admitted to ICU. Underlying CKD, stroke and dementia in-creased the risk of death. Having learning disabilities was strongly associated with increased risk of death (OR=4.75, 95%CI=(1.91,11.84), p=0.001). Having three or four co-morbidities increased the risk of hospitalisation (OR=2.34,95%CI=(1.55,3.54),p<0.001;OR=2.40, 95%CI=(1.55,3.73), p<0.001 respectively) and death (OR=2.61, 95%CI=(1.59,4.28), p<0.001;OR=4.07, 95% CI= (2.48,6.69), p<0.001 respectively).

Conclusions

We confirm that age, sex, ethnicity, obesity, CKD and diabetes are important determinants of risk of COVID-19 hospitalisation or death. For the first time, we also identify people with learning disabilities and multi-morbidity as additional patient cohorts that need to be actively protected during COVID-19 waves.
Safety and immunogenicity of SARS-CoV-2 recombinant protein vaccine formulations in healthy adults: a randomised, placebo-controlled, dose-ranging study
Authors: Goepfert Paul A , Fu Bo , Chabanon Anne-Laure , Bonaparte Matthew I , Davis Matthew G , Essink Brandon J , Frank Ian , Haney Owen , Janosczyk Helene , Keefer Michael C , Koutsoukos Marguerite , Kimmel Murray A , Masotti Roger , Savarino Stephen J , Schuerman Lode , Schwartz Howard , Sher Lawrence D , Smith Jon , Tavares-Da-Silva Fernanda , Gurunathan Sanjay , DiazGranados Carlos A , De Bruyn Guy
Published: January 2021

Background

Effective vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently needed. CoV2 preS dTM is a stabilised pre-fusion S protein vaccine produced in a baculovirus expression system. We present interim safety and immunogenicity results of the first-in-human study of the CoV2 preS dTM vaccine with two different adjuvant formulations.

Methods

This Phase I/II, randomised, double-blind study (NCT04537208) is being conducted in healthy, SARS-CoV-2-seronegative adults in the USA. Participants were stratified by age (18–49 and ≥50 years) and randomised to receive one (on Day[D]1) or two doses (D1, D22) of placebo or candidate vaccine, containing: low-dose (LD, effective dose 1.3 µg) or high-dose (HD, 2.6 µg) antigen with adjuvant AF03 (Sanofi Pasteur) or AS03 (GlaxoSmithKline); or unadjuvanted HD (18–49 years only). Safety was assessed up to D43. SARS-CoV-2 neutralising and binding antibody profiles were assessed in D1, D22 and D36 serum samples.

Findings

The interim safety analyses included 439/441 randomised participants. There were no related unsolicited immediate AEs, serious AEs, medically attended AEs classified as severe, or AE of special interest. More grade 3 solicited reactions were reported than expected after the second dose in the adjuvanted vaccine groups. Neutralising and binding antibody responses after two vaccine doses were higher in adjuvanted versus unadjuvanted groups, in AS03-versus AF03-adjuvanted groups, in HD versus LD groups, and in younger versus older age strata.

Interpretation

The lower than expected immune responses, especially in the older age stratum, and the higher than anticipated reactogenicity post dose 2 were likely due to a higher than anticipated host cell protein content and lower than planned antigen dose in the clinical material. Further development of the AS03-adjuvanted candidate vaccine will focus on identifying the optimal antigen formulation and dose.
RPE65-related retinal dystrophy: mutational and phenotypic spectrum in 45 affected patients
Authors: Lopez-Rodriguez R , Lantero E , Blanco-Kelly F , Avila-Fernandez A , Martin Merida I , del Pozo-Valero M , Perea-Romero I , Zurita O , Jiménez-Rolando B , Swafiri ST , Riveiro-Alvarez R , Trujillo-Tiebas MJ , Carreño Salas E , García-Sandoval B , Corton M , Ayuso C
Published: January 2021

Background

Biallelic pathogenicRPE65variants are related to a spectrum of clinically overlapping inherited retinal dystrophies (IRD). Most affected individuals show a severe progression, with 50% of patients legally blind by 20 years of age. A better knowledge of the mutational spectrum and the phenotype-genotype correlation inRPE65-related IRD is needed.

Methods

Forty-five affected subjects from 27 unrelated families with a clinical diagnosis ofRPE65-related IRD were included. Clinical evaluation consisted on self-reported ophthalmological history and objective ophthalmological examination. Patients’ genotype was classified accordingly to variant class (truncating or missense) or to variant location at different protein domains. Main phenotypic outcome was age at onset (AAO) of the symptomatic disease and a Kaplan–Meier analysis of disease symptom event-free survival was performed.

Results

Twenty-nine differentRPE65variants were identified in our cohort, 7 of them novel. Most frequent variants were p.(Ile98Hisfs*26), p.(Pro111Ser) and p.(Gly187Glu) accounting for the 24% of the detected alleles. Patients carrying two missense alleles showed a later disease onset than those with 1 or 2 truncating variants (Log Rank test p<0.05). While the 60% of patients carrying a missense/missense genotype presented symptoms before or at the first year of life, almost all patients with at least 1 truncating allele (91%) had an AAO ≤1 year (p<0.05).

Conclusion

Our findings suggest an association between the type of theRPE65carried variant and the AAO. Thus, our results provide useful data onRPE65-associated IRD phenotypes which may help to improve clinical and therapeutic management of these patients.
Economic Burden of Periodontal Disease in Europe and the United States of America – An updated forecast
Authors: Botelho João , Proença Luís , Leira Yago , Chambrone Leandro , Mendes José João , Machado Vanessa
Published: January 2021

Introduction

Periodontal disease is a pandemic condition and its severe form affects approximately 10% of the global population. Here, we present a forecast for the economic burden of periodontal disease in 32 European countries and in the United States of America (USA).

Material and methods

In an aggregate population-based cost analysis, taken as reference the most recent available data, we estimated the cost of periodontal disease. Under this, global health, dental and periodontal expenditures were estimated. Additionally, indirect estimates accounted for Disability-Adjusted Life Years (DALY) valued at per capita Gross Domestic Product (GDP), to estimate productivity losses, including periodontal disease, edentulism due to periodontal disease and caries due to periodontal disease.

Results

In 2018 the aggregate cost in Europe was estimated at €17.00B and €2.35B more in the USA (€19.35B). Indirect costs due to periodontal disease amounted to €132.52B in European countries and €103.30B in the USA. The majority of the projected indirect costs were due to edentulism related to periodontal disease and periodontal disease. Indirect costs were the major portion of the estimated economic impact with an average of 0.66% of GDP in Europe and 0.50% in the USA. For the overall costs (direct and indirect), the value amounted to 0.75% of GDP in Europe and 0.60% in the USA.

Conclusion

Periodontal disease caused a €149.52B loss in Europe and €122.65B in the USA, in 2018. These results show that the economic burden of periodontal disease is increasing.

CLINICAL RELEVANCE

Scientific rationale for the study

Considering the pandemic pattern of periodontal diseases we present a forecast for the economic burden of periodontal disease in 32 European countries and in the United States of America (USA).

Principal findings

Periodontal disease caused a €149.52B loss in Europe and €122.65B in the USA, in 2018. For the overall costs (direct and indirect), the value amounted to 0.75% of GDP in Europe and 0.60% in the USA.

Practical implications

These results show that the economic burden of periodontal disease is increasing.
The Impact of U.S. County-Level Factors on COVID-19 Morbidity and Mortality
Authors: Itzhak Nevo , Shahar Tomer , Moskovich Robert , Shahar Yuval
Published: January 2021

Background

The effect of socioeconomic factors, ethnicity, and other variables, on the frequency of COVID-19 cases [morbidity] and induced deaths [mortality] atsub-population, rather than atindividuallevels, is only partially understood.

Objective

To determine which county-level features best predict COVID-19 morbidity and mortality for a given county in the U.S.

Design

A Machine-Learning model that predicts COVID-19 mortality and morbidity using county-level features, followed by a SHAP-values-based importance analysis of the predictive features.

Setting

Publicly available data from various American government and news websites.

Participants

3,071 U.S. counties, from which 53 county-level features, as well as morbidity and mortality numbers, were collected.

Measurements

For each county: Ethnicity, socioeconomic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, air quality indicators, presidential election results, ICU beds.

Results

A Random Forest classifier produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP-values-based analysis indicated that poverty rate, obesity rate, mean commute time to work, and proportion of people that wear masks significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels, heavily influenced mortality rates. The correlation between several of these factors and COVID-19 morbidity and mortality, from 4/2020 to 11/2020 shifted, probably due to COVID-19 being initially associated with more urbanized areas, then with less urbanized ones.

Limitations

Data are still coming in.

Conclusions

Ethnicity, education, and economic disparity measures are major factors in predicting the COVID-19 mortality rate in a county. Between-counties low-variance factors (e.g., age), are not meaningful predictors.

Differing correlations can be explained by the COVID-19 spread from metropolitan to less metropolitan areas.

Primary Funding Source

None.