bluuebunny/crossref_metadata_2025_split huggingface dataset:
Systematic review of the prevalence of Long Covid
Authors: Woodrow Mirembe , Carey Charles , Ziauddeen Nida , Thomas Rebecca , Akrami Athena , Lutje Vittoria , Greenwood Darren C , Alwan Nisreen A 
Published: November 2022

SummaryBackgroundLong Covid occurs in those infected with SARSCoV2 whose symptoms persist or develop beyond the acute phase. We conducted a systematic review to determine the prevalence of persistent symptoms, functional disability or pathological changes in adults or children at least 12 weeks post- infection.MethodsWe searched key registers and databases from 1stJanuary 2020 to 2ndr 2021, limited to publications in English and studies with at least 100 participants. Studies where all participants were critically ill were excluded. Long Covid was extracted as prevalence of at least one symptom or pathology, or prevalence of the most common symptom or pathology, at 12 weeks or later. Heterogeneity was quantified in absolute terms and as a proportion of total variation and explored across pre-defined subgroups (PROSPERO ID CRD42020218351).Results120 studies in 130 publications were included. Length of follow-up varied between 12 weeks - 12 months. Few studies had low risk of bias. All complete and subgroup analyses except one had I2≥ 90%, with prevalence of persistent symptoms range of 0% - 93% (pooled estimate 42.1%, 95% prediction interval : 6.8% to 87.9%). Studies using routine healthcare records tended to report lower prevalence of persistent symptoms/pathology than self-report. However, studies systematically investigating pathology in all participants at follow up tended to report the highest estimates of all three. Studies of hospitalised cases had generally higher estimates than community- based studies.ConclusionsThe way in which Long Covid is defined and measured affects prevalence estimation. Given the widespread nature of SARSCoV2 infection globally, the burden of chronic illness is likely to be substantial even using the most conservative estimates.Funding this systematic review received no specific funding.Key pointsIn a systematic review of 130 publications, prevalence estimates of Long Covid (>12 weeks) after SARSCoV2 infection differed according to how persistent symptoms/pathology were identified and measured, and ranged between 0% - 93% (pooled estimate 42.1%, 95% prediction interval: 6.8% to 87.9%).
Too much is too much: influence of former stress levels on food craving and weight gain during the COVID-19 period
Authors: Granger Rachel , Kubis Hans P. 
Published: November 2022

AbstractThe COVID-19 pandemic and associated social restrictions had an extensive effect on peoples’ lives. Increased rates of weight gain were widely reported, as were declines in the general populations’ mental health, including increases in perceived stress. This study investigated whether higher perceived levels of stress during the pandemic were associated with greater levels of weight gain, and whether poor prior levels of mental health were a factor in higher levels of both stress and weight gain during the pandemic. Underlying changes in eating behaviours and dietary consumption were also investigated. During January-February 2021, UK adults (n=179) completed a self-report online questionnaire to measure perceived levels of stress and changes (current versus pre-COVID-19 restrictions) in weight, eating behaviours, dietary consumption, and physical activity. Participants also reported on how COVID-19 had impacted their lives and their level of mental health prior to the pandemic. Participants with higher levels of stress were significantly more likely to report weight gain and twice as likely to report increased food cravings and comfort food consumption (OR=2.3 and 1.9-2.5, respectively). Participants reporting an increase in food cravings were 6-11 times more likely to snack and to have increased consumption of high sugar or processed foods (OR=6.3, 11.2 and 6.3, respectively). Females reported a far greater number of COVID-19 enforced lifestyle changes and both female gender and having poor mental health prior to the pandemic were significant predictors of higher stress and weight gain during the pandemic. Although COVID-19 and the pandemic restrictions were unprecedented, this study suggests that understanding and addressing the disparity of higher perceived stress in females and individuals’ previous levels of mental health, as well as the key role of food cravings, is key for successfully addressing the continuing societal issue of weight gain and obesity.
Neonatal Paenibacilliosis:Paenibacillus thiaminolyticusas a Novel Cause of Neonatal Sepsis with High Risk of Sequelae in Uganda
Authors: Ericson Jessica E. , Burgoine Kathy , Kumbakumba Elias , Ochora Moses , Hehnly Christine , Bajunirwe Francis , Bazira Joel , Fronterre Claudio , Hagmann Cornelia , Kulkarni Abhaya V. , Kumar M. Senthil , Magombe Joshua , Mbabazi-Kabachelor Edith , Morton Sarah U. , Movassagh Mercedeh , Mugamba John , Mulondo Ronald , Natukwatsa Davis , Kaaya Brian Nsubuga , Olupot-Olupot Peter , Onen Justin , Sheldon Kathryn , Smith Jasmine , Ssentongo Paddy , Ssenyonga Peter , Warf Benjamin , Wegoye Emmanuel , Zhang Lijun , Kiwanuka Julius , Paulson Joseph N. , Broach James R. , Schiff Steven J. 
Published: November 2022

AbstractPaenibacillus thiaminolyticusmay be an underdiagnosed cause of neonatal sepsis. We prospectively enrolled a cohort of 800 neonates presenting with a clinical diagnosis of sepsis at two Ugandan hospitals. Quantitative polymerase chain reaction specific toP. thiaminolyticusand to thePaenibacillusgenus were performed on the blood and cerebrospinal fluid (CSF) of 631 neonates who had both specimen types available. Neonates with virus detected in either specimen type were considered to potentially have paenibacilliosis, (37/631, 6%). We described antenatal, perinatal, and neonatal characteristics, presenting signs, and 12-month developmental outcomes for neonates with paenibacillosis vs. clinical sepsis. Median age at presentation was 3 (interquartile range 1, 7) days. Fever (92%), irritability (84%) and seizures (51%) were common. Eleven (30%) had an adverse outcome: 5 (14%) neonates died during the first year of life; 5 of 32 (16%) survivors developed postinfectious hydrocephalus and one (3%) additional survivor had neurodevelopmental impairment without hydrocephalus. These results highlight the need to consider local pathogen prevalence and the possibility of unusual pathogens when determining antibiotic choice for neonatal sepsis.
Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: An application for type 2 diabetes precision medicine
Authors: Venkatasubramaniam Ashwini , Mateen Bilal A. , Shields Beverley M , Hattersley Andrew T , Jones Angus G , Vollmer Sebastian J. , Dennis John M. 
Published: November 2022

AbstractObjectiveTo compare individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model.Study Design and SettingCohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 trials (SGLT2-inhibitor versus DPP4-inhibitor). For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 UK primary care patients (Clinical Practice Research Datalink).ResultsHeterogeneity in treatment effects was detected in trial participants with both approaches (causal forest: 98.6% & penalized regression: 81.7% predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit >10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1).ConclusionWhen evaluating treatment effect heterogeneity researchers should not rely on causal forest (or other similar machine learning algorithms) alone, and must compare outputs with standard regression.What is new?QuestionWhat is the comparative utility of machine learning compared to standard regression for identifying variation in patient-level outcomes (treatment effect heterogeneity) due to different treatments?FindingsCausal forest and penalized regression models were developed using trial data to predict glycated hemoglobin [HbA1c]) outcomes with SGLT2-inhibitor and DPP4-inhibitor therapy in 1,428 individuals with type 2 diabetes. In external validation (18,741 patients), penalized regression outperformed causal forest at identifying population strata with a superior glycemic response to SGLT2-inhibitors compared to DPP4-inhibitors.ImplicationsStudies estimating treatment effect heterogeneity should not solely rely on machine learning and should compare results with standard regression.
PSKR1 balances the plant growth-defense trade-off in the rhizosphere microbiome
Authors: Song Siyu , Moreira Zayda Morales , Zhang Xuecheng , Diener Andrew C. , Haney Cara H. 
Published: November 2022

AbstractMicrobiota provide benefits to their hosts including nutrient uptake and protection against pathogens. How hosts balance an appropriate immune response to avoid microbiota overgrowth while avoiding autoimmunity is not well understood. Here we show thatArabidopsis pskr1(phytosulfokine receptor 1) loss-of-function mutants display autoimmunity and reduced rhizosphere bacterial growth when inoculated with normally growth-promotingPseudomonas fluorescens. Transcriptional profiling demonstrated that PSKR1 regulates the plant growth-defense trade-off during plant-microbiome interactions: PSKR1 upregulates plant photosynthesis and root growth but suppresses salicylic acid (SA)-mediated defense responses. Genetic epistasis experiments showed thatPSRK1inhibition of microbiota-induced autoimmunity is fully dependent on SA signaling. Finally, using a transgenic reporter, we showed thatP. fluorescensinducesPSKR1expression in roots, suggestingP. fluorescensmight manipulate plant signaling to promote its colonization. Our data demonstrate a genetic mechanism to coordinate beneficial functions of the microbiome while preventing autoimmunity.
GDF3 is an obesity-induced regulator of TGFβ superfamily signaling
Authors: Ramachandran Deepti , Kotikalapudi Nagasuryaprasad , Gipson Gregory R. , Troncone Luca , Vestal Kylie , Maridas David E. , Gulko Anton , Tsai Linus T. , Rosen Vicki , Yu Paul , Thompson Thomas B. , Banks Alexander S. 
Published: November 2022

ABSTRACTGrowth differentiation factor 3 (GDF3) is a relatively understudied member of the TGFβ superfamily that is highly expressed during development. However, the function of GDF3 in adult biology is contentious. We usein vivoapproaches to show that GDF3 loss-of-function in adipose tissue of obese adult mice causes reduced body weight and improved whole-body insulin sensitivity. These effects are accompanied by altered regulation of genes targeted by the TGFβ superfamilyin vivo. Usingin vitroapproaches, we show that GDF3 can influence both arms of the TGFβ superfamily: GDF3 simultaneously inhibits BMP signaling and activates activin-like SMAD 2/3 signaling. We identify the type II receptors mediating this activity. GDF3 binds to the type II receptors BMPR2, ACTRIIA and ACTRIIB and achieves dose-dependent inhibition of multiple BMP proteins including BMP2, BMP7, BMP9, BMP10, and BMP15in vitro. We also find that GDF3 activates TGFβ/activin-like SMAD2/3 signaling. Unbiased expression profiling confirms that GDF3 both attenuates BMP2-regulated gene expression and drives TGFβ/activin-like gene expression. Together these results provide much needed clarity to both the molecular pathways involved in GDF3 signaling and the physiological effects of GDF3 loss of function.
mEnrich-seq: Methylation-guided enrichment sequencing of bacterial taxa of interest from microbiome
Authors: Cao Lei , Kong Yimeng , Fan Yu , Ni Mi , Tourancheau Alan , Ksiezarek Magdalena , Mead Edward A. , Koo Tonny , Gitman Melissa , Zhang Xue-Song , Fang Gang 
Published: November 2022

AbstractMetagenomics has enabled the comprehensive study of microbiomes. However, many applications would benefit from a method that can sequence specific bacterial taxa of interest (pathogens, beneficial microbes, or low-abundance taxa), but not the vast background of other taxa in a microbiome sample. To address this need, we developed mEnrich-seq, a method that can enrich taxa of interest from metagenomic DNA before sequencing. The core idea is to exploit the self vs. non-self genome differentiation provided by natural bacterial DNA methylation and rationally choose methylation-sensitive restriction enzymes (REs), individually or in combination, to deplete host DNA and most background microbial DNA while enriching bacterial taxa of interest. This core idea is integrated with library preparation procedures in a way that only non-digested DNA libraries are sequenced. We performed in-depth evaluations of mEnrich-seq and demonstrated its use in several applications to enrich (up to 117-fold) genomic DNA of pathogenic or beneficial bacteria from human urine and fecal samples, including several species that are hard to culture or of low abundance. We also assessed the broad applicability of mEnrich-seq and found that 3130 (68.03%) of the 4601 strains with mapped methylomes to date can be targeted by at least one commercially available RE, representing 54.78% of the species examined in this analysis. mEnrich-seq provides microbiome researchers with a versatile and cost-effective approach for selective sequencing of diverse taxa of interest directly from the microbiome.
Functional and structural asymmetry suggest a unifying principle for catalysis in integral membrane-bound pyrophosphatases
Authors: Strauss Jannik , Wilkinson Craig , Vidilaseris Keni , Ribeiro Orquidea , Liu Jianing , Hillier James , Malinen Anssi , Gehl Bernadette , Jeuken Lars J.C. , Pearson Arwen R. , Goldman Adrian 
Published: November 2022

AbstractMembrane-bound pyrophosphatases (M-PPases) are homodimeric primary ion pumps that couple the transport of Na+- and/or H+across membranes to the hydrolysis of pyrophosphate. Their role in the virulence of protist pathogens likePlasmodium falciparummakes them an intriguing target for structural and functional studies. Here, we show the first structure of a K+-independent M-PPase, asymmetric and time-dependent substrate binding in time-resolved structures of a K+-dependent M-PPase, and demonstrate pumping-before-hydrolysis by electrometric studies. We suggest how key residues in helix 12, 13, and the exit channel loops affect ion selectivity and K+-activation due to a complex interplay of residues that are involved in subunit-subunit communication. Our findings not only explain ion selectivity in M-PPases but also why they display half-of-the-sites reactivity. Based on this we propose, for the first time, a unified and testable model for ion pumping, hydrolysis, and energy-coupling inallM-PPases, including those that pump both Na+and H+.
A rapid platform for 3D patient-derived cutaneous neurofibroma organoid establishment and screening
Authors: Nguyen Huyen Thi Lam , Kohl Emily , Bade Jessica , Eng Stefan E. , Tosevska Anela , Al Shihabi Ahmad , Hong Jenny J. , Dry Sarah , Boutros Paul C. , Panossian Andre , Gosline Sara , Soragni Alice 
Published: November 2022

AbstractLocalized cutaneous neurofibromas (cNFs) are benign tumors that arise in the dermis of patients affected by Neurofibromatosis Type 1 syndrome (NF1). cNFs are fundamentally benign lesions: they do not undergo malignant transformation or metastasize. Nevertheless, in NF1 patients, they can cover a significant proportion of the body, with some individuals developing hundreds to thousands of lesions. cNFs can cause pain, itching, and disfigurement with substantial socio-emotional repercussions. To date, surgical removal or laser desiccation are the only treatment options, but can result in scarring and the leave a potential for regrowth.To support drug discovery efforts focused on identifying effective systemic therapies for cNF, we introduce an approach to routinely establish and screen cNF tumor organoids. We optimized conditions to support ex vivo growth of genomically-diverse cNFs. Patient-derived cNF organoids closely recapitulate the molecular and cellular heterogeneity of these tumors as measured by immunohistopathology, DNA methylation, RNA-seq and flow cytometry. Our tractable patient-derived cNF organoid platform enables rapid screening of hundreds of compounds in a patient- and tumor-specific manner.
Structured Joint Decomposition (SJD) identifies conserved molecular dynamics across collections of biologically related multi-omics data matrices
Authors: Chen Huan , Liu Jinrui , Sonthalia Shreyash , Stein-O’Brien Genevieve , Xiao Luo , Caffo Brian , Colantuoni Carlo 
Published: November 2022

AbstractMotivationIt is necessary to develop exploratory tools to learn from the unprecedented volume of high-dimensional multi-omic data currently being produced. We have developed an R package, SJD, which identifies components of variation that are shared across multiple matrices. The approach focuses specifically on variation across the samples/cells within each dataset while incorporating biologist-defined hierarchical structure among input experiments that can spanin vivoandin vitrosystems, multi-omic data modalities, and species.ResultsSJD enables the definition of molecular variation that is conserved across systems, those that are shared within subsets of studies, and elements unique to individual matrices. We have included functions to simplify the construction and visualization of highly complexin silicoexperiments involving many diverse multi-omic matrices from multiple species. Here we apply SJD to decompose four RNA-seq experiments focused on neurogenesis in the neocortex. The public datasets used in this analysis and the conserved transcriptomic dynamics in mammalian neurogenesis that we define here can view viewed and explored together athttps://nemoanalytics.org/p?l=ChenEtAlSJD2022&g=DCX.Availability and ImplementationThe SJD package can be found athttps://chuansite.github.io/SJD.Contacthzchenhuan@gmail.com;ccolant1@jhmi.eduSupplementary informationSupplementary data are available at Bioinformatics online.
SEEK to Identify Super Enhancer-Expression Correlated Pairs using Single-cell Multi-omic Data
Authors: Cai Guoshuai 
Published: November 2022

AbstractSuper enhancers (SEs) drive cell identity and disease related genes. However, current methods for studying associations between SE and gene expression are time consuming, costly and with poor scalability.This study formulated a computational approach for screening genome-wide SE-expression associations by analyzing single-cell multi-omic data of transcriptome and H3K27ac histone modification. A pipeline was also constructed for an easy workflow application. Further our application study identified expression correlated SEs (eSEs) in brain and found they mark cell types. Moreover, our analysis provided new insights into the functional role of SEs close toKcnip4andNifb1in frontal cortex neurons and CGE derived inhibitory neurons, linking to neuron development and neurological diseases.Collectively, this study provides a new tool for studying SE-expression associations and identifying significant expression associated SEs, which pave the way for understanding the regulatory role of SEs in gene expression and related cellular and disease development.
Reliable estimation of tree branch lengths using deep neural networks
Authors: Suvorov Anton , Schrider Daniel R. 
Published: November 2022

AbstractA phylogenetic tree represents hypothesized evolutionary history for a set of taxa. Besides the branching patterns (i.e., tree topology), phylogenies contain information about the evolutionary distances (i.e. branch lengths) between all taxa in the tree, which include extant taxa (external nodes) and their last common ancestors (internal nodes). During phylogenetic tree inference, the branch lengths are typically co-estimated along with other phylogenetic parameters during tree topology space exploration. There are well-known regions of the branch length parameter space where accurate estimation of phylogenetic trees is especially difficult. Several novel studies have recently demonstrated that machine learning approaches have the potential to help solve phylogenetic problems with greater accuracy and computational efficiency. In this study, as a proof of concept, we sought to explore the possibility of machine learning models to predict branch lengths. To that end, we designed several deep learning frameworks to estimate branch lengths on fixed tree topologies from multiple sequence alignments or its representations. Our results show that deep learning methods can exhibit superior performance in some difficult regions of branch length parameter space. For example, in contrast to maximum likelihood inference, which is typically used for estimating branch lengths, deep learning methods are more efficient and accurate when inferring long branches that are associated with distantly related taxa and perform well in the aforementioned challenging regions of the parameter space. Together, our findings represent a next step toward accurate, fast, and reliable phylogenetic inference with machine learning approaches.
The gut microbiome has sexually dimorphic effects on bone tissue energy metabolism and multiscale bone quality in C57BL/6J mice
Authors: Vahidi G. , Moody M. , Welhaven H. , Davidson L. , Walk S. T. , Martin S. , June R. K. , Heveran C. M. 
Published: November 2022

AbstractThe gut microbiome impacts bone mass, implying a disruption to bone homeostasis, yet significant uncertainty remains regarding the impacts of the gut microbiome on remodeling bone cells. The gut microbiome is thought to be essential for normal biomineralization, but the specific consequences of the absent gut microbiome on tissue mineralization and multiscale bone quality are not determined. We hypothesized that bone homeostasis and tissue-scale metabolism, tissue mineralization, and whole-bone biomechanics are altered in germ-free (GF) C57BL/6J mice. Further, because many characteristics of the gut microbiome are sexually dimorphic, we hypothesized that the gut microbiome would show important sex differences with regards to its impact on bone quality. Differences between GF and conventional mouse bone extended from bone tissue metabolism to whole bone biomechanics. Cortical bone tissue from male mice had a greater signature of amino acid metabolism whereas female cells had a greater signature of lipid metabolism. These sex differences were also present in GF mice and were indeed even more stark. GF increased cortical femur bone formation for both sexes and decreased bone resorption and osteoclast density only in females. GF similarly increased cortical femur tissue mineralization and altered collagen structure for both sexes but led to greater gains in distal femur trabecular microarchitecture for males. Whole femur strength was similarly increased with GF for both sexes, but males had a greater increase in modulus. GF did not impact fracture toughness for either sex. The altered bone quality with GF is multifactorial and is likely contributed to by differences in tissue-scale composition as well as lower cortical porosity. Together, these data demonstrate that the gut microbiome influences bone cells and multiscale bone quality, but that the specific relationships that underlie these effects to bone are different for females and males.