bluuebunny/crossref_metadata_2025_split huggingface dataset:
Induced pluripotent stem cell-derived extracellular vesicles promote wound repair in a diabetic mouse model via an anti-inflammatory immunomodulatory mechanism
Authors: Levy Daniel , Abadchi Sanaz Nourmohammadi , Shababi Niloufar , Ravari Mohsen Rouhani , Pirolli Nicholas H. , Bergeron Cade , Obiorah Angel , Mokhtari-Esbuie Farzad , Gheshlaghi Shayan , Abraham John M. , Smith Ian M. , Powsner Emily , Solomon Talia , Harmon John W. , Jay Steven M. 
Published: March 2023

AbstractExtracellular vesicles (EVs) derived from mesenchymal stem/stromal cells (MSCs) have recently been widely explored in clinical trials for treatment of diseases with complex pathophysiology. However, production of MSC EVs is currently hampered by donor-specific characteristics and limitedex vivoexpansion capabilities before decreased potency, thus restricting their potential as a scalable and reproducible therapeutic. Induced pluripotent stem cells (iPSCs) represent a self-renewing source for obtaining differentiated iPSC-derived MSCs (iMSCs), circumventing both scalability and donor variability concerns for therapeutic EV production. Thus, we initially sought to evaluate the therapeutic potential of iMSC EVs. Interestingly, while utilizing undifferentiated iPSC EVs as a control, we found that their vascularization bioactivity was similar and their anti-inflammatory bioactivity was superior to donor-matched iMSC EVs in cell-based assays. To supplement this initialin vitrobioactivity screen, we employed a diabetic wound healing mouse model where both the pro-vascularization and anti-inflammatory activity of these EVs would be beneficial. In thisin vivomodel, iPSC EVs more effectively mediated inflammation resolution within the wound bed. Combined with the lack of additional differentiation steps required for iMSC generation, these results support the use of undifferentiated iPSCs as a source for therapeutic EV production with respect to both scalability and efficacy.
Bayesian Inference for Brain Source Imaging with Joint Estimation of Structured Low-rank Noise
Authors: Ghosh Sanjay , Cai Chang , Gao Yijing , Hashemi Ali , Haufe Stefan , Sekihara Kensuke , Raj Ashish , Nagarajan Srikantan S. 
Published: March 2023

ABSTRACTThe inverse problem in brain source imaging is the reconstruction of brain activity from non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG). One key challenge is the efficient recovery of sparse brain activity when the data is corrupted by structured noise that is low-rank noise. This is often the case when there are a few active sources of environmental noise and the MEG/EEG sensor noise is highly correlated. In this paper, we propose a novel robust empirical Bayesian framework which provides us a tractable algorithm for jointly estimating a low-rank noise covariance and brain source activity. Specifically, we use a factor analysis model for the structured noise, and infer a sparse set of variance parameters for source activity, while performing Variational Bayesian inference for the noise. One key aspect of this algorithm is that it does not require any additional baseline measurements to estimate the noise covariance from the sensor data. We perform exhaustive experiments on both simulated and real datasets. Our algorithm achieves superior performance as compared to several existing benchmark algorithms.
Depression: an adaptive disease?
Authors: Yang Shiyu , Zheng Chenqing , Xia Canwei , Gu Langyu 
Published: March 2023

AbstractWhether depression can be adaptive has long been discussed, but systematically investigation of the adaptive evolution of its genetic basis at the genomic level is sparse. Here, we conducted genome-wide analyses on 320 depression genes at two levels, i.e., across the primate phylogeny (large timescale selection), and in modern human populations (recent selection). We identified seven genes under positive selection in the human lineage, and 46 genes under positive selection in modern human populations. Most positively selected variants in modern human populations were at UTR regions and non-coding exons, indicating the importance of gene expression regulation in the adaptive evolution of depression gene networks. Positively selected genes are not only related to immune response, but also function in reproduction and dietary adaptation, extending the pathogen host defense hypothesis. Notably, the proportion of positively selected depression genes was significantly larger than the proportion of positively selected genes at the genomic level in certain modern human populations. We also identified two positively selected variants that happened to be depression-associated variants. We thus propose that recent selection plays important roles on the adaptive evolution of depression genes. Depression can be adaptive, or is a by-product of evolution, which is still an open question.
Accurate Flow Decomposition via Robust Integer Linear Programming
Authors: Dias Fernando H. C. , Tomescu Alexandru I. 
Published: March 2023

AbstractMinimum flow decomposition (MFD) is a common problem across various fields of Computer Science, where a flow is decomposed into a minimum set of weighted paths. However, in Bioinformatics applications, such as RNA transcript or quasi-species assembly, the flow is erroneous, since is obtained from noisy read coverages. Typical generalizations of the MFD problem to handle errors are based on least-squares formulations, or on modeling the erroneous flow values as ranges. All of these are thus focused on error-handling at the level of individual edges.Interpreting the flow decomposition problem as a robust optimization problem, we lift error-handling from individual edges tosolution paths. As such, we introduce a newminimum path-error flow decompositionproblem, for which we give an efficient Integer Linear Programming formulation. Our experimental results reveal that our formulation can account for errors with an accuracy significantly surpassing that of previous error-handling formulations, with computational requirements that remain practical.
The first genome assembly of the amphibian nematode parasite (Aplectana chamaeleonis)
Authors: Han Lei , Liu Tianlu , He Fengping , Hou Zhijun 
Published: March 2023

AbstractCosmocercoid nematodes are common parasites of the digestive tract of amphibians. Genomic resources are important for understanding the evolution of a species and the molecular mechanisms of parasite adaptation. So far, no genome resource of Cosmocercoid has been reported. In 2020, a massive Cosmocercoid infection was found in the small intestine of a toad, causing severe intestinal blockage. We morphologically identified this parasite asA. chamaeleonis. Here, we report the firstA. chamaeleonisgenome with a genome size of 1.04 Gb. The repeat content of thisA. chamaeleonisgenome is 72.45 %, and the total length is 751 Mb. This resource is fundamental for understanding the evolution of Cosmocercoid and provides the molecular basis for Cosmocercoid infection and control.
Amorphous calcium phosphate-coated surfaces as a model for bone microenvironment in prostate cancer
Authors: Martin Rebeca San , Das Priyojit , Xue Tianchun , Brown Morgan Rose , Reis Marques Renata Dos , Essington Michael , Gonzalez Adrian , McCord Rachel Patton 
Published: March 2023

AbstractBone metastasis remains one of the biggest challenges in the treatment of prostate cancer, and other solid tumors such as breast, lung, and colon. Modeling a complex microenvironment in-vitro, such as the bone niche, requires interrogation of cell-cell interactions, specific extracellular matrix proteins and a high calcium environment. Here, we present a fast and cost-effective system in which commercially available, non-adhesive, cell culture vessels are coated with amorphous calcium phosphate (ACP) as a surrogate for bone matrix. We further present modified protocols for subculturing cells, as well as nucleic acid and protein collection in high calcium samples. We find that prostate epithelial cell lines show increased adhesion and proliferation when cultured in these surfaces, as well as independence from androgen starvation. We observe gene expression changes on ACP surfaces in early adenocarcinoma cell lines which may reflect alterations relevant to prostate cancer progression.Summary statementTo model the role of calcium in the microenvironment of the metastatic bone niche, we developed a cost-effective way to coat cell culture vessels in bioavailable calcium, and show that it has an effect on prostate cancer cell survival
Compromised beta-cell identity in type 2 diabetes
Authors: Dutta Pritha , Merabet Nadège , Quax Rick , Carlotti Françoise , Sloot Peter M.A. 
Published: March 2023

AbstractCompromised beta-cell identity is emerging as an important contributor of beta-cell dysfunction in type 2 diabetes (T2D). Several studies suggest that hyperglycemia induces the inactivation of transcription factors involved in mature beta-cell identity. More specifically, chronic hyperglycemia leads to the downregulation of PDX1 and MAFA, two important beta-cell identity transcription factors and regulators of insulin promoter activity. Regulation of these transcription factors depends on interactions between multiple signaling cascades and processes driven by complex non-linear dynamics and taking place in different cellular compartments. To better understand these non-linear dynamics, we developed an integrated mathematical model of the underlying mechanisms regulating these key transcription factors. Our model was able to reproduce experimentally measured variations in the levels of PDX1, MAFA and insulin mRNA under different glucose concentrations. We used this model to simulate scenarios that could allow to restore PDX1 and MAFA levels and therefore insulin gene expression. From these simulations, FOXO1 emerged as an important target for the restoration of beta-cell identity.Author summaryGlucose regulation depends on the secretion of insulin by beta-cells and uptake of glucose by the peripheral cells mediated by the action of insulin. In type 2 diabetes both aspects can be compromised. Defective insulin secretion results from compromised beta-cell function. One of the reasons behind compromised beta-cell function is that beta-cells fail to express one or more of the genes involved in insulin production and secretion and thus maintenance of beta-cell identity. The processes involved in the regulation of insulin production and secretion are complex. In this work, we are particularly interested in the role and downregulation of transcription factors, PDX1 and MAFA, which are critical regulators of insulin production, in relation with compromised beta-cell identity and function in the presence of chronic hyperglycemia. To understand better these complex processes, we use mathematical modelling which enables to generate hypotheses and simulate scenarios to extend our understanding of the mechanisms leading to compromised beta-cell function in the presence of chronic hyperglycemia. Our model and similar models can serve to identify therapeutical targets in beta-cells in order to restore their function.
ProRafts: A machine-learning predictor forraftophilicity, the protein affinity for biomembrane rafts
Authors: Yurtsever Deniz , Keşmir Can , Sperotto Maria Maddalena 
Published: March 2023

AbstractBackgroundProtein raftophilicity refers to the affinity of proteins for cell biomembrane lipid domains, called ‘rafts’. Rafts are fluctuating nanoscale platforms that are enriched in cholesterol and sphingolipids, and that are considered relevant for cell signalling, viral function, and biomembrane trafficking. The dynamic partitioning of proteins into rafts depends on the physical and physico-chemical properties of the biomembranes where such proteins are embedded or attached; however it also depends on specific protein “features”, such as acylation, glypidation, specific amino acid sequence motifs, transmembrane hydrophobic length, and surface accessible area to solvent. In this paper we present a method, and the resulting “ProRafts” predictor, that can be used to predict if a given mammal protein may be “raftophilic” or “non-raftophilic”, without having an a priori knowledge of the physical and physico-chemical properties of the biomembranes where such protein is embedded or attached. ProRafts is based on a machine-learning algorithm, XGBoost, where data regarding the features of known raftophilic human-proteins fed the algorithm.ResultsProRafts enabled to predict correctly more than 80% of human proteins that area prioriknown to be raftophilic; this is a promising result considering the limited size of the training dataset that we could build with data retrieved from protein databases. In addition, although we used protein features of known human raftophilic proteins, it was possible to identify accurately raft-proteins from othermammalsthan humans, such as mouse and rats. This finding suggests that certain protein features are sufficient to predict raftophilicity of proteins from different species. Moreover, our results indicated that phosphorylation may play a more relevant role for protein raftophilicity than indicated by previous studies.ConclusionRaftophilic proteins can be used as biomarkers in medical research, or can serve as targeting sites for therapeutics. In this respect, the machine learning method presented in this paper is a useful tool to guide experimental validations of raftophilicity of proteins in biomembranes, and facilitate the choice of proteins that can be used for experiments on biomimetic membranes.
Shotgun proteomic analysis of the caterpillarLonomia obliqua(Lepitoptera, Saturniidae) hemolymph and effects in rat hippocampal neurons culture
Authors: Maggi Silviane , Pinto Antônio Frederico Michel , Udo Mariana Sayuri Berto , Alencar da Silva Mariana Aguilera , Garcia Raphael Caio Tamborelli , Minetto Luciane , Tasso Leandro , Machado Pablo , Santos Diógenes Santiago , Barcellos Thiago , da Silva Junior Pedro Ismael , Mendonça Ronaldo Zucatelli , Marcourakis Tania , Moura Sidnei 
Published: March 2023

AbstractStudy of substances with potentially neuroprotective has been one of the research focus on drugs development. Toxic proteins ofLonomia obliquacaterpillars, which have caused several accidents in southern Brazil, were identified in the hemolymph with anti-apoptotic activity. This study aims the evaluation of the protein profile and the hemolymph effect on cell viability of rats’ primary cultured hippocampal neurons after apoptosis induction. Semi-quantitative shotgun proteomics approach was used to evaluate the protein profile of 3 caterpillars lots of different origin. Were identified a total of 76 proteins, 71 in hemolymph and 40 in fractions. Antiviral protein predominated in crude hemolymph, following by serine proteases, hemolins and protease inhibitors. In fractions were identified hemolins, serine proteases and protease inhibitors. The treatment of rats’ primary cultured hippocampal neurons with the chromatographic fraction at concentration of 0.05 and 0.10% (v/v) for 24 hours, with subsequently apoptosis induction was able to maintain cell viability significantly higher than positive control. Hemolymph protein composition can show qualitative and quantitative variations intra species when compared different origins animals and consequently exposed to various environmental factors. The results shown on this study may contribute to the identification of proteins with potential use as neuroprotective in degenerative conditions.
Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment
Authors: Malbranke Cyril , Rostain William , Depardieu Florence , Cocco Simona , Monasson Rémi , Bikard David 
Published: March 2023

AbstractWe present here an approach to protein design that enables to leverage (i) scarce functional information such as experimental data (ii) evolutionary information learned from a natural sequence variants and (iii) physics-grounded modeling. Using a Restricted Boltzmann Machine (RBM), we learned a sequence model of a protein family. We use semi-supervision to leverage available functional information during the RBM training. We then propose a strategy to explore the protein representation space that can be informed by external models such as an empirical force field method (FoldX). This method was applied to a domain of the Cas9 protein responsible for recognition of a short DNA motif. We experimentally assessed the functionality of 71 variants that were generated to explore a range of RBM and FoldX energies. We show how a combination of functional, structural and evolutionary information can identify functional variants with high accuracy. Sequences with as many as 50 differences (20% of the protein domain) to the wild-type retained functionality. Overall, 21/71 sequences designed with our method were functional. Interestingly, 6/71 sequences showed an improved activity in comparison with the original wild-type protein sequence. These results demonstrate the interest in further exploring the synergies between machine-learning of protein sequence representations and physics grounded modeling strategies informed by structural information.
Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research
Authors: Pokutnaya Darya , Van Panhuis Willem G , Childers Bruce , Hawkins Marquis S , Arcury-Quandt Alice E , Matlack Meghan , Carpio Kharlya , Hochheiser Harry 
Published: March 2023

AbstractBackgroundInfectious disease computational modeling studies have been widely published during the coronavirus disease 2019 (COVID-19) pandemic, yet they have limited reproducibility. Developed through an iterative testing process with multiple reviewers, the Infectious Disease Modeling Reproducibility Checklist (IDMRC) enumerates the minimal elements necessary to support reproducible infectious disease computational modeling publications. The primary objective of this study was to assess the reliability of the IDMRC and to identify which reproducibility elements were unreported in a sample of COVID-19 computational modeling publications.MethodsFour reviewers used the IDMRC to assess 46 preprint and peer reviewed COVID-19 modeling studies published between March 13th, 2020, and July 31st, 2020. The inter-rater reliability was evaluated by mean percent agreement and Fleiss’ kappa coefficients (κ). Papers were ranked based on the average number of reported reproducibility elements, and average proportion of papers that reported each checklist item were tabulated.ResultsQuestions related to the computational environment (mean κ = 0.90, range = 0.90–0.90), analytical software (mean κ = 0.74, range = 0.68–0.82), model description (mean κ = 0.71, range = 0.58–0.84), model implementation (mean κ = 0.68, range = 0.39–0.86), and experimental protocol (mean κ = 0.63, range = 0.58–0.69) had moderate or greater (κ > 0.41) inter-rater reliability. Questions related to data had the lowest values (mean κ = 0.37, range = 0.23–0.59). Reviewers ranked similar papers in the upper and lower quartiles based on the proportion of reproducibility elements each paper reported. While over 70% of the publications provided data used in their models, less than 30% provided the model implementation.ConclusionsThe IDMRC is the first comprehensive, quality-assessed tool for guiding researchers in reporting reproducible infectious disease computational modeling studies. The inter-rater reliability assessment found that most scores were characterized by moderate or greater agreement. These results suggests that the IDMRC might be used to provide reliable assessments of the potential for reproducibility of published infectious disease modeling publications. Results of this evaluation identified opportunities for improvement to the model implementation and data questions that can further improve the reliability of the checklist.
How influenza vaccination changed over the COVID-19 pandemic
Authors: Yang Yong 
Published: March 2023

AbstractBackgroundVaccination for seasonal influenzas is particularly important during the COVID-19 pandemic, but the influenza vaccination coverage in the U.S. was far lower than the targeted rate.ObjectiveTo examine how people’s actual uptake of the influenza vaccine and the disparity of the vaccination changed during the pandemic.MethodsA survey was conducted online in November 2022. Respondents were asked for influenza vaccination during each of the three latest seasons, prior influenza vaccination history, and COVID-19 vaccination. A linear regression model was used to estimate how the respondents’ change in influenza vaccination was associated with their demographics, COVID-19 vaccination status, and other related variables.ResultsNearly 70% of US adults had influenza vaccine each season during past the three seasons of the COVID-19 pandemic. The prevalence of influenza vaccination varied markedly across demographics. Non-Hispanic Black, Hispanic, and people with low educational attainment were more likely to see relatively negative changes in their level of influenza vaccination. Respondents who uptook their COVID-19 vaccine in 2022 increased their level of influenza vaccine more than those who uptook the vaccine in 2021.ConclusionsOur study indicated that influenza vaccination increased during the pandemic compared with before the pandemic. The disparity of influenza vaccination by race/ethnicity and socioeconomic status may enlarge during the pandemic. Tailored interventions were needed to target some groups to promote their vaccination uptake.
Early left ventricular systolic function is a more sensitive predictor of adverse events after heart transplant
Authors: Sun Zhenxing , Cai Yu , Yang Yujia , Huang Lei , Xie Yuji , Zhu Shuangshuang , Wu Chun , Sun Wei , Zhang Ziming , Li Yuman , Wang Jing , Fang Lingyun , Yang Yali , Lv Qing , Dong Nianguo , Zhang Li , Gu Haotian , Xie Mingxing 
Published: March 2023

AbstractBackgroundFirst-phase ejection fraction (EF1) is a novel measure of early systolic function. This study was to investigate the prognostic value of EF1 in heart transplant recipients.MethodsHeart transplant recipients were prospectively recruited at the Union Hospital, Wuhan, China between January 2015 and December 2019. All patients underwent clinical examination, biochemistry measures [brain natriuretic peptide (BNP) and creatinine] and transthoracic echocardiography. The primary endpoint was a combined event of all-cause mortality and graft rejection.ResultsIn 277 patients (aged 48.6±12.5 years) followed for a median of 38.7 (interquartile range: 18.3) months, there were 35 (12.6%) patients had adverse events including 20 deaths and 15 rejections. EF1 was negatively associated with BNP (β=-0.220, p<0.001) and was significantly lower in patients with events compared to those without. EF1 had the largest area under the curve in ROC analysis compared to other measures. An optimal cut-off value of 25.8% for EF1 had a sensitivity of 96.3% and a specificity of 97.1% for prediction of events. EF1 was the most powerful predictor of events with hazard ratio per 1% change in EF1: 0.628 (95%CI: 0.555-0.710, p<0.001) after adjustment for left ventricular ejection fraction and global longitudinal strain.ConclusionsEarly left ventricular systolic function as measured by EF1 is a powerful predictor of adverse outcomes after heart transplant. EF1 may be useful in risk stratification and management of heart transplant recipients.