Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Differential Genes of DNA Methylation Associated With Alzheimer’s Disease Based on Integrated Bioinformatics and Its Diagnostic Significance

Background Alzheimer’s disease (AD) is a common neurodegenerative disease. The pathogenesis is complex and has not been clearly elucidated, and there is no effective treatment. Recent studies have demonstrated that DNA methylation is closely associated with the pathogenesis of AD, which sheds light on investigating potential biomarkers for the diagnosis of early AD and related possible therapeutic approaches. Methods Alzheimer’s disease patients samples and healthy controls samples were collected from two datasets in the GEO database. Using LIMMA software package in R language to find differentially expressed genes (DEGs). Afterward, DEGs have been subjected to enrichment analysis of GO and KEGG pathways. The PPI networks and Hub genes were created and visualized based on the STRING database and Cytoscape. ROC curves were further constructed to analyze the accuracy of these genes for AD diagnosis. Results Analysis of the GSE109887 and GSE97760 datasets showed 477 significant DEGs. GO and KEGG enrichment analysis showed terms related to biological processes related to these genes. The top ten Hub genes were found on the basis of the PPI network using the CytoHubba plugin, and the AUC areas of these top ranked genes were all greater than 0.7, showing satisfactory diagnostic accuracy. Conclusion The study identified the top 10 Hub genes associated with AD-related DNA methylation, of which RPSA, RPS23, and RPLP0 have high diagnostic accuracy and excellent AD biomarker potential.
N6-Methyladenosine-Related lncRNAs Are Novel Prognostic Markers and Predict the Immune Landscape in Acute Myeloid Leukemia

Background: Acute myelocytic leukemia (AML) is one of the hematopoietic cancers with an unfavorable prognosis. However, the prognostic value of N 6-methyladenosine-associated long non-coding RNAs (lncRNAs) in AML remains elusive. Materials and Methods: The transcriptomic data of m6A-related lncRNAs were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. AML samples were classified into various subgroups according to the expression of m6A-related lncRNAs. The differences in terms of biological function, tumor immune microenvironment, copy number variation (CNV), and drug sensitivity in AML between distinct subgroups were investigated. Moreover, an m6A-related lncRNA prognostic model was established to evaluate the prognosis of AML patients. Results: Nine prognosis-related m6A-associated lncRNAs were selected to construct a prognosis model. The accuracy of the model was further determined by the Kaplan–Meier analysis and time-dependent receiver operating characteristic (ROC) curve. Then, AML samples were classified into high- and low-risk groups according to the median value of risk scores. Gene set enrichment analysis (GSEA) demonstrated that samples with higher risks were featured with aberrant immune-related biological processes and signaling pathways. Notably, the high-risk group was significantly correlated with an increased ImmuneScore and StromalScore, and distinct immune cell infiltration. In addition, we discovered that the high-risk group harbored higher IC50 values of multiple chemotherapeutics and small-molecule anticancer drugs, especially TW.37 and MG.132. In addition, a nomogram was depicted to assess the overall survival (OS) of AML patients. The model based on the median value of risk scores revealed reliable accuracy in predicting the prognosis and survival status. Conclusion: The present research has originated a prognostic risk model for AML according to the expression of prognostic m6A-related lncRNAs. Notably, the signature might also serve as a novel biomarker that could guide clinical applications, for example, selecting AML patients who could benefit from immunotherapy.
A Clinical Decision Support System for Remote Monitoring of Cardiovascular Disease Patients: A Clinical Study Protocol

Introduction Cardiovascular diseases (CVD) are the leading cause of death globally, taking an estimated 17. 9 million lives each year. Cardiac rehabilitation is shown to reduce mortality and hospital readmissions, while improving physical fitness and quality of life. Despite the recommendations and proven benefits, acceptance and adherence remain low. Mobile health (mHealth) solutions may contribute to more personalized and tailored patient recommendations according to their specific needs. This study protocol aims to assess the effectiveness of a user-friendly, comprehensive Clinical Decision Support System (CDSS) for remote patient monitoring of CVD patients, primarily on the reduction of recurrent cardiovascular events. Methods and Analysis The study will follow a multicenter randomized controlled design involving two cardiology units in the Center Region of Portugal. Prospective CVD patients will be approached by the healthcare staff at each unit and checked for eligibility according to the predefined inclusion/exclusion criteria. The CDSS will suggest a monitoring plan for the patient, will advise the mHealth tools (apps and wearables) adapted to patient needs, and will collect data. The clinical study will start in January 2023. Discussion The success of the mHeart.4U intervention will be a step toward the use of technological interfaces as an integrating part of CR programs. Ethics and Dissemination The study will undergo ethical revision by the Ethics Board of the two hospital units where the study will unfold. The study was registered in ClinicalTrials.gov on 18th January 2022 with the number NCT05196802. The study findings will be published in international peer-reviewed scientific journals and encounters and in a user-friendly manner to the society.
Investigating the Impacts of Feedstock Variability on a Carbon-Negative Autothermal Pyrolysis System Using Machine Learning

Feedstock properties impact the economic feasibility and sustainability of biorefinery systems. Scientists have developed pyrolysis kinetics, process, and assessment models that estimate the costs and greenhouse gas (GHG) emissions of various biorefineries. Previous studies demonstrate that feedstock properties have a significant influence on product costs and lifecycle emissions. However, feedstock variability remains a challenge due to a large number of possible feedstock property combinations and limited public availability of feedstock composition data. Here, we demonstrate the use of machine learning (ML) models to generate large feedstock sample data from a smaller sample set for sustainability assessment of biorefinery systems. This study predicts the impact of feedstock properties on the profitability and sustainability of a lignocellulosic biomass autothermal pyrolysis (ATP) biorefinery producing sugar, phenolic oil, and biochar. Generative Adversarial Networks and Kernel Density Estimation machine learning models are used to generate 3,000 feedstock samples of diverse biochemical compositions. Techno-economic and lifecycle assessments estimated that the ATP minimum sugar selling price (MSSP) ranges between $66/metric ton (MT) and $280/MT, and the greenhouse gas (GHG) range from a net negative GHG emission(s) of −0.56 to −0.74 kg CO2e/kg lignocellulosic biomass processed. These results show the potential of ML to enhance sustainability analyses by replacing Monte Carlo-type approaches to generate large feedstock composition datasets that are representative of empirical data.
Key Molecules of Triglycerides Pathway Metabolism Are Disturbed in Patients With Systemic Lupus Erythematosus

Background Elevated triglycerides or triglyceride-rich lipoproteins are an additional cause of cardiovascular (CV) disease. Given that patients with systemic lupus erythematosus (SLE) have a high prevalence of premature CV disease and show an altered lipid profile, our objective was to study whether three molecules that play a central role in the triglyceride metabolism: apolipoprotein C-III (ApoC3), angiopoietin-like protein 4 (ANGPLT4), and lipoprotein lipase (LPL) differ between SLE patients and controls, and how they are related to disease characteristics, including disease damage. Methods Cross-sectional study that included 347 women, 185 of them diagnosed with SLE and 162 age-matched controls. ANGPTL4, ApoC3 and LPL, and standard lipid profiles were analyzed in SLE patients and controls. A multivariable analysis was performed to assess whether ANGPTL4, ApoC3 and LPL molecules differ between patients and controls and to study their relationship with SLE disease damage. Results After fully multivariable analysis that included classic CV risk factors, and the modifications that the disease itself produces over the lipid profile, it was found that ApoC3 was significantly lower (beta coef. -1.2 [95%CI -1.6- -0.8) mg/dl, <0.001), and ANGPTL4 (beta coef. 63 [95%CI 35-90] ng/ml, <0.001) and LPL (beta coef. 79 [95%CI 30-128] ng/ml, p=0.002) significantly higher in patients with SLE compared to controls. Disease damage score was significantly and independently associated with higher serum levels of LPL (beta coef. 23 [95%CI 10-35] ng/ml, p=0.001). Mediation analysis suggested that the relationship between disease damage and LPL was direct and not mediated by ApoC3 or ANGPLT4. Conclusion The ApoC3, ANGPLT4 and LPL axis is disrupted in patients with SLE. Disease damage explains this disturbance.
Development and Validation of Predictive Model—HASBLAD Score—For Major Adverse Cardiovascular Events During Perioperative Period of Non-cardiac Surgery: A Single Center Experience in China

Background Major adverse cardiovascular events (MACEs) represent a significant reason of morbidity and mortality in non-cardiac surgery during perioperative period. The prevention of perioperative MACEs has always been one of the hotspots in the research field. Current existing models have not been validated in Chinese population, and have become increasingly unable to adapt to current clinical needs. Objectives To establish and validate several simple bedside tools for predicting MACEs during perioperative period of non-cardiac surgery in Chinese hospitalized patients. Design We used a nested case-control study to establish our prediction models. A nomogram along with a risk score were developed using logistic regression analysis. An internal cohort was used to evaluate the performance of discrimination and calibration of these predictive models including the revised cardiac risk index (RCRI) score recommended by current guidelines. Setting Peking University Third Hospital between January 2010 and December 2020. Patients Two hundred and fifty three patients with MACEs and 1,012 patients without were included in the training set from January 2010 to December 2019 while 38,897 patients were included in the validation set from January 2020 and December 2020, of whom 112 patients had MACEs. Main Outcome Measures The MACEs included the composite outcomes of cardiac death, non-fatal myocardial infarction, non-fatal congestive cardiac failure or hemodynamically significant ventricular arrhythmia, and Takotsubo cardiomyopathy. Results Seven predictors, including Hemoglobin, CARDIAC diseases, Aspartate aminotransferase (AST), high Blood pressure, Leukocyte count, general Anesthesia, and Diabetes mellitus (HASBLAD), were selected in the final model. The nomogram and HASBLAD score all achieved satisfactory prediction performance in the training set (C statistic, 0.781 vs. 0.768) and the validation set (C statistic, 0.865 vs. 0.843). Good calibration was observed for the probability of MACEs in the training set and the validation set. The two predictive models both had excellent discrimination that performed better than RCRI in the validation set (C statistic, 0.660, P < 0.05 vs. nomogram and HASBLAD score). Conclusion The nomogram and HASBLAD score could be useful bedside tools for predicting perioperative MACEs of non-cardiac surgery in Chinese hospitalized patients.
The Use of Clinical Scores in the Management of Immune Thrombocytopenic Purpura in Children

While the majority of children with recently diagnosed ITP have a benign, self-limiting condition, most often with a spontaneously recovery, 40% of children with ITP progress toward persistent ITP and 10–20% goes toward chronicity. Several clinical scores have been developed with the aim to perform a better monitoring outcome or to differentiate transient vs. persistent ITP (e.g., Donato score). Our paper aims to describe and to compare the most important scores used in the management of ITP in children: bleeding severity scores and chronicity prediction scores. These scores include a combination of different already known risk factors: age, gender, presence of a previous infections or vaccination, bleeding grade, type of onset, platelet count at diagnosis. The real utility of these scores has been a matter of debate and no consensus has been reached so far as to their necessity to be implemented as compulsory tool in the care of children with ITP.
Identification of Candidate Genes Associated With Tolerance to Apple Replant Disease by Genome-Wide Transcriptome Analysis

Apple replant disease (ARD) is a worldwide economic risk in apple cultivation for fruit tree nurseries and fruit growers. Several studies on the reaction of apple plants to ARD are documented but less is known about the genetic mechanisms behind this symptomatology. RNA-seq analysis is a powerful tool for revealing candidate genes that are involved in the molecular responses to biotic stresses in plants. The aim of our work was to find differentially expressed genes in response to ARD in Malus. For this, we compared transcriptome data of the rootstock ‘M9’ (susceptible) and the wild apple genotype M. ×robusta 5 (Mr5, tolerant) after cultivation in ARD soil and disinfected ARD soil, respectively. When comparing apple plantlets grown in ARD soil to those grown in disinfected ARD soil, 1,206 differentially expressed genes (DEGs) were identified based on a log2 fold change, (LFC) ≥ 1 for up– and ≤ −1 for downregulation (p < 0.05). Subsequent validation revealed a highly significant positive correlation (r = 0.91; p < 0.0001) between RNA-seq and RT-qPCR results indicating a high reliability of the RNA-seq data. PageMan analysis showed that transcripts of genes involved in gibberellic acid (GA) biosynthesis were significantly enriched in the DEG dataset. Most of these GA biosynthesis genes were associated with functions in cell wall stabilization. Further genes were related to detoxification processes. Genes of both groups were expressed significantly higher in Mr5, suggesting that the lower susceptibility to ARD in Mr5 is not due to a single mechanism. These findings contribute to a better insight into ARD response in susceptible and tolerant apple genotypes. However, future research is needed to identify the defense mechanisms, which are most effective for the plant to overcome ARD.
Identification of Immune-Related Genes Concurrently Involved in Critical Illnesses Across Different Etiologies: A Data-Driven Analysis

Severe trauma and sepsis can lead to multiple organ dysfunction syndrome, which is a leading cause of death in intensive care units with mortality rates in excess of 50%. In addition to infection, the degree of immuno-inflammatory response also influences the outcome. The genomic changes observed after a variety of pathophysiological insults, such as trauma, sepsis, burns are similar, and consist of innate immune activation and adaptive immunity suppression. However, the characteristics of the shared mechanisms of aforementioned critical illnesses and the clinical relevance remain less explored. In the present study, we performed a data analysis to identify functional genes concurrently involved in critical illnesses across differing etiologies (trauma and sepsis derived from community-acquired pneumonia/abdominal source) and explored the shared signaling pathways these common genes involved in to gain insight into the underlying molecular mechanisms. A number of immune-related biological functions were found to be dysregulated in both trauma and sepsis in the present study, so we continued to identify immune-related common genes, profiled the immune cell proportion, and explored the relationships between them. The diagnostic and prognostic value of the immune-related common genes was also evaluated to address their potential clinical utilization as novel biomarkers. Notably, we identified a list of 14 immune-related genes concurrently dysregulated in trauma and sepsis showing favorable diagnostic value, among which S100P can predict prognosis of sepsis patients. Moreover, a spectrum of immune cell subsets including naïve B cells, CD8+ T cells, CD4+ memory resting T cells, activated NK cells, resting dendritic cells, plasma cells, Tregs, macrophages M0 and macrophages M1 was found to be concurrently dysregulated in both trauma and sepsis, and a close relation between above identified immune-related genes and immune cell subsets was observed. Our data-driven findings lay a foundation for future research to elucidate the pathophysiology regarding the aspect of inflammatory and immune response in critical illnesses, and suggest future studies focus on interpreting the function roles of the identified immune-related genes, as well as the reactive immune cell subsets.
Identification of SOX2 Interacting Proteins in the Developing Mouse Lung With Potential Implications for Congenital Diaphragmatic Hernia

Congenital diaphragmatic hernia is a structural birth defect of the diaphragm, with lung hypoplasia and persistent pulmonary hypertension. Aside from vascular defects, the lungs show a disturbed balance of differentiated airway epithelial cells. The Sry related HMG box protein SOX2 is an important transcription factor for proper differentiation of the lung epithelium. The transcriptional activity of SOX2 depends on interaction with other proteins and the identification of SOX2-associating factors may reveal important complexes involved in the disturbed differentiation in CDH. To identify SOX2-associating proteins, we purified SOX2 complexes from embryonic mouse lungs at 18.5 days of gestation. Mass spectrometry analysis of SOX2-associated proteins identified several potential candidates, among which were the Chromodomain Helicase DNA binding protein 4 (CHD4), Cut-Like Homeobox1 (CUX1), and the Forkhead box proteins FOXP2 and FOXP4. We analyzed the expression patterns of FOXP2, FOXP4, CHD4, and CUX1 in lung during development and showed co-localization with SOX2. Co-immunoprecipitations validated the interactions of these four transcription factors with SOX2, and large-scale chromatin immunoprecipitation (ChIP) data indicated that SOX2 and CHD4 bound to unique sites in the genome, but also co-occupied identical regions, suggesting that these complexes could be involved in co-regulation of genes involved in the respiratory system.
Proteomic Analysis Revealed Metabolic Inhibition and Elongation Factor Tu Deamidation by p-Coumaric Acid in Cronobacter sakazakii

Screening drugs and compounds to fight against Cronobacter sakazakii (C. sakazakii), one of the most common pathogens that can cause fatal necrotizing enterocolitis, septicema and meningitis, is still needed. We found that p-coumaric acid (pCA) has an inhibitory effect on C. sakazakii in vitro and in vivo. Proteomic changes of C. sakazakii BAA-894 exposed to pCA were studied to reveal the antibacterial mechanisms involved. A total of 1,553 proteins were identified in C. sakazakii BAA-894 by label-free proteomics analysis. Fuzzy cluster analysis showed that 33 were up-regulated, and 110 were down-regulated with pCA treatment. Gene Ontology (GO) analysis concluded that pCA caused the change of metabolic state of bacteria and generally in the state of metabolic inhibition. KEGG Enrichment Analysis (KEGG) analysis showed that pCA inhibited energy metabolism and distorted the balance of amino acid metabolism. Posttranslational modification analysis showed that pCA affected the deamidation of three proteins, including Elongation factor Tu, one of the vital proteins in bacteria. Molecular docking suggested the hydrogen bond between the pCA carboxyl group and Elongation factor Tu Asn-64 might contribute to deamidation. Overall, we found that pCA interfered with cellular energy and amino acid metabolism and promoted elongation factor Tu deamidation, suggesting that pCA can be an effective natural substitute to control C. sakazakii.
Point-to-Point Navigation of a Fish-Like Swimmer in a Vortical Flow With Deep Reinforcement Learning

Efficient navigation in complex flows is of crucial importance for robotic applications. This work presents a numerical study of the point-to-point navigation of a fish-like swimmer in a time-varying vortical flow with a hybrid method of deep reinforcement learning (DRL) and immersed boundary–lattice Boltzmann method (IB-LBM). The vortical flow is generated by placing four stationary cylinders in a uniform flow. The swimmer is trained to discover effective navigation strategies that could help itself to reach a given destination point in the flow field, utilizing only the time-sequential information of position, orientation, velocity and angular velocity. After training, the fish can reach its destination from random positions and orientations, demonstrating the effectiveness and robustness of the method. A detailed analysis shows that the fish utilizes highly subtle tail flapping to control its swimming orientation and take advantage of the reduced streamwise flow area to reach it destination, and in the same time avoiding entering the high flow velocity area.
Diprotin A TFA Exerts Neurovascular Protection in Ischemic Cerebral Stroke

Background It has been established that the dipeptidyl peptidase-4 (DPP-4) inhibitor Diprotin A TFA can reduce vascular endothelial (VE)-cadherin disruption by inhibiting the increase in cleaved β-catenin in response to hypoxia, thereby protecting the vascular barrier of human umbilical vein endothelial cells. In this study, we sought to investigate the possible effect of Diprotin A TFA on the VE barrier after cerebral ischemic stroke in mice. Methods C57BL/6J mice were divided into five groups, namely, (1) sham, (2) stroke, (3) stroke + dimethyl sulfoxide (DMSO), (4) stroke + Diprotin A TFA, and (5) stroke + Diprotin A TFA + XAV-939. First, the cerebral ischemia model was established by photothrombotic ischemia, followed by intraperitoneal injection with Diprotin A TFA and XAV-939 at doses of 70 μg/kg and 40 mg/kg 30 min once in the morning and once in the evening for 3 days. Immunofluorescence staining and Western blot methods were used to analyze the expression of vascular and blood-brain barrier (BBB)-associated molecular markers in the peri-infarct area. Results Compared with the vehicle control group, we found that mice injected with Diprotin A TFA exhibited reduced cerebral infarction volume, increased vascular area and length around the brain injury, increased pericyte and basement membrane coverage, upregulated expression of BBB tight junction proteins, and improved their BBB permeability, whereas the group injected with both drug and inhibitor exhibited significantly aggravated vascular injury and BBB permeability. Conclusion Diprotin A TFA can reduce VE-cadherin disruption by inhibiting ischemia-hypoxia-induced β-catenin cleavage to protect blood vessels.