Svanehagen0724

Lipid mediators are crucial for the pathogenesis of rheumatoid arthritis (RA); however, global analyses have not been undertaken to systematically define the lipidome underlying the dynamics of disease evolution, activation, and resolution. Here, we performed untargeted lipidomics analysis of synovial fluid and serum from RA patients at different disease activities and clinical phases (preclinical phase to active phase to sustained remission). We found that the lipidome profile in RA joint fluid was severely perturbed and that this correlated with the extent of inflammation and severity of synovitis on ultrasonography. The serum lipidome profile of active RA, albeit less prominent than the synovial lipidome, was also distinguishable from that of RA in the sustained remission phase and from that of noninflammatory osteoarthritis. Of note, the serum lipidome profile at the preclinical phase of RA closely mimicked that of active RA. Specifically, alterations in a set of lysophosphatidylcholine, phosphatidylcholine, ether-linked phosphatidylethanolamine, and sphingomyelin subclasses correlated with RA activity, reflecting treatment responses to anti-rheumatic drugs when monitored serially. Collectively, these results suggest that analysis of lipidome profiles is useful for identifying biomarker candidates that predict the evolution of preclinical to definitive RA and could facilitate the assessment of disease activity and treatment outcomes.Despite numerous observations regarding the relationship between DNA methylation changes and cancer progression, only a few genes have been verified as diagnostic biomarkers of colorectal cancer (CRC). To more practically detect methylation changes, we performed targeted bisulfite sequencing. Through co-analysis of RNA-seq, we identified cohort-specific DNA methylation markers CpG islands of the intragenic regions of PDX1, EN2, and MSX1. We validated that these genes have oncogenic features in CRC and that their expression levels are increased in correlation with the hypermethylation of intragenic regions. The reliable depth of the targeted bisulfite sequencing data enabled us to design highly optimized quantitative methylation-specific PCR primer sets that can successfully detect subtle changes in the methylation levels of candidate regions. Furthermore, these methylation levels can divide CRC patients into two groups denoting good and poor prognoses. In this study, we present a streamlined workflow for screening clinically significant differentially methylated regions. Our discovery of methylation markers in the PDX1, EN2, and MSX1 genes suggests their promising performance as prognostic markers and their clinical application in CRC patients.RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.In the crustacean Daphnia magna, studying homology-directed repair (HDR) is important to understand genome maintenance during parthenogenesis, effects of environmental toxicants on the genome, and improvement of HDR-mediated genome editing. Here we developed a transgenic D. magna that expresses green fluorescence protein (GFP) upon HDR occurrence. We utilized the previously established reporter plasmid named DR-GFP that has a mutated eGFP gene (SceGFP) and the tandemly located donor GFP gene fragment (iGFP). Upon double-strand break (DSB) introduction on SceGFP, the iGFP gene fragment acts as the HDR template and restores functional eGFP expression. find more We customized this reporter plasmid to allow bicistronic expression of the mCherry gene under the control of the D. magna EF1α-1 promoter/enhancer. By CRISPR/Cas-mediated knock-in of this plasmid via non-homologous joining, we generated the transgenic D. magna that expresses mCherry ubiquitously, suggesting that the DR-GFP reporter gene is expressed in most cells. Introducing DSB on the SceGFP resulted in eGFP expression and this HDR event could be detected by fluorescence, genomic PCR, and quantitative reverse-transcription PCR, suggesting this line could be used for evaluating HDR. The established reporter line might expand our understanding of the HDR mechanism and also improve the HDR-based gene-editing system in this species.The energy transmission through micropolar fluid have a broad range implementation in the field of electronics, textiles, spacecraft, power generation and nuclear power plants. Thermal radiation's influence on an incompressible thermo-convective flow of micropolar fluid across a permeable extensible sheet with energy and mass transition is reported in the present study. The governing equations consist of Navier-Stokes equation, micro rotation, temperature and concentration equations have been modeled in the form of the system of Partial Differential Equations. The system of basic equations is reduced into a nonlinear system of coupled ODE's by using a similarity framework. The numerical solution of the problem has been obtained via PCM (Parametric Continuation Method). The findings are compared to a MATLAB built-in package called bvp4c to ensure that the scheme is valid. It has been perceived that both the results are in best agreement with each other. The effects of associated parameters on the dimensionless velocity, micro-rotation, energy and mass profiles are discussed and depicted graphically. It has been detected that the permeability parameter gives rise in micro-rotation profile.Genetic mutations cause a wide spectrum of human disease by disrupting protein folding, both during and after synthesis. Transient de-novo folding intermediates therefore represent potential drug targets for pharmacological correction of protein folding disorders. Here we develop a FRET-based high-throughput screening (HTS) assay in 1,536-well format capable of identifying small molecules that interact with nascent polypeptides and correct genetic, cotranslational folding defects. Ribosome nascent chain complexes (RNCs) containing donor and acceptor fluorophores were isolated from cell free translation reactions, immobilized on Nickel-NTA/IDA beads, and imaged by high-content microscopy. Quantitative FRET measurements obtained from as little as 0.4 attomole of protein/bead enabled rapid assessment of conformational changes with a high degree of reproducibility. Using this assay, we performed a pilot screen of ~ 50,000 small molecules to identify compounds that interact with RNCs containing the first nucleotide-binding domain (NBD1) of the cystic fibrosis transmembrane conductance regulator (CFTR) harboring a disease-causing mutation (A455E). Screen results yielded 133 primary hits and 1 validated hit that normalized FRET values of the mutant nascent peptide. This system provides a scalable, tractable, structure-based discovery platform for screening small molecules that bind to or impact the folding of protein substrates that are not amenable to traditional biochemical analyses.Long non-coding RNAs (LncRNAs) play vital roles in the tumorigenesis of many cancers. Single nucleotide polymorphisms (SNPs) of the lncRNA also play vital roles in tumorigenesis. We explored lncRNA rs944289 and rs7990916 polymorphisms and analyzed the relationship between these lncRNA polymorphisms with the colorectal cancer (CRC) risk in a Chinese population. We recruited 1003 CRC patients from the Affiliated People's Hospital of Jiangsu University and the Fujian Medical University Union Hospital from October 2014 to August 2017. Genomic DNA was extracted using a DNA Kit from lymphocytes of peripheral blood and the genotyping was performed with a SNPscan method. We found that the rs944289 TT homozygote was associated with the decreased CRC risk in the overall population. LncRNA rs944289 TT decreased the CRC risk in the subgroup of female, male, age ≥ 61, without alcohol intake, smoking and BMI ≥ 24 by logistic regression. The subgroup analysis revealed that lncRNA rs7990916 was not associated with CRC risk except for age  less then  61. Logistic regression analysis revealed that lncRNA rs944289 TT homozygote was associated with the increased risk of rectum cancer (TT vs. CC + CT adjusted OR = 1.29, 95% CI 1.10-1.66, P = 0.041) or colon cancer. In summary, we proved that lncRNA rs944289 might be significantly related to the decreased CRC risk in the Chinese Han populations and lncRNA rs7990916 was not associated with the CRC risk except for patients of age  less then  61. In the future, studies with larger samples should be conducted to validate our results.Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85-90%), specificity of 84% (79-87%), and AUC of 0.92 (0.90-0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.In this study, authors explore the application of modelling and additive layer manufacturing (ALM) for creating and testing materials with interlocking structures aimed to reduce the stress concentration along the edges of a typical lap joint. The effectiveness of this approach is discussed by means of modelling and experimental validation of joints with interlocking structures obtained by ALM. Considering the achieved results, ALM of interlocking structures constitutes an interesting alternative or complement to traditional joining processes, as it may help to minimize stress mismatches in the joining region. It may also prevent the use of adhesive or joining post processes, because the joint is created together with the joined components.