Abstract Increasing evidence has indicated that the gut microbiota is altered in patients diagnosed with gastroesophageal reflux disease (GERD), the exact causal connection between them remains unknown. In this research, we conducted a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary data. The primary analysis employed the inverse-variance weighted (IVW) method. To assess the robustness of our findings, we also conducted additional analyses using the MR-Egger, weighted median, simple mode, and weighted mode methods. Heterogeneity and pleiotropy were examined through the Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis. The present study evaluated the potential causality of gut microbiota in the risk of GERD and found that 10 bacterial taxa, namely class Bifidobacteriaceae, family Christensenellaceae, family ClostridialesvadinBB60group, genus Anaerostipes, genus ChristensenellaceaeR, genus Coprococcus2, genus LachnospiraceaeUCG004, genus Prevotella9, genus Bifidobacteriales, phylum Actinobacteria, may be suggestively causally associated with the risk of GERD.
<p>5A. Classification accuracy of CMS 3 samples on training data set V1 using 4-fold cross validation as function of number of genes. 5B.Classification accuracy of CMS 3 samples on validation data set (V2) as function of number of genes. 5C. Classification accuracy CMS3 samples with out of sample prediction set (V2o) as function of number of genes. 5D. Classification accuracy CMS 3 samples with TCGA dataset (RAN-seq subset) as function of number of genes. 5E. Classification accuracy CMS3 samples with Affymetrix dataset (V2a) as function of number of genes.</p>
<p>Classification accuracy for training data V1 (based on 4-fold cross val-idation, 2A) and the full validation data V2 (2B) for various methods as function of number</p>
Purpose: Sensory referral is commonly experienced by patients after amputation, in which touches to the residual limb are perceived as originating from the phantom limb. This phenomenon offers a compelling interface for noninvasive delivery of sensory feedback in prosthetic limbs. However, a more comprehensive understanding of the complexities of the sensory experience is necessary, especially with the rising popularity of surgical techniques such as targeted muscle reinnervation (TMR) and regenerative peripheral nerve interfaces (RPNI). We hypothesize that in TMR and RPNI, repurposing and providing transected nerves with muscle targets for reinnervation may alter or induce patterns of referred sensation. Methods: To study this, we have developed a novel methodology for detailed sensory characterization of the residual limb, compatible for both upper and lower extremity amputation. We designed a computer program for mapping sensory referral, in which participants interact with a virtual representation of their phantom limb. With a touchscreen laptop, participants can zoom and rotate to see the entire limb. Light touch, deep pressure, and vibratory stimuli are presented to the residual limb, and participants tap on the virtual limb to mark the location of the perceived sensation. Results: Our software interface is intuitive for participants to use and is designed to provide a standardized testing methodology, such that any researcher may quickly and confidently perform rigorous, repeatable sensory characterization of a limb. It also minimizes experimenter bias by eliminating ambiguity in verbal responses; participants tap directly on the virtual limb where the feeling appears to originate without needing to verbally describe that anatomical location. The data points on the virtual limb can then be correlated with the tested points on the residual limb to generate a map of referred sensation. Conclusion: Providing an egocentric virtual body for sensory testing enables participants to indicate with high accuracy where a stimulus is perceived on their phantom limb. With our innovative tool, we can screen for individuals with sensory referral and generate complete sensory referral mappings of the residual limb. Aspects of sensory capabilities can be compared across individuals and groups, including surface area of referring skin and rate of referral to peripheral nerve distributions or phantom limb anatomy. Our systematic methodology will facilitate unbiased explorations of sensory referral after amputation, especially after TMR and RPNI surgery.
The incidence of colorectal cancer in men is higher than that in women. The role of androgen receptor in colorectal cancer was analyzed by bioinformatics to determine the role and mechanism of androgen receptor on colorectal cancer and explore the potential genes and pathways involved in the occurrence and development of colorectal cancer.
Methods
The 'GEOquery' R package, 'stringr' R package, and 'limma' R package were used to download, sort out, and screen gene expression profiles of colorectal cancer tissues and normal tissues. Go and KEGG enrichment analysis of differentially expressed genes were performed by 'Clusterprolifer' R package and 'kegg_plot_function' R package. The protein interaction network among differentially expressed genes was obtained from the STRING database, and a PPI map was drawn with the visual data of Cytoscape. GEPIA database analyzed the correlation between the gene expression level of androgen receptor and colorectal cancer Hub gene. Cytohubba plug-in obtained the top six core genes. The correlation between androgen receptor expression level and the basic information of colorectal cancer patients was analyzed in the UALACN database.
Results
1864 up-regulated and 647 down-regulated differentially expressed genes were screened. The core genes were JUN, FOS, DUSP1, AF3, FOSB, and EGR1. The expression level of androgen receptor was significantly correlated with DUSP1. The expression level of androgen receptor was significantly decreased in colorectal cancer tissues (p<0.05). The expression level of androgen receptor was significantly correlated with age (61–80 years, 81–100 years), clinical stage (I, II, III), sex, and lymph node metastasis (N0, N1) (p<0.05). The expression level of androgen receptor was significantly decreased in mucinousa denocarcinoma type (p<0.05).
Conclusions
Androgen receptor participated in the regulation of colorectal cancer through correlation with DUSP1, which is expected to provide new ideas for the prevention and treatment of colorectal cancer.
Abstract Purpose: Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cancer and implemented the assay in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory. Experimental Design: We performed an in silico experiment to build an optimal CMS classifier using a training set of 1,329 samples from 12 studies and validation set of 1,329 samples from 14 studies. We constructed an assay on the basis of NanoString CodeSets for the top 472 genes, and performed analyses on paired flash-frozen (FF)/FFPE samples from 175 colorectal cancers to adapt the classifier to FFPE samples using a subset of genes found to be concordant between FF and FFPE, tested the classifier's reproducibility and repeatability, and validated in a CLIA-certified laboratory. We assessed prognostic significance of CMS in 345 patients pooled across three clinical trials. Results: The best classifier was weighted support vector machine with high accuracy across platforms and gene lists (>0.95), and the 472-gene model outperforming existing classifiers. We constructed subsets of 99 and 200 genes with high FF/FFPE concordance, and adapted FFPE-based classifier that had strong classification accuracy (>80%) relative to “gold standard” CMS. The classifier was reproducible to sample type and RNA quality, and demonstrated poor prognosis for CMS1–3 and good prognosis for CMS2 in metastatic colorectal cancer (P < 0.001). Conclusions: We developed and validated a colorectal cancer CMS assay that is ready for use in clinical trials, to assess prognosis in standard-of-care settings and explore as predictor of therapy response.
Objective To study the effect of Three- Integration community healthcare practice model on cultivation of students who major in preventive medicine and to improve their comprehensive quality. Methods A total of 300 preventive medicine students and graduates from one medical university in Guangzhou were surveyed with the method of random sampling by using questionnaire. Information was gathered about the impact of this model on the students' social responsibilities,knowledge structure,interpersonal skills,adversity quotient,and practical and innovative capabilities. Moreover,26 staffs from the university and practice bases were interviewed randomly about the effect of community health practice of the Preventive Medicine College students,the cooperation between university and bases,and the improvement of management system under Three- Integration community healthcare practice model. Results The result of questionnaire indicated that 94. 5% students agreed the mode could promote social responsibilities; over 90. 0% students approved that the mode could help them to perfect knowledge structure; over 95% students thought the mode could elevate interpersonal skills; over 87% students agreed the mode contributed to the improvement of adversity quotient; over 84% students considered the mode could enhance their practical and innovative capabilities. The interview result confirmed the good effect of the Three- Integration model but showed but the necessity of improvement in the aspects of manage system,course mode and cooperation connotation. Conclusion Three- Integration facilitates comprehensive quality of undergraduate students in preventive medicine program. However,the system,curriculum design,and connotation remain to be improved.
<p>Nanostring CMS Classifier Performance: Performance of Nanostring 100 gene CMS classifier applied to FFPE samples (6A and 6C) and FF samples (6B and 63D). The top two figures plot 4-class accuracy vs. classification confidence, with the dots marking individual samples either correctly (1.0) or incorrectly (0.0), with color indicat-ing correct CMS. The line contains a generalized additive model (GAM) fit to these data with 95% pointwise confidence bands, and demonstrates that samples classified with greater confidence were more likely to be cor-rectly classified. Figure 6C and 6D plot 4-class accuracy vs. RNA quality, defined as 0nt (FFPE) or RIN (FF). Note that there is little if any association of CMS accuracy with RNA quality, suggesting that the perfor-mance of classifier is robust to RNA quality in this study.</p>
