Damian Sendler Diagnosis labels may influence treatment plans. We looked at the impact of labeling low back pain (LBP) on beliefs about imaging, second opinions, surgery, seriousness, recovery, work and physical activities. We found that labeling LBP had a significant impact.
Damian Jacob Sendler Using a six-arm, online, blinded experiment, participants with and without low back pain were randomized. Labels like “disc bulge” were given to participants, as well as the more specific ones like “arthritis” and “lumbar sprain,” which were given to those who didn’t fit the other categories. The most important result was a shift in attitudes toward imaging.
Dr. Sendler Results: There were 1375 participants (mean [SD] age, 41.7 years [18.4 years]; 54.4 percent women) in total. When compared to the labels “episode of back pain” (6.0 [2.9]), “lumbar sprain”, and “non-specific LBP” the need for imaging was rated lower with the labels “arthritis” (4.2 [2.9]), “degeneration” and “disc bulge” (5.7 [3.1]). In comparison to “disc bulge” “degeneration” and “arthritis” these labels led to higher recovery expectations and lower ratings of the need for a second opinion, surgery, and perceived seriousness. LBP sufferers with a history of seeking medical attention were more likely to be affected by the study results. There were no differences in the six labels’ beliefs about physical activity and work.
“Episode of back pain,” “lumbar sprain,” and “non-specific LBP” reduced the need for imaging, surgery, and a second opinion among the public and patients with LBP, as well as reducing the perceived severity of LBP and increasing recovery expectations, according to the findings. Labels appear to have the greatest impact on those who are most likely to have a negative outcome (participants with current LBP who had a history of seeking care).
Activation of AAC and UCP1 by mitochondrial uncouplers causes proton leakage.
H+ leakage (IH) through the inner membrane of mitochondria generates heat. Long-chain fatty acids act on uncoupling protein 1 (UCP1) in brown fat2-6 and on ADP/ATP carrier (AAC) in other tissues1,7-9 to cause IH, but the mechanism by which this occurs is still not fully elucidated1. IH is induced by protonophores such as 2,4-dinitrophenol (DNP) and cyanide-4-(trifluoromethoxy) phenylhydrazone (FCCP)10,11, as evidence for pharmacological activators of IH through UCP1 and AAC is lacking. The clinical potential of protonophores for treating human disease is limited due to their indiscriminately increasing H+ conductance across all biological membranes10,11 and adverse side effects15, despite their success in animal models in combating obesity, diabetes, and fatty liver. IH induced by DNP, FCCP, and other common protonophores can be directly measured, and we found that it depends on AAC and UCP1. The binding sites for protonophores and long-chain fatty acids are found to overlap with the putative ADP/ATP-binding site by performing a computational analysis on the molecular structures of AAC. Mathematically, we propose an uncoupler-dependent IH mechanism via AAC. AAC and UCP1 can be activated by protonophoric uncouplers, paving the way for the development of new and more specific mitochondrial bioenergetics activaters.
Barrier function and the skin’s microbiota
Damian Jacob Markiewicz Sendler Our skin serves as a first line of defense against toxins, radiation, and pathogens. Besides protecting our internal organs and tissues, defining our appearance, and providing a sensory interface, our skin also acts as a barrier against dehydration. Protecting the skin from disease-causing microorganisms, tuning the immune system, and enhancing the epithelium are all functions of the colonizing microbiota that reside on and in the skin. To better understand how the microbiota affects multiple aspects of skin barrier function, we have highlighted recent advances in our knowledge. We discuss recent findings in the field of pathological host-microbiota interactions and their implications for skin and other systemic diseases. Last but not least, we look at how microbiota-based mechanisms can be targeted in order to prevent or manage skin disorders and impaired wound recovery.
Cellular roles and applications of circular RNAs are discussed in this article
Damian Jacob Sendler
Back-splicing of exons from precursor mRNAs is the most common method for producing circular RNAs. Circular conformation and sequence overlap with linear cognate mRNAs have been overcome by recent technological advances, allowing better understanding of their cellular roles. A variety of biological and pathological processes can be affected by circular RNAs, depending on their location and specific interactions with DNA, RNA, and proteins. Circular RNAs can also influence transcription and splicing, cytoplasmic mRNA stability and translation, signaling pathways, and act as translation templates. New biomedical research applications of RNA circles, such as interfering with cellular processes, modulating immune responses, and directing translation into proteins, are emerging. Here, we present an overview of the current understanding of the regulatory roles of circular RNAs and discuss their future potential applications.
Cancer survival analysis platform Survival Genie, a web-based tool.
Damien Sendler It has become common practice to use genomics data to identify gene signatures and pathways in order to make predictions about cancer survival and treatment choices. Despite the development of a large number of packages and tools, the ability to perform such analysis based on pathways, gene sets, and gene ratios is lacking. In addition, cluster markers from cancer single-cell transcriptomics studies remain an underutilized prognostic option in this era of single-cell omics.. There is a lack of online bioinformatics tools that can assess whether the enrichment of canonical cell types in cancers correlates with survival. As a result, we have created Survival Genie, a web-based tool that can perform survival analysis on RNA-Seq data as well as a variety of other molecular inputs, including gene sets and ratios of tumor-infiltrating immune cells. There are 53 datasets of 27 distinct malignancies from 11 cancer programs in Survival Genie for a comprehensive analysis of adult and pediatric cancers. Gene expression partitioning can be done using any of several methods, including scRNA-seq data or entire gene sets, and the impact of expression levels on survival outcomes can be assessed using a variety of methods. Box plots of low and high-risk groups, Kaplan-Meier plots with univariate Cox proportional hazards model, and correlation of immune cell enrichment and molecular profile are provided by the tool.
Systemic lupus erythematosus can be studied using a transcriptome-wide association study of immune cell transcripts.
These genetic discoveries are hindered by the fact that the disease genes at most of the risk loci have not been identified by genome-wide association studies [GWAS], which have identified more than 100 risk factors for SLE. It was our goal to prioritize the 110 SLE loci identified in the most recent East Asian GWAS meta-analysis of SLE genes.
A total of 105 Japanese individuals were studied to build gene expression predictive models in blood B cells, CD4+ and CD8+ T cells, monocytes, natural killer cells, and peripheral blood cells. The latest genome-wide association meta-analysis of 208 370 East Asians was used to conduct a transcriptome-wide association study (TWAS) and three data-driven computational approaches were used to look for potential candidate genes.
Analysis of GWAS data revealed 127 (74.3 percent) SLE loci to contain 171 significant SLE genes (p1.010-5). Out of these, 114 (66.7 percent) showed significance only in a single cell type. An association between CD83 and SLE was found by TWAS with a p-value of 7.710-8. A new single-variant association was found at rs72836542 (OR=1.11, p=4.510-9) around CD83 in a meta-analysis of genetic associations in the existing 208 370 East Asian and additional 1498 cases and 3330 controls. Among the 110 SLE loci, 276 gene candidates were identified, including 104 genes at newly discovered SLE novel loci. rs61759532 showed an allele-specific effect on ACAP1 expression, and the presence of the SLE risk allele reduced ACAP1 expression in vitro.
SLE gene discovery was enhanced and novel genetic associations were discovered using cell-level TWAS in six different immune cell types. SLE genetic associations can now be explained biologically thanks to gene findings.