Rheumatoid arthritis (RA) is an inflammatory disorder with autoimmune pathogenesis characterized by the immune system attacking the synovium. It is a clinically heterogeneous disease that affects approximately 1.2 million Americans and 20 million people worldwide. It is advantageous to diagnose RA before extensive erosion as treatments are more effective at early stages. RA treatments have made notable progresses, yet a significant number of patients still fail to respond to current medication and most of these come with harmful side effects. While the mechanistic reason for such failure rates remains unknown, the cellular and molecular signatures in the synovial tissues of patients with RA are likely to play a role in the variable treatment response and heterogeneous clinical evolution. While blood-based criteria are currently employed for diagnostics and treatments. such serologic parameters do not necessarily reflect biological actions in the target tissue of the patient and are relatively nonspecific to RA. Synovial tissue-based biomarkers are especially attractive as they can provide a confirmed diagnosis for RA. The shortage of accurate synovial tissue-based identifiers for RA diagnosis encouraged this research. This study analyzed data from several gene expression studies for differentially expressed genes in donor synovial tissue. Bioinformatics tools were used to construct and analyse protein interaction networks. Analysis deduced that regulating hematopoietic stem cell migration could serve as a potential RA diagnostic. VAV1, CD3G, LCK, PTPN6, ITGB2, CXCL13, CD4, and IL7R are found to be previously unclassified, potential biomarkers.

Convolutional codes are error-correcting linear codes that utilize shift registers to encode. These codes have an arbitrary block size and they can incorporate both past and current information bits. DNA codes represent DNA sequences and are defined as sets of words comprised of the alphabet A, C, T, G satisfying certain mathematical bounds and constraints. The application of convolutional code models to DNA codes is a growing field of biocomputation. As opposed to block codes, convolutional codes factor in nearby information bits, which makes them an optimal model for representing biological phenomena. This study explores the properties of both convolutional codes and DNA codes, as well as how convolutional codes are applied to DNA codes. It also proposes revisions to improve a current convolutional code model for DNA sequences.

Rheumatoid Arthritis (RA) is an inflammatory autoimmune disease that affects 23 million people worldwide. It is a clinically heterogeneous disorder characterized by the attack of inflammatory chemicals on the synovial tissue that lines joints. It is advantageous to develop effective, targeted treatments and identify specific diagnostic biomarkers for RA before extensive joint degradation, bone erosion, and cartilage destruction. Current modes of RA treatments have alleviated and notably halted the progression of RA. Despite this, not many patients reach low disease activity status after treatment, and a significant number of patients fail to respond to medication due to drug non-specificity. While the reasons for these rates remain unknown, the cellular and molecular signatures present in the synovial tissue for RA patients likely play a role in the varied treatment response. Thus, a drug that particularly targets specific genes and networks may have a significant effect in halting the progression of RA. This study evaluates and proposes potential drug targets through in silico mathematical modeling of various pathways of interest in RA. To understand how drugs interact with genes, we built a mathematical model with 30 two-gene and three-gene network interactions and analyzed the effect of 92 different perturbations to rate constants. We determined that inhibition of the LCK-CD4, VAV1-CD4, and MLT-ROR pathways could potentially serve as drug targets. We also found that increased activity of the DEC2-IL1β and the NF-kB-interleukin pathway and the decreased activity of the TNF-α-REV-ERB pathway could serve as diagnostic biomarkers.