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We observed the localization of hub lncRNAs by RNA-FISH in human aortic smooth muscle cells and confirmed the upregulation of the hub lncRNAs in DCM patients through quantitative RT-PCR. In conclusion, these findings identified eight candidate lncRNAs associated with DCM disease and revealed their potential involvement in DCM partly through ceRNA crosstalk. Our results facilitate the discovery of therapeutic targets and enhance the understanding of DCM pathogenesis.Seed size and shape traits are important determinants of seed yield and appearance quality in soybean [Glycine max (L.) Merr.]. Understanding the genetic architecture of these traits is important to enable their genetic improvement through efficient and targeted selection in soybean breeding, and for the identification of underlying causal genes. To map seed size and shape traits in soybean, a recombinant inbred line (RIL) population developed from K099 (small seed size) × Fendou 16 (large seed size), was phenotyped in three growing seasons. A genetic map of the RIL population was developed using 1,485 genotyping by random amplicon sequencing-direct (GRAS-Di) and 177 SSR markers. Quantitative trait locus (QTL) mapping was conducted by inclusive composite interval mapping. As a result, 53 significant QTLs for seed size traits and 27 significant QTLs for seed shape traits were identified. Six of these QTLs (qSW8.1, qSW16.1, qSLW2.1, qSLT2.1, qSWT1.2, and qSWT4.3) were identified with LOD scores of 3.80-14.0 and R 2 of 2.36%-39.49% in at least two growing seasons. Among the above significant QTLs, 24 QTLs were grouped into 11 QTL clusters, such as, three major QTLs (qSL2.3, qSLW2.1, and qSLT2.1) were clustered into a major QTL on Chr.02, named as qSS2. The effect of qSS2 was validated in a pair of near isogenic lines, and its candidate genes (Glyma.02G269400, Glyma.02G272100, Glyma.02G274900, Glyma.02G277200, and Glyma.02G277600) were mined. The results of this study will assist in the breeding programs aiming at improvement of seed size and shape traits in soybean.Oral squamous cell carcinoma (OSCC) has a high mortality rate (∼50%), and the 5-year overall survival rate is not optimal. Cyto- and histopathological examination of cancer tissues is the main strategy for diagnosis and treatment. In the present study, we aimed to uncover immunohistochemical (IHC) markers for prognosis in Asian OSCC. From the collected 742 synthetic lethal gene pairs (of various cancer types), we first filtered genes relevant to OSCC, performed 29 IHC stains at different cellular portions and combined these IHC stains into 398 distinct pairs. Next, we identified novel IHC prognostic markers in OSCC among Taiwanese population, from the single and paired IHC staining by univariate Cox regression analysis. Increased nuclear expression of RB1 [RB1(N)↑], CDH3(C)↑-STK17A(N)↑ and FLNA(C)↑-KRAS(C)↑were associated with survival, but not independent of tumor stage, where C and N denote cytoplasm and nucleus, respectively. Furthermore, multivariate Cox regression analyses revealed that CSNK1E(C)↓-SHC1(N)↓ (P = 5.9 × 10-5; recommended for clinical use), BRCA1(N)↓-SHC1(N)↓ (P = 0.030), CSNK1E(C)↓-RB1(N)↑ (P = 0.045), [CSNK1E(C)-SHC1(N), FLNA(C)-KRAS(C)] (P = 0.000, rounded to three decimal places) and [BRCA1(N)-SHC1(N), FLNA(C)-KRAS(C)] (P = 0.020) were significant factors of poor prognosis, independent of lymph node metastasis, stage and alcohol consumption. An external dataset from The Cancer Genome Atlas HNSCC cohort confirmed that CDH3↑-STK17A↑ was a significant predictor of poor survival. Our approach identified prognostic markers with components involved in different pathways and revealed IHC marker pairs while neither single IHC was a marker, thus it improved the current state-of-the-art for identification of IHC markers.Adverse drug reactions (ADRs) remain associated with significant mortality. Delayed hypersensitivity reactions (DHRs) that occur greater than 6 h following drug administration are T-cell mediated with many severe DHRs now associated with human leukocyte antigen (HLA) risk alleles, opening pathways for clinical prediction and prevention. However, incomplete negative predictive value (NPV), low positive predictive value (PPV), and a large number needed to test (NNT) to prevent one case have practically prevented large-scale and cost-effective screening implementation. Additional factors outside of HLA contributing to risk of severe T-cell-mediated DHRs include variation in drug metabolism, T-cell receptor (TCR) specificity, and, most recently, HLA-presented immunopeptidome-processing efficiencies via endoplasmic reticulum aminopeptidase (ERAP). BAPTA-AM price Active research continues toward identification of other highly polymorphic factors likely to impose risk. These include those previously associated with T-cell-mediated HLA-associated infectious or auto-immune disease such as Killer cell immunoglobulin-like receptors (KIR), epistatically linked with HLA class I to regulate NK- and T-cell-mediated cytotoxic degranulation, and co-inhibitory signaling pathways for which therapeutic blockade in cancer immunotherapy is now associated with an increased incidence of DHRs. As such, the field now recognizes that susceptibility is not simply a static product of genetics but that individuals may experience dynamic risk, skewed toward immune activation through therapeutic interventions and epigenetic modifications driven by ecological exposures. This review provides an updated overview of current and proposed genetic factors thought to predispose risk for severe T-cell-mediated DHRs.Alzheimer’s disease (AD) is a neurodegenerative disease with unelucidated molecular pathogenesis. Herein, we aimed to identify potential hub genes governing the pathogenesis of AD. The AD datasets of GSE118553 and GSE131617 were collected from the NCBI GEO database. The weighted gene coexpression network analysis (WGCNA), differential gene expression analysis, and functional enrichment analysis were performed to reveal the hub genes and verify their role in AD. Hub genes were validated by machine learning algorithms. We identified modules and their corresponding hub genes from the temporal cortex (TC), frontal cortex (FC), entorhinal cortex (EC), and cerebellum (CE). We obtained 33, 42, 42, and 41 hub genes in modules associated with AD in TC, FC, EC, and CE tissues, respectively. Significant differences were recorded in the expression levels of hub genes between AD and the control group in the TC and EC tissues (P less then 0.05). The differences in the expressions of FCGRT, SLC1A3, PTN, PTPRZ1, and PON2 in the FC and CE tissues among the AD and control groups were significant (P less then 0.