filtertips 10 ul long, bag
- Known as:
- filtertips 10 ul large, bag
- Catalog number:
- 5131060C
- Product Quantity:
- 1000
- Category:
- -
- Supplier:
- Capp
- Gene target:
- filtertips 10 long bag
Related genes to: filtertips 10 ul long, bag
- Gene:
- AHCYL2 NIH gene
- Name:
- adenosylhomocysteinase like 2
- Previous symbol:
- -
- Synonyms:
- KIAA0828, long-IRBIT, IRBIT2
- Chromosome:
- 7q32.1
- Locus Type:
- gene with protein product
- Date approved:
- 2008-01-17
- Date modifiied:
- 2019-03-26
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- Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. The prognostic relationship between CMLS and STAD was assessed using Kaplan-Meier curves, and the independent prognostic value of CMLS was determined by univariate and multivariate regression analyses. we also evaluated the immune characteristics, immunotherapy response, and drug sensitivity of different CMLS groups. The results of the multiomics analysis classified STAD into three subtypes, with CS1 resulting in the best survival outcome. In total, 10 hub genes (CES3, AHCYL2, APOD, EFEMP1, CYP1B1, ASPN, CPE, CLIP3, MAP1B, and DKK1) were screened and constructed the CMLS was significantly correlated with prognosis in patients with STAD and was an independent prognostic factor for patients with STAD. Using the CMLS risk score, all patients were divided into a high CMLS group and a low CMLS group. Patients in the low-CMLS group had better survival, more enriched immune cells, and higher tumor mutation load scores, suggesting better immunotherapy responsiveness and a possible "hot tumor" phenotype. Patients in the high-CMLS group had a significantly poorer prognosis and were less sensitive to immunotherapy but were likely to benefit more from chemotherapy and targeted therapy. In this study, 10 clustering methods and 10 machine learning methods were combined to analyze the multiomics of STAD, classify STAD into three subtypes, and constructed CMLS-related prognostic model features, which are important for accurate management and effective treatment of STAD. - Source: PubMed
Publication date: 2025/01/30
Wang MiaodongHe QinChen ZeshanQin Yijue - The primary goal of this investigation was to identify mRNA targets affected by dysregulated miRNAs in RIF. This was accomplished by comprehensively analyzing mRNA and miRNA expression profiles in two groups: female subjects with normal reproductive function (control, n = 5) and female subjects experiencing recurrent implantation failure (RIF, n = 5). We conducted transcriptome sequencing and small RNA sequencing on endometrial tissue samples from these cohorts. Subsequently, we validated a selection of intriguing findings using real-time PCR with samples from the same cohort. In total, our analysis revealed that 929 mRNAs exhibited differential expression patterns between the control and RIF patient groups. Notably, our investigation confirmed the significant involvement of dysregulated genes in the context of RIF. Furthermore, we uncovered promising correlation patterns within these mRNA/miRNA pairs. Functional categorization of these miRNA/mRNA pairs highlighted that the differentially expressed genes were predominantly associated with processes such as angiogenesis and cell adhesion. We identified new target genes that are regulated by miR-665, including Blood Vessel Epicardial Substance (BVES) and Adenosylhomocysteinase like 2 (AHCYL2). Our findings suggest that abnormal regulation of genes involved in angiogenesis and cell adhesion, including BVES and AHCYL2, contributes to the endometrial dysfunction observed in women with recurrent implantation failure (RIF) compared to healthy women. - Source: PubMed
Publication date: 2024/02/15
Cho Sung HwanKim Young MyeongAn Hui JeongKim Ji HyangKim Nam Keun - Popular culture and medical lore have long postulated a connection between full moon and exacerbations of psychiatric disorders. We wanted to empirically analyze the hypothesis that suicides are increased during the period around full moons. We analyzed pre-COVID suicides from the Marion County Coroner's Office (n = 776), and show that deaths by suicide are significantly increased during the week of the full moon (p = 0.037), with older individuals (age ≥ 55) showing a stronger effect (p = 0.019). We also examined in our dataset which hour of the day (3-4 pm, p = 0.035), and which month of the year (September, p = 0.09) show the most deaths by suicide. We had blood samples on a subset of the subjects (n = 45), which enabled us to look at possible molecular mechanisms. We tested a list of top blood biomarkers for suicidality (n = 154) from previous studies of ours , to assess which of them are predictive. The biomarkers for suicidality that are predictive of death by suicide during full moon, peak hour of day, and peak month of year, respectively, compared to outside of those periods, appear to be enriched in circadian clock genes. For full moon it is AHCYL2, ACSM3, AK2, and RBM3. For peak hour it is GSK3B, AK2, and PRKCB. For peak month it is TBL1XR1 and PRKCI. Half of these genes are modulated in expression by lithium and by valproate in opposite direction to suicidality, and all of them are modulated by depression and alcohol in the same direction as suicidality. These data suggest that there are temporal effects on suicidality, possibly mediated by biological clocks, pointing to changes in ambient light (timing and intensity) as a therapeutically addressable target to decrease suicidality, that can be coupled with psychiatric pharmacological and addiction treatment preventive interventions. - Source: PubMed
Publication date: 2023/04/03
Bhagar RLe-Niculescu HRoseberry KKosary KDaly CBallew AYard MSandusky G ENiculescu A B - Schizophrenia (SCZ) is a prevalent, severe, and persistent mental disorder with an unknown etiology. Growing evidence indicates that immunological dysfunction is vital in the development of SCZ. Our study aims to uncover potential immune-linked hub genes and immune infiltration characteristics of SCZ, as well as to develop a diagnostic model based on immune-linked central genes. GSE38484 and GSE54913 chip expression data for patients with SCZ and healthy controls were retrieved. Weighted gene co-expression network analysis (WGCNA) was performed to identify major module genes and critical immune-linked genes. Functional enrichment analysis was conducted to elucidate the involvement of key genes in the immunological response to SCZ, along with the examination of their protein interactions. Moreover, 202 peripheral blood samples were examined using the single-sample gene set enrichment analysis (ssGSEA) method to detect distinct immune cell types. Hub immune-linked genes in SCZ were identified using the minimal absolute contraction and selection operator analysis. Receptor profiles of central immune-linked genes were analyzed to distinguish the two groups. Finally, the association between immune-linked hub genes and various types of immune cells was assessed. Our findings revealed ten immune cell types and nine key genes involved in SCZ, including effector memory CD4+ T cells, activated CD8+ T cells, mast cells, naive CD8+ T cells, PBMC, type 17 helper cells (Th17), central memory CD8+ T cells, CD56 bright NK cells, memory B cells, and regulatory T cells. Diagnostic models constructed using LASSO regression exhibited an average area under the curve (AUC) of 0.866. Our results indicate immunological dysfunction as a factor in the development of SCZ. ASGR2, ADRM1, AHANK, S100A8, FUCA1, AKNA, GATA3, AHCYL2, and PTRH2 are the key regulatory genes of immune cells, highlighting their potential as novel therapeutic targets for SCZ. - Source: PubMed
Publication date: 2023/08/08
Lian KunShen ZonglinYang RunxuYe JingShang BinliDong LeiLi HongfangWu JiabingCheng YuqiXu Xiufeng - Strontium (Sr) belongs to the same group in the periodic table as calcium (Ca). Sr level can serve as an index of rumen Ca absorption capacity; however, the effects of Sr on Ca metabolism are unclear. This study aims to investigate the effect of Sr on Ca metabolism in bovine rumen epithelial cells. The bovine rumen epithelial cells were isolated from the rumen of newborn Holstein male calves ( = 3, 1 day old, 38.0 ± 2.8 kg, fasting). The half maximal inhibitory concentration (IC50) of Sr-treated bovine rumen epithelial cells and cell cycle were used to establish the Sr treatment model. Transcriptomics, proteomics, and network pharmacology were conducted to investigate the core targets of Sr-mediated regulation of Ca metabolism in bovine rumen epithelial cells. The data of transcriptomics and proteomics were analyzed using bioinformatic analysis (Gene Ontology and Kyoto Encyclopedia of genes/protein). Quantitative data were analyzed using one-way ANOVA in GraphPad Prism 8.4.3 and the Shapiro-Wilk test was used for the normality test. Results presented that the IC50 of Sr treatment bovine rumen epithelial cells for 24 h was 43.21 mmol/L, and Sr increased intracellular Ca levels. Multi-omics results demonstrated the differential expression of 770 mRNAs and 2436 proteins after Sr treatment; network pharmacology and reverse transcriptase polymerase chain reaction (RT-PCR) revealed Adenosylhomocysteine hydrolase-like protein 2 , Semaphoring 3A , Parathyroid hormone-related protein , Transforming growth factor β (, and Cholesterol side-chain cleavage enzyme as potential targets for Sr-mediated Ca metabolism regulation. Together these results will improve the current comprehension of the regulatory effect of Sr on Ca metabolism and pave a theoretical basis for Sr application in bovine hypocalcemia. - Source: PubMed
Publication date: 2023/05/27
Tan PanpanZhao ChenxuDong YongZhang ZixinMei LinshanKong YeziZeng FangyuanWen YongqiangZhao BaoyuWang Jianguo