Gene expression database

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A comparative gene expression database for invertebrates

A comparative gene expression database for invertebrates

The present comparative gene expression database allows storing, querying and sharing of data not only with the research community, but also in a more restricted way with a group of collaborators or at the level of a single laboratory. In its current state, this tool has been used to track and store the content of gene expression patterns from current and former lab mem- bers allowing new studies based on these resources. As described above, a few steps are sufficient to retrieve the expression pattern of a single gene, compare it to genes expressed in a similar way or even identify putative syn- expression groups in order to predict genetic interac- tions that can be tested with functional experiments. All available information is tightly linked to the user as well as the laboratory he is associated with, ensuring that each user and laboratory gets proper credit for their contributions. We have a strong focus on making simple and intuitive interfaces and we believe that this com- parative gene expression pattern database in its current state will be a useful tool for the research community and students interested in zoology, and evolutionary and developmental biology. Further improvements and addi- tions to the existing database in the future will further enhance its usability for the Evo-Devo community. By integrating scientific data, educational material and gen- eral information about animals on a community plat- form we hope to improve scientific outreach as well as provide students and teachers with means to study and to interact directly with the research community.
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NvERTx: a gene expression database to compare embryogenesis and regeneration in the sea anemone Nematostella vectensis

NvERTx: a gene expression database to compare embryogenesis and regeneration in the sea anemone Nematostella vectensis

More recently, Nematostella has shown to be a powerful model for regeneration. Upon bisection, Nematostella is capable of regenerating the missing body half after ∼ 6 days postamputation (Bossert et al., 2013). Following subpharyngeal amputation (head removal), regeneration occurs via a highly dynamic process: first, there is an initial wound healing phase of ∼ 6 h, then regeneration follows a stereotypic program in which the mesenteries fuse and, via subsequent cell proliferation, reform the missing pharynx and tentacles over the course of 6 days (Amiel et al., 2015). This process has been shown to be both cell proliferation dependent (Passamaneck and Martindale, 2012) and utilize dynamic tissue rearrangement, with large portions of unamputated tissue contributing to the reformed tissue (Amiel et al., 2015). The existence of adult stem cells and the role they might play in regeneration have yet to be uncovered. This process is known to use several developmental signaling pathways originally deployed during embryogenesis (DuBuc et al., 2014; Schaffer et al., 2016; Trevino et al., 2011). It remains unclear, however, if these pathways are deployed the same way, i.e. with similar or divergent regulatory logic. One way to address this question is to systematically compare gene expression profiles during embryonic development and regeneration, to identify groups of genes originally used during embryogenesis that are re-used during regeneration. To facilitate this line of study, we created N.vectensis Embryogenesis and
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CSIOVDB: a microarray gene expression database of epithelial ovarian cancer subtype

CSIOVDB: a microarray gene expression database of epithelial ovarian cancer subtype

age of the CSIOVDB samples is 58 years (Figure 2D). No menopausal information is available. Ovarian cancer grading is assessed either by FIGO (64.4%) or by the University of Texas M. D. Anderson Cancer Center [17] system (1.6%). High-grade ovarian cancers form the majority of CSIOVDB (63.27%; Figure 2E). Optimal (27.35%) and suboptimal (15.19%) surgical debulking status is also noted (Figure 2F), as this status is associated with ovarian cancer survival [18]. Surprisingly, whereas the surgical debulking status is associated with survival, this parameter does not contribute significantly to the molecular differences in ovarian cancer (Suppl. Figure 6D). Overall and disease-free survival data are available for 1,868 and 1,516 samples, respectively, with a median overall survival of 31.67 months and median disease-free survival of 17.09 months (Suppl. Table 2). Finally, molecular subtyping and EMT scores are provided in CSIOVDB. The database comprises 11.75% of ovarian cancer with an Epi-A subtype, 29.04% with Epi-B, 29.01% with Mes, 19.2% with Stem-A and 8.23% with Stem-B ovarian cancer; this spread of tumors mirrors the distribution of previous analyses [5] (Suppl. Figure 6A). Thus, overall, CSIOVDB represents a large and diverse collection of ovarian cancer that could be useful for assessing a gene of interest.
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GENT2: an updated gene expression database for normal and tumor tissues

GENT2: an updated gene expression database for normal and tumor tissues

and expanded the GENT database in terms of datasets and novel useful functions. The number of different tissues increased from 57 to 72, and new functions such as subtype profiling, various statistical tests, and meta-survival analysis were added. We also adopted recent technologies such as such the GWT web frame- work and a Lucene indexing machine to provide more user-friendly web experiences. With those improvements in both data volume and novel functions, GENT2 will continue to be a useful tool to help researchers in the field of cancer genomics. As RNA-seq has become the de facto standard method for exploring gene expression, we plan to add gene expression datasets produced by RNA-seq in the future version of GENT.
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BrassicaEDB: A gene expression database for Brassica crops

BrassicaEDB: A gene expression database for Brassica crops

Abstract: The Brassica family contains several economically important crops, including rapeseed (Brassica napus, 2n = 38, AACC), the second largest source of seed oil and protein meal worldwide. However, research in rapeseed is hampered because it is complicated and time-consuming for researchers to access different types of expression data. We therefore developed the Brassica Expression Database (BrassicaEDB, https://biodb.swu.edu.cn/brassica/) for the research community. We conducted RNA sequencing (RNA-Seq) of 103 tissues from rapeseed cultivar ZhongShuang11 (ZS11) at seven developmental stages (seed germination, seedling, bolting, initial flowering, full- bloom, podding, and maturation). We determined the expression patterns of 101,040 genes via FPKM analysis and displayed the results using the eFP browser. We also analyzed transcriptome data for rapeseed from 70 BioProjects in the SRA database and obtained three types of expression level data (FPKM, TPM, and read counts). We used this information to develop the BrassicaEDB, including eFP, Treatment, Coexpression, and SRA Project modules based on gene expression profiles and Gene Feature, qPCR Primer, and BLAST modules based on gene sequences. The BrassicaEDB provides comprehensive gene expression profile information and a user-friendly visualization interface for Brassica crop researchers. Using this database, researchers can quickly retrieve the expression level data for target genes in different tissues and in response to different treatments to elucidate gene functions and explore the biology of rapeseed at the transcriptome level.
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The Rice Oligonucleotide Array Database: an atlas of rice gene expression

The Rice Oligonucleotide Array Database: an atlas of rice gene expression

These rice microarray platforms have been successfully used in characterizing gene expression profiles from dif- ferent tissues and organs (Wang et al. 2010), different cell types (Jiao et al. 2009), under biotic and abiotic treatment conditions (Jung et al. 2008b; Swarbrick et al. 2008; Jung et al. 2010), identification of alternative splice (Jung et al. 2009) and mutants (Bruce et al. 2009). As a result, an increasing number of rice microarray datasets are being deposited in public repositories such as the Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) (Barrett et al. 2009), the ArrayExpress at the European Bioinformatics Institute (EBI) (Parkinson et al. 2007) and the Center for Information Biology gene EXpression database (CIBEX) at the DNA Data Bank of Japan (DDBJ) (Ikeo et al. 2003). There are also several databases that allow for ef- ficient access and data mining of collections of micro- array data for rice (Table 2). For example, the Rice Expression Profile Database (RiceXPro, http://ricexpro. dna.affrc.go.jp/), which is based on the Agilent 44K microarray, provides an overview of the spatiotemporal gene expression profiles of various organs and tissues (Sato et al. 2011). Genevestigator (https://www.geneves- tigator.ethz.ch/) provides a meta-analysis toolbox to ex- plore gene expressions across a wide variety of biological contexts for rice and other species, but it is commercial and not completely publicly available (Hruz et al. 2008). Other databases including OryzaExpress (Hamada et al. 2011), RicePLEX within the Plant Expression Database (PLEXdb) (Dash et al. 2012), Bio-Array Resource for Plant Biology (BAR) (Toufighi et al. 2005) and Yale Vir- tual Center for Cellular Expression Profiling of Rice (Jiao et al. 2009) are useful for expression pattern analysis of rice genes. Although general agreement between different
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Detecting microRNA activity from gene expression data

Detecting microRNA activity from gene expression data

Arora and Simpson [14] used a combination of three different statistical tests to detect miRNA signatures from gene expression data, the wilcoxon rank sum test, the 'rank ratio test' [16] , and the absolute expression t-test. They used these tests to identify tissue specific miRNAs in both human and mouse, based primarily around TargetScan predictions. Cheng and Li [15] , use an enrichment score, where a ranked vector of genes is compared to a ranked vector of degenerated binding score profiles in which miRNA target prediction binding scores (from miRanda [11]), above and below a certain threshold are set to 1 and 0 respectively. This is similar to the gene set enrichment algorithm (GSEA) [17]. They identified the activity enhancement of miRNAs that were transfected into HeLa cells and showed that their method performed better then GSEA and the wilcoxon test.
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Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia

Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia

Whole genome molecular analysis is a promising source of clinically useful prognostic biomarkers in AML. The prognosis of AML is partly driven by genetic factors, and a combination of multiple genes contributes to the improve- ment of prognostic predictive accuracy. In the present study, we extract the AML gene expression profile dataset and corresponding survival information from GSE12417 and TCGA for whole genome survival analysis. We identified 11 genes associated with AML prognosis and constructed and validated a prognostic signature composed of the 11 genes. An assessment by time-dependent ROC curve analysis demonstrated that the prognostic signature of the 11 genes showed a good performance for predicting 1-, 3-, and 5-year OS of AML patients in the three cohorts.
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<p>Downregulation of RRM2 Attenuates Retroperitoneal Liposarcoma Progression via the Akt/mTOR/4EBP1 Pathway: Clinical, Biological, and Therapeutic Significance</p>

<p>Downregulation of RRM2 Attenuates Retroperitoneal Liposarcoma Progression via the Akt/mTOR/4EBP1 Pathway: Clinical, Biological, and Therapeutic Significance</p>

Activation of intracellular signals is an important fac- tor for tumor development and progression. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of upregulated differentially expressed genes (DEGs) (DEGs were obtained from GSE21122 dataset in GEO database through bioinfor- matics analysis) 16 revealed that RRM2 was related to p53 signaling pathway in LPS (Table S2). In order to further explore the mechanism of RRM2 downregulation in the progression of PLPS, we carried out protein micro- array analysis. We found the downregulation of phospho- Akt (Ser473), phospho-mTOR (Ser2448) and phospho- PRAS40 (Thr246) after RRM2 downregulation through the protein microarray analysis. PRAS40 is not only a substrate for Akt, but also a component of mTOR complex 1 (mTORC1), which connects Akt to the mTOR pathway. 27 We inferred that the changes in biolo- gical behaviors after RRM2 downregulation may be related to Akt/mTORC1 signaling pathway. What is more, p70-S6K and 4EBP1 are two major downstream effectors of mTORC1. According to the protein micro- array analysis, downregulation of RRM2 resulted in upre- gulation of phospho-p70-S6K in both 94T778 and SW872 cells, so it is not regulated through the Akt/ mTOR/p70-S6K pathway. On the other hand, 4EBP1 binds to the eukaryotic translation initiation factor 4E (EIF4E) to prevent the formation of the translation initia- tion complex, mTOR phosphorylates 4EBP1 to separate it from EIF4E, thereby forming the translation initiation complex. 28 Therefore, we can further deduce that the changes in biological behaviors after RRM2 downregula- tion may be connected to Akt/mTORC1/4EBP1 signaling pathway. This speculation was further veri fi ed by Western blot (Figure 11). Similar to our study, it was previously reported that scutellarin suppressed cell pro- liferation and promoted apoptosis in lung adenocarci- noma cells via AKT/mTOR/4EBP1 pathway. 29 In addition, by regulating Akt/mTOR/4EBP1 and other sig- naling pathways, salvianolic acid B protected endothelial progenitor cells from oxidative stress-mediated dysfunction. 30
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Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas

Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas

We have adopted the example of Burkitt lymphoma (BL). This is a highly proliferative neoplasm that occurs sporadically in North America and European countries, but also has a variant associated with HIV infection and an endemic form common in Africa which is associated with Epstein–Barr virus (EBV) [15]. The criteria used to establish a diagnosis of BL have varied since its original description based on morphologic grounds in the en- demic form, but it is now accepted that it is associated with translocation between the MYC oncogene and im- munoglobulin gene [16], normally in the absence of chromosomal translocations involving oncogenes associ- ated with diffuse large B cell lymphoma (DLBCL) [17, 18], and more recent studies have revealed further commonly associated mutations [19–21]. This is a case study of high clinical relevance, since treatment of BL requires intense chemotherapy [e.g. R-CODOX-M/ IVAC; rituximab, cyclophosphamide, vincristine (known as Oncovin), doxorubicin methotrexate, ifosfamide, eto- poside (known as Vepesid) and cytarabine ( known as Ara-C) [22], while in contrast DLBCL outcome is not im- proved by intensification of chemotherapy and is treated with a milder regime as first line therapy (e.g. R-CHOP; rituximab, cyclophosphamide, doxorubicin (known as hydroxydaunomycin), vincristine (known as Oncovin), prednisolone) [23]. However, a group of cases which are in- troduced as “B cell lymphoma, unclassifiable, with features intermediate between diffuse large B cell lymphoma and Burkitt lymphoma” [24] has received increased attention. These are likely to share some but not all pathogenetic features of classic BL, or arise as a result of alternative primary molecular events that nonetheless deregulate the common oncogenic pathways [25, 26]. This group appears to respond poorly to either intensive treatment or R-CHOP-like regimes [27–29], and the underlying mech- anism remains largely unknown and the appropriate treat- ment still needs to be established.
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Gene expression profiles and protein&ndash;protein interaction network analysis in AIDS patients with HIV-associated encephalitis and dementia

Gene expression profiles and protein&ndash;protein interaction network analysis in AIDS patients with HIV-associated encephalitis and dementia

To demonstrate the potential PPI correlations, all DEGs were mapped on the compiled data set of human interactome for the PPI network construction and microarray data enrichments analysis. The human interactome kindly provided by Dr C Lau- danna from the Laboratory of Cell Trafficking and Signal Transduction (University of Verona, Verona, Italy) represents nonredundant, undirected, and no-loop physical protein– protein binary interaction data set in Cytoscape sif format comprising HGNC (HUGO Gene Nomenclature Committee)- curated protein IDs compiled from different sources. Next, a PPI network was constructed by the Cytoscape v2.8 software platform 23 based on the PPI correlations.
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Expression analysis of anthocyanin gene induced under phosphorus starvation in maize genotypes with contrasting phosphorus use efficiency

Expression analysis of anthocyanin gene induced under phosphorus starvation in maize genotypes with contrasting phosphorus use efficiency

The total RNA was extracted by the hot phenol and lithium chloride precipitation method (Pawlowski et al., 1994). Then, 10 micro grams of total RNA was electrophoretically separated on 1.2% (w/v) denaturing formaldehyde agarose gel and blotted onto a nylon membrane (MAGNA Osmonics Inc., Minetonka, MN), following the manufacturer’s instructions. After blotting, the RNA was immobilized on the membrane by UV cross-linking (120 mJ) in a UV Stratalinker (Stratagene, La Jolla, CA, USA). The pre- hybridization was carried out for 2 to 4 h at 42 o C in a solution containing 50% (v/v) formamide, 5X Denhardt’s solution, 0.1% (w/v) SDS, 6X SSPE and 150 g/mL denatured salmon sperm DNA. DNA fragments labeled with 32 P-dCTP using DECA prime II TM DNA labeling kit (Ambion, Austin, TX) was used to probe the membranes. Hybridization was carried out with 10 6 cpm of the gene A1 probe/mL at 42 o C for 16 h in a fresh pre- hybridization buffer. The filters were initially washed twice for 10 min with a low stringency solution consisting of 2X SSC and 0.2% SDS (v/v), followed by a high stringency wash with 0.1X SSC and 0.1% SDS (v/v) at 42 o C for 10 min. Membranes were exposed to Kodak XAR-5 films.
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A database for poplar gene co-expression analysis for systematic understanding of biological processes, including stress responses

A database for poplar gene co-expression analysis for systematic understanding of biological processes, including stress responses

Summarized descriptions of a co-expression module are composed of three parts (Fig. 2). Namely, the fi rst part includes the information of a query gene identifi er, tightness index of the module (referred to as the network F-measure; NF), and module size (the number of genes). The second part represents “Descriptions” of individual genes included in the module, composed of probe identifi ers, representative public identifi ers, and information on the homologous Ara- bidopsis genes; i.e., AGI code, HF, gene names, short descriptions of the function, and GO biological processes, respectively. The third part provides information on “Spe- cifi c Experiments,” in which the co-expressed genes are specifi cally expressed, composed of standardized scores of the genes, sample names, experimental identifi ers of GEO, links to detailed description of experiments, and experiment titles. From the descriptions of the parts, users can obtain information on the module members that are specifi cally expressed under particular experiments.
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Analysis of melanopsin gene expression in the rabbit retina at different ages.

Analysis of melanopsin gene expression in the rabbit retina at different ages.

Total retinal RNA was isolated using the Trizol™ technique (Chomczynski and Sachi, 1987). RNA quality was analyzed on a 0.8% agarose gel and quantified using a Nanodrop spectrophotometer (Thermo-Fisher). A sample of 1 µg RNA was reverse transcribed with the MMLV Reverse Transcriptase kit (Promega), and the resulting cDNA (1 µg) was used for polymerase chain reaction (PCR) using specific primers for the rabbit OPN4 gene, the gan- glion cell-specific-Thy-1 gene, and the constitutive GAPDH gene. PCR primers were designed in our laboratory using the FAST-PCR software: OPN4 (forward 5'-ATTATCAACCTCGCGG TCAG-3', reverse 5'-CCGTCAGTGTGATCATGGAG-', expected size 163 bp); Thy-1 (for- ward 5'-GCTGCTGACAGTCTTGCAGGTG-3', reverse 5'-ACGCGCAGTTCGCAGGTGTA -3', expected size 297 bp), and GADPH (forward 5'-AGGTCATCCACGACCACTTC-3', re- verse 5'-GTGAGTTTCCCGTTCAGCTC-3', expected size 204 bp). PCR OPN4 conditions consisted of an initial denaturation at 95°C for 3 min followed by 35 cycles of denaturation at 95°C for 60 s, annealing at 58.9°C for 30 s, and extension at 72°C for 30 s, and a final elonga- tion at 72°C for 4 min. The annealing temperatures for Thy-1 and GADPH genes were 64.3° and 64°C, respectively.
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However there are also other considerations to be made with regard to preserving this database. Databases differ from the digital objects looked at in the past as they retain information in a highly structured manner, tend to be updated frequently and so change over time, and include integrity constraints that are important for the future interpretation of the data. One tool that may be available in the near future from the Swiss Federal Archive is SIARD (Software-Independent Archiving of Relational Databases). The latest version of SIARD is undergoing acceptance trials and may be available soon. According to the currently available documentation (Comment, 2008)
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RETRACTED ARTICLE: Gene expression profiling analysis of hepatocellular carcinoma

RETRACTED ARTICLE: Gene expression profiling analysis of hepatocellular carcinoma

The Connectivity Map (cmap) database contains the ex- pression data of the genomewide transcription of human cells in the activity of small-molecule intervention, includ- ing the 6100 group of small-molecule interference experi- ments (small interfering group and normal control group) and the expression profile of 7056 [15]. We analyzed gene expression differences between normal cells and hepatoma cells and compared the differentially expressed genes caused by these small interfering expression genes with the hope of identifying similar gene expression profiles of normal cells and hepatoma cells or, vice versa, a small interfering group. The differentially expressed genes of normal cells and hepatoma cells were divided into two cat- egories: upregulation and downregulation. We obtained an enrichment value which stands for similarity through Gene Set Enrichment Analysis compared with the differ- entially expressed genes in the cmap database dealing with small molecules, whose value was between −1 and 1. The closer they were to 1, the more the small molecules were able to simulate the state of normal cells. The closer they were to −1, the more the small molecules were able to simulate the state of the hepatoma cells.
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Expression of protein tyrosine kinase 6 (PTK6) in nonsmall cell lung cancer and their clinical and prognostic significance

Expression of protein tyrosine kinase 6 (PTK6) in nonsmall cell lung cancer and their clinical and prognostic significance

We further applied a validation study of PTK6 expression in a panel of clinical samples. As for the staining pattern, the posi- tive staining reaction for PTK6 in normal and cancerous lung tissue cells was observed in the cytoplasm, and in a combination of the nucleus and cytoplasm. Compared with normal lung tis- sues, NSCLC tissues had a significantly higher staining index of PTK6. For NSCLC tissues, the median staining index of 6 was set as the cut-point to delineate PTK6-low and PTK6-high subgroups. Representative immunostaining images were seen in Figure 2. The associations between PTK6 expression and clinicopathological features of NSCLC are shown in Table 1. No significant association was observed between PTK6 expres- sion with the clinicopathological traits observed in this study. Furthermore, we also found no significant correlation between PTK6 expression with another tyrosine kinase target epidermal growth factor receptor (EGFR) mutation status.
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Random-forest algorithm based biomarkers in predicting prognosis in the patients with hepatocellular carcinoma

Random-forest algorithm based biomarkers in predicting prognosis in the patients with hepatocellular carcinoma

The risk score model was carried out by multivari- ate Cox regression model. The coefficients of the result were used as the weight for each gene to create a risk score model. Risk score = (0.3497 × expression level of SPC25) + (0.0995 × expression level of MCM2) + (0.0327 × expres- sion level of NUF2) + (0.0369 × expression level of AURKA) + (-0.3185 × expression level of BLM). The risk score model was examined in the testing group and full dataset with KM curve and P value (Figs.  5 and 6). The patients with higher risk scores had the worse survival compared with lower ones (Fig.  7). Risk score had the negative correlation with overall survival (OS). The anal- ysis suggested risk score model can be considered as an independent clinical feature for OS of the patients with HCC.
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Nramp1 gene expression in different tissues of Meishan piglets from newborn to weaning.

Nramp1 gene expression in different tissues of Meishan piglets from newborn to weaning.

Based on existing research, differential expressions of Nramp1 in 12 tissues taken from Meishan piglets ranging in age from newborn to weaning were investigated in this study to establish a basic data profile for Nramp1 tissue expression and regulation among piglet age classes. Results indicated that Nramp1 was expressed in all tissues tested regardless of individual tissue specificity, although expression at different developmental stages did vary significantly. This finding was consistent with Nramp1 tissue expression among hybrid boar and indigenous Tibetan and Huai breeds, but differed from that in foreign pig breeds (Zhang et al., 2000; Ying and Zhang, 2007; Chen and Liu, 2009; Ding et al., 2014). Nramp1 was expressed at relatively high levels in the spleen, which is the largest immune response-related organ in the body that develops and produces macrophages and lymphocytes, and is key to both humoral and cellular immunity in Chinese pig breeds. It is logical, therefore, that high expression of Nramp1 in spleen tissue is likely consistent with the body’s capacity for immune response.
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CASE-REPORT Dysregulation of gene expression in a patient with depressive disorder after transient ischemic attack confirmed by a neurophysiological neuromarker.

CASE-REPORT Dysregulation of gene expression in a patient with depressive disorder after transient ischemic attack confirmed by a neurophysiological neuromarker.

A 54-year-old male patient with a past history of schizophrenia (at the age of 20), and of transient ischemic attack (at the age of 53), caused a post-“warning stroke” depressive disorder. Doppler ultrasound proved that carotid artery sclerosis did not cause the TIA. Atrial fibrillation was also ruled out. It is suspected that a vasoconstrictive reaction increased pressure and might have caused the TIA. Directly before the TIA, the patient experienced severe stress at work. As a result of the TIA, the patient developed moderate anomia, which relieved after a week. However, motion slow-down, presenting mostly during speech, remained. The patient reported a depressed mood, sleep disorders with nightmares, and fasciculation in the upper limbs and abdomen. At that time, he underwent functional brain examinations [QEEG, ERPs, standardized low resolution electromagnetic tomography (sLoreta)]. The results demonstrated functional changes related to concentration disorders, impulsiveness, and difficulties in reaction to stimuli GO/NOGO, which are characteristic manifestations of a past TIA episode. A post-“warning stroke” depressive disorder was confirmed by functional, cognitive, emotional, and affectional diagnostics, testing for a depressive disorder neuromarker through the use of QEEG and ERPs. The patient underwent twenty sessions of neurotherapy (neurofeedback), which was not effective. Therefore, we offered additional testing for expression of the genes associated with stress response. The expression of genes coding for HSP (HSPA1A, HSPB1), IL (IL6, IL10), and C-reactive protein (CRP) was tested along with factors that regulate their expression. The results of the tests conducted on this patient were compared with 42 healthy control subjects.
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