China
Africa
Explore chapters and articles related to this topic
The genome in the clinic: Diagnosis, treatment, and education
Published in Priya Hays, Advancing Healthcare Through Personalized Medicine, 2017
The key to understanding personalized medicine’s impact on mental health is to realize that psychiatric illnesses are not diseases, but disorders. “DSM-5 [the diagnostic manual for psychiatry] lists clinical syndromes,” according to Stephen Stahl, professor of psychiatry at the University of California, San Diego. Stahl explains that there has been a move from a diagnostic system based only on symptoms that a patient presents, to a psychiatrist one based on biosignatures and finding links between biomarkers and treatment responses.
The Role of Epigenetics
Published in Dr. Ather Muneer, Mood Disorders, 2018
Importantly, an ever increasing number of studies are shedding light on the effects of early life stress on DNA methylation at the genome-wide level in the brain as well as in peripheral tissues.89 These efforts are helpful in ascertaining biosignatures that may be linked to the long-term pathologic consequences of early life traumatic experiences.90 In this regard, epigenetic modifications in the peripheral tissues may compare to a certain degree with changes in the brain.91 As an example, differential rearing conditions of rhesus macaques was related to distinctive methylation in early adulthood in both the brain and T cells, implying that the reaction to early life adversity was system-wide and genome-wide and continued to adulthood.92 Moreover, the finding that such DNA methylation variations were not restricted to the brain but could be found in the peripheral systems indicated that these alterations were also of pertinence to additional health problems, such as the increased risk for cardiovascular and metabolic diseases found in mood disorders.93 In this regard, it will be highly important to examine and identify traces of inflammatory-immune methylation as a result of early life stress. This will ultimately furnish essential information for the connecting role of epigenetic alterations and inflammatory processes in the causation of mental illnesses and also serve to elucidate the comorbidity of affective disorders with different medical conditions.94Figure 4.3 conceptualizes the emerging understanding of mood disorders and provides a schematic version of the current pathogenic model.
Precision medicine for colorectal cancer
Published in Debmalya Barh, Precision Medicine in Cancers and Non-Communicable Diseases, 2018
Candan Hızel, Şükrü Tüzmen, Arsalan Amirfallah, Gizem Çalıbaşı Koçal, Duygu Abbasoğlu, Haluk Onat, Yeşim Yıldırım, Yasemin Baskın
CRC is linked to mutations in DNA, amino acids, pentose-phosphate pathway carbohydrates, and glycolytic, gluconeogenic, and tricarboxylic acid intermediates (Brown et al., 2016). In the year 2016, Brown et al. (2016) utilized a nontargeted global metabolome perspective to investigate human CRC, adjacent mucosa, and stool. In this research, they identified metabolite profile variations between CRC and adjacent mucosa from patients who had parts of their colon removed. Furthermore, the analyses of the metabolic pathways unveiled connections among complex networks of metabolites (Halama et al., 2015; Brown et al., 2016; Farshidfar et al., 2016). The extensive and thorough characterization of tumor phenotypes facilitated the therapeutic approaches (Halama et al., 2015). Lately, omics strategies have shed light upon tumor biology. Such applications have been to a large degree executed to avail biosignatures to examine the disease and ameliorate therapeutic results. The orchestration of metabolomics for studying tumorigenesis is particularly instrumental, since it demonstrates the biochemical outcome of a number of tumor-specific functional changes associated with the disease (Halama et al., 2015; Farshidfar et al., 2016). In 2015 Halama et al. experimented utilizing nontargeted metabolomics-based mass spectroscopy together with ultrahigh-performance liquid chromatography and gas chromatography in order to perform metabolic phenotyping of four cancer cell lines: two colon cancer (HCT15, HCT116) and two ovarian cancer (OVCAR3, SKOV3). They then applied the MetaP server to statistically analyze their data (Halama et al., 2015). According to Halama et al.’s study, where a total of 225 metabolites were detected in all colon cancer cell lines mentioned herein, 67 of these molecules exceptionally discriminated colon cancer cells from ovarian cancer cells. Metabolic biomarkers identified in this study suggested elevation of β-oxidation and urea cycle metabolism in colon cancer cell lines. As a conclusion Halama et al.’s study provided a panel of specific metabolic identifiers between colon and ovarian cancer cell lines. The novel findings can be considered as potential drug targets. Furthermore, these potential hits can be assessed further in primary cells, biofluids, and tissue samples as biosignatures for CRC (Halama et al., 2015).
Evaluation of Potential Antigen-specific Host Biomarkers in QuantiFERON Supernatants as Candidates for the Diagnosis of Ocular Tuberculosis
Published in Ocular Immunology and Inflammation, 2021
Nonjabulo S. Makhoba, Derrick P. Smit, Gerhard Walzl, Novel N. Chegou
Differences in the concentrations of individual host biomarkers between any two study groups (e.g. possible or probable TB Vs OD) were determined using the Mann–Whitney U test. To determine the diagnostic abilities of combinations of biomarkers in diagnosing OTB, the General discriminant analysis (GDA) procedure was used. Leave-one-out cross-validation was employed to assess the prediction accuracies of biosignatures. Receiver operator characteristics (ROC) curve analysis were used to assess the diagnostic potentials of individual biomarkers and also biomarker combinations for OTB. The significance level of 5% was used as a guide for determining significant associations where applicable and results are presented with 95% confidence intervals. All data were analyzed using Statistica (TIBCO Software Inc., CA, USA).
Could a blood test for PTSD and depression be on the horizon?
Published in Expert Review of Proteomics, 2018
In addition, gene-activity assay is a promising technique and cost-efficient; however, this technology requires more investigation to identify specific genes that change their expression in PTSD and MDD [88]. DNA microarrays can efficiently highlight gene expression profiling of transcriptional reactivity. Currently, studies that analyze gene expression related to PTSD showed relevant signatures in mononuclear cells that may be useful to diagnose a mental disorder [272]. However, improved methods are still required to screen more efficiently through sets of candidate variants, and then, a rigorous validation of variants and gene effects are also needed [273]. Furthermore, replications in larger samples and investigations focusing on selected markers as part of the biosignatures that have been discovered, are required to assess the diagnostic utility and pathological relevance of these methods.
Pursuit of proteomic excellence and the excitement in Košice, Slovakia, at the 11th Central and Eastern European Proteomic Conference (CEEPC)
Published in Expert Review of Proteomics, 2018
Suresh Gadher, Mangesh Bhide, Hana Kovarova
With major focus on ‘societal and medical challenges’, the conference commenced on 27 September 2017 with a plenary lecture from Chris Turck (Max Planck Institute of Psychiatry, Munich, Germany) entitled, ‘Pathway illumination for disease research – from Omics to Biosignatures’. Identification of biosignatures for psychiatric disorders and antidepressant drug response using sensitive high-throughput proteomics and metabolomics platforms were discussed. Molecular pathways in mouse models that represent defined endophenotypes characteristic for human psychiatric disorders including anxiety, posttraumatic stress disorder, and schizophrenia were examined together with drugs that target the monoaminergic and glutamatergic systems to delineate mechanisms relevant for therapeutic response and novel drug targets. The ultimate goal remains to complement imprecise clinical parameters with molecular biosignatures to improve patient diagnosis, stratification, and treatment.