scholarly journals Metabolomic Biomarkers Related to Non-suicidal Self-injury in Patients with Bipolar Disorder

2021 ◽  
Author(s):  
Xiangjie Guo ◽  
Jiao Jia ◽  
Zhiyong Zhang ◽  
Yuting Miao ◽  
Peng Wu ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Metabolic Pathways ◽  
Purine Metabolism ◽  
Partial Least Square ◽  
Least Square ◽  
Healthy Controls ◽  
Glutamate Metabolism ◽  
Metabolism Pathway ◽  
Self Injury ◽  
Important Symptom

Abstract Background: Non-suicidal self-injury (NSSI) is an important symptom of bipolar disorder (BD) and other mental disorders and has attracted the attention of researchers lately. Metabolomics is a relatively new field that can provide complementary insights into data obtained from genomic, transcriptomic, and proteomic analyses of psychiatric disorders. The aim of this study was to identify the metabolic pathways associated with BD with NSSI and assess important diagnostic and predictive indices of NSSI in BD.Method: Nuclear magnetic resonance spectrometry was performed to evaluate the serum metabolic profiles of patients with BD with NSSI (n = 31), patients with BD without NSSI (n = 46), and healthy controls (n = 10). Data were analyzed using an Orthogonal Partial Least Square Discriminant Analysis and a t-test. Differential metabolites were identified (VIP > 1 and p < 0.05), and further analyzed using Metabo Analyst 3.0 to identify associated metabolic pathways.Results: Eight metabolites in the serum and two important metabolic pathways, the urea and glutamate metabolism cycles, were found to distinguish patients with BD with NSSI from healthy controls. Eight metabolites in the serum, glycine and serine metabolism pathway, and the glucose-alanine cycle were found to distinguish patients with BD without NSSI from healthy controls. Five metabolites in the serum and the purine metabolism pathway were found to distinguish patients with BD with NSSI from those with BD without NSSI.Conclusions: Abnormalities in the urea cycle, glutamate metabolism, and purine metabolism played important roles in the pathogenesis of BD with NSSI.

scholarly journals The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mika Jönsson ◽  
Björn Gerdle ◽  
Bijar Ghafouri ◽  
Emmanuel Bäckryd
Keyword(s):  
Cerebrospinal Fluid ◽  
Neuropathic Pain ◽  
Pain Intensity ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Therapeutic Interventions ◽  
Healthy Controls ◽  
Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

scholarly journals Changes in Amino Acid and Acylcarnitine Plasma Profiles for Distinguishing Patients with Multiple Sclerosis from Healthy Controls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Marat F. Kasakin ◽  
Artem D. Rogachev ◽  
Elena V. Predtechenskaya ◽  
Vladimir J. Zaigraev ◽  
Vladimir V. Koval ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Amino Acid ◽  
Discriminant Analysis ◽  
Clinical Decision ◽  
Partial Least Square ◽  
Least Square ◽  
Control Group ◽  
Healthy Controls ◽  
Linear Discriminant ◽  
Tandem Mass Spectrometric

McDonald criteria and magnetic resonance imaging (MRI) are used for the diagnosis of multiple sclerosis (MS); nevertheless, it takes a considerable amount of time to make a clinical decision. Amino acid and fatty acid metabolic pathways are disturbed in MS, and this information could be useful for diagnosis. The aim of our study was to find changes in amino acid and acylcarnitine plasma profiles for distinguishing patients with multiple sclerosis from healthy controls. We have applied a targeted metabolomics approach based on tandem mass-spectrometric analysis of amino acids and acylcarnitines in dried plasma spots followed by multivariate statistical analysis for discovery of differences between MS (n=16) and control (n=12) groups. It was found that partial least square discriminant analysis yielded better group classification as compared to principal component linear discriminant analysis and the random forest algorithm. All the three models detected noticeable changes in the amino acid and acylcarnitine profiles in the MS group relative to the control group. Our results hold promise for further development of the clinical decision support system.

Exhaled breath analysis by use of eNose technology: a novel diagnostic tool for interstitial lung disease

2020 ◽  
Vol 57 (1) ◽  
pp. 2002042 ◽  
Author(s):  
Catharina C. Moor ◽  
Judith C. Oppenheimer ◽  
Gizal Nakshbandi ◽  
Joachim G.J.V. Aerts ◽  
Paul Brinkman ◽  
...  
Keyword(s):  
Interstitial Pneumonia ◽  
Breath Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Diagnostic Tools ◽  
Exhaled Breath ◽  
Healthy Controls ◽  
Characteristic Analysis ◽  
Cross Sectional ◽  
Exhaled Breath Analysis

IntroductionEarly and accurate diagnosis of interstitial lung diseases (ILDs) remains a major challenge. Better noninvasive diagnostic tools are much needed. We aimed to assess the accuracy of exhaled breath analysis using eNose technology to discriminate between ILD patients and healthy controls, and to distinguish ILD subgroups.MethodsIn this cross-sectional study, exhaled breath of consecutive ILD patients and healthy controls was analysed using eNose technology (SpiroNose). Statistical analyses were done using partial least square discriminant analysis and receiver operating characteristic analysis. Independent training and validation sets (2:1) were used in larger subgroups.ResultsA total of 322 ILD patients and 48 healthy controls were included: sarcoidosis (n=141), idiopathic pulmonary fibrosis (IPF) (n=85), connective tissue disease-associated ILD (n=33), chronic hypersensitivity pneumonitis (n=25), idiopathic nonspecific interstitial pneumonia (n=10), interstitial pneumonia with autoimmune features (n=11) and other ILDs (n=17). eNose sensors discriminated between ILD and healthy controls, with an area under the curve (AUC) of 1.00 in the training and validation sets. Comparison of patients with IPF and patients with other ILDs yielded an AUC of 0.91 (95% CI 0.85–0.96) in the training set and an AUC of 0.87 (95% CI 0.77–0.96) in the validation set. The eNose reliably distinguished between individual diseases, with AUC values ranging from 0.85 to 0.99.ConclusionseNose technology can completely distinguish ILD patients from healthy controls and can accurately discriminate between different ILD subgroups. Hence, exhaled breath analysis using eNose technology could be a novel biomarker in ILD, enabling timely diagnosis in the future.

Biomarkers for neuropathic pain – Is the old alpha-1-antitrypsin any good?

2016 ◽  
Vol 12 (1) ◽  
pp. 125-125 ◽  
Author(s):  
E. Bäckryd ◽  
B. Gerdle ◽  
B. Ghafouri
Keyword(s):  
Neuropathic Pain ◽  
Pigment Epithelium ◽  
Partial Least Square ◽  
Least Square ◽  
Protein Isoforms ◽  
Correlation Structure ◽  
Clinical Parameters ◽  
Healthy Controls ◽  
Alpha 1 Antitrypsin ◽  
Pain Patients

Abstract Aims In a previous comparative two-dimensional gel electrophoresis study [1], we described seven cerebrospinal fluid (CSF) proteins highly discriminating between neuropathic pain patients and healthy controls. The aims of the present follow-up work were to examine the multivariate inter-correlations between all identified isoforms of these seven proteins. The focus was not on discriminant analysis but rather on the internal correlation structure between these proteins in healthy controls vs neuropathic pain patients. Our hypothesis was that neuropathic pain is associated with a disrupted correlation structure between these protein isoforms. Moreover, focusing on the patients, we wanted to regress clinical pain parameters (pain intensity and pain duration), using all the proteomic data (260 proteins) of our previous study [1] as predictor variables, thereby testing the hypothesis that the abovementioned seven discriminating proteins and/or their isoforms would be among the proteins having the highest predictive power for clinical parameters. Methods Biochemical identification of isoforms of the above mentioned seven proteins. Principal component analysis (PCA) was used to describe and compare the correlation structures of patients vs. healthy controls, and orthogonal partial least square (OPLS) analysis was used to regress clinical parameters [2,3]. Results We identified 5 isoforms of angiotensinogen, 18 isoforms or fragments of alpha-1-antitrypsin, 5 isoforms of haptoglobin, and 5 isoforms of pigment epithelium derived factor. In patients and using PCA, a down-regulated fragment of alpha-1-antitrypsin correlated to another up-regulated fragment of alpha-1-antitrypsin, indicating a possible physiological relationship. The OPLS regression of clinical parameters in patients also seems to yield interesting results (work in progress). Conclusions Some of the seven proteins that were the main results of our previous study [1] reappear in different ways in the results of the present study. This strengthens our interest in characterizing the nature of their post-translational modifications.

scholarly journals The Interplay between Pathophysiological Pathways in Early-Onset Severe Preeclampsia Unveiled by Metabolomics

Life ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 86
Author(s):  
Lina Youssef ◽  
Francesca Crovetto ◽  
Rui Vasco Simoes ◽  
Jezid Miranda ◽  
Cristina Paules ◽  
...  
Keyword(s):  
Early Onset ◽  
Goodness Of Fit ◽  
Univariate Analysis ◽  
Great Part ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Glutamate Metabolism ◽  
Metabolomic Analysis ◽  
Early Onset Preeclampsia

Introduction: Preeclampsia is a multi-system disorder unique to pregnancy responsible for a great part of maternal and perinatal morbidity and mortality. The precise pathogenesis of this complex disorder is still unrevealed. Methods: We examined the pathophysiological pathways involved in early-onset preeclampsia, a specific subgroup representing its most severe presentation, using LC-MS/MS metabolomic analysis based on multi-level extraction of lipids and small metabolites from maternal blood samples, collected at the time of diagnosis from 14 preeclamptic and six matched healthy pregnancies. Statistical analysis comprised multivariate and univariate approaches with the application of over representation analysis to identify differential pathways. Results: A clear difference between preeclamptic and control pregnancies was observed in principal component analysis. Supervised multivariate analysis using orthogonal partial least square discriminant analysis provided a robust model with goodness of fit (R2X = 0.91, p = 0.002) and predictive ability (Q2Y = 0.72, p < 0.001). Finally, univariate analysis followed by 5% false discovery rate correction indicated 82 metabolites significantly altered, corresponding to six overrepresented pathways: (1) aminoacyl-tRNA biosynthesis; (2) arginine biosynthesis; (3) alanine, aspartate and glutamate metabolism; (4) D-glutamine and D-glutamate metabolism; (5) arginine and proline metabolism; and (6) histidine metabolism. Conclusion: Metabolomic analysis focusing specifically on the early-onset severe form of preeclampsia reveals the interplay between pathophysiological pathways involved in this form. Future studies are required to explore new therapeutic approaches targeting these altered metabolic pathways in early-onset preeclampsia.

scholarly journals Serum Metabolomics Study of Papillary Thyroid Carcinoma Based on HPLC-Q-TOF-MS/MS

2021 ◽  
Vol 9 ◽  
Author(s):  
Yang Du ◽  
Peizhi Fan ◽  
Lianhong Zou ◽  
Yu Jiang ◽  
Xiaowen Gu ◽  
...  
Keyword(s):  
Glutamic Acid ◽  
Metabolic Pathways ◽  
Acid Metabolism ◽  
Methylmalonic Acid ◽  
Partial Least Square ◽  
Receiver Operating Curve ◽  
Control Group ◽  
Cancer Type ◽  
Healthy Controls ◽  
Serum Samples

This study examined metabolite profile differences between serum samples of thyroid papillary carcinoma (PTC) patients and healthy controls, aiming to identify candidate biomarkers and pathogenesis pathways in this cancer type. Serum samples were collected from PTC patients (n = 80) and healthy controls (n = 80). Using principal component analysis (PCA), partial least squares discrimination analysis(PLS-DA), orthogonal partial least square discriminant analysis (OPLS-DA), t-tests, and the volcano plot, a model of abnormal metabolic pathways in PTC was constructed. PCA, PLS-DA, and OPLS-DA analysis revealed differences in serum metabolic profiles between the PTC and control group. OPLS-Loading plot analysis, combined with Variable importance in the projection (VIP)&gt;1, Fold change (FC) &gt; 1.5, and p &lt; 0.05 were used to screen 64 candidate metabolites. Among them, 22 metabolites, including proline betaine, taurocholic acid, L-phenylalanine, retinyl beta-glucuronide, alpha-tocotrienol, and threonine acid were upregulated in the PTC group; meanwhile, L-tyrosine, L-tryptophan, 2-arachidonylglycerol, citric acid, and other 42 metabolites were downregulated in this group. There were eight abnormal metabolic pathways related to the differential metabolites, which may be involved in the pathophysiology of PTC. Six metabolites yielded an area under the receiver operating curve of &gt;0.75, specifically, 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanylphenylalanine, and l-glutamic acid. The Warburg effect was observed in PTC. The levels of 3-hydroxy-cis-5-tetradecenoylcarnitine, aspartylphenylalanine, l-kynurenine, methylmalonic acid, phenylalanine, and L-glutamic acid may help distinguish PTC patients from healthy controls. Aspartic acid metabolism, glutamic acid metabolism, urea cycle, and tricarboxylic acid cycle are involved in the mechanism of PTC.

scholarly journals Serum Metabolomic Signatures Associated with Disease Severity in Allergic Rhinitis

2020 ◽  
Author(s):  
Shaobing Xie ◽  
Hua Zhang ◽  
Zhihai Xie ◽  
Yongzhen Liu ◽  
Kelei Gao ◽  
...  
Keyword(s):  
Allergic Rhinitis ◽  
Disease Severity ◽  
High Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Sphingolipid Metabolism ◽  
Healthy Controls ◽  
Serum Samples ◽  
Metabolomic Profiling ◽  
Serum Metabolomics

Abstract Background: Allergic rhinitis (AR) is a global healthcare problem with obscure pathogenesis, and few studies have evaluated the association between AR and metabolomics. The aim of this study was to identify differences in serum metabolomics profiling of AR patients compared to healthy controls and to explore novel biomarkers reflecting disease severity.Methods: Serum samples were collected form 29 healthy controls and 72 AR patients, including 30 mild AR patients (MAR) and 42 moderate to severe AR patients (MSAR). Metabolomic profiling was performed by ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) in serum samples. Orthogonal partial least square-discriminate analysis (OPLS-DA) was applied to assess the differences between AR patients and controls and for subgroups based on disease severity. Results: These analysis results successfully revealed distinct metabolite signatures which distinguished MAR patients and MSAR patients from health controls. In addition, MSAR patients also could be discriminated from MAR patients basing on their metabolic fingerprints. Most observed metabolite changes were related to pathways associated with glycine, serine and threonine metabolism, pyrimidine metabolism, sphingolipid metabolism, arginine and proline metabolism and fatty acid metabolism. Among these metabolites from dysregulated metabolic pathways, levels of sarcosine, sphingosine-1-phosphate (S1P), cytidine and linoleic acid significantly correlated with AR total nasal symptom score (TNSS) and visual analogue scale (VAS).Conclusions: MSAR patients have a distinctive serum metabolomics profile compared to MAR and health controls. These results suggest that metabolomic profiling may provide novel insights into pathophysiological mechanisms of AR and contribute to its evaluation of disease severity.

MOTIVASI MEMEDIASI PENGARUH PELATIHAN DAN LINGKUNGAN KERJA TERHADAP KINERJA KARYAWAN PT. TELKOM WITEL YOGYAKARTA

2020 ◽  
Vol 28 (1) ◽  
pp. 71-88
Author(s):  
Tyas Tunjung Sari ◽  
Pandu Nuansa Luhur
Keyword(s):  
Work Environment ◽  
Significant Influence ◽  
Structural Equation ◽  
Employee Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Primary Data ◽  
Version 2.0 ◽  
Analyze Data

This study aims to determine the motivation of work to mediate the effect of training and work environment on employee performance at PT. Telkom Witel Yogyakarta Yogyakarta. The purpose of this study is to determine and analyze 1) the effect of training on employee performance at PT. Telkom Witel Yogyakarta 2) the effect of training on employee performance through motivation at PT. Telkom Witel Yogyakarta 3) the influence of the work environment on employee performance at PT. Telkom Witel Yogyakarta 4) the influence of the work environment on employee performance through motivation at PT. Telkom Witel Yogyakarta. This study uses primary data through research on 62 respondents. Structural Equation is used to analyze data, using PLS (Partial Least Square) version 2.0. The results of this study indicate that there are 1) positive and significant influence of training on employee performance 2) positive and significant influence of work environment on employee performance 3) positive and significant effect of training on employee performance through motivation 4) positive and significant influence of work environment on employee performance through motivation.

Locus Of Control Sebagai Varibel Moderasi Hubungan Kompleksitas Tugas Dan Kinerja Auiditor

2018 ◽  
Vol 16 (2) ◽  
pp. 113
Author(s):  
Sri Hastuti ◽  
Siti Sundari
Keyword(s):  
Locus Of Control ◽  
Big Five ◽  
Partial Least Square ◽  
Least Square

Research Objectives to prove the influence of the complexity of the tasks faced by the Auditor on performance in carrying out duties as an Auditor. The complexity of tasks related to various problems in the company requires locus of control from internal and external to maintain independence and competence.The first auditor performance case occurred in 2002 with the disclosure of the Enron case involving the KAP in the big five, Athur Anderson. In 2008 the Telkom case affected the closure of KAP Edy Priyanto, and there were still many other cases which were violations of the accountant's code of ethics.This research is in the form of quantitative, with proof of the complexity of the task and locus of control on the performance of the auditor. Sample 46 Junior auditors from several KAPs in Surabaya, using the Partial Least Square test, the result that the complexity of the task affects the performance of the Auditor and the interaction of the complexity of the task with locus of control does not affect the performance of the Auditor.

ANALYSIS THE IMPACT OF THE IMPLEMENTATION OF QUALITY MANAGEMENT SYSTEM (QMS) AND SAFETY AND HEALTHY AT WORK MANAGEMENT SYSTEM (SHWMS / SMK3) ON THE JOB PERFORMANCE OF EMPLOYEES

2018 ◽  
Vol 3 (01) ◽  
pp. 45
Author(s):  
Nur Hidayat ◽  
Indah Kusuma Hayati
Keyword(s):  
Quality Management ◽  
Job Performance ◽  
Latent Variables ◽  
Management System ◽  
Quality Management System ◽  
Partial Least Square ◽  
Least Square ◽  
Global Challenges ◽  
The Impact ◽  
Work Management

Recently, the evolvement of globalization era has been the global challenges that cannot be avoided either by private or government sectors, and they are requested to be survived encountering such the condition. The implementation of Quality Management System (QMS) in the operational company is the way how to guarantee the quality of products or services offered to the people. One of the purposes of QMS implementation is to provide a prime satisfaction to the customers. The impact of QMS implementation is expected to increase job performance of the employees. Besides the implementation of Quality Management System (QMS), the impact of global challenges has been increasing the competitive efforts to execute more effective production process. However, it has required manpower protection accordingly. This research aims to find out whether the implementation of quality management system and safety and healthy at work management system have impacted on the job performance of employees. Objects of this research are the employees in the production department at PT Guna Senaputra Sejahtera Plant 1 Bogor. Data analysis technique of this research has applied software Smart PLS (Partial Least Square). PLS has estimated a model of correlation among the latent variables and correlation between latent variables and its indicators. Result of data processing has indicated that the implementation of Quality Management System (QMS) and system of safety and healthy at work have positively and significantly impacted job performance of employees.Keywords : Quality Management System (QMS), Safety and Healthy at Work System ( SHWS / SMK3), and Job Performance of Employees

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