A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies

Troisi, Jacopo; Sarno, Laura; Martinelli, Pasquale; Carlo, Costantino Di; Landolfi, Annamaria; Scala, Giovanni; Rinaldi, Maurizio; D’Alessandro, Pietro; Ciccone, Carla; Guida, Maurizio

doi:10.1007/s11306-017-1274-z

Cited by 28 publications

(28 citation statements)

References 39 publications

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“…Instrumental analyses were performed with a GC-MS system (GC-2010 Plus gas chromatograph and QP2010SE mass spectrometer; Shimadzu Corp., Kyoto, Japan). The analytical details are reported in Troisi et al [21][22][23][24][25][26].…”

Section: Metabolite Extraction Derivatization and Analysismentioning

confidence: 99%

“…Metabolite identi cation was performed according to Troisi et al [23], brie y, the linear index difference maximum tolerance was set to 10, while the minimum matching for NIST library search was set to 85% (Level 2 identi cation according to Metabolomics Standards Initiative [MSI]) [27]. Metabolites that emerged as the most relevant in separating cases from controls (see below) were further con rmed using external standards (MSI level = 1).…”

Section: Metabolite Extraction Derivatization and Analysismentioning

confidence: 99%

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Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients 

Troisi¹,

Cavallo²,

Masarone³

et al. 2020

Preprint

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Background: One of the most challenging aspects related to Covid-19 is to establish which are the characteristics that may explain clinical variability among patients. To achieve this objective, we compared serum metabolomic profiles of asymptomatic SARS-CoV-2 carriers to patients affected by mild and severe symptoms.Methods: All subjects underwent quantitative assays for anti-SARS-CoV-2 antibody detection. Serum samples for metabolomic analysis were obtained from 109 healthy controls; 15 seroconverted, asymptomatic subjects (AS); 16 Covid-19 patients with mild symptomatology (MI) and 12 patients with severe symptomatology (SE). Metabolites were identified using Mass Spectrometry coupled to Gas Chromatography. Uni- and multivariate approaches were used to select the most relevant metabolites. Results: Anti SARS-CoV-2 IgG showed an increasing trend from controls to asymptomatic and mild severity patients. Tyrosine, phenylalanine, acetoacetate and fumarate showed a decreased concentration in MI compared to both CTRL and AS subjects; on the contrary, mandelic acid showed an increased concertation. The shortest route among these metabolites resulted the Tyrosine metabolism. Aspartic acid, alanine, isoleucine, valine and proline were decreased in MI patients, while methionine and oxo-leucine resulted increased. An increased concertation of fatty acids lauric and myristic acid, phospholipids (phosphatidyl myo-inositol and lyso-phosphatidyl inositol) and histamine were recorded in MI and SE. Conclusion: The reported metabolite levels could be explained by an increase production of L-DOPA, resulting in a following increased production of noradrenaline and adrenaline that could, at least partially, explain the different clinical presentation of the infection.

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Section: Metabolite Extraction Derivatization and Analysismentioning

confidence: 99%

Section: Metabolite Extraction Derivatization and Analysismentioning

confidence: 99%

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients 

Troisi¹,

Cavallo²,

Masarone³

et al. 2020

Preprint

Self Cite

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“…In recent years, machine learning techniques and especially ANNs in the eld of diagnosis of fetal abnormalities have been provided robust results (19,21,22). But what can accurately demonstrate the capacity of a ANN for early detection of a disease is the optimal ANN architecture (13).…”

Section: -Discussionmentioning

confidence: 99%

An Intelligent Prenatal Screening System for the Prediction of Trisomy-21

Jamshidnezhad

Hosseini

Mohammadi-Asl

et al. 2020

Preprint

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Objective: Early and accurate diagnosis of genetic diseases such as Down syndrome can lead to correct action and prevent irreversible events. Therefore, the use of accurate and low-risk diagnostic methods to detect these diseases in the fetal period is important. In this study, a combined artificial neural network (ANN) and genetic algorithm (GA) was used to predict Down syndrome through first trimester screening test. In order to examine the proposed model, sample data were collected on 381 pregnant women who referred to a private screening reference laboratory for the first trimester screening test and NT ultrasound between 11 and 13 weeks of gestation. The proposed model in this study was a feedforward neural which its structure and input parameters were determined by (GA).Results: The average of 10 times experiments showed that the developed model can accurately identify cases of Down syndrome with specificity of 99.72% and sensitivity of 90.91%, and a mean square error (MSE) of 0.61%. The results of this study showed that the use of GA in optimizing the structure of the neural network technique can increase the accuracy in diagnosing Down syndrome through the information of first trimester screening tests.

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“…These samples were used to train the classification models in the same manner as our previous reports. 13,15,16 The diagnostic performances of the resulting models were evaluated using cross-validation. The second cohort collected samples from a population with unknown EC status to test the screening performance of the trained models (ie, independent test set).…”

Section: Study Protocolmentioning

confidence: 99%

Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women

et al. 2020

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IMPORTANCE Endometrial carcinoma (EC) is the most commonly diagnosed gynecologic cancer. Its early detection is advisable because 20% of women have advanced disease at the time of diagnosis. OBJECTIVE To clinically validate a metabolomics-based classification algorithm as a screening test for EC. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study enrolled 2 cohorts. A multicenter prospective cohort, with 50 cases (postmenopausal women with EC; International Federation of Gynecology and Obstetrics stage I-III and grade G1-G3) and 70 controls (no EC but matched on age, years from menopause, tobacco use, and comorbidities), was used to train multiple classification models. The accuracy of each trained model was then used as a statistical weight to produce an ensemble machine learning algorithm for testing, which was validated with a subsequent prospective cohort of 1430 postmenopausal women. The study was conducted at the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy) and Lega Italiana per la Lotta contro i Tumori clinic in Avellino (Italy). Data collection was conducted from January 2018 to February 2019, and analysis was conducted from January to March 2019. MAIN OUTCOMES AND MEASURES The presence or absence of EC based on evaluation of the blood metabolome. Metabolites were extracted from dried blood samples from all participants and analyzed by gas chromatography-mass spectrometry. A confusion matrix was used to summarize test results. Performance indices included sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy. Confirmation or exclusion of EC in women with a positive test result was by means of hysteroscopy. Participants with negative results were followed up 1 year after enrollment to investigate the appearance of EC signs. RESULTS The study population consisted of 1550 postmenopausal women. The mean (SD) age was 68.2 (11.7) years for participants with no EC in the training cohort, 69.4 (13.8) years for women with EC in the training cohort, and 59.7 (7.7) years for women in the validation cohort. Application of the ensemble machine learning to the validation cohort resulted in 16 true-positives, 2 false-positives, and 0 false-negatives, and it correctly classified more than 99% of samples. Disease prevalence was 1.12% (16 of 1430). CONCLUSIONS AND RELEVANCE In this study, dried blood metabolomic profile was used to assess the presence or absence of EC in postmenopausal women not receiving hormonal therapy with greater than 99% accuracy.

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A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies

Cited by 28 publications

References 39 publications

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients

An Intelligent Prenatal Screening System for the Prediction of Trisomy-21

Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women

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A metabolomics-based approach for non-invasive diagnosis of chromosomal anomalies

Cited by 28 publications

References 39 publications

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients&nbsp;

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients&nbsp;

An Intelligent Prenatal Screening System for the Prediction of Trisomy-21

Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women

Contact Info

Product

Resources

About

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients

Serum metabolomic profile of symptomatic and asymptomatic SARS-CoV-2 infected patients