Mass spectrometry: Future opportunities for profiling and imaging steroids and steroid metabolites

Andrew, Ruth; Homer, Natalie

doi:10.1016/j.coemr.2020.11.005

Cited by 15 publications

(5 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Concerns about the specificity of immunoassays when serum steroid levels are low have led to implementation of MS-based techniques as the gold standard methodology for steroid hormone analysis. Mass spectrometry offers a unique identification profile of each of the study analytes, eliminating interferences, thus allowing greater sensitivity and specificity 27 . GC–MS/MS has been reported to be the more precise and accurate than LC–MS/MS in this type of analysis 18 .…”

Section: Discussionmentioning

confidence: 99%

Steroid hormone levels and bone mineral density in women over 65 years of age

Nunes

Gallardo

Fonseca-Moutinho

2023

Sci Rep

View full text Add to dashboard Cite

Previous studies using immunoassays for steroid measurements have focused on the association between steroid hormone levels and bone mineral density (BMD) in postmenopausal women, obtaining contradictory results. This study aimed to assess this association using a highly sensitive bioanalytical method. A total of 68 postmenopausal women, aged 65–89 years, were enrolled in a cross-sectional study. Measurements of the BMD of the hip and lumbar spine were performed using dual energy X-ray absorptiometry, and serum hormone levels were quantified by gas chromatography and tandem mass spectrometry. Associations between estradiol (E2), testosterone, dehydroepiandrosterone (DHEA), androstenedione and T score levels of the hip and lumbar spine were evaluated, after adjustment for confounding variables. The analysis revealed a statistically significant association between testosterone and the T score of the hip (p = 0.035), but not that of the lumbar spine. No statistically significant associations were found between E2, DHEA, androstenedione and the T scores of the hip and the lumbar spine. Using a highly sensitive hormone assay method, our study identified a significant association between testosterone and BMD of the hip in women over 65 years of age, suggesting that lower testosterone increases the risk of osteoporosis.

show abstract

Section: Discussionmentioning

confidence: 99%

Steroid hormone levels and bone mineral density in women over 65 years of age

Nunes

Gallardo

Fonseca-Moutinho

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In many cases, retrospective analysis can be used to identify which ion m / z values contribute the most to the robustness of the model, thereby facilitating the identification of biomarkers. Clinical use of MS has been praised due to a higher fidelity compared to immunoassays. , With mass spectral analysis expedited by using ML, we anticipate that the trend of coupling the two methods for clinical diagnosis to continue. Below we will list and describe recent uses of ML to classify MS samples outside the scope of single cell MS (Section ), mass cytometry (Section ), and MSI (Section ).…”

Section: Machine Learning Applications For Mass Spectrometrymentioning

confidence: 99%

“…Clinical use of MS has been praised due to a higher fidelity compared to immunoassays. 37 , 38 With mass spectral analysis expedited by using ML, we anticipate that the trend of coupling the two methods for clinical diagnosis to continue. Below we will list and describe recent uses of ML to classify MS samples outside the scope of single cell MS ( Section 3.4 ), mass cytometry ( Section 3.5 ), and MSI ( Section 3.6 ).…”

Section: Machine Learning Applications For Mass Spectrometrymentioning

confidence: 99%

Recent Developments in Machine Learning for Mass Spectrometry

Beck,

Muhoberac,

Randolph

et al. 2024

ACS Meas. Sci. Au

View full text Add to dashboard Cite

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

show abstract

“…The ability to accurately quantify concentrations of a panel of different neurosteroids using mass spectrometry [ 11 , 20 , 29 ] or visualise and map neurosteroids across different brain regions using mass spectrometry-imaging [ 60 ] has allowed for thorough investigation of changes in steroids under different conditions and manipulations, and represents an exceptional advance in the field [ 61 ]. Nonetheless, there are pitfalls associated with mass spectrometry detection methods as careful tissue preparation, specialised instruments and expertise are required for their proper use, especially in assay validation and quality control [ 62 , 63 ], and it is recognised that not all laboratories have the capabilities for mass spectrometry studies.…”

Section: Future Directionsmentioning

confidence: 99%