IndraLab

Statements

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"These metabolites reflect integrated CAM‐CRF conditions, enhancing the understanding of underlying metabolic profiles in apparently healthy individuals and may represent promising metabolic indicators of systemic health."
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"These metabolites reflect integrated CAM‐CRF conditions, enhancing the understanding of underlying metabolic profiles."
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"This longstanding interest is based on the strong association of both CRF and CAM with overall health."
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"The metabolomic profile of each sex and of each group generated according to the CAM‐CRF profile for both sexes was evaluated."
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"In the present study, we identified significant associations between CAM‐CRF profiles and seven metabolites: sebacic acid, ornithine, choline, betaine, N,N ‐dimethylglycine, sarcosine, and glucose."
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"Identifying metabolites associated with integrated CAM‐CRF profiles may help characterize healthy physiological states."
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"In this context, sebacic acid and ornithine merit attention because they characterize groups with distinct CAM‐CRF profiles based on two PCs, accounting for at least 47.41% of total data variability."
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"CAM‐CRF profiles were obtained separately by sex using principal components analysis (PCA) of CAM and CRF."
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"Among these, sebacic acid and ornithine emerged as particularly relevant, as they characterize distinct CAM‐CRF profiles and contributed more substantially to overall data variability."
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"Interestingly, these metabolites, together with sarcosine, which showed marginal significance in females, p  = 0.019, comprise the mitochondrial arm of the choline degradation pathway, suggesting that mitochondrial disturbances may underlie unfavorable CAM‐CRF profiles."
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"Notably, some of these metabolites were associated with the CAM‐CRF profile in females individuals, further emphasizing the physiological differences between the sexes."
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"The significant metabolites observed in the main analyses of this study suggest a link to specific CAM‐CRF profiles and offers insights into overall health."
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"This study aimed to investigate metabolic signatures representing distinct CAM‐CRF profiles in apparently healthy individuals."
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"CAM-CRF using CRF partially reduces background noise but does not eliminate it."
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"The CAM‐CRF characteristics of each generated group were similar between the sexes (Table  xref )."
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"Although the relationships between SM, CAM, and CRF has been studied in different contexts (Kelly et al.,  xref ; Mathew et al.,  xref ; Signini, Castro, Rehder‐Santos, Cristina Millan‐Mattos, et al.,  xref ; Signini, Castro, Rehder‐Santos, Milan‐Mattos, et al.,  xref ; Ziegler et al.,  xref ), an integrate score combining CAM and CRF metrics (CAM‐CRF profile) in a weighted manner has not yet been evaluated."
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"Synthesizing these variables into composite score for each individual may offer a novel approach for identifying serum metabolic signatures that characterize CAM‐CRF profiles."
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"Therefore, finding serum metabolites that represent these profiles could help in future research aimed at simplifying access to the level of response to physical stress and systemic health (without the need for complex tests and analyses to identify a person's CAM‐CRF profile) through the development of specific blood tests to measure these metabolites."
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"To verify the effectiveness of different quality pseudo-labels for CrackCLIP, we use two different types of crack pixel-level pseudo-labels, including CAM-CRF [ xref ] and CAM-Location [ xref ]."
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"Thus, this study aimed to identify distinct CAM‐CRF profiles and investigate the metabolic signatures associated with these profiles in apparently healthy individuals."
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"As expected, due to the nature of the PCA, the groups differed significantly from each other in terms of the CAM‐CRF profile."
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"CAM-CRF [ xref ] is a pixel-level pseudo-label generated by a class activation map with CRF, while CAM-Location [ xref ] is a pixel-level pseudo-label generated by class activation maps, crack patch location, and threshold segmentation combined to generate crack pixel-level pseudo-labels. xref shows the quantitative prediction results of cracks for the CrackCLIP model using different types of pseudo-labels."
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