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Inflammation is widely recognized as the driving force of cachexia induced by chronic diseases; however, therapies targeting inflammation do not always reverse cachexia. Thus, whether inflammation per se plays an important role in the clinical course of cachectic patients is still a matter of debate.
AimsTo give new insights into cachexia’s pathogenesis and diagnosis, we performed a comprehensive literature search on the contribution of inflammatory markers to this syndrome, focusing on the noncommunicable diseases cancer and cardiovascular diseases.
MethodsA systematic review was performed in PubMed using the keywords (“cancer” OR “cardiac” cachexia AND “human” OR “patient” AND “plasma” or “serum”). A total of 744 studies were retrieved and, from these, 206 were selected for full-text screening. In the end, 98 papers focusing on circulating biomarkers of cachexia were identified, which resulted in a list of 113 different mediators.
ResultsData collected from the literature highlight the contribution of interleukin-6 (IL-6) and C-reactive protein (CRP) to cachexia, independently of the underlying condition. Despite not being specific, once the diagnosis of cachexia is established, CRP might help to monitor the effectiveness of anti-cachexia therapies. In cardiac diseases, B-type natriuretic peptide (BNP), renin, and obestatin might be putative markers of body wasting, whereas in cancer, growth differentiation factor (GDF) 15, transforming growth factor (TGF)-β1 and vascular endothelial growth factor (VEGF) C seem to be better markers of this syndrome. Independently of the circulating mediators, NF-κB and JAK/STAT signaling pathways play a key role in bridging inflammation with muscle wasting; however, therapies targeting these pathways were not proven effective for all cachectic patients.
ConclusionThe critical and integrative analysis performed herein will certainly feed future research focused on the better comprehension of cachexia pathogenesis toward the improvement of its diagnosis and the development of personalized therapies targeting specific cachexia phenotypes.
相似文献Hypophysitis is a heterogeneous condition that includes inflammation of the pituitary gland and infundibulum, and it can cause symptoms related to mass effects and hormonal deficiencies. We aimed to evaluate the potential role of machine learning methods in differentiating hypophysitis from non-functioning pituitary adenomas.
MethodsThe radiomic parameters obtained from T1A-C images were used. Among the radiomic parameters, parameters capable of distinguishing between hypophysitis and non-functioning pituitary adenomas were selected. In order to avoid the effects of confounding factors and to improve the performance of the classifiers, parameters with high correlation with each other were eliminated. Machine learning algorithms were performed with the combination of gray-level run-length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray-level co-occurrence entropy.
ResultsA total of 34 patients were included, 17 of whom had hypophysitis and 17 had non-functioning pituitary adenomas. Among the 38 radiomics parameters obtained from post-contrast T1-weighted images, 10 tissue features that could differentiate the lesions were selected. Machine learning algorithms were performed using three selected parameters; gray level run length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray level co-occurrence entropy. Error matrices were calculated by using the machine learning algorithm and it was seen that support vector machines showed the best performance in distinguishing the two lesion types.
ConclusionsOur analysis reported that support vector machines showed the best performance in distinguishing hypophysitis from non-functioning pituitary adenomas, emphasizing the importance of machine learning in differentiating the two lesions.
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