Background: HC-HA is a unique anti-inflammatory matrix different from hyaluronic acid (HA). Results: Soluble HC-HA induces apoptosis of inflammatory neutrophils and macrophages, and immobilized HC-HA promotes M2 polarization upon LPS/TLR ligation while both enhancing macrophage phagocytosis. Conclusion: HC-HA exerts its anti-inflammatory action using multiple mechanisms. Significance: HC-HA is the first known matrix component to polarize M2b.Despite the known anti-inflammatory effect of amniotic membrane, its action mechanism remains largely unknown. HC-HA complex (HC-HA) purified from human amniotic membrane consists of high molecular weight hyaluronic acid (HA) covalently linked to the heavy chain (HC) 1 of inter-␣-trypsin inhibitor. In this study, we show that soluble HC-HA also contained pentraxin 3 and induced the apoptosis of both formyl-Met-Leu-Phe or LPS-activated neutrophils and LPS-activated macrophages while not affecting the resting cells. This enhanced apoptosis was caused by the inhibition of cell adhesion, spreading, and proliferation caused by HC-HA binding of LPS-activated macrophages and preventing adhesion to the plastic surface. Preferentially, soluble HC-HA promoted phagocytosis of apoptotic neutrophils in resting macrophages, whereas immobilized HC-HA promoted phagocytosis in LPSactivated macrophages. Upon concomitant LPS stimulation, immobilized HC-HA but not HA polarized macrophages toward the M2 phenotype by down-regulating IRF5 protein and preventing its nuclear localization and by down-regulating IL-12, TNF-␣, and NO synthase 2. Additionally, IL-10, TGF-␤1, peroxisome proliferator-activated receptor ␥, LIGHT (TNF superfamily 14), and sphingosine kinase-1 were up-regulated, and such M2 polarization was dependent on TLR ligation. Collectively, these data suggest that HC-HA is a unique matrix component different from HA and uses multiple mechanisms to suppress M1 while promoting M2 phenotype. This anti-inflammatory action of HC-HA is highly desirable to promote wound healing in diseases heightened by unsuccessful transition from M1 to M2 phenotypes.Inflammation serves as the first host response to real or perceived threats to tissue homeostasis. It is heralded by the recruitment of neutrophils, which eliminate pathogens and damaged tissues before eventually undergoing apoptosis (1-3). These apoptotic neutrophils are then removed by macrophages via phagocytosis, resulting in the restoration and maintenance of anti-inflammatory and tolerogenic milieu (4). On the contrary, delayed neutrophil apoptosis and/or impaired phagocytic clearance of apoptotic neutrophils by macrophages leads to prolonged inflammation that is the hallmark of a number of diseases (5-8). Hence, facilitation of neutrophil apoptosis and phagocytic clearance of apoptotic neutrophils by macrophages is an important strategy to limit inflammation-mediated tissue damage to restore tissue homeostasis (9 -11).Macrophages undergo phenotypic changes to adapt to the transition from proinflammatory to anti-inflammatory states of wou...

Purpose: Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional imaging. In this study, we sought to apply a radiomics-based prediction model to the problem of diagnosing treatment effect after SRS. Methods and Materials: We included patients in the Johns Hopkins Health System who were treated with SRS for brain metastases who subsequently underwent resection for symptomatic growth. We also included cases of likely treatment effect in which lesions grew but subsequently regressed spontaneously. Lesions were segmented semiautomatically on preoperative T1 postcontrast and T2 fluid-attenuated inversion recovery magnetic resonance imaging, and radiomic features were extracted with software developed in-house. Top-performing features on univariate logistic regression were entered into a hybrid feature selection/classification model, IsoSVM, with parameter optimization and further feature selection performed using leave-one-out cross-validation. Final model performance was assessed by 10-fold cross-validation with 100 repeats. All cases were independently reviewed by a board-certified neuroradiologist for comparison. Results: We identified 82 treated lesions across 66 patients, with 77 lesions having pathologic confirmation. There were 51 radiomic features extracted per segmented lesion on each magnetic resonance imaging sequence. An optimized IsoSVM classifier based on top-ranked radiomic features had sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%. Conclusions: Radiomics holds promise for differentiating between treatment effect and true progression in brain metastases treated with SRS. A predictive model built on radiomic features from an institutional cohort performed well on cross-validation testing. These results warrant further validation in independent datasets. Such work could prove invaluable for guiding management of individual patients and assessing outcomes of novel interventions.

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