Background and Objectives Many types of lasers have been used to treat café‐au‐lait macules (CALMs) since the introduction of the selective photothermolysis theory. However, the efficacy and safety of picosecond lasers, compared with those of nanosecond lasers, have not been researched. To compare the efficacy and safety of 755 nm picosecond laser (PS‐755 nm), Q‐switched (QS) Alexandrite 755 nm nanosecond laser (QS‐755 nm), and QS Nd:YAG 532 nm nanosecond laser (QS‐532 nm) for treating CALMs. Study Design/Materials and Methods Forty‐one patients received several treatments at 3‐month intervals. Lesions were divided into two or three approximately equal parts, which were randomly treated with PS‐755 nm, QS‐755 nm, and QS‐532 nm. The safety and efficacy of three lasers were determined based on blinded visual assessments and self‐reports of patients three months after the comparative trial. Results Visual assessment 3 months after the comparative trial revealed that there was no statistically significant difference among the sites treated by QS‐755 nm (2.84 ± 1.11), QS‐532 nm (2.63 ± 1.06), and PS‐755 nm (2.74 ± 1.05) lasers. Five (26.32%) of 19 patients showed lesion recurrence. Adverse effects included acneiform miliaris, hypopigmentation, and hyperpigmentation, which were resolved within 12 months. Five (26.32%) of 19 patients who showed lesion recurrence 1–5 months after laser treatment had lightened or cleared at least 50% of the lesion. 46.67% of patients were satisfied or very satisfied with the outcome of the overall treatment. Conclusions PS‐755 nm, QS‐755 nm, and QS‐532 nm laser treatments were equally effective in treating and improving CALMs. PS‐755 nm caused fewer adverse effects. Individuals can react differently to different types of lasers. Patch tests should be conducted before the treatment. Lasers Surg. Med. © 2020 Wiley Periodicals LLC
Long noncoding RNA CPS1-IT1 is recently recognized as a tumor suppressor in several cancers. Here, we investigate the role of CPS1-IT1 in human melanoma.Presently, our study reveals the low expression of CPS1-IT1 in human melanoma tissues and cell lines, which is significantly associated with metastasis and tumor stage. Besides, the potential of CPS1-IT1 as a prognosis-predictor is strongly indicated. Functionally, CPS1-IT1 overexpression inhibits cell migration, invasion, epithelial-mesenchymal transition, and angiogenesis in melanoma cells. CYR61, an angiogenic factor that participates in tumor metastasis as well as a recognized oncogene in melanoma, is shown to be confined under CPS1-IT1 overexpression in melanoma cells. Furthermore, enforced expression of Cyr61 in CPS1-IT1-silenced melanoma cells dramatically normalized the protein level of Cyr61 and that of its downstream targets vascular endothelial growth factor and matrix metalloproteinase-9, as well as the repressive effect of CPS1-IT1 overexpression on melanoma cell metastasis. BRG1, a core component of SWI/SNF complex, is implied to interact with both CPS1-IT1 and Cyr61 in melanoma cells. Moreover, CPS1-IT1 negatively regulates Cyr61 expression by blocking the binding of BRG1 to Cyr61 promoter. Jointly, CPS1-IT1 controls melanoma metastasis through impairing Cyr61 expression via competitively binding with BRG1, uncovering a novel potential therapeutic and prognostic biomarker for patients with melanoma. K E Y W O R D SBRG1, CPS1-IT1, Cyr61, melanoma, metastasis
Background The differential diagnosis of eyelid basal cell carcinoma (BCC) and sebaceous carcinoma (SC) is highly dependent on pathologist’s experience. Herein, we proposed a fully automated differential diagnostic method, which used deep learning (DL) to accurately classify eyelid BCC and SC based on whole slide images (WSIs). Methods We used 116 haematoxylin and eosin (H&E)-stained sections from 116 eyelid BCC patients and 180 H&E-stained sections from 129 eyelid SC patients treated at the Shanghai Ninth People’s Hospital from 2017 to 2019. The method comprises two stages: patch prediction by the DenseNet-161 architecture-based DL model and WSI differentiation by an average-probability strategy-based integration module, and its differential performance was assessed by the carcinoma differentiation accuracy and F1 score. We compared the classification performance of the method with that of three pathologists, two junior and one senior. To validate the auxiliary value of the method, we compared the pathologists’ BCC and SC classification with and without the assistance of our proposed method. Results Our proposed method achieved an accuracy of 0.983, significantly higher than that of the three pathologists (0.644 and 0.729 for the two junior pathologists and 0.831 for the senior pathologist). With the method’s assistance, the pathologists’ accuracy increased significantly (P<0.05), by 28.8% and 15.2%, respectively, for the two junior pathologists and by 11.8% for the senior pathologist. Conclusions Our proposed method accurately classifies eyelid BCC and SC and effectively improves the diagnostic accuracy of pathologists. It may therefore facilitate the development of appropriate and timely therapeutic plans.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.