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Methods: Retrospective study of 42 patients.
Results: The mean age at presentation of conjunctival melanoma was 43 years (median, 45 years; range, 9–78 years). There were 20 (48%) males and 22 (52%) females. Nineteen patients (45%) had a known history of a preexisting pigmented conjunctival lesion. Bulbar conjunctiva (n = 28; 67%) was the most common tumor epicenter, and medial ocular surface quadrant (n = 15; 36%) was more commonly involved. The mean tumor basal diameter was 12 mm (median, 10 mm; range, 4–30 mm), and the mean tumor thickness was 4 mm (median, 2 mm; range, 1–30 mm). Majority of the patients had a pigmented tumor (n = 33; 79%). The tumors arose de novo (n = 17, 41%) or were associated with conjunctival nevus (n = 9; 21%) or primary acquired melanosis (n = 16, 38%). Wide excisional biopsy, adjunctive cryotherapy, and amniotic membrane grafting were performed in 27 (71%) patients, 11 (29%) underwent orbital exenteration, and 4 were lost to follow-up prior to definitive treatment. Over a mean follow-up period of 24 months (median, 9 months; range, <1 to 136 months), four (11%) patients had tumor recurrence, seven (18%) had locoregional lymph node metastasis, and four (11%) developed systemic metastasis and died due to metastatic disease.
Conclusion: Conjunctival melanoma predominantly occurs in middle-aged Asian Indians and is associated with a high rate of systemic metastasis and death. 相似文献
Identification of non-invasive biomarkers of disease progression in multiple sclerosis (MS) is critically needed for monitoring the disease progression and for effective therapeutic interventions. Urine is an attractive source for non-invasive biomarkers because it is easily obtained in the clinic. In search of a urine metabolite signature of progression in chronic experimental autoimmune encephalomyelitis (EAE), we profiled urine at the chronic stage of the disease (day 45 post immunization) by global untargeted metabolomics. Using a combination of high-throughput liquid-and-gas chromatography with mass spectrometry, we found 105 metabolites (P < 0.05) significantly altered at the chronic stage, indicating a robust alteration in the urine metabolite profile during disease. Assessment of altered metabolites against the Kyoto Encyclopedia of Genes and Genomes revealed distinct non-overlapping metabolic pathways and revealed phenylalanine-tyrosine and associated metabolism being the most impacted. Combined with previously performed plasma profiling, eight common metabolites were significantly altered in both of the biofluids. Metaboanalyst analysis of these common metabolites revealed that phenylalanine metabolism and Valine, leucine, and isoleucine biosynthetic pathways are central metabolic pathways in both bio-fluids and could be analyzed further, either for the discovery of therapeutics or biomarker development. Overall, our study suggests that urine and plasma metabolomics may contribute to the identification of a distinct metabolic fingerprint of EAE disease discriminating from the healthy control which may aid in the development of an objective non-invasive monitoring method for progressive autoimmune diseases like MS.
Untargeted urinary metabolomics of a chronic mouse model of multiple sclerosis identified Phenylalanine, tyrosine & tryptophan metabolism as the significantly altered metabolic pathway. Eight common metabolites were identified when we combined urinary and plasma metabolic signature, which revealed a perturbation of Phenylalanine metabolism and valine, leucine & isoleucine metabolic pathways, involved in CNS dysfunction during diseases. The identified eight metabolic signature of urine and plasma may be of clinical relevance as potential biomarkers and guide towards the identification of specific metabolic pathways as novel drug targets.