Prognostic Features of Sporadic Creutzfeldt-Jakob Disease: An Analysis of Taiwan's Nationwide Surveillance

2021 ◽  
Author(s):  
Yu Sun ◽  
Ling-Yun Fan ◽  
Chung-Te Huang ◽  
Chih-Ching Liu ◽  
Ta-Fu Chen ◽  
...  
Keyword(s):  
Prognostic Features ◽  
Jakob Disease ◽  
Nationwide Surveillance

Upbeat nystagmus as the initial clinical sign of Creutzfeldt-Jakob disease

2005 ◽  
Vol 32 (S 4) ◽  
Author(s):  
M Strupp ◽  
V.C Zingler ◽  
K Jahn ◽  
M Glaser ◽  
H Kretzschmar ◽  
...  
Keyword(s):  
Clinical Sign ◽  
Jakob Disease ◽  
Upbeat Nystagmus

Melanoma: Prognostic Factors and Factors Predictive of Response to Therapy

2020 ◽  
Vol 27 (17) ◽  
pp. 2792-2813
Author(s):  
Martina Strudel ◽  
Lucia Festino ◽  
Vito Vanella ◽  
Massimiliano Beretta ◽  
Francesco M. Marincola ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Lactate Dehydrogenase ◽  
Checkpoint Inhibitors ◽  
Survival Rates ◽  
Elevated Serum ◽  
Predictive Biomarkers ◽  
Response To Therapy ◽  
Response To Treatment ◽  
Prognostic Features ◽  
Programmed Death

Background: A better understanding of prognostic factors and biomarkers that predict response to treatment is required in order to further improve survival rates in patients with melanoma. Predictive Biomarkers: The most important histopathological factors prognostic of worse outcomes in melanoma are sentinel lymph node involvement, increased tumor thickness, ulceration and higher mitotic rate. Poorer survival may also be related to several clinical factors, including male gender, older age, axial location of the melanoma, elevated serum levels of lactate dehydrogenase and S100B. Predictive Biomarkers: Several biomarkers have been investigated as being predictive of response to melanoma therapies. For anti-Programmed Death-1(PD-1)/Programmed Death-Ligand 1 (PD-L1) checkpoint inhibitors, PD-L1 tumor expression was initially proposed to have a predictive role in response to anti-PD-1/PD-L1 treatment. However, patients without PD-L1 expression also have a survival benefit with anti-PD-1/PD-L1 therapy, meaning it cannot be used alone to select patients for treatment, in order to affirm that it could be considered a correlative, but not a predictive marker. A range of other factors have shown an association with treatment outcomes and offer potential as predictive biomarkers for immunotherapy, including immune infiltration, chemokine signatures, and tumor mutational load. However, none of these have been clinically validated as a factor for patient selection. For combined targeted therapy (BRAF and MEK inhibition), lactate dehydrogenase level and tumor burden seem to have a role in patient outcomes. Conclusions: With increasing knowledge, the understanding of melanoma stage-specific prognostic features should further improve. Moreover, ongoing trials should provide increasing evidence on the best use of biomarkers to help select the most appropriate patients for tailored treatment with immunotherapies and targeted therapies.

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