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However, after PCa make metastases, chemotherapy plays an extremely important role.
The different therapeutic possibilities in PCa make it even more complex to understand all interactions between cancer cells and their surroundings during and after treatment.
Although further work is needed, these observations, in conjunction with the high serum expression levels in patients with far advanced lethal PCA, make the authors believe that the 80 kDa expression level has the potential to serve as biomarker with predictive value.
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PCA makes few assumptions about the underlying structure of the data, simply revealing dimensions that explain a large percentage of the variance.
Prostate-specific antigen (PSA) testing has dramatically changed the composition of prostate cancer (PCa), making it difficult to interpret incidence trends.
The reason for this poor performance is precisely that the traditional PCA makes sense for Gaussian noise and not for sparse noise.
PCA makes use of an orthonormal basis to capture on a small set of vectors (the signal subspace) as much energy as possible from the observed data.
While PSMA is expressed in normal human prostate cells and certain other normal tissues, it is highly upregulated in PCa, making it a promising diagnostic and therapeutic target [2].
Principal component analysis (PCA) makes it possible to visualize correlations in datasets by compressing information into a small number of dimensions.
PCA makes use of such overlap and combines variables that frequently occur together into components.
Patient-controlled analgesia (PCA) made of hydromophone was applied once the patients opened their eyes in the PACU.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com