Despite having a relatively low incidence, renal cell carcinoma (RCC) is one of the most lethal urologic cancers. For successful treatment including surgery, early detection is essential. Currently there is no screening method such as biomarker assays for early diagnosis of RCC. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF) is a recent technical advance that can be used to identify biomarkers for cancers. In this study, we investigated whether SELDI protein profiling and artificial intelligence analysis of serum could distinguish RCC from healthy persons and other urologic diseases (nonRCC). The SELDI-TOF data was acquired from a total of 36 serum samples with weak cation exchange-2 protein chip arrays and filtered using ProteinChip software. We used a decision tree algorithm c4.5 to classify the three groups of sera. Five proteins were identified with masses of 3900, 4107, 4153, 5352, and 5987 Da. These biomarkers can correctly separate RCC from healthy and nonRCC samples.
Sensitive skin is characterized by subjective symptoms that are hard to quantify. However, a neurobiological approach could improve our understanding of the nature of skin sensitivity. In this study, we measured the sensory perception of well-controlled electric currents on the skin that stimulated sensory nerve fibres such as the myelinated A fibre, A delta fibre and unmyelinated c-fibre. The sensory perception thresholds were obtained quantitatively from subjects with sensitive-prone skin and controls. Application of 0.075% capsaicin, known to stimulate the nociceptor c-fibre, was topically applied; then the sensory perception thresholds were measured to determine whether the exposure to nociceptive stimulation could affect the subsequent sensory perception. The results showed that the perception thresholds of skin sensitive-prone subjects were low for the c-fibre measurements at 5 Hz electric current stimulation. Furthermore, a wide variation in sensory perception was noted in the skin sensitive-prone subjects after topical application of capsaicin. In conclusion, the abnormal sensory perception in individuals with sensitive skin appears to be related to neurological instability, where c-fibre nociception plays a role. Thus, quantitative sensory perception threshold measurement was found to be a useful method for the identification of skin sensitive-prone subjects.
Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden layer biases. However, this algorithm often requires a large number of hidden units and thus slowly responds to new observations. Evolutionary extreme learning machine (E-ELM) was proposed to overcome this problem; it used the differential evolution algorithm to select the input weights and hidden layer biases. However, this algorithm required much time for searching optimal parameters with iterative processes and was not suitable for data sets with a large number of input features. In this paper, a new approach for training SLFNs is proposed, in which the input weights and biases of hidden units are determined based on a fast regularized least-squares scheme. Experimental results for many real applications with both small and large number of input features show that our proposed approach can achieve good generalization performance with much more compact networks and extremely high speed for both learning and testing.
These results suggest that MAL-induced PpIX fluorescence imaging using FIA is quite sensitive and specific for detecting tumour and occult tumour in facial BCC lesions. This method of presurgical in vivo imaging is therefore proposed as a useful tool for defining the lateral border between BCC tumour and tumour-free areas.
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.