A significant seasonal correlation was recently shown for brown recluse spider activity. Vetter (2011) observed brown recluse spiders were submitted by the general public predominantly during April–October. For patients with suspected brown recluse spider bites (BRSB), we have observed the same seasonality. Among 45 cases with features consistent of a BRSB, 43 (95.6%) occurred during April–October. Both the Vetter study and our study serve to demonstrate seasonal activity for brown recluse spiders.

GAC techniques show promise in attaining the goal of automatic skin lesion segmentation.

Fuzzy logic image analysis techniques were used to analyze three shades of blue (lavender blue, light blue, and dark blue) in dermoscopic images for melanoma detection. A logistic regression model provided up to 82.7% accuracy for melanoma discrimination for 866 images. With a support vector machines (SVM) classifier, lower accuracy was obtained for individual shades (79.9–80.1%) compared with up to 81.4% accuracy with multiple shades. All fuzzy blue logic alpha cuts scored higher than the crisp case. Fuzzy logic techniques applied to multiple shades of blue can assist in melanoma detection. These vector-based fuzzy logic techniques can be extended to other image analysis problems involving multiple colors or color shades.

Background Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the United States. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including Evolving Artificial Neural Networks and Evolving Artificial Neural Network Ensembles. Results Experiment results based on ten-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process.

Importance Bites from the brown recluse spider (BRS) can cause extreme pain. We propose cytokine release as a cause of the discomfort and a central mechanism through glial cell upregulation to explain measured pain levels and time course. Observations Twenty-three BRS bites were scored at a probable or documented level clinically, and an enzyme-linked immunosorbent assay was used to confirm the presence of BRS venom. The mean (SD) pain level in these cases 24 hours after the spider bite was severe: 6.74 (2.75) on a scale of 0 to 10. Narcotics may be needed to provide relief in some cases. The difference in pain level by anatomic region was not significant. Escalation observed in 22 of 23 cases, increasing from low/none to extreme within 24 hours, is consistent with a cytokine pain pattern, in which pain increases concomitantly with a temporal increase of inflammatory cytokines. Conclusions and Relevance These findings in BRS bites support the hypothesis of cytokine release in inflammatory pain. A larger series is needed to confirm the findings reported here. The extreme pain from many BRS bites motivates us to find better prevention and treatment techniques.