Human melanocytes and keratinocytes exposed to UVB or UVA in vivo show comparable levels of thymine dimers

Epidemiology shows a relationship between solar exposure and all types of skin cancer. Understanding the mechanisms of skin cancer requires knowledge of the photomolecular events that occur within the relevant epidermal cell types in vivo. Studies to date have focused on UVR-induced DNA lesions in keratinocytes, the majority epidermal cell population which gives rise to most skin cancers. Malignant melanoma, arising from melanocytes (5%-10% of epidermal cells), accounts for most skin cancer deaths. We report on new techniques to detect DNA photolesions in human epidermal melanocytes in situ. Previously nonexposed buttock skin of volunteers of skin types I/II was exposed to clinically relevant doses of narrow bandwidth UVB (300 nm) and UVA (320 nm, 340 nm, 360 nm) radiation. Biopsies were taken immediately afterwards and processed for routine histology. Microscope sections were prepared and double-stained with fluorescent-tagged monoclonal antibodies for thymine dimers and melanocytes. UVR dose-response curves for dimer levels within melanocyte nuclei were determined by image analysis and compared with dimer levels in adjacent basal cell keratinocytes. Our data show that UVB and UVA readily induce thymine dimers in melanocytes at levels that are comparable with those found in adjacent keratinocytes. This new technique will enable melanocyte specific studies, such as DNA repair kinetics, to be done in vivo.     

Intravenous long term infusions of the rat without anesthesia

The lumbar-spine radiographs of 67 patients with idiopathic chondrocalcinosis articularis were reviewed for disc calcification and other changes. Calcification was present in 21 (31%) of the patients, seen most frequently at the L 2-3 disc space. The 21 patients as a group were significantly older than the 46 patients without disc calcification, and also had a much higher incidence of chondrocalcinosis in peripheral joints. There was no association with back pain or spinal stiffness. The 21 patients with disc calcification included six patients with a destructive peripheral arthropathy, and three of them had destructive changes affecting the lumbar spine. Three patients with a destructive peripheral arthropathy were also included in the group without disc calcification, and one of these had a destructive arthritis of the lumbar spine. For comparison, there was a 55% incidence of spinal chondrocalcinosis in nine patients with primary hyperparathyroidism and peripheral joint chondrocalcinosis, and a 6% incidence in 100 anteroposterior lumbar spine radiograph 'controls', taken before intravenous urography, although in this latter group the changes were minimal and confined to the margin of a single disc in each case.     

Histometric study of meat products. A literature review

In the present paper, the literature on the development of methods for histometric monitoring the quality of meats is reviewed. The value of specific techniques, statistical interpretation of results and the practicability of automation of histometric analysis are examined more closely. It is concluded from the study of the literature that histometric examination of meats allows an objective assessment of volume percentages of tissue components. When a distinct difference in contrast between various tissues is achieved by specific staining methods, developments in the field of image analysis systems will allow automation of the quantitative histological examination of meats.     

Hybridizing mixtures of experts with support vector machines: Investigation into nonlinear dynamic systems identification

Mixture of experts (ME) models comprise a family of modular neural network architectures aiming at distilling complex problems into simple subtasks. This is done by deploying a separate gating module for softly dividing the input space into overlapping regions to be each assigned to one or more expert networks. Conversely, support vector machines (SVMs) refer to kernel-based methods, neural-network-alike models that constitute an approximate implementation of the structural risk minimization principle. Such learning machines follow the simple, but powerful idea of nonlinearly mapping input data into high-dimensional feature spaces wherein a linear decision surface discriminating different regions is properly designed. In this work, we formally characterize and empirically evaluate a novel approach, named as Mixture of Support Vector Machine Experts (MSVME), whose main purpose is to combine the complementary properties of both SVM and ME models. In the formal characterization, an algorithm based on a maximum likelihood criterion is considered for the MSVME training, and we demonstrate that it is possible to train each expert based on an SVM perspective. Regarding the empirical evaluation, simulation results involving nonlinear dynamic system identification problems are reported, contrasting the performance shown by the MSVME approach with that exhibited by conventional SVM and ME models.