MeCP2 in the enteric nervous system.

BACKGROUND: Rett syndrome (RTT) is an intellectual deficit and movement disorder that develops during early childhood in girls. Affected children are normal until 6-18 months of age, after which symptoms begin to appear. Most cases of RTT are due to mutations in the MeCP2 gene leading to disruption of neuronal communication in the central nervous system. In addition, RTT patients show peripheral ailments such as gastrointestinal (GI), respiratory, and cardiac dysfunction. The etiology of intestinal dysfunction in RTT is not well-understood. Reports on the presence of MeCP2 in the peripheral nervous system are scant. As such we examined the levels of MeCP2 in human and murine GI tissue and assessed MeCP2 expression at various developmental stages. METHODS: Immunohistochemistry for MeCP2, HuC/D, juvenile beta tubulin, and GFAP was performed on human and murine intestine. Western blots of these same tissues were probed with MeCP2, vAChT, nNOS, and beta-actin antibodies. KEY RESULTS: MeCP2 is expressed throughout the GI tract. MeCP2 is expressed specifically in the enteric nervous system of the GI tract. MeCP2 is expressed in the GI tract throughout development with appearance beginning at or before E11.5 in the murine intestine. CONCLUSIONS & INFERENCES: The proof of MeCP2 expression in enteric neurons suggests that the GI dysmotility in Rett may arise from enteric network dysfunction secondary to MeCP2 mutation.

Multispectral retinal image analysis: a novel non-invasive tool for retinal imaging.

PURPOSE: To develop a non-invasive method for quantification of blood and pigment distributions across the posterior pole of the fundus from multispectral images using a computer-generated reflectance model of the fundus. METHODS: A computer model was developed to simulate light interaction with the fundus at different wavelengths. The distribution of macular pigment (MP) and retinal haemoglobins in the fundus was obtained by comparing the model predictions with multispectral image data at each pixel. Fundus images were acquired from 16 healthy subjects from various ethnic backgrounds and parametric maps showing the distribution of MP and of retinal haemoglobins throughout the posterior pole were computed. RESULTS: The relative distributions of MP and retinal haemoglobins in the subjects were successfully derived from multispectral images acquired at wavelengths 507, 525, 552, 585, 596, and 611 nm, providing certain conditions were met and eye movement between exposures was minimal. Recovery of other fundus pigments was not feasible and further development of the imaging technique and refinement of the software are necessary to understand the full potential of multispectral retinal image analysis. CONCLUSION: The distributions of MP and retinal haemoglobins obtained in this preliminary investigation are in good agreement with published data on normal subjects. The ongoing development of the imaging system should allow for absolute parameter values to be computed. A further study will investigate subjects with known pathologies to determine the effectiveness of the method as a screening and diagnostic tool.

Multispectral imaging of the ocular fundus using light emitting diode illumination.

We present an imaging system based on light emitting diode (LED) illumination that produces multispectral optical images of the human ocular fundus. It uses a conventional fundus camera equipped with a high power LED light source and a highly sensitive electron-multiplying charge coupled device camera. It is able to take pictures at a series of wavelengths in rapid succession at short exposure times, thereby eliminating the image shift introduced by natural eye movements (saccades). In contrast with snapshot systems the images retain full spatial resolution. The system is not suitable for applications where the full spectral resolution is required as it uses discrete wavebands for illumination. This is not a problem in retinal imaging where the use of selected wavelengths is common. The modular nature of the light source allows new wavelengths to be introduced easily and at low cost. The use of wavelength-specific LEDs as a source is preferable to white light illumination and subsequent filtering of the remitted light as it minimizes the total light exposure of the subject. The system is controlled via a graphical user interface that enables flexible control of intensity, duration, and sequencing of sources in synchrony with the camera. Our initial experiments indicate that the system can acquire multispectral image sequences of the human retina at exposure times of 0.05 s in the range of 500-620 nm with mean signal to noise ratio of 17 dB (min 11, std 4.5), making it suitable for quantitative analysis with application to the diagnosis and screening of eye diseases such as diabetic retinopathy and age-related macular degeneration.

Quantitative analysis of multi-spectral fundus images.

We have developed a new technique for extracting histological parameters from multi-spectral images of the ocular fundus. The new method uses a Monte Carlo simulation of the reflectance of the fundus to model how the spectral reflectance of the tissue varies with differing tissue histology. The model is parameterised by the concentrations of the five main absorbers found in the fundus: retinal haemoglobins, choroidal haemoglobins, choroidal melanin, RPE melanin and macular pigment. These parameters are shown to give rise to distinct variations in the tissue colouration. We use the results of the Monte Carlo simulations to construct an inverse model which maps tissue colouration onto the model parameters. This allows the concentration and distribution of the five main absorbers to be determined from suitable multi-spectral images. We propose the use of "image quotients" to allow this information to be extracted from uncalibrated image data. The filters used to acquire the images are selected to ensure a one-to-one mapping between model parameters and image quotients. To recover five model parameters uniquely, images must be acquired in six distinct spectral bands. Theoretical investigations suggest that retinal haemoglobins and macular pigment can be recovered with RMS errors of less than 10%. We present parametric maps showing the variation of these parameters across the posterior pole of the fundus. The results are in agreement with known tissue histology for normal healthy subjects. We also present an early result which suggests that, with further development, the technique could be used to successfully detect retinal haemorrhages.

Model-based parameter recovery from uncalibrated optical images.

We propose a novel method for quantitative interpretation of uncalibrated optical images which is derived explicitly from an analysis of the image formation model. Parameters characterising the tissue are recovered from images acquired using filters optimised to minimise the error. Preliminary results are shown for the skin, where the technique was successfully applied to aid the diagnosis and interpretation of non-melanocytic skin cancers and acne; and for the more challenging ocular fundus, for mapping of the pigment xanthophyll.

Monte Carlo modelling of the spectral reflectance of the human eye.

The interpretation of in vivo spectral reflectance measurements of the ocular fundus requires an accurate model of radiation transport within the eye. As well as considering the scattering and absorption processes, it is also necessary to account for appropriate histological variation. This variation results in experimentally measured spectra which vary, both with position in the eye, and between individuals. In this paper the results of a Monte Carlo simulation are presented. Three histological variables are considered: the RPE melanin concentration, the choriodal haemoglobin concentration and the choroidal melanin concentration. By considering these three variables, it is possible to generate model spectra which agree well with in vivo experimental measurements of the nasal fundus. The model has implications for the problem of extracting histological parameters from spectral reflectance measurements. These implications are discussed and a novel approach to interpretation of images of the ocular fundus suggested.

Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions.

BACKGROUND: Spectrophotometric intracutaneous analysis (SIA) is a new technique for imaging pigmented skin lesions and for diagnosing melanoma. The SIAscope produces eight narrow-band spectrally filtered images of the skin over an area of 24 x 24 mm with radiation ranging from 400 to 1000 nm. OBJECTIVES: To present the early results of a clinical trial with SIA. METHODS: Spectrophotometric inputs from the skin were analysed using complex algorithms to return high-resolution information regarding total melanin content of the epidermis and papillary dermis, collagen and haemoglobin content as well as the presence of melanin in the papillary dermis. RESULTS: Simple, highly reproducible and reliable features were identified, e.g. the presence of dermal melanin, collagen holes and 'erythematous blush' with blood displacement. These simple features were found to be highly specific (80.1%) and sensitive (82.7%) for melanoma in a dataset of 348 pigmented lesions (52 melanomas) and compared very favourably with dermatoscopy when analysed using receiver-operator characteristic curves. CONCLUSIONS: This first clinical trial with SIAscopy has yielded very promising results and delivers new, useful information to the clinician diagnosing pigmented skin lesions.

The symbolic atlas of the brain: handling non-visual information associated with neuroanatomy.

The Symbolic Atlas of the brain is a novel software tool which enables the user to store and access non-visual information associated with the brain anatomy. The atlas is potentially capable of storing any information about neuroanatomical objects. The user can construct a number of atlases, each containing a different kind of information related to functionality, pathology, symptoms and other facts. The Prolog database that underpins the storage of knowledge frames provides scope for nearly unconstrained usage and manipulation of the stored data, including reasoning with and about data and complex querying. The access to the stored information is provided in an intuitive way, through a simple 'click' on an anatomical structure either in a two- or three-dimensional atlas, or on an outline of a structure superimposed on a real image slice. A pilot educational and clinical use of the atlas indicates its great potential, especially in the are of education.

Computer screening for early detection of melanoma--is there a future?

Computer image analysis in the study of pigmented lesions is critically examined and discussed in the light of the current published data. The potential for objective analysis by computers as a possible screening aid for the inexperienced clinician is discussed. The future for this technology is exciting if handled with care.

Shape analysis for classification of malignant melanoma.

Melanoma is a malignant skin tumour. If detected and surgically removed early whilst residing in the superficial part of the skin the prognosis is excellent. A seven-point check-list of signs and symptoms has been adopted by the Cancer Research Campaign to help non-dermatologists distinguish benign pigmented lesions from melanoma. The presence of irregularity in shape or outline of a mole is one of these important signs. However, it has recently been shown that not only patients, but also clinicians have difficulty in agreeing upon whether a mole exhibits irregularity or not. Computer image analysis methods have been developed to derive quantitative measures of those shape parameters which dermatologists appear to use in their assessment of shape irregularity. The overall shape of the lesion is expressed by the 'bulkiness' measure. Irregularity of the border is expressed by two fractal dimension measures, one for the 'structural' aspect of the shape and the other for the 'textural' aspect. These measures were used in combination to classify melanomas in the study containing silhouettes of 43 melanomas and 45 benign lesions producing correct classification with 91% sensitivity and 69% specificity. This paper describes computer image analysis aspects of the study.

Extraction of quantitative blur measures for circumscribed lesions in mammograms.

This study investigates ways of improving lesion diagnosis in mammograms by deriving quantitative descriptions of the lesion periphery. The descriptions are derived by computer image analysis methods. The degree of blur at lesion boundaries is of prime concern, as poorly outlined lesions can indicate malignancy. The need for quantitative analysis arises from psychological evidence suggesting that the human visual system cannot precisely estimate the degree of blur. To help find suitable measures a set of 'artificial' lesions has been generated by convolving a step-like edge with a set of Gaussian functions G(sigma) where sigma characterizes the degree of blur. From these generated lesion images the parameters sigma are derived by the process involving deconvolution. As the edge changes are most important in radial directions, the measures of sigma are calculated for each radial profile of the lesion. The derived individual values correspond very closely to those used to generate the lesions. Statistical measures obtained from them allow distinction between edges which are blurred to different extents and yet are impossible to differentiate visually. The artificial lesions will be combined with mammographic data, and similar measures derived. The work will be validated on real lesions for which the histological findings are known from performed biopsies.