Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

This paper introduces an automated computer- assisted system for the diagnosis of cervical intraepithelial neoplasia (CIN) using ultra-large cervical histological digital slides. The system contains two parts: the segmentation of squamous epithelium and the diagnosis of CIN. For the segmentation, to reduce processing time, a multiresolution method is developed. The squamous epithelium layer is first segmented at a low (2X) resolution. The boundaries are further fine tuned at a higher (20X) resolution. The block-based segmentation method uses robust texture feature vectors in combination with support vector machines (SVMs) to perform classification. Medical rules are finally applied. In testing, segmentation using 31 digital slides achieves 94.25% accuracy. For the diagnosis of CIN, changes in nuclei structure and morphology along lines perpendicular to the main axis of the squamous epithelium are quantified and classified. Using multi-category SVM, perpendicular lines are classified into Normal, CIN I, CIN II, and CIN III. The robustness of the system in term of regional diagnosis is measured against pathologists' diagnoses and inter-observer variability between two pathologists is considered. Initial results suggest that the system has potential as a tool both to assist in pathologists' diagnoses, and in training.

Effect of Aspirin or Resistant Starch on Colorectal Neoplasia in the Lynch Syndrome

Background: Observational and epidemiologic data indicate that the use of aspirin reduces the risk of colorectal neoplasia; however, the effects of aspirin in the Lynch syndrome (hereditary nonpolyposis colon cancer) are not known. Resistant starch has been associated with an antineoplastic effect on the colon.

Buccal absorption of enalapril and lisinopril

Objective: The buccal absorption of captopril does not exhibit the classical pH/partition hypothesis, suggesting that mechanisms other than passive diffusion are involved in its absorption; animal studies have suggested that a peptide carrier-mediated transport system may be responsible for its absorption. The present study evaluated the effects of pH on octanol partitioning, and on the buccal absorption of enalapril and lisinopril, using in vitro techniques and buccal partitioning in human volunteer subjects.

The effects of hexetidine (Oraldene (TM)) on the adherence of Candida albicans to human buccal epithelial cells in vitro and ex vivo and on in vitro morphogenesis

Purpose. This study reports the effects of hexetidine (Oraldene(TM)) on two virulence attributes of Candida albicans, namely, in vitro and ex vivo adherence of yeast cells to buccal epithelial cells (BEG) and in vitro morphogenesis.

The role of p63 in epidermal morphogenesis and neoplasia

The p53 family of transcription factors is made up of p53, p63 and p73, which share significant structural homology. In particular, transcriptional complexity and the expression of multiple protein isoforms are an emergent trait of all family members. p63 is the evolutionarily eldest member of the p53 family and the various isoforms have critical roles in the development of stratifying epithelia. Recent results have uncovered additional splice variants, adding to the complexity of the transcriptional architecture of p63. These observations and the emerging extensive interplay between p63 and p53 in development, proliferation and differentiation underline the importance of considering all isoforms and family members in studies of the function of p53 family members.

PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.