Cytokeratin phenotypes at the dento-gingival junction in relative health and inflammation, in smokers and non-smokers

OBJECTIVES: The cells of the junctional epithelium (JE) provide and maintain the epithelial attachment, and remain morphologically and phenotypically distinct from oral sulcular (OSE) and external oral epithelia (EOE), from which they may be regenerated de novo. Expression of cytokeratins (CK) in human epithelia has been shown to be highly site-specific, implying a functional role. The aims of this study were to differentiate between the cyto-keratin profiles of JE, OSE, EOE and pocket epithelia (PE) in health and disease, in smokers and non-smokers.
MATERIALS AND METHODS: The cytokeratin profiles of 40 samples of healthy and clinically inflamed human gingival tissue taken from 15 smokers and 25 non-smokers were studied by immunocytochemistry. Cryostat sections of fresh frozen gingival tissues were stained with a panel of monoclonal antibodies (mAb) and visualised by a biotin-Streptavidin-peroxidase complex technique.
RESULTS: JE and PE expressed an identical range of cytokeratins irrespective of the inflammatory or smoking status, with the exception of CK4 expression, which tended to be increased in smokers. The OSE and EOE expressed non-cornifying and cornifying differentiation cytokeratins respectively, but in the presence of inflammation, both these epithelia showed increased expression of CK19 at a basal level in association with expression of one or more of the simple cytokeratins. JE/PE expressed CK17 in external layers only, approximating the tooth surface. All epithelia expressed CK6,16 the markers of high cell turnover.
CONCLUSIONS: CK19 was a consistent differentiation marker for JE and PE.Expression of CK8,18 was enhanced by inflammation. CK4 expression increased in association with smoking. Markers of differentiation were not always co-expressed equally within a pair. Pairs were not always completely mutually exclusive with frequent co-localisation.     

Multivariate Analysis of Clinical Factors in Restenosis after Coronary Stenting

Ojbective To find the independent predictors for restenosis after coronary stenting. Methods Quantitative angiography was performed on 60 cases (67 successfully dilated lesions) after angio-plasty over 6-months follow-up, and both univariate and multivariate logistic regression analysis were done to i-dentify the correlations of restenosis with clinical factors. Results The total restenosis rate was 31. 3% (21 of 67 lesions), and according to univariate analysis the patients who underwent coronary stenting≥ 3. 5mm had a lower rate of restenosis ( P < 0. 01). Collateral circulation to the obstruction site, high maximal inflation pressure, smoking and the less minimal lumen diameter after PTCA made the rate of restenosis higherower ( P < 0. 05) . Multivariate logistic regression analysis showed that coronary stenting ≥3. 5mm had a low rate of restenosis, but high maximal inflation pressure and smoking made the restenosis rate higher. Conclusion Coronary stent size, maximal inflation pressure and. smokin     

Patel S,Turner PR,Stubberfield C,Barry E,Rohlff CR,Stamps A,McKenzie E,Young K,Tyson K,Terrett J,Box G,Eccles S,Page MJ. Hyaluronidase gene profiling and role of HYAL‐1 overexpression in an orthotopic model of prostate cancer. International Journal of Cancer 2002;97(4): 416–424.

The original article to which this Erratum refers was published in International Journal of Cancer; 2002; 97(4) 416–422.     

METHYLATION STATUS OF BCL6 DISTINGUISHES DNA SAMPLES FROM BENIGN AND MALIGNANT LYMPHOPROLIFERATIVE DISORDERS

INTRODUCTION: Core biopsy of the breast has become the method of choice for tissue diagnosis of screen detected microcalcifications and some mass lesions in many breast assessment centres. Biopsy results are not available until the following day. Imprint cytology of fresh breast core samples allows same-day reporting and patient counselling.
AIM: To determine the accuracy of core imprint cytology when compared with core biopsy diagnosis when used in a breast assessment centre setting.
METHODS: Core imprints (CI) were prepared and reported on all fresh core biopsies (CB) performed at the Sir Charles Gairdner Hospital Breast Centre from May to December 2000. Fresh core samples were placed on a glass microscope slide. Core radiographs were taken for microcalcification lesions (MC). A laboratory technician gently and quickly rolled the cores on the slide with fine forceps. The cores were fixed in formalin, processed and reported next day. The imprint slide was air dried and stained with DiffQuik. CI were reported using four categories: Insufficient, Benign, Indeterminate and Malignant. Counselling and planning for management were possible on the same day in women with malignant diagnoses. Clinicians were advised not to discuss negative or indeterminate CI results with women and to defer to the final CB report.
RESULTS: Cores were performed on 381 lesions. There were 83 carcinomas (38 in MC and 45 in masses) and 56 were called malignant on CI (absolute sensitivity 67.5%; 78.9% for MC and 57.8% for masses). 3 malignancies on CB were negative on CI giving a false negative rate of 3.6%. There were no false positive diagnoses. The predictive value of a benign diagnosis was 95.3%. There were no adverse effects in the histology of CB.
CONCLUSION: CI was an accurate method of providing an immediate diagnosis of malignancy in two thirds of malignancies confirmed on CB.     

Prediction of response to antiretroviral therapy by human experts and by the EuResist data‐driven expert system (the EVE study)

Objectives

The EuResist expert system is a novel data‐driven online system for computing the probability of 8‐week success for any given pair of HIV‐1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment.

Methods

The EuResist system was compared with 10 HIV‐1 drug resistance experts for the ability to predict 8‐week response to 25 treatment cases derived from the EuResist database validation data set. All current and past patient data were made available to simulate clinical practice. The experts were asked to provide a qualitative and quantitative estimate of the probability of treatment success.

Results

There were 15 treatment successes and 10 treatment failures. In the classification task, the number of mislabelled cases was six for EuResist and 6–13 for the human experts [mean±standard deviation (SD) 9.1±1.9]. The accuracy of EuResist was higher than the average for the experts (0.76 vs. 0.64, respectively). The quantitative estimates computed by EuResist were significantly correlated (Pearson r=0.695, P<0.0001) with the mean quantitative estimates provided by the experts. However, the agreement among experts was only moderate (for the classification task, inter‐rater κ=0.355; for the quantitative estimation, mean±SD coefficient of variation=55.9±22.4%).

Conclusions

With this limited data set, the EuResist engine performed comparably to or better than human experts. The system warrants further investigation as a treatment‐decision support tool in clinical practice.