Potential diagnostic value of P16 expression in premalignant and malignant cervical lesions.

BACKGROUND: The goal of this study was to evaluate the results of the expression of p16INK4a in normal uterine cervical epithelium, low-grade cervical intraepithelial neoplasia (CIN), high-grade CIN, squamous cell carcinoma (SCC), and adenocarcinoma of the cervix, in order to help draw a distinction between low risk and high risk patients with cervical lesions. MATERIALS AND METHODS: [corrected] P16INK4a expression was evaluated by immunohistochemistry in 78 paraffin-embedded tissue samples including 39 normal cervical tissues, 11 low-grade CINs, 11 high-grade CINs, 22 cervical SCCs and 8 cervical adenocarcinomas. Two parameters in immunohistochemical p16 expression were evaluated: percentage of p16-positive cells, and reaction intensity. RESULTS: The p16INK4a expression rate was 81.8% in low-grade CINs, 91% in high-grade CINs, 90% in SCCs and 75% in cervical adenocarcinomas. 10% of normal cervical samples expressed p16. Moreover, there was a significant relationship between the histological diagnoses and percentage of positive cells and reaction intensity of p16 (p < 0.005). The intensity of the reaction was the best parameter to evaluate the positivity of p16. CONCLUSIONS: Over-expression of the p16INK4a was typical for dysplastic and neoplastic epithelia of the uterine cervix. However, p16INK4a-negative CINs and carcinomas did exist. Although negative p16INK4a expression does not definitely exclude the patient with cervical lesion from the high-risk group, immunohistochemical study for p16INK4a may be used as a supplementary test for an early diagnosis of cervical cancers.

Probing airway conditions governing ventilation defects in asthma via hyperpolarized MRI image functional modeling.

Image functional modeling (IFM) has been introduced as a method to simultaneously synthesize imaging and mechanical data with computational models to determine the degree and location of airway constriction in asthma. Using lung imaging provided by hyperpolarized (3)He MRI, we advanced our IFM method to require matching not only to ventilation defect location but to specific ventilation throughout the lung. Imaging and mechanical data were acquired for four healthy and four asthmatic subjects pre- and postbronchial challenge. After provocation, we first identified maximum-size airways leading exclusively to ventilation defects and highly constricted them. Constriction patterns were then found for the remaining airways to match mechanical data. Ventilation images were predicted for each pattern, and visual and statistical comparisons were done with measured data. Results showed that matching of ventilation defects requires severe constriction of small airways. The mean constriction of such airways leading to the ventilation defects needed to be 70-80% rather than fully closed. Also, central airway constriction alone could not account for dysfunction seen in asthma, so small airways must be involved.