C-kit overexpression is not associated with KIT gene mutations in chromophobe renal cell carcinoma or renal oncocytoma

Introduction

C-kit overexpression has previously been described in chromophobe renal cell carcinoma (cpRCC) and renal oncocytoma (RO). However, so far no KIT mutations have been found. The objective of our study was to analyse c-kit in a large cohort of renal tumors and to perform KIT mutation analysis in a subset cpRCC and RO cases with overexpression of c-kit.

Materials and methods

We studied the immunohistochemical expression of c-kit on tissue microarrays containing formalin-fixed, paraffin-embedded samples of 948 patients with renal tumors. CpRCC and RO cases with c-kit overexpression (n = 23) were analyzed for KIT mutations in exons 9, 11, 13, 14, 15, and 17.

Results

Expression of c-kit was found in 6/642 (0.9%) clear cell RCC, 3/154 (1.9%) papillary RCC, 54/69 (78.3%) cpRCC, 37/45 (82.2%) RO and 2/30 (6.7%) of other unclassified tumor types. In none of the RO and cpRCC cases analyzed, a KIT gene mutation was found.

Conclusion

C-kit expression is found in the majority of cpRCC and RO, but these tumors do not harbor the usual c-kit activating mutations. This may have implications for the use of tyrosine kinase inhibitors in patients with advanced cpRCC and c-kit expression.     

Thickness distribution of the glenohumeral joint cartilage: a quantitative study using computed tomography

Purpose

Among late signs like sclerosis, cysts and osteophytes, alteration of cartilage is a common problem in osteoarthritis. To detect abnormal states in the glenohumeral joint, the physiologic distribution of the cartilage thickness must be known, which will allow physicians to better advise patients. High-resolution computed tomography (CT) data in soft tissue kernel provide highly accurate quantitative results and are a useful method to determine the geometrical situation of the glenohumeral joint. The objective of this study was to characterize the distribution of the thickness of the glenohumeral joint cartilage using CT.

Methods

To investigate the distribution of thickness of the joint cartilage, CT images in soft tissue kernel of nine specimens were analyzed using image visualization software. Statistical analysis of the obtained data was performed using the ANOVA test.

Results

Results showed different patterns in the glenoid cavity than in humeral head. Cartilage thickness in all glenoids showed maxima in the inferior and anterior portion, whereas central areas are covered with the thinnest cartilage layer. Maximum cartilage thickness in the humeral head was found in the central and superior parts.

Conclusion

We could show that the distribution of cartilage thickness in the glenohumeral joint is not homogenous and that there exist several reproducible patterns. Evaluation of cartilage thickness in the glenohumeral joint is of high interest in basic and clinical research.     

Modelling the dynamics of expiratory airflow to describe chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is characterized by expiratory airflow limitation, but current diagnostic criteria only consider flow till the first second and are therefore strongly debated. We aimed to develop a data-based individualized model for flow decline and to explore the relationship between model parameters and COPD presence. A second-order transfer function model was chosen and the model parameters (namely the two poles and the steady state gain (SSG)) from 474 individuals were correlated with COPD presence. The capability of the model to predict disease presence was explored using 5 machine learning classifiers and tenfold cross-validation. Median (95 % CI) poles in subjects without disease were 0.9868 (0.9858–0.9878) and 0.9333 (0.9256–0.9395), compared with 0.9929 (0.9925–0.9933) and 0.9082 (0.9004–0.9140) in subjects with COPD (p < 0.001 for both poles). A significant difference was also found when analysing the SSG, being lower in COPD group 3.8 (3.5–4.2) compared with 8.2 (7.8–8.7) in subjects without (p < 0.0001). A combination of all three parameters in a support vector machines corresponded with highest sensitivity of 85 %, specificity of 98.1 % and accuracy of 88.2 % to COPD diagnosis. The forced expiration of COPD can be modelled by a second-order system which parameters identify most COPD cases. Our approach offers an additional tool in case FEV₁/FVC ratio-based diagnosis is doubted.     

Long Working Hours and Sleep Disturbances: The Whitehall II Prospective Cohort Study

Study Objective:

To examine whether exposure to long working hours predicts various forms of sleep disturbance; short sleep, difficulty falling asleep, frequent waking, early waking and waking without feeling refreshed.

Design:

Prospective study with 2 measurements of working hours (phase 3, 1991–1994 and phase 5, 1997–1999) and 2 measurements of subjective sleep disturbances (phase 5 and phase 7, 2002–2004).

Setting:

The Whitehall II study of British civil servants.

Participants:

Full time workers free of sleep disturbances at phase 5 and employed at phases 5 and 7 (n = 937–1594) or at phases 3, 5, and 7 (n = 886–1510).

Measurements and Results:

Working more than 55 hours a week, compared with working 35–40 hours a week, was related to incident sleep disturbances; demographics-adjusted odds ratio (95% CI) 1.98 (1.05, 3.76) for shortened sleeping hours, 3.68 (1.58, 8.58) for difficulty falling asleep; and 1.98 (1.04, 3.77) for waking without feeling refreshed. Repeat exposure to long working hours was associated with odds ratio 3.24 (1.45, 7.27) for shortened sleep, 6.66 (2.64, 16.83) for difficulty falling asleep, and 2.23 (1.16, 4.31) for early morning awakenings. Some associations were attenuated after adjustment for other risk factors. To a great extent, similar results were obtained using working hours as a continuous variable. Imputation of missing values supported the findings on shortened sleep and difficulty in falling asleep.

Conclusion:

Working long hours appears to be a risk factor for the development of shortened sleeping hours and difficulty falling asleep.

Citation:

Virtanen M; Ferrie JE; Vahtera J; Elovainio M; Singh-Manoux A; Marmot MG; Kivimäki M. Long working hours and sleep disturbances: the whitehall II prospective cohort study. SLEEP 2009;32(6):737–745.