Influence of short-term dietary measures on dioxin concentrations in human milk.

Breast-feeding may expose infants to high levels of toxic chlorinated dioxins. To diminish intake of these lipophilic compounds by the baby, two diets were tested for their ability to reduce concentrations of dioxins in human milk. The diets were a low-fat/high- carbohydrate/low-dioxin diet. (about 20% of energy intake derived from fat) and a high fat /low-carbohydrate/low-dioxin diet. These diets were tested in 16 and 18 breast-feeding women, respectively. The test diets were followed for 5 consecutive days in the fourth week after delivery. Milk was sampled before and at the end of the dietary regimen, and dioxin concentrations and fatty acid concentrations were determined. Despite significant influences of these diets on the fatty acid profiles, no significant influence on the dioxin concentrations in breast milk could be found. We conclude that short-term dietary measures will not reduce dioxin concentration in human milk

Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed a deep learning-based DCIS grading system. It was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the observers (o1, o2 and o3) ($\kappa_{o1,dl}=0.81, \kappa_{o2,dl}=0.53, \kappa_{o3,dl}=0.40$) than the observers amongst each other ($\kappa_{o1,o2}=0.58, \kappa_{o1,o3}=0.50, \kappa_{o2,o3}=0.42$) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers ($\kappa_{o1,dl}=0.77, \kappa_{o2,dl}=0.75, \kappa_{o3,dl}=0.70$) as the observers amongst each other ($\kappa_{o1,o2}=0.77, \kappa_{o1,o3}=0.75, \kappa_{o2,o3}=0.72$). In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. We believe this is the first automated system that could assist pathologists by providing robust and reproducible second opinions on DCIS grade

A Unifying Framework for Mutual Information Methods for Use in Non-linear Optimisation

Many variants of MI exist in the literature. These vary primarily in how the joint histogram is populated. This paper places the four main variants of MI: Standard sampling, Partial Volume Estimation (PVE), In-Parzen Windowing and Post-Parzen Windowing into a single mathematical framework. Jacobians and Hessians are derived in each case. A particular contribution is that the non-linearities implicit to standard sampling and post-Parzen windowing are explicitly dealt with. These non-linearities are a barrier to their use in optimisation. Side-by-side comparison of the MI variants is made using eight diverse data-sets, considering computational expense and convergence. In the experiments, PVE was generally the best performer, although standard sampling often performed nearly as well (if a higher sample rate was used). The widely used sum of squared differences metric performed as well as MI unless large occlusions and non-linear intensity relationships occurred. The binaries and scripts used for testing are available online

Cisplatin-DNA adduct formation in patients treated with cisplatin-based chemoradiation: lack of correlation between normal tissues and primary tumor

Contains fulltext : 69595.pdf (publisher's version ) (Closed access)PURPOSE: In this study, the formation of cisplatin-DNA adducts after concurrent cisplatin-radiation and the relationship between adduct-formation in primary tumor tissue and normal tissue were investigated. METHODS: Three intravenous cisplatin-regimens, given concurrently with radiation, were studied: daily low-dose (6 mg/m(2)) cisplatin, weekly 40 mg/m(2), three-weekly 100 mg/m(2). A (32)P-postlabeling technique was used to quantify adducts in normal tissue [white blood cells (WBC) and buccal cells] and tumor. RESULTS: Normal tissue samples for adduct determination were obtained from 63 patients and tumor biopsies from 23 of these patients. Linear relationships and high correlations were observed between the levels of two guanosine- and adenosine-guanosine-adducts in normal and tumor tissue. Adduct levels in tumors were two to five times higher than those in WBC (P<0.001). No significant correlations were found between adduct levels in normal tissues and primary tumor biopsies, nor between WBC and buccal cells. CONCLUSIONS: In concurrent chemoradiotherapy schedules, cisplatin adduct levels in tumors were significantly higher than in normal tissues (WBC). No evidence of a correlation was found between adduct levels in normal tissues and primary tumor biopsies. This lack of correlation may, to some extent, explain the inconsistencies in the literature regarding whether or not cisplatin-DNA adducts can be used as a predictive test in anticancer platinum therapy