Background One of the big challenges in onco-radiology is to find a reliable imaging method that may predict early response during the first cycles of any neoadjuvant chemotherapy. Purpose To evaluate the use of real-time harmonic contrast-enhanced ultrasound (CEUS) in predicting early response in breast cancer tumors under neoadjuvant chemotherapy (NAC) treatment. Material and Methods Nineteen consecutive patients with invasive breast cancer were evaluated with a bolus dose of 2.4 mL contrast agent using CEUS, before and after two cycles of epirubicin and docetaxel. The lognormal function was used for quantitative analysis of kinetic data to evaluate early response. Results There was statistically significant difference in time-to-peak ( t) between responders and non-responders (two sample t-test, P = 0.027) where t was significantly longer at the week 5 than at the baseline scan among responders when compared to non-responders. Conclusion In-flow of intravascular contrast agent in tumors is significantly slower in responders at real-time harmonic CEUS, and might be effectively used for the evaluation of early response to chemotherapy in invasive breast cancer. However, further investigations in a larger and more heterogeneous population should be performed to corroborate the reliability of the method.

We study concentration inequalities for the Kullback–Leibler (KL) divergence between the empirical distribution and the true distribution. Applying a recursion technique, we improve over the method of types bound uniformly in all regimes of sample size $n$ and alphabet size $k$, and the improvement becomes more significant when $k$ is large. We discuss the applications of our results in obtaining tighter concentration inequalities for $L_1$ deviations of the empirical distribution from the true distribution, and the difference between concentration around the expectation or zero. We also obtain asymptotically tight bounds on the variance of the KL divergence between the empirical and true distribution, and demonstrate their quantitatively different behaviours between small and large sample sizes compared to the alphabet size.

Invasive breast carcinomas exhibiting earlier peak enhancement and faster elimination of microbubble contrast agent at CEUS are found to be associated with established predictors of poor prognosis.

In many practical settings one can sequentially and adaptively guide the collection of future data, based on information extracted from data collected previously. These sequential data collection procedures are known by different names, such as sequential experimental design, active learning or adaptive sensing/sampling. The intricate relation between data analysis and acquisition in adaptive sensing paradigms can be extremely powerful, and often allows for reliable signal estimation and detection in situations where non-adaptive sensing would fail dramatically. In this work we investigate the problem of estimating the support of a structured sparse signal from coordinate-wise observations under the adaptive sensing paradigm. We present a general procedure for support set estimation that is optimal in a variety of cases and shows that through the use of adaptive sensing one can: (i) mitigate the effect of observation noise when compared to non-adaptive sensing and, (ii) capitalize on structural information to a much larger extent than possible with non-adaptive sensing. In addition to a general procedure to perform adaptive sensing in structured settings we present both performance upper bounds, and corresponding lower bounds for both sensing paradigms.

Peroxisome proliferator-activated receptor gamma (PPARγ), a ligand-activated transcriptional factor involved in the regulation of glucose and lipid metabolism, has gained interest as a potential therapeutic target in multiple sclerosis (MS) due to its potent immunoregulatory properties and the therapeutic efficacy of its ligands in experimental autoimmune encephalitis (EAE). Elevated expression of PPARγ has been observed in the spinal cord of EAE mice and in an in vitro model of antigen-induced demyelination; however, no reports have yet been available on the PPARγ status in the central nervous system of human individuals with MS. Aiming to identify a possible alteration, the present study assessed the levels of PPARγ protein in the cerebrospinal fluid (CSF) of MS patients via ELISA technique. We report a pronounced elevation in the CSF levels of PPARγ in MS patients (n=35) compared to non-inflammatory controls (n=22). This elevation was independent of blood-CSF barrier integrity, but correlated with CSF white blood cell count and IgG index, associating the observed elevation with neuroinflammation. Controlling for potential confounders, the CSF levels of PPARγ further displayed a moderate but significant association with clinical severity. Corroborating with prior experimental findings, these results may contribute to our understanding about the role of PPARγ in MS, and may implicate this protein as a potential CSF biomarker of the disease.