Quercetin (QU), a hyperthermic sensitizer, when combined with cisplatin (CP) affects tumor growth. To determine the effects of QU and CP and their interactions, multimodal treatment in vitro and in vivo models under physiological and hyperthermic conditions was performed. In vitro, different sensitivity of T24 and UMUC human bladder cancer cells was observed after short-term exposure to QU (2 h) and CP (1 h). Effects of both compounds were investigated at low and high micromolar concentrations (1 and 50 µM, respectively) under both thermal conditions. QU acted in additive or synergistic manner in combination with CP between physiological condition and hyperthermia. As determined by 3-(4,5-dimethylthiazol2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, short-term application of QU and CP reduced cell viability. Clonal assay also indicated that combined treatment with QU and CP is lethal to bladder cancer cells in both conditions. In vivo, CP (5 or 10 mg kg−1) and QU (50 mg kg−1) acted synergistically with hyperthermia (43 °C) and inhibited tumor growth, activated immune effectors and increased mice survival. Our results demonstrate that combined treatment with CP and QU may increase death of tumor cells in physiological and hyperthermic conditions which could be clinically relevant in locoregional chemotherapy.

In this study we investigated whether combining external visualizations with extreme case reasoning may facilitate developing of conceptual understanding about wave optics. For purposes of answering our research question we conducted a pretest-posttest quasi-experiment which included 179 students from a first year introductory physics course at the University of Zagreb, Croatia. Students who were guided through extreme case reasoning in their wave optics seminars significantly outperformed their peers who received conventional teaching treatment. Findings from our study suggest that combining external visualizations with extreme case reasoning facilitates development of visually rich internal representations which are a good basis for performing mental simulations about wave optics phenomena. In addition, it has been also found that many students use the “ closer to the source implicates greater effect ” p-prim when reasoning about certain relationships, such as the relationship between fringes’ dimension and slits-screen separation.

Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance— to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.

We propose a framework for designing observers possessing global convergence properties and desired asymptotic behaviors for the state estimation of nonlinear systems. The proposed scheme consists in combining two given continuous-time observers: One, denoted as global, ensures (approximate) convergence of the estimation error for any initial condition ranging in some prescribed set, while the other, denoted as local, guarantees a desired local behavior. We make assumptions on the properties of these two observers, and not on their structures, and then explain how to unite them as a single scheme using hybrid techniques. Two case studies are provided to demonstrate the applicability of the framework. Finally, a numerical example is presented.

We study the design of state observers for nonlinear networked control systems (NCSs) affected by disturbances and measurement noise, via an emulation-like approach. That is, given an observer designed with a specific stability property in the absence of communication constraints, we implement it over a network, and we provide sufficient conditions on the latter to preserve the stability property of the observer. In particular, we provide a bound on the maximum allowable transmission interval (MATI) that guarantees an input-to-state stability (ISS) property for the corresponding estimation error system. The stability analysis is trajectory-based, utilizes small-gain arguments, and exploits a persistently exciting property of the scheduling protocols. This property is key in our analysis and allows us to obtain significantly larger MATI bounds in comparison to the ones found in the literature. Our results hold for a general class of NCSs; however, we show that these results are also applicable to NCSs implemented over a specific physical network called WirelessHART (WH). The latter is mainly characterized by its multihop structure, slotted communication cycles, and the possibility to simultaneously transmit over different frequencies. We show that our results can be further improved by taking into account the intrinsic structure of the WH–NCS model. That is, we explicitly exploit the model structure in our analysis to obtain an even tighter MATI bound that guarantees the same ISS property for the estimation error system. Finally, to illustrate our results, we present analysis and numerical simulations for a class of Lipschitz nonlinear systems and high-gain observers.

Reptiles, especially turtles, are becoming increasingly popular as pets. The haematological evaluation of turtles is an irreplaceable diagnostic tool in veterinary practice. However, the morphologic distinctiveness of turtle blood limits the use of electronic cell-counting devices, making time-consuming, manual counting techniques and evaluation of blood smears necessary. Many samples are dispatched to a laboratory over long distances, where a delay of 24 h or more may occur. At weekends, this interval may exceed 48 h. The objective of the present study was to determine the effect of storage duration at refrigerator temperature (4 °C) on the counts of red blood cells (RBC) and white blood cells (WBC), and on the mean corpuscular volume (MCV) and packed cell volume (PCV) in blood samples from healthy adult red-eared sliders. Blood samples were collected by venipuncture from the occipital venous sinus from six apparently healthy adult red-eared sliders, aged 2 to 4 years. Blood samples were analysed immediately after sampling to obtain the baseline value (BV) of the red blood cell count, white blood cell count and packed cell volume percentage. Blood was stored at 4 °C and the haematological analyses were performed after 24h, 48h and 72h. The results showed the same level of stability for RBC and WBC count, and MCV values during 72 hours of storage at 4 °C and for PCV during 48 hours. Handling of blood samples, and duration of storage of the blood samples can significantly influence the results/values of haematological tests. Consequently, the obtained values of the determined haematological parameters of improperly stored or handled blood samples can give a misleading interpretation of the results on the animal’s health status.

A new method substantially improves the accuracy of mapped brain networks using anatomy and microstructure informed tractography. Diffusion magnetic resonance imaging is a noninvasive imaging modality that has been extensively used in the literature to study the neuronal architecture of the brain in a wide range of neurological conditions using tractography. However, recent studies highlighted that the anatomical accuracy of the reconstructions is inherently limited and challenged its appropriateness. Several solutions have been proposed to tackle this issue, but none of them proved effective to overcome this fundamental limitation. In this work, we present a novel processing framework to inject into the reconstruction problem basic prior knowledge about brain anatomy and its organization and evaluate its effectiveness using both simulated and real human brain data. Our results indicate that our proposed method dramatically increases the accuracy of the estimated brain networks and, thus, represents a major step forward for the study of connectivity.

Diffusion magnetic resonance imaging is a noninvasive imaging modality that has been extensively used in the literature to study the neuronal architecture of the brain in a wide range of neurological conditions using tractography. However, recent studies highlighted that the anatomical accuracy of the reconstructions is inherently limited and challenged its appropriateness. Several solutions have been proposed to tackle this issue, but none of them proved effective to overcome this fundamental limitation. In this work, we present a novel processing framework to inject into the reconstruction problem basic prior knowledge about brain anatomy and its organization and evaluate its effectiveness using both simulated and real human brain data. Our results indicate that our proposed method dramatically increases the accuracy of the estimated brain networks and, thus, represents a major step forward for the study of connectivity.

Cardiovascular disease (CVD) is the biggest cause of sickness and mortality worldwide in both males and females. Clinical statistics demonstrate clear sex differences in risk, prevalence, mortality rates, and response to treatment for different entities of CVD. The reason for this remains poorly understood. Non-coding RNAs (ncRNAs) are emerging as key mediators and biomarkers of CVD. Similarly, current knowledge on differential regulation, expression, and pathology-associated function of ncRNAs between sexes is minimal. Here, we provide a state-of-the-art overview of what is known on sex differences in ncRNA research in CVD as well as discussing the contributing biological factors to this sex dimorphism including genetic and epigenetic factors and sex hormone regulation of transcription. We then focus on the experimental models of CVD and their use in translational ncRNA research in the cardiovascular field. In particular, we want to highlight the importance of considering sex of the cellular and pre-clinical models in clinical studies in ncRNA research and to carefully consider the appropriate experimental models most applicable to human patient populations. Moreover, we aim to identify sex-specific targets for treatment and diagnosis for the biggest socioeconomic health problem globally.

Lung sound (LS) signals are often contaminated by impulsive artifacts that complicate the estimation of lung sound intensity (LSI) using conventional amplitude estimators. Fixed sample entropy (fSampEn) has proven to be robust to cardiac artifacts in myographic respiratory signals. Similarly, fSampEn is expected to be robust to artifacts in LS signals, thus providing accurate LSI estimates. However, the choice of fSampEn parameters depends on the application and fSampEn has not previously been applied to LS signals. This study aimed to perform an evaluation of the performance of the most relevant fSampEn parameters on LS signals, and to propose optimal fSampEn parameters for LSI estimation. Different combinations of fSampEn parameters were analyzed in LS signals recorded in a heterogeneous population of healthy subjects and chronic obstructive pulmonary disease patients during loaded breathing. The performance of fSampEn was assessed by means of its cross-covariance with flow signals, and optimal fSampEn parameters for LSI estimation were proposed.