Long QT syndrome (LQTS) is a rare (1:2500–1:10,000) inherited disorder characterized by the onset of arrhythmogenic syncope, polymorphic ventricular tachycardia, and sudden cardiac death. The aim of this article was to describe an unexpected success with an unusual therapeutic modality of a patient diagnosed with LQTS syndrome (suspected Romano–Ward syndrome) during an 8-year period. A 59-year-old female patient was admitted to the hospital due to chest pain and nausea, and after diagnostic and therapeutical approach, a permanent dual-chamber rate-modulated (DDDR) pacemaker was implanted instead of the implantable cardioverter defibrillator (ICD). During the 8-year period, the patient remained stable, without rhythm disorder. Romano–Ward syndrome as a congenital LQTS carries a high risk of sudden cardiac death and presents an indication for ICD. In this patient, for objective reasons, this could not be performed. Implantation of a DDDR with an appropriate pharmacological therapy, including propranolol, in this case, proved to be a successful therapeutic modality.

Introduction: The head-up tilt table test is noninvasive diagnostic procedure, which is used in the diagnosis of syncope. Syncope presents a benign short-term disorder of cerebral circulation with the sudden loss of consciousness and muscle tone. Aim: The aim is to present not only the role and importance of orthostatic tests in the daily clinical practice of pediatric cardiology, neuropediatrics but also pediatrics in general. Patients and Methods: This study has retrospective descriptive character and included the period from April 1997 to June 2020, during which the registration and analysis of orthostatic stability tests (head-up/tilt table test, tilt table test) was performed. Medical documentation of outpatient and hospitalized patients on the Paediatric Clinic of Clinical Center University of Sarajevo (Register of Tilt Table Test) was used. Results: During this period, 1029 tests were registered and analyzed. Modification test (head-up) was performed in 132 (12.8%) patients, and since 2008, classic tilt table test was performed in 897 (87.1%) patients. Patients were 6.5–19 years old, with a predominance of female patients 611 (59.4%). There were 519 (50.4%) patients who were 15–19 years old and 510 (49.6%) patients who were under 15 years of age. Indications were syncope or suspected syncope in 671 (65.2%) patients, cardiovascular etiology (arrhythmias, chest pain, congenital heart defects [CHDs], surgically corrected CHDs, hypotension, and hypertension) in 195 (19%) patients, neuropediatric pathology (epilepsia, suspected epilepsia, headache, vertigo) in 101 (9.8%) patients, and other indications in 62 (6.03%) patients. From the total number of tests, 862 were first tests (83.4%) and 167 were control tests (16.3%). The positive test was found in 538 (52.3%) patients, most often vasovagal syncope (473 patients or 87.9%). Conclusion: Tilt table test is a reliable diagnostic tool in examining the etiology of syncope, primarily vasovagal, and is an extremely important method primarily in cardiopediatric and neuropediatric daily diagnostics.

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.

INTRODUCTION: Gastrointestinal symptoms in irritable bowel syndrome (IBS) have been correlated with psychological factors using retrospective symptom assessment. However, real-time symptom assessment might reveal the interplay between abdominal and affective symptoms more reliably in a longitudinal perspective. The aim was to evaluate the association between stress and abdominal pain, using the Experience Sampling Method (ESM) as a real-time, repeated measurement method. METHODS: Thirty-seven patients with IBS (26 women; mean age 36.7 years) and 36 healthy controls (HC; 24 women; mean age 31.1 years) completed an electronic ESM during 7 consecutive days. Abdominal pain and stress were scored on an 11-point Numeric Rating Scale at a maximum of 10 random moments each day. RESULTS: Abdominal pain scores were 2.21 points higher in patients with IBS compared with those in HC (P < 0.001), whereas stress levels did not differ significantly (B: 0.250, P = 0.406). In IBS, a 1-point increase in stress was associated with, on average, 0.10 points increase in abdominal pain (P = 0.017). In HC, this was only 0.02 (P = 0.002). Stress levels at t = −1 were not a significant predictor for abdominal pain at t = 0 in both groups, and vice versa. DISCUSSION: Our results demonstrate a positive association between real-time stress and abdominal pain scores and indicate a difference in response to stress and not a difference in experienced stress per se. Furthermore, an in-the-moment rather than a longitudinal association is suggested. This study underlines the importance of considering the individual flow of daily life and supports the use of real-time measurement when interpreting potential influencers of abdominal symptoms in IBS.

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.

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.

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.

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.

We present a real-time feasible Nonlinear Model Predictive Control (NMPC) scheme to control a microgrid described by a detailed Differential Algebraic Equation (DAE). Our NMPC formulation allows to consider secondary voltage and frequency control, steady-state equal load sharing, economic goals and all relevant operational constraints in a single optimization problem. The challenge is to control the fast and large dynamical system in real-time. To achieve this goal, we use the recently introduced Advanced Step Real-Time Iteration (AS-RTI) scheme and its efficient implementation in the acados software package. We present an NMPC scheme which delivers feedback in the range of milliseconds. Thereby, the controller responds efficiently to large disturbances and mismatches in the predictions and effectively controls the fast transient dynamics of the microgrid. Our NMPC approach outperforms a state-of-the-art I-controller usually used in microgrid control and shows minor deviation to a fully converged NMPC approach.