Effectiveness of a health promotion program on overweight in vulnerable children from primary schools (BeE-school): A cluster-randomized controlled trial.

Background: Obesity is a complex, multifactorial disease with rising global prevalence. Considering its significant association with lower socioeconomic status, implementing obesity prevention programs in vulnerable school-age children is particularly pertinent. This cluster-randomized controlled trial aims to evaluate the effectiveness of a health promotion program on overweight in vulnerable children from primary schools.

A Review of Computational Modeling in Wastewater Treatment Processes.

Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed.

Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study.

Accurate lesion classification as benign or malignant in breast ultrasound (BUS) images is a critical task that requires experienced radiologists and has many challenges, such as poor image quality, artifacts, and high lesion variability. Thus, automatic lesion classification may aid professionals in breast cancer diagnosis. In this scope, computer-aided diagnosis systems have been proposed to assist in medical image interpretation, outperforming the intra and inter-observer variability.

A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications.

The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements.

Practical lessons learned from real-world implementation of the molecular classification for endometrial carcinoma.

Objectives: This study aimed to explore the practical organisational aspects and difficulties in the implementation of the molecular classification of endometrial carcinoma (EC), and to demonstrate its potential impact in prognostic risk group classification.

Methods: We conducted a multicentre, retrospective cohort study of 230 patients with EC diagnosed between 2019 and 2022. Sample processing, clinicopathological, treatment and follow-up data were collected.

Group recommender systems for tourism: how does personality predict preferences for attractions, travel motivations, preferences and concerns?

To travel in leisure is an emotional experience, and therefore, the more the information about the tourist is known, the more the personalized recommendations of places and attractions can be made. But if to provide recommendations to a tourist is complex, to provide them to a group is even more. The emergence of personality computing and personality-aware recommender systems (RS) brought a new solution for the cold-start problem inherent to the conventional RS and can be the leverage needed to solve conflicting preferences in heterogenous groups and to make more precise and personalized recommendations to tourists, as it has been evidenced that personality is strongly related to preferences in many domains, including tourism.

Endoscopic lumbar sympathectomy as a treatment option for primary erythromelalgia - case report and review.

Erythromelalgia is a rare disease, involving pain, edema, redness, and hyperthermia in the limbs. It is extremely refractory to drugs, has no defined treatment, and causes psychological comorbidities in the patient. We describe a case of erythromelalgia involving a 17-year-old boy who had been suffering from the disease for almost 4 years prior to finding an effective treatment.

Algorithm Recommendation and Performance Prediction Using Meta-Learning.

In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly.

Expression of microRNAs miR-21 and miR-326 associated with HIF-1α regulation in neurospheres of glioblastoma submitted to ionizing radiation treatment.

Background: Glioblastoma is an incurable neoplasm. Its hypoxia mechanism associated with cancer stem cells (CSCs) demonstrates hypoxia-inducible factor 1α (HIF-1α) expression regulation, which is directly related to tumor malignancy. The aim of this study was to identify a possible tumor malignancy signature associated with regulation of HIF-1α by microRNAs miR-21 and miR-326 in the subpopulation of tumor stem cells which were irradiated by ion in primary culture of patients diagnosed with glioblastoma.

Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images.

Automatic lesion segmentation in breast ultrasound (BUS) images aids in the diagnosis of breast cancer, the most common type of cancer in women. Accurate lesion segmentation in ultrasound images is a challenging task due to speckle noise, artifacts, shadows, and lesion variability in size and shape. Recently, convolutional neural networks have demonstrated impressive results in medical image segmentation tasks.

Navitoclax Enhances the Therapeutic Effects of PLK1 Targeting on Lung Cancer Cells in 2D and 3D Culture Systems.

The efficacy of antimitotics is limited by slippage, whereby treated cells arrested in mitosis exit mitosis without cell division and, eventually, escape apoptosis, constituting a serious resistance mechanism to antimitotics. Strategies to overcome slippage should potentiate the cancer cell killing activity of these antimitotics. Such strategies should accelerate cell death in mitosis before slippage.

Evaluation of Brain Functional Connectivity from Electroencephalographic Signals Under Different Emotional States.

The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals.

Lack of association between 11 gene polymorphisms on weight loss 1 year after Roux-en-y gastric bypass surgery in woman.

Background: Although effective, the impact of bariatric surgery on weight loss is variable, and little is known about the influence of single-nucleotide polymorphisms (SNPs). The present study investigated the association of eleven SNPs related to obesity with weight loss 1 year after Roux-Y gastric bypass (RYGB) surgery in female patients.

Methods: This prospective study included 351 women with obesity.

Real-Time 3D Object Detection and SLAM Fusion in a Low-Cost LiDAR Test Vehicle Setup.

Recently released research about deep learning applications related to perception for autonomous driving focuses heavily on the usage of LiDAR point cloud data as input for the neural networks, highlighting the importance of LiDAR technology in the field of Autonomous Driving (AD). In this sense, a great percentage of the vehicle platforms used to create the datasets released for the development of these neural networks, as well as some AD commercial solutions available on the market, heavily invest in an array of sensors, including a large number of sensors as well as several sensor modalities. However, these costs create a barrier to entry for low-cost solutions for the performance of critical perception tasks such as Object Detection and SLAM.

BP-M345, a New Diarylpentanoid with Promising Antimitotic Activity.

Previously, we reported the in vitro growth inhibitory effect of diarylpentanoid BP-M345 on human cancer cells. Nevertheless, at that time, the cellular mechanism through which BP-M345 exerts its growth inhibitory effect remained to be explored. In the present work, we report its mechanism of action on cancer cells.

Resource-Constrained Onboard Inference of 3D Object Detection and Localisation in Point Clouds Targeting Self-Driving Applications.

Research about deep learning applied in object detection tasks in LiDAR data has been massively widespread in recent years, achieving notable developments, namely in improving precision and inference speed performances. These improvements have been facilitated by powerful GPU servers, taking advantage of their capacity to train the networks in reasonable periods and their parallel architecture that allows for high performance and real-time inference. However, these features are limited in autonomous driving due to space, power capacity, and inference time constraints, and onboard devices are not as powerful as their counterparts used for training.

Effects of Therapy with Fibrin Glue combined with Mesenchymal Stem Cells (MSCs) on Bone Regeneration: A Systematic Review.

Cell therapy strategies using mesenchymal stem cells (MSCs) carried in fibrin glue have shown promising results in regenerative medicine. MSCs are crucial for tissue healing because they have angiogenic, anti-apoptotic and immunomodulatory properties, in addition to the ability to differentiate into several specialized cell lines. Fibrin sealant or fibrin glue is a natural polymer involved in the coagulation process.

The role of circulating miRNAs and CA19-9 in pancreatic cancer diagnosis.

Diagnosis and treatment of pancreatic ductal adenocarcinoma (PA) remains a challenge in clinical practice. The aim of this study was to assess the role of microRNAs (miRNAs-21, -23a, -100, -107, -181c, -210) in plasma and tissue as possible biomarkers in the diagnosis of PA. Samples of plasma (PAp-n = 13), pancreatic tumors (PAt-n = 18), peritumoral regions (PPT-n = 9) were collected from patients during the surgical procedure.

Second-Generation Antimitotics in Cancer Clinical Trials.

Mitosis represents a promising target to block cancer cell proliferation. Classical antimitotics, mainly microtubule-targeting agents (MTAs), such as taxanes and vinca alkaloids, are amongst the most successful anticancer drugs. By disrupting microtubules, they activate the spindle assembly checkpoint (SAC), which induces a prolonged delay in mitosis, expected to induce cell death.

Improving classification based on physical surface tension-neural net for the prediction of psychosocial-risk level in public school teachers.

Background: Psychosocial risks, also present in educational processes, are stress factors particularly critical in state-schools, affecting the efficacy, stress, and job satisfaction of the teachers. This study proposes an intelligent algorithm to improve the prediction of psychosocial risk, as a tool for the generation of health and risk prevention assistance programs.

Methods: The proposed approach, Physical Surface Tension-Neural Net (PST-NN), applied the theory of superficial tension in liquids to an artificial neural network (ANN), in order to model four risk levels (low, medium, high and very high psychosocial risk).

Tetracyclic Thioxanthene Derivatives: Studies on Fluorescence and Antitumor Activity.

Thioxanthones are bioisosteres of the naturally occurring xanthones. They have been described for multiple activities, including antitumor. As such, the synthesis of a library of thioxanthones was pursued, but unexpectedly, four tetracyclic thioxanthenes with a quinazoline-chromene scaffold were obtained.

Deepint.net: A Rapid Deployment Platform for Smart Territories.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases.

Chronic alcoholism associated with diabetes induced apoptosis in the corpus cavernosum of rats.

Purpose: To evaluate the effects of alcohol exposure and diabetes on apoptotic process in the corpus cavernosum.

Methods: Forty eight male Wistar rats were divided into four groups: control, diabetic, alcoholic and diabetic-alcoholic. Samples of the corpus cavernosum were prepared to study protein expression of apoptotic genes (Caspases-3 and 9) by immunohistochemistry and Real-Time PCR.