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. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

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. Recently, such systems using convolutional neural networks have demonstrated impressive results in medical image classification tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the performance comparison of networks. This work is a benchmark for lesion classification in BUS images comparing six state-of-the-art networks: GoogLeNet, InceptionV3, ResNet, DenseNet, MobileNetV2, and EfficientNet. For each network, five input data variations that include segmentation information were tested to compare their impact on the final performance. The methods were trained on a multi-center BUS dataset (BUSI and UDIAT) and evaluated using the following metrics: precision, sensitivity, F1-score, accuracy, and area under the curve (AUC). Overall, the lesion with a thin border of background provides the best performance. For this input data, EfficientNet obtained the best results: an accuracy of 97.65% and an AUC of 96.30%.Clinical Relevance- This study showed the potential of deep neural networks to be used in clinical practice for breast lesion classification, also suggesting the best model choices.

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. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.

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. Although many studies on psychology of tourism can be found, not many predict the tourists' preferences based on the Big Five personality dimensions. This work aims to find how personality relates to the choice of a wide range of tourist attractions, traveling motivations, and travel-related preferences and concerns, hoping to provide a solid base for researchers in the tourism RS area to automatically model tourists in the system without the need for tedious configurations, and solve the cold-start problem and conflicting preferences. By performing Exploratory and Confirmatory Factor Analysis on the data gathered from an online questionnaire, sent to Portuguese individuals from different areas of formation and age groups (n = 1035), we show all five personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns, and that only neuroticism and openness predict traveling motivations.

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. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time.

Expression of microRNAs miR-126, miR-133b and miR-221 associated with apoptosis in rats submitted to focal cerebral ischemia and physical exercise / Expresión de microRNAs miR-126, miR-133b y miR-221 asociados a apoptosis en ratas sometidas a isquemia cerebral focal y ejercicio físico

SUMMARY: Stroke is one of the main causes of death and disability worldwide. The great impact on the quality of life of the population and on the health system justifies that we seek relevant alternatives to reduce the incidence and improve the treatment and recovery of patients affected by this disease. Physical exercise appears as an important tool in this scenario, being already pointed out as a possible therapeutic approach for the prevention of non-contagious chronic diseases. In this context, biomarkers such as miRNAs that respond to physical exercise and are directly related to several epigenetic mechanisms appear. Therefore, explaining the molecular mechanisms involved during physical exercise will lead to a better understanding of each stimulus and the dose to be used to better respond to each situation, thus being a promising approach for the evolution of prescription and control of training and processes recovery from various diseases, including stroke. Forty-eight Wistar rats were used, divided into four experimental groups: control group, ischemia group, physical exercise group and exercise + ischemia group. Real-time PCR methodology was used to analyze the expression of miRNAs: miR-126, miR-133b and miR-221. In our study we observed a significant difference in the expression of miR- 221 between the control group and the others groups. However, microRNAs: miR-126 and miR-133b do not show significant differences in expression between groups.

El ictus es una de las principales causas de muerte y discapacidad en todo el mundo. El gran impacto en la calidad de vida de la población y en el sistema de salud justifica buscar alternativas pertinentes para reducir la incidencia y mejorar el tratamiento y recuperación de los pacientes afectados por esta enfermedad. El ejercicio físico aparece como una herramienta importante en este escenario, siendo ya señalado como un posible abordaje terapéutico para la prevención de enfermedades crónicas no contagiosas. En este contexto, aparecen biomarcadores como los miRNAs que responden al ejercicio físico y están directamente relacionados con varios mecanismos epigenéticos. Por lo tanto, explicar los mecanismos moleculares involucrados durante el ejercicio físico conducirá a una mejor comprensión de cada estímulo y la dosis a utilizar para responder mejor a cada situación, siendo así un enfoque prometedor para la evolución de la prescripción, el control del entrenamiento y los procesos de recuperación de diversas enfermedades, incluido el accidente cerebrovascular. Se utilizaron cuarenta y ocho ratas Wistar, divididas en cuatro grupos experimentales: grupo control, grupo isquemia, grupo ejercicio físico y grupo ejercicio + isquemia. Se utilizó la metodología de PCR en tiempo real para analizar la expresión de miRNAs: miR-126, miR-133b y miR-221. En nuestro estudio observamos una diferencia significativa en la expresión de miR-221 entre el grupo control y los demás grupos. Sin embargo, los microARN: miR-126 y miR-133b no mostraron diferencias significativas en la expresión entre grupos.

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. Materials and methods: We used cellular cultures from surgery biopsies of ten patients with glioblastoma. MicroRNA expressions were analyzed through real-time polymerase chain reaction (PCR ) and correlated with mortality and recurrence. The ROC curve displayed the cutoff point of the respective microRNAs in relation to the clinical prognosis, separating them by group. Results: The miR-21 addressed high level of expression in the irradiated neurosphere group (p = 0.0028). However, miR-21 was not associated with recurrence and mortality. miR-326 can be associated with tumoral recurrence (p = 0.032) in both groups; every 0.5 units of miR-326 increased the chances of recurrence by 1,024 (2.4%). Conclusion: The high expression of miR-21 in the irradiated group suggests its role in the regulation of HIF-1α and in the radioresistant neurospheres. miR-326 increased the chances of recurrence in both groups, also demonstrating that positive regulation from miR-326 does not depend on ionizing radiation treatment.

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. However, the lack of public benchmarks and a standardized evaluation method hampers the networks' performance comparison. This work presents a benchmark of seven state-of-the-art methods for the automatic breast lesion segmentation task. The methods were evaluated on a multi-center BUS dataset composed of three public datasets. Specifically, the U-Net, Dynamic U-Net, Semantic Segmentation Deep Residual Network with Variational Autoencoder (SegResNetVAE), U-Net Transformers, Residual Feedback Network, Multiscale Dual Attention-Based Network, and Global Guidance Network (GG-Net) architectures were evaluated. The training was performed with a combination of the cross-entropy and Dice loss functions and the overall performance of the networks was assessed using the Dice coefficient, Jaccard index, accuracy, recall, specificity, and precision. Despite all networks having obtained Dice scores superior to 75%, the GG-Net and SegResNetVAE architectures outperform the remaining methods, achieving 82.56% and 81.90%, respectively. Clinical Relevance- The results of this study allowed to prove the potential of deep neural networks to be used in clinical practice for breast lesion segmentation also suggesting the best model choices.

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. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.

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. This paper investigates the use of a deep learning-based method in edge devices for onboard real-time inference that is power-effective and low in terms of space-constrained demand. A methodology is proposed for deploying high-end GPU-specific models in edge devices for onboard inference, consisting of a two-folder flow: study model hyperparameters' implications in meeting application requirements; and compression of the network for meeting the board resource limitations. A hybrid FPGA-CPU board is proposed as an effective onboard inference solution by comparing its performance in the KITTI dataset with computer performances. The achieved accuracy is comparable to the PC-based deep learning method with a plus that it is more effective for real-time inference, power limited and space-constrained purposes.

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. This paper explores current vehicle platforms and proposes a low-cost, LiDAR-based test vehicle platform capable of running critical perception tasks (Object Detection and SLAM) in real time. Additionally, we propose the creation of a deep learning-based inference model for Object Detection deployed in a resource-constrained device, as well as a graph-based SLAM implementation, providing important considerations, explored while taking into account the real-time processing requirement and presenting relevant results demonstrating the usability of the developed work in the context of the proposed low-cost platform.

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. Fibrin glue provides a temporary structure that favors angiogenesis, extracellular matrix deposition and cell-matrix interactions. Additionally, fibrin glue maintains the local and paracrine functions of MSCs, providing tissue regeneration through less invasive clinical procedures. Thus, the objective of this systematic review was to assess the potential of fibrin glue combined with MSCs in bone or cartilage regeneration. The bibliographic search was performed in the PubMed/MEDLINE, LILACS and Embase databases, using the descriptors ("fibrin sealant" OR "fibrin glue") AND "stem cells" AND "bone regeneration", considering articles published until 2021. In this case, 12 preclinical and five clinical studies were selected to compose this review, according to the eligibility criteria. In preclinical studies, fibrin glue loaded with MSCs, alone or associated with bone substitute, significantly favored bone defects regeneration compared to scaffold without cells. Similarly, fibrin glue loaded with MSCs presented considerable potential to regenerate joint cartilage injuries and multiple bone fractures, with significant improvement in clinical parameters and absence of postoperative complications. Therefore, there is clear evidence in the literature that fibrin glue loaded with MSCs, alone or combined with bone substitute, is a promising strategy for treating lesions in bone or cartilaginous tissue.

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. The control group consisted of samples from patients submitted to pancreatic surgery for trauma or cadaveric organs (PC-n = 7) and healthy volunteers donated blood (PCp-n = 6). The expression profile of microRNAs was measured in all groups using RT-PCR, serum CA19-9 levels were determined in PA and PC. In tissue samples, there was a difference in the expression of miRNAs-21, -210 (p < 0.05) across the PAt, PC and PPT groups. The PAp showed overexpression of miRNAs-181c, -210 (p < 0.05) when compared to PCp. The combination of miRNAs-21, -210 tissue expression and serum CA19-9 showed 100% accuracy in the diagnosis of PA, as well as miR-181c expression in the plasma (PApxPCp). The expression of microRNAs in plasma proved to be a promising tool for a noninvasive detection test for PA, as well as further studies will evaluate the utility of microRNAs expression as biomarkers for prognostic and response to therapy in PA.

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). The model was trained and tested using the results of tests for measurement of the psychosocial risk levels of 5,443 teachers. Psychosocial, and also physiological and musculoskeletal symptoms, factors were included as inputs of the model. The classification efficiency of the PST-NN approach was evaluated by using the sensitivity, specificity, accuracy and ROC curve metrics, and compared against other techniques as the Decision Tree model, Naïve Bayes, ANN, Support Vector Machines, Robust Linear Regression and the Logistic Regression Model. RESULTS: The modification of the ANN model, by the adaptation of a layer that includes concepts related to the theory of physical surface tension, improved the separation of the subjects according to the risk level group, as a function of the mass and perimeter outputs. Indeed, the PST-NN model showed better performance to classify psychosocial risk level on state-school teachers than the linear, probabilistic and logistic models included in this study, obtaining an average accuracy value of 97.31%. CONCLUSIONS: The introduction of physical models, such as the physical surface tension, can improve the classification performance of ANN. Particularly, the PST-NN model can be used to predict and classify psychosocial risk levels among state-school teachers at work. This model could help to early identification of psychosocial risk and to the development of programs to prevent it.

MicroRNA-27b expression in rats submitted to physical exercise and experimental cerebral ischemia / Expresión de MicroRNA-27b en ratas sometidas a ejercicio físico e isquemia cerebral experimental

SUMMARY: Cerebral ischemia has not only a high mortality rate, which is the second leading cause of death worldwide, but is also responsible for severe disabilities in working age individuals, generating enormous public expending for treatment and rehabilitation of the affected individuals. The role of microRNAs in the pathophysiology of cerebral ischemia has been highlighted in current investigations. In addition, recent studies have also highlighted physical exercise as a possible protective factor both in the prevention and in the effects of cerebral ischemia, placing it as an important study resource. Thus, we investigated the role of physical exercise in experimental cerebral ischemia associated with the expression of microRNA-27b. 16 animals were used, divided into four experimental groups: Control, Physical Exercise, Cerebral Ischemia and Cerebral Ischemia associated with Physical Exercise. The real-time PCR methodology was used to analyze the expression of microRNA-27b. Although there were no statistically significant differences in the expression of microRNA-27b between the groups studied, the increased expression of microRNA-27b in the Physical Exercise group indicates its neuroprotective role in the pathophysiology of cerebral ischemia.

RESUMEN: La isquemia cerebral no solo tiene una alta tasa de mortalidad y es la segunda causa principal de muerte en todo el mundo, sino también es la causa de enfermedades invalidantes en personas en edad laboral, lo que genera un gasto público enorme para el tratamiento y la rehabilitación de las personas afectadas. El papel de los microARN en la fisiopatología de la isquemia cerebral se ha destacado en las investigaciones actuales. Además, estudios recientes también han destacado el ejercicio físico como un posible factor protector tanto en la prevención como en los efectos de la isquemia cerebral, situándolo como un importante recurso de estudio. Por lo tanto, investigamos el papel del ejercicio físico en la isquemia cerebral experimental asociada con la expresión del microARN-27b. Se utilizaron 16 animales, divididos en cuatro grupos experimentales: Control, Ejercicio Físico, Isquemia Cerebral e Isquemia Cerebral asociada al Ejercicio Físico. Se utili- zó la metodología de PCR en tiempo real para analizar la expresión de microARN-27b. Aunque no se observaron diferencias estadísticamente significativas en la expresión de microARN-27b entre los grupos estudiados, la mayor expresión de microARN-27b en el grupo de Ejercicio Físico indica su papel neuroprotector en la fisiopatología de la isquemia cerebral.

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. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris-Vélib' Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

Expression of AIF, PARP, and miRNAs MIR-145, MIR-210, and MIR-486 associated with apoptosis in the corpus cavernosum of rats subjected to chronic alcoholism model / Expresión de AIF, PARP y miRNAs MIR-145, MIR-210 y MIR-486 asociados con apoptosis en el cuerpo cavernoso de ratas sometidas a modelo de alcoholismo crónico

SUMMARY: Previous studies from our group described the consequences of using ethanol on penile erection. Nevertheless, the molecular mechanisms surrounding microRNAs, apoptosis process and their relationship with erectile dysfunction associated with alcohol consumption are still poorly understood. The objective of this analysis was to evaluate the mechanism of apoptosis by the expression of AIF and PARP, as well as their regulatory microRNAs: miR-145, miR-210 and miR-486, in the corpus cavernosum of rats submitted to a semivoluntary alcoholism model. For this study 24 Wistar rats were divided into two groups: control (C) and treated with 20 % ethanol (A) for seven weeks. The corpus cavernosum samples were prepared for immunohistochemical analysis of AIF and PARP protein expression, and microRNAs miR-145, miR-210, miR-486 gene expression in cavernous tissue was performed by real time PCR. The immunohistochemical analysis showed little nuclear positive labeling for the protein PARP and AIF in the corpus cavernosum of control and ethanol treated animals. After analysis of miR-145, -210 and -486 microRNA expression in the 12 animals studied, no results were found with significant statistical difference between the control and alcoholized groups. The expression of AIF and PARP and their regulatory microRNAs involved in apoptotic process (miR-145, miR-210 and miR-486) were not altered in the corpus cavernosum of rats submitted to semivoluntary alcoholism.

RESUMEN: Estudios previos de nuestro grupo describieron las consecuencias del uso de etanol en la erección del pene. Sin embargo, los mecanismos moleculares que rodean a los microARN, el proceso de apoptosis y su relación con la disfunción eréctil asociada con el consumo de alcohol aún no se conocen bien. El objetivo de este análisis fue evaluar el mecanismo de apoptosis mediante la expresión de AIF y PARP, así como sus microARN reguladores: miR-145, miR-210 y miR-486, en el cuerpo cavernoso de ratas sometidas a un modelo de alcoholismo semivoluntario. Se dividieron 24 ratas Wistar en dos grupos: control (C) grupo de ratas tratadas con etanol al 20 % (A) durante siete semanas. Las muestras del cuerpo cavernoso se prepararon para el análisis inmunohistoquímico de la expresión de la proteína AIF y PARP, y la expresión del gen microRNAs miR-145, miR-210, miR-486 en tejido cavernoso se realizó por PCR en tiempo real. El análisis inmunohistoquímico mostró escaso etiquetado nuclear positivo para la proteína PARP y AIF en el cuerpo cavernoso de los animales de control y tratados con etanol. Después del análisis de la expresión de microARN miR-145, -210 y -486 no se encontraron resultados con diferencias estadísticas significativas entre los grupos control y alcoholizados. La expresión de AIF y PARP y sus microARN reguladores involucrados en el proceso apoptótico (miR-145, miR-210 y miR-486) no se alteraron en el cuerpo cavernoso de las ratas sometidas a alcoholismo semivoluntario.

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. RESULTS: The immunoreactivity of Caspases-3 and -9 was diffuse and higher in the treated groups though there was no significant difference between the experimental groups, only when compared with the control group. An increase was observed in the gene expression of Caspases-9 in the diabetic and ethanol-diabetic groups when compared with control and ethanol groups. CONCLUSIONS: The association of these factors (ethanol and diabetes) probably can affect the apoptosis mechanism in lesions of the cavernous tissue in the rat penis. Both gene and protein expression of Caspase-9 in diabetic and ethanol-diabetic groups suggest the involvement of the apoptosis cascade from this study model.

Morphological and molecular analysis of apoptosis in the corpus cavernosum of rats submitted to a chronic alcoholism model.

PURPOSE: To evaluate the effect of chronic alcoholism on morphometry and apoptosis mechanism and correlate with miRNA-21 expression in the corpus cavernosum of rats. METHODS: Twenty-four rats were divided into two experimental groups: Control (C) and Alcoholic group (A). After two weeks of an adaptive phase, rats from group A received only ethanol solution (20%) during 7 weeks. The morphometric and caspase-3 immunohistochemistry analysis were performed in the corpus cavernosum. The miRNA-21 expression was analyzed in blood and cavernous tissue. RESULTS: Chronic ethanol consumption decreased cavernosal smooth muscle area of alcoholic rats. The protein expression of caspase 3 in the corpus cavernosum was higher in A compared to the C group. There was no difference in the expression of miRNA-21 in serum and cavernous tissue between the groups. CONCLUSION: Chronic ethanol consumption reduced smooth muscle area and increased caspase 3 in the corpus cavernosum of rats, without altered serum and cavernosal miR-21 gene expression.

Morphometric analysis and pattern of protein and gene expression of apoptosis in focal cerebral ischemia in rats and the neuroprotetive action of hypothermia and ketoprofen / Análisis morfométrico y modelo de expresión de proteína y genes de apoptosis en isquemia cerebral focal en ratas y la acción neuroprotectora de hipotermia y ketoprofeno

This study aimed to investigate the morphometric and the pattern of protein and gene expression related to the extrinsic apoptotic pathway in experimental focal cerebral ischemia and the hole of neuroprotection with hypothermia and ketoprofen. For this analysis, 120 rats were randomly divided into 3 groups (20 animals each): control - no surgery (20 animals); sham - simulation of surgery (20 animals); ischemic - focal ischemia for 1 hour, without reperfusion (80 animals) and divided into four subgroups with 20 animals each: ischemic + intraischemic hypothermia; ischemic + previous intravenous ketoprofen, and ischemic + hypothermia and ketoprofen. The infarct volume was measured using morphometric analysis of infarct areas defined by triphenyl tetrazolium chloride and the patterns of expression of the apoptosis genes (Fas, c-Flip, caspase-8 and caspase-3) and the apoptosis protein caspase-3 were evaluated by quantitative real-time PCR and immunohistochemistry, respectively. Hypo expression of genes of extrinsic pathway of apoptosis was observed: Fas receptor, c-Flip and caspase-8 in the ischemics areas. Increases in the gene and protein caspase-3 in the ischemic areas were also observed, and these increases were reduced by hypothermia and ketoprofen, also noted in the morphometric study. The caspases-3 increase suggests that this gene plays an important role in apoptosis, probably culminating in cell death and that the neuroprotective effect of hypothermia and ketoprofen is involved.

Este estudio tuvo como objetivo investigar la morfometría y el patrón de expresión de proteínas y genes relacionados con la vía apoptótica extrínseca en la isquemia cerebral focal experimental y el agujero de neuroprotección con hipotermia y ketoprofeno. Se dividieron aleatoriamente 120 ratas en 3 grupos (20 animales cada uno): control - sin cirugía (20 animales); simulación - simulación de cirugía (20 animales); isquemia isquemia focal durante 1 hora, sin reperfusión (80 animales) y dividida en cuatro subgrupos con 20 animales cada uno: isquemia + hipotermia intraisquémica; isquemia + ketoprofeno intravenoso previo, e isquemia + hipotermia y ketoprofeno. El volumen del infarto se midió utilizando un análisis morfométrico de áreas de infarto definidas por cloruro de trifenil tetrazolio y los patrones de expresión de los genes de apoptosis (Fas, c-Flip, caspase-8 y caspase-3) y la proteína de apoptosis caspase-3 fueron evaluados por PCR cuantitativa en tiempo real e inmunohistoquímica, respectivamente. Se observó hipoexpresión de genes de la vía extrínseca de la apoptosis: receptor Fas, c-Flip y caspasa-8 en las áreas isquémicas. También se observaron aumentos en el gen y la proteína caspasa-3 en las áreas isquémicas y estos aumentos se redujeron por hipotermia y ketoprofeno, también observado por estudio morfométrico. El aumento de caspasas-3 sugiere que este gen tiene un papel importante en la apoptosis, y probable causa de muerte celular, involucrando el efecto neuroprotector de la hipotermia y el ketoprofeno.