Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

�EN COLOMBIA SE PUEDE SER��:: INDAGACIONES SOBRE LA PRODUCCIÓN DE LO LGBT DESDE LA ACADEMIA

Describing a movement going from resistance to institutionalization, through the motto of acknowledgement the difference, the paper shows that the coming up of a contemporary subjectivity can be related to the production of knowledge, and how it embodies several forms. The study case is the so called LGBT community in Colombia; its trajectory can be interpreted as the step from a social movement to the statization of its political agendaO presente texto mostra como a aparição de uma subjetividade contemporânea pode relacionar-se com a produção de conhecimento, e como esta adquire diferentes formas, a partir da descrição de um movimento que se desloca da resistência à institucionalização, através da consigna do reconhecimento da diferença. O caso apresentado é o de o que hoje se denomina comunidade LGBT; seu devir na Colômbia é o que se pretende interpretar como passo do movimento social à estatização de sua agenda políticaEl presente texto muestra cómo la aparición de una subjetividad contemporánea puede relacionarse con la producción de conocimiento, y cómo ésta adquiere diferentes formas, a partir de la descripción de un movimiento que se desplaza de la resistencia a la institucionalización, a través de la consigna del reconocimiento de la diferencia. El caso presentado es el de lo que hoy se denomina comunidad LGBT; su devenir en Colombia es lo que se pretende interpretar como paso del movimiento social a la estatización de su agenda polític

"EN COLOMBIA SE PUEDE SER...": INDAGACIONES SOBRE LA PRODUCCIÓN DE LO LGBT DESDE LA ACADEMIA

El presente texto muestra cómo la aparición de una subjetividad contemporánea puede relacionarse con la producción de conocimiento, y cómo ésta adquiere diferentes formas, a partir de la descripción de un movimiento que se desplaza de la resistencia a la institucionalización, a través de la consigna del reconocimiento de la diferencia. El caso presentado es el de lo que hoy se denomina comunidad LGBT; su devenir en Colombia es lo que se pretende interpretar como paso del movimiento social a la estatización de su agenda política

Design and Construction of an ROV for Underwater Exploration

The design of a remotely operated vehicle (ROV) with a size of 18.41 cm &times; 29.50 cm &times; 33.50 cm, and a weight of 15.64 kg, is introduced herein. The main goal is to capture underwater video by remote control communication in real time via Ethernet protocol. The ROV moves under the six brushless motors governed through a smart PID controller (Proportional + Integral + Derivative) and by using pulse-wide modulation with short pulses of 1 &mu;s to improve the stability of the position in relation to the translational, ascent or descent, and rotational movements on three axes to capture images of 800 &times; 640 pixels on a video graphic array standard. The motion control, 3D position, temperature sensing, and video capture are performed at the same time, exploiting the four cores of the Raspberry Pi 3, using the threading library for parallel computing. In such a way, experimental results show that the video capture stage can process up to 42 frames per second on a Raspberry Pi 3. The remote control of the ROV is executed under a graphical user interface developed in Python, which is suitable for different operating systems, such as GNU/Linux, Windows, Android, and OS X. The proposed ROV can reach up to 100 m underwater, thus solving the issue of divers who can only reach 30 m depth. In addition, the proposed ROV can be useful in underwater applications such as surveillance, operations, maintenance, and measurement

Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis

Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient&rsquo;s age, and the patient&rsquo;s gender. The output data correspond to the patient&rsquo;s diagnosis. This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner

Trombosis venosa profunda en extremidades inferiores: revisión de las técnicas de diagnóstico actuales y su simbiosis con el aprendizaje automático para un diagnóstico oportuno

Deep Venous Thrombosis (DVT) is a manifestation of a Thromboembolic Disease (ET). When in a DVT the venous thrombus detaches and travel through the bloodstream can cause a Pulmonary Embolism Thrombus (PET). The existence of Deep Venous Thrombosis (DVT) in the lower extremities has been described as one of the main risk factors for the development of PET. It is considered that up to 90% of pulmonary emboli come from venous thrombi of the lower extremities. The most commonly used techniques for the detection of DVT are clinical probability models, D-dimer and non-invasive imaging tests, such as ultrasound for DVT and computed angiotomography (CT) for pulmonary embolism. However, due to the non-specificity of the symptoms of DVT, the threshold for ordering an ultrasound is low, in addition to being a complicated process that requires the participation of a specialist doctor for its interpretation. In recent decades, machine learning has emerged as support in decision-making for the diagnosis of various diseases, some of the most used technologies in the field of medicine include Support Vector Machine (SVM), Decision Trees and Neural Networks Artificial (RNA). This article reviews the existing technologies for the detection of DVT as well as the main machine learning algorithms commonly used in biomedical applications; The design of a computerized system that uses machine learning techniques as a support tool for the timely detection of a possible DVT is proposed.La Trombosis Venosa Profunda (TVP) es una manifestación de una Enfermedad Tromboembólica (ET). Cuando en una TVP los trombos venosos se desprenden y viajan a través del torrente sanguíneo pueden ocasionar una Trombo Embolia Pulmonar (TEP). La existencia de Trombosis Venosa Profunda (TVP) en las extremidades inferiores se ha descrito como uno de los principales factores de riesgo para el desarrollo de la TEP. Se considera que hasta el 90% de los émbolos pulmonares proceden de trombos venosos de las extremidades inferiores. Las técnicas más utilizadas para la detección de TVP son los modelos de probabilidad clínica, el dímero D y las pruebas de imagen no invasivas, como la ecografía para la TVP y la angiotomografía computadorizada (TC) para el embolismo pulmonar. Sin embargo, debido a la inespecificidad de los síntomas de la TVP, el umbral para ordenar una ecografía es bajo, además de ser un proceso complicado que requiere la participación de un médico especialista para su interpretación. En las últimas décadas el aprendizaje automático ha surgido como apoyo en la toma de decisiones para el diagnóstico de diversas enfermedades, algunas de las tecnologías más utilizadas en el campo de la medicina incluyen Support Vector Machine (SVM), Árboles de decisión y las Redes Neuronales Artificiales (RNA). En el presente artículo se hace una revisión de las tecnologías existentes para la detección de la TVP así como de los principales algoritmos de aprendizaje automático comúnmente utilizados en aplicaciones biomédicas; se propone el diseño de un sistema computarizado que utilice técnicas de aprendizaje automático como herramienta de apoyo para la detección oportuna de un posible padecimiento de TVP

Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms

Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers

Development of a Portable, Reliable and Low-Cost Electrical Impedance Tomography System Using an Embedded System

Electrical impedance tomography (EIT) is a useful procedure with applications in industry and medicine, particularly in the lungs and brain area. In this paper, the development of a portable, reliable and low-cost EIT system for image reconstruction by using an embedded system (ES) is introduced herein. The novelty of this article is the hardware development of a complete low-cost EIT system, as well as three simple and efficient algorithms that can be implemented on ES. The proposed EIT system applies the adjacent voltage method, starting with an impedance acquisition stage that sends data to a Raspberry Pi 4 (RPi4) as ES. To perform the image reconstruction, a user interface was developed by using GNU Octave for RPi4 and the EIDORS library. A statistical analysis is performed to determine the best average value from the samples measured by using an analog-to-digital converter (ADC) with a capacity of 30 kSPS and 24-bit resolution. The tests for the proposed EIT system were performed using materials such as metal, glass and an orange to simulate its application in food industry. Experimental results show that the statistical median is more accurate with respect to the real voltage measurement; however, it represents a higher computational cost. Therefore, the mean is calculated and improved by discarding data values in a transitory state, achieving better accuracy than the median to determine the real voltage value, enhancing the quality of the reconstructed images. A performance comparison between a personal computer (PC) and RPi4 is presented. The proposed EIT system offers an excellent cost-benefit ratio with respect to a traditional PC, taking into account precision, accuracy, energy consumption, price, light weight, size, portability and reliability. The proposed EIT system has potential application in mechanical ventilation, food industry and structural health monitoring

An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells&rsquo; criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells&rsquo; criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%

Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain&ndash;Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta&ndash;Beta Ratio (TBR), Theta&ndash;Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen&rsquo;s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person&rsquo;s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist