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This paper introduces a novel one-dimensional convolutional neural network that utilizes clinical data to accurately detect choledocholithiasis, where gallstones obstruct the common bile duct. Swift and precise detection of this condition is critical to preventing severe complications, such as biliary colic, jaundice, and pancreatitis. This cutting-edge model was rigorously compared with other machine learning methods commonly used in similar problems, such as logistic regression, linear discriminant analysis, and a state-of-the-art random forest, using a dataset derived from endoscopic retrograde cholangiopancreatography scans performed at Olive View-University of California, Los Angeles Medical Center. The one-dimensional convolutional neural network model demonstrated exceptional performance, achieving 90.77% accuracy and 92.86% specificity, with an area under the curve of 0.9270. While the paper acknowledges potential areas for improvement, it emphasizes the effectiveness of the one-dimensional convolutional neural network architecture. The results suggest that this one-dimensional convolutional neural network approach could serve as a plausible alternative to endoscopic retrograde cholangiopancreatography, considering its disadvantages, such as the need for specialized equipment and skilled personnel and the risk of postoperative complications. The potential of the one-dimensional convolutional neural network model to significantly advance the clinical diagnosis of this gallstone-related condition is notable, offering a less invasive, potentially safer, and more accessible alternative.

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The present work describes the training and subsequent implementation on an FPGA board of an LSTM neural network for the modeling and prediction of the exceedances of criteria pollutants such as nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM10 and PM2.5). Understanding the behavior of pollutants and assessing air quality in specific geographical regions is crucial. Overexposure to these pollutants can cause harm to both natural ecosystems and living organisms, including humans. Therefore, it is essential to develop a solution that can accurately evaluate pollution levels. One potential approach is to implement a modified LSTM neural network on an FPGA board. This implementation obtained an 11% improvement compared to the original LSTM network, demonstrating that the proposed architecture is able to maintain its functionality despite reducing the number of neurons in its initial layers. It shows the feasibility of integrating a prediction network into a limited system such as an FPGA board, but easily coupled to a different system. Importantly, this implementation does not compromise the prediction accuracy for both 24 h and 72 h time frames, highlighting an opportunity for further enhancement and refinement.

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Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder-Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.

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Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of ±1.2 mm.

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Background: Our previous work has demonstrated the benefits of transcutaneous immunization in targeting Langerhans cells and preferentially inducing CD8 T-cell responses. Methods: In this randomized phase Ib clinical trial including 20 HIV uninfected volunteers, we compared the safety and immunogenicity of the MVA recombinant vaccine expressing HIV-B antigen (MVA-B) by transcutaneous and intramuscular routes. We hypothesized that the quality of innate and adaptive immunity differs according to the route of immunization and explored the quality of the vector vaccine-induced immune responses. We also investigated the early blood transcriptome and serum cytokine levels to identify innate events correlated with the strength and quality of adaptive immunity. Results: We demonstrate that MVA-B vaccine is safe by both routes, but that the quality and intensity of both innate and adaptive immunity differ significantly. Transcutaneous vaccination promoted CD8 responses in the absence of antibodies and slightly affected gene expression, involving mainly genes associated with metabolic pathways. Intramuscular vaccination, on the other hand, drove robust changes in the expression of genes involved in IL-6 and interferon signalling pathways, mainly those associated with humoral responses, and also some levels of CD8 response. Conclusion: Thus, vaccine delivery route perturbs early innate responses that shape the quality of adaptive immunity. Clinical Trial Registration: http://ClinicalTrials.gov, identifier PER-073-13.

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The present work presents an improved method to align the measurement scale mark in an immersion hydrometer calibration system of CENAM, the National Metrology Institute (NMI) of Mexico, The proposed method uses a vision system to align the scale mark of the hydrometer to the surface of the liquid where it is immersed by implementing image processing algorithms. This approach reduces the variability in the apparent mass determination during the hydrostatic weighing in the calibration process, therefore decreasing the relative uncertainty of calibration.

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Antecedentes: La morbilidad asociada a la ruptura prematura de membranas puede ser mayor en neonatos pretérmino. Objetivo: Determinar la morbilidad materno-perinatal de la Rotura Prematura de Membranas Pretérmino en el Hospital Nacional Dos de Mayo en el periodo 2011-2012. Métodos: Revisión documentaria de las historias clínicas de las madres y neonatos prematuros que cumplieron criterios de selección, encontrando 170 casos en el periodo de estudio. Se muestran los resultados mediante estadística descriptiva. Resultados: La edad de las madres de neonatos prematuros con RPM fue 25,52 años. La forma de terminación del parto en 34,12 por ciento fue la vía vaginal, y en 65,88 por ciento por cesárea. El tiempo de latencia desde la RPM al parto en 9,41 por ciento fue dentro de las 6 primeras horas, en 21,18 por ciento dentro de las primeras 12 horas, y en 69,41 por ciento luego de las 24horas. Se emplearon corticoides para maduración pulmonar en 46,47 por ciento de casos, todos ellos con dexametasona, y en 27,65 por ciento se completó cuatro cursos. El 47,65 por ciento de neonatos fueron varones y 52,35 por ciento mujeres. En 82,35 por ciento de casos el líquido amniótico fue claro, 15,88 por ciento fue verde claro y en 1,76 por ciento fue purulento. No se usó antibióticos en 10 por ciento de casos, se usó sólo un antibiótico en 48,24 por ciento, dos antibióticos en 24,71 por ciento y tres antibióticos en 17,06 por ciento; el antibiótico más empleado fue la cefazolina. En 45,29 por ciento de madres se presentó endometritis; la duración de la hospitalización en las madres fue 5,73 días. El 14,71 por ciento de neonatos pretérmino fue llevado a alojamiento conjunto con la madre, 29,41 por ciento llegó a cuidados intermedios y 55,88 por ciento pasó a UCI; la indicación de hospitalización en UCI fue la sepsis en 55,79 por ciento, insuficiencia respiratoria en 20 por ciento, prematuridad extrema en 12,63 por ciento. Un 22,35 por ciento de neonatos no requirieron de apoyo...

Background: The morbidity associated with premature rupture of membranes may be higher in preterm infants. Objective: To determine maternal and perinatal morbidity of Premature Rupture of Membranes at the National Hospital Dos de Mayo in the period 2011-2012. Methods: Review of medical records documentary of mothers and preterm infants who met selection criteria, finding 170 cases in the study period. Results are shown using descriptive statistics. Results: The age of the mothers of preterm infants with RPM was 25.52 years. The manner of termination of labor was vaginal in 34.12 per cent, and by cesarean in 65.88 per cent. The latency from the RPM to birth was 9.41 per cent within the first 6 hours, in 21.18 per cent within the first 12 hours, and 69.41 per cent after 24 hours. Corticosteroids were used for 11Ing maturation in 46.47 per cent of cases, all with dexamethasone, and 27.65 per cent completed four courses. The 47.65 per cent of infants were male and 52.35 per cent female. In 82.35 per cent of cases the amniotic fluid was clear, light green in 15.88 per cent and in 1.76 per cent was purulent. No antibiotics were used in 10 per cent of cases, only one antibiotic was used in 48.24 per cent, two antibiotics in 24.71 per cent and three in 17.06 per cent, the most used antibiotic was cefazolin. In 45.29 per cent of mothers showed endometritis, the duration of hospitalization in mothers was 5.73 days. The 14.71 per cent of preterm infants was admitted with mother, intermediate care in 29.41 per cent and 55.88 per cent went to ICU, the indication for hospitalization in ICU was in 55.79 per cent sepsis, respiratory failure in 20 per cent, and in 12.63 per cent extreme prematurity. A 22.35 per cent of neonates did not required ventilatory support, 39.41 per cent required mechanical ventilation and 38.24 per cent used CPAP. In only 5.29 per cent of children there were no complications, a 91.18 per cent of cases had metabolic disorders, 90 per cent developed...

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