Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques.

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient-doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes.

A Video Mosaicing-Based Sensing Method for Chicken Behavior Recognition on Edge Computing Devices.

Chicken behavior recognition is crucial for a number of reasons, including promoting animal welfare, ensuring the early detection of health issues, optimizing farm management practices, and contributing to more sustainable and ethical poultry farming. In this paper, we introduce a technique for recognizing chicken behavior on edge computing devices based on video sensing mosaicing. Our method combines video sensing mosaicing with deep learning to accurately identify specific chicken behaviors from videos. It attains remarkable accuracy, achieving 79.61% with MobileNetV2 for chickens demonstrating three types of behavior. These findings underscore the efficacy and promise of our approach in chicken behavior recognition on edge computing devices, making it adaptable for diverse applications. The ongoing exploration and identification of various behavioral patterns will contribute to a more comprehensive understanding of chicken behavior, enhancing the scope and accuracy of behavior analysis within diverse contexts.

The interaction between liver cirrhosis, infection by Streptococcus bovis, and colon cancer.

Whether cirrhotic patients with Streptococcus bovis bacteremia have an increased risk of colorectal neoplasm is uncertain. A multicentric retrospective cohort study was conducted investigating associations between S. bovis biotype and species, cirrhosis, and colorectal neoplasm. Out of 779 patients with S. bovis bacteremia, 69 (8.7%) had cirrhosis. No differences were found in the prevalence of colorectal neoplasm between cirrhotic and non-cirrhotic patients undergoing colonoscopy. Among cirrhotic patients, prevalence of colorectal neoplasms was higher in S. bovis biotype I (S. gallolyticus) bacteremia (80%) than in S. bovis biotype II (33.3%; p < 0.007). In conclusion, risk of colorectal neoplasm is high among cirrhotic patients with S. gallolyticus bacteremia.

Shot Boundary Detection with 3D Depthwise Convolutions and Visual Attention.

Shot boundary detection is the process of identifying and locating the boundaries between individual shots in a video sequence. A shot is a continuous sequence of frames that are captured by a single camera, without any cuts or edits. Recent investigations have shown the effectiveness of the use of 3D convolutional networks to solve this task due to its high capacity to extract spatiotemporal features of the video and determine in which frame a transition or shot change occurs. When this task is used as part of a scene segmentation use case with the aim of improving the experience of viewing content from streaming platforms, the speed of segmentation is very important for live and near-live use cases such as start-over. The problem with models based on 3D convolutions is the large number of parameters that they entail. Standard 3D convolutions impose much higher CPU and memory requirements than do the same 2D operations. In this paper, we rely on depthwise separable convolutions to address the problem but with a scheme that significantly reduces the number of parameters. To compensate for the slight loss of performance, we analyze and propose the use of visual self-attention as a mechanism of improvement.

POSEIDON: A Data Augmentation Tool for Small Object Detection Datasets in Maritime Environments.

Certain fields present significant challenges when attempting to train complex Deep Learning architectures, particularly when the available datasets are limited and imbalanced. Real-time object detection in maritime environments using aerial images is a notable example. Although SeaDronesSee is the most extensive and complete dataset for this task, it suffers from significant class imbalance. To address this issue, we present POSEIDON, a data augmentation tool specifically designed for object detection datasets. Our approach generates new training samples by combining objects and samples from the original training set while utilizing the image metadata to make informed decisions. We evaluate our method using YOLOv5 and YOLOv8 and demonstrate its superiority over other balancing techniques, such as error weighting, by an overall improvement of 2.33% and 4.6%, respectively.

Design, Implementation, and Empirical Validation of a Framework for Remote Car Driving Using a Commercial Mobile Network.

Despite the fact that autonomous driving systems are progressing in terms of their automation levels, the achievement of fully self-driving cars is still far from realization. Currently, most new cars accord with the Society of Automotive Engineers (SAE) Level 2 of automation, which requires the driver to be able to take control of the car when needed: for this reason, it is believed that between now and the achievement of fully automated self-driving car systems, there will be a transition, in which remote driving cars will be a reality. In addition, there are tele-operation-use cases that require remote driving for health or safety reasons. However, there is a lack of detailed design and implementation available in the public domain for remote driving cars: therefore, in this work we propose a functional framework for remote driving vehicles. We implemented a prototype, using a commercial car. The prototype was connected to a commercial 4G/5G mobile network, and empirical experiments were conducted, to validate the prototype's functions, and to evaluate its performance in real-world driving conditions. The design, implementation, and empirical evaluation provided detailed technical insights into this important research and innovation area.

A Comprehensive Study on Pain Assessment from Multimodal Sensor Data.

Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed.

A Survey of Alzheimer's Disease Early Diagnosis Methods for Cognitive Assessment.

Dementia is a syndrome that is characterised by the decline of different cognitive abilities. A high rate of deaths and high cost for detection, treatments, and patients care count amongst its consequences. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support, appropriate medication, and maintenance, as far as possible, of engagement in intellectual, social, and physical activities. The early detection of Alzheimer Disease (AD) is considered to be of high importance for improving the quality of life of patients and their families. In particular, Virtual Reality (VR) is an expanding tool that can be used in order to assess cognitive abilities while navigating through a Virtual Environment (VE). The paper summarises common AD screening and diagnosis techniques focusing on the latest approaches that are based on Virtual Environments, behaviour analysis, and emotions recognition, aiming to provide more reliable and non-invasive diagnostics at home or in a clinical environment. Furthermore, different AD diagnosis evaluation methods and metrics are presented and discussed together with an overview of the different datasets.

Long-term efficacy of 6-month therapy with isoniazid and rifampin compared with isoniazid, rifampin, and pyrazinamide treatment for pleural tuberculosis.

Research into anti-tuberculosis treatment has mainly focused on pulmonary tuberculosis (TB), with few studies on pleural-TB. The aim of the study is to compare the long-term efficacy of a 6-month treatment regimen with isoniazid and rifampicin (6HR) with treatment regimen of isoniazid, rifampicin, and pyrazinamide (6HR2Z) for pleural-TB. A case-control study of 200 HIV-negative patients with pleural-TB prospectively followed in our TB-unit from 1995 to 2018. The primary resistance to isoniazid is < 4% in our geographic area. Pleural-TB diagnosis was based on a positive culture for M. tuberculosis (84 patients), presence of caseating granulomas in pleural biopsy (28), or characteristics of pleural fluid (88). A comparative study of demographic and clinical characteristics between the treatment groups was carried out. Out of the 200 patients followed, (112 males, 88 females; mean age 32.9 ± 18.4 years), 99 patients were treated with 6HR regimen and 101 with 6HR2Z. The groups were comparable, except the 6HR2Z had larger size of pleural effusion. All patients completed the treatment. The group treated with 6HR presented fewer adverse effects (15.3%) than 6HR2Z group (33%), p = 0.005, and lower frequency of severe hepatic toxicity (5% vs 10.9%). Four patients died from causes other than TB during treatment with 6HR2Z, and all other patients were cured during a monitoring period for 8.4 years (IQRs, 3.3-14.3). Six patients in 6HR and 10 in 6HR2Z developed residual pachypleuritis. 6HR is as effective as 6HR2Z treatment for pleural-TB, with fewer adverse effects.

Compressed kNN: K-Nearest Neighbors with Data Compression.

The kNN (k-nearest neighbors) classification algorithm is one of the most widely used non-parametric classification methods, however it is limited due to memory consumption related to the size of the dataset, which makes them impractical to apply to large volumes of data. Variations of this method have been proposed, such as condensed KNN which divides the training dataset into clusters to be classified, other variations reduce the input dataset in order to apply the algorithm. This paper presents a variation of the kNN algorithm, of the type structure less NN, to work with categorical data. Categorical data, due to their nature, can be compressed in order to decrease the memory requirements at the time of executing the classification. The method proposes a previous phase of compression of the data to then apply the algorithm on the compressed data. This allows us to maintain the whole dataset in memory which leads to a considerable reduction of the amount of memory required. Experiments and tests carried out on known datasets show the reduction in the volume of information stored in memory and maintain the accuracy of the classification. They also show a slight decrease in processing time because the information is decompressed in real time (on-the-fly) while the algorithm is running.

A Quantitative Comparison of Calibration Methods for RGB-D Sensors Using Different Technologies.

RGB-D (Red Green Blue and Depth) sensors are devices that can provide color and depth information from a scene at the same time. Recently, they have been widely used in many solutions due to their commercial growth from the entertainment market to many diverse areas (e.g., robotics, CAD, etc.). In the research community, these devices have had good uptake due to their acceptable levelofaccuracyformanyapplicationsandtheirlowcost,butinsomecases,theyworkatthelimitof their sensitivity, near to the minimum feature size that can be perceived. For this reason, calibration processes are critical in order to increase their accuracy and enable them to meet the requirements of such kinds of applications. To the best of our knowledge, there is not a comparative study of calibration algorithms evaluating its results in multiple RGB-D sensors. Specifically, in this paper, a comparison of the three most used calibration methods have been applied to three different RGB-D sensors based on structured light and time-of-flight. The comparison of methods has been carried out by a set of experiments to evaluate the accuracy of depth measurements. Additionally, an object reconstruction application has been used as example of an application for which the sensor works at the limit of its sensitivity. The obtained results of reconstruction have been evaluated through visual inspection and quantitative measurements.

Synthesis and structure-activity studies of the V-ATPase inhibitor saliphenylhalamide (SaliPhe) and simplified analogs.

An efficient total synthesis of the potent V-ATPase inhibitor saliphenylhalamide (SaliPhe), a synthetic variant of the natural product salicylihalamide A (SaliA), has been accomplished aimed at facilitating the development of SaliPhe as an anticancer and antiviral agent. This new approach enabled facile access to derivatives for structure-activity relationship studies, leading to simplified analogs that maintain SaliPhe's biological properties. These studies will provide a solid foundation for the continued evaluation of SaliPhe and analogs as potential anticancer and antiviral agents.

Emergence of quinolone-resistant, topoisomerase-mutant Brucella after treatment with fluoroquinolones in a macrophage experimental infection model.

AIM OF THE STUDY: To determine the activity of fluoroquinolones (FQ) and the selection of FQ-resistant mutants in a macrophage experimental infection model (MEIM). MATERIAL AND METHODS: Canine macrophages were inoculated with Brucella melitensis ATCC 23457 (WT), achieving intracellular counts of around 105 CFU/mL. Cell cultures were incubated in the presence of ciprofloxacin (CIP), levofloxacin (LEV), moxifloxacin (MOX), and doxycycline (DOX). After cell lysis, surviving microorganisms were plated for count purposes, and plated onto antibiotics-containing media for mutant selection. Topoisomerases mutations were detected by PCR and sequencing. RESULTS: Bacterial counts after cell lysis were 14.3% (CIP), 65.3% (LEV), and 75% (MOX) lower compared to the control. Quinolone-resistant mutants emerged in cell cultures containing CIP and LEV with a frequency of around 0.5×10(-3). All mutants showed an Ala87Val change in GyrA. Mutants had FQs MICs around 10×WT. The ability of these mutants for infecting new macrophages and the intracellular lysis after antibiotic exposure did not change significantly. No 2nd step FQ-resistant mutants were selected from 1st step mutants. CONCLUSIONS: Intracellular activity of FQs is low against WT and gyrA-mutant Brucella. FQs easily select gyrA mutants in MEIM. The ability of mutants for infecting new macrophages remains unchanged. In this MEIM, 2nd step mutants do not emerge.

[AN EDUCATIONAL EXPERIENCE BASED ON CLICKERS]. / Una experiencia educativa basada en los clickers.

Active learning or self-learning increases the student's participation and commitment to his studies; these conditions are necessary to improve academic performance. An intervention has been designed based on the experience in the use of clickers in other universities, but without the actual technology. This work has been performed in the School of Nursing affiliated to the University of Malaga (UMA) on students enrolled in their second year of Degree in Adult Nursing Course I. Three sessions of multiple-choice questions were scheduled on the subject "distance learning" in which master classes were not taught. The answers were collected on paper templates. We wanted to determine the degree of relationship between the attendance of sessions and the results obtained by students in the final examination of the subject, as well as, the questions dedicated to assess the "distance learning" matter. The results support a significant statistical difference in the correct answers by students according to the number of sessions attended. These differences are highest among students who did not attend any session and those who attended the three planned sessions.

Inhibition of IκB kinase-ß and IκB kinase-α by heterocyclic adamantyl arotinoids.

We recently reported on a series of retinoid-related molecules containing an adamantyl group, a.k.a. adamantyl arotinoids (AdArs), that showed significant cancer cell growth inhibitory activity and activated RXRα (NR2B1) in transient transfection assays while devoid of RAR transactivation capacity. We have now explored whether these AdArs could also bind and inhibit IKKß, a known target that mediates the induction of apoptosis and cancer cell growth inhibition by related AdArs containing a chalcone functional group. In addition, we have prepared and evaluated novel AdArs that incorporate a central heterocyclic ring connecting the adamantyl-phenol and the carboxylic acid at the polar termini. Our results indicate that the majority of the RXRα activating compounds lacked IKKß inhibitory activity. In contrast, the novel heterocyclic AdArs containing a thiazole or pyrazine ring linked to a benzoic acid motif were potent inhibitors of both IKKα and IKKß, which in most cases paralleled significant growth inhibitory and apoptosis inducing activities.

A comparative study of registration methods for RGB-D video of static scenes.

The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.

Epidemiology and impact of a multifaceted approach in controlling central venous catheter associated blood stream infections outside the intensive care unit.

BACKGROUND: Outside ICUs, CVC-ABSIs epidemiology and the results of strategies for their prevention are not well known. The aim of this study was to investigate the epidemiology and the impact of a multifaceted "bundle" approach in controlling CVC-ABSIs outside ICU. METHODS: From 1991 we performed prevalence studies of device and parenteral nutrition use, and prospective surveillance of all episodes of CVC-ABSIs in a 350-bed teaching hospital. CVC-ABSIs incidence/1,000 inpatient-days was calculated. An estimated CVC-ABSIs incidence/1,000 catheter-days was calculated based on the prevalence rates of catheter use and the total number of inpatient-days in each year. On november 2008, an education programme was instituted for care of catheter lines: reinforcing instructions in aseptic insertion technique, after care and hand-washing; in order to assess the adherence to these measures the quantity of alcohol-based hand-rub consumption/1,000 patient-days was quoted in litres. From January 2009, a checklist intervention for CVC insertion in ICU was started: hand hygiene, using full barrier precautions, cleaning the skin with alcoholic chlorhexidine, avoiding femoral access and removing unnecessary catheters. Compliance with the central line insertion checklist was measured by real-time audits and was achieved in 80% of cases. RESULTS: Prevalence of use of CVC and parenteral nutrition was similar throughout the study. We followed-up 309 CVC-ABSIs cases. Estimated CVC-ABSIs rate progressively increased to 15.1/1,000 catheter-days in 2008 (0.36/1,000 inpatient-days). After the intervention, the alcohol-based hand-rub consumption increased slightly and estimated CVC-ABSIs rate fell to 10.1 /1,000 catheter-days in last three years (0.19/1,000 inpatient-days), showing a 32.9% decrease. The infection rates achieved were lower in Internal Medicine wards: decreased from 14.1/1,000 catheter-days (0.17/patient-days) in 2008 to 5.2/1,000 catheter-days (0.05/1,000 inpatient-days) in last three years, showing a 63.1% decrease. In 2009, the estimated CVC-ABSIs incidence rate was significantly lower in the Internal Medicine ward compared to the Surgery ward: rate ratio (RR) = 0.14, 95%CI: 0.03-0.60), and within the Internal Medicine ward, the estimated CVC-ABSIs incidence rate was significantly lower in 2009 compared to 2008 (RR = 0.20, 95%CI: 0.04-0.91). CONCLUSION: The rate of CVC-ABSIs increased outside-ICU, and the implementation of multifaceted infection control programme decreased their clinical impact.

[Engagement and academics results of the grade students]. / Engagement y resultados académicos de los estudiantes de grado.

The academic engagement is a three-feature psychological welfare (determination, knowledge absorptive capability and dedication) as an intrinsic commitment towards the studies. The main aim of this study is to know the motivational status in students and to compare this with their academic performance. For the purpose of this paper, it was carried out an observational, correlational, cross-sectional study. The target population is the students carrying out the clinical training in the University of Malaga as part of the Nursing Degree. According to the data achieved, the mean score are the followings: determination 3.46 out of 6 (ST 1.05), dedication 5.16 out of 6 (ST 0.97) and knowledge absorptive capability 3.59 out of 6 (ST 1.18). The engagement global mean score of the nursing students enrolled in this University School of Nursing is 4.1 out of 6. It is worthy to mention the levels for dedication, ranging from almost always and always, which involves a remarkable vocation towards their profession. The marks on Practicum II-III have evidenced an association with the engagement determination (p < 0.008), and knowledge absorption capability (p < 0.036). Marks on Practicum I have not evidenced any association.

Species and biotypes of Streptococcus bovis causing infective endocarditis.

INTRODUCTION: Streptococcus bovis/equinus complex (SBEC) is a major cause of infective endocarditis (IE), although its incidence varies greatly depending on the geographical area. The characteristics of IE caused by Streptococcus gallolyticus susp. gallolyticus are well known; there are hardly any descriptions of IE caused by other species or biotypes. METHODS: Retrospective cohort study, from 1990 to 2019, of all SBEC IE in adults in three Spanish hospitals, Lugo (LH), Barcelona (BH) and Ferrol (FH) where the population is mainly rural, urban and mixed, respectively. The incidence of IE was analyzed in 3 areas. Clinical characteristics of IE (277 cases, 258 biotyped) were compared according to SBEC species and biotypes. RESULTS: There are significant differences between the incidence of SBEC IE in HL (27.9/106) vs. HF and HB (8.8 and 7,1, respectively, p<0.001). We found significant differences (SbI vs. SbII) in mean age (68.5 vs. 73 years; p<0.01), duration of symptoms before diagnosis (46.9±46.5 vs. 30.4±40.9 days; p<0.01), presence of comorbidities: 39.1% (78) vs. 54.2% (32; p<0.04), predisposing heart illness:62.3% (124) vs. 81.3% (48; p<0.006), particularly, prosthetic or intravascular devices IE: 24.6% (49) vs. 52.4% (31; p<0.001), bi-valve involvement:23.6% (47) vs. 11.8% (7; p<0.05) and heart failure: 24.6% (49) vs. 38.9% (23; p<0.03). There were no significant differences in embolic events, need for surgery or mortality. The association with CRC was high in both groups: 77.7% vs. 66.6%. CONCLUSIONS: IE due to SBEC has geographical variations in incidence and different clinical characteristics among biotypes. The association with CRC was high.

A Review on Deep Learning Techniques for Video Prediction.

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We first define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions.