Post-COVID-19 respiratory sequelae two years after hospitalization: an ambidirectional study.

Background: COVID-19 lung sequelae can impact the course of patient lives. We investigated the evolution of pulmonary abnormalities in post-COVID-19 patients 18-24 months after hospital discharge. Methods: A cohort of COVID-19 patients admitted to the Hospital das Clínicas da Faculdade de Medicina da USP in São Paulo, Brazil, between March and August of 2020, were followed-up 6-12 months after hospital discharge. A subset of patients with pulmonary involvement and chest computed tomography (CT) scans were eligible to participate in this second follow-up (18-24 months). Data was analyzed in an ambidirectional manner, including retrospective data from the hospitalization, and from the first follow-up (6-12 months after discharge), and compared with the prospective data collected in this new follow-up. Findings: From 348 patients eligible, 237 (68%) participated in this follow-up. Among participants, 139 (58%) patients presented ground-glass opacities and reticulations, and 80 (33%) presented fibrotic-like lesions (traction bronchiectasis and architectural distortion). Five (2%) patients improved compared to the 6-12-month assessment, but 20 (25%) of 80 presented worsening of lung abnormalities. For those with relevant assessments on both occasions, comparing the CT findings between this follow-up with the previous assessment, there was an increase in patients with architectural distortion (43 [21%] of 204 vs 57 [28%] of 204, p = 0.0093), as well as in traction bronchiectasis (55 [27%] of 204 vs 69 [34%] of 204, p = 0.0043). Patients presented a persistent functional impairment with demonstrated restrictive pattern in both follow-ups (87 [42%] of 207 vs 91 [44%] of 207, p = 0.76), as well as for the reduced diffusion capacity (88 [42%] of 208 vs 87 [42%] of 208, p = 1.0). Length of hospitalization (OR 1.04 [1.01-1.07], p = 0.0040), invasive mechanical ventilation (OR 3.11 [1.3-7.5] p = 0.011), patient's age (OR 1.03 [1.01-1.06] p = 0.0074 were consistent predictors for development of fibrotic-like lung lesions in post-COVID-19 patients. Interpretation: Post-COVID-19 lung sequelae can persist and progress after hospital discharge, suggesting airways involvement and formation of new fibrotic-like lesions, mainly in patients who were in intensive care unit (ICU). Funding: São Paulo Research Foundation (22/01769-5) and Instituto Todos pela Saúde (C1721).

Effect of Different Levels of Extruded Coffee (Coffea arabica) Pulp Flour on the Productive Performance and Intestinal Morphometry of Cobb 500 Broiler Chickens.

Coffee pulp is a by-product of the coffee industry. Due to conventional management techniques, it represents a severe environmental problem due to its negative impact on the soil (anaerobic fermentation and pH changes), water sources (the infiltration of pollutants into streams, acidification of water sources, and modification of microorganisms), and biodiversity (soil microbiology, fish, crustaceans, and other vertebrates). Therefore, it is essential to develop protocols for the treatment of this waste so that it can be used again in other productive activities under the circular economy approach. This means that all the waste from a production process can be reused, can generate value for the benefit of the producer, and, in turn, mitigate the environmental impact. The objective of this study was to evaluate the replacement of 5 levels of wheat bran (WB) with extruded coffee pulp flour (ECPF) as an alternative to a conventional fiber source in broiler finisher diets. A total of 300 Cobb 500 chickens in the finishing phase were assessed in the study, grouped in 5 treatments: T1, a conventional diet or control treatment (100% WB and 0% ECPF), T2 (75% WB and 25% ECPF), T3 (50% WB and 50% ECPF), T4 (25% WB and 75% ECPF), and T5 (0% WB and 100% ECPF). Feed intake, weight gain, feed conversion ratio (FCR), and intestinal morphometry (villus length: VL, villus width: VW, crypt depth: CD, villus height/crypt depth ratio: V/C, and villus surface area: VSA) were evaluated at the level of the duodenum, jejunum, and ileum. Feed intake decreased correspondingly as the ECPF in the diet was increased, with statistical differences (p < 0.01) between their averages; the most significant weight gain (834.61 g) was evidenced with the T2 treatment, this being statistically different (p < 0.01) from T4 and T5; similarly, the best FCR (1.58) was evidenced with the T2 treatment, followed by the control treatment T1 (with 1.64); however, they were not statistically different (p > 0.05). All treatment results were similar to the VL control samples in the three intestinal portions, except for the T5 in the jejunum, which showed statistical differences from the control. In VW, the treatment results were similar to the control samples of the jejunum and ileum; however, in the duodenum, the T5 results showed the highest value (172.18 µm), being statistically different (p < 0.05) from the other treatments being evaluated. For CD, it was only in the duodenum that the T2 and T3 treatments were similar to the control. Likewise, for V/C in the duodenum, only the T2 results were similar to the control. There was no significant difference in the VSA among the different treatment groups. T2 showed better production parameters without altering the intestinal villi. In conclusion, ECPF is a potential input for use to replace up to 25% of WB in the feed of broilers in the finishing phase.

¿Existe correlación metabólica hipoglucémica y el síndrome de Percheron? Reporte de caso / Is there a metabolic correlation between hypoglycemia and Percheron syndrome? Case report

Introducción: El síndrome de Percheron es causa de lesiones isquémicas talamicas bilaterales ocasionando una serie de anomalías clínicas como: alteración del estado de conciencia, oftalmoplejias, y alteraciones de la memoria. Caso clínico: Presentamos el caso de una mujer de 49 años con diabetes mellitus, mal control metabólico, episodios hipoglucémicos e hiperglucemicos repetitivos, quien cursa con sintomatologia inespecífica, se realizan estudios imagenológicos no invasivos documentado lesiones talamica bilaterales. Conclusiones: Los cuadros subagudos e isquémicos parciales que involucran la región paramediana del Talamo bilateral podrían tener alguna relación con episodios hipoglucémicos e hiperglucemicos repetitivos, que pueden llegar a cursar con sintomatología leve e inespecífica, lo cual lo convierte un reto diagnóstico para este síndrome.

Introduction: Percheron syndrome is the cause of bilateral thalamic ischemic lesions, causing a series of clinical abnormalities such as an altered state of consciousness, ophthalmoplegia, and memory alterations. Clinical case: We present the case of 49-year-old woman with diabetes mellitus, poor metabolic control, and repetitive hypoglycemic and hyperglycemic episodes with nonspecific symptoms, non-invasive imaging studies were performed, which documented bilateral thalamic lesions. Conclusions: Subacute and partial ischemic symptoms that involve the paramedian region of the bilateral thalamus could have some relationship with repetitive hypoglycemic and hyperglycemic episodes, and can lead to mild and nonspecific symptoms, which makes it a diagnostic challenge for this syndrome.

[Analysis of life quality on patients with thoracolumbar fractures]. / Análisis de calidad de vida en pacientes con fracturas toracolumbares.

Background: Patients with thoracolumbar fractures with TLICS 4 classification are at the limit of surgical fixation with regards to conservative treatment; however, results in our environment are not known, which is why this study has innovative characteristics. Objective: To determine the quality of life in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation in a third level hospital. Material and methods: A cohort prospective study was carried out in patients with TLICS 4 classification thoracolumbar fractures using traditional fixation with regards to no fixation in beneficiaries from the Mexican Institute for Social Security. The SF-12 instrument, which assessed quality of life, was administered; age, sex, days of hospitalization, time of spinal cord injury were searched in the patients' medical history. It was used descriptive and inferential statistics using Student's t or Mann-Whitney U. Results: 20 patients participated and 9 had traditional fixation (45%). All patients had type E spinal cord injuries according to the International Standards for Neurological Classification of Spinal Cord Injury. Mean age of non-fixation was 42.2 ± 12.9 and of fixation 44.9 ± 10.2; in non-fixation 6 (67%) were male. The quality of life score was 29.1 ± 0.9 in the conservative treatment and 28.7 ± 1.3 in the surgical treatment, p < 0.462. Conclusions: No differences in quality of life were observed in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation.

Introducción: los pacientes con fracturas toracolumbares con clasificación TLICS 4 se encuentran en el límite de la fijación quirúrgica con respecto al tratamiento conservador; sin embargo, resultados en nuestro medio no son conocidos, por lo que este estudio tiene características innovadoras. Objetivo: determinar la calidad de vida en pacientes con fracturas toracolumbares clasificación TLICS 4, mediante fijación tradicional con respecto a no fijación en un hospital de tercer nivel. Material y métodos: estudio de cohorte prospectiva en pacientes con fracturas toracolumbares clasificación TLICS 4 mediante fijación tradicional con respecto a no fijación en población derechohabiente del Instituto Mexicano del Seguro Social (IMSS). Se aplicó el instrumento SF-12, que evaluó la calidad de vida; se buscó en los expedientes de los pacientes edad, sexo, días de hospitalización, tiempo de lesión medular. Se usó estadística descriptiva e inferencial mediante t de Student o U de Mann Whitney. Resultados: participaron 20 pacientes y 9 (45%) tuvieron fijación tradicional. Todos los pacientes pertenecían a la clase E según las Normas Internacionales para la Clasificación Neurológica de lesiones de la médula espinal. Edad media de no fijación con 42.2 ± 12.9 y de fijación 44.9 ± 10.2; en no fijación, 6 (67%) eran varones. La puntuación de calidad de vida fue en el tratamiento conservador con 29.1 ± 0.9 y quirúrgico 28.7 ± 1.3, p < 0.462. Conclusiones: no se observaron diferencias en la calidad de vida en pacientes con fracturas toracolumbares clasificación TLICS 4 mediante fijación tradicional y no fijación.

[Analysis of life quality on patients with thoracolumbar fractures]. / Análisis de calidad de vida en pacientes con fracturas toracolumbares.

Background: Patients with thoracolumbar fractures with TLICS 4 classification are at the limit of surgical fixation with regards to conservative treatment; however, results in our environment are not known, which is why this study has innovative characteristics. Objective: To determine the quality of life in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation in a third level hospital. Material and methods: A cohort prospective study was carried out in patients with TLICS 4 classification thoracolumbar fractures using traditional fixation with regards to no fixation in beneficiaries from the Mexican Institute for Social Security. The SF-12 instrument, which assessed quality of life, was administered; age, sex, days of hospitalization, time of spinal cord injury were searched in the patients' medical history. It was used descriptive and inferential statistics using Student's t or Mann-Whitney U. Results: 20 patients participated and 9 had traditional fixation (45%). All patients had type E spinal cord injuries according to the International Standards for Neurological Classification of Spinal Cord Injury. Mean age of non-fixation was 42.2 ± 12.9 and of fixation 44.9 ± 10.2; in non-fixation 6 (67%) were male. The quality of life score was 29.1 ± 0.9 in the conservative treatment and 28.7 ± 1.3 in the surgical treatment, p < 0.462. Conclusions: No differences in quality of life were observed in patients with TLICS 4 thoracolumbar fractures using traditional fixation with regards to no fixation.

Introducción: los pacientes con fracturas toracolumbares con clasificación TLICS 4 se encuentran en el límite de la fijación quirúrgica con respecto al tratamiento conservador; sin embargo, resultados en nuestro medio no son conocidos, por lo que este estudio tiene características innovadoras. Objetivo: determinar la calidad de vida en pacientes con fracturas toracolumbares clasificación TLICS 4, mediante fijación tradicional con respecto a no fijación en un hospital de tercer nivel. Material y métodos: estudio de cohorte prospectiva en pacientes con fracturas toracolumbares clasificación TLICS 4 mediante fijación tradicional con respecto a no fijación en población derechohabiente del Instituto Mexicano del Seguro Social (IMSS). Se aplicó el instrumento SF-12, que evaluó la calidad de vida; se buscó en los expedientes de los pacientes edad, sexo, días de hospitalización, tiempo de lesión medular. Se usó estadística descriptiva e inferencial mediante t de Student o U de Mann Whitney. Resultados: participaron 20 pacientes y 9 (45%) tuvieron fijación tradicional. Todos los pacientes pertenecían a la clase E según las Normas Internacionales para la Clasificación Neurológica de lesiones de la médula espinal. Edad media de no fijación con 42.2 ± 12.9 y de fijación 44.9 ± 10.2; en no fijación, 6 (67%) eran varones. La puntuación de calidad de vida fue en el tratamiento conservador con 29.1 ± 0.9 y quirúrgico 28.7 ± 1.3, p < 0.462. Conclusiones: no se observaron diferencias en la calidad de vida en pacientes con fracturas toracolumbares clasificación TLICS 4 mediante fijación tradicional y no fijación.

Siamese pyramidal deep learning network for strain estimation in 3D cardiac cine-MR.

Strain represents the quantification of regional tissue deformation within a given area. Myocardial strain has demonstrated considerable utility as an indicator for the assessment of cardiac function. Notably, it exhibits greater sensitivity in detecting subtle myocardial abnormalities compared to conventional cardiac function indices, like left ventricle ejection fraction (LVEF). Nonetheless, the estimation of strain poses considerable challenges due to the necessity for precise tracking of myocardial motion throughout the complete cardiac cycle. This study introduces a novel deep learning-based pipeline, designed to automatically and accurately estimate myocardial strain from three-dimensional (3D) cine-MR images. Consequently, our investigation presents a comprehensive pipeline for the precise quantification of local and global myocardial strain. This pipeline incorporates a supervised Convolutional Neural Network (CNN) for accurate segmentation of the cardiac muscle and an unsupervised CNN for robust left ventricle motion tracking, enabling the estimation of strain in both artificial phantoms and real cine-MR images. Our investigation involved a comprehensive comparison of our findings with those obtained from two commonly utilized commercial software in this field. This analysis encompassed the examination of both intra- and inter-user variability. The proposed pipeline exhibited demonstrable reliability and reduced divergence levels when compared to alternative systems. Additionally, our approach is entirely independent of previous user data, effectively eliminating any potential user bias that could influence the strain analyses.

Telemedicine in Brazil: Teleconsultations at the Largest University Hospital in the Country.

The coronavirus disease (COVID-19) pandemic leveraged telemedicine worldwide mainly due to the need for social distancing, patient safety, and infection prevention. The Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP) was a key reference site in the treatment of COVID-19 severe cases in the country. To continue patient's health care, it became necessary to increase the number of teleconsultations and standardize it institutionally. Herein, we briefly described how the HCFMUSP improved the teleconsultation health care service during the COVID-19 pandemic, highlighting the implementation of important innovations and the throughout standardization process, including patients and professional workflow. We also detailed the methodology used to implement or improve teleconsultation in a medical/multidisciplinary specialty at HCFMUSP. All these efforts made the HCFMUSP reach the goal of converting 15% of all face-to-face consultations into teleconsultations only in 2021. In addition, there were more than 370,000 teleconsultations until the end of 2022. Our experience has shown that having a supporting team, a digital certification process, and the data integration were key factors toward the successful implementation of the teleconsultation services. We believe that progressing toward teleconsultation will improve the population covered by health care services in Brazil, as well as contribute to a reduction of waiting time, and solving costs to health care institutions and patients. We expect this report of our experience in teleconsultation implementation could inspire and guide other health care institutions in the development of telemedicine.

Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: Prospective cohort study.

BACKGROUND: Coronavirus disease (COVID-19) survivors exhibit multisystemic alterations after hospitalization. Little is known about long-term imaging and pulmonary function of hospitalized patients intensive care unit (ICU) who survive COVID-19. We aimed to investigate long-term consequences of COVID-19 on the respiratory system of patients discharged from hospital ICU and identify risk factors associated with chest computed tomography (CT) lesion severity. METHODS: A prospective cohort study of COVID-19 patients admitted to a tertiary hospital ICU in Brazil (March-August/2020), and followed-up six-twelve months after hospital admission. Initial assessment included: modified Medical Research Council dyspnea scale, SpO2 evaluation, forced vital capacity, and chest X-Ray. Patients with alterations in at least one of these examinations were eligible for CT and pulmonary function tests (PFTs) approximately 16 months after hospital admission. Primary outcome: CT lesion severity (fibrotic-like or non-fibrotic-like). Baseline clinical variables were used to build a machine learning model (ML) to predict the severity of CT lesion. RESULTS: In total, 326 patients (72%) were eligible for CT and PFTs. COVID-19 CT lesions were identified in 81.8% of patients, and half of them showed mild restrictive lung impairment and impaired lung diffusion capacity. Patients with COVID-19 CT findings were stratified into two categories of lesion severity: non-fibrotic-like (50.8%-ground-glass opacities/reticulations) and fibrotic-like (49.2%-traction bronchiectasis/architectural distortion). No association between CT feature severity and altered lung diffusion or functional restrictive/obstructive patterns was found. The ML detected that male sex, ICU and invasive mechanic ventilation (IMV) period, tracheostomy and vasoactive drug need during hospitalization were predictors of CT lesion severity(sensitivity,0.78±0.02;specificity,0.79±0.01;F1-score,0.78±0.02;positive predictive rate,0.78±0.02; accuracy,0.78±0.02; and area under the curve,0.83±0.01). CONCLUSION: ICU hospitalization due to COVID-19 led to respiratory system alterations six-twelve months after hospital admission. Male sex and critical disease acute phase, characterized by a longer ICU and IMV period, and need for tracheostomy and vasoactive drugs, were risk factors for severe CT lesions six-twelve months after hospital admission.

ClinicalPath: A Visualization Tool to Improve the Evaluation of Electronic Health Records in Clinical Decision-Making.

Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this article, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians' experience in decision-making tasks, made with more confidence and better usage of the physicians' time, allowing them to take other needed care for the patients.

Wearable potentiometric biosensor for analysis of urea in sweat.

Herein, we introduce wearable potentiometric biosensors on screen-printed carbon electrodes (SPCEs) for on-body and on-site monitoring of urea in sweat. The biosensor architecture was judiciously designed to detect urea at different pHs and incorporate a pH sensor, thus containing polyaniline ink, urease bioink and a polyvinylchloride membrane. Urea detection could be performed in the wide range from 5 to 200 mM at pH 7.0, encompassing urea levels in human sweat. The biosensor response was fast (incubation time 5 min), with no interference from other substances in sweat. Reliable urea detection could be done in undiluted human sweat with a skin-worn flexible device using the pH correction strategy afforded by the pH sensor. The performance of the epidermal biosensor was not affected by severe bending strains. The feasibility of mass production was demonstrated by fabricating epidermal flexible biosensors using slot-die coating with a roll-to-roll technique.

New fossil assemblages from the Early Ordovician Fezouata Biota.

The Fezouata Biota (Morocco) is a unique Early Ordovician fossil assemblage. The discovery of this biota revolutionized our understanding of Earth's early animal diversifications-the Cambrian Explosion and the Ordovician Radiation-by suggesting an evolutionary continuum between both events. Herein, we describe Taichoute, a new fossil locality from the Fezouata Shale. This locality extends the temporal distribution of fossil preservation from this formation into the upper Floian, while also expanding the range of depositional environments to more distal parts of the shelf. In Taichoute, most animals were transported by density flows, unlike the in-situ preservation of animals recovered in previously investigated Fezouata sites. Taichoute is dominated by three-dimensionally preserved, and heavily sclerotized fragments of large euarthropods-possibly representing nektobenthic/nektic bivalved taxa and/or hurdiid radiodonts. Resolving whether this dominance reflects a legitimate aspect of the original ecosystem or a preservational bias requires an in-depth assessment of the environmental conditions at this site. Nevertheless, Taichoute provides novel preservational and palaeontological insights during a key evolutionary transition in the history of life on Earth.

Contrast phase recognition in liver computer tomography using deep learning.

Hepatocellular carcinoma (HCC) has become the 4th leading cause of cancer-related deaths, with high social, economical and health implications. Imaging techniques such as multiphase computed tomography (CT) have been successfully used for diagnosis of liver tumors such as HCC in a feasible and accurate way and its interpretation relies mainly on comparing the appearance of the lesions in the different contrast phases of the exam. Recently, some researchers have been dedicated to the development of tools based on machine learning (ML) algorithms, especially by deep learning techniques, to improve the diagnosis of liver lesions in imaging exams. However, the lack of standardization in the naming of the CT contrast phases in the DICOM metadata is a problem for real-life deployment of machine learning tools. Therefore, it is important to correctly identify the exam phase based only on the image and not on the exam metadata, which is unreliable. Motivated by this problem, we successfully created an annotation platform and implemented a convolutional neural network (CNN) to automatically identify the CT scan phases in the HCFMUSP database in the city of São Paulo, Brazil. We improved this algorithm with hyperparameter tuning and evaluated it with cross validation methods. Comparing its predictions with the radiologists annotation, it achieved an accuracy of 94.6%, 98% and 100% in the testing dataset for the slice, volume and exam evaluation, respectively.

Automatic myocardium strain quantification in MR synthetic images with Deep Leaning.

Accurate quantification of myocardium strain in magnetic resonance images is important to correctly diagnose and monitor cardiac diseases. Currently, available methods to estimate motion are based on tracking brightness pattern differences between images. In cine-MR images, the myocardium interior presents an inhered homogeneity, which reduces the accuracy in estimated motion, and consequently strain. Neural networks have recently been shown to be an important tool for a variety of applications, including motion estimation. In this work, we investigate the feasibility of quantifying myocardium strain in cardiac resonance synthetic images using motion generated by a compact and powerful network called Pyramid, Warping, and Cost Volume (PWC). Using the motion generated by the neural network, the radial myocardium strain obtained presents a mean average error of 12.30% +- 6.50%, and in the circumferential direction 1.20% +-0.61 %, better than the two classical methods evaluated. Clinical Relevance- This work demonstrates the feasibility of estimating myocardium strain using motion estimated by a convolutional neural network.

Chronic lung lesions in COVID-19 survivors: predictive clinical model.

OBJECTIVE: This study aimed to propose a simple, accessible and low-cost predictive clinical model to detect lung lesions due to COVID-19 infection. DESIGN: This prospective cohort study included COVID-19 survivors hospitalised between 30 March 2020 and 31 August 2020 followed-up 6 months after hospital discharge. The pulmonary function was assessed using the modified Medical Research Council (mMRC) dyspnoea scale, oximetry (SpO2), spirometry (forced vital capacity (FVC)) and chest X-ray (CXR) during an in-person consultation. Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT. SETTING: A tertiary hospital in Sao Paulo, Brazil. PARTICIPANTS: 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years. PRIMARY OUTCOME MEASURE: A predictive clinical model for lung lesion detection on chest CT. RESULTS: There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. Almost half of them (48%) had significant pulmonary abnormalities, including ground-glass opacities, parenchymal bands, reticulation, traction bronchiectasis and architectural distortion. The machine learning model, including the results of 257 patients with complete data on mMRC, SpO2, FVC, CXR and CT, accurately detected pulmonary lesions by the joint data of CXR, mMRC scale, SpO2 and FVC (sensitivity, 0.85±0.08; specificity, 0.70±0.06; F1-score, 0.79±0.06 and area under the curve, 0.80±0.07). CONCLUSION: A predictive clinical model based on CXR, mMRC, oximetry and spirometry data can accurately screen patients with lung lesions after SARS-CoV-2 infection. Given that these examinations are highly accessible and low cost, this protocol can be automated and implemented in different countries for early detection of COVID-19 sequelae.

Blockchain-Based Architecture Design for Personal Health Record: Development and Usability Study.

BACKGROUND: The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data to the respective owner. In spite of this importance, most of the works focus more on describing how blockchain models can be used in a PHR scenario rather than whether these models are in fact feasible and robust enough to support a large number of users. OBJECTIVE: To achieve a consistent, reproducible, and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share, and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related (net)works. METHODS: We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system: first, under workload, ranging from 100 to 2500 simultaneous record submissions, and second, increasing the network size from 3 to 13 peers. In both experiments, we used throughput and average latency as the primary metrics. We also created a health database, a cryptographic unit, and a server to complement the blockchain network. RESULTS: With a 3-peer network, smart contracts that write on the ledger have throughputs, measured in transactions per second (tps) in an order of magnitude close to 102 tps, while those contracts that only read have rates close to 103 tps. Smart contracts that write also have latencies, measured in seconds, in an order of magnitude close to 101 seconds, while that only read have delays close to 100 seconds. In particular, smart contracts that retrieve, list, and view history have throughputs varying, respectively, from 1100 tps to 1300 tps, 650 tps to 750 tps, and 850 tps to 950 tps, impacting the overall system response if they are equally requested under the same workload. Varying the network size and applying an equal fixed load, in turn, writing throughputs go from 102 tps to 101 tps and latencies go from 101 seconds to 102 seconds, while reading ones maintain similar values. CONCLUSIONS: To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below what the traditional distributed databases achieve, indicating that the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production.

Perspectives of the Biomedical Engineering Program at UASLP after ten years - analysis and criticism.

The Biomedical Engineering (BME) bachelor pro-gram of the Faculty of Sciences in Universidad Autónoma de San Luis Potosí (UASLP) was created in June of 2010, with the aim of training professionals with an integral perspective in the engineering field by considering a multidisciplinary approach to develop and apply technology in the areas of medicine and biology. After 10 years, our BME program has achieved national recognition. Despite of being an emerging program, this achievement has been obtained by the consolidation of our academic staff, the outstanding participation of our students in national and international academic events, and the historical graduation results. In our comprehensive evaluation, we report an overall terminal efficiency (completion rate) of 67% and a graduation rate of 47.2%, where these values are above the average for an engineering program in our institution. Additionally, the BME program provides students with solid skills and background to carry out research activities, which has resulted in a considerable number of alumni pursuing graduate studies or have already completed one. Our results show that 90% of our former students are working after graduation, but only 44% work in the field of biomedical engineering, since the regional labor market starts to saturate given the fact that, at present, students from six generations have completed our BME bachelor program. In this way, few graduates visualize the wide spectrum of job options where a biomedical engineer can impact, by their distinctive comprehensive and multidisciplinary training. Therefore, it is necessary to propose new curricular design strategies to provide our students with an academic training that allows them to enter a globalized world, where there is an even greater spectrum of engineering possibilities related to the fields of medicine and biology, in line with current trends.

Implementation of Tele-ICU during the COVID-19 pandemic.

OBJECTIVE: To describe the implementation of a Tele-ICU program during the COVID-19 pandemic, as well as to describe and analyze the results of the first four months of operation of the program. METHODS: This was a descriptive observational study of the implementation of a Tele-ICU program, followed by a retrospective analysis of clinical data of patients with COVID-19 admitted to ICUs between April and July of 2020. RESULTS: The Tele-ICU program was implemented over a four-week period and proved to be feasible during the pandemic. Participants were trained remotely, and the program had an evidence-based design, the objective being to standardize care for patients with COVID-19. More than 100,000 views were recorded on the free online platforms and the mobile application. During the study period, the cases of 326 patients with COVID-19 were evaluated through the program. The median age was 60 years (IQR, 49-68 years). There was a predominance of males (56%). There was also a high prevalence of hypertension (49.1%) and diabetes mellitus (38.4%). At ICU admission, 83.7% of patients were on invasive mechanical ventilation, with a median PaO2/FiO2 ratio < 150. It was possible to use lung-protective ventilation in 75% of the patients. Overall, in-hospital mortality was 68%, and ICU mortality was 65%. CONCLUSIONS: Our Tele-ICU program provided multidisciplinary training to health care professionals and clinical follow-up for hundreds of critically ill patients. This public health care network initiative was unprecedented and proved to be feasible during the COVID-19 pandemic, encouraging the creation of similar projects that combine evidence-based practices, training, and Tele-ICU.

Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes.

COVID-19 is a highly contagious disease that can cause severe pneumonia. Patients with pneumonia undergo chest X-rays (XR) to assess infiltrates that identify the infection. However, the radiographic characteristics of COVID-19 are similar to the other acute respiratory syndromes, hindering the imaging diagnosis. In this work, we proposed identifying quantitative/radiomic biomarkers for COVID-19 to support XR assessment of acute respiratory diseases. This retrospective study used different cohorts of 227 patients diagnosed with pneumonia; 49 of them had COVID-19. Automatically segmented images were characterized by 558 quantitative features, including gray-level histogram and matrices of co-occurrence, run-length, size zone, dependence, and neighboring gray-tone difference. Higher-order features were also calculated after applying square and wavelet transforms. Mann-Whitney U test assessed the diagnostic performance of the features, and the log-rank test assessed the prognostic value to predict Kaplan-Meier curves of overall and deterioration-free survival. Statistical analysis identified 51 independently validated radiomic features associated with COVID-19. Most of them were wavelet-transformed features; the highest performance was the small dependence matrix feature of "low gray-level emphasis" (area under the curve of 0.87, sensitivity of 0.85, [Formula: see text]). Six features presented short-term prognostic value to predict overall and deterioration-free survival. The features of histogram "mean absolute deviation" and size zone matrix "non-uniformity" yielded the highest differences on Kaplan-Meier curves with a hazard ratio of 3.20 ([Formula: see text]). The radiomic markers showed potential as quantitative measures correlated with the etiologic agent of acute infectious diseases and to stratify short-term risk of COVID-19 patients.

Improving the generalization of deep learning methods to segment the left ventricle in short axis MR images.

Cardiovascular disease is one of the major health problems worldwide. In clinical practice, cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for the evaluation of the function and structure of the left ventricle (LV). More recently, deep learning methods have been used to segment LV with impressive results. On the other hand, this kind of approach is prone to overfit the training data, and it does not generalize well between different data acquisition centers, thus creating constraints to the use in daily routines. In this paper, we explore methods to improve the generalization in the segmentation performed by a convolutional neural network. We applied a U-net based architecture and compared two different pre-processing methods to improve uniformity in the image contrast between five cross-dataset training and testing. Overall, we were able to perform the segmentation of the left ventricle using multiple cross-dataset combinations of train and test, with a mean endocardium dice score of 0.82.Clinical Relevance- This work improves the result between the cross-dataset evaluation of the left ventricle segmentation, reducing the constraints for daily clinical adoption of a fully-automatic segmentation method.

Fully Automated Quantification of Cardiac Indices from Cine MRI Using a Combination of Convolution Neural Networks.

Cardiovascular magnetic resonance imaging (CMRI) is one of the most accurate non-invasive modalities for evaluation of cardiac function, especially the left ventricle (LV). In this modality, the manual or semi-automatic delineation of LV by experts is currently the standard clinical practice for chambers segmentation. Despite these efforts, global quantification of LV remains a challenge. In this work, a combination of two convolutional neural network (CNN) architectures for quantitative evaluation of the LV is described, which estimates the cavity and the myocardium areas, endocardial cavity dimensions in three directions, and the myocardium regional wall thickness in six radial directions. The method was validated in CMRI exams of 56 patients (LVQuan19 dataset) and evaluated by metrics Dice Index, Mean Absolute Error, and Correlation with superior performance compared to the state-of-the-art methods. The combination of the CNN architectures provided a simpler yet fully automated approach, requiring no specialist interaction.Clinical Relevance- With the proposed method, it is possible to perform automatically the full quantification of regional clinically relevant parameters of the left ventricle in short-axis CMRI images with superior performance compared to state-of-the-art methods.