Predicción de arritmias e infartos agudos de miocardio usando aprendizaje automático

Las enfermedades cardiovasculares, como el infarto agudo de miocardio, son una de las tres principales causas de muerte en el mundo según datos de la OMS. De forma similar, las arritmias cardíacas¸ como la fibrilación auricular, son enfermedades muy comunes en la actualidad. El electrocardiograma (ECG) es el medio de diagnóstico cardíaco que se utiliza de forma estandarizada en todo el mundo. Los modelos de aprendizaje automático son muy útiles en problemas de clasificación y predicción. Aplicadas al campo de la salud, las redes neuronales artificiales (ANN) y las redes neuronales convolucionales (CNN) en conjunto con modelos basados en árboles como XGBoost, son de vital ayuda en la prevención y control de enfermedades del corazón. El presente estudio tiene como objetivo comparar y evaluar el aprendizaje basado en los algoritmos ANN, CNN y XGBoost mediante el uso de las bases de datos de ECG Physionet MITBIH y PTB, que proporcionan ECG clasificados con arritmias e infartos agudos de miocardio, respectivamente. Se comparan por separado los tiempos de aprendizaje y el porcentaje de exactitud de los tres algoritmos en las dos bases de datos, y finalmente se cruzan los datos para comparar la validez y seguridad de la predicción./Cardiovascular diseases such as Acute Myocardial Infarction is one of the 3 leading causes of death in the world according to WHO data, in the same way cardiac arrhythmias are very common diseases today, such as atrial fibrillation. The ECG electrocardiogram is the means of cardiac diagnosis that is used in a standardized way throughout the world. Machine learning models are very helpful in classification and prediction problems. Applied to the field of health, ANN, and CNN artificial and neural networks, added to tree-based models such as XGBoost, are of vital help in the prevention and control of heart disease. The present study aims to compare and evaluate learning based on ANN, CNN and XGBoost algorithms by using the Physionet MIT-BIH and PTB ECG databases, which provide ECGs classified with Arrhythmias and Acute Myocardial Infarctions respectively. The learning times and the percentage of Accuracy of the 3 algorithms in the 2 databases are compared separately, and finally the data are crossed to compare the validity and safety of the learning prediction

Roflumilast N-oxide inhibits bronchial epithelial to mesenchymal transition induced by cigarette smoke in smokers with COPD

BACKGROUND: Epithelial to mesenchymal transition (EMT) is under discussion as a potential mechanism of small airway remodelling in COPD. In bronchial epithelium of COPD and smokers markers of EMT were described. In vitro, EMT may be reproduced by exposing well-differentiated human bronchial epithelial cells (WD-HBEC) to cigarette smoke extract (CSE). EMT may be mitigated by an increase in cellular cAMP. OBJECTIVE: This study explored the effects of roflumilast N-oxide, a PDE4 inhibitor on CSE-induced EMT in WD-HBEC and in primary bronchial epithelial cells from smokers and COPD in vitro. METHODS: WD-HBEC from normal donors were stimulated with CSE (2.5%) for 72 h in presence of roflumilast N-oxide (2 nM or 1 μM) or vehicle. mRNA and protein of EMT markers αSMA, vimentin, collagen-1, E-cadherin, ZO-1, KRT5 as well as NOX4 were quantified by real-time quantitative PCR or protein array, respectively. Phosphorylated and total ERK1/2 and Smad3 were assessed by protein array. cAMP and TGFβ1 were measured by ELISA. Reactive oxygen species (ROS) were determined by DCF fluorescence, after 30 min CSE (2.5%). Apoptosis was measured with Annexin V/PI labelling. In some experiments, EMT markers were determined in monolayers of bronchial epithelial cells from smokers, COPD versus controls. RESULTS: Roflumilast N-oxide protected from CSE-induced EMT in WD-HBEC. The PDE4 inhibitor reversed both the increase in mesenchymal and the loss in epithelial EMT markers. Roflumilast N-oxide restored the loss in cellular cAMP following CSE, reduced ROS, NOX4 expression, the increase in TGFβ1 release, phospho ERK1/2 and Smad3. The PDE4 inhibitor partly protected from the increment in apoptosis with CSE. Finally the PDE4 inhibitor decreased mesenchymal yet increased epithelial phenotype markers in HBEC of COPD and smokers. CONCLUSIONS: Roflumilast N-oxide may mitigate epithelial-mesenchymal transition in bronchial epithelial cells in vitro

Selective T3-T4 sympathicotomy versus gray ramicotomy on outcome and quality of life in hyperhidrosis patients : a randomized clinical trial

Compensatory hyperhidrosis is the leading cause of patients' dissatisfaction after thoracic sympathicotomy. The study aimed to reduce compensatory hyperhidrosis to increase patients' satisfaction. A prospective randomized study on palmar hyperhidrosis, May 2016-September 2019. Twenty-one patients T3- T4 sympathicotomy and 21 T3- T4 gray ramicotomy. Data prospectively collected. Analysis at study's end. Focus on the sweating, temperature, quality of life baseline and postoperatively, compensatory hyperhidrosis, hand dryness, patients' satisfaction, and if they would undergo the procedure again and recommend it. No baseline differences between groups. Hyperhidrosis was controlled postoperatively in all patients. No mortality, serious complications, or recurrences. Sympathicotomy worse postoperative quality of life (49.05 (SD: 15.66, IR: 35.50-63.00) versus ramicotomy 24.30 (SD: 6.02, IR: 19.75-27.25). After ramicotomy, some residual sweating on the face, hands, and axillae. Compensatory sweating worse with sympathicotomy. Satisfaction higher with ramicotomy. Better results with ramicotomy than sympathicotomy regarding hand dryness, how many times a day the patients had to shower or change clothes, intention to undergo the procedure again or recommend it to somebody else, and how bothersome compensatory hyperhidrosis was. T3-T4 gray ramicotomy had better results than T3-T4 sympathicotomy, with less compensatory sweating and higher patients' satisfaction

Soluble galectin-3 as a microenvironment-relevant immunoregulator with prognostic and predictive value in lung adenocarcinoma

Despite the success of therapies in lung cancer, more studies of new biomarkers for patient selection are urgently needed. The present study aims to analyze the role of galectin-3 (GAL-3) in the lung tumor microenvironment (TME) using tumorspheres as a model and explore its potential role as a predictive and prognostic biomarker in non-small cell lung cancer (NSCLC) patients. For in vitro studies, lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) primary cultures from early-stage patients and commercial cell lines were cultured, using tumorsphere-forming assays and adherent conditions for the control counterparts. We analyzed the pattern of secretion and expression of GAL-3 using reverse transcription-quantitative real-time PCR (RTqPCR), immunoblot, immunofluorescence, flow cytometry and immunoassay analysis. Our results using three-dimensional (3D) models of lung tumor cells revealed that soluble GAL-3 (sGAL-3) is highly expressed and secreted. To more accurately mimic the TME, a co-culture of tumorspheres and fibroblasts was used, revealing that GAL-3 could be important as an immunomodulatory molecule expressed and secreted in the TME, modulating immunosuppression through regulatory T cells (TREGS). In the translational phase, we confirmed that patients with high expression levels of GAL-3 had more TREGS, which suggests that tumors may be recruiting this population through GAL-3. Next, we evaluated levels of sGAL-3 before surgery in LUAD and LUSC patients, hypothesizing that sGAL-3 could be used as an independent prognostic biomarker for overall survival and relapse-free survival in early-stage LUAD patients. Additionally, levels of sGAL-3 at pretreatment and first response assessment from plasma to predict clinical outcomes in advanced LUAD and LUSC patients treated with first-line pembrolizumab were evaluated, further supporting that sGAL-3 has a high efficiency in predicting durable clinical response to pembrolizumab with an area under curve (AUC) of 0.801 (p=0.011). Moreover, high levels might predict decreased progression-free survival and overall survival to anti-PD-1 therapy, with sGAL-3 being a prognosis-independent biomarker for advanced LUAD

Analysis of Exosomal Cargo Provides Accurate Clinical, Histologic and Mutational Information in Non-Small Cell Lung Cancer

Lung cancer is a malignant disease with high mortality and poor prognosis, frequently diagnosed at advanced stages. Nowadays, immense progress in treatment has been achieved. However, the present scenario continues to be critical, and a full comprehension of tumor progression mechanisms is required, with exosomes being potentially relevant players. Exosomes are membranous vesicles that contain biological information, which can be transported cell-to-cell and modulate relevant processes in the hallmarks of cancer. The present research aims to characterize the exosomes' cargo and study their role in NSCLC to identify biomarkers. We analyzed exosomes secreted by primary cultures and cell lines, grown in monolayer and tumorsphere formations. Exosomal DNA content showed molecular alterations, whereas RNA high-throughput analysis resulted in a pattern of differentially expressed genes depending on histology. The most significant differences were found in XAGE1B, CABYR, NKX2-1, SEPP1, CAPRIN1, and RIOK3 genes when samples from two independent cohorts of resected NSCLC patients were analyzed. We identified and validated biomarkers for adenocarcinoma and squamous cell carcinoma. Our results could represent a relevant contribution concerning exosomes in clinical practice, allowing for the identification of biomarkers that provide information regarding tumor features, prognosis and clinical behavior of the disease. Keywords: non-small cell lung cancer; liquid biopsy; exosomes; extracellular vesicles; cell cultures; adenocarcinoma; squamous cell carcinoma; biomarker; tumorsphere

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Prediction of Arrhythmias and Acute Myocardial Infarctions using Machine Learning

Cardiovascular diseases such as Acute Myocardial Infarction is one of the 3 leading causes of death in the world according to WHO data, in the same way cardiac arrhythmias are very common diseases today, such as atrial fibrillation. The ECG electrocardiogram is the means of cardiac diagnosis that is used in a standardized way throughout the world. Machine learning models are very helpful in classification and prediction problems. Applied to the field of health, ANN, and CNN artificial and neural networks, added to tree-based models such as XGBoost, are of vital help in the prevention and control of heart disease. The present study aims to compare and evaluate learning based on ANN, CNN and XGBoost algorithms by using the Physionet MIT-BIH and PTB ECG databases, which provide ECGs classified with Arrhythmias and Acute Myocardial Infarctions respectively. The learning times and the percentage of Accuracy of the 3 algorithms in the 2 databases are compared separately, and finally the data are crossed to compare the validity and safety of the learning prediction

Analysis of Expression of Vascular Endothelial Growth Factor A and Hypoxia Inducible Factor-1alpha in Patients Operated on Stage I Non-Small-Cell Lung Cancer

Objectives. Recent studies show that expression of hypoxia inducible factor-1alpha (HIF-1α) favours expression of vascular endothelial growth factor A (VEGF-A), and these biomarkers are linked to cellular proliferation, angiogenesis, and metastasis in different cancers. We analyze expression of HIF-1α and VEGF-A to clinicopathologic features and survival of patients operated on stage I non-small-cell lung cancer. Methodology. Prospective study of 52 patients operated on with stage I. Expression of VEGF-A and HIF-1α was performed through real-time quantitative polymerase chain reaction (qRT-PCR). Results. Mean age was 64.7 and 86.5% of patients were male. Stage IA represented 23.1% and stage IB 76.9%. Histology classification was 42.3% adenocarcinoma, 34.6% squamous cell carcinoma, and 23.1% others. Median survival was 81.0 months and 5-year survival 67.2%. There was correlation between HIF-1α and VEGF-A (P=0.016). Patients with overexpression of HIF-1α had a tendency to better survival with marginal statistical significance (P=0.062). Patients with overexpression of VEGF-A had worse survival, but not statistically significant (P=0.133). Conclusion. The present study revealed that VEGF-A showed correlation with HIF-1α. HIF-1α had a tendency to protective effect with a P value close to statistical significance. VEGF-A showed a contrary effect but without statistical significance