Effect of pine bark extract and its phenolic compounds on selected pathogenic and probiotic bacterial strains.

Introduction: Inflammatory bowel disease (IBD) comprises a heterogeneous group of chronic diseases as ulcerative colitis (UC) and Crohn's disease (CD). IBD is the result of a dysregulation of intestinal homeostasis with a host's loss of tolerance toward normal enteric microflora. Plant-based extracts as phenolic compounds can play a role by modulating the intestinal inflammation response. Methods: The in vitro antimicrobial activity of French maritime pine bark extract (PBE) and its phenolic constituents has been investigated in this study. Furthermore, the ability of PBE and phenolic compounds (caffeic acid, chlorogenic acid, ferulic acid, gallic acid and taxifolin) to modulate the microbiota has been assessed. Results: Phenolic compounds and PBE showed a great inhibitory effect on the pathogens growth at the highest concentration assessed (1.25 mg/mL). The growth of E. sakazakii and E. faecalis were affected by the effect of caffeic acid and ferulic acid. Taxifolin showed a very strong activity against Listeria sp. (with a reduction ~98%). Gallic acid revealed antibacterial effect on S. aureus at different concentrations. The inhibitory effect of PBE was highly significant on the growth of E. coli O157:H7. PBE, caffeic acid and chlorogenic acid seem to provide the greatest beneficial effect on the probiotic bacteria. However, the highest concentrations of taxifolin may have impaired the growth of beneficial microbiota. Conclusion: Present findings could be of interest for considering PBE and/or its phenolic constituents as protectors against gastrointestinal disturbances which lead to ulcerative colitis and Crohn's disease.

Development of LuxIA, a Cloud-Based AI Diabetic Retinopathy Screening Tool Using a Single Color Fundus Image.

Purpose: Diabetic retinopathy (DR) is the leading cause of vision impairment in working-age adults. Automated screening can increase DR detection at early stages at relatively low costs. We developed and evaluated a cloud-based screening tool that uses artificial intelligence (AI), the LuxIA algorithm, to detect DR from a single fundus image. Methods: Color fundus images that were previously graded by expert readers were collected from the Canarian Health Service (Retisalud) and used to train LuxIA, a deep-learning-based algorithm for the detection of more than mild DR. The algorithm was deployed in the Discovery cloud platform to evaluate each test set. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were computed using a bootstrapping method to evaluate the algorithm performance and compared through different publicly available datasets. A usability test was performed to assess the integration into a clinical tool. Results: Three separate datasets, Messidor-2, APTOS, and a holdout set from Retisalud were evaluated. Mean sensitivity and specificity with 95% confidence intervals (CIs) reached for these three datasets were 0.901 (0.901-0.902) and 0.955 (0.955-0.956), 0.995 (0.995-0.995) and 0.821 (0.821-0.823), and 0.911 (0.907-0.912) and 0.880 (0.879-0.880), respectively. The usability test confirmed the successful integration of LuxIA into Discovery. Conclusions: Clinical data were used to train the deep-learning-based algorithm LuxIA to an expert-level performance. The whole process (image uploading and analysis) was integrated into the cloud-based platform Discovery, allowing more patients to have access to expert-level screening tools. Translational Relevance: Using the cloud-based LuxIA tool as part of a screening program may give diabetic patients greater access to specialist-level decisions, without the need for consultation.

Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network.

BACKGROUND: Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP), the recognition of negation modifiers represents an important challenge. A wide range of cNLP applications have been developed to detect the negation of medical entities in clinical free-text, however, effective solutions for languages other than English are scarce. This study aimed at developing a solution for negation recognition in Spanish EHRs based on a combination of a customized rule-based NegEx layer and a convolutional neural network (CNN). METHODS: Based on our previous experience in real world evidence (RWE) studies using information embedded in EHRs, negation recognition was simplified into a binary problem ('affirmative' vs. 'non-affirmative' class). For the NegEx layer, negation rules were obtained from a publicly available Spanish corpus and enriched with custom ones, whereby the CNN binary classifier was trained on EHRs annotated for clinical named entities (cNEs) and negation markers by medical doctors. RESULTS: The proposed negation recognition pipeline obtained precision, recall, and F1-score of 0.93, 0.94, and 0.94 for the 'affirmative' class, and 0.86, 0.84, and 0.85 for the 'non-affirmative' class, respectively. To validate the generalization capabilities of our methodology, we applied the negation recognition pipeline on EHRs (6,710 cNEs) from a different data source distribution than the training corpus and obtained consistent performance metrics for the 'affirmative' and 'non-affirmative' class (0.95, 0.97, and 0.96; and 0.90, 0.83, and 0.86 for precision, recall, and F1-score, respectively). Lastly, we evaluated the pipeline against two publicly available Spanish negation corpora, the IULA and NUBes, obtaining state-of-the-art metrics (1.00, 0.99, and 0.99; and 1.00, 0.93, and 0.96 for precision, recall, and F1-score, respectively). CONCLUSION: Negation recognition is a source of low precision in the retrieval of cNEs from EHRs' free-text. Combining a customized rule-based NegEx layer with a CNN binary classifier outperformed many other current approaches. RWE studies highly benefit from the correct recognition of negation as it reduces false positive detections of cNE which otherwise would undoubtedly reduce the credibility of cNLP systems.

Development of a predictive model of venous thromboembolism recurrence in anticoagulated cancer patients using machine learning.

INTRODUCTION: Patients with cancer and venous thromboembolism (VTE) show a high risk of VTE recurrence during anticoagulant treatment. This study aimed to develop a predictive model to assess the risk of VTE recurrence within 6 months at the moment of primary VTE diagnosis in these patients. MATERIALS AND METHODS: Using the EHRead® technology, based on Natural Language Processing (NLP) and machine learning (ML), the unstructured data in electronic health records from 9 Spanish hospitals between 2014 and 2018 were extracted. Both clinically- and ML-driven feature selection were performed to identify predictors for VTE recurrence. Logistic regression (LR), decision tree (DT), and random forest (RF) algorithms were used to train different prediction models, which were subsequently validated in a hold-out data set. RESULTS: A total of 16,407 anticoagulated cancer patients with diagnosis of VTE were identified (54.4 % male and median age 70). Deep vein thrombosis, pulmonary embolism and metastases were observed in 67.2 %, 26.6 %, and 47.7 % of the patients, respectively. During the study follow-up, 11.4 % of the patients developed a recurrent VTE, being more frequent in patients with lung cancer. Feature selection and model training based on ML identified primary pulmonary embolism, deep vein thrombosis, metastasis, adenocarcinoma, hemoglobin and serum creatinine levels, platelet and leukocyte count, family history of VTE, and patients' age as predictors of VTE recurrence within 6 months of VTE diagnosis. The LR model had an AUC-ROC (95 % CI) of 0.66 (0.61, 0.70), the DT of 0.69 (0.65, 0.72) and the RF of 0.68 (0.63, 0.72). CONCLUSIONS: This is the first ML-based predictive model designed to predict 6-months VTE recurrence in patients with cancer. These results hold great potential to assist clinicians to identify the high-risk patients and improve their clinical management.

Determinación de la incidencia de la fruta milagrosa (Synsepalum dulcificum) en la elaboración de chocolate y su efecto en las características físico-químicas y sensoriales / Determination of the incidence of miracle fruit (Synsepalum dulcificum) in chocolate making and its effect on physical-chemical and sensory characteristics

Introducción: Los endulzantes naturales con bajo nivel calórico son aquellos que sirven como reemplazo de la sacarosa y mantienen la característica dulce de los productos, sin embargo, la fruta milagrosa no se ha utilizado en aplicacione sagroindustriales, pese a que esta posee características que pueden sustituir a la azúcar blanca, debido a su poder de endulzante natural. Objetivo: Determinar la incidencia de la fruta milagrosa(Synsepalum dulcificum) y el efecto de dos variedades de cacao (Theobroma cacao) sobre las características físico-químicas y sensoriales en la elaboración de chocolate. Materiales y métodos: A partir de 3 concentraciones de fruta milagrosa y 2 tipos de cacao, empleando un diseño bifactorial A*B, se determinaron las características físico-químicas y sensoriales del chocolate.Resultados: Se encontró diferencia significativa (p<0,05)entre los tratamientos en estudio. La concentración del 40 %+ cacao nacional sitúo los mejores valores para las características físico químicas, en cuanto a los polifenoles totales el menor contenido se sitúo en concentraciones de 20 % tanto para el cacao Nacional y CCN51 con valores de 47.82 y 50.23respectivamente. Sin embargo, el T3 = 30 % + cacao Nacional, obtuvo la mejor valoración en los perfiles sensoriales (sabor, aroma y aceptabilidad)Conclusiones: El uso la fruta milagrosa (S. dulcificum) en lugar del azúcar convencional permite darle valor agregado ala barra de chocolate. Cabe destacar que las distintas concentraciones influyen sobre las características físico-químicas(Humedad, pH y grasa) y en el contenido de polifenoles totales. La inclusión del 30 % de fruta milagrosa como edulcorante natural en la formulación de chocolate permite obtener un producto con buenos atributos sensoriales y aceptables por el consumidor. (AU)

Introduction: Natural sweeteners with low caloric levelare those that serve as a replacement for sucrose and maintain the sweet characteristic of the products, however, miracle fruit has not been used in agroindustrial applications, despite the fact that this has characteristics that can replacewhite sugar, due to its power as a natural sweetener. Objective: To determine the incidence of miracle fruit(Synsepalum dulcificum) and the effect of two varieties of cocoa (Theobroma cacao) on the physicochemical and sensorycharacteristics in chocolate production. Materials and methods: From 3 concentrations of miraculous fruit and 2 types of cocoa, using a bifactorial A*B design, the physicochemical and sensory characteristics ofchocolate were determined.Results: Significant differences were found (p<0.05) between the treatments under study. The concentration of 40 %+ national cocoa had the best values for the physical-chemical characteristics, while the lowest total polyphenol contentwas found in concentrations of 20 % for both national cocoaand CCN51 with values of 47.82 and 50.23, respectively.However, T3 = 30 % + National cocoa, obtained the best valuation in sensory profiles (flavor, aroma and acceptability).Conclusions: The use of miracle fruit (S. dulcificum) instead of conventional sugar allows giving added value to thechocolate bar. It should be noted that the different concentrations influence the physicochemical characteristics (moisure, pH and fat) and the total polyphenol content. The inclusion of 30 % of miracle fruit as a natural sweetener in thechocolate formulation allows obtaining a product with goodsensory attributes and acceptable to the consumer.(AU)

Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence.

Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative motor neuron disease. Although an early diagnosis is crucial to provide adequate care and improve survival, patients with ALS experience a significant diagnostic delay. This study aimed to use real-world data to describe the clinical profile and timing between symptom onset, diagnosis, and relevant outcomes in ALS. Retrospective and multicenter study in 5 representative hospitals and Primary Care services in the SESCAM Healthcare Network (Castilla-La Mancha, Spain). Using Natural Language Processing (NLP), the clinical information in electronic health records of all patients with ALS was extracted between January 2014 and December 2018. From a source population of all individuals attended in the participating hospitals, 250 ALS patients were identified (61.6% male, mean age 64.7 years). Of these, 64% had spinal and 36% bulbar ALS. For most defining symptoms, including dyspnea, dysarthria, dysphagia and fasciculations, the overall diagnostic delay from symptom onset was 11 (6-18) months. Prior to diagnosis, only 38.8% of patients had visited the neurologist. In a median post-diagnosis follow-up of 25 months, 52% underwent gastrostomy, 64% non-invasive ventilation, 16.4% tracheostomy, and 87.6% riluzole treatment; these were more commonly reported (all Ps < 0.05) and showed greater probability of occurrence (all Ps < 0.03) in bulbar ALS. Our results highlight the diagnostic delay in ALS and revealed differences in the clinical characteristics and occurrence of major disease-specific events across ALS subtypes. NLP holds great promise for its application in the wider context of rare neurological diseases.

Coupling between the prelimbic cortex, nucleus reuniens, and hippocampus during NREM sleep remains stable under cognitive and homeostatic demands.

The interplay between the medial prefrontal cortex and hippocampus during non-rapid eye movement (NREM) sleep contributes to the consolidation of contextual memories. To assess the role of the thalamic nucleus reuniens (Nre) in this interaction, we investigated the coupling of neuro-oscillatory activities among prelimbic cortex, Nre, and hippocampus across sleep states and their role in the consolidation of contextual memories using multi-site electrophysiological recordings and optogenetic manipulations. We showed that ripples are time-locked to the Up state of cortical slow waves, the transition from UP to DOWN state in thalamic slow waves, the troughs of cortical spindles, and the peaks of thalamic spindles during spontaneous sleep, rebound sleep and sleep following a fear conditioning task. In addition, spiking activity in Nre increased before hippocampal ripples, and the phase-locking of hippocampal ripples and thalamic spindles during NREM sleep was stronger after acquisition of a fear memory. We showed that optogenetic inhibition of Nre neurons reduced phase-locking of ripples to cortical slow waves in the ventral hippocampus whilst their activation altered the preferred phase of ripples to slow waves in ventral and dorsal hippocampi. However, none of these optogenetic manipulations of Nre during sleep after acquisition of fear conditioning did alter sleep-dependent memory consolidation. Collectively, these results showed that Nre is central in modulating hippocampus and cortical rhythms during NREM sleep.

How can artificial intelligence optimize value-based contracting?

Efforts in the pharmaceutical market have been aimed at ensuring that the benefits obtained from the introduction of new therapies justify the associated costs. In recent years, drug payment models in healthcare have undergone a dramatic shift from focusing on volume (i.e., size of the target clinical population) to focusing on value (i.e., drug performance in real-world settings). In this context, value-based contracts (VBCs) were designed to align the payment of a drug to its clinical performance outside clinical trials by evaluating the effectiveness using real-word evidence (RWE). Despite their widespread implementation, different factors jeopardize the application of VBCs to most marketed drugs in a near future, including the need for easily measurable and relevant outcomes associated with clinical improvements, and access to a large patient population to assess said outcomes. Here, we argue that the extraction and analysis of massive amounts of RWE captured in patients' electronic health records (EHRs) will circumvent these issues and optimize negotiations in VBCs. Particularly, the use of Natural Language Processing (NLP) has proven successful in the analysis of structured and unstructured clinical information in EHRs in multicenter research studies. Thus, the application of NLP to analyze patient-centered information in EHRs in the context of innovative contracting can be utterly beneficial as it enables the real-time evaluation of treatment response and financial impact in real-world settings.

Single-shot optical surface profiling using extended depth of field 3D microscopy.

The measurement of three-dimensional samples at high speed is essential for many applications, either due to the requirement for measuring samples that change fast over time, or due to the requirement of reducing the scanning time, and therefore inspection cost, in industrial environments. Conventionally, the measurement of surface topographies at high resolution typically requires an axial scanning of the sample. We report the implementation of a technique able to reconstruct surface topographies at high resolution, only from the acquisition of a single camera shot, dropping the need to perform an axial scan. A system prototype is reported and assessed as an ultra-fast optical surface profiler. We propose robust calibration and operation methods and algorithms to reconstruct surface topographies of optically-rough samples, and compare the experimental results with a reference confocal optical profiler.

Beta-Adrenergic Antagonist Tolerance in Amyloid Cardiomyopathy.

Background: Beta-adrenergic antagonists or blockers (BB) are a cornerstone of cardiac therapy for multiple indications. However, BB are considered relatively contraindicated in amyloid cardiomyopathy due to poor tolerance. This intolerance is hypothesized to be due to concomitant neuropathy and significant restrictive cardiomyopathy. This study analyzes the incidence and characteristics of BB tolerance in patients with amyloid cardiomyopathy. Methods: Through a single-center retrospective chart review, patients with amyloid cardiomyopathy, confirmed by endomyocardial biopsy or technetium-99 pyrophosphate scan, were identified and clinical data was collected. Statistical methods included Chi-square test and two sample t-tests. Results: Of 135 cardiac amyloidosis patients, 27 patients (20.0%) had no BB use, 56 patients (41.5%) were current BB users, and 52 patients (38.5%) were prior BB users. The most frequent indications for BB use were heart failure, hypertension, coronary artery disease, and arrhythmia. The most common reason for stopping BB therapy was hypotension (62.8%) followed by fatigue, bradycardia, and orthostasis. Neurologic symptoms at the initial BB prescription or most recent evaluation were not significantly different between current and prior BB users. Their cardiovascular profiles were similar by ejection fraction, wall thickness, troponin I, and brain natriuretic peptide. There was no association for BB discontinuation based on amyloid subtype, sex, or race. Conclusion: The majority of patients with amyloid cardiomyopathy were prescribed BB, and over half of these patients still tolerated BB therapy. Current and prior BB users had similar profiles from a cardiovascular and neurologic perspective, with no association identified to predict BB discontinuation.

Sleep as a window on the sensorimotor foundations of the developing hippocampus.

The hippocampal formation plays established roles in learning, memory, and related cognitive functions. Recent findings also suggest that the hippocampus integrates sensory feedback from self-generated movements to modulate ongoing motor responses in a changing environment. Such findings support the view of Bland and Oddie (Behavioural Brain Research, 2001, 127, 119-136) that the hippocampus is a site of sensorimotor integration. In further support of this view, we review neurophysiological evidence in developing rats that hippocampal function is built on a sensorimotor foundation and that this foundation is especially evident early in development. Moreover, at those ages when the hippocampus is first establishing functional connectivity with distant sensory and motor structures, that connectivity is preferentially expressed during periods of active (or REM) sleep. These findings reinforce the notion that sleep, as the predominant state of early infancy, provides a critical context for sensorimotor development, including development of the hippocampus and its associated network.

Guía de práctica clínica para el tratamiento de la leucemia linfocítica crónica: guía para profesionales de la salud / Clinical practice guideline for the treatment of chronic lymphocytic leukemia: a guide for health care professionals

La leucemia linfocítica crónica (LLC) es una neoplasia caracterizada por la proliferación y acumulación clonal de células B maduras, que típicamente co-expresan los antígenos de superficie CD5 ­ CD23, dentro de la sangre, la médula ósea, los ganglios linfáticos, el bazo y otros tejidos . Esta patología es considerada el tipo de leucemia más común en personas adultas en países occidentales, y se considera una enfermedad de adultos mayores, con una mediana de edad al diagnóstico de 70 años .

Chronic lymphocytic leukemia (CLL) is a neoplasm characterized by the proliferation and clonal accumulation of mature B cells, which typically co-express the CD5 - CD23 surface antigens, within the blood, bone marrow, lymph nodes, spleen and other tissues. This pathology is considered the most common type of leukemia in adults in Western countries, and is considered a disease of older adults, with a median age at diagnosis of 70 years.

Guía de práctica clínica para el tratamiento de la leucemia linfocítica crónica / Clinical practice guideline for the treatment of chronic lymphocytic leukemia

La guía está dirigida al personal clínico asistencial especializado que brinda tratamiento a los pacientes con diagnóstico de LLC, en el contexto del SGSSS colombiano. Incluye a los siguientes profesionales potenciales: Hematólogos y Hematólogos-oncólogos. También está dirigida a los centros asistenciales que brindan cuidado a los pacientes con diagnóstico de LLC y a quienes toman decisiones administrativas, tanto en el medio hospitalario como en las aseguradoras, pagadores del gasto en la salud y en la generación de políticas de salud. Finalmente, las recomendaciones pueden ser de interés para pacientes con LLC, sus familiares y cuidadores. Se considera pertinente aclarar que la guía ofrecerá recomendaciones específicas frente a las preguntas definidas, y excede el alcance de esta, definir las competencias profesionales del equipo involucrado en el manejo de esta patología.

The guide is aimed at specialized clinical care personnel who provide treatment to patients diagnosed with CLL, in the context of the Colombian SGSSS. It includes the following potential professionals: hematologists and hematologist-oncologists. It is also addressed to health care centers that provide care to patients diagnosed with CLL and to administrative decision makers, both in the hospital environment and in the insurance companies, health care payers and health policy makers. Finally, the recommendations may be of interest to CLL patients, their families and caregivers. It is considered pertinent to clarify that the guide will offer specific recommendations in response to the questions defined, and it is beyond the scope of this guide to define the professional competencies of the team involved in the management of this pathology.

Assessment of hepatic function, perfusion and parenchyma attenuation with indocyanine green, ultrasound and computed tomography in a healthy rat model: Preliminary determination of baseline parameters in a healthy liver.

BACKGROUND: Defining reference intervals in experimental animal models plays a crucial role in pre-clinical studies. The hepatic parameters in healthy animals provide useful information about type and extension of hepatic damage. However, in the majority of the cases, to obtain them require an invasive techniques. Our study combines these determinations with dynamic functional test and imaging techniques to implement a non-invasive protocol for liver evaluation. The aim of the study was to determine reference intervals for hepatic function, perfusion and parenchyma attenuation with analytical and biochemical blood parameters, indocyanine green, ultrasound and computed tomography in six healthy SD rats. METHODS: Six males healthy SD rats were followed for 4 weeks. To determine hepatic function, perfusion and parenchyma attenuation analytical and biochemical blood parameters, indocyanine green, ultrasound and computed tomography were studied. Results were expressed as Means ± standard error of mean (SEM). The significance of differences was calculated by using student t-test, p < 0.05 was considered statistically significant. RESULTS: Indocyanine green clearance 5 and 10 minutes after its injection was 80.12% and 96.59%, respectively. Approximate rate of decay during the first 5 minutes after injection was 38% per minute. Hepatic perfusion evaluation with the high-frequency ultrasound was related to cardiovascular hemodynamic and renal perfusion. Portal area, hepatic artery resistance index, hepatic artery and portal peak systolic velocity and average between hepatic artery and porta was 3.41 ± 0.62 mm2, 0.57 ± 0.04 mm2/s, 693.24±102.53 mm2/s, 150.72 ± 17.80 mm2/s and 4.82 ± 0.96 mm2/s, respectively. Heart rate, cardiac output, left renal artery diammetre and renal blood flow were 331.01 ± 22.22 bpm, 75.58 ± 8.72 mL/min, 0.88 ± 0.04 mm2 and 13.65 ± 1.95 mm2/s. CT-scan hepatic average volume for each rat were 21.08±3.32, 17.57±2.76, 14.87±2.83 and 13.67±2.45 cm3 with an average attenuation coefficient of 113.51±18.08, 129,19±7.18, 141,47±1.95 y 151,67±1.2 HU. CONCLUSION: Indocyanine green and high-frequency ultrasound could be used in rats as a suitable marker of liver function. Computed tomography, through the study of raw data, help to characterize liver parenchyma, and could be a potential tool for early detection of liver parenchymal alterations and linear follow-up of patients. Further studies in rats with liver disease are necessary to verify the usefulness of these parameters.

Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology.

BACKGROUND: Clinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient's clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them. OBJECTIVE: Our objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured. METHODS: The proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called "EHRead Technology" (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard. RESULTS: The application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs. CONCLUSIONS: We showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.

The impact of COVID-19 on patients with asthma.

BACKGROUND: An association between the severity of coronavirus disease 2019 (COVID-19) and the presence of certain chronic conditions has been suggested. However, unlike influenza and other viruses, the disease burden of COVID-19 in patients with asthma has been less evident. OBJECTIVE: To understand the impact of COVID-19 in patients with asthma. METHODS: Using big-data analytics and artificial intelligence through the SAVANA Manager clinical platform, we analysed clinical data from patients with asthma from January 1 to May 10, 2020. RESULTS: Out of 71 182 patients with asthma, 1006 (1.41%) suffered from COVID-19. Compared to asthmatic individuals without COVID-19, patients with asthma and COVID-19 were significantly older (55 versus 42 years), predominantly female (66% versus 59%), smoked more frequently and had higher prevalence of hypertension, dyslipidaemias, diabetes and obesity. Allergy-related factors such as rhinitis and eczema were less common in asthmatic patients with COVID-19 (p<0.001). In addition, higher prevalence of these comorbidities was observed in patients with COVID-19 who required hospital admission. The use of inhaled corticosteroids (ICS) was lower in patients who required hospitalisation due to COVID-19, as compared to non-hospitalised patients (48.3% versus 61.5%; OR 0.58, 95% CI 0.44-0.77). Although patients treated with biologics (n=865; 1.21%) showed increased severity and more comorbidities at the ear, nose and throat level, COVID-19-related hospitalisations in these patients were relatively low (0.23%). CONCLUSION: Patients with asthma and COVID-19 were older and at increased risk due to comorbidity-related factors. ICS and biologics are generally safe and may be associated with a protective effect against severe COVID-19 infection.

Towards a symbiotic relationship between big data, artificial intelligence, and hospital pharmacy.

The digitalization of health and medicine and the growing availability of electronic health records (EHRs) has encouraged healthcare professionals and clinical researchers to adopt cutting-edge methodologies in the realms of artificial intelligence (AI) and big data analytics to exploit existing large medical databases. In Hospital and Health System pharmacies, the application of natural language processing (NLP) and machine learning to access and analyze the unstructured, free-text information captured in millions of EHRs (e.g., medication safety, patients' medication history, adverse drug reactions, interactions, medication errors, therapeutic outcomes, and pharmacokinetic consultations) may become an essential tool to improve patient care and perform real-time evaluations of the efficacy, safety, and comparative effectiveness of available drugs. This approach has an enormous potential to support share-risk agreements and guide decision-making in pharmacy and therapeutics (P&T) Committees.

Characteristics and Prognosis of COVID-19 in Patients with COPD.

Patients with Chronic Obstructive Pulmonary Disease (COPD) have a higher prevalence of coronary ischemia and other factors that put them at risk for COVID-19-related complications. We aimed to explore the impact of COVID-19 in a large population-based sample of patients with COPD in Castilla-La Mancha, Spain. We analyzed clinical data in electronic health records from 1 January to 10 May 2020 by using Natural Language Processing through the SAVANA Manager® clinical platform. Out of 31,633 COPD patients, 793 had a diagnosis of COVID-19. The proportion of patients with COVID-19 in the COPD population (2.51%; 95% CI 2.33-2.68) was significantly higher than in the general population aged >40 years (1.16%; 95% CI 1.14-1.18); p < 0.001. Compared with COPD-free individuals, COPD patients with COVID-19 showed significantly poorer disease prognosis, as evaluated by hospitalizations (31.1% vs. 39.8%: OR 1.57; 95% CI 1.14-1.18) and mortality (3.4% vs. 9.3%: OR 2.93; 95% CI 2.27-3.79). Patients with COPD and COVID-19 were significantly older (75 vs. 66 years), predominantly male (83% vs. 17%), smoked more frequently, and had more comorbidities than their non-COPD counterparts. Pneumonia was the most common diagnosis among COPD patients hospitalized due to COVID-19 (59%); 19% of patients showed pulmonary infiltrates suggestive of pneumonia and heart failure. Mortality in COPD patients with COVID-19 was associated with older age and prevalence of heart failure (p < 0.05). COPD patients with COVID-19 showed higher rates of hospitalization and mortality, mainly associated with pneumonia. This clinical profile is different from exacerbations caused by other respiratory viruses in the winter season.

Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 in hospital workers.

BACKGROUND: Hospital workers have been the most frequently and severely affected professional group during the COVID-19 pandemic, and have a big impact on transmission. In this context, innovative tools are required to measure the symptoms compatible with COVID-19, the spread of infection, and testing capabilities within hospitals in real time. OBJECTIVE: We aimed to develop and test an effective and user-friendly tool to identify and track symptoms compatible with COVID-19 in hospital workers. METHODS: We developed and pilot tested Hospital Epidemics Tracker (HEpiTracker), a newly designed app to track the spread of COVID-19 among hospital workers. Hospital staff in 9 hospital centers across 5 Spanish regions (Andalusia, Balearics, Catalonia, Galicia, and Madrid) were invited to download the app on their phones and to register their daily body temperature, COVID-19-compatible symptoms, and general health score, as well as any polymerase chain reaction and serological test results. RESULTS: A total of 477 hospital staff participated in the study between April 8 and June 2, 2020. Of note, both health-related (n=329) and non-health-related (n=148) professionals participated in the study; over two-thirds of participants (68.8%) were health workers (43.4% physicians and 25.4% nurses), while the proportion of non-health-related workers by center ranged from 40% to 85%. Most participants were female (n=323, 67.5%), with a mean age of 45.4 years (SD 10.6). Regarding smoking habits, 13.0% and 34.2% of participants were current or former smokers, respectively. The daily reporting of symptoms was highly variable across participating hospitals; although we observed a decline in adherence after an initial participation peak in some hospitals, other sites were characterized by low participation rates throughout the study period. CONCLUSIONS: HEpiTracker is an already available tool to monitor COVID-19 and other infectious diseases in hospital workers. This tool has already been tested in real conditions. HEpiTracker is available in Spanish, Portuguese, and English. It has the potential to become a customized asset to be used in future COVID-19 pandemic waves and other environments. TRIAL REGISTRATION: ClinicalTrials.gov NCT04326400; https://clinicaltrials.gov/ct2/show/NCT04326400.

Diet: A Specific Part of the Western Lifestyle Pack in the Asthma Epidemic.

The Western lifestyle is a complex concept that includes the diet as the main axis of different factors which contribute to a detrimental effect on health, lower life expectancy and low quality-of-life. This type of diet is characterized by being high in calories, mainly provided by saturated fats, and rich in sugars that can lead to changes in immune cells and their responsiveness, by different mechanisms that have yet to be totally clarified. Inflammatory processes are perpetuated through different pathways, in which adipose tissue is a major factor. High fat stores in overweight and obesity accumulate energy but the endocrine function is also producing and releasing different bioactive compounds, adipokines, known to be pro-inflammatory and which play an important role in the pathogenesis of asthma. This review therefore explores the latest evidence regarding the adverse effect of the Western diet on adipose tissue inflammation and its causative effect on the asthma epidemic.