Treatment Adherence and Health Status of Patients With COPD Under Treatment With Salmeterol/Fluticasone via the Elpenhaler® Device: The AHEAD Study.

Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous progressive lung condition characterized by long-term respiratory symptoms and airflow limitation. Appropriate bronchodilation is the cornerstone of COPD treatment, leading to better health status as well as benefits in prognosis and mortality.

Methods: In the current open, noninterventional, observational study, 716 patients diagnosed with COPD of variable severity were administered a fixed-dose combination (FDC) of fluticasone propionate and salmeterol (500 + 50 mcg) through the Elpenhaler® device.

CDK4/6 and aromatase inhibitors as first-line treatment in metastatic high-grade neuroendocrine carcinoma of the breast: A case report.

Key Clinical Message: There is no consensus regarding the therapeutic approach of breast neuroendocrine carcinomas (NECs). As most NECs are hormone receptor positive and HER-2 negative, we suggest that endocrine-based strategies may play a leading role. Here, we report a new treatment strategy by incorporating CDK4/6 inhibitors in the therapeutic armamentarium.

What is the real incidence of right ventricular affection in patients with acute pulmonary embolism?

Background: Echocardiographic markers of right ventricular dysfunction or pressure overload (RV) have been used in risk assessment of patients with acute pulmonary embolism (APE). Nevertheless, the role of echocardiography in these patients is incompletely determined. We evaluated the right ventricular function using 'non-conventional' markers of RV in patients with APE.

Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases.

Interstitial lung diseases (ILDs) comprise a rather heterogeneous group of diseases varying in pathophysiology, presentation, epidemiology, diagnosis, treatment and prognosis. Even though they have been recognized for several years, there are still areas of research debate. In the majority of ILDs, imaging modalities and especially high-resolution Computed Tomography (CT) scans have been the cornerstone in patient diagnostic approach and follow-up.

Recurrent bilateral lung infiltrates in a patient with ulcerative colitis.

https://bit.ly/3AnNJNN.

Effectiveness and quality of life in asthmatic patients treated with budesonide/formoterol via Elpenhaler® device in primary care. The "SKIRON" real world study.

Inhaled corticosteroid (ICS)/long-acting β agonist (LABA) combination therapy is used for the effective control of asthma. Aim of this study was to collect data on the effectiveness, safety, quality of life, and patient satisfaction from a fixed dose combination of budesonide/formoterol administered with the Elpenhaler device following 3-months' treatment. A 3-month real-life, multicentre, one-arm, prospective observational study (SKIRON study-NCT03055793) was conducted, using the following questionnaires: Asthma Control Questionnaire (ACQ-6) for asthma control assessment, MiniAQLQ questionnaire for QoL assessment, and Feeling of Satisfaction with Inhaler questionnaire (FSI-10) for patients' satisfaction with the inhaler device.

Adherence and quality of life assessment in patients with asthma treatment with budesonide/formoterol via the Elpenhaler device: the COMPLETE study.

Background: Asthma is a chronic inflammatory disease of the airways that causes recurring episodes of wheezing, breathlessness, chest tightness and coughing. Inhaled drugs on a daily basis are the cornerstone of asthma treatment, therefore, patient adherence is very important.

Methods: We performed a multicenter, open, non-interventional, observational, prospective study of 716 adult patients diagnosed with asthma receiving FDC (Fixed-dose combination) budesonide/formoterol via the Elpenhaler device.

The Diagnostic Role of Uric Acid to Creatinine Ratio for the Identification of Patients with Adverse Pulmonary Embolism Outcomes.

Background: Uric acid (UA) is the final product of purine metabolism and a marker of oxidative stress that may be involved in the pathophysiology of cardiovascular and thromboembolic disease. The aim of the current study is to investigate the potential value of UA to creatinine ratio (UA/Cr) as a diagnostic tool for the outcome of patients admitted with acute pulmonary embolism (PE) and the correlations with other parameters.

Methods: We evaluated 116 patients who were admitted for PE in a respiratory medicine department.

Review of Artificial Intelligence Techniques in Chronic Obstructive Lung Disease.

Background: Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI.

Objective: The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects.

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.

Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years.

Reduction in Hospitalizations for Respiratory Diseases during the First COVID-19 Wave in Greece.

Introduction: During the first COVID-19 wave, a considerable decline in hospital admissions was observed worldwide.

Aim: This retrospective cohort study aimed to assess if there were any changes in the number of patients hospitalized for respiratory diseases in Greece during the first CO-VID-19 wave.

Methods: In the present study, we evaluated respiratory disease hospitalization rates across 9 tertiary hospitals in Greece during the study period (March-April 2020) and the corresponding period of the 2 previous years (2018-2019) that served as the control periods.

Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature.

Artificial intelligence (AI) when coupled with large amounts of well characterised data can yield models that are expected to facilitate clinical practice and contribute to the delivery of better care, especially in chronic diseases such as asthma.The purpose of this paper is to review the utilisation of AI techniques in all aspects of asthma research, from asthma screening and diagnosis, to patient classification and the overall asthma management and treatment, in order to identify trends, draw conclusions and discover potential gaps in the literature.We conducted a systematic review of the literature using PubMed and DBLP from 1988 up to 2019, yielding 425 articles; after removing duplicate and irrelevant articles, 98 were further selected for detailed review.

Validation of the portable Bluetooth® Air Next spirometer in patients with different respiratory diseases.

Background: Chronic respiratory diseases constitute a considerable part in the practice of pulmonologists and primary care physicians; spirometry is integral for the diagnosis and monitoring of these diseases, yet remains underutilized. The Air Next spirometer (NuvoAir, Sweden) is a novel ultra-portable device that performs spirometric measurements connected to a smartphone or tablet via Bluetooth®.

Methods: The objective of this study was to assess the accuracy and validity of these measurements by comparing them with the ones obtained with a conventional desktop spirometer.

Prediction of oral cancer recurrence using dynamic Bayesian networks.

We propose a methodology for predicting oral cancer recurrence using Dynamic Bayesian Networks. The methodology takes into consideration time series gene expression data collected at the follow-up study of patients that had or had not suffered a disease relapse. Based on that knowledge, our aim is to infer the corresponding dynamic Bayesian networks and subsequently conjecture about the causal relationships among genes within the same time-slice and between consecutive time-slices.

Prediction of time dependent survival in HF patients after VAD implantation using pre- and post-operative data.

Heart failure is one of the most common diseases worldwide. In recent years, Ventricular Assist Devices (VADs) have become a valuable option for patients with advanced HF. Although it has been shown that VADs improve patient survival rates, several complications persist during left VAD (LVAD) support.

A Bayesian Network-based approach for discovering oral cancer candidate biomarkers.

Oral cancer can arise in the head and neck region. Due to the aggressive nature of the disease, which often leads to poor prognosis, Oral Squamous Cell Carcinoma (OSCC) constitutes the 8(th) most common neoplasms in humans. In the present work we formulate gene interaction network from oral cancer genomic data using Dynamic Bayesian Networks (DBNs).

Machine learning applications in cancer prognosis and prediction.

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods.

A multiscale approach for modeling atherosclerosis progression.

Progression of atherosclerotic process constitutes a serious and quite common condition due to accumulation of fatty materials in the arterial wall, consequently posing serious cardiovascular complications. In this paper, we assemble and analyze a multitude of heterogeneous data in order to model the progression of atherosclerosis (ATS) in coronary vessels. The patient's medical record, biochemical analytes, monocyte information, adhesion molecules, and therapy-related data comprise the input for the subsequent analysis.

Prediction of coronary atherosclerosis progression using dynamic Bayesian networks.

In this paper we propose a methodology for predicting the progression of atherosclerosis in coronary arteries using dynamic Bayesian networks. The methodology takes into account patient data collected at the baseline study and the same data collected in the follow-up study. Our aim is to analyze all the different sources of information (Demographic, Clinical, Biochemical profile, Inflammatory markers, Treatment characteristics) in order to predict possible manifestations of the disease; subsequently, our purpose is twofold: i) to identify the key factors that dictate the progression of atherosclerosis and ii) based on these factors to build a model which is able to predict the progression of atherosclerosis for a specific patient, providing at the same time information about the underlying mechanism of the disease.