Forced expiratory volume in one second: A novel predictor of work disability in subjects with suspected obstructive sleep apnea

Whether the association of work disability with obstructive sleep apnea (OSA) is mainly due to the disease, i.e. the number and frequency of apneas-hypoapneas, or to coexisting factors independent from the disease, is not well-established. In this study, we aim to evaluate work ability in a group of subjects undergoing OSA workup and to identify the major contributors of impaired work ability. In a cross-sectional study, we enrolled 146 consecutive subjects who have been working for the last five years and referred to the sleep disorders outpatients’ clinic of the University-Hospital of Ferrara, Italy, with suspected OSA. After completing an interview in which the Work Ability Index (WAI) and the Epworth Sleepiness Scale (ESS) questionnaires were administered to assess work ability and excessive daytime sleepiness, respectively, subjects underwent overnight polysomnography for OSA diagnosing and spirometry. Of the 146 subjects, 140 (96%) completed the tests and questionnaires and, of these, 66 exhibited work disability (WAI < 37). OSA was diagnosed (apnea-hypopnea index 5) in 45 (68%) of the 66 subjects. After controlling for confounders, a lower level of forced expiratory volume at 1 second (FEV1), [odds ratio 0.97 (95% CI 0.95–1.00)], older age [1.09 (95% CI 1.03–1.15)], excessive daytime sleepiness [3.16 (95% CI 1.20–8.34)] and a worse quality of life [0.96 (95% CI 0.94–1.00)], but not OSA [1.04 (95% CI 0.41–2.62)], were associated with work disability. Patients with a higher number of diseases, in which OSA was not included, and a lower quality of life had an increased probability of absenteeism in the previous 12 months. In subjects with suspected OSA, FEV1 can be an important predictor of work disability

Antifibrotic treatment response and prognostic predictors in patients with idiopathic pulmonary fibrosis and exposed to occupational dust

BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) is an aggressive interstitial lung disease with an unpredictable course. Occupational dust exposure may contribute to IPF onset, but its impact on antifibrotic treatment and disease prognosis is still unknown. We evaluated clinical characteristics, respiratory function and prognostic predictors at diagnosis and at 12 month treatment of pirfenidone or nintedanib in IPF patients according to occupational dust exposure. METHODS: A total of 115 IPF patients were recruited. At diagnosis, we collected demographic, clinical characteristics, occupational history. Pulmonary function tests were performed and two prognostic indices [Gender, Age, Physiology (GAP) and Composite Physiologic Index (CPI)] calculated, both at diagnosis and after the 12 month treatment. The date of long-term oxygen therapy (LTOT) initiation was recorded during the entire follow-up (mean = 37.85, range 12-60 months). RESULTS: At baseline, patients exposed to occupational dust [≥ 10 years (n = 62)] showed a lower percentage of graduates (19.3% vs 54.7%; p = 0.04) and a higher percentage of asbestos exposure (46.8% vs 18.9%; p 0.002) than patients not exposed [< 10 years (n = 53)]. Both at diagnosis and after 12 months of antifibrotics, no significant differences for respiratory function and prognostic predictors were found. The multivariate analysis confirmed that occupational dust exposure did not affect neither FVC and DLCO after 12 month therapy nor the timing of LTOT initiation. CONCLUSION: Occupational dust exposure lasting 10 years or more does not seem to influence the therapeutic effects of antifibrotics and the prognostic predictors in patients with IPF

PReDUS: A Privacy Requirements Detector From User Stories

In the context of requirements engineering, stakeholders are often unaware of identifying and managing privacy and security requirements. The purpose of this paper is to present a tool, namely PReDUS, for the detection of privacy content from user stories. The core of the tool is the use of deep learning algorithms that exploit Natural Language Processing techniques and linguistic resources

Detecting privacy requirements from User Stories with NLP transfer learning models

Context: To provide privacy-aware software systems, it is crucial to consider privacy from the very beginning of the development. However, developers do not have the expertise and the knowledge required to embed the legal and social requirements for data protection into software systems. Objective: We present an approach to decrease privacy risks during agile software development by automatically detecting privacy-related information in the context of user story requirements, a prominent notation in agile Requirement Engineering (RE). Methods: The proposed approach combines Natural Language Processing (NLP) and linguistic resources with deep learning algorithms to identify privacy aspects into User Stories. NLP technologies are used to extract information regarding the semantic and syntactic structure of the text. This information is then processed by a pre-trained convolutional neural network, which paved the way for the implementation of a Transfer Learning technique. We evaluate the proposed approach by performing an empirical study with a dataset of 1680 user stories. Results: The experimental results show that deep learning algorithms allow to obtain better predictions than those achieved with conventional (shallow) machine learning methods. Moreover, the application of Transfer Learning allows to considerably improve the accuracy of the predictions, ca. 10%. Conclusions: Our study contributes to encourage software engineering researchers in considering the opportunities to automate privacy detection in the early phase of design, by also exploiting transfer learning models

A Web-based Architecture for tracking Multimedia using SCORM

In the context of a driven and collaborative on-line learning, multimedia resources, in particular videos, are more and more used. Due to their complex nature, there is the increasing need to manage the video contents in order to ensure a more fine-grained tracking on audio-video assets and obtain a continual feedback of student activities. In according to the ADL’s SCORM model, a video is usually assumed to be an atomic Learning Object: this assumption restricts the interaction between client and Learning Management System to a merely ON/OFF tracking process and limits the reusability of such type of contents. In our approach the video resources are considered as SCO or, more in details, as a SCO container; in this manner, each single component asset can be just a frame or a video segment in order to achieve the wanted tracking grain size. In according to the last hypothesis the main focus of the paper has been the design and implementation of an architecture for the tracking of video SCOs in web learning environmen