Thoracic ultrasound use in hospitalized and ambulatory adult patients: a quantitative picture

Abstract Introduction and objectives Thoracic ultrasound (TUS) has been established as a powerful diagnostic and monitoring tool in the Intensive Care Unit (ICU). However, studies outside the critical care setting are scarce. The aim of this study was to investigate the value of TUS for hospitalized or ambulatory community patients. Materials and methods This was a retrospective study conducted from 2016 to 2020 in the TUS clinic at Heraklion University Hospital. TUS examination was performed using a standard ultrasound machine (EUB HITACHI 8500), and a high-frequency microconvex probe (5–8 MHz). Patients had been referred by their primary physician to address a range of different questions. The various respiratory system entities were characterised according to internationally established criteria. Results 762 TUS studies were performed on 526 patients due to underlying malignancy (n = 376), unexplained symptoms/signs (n = 53), pregnancy related issues (n = 42), evaluation of abnormal findings in X-ray (n = 165), recent surgery/trauma (n = 23), recent onset respiratory failure (n = 12), acute respiratory infection (n = 66) and underlying non-malignant disease (n = 25). Pleural effusion was the commonest pathologic entity (n = 610), followed by consolidation (n = 269), diaphragmatic dysfunction/paradox (n = 174) and interstitial syndrome (n = 53). Discrepancies between chest X-ray and ultrasonographic findings were demonstrated in 96 cases. The TUS findings guided invasive therapeutic management in 448 cases and non-invasive management in 43 cases, while follow-up monitoring was decided in 271 cases. Conclusions This study showed that TUS can identify the most common respiratory pathologic entities encountered in hospitalized and community ambulatory patients, and is especially useful in guiding the decision making process in a diverse group of patients

Human herpesvirus-8 seropositivity and clinical correlations in HIV-1-positive and highly exposed, persistently HIV-seronegative individuals in Greece

The prevalence of anti-human herpesvirus 8 (HHV- 8) antibodies was retrospectively assessed in a cohort of 248 consecutive HIV-1-positive patients followed up in an academic unit in Greece during a 14-year period and in 46 highly exposed, persistently HIV-seronegative (HEPS) individuals. The impact of the initial anti-HHV-8 status on tumorgenesis and mortality was studied. The first available serum sample from the department’s pool was tested. Demographics and data regarding history of sexually transmitted diseases, Hepatitis B surface antigen (HbsAg) and hepatitis C (HCV) status were collected. Patients who developed either HHV-8-related or non-HHV-8-related neoplasms during long-term follow-up were also identified. Forty-eight percent of the HIV-1-positive patients and 56% of the HEPS subjects were found anti-HHV-8-positive. No difference was observed regarding the development of HHV-8-related or non-HHV-8-related neoplasia and mortality on grounds of initial anti-HHV- 8 status. Mortality was positively associated with the presence of HBsAg. HCV infection showed a trend to be more common in anti-HHV-8-positive patients. In summary, the seroprevalence of HHV-8 among HIV-1- positive patients is higher than the one reported in the Western world. The initial anti-HHV-8 status is not a prognostic factor in HIV-1- positive individuals. The high seroprevalence in HEPS individuals possibly reflects their risk- prone lifestyle. HbsAg-positive status is a long- term negative prognostic factor in HIV infection

Automated facial video-based recognition of depression and anxiety symptom severity: cross-corpus validation

International audienceThere is a growing interest in computational approaches permitting accurate detection of nonverbal signs of depression and related symptoms (i.e., anxiety and distress) that may serve as minimally intrusive means of monitoring illness progression. The aim of the present work was to develop a methodology for detecting such signs and to evaluate its generalizability and clinical specificity for detecting signs of depression and anxiety. Our approach focused on dynamic descriptors of facial expressions, employing motion history image, combined with appearance-based feature extraction algorithms (local binary patterns, histogram of oriented gradients), and visual geometry group features derived using deep learning networks through transfer learning. The relative performance of various alternative feature description and extraction techniques was first evaluated on a novel dataset comprising patients with a clinical diagnosis of depression (n=20\) and healthy volunteers (n=45). Among various schemes involving depression measures as outcomes, best performance was obtained for continuous assessment of depression severity (as opposed to binary classification of patients and healthy volunteers). Comparable performance was achieved on a benchmark dataset, the audio/visual emotion challenge (AVEC'14). Regarding clinical specificity, results indicated that the proposed methodology was more accurate in detecting visual signs associated with self-reported anxiety symptoms. Findings are discussed in relation to clinical and technical limitations and future improvements

Semeoticons -Reading the face code of cardio-metabolic risk

What if you could discover your health status by looking at yourself in the mirror? Since November 2013, the EU FP7 Project SEMEOTICONS is working to make this possible. The Project is building a multi-sensory device, having the form of a conventional mirror, able to read the semeiotic code of the face and detect possible evidence of the onset of cardio-metabolic diseases. The device, called Wize Mirror, integrates unobtrusive imaging sensors used to capture videos, images and 3D scans of the face. These are processed to assess the risk of a cardio-metabolic disease and thereby suggest possible strategies to prevent its onset

Proof explanation for the semantic web using defeasible logic

In this work we present the design and implementation of a system for proof explanation in the Semantic Web, based on defeasible reasoning. Trust is a vital feature for Semantic Web. If users (humans and agents) are to use and integrate system answers, they must trust them. Thus, systems should be able to explain their actions, sources, and beliefs. Our system produces automatically proof explanations using a popular logic programming system (XSB), by interpreting the output from the proof's trace and converting it into a meaningful representation. It also supports an XML representation (a RuleML language extension) for agent communication, which is a common scenario in the Semantic Web. The system in essence implements a proof layer for nonmonotonic rules on the Semantic Web