Conectar
by Gabriel Ott, Yannik Schaubelt, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff
Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.Complex, chronic or hard-to-heal wounds are a prevalent health problem worldwide, with significant physical, psychological and social consequences. This study aims to identify factors associated with the healing process of these wounds and develop a mobile application for wound care that incorporates these factors. A prospective multicentre cohort study was conducted in nine health units in Portugal, involving data collection through a mobile application by nurses from April to October 2022. The study followed 46 patients with 57 wounds for up to 5 weeks, conducting six evaluations. Healing time was the main outcome measure, analysed using the Mann–Whitney test and three Cox regression models to calculate risk ratios. The study sample comprised various wound types, with pressure ulcers being the most common (61.4%), followed by venous leg ulcers (17.5%) and diabetic foot ulcers (8.8%). Factors that were found to impair the wound healing process included chronic kidney disease (U = 13.50; p = 0.046), obesity (U = 18.0; p = 0.021), non-adherence to treatment (U = 1.0; p = 0.029) and interference of the wound with daily routines (U = 11.0; p = 0.028). Risk factors for delayed healing over time were identified as bone involvement (RR 3.91; p < 0.001), presence of odour (RR 3.36; p = 0.007), presence of neuropathy (RR 2.49; p = 0.002), use of anti-inflammatory drugs (RR 2.45; p = 0.011), stalled wound (RR 2.26; p = 0.022), greater width (RR 2.03; p = 0.002), greater depth (RR 1.72; p = 0.036) and a high score on the healing scale (RR 1.21; p = 0.001). Integrating the identified risk factors for delayed healing into the assessment of patients and incorporating them into a mobile application can enhance decision-making in wound care.
by Germán Mejía-Salgado, Paula Tatiana Muñoz-Vargas, Carlos Cifuentes-González, Gabriela Flórez-Esparza, Rebeca Paquentín-Jiménez, Miguel Ángel Castro-Monreal, Naomi Medina-Galindo, Gilma Norella Hernández-Herrera, Luz Elena Concha-del-Río, Alejandra de-la-Torre
PurposeTo establish the effects of anterior chamber inflammation (ACI) on the corneal endothelium parameters and central corneal thickness (CCT).
MethodsWe conducted a comprehensive literature review using medical databases (PubMed, EMBASE, VHL, and medRxiv) on March 8, 2023, for studies that included patients with ACI who had undergone specular microscopy or pachymetry. Case series with >10 patients, cross-sectional, case-control, and cohort studies were included. The risk of bias was assessed using CLARITY tools and validated scales such as those by Hassan Murad et al. and Hoy et al. A narrative synthesis and a quantitative standardized mean difference meta-analysis, I2 heterogeneity assessment, and publication bias tests were conducted. The study was registered in PROSPERO (CRD42023420148) and approved by the Universidad del Rosario ethical committee (DVO005 2277- CV1712).
ResultsThirty-four studies, encompassing 1,388 eyes with ACI, were included. Compared with healthy controls, overall, ACI eyes show significant mean differences in endothelial parameters (endothelial cell density (ECD), coefficient of variation (CV), and hexagonality (HEX)) (P Conclusion
ACI leads to significant alterations in endothelial parameters and CCT. The primary contributors to these changes are increased IOP, uveitis duration, and intraocular surgeries. Further studies are needed to explore the impact of ACI etiology on the endothelium, potential biases in IOP measurements during acute ACI episodes, and the potential necessity for monitoring the endothelial parameters and CCT in patients with chronic ACI.
The confidence of young people diagnosed with psychosis is often low. Positive self-beliefs may be few and negative self-beliefs many. A sense of defeat and failure is common. Young people often withdraw from many aspects of everyday life. Psychological well-being is lowered. Psychological techniques can improve self-confidence, but a shortage of therapists means that very few patients ever receive such help. Virtual reality (VR) offers a potential route out of this impasse. By including a virtual coach, treatment can be automated. As such, delivery of effective therapy is no longer reliant on the availability of therapists. With young people with lived experience, we have developed a staff-assisted automated VR therapy to improve positive self-beliefs (Phoenix). The treatment is based on established cognitive behavioural therapy and positive psychology techniques. A case series indicates that this approach may lead to large improvements in positive self-beliefs and psychological well-being. We now aim to conduct the first randomised controlled evaluation of Phoenix VR.
80 patients with psychosis, aged between 16 and 30 years old and with low levels of positive self-beliefs, will be recruited from National Health Service (NHS) secondary care services. They will be randomised (1:1) to the Phoenix VR self-confidence therapy added to treatment as usual or treatment as usual. Assessments will be conducted at 0, 6 (post-treatment) and 12 weeks by a researcher blind to allocation. The primary outcome is positive self-beliefs at 6 weeks rated with the Oxford Positive Self Scale. The secondary outcomes are psychiatric symptoms, activity levels and quality of life. All main analyses will be intention to treat.
The trial has received ethical approval from the NHS Health Research Authority (22/LO/0273). A key output will be a high-quality VR treatment for patients to improve self-confidence and psychological well-being.
FreshRSS 