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Integrating factors associated with complex wound healing into a mobile application: Findings from a cohort study

Abstract

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.

A comparative analysis of converters of tabular data into image for the classification of Arboviruses using Convolutional Neural Networks

by Leonides Medeiros Neto, Sebastião Rogerio da Silva Neto, Patricia Takako Endo

Tabular data is commonly used in business and literature and can be analyzed using tree-based Machine Learning (ML) algorithms to extract meaningful information. Deep Learning (DL) excels in data such as image, sound, and text, but it is less frequently utilized with tabular data. However, it is possible to use tools to convert tabular data into images for use with Convolutional Neural Networks (CNNs) which are powerful DL models for image classification. The goal of this work is to compare the performance of converters for tabular data into images, select the best one, optimize a CNN using random search, and compare it with an optimized ML algorithm, the XGBoost. Results show that even a basic CNN, with only 1 convolutional layer, can reach comparable metrics to the XGBoost, which was trained on the original tabular data and optimized with grid search and feature selection. However, further optimization of the CNN with random search did not significantly improve its performance.

Laser and radiofrequency for treating genitourinary syndrome of menopause in breast cancer survivors: a systematic review and meta-analysis protocol

Por: Serquiz · N. · Sarmento · A. C. A. · Almeida · N. R. · Nobre · M. L. · Medeiros · K. S. · Oliveira · R. d. · Costa · A. P. F. · Goncalves · A. K.
Introduction

Breast cancer survivors (BCSs) experience more severe symptoms of genitourinary syndrome of menopause (GSM) than healthy postmenopausal women. As hormonal therapy with oestrogen should be avoided in BCSs, finding an effective and safe therapy to address vaginal symptoms and sexual dysfunction is urgently needed. Physical methods may be promising alternatives for the specificities of this group of women. This review aims to evaluate the efficacy and safety of physical methods (laser and radiofrequency) for treating GSM in BCSs.

Methods and analysis

The PubMed, Embase, Web of Science, SciELO, LILACS, Scopus, Cochrane Central Register of Controlled Trials and ClinicalTrials.gov databases will be searched. A search strategy was developed to retrieve clinical trials that evaluate the efficacy and safety of any physical method (laser or radiofrequency) used for GSM in BCSs. No date or language restrictions will be imposed. Two authors will independently select studies by title, abstract and full text to meet the inclusion criteria. Data will be extracted, and the risk of bias will be evaluated using the Cochrane risk-of-bias tool (RoB 2). Review Manager 5.4.1 will be used for data synthesis. The Grading of Recommendations, Assessment, Development and Evaluation will be used to assess the strength of the evidence.

Ethics and dissemination

This study reviews the published data; thus, obtaining ethical approval is unnecessary. The findings of this systematic review will be published in a peer-reviewed journal.

PROSPERO registration number

CRD42023387680.

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