Abstract

Internet-based interventions can be effective in the treatment of depression. However, internet-based interventions for older adults with depression are scarce and little is known about their feasibility and effectiveness.

Abstract
The estimation of the spectral absorption coefficient of biological tissues provides valuable information that can be used in diagnostic procedures. Such estimation can be made using direct calculations from invasive spectral measurements or though machine learning algorithms based on noninvasive or minimally invasive spectral measurements. Since in a noninvasive approach, the number of measurements is limited, an exploratory study to investigate the use of artificial generated data in machine learning techniques was performed to evaluate the spectral absorption coefficient of the brain cortex. Considering the spectral absorption coefficient that was calculated directly from invasive measurements as reference, the similar spectra that were estimated through different machine learning approaches were able to provide comparable information in terms of pigment, DNA and blood contents in the cortex. The best estimated results were obtained based only on the experimental measurements, but it was also observed that artificially generated spectra can be used in the estimations to increase accuracy, provided that a significant number of experimental spectra are available both to generate the complementary artificial spectra and to estimate the resulting absorption spectrum of the tissue. © 2022 Journal of Biomedical Photonics & Engineering. © J-BPE.

Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and motivate the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.

Abstract
Objective/background: To describe and characterize insomnia symptoms and nightmare profiles in Portugal during the first six weeks of a national lockdown due to COVID-19. Patients/methods: An open cohort study was conducted to collect information of the general population during the first wave of SARS-CoV-2/COVID-19 pandemic in Portugal. We analyzed data from 5011 participants (>= 16 years) who answered a weekly questionnaire about their well-being. Two questions about the frequency of insomnia and nightmares about COVID-19 were consecutively applied during six weeks (March-May 2020). Latent class analysis was conducted and different insomnia and nightmare profiles were identified. Associations between individual characteristics and both profiles were estimated using odds ratios (ORs) and 95% confidence intervals (CI). Results: Five insomnia (No insomnia, Stable-mild, Decreasing-moderate, Stable-severe, Increasing-severe) and three nightmares profiles (Stable-mild, Stable-moderate, Stable-severe) were identified. Being female, younger, perceiving their income as insufficient and feelings of fear towards COVID-19 were associated with higher odds of insomnia (Women: OR = 6.98 95%CI: 4.18-11.64; >= 60 years: OR = 0.30 95%CI: 0.18-0.53; Insufficient income: adjusted OR (aOR) = 8.413 95% CI: 3.93-16.84; Often presenting fear of being infected with SARS-CoV-2 infection: aOR = 9.13 95%CI: 6.36-13.11), and nightmares (Women: OR = 2.60 95%CI: 1.74-3.86; >= 60 years: OR = 0.45 95%CI: 0.28-0.74; Insufficient income: aOR = 2.60 95%CI: 1.20e5.20; Often/almost always presenting fear of being infected with SARS-CoV-2 infection: aOR = 6.62 95%CI: 5.01-8.74). Having a diagnosis of SARS-CoV-2 virus infection was associated with worse patterns of nightmares about the pandemic. Conclusions: Social and psychological individual factors are important characteristics to consider in the developmentof therapeutic strategies to supportpeoplewithsleep problems during the COVID-19 pandemic.

Abstract
Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients' health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.

Abstract
Backed by more powerful computational resources and optimized training routines, deep learning models have attained unprecedented performance in extracting information from chest X-ray data. Preceding other tasks, an automated abnormality detection stage can be useful to prioritize certain exams and enable a more efficient clinical workflow. However, the presence of image artifacts such as lettering often generates a harmful bias in the classifier, leading to an increase of false positive results. Consequently, healthcare would benefit from a system that selects the thoracic region of interest prior to deciding whether an image is possibly pathologic. The current work tackles this binary classification exercise using an attention-driven and spatially unsupervised Spatial Transformer Network (STN). The results indicate that the STN achieves similar results to using YOLO-cropped images, with fewer computational expenses and without the need for localization labels. More specifically, the system is able to distinguish between normal and abnormal CheXpert images with a mean AUC of 84.22%.