The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer.

Liquid biopsy is an emerging technology with a potential role in the screening and early detection of lung cancer. Several liquid biopsy-derived biomarkers have been identified and are currently under ongoing investigation. In this article, we review the available data on the use of circulating biomarkers for the early detection of lung cancer, focusing on the circulating tumor cells, circulating cell-free DNA, circulating micro-RNAs, tumor-derived exosomes, and tumor-educated platelets, providing an overview of future potential applicability in the clinical practice.

Refinement of Animal Model of Colorectal Carcinogenesis through the Definition of Novel Humane Endpoints.

This study aimed to define appropriate humane endpoints (HEs) for an animal model of colorectal carcinogenesis (CRC). Twenty-nine male Wistar rats were divided into two control groups (CTRL1 and CTRL2) injected with ethylenediamine tetraacetic acid (EDTA)-saline solutions and two induced groups (CRC1 and CRC2) injected with 1,2-dimethylhydrazine (DMH) for seven weeks. A score sheet with 14 biological parameters was used to assess animal welfare.

Effect of Low-Doses of Gamma Radiation on Electric Arc-Induced Long Period Fiber Gratings.

This work presents an experimental study on the effects of gamma radiation on Long Period Fiber Gratings (LPFGs) in a low-dose test campaign to evaluate their eventual degradation. The study was carried out with standard single-mode fibers where the grating was inscribed using the Electric-Arc Discharge (EAD) technique. Before the gamma campaign, a detailed optical characterization was performed with repeatability tests to verify the accuracy of the setup and the associated error sources.

Biosensors for Biogenic Amines: A Review.

Biogenic amines (BAs) are well-known biomolecules, mostly for their toxic and carcinogenic effects. Commonly, they are used as an indicator of quality preservation in food and beverages since their presence in higher concentrations is associated with poor quality. With respect to BA's metabolic pathways, time plays a crucial factor in their formation.

Advances in Plasmonic Sensing at the NIR-A Review.

Surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) are among the most common and powerful label-free refractive index-based biosensing techniques available nowadays. Focusing on LSPR sensors, their performance is highly dependent on the size, shape, and nature of the nanomaterial employed. Indeed, the tailoring of those parameters allows the development of LSPR sensors with a tunable wavelength range between the ultra-violet (UV) and near infra-red (NIR).

Acoustic Optical Fiber Sensor Based on Graphene Oxide Membrane.

A Fabry-Pérot acoustic sensor based on a graphene oxide membrane was developed with the aim to achieve a faster and simpler fabrication procedure when compared to similar graphene-based acoustic sensors. In addition, the proposed sensor was fabricated using methods that reduce chemical hazards and environmental impacts. The developed sensor, with an optical cavity of around 246 µm, showed a constant reflected signal amplitude of 6.

Explainability Metrics of Deep Convolutional Networks for Photoplethysmography Quality Assessment.

Photoplethysmography (PPG) is a noninvasive way to monitor various aspects of the circulatory system, and is becoming more and more widespread in biomedical processing. Recently, deep learning methods for analyzing PPG have also become prevalent, achieving state of the art results on heart rate estimation, atrial fibrillation detection, and motion artifact identification. Consequently, a need for interpretable deep learning has arisen within the field of biomedical signal processing.

LNDb challenge on automatic lung cancer patient management.

Lung cancer is the deadliest type of cancer worldwide and late detection is the major factor for the low survival rate of patients. Low dose computed tomography has been suggested as a potential screening tool but manual screening is costly and time-consuming. This has fuelled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules.

The Entropy Universe.

About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents , which aims to review the many variants of entropies applied to time-series.

More than Ownership: The Importance of Relationships with Companion Dogs for the Psychological Adjustment of Fibromyalgia Patients.

Objective: This study explored the role of companion dogs for psychological adjustment to pain in patients with fibromyalgia with different levels of social support. It also considered the potential moderating effects of the quality of the owner-dog relationship and the use of interactions with dogs as a coping strategy.

Setting: A cross-sectional approach was followed using an online questionnaire.

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images.

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis.

Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review.

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions.

The Food Bank of Madrid: A Linear Model for Optimal Nutrition.

This work proposes a mathematical linear programming model that addresses the food provisioning problem of the food bank of Madrid. It aims to determine the most appropriate weekly decisions to meet the macro-nutritional requirements of the beneficiaries of this social service, by minimizing the total cost considering third-party donations. The model has been applied to a realistic case study considering a sociological structure of beneficiaries categorized by age and gender and representing the first decile of incomes of the Spanish population.

Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions.

Objectives: This study evaluated the usefulness of artificial intelligence (AI) algorithms as tools in improving the accuracy of histologic classification of breast tissue.

Methods: Overall, 100 microscopic photographs (test A) and 152 regions of interest in whole-slide images (test B) of breast tissue were classified into 4 classes: normal, benign, carcinoma in situ (CIS), and invasive carcinoma. The accuracy of 4 pathologists and 3 pathology residents were evaluated without and with the assistance of algorithms.

Improving adherence to an online intervention for low mood with a virtual coach: study protocol of a pilot randomized controlled trial.

Background: Internet-based cognitive-behavioral therapy (iCBT) is more effective when it is guided by human support than when it is unguided. This may be attributable to higher adherence rates that result from a positive effect of the accompanying support on motivation and on engagement with the intervention. This protocol presents the design of a pilot randomized controlled trial that aims to start bridging the gap between guided and unguided interventions.

Microaneurysm detection in color eye fundus images for diabetic retinopathy screening.

Diabetic retinopathy (DR) is a diabetes complication, which in extreme situations may lead to blindness. Since the first stages are often asymptomatic, regular eye examinations are required for an early diagnosis. As microaneurysms (MAs) are one of the first signs of DR, several automated methods have been proposed for their detection in order to reduce the ophthalmologists' workload.

Discrimination of Benign and Malignant Lesions in Canine Mammary Tissue Samples Using Raman Spectroscopy: A Pilot Study.

Breast cancer is a health problem that affects individual life quality and the family system. It is the most frequent type of cancer in women, but men are also affected. As an integrative approach, comparative oncology offers an opportunity to learn more about natural cancers in different species.

Gender Differential Transcriptome in Gastric and Thyroid Cancers.

Cancer has an important and considerable gender differential susceptibility confirmed by several epidemiological studies. Gastric (GC) and thyroid cancer (TC) are examples of malignancies with a higher incidence in males and females, respectively. Beyond environmental predisposing factors, it is expected that gender-specific gene deregulation contributes to this differential incidence.

Cross-cultural adaptation and validation of the European Portuguese version of the heartland forgiveness scale.

Background: Forgiveness is linked with well-being, and social and health research has focused on the role and aspects of forgiveness that has been recently suggested as a phenomenon of public health importance. The Heartland Forgiveness Scale (HFS) was developed gathering three subscales to assess the forgiveness of others, forgiveness of self, and forgiveness of situation. The present study aimed to adapt the HFS into European Portuguese, and investigate its reliability and validity.

Using autoencoders as a weight initialization method on deep neural networks for disease detection.

Background: As of today, cancer is still one of the most prevalent and high-mortality diseases, summing more than 9 million deaths in 2018. This has motivated researchers to study the application of machine learning-based solutions for cancer detection to accelerate its diagnosis and help its prevention. Among several approaches, one is to automatically classify tumor samples through their gene expression analysis.

Exploring the Impact of COVID-19 on the Sustainability of Health Critical Care Systems in South America.

The coronavirus disease 2019 (COVID-19) pandemic has created strong pressure on national health critical care systems. After its initial impact in Asia, the highest case growth is now in the Americas. The South American countries face a strong challenge due to the vulnerabilities of their health systems and the fragile socio-economic conditions of their population.

Comparison of upper limb kinematics in two activities of daily living with different handling requirements.

Introduction: Recently, kinematic analysis of the drinking task (DRINK) has been recommended to assess the quality of upper limb (UL) movement after stroke, but the accomplishment of this task may become difficult for poststroke patients with hand impairment. Therefore, it is necessary to study ADLs that involve a simpler interaction with a daily life target, such as the turning on a light task (LIGHT). As the knowledge of movement performed by healthy adults becomes essential to assess the quality of movement of poststroke patients, the main goal of this article was to compare the kinematic strategies used by healthy adults in LIGHT with those that are used in DRINK.

DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images.

Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and follow up of patient, but the screening process can be tiresome and prone to errors. Deep learning approaches have shown promising performance as computer-aided diagnosis (CAD) systems, but their black-box behaviour hinders clinical application. We propose DR|GRADUATE, a novel deep learning-based DR grading CAD system that supports its decision by providing a medically interpretable explanation and an estimation of how uncertain that prediction is, allowing the ophthalmologist to measure how much that decision should be trusted.

Optical Fiber Temperature Sensors and Their Biomedical Applications.

The use of sensors in the real world is on the rise, providing information on medical diagnostics for healthcare and improving quality of life. Optical fiber sensors, as a result of their unique properties (small dimensions, capability of multiplexing, chemical inertness, and immunity to electromagnetic fields) have found wide applications, ranging from structural health monitoring to biomedical and point-of-care instrumentation. Furthermore, these sensors usually have good linearity, rapid response for real-time monitoring, and high sensitivity to external perturbations.