Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.

This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.

Enhancing Cannabinoid Bioavailability in Pain Management: The Role of Cyclodextrins.

Chronic pain (CP), including pain related to cancer, affects approximately 2 billion people worldwide, significantly diminishing quality of life and imposing socio-economic burdens. Current treatments often provide limited relief and may cause adverse effects, demanding more effective alternatives. Natural compounds from L.

Pilot deployment of a cloud-based universal medical image repository in a large public health system: A protocol study.

This paper outlines the protocol for the deployment of a cloud-based universal medical image repository system. The proposal aims not only at the deployment but also at the automatic expansion of the platform, incorporating Artificial Intelligence (AI) for the analysis of medical image examinations. The methodology encompasses efficient data management through a universal database, along with the deployment of various AI models designed to assist in diagnostic decision-making.

Artificial intelligence-based pulmonary embolism classification: Development and validation using real-world data.

This paper presents an artificial intelligence-based classification model for the detection of pulmonary embolism in computed tomography angiography. The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels. The training process consists of two stages: first using a convolutional neural network InceptionResNet V2 and then a recurrent neural network long short-term memory model.

Wearable Near-Eye Tracking Technologies for Health: A Review.

With the rapid advancement of computer vision, machine learning, and consumer electronics, eye tracking has emerged as a topic of increasing interest in recent years. It plays a key role across diverse domains including human-computer interaction, virtual reality, and clinical and healthcare applications. Near-eye tracking (NET) has recently been developed to possess encouraging features such as wearability, affordability, and interactivity.

An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.

Due to iterative matrix multiplications or gradient computations, machine learning modules often require a large amount of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory. In this study, we propose an ultralow-power and real-time machine learning-based motion artifact detection module for functional near-infrared spectroscopy (fNIRS) systems.

Folding Kinetics and Volume Variation of the β-Hairpin Peptide Chignolin upon Ultrafast pH-Jumps.

In-depth characterization of fundamental folding steps of small model peptides is crucial for a better understanding of the folding mechanisms of more complex biomacromolecules. We have previously reported on the folding/unfolding kinetics of a model α-helix. Here, we study folding transitions in chignolin (GYDPETGTWG), a short β-hairpin peptide previously used as a model to study conformational changes in β-sheet proteins.

The potential role of artificial intelligence in the clinical management of Hansen's disease (leprosy).

Missed and delayed diagnoses of Hansen's disease (HD) are making the battle against it even more complex, increasing its transmission and significantly impacting those affected and their families. This strains public health systems and raises the risk of lifelong impairments and disabilities. Worryingly, the three countries most affected by HD witnessed a growth in new cases in 2022, jeopardizing the World Health Organization's targets to interrupt transmission.

Increasing the polarity of β-lapachone does not affect its binding capacity with bovine plasma protein.

Despite ortho-quinones showing several biological and pharmacological activities, there is still a lack of biophysical characterization of their interaction with albumin - the main carrier of different endogenous and exogenous compounds in the bloodstream. Thus, the interactive profile between bovine serum albumin (BSA) with β-lapachone (1) and its corresponding synthetic 3-sulfonic acid (2, under physiological pH in the sulphonate form) was performed. There is one main binding site of albumin for both β-lapachones (n ≈ 1) and a static fluorescence quenching mechanism was proposed.

Artificial intelligence for skin cancer detection and classification for clinical environment: a systematic review.

Background: Skin cancer is one of the most common forms worldwide, with a significant increase in incidence over the last few decades. Early and accurate detection of this type of cancer can result in better prognoses and less invasive treatments for patients. With advances in Artificial Intelligence (AI), tools have emerged that can facilitate diagnosis and classify dermatological images, complementing traditional clinical assessments and being applicable where there is a shortage of specialists.

Economic Evaluations and Equity in the Use of Artificial Intelligence in Imaging Exams for Medical Diagnosis in People With Skin, Neurological, and Pulmonary Diseases: Protocol for a Systematic Review.

Background: Traditional health care systems face long-standing challenges, including patient diversity, geographical disparities, and financial constraints. The emergence of artificial intelligence (AI) in health care offers solutions to these challenges. AI, a multidisciplinary field, enhances clinical decision-making.

fNIRS-EEG BCIs for Motor Rehabilitation: A Review.

Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain-computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others.

Effects of copper and cadmium on stream leaf decomposition: evidence from a microcosm study.

We seek to understand how copper and cadmium act on leaf litter decomposition by their effects on microbial conditioning and litter fragmentation by invertebrates. In this study, we evaluated, in an integrated manner, different biological elements responsible for functioning of streams. Thus, we performed a microcosm assay with different concentrations for the two metals and their combination, evaluating their effects on fungi sporulation rate, consumption rate by shredders, and, consequently, the leaf litter decomposition rates.

Learning based motion artifacts processing in fNIRS: a mini review.

This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA and compromise data integrity. Traditional strategies often result in reduced reliability of the hemodynamic response and statistical power. Recognizing the limited number of studies focusing on learning-based MA removal, we examine 315 studies, identifying seven pertinent to our focus area.

Interaction between a water-soluble anionic porphyrin and human serum albumin unexpectedly stimulates the aggregation of the photosensitizer at the surface of the albumin.

The 5,10,15,20-tetrakis(2,6-difluoro-3-sulfophenyl)porphyrin (TDFPPS) was reported as a potential photosensitizer for photodynamic therapy. The capacity of the photosensitizers to be carried in the human bloodstream is predominantly determined by its extension of binding, binding location, and binding mechanism to human serum albumin (HSA), influencing its biodistribution and ultimately its photodynamic therapy efficacy in vivo. Thus, the present work reports a biophysical characterization on the interaction between the anionic porphyrin TDFPPS and HSA by UV-visible absorption, circular dichroism, steady-state, time-resolved, and synchronous fluorescence techniques under physiological conditions, combined with molecular docking calculations and molecular dynamics simulations.

Glutaric Acidemia Type 1: Diagnosis, Clinical features, and Outcome in a Portuguese Cohort.

Introduction: Glutaric acidemia type 1 (GA1) is a rare autosomal recessive disorder characterized by a deficiency of glutaryl-CoA dehydrogenase, resulting in the accumulation of glutaric acid (GA), 3-hydroxyglutaric acid, and glutarylcarnitine, especially in the brain. GA1-affected children are clinically characterized by macrocephaly. Neurological abnormalities usually appear between 6 and 18 months of age, often triggered by a catabolic event.