Is artificial intelligence prepared for the 24-h shifts in the ICU?

Integrating machine learning (ML) into intensive care units (ICUs) can significantly enhance patient care and operational efficiency. ML algorithms can analyze vast amounts of data from electronic health records, physiological monitoring systems, and other medical devices, providing real-time insights and predictive analytics to assist clinicians in decision-making. ML has shown promising results in predictive modeling for patient outcomes, early detection of sepsis, optimizing ventilator settings, and resource allocation.

Detecting Amyloid Positivity Using Morphometric Magnetic Resonance Imaging.

Background: Early detection of amyloid-β (Aβ) positivity is essential for an accurate diagnosis and treatment of Alzheimer's disease (AD), but it is currently costly and/or invasive.

Objective: We aimed to classify Aβ positivity (Aβ+) using morphometric features from magnetic resonance imaging (MRI), a more accessible and non-invasive technique, in two clinical population scenarios: one containing AD, mild cognitive impairment (MCI) and cognitively normal (CN) subjects, and another only cognitively impaired subjects (AD and MCI).

Methods: Demographic, cognitive (Mini-Mental State Examination [MMSE] scores), regional morphometry MRI (volumes, areas, and thicknesses), and derived morphometric graph theory (GT) features from all subjects (302 Aβ+, age: 73.

How does the electric field induced by tDCS influence motor-related connectivity? Model-guided perspectives.

Over the last decade, transcranial direct current stimulation (tDCS) has been applied not only to modulate local cortical activation, but also to address communication between functionally-related brain areas. Stimulation protocols based on simple two-electrode placements are being replaced by multi-electrode montages to target intra- and inter-hemispheric neural networks using multichannel/high definition paradigms..

Studying Erythromelalgia Using Doppler Flowmetry Perfusion Signals and Wavelet Analysis-An Exploratory Study.

Erythromelalgia (EM) is a rare disease, which is still poorly characterized. In the present paper, we compared the hand perfusion of one female EM patient, under challenges, with a healthy control group. Using a laser Doppler flowmeter (LDF) with an integrated thermal probe, measurements were taken in both hands at rest (Phase I) and after two separate challenges-post-occlusive hyperemia (PORH) in one arm (A) and reduction of skin temperature (cooling) with ice in one hand (B) (Phase II).

Nucleic Acid Aptamer-Based Biosensors: A Review.

Aptamers, short strands of either DNA, RNA, or peptides, known for their exceptional specificity and high binding affinity to target molecules, are providing significant advancements in the field of health. When seamlessly integrated into biosensor platforms, aptamers give rise to aptasensors, unlocking a new dimension in point-of-care diagnostics with rapid response times and remarkable versatility. As such, this review aims to present an overview of the distinct advantages conferred by aptamers over traditional antibodies as the molecular recognition element in biosensors.

Make It Less Complex: Autoencoder for Speckle Noise Removal-Application to Breast and Lung Ultrasound.

Ultrasound (US) imaging is used in the diagnosis and monitoring of COVID-19 and breast cancer. The presence of Speckle Noise (SN) is a downside to its usage since it decreases lesion conspicuity. Filters can be used to remove SN, but they involve time-consuming computation and parameter tuning.

Nanoparticle-Based Treatment in Glioblastoma.

Glioblastoma (GB) is a malignant glioma associated with a mean overall survival of 12 to 18 months, even with optimal treatment, due to its high relapse rate and treatment resistance. The standardized first-line treatment consists of surgery, which allows for diagnosis and cytoreduction, followed by stereotactic fractionated radiotherapy and chemotherapy. Treatment failure can result from the poor passage of drugs through the blood-brain barrier (BBB).

Urinary Biomarkers and Point-of-Care Urinalysis Devices for Early Diagnosis and Management of Disease: A Review.

Biosensing and microfluidics technologies are transforming diagnostic medicine by accurately detecting biomolecules in biological samples. Urine is a promising biological fluid for diagnostics due to its noninvasive collection and wide range of diagnostic biomarkers. Point-of-care urinalysis, which integrates biosensing and microfluidics, has the potential to bring affordable and rapid diagnostics into the home to continuing monitoring, but challenges still remain.

Highlighted Advances in Therapies for Difficult-To-Treat Brain Tumours Such as Glioblastoma.

Glioblastoma multiforme (GBM) remains a challenging disease, as it is the most common and deadly brain tumour in adults and has no curative solution and an overall short survival time. This incurability and short survival time means that, despite its rarity (average incidence of 3.2 per 100,000 persons), there has been an increased effort to try to treat this disease.

Normative model detects abnormal functional connectivity in psychiatric disorders.

Introduction: The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient's signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders.

Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach.

Background: Early and accurate diagnosis of Alzheimer's disease (AD) is essential for disease management and therapeutic choices that can delay disease progression. Machine learning (ML) approaches have been extensively used in attempts to develop algorithms for reliable early diagnosis of AD, although clinical usefulness, interpretability, and generalizability of the classifiers across datasets and MRI protocols remain limited.

Methods: We report a multi-diagnostic and generalizable approach for mild cognitive impairment (MCI) and AD diagnosis using structural MRI and ML.

The Role of Rosmarinic Acid on the Bioproduction of Gold Nanoparticles as Part of a Photothermal Approach for Breast Cancer Treatment.

Breast cancer is a high-burden malignancy for society, whose impact boosts a continuous search for novel diagnostic and therapeutic tools. Among the recent therapeutic approaches, photothermal therapy (PTT), which causes tumor cell death by hyperthermia after being irradiated with a light source, represents a high-potential strategy. Furthermore, the effectiveness of PTT can be improved by combining near infrared (NIR) irradiation with gold nanoparticles (AuNPs) as photothermal enhancers.

A Fast Protocol for Multiparametric Characterisation of Diffusion in the Brain and Brain Tumours.

Multi-parametric tissue characterisation is demonstrated using a 4-minute protocol based on diffusion trace acquisitions. Three diffusion regimes are covered simultaneously: pseudo-perfusion, Gaussian, and non-Gaussian diffusion. The clinical utility of this method for fast multi-parametric mapping for brain tumours is explored.

Comparing SPM12 and CAT12 segmentation pipelines: a brain tissue volume-based age and Alzheimer's disease study.

Background: Brain volumes have been used as research biomarkers both in health and in Alzheimer's disease(AD). In order to improve the comparability between studies and aid future analytical software platform choice in the research setting, here we compare two segmentation pipelines of structural brain magnetic resonance imaging(sMRI): the SPM12 toolbox, and a SPM12 add-on, the CAT12 toolbox.

Methods: We segmented 1.

Benchmarking of the BITalino biomedical toolkit against an established gold standard.

The low-cost multimodal platform BITalino is being increasingly used for educational and research purposes. However, there is still a lack of well-structured work comparing data acquired by this toolkit against a reference device, using established experimental protocols. This work intends to fill the said gap by benchmarking the performance of BITalino against the BioPac MP35 Student Lab Pro device.

Apparent diffusion coefficient in the analysis of prostate cancer: determination of optimal b-value pair to differentiate normal from malignant tissue.

Purpose: Determining optimal b-value pair for differentiation between normal and prostate cancer (PCa) tissues.

Methods: Forty-three patients with diagnosis or PCa symptoms were included. Apparent diffusion coefficient (ADC) was estimated using minimum and maximum b-values of 0, 50, 100, 150, 200, 500s/mm2 and 500, 800, 1100, 1400, 1700 and 2000s/mm2, respectively.

Visualizing the brain on a mixed reality smartphone application.

Augmented and Virtual Reality approaches are getting more and more advanced and consequently their use in various real world areas is increasing. Medicine is one of the fields in which more practical applications are surfacing, mainly approaches that enable new forms of visualization of data obtained from real patients. Our work focuses on providing a new simple, practical and efficient way to visualize the brain of a patient, both in an Augmented Reality and in a Virtual Reality approach, through a smartphone application.

Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox.

Aim. In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures.

Improving malignancy prediction in breast lesions with the combination of apparent diffusion coefficient and dynamic contrast-enhanced kinetic descriptors.

Aim: To assess how the joint use of apparent diffusion coefficient (ADC) and kinetic parameters (uptake phase and delayed enhancement characteristics) from dynamic contrast-enhanced (DCE) can boost the ability to predict breast lesion malignancy.

Materials And Methods: Breast magnetic resonance examinations including DCE and diffusion-weighted imaging (DWI) were performed on 51 women. The association between kinetic parameters and ADC were evaluated and compared between lesion types.

Region of interest demarcation for quantification of the apparent diffusion coefficient in breast lesions and its interobserver variability.

Purpose: We aimed to compare two different methods of region of interest (ROI) demarcation and determine interobserver variability on apparent diffusion coefficient (ADC) in breast lesions.

Methods: Thirty-two patients with 39 lesions were evaluated with a 3.0 Tesla scanner using a diffusion-weighted sequence with several b-values.