Comparing the different sets of item-diagnostic criteria of the Coma Recovery Scale-Revised (CRS-R): a measurement-based approach driven by Rasch analysis.

Objectives: (1) to replicate the assessment of the internal construct validity of the Coma Recovery Scale-Revised (CRS-R) within the Rasch Measurement Theory framework using a larger multicenter sample size; (2) to compare the different sets of item diagnostic criteria against the measurement ruler constructed from Rasch analysis to understand how those criteria relate to the overall level of persons' consciousness.

Design: Multicenter retrospective study.

Setting: Seven centers.

Deep learning algorithm on H&E whole slide images to characterize alterations frequency and spatial distribution in breast cancer.

The tumor suppressor is frequently mutated in hormone receptor-negative, HER2-positive breast cancer (BC), contributing to tumor aggressiveness. Traditional ancillary methods like immunohistochemistry (IHC) to assess functionality face pre- and post-analytical challenges. This proof-of-concept study employed a deep learning (DL) algorithm to predict mutational status from H&E-stained whole slide images (WSIs) of BC tissue.

Clinical Efficacy and Safety of Ultrasound-Guided Injection with Low-Molecular-Weight Peptides from Hydrolyzed Collagen in Patients with Partial Supraspinatus Tendon Tears: A Pilot Study.

Background: This study evaluates the clinical efficacy and safety of two ultrasound (US)-guided injections of a 5 mg/1 mL low-molecular-weight peptide (LWP) solution derived from hydrolyzed bovine collagen in patients with supraspinatus partial tendon tears.

Methods: A total of 21 patients with symptomatic partial tears of the supraspinatus tendon, detected by US, were consecutively enrolled and received one injection at a baseline visit (T0) and one after two weeks (T1). The primary outcome measure was the visual analogue scale (VAS) for pain.

Triheptanoin in patients with long-chain fatty acid oxidation disorders: clinical experience in Italy.

Background: Long-chain fatty acid oxidation disorders (LC-FAOD) are rare and potentially life-threatening diseases that cause deficient energy production and accumulation of toxic metabolites. Despite dietary management, adherence to maximum fasting guidelines, restricted long-chain triglyceride intake and supplementation with medium-chain triglyceride (MCT) oil (current standard of care), most patients experience recurrent decompensation episodes that can require hospitalisation. Herein, we analysed the effectiveness and safety of triheptanoin (a highly purified, synthetic medium odd-chain triglyceride) treatment in a cohort of Italian patients with LC-FAOD.

Development of Machine Learning Algorithms Using EEG Data to Detect the Presence of Chronic Pain.

Chronic pain impacts more than one in five adults in the United States (US) and the costs associated with the condition amount to hundreds of billions of dollars annually. Despite the tremendous impact of chronic pain in the US and worldwide, the standard of care for diagnosis depends on subjective self-reporting of pain state, with no effective objective assessment procedure available. This study investigated the application of signal processing and machine learning to electroencephalography (EEG) data for the development of classification algorithms capable of differentiating subjects in pain from pain free subjects.