Severe hypotension requiring resuscitation in tumour-related carotid sinus syndrome: a case report.

Background: An uncommon cause of reflex syncope is carotid sinus syndrome (CSS). In rare cases, this can be caused by compression of the carotid sinus by a progressive or invasive tumour.

Case Summary: A 57-year-old female was presented at the emergency department with recurrent syncope in the morning.

Injection of Autologous Adipose Stromal Vascular Fraction in Combination with Autologous Conditioned Plasma for the Treatment of Advanced Knee Osteoarthritis Significantly Improves Clinical Symptoms.

(1) Osteoarthritis (OA) is the most common joint disease in the world. It is chronic, systemic, progressive and disabling. Orthobiologics have the potential to positively alter the course of this disease.

Fabrication and biomechanical characterization of a spider silk reinforced fibrin-based vascular prosthesis.

With fibrin-based vascular prostheses, vascular tissue engineering offers a promising approach for the fabrication of biologically active regenerative vascular grafts. As a potentially autologous biomaterial, fibrin exhibits excellent hemo- and biocompatibility. However, the major problem in the use of fibrin constructs in vascular tissue engineering, which has so far prevented their widespread clinical application, is the insufficient biomechanical stability of unprocessed fibrin matrices.

The Effect of Therapeutic Adherence on the Effectiveness of a Digital Therapeutic Exercise Program: A Propensity Score Matching Analysis.

Unlabelled: Nonspecific back pain (NSBP) contributes greatly to the overall burden of disease from musculoskeletal conditions. Digital therapeutics (DTx) aims to address the excess demand for movement and exercise therapy resulting from this spectrum of conditions. This study aims to investigate the differential therapeutic response of NSBP to different use profiles of a digital home exercise program.

Higher performance for women than men in MRI-based Alzheimer's disease detection.

Introduction: Although machine learning classifiers have been frequently used to detect Alzheimer's disease (AD) based on structural brain MRI data, potential bias with respect to sex and age has not yet been addressed. Here, we examine a state-of-the-art AD classifier for potential sex and age bias even in the case of balanced training data.

Methods: Based on an age- and sex-balanced cohort of 432 subjects (306 healthy controls, 126 subjects with AD) extracted from the ADNI data base, we trained a convolutional neural network to detect AD in MRI brain scans and performed ten different random training-validation-test splits to increase robustness of the results.