Sculpting Success: The Importance of Diet and Physical Activity to Support Skeletal Muscle Health during Weight Loss with New Generation Anti-Obesity Medications.

Obesity is a public health crisis, with prevalence rates tripling over the past 60 y. Although lifestyle modifications, such as diet and physical activity, remain the first-line treatments, recent anti-obesity medications (AOMs) have been shown to achieve greater reductions in body weight and fat mass. However, AOMs also reduce fat-free mass, including skeletal muscle, which has been demonstrated to account for 20% to 50% of total weight loss.

Aspergillus-mediated allergic airway inflammation is triggered by dendritic cell recognition of a defined spore morphotype.

Background: Exposure to fungi, especially Aspergillus fumigatus, can elicit potent allergic inflammation that triggers and worsens asthmatic disease. Dendritic cells (DCs) initiate allergic inflammatory responses to allergic stimuli. However, it is unclear if Af spores during isotropic growth (early spore swelling) can activate DCs to initiate allergic responses or if germination is required.

Use of machine learning to identify prognostic variables for outcomes in chronic low back pain treatment: a retrospective analysis.

Objectives: Most patients seen in physical therapy (PT) clinics for low back pain (LBP) are treated for chronic low back pain (CLBP), yet PT interventions suggest minimal effectiveness. The Cochrane Back Review Group proposed 'Holy Grail' questions, one being: 'What are the most important (preventable) predictors of chronicity' for patients with LBP? Subsequently, prognostic factors influencing outcomes for CLBP have been described, however results remain conflicting due to methodological weaknesses.

Methods: This retrospective observational cohort study examined prognostic risk factors for PT outcomes in CLBP treatment using a sub-type of AI.

Task-agnostic exoskeleton control via biological joint moment estimation.

Lower-limb exoskeletons have the potential to transform the way we move, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We introduce a task-agnostic controller that assists the user on the basis of instantaneous estimates of lower-limb biological joint moments from a deep neural network. By estimating both hip and knee moments in-the-loop, our approach provided multi-joint, coordinated assistance through our autonomous, clothing-integrated exoskeleton.