A guide to building a low-cost centrifuge force microscope module for single-molecule force experiments.

The ability to apply controlled forces to individual molecules or molecular complexes and observe their behaviors has led to many important discoveries in biology. Instruments capable of probing single-molecule forces typically cost >US$100,000, limiting the use of these techniques. The centrifuge force microscope (CFM) is a low-cost and easy-to-use instrument that enables high-throughput single-molecule studies.

Intervention description of pharmacist-facilitated medication reviews in Nordic primary care settings: a scoping review.

Background: Multicomponent interventions are increasingly utilized to tackle the complexity of aging and co-morbid patients. However, descriptions of interventions are generally poor, making it difficult for healthcare providers to implement successful programs.

Objectives: This study aimed to explore the completeness of intervention description of pharmacist-facilitated medication reviews (MRs) in Nordic primary care settings.

Investigating the impact of a pharmacist intervention on inappropriate prescribing practices at hospital admission and discharge in older patients: a secondary outcome analysis from a randomized controlled trial.

Background: Inappropriate medication prescribing in older patients increases the risk of poorer health outcomes and increased costs. The IMMENSE trial, integrated a clinical pharmacist into the health care team, to improve medication therapy among older patients, and to investigate the impact on acute revisits to hospital.

Objectives: This study investigated the prevalence of potentially inappropriate medications (PIMs) and prescribing omissions (PPOs) at hospital admission and discharge.

Two-step deep-learning identification of heel keypoints from video-recorded gait.

Accurate and fast extraction of step parameters from video recordings of gait allows for richer information to be obtained from clinical tests such as Timed Up and Go. Current deep-learning methods are promising, but lack in accuracy for many clinical use cases. Extracting step parameters will often depend on extracted landmarks (keypoints) on the feet.