2D fibrillar osteoid niche mimicry through inclusion of visco-elastic and topographical cues in gelatin-based networks.

Given the clinical need for osteoregenerative materials incorporating controlled biomimetic and biophysical cues, a novel highly-substituted norbornene-modified gelatin was developed enabling thiol-ene crosslinking exploiting thiolated gelatin as cell-interactive crosslinker. Comparing the number of physical crosslinks, the degree of hydrolytic degradation upon modification, the network density and the chemical crosslinking type, the osteogenic effect of visco-elastic and topographical properties was evaluated. This novel network outperformed conventional gelatin-based networks in terms of osteogenesis induction, as evidenced in 2D dental pulp stem cell seeding assays, resulting from the presentation of both a local (substrate elasticity, 25-40 kPa) and a bulk (compressive modulus, 25-45 kPa) osteogenic substrate modulus in combination with adequate fibrillar cell adhesion spacing to optimally transfer traction forces from the fibrillar ECM (as evidenced by mesh size determination with the rubber elasticity theory) and resulting in a 1.

Machine learning identifies a profile of inadequate responder to methotrexate in rheumatoid arthritis.

Objectives: Around 30% of patients with RA have an inadequate response to MTX. We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers.

Methods: Models were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (± 6 months).

Machine learning predicts response to TNF inhibitors in rheumatoid arthritis: results on the ESPOIR and ABIRISK cohorts.

Objectives: Around 30% of patients with rheumatoid arthritis (RA) do not respond to tumour necrosis factor inhibitors (TNFi). We aimed to predict patient response to TNFi using machine learning on simple clinical and biological data.

Methods: We used data from the RA ESPOIR cohort to train our models.

Identifying SARS-COV-2 infected patients through canine olfactive detection on axillary sweat samples; study of observed sensitivities and specificities within a group of trained dogs.

There is an increasing need for rapid, reliable, non-invasive, and inexpensive mass testing methods as the global COVID-19 pandemic continues. Detection dogs could be a possible solution to identify individuals infected with SARS-CoV-2. Previous studies have shown that dogs can detect SARS-CoV-2 on sweat samples.

The 2021 Magnonics Roadmap.

Magnonics is a budding research field in nanomagnetism and nanoscience that addresses the use of spin waves (magnons) to transmit, store, and process information. The rapid advancements of this field during last one decade in terms of upsurge in research papers, review articles, citations, proposals of devices as well as introduction of new sub-topics prompted us to present the first roadmap on magnonics. This is a collection of 22 sections written by leading experts in this field who review and discuss the current status besides presenting their vision of future perspectives.