Volumetric bioprinting of the osteoid niche.

Volumetric bioprinting has revolutionized the field of biofabrication by enabling the creation of cubic centimeter-scale living constructs at faster printing times (in the order of seconds). However, a key challenge remains: developing a wider variety of available osteogenic bioinks that allow osteogenic maturation of the encapsulated cells within the construct. Herein, the bioink exploiting a step-growth mechanism (norbornene-norbornene functionalized gelatin in combination with thiolated gelatin-GelNBNBSH) outperformed the bioink exploiting a chain-growth mechanism (gelatin methacryloyl-GelMA), as the necessary photo-initiator concentration was three times lower combined with a more than 50% reduction in required light exposure dose resulting in an improved positive and negative resolution.

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.