Optogenetically controlled inflammasome activation demonstrates two phases of cell swelling during pyroptosis.

Inflammasomes are multiprotein platforms that control caspase-1 activation, which process the inactive precursor forms of the inflammatory cytokines IL-1β and IL-18, leading to an inflammatory type of programmed cell death called pyroptosis. Studying inflammasome-driven processes, such as pyroptosis-induced cell swelling, under controlled conditions remains challenging because the signals that activate pyroptosis also stimulate other signaling pathways. We designed an optogenetic approach using a photo-oligomerizable inflammasome core adapter protein, apoptosis-associated speck-like containing a caspase recruitment domain (ASC), to temporally and quantitatively manipulate inflammasome activation.

Predicting nanocarriers' efficacy in 3D models with Brillouin microscopy.

Thanks to their unique nanoscale properties, nanomedicines can overcome some of the shortcomings of conventional therapies. For better predictive screening, it is important to assess their performance in three-dimensional (3D) multicellular tumour spheroids (MCTS) that can recapitulate the physiological barriers found in real tumours. Today, the evaluation of drug delivery nanosystems in MCTS is mainly explored by means of microscopy techniques that are invasive and require fluorescent labels which modify the composition and fate of the carriers.

Symptom control and health-related quality of life in allergic rhinitis with and without comorbid asthma: A multicentre European study.

Background: Allergic rhinitis (AR) is a major non-communicable disease that affects the health-related quality of life (HRQoL) of patients. However, data on HRQoL and symptom control in AR patients with comorbid asthma (AR + asthma) are lacking.

Methods: In this multicentre, cross-sectional study, patients with AR were screened and administered questionnaires of demographic characteristics and health conditions (symptoms/diagnosis of AR and asthma, disease severity level, and allergic conditions).

Machine learning-based detection of label-free cancer stem-like cell fate.

The detection of cancer stem-like cells (CSCs) is mainly based on molecular markers or functional tests giving a posteriori results. Therefore label-free and real-time detection of single CSCs remains a difficult challenge. The recent development of microfluidics has made it possible to perform high-throughput single cell imaging under controlled conditions and geometries.