Observation of the neutrinoless double β decay is the only practical way to establish that neutrinos are their own antiparticles¹. Because of the small masses of neutrinos, the lifetime of neutrinoless double β decay is expected to be at least ten orders of magnitude greater than the typical lifetimes of natural radioactive chains, which can mimic the experimental signature of neutrinoless double β decay². The most robust identification of neutrinoless double β decay requires the definition of a signature signal—such as the observation of the daughter atom in the decay—that cannot be generated by radioactive backgrounds, as well as excellent energy resolution. In particular, the neutrinoless double β decay of ¹³⁶Xe could be established by detecting the daughter atom, ¹³⁶Ba²⁺, in its doubly ionized state^3,4,5,6,7,8. Here we demonstrate an important step towards a ‘barium-tagging’ experiment, which identifies double β decay through the detection of a single Ba²⁺ ion. We propose a fluorescent bicolour indicator as the core of a sensor that can detect single Ba²⁺ ions in a high-pressure xenon gas detector. In a sensor made of a monolayer of such indicators, the Ba²⁺ dication would be captured by one of the molecules and generate a Ba²⁺-coordinated species with distinct photophysical properties. The presence of such a single Ba²⁺-coordinated indicator would be revealed by its response to repeated interrogation with a laser system, enabling the development of a sensor able to detect single Ba²⁺ ions in high-pressure xenon gas detectors for barium-tagging experiments.

The NLRP3 inflammasome coordinates inflammation in response to different pathogen- and damage-associated molecular patterns, being implicated in different infectious, chronic inflammatory, metabolic and degenerative diseases. In chronic tendinopathic lesions, different non-resolving mechanisms produce a degenerative condition that impairs tissue healing and which therefore complicates their clinical management. Percutaneous needle electrolysis consists of the application of a galvanic current and is an emerging treatment for tendinopathies. In the present study, we found that galvanic current activates the NLRP3 inflammasome and induces an inflammatory response that promotes a collagen-mediated regeneration of the tendon in mice. This study establishes the molecular mechanism of percutaneous electrolysis that can be used to treat chronic lesions and describes the beneficial effects of an induced inflammasome-related response.

Multiphoton microscopy (MPM) is currently acknowledged as a very powerful method for the visualization and analysis of tissues in biomedicine. It allows high resolution, deep optical sectioning and reduced photodamage. MPM does not require labeling and is deployable both in-vivo and ex-vivo, which simplifies the diagnostic procedure compared to traditional histology approaches based on excisional biopsy, tissue fixation and staining. Among the important applications of MPM in medicine, differentiation of healthy from pathological tissues has gained massive interest over the past years, but MPM is also very useful for acquiring new insights on how various pathologies originate and progress. In this work we review the use of MPM in imaging assays focused on investigating unlabeled oral tissues (teeth and oral mucosa) and discuss a series of important results which hold potential for enabling a next generation of oral tissue characterization/diagnostic frameworks. The surveyed literature shows that non-linear optical imaging tools can significantly contribute to achieve a better understanding of oral cavity tissues, by allowing the accurate analysis of morphological structures and relevant biochemical processes.

In this paper, a Second-Harmonic-Generation (SHG) microscope was used to study the relationship between collagen structural arrangement, image quality and polarization sensitivity in human corneas with different organizations. The degree of order (or alternatively, the Structural Dispersion, SD) was quantified using the structure tensor method. SHG image quality was evaluated with different objective metrics. Dependence with polarization was quantified by means of a parameter defined as polarimetric modulation, which employs polarimetric SHG images acquired with four independent polarization states. There is a significant exponential relationship between the quality of the SHG images and the SD of the samples. Moreover, polarization sensitivity strongly depends on collagen arrangement. For quasi- or partially organized specimens, there is a polarization state that noticeably improves the image quality, providing additional information often not seen in other SHG images. This does not occur in non-organized samples. This fact is closely related to polarimetric modulation, which linearly decreases with the SD. Understanding in more detail the relationships that take place between collagen distribution, image quality and polarization sensitivity brings the potential to enable the development of optimized SHG image acquisition protocols and novel objective strategies for the analysis and detection of pathologies related to corneal collagen disorders, as well as surgery follow-ups.

Second Harmonic Generation Microscopy (SHG) is widely acknowledged as a valuable non-linear optical imaging tool, its contrast mechanism providing the premises to non-invasively identify, characterize, and monitor changes in the collagen architecture of tissues. However, the interpretation of SHG data can pose difficulties even for experts histopathologists, which represents a bottleneck for the translation of SHG-based diagnostic frameworks to clinical settings. The use of artificial intelligence methods for automated SHG analysis is still in an early stage, with only few studies having been reported to date, none addressing ocular tissues yet. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We observe that Deep Learning models building on different architectures provide complementary results for the classification of cornea SHG images and demonstrate an AU-ROC = 0.98 for their joint use. These results have potential to be extrapolated to other diagnostics scenarios, such as automated extraction of hydration level of cornea, or identification of corneal edema causes, and thus pave the way for novel methods for precision diagnostics of the cornea with Deep-Learning assisted SHG imaging.