Abstract
Designing a science activity for middle school children is a challenging task, especially if it aims to be interdisciplinary. One may ask if it is possible to craft a positive learning experience from different areas such as history of science, chemistry, or ethics. In this paper, we argue it can be achieved if we use the right tools to engage a young audience. A combination of gamification, hands-on activities, and storytelling techniques was successfully used, providing a unique form to approach chemistry and ethics subjects in an "edutainment" format. The result is a game that challenges participants to solve a crime, while addressing chemistry topics to gather pieces of evidence and facing science-related ethical conundrums. In our pilot study, participants evaluated this activity positively, identifying the innovation and entertaining features as the most relevant in their gaming experience.

Abstract
Forecasting COVID-19 disease severity is key to supporting clinical decision making and assisting resource allocation, particularly in intensive care units (ICUs). Here, we investigated the utility of time- and frequency-related features of the backscattered signal of serum patient samples to predict COVID-19 disease severity immediately after diagnosis. ICU admission was the primary outcome used to define disease severity. We developed a stacking ensemble machine learning model including the backscattered signal features (optical fingerprint), patient comorbidities, and age (AUROC = 0.80), which significantly outperformed the predictive value of clinical and laboratory variables available at hospital admission (AUROC = 0.71). The information derived from patient optical fingerprints was not strongly correlated with any clinical/laboratory variable, suggesting that optical fingerprinting brings unique information for COVID-19 severity risk assessment. Optical fingerprinting is a label-free, real-time, and low-cost technology that can be easily integrated as a front-line tool to facilitate the triage and clinical management of COVID-19 patients.

Abstract
With the advent of personalized medicine, there is a movement to develop "smaller" and "smarter" microdevices that are able to distinguish similar cancer subtypes. Tumor cells display major differences when compared to their natural counterparts, due to alterations in fundamental cellular processes such as glycosylation. Glycans are involved in tumor cell biology and they have been considered to be suitable cancer biomarkers. Thus, more selective cancer screening assays can be developed through the detection of specific altered glycans on the surface of circulating cancer cells. Currently, this is only possible through time-consuming assays. In this work, we propose the "intelligent" Lab on Fiber (iLoF) device, that has a high-resolution, and which is a fast and portable method for tumor single-cell type identification and isolation. We apply an Artificial Intelligence approach to the back-scattered signal arising from a trapped cell by a micro-lensed optical fiber. As a proof of concept, we show that iLoF is able to discriminate two human cancer cell models sharing the same genetic background but displaying a different surface glycosylation profile with an accuracy above 90% and a speed rate of 2.3 seconds. We envision the incorporation of the iLoF in an easy-to-operate microchip for cancer identification, which would allow further biological characterization of the captured circulating live cells.

Abstract
Background: In view of the growing importance of nanotechnologies, the detection/identification of nanoparticles type has been considered of utmost importance. Although the characterization of synthetic/organic nanoparticles is currently considered a priority (eg, drug delivery devices, nanotextiles, theranostic nanoparticles), there are many examples of "naturally" generated nanostructures - for example, extracellular vesicles (EVs), lipoproteins, and virus - that provide useful information about human physiology or clinical conditions. For example, the detection of tumor-related exosomes, a specific type of EVs, in circulating fluids has been contributing to the diagnosis of cancer in an early stage. However, scientists have struggled to find a simple, fast, and low-cost method to accurately detect/identify these nanoparticles, since the majority of them have diameters between 100 and 150 nm, thus being far below the diffraction limit. Methods: This study investigated if, by projecting the information provided from short-term portions of the back-scattered laser light signal collected by a polymeric lensed optical fiber tip dipped into a solution of synthetic nanoparticles into a lower features dimensional space, a discriminant function is able to correctly detect the presence of 100 nm synthetic nanoparticles in distilled water, in different concentration values. Results and discussion: This technique ensured an optimal performance (100% accuracy) in detecting nanoparticles for a concentration above or equal to 3.89 mu g/mL (8.74E+10 particles/mL), and a performance of 90% for concentrations below this value and higher than 1.22E-03 mu g/mL (2.74E+07 particles/mL), values that are compatible with human plasmatic levels of tumor-derived and other types of EVs, as well as lipoproteins currently used as potential biomarkers of cardiovascular diseases. Conclusion: The proposed technique is able to detect synthetic nanoparticles whose dimensions are similar to EVs and other "clinically" relevant nanostructures, and in concentrations equivalent to the majority of cell-derived, platelet-derived EVs and lipoproteins physiological levels. This study can, therefore, provide valuable insights towards the future development of a device for EVs and other biological nanoparticles detection with innovative characteristics.

Abstract
In view of the growing importance of nanotechnologies, the detection of nanoparticles type in several contexts has been considered a relevant topic. Several organisms, including the National Institutes of Health, have been highlighting the urge of developing nanoparticles exposure risk assessment assays, since very little is known about their physiological responses. Although the identi fi cation/characterization of synthetically produced nanoparticles is considered a priority, there are many examples of \ naturally" generated nanostructures that provide useful information about food components or human physiology. In fact, several nanoscale extracellular vesicles are present in physiological fluids with high potential as cancer biomarkers. However, scientists have struggled to fi nd a simple and rapid method to accurately detect/identify nanoparticles, since their majority have diameters between 100-150 nm -far below the di ff raction limit. Currently, there is a lack of instruments for nanoparticles detection and the few instrumentation that is commonly used is costly, bulky, complex and time consuming. Thus, considering our recent studies on particles identi fi cation through back-scattering, we examined if the time/frequency-domain features of the back-scattered signal provided from a 100 nm polystyrene nanoparticles suspension are able to detect their presence only by dipping a polymeric lensed optical fi ber in the solution. This novel technique allowed the detection of synthetic nanoparticles in distilled water versus \ blank solutions" (only distilled water) through Multivariate Statistics and Arti fi cial Intelligence (AI)-based techniques. While the state-of-the-art methods do not o ff er a ff ordable and simple approaches for nanoparticles detection, our technique can contribute for the development of a device with innovative characteristics.