Ferritin nanocages for theranostic applications

Ferritin is a ubiquitous protein involved in iron storage composed of 24 subunits assembled in a hollow spherical nano-cage architecture. Channels formed between the intersection of peptide subunits are lined with polar aminoacids and allow for the entry and exit of cations. Ferritin can be successfully used as an highly biocompatible nanocarrier, due to the ability of being recognized and uptaken by TfR-1 overexpressing tumour cells. Furthermore, both inner or outer surface can be easily functionalized conferring multiple functionalities onto a single molecule. For these reasons, ferritins are emerging as novel biotech platforms for biomedical applications (both diagnostical and therapeutic) due to their ability to encapsulate cargo molecules, broad functionalization possibilities and selective targeting properties. In this framework, the present work has been focused on the development and characterization of engineered recombinant mammalian and archaeal ferritin constructs to expand the scope of their nanotechnological applications. With the aim of investigating the biological and biophysical properties of prokaryotic homopolymers and characterizing the permeability of the prokaryotic protein shell toward diffusants, two ferritins from Archaea have been chosen as model. A set of engineered mutants of Pyrococcus furiosus ferritin (Pf-Ft) and Archaeoglobus fulgidus ferritin (Af-Ft) have been obtained by placing a reactive cysteine residue per subunit in the same topological positions either inside or outside the internal cavity. These mutants differ from each other by the aminoacid composition of ferritin channels and the related “open” versus “closed” ferritin architecture. The molecular diffusion through the ferritin cavity has been characterized by studying within these mutants the cysteine reactivity toward the bulky and negatively charged DTNB molecule (5,5'-dithiobis-2-nitrobenzoic acid). Moreover, Archaeoglobus fulgidus ferritin has been genetically engineered by changing the surface exposed loop connecting helices B and C to mimic the sequence of the analogous human H-chain ferritin loop. This novel “humanized” chimeric construct (named HumAf-Ft) thus combines the unique open structure and self-assembly properties of Af-Ft with the typical humanH-ferritin ability to bind the Transferrin Receptor TfR-1, which is overexpressed in several types of tumor cells. HumAfFt has been structurally and biophysically characterized and the improved cellular uptake has been demonstrated on HeLa cell line. Lastly, to exploit lanthanide fluorescence properties and develop an intrinsically fluorescent nanoparticle, a novel construct has been developed by genetically fusing at the C-terminal end of mouse H-ferritin a lanthanide binding tag (LBT). LBTs are short peptides that selectively bind lanthanide ions at low-nanomolar affinities and, due to the presence of a tryptophan residue, provide strong FRET sensitization. This novel construct (named HFt-LBT) has been designed by locating the tag inside the inner cavity, so that the lanthanide ions diffusing through the surface pores can eventually bind to the LBT sequence. HFt-LBT would thus act both as carrier targeted to TfR-1 receptor and as a FRET sensitizer. Fluorescence improvement and lanthanide binding properties have been investigated by spectrophotometric measurements using Tb+3 as lanthanide probe. The structural characterization has been carried out and cellular uptake by HeLa cell line has been assessed as well

Unstructured Handwashing Recognition using Smartwatch to Reduce Contact Transmission of Pathogens

Current guidelines from the World Health Organization indicate that the SARS-CoV-2 coronavirus, which results in the novel coronavirus disease (COVID-19), is transmitted through respiratory droplets or by contact. Contact transmission occurs when contaminated hands touch the mucous membrane of the mouth, nose, or eyes so hands hygiene is extremely important to prevent the spread of the SARSCoV-2 as well as of other pathogens. The vast proliferation of wearable devices, such as smartwatches, containing acceleration, rotation, magnetic field sensors, etc., together with the modern technologies of artificial intelligence, such as machine learning and more recently deep-learning, allow the development of accurate applications for recognition and classification of human activities such as: walking, climbing stairs, running, clapping, sitting, sleeping, etc. In this work, we evaluate the feasibility of a machine learning based system which, starting from inertial signals collected from wearable devices such as current smartwatches, recognizes when a subject is washing or rubbing its hands. Preliminary results, obtained over two different datasets, show a classification accuracy of about 95% and of about 94% for respectively deep and standard learning techniques

Engineered ferritin for lanthanide binding

Ferritin H-homopolymers have been extensively used as nanocarriers for diverse applications in the targeted delivery of drugs and imaging agents, due to their unique ability to bind the transferrin receptor (CD71), highly overexpressed in most tumor cells. In order to incorporate novel fluorescence imaging properties, we have fused a lanthanide binding tag (LBT) to the C-terminal end of mouse H-chain ferritin, HFt. The HFt-LBT possesses one high affinity Terbium binding site per each of the 24 subunits provided by six coordinating aminoacid side chains and a tryptophan residue in its close proximity and is thus endowed with strong FRET sensitization properties. Accordingly, the characteristic Terbium emission band at 544 nm for the HFt-LBT Tb(III) complex was detectable upon excitation of the tag enclosed at two order of magnitude higher intensity with respect to the wtHFt protein. X-ray data at 2.9 Å and cryo-EM at 7 Å resolution demonstrated that HFt-LBT is correctly assembled as a 24-mer both in crystal and in solution. On the basis of the intrinsic Tb(III) binding properties of the wt protein, 32 additional Tb(III) binding sites, located within the natural iron binding sites of the protein, were identified besides the 24 Tb(III) ions coordinated to the LBTs. HFt-LBT Tb(III) was demonstrated to be actively uptaken by selected tumor cell lines by confocal microscopy and FACS analysis of their FITC derivatives, although direct fluorescence from Terbium emission could not be singled out with conventional, 295–375 nm, fluorescence excitation

La prevención de las patologías del asbesto: perspectivas operativas de la cooperación italiana con los países de américa latina

El propósito del presente artículo es valorar la tesis de que el impacto en la salud de la exposición a fibras de asbesto presentes en los lugares de trabajo y en el ambiente, requiere contramedidas basadas en la evidencia científica y la cooperación internacional. La evidencia científica adquirida en el ámbito internacional sobre el asbesto, la experiencia pluridecenal madurada en Italia sobre este tema, así como la conciencia de que la adopción de medidas para combatir los efectos en la salud causados por la exposición a asbesto, deben ser verificadas considerando la especificidad de los diversos contextos nacionales y locales en América Latina. Constituyen la base para la identificación de las cuatro principales directrices de intervención (Promoción del acceso a la documentación sobre el asbesto; Realización de intervenciones para reducir la exposición al asbesto; Vigilancia sanitaria de los sujetos expuestos; Detección del mesotelioma) que pueden ser desarrolladas en el ámbito de la cooperación técnico-científica entre Italia y los países de América Latina. La integración de las capacidades de los investigadores colombianos e italianos permitirá obtener estos resultados, contribuyendo al proceso de eliminación del asbesto, ya en curso en América Latin

Energy-aware Tiny Machine Learning for Sensor-based Hand-washing Recognition

Tiny wearable devices are nowadays one of the most popular and used devices in everyday life. At the same time, machine learning techniques have reached a level of maturity such that they can be used in the most varied fields. The union of these two technologies represents a valuable opportunity for the development of pervasive computing applications. On the other hand, pushing the machine learning inference on a wearable device can lead to nontrivial issues. In fact, devices with small size and low-energy availability, like those dedicated to wearable platforms, pose strict computational, memory, and power requirements which result in challenging issues to be addressed by designers. The main purpose of this study is to empirically explore the trade-off between energy consumption and classification accuracy of a machine learning-based hand-washing recognition task deployed on a real wearable device. Through extensive experimental results, obtained on a public human activity recognition dataset, we demonstrated that given an identical level of classification performance, a classic SVM classifier can save about 40% of energy with respect to a more complex LSTM network. Moreover, reducing the LSTM complexity, by lowering the number of its internal unit, can make the LSTM network energy cost-effective (with a savings of about 30%) at the cost of a reduction in accuracy of only 2%

SensorLib: an Energy-efficient Sensor-collection Library for Wear OS

In recent years, wearable technology has gained popularity due to features like long battery life, network connectivity, and fitness monitoring. Human Activity Recognition has emerged as a popular use case for smartwatches, enabling the recognition of activities starting from internal sensors. Data acquisition from sensors is crucial in wearable devices because if not properly implemented can reduce battery life or device responsiveness. The paper presents an energy-efficient programming library for real-time sensor sampling on smartwatches using native Wear OS sensor APIs. The library's implementation is evaluated on a real smartwatch for code size, memory utilization, and power consumption. The preliminary results empirically demonstrate that the solution proved to be light and versatile enough to be used on wearable devices without heavily compromising battery life and system performance

Lightweight accurate trigger to reduce power consumption in sensor-based continuous human activity recognition

Wearable devices have become increasingly popular in recent years, and they offer a great opportunity for sensor-based continuous human activity recognition in real-world scenarios. However, one of the major challenges is their limited battery life. In this study, we propose an energy-aware human activity recognition framework for wearable devices based on a lightweight accurate trigger. The trigger acts as a binary classifier capable of recognizing, with maximum accuracy, the presence or absence of one of the interesting activities in the real-time input signal and it is responsible for starting the energy-intensive classification procedure only when needed. The measurement results conducted on a real wearable device show that the proposed approach can reduce energy consumption by up to 95% in realistic case studies, with a cost of performance deterioration of at most 1% or 2% compared to the traditional energy-intensive classification strategy

Unstructured Handwashing Recognition Using Smartwatch to Reduce Contact Transmission of Pathogens

Current guidelines from the World Health Organization indicate that the SARS-CoV-2 coronavirus, which results in the novel coronavirus disease (COVID-19), is transmitted through respiratory droplets or by contact. Contact transmission occurs when contaminated hands touch the mucous membrane of the mouth, nose, or eyes so hands hygiene is extremely important to prevent the spread of the SARS-CoV-2 as well as of other pathogens. The vast proliferation of wearable devices, such as smartwatches, containing acceleration, rotation, magnetic field sensors, etc., together with the modern technologies of artificial intelligence, such as machine learning and more recently deep-learning, allow the development of accurate applications for recognition and classification of human activities such as: walking, climbing stairs, running, clapping, sitting, sleeping, etc. In this work, we evaluate the feasibility of a machine learning based system which, starting from inertial signals collected from wearable devices such as current smartwatches, recognizes when a subject is washing or rubbing its hands. Preliminary results, obtained over two different datasets, show a classification accuracy of about 95% and of about 94% for respectively deep and standard learning techniques

Oneiric activity in schizophrenia: textual analysis of dream reports

This work evaluated the structure of dreams in people affected by schizophrenia. The verbal reports of 123 schizophrenic patients were compared with 123 dream reports from a control group. In accordance with the Jungian conceptualization of, dreams as texts, dream reports were assessed using textual analysis processing techniques. Significant differences were found in textual parameters, showing that the dreams reports of schizophrenic patients differ from those of the control group. It is thus possible that schizophrenia probably underlies changes in the oneiric production and dream reports. This work confirms the value of textual analysis in the study of oneiric material