Efficiency Optimization of Ge-V Quantum Emitters in Single-Crystal Diamond upon Ion Implantation and HPHT Annealing

The authors report on the characterization at the single-defect level of germanium-vacancy (GeV) centers in diamond produced upon Ge− ion implantation and different subsequent annealing processes, with a specific focus on the effect of high-pressure-high-temperature (HPHT) processing on their quantum-optical properties. Different post-implantation annealing conditions are explored for the optimal activation of GeV centers, namely, 900 °C 2 h, 1000 °C 10 h, 1500 °C 1 h under high vacuum, and 2000 °C 15 min at 6 GPa pressure. A systematic analysis of the relevant emission properties, including the emission intensity in saturation regime and the excited state radiative lifetime, is performed on the basis of a set of ion-implanted samples, with the scope of identifying the most suitable conditions for the creation of GeV centers with optimal quantum-optical emission properties. The main performance parameter adopted here to describe the excitation efficiency of GeV centers as single-photon emitters is the ratio between the saturation optical excitation power and the emission intensity at saturation. The results show an up to eightfold emission efficiency increase in HPHT-treated samples with respect to conventional annealing in vacuum conditions, suggesting a suitable thermodynamic pathway toward the repeatable fabrication of ultra-bright GeV centers for single-photon generation purposes

Quantitative MRI Harmonization to Maximize Clinical Impact: The RIN-Neuroimaging Network

Neuroimaging studies often lack reproducibility, one of the cardinal features of the scientific method. Multisite collaboration initiatives increase sample size and limit methodological flexibility, therefore providing the foundation for increased statistical power and generalizable results. However, multisite collaborative initiatives are inherently limited by hardware, software, and pulse and sequence design heterogeneities of both clinical and preclinical MRI scanners and the lack of benchmark for acquisition protocols, data analysis, and data sharing. We present the overarching vision that yielded to the constitution of RIN-Neuroimaging Network, a national consortium dedicated to identifying disease and subject-specific in-vivo neuroimaging biomarkers of diverse neurological and neuropsychiatric conditions. This ambitious goal needs efforts toward increasing the diagnostic and prognostic power of advanced MRI data. To this aim, 23 Italian Scientific Institutes of Hospitalization and Care (IRCCS), with technological and clinical specialization in the neurological and neuroimaging field, have gathered together. Each IRCCS is equipped with high- or ultra-high field MRI scanners (i.e., ≥3T) for clinical or preclinical research or has established expertise in MRI data analysis and infrastructure. The actions of this Network were defined across several work packages (WP). A clinical work package (WP1) defined the guidelines for a minimum standard clinical qualitative MRI assessment for the main neurological diseases. Two neuroimaging technical work packages (WP2 and WP3, for clinical and preclinical scanners) established Standard Operative Procedures for quality controls on phantoms as well as advanced harmonized quantitative MRI protocols for studying the brain of healthy human participants and wild type mice. Under FAIR principles, a web-based e-infrastructure to store and share data across sites was also implemented (WP4). Finally, the RIN translated all these efforts into a large-scale multimodal data collection in patients and animal models with dementia (i.e., case study). The RIN-Neuroimaging Network can maximize the impact of public investments in research and clinical practice acquiring data across institutes and pathologies with high-quality and highly-consistent acquisition protocols, optimizing the analysis pipeline and data sharing procedures

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Diagnostic approach to sleep disordered-breathing among patients with grade III obesity

International audienceSleep apnea test (SAT) is a cost-effective approach to evaluate subjects without associated comorbidities suspected for obstructive sleep apnea (OSA), a disorder particularly common in obese subjects. The association of obesity with awake hypercapnia (carbon dioxide arterial pressure, PaCO2 ≥45 mmHg) defines the obesity-hypoventilation syndrome (OHS), which in turn results in increased morbidity and mortality compared to simple OSA. Isolated hypoventilation during sleep in obese patients (obesity-related sleep hypoventilation, ORSH) is now considered as an early stage of OHS. The aim of this study was to assess the performance of SAT in diagnosing OSA and predicting the presence of ORHS among patients with grade III obesity without awake hypercapnia. Methods: Over a 14-months period, patients with grade III obesity (body mass index≥40 kg/m 2) presenting moderate-to-severe OSA (apnea-hypopnea index [AHI]≥15) upon SAT and normal awake PaCO2 at arterial blood gas analysis, systematically underwent in-lab nocturnal polysomnography combined with transcutaneous carbon dioxide pressure (PtcCO2) monitoring. Results: Among 48 patients included in the study, 16 (33%) presented an AHI<15 upon polysomnography and 14 (29%) had ORSH. The test revealed no difference in ORSH prevalence between patients with AHI <15 or ≥15 (31% vs. 25%). No SAT variables were independently associated with increased PtCO2. Conclusions: This study shows that SAT overestimates OSA severity and ORSH affects one third of patients with grade III obesity without awake hypercapnia and with moderate-to-severe OSA at SAT, suggesting how polysomnography combined with PtCO2 monitoring is the most appropriate diagnostic approach for OSA and ORSH in this population

Le competenze interculturali del personale infermieristico in Toscana: effetti di un intervento formativo

Le relazioni interculturali sono state ampiamente studiate nei contesti scolastici, sebbene il primo ambiente incontrato dai migranti nelle nostre comunità locali sia probabilmente costituito dai servizi per la salute. E mentre molti interventi formativi sono rivolti alla scuola, lo stesso non si può dire per i servizi sociosanitari. Riteniamo che lo studio delle competenze interculturali nell’ambito dei servizi della salute costituisca un filone promettente di ricerca, e che la formazione in quest’ambito costituisca un’esigenza ormai irrinunciabile per le nostre comunità locali

Mitochondrial Ca2+ Signaling and Bioenergetics in Alzheimer&rsquo;s Disease

Alzheimer&rsquo;s disease (AD) is a hereditary and sporadic neurodegenerative illness defined by the gradual and cumulative loss of neurons in specific brain areas. The processes that cause AD are still under investigation and there are no available therapies to halt it. Current progress puts at the forefront the &ldquo;calcium (Ca2+) hypothesis&rdquo; as a key AD pathogenic pathway, impacting neuronal, astrocyte and microglial function. In this review, we focused on mitochondrial Ca2+ alterations in AD, their causes and bioenergetic consequences in neuronal and glial cells, summarizing the possible mechanisms linking detrimental mitochondrial Ca2+ signals to neuronal death in different experimental AD models