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At the SPES (Selective Production of Exotic Species) facility, intense Radioactive Ion Beams (RIBs) are produced by the interaction of a 40 MeV proton beam with a multi-foil uranium carbide target employing the Isotope Separation On-Line (ISOL) technique. The Target Ion Source (TIS) unit constitutes the core of the isotope production process. TIS units are replaced on a periodic basis during operation to maintain high performance. An automated storage system has been designed to accept highly radioactive TIS units and house them during a cooling period prior to decommissioning. The system is conceived to meet strict functional and safety requirements. Its peculiar design allows for improved reliability and availability during critical operations, as well as minimization of staff exposure to ionizing radiation during maintenance tasks. This contribution describes the design and control architecture of the Temporary Storage System (TSS). The equipment is part of a structured framework of remote manipulation, consisting of various machines interlocked with the Access Control System (ACS) and the Machine Protection System (MPS).

The CERN-MEDICIS (MEDical Isotopes Collected from ISolde) facility has delivered its first radioactive ion beam at CERN (Switzerland) in December 2017 to support the research and development in nuclear medicine using non-conventional radionuclides. Since then, fourteen institutes, including CERN, have joined the collaboration to drive the scientific program of this unique installation and evaluate the needs of the community to improve the research in imaging, diagnostics, radiation therapy and personalized medicine. The facility has been built as an extension of the ISOLDE (Isotope Separator On Line DEvice) facility at CERN. Handling of open radioisotope sources is made possible thanks to its Radiological Controlled Area and laboratory. Targets are being irradiated by the 1.4 GeV proton beam delivered by the CERN Proton Synchrotron Booster (PSB) on a station placed between the High Resolution Separator (HRS) ISOLDE target station and its beam dump. Irradiated target materials are also received from external institutes to undergo mass separation at CERN-MEDICIS. All targets are handled via a remote handling system and exploited on a dedicated isotope separator beamline. To allow for the release and collection of a specific radionuclide of medical interest, each target is heated to temperatures of up to 2,300°C. The created ions are extracted and accelerated to an energy up to 60 kV, and the beam steered through an off-line sector field magnet mass separator. This is followed by the extraction of the radionuclide of interest through mass separation and its subsequent implantation into a collection foil. In addition, the MELISSA (MEDICIS Laser Ion Source Setup At CERN) laser laboratory, in service since April 2019, helps to increase the separation efficiency and the selectivity. After collection, the implanted radionuclides are dispatched to the biomedical research centers, participating in the CERN-MEDICIS collaboration, for Research & Development in imaging or treatment. Since its commissioning, the CERN-MEDICIS facility has provided its partner institutes with non-conventional medical radionuclides such as Tb-149, Tb-152, Tb-155, Sm-153, Tm-165, Tm-167, Er-169, Yb-175, and Ac-225 with a high specific activity. This article provides a review of the achievements and milestones of CERN-MEDICIS since it has produced its first radioactive isotope in December 2017, with a special focus on its most recent operation in 2020.

Purpose The chemokine receptor CXCR4 is a promising target for molecular imaging of CXCR4 + cell types, e.g. inflammatory cells, in cardiovascular diseases. We speculated that a specific CXCR4 ligand, [ 68 Ga]pentixafor, along with novel techniques for motion correction, would facilitate the in vivo characterization of CXCR4 expression in small culprit and nonculprit coronary atherosclerotic lesions after acute myocardial infarction by motion-corrected targeted PET/CT. Methods CXCR4 expression was analysed ex vivo in separately obtained arterial wall specimens. [ 68 Ga]Pentixafor PET/CT was performed in 37 patients after stent-based reperfusion for a first acute ST-segment elevation myocardial infarction. List-mode PET data were reconstructed to five different datasets using cardiac and/or respiratory gating. Guided by CT for localization, the PET signals of culprit and various groups of nonculprit coronary lesions were analysed and compared. Results Ex vivo, CXCR4 was upregulated in atherosclerotic lesions, and mainly colocalized with CD68 + inflammatory cells. In vivo, elevated CXCR4 expression was detected in culprit and nonculprit lesions, and the strongest CXCR4 PET signal (median SUV max 1.96; interquartile range, IQR, 1.55–2.31) was observed in culprit coronary artery lesions. Stented nonculprit lesions (median SUV max 1.45, IQR 1.23–1.88; P  = 0.048) and hot spots in naive remote coronary segments (median SUV max 1.34, IQR 1.23–1.74; P  = 0.0005) showed significantly lower levels of CXCR4 expression. Dual cardiac/respiratory gating provided the strongest CXCR4 PET signal and the highest lesion detectability. Conclusion We demonstrated the basic feasibility of motion-corrected targeted PET/CT imaging of CXCR4 expression in coronary artery lesions, which was triggered by vessel wall inflammation but also by stent-induced injury. This novel methodology may serve as a platform for future diagnostic and therapeutic clinical studies targeting the biology of coronary atherosclerotic plaque.

Inhibitors of WEE1 and ATR kinases are considered promising for cancer treatment, either as monotherapy or in combination with chemo- or radiotherapy. Here, we addressed whether simultaneous inhibition of WEE1 and ATR might be advantageous. Effects of the WEE1 inhibitor MK1775 and ATR inhibitor VE822 were investigated in U2OS osteosarcoma cells and in four lung cancer cell lines, H460, A549, H1975, and SW900, with different sensitivities to the WEE1 inhibitor. Despite the differences in cytotoxic effects, the WEE1 inhibitor reduced the inhibitory phosphorylation of CDK, leading to increased CDK activity accompanied by ATR activation in all cell lines. However, combining ATR inhibition with WEE1 inhibition could not fully compensate for cell resistance to the WEE1 inhibitor and reduced cell viability to a variable extent. The decreased cell viability upon the combined treatment correlated with a synergistic induction of DNA damage in S-phase in U2OS cells but not in the lung cancer cells. Moreover, less synergy was found between ATR and WEE1 inhibitors upon co-treatment with radiation, suggesting that single inhibitors may be preferable together with radiotherapy. Altogether, our results support that combining WEE1 and ATR inhibitors may be beneficial for cancer treatment in some cases, but also highlight that the effects vary between cancer cell lines.

Background:Systemic Lupus Erythematosus (SLE) is a complex, relapsing-remitting disease, posing challenges in diagnosis and management. Traditional disease activity indices often fail to capture its dynamic nature, hindering effective therapy guidance. Leveraging Electronic Health Records (EHR) through data mining and machine learning offers a promising approach to understanding disease complexity and prognostic trajectories, specifically disease flares.Objectives:To validate a machine-learning methodology for identifying SLE phenotypes and flare trajectories in an outpatient setting.Methods:An observational retrospective monocenter study was performed using EHR of our Tertiary Care University Hospital. First, we developed a SLE Data Mart combining all HER sources. Then a machine learning algorithm, based on Natural Language Processing (NLP), was created to characterize disease complexity and flares of SLE pts in a primary cohort of adult SLE pts with at least one hospitalization. Further, we validated this algorithm in a second cohort of SLE pts followed only in outpatient setting (internal validation cohort). The inclusion criteria of the validation cohort were: 1) SLE diagnosis (according to ACR/EULAR 2019 criteria); 2) Age > 18; 3) No hospitalizations for SLE disease 4) at least 1 year follow-up, 5) at least 1.5 contacts/year in the period between January 2012 and December 2020; 6) at least one laboratory value available for the patient during follow-up.For each patient, clinical reports including demographics, anamnesis, clinical symptoms, laboratory values, medication orders and therapy, were extracted from the Data Mart, through the NLP pipeline: 1) presence of 8 different SLE clinical domains (hematological, muco-cutaneous, articular, renal, systemic, neurologic, vascular involvement and serositis); 2) disease complexity based on the combination of the involvement of single or multiple organ domains, as well as therapy escalation (low, medium, high); 3) disease flares.Baseline and longitudinal descriptive analyses were performed using median and interquartile values for numerical values and percentage for categorical ones. A p-value<0.05 was considered as significantResults:A total of 255 SLE pts with at least one hospitalization were identified in our EHR and considered as primary cohort, while 91 SLE pts were included in the internal validation cohort. The 2 cohorts were comparable for age, sex and disease duration. The median number of clinical domains involved at baseline was higher in the primary cohort [4 (2.5-5)] than in the validation cohort [2 (1, 2.5)], (<0.01); Differences in clinical phenotype were confirmed in the longitudinal analysis, in which the median number of clinical domains involved was higher in the primary cohort [5 (4-6)] compared to the validation cohort [4 (3-4)],(p<0.01).At baseline, SLE complexity was categorized as low, medium and high (13.7%, 34.5% and 51.8% in the primary cohort and 47.3%, 35.2% and 17.6% in the validation cohort, respectively, p_low < 0.01, pmedium > 0.01, p_high < 0.01).The more complex SLE phenotype (i.e. higher number of domains involved) observed in the primary cohort was also confirmed by the higher number of flares [5.0 (2.0-9.0 vs 3 (1-5)], and therefore the higher number of clinical contacts (17.0 (11.0-25.5) vs 12 [6-19.5]), respectively (p<0.01 for both comparisons). Median number of flares significantly increased with disease complexity in the primary cohort [(3.5 (2.0-6.0), 4.0 (2.0-8.0), 6 (3.0-9.2), p<0.05], while they were comparable in the validation cohort [3 (1.0-5.0), 3 (1.0-5.0), 3 (1.0-6.0)].In addition, the use of steroids was higher in the primary cohort (78.6%), as compared to the validation cohort (52.7%), as well as conventional immunosuppressive treatment intake (73.2% vs 45%) and biologic treatment (29.0% vs 9.8%) (p<0.00001 for all comparisons). The percentage of pts treated with antimalarial was comparable (79.8 vs 87.5%, p=ns).Conclusion:The machine learning algorithm effectively describes SLE heterogeneity, enabling the characterization of clinical phenotypes and longitudinal trajectories based on clinical complexity.REFERENCES:NIL.Figure 1.Acknowledgements:This project received financial support from AstraZeneca.Disclosure of Interests:Silvia Laura Bosello: None declared, Livia Lilli: None declared, Carlotta Masciocchi: None declared, Laura Antenucci: None declared, Jacopo Lenkowicz: None declared, Augusta Ortolan: None declared, Pier Giacomo Cerasuolo: None declared, Lucia Lanzo: None declared, Silvia Piunno: None declared, Gabriella Castellino Astrazeneca, Marco Gorini Astrazeneca, Stefano Patarnello: None declared, Maria Antonietta D’Agostino: None declared.

Background COVID-19 disproportionately affected Latine communities in the United States, particularly Spanish-speaking and immigrant Latines. Our community-based participatory research (CBPR) partnership developed and tested Nuestra Comunidad Saludable , a Spanish-language intervention harnessing community-based peer navigation and mHealth. The objective of this work is to test the intervention using a cluster randomized trial design to increase COVID-19 testing and vaccination among Spanish-speaking Latino communities in the United States. Methods We used a longitudinal, two-group randomized controlled trial design to evaluate the intervention. We recruited 20 peer navigators ( Navegantes ), each with -eight non-overlapping social network members, and randomly assigned them with their social networks to either the intervention or delayed-intervention group (10 Navegantes and 80 social network members per group) for a total of 160 social network participants at baseline. Data were collected from social network member participants at baseline and immediately post-intervention (six months after intervention-group Navegante training). Participant retention rate was 98.1%. Regression modeling was used to assess changes in testing and vaccination outcomes and intervention-related psychosocial determinants between baseline and follow-up. Results Mean age of participants was 42 years (SD = 12.8); 77% identified as cisgender women; 85% spoke only or more Spanish than English; and 44% had beyond high school/GED equivalent educational attainment. At follow-up, there were no significant increases in ever having been tested or ever having been vaccinated. Both intervention and delayed-intervention groups experienced improvements in being up to date on recommended vaccine doses and in the number of doses received. Intervention participants reported improvements in some intervention-related psychosocial determinants such as increased testing intention ( p <0.001), decreased testing barriers ( p= 0.034), and fewer number of testing barriers ( p =0.043). Conclusion The results of this intervention, including the high retention rates, increased intention to get tested, and reduced testing barriers reveal valuable insights for future efforts aimed at addressing the profound challenges faced by Spanish-speaking Latines in accessing COVID-19, in particular, and other healthcare resources, more generally. Peer navigation and mHealth hold promise for promoting community engagement and improving access to services and overall health outcomes for populations facing barriers to healthcare.

Torquetenovirus (TTV), a small, single stranded anellovirus, is currently being explored as a marker of immunocompetence in patients with immunological impairment and inflammatory disorders. TTV has an extremely high prevalence and is regarded as a part of the human virome, the replication of which is controlled by a functioning immune system. The viral load of TTV in plasma of individuals is thought to reflect the degree of immunosuppression. Measuring and quantifying this viral load is especially promising in organ transplantation, as many studies have shown a strong correlation between high TTV loads and increased risk of infection on one side, and low TTV loads and an increased risk of rejection on the other side. As clinical studies are underway, investigating if TTV viral load measurement is superior for gauging antirejection therapy compared to medication-levels, some aspects nevertheless have to be considered. In contrast with medication levels, TTV loads have to be interpreted bearing in mind that viruses have properties including transmission, tropism, genotypes and mutations. This narrative review describes the potential pitfalls of TTV measurement in the follow-up of solid organ transplant recipients and addresses the questions which remain to be answered.

Background:Electronic Health Records (EHRs) contain a wealth of patient data, but they are often unstructured and difficult to analyze. Artificial Intelligence (AI) and its application Natural Language Processing (NLP, which is able to interpret and generate human language) can be helpful to extract longitudinal information on the disease course, especially in complex chronic diseases such as Systemic Lupus Erythematosus (SLE).Objectives:Our aim was to develop an integrated approach that combines clinical knowledge and advanced data science techniques (specifically, automated rule-based system and NLP) to characterize SLE patients in terms of involved disease domains, current symptoms, therapies and disease activityMethods:A standardized, replicable methodology was created, using data from a training set (development cohort) to extract relevant SLE features. The framework combined both AI-based steps with human intelligence (HI). A stepwise sequence was followed (1 and 4 HI-based; 2,3, and 5 AI-based): 1) ontology definition, that specifies relevant SLE attributes that characterize patient status at time of visit. Namely, we decided to extract: a) disease domains (hematological, cutaneous, articular, kidney, serositic, systemic, neurological, vascular involvement); b) current symptoms; c) therapies; d) disease activity expressed as SLEDAI-2K. 2) creation of a structured body of knowledge, where EHRs are selected and preprocessed using segmentation and tagging techniques 3) extraction of information specified in step 1 by an automated NLP algorithm, able to identify from EHRs, for each patient’s contact, the lupic attributes previously defined 4) development of a rule-based framework determining how the SLE attributes, biomarkers and patient’s history are combined to characterize the disease domains (Figure 1) and disease activity 5) implementation of the rule based-framework to classify for each patient’s contact in terms of lupic attributesFinally, the clinical records of 56 patients (excluded from HERs used to develop the algorithm, validation cohort) were examined by a group of physicians who manually extracted SLE attributes. Thereafter, the information was compared with the one extracted by the NLP algorithm: accuracy of algorithm was tested against the gold standard (manual extraction further revised by a second team of expert clinicians). Furthermore, distribution of SLEDAI-2K extracted with the algorithm (proxy SLEDAI) was compared to the SLEDAI-2K manually annotated by physicians (manual SLEDAI).Results:The framework was applied to a cohort of 262 SLE patients, with a median of 18 (11- 28) contacts, in a temporal window of 7 (4-10) years, for a total of 4567 EHRs. In the 56 patients of the validation cohort (n contacts 12.5, 10-17), the most frequently reported involved disease domains were articular (59%), cutaneous (62%), hematological (60%), neurological (20%), kidney (34%), serositic (20%), systemic (16%) and vascular (30%) involvement. Among symptoms, the most frequent were arthromyalgia (78%) and erythema (64%). Antimalarials, traditional immunosuppressant and biologics were used by 79%, 75% and 27% of the patients. These percentages reflected plausible values for an SLE population and this was considered as proof of face validity. Accuracy [n of true positives and negatives/all observations] for the NLP algorithm to extract data was in the range of 99-100% for disease domains, 97-99 % for symptoms, and 93-98% for therapies. Variance distribution of SLEDAI and proxy SLEDAI was not significantly different (Levene’s test 1.58, p=0.21) (Figure 2). When looking at the effort required to extract data from EHRs, the mean time to extract the lupic features from EHRs through the framework was in the range of 10 mins for a cohort of 262 patients, to be compared with an effort of 2 hours per patients through HI.Conclusion:The proposed framework integrates domain expertise and AI-based techniques to deliver a validated longitudinal phenotype characterization for each SLE patients. The application of this technique to elaborate real-life SLE data seems promising and feasible, with a relevant spare of human effort.REFERENCES:NIL.Acknowledgements:This work was funded by AstraZenecaDisclosure of Interests:Augusta Ortolan Janssen, Novartis, Abbvie, UCB Pharma, Livia Lilli: None declared, Silvia Laura Bosello: None declared, Laura Antenucci: None declared, Carlotta Masciocchi: None declared, Jacopo Lenkowicz: None declared, Piergiacomo Cerasuolo: None declared, Lucia Lanzo: None declared, Silvia Piunno: None declared, Gabriella Castellino AstraZeneca, Marco Gorini Astrazeneca, Stefano Patarnello: None declared, Maria Antonietta D’ Agostino Novartis, BMS, Janssen,Pfizer, Amgen, Galapagos, AbbVie, UCB, and Eli Lilly

Background Radiofrequency ablation has been shown to be a safe and effective treatment for scar-related ventricular arrhythmias (VA). Recent preliminary studies have shown that real time integration of late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) images with electroanatomical map (EAM) data may lead to increased procedure efficacy, efficiency, and safety. Methods VOYAGE is a prospective, randomized, multicenter controlled open label study designed to compare in terms of efficacy, efficiency, and safety a CMR aided/guided workflow to standard EAM-guided ventricular tachycardia (VT) ablation. Patients with an ICD or with ICD implantation expected within 1 month, with scar related VT, suitable for CMR and multidetector computed tomography (MDCT) will be randomized to a CMR-guided or CMR-aided approach, whereas subjects unsuitable for imaging or with image quality deemed not sufficient for postprocessing will be allocated to standard of care ablation. Primary endpoint is defined as VT recurrences (sustained or requiring appropriate ICD intervention) during 12 months follow-up, excluding the first month of blanking period. Secondary endpoints will include procedural efficiency, safety, impact on quality of life and comparison between CMR-guided and CMR-aided approaches. Patients will be evaluated at 1, 6 and 12 months. Discussion The clinical impact of real time CMR-guided/aided ablation approaches has not been thoroughly assessed yet. This study aims at defining whether such workflow results in more effective, efficient, and safer procedures. If proven to be of benefit, results from this study could be applied in large scale interventional practice. Trial registration ClinicalTrials.gov, NCT04694079, registered on January 1, 2021.

Latine communities in the United States have been disproportionately affected by COVID-19. It is critical to gain a better understanding of the sociocultural determinants that challenge and facilitate COVID-19 testing, vaccination, and booster uptake within these vulnerable communities to inform culturally congruent strategies and interventions. In summer 2022, our community-based participatory research partnership conducted 30 key informant interviews and 7 focus groups with 64 Spanish-speaking Latine participants in North Carolina. Interviewees consisted of representatives from health and service organizations, most of whom were engaged with direct service to Spanish speakers. Interviews were conducted in either English or Spanish, depending on the preference of the participant; all focus groups were conducted in Spanish. Interviews and focus groups were conducted in person or by videoconference. Twenty themes emerged that we organize into four domains: general perceptions about COVID-19; barriers to COVID-19 testing, vaccination, and booster uptake; facilitators to COVID-19 testing, vaccination, and booster uptake; and recommendations to promote testing, vaccination, and booster uptake. Results underscore important sociocultural determinants of ongoing COVID-19 testing, vaccination, and booster uptake to consider in developing interventions for Spanish-speaking Latines in the United States. Based on this formative work, our partnership developed Nuestra Comunidad Saludable (Our Healthy Community). We are implementing the intervention to test whether trained peer navigators can increase COVID-19 testing, vaccination, and booster uptake among Spanish-speaking Latines through blending in-person interactions and mHealth (mobile health) strategies using social media.