IAS-LAB PUBLICATIONS
Supervised and Unsupervised Soft Sensors for Capsule Recognition in Espresso Coffee Machines
Authors: Tortora Nicolas; De Moliner Antonio; Borsatti Francesco; Oboe Roberto; Susto Gian Antonio
Journal: 2024 IEEE 8TH FORUM ON RESEARCH AND TECHNOLOGIES FOR SOCIETY AND INDUSTRY INNOVATION, RTSI 2024
Published: 2024
DOI: 10.1109/RTSI61910.2024.10761676
Soft sensing technologies are of fundamental importance in production systems and in intelligent devices, especially in the Internet of Things scenario. Soft sensing approaches can be leveraged to create new functionalities or to reduce costs. In this work, we present one of the first soft sensor applications for coffee machines, the first one in the literature for capsule recognition. Coffee maker users expect the machine to deliver the product in the shortest time possible, without sacrificing the quality of the brewing. Traditional heated water tanks (boilers) have been replaced with electrical instantaneous water heaters, allowing the water to be heated as it flows from the tank to the brewing unit. Furthermore, instead of manually loading coffee powder, sealed capsules are now commonly adopted. To maintain the desired water temperature during brewing, it is crucial to modulate the electrical power applied to the heater in a predictive manner. However, distinguishing between different capsule types becomes a challenge in a multi-vendor scenario where capsules may adhere to standard shape constraints but originate from different companies. To address this problem, we propose an approach that utilizes Artificial Intelligence techniques to infer information related to the capsule characteristics and detect anomalies. The soft sensor estimations can be applied to adjust the brewing process or inform users about lower-quality capsules or improper usage of the machine. Experimental results support the effectiveness of the procedure, which analyzes the water flow during the pre-infusion phase.
Pages: 311-316
Keywords: Anomaly Detection; Classification; Fault identification; Home Appliances; Machine Learning; Soft Sensor; Unsupervised Learning;
REDISCOVER International Guidelines on the Perioperative Care of Surgical Patients With Borderline-resectable and Locally Advanced Pancreatic Cancer
Authors: AREA MIN. 06 - Scienze mediche; Non assegn; ITA; ANNALS OF SURGERY###0003-4932; Goal 3: Good health and well-being###25122; HIR-9086-2022; KUD-7288-2024; LPQ-9273-2024; F-4959-2015; R-4268-2019; AFN-0292-2022; DWN-5483-2022; KTZ-9389-2024; J-1823-2018; J-1773-2018; DRW-9172-2022; GGT-4260-2022; GJK-1358-2022; G-1562-2013; ABF-3638-2021; K-2162-2018; KOD-2341-2024; KLJ-2744-2024; FYV-1027-2022; HZK-7677-2023; HKC-8040-2023; JRR-9074-2023; J-2459-2019; FZZ-8447-2022; KFO-0106-2024; FSE-5790-2022; H-2906-2019; HJP-6338-2023; FLI-4548-2022; AAC-6811-2022; GNP-6898-2022; AAC-4725-2022; GDT-6714-2022; AAA-6894-2019; JAZ-9917-2023; FYX-3022-2022; JEV-4121-2023; EJU-6275-2022; LGC-9665-2024; NPJ-6583-2025; EKF-7457-2022; HKT-6511-2023; HQZ-0115-2023; CIT-6486-2022; EPU-6471-2022; AAR-9890-2020; AAL-6053-2021; HLH-2177-2023; CME-7637-2022; EPF-1268-2022; EQU-6208-2022; CLO-5864-2022; EUV-8360-2022; DWH-0618-2022; KLZ-4080-2024; JWH-9189-2024; B-8388-2015; LQH-4784-2024; CMU-9259-2022; CVL-7380-2022; EUJ-2112-2022; AAD-1462-2022; DXR-7353-2022; K-2194-2016; A-3390-2009; AAJ-7492-2020; CQD-9998-2022; AAP-7517-2021; GBP-2726-2022; DXX-3460-2022; CWB-3165-2022; FCO-3699-2022; FCW-1072-2022; KBL-7471-2024; AAC-1285-2020; AAS-9446-2021; FKD-1209-2022; FHT-1571-2022; FKQ-3988-2022; J-4395-2012; GPO-7828-2022; JSI-5198-2023; HLL-1987-2023; IOB-3974-2023; KSL-9685-2024; DIR-8420-2022; DYF-1341-2022; GBT-1583-2022; DJN-5216-2022; DMT-3940-2022; DWP-4435-2022; FSB-9577-2022; ABG-8049-2020; IRR-5847-2023; AAB-9311-2019; DQB-0141-2022; DPI-2359-2022; DRA-6821-2022; DWK-6248-2022; JQM-8624-2023; KDR-4457-2024; GEB-4419-2022; EBQ-4664-2022; LPU-4818-2024; MUK-4229-2025; L-4859-2019; JBJ-5939-2023; ECO-1318-2022; F-3648-2012; AFM-1199-2022; GGB-3610-2022; AIB-1093-2022; DYF-6587-2022; GEK-3872-2022; M-9998-2016; BBB-4422-2020; CLH-7436-2022; LZH-1527-2025; ISB-9310-2023; LPU-9898-2024; FMC-5677-2022; GYD-3896-2022; FQK-5172-2022; DNE-0425-2022; JQF-8815-2023; LFI-6260-2024; LPY-1007-2024; GBT-1096-2022; CKJ-2782-2022; ESC-4793-2022; EKS-2140-2022; 7006650849; 56702795300; 56825486500; 36896756400; 6603166269; 25629557900; 55984566700; 57197168275; 57214806473; 6701399875; 7006060690; 15038468800; 7403266165; 57218107225; 57192231561; 6603009398; 57720010400; 55509111100; 6602432201; 57193674819; 57935226100; 56226539800; 56293790400; 57219095045; 57218457292; 57203362088; 25822653100; 35194297700; 57216505967; 57160542600; 56374392200; 35932693900; 57194129977; 37032225200; 57452776600; 57197741821; 7004108792; 7003624387; 54379670700; 7003632276; 56320259700; 6602740386; 6701643081; 6602003631; 55322507700; 7103299923; 57222346419; 57210357669; 6507901837; 6602425555; 57195511860; 6602086506; 7003736253; 7005721973; 36636920900; 55864601100; 57188534945; 35333796700; 24398308900; 15131946700; 23495947400; 57215119693; 55900112400; 7006841625; 57217432305; 57217695196; 57224531374; 55232872100; 13102992200; 7003345079; 54417289400; 7003789046; 7403032095; 7006047480; 56155187200; 57199535647; 55871121048; 7402486188; 57195246303; 13608518900; 6603562523; 7006292400; 58166829400; 57208366180; 57210635588; 8710516300; 36480327700; 36119245100; 7003426595; 7004553354; 7006802879; 8651098300; 55825926600; 35285979400; 7101729388; 6604060317; 8671220800; 6701774458; 7006352137; 23028738700; 55068064200; 7004693267; 55536670400; 55036628400; 57214082478; 57193535459; 7004367076; 7005266719; 6603930289; 57215377105; 35453095200; 6602096290; 14219817200; 6602679151; 35569306200; 25121507000; 22949910600; 7003990635; 6506423746; 59165675300; 7006733339; 57216735491; 7003530707; 6603935090; 56271517500; 59165685200; 59165675400; 7004715910; 35377646900; 15818940100; 6701825374; 56269958800; 7003748603; 35576925000; 7007041726; 6603624531; 35276925200; 35564036000; 8574078800; 7005105898; 7007092215; 56261277000; 57957781200; 8596019700; 7003737747; 57191431774; 7004428325; 7005488006; 23391018000; 6701758716
Journal: ANNALS OF SURGERY
Published: 2024
DOI: 10.1097/SLA.0000000000006248
Objective: The REDISCOVER consensus conference aimed at developing and validating guidelines on the perioperative care of patients with borderline-resectable (BR-) and locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC). Background: Coupled with improvements in chemotherapy and radiation, the contemporary approach to pancreatic surgery supports the resection of BR-PDAC and, to a lesser extent, LA-PDAC. Guidelines outlining the selection and perioperative care for these patients are lacking. Methods: The Scottish Intercollegiate Guidelines Network (SIGN) methodology was used to develop the REDISCOVER guidelines and create recommendations. The Delphi approach was used to reach a consensus (agreement ≥80%) among experts. Recommendations were approved after a debate and vote among international experts in pancreatic surgery and pancreatic cancer management. A Validation Committee used the AGREE II-GRS tool to assess the methodological quality of the guidelines. Moreover, an independent multidisciplinary advisory group revised the statements to ensure adherence to nonsurgical guidelines. Results: Overall, 34 recommendations were created targeting centralization, training, staging, patient selection for surgery, possibility of surgery in uncommon scenarios, timing of surgery, avoidance of vascular reconstruction, details of vascular resection/reconstruction, arterial divestment, frozen section histology of perivascular tissue, extent of lymphadenectomy, anticoagulation prophylaxis, and role of minimally invasive surgery. The level of evidence was however low for 29 of 34 clinical questions. Participants agreed that the most conducive means to promptly advance our understanding in this field is to establish an international registry addressing this patient population (https://rediscover.unipi.it/). Conclusions: The REDISCOVER guidelines provide clinical recommendations pertaining to pancreatectomy with vascular resection for patients with BR-PDAC and LA-PDAC, and serve as the basis of a new international registry for this patient population.
Volume: 280 Pages: 56-65
Keywords: borderline-resectable pancreatic cancer; locally advanced pancreatic cancer; pancreatectomy with vascular resection; REDISCOVER Guidelines; REDISCOVER registry;
A pharmacoeconomic analysis from Italian guidelines for the management of prolactinomas
Authors: Basile Michele; Valentini Ilaria; Attanasio Roberto; Cozzi Renato; Persichetti Agnese; Samperi Irene; Scoppola Alessandro; Auriemma Renata Simona; De Menis Ernesto; Esposito Felice; Ferrante Emanuele; Iati Giuseppe; Mazzatenta Diego; Poggi Maurizio; Ruda Roberta; Tortora Fabio; Cruciani Fabio; Mitrova Zuzana; Saulle Rosella; Vecchi Simona; Cappabianca Paolo; Paoletta Agostino; Bozzao Alessandro; Caputo Marco; Doglietto Francesco; Ferrau Francesco; Lania Andrea Gerardo; Laureti Stefano; Lello Stefano; Locatelli Davide; Maffei Pietro; Minniti Giuseppe; Peri Alessandro; Ruini Chiara; Settanni Fabio; Silvani Antonio; Veronese Nadia; Grimaldi Franco; Papini Enrico; Cicchetti Americo; Iatì Giuseppe; Rudà Roberta; Ferraù Francesco
Journal: GLOBAL & REGIONAL HEALTH TECHNOLOGY ASSESSMENT
Published: 2024
Background: Prolactinoma, the most common pituitary adenoma, is usually treated with dopamine agonist (DA) therapy like cabergoline. Surgery is second-line therapy, and radiotherapy is used if surgical treatment fails or in relapsing macroprolactinoma. Objective: This study aimed to provide economic evidence for the management of prolactinoma in Italy, using a cost-of-illness and cost-utility analysis that considered various treatment options, including cabergoline, bromocriptine, temozolomide, radiation therapy, and surgical strategies. Methods: The researchers conducted a systematic literature review for each research question on scientific databases and surveyed a panel of experts for each therapeutic procedure’s specific drivers that contributed to its total cost. Results: The average cost of the first year of treatment was €2,558.91 and €3,287.40 for subjects with microprolactinoma and macroprolactinoma, respectively. Follow-up costs from the second to the fifth year after initial treatment were €798.13 and €1,084.59 per year in both groups. Cabergoline had an adequate cost-utility profile, with an incremental cost-effectiveness ratio (ICER) of €3,201.15 compared to bromocriptine, based on a willingness-to-pay of €40,000 per quality-adjusted life year (QALY) in the reference economy. Endoscopic surgery was more cost-effective than cabergoline, with an ICER of €44,846.64. Considering a willingness-to-pay of €40,000/QALY, the baseline findings show cabergoline to have high cost utility and endoscopic surgery just a tad above that. Conclusions: Due to the favorable cost-utility profile and safety of surgical treatment, pituitary surgery should be considered more frequently as the initial therapeutic approach. This management choice could lead to better outcomes and an appropriate allocation of healthcare resources.
Volume: 11 Pages: 1-16
Keywords: Bromocriptine; Cabergoline; Cost-utility; ICER; Prolactinoma;
Development and Validation of a Scoring System to Predict Response to Obeticholic Acid in Primary Biliary Cholangitis
Authors: AREA MIN. 06 - Scienze mediche; ITA; ESP; CLINICAL GASTROENTEROLOGY AND HEPATOLOGY###1542-3565; Goal 3: Good health and well-being###25122; AAB-7965-2019; AAI-2582-2019; EAE-4108-2022; GXH-0637-2022; AAM-5199-2020; ABA-7458-2022; JVS-9756-2024; CGZ-1469-2022; GDC-3815-2022; FZV-1726-2022; OUU-0112-2025; GDY-8839-2022; DJL-4541-2022; EEZ-3131-2022; AAA-5759-2019; LFT-8668-2024; J-8600-2018; DTY-5484-2022; DBM-5248-2022; OWZ-9517-2025; ESM-4915-2022; DKA-8928-2022; GCK-5367-2022; AAC-1949-2019; A-8153-2011; JGT-6942-2023; DII-2349-2022; MZQ-3477-2025; EYC-3101-2022; DUV-7269-2022; ABG-8602-2020; DUQ-0447-2022; JHF-5169-2023; EVK-6893-2022; FZX-8817-2022; IFW-9480-2023; ABH-3830-2020; FPR-5802-2022; DMO-7443-2022; HLO-6987-2023; CSJ-4102-2022; DTW-4107-2022; DML-2022-2022; AFB-8988-2022; EJE-9947-2022; DWY-3867-2022; GZB-9923-2022; GIU-7918-2022; GPU-6858-2022; FQP-0594-2022; K-4255-2019; GPG-1122-2022; LGR-8628-2024; I-3181-2012; Q-9087-2016; IAZ-8837-2023; E-4136-2016; AAC-1011-2019; KJQ-3800-2024; PHG-8971-2026; GVU-0673-2022; JCS-0572-2023; DTZ-5739-2022; AFM-3615-2022; DPL-0586-2022; ABH-5696-2020; DPA-2383-2022; CEV-0715-2022; A-1747-2015; DZH-2825-2022; EIZ-6768-2022; AAE-3161-2022; JHF-5645-2023; ESJ-6426-2022; FQB-6797-2022; MEA-5470-2025; DWP-4630-2022; MAF-2755-2025; GYG-3745-2022; K-7706-2016; CWY-4285-2022; HSQ-4900-2023; BBE-1333-2022; MNU-4399-2025; 55614146500; 18633282000; 57205384809; 57212087318; 57193756577; 56313347500; 36917864000; 60060300400; 55393491500; 25632063300; 59261172100; 53984037300; 6603313804; 6601986579; 13610386800; 6507356405; 57217439024; 16687592200; 56568449700; 8585105000; 57194027077; 6602157816; 7102124960; 16642301200; 57219424448; 14007949000; 35358824500; 57213387891; 7004246898; 20133772200; 55035217000; 57204697129; 7404409907; 7201832597; 57201632087; 7006018364; 57220568199; 57205518137; 59454560300; 7202617798; 7005538636; 59032984400; 6602216982; 8591926400; 57221085405; 55466623500; 7201882466; 6701845300; 8343762300; 59261531900; 56910382300; 7003598093; 6701914554; 7005708819; 57195457983; 35378208900; 35316450500; 57194756806; 55405948300; 36491591400; 24448726900; 59261532000; 15754656200; 6701614804; 7005317954; 6603415492; 6603967304; 7004209865; 6507767839; 6507569573; 6506390073; 16679697200; 6701711353; 7801426054; 6602552254; 7005510028; 55954093900; 57212735274; 57214121395; 7101699492; 54410544100; 7004168218; 6508059783; 49661067500; 57672957000; 59260812600; 7101785311; 21742405800; 58580548300; 59261172200; 59261907500; 6603588881; 35811426500; 57855586000; 56091637400; 37061857600; 57204005406; 57193753872; 14521508500; 7101771483; 9334501400; 57218115125; 16175587900; 57216488321; 57220047476; 58568206300; 8303238100; 55169439200; 57463244200; 59821121600; 22933243100; 6603798810; 7004111132; 58884412500; 57195772862; 57200288343; 7005255660; 36631959800; 58455215400; 56020556000; 59320129900; 59261907600
Journal: CLINICAL GASTROENTEROLOGY AND HEPATOLOGY
Published: 2024
DOI: 10.1016/j.cgh.2024.05.008
Background & Aims: Obeticholic acid (OCA) is the only licensed second-line therapy for primary biliary cholangitis (PBC). With novel therapeutics in advanced development, clinical tools are needed to tailor the treatment algorithm. We aimed to derive and externally validate the OCA response score (ORS) for predicting the response probability of individuals with PBC to OCA. Methods: We used data from the Italian RECAPITULATE (N = 441) and the IBER-PBC (N = 244) OCA real-world prospective cohorts to derive/validate a score including widely available variables obtained either pre-treatment (ORS) or also after 6 months of treatment (ORS+). Multivariable Cox regressions with backward selection were applied to obtain parsimonious predictive models. The predicted outcomes were biochemical response according to POISE (alkaline phosphatase [ALP]/upper limit of normal [ULN]<1.67 with a reduction of at least 15%, and normal bilirubin), or ALP/ULN<1.67, or normal range criteria (NR: normal ALP, alanine aminotransferase [ALT], and bilirubin) up to 24 months. Results: Depending on the response criteria, ORS included age, pruritus, cirrhosis, ALP/ULN, ALT/ULN, GGT/ULN, and bilirubin. ORS+ also included ALP/ULN and bilirubin after 6 months of OCA therapy. Internally validated c-statistics for ORS were 0.75, 0.78, and 0.72 for POISE, ALP/ULN<1.67, and NR response, which raised to 0.83, 0.88, and 0.81 with ORS+, respectively. The respective performances in validation were 0.70, 0.72, and 0.71 for ORS and 0.80, 0.84, and 0.78 for ORS+. Results were consistent across groups with mild/severe disease. Conclusions: We developed and externally validated a scoring system capable to predict OCA response according to different criteria. This tool will enhance a stratified second-line therapy model to streamline standard care and trial delivery in PBC.
Volume: 22 Pages: 2062-+
Keywords: Obeticholic Acid; Predictive Model; Primary Biliary Cholangitis;
Resorbable engineered barrier membranes for oral surgery applications
Authors: Balducci Cristian; Zamuner Annj; Todesco Martina; Bagno Andrea; Pasquato Antonella; Iucci Giovanna; Bertela Federica; Battocchio Chiara; Tortora Luca; Sacchetto Luca; Brun Paola; Bressan Eriberto; Dettin Monica; Bertelà Federica
Journal: JOURNAL OF BIOMEDICAL MATERIALS RESEARCH PART A
Published: 2024
DOI: 10.1002/jbm.a.37752
Population aging, reduced economic capacity, and neglecting the treatments for oral pathologies, are the main causal factors for about 3 billion individuals who are suffering from partial/total edentulism or alveolar bone resorption: thus, the demand for dental implants is increasingly growing. To achieve a good prognosis for implant-supported restorations, adequate peri-implant bone volume is mandatory. The Guided Bone Regeneration (GBR) technique is one of the most applied methods for alveolar bone reconstruction and treatment of peri-implant bone deficiencies. This technique involves the use of different types of membranes in association with some bone substitutes (autologous, homologous, or heterologous). However, time for bone regeneration is often too long and the bone quality is not simply predictable. This study aims at engineering and evaluating the efficacy of modified barrier membranes, enhancing their bioactivity for improved alveolar bone tissue regeneration. We investigated membranes functionalized with chitosan (CS) and chitosan combined with the peptide GBMP1α (CS + GBMP1α), to improve bone growth. OsseoGuard® membranes, derived from bovine Achilles tendon type I collagen crosslinked with formaldehyde, were modified using CS and CS + GBMP1α. The functionalization, carried out with 1-ethyl-3-(3 dimethylaminopropyl)carbodiimide and sulfo-N-Hydroxysuccinimide (EDC/sulfo-NHS), was assessed through FT-IR and XPS analyses. Biological assays were performed by directly seeding human osteoblasts onto the materials to assess cell proliferation, mineralization, gene expression of Secreted Phosphoprotein 1 (SPP1) and Runt-Related Transcription Factor 2 (Runx2), and antibacterial properties. Both CS and CS + GBMP1α functionalizations significantly enhanced human osteoblast proliferation, mineralization, gene expression, and antibacterial activity compared to commercial membranes. The CS + GBMP1α functionalization exhibited superior outcomes in all biological assays. Mechanical tests showed no significant alterations of membrane biomechanical properties post-functionalization. The engineered membranes, especially those functionalized with CS + GBMP1α, are suitable for GBR applications thanks to their ability to enhance osteoblast activity and promote bone tissue regeneration. These findings suggest a potential advancement in the treatment of oral cavity problems requiring bone regeneration.
Volume: 112 Pages: 1960-1974
Keywords: BMP-2; chitosan; GBR membranes; human osteoblasts; peptide;
Eosinophilic esophagitis in adults and adolescents: epidemiology, diagnostic challenges, and management strategies for a type 2 inflammatory disease
Authors: Savarino Edoardo Vincenzo; Barbara Giovanni; Bilo Maria Beatrice; De Bortoli Nicola; Di Sabatino Antonio; Oliva Salvatore; Penagini Roberto; Racca Francesca; Tortora Annalisa; Rumi Filippo; Cicchetti Americo; Bilò Maria Beatrice
Journal: THERAPEUTIC ADVANCES IN GASTROENTEROLOGY
Published: 2024
DOI: 10.1177/17562848241249570
Background: Eosinophilic esophagitis (EoE) is recognized as a chronic type 2 inflammatory disease characterized by the eosinophilic infiltration of the esophageal tissue, posing a significant disease burden and highlighting the necessity for novel management strategies to address unmet clinical needs. Objectives: To critically evaluate the existing literature on the epidemiology and management of EoE, identify evidence gaps, and assess the efficacy of current and emerging treatment modalities. Design: An extensive literature review was conducted, focusing on the epidemiological trends, diagnostic challenges, and therapeutic interventions for EoE. This was complemented by a survey among physicians and consultations with a scientific expert panel, including a patient’s association (ESEO Italia), to enrich the study findings. Data sources and methods: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, scrutinizing epidemiological studies and management research to compile comprehensive insights into the disease’s landscape. The physician survey and expert panel discussions aimed to bridge identified evidence gaps. Results: The review included 59 epidemiological and 51 management studies, uncovering variable incidence and prevalence rates of EoE globally, with an estimated diagnosed prevalence of 41 per 100,000 in Italy. Diagnostic challenges were identified, including nonspecific symptoms and the lack of definitive biomarkers, which complicate the use of endoscopy. Treatment options such as elimination diets, proton-pump inhibitors, and swallowed corticosteroids were found to have varying success rates, while Dupilumab, an emerging therapy targeting interleukin (IL)-4 and IL-13, shows promise. Conclusion: Despite advancements in understanding and managing EoE, significant unmet clinical needs remain, particularly in biomarker identification, therapy personalization, and cost-effectiveness evaluation. A comprehensive, multidimensional approach to patient management is required, emphasizing the importance of early symptom recognition, accurate diagnosis, and tailored treatment strategies. Dupilumab offers potential as a novel treatment, underscoring the need for future research to explore the economic and social dimensions of EoE care pathways.
Volume: 17
Keywords: diagnosis; eosinophilic esophagitis; epidemiology; systematic review; treatment pathway;
VADER: Probing the Dark Side of Dimorphos with LICIACube LUKE
Authors: Zinzi Angelo; Hasselmann P. H. A.; Della Corte V.; Deshapriya J. D. P.; Gai I.; Lucchetti A.; Pajola A.; Rossi A.; Dotto E.; Epifani E. Mazzotta; Daly R. T.; Hirabayashi M.; Farnham T.; Ernst C. M.; Ivanovski S. L.; Li J. -y.; Parro L. M.; Amoroso M.; Beccarelli J.; Bertini I.; Brucato J. R.; Capannolo A.; Caporali S.; Ceresoli M.; Cremonese G.; Dall'Ora M.; Casajus L. Gomez; Gramigna E.; Ieva S.; Impresario G.; Manghi R. Lasagni; Lavagna M.; Lombardo M.; Modenini D.; Negri B.; Palumbo P.; Perna D.; Pirrotta S.; Poggiali G.; Tortora P.; Tusberti F.; Zannoni M.; Zanotti G.; Hasselmann P.H.A.; Deshapriya J.D.P.; Mazzotta Epifani E.; Daly R.T.; Ernst C.M.; Ivanovski S.L.; Li J.-Y.; Parro L.M.; Brucato J.R.; Dall’Ora M.; Gomez Casajus L.; Lasagni Manghi R.
Journal: PLANETARY SCIENCE JOURNAL
Published: 2024
DOI: 10.3847/PSJ/ad3826
The ASI cubesat LICIACube has been part of the first planetary defense mission DART, having among its scopes to complement the DRACO images to better constrain the Dimorphos shape. LICIACube had two different cameras, LEIA and LUKE, and to accomplish its goal, it exploited the unique possibility of acquiring images of the Dimorphos hemisphere not seen by DART from a vantage point of view, in both time and space. This work is indeed aimed at constraining the tridimensional shape of Dimorphos, starting from both LUKE images of the nonimpacted hemisphere of Dimorphos and the results obtained by DART looking at the impacted hemisphere. To this aim, we developed a semiautomatic Computer Vision algorithm, named VADER, able to identify objects of interest on the basis of physical characteristics, subsequently used as input to retrieve the shape of the ellipse projected in the LUKE images analyzed. Thanks to this shape, we then extracted information about the Dimorphos ellipsoid by applying a series of quantitative geometric considerations. Although the solution space coming from this analysis includes the triaxial ellipsoid found by using DART images, we cannot discard the possibility that Dimorphos has a more elongated shape, more similar to what is expected from previous theories and observations. The result of our work seems therefore to emphasize the unique value of the LICIACube mission and its images, making even clearer the need of having different points of view to accurately define the shape of an asteroid.
Volume: 5
Long-term albumin improves the outcomes of patients with decompensated cirrhosis and diabetes mellitus: Post hoc analysis of the ANSWER trial
Authors: Pompili Enrico; Baldassarre Maurizio; Iannone Giulia; Tedesco Greta; Nardelli Silvia; Piano Salvatore; Alessandria Carlo; Neri Sergio; Foschi Francesco G.; Levantesi Fabio; Caraceni Paolo; Bernardi Mauro; Zaccherini Giacomo
Journal: LIVER INTERNATIONAL
Published: 2024
DOI: 10.1111/liv.16020
Type-2 diabetes mellitus is a frequent comorbidity of cirrhosis independently associated with cirrhosis-related complications and mortality. This post hoc analysis of the ANSWER trial database assessed the effects of long-term human albumin (HA) administration on top of the standard medical treatment (SMT) on the clinical outcomes of a subgroup of 85 outpatients with liver cirrhosis, uncomplicated ascites and insulin-treated diabetes mellitus type 2 (ITDM). Compared to patients in the SMT arm, the SMT + HA group showed a better overall survival (86% vs. 57%, p =.016) and lower incidence rates of paracenteses, overt hepatic encephalopathy, bacterial infections, renal dysfunction and electrolyte disorders. Hospital admissions did not differ between the two arms, but the number of days spent in hospital was lower in the SMT + HA group. In conclusion, in a subgroup of ITDM outpatients with decompensated cirrhosis and ascites, long-term HA administration was associated with better survival and a lower incidence of cirrhosis-related complications.
Volume: 44 Pages: 2108-2113
Keywords: ascites; bacterial infections; diabetes mellitus; hepatic encephalopathy; long-term albumin treatment; renal dysfunction; survival;
Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; NQF-9312-2025; DWY-3410-2022; GCV-8309-2022; GBV-7145-2022; IVA-4275-2023; B-4348-2013; DWR-0706-2022; Y-9126-2019; DWO-2405-2022; Z-4828-2019; GWV-3568-2022; A-5940-2015; GBD-1861-2022; DVE-7652-2022; OVT-7176-2025; GGL-1794-2022; H-4394-2019; PCO-3236-2025; EJM-8740-2022; GBC-8404-2022; FZO-1254-2022; FYJ-9637-2022; CDR-2303-2022; C-4378-2014; O-9369-2015; GBF-1843-2022; EKV-4052-2022; DVB-2560-2022; L-8385-2017; IZS-1150-2023; M-2616-2015; AAG-7753-2020; GBO-0318-2022; E-2727-2014; FCH-5665-2022; JFJ-3489-2023; HZM-8546-2023; GBS-0220-2022; H-8587-2015; DVC-6323-2022; LTU-6502-2024; PGG-2427-2026; AGZ-3259-2022; FZL-7353-2022; A-2693-2010; PCA-2324-2025; HLX-2021-2023; HKB-2933-2023; EUO-2530-2022; EUK-3820-2022; J-3686-2012; CPC-6980-2022; AAR-6622-2021; CQF-5798-2022; KLD-3528-2024; DXA-1952-2022; ITP-9423-2023; DVG-8690-2022; GAZ-3876-2022; GBB-1832-2022; JWI-2163-2024; ABF-7029-2021; DWK-1716-2022; CTE-6775-2022; CSK-3817-2022; FBF-5584-2022; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; EYY-4006-2022; DWQ-9372-2022; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; GEK-4486-2022; CYT-5449-2022; OOP-8239-2025; DUU-4676-2022; B-8502-2016; FKB-6410-2022; GFM-0308-2022; A-2699-2012; GAU-7672-2022; KES-2001-2024; DWC-8789-2022; DWZ-6747-2022; DFQ-7859-2022; AAH-9937-2020; DZP-0372-2022; AAX-3485-2021; D-1300-2016; FNO-5530-2022; GNG-7078-2022; FNC-4379-2022; FLD-9518-2022; AAB-4321-2020; DVP-3997-2022; FNA-5485-2022; AAW-4410-2021; DWT-4779-2022; DZP-5216-2022; FNB-0821-2022; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; DMG-4306-2022; IVG-7504-2023; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; AAR-4345-2020; K-4114-2015; OON-3882-2025; DNW-6364-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DXM-5348-2022; GBG-9412-2022; DPD-7597-2022; IZJ-2041-2023; GBV-4959-2022; GWX-9207-2022; FZJ-5145-2022; J-5067-2012; EAA-4768-2022; JAE-9097-2023; L-8068-2014; CFK-4637-2022; DZM-7523-2022; Q-2220-2015; T-7378-2018; AAB-2503-2019; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; JCG-3503-2023; Q-6715-2019; ABD-6783-2021; DZU-8266-2022; EAZ-0566-2022; EIA-6036-2022; O-9396-2015; AAO-6325-2021; O-9495-2015; GDW-2905-2022; CHT-0596-2022; DTO-7937-2022; HTG-8587-2023; CMV-6954-2022; JWO-0785-2024; GBY-6621-2022; FLM-0394-2022; HUJ-7899-2023; B-8712-2017; DJO-8166-2022; LXV-7382-2024; FXS-9180-2022; JMK-1133-2023; P-2194-2018; DYK-4428-2022; ECX-7840-2022; AAH-3743-2019; DTU-2081-2022; FXV-4290-2022; CEY-5520-2022; AAZ-4907-2020; KTI-3074-2024; B-3004-2019; CIW-4665-2022; CGZ-3153-2022; EPI-1133-2022; AFR-7693-2022; O-9391-2015; KDL-3231-2024; OZD-6988-2025; S-1204-2016; GBB-5111-2022; HOH-0341-2023; CMH-0439-2022; EQT-2114-2022; PDR-7717-2025; ERD-3189-2022; CNT-5485-2022; L-6378-2014; IDQ-0489-2023; L-2472-2017; CDE-1189-2022; AAW-1061-2020; FYF-0438-2022; DUJ-9002-2022; EVC-7104-2022; CRZ-8120-2022; C-2920-2017; L-4894-2014; PDR-5897-2025; DWN-8747-2022; ABB-8257-2020; J-1632-2012; EZW-1554-2022; DSX-9005-2022; JZW-7667-2024; DWK-0332-2022; HRR-2616-2023; ITW-2356-2023; GEC-5455-2022; HRX-7202-2023; PGA-1439-2026; IZP-8032-2023; FZY-7746-2022; DWL-3001-2022; ABA-3922-2020; U-7309-2018; AAC-2261-2020; LRV-2049-2024; FMO-3603-2022; GFP-2203-2022; D-1237-2017; DVN-0580-2022; PHJ-0952-2026; AAI-1245-2021; AAN-7016-2021; DNL-3219-2022; FTV-6637-2022; ORK-4232-2025; V-1081-2019; JCV-3612-2023; GCY-0967-2022; GGM-6223-2022; GEF-7978-2022; DWT-7233-2022; FXG-6905-2022; R-3469-2017; IYS-3498-2023; GCB-1754-2022; JCM-8241-2023; JAN-6167-2023; EAO-6360-2022; LGB-5701-2024; GDF-8239-2022; CDT-0258-2022; LXA-1722-2024; JEZ-2766-2023; MVC-8981-2025; CDU-7975-2022; JSZ-6163-2023; ECF-2024-2022; A-9058-2016; IAD-4339-2023; AAW-3335-2020; 58122394000; 57193558463; 57191290632; 23050749700; 24439181000; 7003910265; 57219737156; 36237905600; 7004293616; 7005317106; 55868798300; 57219746647; 55541304900; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2024
DOI: 10.1051/0004-6361/202449609
The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.
Volume: 689
Keywords: galaxies: evolution; galaxies: structure; methods: data analysis; methods: observational; techniques: image processing;
Gender minorities in breast cancer – Clinical trials enrollment disparities: Focus on male, transgender and gender diverse patients
Authors: Miglietta Federica; Pontolillo Letizia; De Angelis Carmine; Caputo Roberta; Marino Monica; Bria Emilio; Di Rienzo Rossana; Verrazzo Annarita; Buonerba Carlo; Tortora Giampaolo; Di Lorenzo Giuseppe; Del Mastro Lucia; Giuliano Mario; Montemurro Filippo; Puglisi Fabio; Guarneri Valentina; De Laurentiis Michelino; Scafuri Luca; Arpino Grazia
Journal: BREAST
Published: 2024
DOI: 10.1016/j.breast.2024.103713
Background: The last years have seen unprecedented improvement in breast cancer (BC) survival rates. However, this entirely apply to female BC patients, since gender minorities (male, transgender/gender-diverse) are neglected in BC phase III registration clinical trials. Methods: We conducted a scoping review of phase III clinical trials of agents with a current positioning within the therapeutic algorithms of BC. Results: We selected 51 phase III trials. Men enrollment was allowed in 35.3% of trials. In none of the trial inclusion/exclusion criteria referred to transgender/gender-diverse people. A numerical higher rate of enrolled men was observed in the contemporary as compared to historical group. We found a statistically significant association between the drug class and the possibility of including men: 100%, 80%, 50%, 33.3%, 25%, 10% and 9.1% of trials testing ICI/PARP-i, ADCs, PI3K/AKT/mTOR-i, anti-HER2 therapy, CDK4/6-i, ET alone, and CT alone. Overall, 77409 patients were enrolled, including 112 men (0.2%). None of the trial reported transgender/gender-diverse people proportion. Studies investigating PARP-i were significantly associated with the highest rate of enrolled men (1.42%), while the lowest rates were observed for trials of CT (0.13%), ET alone (0.10%), and CDK 4/6-I (0.08%), p < 0.001. Conclusions: We confirmed that gender minorities are severely underrepresented among BC registration trials. We observed a lower rate of men in trials envisaging endocrine manipulation or in less contemporary trials. This work sought to urge the scientific community to increase the awareness level towards the issue of gender minorities and to endorse more inclusive criteria in clinical trials.
Volume: 75
Keywords: Breast cancer; Gender diversity; Gender minorities; Male; Transgender and gender diverse people;