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Gangliosides (GGs) represent an important class of biomolecules associated with the central nervous system (CNS). In view of their special role at a CNS level, GGs are valuable diagnostic markers and prospective therapeutic agents. By ion mobility separation mass spectrometry (IMS MS), recently implemented by us in the investigation of human CNS gangliosidome, we previously discovered a similarity between GG profiles in CSF and the brain. Based on these findings, we developed IMS tandem MS (MS/MS) to characterize rare human CSF glycoforms, with a potential biomarker role. To investigate the oligosaccharide and ceramide structures, the ions detected following IMS MS separation were submitted to structural analysis by collision-induced dissociation (CID) MS/MS in the transfer cell. The IMS evidence on only one mobility feature, together with the diagnostic fragment ions, allowed the unequivocal identification of isomers in the CSF. Hence, by IMS MS/MS, GalNAc-GD1c(d18:1/18:1) and GalNAc-GD1c(d18:1/18:0) having both Neu5Ac residues and GalNAc attached to the external galactose were for the first time discovered and structurally characterized. The present results demonstrate the high potential of IMS MS/MS for biomarker discovery and characterization in body fluids, and the perspectives of method implementation in clinical analyses targeting the early diagnosis of CNS diseases through molecular fingerprints.

The radionuclides [sup.43] Sc, [sup.44g/m] Sc, and [sup.47] Sc can be produced cost-effectively in sufficient yield for medical research and applications by irradiating [sup.nat] Ti and [sup.nat] V target materials with protons. Maximizing the production yield of the therapeutic [sup.47] Sc in the highest cross section energy range of 24–70 MeV results in the co-production of long-lived, high-γ-ray-energy [sup.46] Sc and [sup.48] Sc contaminants if one does not use enriched target materials. Mass separation can be used to obtain high molar activity and isotopically pure Sc radionuclides from natural target materials; however, suitable operational conditions to obtain relevant activity released from irradiated [sup.nat] Ti and [sup.nat] V have not yet been established at CERN-MEDICIS and ISOLDE. The objective of this work was to develop target units for the production, release, and purification of Sc radionuclides by mass separation as well as to investigate target materials for the mass separation that are compatible with high-yield Sc radionuclide production in the 9–70 MeV proton energy range. In this study, the in-target production yield obtained at MEDICIS with 1.4 GeV protons is compared with the production yield that can be reached with commercially available cyclotrons. The thick-target materials were irradiated at MEDICIS and comprised of metallic [sup.nat] Ti, [sup.nat] V metallic foils, and [sup.nat] TiC pellets. The produced radionuclides were subsequently released, ionized, and extracted from various target and ion source units and mass separated. Mono-atomic Sc laser and molecule ionization with forced-electron-beam-induced arc-discharge ion sources were investigated. Sc radionuclide production in thick [sup.nat] Ti and [sup.nat] V targets at MEDICIS is equivalent to low- to medium-energy cyclotron-irradiated targets at medically relevant yields, furthermore benefiting from the mass separation possibility. A two-step laser resonance ionization scheme was used to obtain mono-atomic Sc ion beams. Sc radionuclide release from irradiated target units most effectively could be promoted by volatile scandium fluoride formation. Thus, isotopically pure [sup.44g/m] Sc, [sup.46] Sc, and [sup.47] Sc were obtained as mono-atomic and molecular ScF[sub.2] [sup.+] ion beams and collected for the first time at CERN-MEDICIS. Among all the investigated target materials, [sup.nat] TiC is the most suitable target material for Sc mass separation as molecular halide beams, due to high possible operating temperatures and sustained release.

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.

The mass separation of chemical element mixtures is a relevant task for numerous applications in the nuclear power industry. One of the promising approaches to solve this problem is plasma mass separation. In a recent study, the efficiency of plasma mass separation in a configuration with a potential well and a homogeneous magnetic field was experimentally demonstrated. This article examines the possibility of increasing the distance between the deposition regions of charged particles with different masses by varying the profile of the electric field potential. Such correlation can be considered as the control in a system of active particles. A cylindrical coordinate system is used. The electric field is axially symmetrical, and the magnetic field is directed along the axis of the symmetry. The corresponding mathematical problem was solved in a general way. The criteria for increasing the distance between the deposition areas of the “light” and “heavy” components of the mixture have been formulated. A high sensitivity of particle trajectories to the electric field potential in the region of the pericentres of the trajectories of charged particles was detected. Recommendations for the practical implementation of the optimal spatial separation of ion fluxes are proposed.

The radionuclides 43Sc,  44g/mSc, and 47Sc can be produced cost-effectively in sufficient yield for medical research and applications by irradiating  natTi and  natV target materials with protons. Maximizing the production yield of the therapeutic 47Sc in the highest cross section energy range of 24–70 MeV results in the co-production of long-lived, high-γ-ray-energy 46Sc and 48Sc contaminants if one does not use enriched target materials. Mass separation can be used to obtain high molar activity and isotopically pure Sc radionuclides from natural target materials; however, suitable operational conditions to obtain relevant activity released from irradiated  natTi and  natV have not yet been established at CERN-MEDICIS and ISOLDE. The objective of this work was to develop target units for the production, release, and purification of Sc radionuclides by mass separation as well as to investigate target materials for the mass separation that are compatible with high-yield Sc radionuclide production in the 9–70 MeV proton energy range. In this study, the in-target production yield obtained at MEDICIS with 1.4 GeV protons is compared with the production yield that can be reached with commercially available cyclotrons. The thick-target materials were irradiated at MEDICIS and comprised of metallic  natTi,  natV metallic foils, and  natTiC pellets. The produced radionuclides were subsequently released, ionized, and extracted from various target and ion source units and mass separated. Mono-atomic Sc laser and molecule ionization with forced-electron-beam-induced arc-discharge ion sources were investigated. Sc radionuclide production in thick  natTi and  natV targets at MEDICIS is equivalent to low- to medium-energy cyclotron-irradiated targets at medically relevant yields, furthermore benefiting from the mass separation possibility. A two-step laser resonance ionization scheme was used to obtain mono-atomic Sc ion beams. Sc radionuclide release from irradiated target units most effectively could be promoted by volatile scandium fluoride formation. Thus, isotopically pure  44g/mSc, 46Sc, and 47Sc were obtained as mono-atomic and molecular ScF 2+ ion beams and collected for the first time at CERN-MEDICIS. Among all the investigated target materials,  natTiC is the most suitable target material for Sc mass separation as molecular halide beams, due to high possible operating temperatures and sustained release.

Separation facilities ensuring flexible control over produced ore mass quality, with potential production of concentrates with high metal content are examined. The process solutions are justified to develop small gold ore deposits located far from processing factories, in the areas of the underdeveloped or missing energy infrastructure. A mobile processing facility carries out two-stage X-ray radiometric separation of sorted ore classes, with production of tailings, middlings and concentrate via float-and-sink separation. Middlings are treated on-the-spot by heap and trench leaching, and concentrate is shipped to a long-distance processing factory. Separate processing of different-grade mineral raw material enables high metal recovery at a reasonable cost.

149 Tb represents a powerful alternative to currently used α-emitters: the relatively short half-life (T 1/2  = 4.1 h), low α-energy (3.97 MeV, I α  = 16.7 %), absence of α-emitting daughters and stable coordination via DOTA are favorable features for potential clinical application. In this letter, we wish to highlight the unique characteristics of 149 Tb for PET imaging, based on its positron emission (E β+mean  = 730 keV, I β+  = 7.1 %) in addition to it’s a therapeutic value. To this end, a preclinical study with a tumor-bearing mouse is presented. The perspective of alpha-PET makes 149 Tb highly appealing for radiotheragnostic applications in future clinical trials.

Olivine-type lithium iron phosphate (LiFePO4, LFP) lithium-ion batteries (LIBs) have become a popular choice for electric vehicles (EVs) and stationary energy storage systems. In the context of recycling, this study addresses the complex challenge of separating black mass of spent LFP batteries from its main composing materials to allow for direct recycling. In this study, 71% copper and 81% aluminium foil impurities were removed by sieving black mass to <250 µm. Next, the application of froth flotation as a separation technique was explored, examining the influence of chemical agents, pre-treatment, and multi-step processes. Frother agent addition improved material recovery in the froth, while collector addition influenced the separation efficiency and enhanced graphite recovery. Pre-treatment, particularly sonication, was found to break down agglomerates and further improve separation. Multi-step flotation increased the purity of recovered fractions. The optimized process for a black mass < 250 µm, involving sonication pre-treatment and double flotation, resulted in enriched carbonaceous material (80.3 mol%) in froth fractions and high LFP concentration (81.9 mol%) in tailings fractions. The recovered spent LFP cathode material contained 37.20 wt% Fe2P2O7, a degradation product of LiFePO4. This research offers valuable insights for the development of efficient battery recycling methods for LFP batteries.

Thulium-167 is a promising radionuclide for nuclear medicine applications with potential use for both diagnosis and therapy (“theragnostics”) in disseminated tumor cells and small metastases, due to suitable gamma-line as well as conversion/Auger electron energies. However, adequate delivery methods are yet to be developed and accompanying radiobiological effects to be investigated, demanding the availability of 167 Tm in appropriate activities and quality. We report herein on the production of radionuclidically pure 167 Tm from proton-irradiated natural erbium oxide targets at a cyclotron and subsequent ion beam mass separation at the CERN-MEDICIS facility, with a particular focus on the process efficiency. Development of the mass separation process with studies on stable 169 Tm yielded 65 and 60% for pure and erbium-excess samples. An enhancement factor of thulium ion beam over that of erbium of up to several 10 4 was shown by utilizing laser resonance ionization and exploiting differences in their vapor pressures. Three 167 Tm samples produced at the IP2 irradiation station, receiving 22.8 MeV protons from Injector II at Paul Scherrer Institute (PSI), were mass separated with collected radionuclide efficiencies between 11 and 20%. Ion beam sputtering from the collection foils was identified as a limiting factor. In-situ gamma-measurements showed that up to 45% separation efficiency could be fully collected if these limits are overcome. Comparative analyses show possible neighboring mass suppression factors of more than 1,000, and overall 167 Tm/Er purity increase in the same range. Both the actual achieved collection and separation efficiencies present the highest values for the mass separation of external radionuclide sources at MEDICIS to date.

Winner of the \"Outstanding Academic Title\" recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging. 1. A novel stacked model ensemble for improved Tuberculosis (TB) detection in chest radiographs Sivaramakrishnan Rajaraman, Sema Candemir, Zhiyun Xue, Philip Alderson, George Thoma, Sameer Antani 2. The Role of Artificial Intelligence (AI) in Medical Imaging: General Radiologic and Urologic Applications Diboro Kanabolo, Mohan S. Gundeti, 3. Early Detection of Epileptic Seizures based on scalp EEG signals Abhishek Agrawal, Lalit Garg, Eliazar Elisha Audu, Ram Bilas Pachori, Justin HG Dauwels 4. Fractal analysis in histology classification of non-small cell lung cancer Ravindra Patil, Geetha M , Srinidhi Bhat, Dinesh M S, Leonard Wee and Andre Dekker 5. Multi feature-based classification of osteoarthritis in knee joint x ray images Ravindra S. Hegadi, Dattatray N. Navale, Trupti D. Pawar, Darshan D. Ruikar 6. Detection and classification of non-proliferative Diabetic Retinopathy Lesions Ramesh R Manza, Bharti W. Gawali, Pravin Yannawar, K.C. Santosh 7. Segmentation and analysis of CT images for bone fracture detection and labelling Darshan D. Ruikar, K.C. Santosh, Ravindra S. Hegadi 8. 3D imaging in biomedical applications: a systematic review Darshan D. Ruikar, Dattatray D. Sawat, K.C. Santosh, Ravindra S. Hegadi 9. Evolution of Digital sliding of pathology in medical imaging M Ravi, Ravindra S Hegadi 10. Pathological medical image segmentation: a quick review based on parametric techniques M Ravi, Ravindra S Hegadi Dr. K.C. Santosh is an Assistant Professor and Graduate Program Coordinator (GPC) of the department of computer science at the University of South Dakota (USD). Also, Dr. Santosh serves School of Computing and IT, Taylor's University as an Adjunct Associate Professor. Before joining USD, Dr. Santosh worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research centre, Universite de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also worked as a research scientist at the INRIA Nancy Grand Est research centre, France, where, in just 3 years, he has received his PhD diploma in computer science. Before that, he worked as a graduate research scholar at the SIIT, Thammasat University, Thailand. Dr. Santosh demonstrated expertise in artificial intelligence, machine learning, computer vision, pattern recognition and image processing with various applications in medical image analysis, graphics recognition, document information content exploitation, biometrics and forensics. He published more than 120 peer-reviewed research articles; two authored books (Springer) and edited several books (Springer, Elsevier and CRC press), journal issues (Springer) and conference proceedings (Springer). Dr. Santosh serves as an associate editor of the International Journal of Machine Learning & Cybernetics (Springer). For more information, visit: kc-santosh.org. Sameer Antani , PhD is a versatile researcher leading several scientific and technical research projects. He applies his expertise in biomedical image informatics, automatic medical image interpretation, machine learning, information retrieval, computer vision, and related topics in computer science and engineering technology toward advancing the role of computational sciences in biomedical research, education, and clinical care. His current R&D projects include: an　automatic screening system　for detecting presence of Tuberculosis (TB) and other pulmonary abnormalities in digital chest x-ray images; an automatic cell counting system for　malaria　screening; retrieval of fMRI data based on activation similarity; and, the　OPEN-i SM 　biomedical image retrieval system that provides text and visual search capability to retrieve over 3.2 million images and videos from approximately 1.2 million Open Access biomedical research articles from NLM’s　PubMed Central®　repository.His other work includes contributions to cervical cancer diagnostics through cervicography and histology image analysis; retrieval of spine x-rays from an image database using visual and shape queries; and, next generation scientific publishing. Dr. Antani is a Senior Member of the　International Society of Photonics and Optics (SPIE), Institute of Electrical and Electronics Engineers (IEEE)　and the　IEEE Computer Society. He　serves as the Vice Chair for Computational Medicine on the　IEEE Technical Committee on Computational Life Sciences (TCCLS), and as an Associate Editor for　the IEEE Journal of Biomedical and Health Informatics 　 D S Guru , is a professor at the Department of Studies in Computer Science. He is known for his contributions to the field of Image Processing and Pattern Recognition. He is a recipient of BOYSCAST fellowship awarded by Department of Science and Technology, Govt of India and also Award for Research Publications by Department of Science and Technology, Karnataka Government. He has been recognized as best ethical teacher at higher learning by Rotary North Mysore. He has supervised more than 15 Ph.D. students and currently supervising many more. He holds rank positions both at Bachelors and Masters Educations. He earned his Doctorate from University of Mysore and he did his post-doctoral work at PRIP lab, Michigan State University. He has been a reviewer for international journals of Elsevier Science, Springer and IEEE Transactions. He has chaired, delivered lectures at many international conferences and workshops. He is a co-author for three text books, co-editor of three proceedings and authored many research articles both in peer reviewed journals and proceedings. 　 N Nilanjan Dey, PhD. 　 is an Assistant Professor in the Department of Information Technology at Techno India College of Technology, Kolkata, India. He is a visiting fellow of the University of Reading, UK.　 He is a Visiting Professor at Wenzhou Medical University, China and Duy Tan University, Vietnam, He was an　 honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD. from Jadavpur University in 2015. 　 He has authored/edited more than 45 books with Elsevier, Wiley, CRC Press and Springer, and published more than 300 papers. He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, IGI Global , Associate Editor of 　 IEEE Access and International Journal of Information Technology, Springer. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing, Springer Nature , Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier , Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC . 　 His main research interests include Medical Imaging, Machine learning, Computer Aided Diagnosis, Data Mining, etc. He is the Indian 　Ambassador of International Federation for Information Processing (IFIP) – Young ICT Group . Recently, he has been awarded as one among the top 10 most published academics in the field of 　Computer Science in India (2015-17).