Generation of 1.5 Million Beam Loss Threshold Values

CERN's Large Hadron Collider will store an unprecedented amount of energy in its circulating beams. Beamloss monitoring (BLM) is, therefore, critical for machine protection. It must protect against the consequences (equipment damage, quenches of superconducting magnets) of excessive beam loss. About 4000 monitors will be installed at critical loss locations. Each monitor has 384 beam abort thresholds associated; for 12 integrated loss durations ($40\mu$s to 83 s) and 32 energies (450GeV to 7 TeV). Depending on monitor location, the thresholds vary by orders of magnitude. For simplification, the monitors are grouped in 'families'. Monitors of one family protect similar magnets against equivalent loss scenarios. Therefore, they are given the same thresholds. The start-up calibration of the BLM system is required to be within a factor of five in accuracy; and the final accuracy should be a factor of two. Simulations (backed-up by control measurements) determine the relation between the BLM signal, the deposited energy and the critical energy deposition for damage or quench (temperature of the coil). The paper presents the strategy of determining 1.5 million threshold values

Performance of upstream interaction region detectors for the FIRST experiment at GSI

The FIRST (Fragmentation of Ions Relevant for Space and Therapy) experiment at GSI has been designed to study carbon fragmentation, measuring 12C double differential cross sections (∂2σ/ ∂θ∂E) for different beam energies between 100 and 1000 MeV/u. The experimental setup integrates newly designed detectors in the, so called, Interaction Region around the graphite target. The Interaction Region upstream detectors are a 250 μm thick scintillator and a drift chamber optimized for a precise measurement of the ions interaction time and position on the target. In this article we review the design of the upstream detectors along with the preliminary results of the data taking performed on August 2011 with 400 MeV/u fully stripped carbon ion beam at GSI. Detectors performances will be reviewed and compared to those obtained during preliminary tests, performed with 500 MeV electrons (at the BTF facility in the INFN Frascati Laboratories) and 80 MeV/u protons and carbon ions (at the INFN LNS Laboratories in Catania)

Model‐based control of mechanical ventilation: design and clinical validation

Background. We developed a model‐based control system using end‐tidal carbon dioxide fraction (FE′CO2) to adjust a ventilator during clinical anaesthesia. Methods. We studied 16 ASA I-II patients (mean age 38 (range 20-59) yr; weight 67 (54-87) kg) during i.v. anaesthesia for elective surgery. After periods of normal ventilation the patients were either hyper‐ or hypoventilated to assess precision and dynamic behaviour of the control system. These data were compared with a previous group where a fuzzy‐logic controller had been used. Responses to different clinical events (invalid carbon dioxide measurement, limb tourniquet release, tube cuff leak, exhaustion of carbon dioxide absorbent, simulation of pulmonary embolism) were also noted. Results. The model‐based controller correctly maintained the setpoint. No significant difference was found for the static performance between the two controllers. The dynamic response of the model‐based controller was more rapid (P<0.05). The mean rise time after a setpoint increase of 1 vol% was 313 (sd 90) s and 142 (17) s for fuzzy‐logic and model‐based control, respectively, and after a 1 vol% decrease was 355 (127) s and 177 (36) s, respectively. The new model‐based controller had a consistent response to clinical artefacts. Conclusion. A model‐based FE′CO2 controller can be used in a clinical setting. It reacts appropriately to artefacts, and has a better dynamic response to setpoint changes than a previously described fuzzy‐logic controller. Br J Anaesth 2004; 92: 800-

Dose- and Volume-Limiting Late Toxicity of FLASH Radiotherapy in Cats with Squamous Cell Carcinoma of the Nasal Planum and in Mini Pigs

Purpose: The FLASH effect is characterized by normal tissue sparing without compromising tumor control. Although demonstrated in various preclinical models, safe translation of FLASH-radiotherapy stands to benefit from larger vertebrate animal models. Based on prior results, we designed a randomized phase III trial to investigate the FLASH effect in cat patients with spontaneous tumors. In parallel, the sparing capacity of FLASH-radiotherapy was studied on mini pigs by using large field irradiation. Experimental Design: Cats with T1-T2, N0 carcinomas of the nasal planum were randomly assigned to two arms of electron irradiation: arm 1 was the standard of care (SoC) and used 10 × 4.8 Gy (90% isodose); arm 2 used 1 × 30 Gy (90% isodose) FLASH. Mini pigs were irradiated using applicators of increasing size and a single surface dose of 31 Gy FLASH. Results: In cats, acute side effects were mild and similar in both arms. The trial was prematurely interrupted due to maxillary bone necrosis, which occurred 9 to 15 months after radiotherapy in 3 of 7 cats treated with FLASH-radiotherapy (43%), as compared with 0 of 9 cats treated with SoC. All cats were tumor-free at 1 year in both arms, with one cat progressing later in each arm. In pigs, no acute toxicity was recorded, but severe late skin necrosis occurred in a volume-dependent manner (7–9 months), which later resolved. Conclusions: The reported outcomes point to the caveats of translating single-high-dose FLASH-radiotherapy and emphasizes the need for caution and further investigations. See related commentary by Maity and Koumenis, p. 363

A joint physics and radiobiology DREAM team vision - Towards better response prediction models to advance radiotherapy.

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines

Characterization of the PTW 34031 ionization chamber (PMI) at RCNP with high energy neutrons ranging from 100 – 392 MeV

Radiation monitoring at high energy proton accelerators poses a considerable challenge due to the complexity of the encountered stray radiation fields. These environments comprise a wide variety of different particle types and span from fractions of electron-volts up to several terra electron-volts. As a consequence the use of Monte Carlo simulation programs like FLUKA is indispensable to obtain appropriate field-specific calibration factors. At many locations of the LHC a large contribution to the particle fluence is expected to originate from high-energy neutrons and thus, benchmark experiments with mono-energetic neutron beams are of high importance to verify the aforementioned detector response calculations. This paper summarizes the results of a series of benchmark experiments with quasi mono-energetic neutrons of 100, 140, 200, 250 and 392 MeV that have been carried out at RCNP - Osaka University, during several campaigns between 2006 and 2014

A joint physics and radiobiology DREAM team vision - towards better response prediction models to advance radiotherapy

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team's consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines. [Abstract copyright: Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

Induction of effective and antigen-specific antitumour immunity by a liposomal ErbB2/HER2 peptide-based vaccination construct

Efficient delivery of tumour-associated antigens to appropriate cellular compartments of antigen-presenting cells is of prime importance for the induction of potent, cell-mediated antitumour immune responses. We have designed novel multivalent liposomal constructs that co-deliver the p63–71 cytotoxic T Lymphocyte epitope derived from human ErbB2 (HER2), and HA307–319, a T-helper (Th) epitope derived from influenza haemagglutinin. Both peptides were conjugated to the surface of liposomes via a Pam3CSS anchor, a synthetic lipopeptide with potent adjuvant activity. In a murine model system, vaccination with these constructs completely protected BALB/c mice from subsequent s.c. challenge with ErbB2-expressing, but not ErbB2-negative, murine renal carcinoma (Renca) cells, indicating the induction of potent, antigen-specific immune responses. I.v. re-challenge of tumour-free animals 2 months after the first tumour cell inoculation did not result in the formation of lung tumour nodules, suggesting that long-lasting, systemic immunity had been induced. While still protecting the majority of vaccinated mice, a liposomal construct lacking the Th epitope was less effective than the diepitope construct, also correlating with a lower number of CD8+ IFN-γ+ T-cells identified upon ex vivo peptide restimulation of splenocytes from vaccinated animals. Importantly, in a therapeutic setting treatment with the liposomal vaccines resulted in cures in the majority of tumour-bearing mice and delayed tumour growth in the remaining ones. Our results demonstrate that liposomal constructs which combine Tc and Th peptide antigens and lipopeptide adjuvants can induce efficient, antigen-specific antitumour immunity, and represent promising synthetic delivery systems for the design of specific antitumour vaccines

A joint physics and radiobiology DREAM team vision - towards better response prediction models to advance radiotherapy

Radiotherapy developed empirically through experience balancing tumour control and normal tissue toxicities. Early simple mathematical models formalized this practical knowledge and enabled effective cancer treatment to date. Remarkable advances in technology, computing, and experimental biology now create opportunities to incorporate this knowledge into enhanced computational models. The ESTRO DREAM (Dose Response, Experiment, Analysis, Modelling) workshop brought together experts across disciplines to pursue the vision of personalized radiotherapy for optimal outcomes through advanced modelling. The ultimate vision is leveraging quantitative models dynamically during therapy to ultimately achieve truly adaptive and biologically guided radiotherapy at the population as well as individual patient-based levels. This requires the generation of models that inform response-based adaptations, individually optimized delivery and enable biological monitoring to provide decision support to clinicians. The goal is expanding to models that can drive the realization of personalized therapy for optimal outcomes. This position paper provides their propositions that describe how innovations in biology, physics, mathematics, and data science including AI could inform models and improve predictions. It consolidates the DREAM team’s consensus on scientific priorities and organizational requirements. Scientifically, it stresses the need for rigorous, multifaceted model development, comprehensive validation and clinical applicability and significance. Organizationally, it reinforces the prerequisites of interdisciplinary research and collaboration between physicians, medical physicists, radiobiologists, and computational scientists throughout model development. Solely by a shared understanding of clinical needs, biological mechanisms, and computational methods, more informed models can be created. Future research environment and support must facilitate this integrative method of operation across multiple disciplines

Biofluid Biomarkers in Huntington's Disease

Huntington's disease (HD) is a chronic progressive neurodegenerative condition where new markers of disease progression are needed. So far no disease-modifying interventions have been found, and few interventions have been proven to alleviate symptoms. This may be partially explained by the lack of reliable indicators of disease severity, progression, and phenotype.Biofluid biomarkers may bring advantages in addition to clinical measures, such as reliability, reproducibility, price, accuracy, and direct quantification of pathobiological processes at the molecular level; and in addition to empowering clinical trials, they have the potential to generate useful hypotheses for new drug development.In this chapter we review biofluid biomarker reports in HD, emphasizing those we feel are likely to be closest to clinical applicability