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Purpose: Proton beam therapy (PBT) plays an important role in the management of primary spine tumors. The purpose of this consensus statement was to summarize safe and optimal delivery of PBT for spinal tumors., Methods and Materials: The Particle Therapy Cooperative Group Skull Base/Central nervous system/Sarcoma Subcommittee consisting of radiation oncologists and medical physicists with specific expertise in spinal irradiation developed expert recommendations discussing treatment planning considerations and current approaches in the treatment of primary spinal tumors., Results: Computed tomography simulation: factors that require significant consideration include (1) patient comfort, (2) setup reproducibility and stability, and (3) accessibility of appropriate beam angles., Spine Stabilization Hardware: If present, hardware should be placed with cross-links well above/below the level of the primary tumor to reduce the metal burden at the level of the tumor bed. New materials that can reduce uncertainties include polyether-ether-ketone and composite polyether-ether-ketone-carbon fiber implants., Field Arrangement: Appropriate beam selection is required to ensure robust target coverage and organ at risk sparing. Commonly, 2 to 4 treatment fields, typically from posterior and/or posterior-oblique directions, are used., Treatment Planning Methodology: Robust optimization is recommended for all pencil beam scanning plans (the preferred treatment modality) and should consider setup uncertainty (between 3 and 7 mm) and range uncertainty (3%-3.5%). In the presence of metal hardware, use of an increased range uncertainty up to 5% is recommended., Conclusions: The Particle Therapy Cooperative Group Skull Base/Central nervous system/Sarcoma Subcommittee has developed recommendations to enable centers to deliver PBT safely and effectively for the management of primary spinal tumors., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly data-hungry, compute-intensive, and hard to interpret. Hyperdimensional computing (HDC) has recently emerged as an exciting alternative. The key idea is that random vectors of high dimensionality can represent concepts such as sequence identity or phylogeny. These vectors can then be combined using simple operators for learning, reasoning, or querying by exploiting the peculiar properties of high-dimensional spaces. Our work reviews and explores HDC's potential for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds great potential for various omics data searching, biosignal analysis, and health applications., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Stock et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Bacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to identify depolymerase sequences and their enzymatic domains precisely. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which is subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can greatly enhance our understanding of phage-host interactions at the level of depolymerases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Concha-Eloko et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking., Methods: Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS., Findings: Machine learning models achieved a ROC-AUC of 0⋅71 ± 0⋅01, an AUC-PR of 0⋅26 ± 0⋅02, a Brier score of 0⋅1 ± 0⋅01 and an expected calibration error of 0⋅07 ± 0⋅04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history., Conclusions: Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study., Competing Interests: The authors declare no competing non-financial interests but the following competing financial interests: - Dana Horakova received speaker honoraria and consulting fees from Biogen, Merck, Teva, Roche, Sanofi Genzyme, and Novartis, as well as support for research activities from Biogen and Czech Minsitry of Education [project Progres Q27/LF1]. - Francesco Patti received speaker honoraria and advisory board fees from Almirall, Bayer, Biogen, Celgene, Merck, Novartis, Roche, Sanofi-Genzyme and TEVA. He received research funding from Biogen, Merck, FISM (Fondazione Italiana Sclerosi Multipla), Reload Onlus Association and University of Catania. - Guillermo Izquierdo received speaking honoraria from Biogen, Novartis, Sanofi, Merck, Roche, Almirall and Teva. - Sara Eichau received speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, Roche and Teva. - Marc Girard received consulting fees from Teva Canada Innovation, Biogen, Novartis and Genzyme Sanofi; lecture payments from Teva Canada Innovation, Novartis and EMD. He has also received a research grant from Canadian Institutes of Health Research. - Alessandra Lugaresi has served as a Biogen, Bristol Myers Squibb, Merck Serono, Novartis, Roche, Sanofi/ Genzyme and Teva Advisory Board Member. She received congress and travel/accommodation expense compensations or speaker honoraria from Biogen, Merck, Mylan, Novartis, Roche, Sanofi/Genzyme, Teva and Fondazione Italiana Sclerosi Multipla (FISM). Her institutions received research grants from Novartis and Sanofi Genzyme. - Pierre Grammond has served in advisory boards for Novartis, EMD Serono, Roche, Biogen idec, Sanofi Genzyme, Pendopharm and has received grant support from Genzyme and Roche, has received research grants for his institution from Biogen idec, Sanofi Genzyme, EMD Serono. - Tomas Kalincik served on scientific advisory boards for BMS, Roche, Janssen, Sanofi Genzyme, Novartis, Merck and Biogen, steering committee for Brain Atrophy Initiative by Sanofi Genzyme, received conference travel support and/or speaker honoraria from WebMD Global, Eisai, Novartis, Biogen, Sanofi-Genzyme, Teva, BioCSL and Merck and received research or educational event support from Biogen, Novartis, Genzyme, Roche, Celgene and Merck. - Raed Alroughani received honoraria as a speaker and for serving on scientific advisory boards from Bayer, Biogen, GSK, Merck, Novartis, Roche and Sanofi-Genzyme. - Francois Grand’Maison received honoraria or research funding from Biogen, Genzyme, Novartis, Teva Neurosciences, Mitsubishi and ONO Pharmaceuticals. - Murat Terzi received travel grants from Novartis, Bayer-Schering, Merck and Teva; has participated in clinical trials by Sanofi Aventis, Roche and Novartis. - Jeannette Lechner-Scott travel compensation from Novartis, Biogen, Roche and Merck. Her institution receives the honoraria for talks and advisory board commitment as well as research grants from Biogen, Merck, Roche, TEVA and Novartis. - Samia J. Khoury received compensation for participation in the Novartis Maestro program. - Vincent van Pesch has received travel grants from Merck, Biogen, Sanofi, Bristol Myers Squibb, Almirall and Roche; his institution receives honoraria for consultancy and lectures and research grants from Roche, Biogen, Sanofi, Merck, Bristol Myers Squibb, Janssen, Almirall and Novartis Pharma. - Radek Ampapa received conference travel support from Novartis, Teva, Biogen, Bayer and Merck and has participated in a clinical trials by Biogen, Novartis, Teva and Actelion. - Daniele Spitaleri received honoraria as a consultant on scientific advisory boards by Bayer-Schering, Novartis and Sanofi-Aventis and compensation for travel from Novartis, Biogen, Sanofi Aventis, Teva and Merck. - Claudio Solaro served on scientific advisory boards for Merck, Genzyme, Almirall, and Biogen; received honoraria and travel grants from Sanofi Aventis, Novartis, Biogen, Merck, Genzyme and Teva. - Davide Maimone served on scientific advisory boards for Bayer, Biogen, Merck, Sanofi-Genzyme, Novartis, Roche, and Almirall; received honoraria and travel grants from Sanofi Genzyme, Novartis, Biogen, Merck, and Roche. - Gerardo Iuliano (retired - no PI successor but has approved ongoing use of data) had travel/accommodations/meeting expenses funded by Bayer Schering, Biogen, Merck, Novartis, Sanofi Aventis, and Teva. - Bart Van Wijmeersch received research and travel grants, honoraria for MS-Expert advisor and Speaker fees from Bayer-Schering, Biogen, Sanofi Genzyme, Merck, Novartis, Roche and Teva. - Tamara Castillo Triviño received speaking/consulting fees and/or travel funding from Bayer, Biogen, Merck, Novartis, Roche, Sanofi-Genzyme and Teva. - Jose Luis Sanchez-Menoyo accepted travel compensation from Novartis, Merck and Biogen, speaking honoraria from Biogen, Novartis, Sanofi, Merck, Almirall, Bayer and Teva and has participated in clinical trials by Biogen, Merck and Roche - Guy Laureys received travel and/or consultancy compensation from Sanofi-Genzyme, Roche, Teva, Merck, Novartis, Celgene, Biogen. - Anneke van der Walt served on advisory boards and receives unrestricted research grants from Novartis, Biogen, Merck and Roche She has received speaker’s honoraria and travel support from Novartis, Roche, and Merck. She receives grant support from the National Health and Medical Research Council of Australia and MS Research Australia. - Jiwon Oh has received research funding from the MS Society of Canada, National MS Society, Brain Canada, Biogen, Roche, EMD Serono (an affiliate of Merck KGaA); and personal compensation for consulting or speaking from Alexion, Biogen, Celgene (BMS), EMD Serono (an affiliate of Merck KGaA), Novartis, Roche, and Sanofi-Genzyme. - Ayse Altintas received speaker honoraria from Merck, Alexion,; received travel and registration grants from Merck, Biogen - Gen Pharma, Roche, Sanofi-Genzyme. - Yara Fragoso received honoraria as a consultant on scientific advisory boards by Novartis, Teva, Roche and Sanofi-Aventis and compensation for travel from Novartis, Biogen, Sanofi Aventis, Teva, Roche and Merck. - Tunde Csepany received speaker honoraria/ conference travel support from Bayer Schering, Biogen, Merck, Novartis, Roche, Sanofi-Aventis and Teva. - Suzanne Hodgkinson received honoraria and consulting fees from Novartis, Bayer Schering and Sanofi, and travel grants from Novartis, Biogen Idec and Bayer Schering. - Norma Deri received funding from Bayer, Merck, Biogen, Genzyme and Novartis. - Bruce Taylor received funding for travel and speaker honoraria from Bayer Schering Pharma, CSL Australia, Biogen and Novartis, and has served on advisory boards for Biogen, Novartis, Roche and CSL Australia. - Fraser Moore participated in clinical trials sponsored by EMD Serono and Novartis. - Orla Gray received honoraria as consultant on scientific advisory boards for Genzyme, Biogen, Merck, Roche and Novartis; has received travel grants from Biogen, Merck, Roche and Novartis; has participated in clinical trials by Biogen and Merck. - Csilla Rozsa received speaker honoraria from Bayer Schering, Novartis and Biogen, congress and travel expense compensations from Biogen, Teva, Merck and Bayer Schering. - Allan Kermode received speaker honoraria and scientific advisory board fees from Bayer, BioCSL, Biogen, Genzyme, Innate Immunotherapeutics, Merck, Novartis, Sanofi, Sanofi-Aventis, and Teva. - Magdolna Simo received speaker honoraria from Novartis, Biogen, Bayer Schering; congress/travel compensation from Teva, Biogen, Merck, Bayer Schering. - Todd Hardy has received speaking fees or received honoraria for serving on advisory boards for Biogen, Merck, Teva, Novartis, Roche, Bristol-Myers Squibb and Sanofi-Genzyme, is Co-Editor of Advances in Clinical Neurosciences and Rehabilitation, and serves on the editorial board of Journal of Neuroimmunology and Frontiers in Neurology. - Nikolaos Grigoriadis received honoraria, consultancy/lecture fees, travel support and research grants from Biogen Idec, Biologix, Novartis, TEVA, Bayer, Merck Serono, Genesis Pharma, Sanofi – Genzyme, ROCHE, Cellgene, ELPEN and research grants from Hellenic Ministry of Development., (Copyright: © 2024 De Brouwer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: To investigate the current practice patterns in image-guided particle therapy (IGPT) for cranio-spinal irradiation (CSI)., Methods: A multi-institutional survey was distributed to European particle therapy centres to analyse all aspects of IGPT. Based on the survey results, a Delphi consensus analysis was developed to define minimum requirements and optimal workflow for clinical practice. The centres participating in the institutional survey were invited to join the Delphi process., Results: Eleven centres participated in the survey. Imaging for treatment planning was rather similar among the centres with Computed Tomography (CT) being the main modality. For positioning verification, 2D IGPT was more commonly used than 3D IGPT. Two centres performed routinely imaging for plan adaptation, by the rest ad hoc. Eight centres participated in the Delphi consensus analysis. The full consensus was reached on the use of CT imaging without contrast for treatment planning and the role of magnetic resonance imaging (MRI) in target and organs-at-risk delineation. There was an agreement on the necessity to perform patient position verification and correction before each isocentre. The most important outcome was the clear need for standardization and harmonization of the workflow., Conclusion: There were differences in CSI IGPT clinical practice among the European particle therapy centres. Moreover, the optimal workflow as identified by experts was not yet reached. There is a strong need for consensus guidelines. The state-of-the-art imaging technology and protocols need to be implemented into clinical practice to improve the quality of IGPT for CSI., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Purpose: Carbon ion radiotherapy (CIRT) relies on relative biological effectiveness (RBE)-weighted dose calculations. Japanese clinics predominantly use the microdosimetric kinetic model (MKM), while European centers utilize the local effect model (LEM). Despite both models estimating RBE-distributions in tissue, their physical and mathematical assumptions differ, leading to significant disparities in RBE-weighted doses. Several European clinics adopted Japanese treatment schedules, necessitating adjustments in dose prescriptions and organ at risk (OAR) constraints. In the context of these two clinically used standards for RBE-weighted dose estimation, the objective of this study was to highlight specific scenarios for which the translations between models diverge, as shortcomings between them can influence clinical decisions., Methods: Our aim was to discuss planning strategies minimizing those discrepancies, ultimately striving for more accurate and robust treatments. Evaluations were conducted in a virtual water phantom and patient CT-geometry, optimizing LEM RBE-weighted dose first and recomputing MKM thereafter. Dose-averaged linear energy transfer (LETd) distributions were also assessed., Results: Results demonstrate how various parameters influence LEM/MKM translation. Similar LEM-dose distributions lead to markedly different MKM-dose distributions and variations in LETd. Generally, a homogeneous LEM RBE-weighted dose aligns with lower MKM values in most of the target volume. Nevertheless, paradoxical MKM hotspots may emerge (at the end of the range), potentially influencing clinical outcomes. Therefore, translation between models requires great caution., Conclusions: Understanding the relationship between these two clinical standards enables combining European and Japanese based experiences. The implementation of optimal planning strategies ensures the safety and acceptability of the clinical plan for both models and therefore enhances plan robustness from the RBE-weighted dose and LETd distribution point of view. This study emphasizes the importance of optimal planning strategies and the need for comprehensive CIRT plan quality assessment tools. In situations where simultaneous LEM and MKM computation capabilities are lacking, it can provide guidance in plan design, ultimately contributing to enhanced CIRT outcomes., (© 2024 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools do not accurately predict interactions at the strain level in a way that is relevant and properly evaluated for practical use. We present PhageHostLearn, a machine learning system that predicts strain-level interactions between receptor-binding proteins and bacterial receptors for Klebsiella phage-bacteria pairs. We evaluate this system both in silico and in the laboratory, in the clinically relevant setting of finding matching phages against bacterial strains. PhageHostLearn reaches a cross-validated ROC AUC of up to 81.8% in silico and maintains this performance in laboratory validation. Our approach provides a framework for developing and evaluating phage-host prediction methods that are useful in practice, which we believe to be a meaningful contribution to the machine-learning-guided development of phage therapeutics and diagnostics., (© 2024. The Author(s).)

Motivation: In recent years, many algorithms for inferring gene regulatory networks from single-cell transcriptomic data have been published. Several studies have evaluated their accuracy in estimating the presence of an interaction between pairs of genes. However, these benchmarking analyses do not quantify the algorithms' ability to capture structural properties of networks, which are fundamental, e.g., for studying the robustness of a gene network to external perturbations. Here, we devise a three-step benchmarking pipeline called STREAMLINE that quantifies the ability of algorithms to capture topological properties of networks and identify hubs., Results: To this aim, we use data simulated from different types of networks as well as experimental data from three different organisms. We apply our benchmarking pipeline to four inference algorithms and provide guidance on which algorithm should be used depending on the global network property of interest., Availability and Implementation: STREAMLINE is available at https://github.com/ScialdoneLab/STREAMLINE. The data generated in this study are available at https://doi.org/10.5281/zenodo.10710444., (© The Author(s) 2024. Published by Oxford University Press.)

Purpose: Experimental patient-specific QA (PSQA) is a time and resource-intensive process, with a poor sensitivity in detecting errors. Radiation therapy facilities aim to substitute it by means of independent dose calculation (IDC) in combination with a comprehensive beam delivery QA program. This paper reports on the commissioning of the IDC software tool myQA iON (IBA Dosimetry) for proton therapy and its clinical implementation at the MedAustron Ion Therapy Center., Methods: The IDC commissioning work included the validation of the beam model, the implementation and validation of clinical CT protocols, and the evaluation of patient treatment data. Dose difference maps, gamma index distributions, and pass rates (GPR) have been reviewed. The performance of the IDC tool has been assessed and clinical workflows, simulation settings, and GPR tolerances have been defined., Results: Beam model validation showed agreement of ranges within ± 0.2 mm, Bragg-Peak widths within ± 0.1 mm, and spot sizes at various air gaps within ± 5% compared to physical measurements. Simulated dose in 2D reference fields deviated by -0.3% ± 0.5%, while 3D dose distributions differed by 1.8% on average to measurements. Validation of the CT calibration resulted in systematic differences of 2.0% between IDC and experimental data for tissue like samples. GPRs of 99.4 ± 0.6% were found for head, head and neck, and pediatric CT protocols on a 2%/2 mm gamma criterion. GPRs for the adult abdomen protocol were at 98.9% on average with 3%/3 mm. Root causes of GPR outliers, for example, implants were identified and evaluated., Conclusion: IDC has been successfully commissioned and integrated into the MedAustron clinical workflow for protons in 2021. IDC has been stepwise and safely substituting experimental PSQA since February 2021. The initial reduction of proton experimental PSQA was about 25% and reached up to 90% after 1 year., (© 2024 The Authors. Journal of Applied Clinical Medical Physics is published by Wiley Periodicals, Inc. on behalf of The American Association of Physicists in Medicine.)

Careful consideration of how we approach design is crucial to all areas of biotechnology. However, choosing or developing an effective design methodology is not always easy as biology, unlike most areas of engineering, is able to adapt and evolve. Here, we put forward that design and evolution follow a similar cyclic process and therefore all design methods, including traditional design, directed evolution, and even random trial and error, exist within an evolutionary design spectrum. This contrasts with conventional views that often place these methods at odds and provides a valuable framework for unifying engineering approaches for challenging biological design problems., (© 2024. The Author(s).)

Purpose: Intracerebral radiation-induced contrast enhancement (RICE) can occur after photon as well as proton beam therapy (PBT). This study evaluated the incidence, characteristics, and risk factors of RICE after PBT delivered to, or in direct proximity to, the brain and its effect on health-related quality of life (HRQoL)., Methods and Materials: Four hundred twenty-one patients treated with pencil beam scanning PBT between 2017 and 2021 were included. Follow-up included clinical evaluation and contrast-enhanced magnetic resonance imaging at 3, 6, and 12 months after treatment completion and annually thereafter. RICE was graded according to Common Terminology Criteria for Adverse Events version 4, and HRQoL parameters were assessed via European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ)-C30 questionnaires., Results: The median follow-up was 24 months (range, 6-54), and median dose to 1% relative volume of noninvolved central nervous system (D1%CNS) was 54.3 Gy relative biologic effectiveness (RBE; range, 30-76 Gy RBE). The cumulative RICE incidence was 15% (n = 63), of which 10.5% (n = 44) were grade 1, 3.1% (n = 13) were grade 2, and 1.4% (n = 6) were grade 3. No grade 4 or 5 events were observed. Twenty-six of 63 RICE (41.3%) had resolved at the latest follow-up. The median onset after PBT and duration of RICE in patients in whom the lesions resolved were 11.8 and 9.0 months, respectively. On multivariable analysis, D1%CNS > 57.6 Gy RBE, previous in-field radiation, and diabetes mellitus were identified as significant risk factors for RICE development. Previous radiation was the only factor influencing the risk of symptomatic RICE. After PBT, general HRQoL parameters were not compromised. In a matched cohort analysis of 54/50 patients with and without RICE, no differences in global health score or functional and symptom scales were seen., Conclusions: The overall incidence of clinically relevant RICE after PBT is very low and has no significant negative effect on long-term patient QoL., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

To minimize radiation-induced lumbosacral neuropathy (RILSN), we employed sacral-nerve-sparing optimized carbon-ion therapy strategy (SNSo-CIRT) in treating 35 patients with pelvic sarcomas/chordomas. Plans were optimized using Local Effect Model-I (LEM-I), prescribed D RBE|LEM-I|D50% (median dose to HD-PTV) = 73.6 (70.4-76.8) Gy (RBE)/16 fractions. Sacral nerves were contoured between L5-S3 levels. D RBE|LEM-I to 5% of sacral nerves-to-spare (outside HD-CTV) (D RBE|LEM-I|D5% ) were restricted to <69 Gy (RBE). The median follow-up was 25 months (range of 2-53). Three patients (9%) developed late RILSN (≥G3) after an average period of 8 months post-CIRT. The RILSN-free survival at 2 years was 91% (CI, 81-100). With SNSo-CIRT, D RBE|LEM-I|D5% for sacral nerves-to-spare = 66.9 ± 1.9 Gy (RBE), maintaining D RBE|LEM-I to 98% of HD-CTV (D RBE|LEM-I|D98% ) = 70 ± 3.6 Gy (RBE). Two-year OS and LC were 100% and 93% (CI, 84-100), respectively. LETd and D RBE with modified-microdosimetric kinetic model (mMKM) were recomputed retrospectively. D RBE|LEM-I and D RBE|mMKM were similar, but D RBE -filtered-LETd was higher in sacral nerves-to-spare in patients with RILSN than those without. At D RBE|LEM-I cutoff = 64 Gy (RBE), 2-year RILSN-free survival was 100% in patients with <12% of sacral nerves-to-spare voxels receiving LETd > 55 keV/µm than 75% (CI, 54-100) in those with ≥12% of voxels ( p < 0.05). D RBE -filtered-LETd holds promise for the SNSo-CIRT strategy but requires longer follow-up for validation.

Background and Purpose: Nuclear interaction correction (NIC) and trichrome fragment spectra modelling improve relative biological effectiveness-weighted dose (D RBE ) and dose-averaged linear energy transfer (LET d ) calculation for carbon ions. The effect of those novel approaches on the clinical dose and LET distributions was investigated., Materials and Methods: The effect of the NIC and trichrome algorithm was assessed, creating single beam plans for a virtual water phantom with standard settings and NIC + trichrome corrections. Reference D RBE and LET d distributions were simulated using FLUKA version 2021.2.9. Thirty clinically applied scanned carbon ion treatment plans were recalculated applying NIC, trichrome and NIC + trichrome corrections, using the LEM low dose approximation and compared to clinical plans ( base RS ). Four treatment sites were analysed: six prostate adenocarcinoma, ten head and neck, nine locally advanced pancreatic adenocarcinoma and five sacral chordoma. The FLUKA and clinical plans were compared in terms of D RBE deviations for D 98% , D 50% , D 2% for the clinical target volume (CTV) and D 50% in ring-like dose regions retrieved from isodose curves in base RS plans. Additionally, region-based median LET d deviations and global gamma parameters were evaluated., Results: Dose deviations comparing base RS and evaluation plans were within ± 1% supported by γ-pass rates over 97% for all cases. No significant LET d deviations were reported in the CTV, but significant median LET d deviations were up to 80% for very low dose regions., Conclusion: Our results showed improved accuracy of the predicted D RBE and LET d . Considering clinically relevant constraints, no significant modifications of clinical protocols are expected with the introduction of NIC + trichrome., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lars Glimelius, Daniel Simon Colomar, Walter Ikegami Andersson reports financial support was provided by RaySearch Laboratories AB. Barbara Knäusl is associate editor in phiRO. The remaining authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

This study investigated the role of causative infectious agents in ulceration of the non-glandular part of the porcine stomach (pars oesophagea). In total, 150 stomachs from slaughter pigs were included, 75 from pigs that received a meal feed, 75 from pigs that received an equivalent pelleted feed with a smaller particle size. The pars oesophagea was macroscopically examined after slaughter. (q)PCR assays for H. suis, F. gastrosuis and H. pylori-like organisms were performed, as well as 16S rRNA sequencing for pars oesophagea microbiome analyses. All 150 pig stomachs showed lesions. F. gastrosuis was detected in 115 cases (77%) and H. suis in 117 cases (78%), with 92 cases (61%) of co-infection; H. pylori-like organisms were detected in one case. Higher infectious loads of H. suis increased the odds of severe gastric lesions (OR = 1.14, p = 0.038), while the presence of H. suis infection in the pyloric gland zone increased the probability of pars oesophageal erosions [16.4% (95% CI 0.6-32.2%)]. The causal effect of H. suis was mediated by decreased pars oesophageal microbiome diversity [-1.9% (95% CI - 5.0-1.2%)], increased abundances of Veillonella and Campylobacter spp., and decreased abundances of Lactobacillus, Escherichia-Shigella, and Enterobacteriaceae spp. Higher infectious loads of F. gastrosuis in the pars oesophagea decreased the odds of severe gastric lesions (OR = 0.8, p = 0.0014). Feed pelleting had no significant impact on the prevalence of severe gastric lesions (OR = 1.72, p = 0.28). H. suis infections are a risk factor for ulceration of the porcine pars oesophagea, probably mediated through alterations in pars oesophageal microbiome diversity and composition., (© 2024. The Author(s).)