Hasil untuk "Therapeutics. Pharmacology"

Alana Deng, Sugitha Janarthanan, Yan Sun et al.

Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher-student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework outperforms state-of-the-art methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.

Abeer Badawi, Md Tahmid Rahman Laskar, Elahe Rahimi et al.

The escalating global mental health crisis, marked by persistent treatment gaps, availability, and a shortage of qualified therapists, positions Large Language Models (LLMs) as a promising avenue for scalable support. While LLMs offer potential for accessible emotional assistance, their reliability, therapeutic relevance, and alignment with human standards remain challenging to address. This paper introduces a human-grounded evaluation methodology designed to assess LLM generated responses in therapeutic dialogue. Our approach involved curating a dataset of 500 mental health conversations from datasets with real-world scenario questions and evaluating the responses generated by nine diverse LLMs, including closed source and open source models. More specifically, these responses were evaluated by two psychiatric trained experts, who independently rated each on a 5 point Likert scale across a comprehensive 6 attribute rubric. This rubric captures Cognitive Support and Affective Resonance, providing a multidimensional perspective on therapeutic quality. Our analysis reveals that LLMs provide strong cognitive reliability by producing safe, coherent, and clinically appropriate information, but they demonstrate unstable affective alignment. Although closed source models (e.g., GPT-4o) offer balanced therapeutic responses, open source models show greater variability and emotional flatness. We reveal a persistent cognitive-affective gap and highlight the need for failure aware, clinically grounded evaluation frameworks that prioritize relational sensitivity alongside informational accuracy in mental health oriented LLMs. We advocate for balanced evaluation protocols with human in the loop that center on therapeutic sensitivity and provide a framework to guide the responsible design and clinical oversight of mental health oriented conversational AI.

Ofélia Godinho, Olga Maria Lage, Sandra Quinteira

Background/Objectives: It is of the utmost importance to study environmental bacteria, as these microorganisms remain poorly characterized regarding their diversity, antimicrobial resistance, and impact on the global ecosystem. This knowledge gap is particularly pronounced for marine bacteria. In this study, we aimed to isolate bacteria from different marine samples and to gain insights into the environmental bacterial resistome, an aspect that remains largely neglected. Methods: Bacteria were isolated from several marine sources using two different culture media, and their identification was based on 16S rRNA gene analysis. Whole-genome sequencing was performed for selected isolates belonging to novel taxa. Antimicrobial susceptibility to seven antibiotics was evaluated using the disk diffusion method. Results: A total of 171 bacterial isolates belonging to the phyla <i>Pseudomonadota</i>, <i>Bacteroidota</i>, <i>Planctomycetota</i>, <i>Actinomycetota</i>, and <i>Bacillota</i> were obtained from diverse marine samples. The most abundant group belonged to the class <i>Alphaproteobacteria</i>. Thirty isolates represented novel taxa, comprising 16 new species and one new genus. Despite the challenges associated with determining antibiotic resistance profiles in environmental bacteria, only one isolate (1.8%) was pan-susceptible, whereas 54 (98.2%) showed resistance to at least one of the tested antibiotics. Moreover, 33 isolates exhibited a multidrug-resistant phenotype. Genome analysis of four novel taxa revealed the presence of an incomplete AdeFGH efflux pump. Conclusions: This study highlights the high bacterial diversity in marine environments, the striking prevalence of antibiotic resistance, and the major methodological challenges in studying environmental bacteria. Importantly, it emphasizes the relevance of culturomics-based approaches for uncovering hidden microbial diversity and characterizing environmental resistomes.

Anna Maria Spagnolo, Francesco Palma, Giulia Amagliani et al.

The development of antimicrobial resistance (AMR) and the emergence of multiresistant pathogens represent a growing global threat to both human and animal health. Beyond the excessive and improper use of antimicrobials in human medicine, irrational use in veterinary medicine, agriculture, and aquaculture significantly contributes to the selection and spread of resistant microorganisms, which can enter the food chain and reach humans through food consumption or handling. Based on results from a recent meta-analysis, the prevalence of antimicrobial-resistant foodborne pathogens in food samples exceeds 10%. The veterinary sector is of particular concern, as a large proportion of antimicrobials are used in animal production, generating strong selective pressure and favoring the dissemination of AMR along the food chain. In an increasingly interconnected global context, resistant pathogens and resistance determinants can disseminate rapidly across sectors and national borders, making strategies confined to a single sector insufficient; therefore, effectively addressing AMR requires a One Health approach encompassing the human, veterinary, and environmental domains. Key mitigation strategies include strengthening antimicrobial stewardship programs, also in animal production, reducing routine prophylactic use of antimicrobials, and improving surveillance, coordinated across sectors and, where possible, further supported by advanced technologies such as artificial intelligence and machine learning. Further efforts are also needed to improve microbiological diagnostics, particularly through rapid and molecular methods, to support timely, targeted therapies and reduce inappropriate empirical treatments. In parallel, investment in new therapeutic options, including innovative molecules, drug combinations, and alternative approaches, remains crucial to effectively countering the growing burden of antimicrobial resistance.

Mohammadsaleh Refahi, Bahrad A. Sokhansanj, James R. Brown et al.

Accurate prediction of drug-target binding affinity can accelerate drug discovery by prioritizing promising compounds before costly wet-lab screening. While deep learning has advanced this task, most models fuse ligand and protein representations via simple concatenation and lack explicit geometric regularization, resulting in poor generalization across chemical space and time. We introduce FIRM-DTI, a lightweight framework that conditions molecular embeddings on protein embeddings through a feature-wise linear modulation (FiLM) layer and enforces metric structure with a triplet loss. An RBF regression head operating on embedding distances yields smooth, interpretable affinity predictions. Despite its modest size, FIRM-DTI achieves state-of-the-art performance on the Therapeutics Data Commons DTI-DG benchmark, as demonstrated by an extensive ablation study and out-of-domain evaluation. Our results underscore the value of conditioning and metric learning for robust drug-target affinity prediction.

Sir-Lord Wiafe, Najme Soleimani, Masoud Seraji et al.

Understanding interactions in complex systems requires capturing the relative timing of coupling, not only its strength. Phase synchronization captures this timing, yet most methods either reduce the phase to its cosine or collapse it into scalar indices such as the phase-locking value, discarding relative timing. We propose a complex-valued phase synchrony (CVPS) framework that estimates phase with an adaptive Gabor wavelet and preserves both cosine and sine components. Simulations confirm that CVPS recovers true phase offsets and tracks non-stationary dynamics more faithfully than Hilbert-based methods. Because antipsychotics are known to modulate the timing of cortical interactions, they provide a rigorous context to evaluate whether CVPS can capture such pharmacological effects. CVPS further reveals cortical neuro-hemodynamic drivers, with occipital-to-parietal and prefrontal-to-striatal lead--lag flows consistent with known receptor targets, confirming its ability to capture pharmacological timing. CVPS, therefore, offers a robust, generalizable framework for detecting relative timing in complex systems such as the brain.

Jinyeong Park, Jaegyoon Ahn, Jonghwan Choi et al.

Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence(AI)-based drug discovery. Combining deep generative models with reinforcement learning has emerged as an effective strategy for generating molecules with specific properties. Despite its potential, this approach is ineffective in exploring the vast chemical space and optimizing particular chemical properties. To overcome these limitations, we present Mol-AIR, a reinforcement learning-based framework using adaptive intrinsic rewards for effective goal-directed molecular generation. Mol-AIR leverages the strengths of both history-based and learning-based intrinsic rewards by exploiting random distillation network and counting-based strategies. In benchmark tests, Mol-AIR demonstrates superior performance over existing approaches in generating molecules with desired properties without any prior knowledge, including penalized LogP, QED, and celecoxib similarity. We believe that Mol-AIR represents a significant advancement in drug discovery, offering a more efficient path to discovering novel therapeutics.

Jiashuo Cao, Wujie Gao, Yun Suen Pai et al.

The advent of technology-enhanced interventions has significantly transformed mental health services, offering new opportunities for delivering psychotherapy, particularly in remote settings. This paper reports on a pilot study exploring the use of Virtual Reality (VR) as a medium for remote counselling. The study involved four experienced psychotherapists who evaluated three different virtual environments designed to support remote counselling. Through thematic analysis of interviews and feedback, we identified key factors that could be critical for designing effective virtual environments for counselling. These include the creation of clear boundaries, customization to meet specific therapeutic needs, and the importance of aligning the environment with various therapeutic approaches. Our findings suggest that VR can enhance the sense of presence and engagement in remote therapy, potentially improving the therapeutic relationship. In the paper we also outline areas for future research based on these pilot study results.

Talip Ucar, Aubin Ramon, Dino Oglic et al.

We investigate the potential of patent data for improving the antibody humanness prediction using a multi-stage, multi-loss training process. Humanness serves as a proxy for the immunogenic response to antibody therapeutics, one of the major causes of attrition in drug discovery and a challenging obstacle for their use in clinical settings. We pose the initial learning stage as a weakly-supervised contrastive-learning problem, where each antibody sequence is associated with possibly multiple identifiers of function and the objective is to learn an encoder that groups them according to their patented properties. We then freeze a part of the contrastive encoder and continue training it on the patent data using the cross-entropy loss to predict the humanness score of a given antibody sequence. We illustrate the utility of the patent data and our approach by performing inference on three different immunogenicity datasets, unseen during training. Our empirical results demonstrate that the learned model consistently outperforms the alternative baselines and establishes new state-of-the-art on five out of six inference tasks, irrespective of the used metric.

Baihan Lin, Djallel Bouneffouf, Yulia Landa et al.

The therapeutic working alliance is a critical predictor of psychotherapy success. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. Our approach leverages advanced large language models (LLMs) to analyze session transcripts and map them to distributed representations. These representations capture the semantic similarities between the dialogues and psychometric instruments, such as the Working Alliance Inventory. Analyzing a dataset of over 950 sessions spanning diverse psychiatric conditions -- including anxiety (N=498), depression (N=377), schizophrenia (N=71), and suicidal tendencies (N=12) -- collected between 1970 and 2012, we demonstrate the effectiveness of our method in providing fine-grained mapping of patient-therapist alignment trajectories, offering interpretable insights for clinical practice, and identifying emerging patterns related to the condition being treated. By employing various deep learning-based topic modeling techniques in combination with prompting generative language models, we analyze the topical characteristics of different psychiatric conditions and how these topics evolve during each turn of the conversation. This integrated framework enhances the understanding of therapeutic interactions, enables timely feedback for therapists on the quality of therapeutic relationships, and provides clear, actionable insights to improve the effectiveness of psychotherapy.

Daniel Sanchez‐Morillo, Antonio León‐Jiménez, María Guerrero‐Chanivet et al.

Abstract Engineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x‐rays and high‐resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex‐workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty‐one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief‐F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin‐converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.

Anil Philip Kunnath, Mohamed Suodha Suoodh, Dinesh Kumar Chellappan et al.

The global issue of antimicrobial resistance poses significant challenges to public health. The World Health Organization (WHO) has highlighted it as a major global health threat, causing an estimated 700,000 deaths worldwide. Understanding the multifaceted nature of antibiotic resistance is crucial for developing effective strategies. Several physiological and biochemical mechanisms are involved in the development of antibiotic resistance. Bacterial cells may escape the bactericidal actions of the drugs by entering a physiologically dormant state known as bacterial persistence. Recent findings in this field suggest that bacterial persistence can be one of the main sources of chronic infections. The antibiotic tolerance developed by the persister cells could tolerate high levels of antibiotics and may give rise to persister offspring. These persister offspring could be attributed to antibiotic resistance mechanisms, especially in chronic infections. This review attempts to shed light on persister-induced antibiotic resistance and the current therapeutic strategies.

Jawza F. Alsabhan, Haya M. Almalag, Norah O. Abanmy et al.

Background: The goal of this study was to identify and evaluate the use of Arabic YouTube videos on BD as a resource for patient education. Methods: A cross-sectional evaluation of YouTube videos as a source of information for patients with BD in Arabic was performed. The study was observational and, because it did not involve human subjects, it followed the STROBE guidelines whenever possible. The quality of the videos was assessed using the validated DISCERN instrument. The search strategy involved entering the term “bipolar disorder” in the YouTube search bar, and only YouTube videos in Arabic were included. Results: A total of 58 videos were included in this study after removing duplicates and videos unrelated to BD (Figure 1). The most common source of videos was others (38%), followed by physician (33%), educational (26%), and hospital (3%). Resources covering symptoms and prognosis were mostly in the “others” category (41%). The resources or videos that covered treatment options were mainly created by physicians (41%). However, resources or videos that included a personal story mainly belonged to the “others” category (67%). Conclusion: Visual health-related instructional resources still have a significant shortage. This study highlights the poor quality of videos about serious illnesses like BD. Evaluation and promotion of the creation of visual health-related educational resources should be the primary goal of future study.

Thomas F. Heston

"Prompt Engineering for Students of Medicine and Their Teachers" brings the principles of prompt engineering for large language models such as ChatGPT and Google Bard to medical education. This book contains a comprehensive guide to prompt engineering to help both teachers and students improve education in the medical field. Just as prompt engineering is critical in getting good information out of an AI, it is also critical to get students to think and understand more deeply. The principles of prompt engineering that we have learned from AI systems have the potential to simultaneously revolutionize learning in the healthcare field. The book analyzes from multiple angles the anatomy of a good prompt for both AI models and students. The different types of prompts are examined, showing how each style has unique characteristics and applications. The principles of prompt engineering, applied properly, are demonstrated to be effective in teaching across the diverse fields of anatomy, physiology, pathology, pharmacology, and clinical skills. Just like ChatGPT and similar large language AI models, students need clear and detailed prompting in order for them to fully understand a topic. Using identical principles, a prompt that gets good information from an AI will also cause a student to think more deeply and accurately. The process of prompt engineering facilitates this process. Because each chapter contains multiple examples and key takeaways, it is a practical guide for implementing prompt engineering in the learning process. It provides a hands-on approach to ensure readers can immediately apply the concepts they learn

Mayar Ahmed, Alex M. Maldonado, Jacob D. Durrant

Molecular dynamics (MD) simulations and computer-aided drug design (CADD) have advanced substantially over the past two decades, thanks to continuous computer hardware and software improvements. Given these advancements, MD simulations are poised to become even more powerful tools for investigating the dynamic interactions between potential small-molecule drugs and their target proteins, with significant implications for pharmacological research.

Qi-Ming Ding, Yi-Ming Huang, Xiao Yuan

Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate and biologically relevant docking results, especially for larger molecular docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware compatibility in the noisy intermediate-scale quantum era, indicating their potential for efficient implementation under practical docking scenarios. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.

Giulia Chiari, Giada Fiandaca, Marcello Edoardo Delitala

In the study of therapeutic strategies for the treatment of cancer, eco-evolutionary dynamics are of particular interest, since characteristics of the tumour population, interaction with the environment and effects of the treatment, influence the geometric and epigenetic characterization of the tumour with direct consequences on the efficacy of the therapy and possible relapses. In particular, when considering radiotherapy, oxygen concentration plays a central role both in determining the effectiveness of the treatment and the selective pressure due to hypoxia. We propose a mathematical model, settled in the framework of epigenetically-structured population dynamics and formulated in terms of systems of coupled non-linear integro-differential equations, that aims to catch these phenomena and to provide a predictive tool for the tumour mass evolution and therapeutic effects. The outcomes of the simulations show how the model is able to explain the impact of environmental selection and therapies on the evolution of the mass, motivating observed dynamics such as relapses and therapeutic failures. Furthermore it offers a first hint for the development of therapies which can be adapted to overcome problems of resistance and relapses.

Lamichhane G, Sharma G, Sapkota B et al.

Gopal Lamichhane,1,2,&ast; Grinsun Sharma,1,3,&ast; Biswash Sapkota,1,4,&ast; Mahendra Adhikari,1,5,&ast; Sandhaya Ghimire,1,&ast; Prakash Poudel,1,6 Hyun-Ju Jung2 1School of Health and Allied Sciences, Faculty of Health Sciences, Pokhara University, Pokhara, 33700, Nepal; 2Department of Oriental Pharmacy and Wonkwang-Oriental Medicines Research Institute, Wonkwang University, Iksan, Jeollabuk-do, 570-749, South Korea; 3Institute of Pharmaceutical Research and Development, College of Pharmacy, Wonkwang University, Iksan, 570-749, South Korea; 4Department of Pharmacy and Clinical Pharmacology, Madan Bhandari Academy of Health Sciences, Hetauda, 44107, Nepal; 5Department of Pharmacy, Institute of Pharmaceutics and Biopharmaceutics, Heinrich Heine University Düsseldorf, Düsseldorf, 40225, Germany; 6Pharmacy Program, Gandaki University, Pokhara, 33700, Nepal&ast;These authors contributed equally to this workCorrespondence: Prakash Poudel; Hyun Ju Jung, Email poudelprakesh@gmail.com; hyun104@wku.ac.krIntroduction: Herbal products have been widely used for the treatment of diseases throughout the ages. In this research, we investigated antioxidant, antibacterial, anti-adipogenic, and anti-inflammatory activities of methanolic extracts of five ethnomedicinally important plants; namely, Alnus nepalensis, Dryopteris sparsa, Artocarpus lacucha, Litsea monopetala, and Lyonia ovalifolia.Methods: We investigated the DPPH free radical scavenging potential, sensitivity of selected bacterial strains towards the extracts using a disc diffusion assay, anti-inflammatory activity in RAW-264.7 cells, and anti-adipogenic activity by the ORO assay in 3T3-L1 preadipocytes.Results and discussion: The extract of A. nepalensis showed significant antioxidant activity (IC50=4.838 μg/mL), followed by A. lacucha, L. monopetala, and L. ovalifolia, exhibiting comparable IC50 values to that of ascorbic acid (IC50=5.063 μg/mL). Alnus nepalensis also showed good antibacterial activity in disc diffusion methods, with remarkable zones of inhibition in A. baumannii (14.66 mm) and P. mirabilis (15.50 mm) bacterial species. In addition, A. nepalensis was found to increase adipogenesis in 3T3-L1 cells, evidenced by increased lipid deposition in differentiated 3T3-L1 cells. A similar pattern of increased adipogenesis was observed on treatment with L. ovalifolia extracts. On the other hand, A. lacucha effectively reduced lipid deposition in 3T3-L1 cells at 100 μg/mL (75.18± 6.42%) by inhibiting adipogenesis, showing its potential use in the management of obesity. Furthermore, A. lacucha 100 μg/mL (15.91± 0.277 μM) and L. monopetala 75 μg/mL (12.52± 0.05 μM) and 100 μg/mL (11.77± 0.33 μM) significantly inhibited LPS-induced nitric oxide production in RAW 264.7 cells. Also, A. nepalensis and L. ovalifolia inhibited NO production significantly, endorsing their anti-inflammatory potential.Conclusion: The findings from these in-vitro studies suggest that the selected five plants possess remarkable antioxidant, antibacterial, anti-adipogenic, and anti-inflammatory activities. This study opens the door to conduct further advanced in-vivo experiments to find possible lead compounds for the development of valuable therapeutic agents for common health problems.Keywords: Alnus nepalensis, Dryopteris sparsa, Artocarpus lacucha, Litsea monopetala, Lyonia ovalifolia

Gao Z, Bao J, Wu L et al.

Zhicheng Gao,1,2,&ast; Jiaqi Bao,1,2,&ast; Liuyang Wu,2 Kaiyu Shen,1 Qiqi Yan,2 Lifang Ye,2 Lihong Wang2 1The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, 310053, People’s Republic of China; 2Heart Center, Department of Cardiovascular Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Lihong Wang, Heart Center, Department of Cardiovascular Medicine, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, People’s Republic of China, Tel +86 85894275, Email wanglhnew@126.comPurpose: Lymphocyte to C-reactive protein ratio (LCR) is a recognized systemic inflammatory marker and novel prognostic indicator for several cancers. This study investigated the relationship between preoperative LCR and new-onset atrial fibrillation (NOAF) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI).Patients and Methods: Patients with AMI (n=662) with no history of atrial fibrillation (AF) were enrolled and classified into NOAF and non-NOAF groups based on the occurrence of postoperative NOAF during hospitalization. Logistic regression models were used to analyze NOAF risk factors and to assess the association between preoperative LCR and NOAF incidence. We constructed a new nomogram from the selected NOAF risk factors, and tested its predictive performance, degree of calibration, and clinical utility using receiver operating characteristic and calibration curves, decision curve analysis, and clinical impact curves.Results: Overall, 84 (12.7%) patients developed NOAF during hospitalization. The LCR was significantly lower in the NOAF group. Preoperative LCR accurately predicted NOAF after AMI and was correlated with increased NOAF risk. Age, body mass index, diabetes, serum albumin levels, uric acid levels, left atrium (LA) diameter, left ventricular ejection fraction, left circumflex artery stenosis > 50%, and Killip class II status were independent predictors of NOAF after AMI. In addition, a new nomogram combined with LCR was constructed to stratify the risk of NOAF in patients with AMI. The performance of the new nomogram was satisfactory, as shown by the receiver operating characteristic curve, calibration curve, decision curve analysis and clinical impact curve.Conclusion: Preoperative LCR was an independent predictor of NOAF in patients with AMI after PCI. The novel nomogram combined with LCR could rapidly and individually identify and treat patients at a high risk of NOAF.Keywords: C-reactive protein, acute myocardial infarction, atrial fibrillation, nomogram, left ventricular ejection fraction

Salime Boucher, Eric Esarey, Cameron Geddes et al.

Conventional cancer therapies include surgery, radiation therapy, chemotherapy, and, more recently, immunotherapy. These modalities are often combined to improve the therapeutic index. The general concept of radiation therapy is to increase the therapeutic index by creating a physical dose differential between tumors and normal tissues through precision dose targeting, image guidance, and high radiation beams that deliver radiation dose with high conformality, e.g., protons and ions. However, treatment and cure are still limited by normal tissue radiation toxicity, with many patients experiencing acute and long-term side effects. Recently, however, a fundamentally different paradigm for increasing the therapeutic index of radiation therapy has emerged, supported by preclinical research, and based on the FLASH radiation effect. FLASH radiation therapy (FLASH-RT) is an ultra-high dose-rate delivery of a therapeutic radiation dose within a fraction of a second. Experimental studies have shown that normal tissues seem to be universally spared at these high dose rates, whereas tumors are not. The dose delivery conditions are not yet fully characterized. Still, it is currently estimated that large doses of 10 Gy or more delivered in 200 ms or less produce normal tissue sparing effects yet effectively kill tumor cells. There is a great opportunity, but also many technical challenges, for the accelerator community to create the required dose rates with novel and compact accelerators to ensure the safe delivery of FLASH radiation beams.