Historical insights at scale: A corpus-wide machine learning analysis of early modern astronomic tables.

Understanding the evolution and dissemination of human knowledge over time faces challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of historical archives presents an opportunity for AI-supported analysis. This study advances historical analysis by using an atomization-recomposition method that relies on unsupervised machine learning and explainable AI techniques. Focusing on the "Sacrobosco Collection," consisting of 359 early modern printed editions of astronomy textbooks from European universities (1472-1650), totaling 76,000 pages, our analysis uncovers temporal and geographic patterns in knowledge transformation. We highlight the relevant role of astronomy textbooks in shaping a unified mathematical culture, driven by competition among educational institutions and market dynamics. This approach deepens our understanding by grounding insights in historical context, integrating with traditional methodologies. Case studies illustrate how communities embraced scientific advancements, reshaping astronomic and geographical views and exploring scientific roots amidst a changing world.

Semaglutide treatment for PRevention Of Toxicity in high-dosE Chemotherapy with autologous haematopoietic stem-cell Transplantation (PROTECT): study protocol for a randomised, double-blind, placebo-controlled, investigator-initiated study.

INTRODUCTION: Cancer treatment with high-dose chemotherapy damages the mucosal barrier of the gastrointestinal (GI) tract and is associated with severe toxicity involving mucositis, severe inflammation and organ dysfunction. Currently, there is no effective prophylaxis against this. Glucagon-like peptide 1 (GLP-1), a well-known regulator of blood glucose, has been suggested in mouse studies to possess trophic effects on gut epithelial cells as well as anti-inflammatory properties. In line with this, endogenous GLP-1 levels have been shown to be inversely correlated with toxicities after haematopoietic stem cell transplantation (HSCT) and treatment with a GLP-1 receptor agonist (GLP-1RA) was shown to limit chemotherapy-induced mucositis in rodents. This present study investigates the effects of the GLP-1RA semaglutide on GI mucositis severity score in patients with lymphoma undergoing high-dose chemotherapy followed by autologous (auto) HSCT. METHODS AND ANALYSIS: This is a randomised, double-blind, placebo-controlled, two-centre investigator-initiated clinical study. Forty adult patients with malignant lymphoma referred for auto-HSCT will be randomised in a 1:1 manner to receive either semaglutide or placebo once-weekly for 8 weeks. This includes a run-in period of 3-4 weeks with semaglutide 0.25 mg prior to high-dose chemotherapy treatment followed by a period of 4-5 weeks with semaglutide 0.5 mg including the 1 week of high-dose chemotherapy treatment. Clinical assessment of endpoint measurements and safety will be performed weekly during treatment and in a follow-up period of 10 weeks. The primary endpoint is GI mucositis severity (mean severity grade (0-II) during week 1-4 after auto-HSCT). Secondary endpoints include C-reactive protein increment, quality of life and safety. Fever, bacteraemia, antibiotic use, weight loss, morphine consumption, duration of hospitalisation, use of parenteral nutrition, change in muscle mass and clinical and laboratory evidence of organ toxicities will also be assessed. ETHICS AND DISSEMINATION: The study complies with Danish and European Union legislation and is approved by the Danish Medicines Agency, the Danish National Medical Research Ethics Committee (EU CT #2022-502139-20-00) and the Danish Data Protection Agency. The study is monitored by the Capital Region of Denmark's good clinical practice unit. All results, positive, negative and inconclusive, will be disseminated at national and international scientific meetings and in peer-reviewed scientific journals. TRIAL REGISTRATION NUMBER: NCT06449625.

Upregulation of Insulin-like Growth Factor-I in Response to Chemotherapy in Children with Acute Lymphoblastic Leukemia.

The treatment of childhood cancer is challenged by toxic side effects mainly due to chemotherapy-induced organ damage and infections, which are accompanied by severe systemic inflammation. Insulin-like growth factor I (IGF-I) is a key regulating factor in tissue repair. This study investigated associations between the circulating IGF-I levels and chemotherapy-related toxicity in pediatric acute lymphoblastic leukemia (ALL). In this prospective study, we included 114 patients (age: 1-17 years) with newly diagnosed ALL treated according to The Nordic Society of Paediatric Haematology and Oncology (NOPHO) ALL2008 protocol between 2013 and 2018. The patients' plasma levels of IGF-I, and the primary binding protein, IGFBP-3, were measured weekly during the first six weeks of treatment, including the induction therapy. The patients' systemic inflammation was monitored by their C-reactive protein (CRP) and interleukin (IL)-6 levels and their intestinal epithelial damage by their plasma citrulline levels. IGF-I and IGFBP-3 were converted into sex-and age-adjusted standard deviation scores (SDS) using 1621 healthy children as reference. At ALL diagnosis, IGF-I levels were decreased (median (quartiles): -1.2 SDS (-1.9 to -0.5), p = 0.001), but increased significantly following the initiation of chemotherapy, peaking on day 8 (0.0 SDS (from -0.8 to 0.7), p < 0.001). This increase correlated with the levels of CRP (rho = 0.37, p < 0.001) and IL-6 (rho = 0.39, p = 0.03) on day 15, when these markers reached maximum levels. A larger IGF-I increase from day 1 to 15 correlated with a slower recovery rate of the intestinal damage marker citrulline from day 15 to 29 (rho = -0.28, p = 0.01). Likewise, IGFBP-3 was reduced at diagnosis, followed by an increase after treatment initiation, and was highly correlated with same-day IGF-I levels. This study demonstrates a chemotherapy-induced increase in IGF-I, with a response that appears to reflect the severity of tissue damage and systemic inflammation, preceding CRP and IL-6 increases. IGF-I may have potential as an early reactive biomarker for acute toxicity in patients with ALL.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study.

INTRODUCTION: Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability. METHODS: This retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort. RESULTS: Models trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %. DISCUSSION: Model explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy. CONCLUSION: Although deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.

Dietary intake and risk of metabolic syndrome in long-term survivors of pediatric allogeneic hematopoietic stem cell transplantation.

We explored the dietary intake and metabolic syndrome (MetS) in 85 survivors of pediatric stem cell transplantation (median age 30 years, median follow-up time 20 years). Overall, the distribution of fatty acid deviated from the recommendations with a higher intake of saturated fat and a lower intake of unsaturated fat but was comparable to that of the background population. The prevalence of MetS was 27%, corresponding to that of the elderly background population. We compared the intake of macronutrients between those with MetS and those without MetS and found that overall fat intake was higher in patients with MetS (36.7E% [range, 27.2-51.2E%] vs. 33,5E% (range, 23.4-45.1E%), P = 0.016). Within the subgroup of patients treated with total body irradiation (TBI), we found a higher fat intake in those with MetS (36.8E% (range, 27.2-51.2E%) versus 32.0E% (range, 24.6-42.1E%), P = 0.013). This was confirmed in a multivariate analysis adjusted for TBI, sex, and age at follow-up (OR 1.20 (1.06-1.39), P = 0.008). Our findings suggest that conditioning with the use of TBI may induce a state of hypersensitivity to the potentially harmful effects of fat in the diet and suggest that this risk of MetS after TBI treatment may be modifiable by dietary changes.

DNA Methylation Profiling of Salivary Gland Tumors Supports and Expands Conventional Classification.

Tumors of the major and minor salivary glands histologically encompass a diverse and partly overlapping spectrum of frequent diagnostically challenging neoplasms. Despite recent advances in molecular testing and the identification of tumor-specific mutations or gene fusions, there is an unmet need to identify additional diagnostic biomarkers for entities lacking specific alterations. In this study, we collected a comprehensive cohort of 363 cases encompassing 20 different salivary gland tumor entities and explored the potential of DNA methylation to classify these tumors. We were able to show that most entities show specific epigenetic signatures and present a machine learning algorithm that achieved a mean balanced accuracy of 0.991. Of note, we showed that cribriform adenocarcinoma is epigenetically distinct from classical polymorphous adenocarcinoma, which could support risk stratification of these tumors. Myoepithelioma and pleomorphic adenoma form a uniform epigenetic class, supporting the theory of a single entity with a broad but continuous morphologic spectrum. Furthermore, we identified a histomorphologically heterogeneous but epigenetically distinct class that could represent a novel tumor entity. In conclusion, our study provides a comprehensive resource of the DNA methylation landscape of salivary gland tumors. Our data provide novel insight into disputed entities and show the potential of DNA methylation to identify new tumor classes. Furthermore, in future, our machine learning classifier could support the histopathologic diagnosis of salivary gland tumors.

A Euclidean transformer for fast and stable machine learned force fields.

Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the reliability of MLFFs in molecular dynamics (MD) simulations is facing growing scrutiny due to concerns about instability over extended simulation timescales. Our findings suggest a potential connection between robustness to cumulative inaccuracies and the use of equivariant representations in MLFFs, but the computational cost associated with these representations can limit this advantage in practice. To address this, we propose a transformer architecture called SO3KRATES that combines sparse equivariant representations (Euclidean variables) with a self-attention mechanism that separates invariant and equivariant information, eliminating the need for expensive tensor products. SO3KRATES achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales. To showcase this capability, we generate stable MD trajectories for flexible peptides and supra-molecular structures with hundreds of atoms. Furthermore, we investigate the PES topology for medium-sized chainlike molecules (e.g., small peptides) by exploring thousands of minima. Remarkably, SO3KRATES demonstrates the ability to strike a balance between the conflicting demands of stability and the emergence of new minimum-energy conformations beyond the training data, which is crucial for realistic exploration tasks in the field of biochemistry.

Intestinal mucositis, systemic inflammation and bloodstream infections following high-dose methotrexate treatment in childhood acute lymphoblastic leukaemia: Comparison between the NOPHO ALL 2008 protocol and the ALLTogether1 protocol.

Severe intestinal mucositis (IM) increases the risk of bloodstream infections (BSI) and inflammatory toxicity during acute lymphoblastic leukaemia (ALL) induction treatment. However, the implications of IM in subsequent ALL therapy phases after achieving remission remain unknown. This study investigated the relationship between IM (measured by plasma citrulline and the chemokine CCL20) and the development of BSI and systemic inflammation (reflected by C-reactive protein, CRP) in children with ALL during high-dose methotrexate (HDMTX) treatment, an important part of ALL consolidation therapy. The study compared patients treated according to the NOPHO ALL 2008 protocol (n = 52) and the ALLTogether1 protocol (n = 42), both with identical HDMTX procedures but different scheduling. One week post-HDMTX, citrulline dropped to median levels of 14.5 and 16.9 µM for patients treated according to the NOPHO ALL 2008 and ALLTogether1 protocols, respectively (p = 0.11). In a protocol and neutrophil count-adjusted analysis, hypocitrullinaemia (<10 µmol/L) was associated with increased odds of BSI within 3 weeks from HDMTX (OR = 26.2, p = 0.0074). Patients treated according to the NOPHO ALL 2008 protocol exhibited increased mucosal- and systemic inflammation post-HDMTX compared to patients treated according to ALLTogether1, with increased CCL20 (14.6 vs. 3.7 pg/mL, p < 0.0001) and CRP levels (10.0 vs. 1.0 mg/L, p < 0.0001). Both citrulline and CCL20 correlated with CRP for these patients (rs = -0.44, p = 0.0016 and rs = 0.35, p = 0.016, respectively). These results suggest that hypocitrullinaemia following HDMTX increases the risk of BSI, confirming previous observations from more intensive treatments. Moreover, these data indicate that the patients' vulnerability to mucositis and inflammatory toxicity after chemotherapy varies with treatment protocol.

Bone remodeling in survivors of pediatric hematopoietic stem cell transplantation: Impact of heavy resistance training.

BACKGROUND: Early-onset osteoporosis is a frequent late effect after pediatric hematopoietic stem cell transplantation (HSCT). It remains unknown if physical training can improve bone formation in these patients, as the transplantation procedure may cause sustained dysregulation of the bone-forming osteoblast progenitor cells. OBJECTIVE: We aimed to explore the effect of resistance training on bone remodeling in long-term survivors of pediatric HSCT. PROCEDURE: In this prospective, controlled intervention study, we included seven HSCT survivors and 15 age- and sex-matched healthy controls. The participants completed a 12-week heavy load, lower extremity resistance training intervention with three weekly sessions. We measured fasting serum levels of the bone formation marker "N-terminal propeptide of type I procollagen" (P1NP), and the bone resorption marker "C-terminal telopeptide of type I collagen" (CTX). The hypothesis was planned before data collection began. The trial was registered at Clinicaltrials.gov before including the first participant, with trial registration no. NCT04922970. RESULTS: Resistance training led to significantly increased levels of fasting P1NP in both patients (from 57.62 to 114.99 ng/mL, p = .03) and controls (from 66.02 to 104.62 ng/mL, p < .001). No significant changes in fasting CTX levels were observed. CONCLUSIONS: Despite previous high-dose cytotoxic therapy, long-term survivors of pediatric HSCT respond to resistance training with improvement of bone formation, comparable to that of healthy controls. This suggests that resistance training might be a promising non-pharmacological approach to prevent the early decline in bone mass, and should be considered as part of a follow-up program to counteract long-term sequela after pediatric HSCT.

Cytomegalovirus infection is associated with thymic dysfunction and chronic graft-versus-host disease after pediatric hematopoietic stem cell transplantation.

Pediatric hematopoietic stem cell transplantation (HSCT) is challenged by chronic graft-versus-host disease (cGvHD) significantly affecting survival and long-term morbidity, but underlying mechanisms including the impact of post-HSCT CMV infection are sparsely studied. We first investigated the impact of CMV infection for development of cGvHD in 322 children undergoing standard myeloablative HSCT between 2000 and 2018. Clinically significant CMV infection (n = 61) was an independent risk factor for chronic GvHD in a multivariable Cox regression analysis (HR = 2.17, 95% CI = 1.18-3.97, P = 0.013). We next explored the underlying mechanisms in a subcohort of 39 children. CMV infection was followed by reduced concentration of recent thymic emigrants (17.5 vs. 51.9 × 106/L, P = 0.048) and naïve CD4+ and CD8+ T cells at 6 months post-HSCT (all P < 0.05). Furthermore, CD25highFOXP3+ Tregs tended to be lower in patients with CMV infection (2.9 vs. 9.6 × 106/L, P = 0.055), including Tregs expressing the naivety markers CD45RA and Helios. CD8+ T-cell numbers rose after CMV infection and was dominated by exhausted PD1-expressing cells (66% vs. 39%, P = 0.023). These findings indicate that post-HSCT CMV infection is a main risk factor for development of chronic GvHD after pediatric HSCT and suggest that this effect is caused by reduced thymic function with a persistently impaired production of naïve and regulatory T cells in combination with increased peripheral T-cell exhaustion.

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces.

Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of a heatmap identifying input features (e.g. pixels) that are relevant to the model's decision. These explanations, however, entangle the potentially multiple factors that enter into the overall complex decision strategy. We propose to disentangle explanations by extracting at some intermediate layer of a neural network, subspaces that capture the multiple and distinct activation patterns (e.g. visual concepts) that are relevant to the prediction. To automatically extract these subspaces, we propose two new analyses, extending principles found in PCA or ICA to explanations. These novel analyses, which we call principal relevant component analysis (PRCA) and disentangled relevant subspace analysis (DRSA), maximize relevance instead of e.g. variance or kurtosis. This allows for a much stronger focus of the analysis on what the ML model actually uses for predicting, ignoring activations or concepts to which the model is invariant. Our approach is general enough to work alongside common attribution techniques such as Shapley Value, Integrated Gradients, or LRP. Our proposed methods show to be practically useful and compare favorably to the state of the art as demonstrated on benchmarks and three use cases.

[Explainable artificial intelligence in pathology]. / Erklärbare Künstliche Intelligenz in der Pathologie.

With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI).

Toward Explainable Artificial Intelligence for Precision Pathology.

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.

Diffeomorphic Counterfactuals With Generative Models.

Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. More specifically, we perform a suitable diffeomorphic coordinate transformation and then perform gradient ascent in these coordinates to find counterfactuals which are classified with great confidence as a specified target class. We propose two methods to leverage generative models to construct such suitable coordinate systems that are either exactly or approximately diffeomorphic. We analyze the generation process theoretically using Riemannian differential geometry and validate the quality of the generated counterfactuals using various qualitative and quantitative measures.

Markers of intestinal mucositis to predict blood stream infections at the onset of fever during treatment for childhood acute leukemia.

Despite chemotherapy-induced intestinal mucositis being a main risk factor for blood stream infections (BSIs), no studies have investigated mucositis severity to predict BSI at fever onset during acute leukemia treatment. This study prospectively evaluated intestinal mucositis severity in 85 children with acute leukemia, representing 242 febrile episodes (122 with concurrent neutropenia) by measuring plasma levels of citrulline (reflecting enterocyte loss), regenerating islet-derived-protein 3α (REG3α, an intestinal antimicrobial peptide) and CCL20 (a mucosal immune regulatory chemokine) along with the general neutrophil chemo-attractants CXCL1 and CXCL8 at fever onset. BSI was documented in 14% of all febrile episodes and in 20% of the neutropenic febrile episodes. In age-, sex-, diagnosis- and neutrophil count-adjusted analyses, decreasing citrulline levels and increasing REG3α and CCL20 levels were independently associated with increased odds of BSI (OR = 1.6, 1.5 and 1.7 per halving/doubling, all p < 0.05). Additionally, higher CXCL1 and CXCL8 levels increased the odds of BSI (OR = 1.8 and 1.7 per doubling, all p < 0.0001). All three chemokines showed improved diagnostic accuracy compared to C-reactive protein and procalcitonin. These findings underline the importance of disrupted intestinal integrity as a main risk factor for BSI and suggest that objective markers for monitoring mucositis severity may help predicting BSI at fever onset.

Physical capacity in children and adolescents with newly diagnosed cancer: A systematic review and meta-analysis.

OBJECTIVE: To review the body of evidence on cardiorespiratory fitness, muscle strength, and physical performance in children with newly diagnosed cancer, five databases (MEDLINE, Embase, CINAHL, CENTRAL, and Web of Science) were searched on December 19, 2022. METHODS: Thirteen studies, embodying 594 participants within 1 month of cancer diagnosis and 3674 healthy controls were included. Eighteen different outcomes on cardiorespiratory fitness (n = 2), muscle strength (n = 5), physical performance (n = 10), and adverse events (n = 1) were analyzed. RESULTS: Fifteen out of 17 outcomes on physical capacity showed severe impairments compared with healthy controls. Where possible, random-effects meta-analysis was conducted to synthesize the results. No adverse events were reported related to testing. CONCLUSION: Children with cancer have impaired cardiorespiratory fitness, muscle strength, and physical performance within the first month after diagnosis. However, the evidence is based on a small number of studies with large clinical heterogeneity, limiting the certainty of evidence.

sVEGF-R1 in acute non-infectious toxicity syndromes after pediatric allogeneic hematopoietic stem cell transplantation.

BACKGROUND: Allogeneic hematopoietic stem cell transplantation (HSCT) is challenged by acute non-infectious toxicities, including sinusoidal obstruction syndrome (SOS), engraftment syndrome (ES) and capillary leak syndrome (CLS) among others. These complications are thought to be driven by a dysfunctional vascular endothelium, but the pathophysiological mechanisms remain incompletely understood, and the diagnoses are challenged by purely clinical diagnostic criteria that are partly overlapping, limiting the possibilities for progress in this field. There is, however, increasing evidence suggesting that these challenges may be met through the development of diagnostic biomarkers to improve diagnostic accuracy of pathogenetically homogenous entities, improved pre-transplant risk assessment and the early identification of patients with increased need for specific treatment. Soluble vascular endothelial growth factor receptor-1 (sVEGF-R1) is emerging as an important biomarker of endothelial damage in patients with trauma and sepsis but has not been studied in HSCT. OBJECTIVES: To investigate sVEGF-R1 as a marker of endothelial damage in pediatric HSCT patients by exploring associations with SOS, CLS, ES, and acute graft-versus-host disease (aGvHD). METHODS: We prospectively included 113 children undergoing myeloablative HSCT and measured sVEGF-R1 in plasma samples obtained weekly during the early period of transplantation and 3 months post-transplant. RESULTS: All over, sVEGF-R1 levels were significantly increased from day +7 after graft infusion, peaking at day +30, most pronounced in patients receiving busulfan. Patients considered to be at increased risk of SOS and therefore commenced on prophylactic defibrotide had significantly elevated levels of sVEGF-R1 before start of conditioning (446 pg/mL vs. 281 pg/mL, p = 0.0035), and this treatment appeared to stabilize sVEGF-R1 levels compared to patients not treated with defibrotide. Thirteen (11.5%) children meeting the modified Seattle criteria for SOS at median day +8 (1-18), had significantly elevated sVEGF-R1 levels on day +14 (489 pg/mL vs. 327 pg/mL, p = 0.007). In contrast. sVEGF-R1 levels in the much broader group of patients (45.1%) meeting EBMT-SOS criteria, including patients with very mild disease, did not overall differ in sVEGF-R1 levels, but higher sVEGF-R1 levels were seen in EBMT-SOS patients with an increased need for diuretic treatment. Importantly, sVEGF-R1 levels were not associated with ES and CLS but were significantly increased on day +30 in patients with grade III-IV aGvHD (OR = 4.2 pr. quartile, p = 0.023). CONCLUSION: VEGF-R1 levels are found to be increased in pediatric patients developing SOS, reflecting the severity of morbidity. sVEGF-R1 were unassociated with both CLS and ES. The potential of sVEGF-R1 as a clinically useful biomarker for SOS should be further explored to improve pre-transplant SOS-risk assessment, SOS-severity grading, and to guide treatment.