Ammonium‐nitrate‐fuel‐oil (ANFO) explosive, one of the most used mining explosives, exhibits highly non‐ideal behaviour. The non‐ideality of the detonation is manifested in the strong dependence of the detonation velocity on the charge radius and existence and the characteristics of confinement. This can lead to the detonation velocities as low as one‐third of the ideal velocity. The literature reported experimental detonation velocities of cylindrical ANFO charges confined in different confiners (aluminium, copper, steel, polymethyl methacrylate, and polyvinyl chloride) are analysed in this paper. An empirical confinement model, which relates the detonation velocity to the charge radius and the mass of the confiner to the mass of explosive ratio per unit length, is proposed. The model predicts the detonation velocity of unconfined and confined ANFO charges with a mean average percentage error of 8.8 %.

The issue of spinal deformities, which occurs during phases of intense growth in children, has been recognized within the broader community. Certain categories of children are particularly vulnerable to long-term health risks in this regard, making it important to find ways to intervene early and identify such cases. This study was conducted to assess the current state of spinal posture during one of the most sensitive phases of physiological development in a special group of preschool children. In this case, the sample included 92 preschool children aged 4 to 6 years, who have been living and growing up from an early age in the SOS Children's Village in Sarajevo, without the presence of their biological parents. The assessment of poor posture was applied according to Napoleon Wolanski (1975), which is based on determining the relationships between segmental dimensions as follows: D1 – assessment of head posture (HPA), D2 – assessment of shoulder posture (SPA), D3 – assessment of chest posture (CPA), D4 – assessment of scapula posture (SBPA), D5 – assessment of spinal posture (SP), D6 – assessment of abdominal posture (APA), D7 – assessment of leg posture (LPA), D8 – assessment of foot posture (FPA). Deviations are classified according to their severity and are assessed with so-called negative points, where: 0 points indicate no deviation, 1 point indicates a mild deviation, and 2 points indicate a significant deviation. The posture results show that 15.2% of the children have scoliosis, while 13.0% have lordosis, and 7.6% of preschoolers have kyphosis. Additionally, 30.4% of the children have flat feet, which predisposes them to long-term spinal problems, and 33.7% have significant deviations in leg posture. Numerical values of the results indicate that none of the children included in the testing had an ideal shoulder posture, i.e., a score of 0, which implies no deviation from normal, while the head posture results were also extremely poor.

Background and objective In recent years, the complex interplay between systemic health and oral well-being has emerged as a focal point for researchers and healthcare practitioners. Among the several important connections, the convergence of Type 2 Diabetes Mellitus (T2DM), dyslipidemia, chronic periodontitis, and peripheral blood mononuclear cells (PBMCs) is a remarkable example. These components collectively contribute to a network of interactions that extends beyond their domains, underscoring the intricate nature of human health. In the current study, bioinformatics analysis was utilized to predict the interactomic hub genes involved in type 2 diabetes mellitus (T2DM), dyslipidemia, and periodontitis and their relationships to peripheral blood mononuclear cells (PBMC) by machine learning algorithms. Materials and Methods Gene Expression Omnibus datasets were utilized to identify the genes linked to type 2 diabetes mellitus(T2DM), dyslipidemia, and Periodontitis (GSE156993).Gene Ontology (G.O.) Enrichr, Genemania, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for analysis for identification and functionalities of hub genes. The expression of hub D.E.G.s was confirmed, and an orange machine learning tool was used to predict the hub genes. Result The decision tree, AdaBoost, and Random Forest had an A.U.C. of 0.982, 1.000, and 0.991 in the R.O.C. curve. The AdaBoost model showed an accuracy of (1.000). The findings imply that the AdaBoost model showed a good predictive value and may support the clinical evaluation and assist in accurately detecting periodontitis associated with T2DM and dyslipidemia. Moreover, the genes with p-value < 0.05 and A.U.C.>0.90, which showed excellent predictive value, were thus considered hub genes. Conclusion The hub genes and the D.E.G.s identified in the present study contribute immensely to the fundamentals of the molecular mechanisms occurring in the PBMC associated with the progression of periodontitis in the presence of T2DM and dyslipidemia. They may be considered potential biomarkers and offer novel therapeutic strategies for chronic inflammatory diseases.

Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate detection of different types of somatic SVs using a phased breakpoint graph approach. To benchmark various short- and long-read SV detection methods, we sequenced five tumor/normal cell line pairs with Illumina, Nanopore, and PacBio sequencing platforms; on this benchmark Severus showed the highest F1 scores (harmonic mean of the precision and recall) as compared to long-read and short-read methods. We then applied Severus to three clinical cases of pediatric cancer, demonstrating concordance with known genetic findings as well as revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Quantum computing represents a paradigm shift in computation, offering the potential to solve complex problems intractable for classical computers. Although current quantum processors already consist of a few hundred qubits, their scalability remains a significant challenge. Modular quantum computing architectures have emerged as a promising approach to scale up quantum computing systems. This article delves into the critical aspects of distributed multi-core quantum computing, focusing on quantum circuit mapping, a fundamental task to successfully execute quantum algorithms across cores while minimizing inter-core communications. We derive the theoretical bounds on the number of non-local communications needed for random quantum circuits and introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications. Our exhaustive evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a 4.9× and 1.6× improvement in terms of execution time and non-local communications, respectively, compared to the best-performing algorithm. HQA emerges as a very promising scalable approach for mapping quantum circuits into multi-core architectures, positioning it as a valuable tool for harnessing the potential of quantum computing at scale.

Flat-panel detector computed tomography (FDCT) is increasingly used in (neuro)interventional angiography suites. This study aimed to compare FDCT perfusion (FDCTP) with conventional multidetector computed tomography perfusion (MDCTP) in patients with acute ischemic stroke. In this study, 19 patients with large vessel occlusion in the anterior circulation who had undergone mechanical thrombectomy, baseline MDCTP and pre-interventional FDCTP were included. Hypoperfused tissue volumes were manually segmented on time to maximum (Tmax) and time to peak (TTP) maps based on the maximum visible extent. Absolute and relative thresholds were applied to the maximum visible extent on Tmax and relative cerebral blood flow (rCBF) maps to delineate penumbra volumes and volumes with a high likelihood of irreversible infarcted tissue (“core”). Standard comparative metrics were used to evaluate the performance of FDCTP. Strong correlations and robust agreement were found between manually segmented volumes on MDCTP and FDCTP Tmax maps (r = 0.85, 95% CI 0.65–0.94, p < 0.001; ICC = 0.85, 95% CI 0.69–0.94) and TTP maps (r = 0.91, 95% CI 0.78–0.97, p < 0.001; ICC = 0.90, 95% CI 0.78–0.96); however, direct quantitative comparisons using thresholding showed lower correlations and weaker agreement (MDCTP versus FDCTP Tmax 6 s: r = 0.35, 95% CI −0.13–0.69, p = 0.15; ICC = 0.32, 95% CI 0.07–0.75). Normalization techniques improved results for Tmax maps (r = 0.78, 95% CI 0.50–0.91, p < 0.001; ICC = 0.77, 95% CI 0.55–0.91). Bland-Altman analyses indicated a slight systematic underestimation of FDCTP Tmax maximum visible extent volumes and slight overestimation of FDCTP TTP maximum visible extent volumes compared to MDCTP. FDCTP and MDCTP provide qualitatively comparable volumetric results on Tmax and TTP maps; however, direct quantitative measurements of infarct core and hypoperfused tissue volumes showed lower correlations and agreement.

In healthcare facilities, infections caused by Staphylococcus aureus (S. aureus) from textile materials are a cause for concern, and nanomaterials are one of the solutions; however, their impact on safety and biocompatibility with the human body must not be neglected. This study aimed to develop a novel multilayer coating with poly(allylamine hydrochloride) (PAH) and immobilized ZnO nanoparticles (ZnO NPs) to make efficient antibacterial and biocompatible cotton, polyester, and nylon textiles. For this purpose, the coated textiles were characterized with profilometry, contact angles, and electrokinetic analyzer measurements. The ZnO NPs on the textiles were analyzed by scanning electron microscopy and inductively coupled plasma mass spectrometry. The antibacterial tests were conducted with S. aureus and biocompatibility with immortalized human keratinocyte cells. The results demonstrated successful PAH/ZnO coating formation on the textiles, demonstrating weak hydrophobic properties. Furthermore, PAH multilayers caused complete ZnO NP immobilization on the coated textiles. All coated textiles showed strong growth inhibition (2–3-log reduction) in planktonic and adhered S. aureus cells. The bacterial viability was reduced by more than 99%. Cotton, due to its better ZnO NP adherence, demonstrated a slightly higher antibacterial performance than polyester and nylon. The coating procedure enables the binding of ZnO NPs in an amount (<30 µg cm−2) that, after complete dissolution, is significantly below the concentration causing cytotoxicity (10 µg mL−1).

The state of charge (SOC) of lithium-ion batteries needs to be accurately estimated for safety and reliability purposes. For battery packs made of a large number of cells, it is not always feasible to design one SOC estimator per cell due to limited computational resources. Instead, only the minimum and the maximum SOC need to be estimated. The challenge is that the cells having the minimum and maximum SOC typically change over time. In this context, we present a low-dimensional hybrid estimator of the minimum (maximum) SOC, whose convergence is analytically guaranteed. We consider for this purpose a battery consisting of cells interconnected in series, which we model by electrical equivalent circuit models. We then present the hybrid estimator, which runs an observer designed for a single cell at any time instant, selected by a switching-like logic mechanism. We establish a practical exponential stability property for the estimation error on the minimum (maximum) SOC thereby guaranteeing the ability of the hybrid scheme to generate accurate estimates of the minimum (maximum) SOC. The analysis relies on non-smooth hybrid Lyapunov techniques. A numerical illustration is provided to showcase the relevance of the proposed approach.

The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed.

The culling of animals that are infected, or suspected to be infected, with COVID-19 has fuelled outcry. What might have contributed to the ongoing debates and discussions about animal rights protection amid global health crises is the lack of a unified understanding and internationally agreed-upon definition of “One Health”. The term One Health is often utilised to describe the imperative to protect the health of humans, animals, and plants, along with the overarching ecosystem in an increasingly connected and globalized world. However, to date, there is a dearth of research on how to balance public health decisions that could impact all key stakeholders under the umbrella of One Health, particularly in contexts where human suffering has been immense. To shed light on the issue, this paper discusses whether One Health means “human-centred connected health” in a largely human-dominated planet, particularly amid crises like COVID-19. The insights of this study could help policymakers make more informed decisions that could effectively and efficiently protect human health while balancing the health and well-being of the rest of the inhabitants of our shared planet Earth.

Abstract By providing important indicators, financial indices help investors make educated judgements regarding their assets, much like vital sign monitors for the financial markets. The best way for investors to keep up with the market and make strategic adjustments is to keep an eye on these indexes. Researching the most important financial indexes for making educated investing decisions is, thus, quite relevant. Finding the most essential financial indices from an investing standpoint and assigning a weight to each of those indexes are the main goals of this research. A weighted score is derived by combining four financial indices in a Multi-Criteria Decision-Making (MCDM) technique. These objectives are then pursued. Triangular Fuzzy Numbers (TFNs) and the Fuzzy Analytic Hierarchy Process (F-AHP) are used to determine the weights of criteria in this technique. Using these methods together, the research hopes to provide a thorough analysis of the role that different financial indexes have in informing investment choices. This study emphasizes the paramount importance of considering the Price Earning to Growth (PEG) ratio when making investment decisions, followed by the Debt Equity Ratio. Price to Book Value and Dividend Yield, while relevant, carry comparatively less weightage in the overall assessment. Investors are advised to use these insights as a guideline in their financial analysis and decision-making processes.

Background In Bosnia and Herzegovina, domestic and wild carnivores represent a significant driver for the transmission and ecology of zoonotic pathogens, especially those of parasitic aetiology. Nevertheless, there is no systematic research of Trichinella species in animals that have been conducted in Bosnia and Herzegovina, even though trichinellosis is considered the most important parasitic zoonosis. The available results of the few studies carried out in Bosnia and Herzegovina are mainly related to the confirmation of parasitic larvae in the musculature of domestic pigs and wild boars or data related to trichinellosis in humans. The objective of our study was to present the findings of a comprehensive investigation into the species composition of Trichinella among 11 carnivorous species within the territory of Bosnia and Herzegovina, as follows: red fox ( Vulpes vulpes ), grey wolf ( Canis lupus ), brown bear ( Ursus arctos ), wildcat ( Felis silvestris ), pine marten ( Martes martes ), European badger ( Meles meles ), weasel ( Mustela nivalis ), European polecat ( Mustela putorius ), Eurasian lynx ( Lynx lynx ), but also dog ( Canis lupus familiaris ) and cat ( Felis catus ). Results In the period 2013–2023, carnivore musculature samples ( n  = 629), each consisting of 10 g of muscle tissue, were taken post-mortem and individually examined using the artificial digestion method. In the positive samples ( n  = 128), molecular genotyping and identification of parasitic larvae of Trichinella spp. were performed using a PCR-based technique up to the species/genotype level. Positive samples were used for basic PCR detection of the genus Trichinella (rrnS rt-PCR technique) and genotyping (rrnl-EVS rt-PCR technique). The Trichinella infection was documented for the first time in Bosnia and Herzegovina among red foxes, grey wolves, brown bears, dogs, badgers and Eurasian lynx, with a frequency rate of 20.3%. Additionally, the presence of T. britovi infection was newly confirmed in Bosnia and Herzegovina, marking the initial documented cases. Furthermore, both T. britovi and T. pseudospiralis infections were observed in the wildcat population, whereas T. britovi and T. spiralis infections were detected in pine martens. Consistent with previous research, our findings align particularly regarding carnivores, with data from other countries such as Germany, Finland, Romania, Poland and Spain, where T. britovi exhibits a wider distribution (62.5–100%) compared to T. spiralis (0.0–37.5%). T. britovi is more common among sylvatic carnivores (89.0%), while T. spiralis prevails in wild boars (62.0%), domestic swine (82.0%) and rodents (75.0%). Conclusion The results of our study represent the first molecular identification of species of the genus Trichinella in Bosnia and Herzegovina. Additionally, our findings underscore the necessity for targeted epidemiological studies to thoroughly assess trichinellosis prevalence across diverse animal populations. Considering the relatively high frequency of trichinellosis infection in investigated animal species and its public health implications, there is an evident need for establishing an effective trichinellosis surveillance system in Bosnia and Herzegovina.

Background: Cancer research autopsy genomic studies offer insight into the metastatic cancer landscape but come with complexities that relate to the sampling and processing of post-mortem tissue. Clarifying the effect of autopsy variables on pre- and post-sequencing quality control (QC) is an unmet need that may inform tissue collection strategies. Methods: The effect of age, sex, post-mortem interval (PMI), and sample type (primary, metastatic, or normal) on pre-sequencing QC (nucleic acid concentration and integrity) was examined in 2678 samples (88.6% metastatic, 8.0% primary, 3.4% normal) from 83 patients with melanoma, lung, renal, or prostate cancer in the PEACE study. In the lung cohort, 160 surgical samples were also included through the TRACERx study, allowing surgery-autopsy tissue comparisons. Post-sequencing QC metrics were evaluated for lung samples that underwent DNA (n=522) or RNA (n=366) sequencing. Results: RNA concentration and RIN were greater in surgical samples than those collected at autopsy. Across cohorts, metastatic autopsy samples had greater nucleic acid concentrations than primary or normal autopsy samples, but not integrity. DNA and RNA concentration and integrity differed significantly between primary tumor types. When comparing samples of different metastatic sites from the whole cohort, concentration was lowest in bone (DNA) or the digestive tract (RNA), while integrity was greatest in the brain and lowest in the digestive tract (DIN, RIN). Although autopsy variables like age, sex and PMI correlated with pre-sequencing QC metrics in univariate analysis, they were not found to significantly correlate with these metrics in multivariate analysis, which identified that only primary cancer type and metastatic site were independent determinants of concentration and integrity. Similarly, for post-DNA (whole exome) sequencing QC, only the metastatic site was found to independently influence sequencing QC metrics like total number of sequences, average sequence length, and FastQC score. For RNA sequencing, only the metastatic site was found to influence sequencing QC metrics like total number of sequences, percentage of non-duplicated sequences, one hit-one genome percentage, and the alignment percentage on the human genome. Discussion: The lack of influence of PMI on QC in the largest QC-focused autopsy cancer study to date suggests that quality tissue can be obtained from non-rapid autopsy programs, which are more feasible and less resource-intensive than rapid programs. Citation Format: Petros Fessas, Sonya Hessey, Corentin Richard, Cristina Naceur-Lombardelli, Sophia Ward, David A. Moore, Karolina Nowakowska, Blanca Trujillo, Irene Lobon, Scott T. Shepherd, Fiona Byrne, Samra Turajlic, Gerhardt Attard, Charles Swanton, Mariam Jamal-Hanjani. The effect of cancer research autopsy parameters on DNA and RNA sequencing quality [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2926.

Background: Genetic evolution of clear cell renal cell carcinoma (ccRCC) follows distinct trajectories, with varying levels of intratumor heterogeneity (ITH) and chromosomal complexity (WGII). While these patterns associate with clinical outcomes, it remains unknown whether they fully reconcile tumor behavior and how genetic and transcriptional features co-evolve in relation to the tumor microenvironment (TME). Methods: To analyze the patterns of transcriptional and TME heterogeneity, we performed bulk whole-transcriptome sequencing on 244 samples, including 22 metastatic and 12 tumor-adjacent normal samples, from 79 ccRCC patients recruited to the TRACERx Renal study. We integrated transcriptional data with previously published genetic, phylogenetic, spatial and clinical information. Results: Transcriptional distances between paired samples from the same primary tumor mirrored but were not fully determined by genetic distance (p-value < 0.001); and increased from primary-primary to primary-metastasis and primary-normal pairs. Within primary-metastasis pairs, metastasis-seeding primary tumor regions were transcriptionally closest to their matched metastasis (p-value < 0.001), suggesting that an important fraction of metastatic transcriptional traits were acquired in the primary tumor. Regarding the tumor clonal structure, transcriptional evolution followed a conserved path through increasing cell proliferation and oxidative phosphorylation and downregulating DNA repair from earlier to later clones. Further, within tumors with increasing WGII we observed upregulation and downregulation of repressors and downstream effectors, respectively, of the canonical cGAS-STING pathway. Combining the presence of this transcriptional pattern with WGII predicted shorter PFS in TRACERx Renal (p-value < 0.001) and in TCGA-KIRC (p-value < 0.001). Clonal evolution was also linked to changes in TME, with each of the previously defined genetic evolutionary trajectories associated to a specific TME (p-value < 0.001). For example, ccRCCs on a PBRM1-SETD2 trajectory demonstrated increased infiltration of cytotoxic immune cells. TME ITH was pervasive and associated with shorter PFS (p-value = 0.03). A recurrent trend from earlier to later clones was progressive T cell depletion (p-value < 0.001). The evolution of the TCR repertoire mirrored the tumor clonal structure (p-value = 0.002), suggesting the thus far elusive antigenic source in ccRCC is heritable. Accordingly, the TCR repertoire in metastasis-seeding primary tumor regions resembled the closest the TCR repertoire of matched metastasis (p-value = 0.06). Conclusion: Integrated analysis of genetic and transcriptional data in TRACERx Renal showed i) transcriptional and TME ITH not fully recapitulated by genetic ITH, ii) conserved paths of transcriptional and TME evolution and iii) a heritable nature of part of the ccRCC antigen source. Citation Format: Ángel Fernández Sanromán, Lewis Au, Benjy Jek Yang Tan, Charlotte Spencer, Anne-Laure Catin, Irene Lobon, Husayn Pallikonda, Kevin Litchfield, Fiona Byrne, James Larkin, Annika Fendler, Samra Turajlic. Integrated analysis of genetic, transcriptional and TME evolution of ccRCC: TRACERx Renal [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1621.

Diverse clinical presentations of clear cell renal cell carcinoma (ccRCC) confound clinical decision making, leading to over and undertreatment. Clonal evolution of ccRCC proceeds through distinct trajectories characterised by differing levels of genomic intratumoral heterogeneity (gITH) and chromosomal complexity (weighted genomic instability index, wGII). However, accurate evaluation of these indices requires multiregional profiling of fresh tumour; cost prohibitive and logistically challenging in the clinical setting. Clinical histopathology workflows routinely capture multiple tumour areas enabling the use artificial intelligence (AI) to predict tumour evolutionary features directly from clinical grade H&E whole slide image (WSIs). ccRCC displays profound genetic and histological ITH but the link between these entities remains unclear. We leverage the TRACERx Renal cohort, comprising 1485 WSIs from 81 tumours to predict WGII and gITH and to gain insights into the relationship between genetic and histological ITH. Critically, each WSI is associated with a wGII and gITH label derived from a closely linked fresh tumour sample. For both prediction tasks, we extracted meaningful features for each WSI using self-supervised representation learning “MoCo”. Since high wGII confers poor prognosis we focussed on predicting binary stratification label of high wGII or low wGII (relative to the cohort median). First we predicted wGII as a continuous variable using a supervised multiple instance learning regression model trained on the MoCo features, and then classified the predicted wGII into “high” or “low” achieving 0.80 AUROC. To predict gITH we postulated that the degree of gITH would correlate with histological ITH. Using an unsupervised clustering of refined MoCo features we defined 24 histological clusters. The number of computationally derived histological clusters within a single tumour positively correlated with gITH (pearson’s 0.56). We used the number of clusters to classify WSIs into prognostic binary groups of high or low gITH (relative to the cohort median) achieving an AUROC of 0.80. To understand the biological relationship between histological and genetic ITH we pathologically characterised the histological clusters: a pathologist annotated WSIs with tumour architecture and cytomorphology. Image tiles were associated with the annotations using spatial coordinates, illuminating phenotypic traits of different evolutionary trajectories and providing an interpretability framework for our AI pipelines. Since the tumour evolutionary course dictates disease progression tempo, applying evolutionary classification in clinic can fundamentally improve patient care. Here, for the first time, we provide a framework to translate fundamental evolutionary principles underpinning tumour biology and clinical progression into a prognostic computational pathology biomarker possible to clinically implement. Citation Format: Charlotte E. Spencer, Axel Camara, Auriane Riou, Lewis Au, Jose I. Lopez, Zayd Tippu, Charles Maussion, Kenneth Ho, Amy Strange, Emma Nye, Veronique Birault, Lydwine Van-praet, Kim Edmonds, Eleanor Carlyle, Steve Hazell, Sarah Rudman, James Larkin, Samra Turajlic. Predicting tumor evolution from digital histology using AI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4298.