Heterogeneous Entity Research Articles

Discovery and Evaluation of Extracellular MicroRNA Biomarkers in Plasma, Ascites, and Urine.

MicroRNAs (miRNAs) comprise a large group of small noncoding RNAs within a heterogeneous entity of noncoding RNAs, forming potent functional tools regulating the crucial biological processes within cells and the body. Cell-free miRNAs have become one of the novel promising diagnostic, predictive, and prognostic biomarkers for various diseases extensively investigated in recent years. This is due to their presence within extracellular fractions of various body fluids suggesting their potential as noninvasive "liquid biopsy" in case of their dysregulated expression.Among the body fluids, blood plasma and serum along with urine are the most commonly investigated sources of various types of cell-free miRNAs. Another body fluid, i.e., ascites (effusion, peritoneal/pleural fluid) may be the clinically important fluid particularly associated with carcinogenesis in ovarian carcinomas and hepatocellular carcinomas or in case of liver cirrhosis.Here, we provide a protocol for an expression profiling study based on qPCR analyses aimed at finding novel candidate miRNAs via small-scale or large-scale screening and evaluation experiments using liquid biopsies of blood plasma, ascites, and urine. Using this approach may be worth in cases where no (or limited) information is available on miRNA expression in particular diseases and geographic regions, for validation of previously published miRNAs with promising diagnostic potential, particularly in situations where follow-up study is aimed at validating miRNAs coming from (micro) array or NGS experiments, or where funding for large-scale experiments is not available. We demonstrate that assessment of plasma, ascites, and urine miRNAs expression may represent a feasible method to explore the potential for finding novel diagnostic, predictive, and prognostic biomarkers for various diseases.

Heterogeneous Entity Matching with Complex Attribute Associations using BERT and Neural Networks

Heterogeneous Entity Matching with Complex Attribute Associations using BERT and Neural Networks

Механизм экономических взаимодействий: содержание категории и сущностные характеристики

The mechanism of economic interactions is represented by mechanisms of three main types: vertical, horizontal and network interactions. The essence of these types of interaction mechanisms, the features of their organization and formation are demonstrated. The mechanism of vertical interactions implies the presence of a certain center that determines the «rules of the game» within the boundaries of the economic space it controls. This type of mechanisms includes, for example, the mechanism of interaction between the state and economic entities, the mechanism of interaction between management companies and various subdivisions in holding-type structures. The mechanism of horizontal interactions is focused on the initiation and maintenance of intersystem and intrasystem interactions. Also there are the issues of organizing interpersonal interactions of individuals acting as independent economic entities in the sphere of influence of this mechanism. The mechanism of network interactions ensures the formation of network communities of an open type, within which there is a trusting cooperation of heterogeneous entities that share the values of these communities and are ready to interact with each other in order to achieve common interests within the framework of formal and informal ties with different levels of scale, intensity and frequency of transactions. As the essential characteristics of these mechanisms, the following are distinguished: the predominant direction of influences, the presence of entities that implement the functions of management, regulation and coordination, the predominant type of interactions, economic spaces within which interactions are organized, the functions of mechanisms.

Curriculum and History Reflections on the Pakistan Studies Text Books Taught in Khyber Pakhtunkhwa, Pakistan

Though history as a discipline and historical instances are used for different agendas since centuries, it provided new grounds to ‘nation building’ after the emergence of Nation State in the previous colonies during the 20th century. The academicians and writers started writing and interpreting history to serve ‘national interests’ and promote ‘national sense’. Each State tried to sort out common features in the different ethnic and social groups living in that political boundary. The creation of Pakistan in August 1947 was a sort of unique experience when Islam was used as a common factor to unite heterogeneous ethnic entities. The ideological fathers, with the colonial legacy of identity crisis tried to promote common features for the Pakistani Nation. For this purpose, the new state managers formed different commissions which provided guidelines for textbooks writing. The successive governments followed the policy which developed ‘historical content’ in all textbooks for ‘constructing image’ of different individuals, institutions and creeds. This ‘insertion’ in textbooks might have some positive impact in promoting ‘Pakistani nation’ but the process culminated in alienation of multiple ethnicities on one hand and use of historical accounts as a propaganda tool on the other hand. The present paper is an attempt to scrutinize the historical content in the textbooks of Pakistan Studies taught at secondary level in public schools in Khyber Pakhtunkhwa. The scrutiny would be dealing with two aspects: a) authenticity of the material; b) presentation. The paper would cover textbooks for grade 9 and 10, published by Khyber Pakhtunkhwa textbook board.

The ends of an assemblage of health

This paper identifies and responds to three key challenges that have emerged in discussions of the assemblage across health geography. These challenges concern the problem of identifying the borders or limits of an assemblage of health; the problem of clarifying how such assemblages change over time; and the more general problem of identifying the affective and material character of the assemblage such that one may distinguish ‘therapeutic’ from ‘oppressive’ arrangements. The paper argues that each challenge calls for a novel analytics of power grounded in assessments of the generative forces of stratification and selection expressed within an assemblage. Assemblages of health are composed in relations of power, affect and desire that stratify the assemblage in ongoing processes of selection, acting upon heterogeneous entities (material and immaterial, intensive and extensive, human and nonhuman), bringing them into contact, causing them to affect one another, transforming their activity. Analysis of these processes provides potent tools for rethinking how relations, events, spaces and encounters mediate experiences of health and illness, and novel grounds for intervening in the formation of an assemblage of health.

Random fixed points, systemic risk and resilience of heterogeneous financial network.

We consider a large random network, in which the performance of a node depends upon that of its neighbours and some external random influence factors. This results in random vector valued fixed-point (FP) equations in large dimensional spaces, and our aim is to study their almost-sure solutions. An underlying directed random graph defines the connections between various components of the FP equations. Existence of an edge between nodes i,j implies the i-th FP equation depends on the j-th component. We consider a special case where any component of the FP equation depends upon an appropriate aggregate of that of the random 'neighbour' components. We obtain finite dimensional limit FP equations in a much smaller dimensional space, whose solutions aid to approximate the solution of FP equations for almost all realizations, as the number of nodes increases. We use Maximum theorem for non-compact sets to prove this convergence.We apply the results to study systemic risk in an example financial network with large number of heterogeneous entities. We utilized the simplified limit system to analyse trends of default probability (probability that an entity fails to clear its liabilities) and expected surplus (expected-revenue after clearing liabilities) with varying degrees of interconnections between two diverse groups. We illustrated the accuracy of the approximation using exhaustive Monte-Carlo simulations.Our approach can be utilized for a variety of financial networks (and others); the developed methodology provides approximate small-dimensional solutions to large-dimensional FP equations that represent the clearing vectors in case of financial networks.

A fair and interpretable network for clinical risk prediction: a regularized multi-view multi-task learning approach.

In healthcare domain, complication risk profiling which can be seen as multiple clinical risk prediction tasks is challenging due to the complex interaction between heterogeneous clinical entities. With the availability of real-world data, many deep learning methods are proposed for complication risk profiling. However, the existing methods face three open challenges. First, they leverage clinical data from a single view and then lead to suboptimal models. Second, most existing methods lack an effective mechanism to interpret predictions. Third, models learned from clinical data may have inherent pre-existing biases and exhibit discrimination against certain social groups. We then propose a multi-view multi-task network (MuViTaNet) to tackle these issues. MuViTaNet complements patient representation by using a multi-view encoder to exploit more information. Moreover, it uses a multi-task learning to generate more generalized representations using both labeled and unlabeled datasets. Last, a fairness variant (F-MuViTaNet) is proposed to mitigate the unfairness issues and promote healthcare equity. The experiments show that MuViTaNet outperforms existing methods for cardiac complication profiling. Its architecture also provides an effective mechanism for interpreting the predictions, which helps clinicians discover the underlying mechanism triggering the complication onsets. F-MuViTaNet can also effectively mitigate the unfairness with only negligible impact on accuracy.

Recurrence and outcomes of non‐functional pancreatic neuroendocrine tumours post‐resection: an Australian retrospective, multicentre cohort study

Pancreatic neuroendocrine tumours (PNETs) are heterogenous entities with variable clinical outlook. The prevalence of PNETs is increasing in Australia. Despite this, data on peri-operative management and post-operative prognosis for Australian patients is scant in the literature. Patients from two tertiary hospitals in Victoria were recruited. Inclusion criteria included patients who underwent curative surgical resection for primary, non-functioning, PNETs without metastases from January 2011 to December 2021. Patients were identified via histopathological reports, CMBS and ICD-10 codes. Data were sourced from Electronic Medical Records, outpatient notes and letters. Sixty-three patients (34 Male, 29 Female) underwent surgical resection for PNETs. Fifty-three patients (84.1%) had a post-operative complication, and 21 (33.3%) had severe complications. Two patients had disease recurrence. Head PNETs had higher Ki-67% (5.33 vs. 2.72, P=0.29), and likelihood of nodal spread (9 (36%) vs. 4 (16%), P=0.054). Pancreatic Head resections were also associated with more frequent ICU admissions (21 (84%) vs. 18 (54.5), P=0.024), longer ICU stays (4.05 vs. 2.17 days, P=0.10) and hospital stays (26.76 vs. 8.27 days, P=<0.001). Within the limitations of this study, it demonstrates that surgical resection of PNET carries a significant morbidity with a low rate of recurrence. Additionally, Pancreatic head NETs may be associated with higher grades and increased likelihood of nodal metastases. Considering this, careful patient selection is paramount.

1025 PROGNOSTIC IMPACT OF STATINS AND DUAL ANTIPLATELET THERAPY ON LONG-TERM PROGNOSIS IN MINOCA PATIENTS

Abstract Background Myocardial Infarction with Non-Obstructive Coronary Artery disease (MINOCA) is a heterogeneous entity with relevant long-term major adverse cardiovascular events (MACE). There is solid evidence that secondary prevention strategies improve prognosis of patients with obstructive myocardial infarction. However, evidence-based treatments for MINOCA are lacking as no published randomized clinical trials have ever exclusively enrolled this population. In fact, treatment recommendations in current guidelines are mainly based on expert opinions and MINOCA patients are frequently discharged with statins and dual antiplatelet therapy (DAPT). Purpose To evaluate the effects of statins and DAPT as secondary prevention treatments on long-term outcomes in MINOCA patients. Methods We enrolled all consecutive MINOCA patients admitted to our Centre from 2016 to 2021. The diagnosis of MINOCA was made according to the current European Society of Cardiology diagnostic criteria (angiographic conventional cut-off of &amp;lt; 50% coronary stenosis without a clinically apparent alternative diagnosis). Second-level diagnostic work-up including cardiac magnetic resonance was performed to exclude non-ischemic troponin elevation cause. All-cause mortality and MACE (a composite of all-cause mortality, hospitalization for heart failure, myocardial re-infarction and stroke) were collected during follow-up. The prognostic impact of statins and DAPT at discharge was assessed. The relationship between treatments and outcomes was evaluated by using Kaplan-Meier survival analysis. Results 278 MINOCA patients were enrolled, of whom 203 (73%) were discharged on statins and 123 (44.2%) on DAPT. After a median follow-up of 36 ± 14.8 months, the overall all-cause mortality was 11.8% and the composite endpoint (MACE) was achieved in 28.4% of the entire population. Kaplan-Meier curves showed that patients treated with statins had a significantly lower rate of all-cause mortality (9.3% vs 18.2%, p=0.04) and MACE (24.6% vs 39.2%, p=0.02). On the other hand, rates of death (9.8% vs 13.2%, p=0.4) and MACE (23.6% vs 31.6%, p=0.1) were similar in MINOCA patients treated with DAPT or single antiplatelet therapy. Conclusions Among MINOCA patients, DAPT at discharge neither reduced long-term all-cause mortality nor MACE. In contrast, statin treatment provided beneficial effects on long-term outcomes. These results should be considered preliminary and require confirmation from randomized clinical trials.

855 PREDICTORS OF LATE GADOLINIUM ENHANCEMENT DEVELOPMENT AND EXTENSION IN MYOCARDIAL INFARCTION WITH NON-OBSTRUCTIVE CORONARY ARTERIES

Abstract Background Myocardial infarction with non-obstructive coronary arteries (MINOCA) is a heterogeneous clinical entity and in its complex diagnostic approach cardiovascular magnetic resonance (CMR) plays a pivotal role. Purpose To characterize the differences of MINOCA patients with and without late gadolinium enhancement (LGE) at CMR and to identify the predictors for ischemic LGE development and extension. Methods We assessed 461 MINOCA cases from January 2016 to June 2021. MINOCA were defined according to the current European guidelines criteria. We excluded acute myocarditis, Tako-tsubo syndromes, cardiomyopathies, or non-pathological CMR. According to CMR imaging findings, our cohort was divided into two CMR phenotypes based on regional myocardial necrosis detected throughout LGE (“LGE-positive MINOCA”) or regional ischemic injury without LGE (“LGE-negative MINOCA”). Extended LGE was considered as the presence of &amp;gt;2 segments with transmural LGE. Multivariate logistic regression analysis was used to determine the predictors of LGE and extended LGE. Results The final cohort included 175 MINOCA: 121 (69.1%) constituted the LGE-positive group. The mean time delay between acute clinical presentation and CMR was 6 ± 2.9 days. At admission, MINOCA LGE-patients more frequently presented angina and ST segment elevation (24% vs 7.4%, p = 0.01), compared to the LGE negative ones. Furthermore, the LGE positive group had a significantly greater infarct size, measured by peak hs-Troponin I values and left ventricular function. The only predictor of LGE was the peak troponin value (OR 1.64, 95% CI 1.18–2.28, p = 0.003), while predictors of extended LGE were ST-segment elevation at admission (OR 7.44, 95% CI 1.57–35.22, p = 0.01), peak troponin values (OR 1.07, 95% CI 1.02–1.13, p = 0.01) and the presence of non-obstructive coronary artery disease at coronary angiography (OR 5.49, 95% CI 1.20–25.09, p = 0.028). Conclusion The presence and extension of LGE at early CMR evaluation is an important feature in the setting of MINOCA. In addition, simple baseline characteristics (such as ST elevation, peak troponin value and LVEF) may aid the identification of a greater ischemic necrosis burden at CMR and therefore these high-risk MINOCA subjects could be benefit from a stricter management effort.

857 DEVELOPMENT AND VALIDATION OF A DIAGNOSTIC ECHOCARDIOGRAPHIC MASS (DEM) SCORE IN THE COMPLEX APPROACH TO CARDIAC MASSES

Abstract Background Cardiac masses (CM) are an extremely heterogeneous clinical entity, including benign and malignant neoformations. 2D Echocardiography is, nowadays, the first-line approach to define nature and management of CM. Purpose The purpose of our study was to identify the echocardiographic predictors of malignancy and create a multiparametric score to further increase the diagnostic yield and accurately suggest the nature of CM. Materials and methods 249 consecutive patients undergoing a complete echocardiographic assessment for suspected cardiac mass in our center were enrolled from January 2004 to December 2020. A definitive diagnosis was achieved by histological examination or, in case of cardiac thrombi, with radiological evidence of thrombus resolution after an appropriate anticoagulant treatment. Logistic regression was performed to evaluate the ability of echocardiography to discriminate benign versus malignant masses. Results A scoring system was developed in a derivation cohort of 178 (70%) and validated in 71 (30%) patients. A weighted score [Diagnostic Echocardiographic Mass (DEM) Score] ranging from 0 to 9 was obtained from 6 variables: infiltration, polylobate mass, moderate-severe pericardial effusion, inhomogeneity, sessile and non-left localization. The AUC for the score was 0.965 (95% CI 0.938-0.993). In a logistic regression analysis using the DEM score as a predictor, the likelihood of malignancy increased more than 4 times for a 1-unit increase of the score (OR=4.468; 95% CI 2.733-7.304). The prognostic validity of the score was confirmed by its ability to predict survival during follow-up (median time of 31 months). Conclusions The application of a multiparametric echocardiographic score in the approach to CM accurately predicts mass malignancy, thereby reducing the need for second-level investigations, and minimizing the diagnostic delay in such a complex clinical scenario.

Congenital Pseudoarthrosis of the Tibia: A Narrative Review.

Congenital pseudoarthrosis of the tibia, also known as CPT, is a rare bone disease mostly occurring in the pediatric age group of 10. The case generally represents anterolateral bowing of the tibia with refractures. This condition is quite rare and has very few cases per year. CPT is still a challenging concern in orthopedics. Bone reunion often requires repeated surgical measurements. With advancements, we have new treatment plans that include induced membrane techniques and bone marrow stromal cell grafts of various induced and morphogenetic protein, which still requires confirmation. Different treatment strategies based on surgical, mechanical, and biological concepts have been shown with varying success rates. Ilizarov technique and vascularised fibular grafts have vastly increased the effectiveness in treating CPT of the tibia. Constant and recurrent refracture, residual deformities, and difficulty obtaining bone union remain the challenges in managing CPT. Hence, phasing CPT as bowing the tibia in an anterolateral fashion is more appropriate because it's a heterogeneous entity with varying prognoses.

Multi-Level Knowledge-Aware Contrastive Learning Network for Personalized Recipe Recommendation

Personalized recipe recommendation is attracting more and more attention, which can help people make choices from the exploding growth of online food information. Unlike other recommendation tasks, the target of recipe recommendation is a non-atomic item, so attribute information is especially important for the representation of recipes. However, traditional collaborative filtering or content-based recipe recommendation methods tend to focus more on user–recipe interaction information and ignore higher-order semantic and structural information. Recently, graph neural networks (GNNs)-based recommendation methods provided new ideas for recipe recommendation, but there was a problem of sparsity of supervised signals caused by the long-tailed distribution of heterogeneous graph entities. How to construct high-quality representations of users and recipes becomes a new challenge for personalized recipe recommendation. In this paper, we propose a new method, a multi-level knowledge-aware contrastive learning network (MKCLN) for personalized recipe recommendation. Compared with traditional comparative learning, we design a multi-level view to satisfy the requirement of fine-grained representation of users and recipes, and use multiple knowledge-aware aggregation methods for node fusion to finally make recommendations. Specifically, the local-level includes two views, interaction view and semantic view, which mine collaborative information and semantic information for high-quality representation of nodes. The global-level learns node embedding by capturing higher-order structural information and semantic information through a network structure view. Then, a kind of self-supervised cross-view contrastive learning is invoked to make the information of multiple views collaboratively supervise each other to learn fine-grained node embeddings. Finally, the recipes that satisfy personalized preferences are recommended to users by joint training and model prediction functions. In this study, we conduct experiments on two real recipe datasets, and the experimental results demonstrate the effectiveness and advancement of MKCLN.

Molecular cartooning with knowledge graphs.

Molecular "cartoons," such as pathway diagrams, provide a visual summary of biomedical research results and hypotheses. Their ubiquitous appearance within the literature indicates their universal application in mechanistic communication. A recent survey of pathway diagrams identified 64,643 pathway figures published between 1995 and 2019 with 1,112,551 mentions of 13,464 unique human genes participating in a wide variety of biological processes. Researchers generally create these diagrams using generic diagram editing software that does not itself embody any biomedical knowledge. Biomedical knowledge graphs (KGs) integrate and represent knowledge in a semantically consistent way, systematically capturing biomedical knowledge similar to that in molecular cartoons. KGs have the potential to provide context and precise details useful in drawing such figures. However, KGs cannot generally be translated directly into figures. They include substantial material irrelevant to the scientific point of a given figure and are often more detailed than is appropriate. How could KGs be used to facilitate the creation of molecular diagrams? Here we present a new approach towards cartoon image creation that utilizes the semantic structure of knowledge graphs to aid the production of molecular diagrams. We introduce a set of "semantic graphical actions" that select and transform the relational information between heterogeneous entities (e.g., genes, proteins, pathways, diseases) in a KG to produce diagram schematics that meet the scientific communication needs of the user. These semantic actions search, select, filter, transform, group, arrange, connect and extract relevant subgraphs from KGs based on meaning in biological terms, e.g., a protein upstream of a target in a pathway. To demonstrate the utility of this approach, we show how semantic graphical actions on KGs could have been used to produce three existing pathway diagrams in diverse biomedical domains: Down Syndrome, COVID-19, and neuroinflammation. Our focus is on recapitulating the semantic content of the figures, not the layout, glyphs, or other aesthetic aspects. Our results suggest that the use of KGs and semantic graphical actions to produce biomedical diagrams will reduce the effort required and improve the quality of this visual form of scientific communication.

Human-Machine Shared Situational Awareness

Abstract As unmanned platforms are more and more present in battlefield, alongside humans, in different configurations (from tele-operated to fully autonomous platforms, manned-unmanned teaming, swarming, etc.) and domains (space, air, land, sea, and underwater), it is also necessary for decision-making process to adapt to this new reality. This process will no longer be exclusively human and this requires that humans and machines share a common, meaningful, and timely understanding of the context in which they act and interact, namely a common situational awareness. We will investigate how this objective could be achieved by means of AI-related techniques of inference and reasoning based on ontologies that will enable all levels of information sharing (data, knowledge, and models) among all participants, humans and non-humans. The success of this undertaking shall be reflected by the achievement of a high level of interoperability between heterogeneous entities, within which they will be able to take advantage of each other’s best developed abilities.

Protein biomarkers of cardiac remodeling and inflammation associated with HFpEF and incident events

There is increasing evidence that HFpEF is a heterogeneous clinical entity and distinct molecular pathways may contribute to pathophysiology. Leveraging unbiased proteomics to identify novel biomarkers, this study seeks to understand the underlying molecular mechanisms of HFpEF. The discovery cohort consisted of HFpEF cases and non-HF controls from the CATHGEN study (N = 176); the validation cohort consisted of participants from the TECOS trial of patients with diabetes (N = 109). Proteins associated with HFpEF were included in a LASSO model to create a discriminative multi-protein model and assessed in the validation cohort. Survival models and meta-analysis were used to test the association of proteins with incident clinical outcomes, including HF hospitalization, mortality and HFpEF hospitalization in CATHGEN, TECOS and the Jackson Heart Study. In the derivation set, 190 proteins were associated with HFpEF in univariate analysis, of which 65 remained significant in the multivariate model. Twenty (30.8%) of these proteins validated in TECOS, including LCN2, U-PAR, IL-1ra, KIM1, CSTB and Gal-9 (OR 1.93–2.77, p < 0.01). LASSO regression yielded a 13-protein model which, when added to a clinical model inclusive of NT-proBNP, improved the AUC from 0.82 to 0.92 (p = 1.5 × 10–4). Five proteins were associated with incident HF hospitalization, four with HFpEF hospitalization and eleven with mortality (p < 0.05). We identified and validated multiple circulating biomarkers associated with HFpEF as well as HF outcomes. These biomarkers added incremental discriminative capabilities beyond clinical factors and NT-proBNP.

Arthroscopic Management of the Hamate Proximal Pole Arthrosis.

In this retrospective study, all patients with arthrosis of the proximal pole of the hamate encountered in a 10-year period treated with arthroscopy were reviewed. Patient demographics, arthroscopic details, associated injuries, and procedures were reviewed. Functional and patient-reported clinical outcomes after arthroscopic osteochondroplasty were analyzed. An analysis of 39 patients who underwent arthroscopic osteochondroplasty showed a type II lunate prevalence of 87.2%. On wrist arthroscopy, a triangular fibrocartilaginous complex tear was the most common diagnosis (64.1%), followed by scapholunate ligament tear (61.5%) and lunotriquetral ligament tear (35.9%). All patients were managed arthroscopically with 100% procedural success. Average follow-up of 42.8 ± 37.7 months showed statistically significant improvements in the visual analog score and Mayo wrist score postoperatively (P < 0.01). Three patients (7.7%) failed the procedure at a mean of 23 ± 31.4 months. Risk factors for salvage operations were nondominant wrist involvement 100% (P = 0.04), lunotriquetral ligament injury 100% (P = 0.04), the absence of triangular fibrocartilaginous complex injury 100% (P = 0.04), and the presence of scapholunate advanced collapse 100% (P < 0.01). Wrist arthroscopy is valuable for accurately diagnosing and treating hamate arthrosis and identifying a wide variety of pathologies associated. This study showed arthroscopic osteochondroplasty produced satisfactory clinical results. Furthermore, the presence of scapholunate advanced collapse wrist offers its prognostic value in determining treatment failure.

Rupture risk parameters upon biomechanical analysis independently change from vessel geometry during abdominal aortic aneurysm growth

ObjectiveThe indication for abdominal aortic aneurysm (AAA) repair is based on a diameter threshold. However, mechanical properties, such as peak wall stress (PWS) and peak wall rupture index (PWRI), influence the individual rupture risk. This study aims to correlate biomechanical and geometrical AAA characteristics during aneurysm growth applying a new linear transformation-based comparison of sequential imaging. MethodsPatients with AAA with two sequential computed tomography angiographies (CTA) were identified from a single-center aortic database. Patient characteristics included age, gender, and comorbidities. Semiautomated segmentation of CTAs was performed using Endosize (Therenva) for geometric variables (diameter, neck configuration, α/β angle, and vessel tortuosity) and for finite element method A4 Clinics Research Edition (Vascops) for additional variables (intraluminal thrombus [ILT]), vessel volume, PWS, PWRI). Maximum point coordinates from at least one CTA 6 to 24 months before their final were predicted for the final preoperative CTA using linear transformation along fix and validation points to estimate spatial motion. Pearson’s correlation and the t test were used for comparison. ResultsThirty-two eligible patients (median age, 70 years) were included. The annual AAA growth rate was 3.7 mm (interquartile range [IQR], 2.25-5.44; P < .001) between CTs. AAA (+17%; P < .001) and ILT (+43%; P < .001) volume, maximum ILT thickness (+35%; P < .001), β angle (+1.96°; P = .017) and iliac tortuosity (+0.009; P = .012) increased significantly. PWS (+12%; P = .0029) and PWRI (+16%; P < .001) differed significantly between both CTAs. Both mechanical parameters correlated most significantly with the AAA volume increase (r = 0.68 [P < .001] and r = 0.6 [P < .001]). Changes in PWS correlated best with the aneurysm neck configuration. The spatial motion of maximum ILT thickness was 14.4 mm (IQR, 7.3-37.2), for PWS 8.4 mm (IQR, 3.8-17.3), and 11.5 mm (IQR, 5.9-31.9) for PWRI. Here, no significant correlation with any of the aforementioned parameters, patient age, or time interval between CTs were observed. ConclusionsPWS correlates highly significant with vessel volume and aneurysm neck configuration. Spatial motion of maximum ILT thickness, PWS, and PWRI is detectable and predictable and might expose different aneurysm wall segments to maximum stress throughout aneurysm growth. Linear transformation could thus add to patient-specific rupture risk analysis. Clinical RelevanceAbdominal aortic aneurysm rupture risk assessment is a key feature in future individualized therapy approaches for patients, since more and more data are obtained concluding a heterogeneous disease entity that might not be addressed ideally looking only at diameter enlargement. The approach presented in this pilot study demonstrates the feasibility and importance of measuring peak wall stress and rupture risk indices based on predicted and actual position of maximum stress points including intraluminal thrombus.

Pre-Conditioning Easix Is Associated with Survival after Autologous Hematopoietic Stem Cell Transplant in Mature T-Cell Lymphomas

Introduction: Mature T-cell lymphomas (MTCLs) comprise a group of rare, heterogeneous entities with poor prognosis. Optimal use of autologous (auto) and allogeneic (allo) hematopoietic stem cell transplantation (HSCT) remains an area of active study (Marchi & O'Connor 2019). Prognostic scores help guide clinical decision making and are useful to assess baseline risk in retrospective studies. The Endothelial Activation and Stress Index (EASIX) has shown promise in predicting post-alloHSCT survival (Shouval et al. 2019, Luft et al. 2020). However, we have little data on the association between EASIX and post-HSCT survival specific to the MTCL population. Here, we assess the relationship between EASIX prior to conditioning chemotherapy (EASIX-pre) and post-HSCT survival. Methods: Subjects with MTCL who underwent auto- or alloHSCT were identified from a Montefiore Medical Center database. Data were collected on the following by manual chart review: date of birth, sex, race, ethnicity, MTCL subtype, date of stem cell infusion (D0), stem cell source, conditioning intensity, and survival. To be included in the auto- or alloHSCT cohort, a subject's MTCL subtype had to have auto- or alloHSCT as a recommendation in the NCCN Guidelines Version 2.2022 (Horwitz et al. 2022). Conditioning intensity was determined by the Center for International Blood and Bone Marrow Transplant Research operational definition (Giralt et al. 2009). EASIX prior to conditioning (EASIX-pre) was calculated as (lactate dehydrogenase [U/L] x creatinine [mg/dL])/platelets [109 cells/L] at D-21. To estimate lab values at D-21 we used linear interpolation on data available from D-81 to D-9. Missing numerical variables were imputed using scikit-learn's IterativeImputer with the RandomForestRegressor estimator and default parameters, a method similar to missForest. The Nelson-Aalen estimate of the cumulative hazard and event status at last follow up were included in imputation (White & Royston 2009). Missing categorical variables were placed into an unknown category. Univariable Cox models were made for each variable. Variables whose Cox models had likelihood ratio test p < 0.20 were used for adjustment in multivariable models. Survival was measured from D0 to last follow up. Events included both death and discharge to hospice with unknown date of death. Data were analyzed using Python, lifelines, and sci-kit learn (Davidson-Pilon 2019, Pedregosa et al. 2011). Results: 23 subjects underwent autoHSCT 9/19/2006-3/24/2021. Median follow up among censored subjects was 2.30 years. 9 subjects had peripheral T-cell lymphoma, NOS (PTCL, NOS), 7 had angioimmunoblastic T-cell lymphoma, 4 had ALK-negative anaplastic large cell lymphoma, and 3 had other subtypes. EASIX 10th, 50th, and 90th percentiles were 0.55, 1.51, and 3.68. The univariable model for age at D0 had likelihood ratio test p < 0.20 and was used for adjustment in the multivariable Cox model; other potential adjustment variables did not meet this criterion. EASIX-pre adjusted for age at D0 had HR 1.37 (95% CI 1.06-1.76, p = 0.0155, Fig. 1a). 32 subjects underwent alloHSCT 4/22/2008-4/5/2021. Median follow up among censored subjects was 3.36 years. 16 subjects had adult T-cell leukemia/lymphoma, 6 had hepatosplenic T-cell lymphoma, 4 had mycosis fungoides/Sezary syndrome, 4 had PTCL, NOS, and 2 had other subtypes. EASIX 10th, 50th, and 90th percentiles were 0.55, 1.16, and 2.82. The univariable models for age at D0, race, and conditioning intensity had p < 0.20 and were included for adjustment in a multivariable Cox model. In the multivariable model, EASIX-pre had HR 1.12 (95% CI 1.00-1.26, p = 0.0584, Fig. 1b). Conclusion: In our MTCL cohort, at an alpha level of 0.05, EASIX-pre was independently associated with post-autoHSCT but not post-alloHSCT survival. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Integrated Personalized Prediction Model Identifies a Subgroup of Wild-Type TP53therapy-Related Myeloid Neoplasm Patients with Poor Outcome

Background: Therapy-related myeloid neoplasm (t-MN), is a heterogenous entity that includes therapy-related myelodysplastic syndromes (t-MDS), myelodysplastic syndrome/myeloproliferative neoplasms (t-MDS/MPN), and acute myeloid leukemia (t-AML) phenotypes. TP53 mutations (TP53mut) are known to be associated with poor survival in myeloid neoplasms (MN), with the recently revised World Health Organization (WHO) and the International Consensus Classification recommending major changes to the MN classification based on TP53mut status. The aim of this study was to employ machine learning approach to risk-stratify t-MN using the variables available at the time of diagnosis. The secondary aim was to identify risk factors, with a focus on the TP53 wild-type (TP53wt) cohort. Methods: We retrospectively analyzed clinicopathological features and outcomes of 380 WHO-defined t-MN patients treated at Mayo Clinic, Rochester (US cohort, n=200) or South Australia MDS Registry (AU cohort, n=180) who had cytogenetics and NGS at diagnosis. Karyotypes were classified using the International System for Cytogenetic Nomenclature Criteria. Number of cytogenetic aberrancies were counted according to Hasse et al. Leukemia 2019. A random forest survival algorithm was used to build a prognostic model using the training cohort (US cohort). The model algorithm randomly bootstraps the original data into two thirds, where the model is developed, and one third, where the model is internally validated. The process is repeated multiple times to assure reproducibility of the final result. The model was subsequently validated in an independent cohort from South Australia. The risk score optimal cut-off was determined using the maximally selected rank statistics. Results: The median age for the entire cohort was 67.3 (59.7-74.2) years. The most common cytogenetic abnormalities were complex karyotype (CK, 47.7%), monosomal karyotype (MK, 44.7%), deletion 7q (del 7q, 33.2%), and deletion 5q (del5q, 23.6%). Most common somatic mutations were TP53 (n=141; 37%), ASXL1 (n=67; 18%), TET2 (n=66;17%) and DNMT3A (n=64;17%), SRSF2 (n=46; 12%), RAS (44; 12%) and RUNX1 (n=43; 11%). Haematological malignancy (53.5%) followed by solid cancer (40.0%) and autoimmune disease (3%) were the most common primary diseases. Despite differences in frequency of CK, MK, del 7q and 5q between the training and validation cohort, the median overall survival (OS) was not significantly different between the US and the AU cohorts (17 vs. 11 months, P=0.51). The US cohort was used to build a new prognostic model. A total of 88 variables available at t-MN diagnosis (15 clinical, 32 mutations, and 44 cytogenetic) were included. TP53mut was the most important variable driving poor survival. Other variables included absolute neutrophil count, number of chromosomal abnormalities, chromosome 18 abnormality, del 5q, platelets, the number of cytogenetic clones and hemoglobin. The concordance index of the model in the training cohort was 0.65. In the US cohort, 83 (41%) and 117 (58%) patients were TP53mut and TP53wt, respectively. Median survival for TP53mut patients was 9.9 months (Figure 1A). Within the TP53wt subcohort, the model designated 52 (44%) and 65 (56%) patients as high- and low-risk groups, respectively. Strikingly, the survival of high-risk TP53wt patients was comparable to TP53mut cohort, and significantly inferior compared to the low-risk group (11.1 vs. 40.6; P<0.0001). These results were validated in the AU cohort: the proportion of TP53mut, high-risk TP53wt, and low-risk TP53wt patients was 58 (32.2%), 61 (33.9%), and 61 (33.9%), respectively. Median survival of the 3 cohorts was 7.8 months, 11.3 months, and 27.9 months respectively (P<0.0001, Figure 1B). The c-index for AU cohort was 0.66. Conclusion: The integrated prognostic model that used only diagnostic information, stratified t-MN patients into 3 approximately equal categories: TP53mut, constituting a third of the cohort, was the principal driver for inferior survival. Importantly, TP53wt cohort could further be stratified into 2 halves: the high-risk TP53wt patients had survival comparable to TP53mut cohort, whereas the low-risk cohort had an estimated 5-year survival approaching 45%. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal