Generative Artificial Intelligence: Enhancing Patient Education in Cardiovascular Imaging.

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.

Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference.

Pelvic ring disruptions result from blunt injury mechanisms and are potentially lethal mainly due to associated injuries and massive pelvic hemorrhage. The severity of pelvic fractures in trauma victims is frequently assessed by grading the fracture according to the Tile AO/OTA classification in whole-body Computed Tomography (CT) scans. Due to the high volume of whole-body CT scans generated in trauma centers, the overall information content of a single whole-body CT scan and low manual CT reading speed, an automatic approach to Tile classification would provide substantial value, e. g., to prioritize the reading sequence of the trauma radiologists or enable them to focus on other major injuries in multi-trauma patients. In such a high-stakes scenario, an automated method for Tile grading should ideally be transparent such that the symbolic information provided by the method follows the same logic a radiologist or orthopedic surgeon would use to determine the fracture grade. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grading. To achieve interpretability despite processing high-dimensional whole-body CT images, we design a neurosymbolic algorithm that operates similarly to human interpretation of CT scans. The algorithm first detects relevant pelvic fractures on CTs with high specificity using Faster-RCNN. To generate robust fracture detections and associated detection (un)certainties, we perform test-time augmentation of the CT scans to apply fracture detection several times in a self-ensembling approach. The fracture detections are interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. We apply a Bayesian causal model to recover likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides fracture location and types, as well as information on important counterfactuals that would invalidate the system's recommendation. Our approach achieves an AUC of 0.89/0.74 for translational and rotational instability,which is comparable to radiologist performance. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box methods.

Explainable AI decision support improves accuracy during telehealth strep throat screening.

BACKGROUND: Artificial intelligence-based (AI) clinical decision support systems (CDSS) using unconventional data, like smartphone-acquired images, promise transformational opportunities for telehealth; including remote diagnosis. Although such solutions' potential remains largely untapped, providers' trust and understanding are vital for effective adoption. This study examines how different human-AI interaction paradigms affect clinicians' responses to an emerging AI CDSS for streptococcal pharyngitis (strep throat) detection from smartphone throat images. METHODS: In a randomized experiment, we tested explainable AI strategies using three AI-based CDSS prototypes for strep throat prediction. Participants received clinical vignettes via an online survey to predict the disease state and offer clinical recommendations. The first set included a validated CDSS prediction (Modified Centor Score) and the second introduced an explainable AI prototype randomly. We used linear models to assess explainable AI's effect on clinicians' accuracy, confirmatory testing rates, and perceived trust and understanding of the CDSS. RESULTS: The study, involving 121 telehealth providers, shows that compared to using the Centor Score, AI-based CDSS can improve clinicians' predictions. Despite higher agreement with AI, participants report lower trust in its advice than in the Centor Score, leading to more requests for in-person confirmatory testing. CONCLUSIONS: Effectively integrating AI is crucial in the telehealth-based diagnosis of infectious diseases, given the implications of antibiotic over-prescriptions. We demonstrate that AI-based CDSS can improve the accuracy of remote strep throat screening yet underscores the necessity to enhance human-machine collaboration, particularly in trust and intelligibility. This ensures providers and patients can capitalize on AI interventions and smartphones for virtual healthcare.

Strep pharyngitis, or strep throat, is a bacterial infection that can cause a sore throat. Artificial intelligence (AI) can use photos taken on a person's phone to help diagnose strep throat, offering an additional way for doctors to screen patients during virtual appointments. However, it is currently unclear whether doctors will trust AI recommendations or how they might use them in decision-making. We surveyed clinicians about their use of an AI system for strep throat screening with smartphone images. We compared different ways of providing AI recommendations to standard medical guidelines. We found that all tested AI methods helped clinicians to identify strep throat cases. However, clinicians trusted AI less than their usual clinical guidelines, leading to more requests for follow-up in-person testing. Our results show how AI may improve the accuracy of pharyngitis assessment. Still, further research is needed to ensure doctors trust and collaborate with AI to improve remote healthcare.

Optimizing in silico drug discovery: simulation of connected differential expression signatures and applications to benchmarking.

BACKGROUND: We present a novel simulation method for generating connected differential expression signatures. Traditional methods have struggled with the lack of reliable benchmarking data and biases in drug-disease pair labeling, limiting the rigorous benchmarking of connectivity-based approaches. OBJECTIVE: Our aim is to develop a simulation method based on a statistical framework that allows for adjustable levels of parametrization, especially the connectivity, to generate a pair of interconnected differential signatures. This could help to address the issue of benchmarking data availability for connectivity-based drug repurposing approaches. METHODS: We first detailed the simulation process and how it reflected real biological variability and the interconnectedness of gene expression signatures. Then, we generated several datasets to enable the evaluation of different existing algorithms that compare differential expression signatures, providing insights into their performance and limitations. RESULTS: Our findings demonstrate the ability of our simulation to produce realistic data, as evidenced by correlation analyses and the log2 fold-change distribution of deregulated genes. Benchmarking reveals that methods like extreme cosine similarity and Pearson correlation outperform others in identifying connected signatures. CONCLUSION: Overall, our method provides a reliable tool for simulating differential expression signatures. The data simulated by our tool encompass a wide spectrum of possibilities to challenge and evaluate existing methods to estimate connectivity scores. This may represent a critical gap in connectivity-based drug repurposing research because reliable benchmarking data are essential for assessing and advancing in the development of new algorithms. The simulation tool is available as a R package (General Public License (GPL) license) at https://github.com/cgonzalez-gomez/cosimu.

Direct oral challenge for immediate and non-immediate beta-lactam allergy in children: A real-world multicenter study.

BACKGROUND: Allergy to beta-lactam antibiotics (BLA) is frequently suspected in children, but a drug provocation test (DPT) rules it out in over 90% of cases. Direct oral DPT (DODPT), without skin or other previous tests, is increasingly been used to delabel non-immediate BLA reactions. This real-world study aimed to assess the safety and effectiveness of DODPT in children with immediate and non-immediate reactions to BLAs. METHODS: Ambispective registry study in children (<15 years), attended between 2016 and 2023 for suspected BLA allergy in 15 hospitals in Spain that routinely perform DODPT. RESULTS: The study included 2133 patients with generally mild reactions (anaphylaxis 0.7%). Drug provocation test with the implicated BLA was performed in 2014 patients (94.4%): 1854 underwent DODPT (86.9%, including 172 patients with immediate reactions). One hundred forty-five (7.2%) had symptoms associated with DPT, although only four reactions were severe: two episodes of anaphylaxis and two of drug-induced enterocolitis syndrome, which resolved rapidly with treatment. Of the 141 patients with mild reactions in the first DPT, a second DPT was considered in 87 and performed in 57, with 52 tolerating it without symptoms. Finally, BLA allergy was ruled out in 90.9% of the sample, confirmed in 3.4%, and remained unverified, usually due to loss to follow-up, in 5.8%. CONCLUSIONS: Direct oral DPT is a safe, effective procedure even in immediate mild reactions to BLA. Many reactions observed in DPT are doubtful and require confirmation. Severe reactions are exceptional and amenable to treatment. Direct oral DPT can be considered for BLA allergy delabeling in pediatric primary care.

Active lupus in Argentina: Results of a multicenter and national registry.

OBJECTIVE: To evaluate the association between patients' characteristics and disease activity in an Argentine lupus registry. METHODS: Cross-sectional study. Disease activity was stratified into: Remission off-treatment: SLEDAI = 0, without prednisone and immunosuppressive drugs. Low disease activity Toronto Cohort (LDA-TC): SLEDAI ≤2, without prednisone or immunosuppressive drugs. Modified lupus low disease activity (mLLDAS): SLEDAI score of ≤4, with no activity in major organ systems and no new features, prednisone of ≤10 mg/day and/or immunosuppressive drugs (maintenance dose) and Active disease: SLEDAI score of >4 and prednisone >10 mg/day and immunosuppressive drugs. A descriptive analysis and logistic regression model were performed. RESULTS: A total of 1346 patients were included. Of them, 1.6% achieved remission off steroids, 0.8% LDA-TC, 12.1% mLLDAS and the remaining 85.4% had active disease. Active disease was associated with younger age (p ≤ 0.001), a shorter time to diagnosis (p ≤ 0.001), higher frequency of hospitalizations (p ≤ 0.001), seizures (p = 0.022), serosal disease (p ≤ 0.001), nephritis (p ≤ 0.001), higher SDI (p ≤ 0.001), greater use of immunosuppressive therapies and higher doses of prednisone compared to those on mLLDAS. In the multivariable analysis, the variables associated with active disease were the presence of pleuritis (OR 2.1, 95% CI 1.2-3.9; p = 0.007), persistent proteinuria (OR 2.5, 95% CI 1.2-5.5; p ≤ 0.011), nephritis (OR 2.5, 95% CI 1.2-5.6; p = .018) and hospitalizations (OR 8.9, 95% CI 5.3-16.0; p ≤ 0.001) whereas age at entry into the registry was negatively associated with it (OR 0.9, 95% CI 0.9-1.0; p = 0.029). CONCLUSION: Active disease was associated with shorter time to diagnosis, worse outcomes (SDI and hospitalizations) and renal, neurological and serosal disease.

Frecuencia de eventos cardiovasculares en pacientes con lupus eritematoso sistémico / Frequency of cardiovascular events in patients with systemic lupus erythematosus

Resumen Introducción: el progreso en los tratamientos para el lupus eritematoso sistémico (LES) resultó en una disminución de la mortalidad; sin embargo, la enfermedad cardiovascular y las complicaciones infecciosas aún son las principales causas de muerte. La evidencia apoya la participación del sistema inmunológico en la generación de la placa aterosclerótica, así como su conexión con las enfermedades autoinmunes. Objetivos: describir la frecuencia de eventos cardiovasculares (ECV) en el Registro de Lupus Eritematoso Sistémico de la Sociedad Argentina de Reumatología (RELESSAR) transversal, así como sus principales factores de riesgo asociados. Materiales y métodos: estudio descriptivo y transversal para el cual se tomaron los pacientes ingresados en el registro RELESSAR transversal. Se describieron las variables sociodemográficas y clínicas, las comorbilidades, score de actividad y daño. ECV se definió como la presencia de al menos una de las siguientes patologías: enfermedad arterial periférica, cardiopatía isquémica o accidente cerebrovascular. El evento clasificado para el análisis fue aquel posterior al diagnóstico del LES. Se conformaron dos grupos macheados por edad y sexo 1:2. Resultados: 1515 pacientes mayores de 18 años participaron del registro. Se describieron 80 pacientes con ECV (5,3%). En este análisis se incluyeron 240 pacientes conformando dos grupos. La edad media fue de 47,8 (14,4) y 47,6 (14,2) en el grupo con y sin ECV respectivamente. Los pacientes con ECV tuvieron mayor duración del LES en meses, mayor índice de Charlson, mayor SLICC (Systemic Lupus International Collaborating Clinics/American College of Rheumatology), mayor frecuencia de manifestaciones neurológicas, síndrome antifosfolípido, hospitalizaciones y uso de ciclofosfamida. Las únicas variables asociadas en el análisis multivariado fueron el índice de Charlson (p=0,004) y el SLICC (p<0,001). Conclusiones: los ECV influyen significativamente en nuestros pacientes, y se asocian a mayor posibilidad de daño irreversible y comorbilidades.

Abstract Introduction: progress in treatments for systemic lupus erythematosus (SLE) has resulted in a decrease in mortality; however, cardiovascular and infectious diseases remain the leading causes of death. Evidence supports the involvement of the immune system in the generation of atherosclerotic plaque, as well as its connection to autoimmune diseases. Objectives: to describe the frequency of cardiovascular disease (CVD) in the cross-sectional RELESSAR registry, as well as its associated variables. Materials and methods: a descriptive and cross-sectional study was performed using patients admitted to the cross-sectional RELESSAR registry. Sociodemographic variables, clinical variables, comorbidities, activity and damage scores were described. CVD was defined as at least one of the following: peripheral arterial disease, ischemic heart disease, or cerebrovascular accident. All patients with at least one CVD were included in our analysis (heart attack, central nervous system vascular disease, and peripheral arteries atherosclerotic disease). The event classified for the analysis was that after the diagnosis of SLE. SLE diagnosis was previous to CVD. Two groups matched by age and sex, 1:2 were formed. Results: a total of 1515 patients older than 18 years participated in the registry. Eighty patients with CVD (5.3%) were described in the registry. Two-hundred and forty patients were included, according to two groups. The mean age was 47.8 (SD 14.4) and 47.6 (SD 14.2) in patients with and without CVD, respectively. Patients with CVD had a longer duration of SLE in months, a higher Charlson index, a higher SLICC, increased frequency of neurological manifestations, antiphospholipid syndrome, hospitalizations, and use of cyclophosphamide. The associated variables in the multivariate were the Charlson Index (p=0.004) and the SLICC (p<0.001). Conclusions: CVDs have a significant influence on our patients, being associated with a greater possibility of damage and comorbidities.

Visualization in 2D/3D registration matters for assuring technology-assisted image-guided surgery.

PURPOSE: Image-guided navigation and surgical robotics are the next frontiers of minimally invasive surgery. Assuring safety in high-stakes clinical environments is critical for their deployment. 2D/3D registration is an essential, enabling algorithm for most of these systems, as it provides spatial alignment of preoperative data with intraoperative images. While these algorithms have been studied widely, there is a need for verification methods to enable human stakeholders to assess and either approve or reject registration results to ensure safe operation. METHODS: To address the verification problem from the perspective of human perception, we develop novel visualization paradigms and use a sampling method based on approximate posterior distribution to simulate registration offsets. We then conduct a user study with 22 participants to investigate how different visualization paradigms (Neutral, Attention-Guiding, Correspondence-Suggesting) affect human performance in evaluating the simulated 2D/3D registration results using 12 pelvic fluoroscopy images. RESULTS: All three visualization paradigms allow users to perform better than random guessing to differentiate between offsets of varying magnitude. The novel paradigms show better performance than the neutral paradigm when using an absolute threshold to differentiate acceptable and unacceptable registrations (highest accuracy: Correspondence-Suggesting (65.1%), highest F1 score: Attention-Guiding (65.7%)), as well as when using a paradigm-specific threshold for the same discrimination (highest accuracy: Attention-Guiding (70.4%), highest F1 score: Corresponding-Suggesting (65.0%)). CONCLUSION: This study demonstrates that visualization paradigms do affect the human-based assessment of 2D/3D registration errors. However, further exploration is needed to understand this effect better and develop more effective methods to assure accuracy. This research serves as a crucial step toward enhanced surgical autonomy and safety assurance in technology-assisted image-guided surgery.

Direct Gene Expression Profile Prediction for Uveal Melanoma from Digital Cytopathology Images via Deep Learning and Salient Image Region Identification.

Objective: To demonstrate that deep learning (DL) methods can produce robust prediction of gene expression profile (GEP) in uveal melanoma (UM) based on digital cytopathology images. Design: Evaluation of a diagnostic test or technology. Subjects Participants and Controls: Deidentified smeared cytology slides stained with hematoxylin and eosin obtained from a fine needle aspirated from UM. Methods: Digital whole-slide images were generated by fine-needle aspiration biopsies of UM tumors that underwent GEP testing. A multistage DL system was developed with automatic region-of-interest (ROI) extraction from digital cytopathology images, an attention-based neural network, ROI feature aggregation, and slide-level data augmentation. Main Outcome Measures: The ability of our DL system in predicting GEP on a slide (patient) level. Data were partitioned at the patient level (73% training; 27% testing). Results: In total, our study included 89 whole-slide images from 82 patients and 121 388 unique ROIs. The testing set included 24 slides from 24 patients (12 class 1 tumors; 12 class 2 tumors; 1 slide per patient). Our DL system for GEP prediction achieved an area under the receiver operating characteristic curve of 0.944, an accuracy of 91.7%, a sensitivity of 91.7%, and a specificity of 91.7% on a slide-level analysis. The incorporation of slide-level feature aggregation and data augmentation produced a more predictive DL model (P = 0.0031). Conclusions: Our current work established a complete pipeline for GEP prediction in UM tumors: from automatic ROI extraction from digital cytopathology whole-slide images to slide-level predictions. Our DL system demonstrated robust performance and, if validated prospectively, could serve as an image-based alternative to GEP testing.

Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review.

Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users and the knowledge imbalance between those users and ML designers. To investigate the state of transparent ML in medical image analysis, we conducted a systematic review of the literature from 2012 to 2021 in PubMed, EMBASE, and Compendex databases. We identified 2508 records and 68 articles met the inclusion criteria. Current techniques in transparent ML are dominated by computational feasibility and barely consider end users, e.g. clinical stakeholders. Despite the different roles and knowledge of ML developers and end users, no study reported formative user research to inform the design and development of transparent ML models. Only a few studies validated transparency claims through empirical user evaluations. These shortcomings put contemporary research on transparent ML at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research, we introduce the INTRPRT guideline, a design directive for transparent ML systems in medical image analysis. The INTRPRT guideline suggests human-centered design principles, recommending formative user research as the first step to understand user needs and domain requirements. Following these guidelines increases the likelihood that the algorithms afford transparency and enable stakeholders to capitalize on the benefits of transparent ML.

Extracellular Volume Fraction by Computed Tomography Predicts Long-Term Prognosis Among Patients With Cardiac Amyloidosis.

BACKGROUND: Light chain (AL) and transthyretin (ATTR) amyloid fibrils are deposited in the extracellular space of the myocardium, resulting in heart failure and premature mortality. Extracellular expansion can be quantified by computed tomography, offering a rapid, cheaper, and more practical alternative to cardiac magnetic resonance, especially among patients with cardiac devices or on renal dialysis. OBJECTIVES: This study sought to investigate the association of extracellular volume fraction by computed tomography (ECVCT), myocardial remodeling, and mortality in patients with systemic amyloidosis. METHODS: Patients with confirmed systemic amyloidosis and varying degrees of cardiac involvement underwent electrocardiography-gated cardiac computed tomography. Whole heart and septal ECVCT was analyzed. All patients also underwent clinical assessment, electrocardiography, echocardiography, serum amyloid protein component, and/or technetium-99m (99mTc) 3,3-diphosphono-1,2-propanodicarboxylic acid scintigraphy. ECVCT was compared across different extents of cardiac infiltration (ATTR Perugini grade/AL Mayo stage) and evaluated for its association with myocardial remodeling and all-cause mortality. RESULTS: A total of 72 patients were studied (AL: n = 35, ATTR: n = 37; median age: 67 [IQR: 59-76] years, 70.8% male). Mean septal ECVCT was 42.7% ± 13.1% and 55.8% ± 10.9% in AL and ATTR amyloidosis, respectively, and correlated with indexed left ventricular mass (r = 0.426; P < 0.001), left ventricular ejection fraction (r = 0.460; P < 0.001), N-terminal pro-B-type natriuretic peptide (r = 0.563; P < 0.001), and high-sensitivity troponin T (r = 0.546; P < 0.001). ECVCT increased with cardiac amyloid involvement in both AL and ATTR amyloid. Over a mean follow-up of 5.3 ± 2.4 years, 40 deaths occurred (AL: n = 14 [35.0%]; ATTR: n = 26 [65.0%]). Septal ECVCT was independently associated with all-cause mortality in ATTR (not AL) amyloid after adjustment for age and septal wall thickness (HR: 1.046; 95% CI: 1.003-1.090; P = 0.037). CONCLUSIONS: Cardiac amyloid burden quantified by ECVCT is associated with adverse cardiac remodeling as well as all-cause mortality among ATTR amyloid patients. ECVCT may address the need for better identification and risk stratification of amyloid patients, using a widely accessible imaging modality.

The Medical Segmentation Decathlon.

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)-a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training.

Smart pooling: AI-powered COVID-19 informative group testing.

Massive molecular testing for COVID-19 has been pointed out as fundamental to moderate the spread of the pandemic. Pooling methods can enhance testing efficiency, but they are viable only at low incidences of the disease. We propose Smart Pooling, a machine learning method that uses clinical and sociodemographic data from patients to increase the efficiency of informed Dorfman testing for COVID-19 by arranging samples into all-negative pools. To do this, we ran an automated method to train numerous machine learning models on a retrospective dataset from more than 8000 patients tested for SARS-CoV-2 from April to July 2020 in Bogotá, Colombia. We estimated the efficiency gains of using the predictor to support Dorfman testing by simulating the outcome of tests. We also computed the attainable efficiency gains of non-adaptive pooling schemes mathematically. Moreover, we measured the false-negative error rates in detecting the ORF1ab and N genes of the virus in RT-qPCR dilutions. Finally, we presented the efficiency gains of using our proposed pooling scheme on proof-of-concept pooled tests. We believe Smart Pooling will be efficient for optimizing massive testing of SARS-CoV-2.

Multicenter lupus register from Argentina, the RELESSAR database: Influence of ethnicity on disease phenotype.

OBJECTIVE: The objective is to describe the main characteristics of patients with systemic lupus erythematosus (SLE) in Argentina and to examine the influence of ethnicity on the expression of the disease. PATIENTS AND METHODS: RELESSAR is a multicentre register carried out by 106 researchers from 67 rheumatologic Argentine centres. It is a cross-sectional study of SLE (1982/1997 ACR) patients. RELESSAR electronic database includes demographic, cumulative SLE manifestations, SELENA-SLEDAI, SLICC-SDI, Katz's severity and Charlson's comorbidity indexes and treatment patterns. RESULTS: We included 1,610 patients, 91.7% were female with a median age at diagnosis of 28.1 ± 12.8; 96.2% met ≥4 ACR 1982/97 criteria. Frequent manifestations were arthritis (83.5%), malar rash (79.5%), photosensitivity (75.3%), haematological (63.8%) and renal disease (47.4%), antinuclear antibodies (96%), anti-dsDNA (66.5%) and anti-Smith antibodies (29%). The mean Selena-SLEDAI score at last visit was 3.18 (SD 4.3) and mean SDI was 1 (SD 1.3). The accumulated treatments most frequently used were antimalarials (90.4%), corticosteroids (90%), azathioprine (31.8%), intravenous cyclophosphamide (30.2%), mycophenolate mofetil or mycophenolic acid (24.5%), methotrexate (19.3%), belimumab 5.3% and rituximab 5.1%. Refractory lupus was diagnosed in 9.3% of the cases. The main causes of death were lupus activity (25.0%), activity and concomitant infections (25.0%), infections (18.2%), vascular disease (13.6%) and cancer (4.5%). Mortality was associated with higher SLEDAI, Katz, damage indexes and comorbidities. Of the 1610 patients included, 44.6% were Caucasian, 44.5% Mestizo, 8.1% Amerindian and 1.2% Afro-Latin American. Mestizo patients had higher male representation, low socioeconomic status, more inadequate medical coverage, fewer formal years of education and shorter disease duration. Polyadenopathies and Raynaud's phenomenon were more frequent in Caucasians. In the logistic regression analysis higher damage index (OR 1.28, CI 95% 1.02-1.61, p = 0.03) remained associated to mestizo ethnicity. CONCLUSIONS: This study represents the largest number of adult patients with SLE studied in Argentina. Caucasian patients were differentiated by having Raynaud's phenomenon and polyadenopathy more frequently, while patients of Mestizo origin had higher damage indexes.

Lupus en Argentina. Pacientes no respondedores al tratamiento estándar y belimumab como posible opción. Datos del registro RELESSAR / Lupus in Argentina. Non-responders to standard treatment and belimumab as a possible option. RELESSAR record data

Introducción: el lupus es una enfermedad compleja y varias veces de difícil abordaje. Alcanzar la remisión es uno de los objetivos, incorporando opciones terapéuticas. Objetivos: describir las características generales de los pacientes según el estado de la enfermedad y el uso de belimumab. Materiales y métodos: estudio de corte transversal, registro RELESSAR. Se definió el estado de la enfermedad como: remisión: SLEDAI=0 y sin corticoides; baja actividad de la enfermedad: SLEDAI >0 y ≤4 y sin corticoides; control no óptimo: SLEDAI >4 y cualquier dosis de corticoides. Resultados: se incluyeron 1.277 pacientes, 23,4% en remisión, 12,6% en baja actividad y 63,8% con control no óptimo. En este último grupo eran más jóvenes y con menor duración de la enfermedad; presentaban mayores índices de actividad y cronicidad, y mayor empleo de inmunosupresores. Solo el 22,3% de los pacientes con criterio potencial de uso de belimumab (lupus eritematoso sistémico activo a pesar del tratamiento estándar) lo recibía en ese momento. Las variables asociadas a hospitalizaciones fueron: terapia con corticoides, ciclofosfamida y mayor SLICC. Conclusiones: se refleja la complejidad del manejo de estos pacientes y se visualizan aspectos estructurales como la desigualdad. El uso del belimumab resultaría beneficioso en los pacientes seleccionados.

Introduction: lupus is a complex disease and often difficult to approach. Achieving remission is one of the objectives, incorporating therapeutic options. Objectives: to describe the characteristics of the patients and the use of belimumab, according to the status of the disease. Materials and methods: cross-sectional study. Patients of the RELESSAR registry. Stratification: Remission: SLEDAI=0 and without corticosteroids. Low disease activity SLEDAI> 0 and ≤4 and without corticosteroids and non-optimal control: SLEDAI> 4 and any dose of corticosteroids. Results: a total of 1,277 patients were included, 23.4% in remission, 12.6% in low disease activity and 63.8% in non-optimal control. The last group was younger and had a shorter duration of the disease. They had higher activity and chronicity indices and greater use of immunosuppressants. Only 22.3% of the patients with potential criteria for the use of belimumab (activity disease despite standard treatment) were receiving it. The variables associated with hospitalizations were: corticosteroids, cyclophosphamide and higher SLICC. Those associated with severe infection: mycophenolate mofetil, azathioprine, corticosteroids, and higher SLICC. Conclusions: the complexity of the management of these patients is reflected, visualizing structural aspects such as inequality. The use of belimumab could be beneficial in selected patients.

Lupus en Argentina. Pacientes no respondedores al tratamiento estándar y belimumab como posible opción. Datos del registro RELESSAR / Lupus in Argentina. Non-responders to standard treatment and belimumab as a possible option. RELESSAR record data

Introducción: el lupus es una enfermedad compleja y varias veces de difícil abordaje. Alcanzar la remisión es uno de los objetivos, incorporando opciones terapéuticas. Objetivos: describir las características generales de los pacientes según el estado de la enfermedad y el uso de belimumab. Materiales y métodos: estudio de corte transversal, registro RELESSAR. Se definió el estado de la enfermedad como: remisión: SLEDAI=0 y sin corticoides; baja actividad de la enfermedad: SLEDAI >0 y ≤4 y sin corticoides; control no óptimo: SLEDAI >4 y cualquier dosis de corticoides. Resultados: se incluyeron 1.277 pacientes, 23,4% en remisión, 12,6% en baja actividad y 63,8% con control no óptimo. En este último grupo eran más jóvenes y con menor duración de la enfermedad; presentaban mayores índices de actividad y cronicidad, y mayor empleo de inmunosupresores. Solo el 22,3% de los pacientes con criterio potencial de uso de belimumab (lupus eritematoso sistémico activo a pesar del tratamiento estándar) lo recibía en ese momento. Las variables asociadas a hospitalizaciones fueron: terapia con corticoides, ciclofosfamida y mayor SLICC. Conclusiones: se refleja la complejidad del manejo de estos pacientes y se visualizan aspectos estructurales como la desigualdad. El uso del belimumab resultaría beneficioso en los pacientes seleccionados.

Introduction: lupus is a complex disease and often difficult to approach. Achieving remission is one of the objectives, incorporating therapeutic options. Objectives: to describe the characteristics of the patients and the use of belimumab, according to the status of the disease. Materials and methods: cross-sectional study. Patients of the RELESSAR registry. Stratification: Remission: SLEDAI=0 and without corticosteroids. Low disease activity SLEDAI> 0 and ≤4 and without corticosteroids and non-optimal control: SLEDAI> 4 and any dose of corticosteroids. Results: a total of 1,277 patients were included, 23.4% in remission, 12.6% in low disease activity and 63.8% in non-optimal control. The last group was younger and had a shorter duration of the disease. They had higher activity and chronicity indices and greater use of immunosuppressants. Only 22.3% of the patients with potential criteria for the use of belimumab (activity disease despite standard treatment) were receiving it. The variables associated with hospitalizations were: corticosteroids, cyclophosphamide and higher SLICC. Those associated with severe infection: mycophenolate mofetil, azathioprine, corticosteroids, and higher SLICC. Conclusions: the complexity of the management of these patients is reflected, visualizing structural aspects such as inequality. The use of belimumab could be beneficial in selected patients.

Impact of house dust mite-driven asthma on children's school performance and activity.

Evidence regarding asthma's impact on children's daily lives is limited. This prospective and cross-sectional, observational, multicenter study assessed school/work and activity impairment in children and adolescents with allergic asthma and their caregivers and allergen immunotherapy (AIT) effects. Included patients were schooled children and adolescents (5 to 17 years) with allergic asthma due to house dust mites (HDM). Impairment of school/work (i.e., absenteeism and presenteeism) and activity was measured in patients and their caregivers using the Work Productivity Impairment Questionnaire plus Classroom Impairment Questions: Allergy Specific (WPAI + CIQ:AS). HDM allergic patients with school impairment received subcutaneous AIT with a MicroCrystalline Tyrosine-associated allergoid. WPAI + CIQ:AS and effectiveness variables were compared between baseline and 1-year post-AIT. Of the 113 patients included, 59 (52.2%) and 51 (45.1%) showed school and activity impairment, respectively, missing a mean (SD) of 37.6 (24.4) % and 42.6 (25.6) % of school and activity time, respectively. Twenty-six (23%) caregivers reported activity impairment and, of the 79 (69.9%) employed, 30 (38%) reported work impairment. Of the 65 patients with school/activities impairment, 41 (63.1%) received AIT, of which 21 (51.2%) completed 1 year of treatment. Effectiveness variables and WPAI + CIQ:AS significantly improved: Mean (SD) school impairment decreased from 39.7 (26.7) to 2.1 (7.1) % (p < 0.001) and activity impairment from 46.2 (34.6) to 1.4 (3.6) % (p < 0.001). CONCLUSION: Allergic asthma due to HDMs results in school/work and activity impairment in children and adolescents and their caregivers. One year of AIT provided clinical benefits and reduced school and activity impairment. WHAT IS KNOWN: • Allergic asthma impairs children's school performance and daily activities. • Allergen immunotherapy modifies allergic disease course and ameliorates its symptoms. WHAT IS NEW: • Asthma symptoms due to allergy to house dust mites impair children's school attendance and productivity and daily activity and their caregivers' work performance and daily lives. • Allergen immunotherapy with a house dust mite MicroCrystalline Tyrosine (MCT)-associated allergoid seems to provide clinical benefits, associated with decreased school and activity impairment, supporting it as an effective treatment option.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.

"Malignant" origin of the left coronary artery, case report / Origen «maligno¼ de arteria coronaria izquierda, reporte de caso

Anomalous origin of the coronary arteries are very infrequent, however their diagnosis has been increasing due to the increase in the use of coronary computer tomography angiography (CCTA) within the algorithm of patients with suspected coronary disease; We present a case of a patient with acute on chronic chest pain in whom an anomalous origin was diagnosed with an interarterial "malignant" course of the left coronary artery, who was taken to surgery with complete improvement of symptoms and quality of life.

El origen anómalo de las arterias coronarias (OAAC) es muy infrecuente, sin embargo, su diagnóstico ha ido en aumento por el incremento en el uso de la angiotomografía coronaria por tomografía dentro del algoritmo del paciente con sospecha de enfermedad coronaria. Presentamos el caso de un paciente con dolor torácico crónico agudizado en quien se diagnosticó un OAAC de la coronaria izquierda con curso interarterial «maligno¼, que fue llevado a cirugía, con mejoría completa de síntomas y en calidad de vida.

Acoplamiento molecular y modelado tridimensional por homología de flavonoides derivados de amentoflavona con las neuraminidasas H1N1 y H5N1 del virus de gripe aviar / Molecular docking and threedimensional homology modeling of flavonoids derived from amentoflavone with H1N1 and H5N1 neuraminidases of avian influenza virus / Modelagem de homologia tridimensional e de acoplamento molecular de flavonóides derivados de amentoflavona com as neuraminidases H1N1 e H5N1 do vírus da gripe aviária

Resumen El virus de la influenza A es el responsable de la gripe aviar, condición patológica que afecta principalmente aves, caballos y mamíferos marinos, sin embargo, el subtipo H5NI tiene la capacidad de infectar a los humanos de forma rápida, exponiéndolos a un posible evento pandémico. Por tanto, el objetivo de este estudio fue realizar el acoplamiento molecular y modelado tridimensional por homología de flavonoides derivados de amentoflavona con las neuraminidasas H1N1 y H5N1 del virus de gripe aviar. Inicialmente, se obtuvo por homología la estructura 3D de la neuraminidasa H1N1. Seguido, se realizó un acoplamiento molecular de H1N1 con seis ligandos (F36, Ginkgetin, 3S,3R, 5S,5R, 6S y 6R), y más adelante H5N1 y los ligandos F36, Ginkgetin, 5R y 6R. Finalmente, a los complejos obtenidos se les realizó un análisis de interacciones. Los resultados dejaron en evidencia una relación entre la actividad inhibitoria y las interacciones tipo puente de hidrógeno e hidrofóbicas formadas entre el sitio activo de las neuraminidasas y los ligandos. Además, se observó una mejora en la actividad inhibitoria de los ligandos para la estereoquímica tipo R y sustituyentes poco voluminosos. De ahí que se propongan la evaluación experimental de los ligandos 5R y 6R como potenciales inhibidores de H5N1.

Abstract The influenza A virus is responsible for bird flu; a pathological condition that mainly affects birds, horses, and marine mammals, however, the H5N' subtype can infect humans quickly; exposing them to a possible pandemic event. Therefore, the objective of this study was to carry out the molecular docking and three-dimensional homology modeling of flavonoids derived from amentoflavone with H'NI and H5NI neuraminidases of the avian influenza virus. Initially, the 3D structure of H1N1 neuraminidase was obtained by homology. Then, the molecular docking of H1N1 was carried out with six ligands (F36, Ginkgetin, 3S, 3R, 5S, 5R, 6S, and 6R), and subsequently H5N1 and F36, Ginkgetin, 5R, and 6R ligands. Finally, an interaction analysis of the proteinligand complex was performed. The results showed a relationship between the inhibitory activity of ligands and the hydrophobic and hydrogen bridge-type interactions. In addition, an improvement in the inhibitory activity of the ligands for R-type stereochemistry and small bulky substituents was observed. Thus, the experimental evaluation of the 5R and 6R ligands as potential H5N' inhibitors is proposed.

Resumo O vírus influenza A é responsável pela gripe aviária; condição patológica que afeta principalmente pássaros, cavalos e mamíferos marinhos, no entanto, o subtipo H5N' tem a capacidade de infectar humanos rapidamente; assim, expondo-os a um possível evento pandémico. Portanto, o objetivo deste estudo foi realizar o acoplamento e modelagem de homologia tridimensional de flavonóides derivados da amentoflavona com as neuraminidases H1N1 e H5N1 do vírus da influenza aviária. Inicialmente, a estrutura 3D da neuraminidase H1N1 foi obtida por homologia. Em seguida, o acoplamento molecular de H1N1 foi realizado com seis ligantes (F36, Ginkgetin, 3S, 3R, 5S, 5R, 6S e 6R) e, posteriormente, H5NI e os ligantes F36, Ginkgetin, 5R e 6R. Finalmente, uma análise de interação foi realizada nos complexos obtidos. Os resultados mostraram uma relação entre a atividade inibitória e as interações hidrofóbicas e do tipo ponte de hidrogénio formadas entre o sítio ativo das neuraminidases e os ligantes. Além disso, foi observada uma melhoria na atividade inibitória dos ligantes para a estereoquímica do tipo R e pequenos substituintes volumosos. Assim, é proposta a avaliação experimental dos ligantes 5R e 6R como potenciais inibidores do H5NI.