Drug capture materials based on genomic DNA-functionalized magnetic nanoparticles

Chemotherapy agents are notorious for producing severe side-effects. One approach to mitigating this off-target damage is to deliver the chemotherapy directly to a tumor via transarterial infusion, or similar procedures, and then sequestering any chemotherapeutic in the veins draining the target organ before it enters the systemic circulation. Materials capable of such drug capture are yet to be fully realized. Here, we report the covalent attachment of genomic DNA to iron-oxide nanoparticles. With these magnetic materials, we captured three common chemotherapy agents—doxorubicin, cisplatin, and epirubicin—from biological solutions. We achieved 98% capture of doxorubicin from human serum in 10 min. We further demonstrate that DNA-coated particles can rescue cultured cardiac myoblasts from lethal levels of doxorubicin. Finally, the in vivo efficacy of these materials was demonstrated in a porcine model. The efficacy of these materials demonstrates the viability of genomic DNA-coated materials as substrates for drug capture applications

Inversão sísmica bayesiana com modelagem a priori integrada com física de rocha

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro de Ciências Físicas e Matemáticas, Programa de Pós-Graduação em Física, Florianópolis, 2017.A inversão sísmica conjunta para as propriedades elásticas e petrofísicas é um problema inverso com solução não única. Existem vários fatores que afetam a precisão dos resultados como a relação estatística de física de rocha, os erros dos dados experimentais e de modelagem. Apresentamos uma metodologia para incorporar um modelo linearizado de física de rocha em uma distribuição Gaussiana multivariada. A proposta é usada para definir um modelo de mistura Gaussiana para a distribuiçãoconjunta a priori das propriedades elásticas e petrofísicas, no qual cada componente é interpretada como uma litofácies. Este processo permite introduzir uma correlação teórica entre as propriedades, com interpretação geológica específica dos parâmetros da física de rocha para cada fácies. Com base nesta modelagem a priori e no modelo convolucional, obtemos analiticamente as distribuições condicionais da amostragem de Gibbs. Em seguida, combinamos o algoritmo de amostragem com métodos de simulação geoestatística para obter a distribuição a posteriori de Bayes. Aplicamos a proposta em um conjunto de dados sísmicos reais, com três poços, para obter múltiplas realizações geoestatísticas tridimensionais das propriedades e das litofácies. A proposta é validada através de testes de poço cego e comparações com a inversão Bayesiana tradicional. Usando a probabilidade das litofácies, também calculamos a isosuperfície de probabilidade do reservatório de óleo principal do campo estudado. Além da proposta de inversão sísmica conjunta, apresentamos também uma formulação revisitada para o método de simulação geoestatística FFT-Moving Average. Nessa formulação, o filtro de correlação é derivado através de apenas um único ruído aleatório, o que permite a aplicação do método sem qualquer suposição sobre as características do ruído.Abstract : Joint seismic inversion for elastic and petrophysical properties is an inverse problem with a nonunique solution. There are several factors that affect the accuracy of the results such as the statistical rock-physics relation and observation errors. We present a general methodology to incorporate a linearized rock-physics model into a multivariate Gaussian distribution. The proposal is used to define a Gaussian mixture model for the joint prior distribution of the elastic and petrophysical properties, in which each component is interpreted as a lithofacies. This process allows to introduce a theoretical correlation between the properties with specific geological interpretation for the rock physicsparameters of each facies. Based on the prior model and on the convolutional model, we analytically obtain the conditional distributions of the Gibbs sampling. Then, we combine the sampling algorithm with geostatistical simulation methods to calculate the Bayesian posterior distribution. We applied the proposal to a real seismic data set with three wells to obtain multiple three-dimensional geostatistical simulations of the properties and the lithofacies. The proposal is validated through a blind well test and a comparison with the traditional Bayesian inversion. Using the probability of the reservoir lithofacies, we also calculated a 3D isosurface probability model of the main oil reservoir in the studied field

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.Comment: Technical report of BraSy

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors

Apparent diffusion coefficient and pituitary macroadenomas: pre-operative assessment of tumor atypia.

Rationale and objectivesPituitary macroadenomas are predominantly benign intracranial neoplasms that can be locally aggressive with invasion of adjacent structures. Biomarkers of aggressive behavior have been identified in the pathology literature, including the proliferative marker MIB-1. In the radiology literature, diffusion weighted imaging and low ADC values provide similar markers of aggressive behavior in brain tumors. The purpose of this study was to determine if there is a correlation between ADC and MIB-1 in pituitary macroadenomas.Materials and methodsA retrospective review of diffusion imaging and immunohistochemical characteristics of pituitary macroadenomas was performed. The ADC ratio and specimen Ki-67 (MIB-1) indices were measured. Linear regression analysis of normalized ADC values and MIB-1 indices was used to compare these parameters.ResultsThere were 17 patients with available ADC maps and MIB-1 indices. Local invasion was confirmed by imaging and intraoperative visualization in 11 patients. The mean ADC ratio for the invasive group was 0.68, with a mean MIB-1 index of 2.21 %. In the noninvasive group, the mean ADC ratio was 1.05, with a mean MIB-1 index of 0.9 %. Linear regression analysis of normalized ADC values versus MIB-1 demonstrates a negative correlation, with a linear slope significantly different from zero (p = 0.003, correlation coefficient of 0.77, and r squared = 0.59).ConclusionWe determine a strong correlation of low ADC values and MIB-1, demonstrating the potential of diffusion imaging as a possible biomarker for atypical, proliferative adenomas, which may ultimately affect the surgical approach and postoperative management

Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation

Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models