Design and Application of Matrix Metalloproteinase-9-Responsive Peptide Nanostructures

Matrix metalloproteinase (MMP)-responsive materials have been investigated since the late 1990’s as scaffolds for tissue engineering and since then, have evolved into sophisticated nanomaterials for cancer-targeting therapy. In this thesis titled, “Design and Application of Matrix Metalloproteinase-9-Responsive Peptide Nanostructures,” we aim to answer the following key questions: can MMP-responsive nanomaterials improve the efficacy of anti-cancer treatments? How can we achieve specificity towards MMPs using nanomaterials? Finally, what are the advantages in using peptides as building blocks to create MMP-responsive nanostructures? Each chapter in the thesis will address one or more of the key questions and draw conclusions at the end

MMP-9 triggered self-assembly of doxorubicin nanofiber depots halts tumor growth

A central challenge in cancer care is to ensure that therapeutic compounds reach their targets. One approach is to use enzyme-responsive biomaterials, which reconfigure in response to endogenous enzymes that are overexpressed in diseased tissues, as potential site-specific anti-tumoral therapies. Here we report peptide micelles that upon MMP-9 catalyzed hydrolysis reconfigure to form fibrillar nanostructures. These structures slowly release a doxorubicin payload at the site of action. Using both in vitro and in vivo models we demonstrate that the fibrillar depots are formed at the sites of MMP-9 overexpression giving rise to enhanced efficacy of doxorubicin, resulting in inhibition of tumor growth in an animal model

Ten Steps to Organize a Virtual Scientific Symposium and Engage Your Global Audience

The paper describes guidelines for the planning, organization, and successful execution of virtual, global scientific conferences for global audiences. The guidelines are based on experience and lessons learned during the organization of the 3-day 2020 Virtual Systems Chemistry Symposium hosted on Zoom webinar and Twitter, held on May 2020 with over 1000 registered participants from 46 different countries

Selective UMLS knowledge infusion for biomedical question answering

Abstract One of the artificial intelligence applications in the biomedical field is knowledge-intensive question-answering. As domain expertise is particularly crucial in this field, we propose a method for efficiently infusing biomedical knowledge into pretrained language models, ultimately targeting biomedical question-answering. Transferring all semantics of a large knowledge graph into the entire model requires too many parameters, increasing computational cost and time. We investigate an efficient approach that leverages adapters to inject Unified Medical Language System knowledge into pretrained language models, and we question the need to use all semantics in the knowledge graph. This study focuses on strategies of partitioning knowledge graph and either discarding or merging some for more efficient pretraining. According to the results of three biomedical question answering finetuning datasets, the adapters pretrained on semantically partitioned group showed more efficient performance in terms of evaluation metrics, required parameters, and time. The results also show that discarding groups with fewer concepts is a better direction for small datasets, and merging these groups is better for large dataset. Furthermore, the metric results show a slight improvement, demonstrating that the adapter methodology is rather insensitive to the group formulation

Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT

Accurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Monte-Carlo (MC) simulation can yield accurate scatter estimation, but is computationally expensive. Recent deep learning-based approaches can yield accurate scatter estimates quickly, yet full MC simulation is still required to generate scatter estimates as ground truth labels for all training data. Here we propose a physics-guided weakly supervised training framework for fast and accurate scatter estimation in quantitative SPECT by using a 100 x shorter MC simulation as weak labels and enhancing them with deep neural networks. Our weakly supervised approach also allows quick fine-tuning of the trained network to any new test data for further improved performance with an additional short MC simulation (weak label) for patient-specific scatter modelling. Our method was trained with 18 XCAT phantoms with diverse anatomies / activities and then was evaluated on 6 XCAT phantoms, 4 realistic virtual patient phantoms, 1 torso phantom and 3 clinical scans from 2 patients for Lu-177 SPECT with single / dual photopeaks (113, 208 keV). Our proposed weakly supervised method yielded comparable performance to the supervised counterpart in phantom experiments, but with significantly reduced computation in labeling. Our proposed method with patient-specific fine-tuning achieved more accurate scatter estimates than the supervised method in clinical scans. Our method with physics-guided weak supervision enables accurate deep scatter estimation in quantitative SPECT, while requiring much lower computation in labeling, enabling patient-specific fine-tuning capability in testing

Structural evolution of hexagonal boron nitride powder by Bead-milling

The structural evolution of h-BN powder by bead-milling is investigated. A unique initial stage in h-BN milling is discovered in which the x-ray diffraction (XRD) out-of-plane (0 0 2) and (0 0 4) peak intensities increase distinctly up to 3 and 4.8 times respectively, corresponding to an effective c-axis ordering of the h-BN particles. The structural evolution of h-BN due to milling is correlated to the change in the dominant milling mechanism from out-of-plane cleaving to in-plane cutting. An abnormal contraction of out-of-plane (0 0 2) interplanar spacing up to 0.044 angstrom due to bead-milling is reported, which could be attributed to the introduction of oxygen functional groups (B-O and N-O) on the h-BN powder.11Nsciescopu