Multistage nanoparticle delivery system for deep penetration into solid tumor and electrically controlled catalytic nanowire growth

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemistry, 2011.Cataloged from PDF version of thesis.Includes bibliographical references.Assembly of functional nanocomponents offers promising applications in drug delivery to solid tumors and bottom-up synthesis and integration of nanodevices. This thesis presents a novel multistage nanoparticle delivery system consisting of an assembly of nanoparticles that can change its size to facilitate transport into solid tumors. Current FDA-approved nanotherapeutics, which function based on the enhanced permeation and retention (EPR) effect, suffer from poor penetration into the extravascular regions of the tumor due to the dense collagen matrix, resulting in heterogeneous therapeutic effects and likely contributing to tumor regression and development of resistance. We propose a multistage nanoparticle system that "shrinks" when it extravasates into the tumor and is exposed to the tumor microenvironment, allowing enhanced penetration into the tumor parenchyma. This "shrinkage" is preferentially triggered in the tumor through cleavage by MMPs, proteases highly expressed in the tumor microenvironment. A multistage nanoparticle system allows us to engineer the size and surface properties of each stage independently for preferential transvascular transport into tumors and high diffusion in the tumor's interstitial space. To our knowledge, this work is the first demonstration of a size-changing nanoparticle delivery system in vivo. Multistage nanoparticle delivery systems provide a promising approach to improving the delivery of anticancer agents into solid tumors and as a result the enhancement of the drug's therapeutic efficacy. Another area that necessitates the controlled assembly of nanocomponents is in the integration of nanodevices and nanocircuitry. We have developed a method of combining the synthesis and assembly of semiconducting nanowires in a single step using electrically controlled catalytic nanowire growth. Our results demonstrate electric field-modulated nanowire growth that can be used as a simple and inexpensive method for fabricating and integrating nanoscale devices.by Cliff R. Wong.Ph.D

Avidin as a model for charge driven transport into cartilage and drug delivery for treating early stage post-traumatic osteoarthritis

Local drug delivery into cartilage remains a challenge due to its dense extracellular matrix of negatively charged proteoglycans enmeshed within a collagen fibril network. The high negative fixed charge density of cartilage offers the unique opportunity to utilize electrostatic interactions to augment transport, binding and retention of drug carriers. With the goal of developing particle-based drug delivery mechanisms for treating post-traumatic osteoarthritis, our objectives were, first, to determine the size range of a variety of solutes that could penetrate and diffuse through normal cartilage and enzymatically treated cartilage to mimic early stages of OA, and second, to investigate the effects of electrostatic interactions on particle partitioning, uptake and binding within cartilage using the highly positively charged protein, Avidin, as a model. Results showed that solutes having a hydrodynamic diameter ≤10 nm can penetrate into the full thickness of cartilage explants while larger sized solutes were trapped in the tissue's superficial zone. Avidin had a 400-fold higher uptake than its neutral same-sized counterpart, NeutrAvidin, and >90% of the absorbed Avidin remained within cartilage explants for at least 15 days. We report reversible, weak binding (K[subscript D] ~ 150 μm) of Avidin to intratissue sites in cartilage. The large effective binding site density (N[subscript T] ~ 2920 μm) within cartilage matrix facilitates Avidin's retention, making its structure suitable for particle based drug delivery into cartilage

Knowledge-Rich Self-Supervision for Biomedical Entity Linking

Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision ($\tt KRISS$) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised mentions as prototypes for each entity and conducts linking by mapping the test mention to the most similar prototype. Our approach can easily incorporate entity descriptions and gold mention labels if available. We conducted extensive experiments on seven standard datasets spanning biomedical literature and clinical notes. Without using any labeled information, our method produces $\tt KRISSBERT$, a universal entity linker for four million UMLS entities that attains new state of the art, outperforming prior self-supervised methods by as much as 20 absolute points in accuracy

Radio-Frequency Interference (RFI) Mitigation for the Soil, Moisture Active/Passive (SMAP) Radiometer

The presence of anthropogenic RFI is expected to adversely impact soil moisture measurement by NASA s Soil Moisture Active Passive mission. The digital signal processing approach and preliminary design for detecting and mitigating this RFI is presented in this paper. This approach is largely based upon the work of Johnson and Ruf

Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events

Large language models (LLMs), such as GPT-4, have demonstrated remarkable capabilities across a wide range of tasks, including health applications. In this paper, we study how LLMs can be used to scale biomedical knowledge curation. We find that while LLMs already possess decent competency in structuring biomedical text, by distillation into a task-specific student model through self-supervised learning, substantial gains can be attained over out-of-box LLMs, with additional advantages such as cost, efficiency, and white-box model access. We conduct a case study on adverse drug event (ADE) extraction, which is an important area for improving care. On standard ADE extraction evaluation, a GPT-3.5 distilled PubMedBERT model attained comparable accuracy as supervised state-of-the-art models without using any labeled data. Despite being over 1,000 times smaller, the distilled model outperformed its teacher GPT-3.5 by over 6 absolute points in F1 and GPT-4 by over 5 absolute points. Ablation studies on distillation model choice (e.g., PubMedBERT vs BioGPT) and ADE extraction architecture shed light on best practice for biomedical knowledge extraction. Similar gains were attained by distillation for other standard biomedical knowledge extraction tasks such as gene-disease associations and protected health information, further illustrating the promise of this approach

Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology

Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this paper, we conduct a systematic study on scaling clinical trial matching using large language models (LLMs), with oncology as the focus area. Our study is grounded in a clinical trial matching system currently in test deployment at a large U.S. health network. Initial findings are promising: out of box, cutting-edge LLMs, such as GPT-4, can already structure elaborate eligibility criteria of clinical trials and extract complex matching logic (e.g., nested AND/OR/NOT). While still far from perfect, LLMs substantially outperform prior strong baselines and may serve as a preliminary solution to help triage patient-trial candidates with humans in the loop. Our study also reveals a few significant growth areas for applying LLMs to end-to-end clinical trial matching, such as context limitation and accuracy, especially in structuring patient information from longitudinal medical records.Comment: 24 pages, 5 figures, accepted at Machine Learning for Healthcare (MLHC) 202

Compact high-quality CdSe–CdS core–shell nanocrystals with narrow emission linewidths and suppressed blinking

High particle uniformity, high photoluminescence quantum yields, narrow and symmetric emission spectral lineshapes and minimal single-dot emission intermittency (known as blinking) have been recognized as universal requirements for the successful use of colloidal quantum dots in nearly all optical applications. However, synthesizing samples that simultaneously meet all these four criteria has proven challenging. Here, we report the synthesis of such high-quality CdSe–CdS core–shell quantum dots in an optimized process that maintains a slow growth rate of the shell through the use of octanethiol and cadmium oleate as precursors. In contrast with previous observations, single-dot blinking is significantly suppressed with only a relatively thin shell. Furthermore, we demonstrate the elimination of the ensemble luminescence photodarkening that is an intrinsic consequence of quantum dot blinking statistical ageing. Furthermore, the small size and high photoluminescence quantum yields of these novel quantum dots render them superior in vivo imaging agents compared with conventional quantum dots. We anticipate these quantum dots will also result in significant improvement in the performance of quantum dots in other applications such as solid-state lighting and illumination.National Institutes of Health (U.S.) (Grant 5-U54-CA119349)National Institutes of Health (U.S.) (Grant 5R01CA126642)Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies (W911NF-07-D-0004)National Science Foundation (U.S.) (Collaborative Research in Chemistry Program CHE-0714189)National Science Foundation (U.S.) (Award DMR-08-19762)National Science Foundation (U.S.) (Grant CHE-9808061)National Science Foundation (U.S.) (Grant DBI-9729592

Cross-sectional associations between sleep duration, sedentary time, physical activity, and adiposity indicators among Canadian preschool-aged children using compositional analyses

Abstract Background Sleep duration, sedentary behaviour, and physical activity are three co-dependent behaviours that fall on the movement/non-movement intensity continuum. Compositional data analyses provide an appropriate method for analyzing the association between co-dependent movement behaviour data and health indicators. The objectives of this study were to examine: (1) the combined associations of the composition of time spent in sleep, sedentary behaviour, light-intensity physical activity (LPA), and moderate- to vigorous-intensity physical activity (MVPA) with adiposity indicators; and (2) the association of the time spent in sleep, sedentary behaviour, LPA, or MVPA with adiposity indicators relative to the time spent in the other behaviours in a representative sample of Canadian preschool-aged children. Methods Participants were 552 children aged 3 to 4 years from cycles 2 and 3 of the Canadian Health Measures Survey. Sedentary time, LPA, and MVPA were measured with Actical accelerometers (Philips Respironics, Bend, OR USA), and sleep duration was parental reported. Adiposity indicators included waist circumference (WC) and body mass index (BMI) z-scores based on World Health Organization growth standards. Compositional data analyses were used to examine the cross-sectional associations. Results The composition of movement behaviours was significantly associated with BMI z-scores (p = 0.006) but not with WC (p = 0.718). Further, the time spent in sleep (BMI z-score: γ sleep  = −0.72; p = 0.138; WC: γ sleep  = −1.95; p = 0.285), sedentary behaviour (BMI z-score: γ SB  = 0.19; p = 0.624; WC: γ SB  = 0.87; p = 0.614), LPA (BMI z-score: γ LPA  = 0.62; p = 0.213, WC: γ LPA  = 0.23; p = 0.902), or MVPA (BMI z-score: γ MVPA  = −0.09; p = 0.733, WC: γ MVPA  = 0.08; p = 0.288) relative to the other behaviours was not significantly associated with the adiposity indicators. Conclusions This study is the first to use compositional analyses when examining associations of co-dependent sleep duration, sedentary time, and physical activity behaviours with adiposity indicators in preschool-aged children. The overall composition of movement behaviours appears important for healthy BMI z-scores in preschool-aged children. Future research is needed to determine the optimal movement behaviour composition that should be promoted in this age group