Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 68-72). / 'Smart' targeted drug carriers have long been sought after in the treatment of epidermal growth factor (EGF)-overexpressing cancers due to the potential advantages, relative to current clinical therapies (generally limited to surgery, radiation therapy, traditional chemotherapy, and EGF receptor inhibitors (EGFRIs)), of using such 'smart' targeted drug delivery systems. However, progress toward this goal has been challenged by the difficulty of creating a drug carrier that can autonomously detect and respond to tumor cells in the body. 'Smart' micron-size drug-encapsulating epidermal growth factor (EGF)-sensitive liposomes for EGF-overexpressing cancer therapies have been developed and studied. These drug-encapsulating liposomes remain inert until they are exposed to an abnormal concentration of EGF. As a drug delivery system, these drug-encapsulating liposomes could release pharmaceutical agents specifically in the immediate neighborhood of tumors overexpressing EGF, thereby maximizing the effective amount of drug received by the tumor while minimizing the effective systemic toxicity of the drug. Additionally, quantitative mathematical models were developed to characterize multiple critical rate processes (including drug leakage from drug-encapsulating liposomes and distribution of (drug-encapsulating) liposomes in blood vessels) associated i with (drug-encapsulating) liposomes in general. / (cont.) These quantitative mathematical models provide a low-cost and rapid method for screening novel drug-encapsulating liposome compositions, configurations, and synthetic methods to identify liposome compositions, configurations, and synthetic methods that would deliver optimal performance. The results provide a stepping stone toward the development of EGF-sensitive liposomes for clinical use. More generally, they also present implications for future development of other targeted drug delivery vehicles. / by Albert Wong. / S.M.

Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2016. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 121-132). / The tectorial membrane (TM) is an extracellular matrix that overlies the mechanically sensitive hair bundles of sensory receptor cells in the inner ear. Based on this strategic position, it has long been accepted that the TM plays a critical role in the stimulation of sensory hair cells. Early measurements demonstrated elastic properties of the TM and suggested that the TM is resonant. More recent measurements have shown that longitudinal coupling of the TM generates traveling waves that contribute to cochlear tuning. Here we show the importance of (1) viscosity in controlling the spread of excitation in TM traveling waves, as well as the importance of matrix porosity in determining (2) the viscosity of genetically modified TMs, and (3) local interactions with hair bundles. To understand the longitudinal spread of mechanical excitation via TM traveling waves, we develop chemical manipulations that systematically and reversibly alter TM stiffness and viscosity. Increasing TM viscosity or decreasing stiffness reduces longitudinal spread of mechanical excitation, thereby coupling a smaller range of best frequencies, which would sharpen tuning. In contrast, increasing viscous loss or decreasing stiffness would tend to broaden tuning in resonance based TM models. Thus, TM wave and resonance mechanisms are fundamentally different in the way they control frequency selectivity. To understand the molecular origin of TM viscosity, we investigate traveling waves in genetically modified TMs. We show that nanoscale pores of TectaY1870C/+ TMs are significantly larger than those of Tectb -/- TMs. The larger pore size reduces shear viscosity, thereby reducing traveling wave speed and increasing spread of excitation. These results demonstrate the previously unrecognized importance of TM porosity in cochlear tuning. To understand how TM porosity affects the local interaction between the TM and hair cells, we apply oscillatory forces to the TM with spherical probe tips. The effective stiffness of the TM is small at low frequencies where the porous matrix and surrounding fluid can move independently. By contrast, the effective stiffness of the TM is large at high frequencies, where these two phases are entrained by viscosity to move together. Interestingly, the transition frequency is in the audio frequency range only for hair bundle sized tips. Furthermore, the transition region is characterized by increased phase lead between the stimulus force and applied displacement that may play an essential role in the stability of micromechanical feedback paths and ultimately the sensitivity of hearing. In conclusion, these results show that traveling wave properties and local interactions with the hair bundles depend critically on TM porosity, thus fundamentally changing the way we think about molecular mechanisms underlying cochlear frequency selectivity and sensitivity. / by Jonathan Blake Sellon. / Ph. D.

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007. / Includes bibliographical references (leaves 93-101). / Embryonic stem cells serve as powerful models for the study of development and disease and hold enormous potential for future therapeutics. Yet, over two decades after mouse embryonic stem cells (mESCs) were first isolated, there is still little known about the role of cell-cell signaling in self-renewal. Since traditional cell-culture techniques do not provide significant control of the stem cell microenvironment, the goal of this thesis was to develop a cell patterning technology that allows us to precisely control stem cell signaling and monitor cell proliferation over time. In the first aim of this thesis, we describe the development of our first cell patterning technology using dielectrophoresis (DEP). DEP uses nonuniform electric fields to trap cells on or between electrodes. We first used beads as model particles to validate the strength of our DEP square trap, and then demonstrated efficient cell patterning with multiple cell types. In the second aim of this thesis, we describe the development of a novel cell patterning technology that we created, called the Bio Flip Chip (BFC). / (cont.) The BFC is a microfabricated polymer chip, containing thousands of microwells, that enables cell patterning with single-cell resolution anywhere on a substrate and onto any substrate. In the last aim of this thesis, we used our BFC technology to control the stem cell microenvironment, allowing us to incrementally and independently modulate cell-cell contact. We present the first quantitative evidence that cell-cell contact depresses mESC colony formation and show that E-cadherin signaling is responsible for this negative regulatory pathway. / by Adam Rosenthal. / Ph.D.

Thesis: Ph. D. in Biomedical Engineering, Harvard-MIT Program in Health Sciences and Technology, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 125-136). / The conventional pathologic analysis of malignancies involves a qualitative characterization and integration of several factors including tumor size, general degree of differentiation, tumor heterogeneity, mitotic rate, and lymphovascular invasion. For some cancers, biomarkers such as hormone receptor expression or receptor kinase over-expression can provide additional prognostic and therapeutic guidance. Unfortunately, all of these qualitative histologic approaches, while generally accepted for directing patient care, often exhibit significant inter-observer variability resulting in inconsistent inter- and intra-institutional predictions of tumor behavior (including metastases and/or recurrence), resulting in incorrect diagnoses or treatment. Because cellular morphology is an integrated reflection of genetic and epigenetic expression, we hypothesize that a more accurate quantitative accounting and measurement of histologic features can provide a more robust and reliable prediction of tumor behavior. Computational imaging utilizes software to augment or replace the role of traditional optical elements in imaging systems and has an ability to significantly increase the accuracy, robustness and cost-efficiency of digital pathology. In this thesis, we develop and test three novel computational imaging algorithms including, to the best of our knowledge, the first system for lensless computational imaging through deep learning. We then test our hypothesis by applying augmented image retrieval, analysis algorithms, and machine learning on a validated dataset of breast cancer images where the clinical outcomes of the primary tumor are known. In particular, we analyze algorithms related to identifying mitoses as a central proof of concept. / by Justin Lee. / Ph. D. in Biomedical Engineering

Thesis (Ph.D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, February 1999. / Includes bibliographical references (leaves 229-239). / by Douglas R. Daum. / Ph.D.

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2007. / Includes bibliographical references (leaf 98). / Companies must receive marketing authorization by the Food and Drug Administration (FDA) before they can begin commercial distribution of a new type of medical device in the United States. The premarket approval application (PMA) is the process by which this occurs. Companies submit a PMA after they have completed laboratory test, animal studies and human clinical trials to demonstrate the safety and effectiveness of the device for a specific condition, or therapeutic indication. Despite legislation in the early part of this decade to reduce the timeframe for FDA review of PMAs, these timeframes continue to vary dramatically and unpredictably from months to many years. The focus of this thesis is to examine factors which influence this timeframe. Hypotheses about factors that impact PMA review timeframes were developed by analyzing the review process and through interviews with industry representatives and FDA officials. The following factors were evaluated: year of submission to FDA, size of firm seeking approval, presence of prior approved PMAs by firm seeking approval, product category, first-of-a-kind device, number of amendments, expedited review status, advisory panel review, unanimous advisory panel vote, and confirmation of primary efficacy endpoints in pivotal clinical trials. The year of submission was considered a control variable. The other factors fall into one of three categories: applicant characteristics, device characteristics, and process characteristics. Analysis was limited to PMAs received by FDA from 2000 through 2005. Two levels of analysis were conducted. / (cont.) First, the directional impact of each factor on PMA review time was evaluated. Second, regression analysis was used to develop predictive models for PMA review time, in days, and to test which factors have meaningful associations when controlling for other factors. Factors that have highly statistically significant associations with longer review timeframes include: a larger number of amendments, and designation as an orthopedic device. Designation as an orthopedic device has a particularly dramatic impact on PMA review time. Orthopedic devices have a mean PMA review time of 647 days, 240 days longer (66% more) than the average for all other categories combined. Even after controlling for process, device, and applicant factors, the impact of an orthopedic designation remains large, increasing the review time by 175 days (p<0.01). In a univariate regression model, each additional amendment is associated with 20.2 additional days (p<0.0001) of review time. After controlling for other factors, each additional amendment is associated with 17.5 additional days (p<0.0001) of review time. Although the number of amendments cannot be known - or predicted - in advance of PMA submission, its significance (R-squared of 0.25 in a univariate regression model) in predicting PMA review timeframes reinforces the notion that quality - primarily of the dossier, in terms of its organization, clarity and completeness, but also of the adequacy of the underlying data to substantiate safety and effectiveness - is critically important to the achieving a shorter PMA review time. Only one factor has an association that in the opposite direction to that hypothesized. PMAs with an expedited review status have mildly longer PMA review times, by 37 days, than those that were not expedited. / (cont.) This result can be explained in part by the larger number of amendments on these PMAs (corr=0.32). When controlling for the number of amendments and other important factors, an expedited review designation has a significant impact on PMA review timeframes in the opposite, but hypothesized direction - it shortens PMA review times by 146 days (p<0.01). / by Inder Raj Singh. / S.M.

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2005. / Page 150 blank. / Includes bibliographical references. / Current clinical practice calls for the development of techniques to diagnose diseases in its early stages, when treatment is most effective and significant irreversible damage can either be prevented or delayed. Optical coherence tomography (OCT) is an emerging medical diagnostic technology being investigated for applications in a number of medical fields including ophthalmology, cardiology, and gastroenterology. OCT is analogous to ultrasound except that it uses light waves rather than sound waves. OCT can achieve a much higher resolution than ultrasound in measuring the underlying tissue microstructures. Another advantage of OCT is that it can achieve imaging in a non-contact and non-invasive manner. With typical axial resolution of 10 [mu]m, OCT already provides significantly more detailed structural information than any other conventional clinical imaging technique. The development of OCT with even higher resolution would potentially have significant impact in diagnosing diseases in such fields as ophthalmology, cardiology, gastroenterology, and oncology. Ultrahigh resolution OCT systems have been developed for animal research and clinical ophthalmology. Ultrahigh resolution OCT improves the axial resolution of OCT from the standard 10 [mu]m to 1 [mu]m for animal studies and 3 [mu]m for clinical studies. This improved imaging resolution approaches that of histopathology. Therefore, OCT can potentially function as "optical biopsy" since it permits the imaging of tissue microstructure with resolutions approaching that of histopathology except that imaging can be performed in real time, without the need of tissue removal. / (cont.) Using ultrahigh resolution OCT systems, animal imaging studies have been performed on mouse and rat models of retinal diseases and clinical imaging studies have been performed on more than 800 patients at the ophthalmology clinic. The results from patient imaging studies on a wide variety of retinal diseases suggest that ultrahigh resolution OCT can improve the diagnosis and management of retinal diseases as well as possibly increase the understanding of ocular disease pathogenesis. Therefore, ultrahigh resolution OCT has the potential to become an important tool in ophthalmology research and clinics. / by Tony Hong-Tyng Ko. / Ph.D.

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2010. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student submitted PDF version of thesis. / Includes bibliographical references (p. 120-128). / Oxygen consumption is an essential process of the functioning brain. The rate at which the brain consumes oxygen is known as the cerebral metabolic rate of oxygen (CMRO₂). CMRO₂ is intimately related to brain health and function, and will change in settings of disease and functional activation. Accurate CMRO₂ measurement will enable detailed investigation of neuropathology and facilitate our understanding of the brain's underlying functional architecture. Despite the importance of CMRO₂ in both clinical and basic neuroscience settings, a robust CMRO₂ mapping technique amenable to functional and clinical MRI has not been established. To address this issue, a novel method called QUantitative Imaging of eXtraction of Oxygen and TIssue Consumption, or QUIXOTIC, is introduced. The key innovation in QUIXOTIC is the use of velocity-selective spin labeling to isolate MR signal exclusively from post-capillary venular blood on a voxel-by-voxel basis. This isolated signal can be related to venular oxygen saturation, oxygen extraction fraction, and ultimately CMRO₂. This thesis first explores fundamental theory behind the QUIXOTIC technique, including design of a novel MRI pulse sequence, explanation of the principal sequence parameters, and results from initial human experiences. A human trial follows, in which QUIXOTIC is used to measure cortical gray matter CMRO₂ in ten healthy volunteers. / (cont.) QUIXOTIC-measured CMRO₂ is found to be within the expected physiological range and is comparable to values reported by other techniques. QUIXOTIC is then applied to evaluate CMRO₂ response to carbon-dioxide-induced hypercapnia in awake humans. In this study, CMRO₂ is observed to decrease in response to mild hypercapnia. Finally, pilot studies that show feasibility of QUIXOTIC-based functional MRI (fMRI) and so-called "turbo" QUIXOTIC are presented and discussed. / by Divya Sanam Bolar. / Ph.D.

Thesis (Ph. D. in Electrical and Medical Engineering)--Harvard-MIT Program in Health Sciences and Technology, 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references. / Ambulation is a very important part of everyday life and its absence has a detrimental effect on an individual's quality of life. While much is understood about the neurobiological systems involved in locomotion through detailed anatomical connectivity and lesion studies, it is not well understood how neurons across different regions of the nervous system share information and coordinate their firing activity to achieve ambulation. Moreover, while it is clear that understanding the processes involved in healthy ambulation are essential to understanding how diseases affect an individual's ability to walk, diseases such as stroke tend to "take out" large portions of the underlying system. Until technologies are developed to allow restoration of damaged neural tissue back to its original state, physical therapy (which aims to restore function by establishing new motor-cortical connections among the remaining neurons) remains the most viable option for patients. The aim of this thesis is to elucidate some of the underlying neurobiological mechanisms of walking and to develop tools for rehabilitation robotics that allow finer quantification of patient improvement. To elucidate the neural mechanisms of locomotion, we studied how task relevant information (e.g. positions, velocities, and forces) modulate single unit neural activity from hindlimb/trunk region of the rat motor cortex during adaptations to robot-applied elastic loads and closed-loop brain-machine-interface (BMI) control during treadmill locomotion. Using the Point Process-Generalized Linear Model (PP-GLM) statistical framework we systematically tested parametric and non-parametric point process models of increased complexity for 573 individual neurons recorded over multiple days in six animals. The developed statistical model captures within gait-cycle modulation, load-specific modulation, and intrinsic neural dynamics. Our proposed model accurately describes the firing statistics of 98.5% (563/573) of all the recorded units and allows characterization of the neural receptive fields associated with gait phase and loading force. Understanding how these receptive fields change during training and with experience will be central to developing rehabilitation strategies that optimize motor adaptations and motor learning. The methods utilized for this analysis were developed into an open source neural Spike Train Analysis Toolbox (nSTAT) for Matlab (Mathworks, Natick MA). Systematic analyses have demonstrated the effectiveness of physical therapy, but have been unable to determine which approaches tend to be most effective in restoring function. This is likely due to the multitude of approaches, diseases, and assessment scales used. To address this issue, we develop an extension of the Force Field Adaptation Paradigm, originally developed to quantitatively assess upper extremity motor adaptation, to the lower extremity. The algorithm is implemented on the Lokomat (Hocoma HG) lower extremity gait orthosis and is currently being utilized to assess short-term motor adaptation in 40 healthy adult subjects (ClinicalTrials.gov NCT01361867). Establishing an understanding of how healthy adults' motor systems adapt to external perturbations will be important to understanding how the adaptive mechanisms involved in gait integrate information and how this process is altered by disease. / by Iahn Cajigas González. / Ph.D.in Electrical and Medical Engineering

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 33-35). / Introduction. Models for mortality prediction are traditionally developed from prospective multi-center observational studies involving a heterogeneous group of patients to optimize external validity. We hypothesize that local customized modeling using retrospective data from a homogeneous subset of patients will provide a more accurate prediction than this standard approach. We tested this hypothesis on patients admitted to the ICU with acute kidney injury (AKI), and evaluated variables from the first 72 hours of admission. Methods. The Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) is a database of patients admitted to the Beth Israel Deaconess Medical Center ICU. Using the MIMIC II database, we identified patients who developed acute kidney injury and who survived at least 72 hours in the ICU. 118 variables were extracted from each patient. Second and third level customization of the Simplified Organ Failure Score (SAPS) was performed using logistic regression analysis and the best fitted models were compared in terms of Area under the Receiver Operating Characteristic Curve (AUC) and Hosmer-Lemeshow Goodness-of-Fit test (HL). The patient cohort was divided into a training and test data with a 70:30 split. Ten-fold cross-validation was performed on the training set for every combination of variables that were evaluated. The best fitted model from the cross-validation was then evaluated using the test set, and the AUC and the HL p value on the test set were reported. Results. A total of 1400 patients were included in the study. Of these, 970 survived and 430 died in the hospital (30.7% mortality). We observed progressive improvement in the performance of SAPS on this subset of patients (AUC=0.6419, HL p=0) with second level (AUC=0.6639, HL p=0.2056), and third level (AUC=0.7419, HL p=0.6738) customization. The best fitted model incorporated variables from the first 3 days of ICU admission. The variables that were most predictive of hospital mortality in the multivariate analysis are the maximum blood urea nitrogen and the minimum systolic blood pressure from the third day. Conclusion. A logistic regression model built using local data for patients with AKI performed better than SAPS, the current standard mortality prediction scoring system. / by Leo Anthony G. Celi. / S.M.