Fluorescence Guided Tumor Imaging: Foundations for Translational Applications

Optical imaging for medical applications is a growing field, and it has the potential to improve medical outcomes through its increased sensitivity and specificity, lower cost, and small instrumentation footprint as compared to other imaging modalities. The method holds great promise, ranging from direct clinical use as a diagnostic or therapeutic tool, to pre-clinical applications for increased understanding of pathology. Additionally, optical imaging uses non-ionizing radiation which is safe for patients, so it can be used for repeated imaging procedures to monitor therapy, guide treatment, and provide real-time feedback. The versatile features of fluorescence-based optical imaging make it suited for cancer related imaging applications to increase patient survival and improve clinical outcomes. This dissertation focuses on the development of image processing methods to obtain semi-quantitative fluorescence imaging data. These methods allow for the standardization of fluorescence imaging data for tumor characterization. When a fluorophore is located within tissue, changes in the fluorescence intensity can be used to isolate structures of interest. Typically, this is done through the accumulation of a dye in a target tissue either by the enhanced permeation and retention effect (EPR), or through targeted peptide sequences that bind receptors present in specific tissue types. When imaged, the contrast generated by a fluorescent probe can be used to indicate the presence or absence of a structure, bio-chemical compound, or receptor. Fluorescence intensity contrast can answer many biological and clinical questions effectively; however, we were interested in analyzing more than solely contrast when using planar fluorescence imaging. To better understand tumor properties, we developed a series of algorithms that harness additional pieces of information present in the fluorescence signal. We demonstrated that adding novel image processing algorithms enhanced the knowledge obtained from planar fluorescence images. Through this work, we gained an understanding of alternative approaches for processing planar fluorescence imaging data with the goal of improving future cancer diagnostics and therapeutics

Noninvasive depth estimation using tissue optical properties and a dual-wavelength fluorescent molecular probe in vivo

Translation of fluorescence imaging using molecularly targeted imaging agents for real-time assessment of surgical margins in the operating room requires a fast and reliable method to predict tumor depth from planar optical imaging. Here, we developed a dual-wavelength fluorescent molecular probe with distinct visible and near-infrared excitation and emission spectra for depth estimation in mice and a method to predict the optical properties of the imaging medium such that the technique is applicable to a range of medium types. Imaging was conducted at two wavelengths in a simulated blood vessel and an in vivo tumor model. Although the depth estimation method was insensitive to changes in the molecular probe concentration, it was responsive to the optical parameters of the medium. Results of the intra-tumor fluorescent probe injection showed that the average measured tumor sub-surface depths were 1.31 ± 0.442 mm, 1.07 ± 0.187 mm, and 1.42 ± 0.182 mm, and the average estimated sub-surface depths were 0.97 ± 0.308 mm, 1.11 ± 0.428 mm, 1.21 ± 0.492 mm, respectively. Intravenous injection of the molecular probe allowed for selective tumor accumulation, with measured tumor sub-surface depths of 1.28 ± 0.168 mm, and 1.50 ± 0.394 mm, and the estimated depths were 1.46 ± 0.314 mm, and 1.60 ± 0.409 mm, respectively. Expansion of our technique by using material optical properties and mouse skin optical parameters to estimate the sub-surface depth of a tumor demonstrated an agreement between measured and estimated depth within 0.38 mm and 0.63 mm for intra-tumor and intravenous dye injections, respectively. Our results demonstrate the feasibility of dual-wavelength imaging for determining the depth of blood vessels and characterizing the sub-surface depth of tumors in vivo

Gradient-based algorithm for determining tumor volumes in small animals using planar fluorescence imaging platform

Planar fluorescence imaging is widely used in biological research because of its simplicity, use of nonionizing radiation, and high-throughput data acquisition. In cancer research, where small animal models are used to study the in vivo effects of cancer therapeutics, the output of interest is often the tumor volume. Unfortunately, inaccuracies in determining tumor volume from surface-weighted projection fluorescence images undermine the data, and alternative physical or conventional tomographic approaches are prone to error or are tedious for most laboratories. Here, we report a method that uses a priori knowledge of a tumor xenograft model, a tumor-targeting near infrared probe, and a custom-developed image analysis planar view tumor volume algorithm (PV-TVA) to estimate tumor volume from planar fluorescence images. Our algorithm processes images obtained using near infrared light for improving imaging depth in tissue in comparison with light in the visible spectrum. We benchmarked our results against the actual tumor volume obtained from a standard water volume displacement method. Compared with a caliper-based method that has an average deviation from an actual volume of 18% (204.34 ± 115.35 mm(3)), our PV-TVA average deviation from the actual volume was 9% (97.24 ± 70.45 mm(3); P < .001). Using a normalization-based analysis, we found that bioluminescence imaging and PV-TVA average deviations from actual volume were 36% and 10%, respectively. The improved accuracy of tumor volume assessment from planar fluorescence images, rapid data analysis, and the ease of archiving images for subsequent retrieval and analysis potentially lend our PV-TVA method to diverse cancer imaging applications

Integrating scientific and local knowledge to inform risk-based management approaches for climate adaptation

AbstractRisk-based management approaches to climate adaptation depend on the assessment of potential threats, and their causes, vulnerabilities, and impacts. The refinement of these approaches relies heavily on detailed local knowledge of places and priorities, such as infrastructure, governance structures, and socio-economic conditions, as well as scientific understanding of climate projections and trends. Developing processes that integrate local and scientific knowledge will enhance the value of risk-based management approaches, facilitate group learning and planning processes, and support the capacity of communities to prepare for change. This study uses the Vulnerability, Consequences, and Adaptation Planning Scenarios (VCAPS) process, a form of analytic-deliberative dialogue, and the conceptual frameworks of hazard management and climate vulnerability, to integrate scientific and local knowledge. We worked with local government staff in an urbanized barrier island community (Sullivan’s Island, South Carolina) to consider climate risks, impacts, and adaptation challenges associated with sea level rise and wastewater and stormwater management. The findings discuss how the process increases understanding of town officials’ views of risks and climate change impacts to barrier islands, the management actions being considered to address of the multiple impacts of concern, and the local tradeoffs and challenges in adaptation planning. We also comment on group learning and specific adaptation tasks, strategies, and needs identified

RLS and blood donation.

BACKGROUND AND PURPOSE: The link between brain iron deficiency and RLS is now well established. In a related observation, several conditions that can deplete iron stores have been linked to increased probability of RLS. Blood donation has been linked to iron deficiency. It has thus been hypothesized that donating blood may be a risk factor for developing RLS. PATIENTS AND METHODS: Two thousand and five UK blood donors, ranging from first-time donors to some who had donated more than 70 times, completed the validated Cambridge-Hopkins RLS questionnaire (CH-RLSq) following their donation session. The questionnaire included a set of questions designed to diagnose RLS. The donors' histories of blood donations were determined both from self-report and from the National Blood Service database. RESULTS: A number of statistical models were constructed to determine whether the probability of RLS diagnosis was related to the history of blood donations. Controlling for age and sex, no evidence was found to suggest that a greater number or frequency of blood donations increased the risk of RLS. Even amongst sub-groups especially vulnerable to iron depletion through blood donation, such as vegetarians or low weight individuals, no evidence for an increased risk of RLS could be found. CONCLUSIONS: We found no evidence that the frequency or number of blood donations up to the UK maximum of three times a year would increase the risk of RLS

Pseudo-monoamniotic Pregnancy: Case Report and Review of Etiologic Considerations

Pseudomonoamniotic gestations are increasingly recognized through sonographic surveillance of monochorionic twins, though etiologic factors remain undefined. We present a case of spontaneous pseudomonoamniotic twins and propose umbilical cord insertion proximity as a sonographic marker. Systematic review of the literature was performed and additional cases with similar findings were noted. Approximately 75% of reported cases (28/37) were deemed spontaneous and several included short inter-cord distances. Shunting of blood away from the membranes in the region between the cord insertions may be responsible for membrane rupture. Further investigation is needed into short inter-cord distance as a marker for monochorionic twins at risk to become a pseudomonoamniotic gestation

Adoption of Mobile Apps for Depression and Anxiety: Cross-Sectional Survey Study on Patient Interest and Barriers to Engagement

BACKGROUND: Emerging research suggests that mobile apps can be used to effectively treat common mental illnesses like depression and anxiety. Despite promising efficacy results and ease of access to these interventions, adoption of mobile health (mHealth; mobile device-delivered) interventions for mental illness has been limited. More insight into patients\u27 perspectives on mHealth interventions is required to create effective implementation strategies and to adapt existing interventions to facilitate higher rates of adoption. OBJECTIVE: The aim of this study was to examine, from the patient perspective, current use and factors that may impact the use of mHealth interventions for mental illness. METHODS: This was a cross-sectional survey study of veterans who had attended an appointment at a single Veterans Health Administration facility in early 2016 that was associated with one of the following mental health concerns: unipolar depression, any anxiety disorder, or posttraumatic stress disorder. We used the Veteran Affairs Corporate Data Warehouse to create subsets of eligible participants demographically stratified by gender (male or female) and minority status (white or nonwhite). From each subset, 100 participants were selected at random and mailed a paper survey with items addressing the demographics, overall health, mental health, technology ownership or use, interest in mobile app interventions for mental illness, reasons for use or nonuse, and interest in specific features of mobile apps for mental illness. RESULTS: Of the 400 potential participants, 149 (37.3%, 149/400) completed and returned a survey. Most participants (79.9%, 119/149) reported that they owned a smart device and that they use apps in general (71.1%, 106/149). Most participants (73.1%, 87/149) reported interest in using an app for mental illness, but only 10.7% (16/149) had done so. Paired samples t tests indicated that ratings of interest in using an app recommended by a clinician were significantly greater than general interest ratings and even greater when the recommending clinician was a specialty mental health provider. The most frequent concerns related to using an app for mental illness were lacking proof of efficacy (71.8%, 107/149), concerns about data privacy (59.1%, 88/149), and not knowing where to find such an app (51.0%, 76/149). Participants expressed interest in a number of app features with particularly high-interest ratings for context-sensitive apps (85.2%, 127/149), and apps focused on the following areas: increasing exercise (75.8%, 113/149), improving sleep (73.2%, 109/149), changing negative thinking (70.5%, 105/149), and increasing involvement in activities (67.1%, 100/149). CONCLUSIONS: Most respondents had access to devices to use mobile apps for mental illness, already used apps for other purposes, and were interested in mobile apps for mental illness. Key factors that may improve adoption include provider endorsement, greater publicity of efficacious apps, and clear messaging about efficacy and privacy of information. Finally, multifaceted apps that address a range of concerns, from sleep to negative thought patterns, may be best received

Impact of early childhood infection on child development and school performance:a population-based study

BACKGROUND: Childhood infection might be associated with adverse child development and neurocognitive outcomes, but the results have been inconsistent.METHODS: Two population-based record-linkage cohorts of all singleton children born at term in New South Wales, Australia, from 2001 to 2014, were set up and followed up to 2019 for developmental outcome (N=276 454) and school performance (N=644 291). The primary outcome was developmentally high risk (DHR) at age 4-6 years and numeracy and reading below the national minimum standard at age 7-9 years. Cox regression was used to assess the association of childhood infection ascertained from hospital records with each outcome adjusting for maternal, birth and child characteristics, and sensitivity analyses were conducted assessing E-values and sibling analysis for discordant exposure.RESULTS: A higher proportion of children with an infection-related hospitalisation were DHR (10.9% vs 8.7%) and had numeracy (3.7% vs 2.7%) and reading results (4.3% vs 3.1%) below the national minimum standard, compared with those without infection-related hospitalisation. In the multivariable analysis, children with infection-related hospitalisation were more likely to be DHR (adjusted HR 1.12, 95% CI 1.08 to 1.15) and have numeracy (adjusted HR 1.22, 95% CI 1.18 to 1.26) and reading results (adjusted HR 1.16, 95% CI 1.12 to 1.20) below the national minimum standard. However, these results may be impacted by unmeasured confounding, based on E-values of 1.48-1.74, and minimal association with education outcome was found in the sibling analysis.CONCLUSIONS: Infection-related hospitalisation was modestly associated with adverse child development and school performance, but the association may be explained by shared familial factors, particularly in those with most socioeconomic disadvantages.</p

Accurate simulation of direct laser acceleration in a laser wakefield accelerator

In a laser wakefield accelerator (LWFA), an intense laser pulse excites a plasma wave that traps and accelerates electrons to relativistic energies. When the pulse overlaps the accelerated electrons, it can enhance the energy gain through direct laser acceleration (DLA) by resonantly driving the betatron oscillations of the electrons in the plasma wave. The particle-in-cell (PIC) algorithm, although often the tool of choice to study DLA, contains inherent errors due to numerical dispersion and the time staggering of the electric and magnetic fields. Further, conventional PIC implementations cannot reliably disentangle the fields of the plasma wave and laser pulse, which obscures interpretation of the dominant acceleration mechanism. Here, a customized field solver that reduces errors from both numerical dispersion and time staggering is used in conjunction with a field decomposition into azimuthal modes to perform PIC simulations of DLA in an LWFA. Comparisons with traditional PIC methods, model equations, and experimental data show improved accuracy with the customized solver and convergence with an order-of-magnitude fewer cells. The azimuthal-mode decomposition reveals that the most energetic electrons receive comparable energy from DLA and LWFA.Comment: 10 pages, 5 figures, to submit to Physics of Plasma