Multimodal Magnetic Resonance and Near-Infrared-Fluorescent Imaging of Intraperitoneal Ovarian Cancer Using a Dual-Mode-Dual-Gadolinium Liposomal Contrast Agent.

The degree of tumor removal at surgery is a major factor in predicting outcome for ovarian cancer. A single multimodality agent that can be used with magnetic resonance (MR) for staging and pre-surgical planning, and with optical imaging to aid surgical removal of tumors, would present a new paradigm for ovarian cancer. We assessed whether a dual-mode, dual-Gadolinium (DM-Dual-Gd-ICG) contrast agent can be used to visualize ovarian tumors in the peritoneal cavity by multimodal MR and near infra-red imaging (NIR). Intraperitoneal ovarian tumors (Hey-A8 or OVCAR3) in mice enhanced on MR two days after intravenous DM-Dual Gd-ICG injection compared to controls (SNR, CNR, p < 0.05, n = 6). As seen on open abdomen and excised tumors views and confirmed by optical radiant efficiency measurement, Hey-A8 or OVCAR3 tumors from animals injected with DM-Dual Gd-ICG had increased fluorescence (p < 0.05, n = 6). This suggests clinical potential to localize ovarian tumors by MR for staging and surgical planning, and, by NIR at surgery for resection

Imaging of Pulmonary Embolism and t-PA Therapy Effects Using MDCT and Liposomal Iohexol Blood Pool Agent

HYPOTHESIS AND OBJECTIVES: PEGylated liposomal blood pool contrast agents maintain contrast enhancement over several hours. This study aimed to evaluate (long-term) imaging of pulmonary arteries, comparing conventional iodinated contrast with a liposomal blood pool contrast agent. Secondly, visualization of the (real-time) therapeutic effects of tissue-Plasminogen Activator (t-PA) on pulmonary embolism (PE) was attempted. MATERIALS AND METHODS: Six rabbits (approximate 4 kg weight) had autologous blood clots injected through the superior vena cava. Imaging was performed using conventional contrast (iohexol, 350 mg I/ml, GE HealthCare, Princeton, NJ) at a dose of 1400 mgI per animal and after wash-out, animals were imaged using an iodinated liposomal blood pool agent (88 mg I/mL, dose 900 mgI/animal). Subsequently, five animals were injected with 2mg t-PA and imaging continued for up to 4 ½ hours. RESULTS: Both contrast agents identified PE in the pulmonary trunk and main pulmonary arteries in all rabbits. Liposomal blood pool agent yielded uniform enhancement, which remained relatively constant throughout the experiments. Conventional agents exhibited non uniform opacification and rapid clearance post injection. Three out of six rabbits had mistimed bolus injections, requiring repeat injections. Following t-PA, Pulmonary embolus volume (central to segmental) decreased in four of five treated rabbits (range 10–57%, mean 42%). One animal showed no response to t-PA. CONCLUSIONS: Liposomal blood pool agents effectively identified acute PE without need for re-injection. PE resolution following t-PA was quantifiable over several hours. Blood pool agents offer the potential for repeated imaging procedures without need for repeated (nephrotoxic) contrast injections

Non Inflammatory Boronate Based Glucose-Responsive Insulin Delivery Systems

Boronic acids, known to bind diols, were screened to identify non-inflammatory cross-linkers for the preparation of glucose sensitive and insulin releasing agglomerates of liposomes (Agglomerated Vesicle Technology-AVT). This was done in order to select a suitable replacement for the previously used cross-linker, ConcanavalinA (ConA), a lectin known to have both toxic and inflammatory effects in vivo. Lead-compounds were selected from screens that involved testing for inflammatory potential, cytotoxicity and glucose-binding. These were then conjugated to insulin-encapsulating nanoparticles and agglomerated via sugar-boronate ester linkages to form AVTs. In vitro, the particles demonstrated triggered release of insulin upon exposure to physiologically relevant concentrations of glucose (10 mmoles/L–40 mmoles/L). The agglomerates were also shown to be responsive to multiple spikes in glucose levels over several hours, releasing insulin at a rate defined by the concentration of the glucose trigger

Computed Tomography Imaging of Primary Lung Cancer in Mice Using a Liposomal-Iodinated Contrast Agent

To investigate the utility of a liposomal-iodinated nanoparticle contrast agent and computed tomography (CT) imaging for characterization of primary nodules in genetically engineered mouse models of non-small cell lung cancer.Primary lung cancers with mutations in K-ras alone (Kras(LA1)) or in combination with p53 (LSL-Kras(G12D);p53(FL/FL)) were generated. A liposomal-iodine contrast agent containing 120 mg Iodine/mL was administered systemically at a dose of 16 µl/gm body weight. Longitudinal micro-CT imaging with cardio-respiratory gating was performed pre-contrast and at 0 hr, day 3, and day 7 post-contrast administration. CT-derived nodule sizes were used to assess tumor growth. Signal attenuation was measured in individual nodules to study dynamic enhancement of lung nodules.A good correlation was seen between volume and diameter-based assessment of nodules (R(2)>0.8) for both lung cancer models. The LSL-Kras(G12D);p53(FL/FL) model showed rapid growth as demonstrated by systemically higher volume changes compared to the lung nodules in Kras(LA1) mice (p<0.05). Early phase imaging using the nanoparticle contrast agent enabled visualization of nodule blood supply. Delayed-phase imaging demonstrated significant differential signal enhancement in the lung nodules of LSL-Kras(G12D);p53(FL/FL) mice compared to nodules in Kras(LA1) mice (p<0.05) indicating higher uptake and accumulation of the nanoparticle contrast agent in rapidly growing nodules.The nanoparticle iodinated contrast agent enabled visualization of blood supply to the nodules during the early-phase imaging. Delayed-phase imaging enabled characterization of slow growing and rapidly growing nodules based on signal enhancement. The use of this agent could facilitate early detection and diagnosis of pulmonary lesions as well as have implications on treatment response and monitoring

New Dual Mode Gadolinium Nanoparticle Contrast Agent for Magnetic Resonance Imaging

BACKGROUND: Liposomal-based gadolinium (Gd) nanoparticles have elicited significant interest for use as blood pool and molecular magnetic resonance imaging (MRI) contrast agents. Previous generations of liposomal MR agents contained gadolinium-chelates either within the interior of liposomes (core-encapsulated gadolinium liposomes) or presented on the surface of liposomes (surface-conjugated gadolinium liposomes). We hypothesized that a liposomal agent that contained both core-encapsulated gadolinium and surface-conjugated gadolinium, defined herein as dual-mode gadolinium (Dual-Gd) liposomes, would result in a significant improvement in nanoparticle-based T1 relaxivity over the previous generations of liposomal agents. In this study, we have developed and tested, both in vitro and in vivo, such a dual-mode liposomal-based gadolinium contrast agent. METHODOLOGY/PRINCIPAL FINDINGS: THREE TYPES OF LIPOSOMAL AGENTS WERE FABRICATED: core-encapsulated, surface-conjugated and dual-mode gadolinium liposomes. In vitro physico-chemical characterizations of the agents were performed to determine particle size and elemental composition. Gadolinium-based and nanoparticle-based T1 relaxivities of various agents were determined in bovine plasma. Subsequently, the agents were tested in vivo for contrast-enhanced magnetic resonance angiography (CE-MRA) studies. Characterization of the agents demonstrated the highest gadolinium atoms per nanoparticle for Dual-Gd liposomes. In vitro, surface-conjugated gadolinium liposomes demonstrated the highest T1 relaxivity on a gadolinium-basis. However, Dual-Gd liposomes demonstrated the highest T1 relaxivity on a nanoparticle-basis. In vivo, Dual-Gd liposomes resulted in the highest signal-to-noise ratio (SNR) and contrast-to-noise ratio in CE-MRA studies. CONCLUSIONS/SIGNIFICANCE: The dual-mode gadolinium liposomal contrast agent demonstrated higher particle-based T1 relaxivity, both in vitro and in vivo, compared to either the core-encapsulated or the surface-conjugated liposomal agent. The dual-mode gadolinium liposomes could enable reduced particle dose for use in CE-MRA and increased contrast sensitivity for use in molecular imaging

A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records.

Child physical abuse is a leading cause of traumatic injury and death in children. In 2017, child abuse was responsible for 1688 fatalities in the United States, of 3.5 million children referred to Child Protection Services and 674,000 substantiated victims. While large referral hospitals maintain teams trained in Child Abuse Pediatrics, smaller community hospitals often do not have such dedicated resources to evaluate patients for potential abuse. Moreover, identification of abuse has a low margin of error, as false positive identifications lead to unwarranted separations, while false negatives allow dangerous situations to continue. This context makes the consistent detection of and response to abuse difficult, particularly given subtle signs in young, non-verbal patients. Here, we describe the development of artificial intelligence algorithms that use unstructured free-text in the electronic medical record-including notes from physicians, nurses, and social workers-to identify children who are suspected victims of physical abuse. Importantly, only the notes from time of first encounter (e.g.: birth, routine visit, sickness) to the last record before child protection team involvement were used. This allowed us to develop an algorithm using only information available prior to referral to the specialized child protection team. The study was performed in a multi-center referral pediatric hospital on patients screened for abuse within five different locations between 2015 and 2019. Of 1123 patients, 867 records were available after data cleaning and processing, and 55% were abuse-positive as determined by a multi-disciplinary team of clinical professionals. These electronic medical records were encoded with three natural language processing (NLP) algorithms-Bag of Words (BOW), Word Embeddings (WE), and Rules-Based (RB)-and used to train multiple neural network architectures. The BOW and WE encodings utilize the full free-text, while RB selects crucial phrases as identified by physicians. The best architecture was selected by average classification accuracy for the best performing model from each train-test split of a cross-validation experiment. Natural language processing coupled with neural networks detected cases of likely child abuse using only information available to clinicians prior to child protection team referral with average accuracy of 0.90±0.02 and average area under the receiver operator characteristic curve (ROC-AUC) 0.93±0.02 for the best performing Bag of Words models. The best performing rules-based models achieved average accuracy of 0.77±0.04 and average ROC-AUC 0.81±0.05, while a Word Embeddings strategy was severely limited by lack of representative embeddings. Importantly, the best performing model had a false positive rate of 8%, as compared to rates of 20% or higher in previously reported studies. This artificial intelligence approach can help screen patients for whom an abuse concern exists and streamline the identification of patients who may benefit from referral to a child protection team. Furthermore, this approach could be applied to develop computer-aided-diagnosis platforms for the challenging and often intractable problem of reliably identifying pediatric patients suffering from physical abuse

METALORGANIC CHEMICAL VAPOR DEPOSITION : EXAMPLES OF THE INFLUENCE OF PRECURSOR STRUCTURE ON FILM PROPERTIES

The influence of precursor structure and reactivity on properties of compound semiconductors grown by metalorganic chemical vapor deposition (MOCVD) is discussed and illustrated with examples for growth of GaAs, ZnSe, and AlxGa1-xN. Gas-phase and surface reactions of organometallic arsenic compounds provide understanding of variation in carbon incorporation levels with precursor structure. Surface spectroscopy studies reveal the critical role of hydrogen-arsenic bonds in reducing carbon levels. MOCVD of ZnSe with different organoselenium compounds demonstrate that the growth rate behaves as expected from the Se-ligand bond strengths, but also that unexpected minor pathways can make a precursor unsuitable by causing increased carbon incorporation. Interactions between organometallic precursors mean that the structure of the precursors cannot be manipulated independently for the individual precursors, but must be considered in terms of the overall growth chemistry. The use of a single-source precursor, versus separate precursors for MOCVD of compound semiconductors is discussed and illustrated with data for the growth of AIN and GaN