Optical Properties of Porphyrin: Graphene Oxide Composites

In this work we aim to (via a non-invasive functionalization approach) tune and alter the intrinsic features of optically “transparent” graphene, by integrating water-soluble porphyrin aggregates. We explore the potential to combine porphyrin aggregates and graphene oxide to assess the advantages of such as a composite compared to the individual systems. We apply a range of optical spectroscopy methods including photo-absorption, fluorescence assess ground-state and excited state interactions. Our studies show that comparing resonant Raman scattering with optical transmission and fluorescence microscopy that the presence of influences the microscopic structures of the resulting composites

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient’s quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization

Feature Fusion of Raman Chemical Imaging and Digital Histopathology using Machine Learning for Prostate Cancer Detection

The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP - RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.Comment: 19 pages, 8 tables, 18 figure

High Cysteinyl Leukotriene Receptor 1 Expression Correlates with Poor Survival of Uveal Melanoma Patients and Cognate Antagonist Drugs Modulate the Growth, Cancer Secretome, and Metabolism of Uveal Melanoma Cells

Simple Summary This research investigates the disease relevance and therapeutic potential of cysteinyl leukotriene receptors in uveal melanoma (UM), a rare eye cancer that often spreads to the liver. Unfortunately, there are no therapies available to stop the spread of UM and patients are often faced with an extremely poor prognosis. We assess whether the cysteinyl leukotriene receptors (CysLT(1) and CysLT(2)) are relevant to the progression of UM. Using UM patient samples, we identified that increased levels of CysLT(1) in tumours is associated with reduced patient survival. Using UM cell lines and zebrafish models, we found that drugs targeting CysLT(1), but not CysLT(2), can alter hallmarks of cancer including cell growth, proliferation, and metabolism. This study is the first to examine the relationship of the CysLT receptors with clinical features of UM. Our data strengthen the importance of CysLT signalling in UM and suggest that antagonism of CysLT(1) may be of therapeutic interest in the disease. Metastatic uveal melanoma (UM) is a rare, but often lethal, form of ocular cancer arising from melanocytes within the uveal tract. UM has a high propensity to spread hematogenously to the liver, with up to 50% of patients developing liver metastases. Unfortunately, once liver metastasis occurs, patient prognosis is extremely poor with as few as 8% of patients surviving beyond two years. There are no standard-of-care therapies available for the treatment of metastatic UM, hence it is a clinical area of urgent unmet need. Here, the clinical relevance and therapeutic potential of cysteinyl leukotriene receptors (CysLT(1) and CysLT(2)) in UM was evaluated. High expression of CYSLTR1 or CYSLTR2 transcripts is significantly associated with poor disease-free survival and poor overall survival in UM patients. Digital pathology analysis identified that high expression of CysLT(1) in primary UM is associated with reduced disease-specific survival (p = 0.012; HR 2.76; 95% CI 1.21-6.3) and overall survival (p = 0.011; HR 1.46; 95% CI 0.67-3.17). High CysLT(1) expression shows a statistically significant (p = 0.041) correlation with ciliary body involvement, a poor prognostic indicator in UM. Small molecule drugs targeting CysLT(1) were vastly superior at exerting anti-cancer phenotypes in UM cell lines and zebrafish xenografts than drugs targeting CysLT(2). Quininib, a selective CysLT(1) antagonist(,) significantly inhibits survival (p < 0.0001), long-term proliferation (p < 0.0001), and oxidative phosphorylation (p < 0.001), but not glycolysis, in primary and metastatic UM cell lines. Quininib exerts opposing effects on the secretion of inflammatory markers in primary versus metastatic UM cell lines. Quininib significantly downregulated IL-2 and IL-6 in Mel285 cells (p < 0.05) but significantly upregulated IL-10, IL-1 beta, IL-2 (p < 0.0001), IL-13, IL-8 (p < 0.001), IL-12p70 and IL-6 (p < 0.05) in OMM2.5 cells. Finally, quininib significantly inhibits tumour growth in orthotopic zebrafish xenograft models of UM. These preclinical data suggest that antagonism of CysLT(1), but not CysLT(2), may be of therapeutic interest in the treatment of UM

Prognostic value of the 6-gene OncoMasTR test in hormone receptor–positive HER2-negative early-stage breast cancer: Comparative analysis with standard clinicopathological factors

Aim: The aim of the study was to assess the prognostic performance of a 6-gene molecular score (OncoMasTR Molecular Score [OMm]) and a composite risk score (OncoMasTR Risk Score [OM]) and to conduct a within-patient comparison against four routinely used molecular and clinicopathological risk assessment tools: Oncotype DX Recurrence Score, Ki67, Nottingham Prognostic Index and Clinical Risk Category, based on the modified Adjuvant! Online definition and three risk factors: patient age, tumour size and grade. Methods: Biospecimens and clinicopathological information for 404 Irish women also previously enrolled in the Trial Assigning Individualized Options for Treatment [Rx] were provided by 11 participating hospitals, as the primary objective of an independent translational study. Gene expression measured via RT-qPCR was used to calculate OMm and OM. The prognostic value for distant recurrence-free survival (DRFS) and invasive disease-free survival (IDFS) was assessed using Cox proportional hazards models and Kaplan-Meier analysis. All statistical tests were two-sided ones. Results: OMm and OM (both with likelihood ratio statistic [LRS] P Discussion: Both OncoMasTR scores were significantly prognostic for DRFS and IDFS and provided additional prognostic information to the molecular and clinicopathological risk factors/tools assessed. OM was also the most accurate risk classification tool for identifying DR. A concise 6-gene signature with superior risk stratification was shown to increase prognosis reliability, which may help clinicians optimise treatment decisions. Trial registration: ClinicalTrials.gov NCT02050750 NCT00310180.</p

Single-Molecule Nonresonant Wide-Field Surface-Enhanced Raman Scattering from Ferroelectrically Defined Au Nanoparticle Microarrays

Single-molecule detection by surface-enhanced Raman scattering (SERS) is a powerful spectroscopic technique that is of interest for the sensor development field. An important aspect of optimizing the materials used in SERS-based sensors is the ability to have a high density of "hot spots" that enhance the SERS sensitivity to the single-molecule level. Photodeposition of gold (Au) nanoparticles through electric-field-directed self-assembly on a periodically proton-exchanged lithium niobate (PPELN) substrate provides conditions to form well-ordered microscale features consisting of closely packed Au nanoparticles. The resulting Au nanoparticle microstructure arrays (microarrays) are plasmon-active and support nonresonant single-molecule SERS at ultralow concentrations (<10-9-10-13 M) with excitation power densities <1 × 10-3 W cm-2 using wide-field imaging. The microarrays offer excellent SERS reproducibility, with an intensity variation of <7.5% across the substrate. As most biomarkers and molecules do not support resonance enhancement, this work demonstrates that PPELN is a suitable template for high-sensitivity, nonresonant sensing applications.Science Foundation IrelandUCD School of PhysicsSwedish Scientific Research CouncilADOPT Linnaeus Centre for Advanced Optics and Photonics in Stockhol

Photoluminescence Blinking from Single CdSeS/ZnS Quantum Dots in a Conducting Polymer Matrix

Quantum dot nanocrystals (NQDs) present within organic conducting (polymer) host environments form hybrid organic–inorganic materials that are applied in a range of technologies such as light emitting diodes or solar cells. Understanding hole-transport and exciton dynamics in these hybrid materials is central to device performance and efficiency. Integral to hole-transport is the understanding of multiexciton processes such as charged excitons as well as neighbor–neighbor NQD interactions (on the nano and micrometer length scales). Studied here are the photoluminescence dynamics of single alloyed NQDs in conducting (or insulating) polymer environments. We find that conducting polymers (through hole transport) affect the presence and dynamics of charged excitons relative to insulating environments. The presence of such charged excitons induces a change in blinking dynamics with a corresponding increase in photoluminescence correlation between neighboring NQDs found using spatiotemporal statistical analysis. Understanding such phenomena advances the understanding of photoluminescence processes central to device design

Graphene oxide intercalation into self-assembled porphyrin J-aggregates

Studies are undertaken to examine graphene oxide intercalation into self-assembled J-aggregate porphyrin structures. Fluorescence lifetime and fluorescence anisotropy imaging were applied along with scanning electron microscopy to study the structure and optical properties of a graphene oxide/TMPyP hybrid composite material. It was seen that the presence of graphene oxide alters the macroscale and nanoscale self-assembled structures of TMPyP in addition graphene oxide also alters the optical activity reducing the emission intensity and exciton recombination lifetime. Evidence exists to support a model where planer-symmetric graphene oxide and TMPyP co-operate in the formation of self-assembled macro and nanostructures forming a composite with strong graphene oxide/TMPyP interaction

Photoinduced Enhanced Raman from Lithium Niobate on Insulator Template

© 2018 American Chemical Society. Photoinduced enhanced Raman spectroscopy from a lithium niobate on insulator (LNOI)-silver nanoparticle template is demonstrated both by irradiating the template with 254 nm ultraviolet (UV) light before adding an analyte and before placing the substrate in the Raman system (substrate irradiation) and by irradiating the sample in the Raman system after adding the molecule (sample irradiation). The photoinduced enhancement enables up to an ∼sevenfold increase of the surface-enhanced Raman scattering signal strength of an analyte following substrate irradiation, whereas an ∼threefold enhancement above the surface-enhanced signal is obtained for sample irradiation. The photoinduced enhancement relaxes over the course of ∼10 h for a substrate irradiation duration of 150 min before returning to initial signal levels. The increase in Raman scattering intensity following UV irradiation is attributed to photoinduced charge transfer from the LNOI template to the analyte. New Raman bands are observed following UV irradiation, the appearance of which is suggestive of a photocatalytic reaction and highlight the potential of LNOI as a photoactive surface-enhanced Raman spectroscopy substrate.Science Foundation Irelan

Graphene oxide intercalation into self-assembled porphyrin J-aggregates

Studies are undertaken to examine graphene oxide intercalation into self-assembled J-aggregate porphyrin structures. Fluorescence lifetime and fluorescence anisotropy imaging were applied along with scanning electron microscopy to study the structure and optical properties of a graphene oxide/TMPyP hybrid composite material. It was seen that the presence of graphene oxide alters the macroscale and nanoscale self-assembled structures of TMPyP in addition graphene oxide also alters the optical activity reducing the emission intensity and exciton recombination lifetime. Evidence exists to support a model where planer-symmetric graphene oxide and TMPyP co-operate in the formation of self-assembled macro and nanostructures forming a composite with strong graphene oxide/TMPyP interaction