Glioblastoma is the most malignant and common high-grade brain tumor with a 14-month overall survival length. According to recent World Health Organization Central Nervous System tumor classification (2021), the diagnosis of glioblastoma requires extensive molecular genetic tests in addition to the traditional histopathological analysis of Formalin- Fixed Paraffin-Embedded (FFPE) tissues. Time-consuming and expensive molecular tests as well as the need for clinical neuropathology expertise are the challenges in the diagnosis of glioblastoma. Hence, an automated and rapid analytical detection technique for identifying brain tumors from healthy tissues is needed to aid pathologists in achieving an errorfree diagnosis of glioblastoma in clinics. Here, we report on our clinical test results of Raman spectroscopy and machine learning-based glioblastoma identification methodology for a cohort of 20 glioblastoma and 18 white matter tissue samples. We used Raman spectroscopy to distinguish FFPE glioblastoma and white matter tissues applying our previously reported protocols about optimized FFPE sample preparation and Raman measurement parameters. One may analyze the composition and identify the subtype of brain tumors using Raman spectroscopy since this technique yields detailed molecule-specific information from tissues. We measured and classified the Raman spectra of neoplastic and non-neoplastic tissue sections using machine learning classifiers including support vector machine and random forest with 86.6% and 83.3% accuracies, respectively. These proof-of-concept results demonstrate that this technique might be eventually used in the clinics to assist pathologists once validated with a larger and more diverse glioblastoma cohort and improved detection accuracies.
The real-time polymerase chain reaction (RT-PCR) analysis using nasal swab samples is the gold standard approach for COVID-19 diagnosis. However, due to the high false-negative rate at lower viral loads and complex test procedure, PCR is not suitable for fast mass screening. Therefore, the need for a highly sensitive and rapid detection system based on easily collected fluids such as saliva during the pandemic has emerged. In this study, we present a surface-enhanced Raman spectroscopy (SERS) metasurface optimized with genetic algorithm (GA) to detect SARS-CoV-2 directly using unprocessed saliva samples. During the GA optimization, the electromagnetic field profiles were used to calculate the field enhancement of each structure and the fitness values to determine the performance of the generated substrates. The obtained design was fabricated using electron beam lithography, and the simulation results were compared with the test results using methylene blue fluorescence dye. After the performance of the system was validated, the SERS substrate was tested with inactivated SARS-CoV-2 virus for virus detection, viral load analysis, cross-reactivity, and variant detection using machine learning models. After the inactivated virus tests are completed, with 36 PCR positive and 33 negative clinical samples, we were able to detect the SARS-CoV-2 positive samples from Raman spectra with 95.2% sensitivity and specificity.
Raman spectroscopy is a highly sensitive and specific technique for identifying tissue compositions. Raman-based characterization of normal and abnormal tissues is impeded due to the variability in routine tissue preparation techniques, fluorescent background, and molecular heterogeneity. Thus, sample preparation and Raman measurement conditions for tissue sections must be optimized. Here, we present an optimized Raman protocol and sample preparation method for brain tissue sections. This protocol allows the characterization of tissues and recognition of brain tumors by refining laser power, accumulation/exposure times, excitation wavelength, glass/CaF2 substrate, deparaffinization solvent, and the thickness of sections.
Surface-enhanced Raman spectroscopy (SERS) enables the surface plasmon-based amplification and detection of Raman signals from biomarkers, which emerge at ultralow concentrations in the early phases of diseases. Thus, SERS chips could be used for early detection of diseases from their biomarkers obtained from liquid or tissue biopsies. While this surface enhancement capability of nanoscale gold or silver layers on different substrates were demonstrated in previous experiments and electromagnetic models, the position of the biomarker molecules on the SERS chips cannot be known or estimated a priori. As a result, SERS chips must be designed over millimeter-scale areas such that the signal amplification must be large (106 times or higher with respect to no SERS) and must span the entire slide. Simultaneous surface-enhancement of Raman signals and distributing this enhancement factor (EF) over the sample surface requires an iterative and “learning” design procedure for the geometries of nanoscale metallic features that could maximize both EF and its area simultaneously. In this study, we develop genetic algorithms and use finite-difference time-domain (FDTD) modeling to optimize the geometry of gold nanostructures (NS) on glass microscope slides to functionalize these slides as SERS-active surfaces for SERS-based enhancement of Raman spectra. By using FDTD models, we calculated the enhancement factors in 3D on glass surface for 785 nm laser for Raman spectrum measurements and used genetic algorithms (GA) to iterate on the metal NS geometry to maximize the average and the hot spot EF over the periodic patterns on the slide. Field enhancement factors as high as 1017 and 1015 were calculated for hot-spots and for whole-slide averages, respectively. The optimized structures indicate that GA could help maximize label-free and whole-slide Raman signal enhancement factors for single-cell SERS detection.
Monolithic integration of nonreciprocal optical devices on semiconductor substrates has been a long-sought goal of the photonics community. One promising route to achieve this goal is to deposit high quality magneto-optical (MO) oxide thin films directly on a semiconductor substrate. In this article, we will review our ongoing progress in material development and device engineering towards enabling a monolithically integrated, high-performance magneto-optical nonreciprocal photonics platform. In particular, we will discuss our recent work which has led to a new pulsed laser deposition (PLD) technique of Ce or Bi substituted yttrium iron garnet (YIG) thin films with reduced thermal budget, simplified growth protocols and improved magneto-optical characteristics. These materials were incorporated in monolithic resonator and interferometer based isolator devices to demonstrate on-chip optical isolation with improved device figure of merit. Challenges and opportunities for monolithic magneto-optical devices will be discussed in the context of our latest material and device performance metrics.
State-of-the-art copper interconnects suffer from increasing spatial power dissipation due to chip downscaling and RC
delays reducing operation bandwidth. Wide bandwidth, minimized Ohmic loss, deep sub-wavelength confinement and
high integration density are key features that make metal-insulator-metal waveguides (MIM) utilizing plasmonic modes
attractive for applications in on-chip optical signal processing. Size-mismatch between two fundamental components
(micron-size fibers and a few hundred nanometers wide waveguides) demands compact coupling methods for
implementation of large scale on-chip optoelectronic device integration. Existing solutions use waveguide tapering,
which requires more than 4λ-long taper distances. We demonstrate that nanoantennas can be integrated with MIM for
enhancing coupling into MIM plasmonic modes. Two-dimensional finite-difference time domain simulations of antennawaveguide
structures for TE and TM incident plane waves ranging from λ = 1300 to 1600 nm were done. The same
MIM (100-nm-wide Ag/100-nm-wide SiO2/100-nm-wide Ag) was used for each case, while antenna dimensions were
systematically varied. For nanoantennas disconnected from the MIM; field is strongly confined inside MIM-antenna gap
region due to Fabry-Perot resonances. Major fraction of incident energy was not transferred into plasmonic modes. When
the nanoantennas are connected to the MIM, stronger coupling is observed and E-field intensity at outer end of core is
enhanced more than 70 times.
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