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1. Hyperspectral confocal microscopy in the short-wave infrared range

   https://doi.org/10.1364/OL.498290  

   Sung, Yongjin; Wang, Weizhong (July 2023, Optics Letters)

   We demonstrate hyperspectral confocal microscopy in the short-wave infrared (SWIR) range of 1100–1600 nm using a wavelength-scanning laser in tandem with laser scanning confocal microscopy. Confocal microscopy in the SWIR range allows for high-resolution inspection of an integrated circuit (IC) chip, while hyperspectral imaging, together with a chemometric analysis, enables us to identify functional circuit block groups in the acquired image. With the extended capability, the developed instrument can be potentially used for inline inspection and non-invasive failure analysis of IC chips. 

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2. Optical projection tomography of fluorescent microscopic specimens using lateral translation of tube lens

   https://doi.org/10.1364/OL.491499  

   Sung, Yongjin (May 2023, Optics Letters)

   Optical projection tomography (OPT) is a three-dimensional (3D) fluorescence imaging technique, in which projection images are acquired for varying orientations of a sample using a large depth of field. OPT is typically applied to a millimeter-sized specimen, because the rotation of a microscopic specimen is challenging and not compatible with live cell imaging. In this Letter, we demonstrate fluorescence optical tomography of a microscopic specimen by laterally translating the tube lens of a wide-field optical microscope, which allows for high-resolution OPT without rotating the sample. The cost is the reduction of the field of view to about halfway along the direction of the tube lens translation. Using bovine pulmonary artery endothelial cells and 0.1 µm beads, we compare the 3D imaging performance of the proposed method with that of the conventional objective-focus scan method. 

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3. Assessment of two brands of fentanyl test strips with 251 synthetic opioids reveals “blind spots” in detection capabilities

   https://doi.org/10.1186/s12954-023-00911-w  

   Hayes, Kathleen L.; Lieberman, Marya (December 2023, Harm Reduction Journal)

   Abstract BackgroundFentanyl test strips (FTS) are a commonly deployed tool in drug checking, used to test for the presence of fentanyl in street drug samples prior to consumption. Previous reports indicate that in addition to fentanyl, FTS can also detect fentanyl analogs like acetyl fentanyl and butyryl fentanyl, with conflicting reports on their ability to detect fentanyl analogs like Carfentanil and furanyl fentanyl. Yet with hundreds of known fentanyl analogs, there has been no large-scale study rationalizing FTS reactivity to different fentanyl analogs. MethodsIn this study, 251 synthetic opioids—including 214 fentanyl analogs—were screened on two brands of fentanyl test strips to (1) assess the differences in the ability of two brands of fentanyl test strips to detect fentanyl-related compounds and (2) determine which moieties in fentanyl analog chemical structures are most crucial for FTS detection. Two FTS brands were assessed in this study: BTNX Rapid Response and WHPM DanceSafe. ResultsOf 251 screened compounds assessed, 121 compounds were detectable at or below 20,000 ng/mL by both BTNX and DanceSafe FTS, 50 were not detectable by either brand, and 80 were detectable by one brand but not the other (n = 52 BTNX,n = 28 DanceSafe). A structural analysis of fentanyl analogs screened revealed that in general, bulky modifications to the phenethyl moiety inhibit detection by BTNX FTS while bulky modifications to the carbonyl moiety inhibit detection by DanceSafe FTS. ConclusionsThe different “blind spots” are caused by different haptens used to elicit the antibodies for these different strips. By utilizing both brands of FTS in routine drug checking, users could increase the chances of detecting fentanyl analogs in the “blind spot” of one brand. 

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4. A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures

   https://doi.org/10.3390/s23094370  

   Díaz-Martínez, Víctor; Orozco-Sandoval, Jairo; Manian, Vidya; Dhatt, Balpreet K; Walia, Harkamal (May 2023, Sensors)

   A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars. 

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5. On-chip hyperspectral detectors for fluorescence lifetime imaging

   https://doi.org/10.1117/12.3002476  

   Ahamed, Ahasan; Rawat, Amita; McPhillips, Lisa N; Marcu, Laura; Islam, M Saif (March 2024, Proc. SPIE 12853, High-Speed Biomedical Imaging and Spectroscopy IX)

   Goda, Keisuke; Tsia, Kevin K (Ed.)

   Fluorescence Lifetime Imaging (FLIM) is a powerful technique that measures the decay time of fluorophores present in tissue samples alluding to their constituent molecules. FLIM has gained popularity in biomedical imaging for applications such as detecting cancerous tumors, surgical guidance, etc. However, conventional FLIM systems are limited by a reduced number of spectral bands and long acquisition time. Moreover, the large footprint, complexity, and cost of the instrumentation make it difficult for clinical applications. In this paper, we demonstrate a reconstruction-based hyperspectral detector that can resolve decay time and intensities in broad spectral ranges while providing high sensitivity, high gain, and fast response time. The hyperspectral detector is comprised of an array of efficient, ultrafast avalanche photodetectors integrated with nanophotonic structures. We utilize different nanostructures in the detectors to modulate light-matter interactions in spectral channels. This allows us to computationally reconstruct the spectral profile of the incoming fluorescence spectrum without the need for additional filters or dispersive optics. Also, the nanophotonic structures enhance efficiency (by a factor of 2 to 10 over different wavelengths) while providing fast response time. An innovative detector design has been employed to reduce the breakdown of the avalanche photodetectors to -7.8V while maintaining high gain (~50) across the spectral range. Therefore, enabling low light detection with a high signal-to-noise ratio for FLIM applications. Added spectral channels would provide valuable information about tissue materials, morphology, and disease diagnosis. Such innovative hyperspectral sensors can now be integrated on-chip capable of miniaturizing the FLIM system and making it a commercially viable tool for clinical use. This technology has the potential to revolutionize the current FLIM system with improved detection capabilities opening doors for new horizons. 

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