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1. A modular microscopic smartphone attachment for imaging and quantification of multiple fluorescent probes using machine learning

   https://doi.org/10.1039/D0AN02451A  

   Sami, Muhammad A.; Tayyab, Muhammad; Parikh, Priya; Govindaraju, Harshitha; Hassan, Umer (April 2021, The Analyst)

   Portable smartphone-based fluorescent microscopes are becoming popular owing to their ability to provide major functionalities offered by regular benchtop microscopes at a fraction of the cost. However, smartphone-based microscopes are still limited to a single fluorophore, fixed magnification, the inability to work with a different smartphones, and limited usability to either glass slides or cover slips. To overcome these challenges, here we present a modular smartphone-based microscopic attachment. The modular design allows the user to easily swap between different sets of filters and lenses, thereby enabling utility of multiple fluorophores and magnification levels. Our microscopic smartphone attachment can also be used with different smartphones and was tested with Nokia Lumia 1020, Samsung Galaxy S9+, and an iPhone XS. Further, we showed imaging results of samples on glass slides, cover slips, and microfluidic devices. A 1951 USAF resolution test target was used to quantify the maximum resolution of the microscope which was found to be 3.9 μm. The performance of the smartphone-based microscope was compared with a benchtop microscope and we found an R 2 value of 0.99 using polystyrene beads and blood cells isolated from human blood samples collected from Robert Wood Johnson Medical Hospital. Additionally, to count the particles (cells and beads) imaged from the smartphone-based fluorescent microscope, we developed artificial neural networks (ANNs) using multiple training algorithms, and evaluated their performances compared to the control (ImageJ). Finally, we did ANOVA and Tukey's post-hoc analysis and found a p -value of 0.97 which shows that no statistical significant difference exists between the performance of the trained ANN and control (ImageJ). 

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2. Plastic-based acoustofluidic devices for high-throughput, biocompatible platelet separation

   https://doi.org/10.1039/c8lc00527c  

   Gu, Yuyang; Chen, Chuyi; Wang, Zeyu; Huang, Po-Hsun; Fu, Hai; Wang, Lin; Wu, Mengxi; Chen, Yuchao; Gao, Tieyu; Gong, Jianying; et al (January 2019, Lab on a Chip)

   Platelet separation is a crucial step for both blood donation and treatment of essential thrombocytosis. Here we present an acoustofluidic device that is capable of performing high-throughput, biocompatible platelet separation using sound waves. The device is entirely made of plastic material, which renders the device disposable and more suitable for clinical use. We used this device to process undiluted human whole blood, and we demonstrate a sample throughput of 20 mL min −1 , a platelet recovery rate of 87.3%, and a red/white blood cell removal rate of 88.9%. We preserved better platelet function and integrity for isolated platelets than those which are isolated using established methods. Our device features advantages such as rapid fabrication, high throughput, and biocompatibility, so it is a promising alternative to existing platelet separation approaches. 

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3. A computer vision enhanced smart phone platform for microfluidic urine glucometry

   https://doi.org/10.1039/d3an01356a  

   Meng, Zhuolun; Tayyab, Muhammad; Lin, Zhongtian; Raji, Hassan; Javanmard, Mehdi (March 2024, The Analyst)

   An innovative disposable microfluidic device was designed, created, and mounted in a 3D-printed chassis to capture images. The images were processed using a custom detector which automatically identifies target glucose strips and colorimetric values. 

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4. A microfluidic approach for hemoglobin detection in whole blood

   https://doi.org/10.1063/1.4997185  

   Taparia, Nikita; Platten, Kimsey_C; Anderson, Kristin_B; Sniadecki, Nathan_J (October 2017, AIP Advances)

   Diagnosis of anemia relies on the detection of hemoglobin levels in a blood sample. Conventional blood analyzers are not readily available in most low-resource regions where anemia is prevalent, so detection methods that are low-cost and point-of-care are needed. Here, we present a microfluidic approach to measure hemoglobin concentration in a sample of whole blood. Unlike conventional approaches, our microfluidic approach does not require hemolysis. We detect the level of hemoglobin in a blood sample optically by illuminating the blood in a microfluidic channel at a peak wavelength of 540 nm and measuring its absorbance using a CMOS sensor coupled with a lens to magnify the image onto the detector. We compare measurements in microchannels with channel heights of 50 and 115 μm and found the channel with the 50 μm height provided a better range of detection. Since we use whole blood and not lysed blood, we fit our data to an absorption model that includes optical scattering in order to obtain a calibration curve for our system. Based on this calibration curve and data collected, we can measure hemoglobin concentration within 1 g/dL for severe cases of anemia. In addition, we measured optical density for blood flowing at a shear rate of 500 s-1 and observed it did not affect the nonlinear model. With this method, we provide an approach that uses microfluidic detection of hemoglobin levels that can be integrated with other microfluidic approaches for blood analysis. 

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5. Using Monte Carlo simulated PPGs signals to train a deep learning model to predict hemoglobin levels

   https://doi.org/10.1117/12.2677499  

   Alvarez, Michael; Arzuaga, Emmanuel; Sierra, Heidy (September 2023, SPIE NANOSCIENCE + ENGINEERING | 20-25 AUGUST 2023 Emerging Topics in Artificial Intelligence (ETAI) 2023)

   Volpe, Giovanni; Pereira, Joana B; Brunner, Daniel; Ozcan, Aydogan (Ed.)

   Measuring Hemoglobin (Hb) levels is required for the assessment of different health conditions, such as anemia, a condition where there are insufficient healthy red blood cells to carry enough oxygen to the body's tissues. Measuring Hb levels requires the extraction of a blood sample, which is then sent to a laboratory for analysis. This is an invasive procedure that may add challenges to the continuous monitoring of Hb levels. Noninvasive techniques, including imaging and photoplethysmography (PPG) signals combined with machine learning techniques, are being investigated for continuous measurements of Hb. However, the availability of real data to train the algorithms is limited to establishing a generalization and implementation of such techniques in healthcare settings. In this work, we present a computational model based on Monte Carlo simulations that can generate multispectral PPG signals that cover a broad range of Hb levels. These signals are then used to train a Deep Learning (DL) model to estimate hemoglobin levels. Through this approach, valuable insights about the relationships between PPG signals, oxygen saturation, and Hb levels are learned by the DL model. The signals were generated by propagating a source in a volume that contains the skin tissue properties and the target physiological parameters. The source consisted of plane waves using the 660 nm and 890 nm wavelengths. A range of 6 g/dL to 18 dL Hb values was used to generate 468 PPGs to train a Convolutional Neural Network (CNN). The initial results show high accuracy in detecting low levels of Hb. To the best of our knowledge, the complexity of biological interactions involved in measuring hemoglobin levels has yet to be fully modeled. The presented model offers an alternative approach to studying the effects of changes in Hb levels on the PPGs signal morphology and its interaction with other physiological parameters that are present in the optical path of the measured signals. 

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