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Picomolar Dopamine Detection using Colloidal Surface-Enhanced Raman Scattering Based on Benzene-Dithiol–Linked Gold Nanoparticles in Solution
(2026-04-01)
Sucharitakul, Waraporn
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Danvirutai, Pobporn
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Pinlaor, Somchai
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Piyawattanametha, Wibool
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Srichan, Chavis
Sensitive detection of dopamine at ultralow concentrations is essential for the development of advanced chemical and neurochemical sensing technologies. Surface-enhanced Raman scattering (SERS) offers molecular specificity and high sensitivity, but its analytical reliability is often limited by poor signal reproducibility arising from uncontrolled nanoparticle aggregation. In this work, we report a solvent-based colloidal SERS platform for picomolar dopamine detection using benzene-1,4-dithiol (BDT)–linked gold nanoparticles (AuNPs). BDT serves as a molecular linker that defines sub-nanometer plasmonic nanogaps and simultaneously acts as an intrinsic Raman reference. Dopamine molecules introduced in dilute solution are efficiently sampled within these nanogaps through noncovalent interactions, resulting in strong and reproducible SERS enhancement. The proposed platform demonstrates a detection limit in the picomolar range, a wide linear dynamic response, and excellent signal stability without the use of biological or artificial matrices. This study presents a solvent-based SERS strategy for quantitative liquid-phase dopamine detection in aqueous environments, offering a foundation for further development in sensor calibration and integration with biological matrices.
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Development of a Telehealth-Enabled Portable Optical Endomicroscopy System with Targeted Peptides: A Preclinical Feasibility Study for Cervical Cancer Detection
(2026-04-01)
Thaijiam, Chanchai
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Navaitthiporn, Nitipon
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Rithcharung, Preeyarat
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Piyawattanametha, Nicholas
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Komai, Shoji
Background/Objectives: We developed a telehealth-enabled fiber-bundle endomicroscopy platform and evaluated its preclinical feasibility for targeted fluorescence imaging in cervical cancer models. Methods: The platform integrates a portable fiber-bundle endomicroscopy (FBE) system, fluorescein isothiocyanate (FITC)-labeled candidate peptides, and a secure web-based telehealth platform for remote consultation. The FBE probe achieved a field of view of 1,700 µm and a lateral resolution of 4 µm, enabling cellular-level fluorescence imaging in a compact, portable format. Four FITC-labeled peptides (SHS1*, SHS2*, FPP*, and CRL*) were evaluated in A549, SiHa, and CaSki cell lines. Ex vivo testing was performed on commercial cervical tissue-array samples. The telehealth platform was assessed for secure medical-image/video transmission and end-to-end latency in a simulated remote-consultation setting. Results: Among the tested probes, FPP*-FITC and CRL*-FITC showed higher fluorescence-positive fractions in the p16-overexpressing cervical cancer cell lines than in the A549 comparator line, with the strongest signals observed in CaSki cells. In ex vivo testing, CRL*-FITC generated higher fluorescence intensity in malignant cervical tissue-array samples than in non-malignant comparator tissues, with a reported 4.6- to 7.4-fold difference in mean signal intensity (p < 0.001). The telehealth platform supported the secure transmission of medical images and video and demonstrated an end-to-end latency of <500 ms in a simulated remote consultation setting. Conclusions: These results support the technical and preclinical feasibility of integrating targeted fluorescence imaging, portable fiber-bundle endomicroscopy, and telehealth into a single platform. This study should therefore be interpreted as a preclinical feasibility study evaluating optical, molecular, and telehealth integration, rather than as a clinically validated cervical cancer screening test.
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Mammogram Analysis with YOLO Models on an Affordable Embedded System
(2026-01-01)
Intasam, Anongnat
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Piyawattanametha, Nicholas
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Promworn, Yuttachon
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Jiranantanakorn, Titipon
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Thawornwanchai, Soonthorn
Background/Objectives: Breast cancer persists as a leading cause of female mortality globally. Mammograms are a key screening tool for early detection, although many resource-limited hospitals lack access to skilled radiologists and advanced diagnostic tools. Deep learning-based computer-aided detection (CAD) systems can assist radiologists by automating lesion detection and classification. This study investigates the performance of various You Only Look Once (YOLO) models and a Hybrid Convolutional-Transformer Architecture (YOLOv5, YOLOv8, YOLOv10, YOLOv11, and Real-Time-DEtection Transformer (RT-DETR)) for detecting mammographic lesions on an affordable embedded system. Methods: We developed a custom web-based annotation tool to enhance mammogram labeling accuracy, using a dataset of 3169 patients from Thailand and expert annotations from three radiologists. Lesions were classified into six categories: Masses Benign (MB), Calcifications Benign (CB), Associated Features Benign (AFB), Masses Malignant (MM), Calcifications Malignant (CM), and Associated Features Malignant (AFM). Results: Our results show that the YOLOv11n model is the optimal choice for the NVIDIA Jetson Nano, achieving an accuracy of 0.86 and an inference speed of 6.16 ± 0.31 frames per second. A comparative analysis with a graphics processing unit (GPU)-powered system revealed that the Jetson Nano achieves comparable detection performance at a fraction of the cost. Conclusions: The current research landscape has not yet integrated advanced YOLO versions for embedded deployment in mammography. This method could facilitate screening in clinics without high-end workstations, demonstrating the feasibility of deploying CAD systems in low-resource environments and underscoring its potential for real-world clinical applications.
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Rapid Detection of Perfluorooctanesulfonic Acid Using Surface-Enhanced Raman Spectroscopy and Deep Learning
(2025-10-07)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yao, Cheng You
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Yang, Chia Wei
Per- and polyfluoroalkyl substances (PFAS) are a large group of human-made chemicals that have been widely used in industry and consumer products. Perfluorooctanesulfonic acid (PFOS) is a ubiquitous type of PFAS, which is extremely stable chemicals that have been persistent in the environment for many years. The accumulation of PFOS in the human body can lead to various unfavorable health issues related to the immune, metabolic, and endocrine systems. The conventional PFOS detection method utilizes liquid chromatography coupled with a mass spectroscopy system that typically involves a lengthy and complex procedure. Herein, we propose to develop a low-cost and rapid test approach based on surface-enhanced Raman spectroscopy (SERS) and deep learning for PFOS detection. The gold nanoparticle SERS substrates utilized in this study can significantly enhance the Raman signal of PFOS in solution at a low concentration. PFOS detection and quantification in water using the SERS-based substrate are carried out by measuring Raman peak intensities of PFOS in solution at a range of low concentrations and comparing them to the signal of a blank SERS substrate background. The results show that the SERS substrate can achieve a detection limit as low as 0.0005 ppb. In addition, we propose a demultiplexing deep learning model, which can generate high signal-to-noise ratio (SNR) PFOS spectra from the noisy mixture of PFOS and background Raman spectra. Average cross-correlation and mean absolute error (MAE) are utilized to evaluate the similarity between the demultiplexed and denoised PFOS Raman spectra (output of deep learning) and their ground truths. The proposed model can achieve an encouraging result with high average cross-correlation and low average MAE of 0.9622 ± 0.0667 and 0.0034 ± 0.0024, respectively.
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Monocular depth estimation based on deep learning for intraoperative guidance using surface-enhanced Raman scattering imaging
(2025-02-01)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yao, Cheng You
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Yang, Chia Wei
Imaging of surface-enhanced Raman scattering (SERS) nanoparticles (NPs) has been intensively studied for cancer detection due to its high sensitivity, unconstrained low signal-to-noise ratios, and multiplexing detection capability. Furthermore, conjugating SERS NPs with various biomarkers is straightforward, resulting in numerous successful studies on cancer detection and diagnosis. However, Raman spectroscopy only provides spectral data from an imaging area without co-registered anatomic context. This is not practical and suitable for clinical applications. Here, we propose a custom-made Raman spectrometer with computer-vision-based positional tracking and monocular depth estimation using deep learning (DL) for the visualization of 2D and 3D SERS NPs imaging, respectively. In addition, the SERS NPs used in this study (hyaluronic acid-conjugated SERS NPs) showed clear tumor targeting capabilities (target CD44 typically overexpressed in tumors) by an ex vivo experiment and immunohistochemistry. The combination of Raman spectroscopy, image processing, and SERS molecular imaging, therefore, offers a robust and feasible potential for clinical applications.
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A Pure CMOS Stack Electrostatic Micromirror Featuring Simplified Fabrication and Stress-Adjusted Modeling
(2025-01-01)
Chen, Wenhao
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Tavakkoli, Hadi
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Zhao, Bin
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Zhang, Maojie
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Piyawattanametha, Wibool
We report a pure CMOS stack-made comb-actuated electrostatic micromirror for non-resonant scanning using a standard 0.18 μm 1-polysilicon 6-metal CMOS foundry process without complex post-processing steps. The staggered vertical combs are formed by different metal layers to provide a nonlinear electrostatic torque, enabling the device to achieve a 12.4° optical scan angle under 70V with a mirror size of 0.4×0.4 mm<sup>2</sup>. Considering CMOS process-induced stress, a modified theoretical model of the comb actuators agrees with experimental data, which gives a guideline for the design optimization of non-resonant CMOS-MEMS mirrors. This innovative integration of comb-drive actuator and mirror structure within the limited thickness of the standard CMOS stack (11 μm) and improved manufacturability boost a new generation of compact, high-performance optical MEMS devices.
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Non-invasive techniques with vital signs for glucose monitoring
(2025-01-01)
Mahittikorn, Pawarit
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Tangkiatphaibun, Parawee
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Thitathan, Thitisart
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Udomtanasub, Pholchanok
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Piyawattanametha, Wibool
This study is focusing on integrating finger sleeves for machine-learning with Near-infrared (NIR) spectroscopy and additional sensors techniques. It applied light emitting diodes (LEDs) at 660 nm, 880 nm, 940 nm wavelengths and photodetectors and a galvanic skin response (GSR) and a temperature sensor to read the signal from patients’ fingers. These sensors are attached to the finger sleeves to make it easy to wear for this continuous glucose monitoring. After the data was collected from the NIR spectroscopy and multiple sensors it has used in the machine learning models to predict the blood sugar level. For the machine learnings that was selected in this study are Linear regression, and Random forest model. Which the R<sup>2</sup> result was 0.07 and -0.27 respectively.
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Multiplexed Surface-Enhanced Raman Mapping via DIY Spectrometer for High-Specificity Molecular Detection
(2025-01-01)
Tangkiatphaibun, Parawee
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Suttikittipong, Pasin
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Udomtanasub, Pholchanok
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Piyawattanametha, Aaron
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Piyawattanametha, Nicholas
In this research, we aim to build a low-cost Do-It-Yourself(DIY) Raman spectrometer system. Due to the previous limitation, the surface-enhanced Raman spectroscopy(SERS) multiplexing for enhance the Raman fingerprint of the benchmark substrate. The previous research was done at a single point, then 3×2 multiplex mapping only. This study enables the 4×4 multiplex mapping method using SERS substrates. Expanding beyond previous work on single-point detection, this study demonstrates a 4×4 multiplex mapping method using SERS substrates (S440) nanoparticle-based substrate. This allows a deeper range of the DIY Raman to get multiple Raman fingerprints from the same substrate and leads to the future artificial intelligence(AI) identification of the substrate complexity and the quantitative analysis. The system is based on a 785nm fiber laser and a custom optics setup.
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Comparison of MEMS-based photoacoustic microscopy in biomedical imaging
(2025-01-01)
Suttikittipong, Pasin
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Tangkiatphaibun, Parawee
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Piyawattanametha, Nicholas
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Piyawattanametha, Wibool
Photoacoustic microscopy (PAM) has emerged as a promising biomedical imaging technique, renowned for its capability to visualize the microvasculature and measure oxygen saturation levels in biological tissues, both non-invasively and in real-time. PAM combines the contrast benefits of optical imaging with the penetrating benefits of ultrasound. It offers high spatial resolution and deep tissue imaging, which is what microscopic and macroscopic imaging can’t do separately. This review presents fundamental knowledge of PAM’s theoretical model and the sensing mechanism. Using a Micro-Electro-Mechanical Systems or Microelectromechanical Systems (MEMS), various PAM optimizing techniques are covered, ranging from design, materials, and algorithms. Discussions also include opinions on future MEMS-based PAM technology development tendencies.
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Multihead Attention U-Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation
(2024-10-01)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yang, Chia Wei
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Yao, Cheng You
Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning-based approach for MPI-CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoworms (NWs-ICG) as the tracer. The NWs-ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U-Net model to perform segmentation on the MPI-CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI-CT dataset.

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