Treebupachatsakul, Treesukon

Preferred name

Alternative Name

Treebupachatsakul, T.

Main Affiliation

School of Engineering

treesukon.tr@kmitl.ac.th

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Classification model for predicting inflammation of the urinary bladder and acute nephritis of the renal pelvis
(2022-01-01)
Lochotinunt, Chanin
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Pechprasarn, Suejit
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Treebupachatsakul, Treesukon
Urinary tract diseases can occur in many organs of the urinary system, such as kidneys, urinary bladder, renal pelvis, ureters, and urethra. The most common disease in the urinary system is bladder inflammation, cystitis, and acute nephritis. In this research, the classification artificial intelligent model is applied to predict 2 symptoms of inflammation of the urinary bladder and acute nephritis of the renal pelvis from 6 parameters, including body temperature of patient, nausea, lumbar pain, urinary pushing, micturition pains, and burning of the urethra. Here, the principal components analysis or PCA are also applied to identify the critical parameters employed to train the machine learning model. Here, we propose to compare several machine learning classification models and show the proper model accurately diagnosing these two symptoms.
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Microfluidic channel from gelatin using laser printer
(2020-10-29)
Lochotinunt, Chanin
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Teechot, Thitirat
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Pechprasarn, Suejit
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Treebupachatsakul, Treesukon
Microfluidic channel is a tool for manipulating and controlling fluids under small precise volumes and spaces. Nowadays, microfluidic fabrication has used varieties of materials such as silicon, glass, polymer, and ceramic. These materials generate waste and pollution in our environment. Moreover, the process of making microfluidic is sophisticated. Therefore, using the green-material, Gelatin is an attractive alternative to fabricate microfluidic because it is abundant, cheap, environmentally friendly, and reusable. Here, the Gelatin is employed for fabricating microfluidic by which the channel features were prepared using a laser printer. The fabrication procedure consists of the following steps (1) design the microfluidic channel features and print them on a transparency plastic sheet using a laser printer. (2) Pour aqueous gelatin solution on this printed template plastic sheet and (3) make the liquid gelatin setting by cooling it down in a refrigerator or leave it at room temperature. These steps allowed us to fabricate the smallest channel of 1.78mm (width) x 0.19mm (height) from 3pt line with 30 times layer printed, which was applicable to flow the liquid through the microfluidic.
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Generalized figure of merit for plasmonic dip measurement-based surface plasmon resonance sensors
(2022-04-01)
Treebupachatsakul, Treesukon
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Boosamalee, Apivitch
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Chaithatwanitch, Kamejira
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Pechprasarn, Suejit
We propose a theoretical framework to analyze quantitative sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip position, and figure of merits for different surface plasmon operating conditions for a Kretschmann configuration. Several definitions and expressions of the figure of merit have been reported in the literature. Moreover, the optimal operating conditions for each figure of merit are, in fact, different. In addition, there is still no direct figure of merit comparison between different expressions and definitions to identify which definition provides a more accurate performance prediction. Here shot-noise model and Monte Carlo simulation mimicking the noise behavior in SPR experiments have been applied to quantify standard deviation in the SPR plasmonic dip measurements to evaluate the performance responses of the figure of merits. Here, we propose and formulate a generalized figure of merit definition providing a good performance estimation to the detection limit. The measurement parameters employed in the figure of merit formulation are identified by principal component analysis and machine learning. We also show that the proposed figure of merit can provide a good estimation for the surface plasmon resonance performance of plasmonic materials, including gold and aluminum, with no need for a resource-demanding computation.
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Cuff-Less Blood Pressure Prediction from ECG and PPG Signals Using Fourier Transformation and Amplitude Randomization Preprocessing for Context Aggregation Network Training
(2022-03-01)
Treebupachatsakul, Treesukon
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Boosamalee, Apivitch
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Shinnakerdchoke, Siratchakrit
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Pechprasarn, Suejit
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Thongpance, Nuntachai
This research proposes an algorithm to preprocess photoplethysmography (PPG) and electrocardiogram (ECG) signals and apply the processed signals to the context aggregation network-based deep learning to achieve higher accuracy of continuous systolic and diastolic blood pressure monitoring than other reported algorithms. The preprocessing method consists of the following steps: (1) acquiring the PPG and ECG signals for a two second window at a sampling rate of 125 Hz; (2) separating the signals into an array of 250 data points corresponding to a 2 s data window; (3) randomizing the amplitude of the PPG and ECG signals by multiplying the 2 s frames by a random amplitude constant to ensure that the neural network can only learn from the frequency information accommodating the signal fluctuation due to instrument attachment and installation; (4) Fourier transforming the windowed PPG and ECG signals obtaining both amplitude and phase data; (5) normalizing both the amplitude and the phase of PPG and ECG signals using z-score normalization; and (6) training the neural network using four input channels (the amplitude and the phase of PPG and the amplitude and the phase of ECG), and arterial blood pressure signal in time-domain as the label for supervised learning. As a result, the network can achieve a high continuous blood pressure monitoring accuracy, with the systolic blood pressure root mean square error of 7 mmHg and the diastolic root mean square error of 6 mmHg. These values are within the error range reported in the literature. Note that other methods rely only on mathematical models for the systolic and diastolic values, whereas the proposed method can predict the continuous signal without degrading the measurement performance and relying on a mathematical model.
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Gelatin-Based Microfluidic Channel for Quantitative E. Coli Detection Using Blue Fluorescence of 4-Methyl-Umbelliferone Product and a Smartphone Camera
(2022-07-01)
Treebupachatsakul, Treesukon
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Lochotinunt, Chanin
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Teechot, Thitirat
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Pensupa, Nattha
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Pechprasarn, Suejit
Escherichia coli (E. coli) is a foodborne pathogen that can produce potent toxins, causing severe illnesses due to contaminated food and water consumption. This research has utilized a fluorescence measurement to quantify E. coli colonies from blue fluorescence emitted by 4-methyl-umbelliferone (4MU). The 4MU is the product of the catalytic reaction between beta-D-glucuronidase (GUD) secreted by multiple strains of Escherichia coli and its substrate 4-methylumbelliferyl-beta-D-glucuronide (MUG). Here, we apply the 4MU enzymatic reaction and propose simple instrumentation for label-free, real-time, in-situ, and quantitative E. coli measurement. The detection platform consists of a smartphone camera, an ultraviolet light source for fluorescence excitation, and MUG suspended microfluidic channels. The underlining mechanism for the proposed E. coli measurement is the passive diffusion process of the MUG secreted by E. coli and the GUD suspended in the gelatin, forming the blue fluorescence 4MU product in the channels. We have also proposed a cost-effective and eco-friendly fabrication method for preparing the MUG suspended gelatin microfluidic channels using a laser printer. Gelatin is an ultraviolet light-absorbing material in nature, providing an embedded optical filter. Here, we demonstrate that a smartphone camera can be utilized to image the fluorescence emission of the 4MU excited by the ultraviolet light in the gelatin film. The proposed E. coli detection technique allows the amount of E. coli colonies to be quantified without liquid sampling, cell-culturing, inoculation, and sophisticated equipment. Furthermore, the proposed method has a trade-off between response time and detection limit.
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Detection Limit of Surface Plasmon Resonance Sensor for Quantitative Foodborne E.coli Detection Using Effective Refractive Index Theory : The theoretical limit of E.coli detection of surface plasmon resonance
(2021-01-01)
Pensupa, Nattha
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Treebupachatsakul, Treesukon
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Lochotinunt, Chanin
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Pechprasarn, Suejit
Surface plasmon resonance has been a gold standard for label-free biomedical and biochemical measurements, such as protein binding kinetics, protein-protein interactions. The surface plasmon resonance has also been utilized in food safety screening, including Escherichia coli and Salmonella detection. The theory of surface plasmon resonance has been well established and demonstrated its ultra-sensitivity to detect small nucleotides, proteins, and molecules. However, the results, so far, for the E.coli detection under the surface plasmon resonance have not shown an impressive detection limit. The typical detection limit of E. c o l i under the conventional surface plasmon detection platform is around 103CFUml. The detection limit can be enhanced using a secondary binding agent to E.coli, including conjugated nanoparticles. Here, we propose a theoretical framework using effective refractive index theory to explain the detection mechanism and give an insight into the underlining obstacles that degrades the detection limit of the surface plasmon resonance.
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Performance Analysis of Machine Learning Models for Angular Interrogation of Surface Plasmon Resonance
(2022-01-01)
Shinnakerdchoke, Siratchakrit
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Thadson, Kitsada
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Pechprasarn, Suejit
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Treebupachatsakul, Treesukon
Surface plasmon resonance (SPR) paves the way for several cutting-edge sensing technologies well-known for being label-free and real-time monitoring. The angular scanning technique, one of the most common SPR applications, was performed by illuminating the SPR-based sensor with multiple incident angles of a single-wavelength laser beam. For refractive index sensing, the optical reflectance is absorbed in a specific angle, known as a plasmonic angle, which can be observed as a dark band when captured using a camera. Various methods have been proposed to locate the plasmonic position based on the detected image. This manuscript presented an analysis of the performance of machine learning on the identification of plasmonic angles based on the reflectance spectra for refractive index sensing. The reflectance curves are generated using Fresnel equations and the transfer matrix method with shot noise. After training and validating, the rational quadratic gaussian process regression model provides the most accurate model for predicting the plasmonic angle positions. The model can predict the plasmonic angles accurately for all studied refractive indices with a root mean square error of 3.83 \times 10^{\mathbf{-4}} RIU. Furthermore, the analysis of noise performance illustrated that a low number of photons could significantly degrade the model's accuracy and precision. The theoretical performance can be achieved at the photon energy level of 8.14 pJ.
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Auto focusing ophthalmoscope for smartphone
(2020-10-29)
Navaitthiporn, Nitipon
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Rithcharung, Preeyarat
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Wongpa, Chuttima
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Treebupachatsakul, Treesukon
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Pechprasarn, Suejit
Several retinal pathologies can cause severe damages and may lead to permanent vision loss. Early diagnosis is highly recommended to take a precautionary measure to reduce the risk of ocular diseases. Therefore, an eye test by an ophthalmologist plays a crucial role. This does, however, require paying visit to a hospital, which becomes a burden for elders and patients with physical disabilities. Consequently, these patients usually get a late diagnosis and treatment, which the symptoms may have progressed and developed. Here, we develop an optical toolkit with engineered software for smartphones enabling the smartphone to take images of human retina as a direct ophthalmoscope. Thanks to the modern smartphone specifications, which usually come with high resolution cameras and sufficient computing power for the software. The optical toolkit illuminates an eye with an appropriate wavelength and light intensity and it has been provisionally tested for health and safety based on the ISO-10942 and IEC 62471 standards. An image processing algorithm is embedded in the software providing an auto focusing capability.
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Sensing Mechanisms of Rough Plasmonic Surfaces for Protein Binding of Surface Plasmon Resonance Detection
(2023-04-01)
Treebupachatsakul, Treesukon
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Shinnakerdchoke, Siratchakrit
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Pechprasarn, Suejit
Surface plasmon resonance (SPR) has been utilized in various optical applications, including biosensors. The SPR-based sensor is a gold standard for protein kinetic measurement due to its ultrasensitivity on the plasmonic metal surface. However, a slight change in the surface morphology, such as roughness or pattern, can significantly impact its performance. This study proposes a theoretical framework to explain sensing mechanisms and quantify sensing performance parameters of angular surface plasmon resonance detection for binding kinetic sensing at different levels of surface roughness. The theoretical investigation utilized two models, a protein layer coating on a rough plasmonic surface with and without sidewall coatings. The two models enable us to separate and quantify the enhancement factors due to the localized surface plasmon polaritons at sharp edges of the rough surfaces and the increased surface area for protein binding due to roughness. The Gaussian random surface technique was employed to create rough metal surfaces. Reflectance spectra and quantitative performance parameters were simulated and quantified using rigorous coupled-wave analysis and Monte Carlo simulation. These parameters include sensitivity, plasmonic dip position, intensity contrast, full width at half maximum, plasmonic angle, and figure of merit. Roughness can significantly impact the intensity measurement of binding kinetics, positively or negatively, depending on the roughness levels. Due to the increased scattering loss, a tradeoff between sensitivity and increased roughness leads to a widened plasmonic reflectance dip. Some roughness profiles can give a negative and enhanced sensitivity without broadening the SPR spectra. We also discuss how the improved sensitivity of rough surfaces is predominantly due to the localized surface wave, not the increased density of the binding domain.
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Analysis of effects of surface roughness on sensing performance of surface plasmon resonance detection for refractive index sensing application
(2021-09-01)
Treebupachatsakul, Treesukon
;
Shinnakerdchoke, Siratchakrit
;
Pechprasarn, Suejit
This paper provides a theoretical framework to analyze and quantify roughness effects on sensing performance parameters of surface plasmon resonance measurements. Rigorous coupled-wave analysis and the Monte Carlo method were applied to compute plasmonic reflectance spectra for different surface roughness profiles. The rough surfaces were generated using the low pass frequency filtering method. Different coating and surface treatments and their reported root‐mean-square roughness in the literature were extracted and investigated in this study to calculate the refractive index sensing performance parameters, including sensitivity, full width at half maximum, plasmonic dip intensity, plasmonic dip position, and figure of merit. Here, we propose a figure‐of-merit equation considering optical intensity contrast and signal‐to‐noise ratio. The proposed figure-of‐merit equation could predict a similar refractive index sensing performance compared to experimental results reported in the literature. The surface roughness height strongly affected all the performance parameters, resulting in a degraded figure of merit for surface plasmon resonance measurement.

Treebupachatsakul, Treesukon

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