Hannanta-Anan, Pimkhuan

Preferred name

Alternative Name

Hannanta-anan, Pimkhuan

Hannanta-Anan, P.

Main Affiliation

School of Engineering

pimkhuan.ha@kmitl.ac.th

Now showing 1 - 10 of 12

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Deep Learning–Assisted Digital Microfluidic Platform for Automated CRISPR/Cas12 Detection of Mycobacterium tuberculosis
(2026-06-17)
Singh, Indira
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Compiro, Peeraphan
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Keawsapsak, Pornchai
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Hannanta-anan, Pimkhuan
CRISPR-based diagnostics offer high sensitivity and specificity for nucleic acid detection, but their translation to point-of-care use remains limited by dependence on benchtop instrumentation, manual reagent handling, and costly optical components. To address these limitations, this study developed a fully integrated, low-cost digital microfluidic (DMF) system capable of automating the complete CRISPR/Cas12 workflow using on-board electronics and smartphone-based imaging. The system incorporates a programmable electrode array capable of precise droplet actuation for sample preparation and reagent mixing, a closed-loop heating module that maintains a stable reaction temperature of 39 °C, and a compact 3D-printed fluorescence imaging unit for end-point signal acquisition. To facilitate rapid and objective interpretation, we implemented a YOLOv11 deep learning model to classify fluorescence outputs into positive or negative results, achieving a mean average precision at 50% of 0.889. We applied the platform to the detection of Mycobacterium tuberculosis (MTB) DNA. The on-chip CRISPR assays reliably detected MTB across a dynamic range from 1 ng/μL down to 10<sup>–8</sup> ng/μL, with no signal observed in no-template controls. Overall, the device delivers analytical performance comparable to conventional tube-based CRISPR assays while offering portability, reduced user intervention, and minimized risk of handling errors. These results highlight the potential of the integrated DMF–CRISPR system as a practical and accessible solution for point-of-care molecular diagnostics.
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Multi-Agent Q-Leaming for Power Allocation in Interference Channel
(2022-01-01)
Wongphatcharatham, Tanutsorn
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Phakphisut, Watid
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Wijitpornchai, Thongchai
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Areeprayoonkij, Poonlarp
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Jaruvitayakovit, Tanun
Signal transmission in wireless networks suffers from unwanted interference. To maximize signal to interference plus noise ratio, transmit power of each transmitter needs to be optimally allocated. Here, we propose to use multi-agent Q-learning to optimize such transmit power within interference channel. Our simulation indicated that multi-agent Q-Iearning resulted in better sum-rate than the traditional methods such as the maximum power allocation and the random power allocation. Our work offers a novel and practical computational approach to optimizing signal transmission in wireless networks.
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Integrated Optothermal Paperfluidic Platform for Single-Step, Model-Driven CRISPR-Cas12a Diagnostics of Mycobacterium tuberculosis
(2026-06-02)
Thepbut, Aujchara
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Rupprom, Thitipa
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Thant, Lin T.
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Nyein, Hein K.
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Compiro, Peeraphan
CRISPR-Cas12a molecular diagnostics offer high sensitivity and specificity for pathogen detection, yet their translation to point-of-care (POC) settings is often hindered by complex reagent handling, precise thermal requirements, and reliance on bulky laboratory equipment. In this study, we present an integrated opto-thermal paperfluidic platform engineered for the autonomous, single-step CRISPR-Cas12a detection of Mycobacterium tuberculosis (MTB). The platform’s architecture was optimized by comparing laser and thermal transfer printing, identifying the latter as the superior method for creating robust, leak-proof hydrophobic barriers while preserving the structural integrity of the cellulose substrate. To achieve autonomous operation, we developed a hybrid mathematical model─incorporating boundary resistance and thermal evaporation─to design an “intrinsic timer” that aligns capillary flow front progression with the 15 min CRISPR reaction kinetics. Thermal regulation and signal readout are managed by a custom-engineered, 3D-printed hardware module that provides closed-loop 39 °C incubation and high-contrast fluorescence imaging. By utilizing a red-shifted ROX–quencher reporter probe to overcome paper autofluorescence and sucrose-based lyoprotection for reagent stabilization, the platform achieved a limit of detection of 0.0335 ng/μL with prestored reagents. Our integrated detection system requires only a single sample-loading step and provides results in 15 min, offering a practical, low-cost solution for decentralized tuberculosis surveillance in resource-limited environments.
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Computational framework for single-cell spatiotemporal dynamics of optogenetic membrane recruitment
(2022-07-18)
Kuznetsov, Ivan A.
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Berlew, Erin E.
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Glantz, Spencer T.
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Hannanta-Anan, Pimkhuan
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Chow, Brian Y.
We describe a modular computational framework for analyzing cell-wide spatiotemporal signaling dynamics in single-cell microscopy experiments that accounts for the experiment-specific geometric and diffractive complexities that arise from heterogeneous cell morphologies and optical instrumentation. Inputs are unique cell geometries and protein concentrations derived from confocal stacks and spatiotemporally varying environmental stimuli. After simulating the system with a model of choice, the output is convolved with the microscope point-spread function for direct comparison with the observable image. We experimentally validate this approach in single cells with BcLOV4, an optogenetic membrane recruitment system for versatile control over cell signaling, using a three-dimensional non-linear finite element model with all parameters experimentally derived. The simulations recapitulate observed subcellular and cell-to-cell variability in BcLOV4 signaling, allowing for inter-experimental differences of cellular and instrumentation origins to be elucidated and resolved for improved interpretive robustness. This single-cell approach will enhance optogenetics and spatiotemporally resolved signaling studies.
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Fabrications of Low-Cost Poly(methyl methacrylate) Microfluidic Devices for Precise Passive Flow Control
(2024-01-01)
Kaewtae, Wattana
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Tanasapsakul, Warinthorn
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Pattamang, Pattaraluck
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Ranron, Norabadee
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Sripumkhai, Witsaroot
Microfluidics serve as effective platforms for conducting point-of-care diagnostics and biochemical assays which require precise control over flow rates and incubation times. To enable their uses in resource-limited settings, a pump-free approach is essential for driving fluid flow within the microfluidics. In this study, we introduced cost-effective methodologies for fabricating poly(methyl methacrylate) (PMMA)-based microfluidic devices driven passively by capillary pressure. We explored three fabrication techniques including two-layer CNC micro-milling, two-layer laser engraving, and three-layer laser cutting. Among these methods, three-layer laser cutting proved to be the most reproducible method, yielding devices with consistent channel dimensions and contact angles. Consequently, this fabrication technique enables better control over flow rates within microfluidic systems. Our findings demonstrate the feasibility of fabricating low-cost, pump-free microfluidic devices for precise flow control, with potential applications in disease detection and biomedical research.
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An RT-RPA-Cas12a platform for rapid and sensitive detection of tilapia lake virus
(2022-11-15)
Sukonta, Thanwarat
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Senapin, Saengchan
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Taengphu, Suwimon
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Hannanta-anan, Pimkhuan
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Kitthamarat, Mintra
Tilapia lake virus (TiLV) or Tilapia tilapinevirus is a highly contagious pathogen that causes severe symptoms and massive mortalities in tilapia, affecting numerous tilapia-farming communities across the continents. Though TiLV has grown into a serious threat that could undermine the aquaculture-based economy and global food security, effective therapeutics and vaccines are not yet commercially available, rendering prompt detection and control the most practical strategy. Current TiLV diagnostic techniques, however, face a number of constraints that restrict their utility, including low throughput and high resource demands imposed upon users. To fill in this critical gap, we developed a novel CRISPR-Cas12a method coupled with reverse-transcriptase recombinase polymerase amplification (RT-RPA-Cas12a) for TiLV detection, using Segment 9 as the target. The platform could detect as few as 200 copies of RNA and produced no false positive results when tested with other fish pathogens. The assay could be carried out at a constant temperature between 37 and 42 °C in less than an hour, allowing naked-eye interpretation of the results through lateral flow and smartphone-based readouts. Taken together, our RT-RPA-Cas12a platform is accurate, streamlined, and user-friendly, and thus has the potential to be a valuable asset in combating the growing threat posed by TiLV.
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Smartphone-compatible, CRISPR-based platforms for sensitive detection of acute hepatopancreatic necrosis disease in shrimp
(2022-12-01)
Naranitus, Punyaporn
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Aiamsa-at, Praphutson
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Sukonta, Thanwarat
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Hannanta-anan, Pimkhuan
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Chaijarasphong, Thawatchai
Acute Hepatopancreatic Necrosis Disease (AHPND), caused by bacterial isolates expressing PirAB binary toxins, represents the severest and most economically destructive disease affecting penaeid shrimp. Its rapid disease progression and associated massive mortalities call for vigilant monitoring and early diagnosis, but molecular detection methods that simultaneously satisfy the requirements of sensitivity, specificity, and portability are still scarce. In this work, the CRISPR-Cas12a technology was harnessed for the development of two fluorescent assays compatible with naked-eye visualization. The first assay, AP4-Cas12a, was based on the OIE-recommended AP4 two-tubed nested PCR method and was designed to bypass the time-consuming and potentially hazardous agarose gel electrophoresis step. Using AP4-Cas12a, the detection limit of 10 copies per reaction could be achieved within less than 30 minutes post-PCR. The second assay, RPA-Cas12a, utilized recombinase polymerase amplification (RPA) to rapidly and isothermally amplify the target DNA, followed by amplicon detection by Cas12a, resulting in a protocol that can be completed in less than an hour at a constant temperature of 37°C. The detection limit of RPA-Cas12a is 100 copies of plasmid DNA or 100 fg of bacterial genomic DNA per reaction. Importantly, we validated that both assays are compatible with a previously reported smartphone-based device for facile visualization of fluorescence, thereby providing an affordable option that requires less consumables than lateral flow detection. Using this portable device for readouts, the AP4-Cas12a and RPA-Cas12a methods showed excellent concordance with the AP4-agarose gel electrophoresis approach in the evaluation of clinical samples. Therefore, the developed Cas12a assays have the potential to streamline both in-laboratory and onsite diagnosis of AHPND.
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Explainable machine learning reveals diverse yield-determining factors among Thai rice farmer cohorts: Implications for targeted agricultural support
(2026-06-01)
Suriyalaksh, Manusnan
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Prommawin, Benjapon
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Hannanta-Anan, Pimkhuan
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Sreewongchai, Tanee
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Promchote, Parichart
Rice yield prediction and optimization remain crucial challenges in Thailand’s agricultural sector. This study presents an explainable machine learning framework for predicting farm-level rice yields and identifying key factors affecting productivity. We collected comprehensive data from 1,722 smallholder farmers in central Thailand, encompassing 58 agronomic and economic variables. Four automated machine learning (AutoML) frameworks – AutoGluon, auto-sklearn, h2o, and mljar – were evaluated using 5-fold cross-validation, with AutoGluon achieving the best performance (root mean square error: 0.532 tonnes/hectare, mean absolute error: 0.372 tonnes/hectare, R<sup>2:</sup> 0.538). Using global SHapley Additive exPlanations (SHAP) analysis, we identified farmers’ social networks, rental costs during harvest, and total harvesting expenses as the most influential predictors of rice yields. Notably, stronger social network connectivity was associated with higher yields, suggesting that information sharing and collective knowledge within farming communities play a key role in improving productivity. Clustering analysis based on individual SHAP values revealed six distinct farmer cohorts, each characterized by unique patterns of feature importance. These cohort-specific insights demonstrate the potential of combining AutoML with explainability techniques to move beyond uniform agricultural recommendations towards precision support tailored to the specific needs of different farmer cohorts.
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Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2
(2021-02-02)
Samacoits, Aubin
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Nimsamer, Pattaraporn
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Mayuramart, Oraphan
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Chantaravisoot, Naphat
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Sitthi-Amorn, Pitchaya
Rapid, accurate, and low-cost detection of SARS-CoV-2 is crucial to contain the transmission of COVID-19. Here, we present a cost-effective smartphone-based device coupled with machine learning-driven software that evaluates the fluorescence signals of the CRISPR diagnostic of SARS-CoV-2. The device consists of a three-dimensional (3D)-printed housing and low-cost optic components that allow excitation of fluorescent reporters and selective transmission of the fluorescence emission to a smartphone. Custom software equipped with a binary classification model has been developed to quantify the acquired fluorescence images and determine the presence of the virus. Our detection system has a limit of detection (LoD) of 6.25 RNA copies/μL on laboratory samples and produces a test accuracy of 95% and sensitivity of 97% on 96 nasopharyngeal swab samples with transmissible viral loads. Our quantitative fluorescence score shows a strong correlation with the quantitative reverse transcription polymerase chain reaction (RT-qPCR) Ct values, offering valuable information of the viral load and, therefore, presenting an important advantage over nonquantitative readouts.
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A Cost-Effective Digital Microfluidic System for CRISPR Diagnostic Automation
(2024-01-01)
Vunkong, Jirachaya
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Hannanta-Anan, Pimkhuan
CRISPR diagnostics offer precise detection of genetic material with high specificity and accuracy while adaptable for field deployment. However, the labor-intensive steps required can reduce usability and increase the risk of human error. To overcome these limitations, we developed a low-cost digital microfluidic (DMF) platform integrated with a custom fluorescence imaging module for the automation of CRISPR diagnostics. Our DMF platform was built from a basic PCB array coated with a layer of parafilm and silicone oil. The fluorescence module was custom designed and fabricated through 3D printing. Our integrated platform effectively facilitated movement, mixing, and fluorescence imaging of water and CRISPR-Cas reagents. This affordable DMF system demonstrates its initial feasibility for automating CRISPR diagnostics, enhancing accessibility and user- friendliness.

Hannanta-Anan, Pimkhuan

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