DSpace collection: 博碩士論文

數位液滴級聯催化髮夾擴增技術應用於微小核醣核酸之定量檢測;數位液滴級聯催化髮夾擴增技術應用於微小核醣核酸之定量檢測

title: 數位液滴級聯催化髮夾擴增技術應用於微小核醣核酸之定量檢測;數位液滴級聯催化髮夾擴增技術應用於微小核醣核酸之定量檢測 abstract: 微小核醣核酸（miRNA）為具有分析價值的癌症相關生物標記，惟現行檢測技術常受限於對外源性酵素的依賴、繁複的溫控需求及微量樣本分析能力不足等限制。為此，本研究旨在建立一套「數位液滴級聯催化髮夾擴增檢測流程」，發展具備穩定度與分析效率之無酶等溫檢測平台。針對傳統催化髮夾自組裝（Catalytic Hairpin Assembly， CHA）反應動力學受限之物理特性，本研究設計了級聯催化髮夾擴增系統。該設計利用二級CHA產物作為一級反應之次級觸發因子（Secondary Trigger），形成自催化反饋迴路，具提升訊號生成速率與光學判讀可讀性之特性。同時，本研究進一步結合數位液滴技術，將反應體系分割為單分散微液滴，藉由微觀空間之區隔化效應降低背景干擾，並實現miRNA之數位濃度推算。本研究以miRNA-21作為模型標的進行平台驗證，實驗結果顯示，整合級聯擴增與數位液滴區隔化技術之檢測流程，能有效改善無酶反應之信號累積表現，成功對飛莫耳（fM）等級之微量miRNA-21進行分析。綜上所述，本研究初步建構之檢測流程，不僅提供非酵素核酸檢測新的可行方案，亦為未來生物標記分析與定點照護應用奠定基礎。;MicroRNAs (miRNAs) serve as important cancer-related biomarkers holding clinical analytical value; however， current detection methodologies are often constrained by their reliance on exogenous enzymes， complex thermal cycling requirements， and limited performance for low-abundance targets. To address these challenges， this study establishes a "Droplet Digital Cascade Catalytic Hairpin Assembly quantitative detection workflow"， developing an enzyme-free isothermal sensing platform. To improve the kinetic rates inherent in traditional Catalytic Hairpin Assembly (CHA)， a dual-hairpin cascade amplification system was designed. This design employs the product of the secondary CHA as a secondary trigger for the primary reaction， establishing an autocatalytic feedback loop to enhance signal generation rates and readability. Furthermore， by integrating Droplet Digital technology， the reaction system is partitioned into monodisperse microdroplets， utilizing spatial compartmentalization to suppress background interference and enable Poisson-based miRNA concentration estimation. Utilizing miRNA-21 as a model target， experimental results demonstrate that this integrated workflow enhances the analytical performance of non-enzymatic assays， achieving femtomolar (fM) detection limits. In conclusion， by synergizing the cascade strategy with droplet partitioning， this study provides a reliable methodology for non-enzymatic nucleic acid sensing， offering technical support for potential point-of-care applications.

基於空間注意力引導與相位增強光流法之抗體動干擾非接觸式心衝擊圖重構;基於空間注意力引導與相位增強光流法之抗體動干擾非接觸式心衝擊圖重構

title: 基於空間注意力引導與相位增強光流法之抗體動干擾非接觸式心衝擊圖重構;基於空間注意力引導與相位增強光流法之抗體動干擾非接觸式心衝擊圖重構 abstract: 近年來，隨著遠距醫療（Telemedicine）與非接觸式生理技術的蓬勃發展，透過常規光學視訊攝影機連續量測人體心血管動力學已成為重要研究趨勢。其中，心衝擊圖（Ballistocardiogram， BCG）源於心室收縮射血對主動脈壁產生之微觀機械的反作用力，其物理本質為體表機械加速度（a(t)=\frac{d^2y(t)}{dt^2}），具備評估心臟收縮動能與動態心律的巨大潛力。然而，傳統基於空間強度梯度之特徵追蹤技術（如 Kanade–Lucas–Tomasi， KLT 光流法），在提取微弱體表 BCG 時面臨三大嚴苛物理瓶頸：第一、人體平坦皮膚缺乏高對比紋理，容易引發結構張量矩陣奇異（\mathbit{Z}^T\mathbit{Z}\rightarrow0），導致相機熱雜訊被放大為隨機漫步漂移；第二、相機 8-bit 模數轉換器（ADC）之空間灰階量化截斷與像素鎖定效應（Pixel-Locking Effect），導致連續亞像素位移退化為鋸齒階梯狀波形；第三、將幾何位移轉換為體表加速度時，時間微分算子導致高頻雜訊功率譜密度隨頻率呈現幾何級數發散，產生劇烈數值脈衝並徹底吞沒生理 J 峰波形。為了徹底克服上述物理限制，本研究提出一套基於「空間注意力引導」與「空間域複數局部相位」之抗體動非接觸式 BCG 加速度重構演算法。系統於受測者額頭黏貼高對比圓形黑色標記貼片並裁剪固定 128\times128 像素之感興趣區域（ROI）。演算法首先利用二維複數可操縱金字塔（Complex Steerable Pyramid， CSP）對影像進行 4 尺度與 4 方向之多解析度解構，將微動映射為空間解析信號之連續相角旋轉；接著，基於最佳線性不偏估計（BLUE）框架推導出振幅平方空間注意力加權機制（W(x，y)\propto A^2(x，y)），自動抑制低對比皮膚背景並聚焦於高訊噪比之貼片邊緣，藉由大數法則空間統計池化在微分前有效壓制微動底噪。在運動降維與特徵選擇方面，本研究將各定向子帶微動速度先經一階時間微分轉換為體表加速度，隨後於各尺度內部透過主成分分析（PCA）完成多方向運動解耦以提取代表性 BCG 加速度波形，結合動態窄帶諧噪比（Dynamic HNR）與四階統計峰度（Kurtosis）構建綜合訊號品質指標（SQI），自適應選出最佳生理尺度通道。最後，演算法利用三次樣條升頻插值（1000\mathrm{\ Hz}）與生理不反應期雙重約束，精準定位 BCG 加速度 J 峰並提取拍對拍心搏間距（Inter-Beat Interval， IBI）。本研究透過頻率域相位平移合成平台與各向同性 2D 高斯斑紋消融實驗，驗證了演算法在極端低照度與感測器熱雜訊下突破 8-bit 量化極限的數值穩定性。在 10位健康受試者之人體臨床驗證中，以同步接觸式第一導程心電圖（Lead-I ECG）之 RR 間期作為黃金標準：在靜止情境下，本方法提取之 IBI 展現極高的線性相關性（r=0.991）與決定係數（R^2=0.982），Bland-Altman 95% 一致性界限（LoA）顯著收窄至 [-18.9，19.2] ms，大幅優於傳統 KLT；在受試者進行微幅點頭與轉頭之動態體動干擾情境下，相較於 KLT 相關性之徹底崩潰（r=0.191），本方法仍展現強韌之動態追蹤效能（r=0.936，R^2=0.876）。本研究從數理上解決了KLT光流之微分發散與紋理依賴難題，為高精度非接觸式心血管功能監控與穿戴式替代方案提供了具備嚴謹理論基礎之工程解決架構。;Non-contact physiological monitoring using conventional optical cameras has emerged as a transformative paradigm in telemedicine and pervasive healthcare. Ballistocardiogram (BCG) signals， which originate from the mechanical recoil forces exerted on the vascular walls during systolic cardiac blood ejection， physically represent micro-scale bodily accelerations (a(t)=\frac{d^2y(t)}{dt^2}) that offer profound insights into myocardial contractility and dynamic cardiac intervals. However， conventional video-based motion tracking methods based on spatial intensity gradients (e.g.， the Kanade–Lucas–Tomasi， KLT optical flow tracker) face three severe physical and numerical constraints when extracting micro-scale bodily movements: first， the lack of prominent geometric textures on smooth human facial skin causes ill-conditioned structure tensors (\mathbit{Z}^T\mathbit{Z}\rightarrow0)， heavily amplifying camera thermal noise into random-walk drifts; second， the discrete spatial quantization of 8-bit analog-to-digital converters (ADCs) and pixel-locking effects degrade continuous sub-pixel displacements into staircase artifacts; third， when converting geometric displacement to acceleration， time differentiation acts as a high-pass operator， causing high-frequency noise power spectral density to explode and generate impulse spikes that completely obscure the underlying physiological J-peaks. To overcome these fundamental limitations， this study proposes a motion-robust non-contact BCG acceleration reconstruction framework guided by spatial attention and complex local phase optical flow. The proposed system employs a high-contrast circular black marker patch placed on the subject’s forehead to define a consistent 128\times128 pixel region of interest (ROI). Using a 2D Complex Steerable Pyramid (CSP) across 4 scales and 4 orientations， bodily micro-motions are translated into continuous sub-pixel phase rotations via the spatial analytic signal representation. An amplitude-squared spatial attention weighting mechanism (W(x，y)\propto A^2(x，y)) is theoretically derived under the Best Linear Unbiased Estimator (BLUE) framework to suppress textureless skin noise and focus computation on high-SNR marker boundaries， drastically lowering the front-end noise floor prior to differentiation via the Law of Large Numbers. Furthermore， directional micro-velocities are converted to bodily surface accelerations via first-order time differentiation. Intra-scale 4-orientation Principal Component Analysis (PCA) is then performed to decouple multi-axis recoil trajectories and project dominant acceleration signals. Subsequently， an adaptive Signal Quality Index (SQI)—integrating dynamic narrow-band Harmonic-to-Noise Ratio (HNR) with statistical Kurtosis—is formulated to dynamically select the optimal spatial scale channel. Finally， sub-frame cubic spline upsampling (1000\mathrm{\ Hz}) coupled with physiological refractory period constraints is utilized for accurate J-peak localization and beat-to-beat Inter-Beat Interval (IBI) extraction. The numerical stability and resolution beyond 8-bit quantization limits were validated through synthetic Fourier phase-shift simulations and isotropic 2D Gaussian speckle ablation studies under severe noise and low-illumination conditions. In clinical human trials involving 10 subjects synchronized with Lead-I Electrocardiogram (ECG) ground truth， the proposed method achieved superior beat-to-beat IBI tracking fidelity at rest (r=0.991， R^2=0.982)， with the Bland-Altman 95% Limits of Agreement (LoA) significantly narrowed to [-18.9，19.2] ms. Under non-voluntary head motion disturbances， while KLT tracking collapsed entirely (r=0.191)， the proposed framework preserved high tracking robustness (r=0.936， R^2=0.876). This research resolves the fundamental derivative noise divergence and texture dependence of intensity-based optical flow， presenting a mathematically grounded and motion-resilient engineering solution for non-contact cardiovascular monitoring.

人源性體外耳蝸類器官新方法學平台之建立 Establishment of a Human In-vitro Cochlear Organoid Platform as a New Approach Methodology;人源性體外耳蝸類器官新方法學平台之建立 Establishment of a Human In-vitro Cochlear Organoid Platform as a New Approach Methodology

title: 人源性體外耳蝸類器官新方法學平台之建立 Establishment of a Human In-vitro Cochlear Organoid Platform as a New Approach Methodology;人源性體外耳蝸類器官新方法學平台之建立 Establishment of a Human In-vitro Cochlear Organoid Platform as a New Approach Methodology abstract: 近年來，全球感音性聽力損失（sensorineural hearing loss， SNHL）患者人數持續增加，然由於哺乳類聽毛細胞於受損後幾乎不具再生能力，且現行臨床治療仍以輔具補償為主，缺乏可促進功能修復之有效策略，使聽損研究長期面臨臨床轉譯瓶頸。傳統臨床前研究高度依賴動物模型，尤以小鼠為主要系統，然而其在耳蝸發育歷程、離子微環境組成及藥物反應上與人類存在本質差異，限制研究成果向臨床應用延伸的可靠性。隨著 FDA Modernization Act 3.0 對新方法學（New Approach Methodologies， NAMs）與動物實驗替代策略的推動，建立具人源相關性且可重現耳蝸發育微環境的體外模型，已成為聽覺醫療研發的重要方向。本研究建構於本實驗室既有之小鼠耳蝸類器官培養與離子交換微環境調控技術基礎，進一步導入人類誘導多能幹細胞（induced pluripotent stem cells， iPSCs），結合3D 生物列印工程與離子微環境設計，建立一套以微環境重建為核心的體外耳蝸類器官新方法學平台。本研究之關鍵並非單一細胞分化流程之優化，而在於回應既有文獻所指出的核心限制：耳前驅細胞（Otic Progenitor Cells， OPCs）中之上皮前驅細胞與神經前驅細胞於體外條件下通常需分別誘導，難以在同一培養系統中穩定共存與協同發育。為突破此限制，本研究以胚胎發育階段之耳蝸微環境為設計藍本，透過三維結構支撐、離子梯度調控與動態培養條件之整合，重建可支持多譜系前驅細胞共存、互動與組織化的培養系統，促進具層次結構之耳蝸類器官形成。預期本研究所建立之人源體外耳蝸類器官平台，將可更貼近人類耳蝸發育與微環境特性，作為探討聽毛細胞分化機制、評估聽損再生策略，以及進行聽力相關藥物與醫材臨床前測試的關鍵研究工具。整體而言，本研究不僅延續既有動物模型成果，更透過人源化與微環境工程導向之方法學設計，展現作為動物實驗替代方案的潛力，並為未來聽覺醫療產品開發與臨床轉譯提供具系統性與前瞻性的研究基礎。;In recent years， the prevalence of sensorineural hearing loss (SNHL) has continued to increase worldwide. However， because mammalian cochlear hair cells possess little regenerative capacity after damage， current clinical treatments remain largely dependent on hearing aids and cochlear implants， which compensate for hearing function rather than restore it. Consequently， the development of regenerative therapies has become a major challenge in hearing research. Conventional preclinical studies rely heavily on animal models， particularly mice. Nevertheless， fundamental differences in cochlear development， ionic microenvironment， and drug responses between mice and humans limit the translational value of these models. With the implementation of the FDA Modernization Act 3.0， which promotes New Approach Methodologies (NAMs) and alternatives to animal testing， the development of physiologically relevant human in vitro models has become increasingly important. Building upon our laboratory’s previous work on mouse cochlear organoids and ionic microenvironment engineering， this study integrates induced pluripotent stem cells (iPSCs) with 3D bioprinting and microenvironmental engineering to establish a human cochlear organoid platform that better recapitulates cochlear development. Rather than focusing solely on optimizing differentiation protocols， this study addresses a critical limitation reported in previous studies: epithelial and neural otic progenitor cells are typically induced separately in vitro， making it difficult to achieve their stable coexistence and coordinated development within a single culture system. Inspired by the embryonic cochlear microenvironment， we reconstructed a biomimetic culture platform by integrating a three-dimensional structural scaffold， ionic microenvironment regulation， and dynamic culture conditions. This engineered microenvironment is designed to support the coexistence， interaction， and spatial organization of multiple otic progenitor lineages， thereby promoting the formation of structurally organized cochlear organoids. The proposed human cochlear organoid platform is expected to more faithfully mimic human cochlear development and provide a valuable in vitro model for investigating hair cell differentiation， evaluating regenerative strategies for hearing loss， and conducting preclinical testing of hearing-related drugs and medical devices. Overall， this study extends previous animal-based research by introducing a humanized， microenvironment-oriented engineering strategy， demonstrating its potential as an alternative to animal models and providing a promising foundation for future translational research and hearing healthcare innovation.

基於卷積神經網路與邊緣部署之多重表面增強拉曼光譜定量分析模型;基於卷積神經網路與邊緣部署之多重表面增強拉曼光譜定量分析模型

title: 基於卷積神經網路與邊緣部署之多重表面增強拉曼光譜定量分析模型;基於卷積神經網路與邊緣部署之多重表面增強拉曼光譜定量分析模型 abstract: 表面增強拉曼散射(SERS)技術憑藉其指紋光譜特性與極高的靈敏度，已成為精準醫療與多目標生物感測的重要工具。然而，現有的 SERS 檢測常受限於大型儀器的成本與體積，且在複雜混合樣本的定量分析上，傳統方法往往難以克服光譜重疊與基線漂移的干擾。本研究針對上述限制，建立可於邊緣裝置部署之定量分析模型與其驗證流程。在演算法層面，本研究提出寬主幹卷積神經網路架構(WideTrunkCNN)，針對多種 SERS 訊號分子之合成混合光譜進行訊號分子辨識與相對組成比例估計，並嵌入一維卷積區塊注意力模組(CBAM)以提供可映射回波數座標之注意力權重，供模型判斷依據之初步檢視。經架構診斷與改良確立網路架構後，再依真實配方特性調整訓練資料之合成分布，最終模型於獨立合成測試集(n = 1，000)上達到決定係數 R² = 0.9347、RMSE = 0.0392。純成分光譜經 airPLS 基線校正以抑制螢光背景後，作為合成混合光譜之基底。模型則以 CNN 進行自動化特徵萃取，並以主成分分析檢視多種訊號分子純光譜之可區別程度。為初步檢驗模型於真實混合物量測情境之適用性，本研究另以多筆獨立配製之真實混合物樣本進行驗證，模型於未經事後校正之情況下達 R²=0.64。於邊緣部署方面，本研究將模型匯出為 ONNX 格式並於微型電腦( Raspberry Pi )完成實機推論驗證，採用ONNX Runtime 可較 PyTorch 加速 8至13 倍。本研究涵蓋多重 SERS 定量之演算法開發、真實樣本驗證與邊緣推論可行性評估，在未來的研究上將進行真實樣本規模擴增與跨批次驗證等項目，進一步最佳化使本研究能推展至更多元之檢測與實際臨床應用。;Surface-enhanced Raman scattering (SERS) offers high sensitivity and molecular fingerprinting capability for precision medicine and multiplexed biosensing. However， quantitative analysis of complex SERS mixtures remains challenging because of spectral overlap and baseline drift， while conventional Raman systems are often costly and bulky. This study developed an edge-deployable quantitative analysis model and validation framework for multiplex SERS spectra. A wide-trunk convolutional neural network (WideTrunkCNN) was developed to identify multiple SERS reporter molecules and estimate their relative compositional proportions from synthetic mixture spectra. A one-dimensional Convolutional Block Attention Module (1D-CBAM) was incorporated to provide wavenumber-resolved attention weights for preliminary interpretation of model predictions. Pure-component spectra were processed using airPLS baseline correction and used to generate synthetic mixture spectra. Principal component analysis was also performed to examine the separability of the reporter spectrum. After refining the architecture and adjusting the synthetic training distribution to match experimentally prepared mixture characteristics， the final model achieved R² = 0.9347 and RMSE = 0.0392 on an independent synthetic test set (n = 1，000). Several independently prepared real-mixture samples were further evaluated， yielding R² = 0.64 without post-hoc calibration. For edge deployment， the model was exported to ONNX and tested on a Raspberry Pi， where ONNX Runtime achieved an approximately 8–13-fold inference speedup compared with PyTorch. Overall， this study demonstrates the feasibility of combining deep learning-based multiplex SERS quantification with edge inference. Future work will focus on larger-scale real-sample validation， cross-batch evaluation， and further optimization for broader detection and potential clinical applications.