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利用 RNA 次代定序資料候選轉錄因子結合位點之基因多型性與退化性關節炎之關聯性研究

為了解決M1 Pro python的問題，作者王定廉 這樣論述：

研究背景：GWAS研究已被廣泛用於鑑定遺傳位點與疾病的相關性，但可能因為過於嚴格得統計檢定力（p < 5 × 10−8）導致可能具有功能性的位點被排除。而利用NGS資料庫候選轉錄因子結合位點則可避免具功能性的位點被排除。轉錄因子結合位點的變異會間接影響疾病，因此，找出轉錄因子結合位點將可為遺傳疾病的調控機轉提供新的視野。研究目的：利用TWB NGS資料庫找出台灣人特有NF-κB結合位點的變異與退化性關節炎風險之相關性。研究方法：本研究參考Fisch學者等人之RNA-seq研究，使用其中NF-κB為候選轉錄因子；並利用JASPAR權重矩陣，以A、T=30%，C、G=20%為切點，候選其辨識序列

，並自NCBI網站下載GRCh37-hg19之人體基因序列資料，將辨識序列與其進行比對，找出潛在的結合位點。運用生物資訊學blast mapping方法，將潛在的結合位點與台灣人體生物資料庫次世代定序（Next Generation Sequencing, NGS）資料進行比對，再與GEO資料庫中NF-κB ChIP-seq資料進行比對找出台灣人特有之轉錄因子結合位置的SNPs。先以台灣人體生物資料庫GWAS資料（n = 88,347），排除其他類型關節炎者，保留個案依據自填式問卷資料進行分組，病例組（n = 3,878）及對照組（n = 83,436）進行候選SNPs與OA之相關性研究；再以

105年至110年三軍總醫院健檢中心參與研究之個案（n = 1,167），排除無X光資料者，保留個案依據KL分級進行分組，病例組（n = 533）及對照組（n = 614）進行候選SNPs與OA之相關性研究。將具有顯著差異之SNPs利用GTEx-Portal資料庫查詢其功能表現。研究結果：本研究使用NF-κB為OA相關之候選轉錄因子，候選其辨識序列為5’-KGGRMTTYCCM-3’。經由GRCh37-hg19之人體基因序列資料比對，共有33,731個潛在的結合位點。將潛在的結合位點與台灣人體生物資料庫次世代定序（Next Generation Sequencing, NGS）資料進行比對，再

與GEO資料庫中NF-κB ChIP-seq資料進行比對，找出9個候選SNPs。經由TWB GWAS資料及本研究室病例對照研究發現，位於NF-κB結合位點 rs73164856的T對偶基因，在男性具有保護作用(OR=0.55，95%CI=0.33-0.91)，在嚴重退化性關節炎分組KL=3及KL=4中，其保護作用更為明顯(OR=0.16，95% CI=0.04-0.70)。位於NF-κB結合位點 rs545654在女性嚴重退化性關節炎分組中，T對偶基因罹病風險較高(OR=2.11，95% CI = 1.20 - 3.69 )。在GTEx功能性資料庫中，亦顯示rs73164856 T對偶基因會使

AKR1B15基因表現量降低(p= 0.00019)；rs545654 T對偶基因則將使血液中nNOS基因表現量升高(p=1.2e-17)。結論：透過本研究之新穎轉錄因子結合位點候選基因多型性策略所發現台灣人特有之致病基因位點，將為台灣人致病機轉及治療研究提供新的視野。

Estimation Of Various Walking Intensities And Plantar Tissue Stiffness Based On Plantar Pressure Data By Using Artificial Intelligence Technology

為了解決M1 Pro python的問題，作者SUNARDI 這樣論述：

Walking has been shown to benefit individuals include Diabetes Mellitus (DM) patients and peripheral artery disease. However, brisk walking and continuous walking could produce repetitive loads and stresses on the plantar foot resulting in increased plantar tissue stiffness and peak plantar pressur

e (PPP), leading to a high risk of foot ulcer formation and tissue injury. Therefore, quantifying the walking intensity is essential for rehabilitation interventions to indicate suitable walking exercise.This study is divided into three objectives. First, this study aims to identify differences in w

alking speeds to the plantar pressure response using deep learning methods, including Resnet50, InceptionV3, and MobileNets. Second, this study proposed a machine learning model to classify the walking speed and duration using plantar region pressure images. Third, prediction of plantar tissue stiff

ness based on plantar stress pattern using vision transformer. The F-scan system (Tekscan, South Boston, MA, USA) was used to measure plantar pressures during walking. An elastographic ultrasound (Aloka Pro Sound Alpha 7, Hitachi Healthcare Americas, Twinsburg, OH, USA) with a linear array transduce

r (UST-5412, 5–13 MHz, Hitachi Healthcare Americas) was used to measure plantar tissue mechanical property. In the first study, the deep learning models were used to classify the plantar pressure images of healthy people walking on a treadmill. The design consisted of three walking speeds (0.8 m/s,

1.6 m/s, and 2.4 m/s). The second study, an Artificial Neural Network (ANN), was adopted to develop a model for walking intensity classification using different plantar region pressure images, including the first toe (T1), the first metatarsal head (M1), the second metatarsal head (M2), and the heel

(HL). The classification consisted of three walking speeds (i.e., slow at 0.8 m/s, moderate at 1.6 m/s, and fast at 2.4 m/s) and two walking durations (i.e., 10 min and 20 min). The third study used vision transformers to predict the relationship between plantar tissue stiffness with a plantar pres

sure pattern image.The experimental results show that artificial intelligence technology could predict walking intensity and analyze the relationship between plantar tissue stiffness and plantar pressure pattern image. The first study indicated that Resnet50 had the highest accuracy compared to Ince

ptioanV3 and MobileNets on analyzing plantar pressure distribution images. Furthermore, the experimental results of estimation of walking speed and duration based on four regions of plantar pressure (i.e., T1, M1, M2, and HL) with an ANN showed that the T1 region was more easily recognized by the AN

N model, as evidenced by the highest F1-score value than other regions. Meanwhile, detection of the relationship between plantar tissue stiffness with plantar pressure pattern images showed that vision transformers could map the relationship between plantar tissue stiffness and plantar stress patter

n images.