• Korean Journal of Clinical Laboratory Science
• /
• v.56 no.1
• /
• pp.43-51
• /
• 2024

[⁶⁸Ga]Ga-FAPI-04 is a promising radiopharmaceutical that binds specifically to fibroblast activation protein, which is overexpressed in more than 90% of malignant epithelial tumors but not in normal healthy tissue. This study aimed to develop an efficient method for producing ⁶⁸Ga-labelled FAPI-04 using a cassette-based automated synthesizer. [⁶⁸Ga]GaCl₃ was eluted from an Eckert & Ziegler Medical germanium-68/gallium-68 generator using 2.5 mL of 0.1 M HCl. The synthesis of the [⁶⁸Ga]Ga-FAPI-04 was performed using different concentrations of HEPES (1~2.5 M; 4-(2-hydroxyethyl) piperazine-1-ethanesulfonic acid) in 3~10 minutes; amounts of FAPI-04 precursor (5~50 ㎍) and reaction temperature (25℃~100℃) were optimized on the BIKBox^® synthesizer. The labeling efficiency of [⁶⁸Ga]Ga-FAPI-04 was greater than 96% (decay corrected) using 25 ㎍ FAPI-04 synthesized in 10 minutes at 100℃ in 2 M HEPES (pH 3.85), and its stability was greater than 99% at 6 hours. The total synthesis time of [⁶⁸Ga]Ga-FAPI-04 was 32.4 minutes, and the product met all quality control criteria. In this study, we developed and optimized a labeling method using [⁶⁸Ga]Ga-FAPI-04 using a cassette-based synthesizer. The devised method is expected to be useful for supplying [⁶⁸Ga]Ga-FAPI-04 for diagnosis in clinical practice.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.5
• /
• pp.177-187
• /
• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• Journal of Radiopharmaceuticals and Molecular Probes
• /
• v.9 no.1
• /
• pp.49-55
• /
• 2023

Tumors are encircled by various non-cancerous cell types in the extracellular matrix, including fibroblasts, endothelial cells, immune cells, and cytokines. Fibroblasts are the most critical cells in the tumor stroma and play an important role in tumor development, which has been highlighted in some epithelial cancers. Many studies have shown a tight connection between cancerous cells and fibroblasts in the last decade. Regulatory factors secreted into the tumor environment by special fibroblast cells, cancer-associated fibroblasts (CAFs), play an important role in tumor and vessel development, metastasis, and therapy resistance. This review addresses the development of FAP inhibitors, emphasizing the first, second, and latest generations. First-generation inhibitors exhibit low selectivity and chemical stability, encouraging researchers to develop new scaffolds based on preclinical and clinical data. Second-generation enzymes such as UAMC-1110 demonstrated enhanced FAP binding and better selectivity. Targeted treatment and diagnostic imaging have become possible by further developing radionuclide-labeled fibroblast activation protein inhibitors (FAPIs). Although all three FAPIs (01, 02, and 04) showed excellent preclinical and clinical findings. The final optimization of these FAPI scaffolds resulted in FAPI-46 with the highest tumor-to-background ratio and better binding affinity.

• International Journal of Aeronautical and Space Sciences
• /
• v.17 no.2
• /
• pp.184-194
• /
• 2016

In existing identification methods for on-orbit spacecraft, such as eigensystem realization algorithm (ERA) and subspace method identification (SMI), singular value decomposition (SVD) is used frequently to estimate the modal parameters. However, these identification methods are often used to process the linear time-invariant system, and there is a lower computation efficiency using the SVD when the system order of spacecraft is high. In this study, to improve the computational efficiency in identifying time-varying modal parameters of large spacecraft, a faster recursive algorithm called fast approximated power iteration (FAPI) is employed. This approach avoids the SVD and can be provided as an alternative spacecraft identification method, and the latest modal parameters obtained can be applied for updating the controller parameters timely (e.g. the self-adaptive control problem). In numerical simulations, two large flexible spacecraft models, the Engineering Test Satellite-VIII (ETS-VIII) and Soil Moisture Active/Passive (SMAP) satellite, are established. The identification results show that this recursive algorithm can obtain the time-varying modal parameters, and the computation time is reduced significantly.

• Korean Journal of Radiology
• /
• v.25 no.1
• /
• pp.86-102
• /
• 2024

Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnetic resonance imaging [MRI], and 18fluorodeoxyglucose positron emission tomography [PET]/CT) have limitations. The advent of new imaging techniques and novel molecular imaging agents have revealed molecular processes in the tumor microenvironment as an application for the early diagnosis and assessment of PM as well as real-time guided surgical resection, which has changed clinical management. In contrast to clinical imaging, which is purely qualitative and subjective for interpreting macroscopic structures, radiomics and artificial intelligence (AI) capitalize on high-dimensional numerical data from images that may reflect tumor pathophysiology. A predictive model can be used to predict the occurrence, recurrence, and prognosis of PM, thereby avoiding unnecessary exploratory surgeries. This review summarizes the role and status of different imaging techniques, especially new imaging strategies such as spectral photon-counting CT, fibroblast activation protein inhibitor (FAPI) PET/CT, near-infrared fluorescence imaging, and PET/MRI, for early diagnosis, assessment of surgical indications, and recurrence monitoring in patients with PM. The clinical applications, limitations, and solutions for fluorescence imaging, radiomics, and AI are also discussed.