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All Journal BULETIN FISIKA Glosains: Jurnal Sains Global Indonesia
Asep Yoyo Wardaya
Universitas Diponegoro
Astronomy Energy Materials Science & Nanotechnology Medicine & Pharmacology Physics

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Studi Kelayakan Fitting Tool Farmakokinetik Berbasis Python untuk Estimasi Time-Integrated Activity Coefficient (TIAC) Deka Andini; Choirul Anam; Asep Yoyo Wardaya; Nur Rahmah Hidayati; Bisma Barron Patrianesha
BULETIN FISIKA Vol. 27 No. 2 (2026): BULETIN FISIKA
Publisher : Departement of Physics Faculty of Mathematics and Natural Sciences, and Institute of Research and Community Services Udayana University, Kampus Bukit Jimbaran Badung Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/BF.2026.v27.i02.p12

Abstract

Studi ini bertujuan  untuk  studi kelayakan implementasi fitting tool berbasis Python pada estimasi nilai TIAC  di organ ginjal. Data biokinetik ginjal kiri dan kanan  dari 8 pasien yang menjalani terapi [177Lu]Lu-DOTATATE yang digunakan pada studi ini diperoleh dari literatur: PMID33443063. Data biokinetik diambil dari pencitraan dual SPECT/CT pada empat titik waktu. Time-activity curve diperoleh dari proses fitting menggunakan mono-exponential fit function dan fungsi objektif untuk optimasi kurva. Evaluasi fitting menggunakan kriteria kontrol goodness of fit (GoF) yaitu inspeksi visual kurva, koefisien determinasi (R2), dan koefisien variasi (KV). Nilai TIAC dihitung menggunakan integrasi numerik area under the curve (AUC) dari kurva hasil fitting. Kemudian, nilai TIAC  yang dihitung menggunakan  fitting tool dibandingkan dengan nilai TIAC dari software SAAM II sebagai referensi menggunakan % relative deviation (RD). Hasil studi  menggunakan fitting tool menunjukkan  inspeksi visual kurva melewati  sebagian besar titik data , nilai R2 mendekati 1 dan koefisien variasi parameter kurva secara umum < 50%. Evaluasi GoF pada pasien ke-5 dan ke-8 tidak memenuhi kriteria akibat underfitting. Hasil perhitungan TIAC dari fitting tool sebesar 1.013 ± 0.459 (jam) dan TIAC dari SAAM II sebesar 0.981 ± 0.390 (jam) serta nilai %RD < 1% . Studi awal kelayakan fitting tool memiliki kesesuaian yang baik terhadap SAAM II memenuhi kriteria kontrol GoF dan  %RD <10%. Fitting tool ini berpotensi sebagai software bantu dosimetri internal yang mudah diintegrasikan dengan workflow klinis, akses lebih mudah dan biaya terjangkau.
Neural Phase Synchronization During Sleep: Comparison of Ischemic Stroke Patients and Healthy Controls Using Kuramoto Model Analysis Berthianna Nurcresia; Asep Yoyo Wardaya; Qidir Maulana Binu Soesanto; Tua Raja Simbolon; Mardjono Tjahjadi
Glosains: Jurnal Sains Global Indonesia Vol. 7 No. 4 (2026): Glosains: Jurnal Sains Global Indonesia
Publisher : Sekolah Tinggi Agama Islam Kuningan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59784/glosains.v7i4.907

Abstract

Background: Neural phase synchronization is essential for coordinating communication across brain regions during sleep, and ischemic stroke may disrupt these collective neural dynamics. Objective: This study aimed to compare neural oscillator synchronization during sleep between healthy controls and patients with ischemic stroke using the Kuramoto model applied to polysomnography (PSG) data. Methods: Secondary EEG data from 150 healthy subjects and 97 patients with ischemic stroke were processed through quality control, signal preprocessing, delta-band filtering (0.5–4 Hz), phase extraction using the Hilbert transform, and calculation of the Kuramoto order parameter (r). Differences in mean synchronization and temporal variability across groups and sleep stages were examined using linear mixed-effects models. Results: After quality control, 147 healthy controls and 94 patients with ischemic stroke were included. Healthy controls consistently showed higher mean r values and greater temporal variability than the ischemic stroke group across N1, N2, N3, and REM sleep. Group, sleep stage, and group-by-stage interaction effects were significant for both mean r and its variability (all p < 0.001). Conclusion: Ischemic stroke was associated with reduced phase synchronization and more restricted neural synchronization dynamics during sleep. PSG-based Kuramoto analysis provides a quantitative framework for characterizing altered neural network dynamics after ischemic stroke, although differences between dataset sources and acquisition systems should be considered when interpreting the findings.
Co-Authors Berthianna Nurcresia Bisma Barron Patrianesha Choirul Anam AM Diponegoro Deka Andini Mardjono Tjahjadi Nur Rahmah Hidayati Qidir Maulana Binu Soesanto Tua Raja Simbolon