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All Journal Indonesian Journal of Tropical and Infectious Disease Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Aisya, Rohadatul
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Medicine & Pharmacology Public Health

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Red Laser-Activated Silver Nanoparticles from Green Synthesis Extract of Butterfly Pea for Antimicrobial Photodynamic Therapy Against Staphylococcus aureus Astuti, Suryani Dyah; Farhah, Ghinaa Rihadatul Aisy; Salwa, Umaimah Mitsalia Ummi; Aisya, Rohadatul; Zaidan, Andi Hamim; Yaqubi, Ahmad Khalil
Indonesian Journal of Tropical and Infectious Disease Vol. 12 No. 3 (2024)
Publisher : Institute of Topical Disease Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/ijtid.v12i3.62884

Abstract

This study investigated the potential of photodynamic therapy (PDT) using green-synthesized silver nanoparticles (AgNPs) derived from butterfly pea extract (Clitoria ternatea L.) to combat Staphylococcus aureus (S. aureus). The use of a red diode laser as a method for enhancing the antimicrobial activity of AgNPs presents a novel approach to treating bacterial infections.  The red diode laser is crucial, as it activates the AgNPs, enhancing their antimicrobial properties. This combination of light, natural extract, and nanoparticles underscores the innovative approach of using PDT in treating bacterial infections. By integrating these elements, the study aims to provide insights into effective, biocompatible treatments for antibiotic-resistant bacteria.  The primary objective of this study is to synthesize and characterize AgNPs using butterfly pea extract and evaluate their effectiveness against S. aureus when combined with red laser irradiation.  Silver nanoparticles were synthesized using an environmentally friendly method that processes butterfly pea extract as the reducing agent for the synthesis of the nanoparticles.  Using UV-Vis spectrophotometry to track the creation of silver nanoparticles (AgNPs), it was determined that the butterfly pea extract was an effective source of nanoparticles. The particle size distribution and peak absorbance wavelength were determined by characterization utilizing a Particle Size Analyzer (PSA). Tryptic soy agar (TSA) plates were used to investigate the antibacterial activity of AgNPs against Staphylococcus aureus (S. aureus). The effectiveness of photoinactivation against S. aureus was evaluated by exposing AgNPs at a concentration of 1 mM to a red diode laser for 90 seconds. The results showed that the produced AgNPs had potential antibacterial capabilities when combined with red light therapy. The results demonstrated that the synthesized silver nanoparticles can effectively kill or inhibit the growth of Staphylococcus aureus (S. aureus) when exposed to a red diode laser for 90 seconds. The findings suggest that photodynamic therapy using green-synthesized AgNPs and red laser irradiation could be a promising approach to controlling bacterial infections like S. aureus. Further research is recommended to explore the underlying mechanisms of photoinactivation and to optimize treatment parameters for in vivo applications on experimental animals.
Application Of Electrical Impedance Tomography For Detecting Meat (Body Tissue): A Study On Frequency And Amplitude Variations Aisya, Rohadatul; Samatha, Syifa Candiki; Ain, Khusnul; Astuti, Suryani Dyah
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.54

Abstract

Electrical Impedance Tomography (EIT) is an emerging non-invasive imaging technique with significant potential for detecting tissue anomalies; however, its performance is highly sensitive to variations in the frequency and amplitude of the injected electrical signals, which can lead to challenges in accurately differentiating between tissue types and detecting subtle pathological changes. This study aims to optimize EIT performance by systematically investigating the impact of signal frequency and amplitude on image reconstruction quality, thereby enhancing diagnostic accuracy. A portable multi-frequency EIT system was developed using Analog Discovery 2 and MATLAB, featuring a 16-electrode configuration arranged evenly around a tissue phantom, with beef tissue serving as an analog for human tissue due to its comparable conductivity properties. The experimental protocol varied signal amplitudes from 0.4 mA to 1.0 mA and frequencies from 50 kHz to 120 kHz, while two reconstruction algorithms the Gauss-Newton method and the GREIT algorithm were employed to evaluate image quality. Results demonstrated that the Gauss-Newton method achieved superior image clarity, with an approximate 18% improvement in reconstruction accuracy and a 20% reduction in noise at an optimal setting of 100 kHz frequency and 0.8 mA amplitude. Although the GREIT method provided faster reconstruction times, its lower sensitivity to amplitude variations resulted in less detailed anomaly detection. Overall, these findings underscore the critical importance of optimizing electrical parameters in EIT systems to enhance diagnostic capabilities. Future research should focus on integrating machine learning algorithms for real-time image processing and expanding the evaluation to include diverse tissue models to further improve the clinical applicability and robustness of EIT-based diagnostics.
Co-Authors Andi Hamim Zaidan Farhah, Ghinaa Rihadatul Aisy Khusnul Ain Salwa, Umaimah Mitsalia Ummi Samatha, Syifa Candiki Suryani Dyah Astuti Yaqubi, Ahmad Khalil