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All Journal Journal of Energy, Material, and Instrumentation Technology Pharmaceutical Sciences and Research (PSR)
Gifari, Muhammad Wildan
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Control & Systems Engineering Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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Artificial Intelligence toward Personalized Medicine Gifari, Muhammad Wildan; Samodro, Pugud; Kurniawan, Dhadhang Wahyu
Pharmaceutical Sciences and Research Vol. 8, No. 2
Publisher : UI Scholars Hub

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Abstract

In current medical practice when a patient feels symptoms he/she would consult the doctor. The doctor then gives medication in a one-fits-all fashion. However, recent genetics studies had shown that different genetic makeup can results in different effects on medication, so the medication should be customed for every individual. The main idea of “personalized medicine” is to provide the right intervention including medication to the right patient at the right time and dose. With this approach, the medication paradigm would shift from curative to preventive. The rise of personalized medicine had been possible because the information from ever-increasing biomolecular (proteomics, genomics, and other omics) and health-related data are successfully “mined” by Artificial Intelligence (AI) tools. In this paper, we proposed that AI systems toward personalized medicine must have acceptable performance, be readily interpretable by the clinical community, and be validated in a large cohort. We examined a few landmark papers with the keyword “AI for personalized medicine application”; 1) automatic image-based patient classification, 2) automatic gene-based cancer classification, and 3) automatic health-record heart failure with preserved ejection fraction patient phenotyping. All the examples are evaluated by their performance, interpretability, and clinical validity. From the analysis, we concluded that AI for personalized medicine could benefit by five factors: (1) standardization and pooling of genetics and health data, nationally and internationally, (2) the use of multi-modalities data, (3) disease specialist to guide the development of AI model, (4) investigation of AI-finding by clinical community, and (5) follow-up of AI-finding by the large clinical trial.
Artificial Intelligence for Detecting Non-alcoholic Steatohepatitis (NASH) Gifari, Muhammad Wildan; Ramadhani, Yogi; Kurniawan, Dhadhang Wahyu
Pharmaceutical Sciences and Research Vol. 12, No. 1
Publisher : UI Scholars Hub

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Abstract

Non-alcoholic steatohepatitis (NASH), an inflammatory disease of the liver, has recently raised concern among healthcare professionals worldwide due to its asymptomatic features, making early diagnosis challenging. If left unnoticed, NASH often progresses to lethal diseases such as liver fibrosis or hepatocellular carcinoma. Recent developments in the field of artificial intelligence (AI) might facilitate the early diagnosis of NASH in a more efficient manner, forming a promising strategy to diagnose patients. In simple terms, AI is any machine that is capable of human-level intelligence, including visual perception, speech recognition, or decision making. A subclass of AI, which particularly deals with knowledge-based systems to find a relationship between different datasets, is called machine learning (ML). ML is based on the capability of a system to define or learn a relationship between the input and output data and then apply the learned relationship to any future datasets with a similar structure. The capability to maintain and analyze large datasets and aid in the prediction of outcomes makes ML particularly interesting for the application in NASH by, for instance, analyzing image data from patients, using biomarkers to predict clinical disease progression or by determining the efficacy of applied therapeutics. In this review, we will highlight the recent developments in the AI-based diagnosis and treatment of liver diseases. First, we provide a brief introduction to AI and ML before generalizing the use of AI in the diagnosis and treatment of different liver diseases. Then, we will specifically elaborate on the use of AI in the detection of NASH and its precursor, non-alcoholic fatty liver disease (NAFLD), focusing on the prediction and diagnosis of NASH and NAFLD as well as on the automation of imaging processes. Finally, we will highlight the clinical importance of AI in the detection of NASH before concluding with the future challenges for the application of AI in the field of NASH detection and treatment.
Morphological Study of Electrospun Polyvinylpyrrolidone Fibers at High Concentration Using Water and Ethanol Solvents Nugroho, Doni Bowo; Kamal, Nada Nadzira Ayasha; Sidabalok, Jenni Bunga Enjelita; Wati, Rosita; Resfita, Nova; Gifari, Muhammad Wildan
Journal of Energy, Material, and Instrumentation Technology Vol 6 No 4 (2025): Journal of Energy, Material, and Instrumentation Technology (In Press)
Publisher : Departement of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

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Polyvinylpyrrolidone (PVP) is widely used in biomedical applications, and electrospinning is a common method for fabricating PVP nanofibers. While most studies focus on low to moderate concentrations (5–12 wt%), this work investigates the electrospinning of high-concentration PVP solutions, 50% (m/v), using distilled water and ethanol under applied voltages of 8 and 12 kV. Fiber morphology was characterized by scanning electron microscopy (SEM) and diameter distributions analyzed with ImageJ. Results showed that water-based solutions produced discontinuous fibers with ribbons, beads, and film-like structures, while ethanol-based solutions formed irregular fiber networks at 8 kV but transformed into globular particles at 12 kV due to jet instability. Diameter distribution of water-based fibers was broader (0.31–1.83 µm), whereas ethanol-based fibers exhibited a narrower but larger range (1.29–3.54 µm). These findings indicate that excessive polymer concentration leads to unstable structures, contrasting with continuous fibers reported at lower concentrations. The study highlights the limitations of electrospinning PVP at high concentrations and suggests potential applications in porous films and drug-release systems rather than uniform nanofibers.
Fabrication of Bioceramic Carbonated Hydroxyapatite–Chitosan Composite Scaffold Derived from River Snail Shells via Freeze-Drying for Bone Grafting Applications Wati, Rosita; Alnovera, Vayza Deva; Herbanu, Aldi; Endah; Tresnaningtyas, Sekar Asri; Gifari, Muhammad Wildan; Siburian, Marsudi
Journal of Energy, Material, and Instrumentation Technology Vol 6 No 4 (2025): Journal of Energy, Material, and Instrumentation Technology (In Press)
Publisher : Departement of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

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Abstract

Co-Authors Alnovera, Vayza Deva Dhadhang Wahyu Kurniawan Doni Bowo Nugroho Endah Herbanu, Aldi Kamal, Nada Nadzira Ayasha Resfita, Nova Rosita Wati Samodro, Pugud Siburian, Marsudi Sidabalok, Jenni Bunga Enjelita Tresnaningtyas, Sekar Asri Yogi Ramadhani