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All Journal Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan Indonesian Journal of Clinical Pathology and Medical Laboratory (IJCPML) Narra J
Anggraini Iriani
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Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Public Health

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NILAI RUJUKAN SOLUBLE TRANSFERRIN RECEPTOR (sTfR) {(Soluble Transferrin Receptor Refence Value (sTfR)} Anggraini Iriani; Endah Purnamasari; Riadi Wirawan
INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY Vol 21, No 3 (2015)
Publisher : Indonesian Association of Clinical Pathologist and Medical laboratory

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24293/ijcpml.v21i3.1268

Abstract

Iron in plasma is carried by transferrin delivered to cells through the interaction with a specific membrane receptor, namelytransferrin receptor. The soluble transferrin receptor (sTfR) is a transferrin receptor monomer which lost its first 100 amino acids, andcirculates in the form of transferrin and its receptor complex. Erythroblasts and reticulocytes are the main source of serum TfR Theconcentration of sTfR in serum is useful to diagnose iron deficiency, especially for patient with chronic disease. A new parameter sTfRis reported to be a surrogate marker of bone marrow iron store. The sTfR concentration can describe the functional iron status whileferritin reflects the iron storage status. The aim of this study was to know a reference interval of sTfR in normal adults by provision.Subjects were 157 healthy adults from clinical medical check up who had met the inclusion criteria and were willing to participate asresearch subjects. Soluble Transferrin Receptor (sTfR) examination was performed using reagents from Roche. The statistical calculationswere performed by SPSS 22. The results showed that there was no significant difference between sTfR levels in men and women as wellas in the age group ≤40 years and >40 years. The STfR reference value in this study was calculated based on 95% CI (X±2SD), is0.197–0.598 mg/dL. It can be concluded that the sTfR reference value is 0.197–0.598 mg/dL.
Determination of Complete Blood Count Reference Values of Mindray BC-760 Hematology Analyzer Iriani, Anggraini; Armenia, Dhinasty; Putri, Dian Eka; Shafwandi, Ritki Fazapadena; Murdianto, Rendy; Aditya, Legina; Purnamasari, Endah; Bahri, Syukrini; Aprilio, Henri; Poerwantoro, Bambang
Jurnal Profesi Medika : Jurnal Kedokteran dan Kesehatan Vol 18 No 1 (2024): Jurnal Profesi Medika : Jurnal Kedokteran dan Kesehatan
Publisher : Fakultas Kedokteran UPN Veteran Jakarta Kerja Sama KNPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33533/jpm.v18i1.7644

Abstract

Hematological assessment serves as a standard examination in supporting diagnostics in clinical practice. The advanced hematology analyzer instrument namely Mindray BC 760 has recently introduced several new hematological parameters which previously unavailable. This study set out to establish the value of the reference interval of those new parameters on the Mindray BC 760 device in the Indonesian adult population. This observational study took place at Yarsi Hospital, Jakarta from March to August 2023. A total of 352 subjects who underwent medical check-ups at pathology clinics laboratory in the hospital were enrolled. All participants comprised both females and males aged > 17 years and were confirmed to be healthy through examination. Hematological assessment using Mindray BC 760 device. A remarkable significant difference was observed between females and males (p< 0.05) in terms of Hb, RBC, MCH, MCHC, RHE, RET, PLT, PDW, PCT, PLCC, Monocyte and eosinophil count, and neutrophil%. Conversely, MCV, RDW-SD, RET%, IRF, LFR, MFR, HFR, NRBC, NRBC%, WBC, NEU, LYM, BAS, IMG, LYM%, BAS%, IMG%, MPV, IPF showed no significant differences based on gender. It is recommended to adjust of reference interval value according to gender for several novel hematological parameters assessed through the Mindray BC 760 instrument.
The Role of Fibrin Monomer Compared to D-dimer and CRP in Determining COVID-19 Severity Iriani, Anggraini; Sukorini, Usi; Fatina, Marsya Kaila; Aflah, Naja F; Aiman, Sarah A; Gemilang, Rizka K; Kamelia, Telly
INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY Vol. 30 No. 2 (2024)
Publisher : Indonesian Association of Clinical Pathologist and Medical laboratory

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24293/ijcpml.v30i2.2110

Abstract

Fibrin Monomer (FM), as a product of thrombin activity in cleaving fibrinogen, can be used as an early marker of thrombotic events in COVID-19 patients. D-dimer is a commonly used marker of hemostasis as a product of plasmin activity in cleaving polymeric fibrin. D-dimer is often used to help decide whether to initiate anticoagulant administration. This study aims to know whether FM can be used as a marker for thrombotic events such as D-dimer in COVID-19 patients; CRP levels were also examined to determine how inflammation affected the two hemostatic indicators. A total of 93 patients were confirmed with COVID-19 by PCR. The median (min-max) FM in the severe stage was 4.53 (2.26-58.20)ug/mL, whereas, in the mild-moderate stage, it was 4.21 (2.19-32.35 ug/mL. There are significant differences in median D-dimer levels in severe stages to mild-moderate, respectively 0.46 (0.14–7.58) and 0.7890, and ages. The level of FM that can be used to differentiate the severe stage  is > 4.46 ug/mL (sensitivity 56.3%, specificity 58.0%) as in the D-dimer level is > 0.58 ug/mL ((sensitivity 75.0%, specificity 65.2%). There is a moderate positive correlation between fibrin monomer and D-dimer, a weak positive correlation between D-dimer and CRP, and no correlation between FM and CRP. This study concludes that the FM median level is higher in severe COVID-19 than in D-dimer. Fibrin monomer levels have a positive correlation with D-dimer. Fibrin Monomer levels are not affected by CRP.  
Designing the CORI score for COVID-19 diagnosis in parallel with deep learning-based imaging models Kamelia, Telly; Zulkarnaien, Benny; Septiyanti, Wita; Afifi, Rahmi; Krisnadhi, Adila; Rumende, Cleopas M.; Wibisono, Ari; Guarddin, Gladhi; Chahyati, Dina; Yunus, Reyhan E.; Pratama, Dhita P.; Rahmawati, Irda N.; Nareswari, Dewi; Falerisya, Maharani; Salsabila, Raissa; Baruna, Bagus DI.; Iriani, Anggraini; Nandipinto, Finny; Wicaksono, Ceva; Sini, Ivan R.
Narra J Vol. 5 No. 2 (2025): August 2025
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v5i2.1606

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has triggered a global health crisis and placed unprecedented strain on healthcare systems, particularly in resource-limited settings where access to RT-PCR testing is often restricted. Alternative diagnostic strategies are therefore critical. Chest X-rays, when integrated with artificial intelligence (AI), offers a promising approach for COVID-19 detection. The aim of this study was to develop an AI-assisted diagnostic model that combines chest X-ray images and clinical data to generate a COVID-19 Risk Index (CORI) Score and to implement a deep learning model based on ResNet architecture. Between April 2020 and July 2021, a multicenter cohort study was conducted across three hospitals in Jakarta, Indonesia, involving 367 participants categorized into three groups: 100 COVID-19 positive, 100 with non-COVID-19 pneumonia, and 100 healthy individuals. Clinical parameters (e.g., fever, cough, oxygen saturation) and laboratory findings (e.g., D-dimer and C-reactive protein levels) were collected alongside chest X-ray images. Both the CORI Score and the ResNet model were trained using this integrated dataset. During internal validation, the ResNet model achieved 91% accuracy, 94% sensitivity, and 92% specificity. In external validation, it correctly identified 82 of 100 COVID-19 cases. The combined use of imaging, clinical, and laboratory data yielded an area under the ROC curve of 0.98 and a sensitivity exceeding 95%. The CORI Score demonstrated strong diagnostic performance, with 96.6% accuracy, 98% sensitivity, 95.4% specificity, a 99.5% negative predictive value, and a 91.1% positive predictive value. Despite limitations—including retrospective data collection, inter-hospital variability, and limited external validation—the ResNet-based AI model and the CORI Score show substantial promise as diagnostic tools for COVID-19, with performance comparable to that of experienced thoracic radiologists in Indonesia.
Co-Authors Aditya, Legina Afifi, Rahmi Aflah, Naja F Aiman, Sarah A Aprilio, Henri Ari Wibisono Armenia, Dhinasty Bahri, Syukrini Bambang Poerwantoro Baruna, Bagus DI. Chahyati, Dina Dian Eka Putri, Dian Eka Endah purnamasari Endah Purnamasari Falerisya, Maharani Fatina, Marsya Kaila Gemilang, Rizka K Guarddin, Gladhi Krisnadhi, Adila Murdianto, Rendy Nandipinto, Finny Nareswari, Dewi Pratama, Dhita P. Rahmawati, Irda N. Riadi Wirawan Rumende, Cleopas M. Salsabila, Raissa Septiyanti, Wita Shafwandi, Ritki Fazapadena Sini, Ivan R. Suzanna Immanuel Telly Kamelia Usi Sukorini Wicaksono, Ceva Yunus, Reyhan E. Zulkarnaien, Benny