Iranian Journal of Blood and Cancer

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Pancytopenia Revealing Phenylketonuria: Coincidence or First Case Report

Rim Ben Abdelaziz, Fathi Mellouli, Mohamed Tahar Lamouchi, Sana Ben Messaoud, Monia Ben Khaled, Raoudha Doghri, Hela Boudabous, Amel Ben Chehida, Hatem Azzouz, Mohamed Bejaoui, Neji Tebib,
Volume 10, Issue 1 ( March 2018 2018)

Abstract

Pancytopenia in childhood can be caused by a variety of underlying diseases including hematological and non-hematological entities. Phenylketonuria (PKU) is an inborn error of phenylalanine metabolism. No association between PKU and pancytopenia has ever been reported. We report the first case of PKU revealed by a pancytopenia at presentation. The patient was an infant girl born to healthy non-consanguineous parents with unremarkable family history. A hereditary metabolic disease workup was performed due to the presence of unexplained hematological features and a global developmental delay. Plasma aminoacid profile by thin-layer chromatography showed elevation of phenylalanine and urine organic acid chromatography showed accumulation of metabolites of phenylalanine; whereas, methylmalonic acid or other abnormal organic acids were not found. This is the first case of untreated PKU associated with pancytopenia who improved with low-phenylalanine diet.

Intelligent Clinical Support System for Anemia Detection and Diagnosis

Abhishek Rao Rao, Rohit Kumar Tiwari Tiwari, Sushil Kumar Saroj Saroj,
Volume 18, Issue 1 (March-2026 2026)

Abstract

Anemia is a common worldwide health concern affecting almost 25% of people, including 37% of pregnant women, 40% of children between the age of 6 and 59 months, and 30% of women between the age of 15 and 49 years. Anemia is primarily diagnosed through blood tests that measure hemoglobin levels. However, these conventional diagnostic methods rely on invasive blood sampling, which is costly, time-consuming and inaccessible in resource-limited areas. To address these challenges, this study proposes a non-invasive method for anemia detection using Haar Wavelet Transform for feature extraction from conjunctiva images of patients. The essential statistical features are extracted from the conjunctiva images using Haar Wavelet Transform that captures the crucial information indicating anemia. These extracted Haar features are used for training various machine learning methods like k-Nearest Neighbour, Decision Tree, XGBoost, Gradient Boosting, Random Subspace, and Random Forest. The accuracy achieved through k-Nearest Neighbour, Decision Tree, XGBoost, Gradient Boosting, Random Subspace and Random Forest are 99.18%, 97.66%, 95.08%, 79.25%, 95.55%, and 98.24% respectively. The k-NN method outperformed the others with an accuracy of 99.18%. This remarkable performance suggests that non-invasive machine learning techniques based on conjunctiva image analysis could serve as a promising alternative for anemia detection.

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