Haemoglobin (Hb) electrophoresis is a method of blood testing used to detect thalassaemia. However, the interpretation of the result of the electrophoresis test itself is a complex task. Expert haematologists, specifically in developing countries, are relatively few in number and are usually overburdened. To assist them with their workload, in this paper we present a novel method for the automated assessment of thalassaemia using Hb electrophoresis images. Moreover, in this study we compile a large Hb electrophoresis image dataset, consisting of 103 strips containing 524 electrophoresis images with a clear consensus on the quality of electrophoresis obtained from 824 subjects. The proposed methodology is split into two parts: (1) single-patient electrophoresis image segmentation by means of the lane extraction technique, and (2) binary classification (normal or abnormal) of the electrophoresis images using state-of-the-art deep convolutional neural networks (CNNs) and using the concept of transfer learning. Image processing techniques including filtering and morphological operations are applied for object detection and lane extraction to automatically separate the lanes and classify them using CNN models. Seven different CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, SqueezeNet and MobileNetV2) were investigated in this study. InceptionV3 outperformed the other CNNs in detecting thalassaemia using Hb electrophoresis images. The accuracy, precision, recall, f1-score, and specificity in the detection of thalassaemia obtained with the InceptionV3 model were 95.8%, 95.84%, 95.8%, 95.8% and 95.8%, respectively. MobileNetV2 demonstrated an accuracy, precision, recall, f1-score, and specificity of 95.72%, 95.73%, 95.72%, 95.7% and 95.72% respectively. Its performance was comparable with the best performing model, InceptionV3. Since it is a very shallow network, MobileNetV2 also provides the least latency in processing a single-patient image and it can be suitably used for mobile applications. The proposed approach, which has shown very high classification accuracy, will assist in the rapid and robust detection of thalassaemia using Hb electrophoresis images.

Objective: Aim was to evaluate the sensitivity and specificity of Mentzer index in differentiating beta thalassemia minor from anemia of Iron deficiency. Materials and Methods: A cross-sectional study conducted in Hematology unit of Hayatabad Medical Complex .Sampling was done non-consecutively. A total of 860 cases with value of Hemoglobin less than 11 Gm/DL were counted. In all the selected cases full blood count were checked and Mentzer Index mean corpuscular volume [MCV] per RBCs count )was calculated. Value of <13 suggests the diagnosis of β Thalassemia minor/trait while more than 13 value is more indicative of Iron deficiency anemia (IDA). Confirmation of diagnoses was made by hemoglobin study using HPLC. Values for specificity and sensitivity for Mentzer Index were calculated for the two differentials of microcytic hypochromic anemia presenting with microcytosis and hypochromia. Results: Mean hemoglobin level of patients was 9.01±1.85. Minimum and maximum hemoglobin value was 2.90 and 11 g/dl. Mean RCB count was 4.66±4.59 minimum and maximum RCB count was 1.07 and 136. Mean MCV value was 63.89±9.01. Minimum and maximum MCV value was 39.40 and 92.10 respectively. Men Mentzer index was 15.70±7.68. Minimum and maximum Mentzer index value was 7.80 and 10.7 respectively. Based on Mentzer index criteria 489(56.86%) patients had iron deficiency anemia and 371(43.14%) patients had higher suspicion of beta thalassemia. Conclusion: It was concluded that for differentiation among Beta thalassemia minor and anemia of iron deficiency , Mentzer Index can be beneficial to discriminate with a high sensitivity as well as specificity percentage and thus through a cost effective way, only suspected cases of beta thalassemia trait/minor can be further confirmed by Hb Electrophoreses. Keywords: Mentzer Index, iron deficiency Anemia, beta Thalassemia minor

Objectives: The objective of the present study was to review the blood components acquisition patterns and practices of transfusion in a tertiary care hospital. Material and Methods: An observational cross sectional retrospective study was conducted in the blood bank of Hayatabad Medical Complex (HMC) Peshawar, for a period of six months. The requisition forms for blood components and details of patients acquiring blood products for transfusion recorded in HMC blood bank Blood components issue register were the sources of data. The data was analyzed using software SPSS version 20. Results: In the study period of six months a total 16025 blood components were transfused with an average of 2670 units per month. whole blood was the maximum blood product transfused and followed by RCC, platelets and FFPs are least utilized blood products. Among total patients 50 % were males while 50% were females. Pediatric units utilized maximum (21.2%) units followed by Gynae/Obs. (19.1%), Medical units demanded 16.7% , Accident and Emergency (A&E) unit 14.1%, surgical units 8.8% , intensive care unit (ICU) 6.8%, Orthopedic units consumed 3.3% , cardiology 3.1%, gastroenterology 2.6 %, oncology 2.4% and endocrinology consumed 1.5 %.

Objectives: To study the changes in red cell indices and counts over a period of 42 days in blood stored in blood bags. Materials and Methods: This research was done in Blood Bank, Khyber Teaching Hospital, Peshawar in collaboration with the main laboratory, Khyber Teaching Hospital, Peshawar. About 450 ml of whole blood was taken from 200 donors into Citrate Phosphate Dextrose Adenine blood bags. The storage temperature for the blood bags was maintained between 2–8°C. Blood samples were collected from blood bags. At days 1 and 42, using a haematology analyser, parameters including haemoglobin, RBC count, WBC count, haematocrit, mean corpuscular volume, mean corpuscular haemoglobin, mean corpuscular haemoglobin concentration and platelet counts were measured. Mean and standard deviation were used for quantitative variables. Frequency and percentages were used for qualitative variables. Results: Through days 1 to 42, Haemoglobin (Hb) level decreased from 13.4gm/dl to 12.78 gm/dl, white cell count (WBC) decreased from 6.03x109/l to 2.95 x109/l, platelets count fell from 207 x109/l to 121 x109/l, neutrophils decreased from 59.6% to 23.8% while lymphocyte count increased from 28.36% to 66.3%. Conclusion: There is a decrease in haemoglobin level and white cell count over a period of 42 days of storage of blood bags. Lymphocyte counts increased during the storage duration of blood bags.