How I investigate acquired megaloblastic anemia

Hematological Complications of Human Immunodeficiency Virus (HIV) Infection: An Update From an HIV-Endemic Setting

Medical professionals, particularly in regions with a high burden of human immunodeficiency virus (HIV), should be alert to the hematological complications of HIV, which may include cytopenias, malignancy, and coagulation disturbances. Patients may present with these conditions as the first manifestation of HIV infection. Hematological abnormalities are often multifactorial with opportunistic infections, drugs, malignancy, and HIV infection itself contributing to the clinical presentation, and the diagnosis should consider all these factors. Life-threatening hematological complications requiring urgent diagnosis and management include thrombotic thrombocytopenic purpura, superior mediastinal syndrome, spinal cord compression, and tumor lysis syndrome due to aggressive lymphoma. Antiretroviral therapy is the therapeutic backbone, including for patients with advanced HIV, in addition to specific therapy for the complication. This article reviews the impact of HIV on the hematological system and provides a clinical and diagnostic approach, including the role of a bone marrow biopsy, focusing on perspectives from sub-Saharan Africa.

Individuals Diagnosed With Type 2 Diabetes Mellitus and the Status of Vitamin B12 Deficiency: A Review

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder with a multifactorial etiology and a significant global burden. In recent years, emerging evidence has suggested a potential link between T2DM and vitamin B12 deficiency, raising concerns about its impact on disease progression, management, and associated complications. This comprehensive review critically examines the current understanding of the prevalence, risk factors, clinical implications, and management strategies related to vitamin B12 deficiency in individuals diagnosed with T2DM. The review begins by providing an overview of the epidemiology of T2DM and its associated complications, underscoring the need for comprehensive management approaches. Subsequently, it delves into the physiology of vitamin B12, including its sources, absorption mechanisms, and biological functions, laying the groundwork for understanding the potential implications of deficiency in T2DM. A thorough analysis of the literature is conducted to elucidate the prevalence and risk factors of vitamin B12 deficiency in individuals with T2DM, considering factors such as age, duration of diabetes, medication use (e.g., metformin), dietary patterns, and comorbidities. Special attention is given to the role of metformin, the first-line therapy for T2DM, in precipitating or exacerbating vitamin B12 deficiency through mechanisms involving alterations in the gut microbiota and intestinal absorption. The review further explores the clinical manifestations and diagnostic challenges associated with vitamin B12 deficiency in the context of T2DM, emphasizing the importance of recognizing subtle symptoms and implementing appropriate screening protocols. It discusses the potential implications of vitamin B12 deficiency on glycemic control, diabetic neuropathy, cognitive function, cardiovascular health, and overall quality of life in individuals with T2DM. In addressing the management of vitamin B12 deficiency in T2DM, the review examines various therapeutic strategies, including oral and parenteral supplementation, dietary modifications, and lifestyle interventions. It critically evaluates the evidence supporting routine screening for vitamin B12 deficiency in individuals with T2DM and discusses controversies surrounding optimal supplementation protocols, dosing regimens, and monitoring strategies. Furthermore, the review highlights gaps in current knowledge and identifies areas for future research, such as the long-term effects of vitamin B12 supplementation on clinical outcomes in T2DM, the impact of genetic factors on vitamin B12 metabolism, and the potential role of personalized interventions. Overall, this review consolidates existing evidence and provides insights into the complex relationship between T2DM and vitamin B12 deficiency, aiming to inform clinical practice, enhance patient care, and guide future research endeavors in this important area of metabolic medicine.

Considerations for the Consumption of Vitamin and Mineral Supplements in Athlete Populations

Vitamins and minerals are of fundamental importance to numerous human functions that are essential to optimise athlete performance. Athletes incur a high turnover of key vitamins and minerals and are therefore dependent on sufficient energy intake to replenish nutrient stores. However, many athletes are poor at servicing their energy replenishment needs, especially female athletes, and although a 'food first approach' to meeting nutrient requirements is the primary goal, it may be important for some athletes to consider a vitamin and/or mineral supplement to meet their daily needs. When working to determine if an athlete requires vitamin or mineral supplements, practitioners should use a robust framework to assess the overall energy requirements, current dietary practices and the biological and clinical status of their athletes. Of note, any supplementation plan should account for the various factors that may impact the efficacy of the approach (e.g. athlete sex, the nutrient recommended dietary intake, supplement dose/timing, co-consumption of other foods and any food-drug interactions). Importantly, there are numerous vitamins and minerals of key importance to athletes, each having specific relevance to certain situations (e.g. iron and B vitamins are significant contributors to haematological adaptation, calcium and vitamin D are important to bone health and folate is important in the female athlete); therefore, the appropriate supplement for a given situation should be carefully considered and consumed with the goal to augment an athlete's diet.

Computational analysis of peripheral blood smears detects disease-associated cytomorphologies

Many hematological diseases are characterized by altered abundance and morphology of blood cells and their progenitors. Myelodysplastic syndromes (MDS), for example, are a group of blood cancers characterised by cytopenias, dysplasia of hematopoietic cells and blast expansion. Examination of peripheral blood slides (PBS) in MDS often reveals changes such as abnormal granulocyte lobulation or granularity and altered red blood cell (RBC) morphology; however, some of these features are shared with conditions such as haematinic deficiency anemias. Definitive diagnosis of MDS requires expert cytomorphology analysis of bone marrow smears and complementary information such as blood counts, karyotype and molecular genetics testing. Here, we present Haemorasis, a computational method that detects and characterizes white blood cells (WBC) and RBC in PBS. Applied to over 300 individuals with different conditions (SF3B1-mutant and SF3B1-wildtype MDS, megaloblastic anemia, and iron deficiency anemia), Haemorasis detected over half a million WBC and millions of RBC and characterized their morphology. These large sets of cell morphologies can be used in diagnosis and disease subtyping, while identifying novel associations between computational morphotypes and disease. We find that hypolobulated neutrophils and large RBC are characteristic of SF3B1-mutant MDS. Additionally, while prevalent in both iron deficiency and megaloblastic anemia, hyperlobulated neutrophils are larger in the latter. By integrating cytomorphological features using machine learning, Haemorasis was able to distinguish SF3B1-mutant MDS from other MDS using cytomorphology and blood counts alone, with high predictive performance. We validate our findings externally, showing that they generalize to other centers and scanners. Collectively, our work reveals the potential for the large-scale incorporation of automated cytomorphology into routine diagnostic workflows.

Association between metformin and vitamin B12 deficiency in patients with type 2 diabetes

Diabetes mellitus (DM) is still one of the most common diseases worldwide, and its prevalence is still increasing globally. According to the American and European recommendations, metformin is considered a first-line oral hypo-glycemic drug for controlling type 2 DM (T2DM) patients. Metformin is the ninth most often prescribed drug in the world, and at least 120 million diabetic people are estimated to receive the drug. In the last 20 years, there has been increasing evidence of vitamin B12 deficiency among metformin-treated diabetic patients. Many studies have reported that vitamin B12 deficiency is related to the ma-labsorption of vitamin B12 among metformin-treated T2DM patients. Vitamin B12 deficiency may have a very bad complication for the T2DM patient. In this review, we will focus on the effect of metformin on the absorption of vitamin B12 and on its proposed mechanisms in hindering vitamin B12 absorption. In addition, the review will describe the clinical outcomes of vitamin B12 deficiency in metformin-treated T2DM.

A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia

The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an F1 score of 98.84%.