Advances in Hematology Laboratory—2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Laboratory Medicine".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 913

Special Issue Editor


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Laboratory, Galdakao-Usansolo Hospital, 48960 Galdakao, Spain
Interests: medicine laboratory; clinical analysis technology; robotics in hematology; iron and anemia; erythropoiesis; glycohemoglobin; infection Inflammation; health
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Special Issue Information

Dear Colleagues,

Complete blood counts (CBCs) and leukocyte differentials are among the most frequently requested laboratory tests. These analyses are highly automated, and the evolution of automation in the hematology laboratory has enabled not only high throughput, greater reliability, and accuracy in the results, but also, as automated hematology systems become more sophisticated with the introduction of new physical principles for cellular analysis, a generation of new parameters to characterize blood cells in more detail. Several parameters have been introduced to CBCs, such as nucleated red blood cells, immature granulocytes, immature platelet fraction, and new parameters for the detection of ineffective erythropoiesis (reticulocyte hemoglobin, red cell subsets). Leucocyte morphometric parameters (cell population data) aid in the preliminary diagnosis of diseases (i.e., differentiating between abnormal lymphocytes in leukemia and viral conditions) and could be useful to ascertain infection and sepsis, and the discrimination of the etiology.

Different technologies can be used to enumerate platelets, and modern counters can provide not only platelet count, but also derived indices relating to the morphology, size, or cytoplasm complexity, related to their functional activity.

The primary aim of this Special Issue is to update the information on the new parameters reported along with CBCs and leukocyte differentials to review the possible clinical applications of these parameters provided by modern hematologic instruments.

Dr. Eloisa Urrechaga
Guest Editor

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Keywords

  • complete blood counts
  • leukocyte differential
  • reticulocyte indices
  • immature platelet fraction
  • cell population data
  • blood cell analyzers

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Published Papers (1 paper)

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Research

12 pages, 736 KB  
Article
The Role of Reticulocyte-Derived Parameters in the Detection of Iron-Restricted Erythropoiesis in the Elderly
by Eloísa Urrechaga and Mónica Fernández
Diagnostics 2026, 16(6), 928; https://doi.org/10.3390/diagnostics16060928 - 20 Mar 2026
Viewed by 462
Abstract
Background: Mindray BC-6800 Plus TM (Mindray, Shenzhen, China) measures reticulocyte counts and provides the reticulocyte hemoglobin (RHe, reticulocyte Hb expression) and mean reticulocyte volume (MRV). We studied the performance of those reticulocyte-derived parameters for the detection of iron-restricted erythropoiesis in older patients, [...] Read more.
Background: Mindray BC-6800 Plus TM (Mindray, Shenzhen, China) measures reticulocyte counts and provides the reticulocyte hemoglobin (RHe, reticulocyte Hb expression) and mean reticulocyte volume (MRV). We studied the performance of those reticulocyte-derived parameters for the detection of iron-restricted erythropoiesis in older patients, compared with standard laboratory tests. Methods: A total of 220 anemic patients, age > 65 years, were recruited in the context of routine health controls. Group differences were assessed using analysis of variance (ANOVA), with p values < 0.05 considered statistically significant. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic performance of RHe and MRV for detecting iron-restricted erythropoiesis. The reference standard for iron deficiency was sTfR > 52 nmol/L. A multivariable logistic regression model was constructed for iron-restricted erythropoiesis, including MRV, Ret-He and s-ferritin as independent covariates, and adjusted for inflammatory status and renal function. Results: Overall, 30.1% in the group had IDA and 29.0% had mixed IDA/ACD, so 59.1% had absolute or functional iron deficiency, while 40.9% had adequate iron supply. RHe and MRV values differed significantly between both groups (p = 0.0001). For s-ferritin, ROC analysis yielded an AUC of 0.685 (95% CI 0.606–0.767), with the best Youden index at a cut-off of 100 µg/L, corresponding to 72.5% sensitivity and 65.9% specificity. An MRV cut-off of 97.4 fL identified iron-restricted erythropoiesis with 88.2% sensitivity and 82.7% specificity (AUC 0.878, 95% CI 0.799–0.957); RHe AUC 0.860, 95% CI 0.777–0.947; cut-off 30.4 pg; sensitivity 82.4%, specificity 79.8%). In multivariable logistic regression adjusted for CRP and eGFR, s-ferritin was not an independent predictor of iron-restricted erythropoiesis, whereas MRV and RHe remained significant. The overall model demonstrated good discrimination, with an AUC 0.808 (95% CI 0.804–0.814). Conclusions: RHe and MRV are reliable parameters for assessing iron supply to erythropoiesis in older patients and can assist in distinguishing iron-restricted erythropoiesis in complex, inflammation-driven settings. Full article
(This article belongs to the Special Issue Advances in Hematology Laboratory—2nd Edition)
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