Circulating platelet count and glycans

Evaluation of artificial intelligence-assisted morphological analysis for platelet count estimation

INTRODUCTION

This study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC-80 digital morphology analyzer.

METHODS

Digital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT-M1) and estimate factor (PLT-M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT-I and PLT-O), digital morphology analyzer (PLT-M1 and PLT-M2), and flow cytometry (PLT-IRM) were compared.

RESULTS

There was no significant deviation observed before and after verification for both PLT-M1 and PLT-M2 across the analysis range (average bias: -0.845/-0.682, 95% limit of agreement (LOA): -28.675-26.985/-29.420-28.056). When platelet alarms appeared, PLT-M1/PLT-M2 showed the strongest correlation with PLT-IRM than PLT-I with PLT-IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT-M1/PLT-M2 and PLT-IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT-M1 and PLT-M2 is related to the number of RBCs. Compared with PLT-I, PLT-M1/PLT-M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low-value PLT.

CONCLUSION

The novel platelet count estimation on the MC-80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.

Mechanistic and Therapeutic Implications of Protein and Lipid Sialylation in Human Diseases

Glycan structures of glycoproteins and glycolipids on the surface glycocalyx and luminal sugar layers of intracellular membrane compartments in human cells constitute a key interface between intracellular biological processes and external environments. Sialic acids, a class of alpha-keto acid sugars with a nine-carbon backbone, are frequently found as the terminal residues of these glycoconjugates, forming the critical components of these sugar layers. Changes in the status and content of cellular sialic acids are closely linked to many human diseases such as cancer, cardiovascular, neurological, inflammatory, infectious, and lysosomal storage diseases. The molecular machineries responsible for the biosynthesis of the sialylated glycans, along with their biological interacting partners, are important therapeutic strategies and targets for drug development. The purpose of this article is to comprehensively review the recent literature and provide new scientific insights into the mechanisms and therapeutic implications of sialylation in glycoproteins and glycolipids across various human diseases. Recent advances in the clinical developments of sialic acid-related therapies are also summarized and discussed.

Pretreatment level of serum sialic acid predicts both qualitative and quantitative bone metastases of prostate cancer

Background

Recently, serum sialic acid (SA) has emerged as a distinct prognostic marker for prostate cancer (PCa) and bone metastases, warranting differential treatment and prognosis for low-volume (LVD) and high-volume disease (HVD). In clinical settings, evaluating bone metastases can prove advantageous.

Objectives

We aimed to establish the correlation between SA and both bone metastasis and HVD in newly diagnosed PCa patients.

Methods

We conducted a retrospective analysis of 1202 patients who received a new diagnosis of PCa between November 2014 and February 2021. We compared pretreatment SA levels across multiple groups and investigated the associations between SA levels and the clinical parameters of patients. Additionally, we compared the differences between HVD and LVD. We utilized several statistical methods, including the non-parametric Mann-Whitney U test, Spearman correlation, receiver operating characteristic (ROC) curve analysis, and logistic regression.

Results

The results indicate that SA may serve as a predictor of bone metastasis in patients with HVD. ROC curve analysis revealed a cut-off value of 56.15 mg/dL with an area under the curve of 0.767 (95% CI: 0.703-0.832, P < 0.001) for bone metastasis versus without bone metastasis and a cut-off value of 65.80 mg/dL with an area under the curve of 0.766 (95% CI: 0.644-0.888, P = 0.003) for HVD versus LVD. Notably, PCa patients with bone metastases exhibited significantly higher SA levels than those without bone metastases, and HVD patients had higher SA levels than LVD patients. In comparison to the non-metastatic and LVD cohorts, the cohort with HVD exhibited higher levels of alkaline phosphatase (AKP) (median, 122.00 U/L), fibrinogen (FIB) (median, 3.63 g/L), and prostate-specific antigen (PSA) (median, 215.70 ng/mL), as well as higher Gleason scores (> 7). Multivariate logistic regression analysis demonstrated that an SA level of > 56.15 mg/dL was independently associated with the presence of bone metastases in PCa patients (OR = 2.966, P = 0.018), while an SA level of > 65.80 mg/dL was independently associated with HVD (OR = 1.194, P = 0.048).

Conclusion

The pretreatment serum SA level is positively correlated with the presence of bone metastases.

Beyond Hemostasis: Platelet Innate Immune Interactions and Thromboinflammation

There is accumulating evidence that platelets play roles beyond their traditional functions in thrombosis and hemostasis, e.g., in inflammatory processes, infection and cancer, and that they interact, stimulate and regulate cells of the innate immune system such as neutrophils, monocytes and macrophages. In this review, we will focus on platelet activation in hemostatic and inflammatory processes, as well as platelet interactions with neutrophils and monocytes/macrophages. We take a closer look at the contributions of major platelet receptors GPIb, α_IIbβ₃, TLT-1, CLEC-2 and Toll-like receptors (TLRs) as well as secretions from platelet granules on platelet-neutrophil aggregate and neutrophil extracellular trap (NET) formation in atherosclerosis, transfusion-related acute lung injury (TRALI) and COVID-19. Further, we will address platelet-monocyte and macrophage interactions during cancer metastasis, infection, sepsis and platelet clearance.