Schistocytes are fragmented red blood cells produced as a result of mechanical damage to erythrocytes, usually due to microangiopathic thrombotic diseases or mechanical factors. The early laboratory detection of schistocytes has a critical impact on the timely diagnosis, effective treatment, and positive prognosis of diseases such as thrombocytopenic purpura and hemolytic uremic syndrome. Due to the rapid development of science and technology, laboratory hematology has also advanced. The accuracy and efficiency of tests performed by fully automated hematology analyzers and fully automated morphology analyzers have been considerably improved. In recent years, substantial improvements in computing power and machine learning (ML) algorithm development have dramatically extended the limits of the potential of autonomous machines. The rapid development of machine learning and artificial intelligence (AI) has led to the iteration and upgrade of automated detection of schistocytes. However, along with significantly facilitated operation processes, AI has brought challenges. This review summarizes the progress in laboratory schistocyte detection, the relationship between schistocytes and clinical diseases, and the progress of AI in the detection of schistocytes. In addition, current challenges and possible solutions are discussed, as well as the great potential of AI techniques for schistocyte testing in peripheral blood.

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.

BACKGROUND: Non-anemic thalassemia trait (TT) accounted for a high proportion of TT cases in South China.
OBJECTIVE: To use artificial intelligence (AI) analysis of erythrocyte morphology and machine learning (ML) to identify TT gene carriers in a non-anemic population.
METHODS: Digital morphological data from 76 TT gene carriers and 97 controls were collected. The AI technology-based Mindray MC-100i was used to quantitatively analyze the percentage of abnormal erythrocytes. Further, ML was used to construct a prediction model.
RESULTS: Non-anemic TT carriers accounted for over 60% of the TT cases. Random Forest was selected as the prediction model and named TT@Normal. The TT@Normal algorithm showed outstanding performance in the training, validation, and external validation sets and could efficiently identify TT carriers in the non-anemic population. The top three weights in the TT@Normal model were the target cells, microcytes, and teardrop cells. Elevated percentages of abnormal erythrocytes should raise a strong suspicion of being a TT gene carrier. TT@Normal could be promoted and used as a visualization and sharing tool. It is accessible through a URL link and can be used by medical staff online to predict the possibility of TT gene carriage in a non-anemic population.
CONCLUSIONS: The ML-based model TT@Normal could efficiently identify TT carriers in non-anemic people. Elevated percentages of target cells, microcytes, and teardrop cells should raise a strong suspicion of being a TT gene carrier.

BACKGROUND: Overhydrated hereditary stomatocytosis (OHSt) is a rare disorder characterized by abnormalities in erythrocytic volume homeostasis. Early and accurate diagnosis is essential for appropriate management and genetic counseling.
METHODS: We present the case of a child with beta-thalassemia and a history of multiple blood transfusions. Clinical presentation, laboratory findings, and genetic testing were reviewed. Peripheral blood smear examination and genetic analysis were performed.
RESULTS: The patient was admitted with severe anemia, and peripheral blood smear examination revealed the presence of up to 50% stomatocytes. Laboratory investigations showed abnormalities in red blood cell parameters, including decreased hemoglobin levels and increased mean corpuscular volume. Genetic testing identified a heterozygous mutation in the RHAG gene, confirming the diagnosis of OHSt. The presence of stomatocytes in the peripheral blood smear was transient, correlating with episodes of hemolysis and its control.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. Due to the lack of specific therapeutic targets, treatment options are limited, and the recurrence and metastasis rate is high, the overall survival of patients is poor. However, with the discovery of some new targets and the corresponding immune regulation after targeting these targets, TNBC has a new hope in treatment. The peptide has a simple structure, strong binding affinity, and high stability, and has great potential in targeted therapy and immune regulation against TNBC. This review will discuss how single peptides and peptide combinations target triple-negative breast cancer to exert immunomodulatory effects. Among them, single peptides target specific receptors on TNBC cells, act as decoys to target key ligands in the regulatory pathway, and target TME-related cells. The combinations of peptides work in the form of cancer vaccines, engineered exosomes, microRNAs and other immune-related molecular pathways, immune checkpoint inhibitors, chimeric antigen receptor T cells, and drug-peptide conjugates. This article is mainly dedicated to exploring new treatment methods for TNBC to improve the curative effect and prolong the survival time of patients.

Cell migration plays important roles in many biological processes, but how migrating cells orchestrate intracellular molecules and subcellular structures to regulate their speed and direction is still not clear. Here, by characterizing the intracellular diffusion and the three-dimensional lamellipodium structures of fish keratocyte cells, we observe a strong positive correlation between the intracellular diffusion and cell migration speed and, more importantly, discover a switching of cell migration modes with reversible intracellular diffusion variation and lamellipodium structure deformation. Distinct from the normal fast mode, cells migrating in the newly-found slow mode have a deformed lamellipodium with swollen-up front and thinned-down rear, reduced intracellular diffusion and compartmentalized macromolecule distribution in the lamellipodium. Furthermore, in turning cells, both lamellipodium structure and intracellular diffusion dynamics are also changed, with left-right symmetry breaking. We propose a mechanism involving the front-localized actin polymerization and increased molecular crowding in the lamellipodium to explain how cells spatiotemporally coordinate the intracellular diffusion dynamics and the lamellipodium structure in regulating their migrations.

Erythrophagocytosis occurring in the spleen is a critical process for removing senescent and diseased red blood cells (RBCs) from the microcirculation. Although some progress has been made in understanding how the biological signaling pathways mediate the phagocytic processes, the role of the biophysical interaction between RBCs and macrophages, particularly under pathological conditions such as sickle cell disease, has not been adequately studied. Here, we combine computational simulations with microfluidic experiments to quantify RBC-macrophage adhesion dynamics under flow conditions comparable to those in the red pulp of the spleen. We also investigate the RBC-macrophage interaction under normoxic and hypoxic conditions. First, we calibrate key model parameters in the adhesion model using microfluidic experiments for normal and sickle RBCs under normoxia and hypoxia. We then study the adhesion dynamics between the RBC and the macrophage. Our simulation illustrates three typical adhesion states, each characterized by a distinct dynamic motion of the RBCs, namely firm adhesion, flipping adhesion, and no adhesion (either due to no contact with macrophages or detachment from the macrophages). We also track the number of bonds formed when RBCs and macrophages are in contact, as well as the contact area between the two interacting cells, providing mechanistic explanations for the three adhesion states observed in the simulations and microfluidic experiments. Furthermore, we quantify, for the first time to our knowledge, the adhesive forces between RBCs (normal and sickle) and macrophages under different oxygenated conditions. Our results show that the adhesive forces between normal cells and macrophages under normoxia are in the range of 33-58 pN and 53-92 pN for sickle cells under normoxia and 155-170 pN for sickle cells under hypoxia. Taken together, our microfluidic and simulation results improve our understanding of the biophysical interaction between RBCs and macrophages in sickle cell disease and provide a solid foundation for investigating the filtration function of the splenic macrophages under physiological and pathological conditions.

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OBJECTIVE: The correlation between fatty acids (FAs) and depression is not yet conclusive. This study examined the relationship of FAs composition with the presence and clinical characteristics of first-diagnosed, drug-naïve patients with depression.
METHODS: A total of 139 first-diagnosed, drug-naïve patients with depression and 55 healthy controls (HCs) were included in the cross-sectional study. The levels of erythrocyte membrane FAs were compared and then the correlation between clinical symptoms and fatty acid levels in depression was investigated.
RESULTS: Compared to HCs, patients with depression had higher C18:1n9t (z = -2.033, p = 0.042), C20:4n6 (z = -2.104, p = 0.035), C20:3n6 (z = -2.104, p = 0.035) and n-6 polyunsaturated fatty acids (PUFAs) (z = -2.127, p = 0.033), whereas the levels of C18:1n9c (z = -3.348, p = 0.001) were significantly lower. Higher C20:3n6, C20:4n6, C18:1n9t and n-6 PUFAs levels were associated with higher severity of depressive and anxiety symptoms in patients with depression, and the correlation remained after adjusting for the related confounding factors (p < 0.05).
CONCLUSIONS: Patients with first-diagnosed, drug-naïve depression show abnormal erythrocyte fatty acid composition. Trans fatty acids (TFAs) and n-6 PUFAs levels are closely related to clinical characteristics of depression. This study indicated that increased n-6 PUFAs and TFAs are characteristic changes of first-diagnosed, drug-naïve depression.

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