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Hematopoiesis is a process by which all blood cells are formed. The mechanisms controlling it have been studied for decades. Surprisingly, while hematopoietic stem cells are among the most extensively studied stem cell types, the complete understanding of how they are regulated during development, adulthood, or in non-homeostatic conditions remains elusive. In this review, our primary focus is on research findings that explore where hematopoietic precursors are found in adults outside their primary niches in the bone marrow. This phenomenon is termed extramedullary hematopoiesis (EMH). Early in development hematopoietic stem cells migrate through different regions within and outside the embryo and later the fetus. Although, the primary home for hematopoietic progenitors is the adult bone marrow, it is now recognized that other adult organs may act as hematopoietic progenitor reservoirs both in mice and humans. The first reports about this topic were principally originated from clinical observations, in cases where the bone marrow was malfunctioning, leading to an aberrant hematopoiesis outside the bone marrow. It is worth highlighting that those extramedullary organs, like the small intestine or fat tissue, contain subsets of fully functioning hematopoietic progenitors demonstrated by both in vitro and in vivo studies. Nonetheless, there are still some unanswered questions regarding the source of these cells, how they differ in function compared to their counterparts in the bone marrow, and the specific roles they play within the tissues where they are located.

Reclassification of massive datasets acquired through different approaches, such as web scraping, is a big challenge to demonstrate the effectiveness of a machine learning model. Notably, there is a strong influence of the quality of the dataset used for training those models. Thus, we propose a threshold algorithm as an efficient method to remove stopwords. This method employs an unsupervised classification technique, such as K-means, to accurately categorize user reviews from the IMDb dataset into their most suitable categories, generating a well-balanced dataset. Analysis of the performance of the algorithm revealed a notable influence of the text vectorization method used concerning the generation of clusters when assessing various preprocessing approaches. Moreover, the algorithm demonstrated that the word embedding technique and the removal of stopwords to retrieve the clustered text significantly impacted the categorization. The proposed method involves confirming the presence of a suggested stopword within each review across various genres. Upon satisfying this condition, the method assesses if the word’s frequency exceeds a predefined threshold. The threshold algorithm yielded a mapping genre success above 80% compared to precompiled lists and a Zipf’s law-based method. In addition, we employed the mini-batch K-means method for the clustering formation of each differently preprocessed dataset. This approach enabled us to reclassify reviews more coherently. Summing up, our methodology categorizes sparsely labeled data into meaningful clusters, in particular, by using a combination of the proposed stopword removal method and TF-IDF. The reclassified and balanced datasets showed a significant improvement, achieving 94% accuracy compared to the original dataset.

Abstract Acute lymphocytic leukemia is the most common type of cancer in pediatric individuals. Glucose regulated protein (GRP78) is an endoplasmic reticulum chaperone that facilitates the folding and assembly of proteins and regulates the unfolded protein response pathway. GRP78 has a role in survival of cancer and metastasis and cell-surface associated GRP78 (sGRP78) is expressed on cancer cells but not in normal cells. Here, we explored the presence of sGRP78 in pediatric B-ALL at diagnosis and investigated the correlation with bona fide markers of leukemia. By using a combination of flow cytometry and high multidimensional analysis, we found a distinctive cluster containing high levels of sGRP78, CD10, CD19, and CXCR4 in bone marrow samples obtained from High-risk leukemia patients, which was absent in the compartment of Standard-risk leukemia. We confirmed that sGRP78+CXCR4+ blood-derived cells were more frequent in High-risk leukemia patients. Finally, we analyzed the dissemination capacity of sGRP78 leukemia cells in a model of xenotransplantation. sGRP78+ cells emigrated to the bone marrow and lymph nodes, maintaining the expression of CXCR4. Testing the presence of sGRP78 and CXCR4 together with conventional markers may help to achieve a better categorization of High and Standard-risk pediatric leukemia at diagnosis.

The process by which blood cells are generated has been widely studied in homeostasis and during pathogen-triggered inflammatory response. Recently, murine lungs have been shown to be a significant source of hematopoietic progenitors in a process known as extramedullary hematopoiesis. Using multiparametric flow cytometry, we have identified mesenchymal, endothelial, and hematopoietic progenitor cells that express the secreted small protein Isthmin 1 (ISM1). Further characterization of hematopoietic progenitor cells indicated that ISM1+ Lineage- Sca-1+ c-kit+ (ISM1+ LSK) cells are enriched in short-term hematopoietic stem cells (ST-HSCs). Moreover, most Sca-1+ ISM1+ cells express the residence marker CD49a, and this correlated with their localization in the extravascular region of the lung, indicating that ISM1+ cells are lung-resident cells. We also observed that ISM1+ cells express TLR4, TLR5, and TLR9, and, in a mouse model of sepsis induced by P. aeruginosa, we observed that all the LSK and ISM1+LSK cells were affected. We conclude that ISM1 is a novel biomarker associated with progenitor-like cells. ISM1+ cells are involved in the response to a bacterial challenge, suggesting an association between ISM1-producing cells and dangerous inflammatory responses like sepsis.