Novel Pipeline for Diagnosing Acute Lymphoblastic Leukemia Sensitive to Related Biomarkers

Acute Lymphoblastic Leukemia (ALL) is one of the most common types of childhood blood cancer. The quick start of the treatment process is critical to saving the patient's life, and for this reason, early diagnosis of this disease is essential. Examining the blood smear images of these patients is one of the methods used by expert doctors to diagnose this disease. Deep learning-based methods have numerous applications in medical fields, as they have significantly advanced in recent years. ALL diagnosis is not an exception in this field, and several machine learning-based methods for this problem have been proposed. In previous methods, high diagnostic accuracy was reported, but our work showed that this alone is not sufficient, as it can lead to models taking shortcuts and not making meaningful decisions. This issue arises due to the small size of medical training datasets. To address this, we constrained our model to follow a pipeline inspired by experts' work. We also demonstrated that, since a judgement based on only one image is insufficient, redefining the problem as a multiple-instance learning problem is necessary for achieving a practical result. Our model is the first to provide a solution to this problem in a multiple-instance learning setup. We introduced a novel pipeline for diagnosing ALL that approximates the process used by hematologists, is sensitive to disease biomarkers, and achieves an accuracy of 96.15%, an F1-score of 94.24%, a sensitivity of 97.56%, and a specificity of 90.91% on ALL IDB 1. Our method was further evaluated on an out-of-distribution dataset, which posed a challenging test and had acceptable performance. Notably, our model was trained on a relatively small dataset, highlighting the potential for our approach to be applied to other medical datasets with limited data availability

Downregulation of ITM2A Gene Expression in Macrophages of Patients with Ankylosing Spondylitis

Objectives: Ankylosing spondylitis (AS) is a rheumatic disorder that is mostly determined by genetic and environmental factors. Given the known importance of macrophage in AS pathogenesis, we investigated the transcriptional profile of macrophage cells in the disease. Methods and Results: Two approaches of differential expression and subsequently, weighted gene co-expression network analysis was utilized to analyze a publicly available microarray dataset of macrophages. Integral membrane protein 2A (ITM2A) was among the most significant genes with a decreased trend in the common results of both methods. In order to confirm the finding, the expression of ITM2A was evaluated in monocyte-derived (M2-like) and M1 macrophages obtained from 14 AS patients and 14 controls. Macrophages were differentiated from whole-blood separated monocytes by 7 days incubating with macrophage colony-stimulating factor and then macrophages specific markers were verified with the flow cytometer. M1 polarization was induced by IFN-gamma and lipopolysaccharide. Finally, relative gene expression analysis by real-time polymerase chain reaction revealed a significant downregulation of the ITM2A gene in both M2 like and M1 macrophages of the AS group compared to the control. Conclusion: Since ITM2A plays a critical role in osteo- and chondrogenic cellular differentiation, our finding may provide new insights into AS pathogenesis.Peer reviewe

Downregulation Of Extracellular Matrix And Cell Adhesion Molecules In Cumulus Cells Of Infertile Polycystic Ovary Syndrome Women With And Without Insulin Resistance

Objective The extracellular matrix (ECM) of the cumulus oocyte complex (COC) is composed of several molecules that have different roles during follicle development. This study aims to explore gene expression profiles for ECM and cell adhesion molecules in the cumulus cells of polycystic ovary syndrome (PCOS) patients based on their insulin sensitivity following controlled ovarian stimulation (COS). Materials And Methods In this prospective case-control study enrolled 23 women less than 36 years of age who participated in an intracytoplasmic sperm injection (ICSI) program. Patients were subdivided into 3 groups: control (n=8, fertile women with male infertility history), insulin resistant (IR) PCOS (n=7), and insulin sensitive (IS) PCOS (n=8). We compared 84 ECM component and adhesion molecule gene expressions by quantitative real-time polymerase chain reaction array (qPCR-array) among the groups. Results We noted that 21 of the 84 studied genes differentially expressed among the groups, from which 18 of these genes downregulated. Overall, comparison of PCOS cases with controls showed downregulation of extracellular matrix protein 1 (ECM1); catenin (cadherin-associated protein), alpha 1 (CTNNA1); integrin, alpha 5 (ITGA5); laminin, alpha 3 (LAMA3); laminin, beta 1 (LAMB1); fibronectin 1 (FN1); and integrin, alpha 7 (ITGA7). In the IS group, there was upregulation of ADAM metallopeptidase with thrombospondin type 1 motif, 8 (ADAMTS8) and neural cell adhesion molecule 1 (NCAM1) compared with the controls (P<0.05). Conclusion Downregulation of ECM and cell adhesion molecules seem to be related to PCOS. Gene expression profile alterations in cumulus cells from both the IS and IR groups of PCOS patients seems to be involved in the composition and regulation of ECM during the ovulation process. This study highlights the association of ECM gene alteration as a viewpoint for additional understanding of the etiology of PCOS

Defining microRNA signatures of hair follicular stem and progenitor cells in healthy and androgenic alopecia patients

[Background]: The exact pathogenic mechanism causes hair miniaturization during androgenic alopecia (AGA) has not been delineated. Recent evidence has shown a role for non-coding regulatory RNAs, such as microRNAs (miRNAs), in skin and hair disease. There is no reported information about the role of miRNAs in hair epithelial cells of AGA.[Objectives]: To investigate the roles of miRNAs affecting AGA in normal and patient’s epithelial hair cells.[Methods]: Normal follicular stem and progenitor cells, as well as follicular patient’s stem cells, were sorted from hair follicles, and a miRNA q-PCR profiling to compare the expression of 748 miRNA (miRs) in sorted cells were performed. Further, we examined the putative functional implication of the most differentially regulated miRNA (miR-324-3p) in differentiation, proliferation and migration of cultured keratinocytes by qRT-PCR, immunofluorescence, and scratch assay. To explore the mechanisms underlying the effects of miR-324-3p, we used specific chemical inhibitors targeting pathways influenced by miR-324-3p.[Result]: We provide a comprehensive assessment of the "miRNome" of normal and AGA follicular stem and progenitor cells. Differentially regulated miRNA signatures highlight several miRNA candidates including miRNA-324-3p as mis regulated in patient’s stem cells. We find that miR-324-3p promotes differentiation and migration of cultured keratinocytes likely through the regulation of mitogen-activated protein kinase (MAPK) and transforming growth factor (TGF)-β signaling. Importantly, pharmacological inhibition of the TGF-β signaling pathway using Alk5i promotes hair shaft elongation in an organ-culture system.[Conclusion]: Together, we offer a platform for understanding miRNA dynamic regulation in follicular stem and progenitor cells in baldness and highlight miR-324-3p as a promising target for its treatment.This study was funded by a grant provided from Royan Institute and Disease Models & Mechanisms Travelling Fellowship by Biologists Company.Peer reviewe

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Comparative In Vitro Evaluation of Human Dental Pulp and Follicle Stem Cell Commitment

Objective: Pulp and periodontal tissues are well-known sources of mesenchymal stem cells (MSCs) that provide a promising place in tissue engineering and regenerative medicine. The molecular mechanisms underlying commitment and differentiation of dental stem cells that originate from different dental tissues are not fully understood. In this study, we have compared the expression levels of pluripotency factors along with immunological and developmentally-related markers in the culture of human dental pulp stem cells (hDPSCs), human dental follicle stem cells (hDFSCs), and human embryonic stem cells (hESCs). Materials and Methods: In this experimental study, isolated human dental stem cells were investigated using quantitative polymerase chain reaction (qPCR), immunostaining, and fluorescence-activated cell sorting (FACS). Additionally, we conducted gene ontology (GO) analysis of differentially expressed genes and compared them between dental stem cells and pluripotent stem cells. Results: The results demonstrated that pluripotency (OCT4 and SOX2) and immunological (IL-6 and TLR4) factors had higher expressions in hDFSCs, with the exception of the JAGGED- 1/NOTCH1 ratio, c-MYC and NESTIN which expressed more in hDPSCs. Immunostaining of OCT4, SOX2 and c-MYC showed cytoplasmic and nucleus localization in both groups at similar passages. GO analysis showed that the majority of hDFSCs and hDPSCs populations were in the synthesis (S) and mitosis (M) phases of the cell cycle, respectively. Conclusion: This study showed different status of heterogeneous hDPSCs and hDFSCs in terms of stemness, differentiation fate, and cell cycle phases. Therefore, the different behaviors of dental stem cells should be considered based on clinical treatment variations