Biology subjects

Juneja, S.

Publications and source records attributed to Juneja, S..

3 recordsLinked to original sources

bioRxiv2026

Megakaryocytic proteomic signatures define myeloproliferative neoplasms

The classical Philadelphia-negative myeloproliferative neoplasms (MPNs) are clonal stem cell disorders of the bone marrow with significant niche dysregulation driven in part by abnormal megakaryocytes. The three classical MPNs: polycythemia vera (PV), essential thrombocythemia (ET) and primary myelofibrosis (PMF), display established megakaryocytic morphological differences which aid in their diagnostic classification. The paucity of megakaryocytes in the bone marrow aspirate has previously limited a proteomic analysis of megakaryocytes in clinical samples. We aimed to undertake an unbiased proteomic analysis of in situ megakaryocytes from the bone marrow of patients with PV, ET, and PMF. Using a novel, deep visual proteomics strategy, we dissected and analyzed megakaryocytes from 60 MPN patients and 20 controls (patients with limited stage non-Hodgkin lymphoma who had uninvolved bone marrow). We reveal that megakaryocyte proteomics from MPN patients are distinct to those from control patients, and further, that the megakaryocytes from the three MPNs have unique, disease-specific signatures. The disease-specific megakaryocyte proteome highlights novel pathways of disease.

cancer biology↗

bioRxiv2026

A Hybrid Residual-Swin Transformer Design with Attention for Prostate Cancer Segmentation

Prostate cancer, a leading cause of cancer-related deaths among men globally, necessitates the development of precise diagnostic and treatment strategies. Accurate segmentation of prostate cancer in medical imaging, particularly in MRI scans, is crucial for early diagnosis and clinical decisions. Traditional manual segmentation techniques, while efficient, are labor-intensive and necessitate significant expertise, resulting in an increasing demand for automated alternatives. The RSAUNet is a novel deep learning architecture designed to improve prostate cancer segmentation. It incorporates essential components, including Residual Blocks, Swin Transformer Blocks, and Attention Mechanisms within a U-Net architecture. This markedly enhances the model's capacity to discern complex anatomical features and accurately segment malignant tissues. Using sophisticated deep Learning methods, RSAUNet addresses the complexities of prostate imaging, delivering reliable, consistent segmentation results. The model was evaluated against various cutting-edge techniques on extensive multi-parametric MRI datasets, attaining an impressive Dice coefficient (DC) of 0.998 and a Jaccard Index (IoU) of 0.965. These findings highlight the innovative characteristics of RSAUNet and its capacity to transform prostate cancer diagnosis and treatment strategies. The proposed model surpasses current methods and shows potential for practical clinical applications, providing an efficient, precise, and scalable solution for automated prostate cancer segmentation and fostering optimism for the future of medical imaging and diagnosis.

cancer biology↗

bioRxiv2018

Effective repair of joint cartilage using human pluripotent stem cell-derived tissue

Adult articular cartilage lacks significant regenerative capacity, and damage to this tissue often leads to progressive joint degeneration (osteoarthritis). We developed strategies to generate articular cartilage from human pluripotent stem cells (hPSCs) as a source of clinically relevant tissues for joint repair1. Previously, we demonstrated that these chondrocytes retain cartilage forming potential following subcutaneous implantation in mice. In this report, we evaluated the potential of human embryonic stem cell (hESC)-derived articular cartilage tissue to repair osteochondral defects created in the rat knee. Following implantation, the hESCderived cartilage maintained a proteoglycan and type II collagen-rich matrix, and was well integrated with native rat tissue at the basal and lateral surfaces. The ability to generate cartilage tissue with integrative and reparative properties from an unlimited and robust cell source represents a significant clinical advance for cartilage repair that can be applied to large animal models and ultimately to patient care.

developmental biology↗