Biology subjects

Rajeev, A.

Publications and source records attributed to Rajeev, A..

3 recordsLinked to original sources

bioRxiv2026

Integrated Transcriptomic and CRISPR Dependency Analysis Prioritizes a CDK1-AURKB Mitotic Vulnerability Axis in Diffuse Intrinsic Pontine Glioma

Diffuse intrinsic pontine glioma (DIPG), now classified within diffuse midline glioma, H3K27-altered, remains a lethal pediatric brainstem tumor with limited therapeutic options. Here, we integrated public DIPG transcriptomic datasets, protein-protein interaction modeling, functional enrichment, immune deconvolution, survival analysis, and DepMap CRISPR dependency data to nominate candidate mitotic vulnerabilities. Differential expression analysis comparing 27 DIPG tumors with 6 brainstem low-grade glioma comparator samples identified a proliferative transcriptional program enriched for chromosome segregation, nuclear division, and cell-cycle pathways. Network analysis prioritized a compact mitotic hub module containing CDK1, AURKB, TOP2A, CDC20, CDCA8, and related G2/M regulators. CIBERSORT analysis of an independent DIPG cohort inferred low cytotoxic T-cell signal, consistent with an immune-cold phenotype, although immune-cell fractions require orthogonal validation. Survival analysis showed that neither inferred immune scores nor a composite mitotic hub score significantly stratified overall survival. DepMap CRISPR gene-effect data nominated CDK1, AURKB, TOP2A, and BIRC5 as candidate dependencies across brain tumor models. These findings provide a computational framework for prioritizing mitotic vulnerabilities in DIPG and support experimental validation in disease-relevant models.

bioinformatics↗

bioRxiv2026

A brain-inspired framework for memory prioritization in neural networks based on valence

Improving long-term memory in artificial neural networks remains an open challenge. To address this, we developed a novel brain-inspired framework for memory prioritization based on the principle of emotional valence. Our framework includes: (i) a valence-weighted cross-entropy loss that scales the learning signal by the valence magnitude, analogous to neuromodulation; (ii) an amygdala-inspired module that learns high-dimensional valence embeddings; and (iii) a hippocampus-inspired module that integrates valence embeddings into the attention mechanism to modulate information retrieval. We demonstrated the generalization of our framework across spatial, episodic, and language-based memory tasks, consistently improving memory prioritization and long-term retention of high-salience information. In addition to improving long-term memory, we also showed that our framework can help mitigate the "lost-in-the-middle" problem in language modeling. More generally, this research provides further evidence of the potential of brain-inspired algorithms to advance the field of machine learning.

neuroscience↗

bioRxiv2024

3D bioprinting of Liver Microenvironment Model Using Photocrosslinkable Decellularized Extracellular Matrix based Hydrogel

The liver, as one of the vital organs in the body, plays a crucial role in various bodily functions. Numerous factors can cause liver damage, that the sole remedy for severe liver conditions is the transplantation of healthy liver tissue. In response to the transplantation challenges, innovative approaches involving hydrogel-based technologies have emerged, leading to the creation of highly functionalized tissues. The development of three-dimensional printing and patterning of cell-laden biomaterial matrices offers promising advances for creating tissue-specific structures in tissue engineering and bioprinting. However, the matrix materials currently employed in bioprinting liver microtissue often fail to capture the complexity of the natural extracellular matrix (ECM), hindering their ability to restore innate cellular shapes and functions. Liver ECM-based hydrogels are increasingly recognized for their potential as biomimetic 3D cell culture systems that facilitate the exploration of liver disease, metabolism, and toxicity mechanisms. Yet, the conventional production of these hydrogels relies on slow thermal gelation processes, which restrict the manipulation of their mechanical characteristics. In this research, we introduce a novel approach with a functionalized photocrosslinkable liver decellularized extracellular matrix (dECM). By combining liver dECM methacrylate (LdECMMA) with gelatin methacrylate (GelMA), we achieved accelerated crosslinking under visible light irradiation and the ability to tune the mechanical, rheological, and physiological properties of the material. We encapsulated human hepatocellular carcinoma cells within an optimal concentration of the GelMA-LdECMMA hybrid hydrogel and examined cell proliferation and function over an extended period. The findings revealed that the GelMA-LdECMMA hybrid hydrogel enhances liver cell proliferation and function, holding significant promise for applications in drug screening and liver cancer metastasis research.

bioengineering↗