Biology subjects

Habiba, U.

Publications and source records attributed to Habiba, U..

3 recordsLinked to original sources

bioRxiv2025

Induction of meningioma stem signature via hydrogel reprogramming and application of meningioma stem cell marker CXCR4 to pathological diagnosis and treatment.

BackgroundsMeningioma accounts for about 40% of brain tumors, but there was no effective treatment for recurrent or inoperable cases. We have previously found that when cancer cells are cultured on certain hydrogels, cancer stem cells are efficiently induced in various cancer types and we named this process as hydrogel activated reprogramming (HARP) phenomenon. In this study, we aimed to identify a key molecule that induced meningioma stem cells via hydrogels. MethodsMeningioma cells cultured on hydrogels were analyzed for expression of regular stem cell markers and their tumor genericity. Microarray analysis was performed to identify meningioma stem cell specific markers and to examine the application of such marker molecules as a therapeutic targets or pathological diagnosis for grading. ResultsRegular stem cell markers such as Nanog, and Oct3/4 were induced by culturing meningioma cells on hydrogels, and comprehensive gene expression analysis identified molecules involved in cancer stem cell activity. Among them, CXCR4 was selected as a therapeutic target molecule. Stimulation of CXCR4 via CXCL12 led to an increase in stem cell markers. In human meningioma pathological specimens and cultured cell lines, there was a correlation between CXCR4 expression levels and NF2 mutations and/or deletions. CXCR4 immunohistochemistry also detected along with the brain invasion. Thus, CXCR4 immunohistochemistry may be useful to suggest typical CNS WHO grade 1 meningioma, that do not require molecular analysis. ConclusionsWe have defined meningioma stem cell signature via HARP phenomenon and identified CXCR4 with biological significance as being diagnostic target. Key PointsO_LIWe induce meningioma stem signature via Hydrogel activated reprogramming (HARP) phenomena. C_LIO_LIWe identified CXCR4 as a candidate molecule as stemness marker and therapeutic target. C_LIO_LITranslational research using pathological specimens reveal CXCR4 immunohistochemistry has been shown to play an auxiliary role in the diagnosis of meningiomas. C_LI Importance of the StudyIn addition to morphology, immunohistochemistry and gene alteration are being adopted as diagnostic criteria for CNS tumors. From CNS5 onwards, epigenetic changes such as methylation analysis are being adopted as diagnostic criteria. We induced epigenetic changes in meningioma cells and induced cancer stemness, and this technique will be of great importance in the research and diagnosis of meningioma. In addition, microarray analysis was used to select CXCR4 from the molecules that increased simultaneously during stem cell induction on the three hydrogels, and further analysis revealed that CXCR4 immunostaining may indicate the distribution of meningioma stem cells, also making it useful for diagnosis. This report will contribute to the advancement of meningioma research and diagnosis by conducting basic research and using histopathological specimens.

bioengineering↗

bioRxiv2024

Exploring Effector Protein Dynamics and Natural Fungicidal Potential in Rice Blast Pathogen Magnaporthe oryzae

Rice blast, caused by Magnaporthe oryzae, is a severe agricultural disease leading to significant global economic losses. Genetic and genomic investigations have identified crucial genes and pathways involved in its pathogenesis, particularly highlighting effector proteins like AvrPik variants and MAX proteins. These proteins interact with specific Pik alleles on rice chromosome 11, influencing host immune responses. This study focused on 35 plant-derived metabolites known for their antifungal properties, evaluating their potential as fungicidal agents against M. oryzae. Molecular docking analyses identified Hecogenin and Cucurbitacin E as highly effective binders to MAX40 and APIKL2A proteins, respectively, which are pivotal for fungal virulence and immune evasion. Molecular dynamics simulations further validated strong and stable interactions, affirming the therapeutic potential of these compounds. Additional assessments including Lipinskis rule of five criteria and toxicity predictions indicated their suitability for agricultural use. These findings underscore the promise of Hecogenin and Cucurbitacin E as lead candidates in developing novel fungicidal strategies against rice blast, offering prospects for enhanced crop protection and agricultural sustainability.

bioinformatics↗

bioRxiv2023

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students concentration via hyper-parameter tuning

Brain-computer interface (BCI) research has gained increasing attention in educational contexts, offering the potential to monitor and enhance students cognitive states. Real-time classification of students confusion levels using electroencephalogram (EEG) data presents a significant challenge in this domain. Since real-time EEG data is dynamic and highly dimensional, current approaches have some limitations for predicting mental states based on this data. This paper introduces an optimal deep learning (DL) model for the BCI, ODL-BCI, optimized through hyperparameter tuning techniques to address the limitations of classifying students confusion in real time. Leveraging the "confused student EEG brainwave" dataset, we employ Bayesian optimization to fine-tune hyperparameters of the proposed DL model. The model architecture comprises input and output layers, with several hidden layers whose nodes, activation functions, and learning rates are determined utilizing selected hyperparameters. We evaluate and compare the proposed model with some state-of-the-art methods and standard machine learning (ML) classifiers, including Decision Tree, AdaBoost, Bagging, MLP, Nave Bayes, Random Forest, SVM, and XG Boost, on the EEG confusion dataset. Our experimental results demonstrate the superiority of the optimized DL model, ODL-BCI. It boosts the accuracy between 4% and 9% over the current approaches, outperforming all other classifiers in the process. The ODL-BCI implementation source codes can be accessed by anyone at https://github.com/MdOchiuddinMiah/ODL-BCI.

neuroscience↗