Biology subjects

Devi, P.

Publications and source records attributed to Devi, P..

4 recordsLinked to original sources

bioRxiv2026

Integration of Morphometric and Machine Learning Approaches Strengthens Yield Prediction and Genetic Divergence Assessment in Annona reticulata under Semi-Arid Conditions.

This study integrated morphometric characterization and machine-learning modelling to identify key predictors of yield in Annona reticulata under semi-arid conditions. Thirty-one canopy, fruit, seed, and biochemical traits were evaluated across 62 genotypes, revealing substantial phenotypic diversity, particularly in structural attributes such as tree growth nature and branch angle. Principal Component Analysis and hierarchical clustering differentiated genotypes into three ideotypes representing high-yielding, structurally stable, and quality-oriented groups. Random Forest modelling and SHapley Additive exPlanations (SHAP) interpretation consistently highlighted leaf breadth, leaf length, fruit shape, and pulp-associated traits as dominant yield predictors, underscoring the coordinated influence of source-sink balance. Integration of SHAP importances with trait stability (CV%) further revealed that moderately variable traits provide reliable selection indices. These findings demonstrate that yield performance is governed by multivariate trait networks rather than isolated descriptors. The proposed framework provides a robust basis for precision phenotyping and strategic parent selection to develop high-yielding, nutritionally enriched, and climate-resilient custard apple cultivars.

plant biology↗

bioRxiv2025

Harnessing the CD2 axis to broaden and enhance the efficacy of CAR T cell therapies

Patients with T-cell lymphomas and leukemias have overall poor outcomes due to the lack of targeted and effective treatments, particularly in the relapsed and refractory settings. Development of chimeric antigen receptor (CAR) T-cells against T-cell neoplasms is limited by a lack of discriminating T-cell antigens that allow for effective anti-tumor responses while preventing CAR T-cell fratricide. We hypothesized that targeting CD2, a pan-T-cell antigen, using anti-CD2 CAR T-cells engineered without CD2 expression (CART2), would support CAR T-cell manufacturability and preclinical efficacy. Optimized CD2-knockout CART2, generated using CRISPR-Cas9, eradicated primary patient-derived CD2+ hematological neoplasms in vitro and in vivo, secreted effector cytokines, and exhibited adequate proliferative capacity. Nevertheless, CD2 has a key costimulatory function, and its deletion could lead to CAR T-cell dysfunction. Therefore, we tested the role of the CD2:CD58 axis in CAR T-cells, using the anti-CD19 CART models. We demonstrate that CD2 loss attenuates CART19 efficacy by reducing avidity for tumor antigen, co-stimulation, and ultimately in vivo activity. Analogously, we show that tumor CD58 loss reduces CART19 efficacy. To overcome this issue, we developed a novel PD-1:CD2 switch receptor that rescues intracellular CD2 signaling, particularly when PD-L1 is engaged, resulting in improved in vivo outcomes. Collectively, we studied the role of CD2 both as a target for CAR T cell therapy and as a critical costimulatory protein, whose signaling can be rescued using the PD-1:CD2 switch receptor. This receptor can be incorporated into CAR T-cells and provides an effective strategy to overcome CD2-signaling deficiencies.

cancer biology↗

bioRxiv2020

In silico Proteome analysis of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (2019-nCoV), is a positive-sense, single-stranded RNA coronavirus. The virus is the causative agent of coronavirus disease 2019 (COVID-19) and is contagious through human-to-human transmission. The present study reports sequence analysis, complete coordinate tertiary structure prediction and in silico sequence-based and structure-based functional characterization of full SARS-CoV-2 proteome based on the NCBI reference sequence NC_045512 (29903 bp ss-RNA) which is identical to GenBank entry MN908947 and MT415321. The proteome includes 12 major proteins namely orf1ab polyprotein (includes 15 proteins), surface glycoprotein, ORF3a protein, envelope protein, membrane glycoprotein, ORF6 protein, ORF7a protein, orf7b, ORF8, Nucleocapsid phosphoprotein and ORF10 protein. Each protein of orf1ab polyprotein group has been studied separately. A total of 25 polypeptides have been analyzed out of which 15 proteins are not yet having experimental structures and only 10 are having experimental structures with known PDB IDs. Out of 15 newly predicted structures six (6) were predicted using comparative modeling and nine (09) proteins having no significant similarity with so far available PDB structures were modeled using ab-initio modeling. Structure verification using recent tools QMEANDisCo 4.0.0 and ProQ3 for global and local (per-residue) quality estimates indicate that the all-atom model of tertiary structure of high quality and may be useful for structure-based drug designing targets. The study has identified nine major targets (spike protein, envelop protein, membrane protein, nucleocapsid protein, 2-O-ribose methyltransferase, endoRNAse, 3-to-5 exonuclease, RNA-dependent RNA polymerase and helicase) for which drug design targets could be considered. There are other 16 nonstructural proteins (NSPs), which may also be percieved from the drug design angle. The protein structures have been deposited to ModelArchive. Tunnel analysis revealed the presence of large number of tunnels in NSP3, ORF 6 protein and membrane glycoprotein indicating a large number of transport pathways for small ligands influencing their reactivity.

bioinformatics↗

bioRxiv2018

Using vocational education to provide development solutions in the Pacific: An emphasis on climate change and health

This article reports on the results of the EU PacTVET project, which explored the use of Technical Vocational Education and Training (TVET) to provide a better understanding on the development solution for the impact of climate change on human health in the region. It describes the findings of a 2014-2018 project on the use of vocational education to provide development solutions in the Pacific with an emphasis on climate change and health. An exploratory design was used to investigate how vocational education developed solutions for climate change and health in the 15 Pacific - African Caribbean and Pacific (P-ACP) countries: Cook Islands, Federated States of Micronesia (FSM), Fiji, Kiribati, Nauru, Niue, Palau, Papua New Guinea (PNG), Republic of Marshall Islands (RMI), Samoa, Solomon Islands, Timor-Leste, Tonga, Tuvalu and Vanuatu. Information collected via personal communication with relevant stakeholders, qualitative interviews, documents review, and survey (n=48) of youths and young women in Fiji. Data analysis was performed using thematic analytical strategy and frequency analysis. The study found that vocational education plays a significant role in building the capacity of people to become more sustainable and resilient in their life now and in the future. Also, getting an accredited qualification on health resilience and/or job in the health sector may help them to respond effectively and efficiently in the event of climate change and/or disasters caused by natural hazards. The same factors were explored quantitatively using descriptive analytical strategy, and concluded TVET education, to have a positive influence on climate change and health. As a result, vocational education could provide development solutions for health adaptation in the Pacific. These results indicate global actions for vocational education, that would perfect the course of resilience for these 15 P-ACP in the Pacific and alike in the U.S.

scientific communication and education↗