Biology subjects

Sood, V.

Publications and source records attributed to Sood, V..

At least 19 recordsLinked to original sources

bioRxiv2026

The mitochondrial transaminase GOT2 supports Japanese encephalitis virus replication in neuronal cells

Japanese encephalitis virus (JEV) is the leading cause of viral encephalitis in Asia, yet the host pathways that sustain its replication in the central nervous system remain poorly defined, and no antiviral therapies exist. Here we combine time-resolved transcriptomics and proteomics in mouse neuroblastoma (Neuro-2a) cells with time-resolved proteomics of infected mouse brain, integrated using directionality-penalized p-value merging. JEV infection was accompanied by progressive induction of interferon-stimulated proteins and by changes in the abundance of enzymes of central carbon and energy metabolism, spanning the tricarboxylic acid cycle, glycolysis, oxidative phosphorylation and glutaminolysis. Temporal clustering of the brain proteome linked oxidative phosphorylation remodelling to a dependence on glutamine metabolism. Glutamine antagonism with DON reduced viral RNA, whereas glycolytic inhibition and AKT modulation did not significantly alter it under the conditions tested. Silencing of the mitochondrial transaminase GOT2 reduced JEV RNA, viral protein, and infectious virus production in human SH-SY5Y cells. Our findings identify a glutamine-GOT2 axis as a metabolic dependency of JEV and a candidate host-directed antiviral target.

microbiology↗

bioRxiv2026

A distribution-aware and functionally relevant novel framework for generation and discovery of bioactive peptides

Recent advances in artificial intelligence have accelerated the discovery of bioactive peptides by enabling computational exploration of the vast peptide sequence space. However, existing peptide generation approaches generally rely on either distribution-learning models, which generate biologically realistic sequences but do not consistently optimize functional activity, or optimization-based methods, which maximize prediction confidence while often deviating from the underlying distribution of experimentally validated peptides. To address this limitation, a two-phase generative-evolutionary framework is proposed that integrates distribution learning with evolutionary optimization. In the first phase, Variational Autoencoders (VAE), Autoregressive Transformers (ART), and Token Diffusion Transformers (TDT) are used to generate biologically plausible seed peptides. In the second phase, these peptides were used as initial seed for Hill Climbing optimization procedure that iteratively improves fitness function score. The proposed two-phase framework was evaluated using a dataset of experimentally validated IL-2-inducing peptides. Evaluation using independent IL-2 prediction models showed that Autoregressive Transformer combined with Hill Climbing achieved the best overall performance, achieving the mean IL-2 induction confidence score of 0.96 while reducing KL divergence from 2.26 for standalone Hill Climbing to 0.75. A case study on an independent IL-13 inducing peptide dataset showed similar trends, with ART initialized Hill Climbing achieving the mean IL-13 induction score of 0.99 while reducing KL divergence from 1.76 to 0.59. Overall, the framework provides a generalizable approach for balancing functional optimization and distributional realism and can be applied to peptide discovery and data augmentation in imbalanced biological datasets thereby generating high confidence peptides for wet lab validation. HighlightsO_LIProposed a two-phase framework for bioactive peptide generation with potential to address class imbalance in peptide classification tasks. C_LIO_LIPerformed a systematic comparison of distribution-learning and optimization-based approaches for peptide generation. C_LIO_LICombined distribution-learning models for sequence generation with optimization algorithms for improving peptide functional properties. C_LIO_LIDemonstrated the applicability of the proposed framework across multiple bioactive peptide datasets. C_LI

bioinformatics↗

bioRxiv2026

A Detailed Model for Understanding the Human Neocortex

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapting data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 {micro}m, a height of 2622 {micro}m, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

neuroscience↗

bioRxiv2025

Disrupted α-ketoglutarate homeostasis trains monocyte-derived macrophages towards M2-like phenotype in long-term treated HIV-infection

Cells of the myeloid lineage, particularly monocytes and macrophages, are central to HIV pathogenesis, contributing to viral persistence and immune regulation during suppressive therapy. We hypothesized that metabolic reprogramming and altered chemokine signaling in people with HIV (PWH) on long-term ART impair monocyte trafficking and macrophage polarization. Using single-cell RNA sequencing, immunophenotyping, and metabolic modeling, we identified altered receptor expression and disrupted metabolic flux linked to reduced monocyte migration. Plasma secretome profiling revealed a nonclassical inflammatory microenvironment, while integrative multi-omics and single-cell proteomics of monocyte-derived macrophages (MDMs) demonstrated metabolic rewiring of the Glycolysis-TCA Anaplerosis Axis, orchestrated in part by elevated -ketoglutarate (AKG). Differentiation with PWH serum or AKG, skewed MDMs toward an M2-like phenotype, and enhanced HIV susceptibility. Together, these systems-level and mechanistic analyses reveal that metabolic training drives macrophage dysfunction in well-treated PWH, sustaining low-grade inflammation and highlighting potential therapeutic targets.

systems biology↗

bioRxiv2025

Effective prediction of IL-17 inducing peptides using hybrid approach: iIL17pred

BackgroundInterleukin 17 (IL-17) plays a crucial role in regulating the immune system and is associated with numerous diseases. Modulating IL-17 levels has demonstrated potential in mitigating disease symptoms, positioning it as a compelling target for drug development. Therefore, identifying and characterizing novel drug molecules capable of influencing IL-17 levels is critical. Recent advances in therapeutic peptides underscore their promise as attractive drug candidates, inspiring the development of IL-17 modulating peptides. ResultsThis study aimed to enhance existing methods for efficiently classifying IL-17 inducing peptides. Positive and negative datasets were obtained from the Immune Epitope Database, and peptide features were extracted using the pfeature algorithm. A three-stage hybrid approach combining BLAST, MERCI, and machine learning techniques was employed to accurately classify IL-17-inducing peptides. ConclusionsExtensive benchmarking experiments revealed that our proposed method outperforms existing techniques i.e IL17eScan, across key performance metrics, including sensitivity, accuracy, and area under the curve-receiver operating characteristics (AUC-ROC). Our algorithm achieved an accuracy of 88.08% and a Matthews Correlation Coefficient (MCC) of 0.68 on an external dataset, significantly surpassing the accuracy of 78.57% and MCC of 0.57 achieved by existing methods under comparable conditions. This demonstrates a substantial improvement in IL-17 peptide classification. The results are accessible via a user-friendly web server (http://www.soodlab.com/iil17pred/). These findings hold significant potential for predicting IL-17-inducing peptides, which can be further validated experimentally.

bioinformatics↗

bioRxiv2024

Identification of molecular determinants of gene-specific bursting patterns by high-throughput imaging screens

Stochastic transcriptional bursting is a universal property of active genes. While different genes exhibit distinct bursting patterns, the molecular mechanisms for gene-specific stochastic bursting are largely unknown. We have developed and applied a high-throughput-imaging based screening strategy to identify cellular factors and molecular mechanisms that determine the bursting behavior of human genes. Focusing on epigenetic regulators, we find that protein acetylation is a strong acute modulator of burst frequency, burst size and heterogeneity of bursting. Acetylation globally affects the Off-time of genes but has gene-specific effects on the On-time. Yet, these effects are not strongly linked to promoter acetylation, which do not correlate with bursting properties, and forced promoter acetylation has variable effects on bursting. Instead, we demonstrate acetylation of the Integrator complex as a key determinant of gene bursting. Specifically, we find that elevated Integrator acetylation decreases bursting frequency. Taken together our results suggest a prominent role of non-histone proteins in determining gene bursting properties, and they identify histone-independent acetylation of a transcription cofactor as an allosteric modulator of bursting via a far-downstream bursting checkpoint.

molecular biology↗

bioRxiv2023

HiTIPS: High-Throughput Image Processing Software for the Study of Nuclear Architecture and Gene Expression

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software for image analysis workflows does not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new algorithms for existing analysis pipelines and to adding new analysis pipelines through separate plugins. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI analysis platform for a variety of cell biology applications.

bioinformatics↗

bioRxiv2023

Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus

The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extend in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce and reconcile experimental findings. Finally, we make data, code and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This neuroscience community-driven model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.

neuroscience↗

bioRxiv2023

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool which can complement laboratory approaches. We present a model comprising eight somatosensory cortex subregions, 4.2 million morphological and electrically-detailed neurons, and 13.2 billion local and mid-range synapses. In silico tools enabled reproduction and extension of complex laboratory experiments under a single parameterization, providing strong validation. The model reproduced millisecond-precise stimulus-responses, stimulus-encoding under targeted optogenetic activation, and selective propagation of stimulus-evoked activity to downstream areas. The models direct correspondence with biology generated predictions about how multiscale organization shapes activity; for example, how cortical activity is shaped by high-dimensional connectivity motifs in local and mid-range connectivity, and spatial targeting rules by inhibitory subpopulations. The latter was facilitated using a rewired connectome which included specific targeting rules observed for different inhibitory neuron types in electron microscopy. The model also predicted the role of inhibitory interneuron types and different layers in stimulus encoding. Simulation tools and a large subvolume of the model are made available to enable further community-driven improvement, validation and investigation.

neuroscience↗

bioRxiv2022

SUMO - In Silico Sequence Assessment Using Multiple Optimization Parameters

To select the most promising screening hits from antibody and VHH display campaigns for subsequent in-depth profiling and optimization, it is highly desirable to assess and select sequences on properties beyond only their binding signals from the sorting process. In addition, developability risk criteria, sequence diversity and the anticipated complexity for sequence optimization are relevant attributes for hit selection and optimization. Here, we describe an approach for the in silico developability assessment of antibody and VHH sequences. This method not only allows for ranking and filtering multiple sequences with regard to their predicted developability properties and diversity, but also visualizes relevant sequence and structural features of potentially problematic regions and thereby provides rationales and starting points for multi-parameter sequence optimization.

bioinformatics↗

bioRxiv2022

Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and a 211,712 neuron subvolume is made openly available to the community.

neuroscience↗

bioRxiv2022

Human blood plasma proteins modeling and binding affinities with Δ9-tetrahydrocannabinol active metabolites: In silico approach

Tetrahydrocannabinol (THC) is a key psychotropic constituent of cannabis sativa. It is also known as {Delta}9-tetrahydrocannabinol ({Delta}9-THC). Previous study suggested that owing to its high lipophilicity, it piles up in adipose tissue and it is disseminated into blood stream for prolonged time. Research suggests that numerous diseases such as multiple sclerosis, neurodegenerative disorders, epilepsy, schizophrenia, osteoporosis, cancer, glaucoma and cardiovascular disorders can be treated using this substance. However, apart from having therapeutic potential, many studies have reported detrimental outcomes along with addiction of {Delta}9-THC for short-term and long-term consumption. Thus, in this study, we determined the binding affinities of {Delta}9-THC and its two active metabolites, 11-Hydroxy-{Delta}9-tetrahydrocannabinol (11-OH-{Delta}9-THC) and 8beta,11-dihydroxy-{Delta}9-tetrahydrocannabinol (8{beta},11-diOH-{Delta}9-THC) with 401 human blood plasma proteins using molecular docking analysis. Results show that {Delta}9-THC has greater binding potential with plasma proteins as compared to other two metabolites. Overall, ADGRE5, ALB, APOA5, APOD, CP, PON1 and PON3 proteins showed the highest binding affinities with three cannabis metabolites.

bioinformatics↗

bioRxiv2022

Time series analysis of SARS-CoV-2 genomes and correlations among highly prevalent mutations

The efforts of the scientific community to tame the recent SARS-CoV-2 pandemic seems to have been diluted by the emergence of new viral strains. Therefore, it becomes imperative to study and understand the effect of mutations on viral evolution, fitness and pathogenesis. In this regard, we performed a time-series analysis on 59541 SARS-CoV-2 genomic sequences from around the world. These 59541 genomes were grouped according to the months (January 2020-March 2021) based on the collection date. Meta-analysis of this data led us to identify highly significant mutations in viral genomes. Correlation and Hierarchical Clustering of the highly significant mutations led us to the identification of sixteen mutation pairs that were correlated with each other and were present in >30% of the genomes under study. Among these mutation pairs, some of the mutations have been shown to contribute towards the viral replication and fitness suggesting the possible role of other unexplored mutations in viral evolution and pathogenesis. Additionally, we employed various computational tools to investigate the effects of T85I, P323L, and Q57H mutations in Non-structural protein 2 (Nsp2), RNA-dependent RNA polymerase (RdRp) and Open reading frame 3a (ORF3a) respectively. Results show that T85I in Nsp2 and Q57H in ORF3a mutations are deleterious and destabilize the parent protein whereas P323L in RdRp is neutral and has a stabilizing effect. The normalized linear mutual information (nLMI) calculations revealed the significant residue correlation in Nsp2 and ORF3a in contrast to reduce correlation in RdRp protein.

bioinformatics↗

bioRxiv2021

A novel binary k-mer approach for classification of coding and non-coding RNAs across diverse species

Classification among coding (CDS) and non-coding RNA (ncRNA) sequences is a challenge and several machine learning models have been developed for the same. Since the frequency of curated coding sequences is many-folds as compared to that of the ncRNAs, we devised a novel approach to work with the complete datasets from fifteen diverse species. In our proposed novel binary approach, we replaced all the As and Ts with 0s and Gs and Cs with 1s to obtain a binary form of coding and ncRNAs. The k-mer analysis of these binary sequences revealed that the frequency of binary patterns among the coding and ncRNAs can be used as features to distinguish among them. Using insights from these distinguishing frequencies, we used k-nearest neighbour classifier to classify among them. Our strategy is not only time-efficient but leads to significantly increased performance metrics including Matthews correlation coefficient (MCC) for some species like P. paniscus, M. mulatta, M. lucifugus, G. gallus, C. japonica, C. abingdonii, A. carolinensis, D. melanogaster and C. elegans when compared with the conventional ATGC approach. Additionally, we also show that the values of MCC obtained for diverse species tested on the model based on H. sapiens correlated with the geological evolutionary timeline thereby further strengthening our approach. Therefore, we propose that CDS and ncRNAs can be efficiently classified using "2-character" frequency as compared to "4-character" frequency of ATGC approach. Thus, our highly efficient binary approach can replace the more complex ATGC approach successfully.

genomics↗

bioRxiv2020

Megakaryopoiesis in Dengue virus infected K562 cell promotes viral replication which inhibits endomitosis and accumulation of ROS associated with differentiation

In the human host blood Monocytes and bone marrow Megakaryocytes are implicated as major sites supporting high replication. The human K562 cell line supports DENV replication and represent Megakaryocyte-Erythrocyte progenitors (MEP), replicating features of in vivo Megakaryopoiesis upon stimulation with Phorbol esters. In this article, we report results that indicate the mutual influence of Megakaryopoiesis and DENV replication on each other, through comparison of PMA-induced differentiation of either mock-infected or DENV-infected K562 cells. We present data showing PMA-induced differentiation to drastically increase DENV replication and a concomitant augmented secretion of infectious virus. Although the mechanism is not clear yet, we show that it is not through an increased uptake of virus by differentiated cells. On the other hand, DENV replication in cells undergoing PMA-induced differentiation, interferes with major differentiation markers of Megakaryopoiesis including activation of ERK1/2 MAP Kinase, endomitosis and surface expression of platelet-specific proteins without any drastic effect on cell death. Among signaling intermediaries of the JAK-STAT pathway, we observed infection associated degradation of SOC3 protein similar to earlier observations with STAT2. DENV infection leads to accumulation of Reactive-oxygen species (ROS) in different cells including K562. PMA-induced differentiation of uninfected K562 cells also leads to intracellular ROS accumulation. Interestingly, we observed ROS accumulation to be suppressed by concomitant DENV replication in K562 cells undergoing PMA-induced differentiation. This is the first report of a model system where DENV replication suppresses intracellular ROS accumulation. The implications of these results for Megakaryopoiesis and viral replication would be discussed.Competing Interest StatementThe authors have declared no competing interest.View Full Text

microbiology↗

bioRxiv2020

In-silico analysis of SARS-CoV-2 genomes: Insights from SARS encoded non-coding RNAs

Recently a novel coronavirus (SARS-CoV-2) emerged from Wuhan, China and has infected more than 571000 people leading to more than 26000 deaths. Since SARS-CoV-2 genome sequences show similarity with those of SARS, we sought to analyze all the available SARS-CoV-2 genomes based on the insights obtained from SARS genome specifically focusing on non-coding RNAs. Here, results are presented from the dual approach i.e identifying host encoded miRNAs that might regulate viral pathogenesis as well as identifying viral encoded miRNAs that might regulate host cell signaling pathways and aid in viral pathogenesis. Analysis utilizing first approach resulted in the identification of 10 host encoded miRNAs that could target the genome of both the viruses (SARS-CoV-2 and SARS reference genome). Interestingly our analysis revealed that there is significantly higher number of host miRNAs that could target SARS-CoV-2 genome as compared to the SARS reference genome. Results from second approach involving SARS-CoV-2 and SARS reference genome identified a set of virus encoded miRNAs which might regulate host signaling pathways. Our analysis further identified a similar "GA" rich motif in SARS-CoV-2 genome that was shown to play a vital role in lung pathogenesis during severe SARS infections. Hence, we successfully identified human and virus encoded miRNAs that might regulate pathogenesis of both these coronaviruses and the fact that more number of host miRNAs could target SARS-CoV-2 genomes possibly reveal as to why this virus follows mild pathogenesis in healthy individuals. We identified non-coding sequences in SARS-CoV-2 genomes that were earlier reported to contribute towards SARS pathology. The study provides insights into the overlapping sequences among these viruses for their effective inhibition as well as identifying new drug targets that could be used for development of new antivirals.

microbiology↗

bioRxiv2019

Physicochemical changes in plasma membrane mirror nanoparticle-mediated cytotoxicity

The aim of this chapter was to understand the influence of nanoparticle challenge on physicochemical characteristics of the cells and to correlate these changes with cytotoxic response of nanoparticles. A nanoparticle surface charge and a concentration-dependent cytotoxic response were observed in the breast cancer cell lines MDA MB 231 and SKBR3. The cationic gold nanoparticles were more cytotoxic to cells as compared to anionic gold nanoparticles. It was also observed that cationic nanoparticles compromised the integrity of the plasma membrane at higher concentrations. Cationic nanoparticle challenge also caused changes in physicochemical characteristics of plasma membrane. Higher concentration of cationic nanoparticles caused an irreversible change in the surface charge density of cells. However, anionic gold nanoparticles did not show any such effect. It was observed that the ROS-mediated oxidative stress was the mechanism of cationic gold nanoparticle-mediated cytotoxic effect. Mitochondrial depolarization was observed in both anionic and cationic nanoparticle challenge. Therefore, the role of mitochondrial ROS in nanoparticle-mediated cytotoxicity is questionable. Finally, a generalized model involving modulation of intracellular Ca2+ can potentially provide an explanation for the observed pluralistic response of the cells towards nanoparticle challenge.

bioengineering↗

bioRxiv2018

Role of Physicochemical Properties of Protein in Modulating the Nanoparticle-Bio interface

Nanoparticles, on exposure to the biological milieu, tend to interact with macromolecules to form a biomolecular corona. The biomolecular corona confers a unique biological identity to nanoparticles, and its protein composition plays a deterministic role in the biological fate of nanoparticles. The physiological behavior of proteins stems from their physicochemical aspects including surface charge, hydrophobicity, and structural stability. However, there is insufficient understanding about the role of physicochemical properties of proteins in biomolecular corona formation. We hypothesized that the physicochemical properties of proteins would influence their interaction with nanoparticles and have a deterministic effect on nanoparticle-cell interactions. To test our hypothesis, we used model proteins from different structural classes to understand the effect of secondary structure elements of proteins on the nanoparticle-protein interface. Further, we modified the surface of proteins to study the role of protein surface characteristics in governing the nanoparticle-protein interface. For this study, we used mesoporous silica nanoparticles as a model nanoparticle system. We observed that the surface charge of proteins governs the nature of the primary interaction as well as the extent of subsequent secondary interactions causing structural rearrangements of the protein. We also observed that the secondary structural contents of proteins significantly affected both the extent of secondary interactions at the nanoparticle-protein interface and the dispersion state of the nanoparticle-protein complex. Further, we also studied the interactions of different protein-coated nanoparticles with different types of cell (fibroblast, carcinoma, and macrophage). We observed that different cells internalized nanoparticle-protein complex as a function of secondary structural components of the protein. The type of model protein had a significant effect on their internalization by macrophages. Overall, we observed that the physicochemical characteristics of proteins had a significant role in modulating the nanoparticle-bio-interface at the level of both biomolecular corona formation and nanoparticle internalization by cells.

bioengineering↗