Machine-Learning-Based Approach to Decode the Influence of Nanomaterial Properties on Their Interaction with Cells

Quantitative prediction of toxicological points of departure using two-stage machine learning models: A new approach methodology (NAM) for chemical risk assessment

Point of departure (POD) is a concept used in risk assessment to calculate the reference dose of exposure that is likely to have no appreciable risk on health. POD can be directly utilized from no observed adverse effect levels (NOAEL) which is the dose or exposure level at which there is little or no risk of adverse effects. However, NOAEL values are unavailable for most of the chemicals due to inconsistent animal toxicity data. Hence, the current study utilizes a two-stage machine learning (ML) model for predicting NOAEL values, based on data curated from diverse toxicity exposures. In the first stage, a random forest regressor is used for supervised outlier detection and removal addressing any variability in data and poor correlations. The refined data is then used for toxicity prediction using several ML models; random forest and XGBoost show relatively higher performance with an R2 value of 0.4 and 0.43, respectively, for predicting NOAEL in chronic toxicity. Similarly, feature combinations with absorption distribution metabolism and excretion (ADME) indicate better NOAEL prediction for acute toxicity. External validation is performed by predicting NOAEL values for cosmetic pigments and calculating reference doses (RfD). Notably, pigments like orange and red show higher RfD values, indicating broader safety margins. This study provides a practical framework for addressing variability and data limitations in toxicity prediction while offering insights into its applicability in risk evaluation.

Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration

Nanomaterials represent an innovation in cancer imaging by offering enhanced contrast, improved targeting capabilities, and multifunctional imaging modalities. Recent advancements in material engineering have enabled the development of nanoparticles tailored for various imaging techniques, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US). These nanoscale agents improve sensitivity and specificity, enabling early cancer detection and precise tumor characterization. Monte Carlo (MC) simulations play a pivotal role in optimizing nanomaterial-based imaging by modeling their interactions with biological tissues, predicting contrast enhancement, and refining dosimetry for radiation-based imaging techniques. These computational methods provide valuable insights into nanoparticle behavior, aiding in the design of more effective imaging agents. Moreover, artificial intelligence (AI) and machine learning (ML) approaches are transforming cancer imaging by enhancing image reconstruction, automating segmentation, and improving diagnostic accuracy. AI-driven models can also optimize MC-based simulations by accelerating data analysis and refining nanoparticle design through predictive modeling. This review explores the latest advancements in nanomaterial-based cancer imaging, highlighting the synergy between nanotechnology, MC simulations, and AI-driven innovations. By integrating these interdisciplinary approaches, future cancer imaging technologies can achieve unprecedented precision, paving the way for more effective diagnostics and personalized treatment strategies.

Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges

The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

Leveraging Artificial Intelligence and Machine Learning for Characterizing Protein Corona, Nanobiological Interactions, and Advancing Drug Discovery

Proteins are essential for all living organisms, playing key roles in biochemical reactions, structural support, signal transduction, and gene regulation. Their importance in biomedical research is highlighted by their role as drug targets in various diseases. The interactions between proteins and nanoparticles (NPs), including the protein corona's formation, significantly affect NP behavior, biodistribution, cellular uptake, and toxicity. Comprehending these interactions is pivotal for advancing the design of NPs to augment their efficacy and safety in biomedical applications. While traditional nanomedicine design relies heavily on experimental work, the use of data science and machine learning (ML) is on the rise to predict the synthesis and behavior of nanomaterials (NMs). Nanoinformatics combines computational simulations with laboratory studies, assessing risks and revealing complex nanobio interactions. Recent advancements in artificial intelligence (AI) and ML are enhancing the characterization of the protein corona and improving drug discovery. This review discusses the advantages and limitations of these approaches and stresses the importance of comprehensive datasets for better model accuracy. Future developments may include advanced deep-learning models and multimodal data integration to enhance protein function prediction. Overall, systematic research and advanced computational tools are vital for improving therapeutic outcomes and ensuring the safe use of NMs in medicine.

Nanotoxicology: developments and new insights

The use of nanoparticles (NPs) in treatment of diseases have increased exponentially recently, giving rise to the science of nanomedicine. The safety of these NPs in humans has also led to the science of nanotoxicology. Due to a dearth of both readily available models and precise bio-dispersion characterization techniques, nanotoxicological research has obviously been constrained. However, the ensuing years were notable for the emergence of improved synthesis methods and characterization tools. Major advances have been made in linking certain physical variables, paralleling improvements in characterization size, shape, or coating factors to the resulting physiological reactions. Although significant progress has been a contribution to the development of nanotoxicology, however, it faces numerous difficulties and technical constraints distinct from those of conventional toxicological assessment as it attempts to improve the therapeutic effects of medicines. Determining thorough characterization standards, standardizing dosimetry, assessing the kinetics of ions dissolving and enhancing the accuracy of in vitro-in vivo correlation efficiency, also defining restrictions on exposure protection are some of the most important and pressing concerns. This article will explore the past advancement and potential prospects of nanotoxicology, standard models, emphasizing significant findings from earlier studies and examining current challenges, giving insight on the way forward.

Comprehensive insights into mechanism of nanotoxicity, assessment methods and regulatory challenges of nanomedicines

Nanomedicine has the potential to transform healthcare by offering targeted therapies, precise diagnostics, and enhanced drug delivery systems. The National Institutes of Health has coined the term "nanomedicine" to describe the use of nanotechnology in biological system monitoring, control, diagnosis, and treatment. Nanomedicine continues to receive increasing interest for the rationalized delivery of therapeutics and pharmaceutical agents to achieve the required response while reducing its side effects. However, as nanotechnology continues to advance, concerns about its potential toxicological effects have also grown. This review explores the current state of nanomedicine, focusing on the types of nanoparticles used and their associated properties that contribute to nanotoxicity. It examines the mechanisms through which nanoparticles exert toxicity, encompassing various cellular and molecular interactions. Furthermore, it discusses the assessment methods employed to evaluate nanotoxicity, encompassing in-vitro and in-vivo models, as well as emerging techniques. The review also addresses the regulatory issues surrounding nanotoxicology, highlighting the challenges in developing standardized guidelines and ensuring the secure translation of nanomedicine into clinical settings. It also explores into the challenges and ethical issues associated with nanotoxicology, as understanding the safety profile of nanoparticles is essential for their effective translation into therapeutic applications.

Engineering of Green Carbon Dots for Biomedical and Biotechnological Applications

Carbon dots (CDs) are attracting increasing research attention due to their exceptional attributes, including their biocompatibility, water solubility, minimal toxicity, high photoluminescence, and easy functionalization. Green CDs, derived from natural sources such as fruits and vegetables, present advantages over conventionally produced CDs, such as cost-effectiveness, stability, simplicity, safety, and environmental friendliness. Various methods, including hydrothermal and microwave treatments, are used to synthesize green CDs, which demonstrate strong biocompatibility, stability, and luminescence. These properties give green CDs versatility in their biological applications, such as bioimaging, biosensing, and drug delivery. This review summarizes the prevalent synthesis methods and renewable sources regarding green CDs; examines their optical features; and explores their extensive biological applications, including in bioimaging, biosensing, drug/gene delivery, antimicrobial and antiviral effects, formatting of mathematical components, cancer diagnosis, and pharmaceutical formulations.

Computer-aided nanodrug discovery: recent progress and future prospects

Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through in silico methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.

Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review

In the rapidly evolving landscape of nanomedicine, aptamers have emerged as powerful molecular tools, demonstrating immense potential in targeted therapeutics, diagnostics, and drug delivery systems. This paper explores the computational features of aptamers in nanomedicine, highlighting their advantages over antibodies, including selectivity, low immunogenicity, and a simple production process. A comprehensive overview of the aptamer development process, specifically the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process, sheds light on the intricate methodologies behind aptamer selection. The historical evolution of aptamers and their diverse applications in nanomedicine are discussed, emphasizing their pivotal role in targeted drug delivery, precision medicine and therapeutics. Furthermore, we explore the integration of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), Internet of Medical Things (IoMT), and nanotechnology in aptameric development, illustrating how these cutting-edge technologies are revolutionizing the selection and optimization of aptamers for tailored biomedical applications. This paper also discusses challenges in computational methods for advancing aptamers, including reliable prediction models, extensive data analysis, and multiomics data incorporation. It also addresses ethical concerns and restrictions related to AI and IoT use in aptamer research. The paper examines progress in computer simulations for nanomedicine. By elucidating the importance of aptamers, understanding their superiority over antibodies, and exploring the historical context and challenges, this review serves as a valuable resource for researchers and practitioners aiming to harness the full potential of aptamers in the rapidly evolving field of nanomedicine.

The Prospect of Biomimetic Immune Cell Membrane-Coated Nanomedicines for Treatment of Serious Bacterial Infections and Sepsis

Invasive bacterial infections and sepsis are persistent global health concerns, complicated further by the escalating threat of antibiotic resistance. Over the past 40 years, collaborative endeavors to improve the diagnosis and critical care of septic patients have improved outcomes, yet grappling with the intricate immune dysfunction underlying the septic condition remains a formidable challenge. Anti-inflammatory interventions that exhibited promise in murine models failed to manifest consistent survival benefits in clinical studies through recent decades. Novel therapeutic approaches that target bacterial virulence factors, for example with monoclonal antibodies, aim to thwart pathogen-driven damage and restore an advantage to the immune system. A pioneering technology addressing this challenge is biomimetic nanoparticles-a therapeutic platform featuring nanoscale particles enveloped in natural cell membranes. Borne from the quest for a durable drug delivery system, the original red blood cell-coated nanoparticles showcased a broad capacity to absorb bacterial and environmental toxins from serum. Tailoring the membrane coating to immune cell sources imparts unique characteristics to the nanoparticles suitable for broader application in infectious disease. Their capacity to bind both inflammatory signals and virulence factors assembles the most promising sepsis therapies into a singular, pathogen-agnostic therapeutic. This review explores the ongoing work on immune cell-coated nanoparticle therapeutics for infection and sepsis. SIGNIFICANCE STATEMENT: Invasive bacterial infections and sepsis are a major global health problem made worse by expanding antibiotic resistance, meaning better treatment options are urgently needed. Biomimetic cell-membrane-coated nanoparticles are an innovative therapeutic platform that deploys a multifaceted mechanism to action to neutralize microbial virulence factors, capture endotoxins, and bind excessive host proinflammatory cytokines, seeking to reduce host tissue injury, aid in microbial clearance, and improve patient outcomes.

Decoding Nanomaterial-Biosystem Interactions through Machine Learning

The interactions between biosystems and nanomaterials regulate most of their theranostic and nanomedicine applications. These nanomaterial-biosystem interactions are highly complex and influenced by a number of entangled factors, including but not limited to the physicochemical features of nanomaterials, the types and characteristics of the interacting biosystems, and the properties of the surrounding microenvironments. Over the years, different experimental approaches coupled with computational modeling have revealed important insights into these interactions, although many outstanding questions remain unanswered. The emergence of machine learning has provided a timely and unique opportunity to revisit nanomaterial-biosystem interactions and to further push the boundary of this field. This minireview highlights the development and use of machine learning to decode nanomaterial-biosystem interactions and provides our perspectives on the current challenges and potential opportunities in this field.

AI-enhanced biomedical micro/nanorobots in microfluidics

Human beings encompass sophisticated microcirculation and microenvironments, incorporating a broad spectrum of microfluidic systems that adopt fundamental roles in orchestrating physiological mechanisms. In vitro recapitulation of human microenvironments based on lab-on-a-chip technology represents a critical paradigm to better understand the intricate mechanisms. Moreover, the advent of micro/nanorobotics provides brand new perspectives and dynamic tools for elucidating the complex process in microfluidics. Currently, artificial intelligence (AI) has endowed micro/nanorobots (MNRs) with unprecedented benefits, such as material synthesis, optimal design, fabrication, and swarm behavior. Using advanced AI algorithms, the motion control, environment perception, and swarm intelligence of MNRs in microfluidics are significantly enhanced. This emerging interdisciplinary research trend holds great potential to propel biomedical research to the forefront and make valuable contributions to human health. Herein, we initially introduce the AI algorithms integral to the development of MNRs. We briefly revisit the components, designs, and fabrication techniques adopted by robots in microfluidics with an emphasis on the application of AI. Then, we review the latest research pertinent to AI-enhanced MNRs, focusing on their motion control, sensing abilities, and intricate collective behavior in microfluidics. Furthermore, we spotlight biomedical domains that are already witnessing or will undergo game-changing evolution based on AI-enhanced MNRs. Finally, we identify the current challenges that hinder the practical use of the pioneering interdisciplinary technology.

Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials

This comprehensive review explores the emerging landscape of Nano-QSAR (quantitative structure-activity relationship) for assessing the risk and potency of nanomaterials in agricultural settings. The paper begins with an introduction to Nano-QSAR, providing background and rationale, and explicitly states the hypotheses guiding the review. The study navigates through various dimensions of nanomaterial applications in agriculture, encompassing their diverse properties, types, and associated challenges. Delving into the principles of QSAR in nanotoxicology, this article elucidates its application in evaluating the safety of nanomaterials, while addressing the unique limitations posed by these materials. The narrative then transitions to the progression of Nano-QSAR in the context of agricultural nanomaterials, exemplified by insightful case studies that highlight both the strengths and the limitations inherent in this methodology. Emerging prospects and hurdles tied to Nano-QSAR in agriculture are rigorously examined, casting light on important pathways forward, existing constraints, and avenues for research enhancement. Culminating in a synthesis of key insights, the review underscores the significance of Nano-QSAR in shaping the future of nanoenabled agriculture. It provides strategic guidance to steer forthcoming research endeavors in this dynamic field.

Quantitative image analysis of intracellular protein translocation in 3-dimensional tissues for pharmacodynamic studies of immunogenic cell death

A recent development in cancer chemotherapy is to use cytotoxics to induce tumor-specific immune response through immunogenic cell death (ICD). In ICD, calreticulin is translocated from endoplasmic reticulum to cell membrane (ecto-CRT) which serves as the 'eat-me-signal' to antigen-presenting cells. Ecto-CRT measurements, e.g., by ecto-CRT immunostaining plus flow cytometry, can be used to study the pharmacodynamics of ICD in single cells, whereas ICD studies in intact 3-dimensional tissues such as human tumors require different approaches. The present study described a method that used (a) immunostaining with fluorescent antibodies followed by confocal microscopy to obtain the spatial locations of two molecules-of-interest (CRT and a marker protein WGA), and (b) machine-learning (trainable WEKA segmentation) and additional image processing tools to locate the target molecules, remove the interfering signals in the nucleus, cytosol and extracellular space, enable the distinction of the inner and outer edges of the cell membrane and thereby identify the cells with ecto-CRT. This method, when applied to 3-dimensional human bladder cancer cell spheroids, yielded drug-induced ecto-CRT measurements that were qualitatively comparable to the flow cytometry results obtained with single cells disaggregated from spheroids. This new method was applied to study drug-induced ICD in short-term cultures of surgical specimens of human patient bladder tumors.

Feeding Next-Generation Nanomedicines to Europe: Regulatory and Quality Challenges

New and innovative nanomedicines have been developed and marketed over the past half-century, revolutionizing the prognosis of many human diseases. Although a univocal regulatory definition is not yet available worldwide, the term "nanomedicines" generally identifies medicinal products that use nanotechnology in their design or production. Due to the intrinsic high structural complexity of these products, the scientific and regulatory communities are reflecting on how to revise the regulatory framework to provide a more appropriate benefit/risk balance to authorize them on the market, considering the impact of their peculiar physicochemical features in the evaluation of efficacy and safety patterns. Herein, a critical perspective is provided on the current open issues regarding regulatory qualification and physicochemical characterization of nanosystems considering the current European regulatory framework on nanomedicine products. Practicable paths for improving their quality assurance and predicting their fate in vivo are also argued. Strengthening the multilevel alliance among academic institutions, industrial stakeholders, and regulatory authorities seems strategic to support innovation by standard approaches (e.g., qualification, characterization, risk assessment), and to expand current knowledge, also benefiting from the new opportunities offered by artificial intelligence and digitization in predictive modelling of the impact of nanomedicine characteristics on their fate in vivo.

Using Machine Learning to Predict Adverse Effects of Metallic Nanomaterials to Various Aquatic Organisms

The wide production and use of metallic nanomaterials (MNMs) leads to increased emissions into the aquatic environments and induces high potential risks. Experimentally evaluating the (eco)toxicity of MNMs is time-consuming and expensive due to the multiple environmental factors, the complexity of material properties, and the species diversity. Machine learning (ML) models provide an option to deal with heterogeneous data sets and complex relationships. The present study established an in silico model based on a machine learning properties-environmental conditions-multi species-toxicity prediction model (ML-PEMST) that can be applied to predict the toxicity of different MNMs toward multiple aquatic species. Feature importance and interaction analysis based on the random forest method indicated that exposure duration, illumination, primary size, and hydrodynamic diameter were the main factors affecting the ecotoxicity of MNMs to a variety of aquatic organisms. Illumination was demonstrated to have the most interaction with the other features. Moreover, incorporating additional detailed information on the ecological traits of the test species will allow us to further optimize and improve the predictive performance of the model. This study provides a new approach for ecotoxicity predictions for organisms in the aquatic environment and will help us to further explore exposure pathways and the risk assessment of MNMs.

Translational Challenges and Prospective Solutions in the Implementation of Biomimetic Delivery Systems

Biomimetic delivery systems (BDSs), inspired by the intricate designs of biological systems, have emerged as a groundbreaking paradigm in nanomedicine, offering unparalleled advantages in therapeutic delivery. These systems, encompassing platforms such as liposomes, protein-based nanoparticles, extracellular vesicles, and polysaccharides, are lauded for their targeted delivery, minimized side effects, and enhanced therapeutic outcomes. However, the translation of BDSs from research settings to clinical applications is fraught with challenges, including reproducibility concerns, physiological stability, and rigorous efficacy and safety evaluations. Furthermore, the innovative nature of BDSs demands the reevaluation and evolution of existing regulatory and ethical frameworks. This review provides an overview of BDSs and delves into the multifaceted translational challenges and present emerging solutions, underscored by real-world case studies. Emphasizing the potential of BDSs to redefine healthcare, we advocate for sustained interdisciplinary collaboration and research. As our understanding of biological systems deepens, the future of BDSs in clinical translation appears promising, with a focus on personalized medicine and refined patient-specific delivery systems.

Antibacterial micro/nanomotors: advancing biofilm research to support medical applications

Multi-drug resistant (MDR) bacterial infections are gradually increasing in the global scope, causing a serious burden to patients and society. The formation of bacterial biofilms, which is one of the key reasons for antibiotic resistance, blocks antibiotic penetration by forming a physical barrier. Nano/micro motors (MNMs) are micro-/nanoscale devices capable of performing complex tasks in the bacterial microenvironment by transforming various energy sources (including chemical fuels or external physical fields) into mechanical motion or actuation. This autonomous movement provides significant advantages in breaking through biological barriers and accelerating drug diffusion. In recent years, MNMs with high penetrating power have been used as carriers of antibiotics to overcome bacterial biofilms, enabling efficient drug delivery and improving the therapeutic effectiveness of MDR bacterial infections. Additionally, non-antibiotic antibacterial strategies based on nanomaterials, such as photothermal therapy and photodynamic therapy, are continuously being developed due to their non-invasive nature, high effectiveness, and non-induction of resistance. Therefore, multifunctional MNMs have broad prospects in the treatment of MDR bacterial infections. This review discusses the performance of MNMs in the breakthrough and elimination of bacterial biofilms, as well as their application in the field of anti-infection. Finally, the challenges and future development directions of antibacterial MNMs are introduced.

Electrospun polyvinyl alcohol-chitosan dressing stimulates infected diabetic wound healing with combined reactive oxygen species scavenging and antibacterial abilities

Diabetic wounds (DW) are constantly challenged by excessive reactive oxygen species (ROS) accumulation and susceptibility to bacterial contamination. Therefore, the elimination of ROS in the immediate vicinity and the eradication of local bacteria are critical to stimulating the efficient healing of diabetic wounds. In the current study, we encapsulated mupirocin (MP) and cerium oxide nanoparticles (CeNPs) into a polyvinyl alcohol/chitosan (PVA/CS) polymer, and then a PVA/chitosan nanofiber membrane wound dressing was fabricated using electrostatic spinning, which is a simple and efficient method for fabricating membrane materials. The PVA/chitosan nanofiber dressing provided a controlled release of MP, which produced rapid and long-lasting bactericidal activity against both methicillin-sensitive S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA) strains. Simultaneously, the CeNPs embedded in the membrane exhibited the desired ROS scavenging capacity to maintain the local ROS at a normal physiological level. Moreover, the biocompatibility of the multifunctional dressing was evaluated both in vitro and in vivo. Taken together, PVA-CS-CeNPs-MP integrated the desirable features of a wound dressing, including rapid and broad-spectrum antimicrobial and ROS scavenging activities, easy application, and good biocompatibility. The results validated the effectiveness of our PVA/chitosan nanofiber dressing, highlighting its promising translational potential in the treatment of diabetic wounds.

Herbal concoction Unveiled: A computational analysis of phytochemicals' pharmacokinetic and toxicological profiles using novel approach methodologies (NAMs)

Herbal medications have an extensive history of use in treating various diseases, attributed to their perceived efficacy and safety. Traditional medicine practitioners and contemporary healthcare providers have shown particular interest in herbal syrups, especially for respiratory illnesses associated with the SARS-CoV-2 virus. However, the current understanding of the pharmacokinetic and toxicological properties of phytochemicals in these herbal mixtures is limited. This study presents a comprehensive computational analysis utilizing novel approach methodologies (NAMs) to investigate the pharmacokinetic and toxicological profiles of phytochemicals in herbal syrup, leveraging in-silico techniques and prediction tools such as PubChem, SwissADME, and Molsoft's database. Although molecular dynamics, docking, and broader system-wide analyses were not considered, future studies hold potential for further investigation in these areas. By combining drug-likeness with molecular simulation, researchers identify diverse phytochemicals suitable for complex medication development examining their pharmacokinetic-toxicological profiles in phytopharmaceutical syrup. The study focuses on herbal solutions for respiratory infections, with the goal of adding to the pool of all-natural treatments for such ailments. This research has the potential to revolutionize environmental and alternative medicine by leveraging in-silico models and innovative analytical techniques to identify novel phytochemicals with enhanced therapeutic benefits and explore network-based and systems biology approaches for a deeper understanding of their interactions with biological systems. Overall, our study offers valuable insights into the computational analysis of the pharmacokinetic and toxicological profiles of herbal concoction. This paves the way for advancements in environmental and alternative medicine. However, we acknowledge the need for future studies to address the aforementioned topics that were not adequately covered in this research.

Anti-Biofilm: Machine Learning Assisted Prediction of IC50 Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing

Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of anti-biofilm agents helps in tackling the menace of antibiotic resistance. The identification of efficient anti-biofilm chemicals remains a challenge. Therefore, in this study, we developed 'anti-Biofilm', a machine learning technique (MLT) based predictive algorithm for identifying and analyzing the biofilm inhibition of small molecules. The algorithm is developed using experimentally validated anti-biofilm compounds with half maximal inhibitory concentration (IC₅₀) values extracted from aBiofilm resource. Out of the five MLTs, the Support Vector Machine performed best with Pearson's correlation coefficient of 0.75 on the training/testing data set. The robustness of the developed model was further checked using an independent validation dataset. While analyzing the chemical diversity of the anti-biofilm compounds, we observed that they occupy diverse chemical spaces with parent molecules like furanone, urea, phenolic acids, quinolines, and many more. Use of diverse chemicals as input further signifies the robustness of our predictive models. The three best-performing machine learning models were implemented as a user-friendly 'anti-Biofilm' web server (https://bioinfo.imtech.res.in/manojk/antibiofilm/) with different other modules which make 'anti-Biofilm' a comprehensive platform. Therefore, we hope that our initiative will be helpful for the scientific community engaged in identifying effective anti-biofilm agents to target the problem of antimicrobial resistance.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning

Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q²  = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes.

Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure-activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.

Coronavirus-mimicking nanoparticles (CorNPs) in artificial saliva droplets and nanoaerosols: Influence of shape and environmental factors on particokinetics/particle aerodynamics

Severe acute respiratory syndrome coronavirus 2, abbreviated as SARS-CoV-2, has been associated with the transmission of infectious COVID-19 disease through breathing and speech droplets emitted by infected carriers including asymptomatic cases. As part of SARS-CoV-2 global pandemic preparedness, we studied the transmission of aerosolized air mimicking the infected person releasing speech aerosol with droplets containing CorNPs using a vibrating mesh nebulizer as human patient simulator. Generally speech produces nanoaerosols with droplets of <5 μm in diameter that can travel distances longer than 1 m after release. It is assumed that speech aerosol droplets are a main element of the current Corona virus pandemic, unlike droplets larger than 5 m, which settle down within a 1 m radius. There are no systemic studies, which take into account speech-generated aerosol/droplet experimental validation and their aerodynamics/particle kinetics analysis. In this study, we cover these topics and explore role of residual water in aerosol droplet stability by exploring drying dynamics. Furthermore, a candle experiment was designed to determine whether air pollution might influence respiratory virus like nanoparticle transmission and air stability.

Recent Advances in Nano-Enabled Seed Treatment Strategies for Sustainable Agriculture: Challenges, Risk Assessment, and Future Perspectives

Agro seeds are vulnerable to environmental stressors, adversely affecting seed vigor, crop growth, and crop productivity. Different agrochemical-based seed treatments enhance seed germination, but they can also cause damage to the environment; therefore, sustainable technologies such as nano-based agrochemicals are urgently needed. Nanoagrochemicals can reduce the dose-dependent toxicity of seed treatment, thereby improving seed viability and ensuring the controlled release of nanoagrochemical active ingredients However, the applications of nanoagrochemicals to plants in the field raise concerns about nanomaterial safety, exposure levels, and toxicological implications to the environment and human health. In the present comprehensive review, the development, scope, challenges, and risk assessments of nanoagrochemicals on seed treatment are discussed. Moreover, the implementation obstacles for nanoagrochemicals use in seed treatments, their commercialization potential, and the need for policy regulations to assess possible risks are also discussed. Based on our knowledge, this is the first time that we have presented legendary literature to readers in order to help them gain a deeper understanding of upcoming nanotechnologies that may enable the development of future generation seed treatment agrochemical formulations, their scope, and potential risks associated with seed treatment.

Application of machine learning on understanding biomolecule interactions in cellular machinery

Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

INTRODUCTION

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

The Influence of Mitochondrial Energy and 1C Metabolism on the Efficacy of Anticancer Drugs: Exploring Potential Mechanisms of Resistance

Mitochondria are the main energy factory in living cells. To rapidly proliferate and metastasize, neoplastic cells increase their energy requirements. Thus, mitochondria become one of the most important organelles for them. Indeed, much research shows the interplay between cancer chemoresistance and altered mitochondrial function. In this review, we focus on the differences in energy metabolism between cancer and normal cells to better understand their resistance and how to develop drugs targeting energy metabolism and nucleotide synthesis. One of the differences between cancer and normal cells is the higher nicotinamide adenine dinucleotide (NAD+) level, a cofactor for the tricarboxylic acid cycle (TCA), which enhances their proliferation and helps cancer cells survive under hypoxic conditions. An important change is a metabolic switch called the Warburg effect. This effect is based on the change of energy harvesting from oxygen-dependent transformation to oxidative phosphorylation (OXPHOS), adapting them to the tumor environment. Another mechanism is the high expression of one-carbon (1C) metabolism enzymes. Again, this allows cancer cells to increase proliferation by producing precursors for the synthesis of nucleotides and amino acids. We reviewed drugs in clinical practice and development targeting NAD+, OXPHOS, and 1C metabolism. Combining novel drugs with conventional antineoplastic agents may prove to be a promising new way of anticancer treatment.

Construction and Validation of a Protein-associated Prognostic Model for Gastrointestinal Cancer

Background Gastrointestinal cancer (GIC) is a prevalent and lethal malignant tumor. It is obligatory to investigate innovative biomarkers for the diagnosis and prognosis. Proteins play a crucial role in regulating the occurrence and progression of GIC. However, the prognostic value of proteins is unclear in GIC.

OBJECTIVE

This paper aims to identify the hub prognosis-related proteins (PAPs) and construct a prognosis model for GIC patients for clinical application.

METHODS

Protein expression data of GIC was obtained from The Cancer Proteome Atlas (TCPA) and downloaded the clinicopathological data from The Cancer Genome Atlas database (TCGA). Besides, hub proteins were filtrated via univariate and multivariate Cox regression analysis. Moreover, survival analysis and nomogram were used to predict overall survival (OS). We used the calibration curves to assess the consistency of predictive and actual survival rates. The consistency index (C-index) was used to evaluate the prognostic ability of the predictive model. Furthermore, functional enrichment analysis and protein co-expression of PAPs were used to explore their roles in GIC.

RESULTS

Finally, a prognosis model was conducted based on ten PAPs (CYCLIND1, DVL3, NCADHERIN, SYK, ANNEXIN VII, CD20, CMET, RB, TFRC, and PREX1). The risk score calculated by the model was an independent prognostic predictor. Compared with the high-risk subgroup, the low-risk subgroup had better OS. In the TCGA cohort, the area under the curve value of the receiver operating characteristic curve of the prognostic model was 0.692. The expression of proteins and risk score had a significant association with the clinicopathological characteristics of GIC. Besides, a nomogram based on GIC clinicopathological features and risk scores could properly predict the OS of individual GIC patients. The C-index is 0.71 in the TCGA cohort and 0.73 in the GEO cohort.

CONCLUSION

The results indicate that the risk score is an independent prognostic biomarker and is related to the malignant clinical features of GIC patients. Besides, several PAPs associated with the survival and clinicopathological characteristics of GIC might be potential biomarkers for GIC diagnosis and treatment.

Effect of Citrate- and Gold-Stabilized Superparamagnetic Iron Oxide Nanoparticles on Head and Neck Tumor Cell Lines during Combination Therapy with Ionizing Radiation

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. They are associated with alcohol and tobacco consumption, as well as infection with human papillomaviruses (HPV). Therapeutic options include radiochemotherapy, surgery or chemotherapy. Nanoparticles are becoming more and more important in medicine. They can be used diagnostically, but also therapeutically. In order to provide therapeutic alternatives in the treatment of HNSCC, the effect of citrate-coated superparamagnetic iron oxide nanoparticles (Citrate-SPIONs) and gold-coated superparamagnetic iron oxide nanoparticles (Au-SPIONs) in combination with ionizing irradiation (IR) on two HPV positive and two HPV negative HNSCC and healthy fibroblasts and keratinocytes cell lines were tested. Effects on apoptosis and necrosis were analyzed by using flow cytometry. Cell survival studies were performed with a colony formation assay. To better understand where the SPIONs interact, light microscopy images and immunofluorescence studies were performed. The HNSCC and healthy cell lines showed different responses to the investigated SPIONs. The cytotoxic effects of SPIONs, in combination with IR, are dependent on the type of SPIONs, the dose administered and the cell type treated. They are independent of HPV status. Reasons for the different cytotoxic effect are probably the different compositions of the SPIONs and the related different interaction of the SPIONs intracellularly and paramembranously, which lead to different strong formations of double strand breaks.

m1A-pred: Prediction of Modified 1-methyladenosine Sites in RNA Sequences through Artificial Intelligence

BACKGROUND

The process of nucleotides modification or methyl groups addition to nucleotides is known as post-transcriptional modification (PTM). 1-methyladenosine (m1A) is a type of PTM formed by adding a methyl group to the nitrogen at the 1st position of the adenosine base. Many human disorders are associated with m1A, which is widely found in ribosomal RNA and transfer RNA.

OBJECTIVE

The conventional methods such as mass spectrometry and site-directed mutagenesis proved to be laborious and burdensome. Systematic identification of modified sites from RNA sequences is gaining much attention nowadays. Consequently, an extreme gradient boost predictor, m1A-Pred, is developed in this study for the prediction of modified m1A sites.

METHODS

The current study involves the extraction of position and composition-based properties within nucleotide sequences. The extraction of features helps in the development of the features vector. Statistical moments were endorsed for dimensionality reduction in the obtained features.

RESULTS

Through a series of experiments using different computational models and evaluation methods, it was revealed that the proposed predictor, m1A-pred, proved to be the most robust and accurate model for the identification of modified sites.

AVAILABILITY AND IMPLEMENTATION

To enhance the research on m¹A sites, a friendly server was also developed, which was the final phase of this research.

Metformin Sensitizes Cisplatin-induced Apoptosis Through Regulating Nucleotide Excision Repair Pathway In Cisplatin-resistant Human Lung Cancer Cells

Background:

Lung cancer is a leading cause of cancer death globally. Platinum-based chemotherapeutic medications are essential for treating advanced NSCLC, despite that drug resistance severely limits its effectiveness.

Objective:

In this study, we investigated the cytotoxic effect of metformin on cisplatin-resistant NSCLC cells (A549/DDP) and its potential mechanisms.

Methods:

Anti-lung cancer efficacy of metformin, cisplatin, and metformin combined with cisplatin was examined in A549 and A549/DDP cells. The cell counting kit-8 (CCK-8) assay was applied for measuring cell proliferation. CalcuSyn software was used to calculate the combination index and estimate the synergistic effect of metformin and cisplatin on cell proliferation. The cell apoptosis was analyzed by flow cytometry and the expression of apoptosis-related proteins, Bcl-2, Bax and caspase-3 were analyzed using Western blot. Futhermore, the expression of key nucleotide excision repair (NER) proteins, ERCC1, XPF, and XPA, was also analyzed using Western blot.

Results:

We found that metformin had dose-dependent antiproliferative effects on A549/DDP and A549 cells. The combination of metformin and cisplatin had higher effectiveness in inhibiting A549/DDP and A549 cell growth than either of the two drugs alone. Flow cytometry analysis indicated that the combined treatment could cause more cell apoptosis than the single-drug treatment. Consistently, the combined treatment decreased the expression of Bcl-2 protein and elevated the expression of Bax, and cleaved caspase-3 proteins. The expression level of ERCC1, XPF, and XPA proteins were lower in the combined treatment than in either of metformin and cisplatin treatment alone.

Conclusions:

Our study suggested that metformin and cisplatin had synergistic antitumorigenic effects in A549/DDP cells. The combination of cisplatin and metformin could be promising drug candidates to sensitize cisplatin-induced apoptosis through regulating nucleotide excision repair pathways in lung cancer.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Micropatterned Neurovascular Interface to Mimic the Blood–Brain Barrier’s Neurophysiology and Micromechanical Function: A BBB-on-CHIP Model

A hybrid blood–brain barrier (BBB)-on-chip cell culture device is proposed in this study by integrating microcontact printing and perfusion co-culture to facilitate the study of BBB function under high biological fidelity. This is achieved by crosslinking brain extracellular matrix (ECM) proteins to the transwell membrane at the luminal surface and adapting inlet–outlet perfusion on the porous transwell wall. While investigating the anatomical hallmarks of the BBB, tight junction proteins revealed tortuous zonula occludens (ZO-1), and claudin expressions with increased interdigitation in the presence of astrocytes were recorded. Enhanced adherent junctions were also observed. This junctional phenotype reflects in-vivo-like features related to the jamming of cell borders to prevent paracellular transport. Biochemical regulation of BBB function by astrocytes was noted by the transient intracellular calcium effluxes induced into endothelial cells. Geometry-force control of astrocyte–endothelial cell interactions was studied utilizing traction force microscopy (TFM) with fluorescent beads incorporated into a micropatterned polyacrylamide gel (PAG). We observed the directionality and enhanced magnitude in the traction forces in the presence of astrocytes. In the future, we envisage studying transendothelial electrical resistance (TEER) and the effect of chemomechanical stimulations on drug/ligand permeability and transport. The BBB-on-chip model presented in this proposal should serve as an in vitro surrogate to recapitulate the complexities of the native BBB cellular milieus.

Where Is Nano Today and Where Is It Headed? A Review of Nanomedicine and the Dilemma of Nanotoxicology

Worldwide nanotechnology development and application have fueled many scientific advances, but technophilic expectations and technophobic demands must be counterbalanced in parallel. Some of the burning issues today are the following: (1) Where is nano today? (2) How good are the communication and investment networks between academia/research and governments? (3) Is there any spotlight application for nanotechnology? Nanomedicine is a particular arm of nanotechnology within the healthcare landscape, focused on diagnosis, treatment, and monitoring of emerging (such as coronavirus disease 2019, COVID-19) and contemporary (including diabetes, cardiovascular diseases, neurodegenerative disorders, and cancer) diseases. However, it may only represent the bright side of the coin. In fact, in the recent past, the concept of nanotoxicology has emerged to address the dark shadows of nanomedicine. The nanomedicine field requires more nanotoxicological studies to identify undesirable effects and guarantee safety. Here, we provide an overall perspective on nanomedicine and nanotoxicology as central pieces of the giant puzzle of nanotechnology. First, the impact of nanotechnology on education and research is highlighted, followed by market trends and scientific output tendencies. In the next section, the nanomedicine and nanotoxicology dilemma is addressed through the interplay of in silico, in vitro, and in vivo models with the support of omics and microfluidic approaches. Lastly, a reflection on the regulatory issues and clinical trials is provided. Finally, some conclusions and future perspectives are proposed for a clearer and safer translation of nanomedicines from the bench to the bedside.

Interfacial Water in the SARS Spike Protein: Investigating the Interaction with Human ACE2 Receptor and In Vitro Uptake in A549 Cells

The severity of global pandemic due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has engaged the researchers and clinicians to find the key features triggering the viral infection to lung cells. By utilizing such crucial information, researchers and scientists try to combat the spread of the virus. Here, in this work, we performed in silico analysis of the protein-protein interactions between the receptor-binding domain (RBD) of the viral spike protein and the human angiotensin-converting enzyme 2 (hACE2) receptor to highlight the key alteration that happened from SARS-CoV to SARS-CoV-2. We analyzed and compared the molecular differences between spike proteins of the two viruses using various computational approaches such as binding affinity calculations, computational alanine, and molecular dynamics simulations. The binding affinity calculations showed that SARS-CoV-2 binds a little more firmly to the hACE2 receptor than SARS-CoV. The major finding obtained from molecular dynamics simulations was that the RBD-ACE2 interface is populated with water molecules and interacts strongly with both RBD and ACE2 interfacial residues during the simulation periods. The water-mediated hydrogen bond by the bridge water molecules is crucial for stabilizing the RBD and ACE2 domains. Near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) confirmed the presence of vapor and molecular water phases in the protein-protein interfacial domain, further validating the computationally predicted interfacial water molecules. In addition, we examined the role of interfacial water molecules in virus uptake by lung cell A549 by binding and maintaining the RBD/hACE2 complex at varying temperatures using nanourchins coated with spike proteins as pseudoviruses and fluorescence-activated cell sorting (FACS) as a quantitative approach. The structural and dynamical features presented here may serve as a guide for developing new drug molecules, vaccines, or antibodies to combat the COVID-19 pandemic.

Investigation of the Associations between a Nanomaterial's Microrheology and Toxicology

With the advent of Nanotechnology, the use of nanomaterials in consumer products is increasing on a daily basis, due to which a deep understanding and proper investigation regarding their safety and risk assessment should be a major priority. To date, there is no investigation regarding the microrheological properties of nanomaterials (NMs) in biological media. In our study, we utilized in silico models to select the suitable NMs based on their physicochemical properties such as solubility and lipophilicity. Then, we established a new method based on dynamic light scattering (DLS) microrheology to get the mean square displacement (MSD) and viscoelastic property of two model NMs that are dendrimers and cerium dioxide nanoparticles in Dulbecco's Modified Eagle Medium (DMEM) complete media at three different concentrations for both NMs. Subsequently, we established the cytotoxicological profiling using water-soluble tetrazolium salt-1 (WST-1) and a reactive oxygen species (ROS) assay. To take one step forward, we further looked into the tight junction properties of the cells using immunostaining with Zonula occluden-1 (ZO-1) antibodies and found that the tight junction function or transepithelial resistance (TEER) was affected in response to the microrheology and cytotoxicity. The quantitative polymerase chain reaction (q-PCR) results in the gene expression of ZO-1 after the 24 h treatment with NPs further validates the findings of immunostaining results. This new method that we established will be a reference point for other NM studies which are used in our day-to-day consumer products.

Merging data curation and machine learning to improve nanomedicines

Nanomedicine design is often a trial-and-error process, and the optimization of formulations and in vivo properties requires tremendous benchwork. To expedite the nanomedicine research progress, data science is steadily gaining importance in the field of nanomedicine. Recently, efforts have explored the potential to predict nanomaterials synthesis and biological behaviors via advanced data analytics. Machine learning algorithms process large datasets to understand and predict various material properties in nanomedicine synthesis, pharmacologic parameters, and efficacy. "Big data" approaches may enable even larger advances, especially if researchers capitalize on data curation methods. However, the concomitant use of data curation processes needed to facilitate the acquisition and standardization of large, heterogeneous data sets, to support advanced data analytics methods such as machine learning has yet to be leveraged. Currently, data curation and data analytics areas of nanotechnology-focused data science, or 'nanoinformatics', have been proceeding largely independently. This review highlights the current efforts in both areas and the potential opportunities for coordination to advance the capabilities of data analytics in nanomedicine.

Emerging cold plasma treatment and machine learning prospects for seed priming: a step towards sustainable food production

Seeds are vulnerable to physical and biological stresses during the germination process. Seed priming strategies can alleviate such stresses. Seed priming is a technique of treating and drying seeds prior to germination in order to accelerate the metabolic process of germination. Multiple benefits are offered by seed priming techniques, such as reducing fertilizer use, accelerating seed germination, and inducing systemic resistance in plants, which are both cost-effective and eco-friendly. For seed priming, cold plasma (CP)-mediated priming could be an innovative alternative to synthetic chemical treatments. CP priming is an eco-friendly, safe and economical, yet relatively less explored technique towards the development of seed priming. In this review, we discussed in detail the application of CP technology for seed priming to enhance germination, the quality of seeds, and the production of crops in a sustainable manner. Additionally, the combination treatment of CP with nanoparticle (NP) priming is also discussed. The large numbers of parameters need to be monitored and optimized during CP treatment to achieve the desired priming results. Here, we discussed a new perspective of machine learning for modeling plasma treatment parameters in agriculture for the development of synergistic protocols for different types of seed priming.

The Janus of Protein Corona on nanoparticles for tumor targeting, immunotherapy and diagnosis

The therapeutics based on nanoparticles (NPs) are considered as the promising strategy for tumor detection and treatment. However, one of the most challenges is the adsorption of biomolecules on NPs after their exposition to biological medium, leading unpredictable in vivo behaviors. The interactions caused by protein corona (PC) will influence the biological fate of NPs in either negative or positive ways, including (i) blood circulation, accumulation and penetration of NPs at targeting sites, and further cellular uptake in tumor targeting delivery; (ii) interactions between NPs and receptors on immune cells for immunotherapy. Besides, PC on NPs could be utilized as new biomarker in tumor diagnosis by identifying the minor change of protein concentration led by tumor growth and invasion in blood. Herein, the mechanisms of these PC-mediated effects will be introduced. Moreover, the recent advances about the strategies will be reviewed to reduce negative effects caused by PC and/or utilize positive effects of PC on tumor targeting, immunotherapy and diagnosis, aiming to provide a reasonable perspective to recognize PC with their applications.

The Chemical Profiling, Docking Study, and Antimicrobial and Antibiofilm Activities of the Endophytic fungi Aspergillus sp. AP5

Growing data suggest that Aspergillus niger, an endophytic fungus, is a rich source of natural compounds with a wide range of biological properties. This study aimed to examine the antimicrobial and antibiofilm capabilities of the Phragmites australis-derived endophyte against a set of pathogenic bacteria and fungi. The endophytic fungus Aspergillus sp. AP5 was isolated from the leaves of P. australis. The chemical profile of the fungal crude extract was identified by spectroscopic analysis using LC-HRESIMS. The fungal-derived extract was evaluated for its antimicrobial activity towards a set of pathogenic bacterial and fungal strains including Staphylococcus aureus, Pseudomonas aeruginosa, Proteus vulgaris, Klebsiella sp., Candida albicans, and Aspergillus niger. Moreover, antibiofilm activity toward four resistant biofilm-forming bacteria was also evaluated. Additionally, a neural-networking pharmacophore-based visual screening predicted the most probable bioactive compounds in the obtained extract. The AP5-EtOAc extract was found to have potent antibacterial activities against S. aureus, E. coli, and Klebsiella sp., while it exhibited low antibacterial activity toward P. Vulgaris and P. aeruginosa and displayed anticandidal activity. The AP5-EtOAc extract had significant antibiofilm activity in S. aureus, followed by P. aeruginosa. The active metabolites' antifungal and/or antibacterial activities may be due to targeting the fungal CYP 51 and/or the bacterial Gyr-B.

Recent updates on applications of Lipid-based nanoparticles for site-specific drug delivery

BACKGROUND

Site-specific drug delivery is a widespread and demanding area nowadays. Lipid-based nanoparticulate drug delivery systems have shown promising effects for targeting drugs among lymphatic systems, brain tissues, lungs, and skin. Recently, lipid nanoparticles are used for targeting the brain via the mucosal route for local therapeutic effects. Lipid nanoparticles (LNPs) can help in enhancing the efficacy and lowering the toxicities of anticancer drugs to treat the tumors, particularly in lymph after metastases of tumors. LNPs contain a non-polar core that can improve the absorption of lipophilic drugs into the lymph node and treat tumors. Cellular uptake of drugs can also be enhanced using LNPs and therefore, LNPs are the ideal carrier for treating intracellular infections such as leishmaniasis, tuberculosis and parasitic infection in the brain, etc. Furthermore, specific surface modifications with molecules like mannose, or PEG could improve the macrophage uptake and hence effectively eradicate parasites hiding in macrophages.

METHOD

An electronic literature search was conducted to update the advancements in the field of site-specific drug delivery utilizing lipid-based nanoparticles. A search of the Scopus database (https://www.scopus.com/home.uri) was conducted using the following keywords: lipid-based nanoparticles; site specific delivery.

CONCLUSION

Solid lipid nanoparticles have shown site-specific targeted delivery to various organs including the liver, oral mucosa, brain, epidermis, pulmonary and lymphatic systems. These lipid-based systems showed improved bioavailability as well as reduced side effects. Therefore, the focus of this article is to review the recent research studies on LNPs for site-specific or targeting drug delivery.

Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence

Today, a growing number of computational aids and simulations are shaping model-informed drug development. Artificial intelligence, a family of self-learning algorithms, is only the latest emerging trend applied by academic researchers and the pharmaceutical industry. Nanomedicine successfully conquered several niche markets and offers a wide variety of innovative drug delivery strategies. Still, only a small number of patients benefit from these advanced treatments, and the number of data sources is very limited. As a consequence, “big data” approaches are not always feasible and smart combinations of human and artificial intelligence define the research landscape. These methodologies will potentially transform the future of nanomedicine and define new challenges and limitations of machine learning in their development. In our review, we present an overview of modeling and artificial intelligence applications in the development and manufacture of nanomedicines. Also, we elucidate the role of each method as a facilitator of breakthroughs and highlight important limitations.