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

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.

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.

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.

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.

Toxicological assessment of agrochemicals on bees using machine learning tools

Machine learning (ML) is a branch of artificial intelligence (AI) that enables the analysis of complex multivariate data. ML has significant potential in risk assessments of non-target insects for modeling the multiple factors affecting insect health, including the adverse effects of agrochemicals. Here, the potential of ML for risk assessments of glyphosate (herbicide; formulation) and imidacloprid (insecticide, neonicotinoid; formulation) on the stingless bee Melipona quadrifasciata was explored. The collective behavior of forager bees was analyzed after in vitro exposure to agrochemicals. ML algorithms were applied to identify the agrochemicals that the bees have been exposed to based on multivariate behavioral features. Changes in the in situ detection of different proteins in the midgut were also studied. Imidacloprid exposure leads to the greatest changes in behavior. The ML algorithms achieved a higher accuracy (up to 91%) in identifying agrochemical contamination. The two agrochemicals altered the detection of cells positive for different proteins, which can be detrimental to midgut physiology. This study provides a holistic assessment of the sublethal effects of glyphosate and imidacloprid on a key pollinator. The procedures used here can be applied in future studies to monitor and predict multiple environmental factors affecting insect health in the field.

Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?

This Perspective outlines recent progress and future directions for using machine learning (ML), a data-driven method, to address critical questions in the design, synthesis, processing, and characterization of biomacromolecules. The achievement of these tasks requires the navigation of vast and complex chemical and biological spaces, difficult to accomplish with reasonable speed. Using modern algorithms and supercomputers, quantum physics methods are able to examine systems containing a few hundred interacting species and determine the probability of finding them in a particular region of phase space, thereby anticipating their properties. Likewise, modern approaches in chemistry and biomolecular simulation, supported by high performance computing, have culminated in producing data sets of escalating size and intrinsically high complexity. Hence, using ML to extract relevant information from these fields is of paramount importance to advance our understanding of chemical and biomolecular systems. At the heart of ML approaches lie statistical algorithms, which by evaluating a portion of a given data set, identify, learn, and manipulate the underlying rules that govern the whole data set. The assembly of a quality model to represent the data followed by the predictions and elimination of error sources are the key steps in ML. In addition to a growing infrastructure of ML tools to address complex problems, an increasing number of aspects related to our understanding of the fundamental properties of biomacromolecules are exposed to ML. These fields, including those residing at the interface of polymer science and biology (i.e., structure determination, de novo design, folding, and dynamics), strive to adopt and take advantage of the transformative power offered by approaches in the ML domain, which clearly has the potential of accelerating research in the field of biomacromolecules.

Protective Effects of N-acetylcysteine Niosome Nanoparticles on Paraquatinduced Nephrotoxicity in Male Rats

INTRODUCTION

Paraquat (PQ), as a bipyridyl compound, is widely used as an effective herbicide that produces reactive oxygen species (ROS), affecting the unsaturated lipids of cell membranes leading to cell mortality. N-acetylcysteine (NAC) is a medication that has a beneficial role in reducing the intoxication of kidneys caused by PQ. Niosomes are bilayer vesicles that enhance the bioavailability of drugs. This study aimed to compare the effects of NAC and niosome of NAC (NACNPs) on PQ-induced kidney toxicity concerning its antioxidant activity.

METHODS

In this experimental study, after formulating NACNP, 30 Wistar male rats weighing 180 to 250 gm were classified into five groups: the control group was treated with normal saline, while the other four groups received 35mg/kg/day of PQ via intraperitoneal route and, was treated with 25mg/kg/day NAC, 25mg/kg/day niosome and 25 mg/kg/day NACNP by gavage, Then, oxidative stress biomarkers such as total antioxidant capacity (TAC), catalase activity (CAT), lipid peroxidation (LPO), and total thiol group (TTG), plus blood urea nitrogen (BUN) and creatinine levels were evaluated in kidney tissue homogenate and examined histopathologically.

RESULTS

The results revealed that TTG increased significantly in NAC & NACNP groups than in the PQ group. Further, in the PQ group, LPO increased significantly compared with the control, NAC, and NACNP groups, while in the NAC and NACNP group, LPO diminished compared with the PQ group. There was no significant difference in TAC between groups. Blood urea nitrogen (BUN) and creatinine levels dropped in NACNP compared with the PQ group and the NAC. Histological studies also approved PQ-induced damage and the protective effect of NACNP.

CONCLUSION

The results indicated that NACNP could modulate oxidative stress status and kidney function against PQ toxicity.

Diverse nanocomposites as a potential dressing for diabetic wound healing

Wound healing is a programmed process of continuous events which is impaired in the case of diabetic patients. This impaired process of healing in diabetics leads to amputation, longer hospitalisation, immobilisation, low self-esteem, and mortality in some patients. This problem has paved the way for several innovative strategies like the use of nanotechnology for the treatment of wounds in diabetic patients. The use of biomaterials, nanomaterials have advanced approaches in tissue engineering by designing multi-functional nanocomposite scaffolds. Stimuli-responsive scaffolds that interact with the wound microenvironment and controlled release of bioactive molecules have helped in overcoming barriers in healing. The use of different types of nanocomposite scaffolds for faster healing of diabetic wounds is constantly being studied. Nanocomposites have helped in addressing specific issues with respect to healing and improving angiogenesis. Method: A literature search was followed to retrieve the articles on strategies for wound healing in diabetes across several databases like PubMed, EMBASE, Scopus and Cochrane database. The search was performed in May 2022 by two researchers independently. They keywords used were "diabetic wounds, nanotechnology, nanocomposites, nanoparticles, chronic diabetic wounds, diabetic foot ulcer, hydrogel". Exclusion criteria included insulin resistance, burn wound, dressing material.

A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

The purpose of this article is to perform in-depth research and analysis on the artificial intelligence coordination and optimization mechanism of college counseling student management using big data technology. This study places the collaborative ideological and political work of colleges and universities in the context of big data, and by analyzing its basic connotation and changes in the real situation, it explores the development progression of colleges and universities making full use of big data resources to cultivate a collaborative education model, which is conducive to promoting colleges and universities to cultivate a whole staff, whole process, and all-round accurate ideological education and value-led services and to shape excellent young college students with comprehensive growth. The first is to scientifically build a multilevel linked big data management platform for counselor professionalization construction, plan the technical architecture of the organizational platform, build a cloud database of counselor career files, and extract valuable information and data from the organizational activities at the macrolevel and personal activities at the microlevel with counselor professionalization construction activities; the second is to realize the integrated application of information resources for counselor team construction. The second is to realize the integrated application of counselor team construction information resources, visualise and accurately analyze and evaluate the counselor group's focus on career development and individual counselors' feedback on career capacity construction, and improve the overall construction, personalized education management level, and self-improvement development ability. Fourth, in the professionalization of counselors, attention should be paid to the scientific selection and prevention of risks of big data application, ensuring the authenticity and reliability of data and leakage prevention and control, etc.

Synthesis and Fungicidal Activity of Hydrated Geranylated Phenols against Botrytis cinerea

Botrytis cinerea is a ubiquitous fungus that affects hundreds of plants, resulting in economic losses to the horticulture and fruit industry. The search for new antifungal agents is a matter of current interest. Thus, in this work a series of geranylated phenols in which the side alkyl chain has been hydrated have been synthesized, and their activity against B. cinerea has been evaluated. The coupling of phenol and geraniol has been accomplished under microwave irradiation obtaining the highest reaction yields in the shortest reaction times. Hydration of the side chain was carried out in dioxane with p-toluenesulfonic acid polymer-bound as the catalyst. All synthesized compounds were tested against B. cinerea using the growth inhibition assay and EC50 values were determined. The results show that activity depends on the number and nature of functional groups in the phenol ring and hydration degree of the geranyl chain. The most active compound is 1,4-dihydroquinone with one hydroxyl group attached at the end of the alkyl chain. Results from a molecular docking study suggest that hydroxyl groups in the phenol ring and alkyl chain are important in the binding of compounds to the active site, and that the experimental antifungal activity correlates with the number of H-bond that can be formed in the binding site.

Potential of Nanocarrier-Based Drug Delivery Systems for Brain Targeting: A Current Review of Literature

The advent of nanotechnologies such as nanocarriers and nanotherapeutics has changed the treatment strategy and developed a more efficacious novel drug delivery system. Various drug delivery systems are focused on drug-targeting of brain cells. However, the manifestation of the brain barrier is the main hurdle for the effective delivery of chemotherapeutics, ultimately causing treatment failure of various drugs. To solve this problem, various nanocarrier-based drug delivery system has been developed for brain targeting. This review outlines nanocarrier-based composites for different brain diseases and highlights nanocarriers for drug targeting towards brain cells. It also summarizes the latest developments in nanocarrier-based delivery systems containing liposomal systems, dendrimers, polymeric micelles, polymeric nanocarriers, quantum dots (QDs), and gold nanoparticles. Besides, the optimal properties of nanocarriers and therapeutic implications for brain targeting have been extensively studied. Finally, the potential applications and research opportunities for nanocarriers in brain targeting are discussed.

Anticancer Potential of Green Synthesized Silver Nanoparticles of the Soft Coral Cladiella pachyclados Supported by Network Pharmacology and In Silico Analyses

Cladiella-derived natural products have shown promising anticancer properties against many human cancer cell lines. In the present investigation, we found that an ethyl acetate extract of Cladiella pachyclados (CE) collected from the Red Sea could inhibit the human breast cancer (BC) cells (MCF and MDA-MB-231) in vitro (IC50 24.32 ± 1.1 and 9.55 ± 0.19 µg/mL, respectively). The subsequent incorporation of the Cladiella extract into the green synthesis of silver nanoparticles (AgNPs) resulted in significantly more activity against both cancer cell lines (IC50 5.62 ± 0.89 and 1.72 ± 0.36, respectively); the efficacy was comparable to that of doxorubicin with much-enhanced selectivity. To explore the mode of action of this extract, various in silico and network-pharmacology-based analyses were performed in the light of the LC-HRESIMS-identified compounds in the CE extract. Firstly, using two independent machine-learning-based prediction software platforms, most of the identified compounds in CE were predicted to inhibit both MCF7 and MDA-MB-231. Moreover, they were predicted to have low toxicity towards normal cell lines. Secondly, approximately 242 BC-related molecular targets were collected from various databases and used to construct a protein-protein interaction (PPI) network, which revealed the most important molecular targets and signaling pathways in the pathogenesis of BC. All the identified compounds in the extract were then subjected to inverse docking against all proteins hosted in the Protein Data bank (PDB) to discover the BC-related proteins that these compounds can target. Approximately, 10.74% of the collected BC-related proteins were potential targets for 70% of the compounds identified in CE. Further validation of the docking results using molecular dynamic simulations (MDS) and binding free energy calculations revealed that only 2.47% of the collected BC-related proteins could be targeted by 30% of the CE-derived compounds. According to docking and MDS experiments, protein-pathway and compound-protein interaction networks were constructed to determine the signaling pathways that the CE compounds could influence. This paper highlights the potential of marine natural products as effective anticancer agents and reports the discovery of novel anti-breast cancer AgNPs.

Evaluation of the Biological Activity of Folic Acid-Modified Paclitaxel-Loaded Gold Nanoparticles

Purpose

Gold nanoparticles (AuNPs) with good physical and biological properties are often used in medicine, diagnostics, food, and similar industries. This paper explored an AuNPs drug delivery system that had good target selectivity for folate-receptor overexpressing cells to induce apoptosis.

Methods

A novel drug delivery system, Au@MPA-PEG-FA-PTX, was developed carrying paclitaxel (PTX) on folic acid (FA) and polyethylene glycol (PEG)-modified AuNPs. The nanomaterial was characterized by transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), and ultraviolet-visible spectroscopy (UV-Vis). Also, the biological activity of the AuNPs drug delivery system was examined using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay in HL-7702, Hela, SMMC-7721, and HCT-116 cells. Furthermore, apoptotic activity using annexin V-FITC, mitochondrial membrane potential (MMP), and reactive oxygen species (ROS) levels was estimated by flow cytometry and fluorescence microscopy.

Results

Au@MPA-PEG-FA-PTX exhibited a distinct core-shell structure with a controllable size of 28±1 nm. Also, the AuNPs maintained good dispersion and spherical shape uniformity before and after modification. The MTT assay revealed good antitumor activity of the Au@MPA-PEG-FA-PTX against the Hela, SMMC-7721, and HCT-116 cells, while Au@MPA-PEG-FA-PTX produced better pharmacological effects than PTX in isolation. Further mechanistic investigation revealed that effective internalization of AuNPs by folate-receptor overexpressing cancer cells induced cell apoptosis through excessive production of intracellular ROS.

Conclusion

The AuNPs drug delivery system showed good target selectivity for folate-receptor overexpressing cancer cells to induce target cell-specific apoptosis. These AuNPs may have great potential as theranostic agents such as in cancer.

In Silico Modeling as a Perspective in Developing Potential Vaccine Candidates and Therapeutics for COVID-19

The potential of computational models to identify new therapeutics and repurpose existing drugs has gained significance in recent times. The current ‘COVID-19’ pandemic caused by the new SARS CoV2 virus has affected over 200 million people and caused over 4 million deaths. The enormity and the consequences of this viral infection have fueled the research community to identify drugs or vaccines through a relatively expeditious process. The availability of high-throughput datasets has cultivated new strategies for drug development and can provide the foundation towards effective therapy options. Molecular modeling methods using structure-based or computer-aided virtual screening can potentially be employed as research guides to identify novel antiviral agents. This review focuses on in-silico modeling of the potential therapeutic candidates against SARS CoVs, in addition to strategies for vaccine design. Here, we particularly focus on the recently published SARS CoV main protease (Mpro) active site, the RNA-dependent RNA polymerase (RdRp) of SARS CoV2, and the spike S-protein as potential targets for vaccine development. This review can offer future perspectives for further research and the development of COVID-19 therapies via the design of new drug candidates and multi-epitopic vaccines and through the repurposing of either approved drugs or drugs under clinical trial.

Specific Binding of Novel SPION-Based System Bearing Anti-N-Cadherin Antibodies to Prostate Tumor Cells

Purpose

Epithelial–mesenchymal (EMT) transition plays an important role in metastasis and is accompanied by an upregulation of N-cadherin expression. A new nanoparticulate system (SPION/CCh/N-cad) based on superparamagnetic iron oxide nanoparticles, stabilized with a cationic derivative of chitosan and surface-modified with anti-N-cadherin antibody, was synthetized for the effective capture of N-cadherin expressing circulating tumor cells (CTC).

Methods

The morphology, physicochemical, and magnetic properties of the system were evaluated using dynamic light scattering (DLS), fluorescence spectroscopy, Mössbauer spectroscopy, magnetometry, and fluorescence spectroscopy. Atomic force microscopy (AFM), confocal microscopy and flow cytometry were used to study the interaction of our nanoparticulate system with N-cadherin expressed in prostate cancer cell lines (PC-3 and DU 145). A purpose-built cuvette was used in the cancer cell capture experiments.

Results

The obtained nanoparticles were a spherical, stable colloid, and exhibited excellent magnetic properties. Biological experiments confirmed that the novel SPION/CCh/N-cad system interacts specifically with N-cadherin present on the cell surface. Preliminary studies on the magnetic capture of PC-3 cells using the obtained nanoparticles were successful. Incubation times as short as 1 minute were sufficient for the synthesized system to effectively bind to the PC-3 cells.

Conclusion

Results obtained for our system suggest a possibility of using it to capture CTC in the flow conditions.

Recent Advances in the Use of Mesoporous Silica Nanoparticles for the Diagnosis of Bacterial Infections

Public awareness of infectious diseases has increased in recent months, not only due to the current COVID-19 outbreak but also because of antimicrobial resistance (AMR) being declared a top-10 global health threat by the World Health Organization (WHO) in 2019. These global issues have spiked the realization that new and more efficient methods and approaches are urgently required to efficiently combat and overcome the failures in the diagnosis and therapy of infectious disease. This holds true not only for current diseases, but we should also have enough readiness to fight the unforeseen diseases so as to avoid future pandemics. A paradigm shift is needed, not only in infection treatment, but also diagnostic practices, to overcome the potential failures associated with early diagnosis stages, leading to unnecessary and inefficient treatments, while simultaneously promoting AMR. With the development of nanotechnology, nanomaterials fabricated as multifunctional nano-platforms for antibacterial therapeutics, diagnostics, or both (known as "theranostics") have attracted increasing attention. In the research field of nanomedicine, mesoporous silica nanoparticles (MSN) with a tailored structure, large surface area, high loading capacity, abundant chemical versatility, and acceptable biocompatibility, have shown great potential to integrate the desired functions for diagnosis of bacterial infections. The focus of this review is to present the advances in mesoporous materials in the form of nanoparticles (NPs) or composites that can easily and flexibly accommodate dual or multifunctional capabilities of separation, identification and tracking performed during the diagnosis of infectious diseases together with the inspiring NP designs in diagnosis of bacterial infections.