Revolutionizing Brain Tumor Care: Emerging Technologies and Strategies.

Glioblastoma multiforme (GBM) is one of the most aggressive forms of brain tumor, characterized by a daunting prognosis with a life expectancy hovering around 12-16 months. Despite a century of relentless research, only a select few drugs have received approval for brain tumor treatment, largely due to the formidable barrier posed by the blood-brain barrier. The current standard of care involves a multifaceted approach combining surgery, irradiation, and chemotherapy. However, recurrence often occurs within months despite these interventions. The formidable challenges of drug delivery to the brain and overcoming therapeutic resistance have become focal points in the treatment of brain tumors and are deemed essential to overcoming tumor recurrence. In recent years, a promising wave of advanced treatments has emerged, offering a glimpse of hope to overcome the limitations of existing therapies. This review aims to highlight cutting-edge technologies in the current and ongoing stages of development, providing patients with valuable insights to guide their choices in brain tumor treatment.

An efficient hybrid deep learning architecture for predicting short antimicrobial peptides.

Short-length antimicrobial peptides (AMPs) have been demonstrated to have intensified antimicrobial activities against a wide spectrum of microbes. Therefore, exploration of novel and promising short AMPs is highly essential in developing various types of antimicrobial drugs or treatments. In addition to experimental approaches, computational methods have been developed to improve screening efficiency. Although existing computational methods have achieved satisfactory performance, there is still much room for model improvement. In this study, we proposed iAMP-DL, an efficient hybrid deep learning architecture, for predicting short AMPs. The model was constructed using two well-known deep learning architectures: the long short-term memory architecture and convolutional neural networks. To fairly assess the performance of the model, we compared our model with existing state-of-the-art methods using the same independent test set. Our comparative analysis shows that iAMP-DL outperformed other methods. Furthermore, to assess the robustness and stability of our model, the experiments were repeated 10 times to observe the variation in prediction efficiency. The results demonstrate that iAMP-DL is an effective, robust, and stable framework for detecting promising short AMPs. Another comparative study of different negative data sampling methods also confirms the effectiveness of our method and demonstrates that it can also be used to develop a robust model for predicting AMPs in general. The proposed framework was also deployed as an online web server with a user-friendly interface to support the research community in identifying short AMPs.

The potential of baicalin to enhance neuroprotection and mitochondrial function in a human neuronal cell model.

Baicalin is a flavone glycoside derived from flowering plants belonging to the Scutellaria genus. Previous studies have reported baicalin's anti-inflammatory and neuroprotective properties in rodent models, indicating the potential of baicalin in neuropsychiatric disorders where alterations in numerous processes are observed. However, the extent of baicalin's therapeutic effects remains undetermined in a human cell model, more specifically, neuronal cells to mimic the brain environment in vitro. As a proof of concept, we treated C8-B4 cells (murine cell model) with three different doses of baicalin (0.1, 1 and 5 µM) and vehicle control (DMSO) for 24 h after liposaccharide-induced inflammation and measured the levels of TNF-α in the medium by ELISA. NT2-N cells (human neuronal-like cell model) underwent identical baicalin treatment, followed by RNA extraction, genome-wide mRNA expression profiles and gene set enrichment analysis (GSEA). We also performed neurite outgrowth assays and mitochondrial flux bioanalysis (Seahorse) in NT2-N cells. We found that in C8-B4 cells, baicalin at ≥ 1 µM exhibited anti-inflammatory effects, lowering TNF-α levels in the cell culture media. In NT2-N cells, baicalin positively affected neurite outgrowth and transcriptionally up-regulated genes in the tricarboxylic acid cycle and the glycolysis pathway. Similarly, Seahorse analysis showed increased oxygen consumption rate in baicalin-treated NT2-N cells, an indicator of enhanced mitochondrial function. Together, our findings have confirmed the neuroprotective and mitochondria enhancing effects of baicalin in human-neuronal like cells. Given the increased prominence of mitochondrial mechanisms in diverse neuropsychiatric disorders and the paucity of mitochondrial therapeutics, this suggests the potential therapeutic application of baicalin in human neuropsychiatric disorders where these processes are altered.

Network-based drug repurposing for schizophrenia.

Despite recent progress, the challenges in drug discovery for schizophrenia persist. However, computational drug repurposing has gained popularity as it leverages the wealth of expanding biomedical databases. Network analyses provide a comprehensive understanding of transcription factor (TF) regulatory effects through gene regulatory networks, which capture the interactions between TFs and target genes by integrating various lines of evidence. Using the PANDA algorithm, we examined the topological variances in TF-gene regulatory networks between individuals with schizophrenia and healthy controls. This algorithm incorporates binding motifs, protein interactions, and gene co-expression data. To identify these differences, we subtracted the edge weights of the healthy control network from those of the schizophrenia network. The resulting differential network was then analysed using the CLUEreg tool in the GRAND database. This tool employs differential network signatures to identify drugs that potentially target the gene signature associated with the disease. Our analysis utilised a large RNA-seq dataset comprising 532 post-mortem brain samples from the CommonMind project. We constructed co-expression gene regulatory networks for both schizophrenia cases and healthy control subjects, incorporating 15,831 genes and 413 overlapping TFs. Through drug repurposing, we identified 18 promising candidates for repurposing as potential treatments for schizophrenia. The analysis of TF-gene regulatory networks revealed that the TFs in schizophrenia predominantly regulate pathways associated with energy metabolism, immune response, cell adhesion, and thyroid hormone signalling. These pathways represent significant targets for therapeutic intervention. The identified drug repurposing candidates likely act through TF-targeted pathways. These promising candidates, particularly those with preclinical evidence such as rimonabant and kaempferol, warrant further investigation into their potential mechanisms of action and efficacy in alleviating the symptoms of schizophrenia.

Use of gene regulatory network analysis to repurpose drugs to treat bipolar disorder.

BACKGROUND: Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies. METHODS: This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database. RESULTS: Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments. LIMITATIONS: Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes. CONCLUSIONS: Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

Transmission electron microscopy study on the phase transformation of metastable precipitates to stable phases.

Nanosized precipitates play a critical role in increasing the strength of metallic alloys. There are many reports that the initial precipitates are metastable phases holding a different composition and crystal structure from the equilibrium precipitate. The metastable precipitate transforms to its stable phase during heat treatment. A transmission electron microscope enables researchers to study the phase transition of metastable precipitates to stable phases due to its fine resolution in identifying crystal structures and chemical compositions. This review introduces the various phase transformation mechanisms of metastable precipitates to stable phases obtained from the analysis using a transmission electron microscope. The role of dislocation movement in the phase transition is further discussed.

Effects of antipsychotic drugs on energy metabolism.

Schizophrenia (SCZ) is a complex neuropsychiatric disorder associated with altered bioenergetic pathways and mitochondrial dysfunction. Antipsychotic medications, both first and second-generation, are commonly prescribed to manage SCZ symptoms, but their direct impact on mitochondrial function remains poorly understood. In this study, we investigated the effects of commonly prescribed antipsychotics on bioenergetic pathways in cultured neurons. We examined the impact of risperidone, aripiprazole, amisulpride, and clozapine on gene expression, mitochondrial bioenergetic profile, and targeted metabolomics after 24-h treatment, using RNA-seq, Seahorse XF24 Flux Analyser, and gas chromatography-mass spectrometry (GC-MS), respectively. Risperidone treatment reduced the expression of genes involved in oxidative phosphorylation, the tricarboxylic acid cycle, and glycolysis pathways, and it showed a tendency to decrease basal mitochondrial respiration. Aripiprazole led to dose-dependent reductions in various mitochondrial function parameters without significantly affecting gene expression. Aripiprazole, amisulpride and clozapine treatment showed an effect on the tricarboxylic acid cycle metabolism, leading to more abundant metabolite levels. Antipsychotic drug effects on mitochondrial function in SCZ are multifaceted. While some drugs have greater effects on gene expression, others appear to exert their effects through enzymatic post-translational or allosteric modification of enzymatic activity. Understanding these effects is crucial for optimising treatment strategies for SCZ. Novel therapeutic interventions targeting energy metabolism by post-transcriptional pathways might be more effective as these can more directly and efficiently regulate energy production.

Development and validation of a rabbit model of Pseudomonas aeruginosa non-ventilated pneumonia for preclinical drug development.

Background: New drugs targeting antimicrobial resistant pathogens, including Pseudomonas aeruginosa, have been challenging to evaluate in clinical trials, particularly for the non-ventilated hospital-acquired pneumonia and ventilator-associated pneumonia indications. Development of new antibacterial drugs is facilitated by preclinical animal models that could predict clinical efficacy in patients with these infections. Methods: We report here an FDA-funded study to develop a rabbit model of non-ventilated pneumonia with Pseudomonas aeruginosa by determining the extent to which the natural history of animal disease reproduced human pathophysiology and conducting validation studies to evaluate whether humanized dosing regimens of two antibiotics, meropenem and tobramycin, can halt or reverse disease progression. Results: In a rabbit model of non-ventilated pneumonia, endobronchial challenge with live P. aeruginosa strain 6206, but not with UV-killed Pa6206, caused acute respiratory distress syndrome, as evidenced by acute lung inflammation, pulmonary edema, hemorrhage, severe hypoxemia, hyperlactatemia, neutropenia, thrombocytopenia, and hypoglycemia, which preceded respiratory failure and death. Pa6206 increased >100-fold in the lungs and then disseminated from there to infect distal organs, including spleen and kidneys. At 5 h post-infection, 67% of Pa6206-challenged rabbits had PaO2 <60 mmHg, corresponding to a clinical cut-off when oxygen therapy would be required. When administered at 5 h post-infection, humanized dosing regimens of tobramycin and meropenem reduced mortality to 17-33%, compared to 100% for saline-treated rabbits (P<0.001 by log-rank tests). For meropenem which exhibits time-dependent bactericidal activity, rabbits treated with a humanized meropenem dosing regimen of 80 mg/kg q2h for 24 h achieved 100% T>MIC, resulting in 75% microbiological clearance rate of Pa6206 from the lungs. For tobramycin which exhibits concentration-dependent killing, rabbits treated with a humanized tobramycin dosing regimen of 8 mg/kg q8h for 24 h achieved Cmax/MIC of 9.8 ± 1.4 at 60 min post-dose, resulting in 50% lung microbiological clearance rate. In contrast, rabbits treated with a single tobramycin dose of 2.5 mg/kg had Cmax/MIC of 7.8 ± 0.8 and 8% (1/12) microbiological clearance rate, indicating that this rabbit model can detect dose-response effects. Conclusion: The rabbit model may be used to help predict clinical efficacy of new antibacterial drugs for the treatment of non-ventilated P. aeruginosa pneumonia.

Myositis-associated interstitial lung disease.

PURPOSE OF REVIEW: In idiopathic inflammatory myopathies (IIMs), interstitial lung disease (ILD) is common and the autoantibody profile, made up of myositis-specific and myositis-associated (MSA and MAA) antibodies, can predict the clinical phenotype and progression over time. This review will focus on the characteristics and management of antisynthetase syndrome related ILD and anti-MDA5 positive ILD, which are the most clinically relevant subtypes. RECENT FINDINGS: The prevalence of ILD in IIM has been estimated in Asia, North America and Europe at 50, 23 and 26%, respectively, and is increasing. In antisynthetase syndrome related ILD, the clinical presentation, progression and prognosis varies among anti-ARS antibodies. ILD is more common and severe in patients with anti-PL-7/anti-PL-12 antibodies when compared with anti Jo-1 patients. The prevalence of anti-MDA5 antibodies is higher in Asians (11-60%) than in whites (7-16%). Sixty-six percent of antisynthetase syndrome patients had 'chronic ILD' compared with the more rapidly progressive ILD (RP-ILD) seen in 69% of patients with anti-MDA5 antibodies. SUMMARY: ILD is most common in the antisynthetase subtype of IIM and can be a chronic indolent or RP- ILD. The MSA and MAAs are associated with different clinical phenotypes of ILD. Treatments typically involve combinations of corticosteroids and other immunosuppressants.

Stimulation of ectopically expressed muscarinic receptors induces IFN-γ but suppresses IL-2 production by inhibiting activation of pAKT pathways in primary T cells.

T cell antigen receptor stimulation induces tyrosine phosphorylation of downstream signaling molecules and the phosphatidylinositol, Ras, MAPK, and PI3 kinase pathways, leading to T cell activation. Previously, we reported that the G-protein-coupled human muscarinic receptor could bypass tyrosine kinases to activate the phosphatidylinositol pathway and induce interleukin-2 production in Jurkat leukemic T cells. Here, we demonstrate that stimulating G-protein-coupled muscarinic receptors (M1 and synthetic hM3Dq) can activate primary mouse T cells if PLCß1 is coexpressed. Resting peripheral hM3Dq+PLCß1 (hM3Dq/ß1) T cells did not respond to clozapine, an hM3Dq agonist, unless they were preactivated by TCR and CD28 stimulation which increased hM3Dq and PLCß1 expression. This permitted large calcium and phosphorylated ERK responses to clozapine. Clozapine treatment induced high IFN-γ, CD69, and CD25 expression, but surprisingly did not induce substantial IL-2 in hM3Dq/ß1 T cells. Importantly, costimulation of both muscarinic receptors plus the TCR even led to reduced IL-2 expression, suggesting a selective inhibitory effect of muscarinic receptor costimulation. Stimulation of muscarinic receptors induced strong nuclear translocation of NFAT and NFκB and activated AP-1. However, stimulation of hM3Dq led to reduced IL-2 mRNA stability which correlated with an effect on the IL-2 3'UTR activity. Interestingly, stimulation of hM3Dq resulted in reduced pAKT and its downstream pathway. This may explain the inhibitory impact on IL-2 production in hM3Dq/ß1T cells. Moreover, an inhibitor of PI3K reduced IL-2 production in TCR-stimulated hM3Dq/ß1 CD4 T cells, suggesting that activating the pAKT pathway is critical for IL-2 production in T cells.

Dietary restriction of cysteine and methionine sensitizes gliomas to ferroptosis and induces alterations in energetic metabolism.

Ferroptosis is mediated by lipid peroxidation of phospholipids containing polyunsaturated fatty acyl moieties. Glutathione, the key cellular antioxidant capable of inhibiting lipid peroxidation via the activity of the enzyme glutathione peroxidase 4 (GPX-4), is generated directly from the sulfur-containing amino acid cysteine, and indirectly from methionine via the transsulfuration pathway. Herein we show that cysteine and methionine deprivation (CMD) can synergize with the GPX4 inhibitor RSL3 to increase ferroptotic cell death and lipid peroxidation in both murine and human glioma cell lines and in ex vivo organotypic slice cultures. We also show that a cysteine-depleted, methionine-restricted diet can improve therapeutic response to RSL3 and prolong survival in a syngeneic orthotopic murine glioma model. Finally, this CMD diet leads to profound in vivo metabolomic, proteomic and lipidomic alterations, highlighting the potential for improving the efficacy of ferroptotic therapies in glioma treatment with a non-invasive dietary modification.

A cell state specific metabolic vulnerability to GPX4-dependent ferroptosis in glioblastoma.

Glioma cells hijack developmental transcriptional programs to control cell state. During neural development, lineage trajectories rely on specialized metabolic pathways. However, the link between tumor cell state and metabolic programs is poorly understood in glioma. Here we uncover a glioma cell state-specific metabolic liability that can be leveraged therapeutically. To model cell state diversity, we generated genetically engineered murine gliomas, induced by deletion of p53 alone (p53) or with constitutively active Notch signaling (N1IC), a pathway critical in controlling cellular fate. N1IC tumors harbored quiescent astrocyte-like transformed cell states while p53 tumors were predominantly comprised of proliferating progenitor-like cell states. N1IC cells exhibit distinct metabolic alterations, with mitochondrial uncoupling and increased ROS production rendering them more sensitive to inhibition of the lipid hydroperoxidase GPX4 and induction of ferroptosis. Importantly, treating patient-derived organotypic slices with a GPX4 inhibitor induced selective depletion of quiescent astrocyte-like glioma cell populations with similar metabolic profiles.

Use of a gene expression signature to identify trimetazidine for repurposing to treat bipolar depression.

OBJECTIVES: The aim of this study was to repurpose a drug for the treatment of bipolar depression. METHODS: A gene expression signature representing the overall transcriptomic effects of a cocktail of drugs widely prescribed to treat bipolar disorder was generated using human neuronal-like (NT2-N) cells. A compound library of 960 approved, off-patent drugs were then screened to identify those drugs that affect transcription most similar to the effects of the bipolar depression drug cocktail. For mechanistic studies, peripheral blood mononuclear cells were obtained from a healthy subject and reprogrammed into induced pluripotent stem cells, which were then differentiated into co-cultured neurons and astrocytes. Efficacy studies were conducted in two animal models of depressive-like behaviours (Flinders Sensitive Line rats and social isolation with chronic restraint stress rats). RESULTS: The screen identified trimetazidine as a potential drug for repurposing. Trimetazidine alters metabolic processes to increase ATP production, which is thought to be deficient in bipolar depression. We showed that trimetazidine increased mitochondrial respiration in cultured human neuronal-like cells. Transcriptomic analysis in induced pluripotent stem cell-derived neuron/astrocyte co-cultures suggested additional mechanisms of action via the focal adhesion and MAPK signalling pathways. In two different rodent models of depressive-like behaviours, trimetazidine exhibited antidepressant-like activity with reduced anhedonia and reduced immobility in the forced swim test. CONCLUSION: Collectively our data support the repurposing of trimetazidine for the treatment of bipolar depression.

eMIC-AntiKP: Estimating minimum inhibitory concentrations of antibiotics towards Klebsiella pneumoniae using deep learning.

Nowadays, antibiotic resistance has become one of the most concerning problems that directly affects the recovery process of patients. For years, numerous efforts have been made to efficiently use antimicrobial drugs with appropriate doses not only to exterminate microbes but also stringently constrain any chances for bacterial evolution. However, choosing proper antibiotics is not a straightforward and time-effective process because well-defined drugs can only be given to patients after determining microbic taxonomy and evaluating minimum inhibitory concentrations (MICs). Besides conventional methods, numerous computer-aided frameworks have been recently developed using computational advances and public data sources of clinical antimicrobial resistance. In this study, we introduce eMIC-AntiKP, a computational framework specifically designed to predict the MIC values of 20 antibiotics towards Klebsiella pneumoniae. Our prediction models were constructed using convolutional neural networks and k-mer counting-based features. The model for cefepime has the most limited performance with a test 1-tier accuracy of 0.49, while the model for ampicillin has the highest performance with a test 1-tier accuracy of 1.00. Most models have satisfactory performance, with test accuracies ranging from about 0.70-0.90. The significance of eMIC-AntiKP is the effective utilization of computing resources to make it a compact and portable tool for most moderately configured computers. We provide users with two options, including an online web server for basic analysis and an offline package for deeper analysis and technical modification.

iNSP-GCAAP: Identifying nonclassical secreted proteins using global composition of amino acid properties.

Nonclassical secreted proteins (NSPs) refer to a group of proteins released into the extracellular environment under the facilitation of different biological transporting pathways apart from the Sec/Tat system. As experimental determination of NSPs is often costly and requires skilled handling techniques, computational approaches are necessary. In this study, we introduce iNSP-GCAAP, a computational prediction framework, to identify NSPs. We propose using global composition of a customized set of amino acid properties to encode sequence data and use the random forest (RF) algorithm for classification. We used the training dataset introduced by Zhang et al. (Bioinformatics, 36(3), 704-712, 2020) to develop our model and test it with the independent test set in the same study. The area under the receiver operating characteristic curve on that test set was 0.9256, which outperformed other state-of-the-art methods using the same datasets. Our framework is also deployed as a user-friendly web-based application to support the research community to predict NSPs.

Optical diffraction tomography and image reconstruction to measure host cell alterations caused by divergent Plasmodium species.

Malaria is a life-threatening infectious disease caused by parasites of the genus Plasmodium. Understanding the biological features of various parasite forms is important for the optical diagnosis and defining pathological states, which are often constrained by the lack of ambient visualization approaches. Here, we employ a label-free tomographic technique to visualize the host red blood cell (RBC) remodeling process and quantify changes in biochemical properties arising from parasitization. Through this, we provide a quantitative body of information pertaining to the influence of host cell environment on growth, survival, and replication of P. falciparum and P. vivax in their respective host cells: mature erythrocytes and young reticulocytes. These exquisite three-dimensional measurements of infected red cells demonstrats the potential of evolving 3D imaging to advance our understanding of Plasmodium biology and host-parasite interactions.

Metformin is Protective Against the Development of Mood Disorders.

INTRODUCTION: Mood disorders are a major cause of disability, and current treatment options are inadequate for reducing the burden on a global scale. The aim of this project was to identify drugs suitable for repurposing to treat mood disorders. METHODS: This mixed-method study utilized gene expression signature technology and pharmacoepidemiology to investigate drugs that may be suitable for repurposing to treat mood disorders. RESULTS: The transcriptional effects of a combination of drugs commonly used to treat mood disorders included regulation of the steroid and terpenoid backbone biosynthesis pathways, suggesting a mechanism involving cholesterol biosynthesis, and effects on the thyroid hormone signaling pathway. Connectivity Map analysis highlighted metformin, an FDA-approved treatment for type 2 diabetes, as a drug having global transcriptional effects similar to the mood disorder drug combination investigated. In a retrospective cohort study, we found evidence that metformin is protective against the onset of mood disorders. DISCUSSION: These results provide proof-of-principle of combining gene expression signature technology with pharmacoepidemiology to identify potential novel drugs for treating mood disorders. Importantly, metformin may have utility in the treatment of mood disorders, warranting future randomized controlled trials to test its efficacy.

EPMA quantification on the chemical composition of retained austenite in a Fe-Mn-Si-C-based multi-phase steel.

An electron probe X-ray microanalyzer (EPMA) is an essential tool for studying chemical composition distribution in the microstructure. Quantifying chemical composition using standard specimens is commonly used to determine the composition of individual phases. However, the local difference in chemical composition in the standard specimens brings the deviation of the quantified composition from the actual one. This study introduces how to overcome the error of quantification in EPMA in the practical aspect. The obtained results are applied to evaluate the chemical composition of retained austenite in multi-phase steel. Film-type austenite shows higher carbon content than blocky-type one. The measured carbon contents of the retained austenite show good coherency with the calculated value from the X-ray diffraction.

Predicting Antimalarial Activity in Natural Products Using Pretrained Bidirectional Encoder Representations from Transformers.

Malaria is a threatening disease that has claimed many lives and has a high prevalence rate annually. Through the past decade, there have been many studies to uncover effective antimalarial compounds to combat this disease. Alongside chemically synthesized chemicals, a number of natural compounds have also been proven to be as effective in their antimalarial properties. Besides experimental approaches to investigate antimalarial activities in natural products, computational methods have been developed with satisfactory outcomes obtained. In this study, we propose a novel molecular encoding scheme based on Bidirectional Encoder Representations from Transformers and used our pretrained encoding model called NPBERT with four machine learning algorithms, including k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGB), and Random Forest (RF), to develop various prediction models to identify antimalarial natural products. The results show that SVM models are the best-performing classifiers, followed by the XGB, k-NN, and RF models. Additionally, comparative analysis between our proposed molecular encoding scheme and existing state-of-the-art methods indicates that NPBERT is more effective compared to the others. Moreover, the deployment of transformers in constructing molecular encoders is not limited to this study but can be utilized for other biomedical applications.

Coaggregation of micro polystyrene particles and suspended minerals under concentrated salt solution: A perspective of terrestrial-to-ocean transfer of microplastics.

This study evaluates the colloidal stability of polystyrene microplastics (PSMPs) in the presence of various mineral colloids. Although PSMPs were highly dispersive, they were found to be involved in the aggregation of each mineral colloid. The efficiency of mineral colloids to stimulate the coaggregation of PSMPs follows the order bentonite > kaolinitic soil clay > illitic soil clay > kaolinite > goethite > haematite. Surface charge density is likely a crucial factor that determines the efficiency of mineral colloids. In concentrated salt solution, PSMPs together with mineral colloids can be involved in various continuous and simultaneous electrochemical processes such as charge neutralization, double electric layer compression, van der Waals attraction stimulation and heteroaggregation. These processes may also occur in the estuary environments, where suspended mineral colloids may play an ultimate role in reducing the transport of microplastics into oceans while also intensifying microplastic enrichment in coastal sediments.