Molecular Simulation Studies of Pharmaceutical Pollutant Removal (Rosuvastatin and Simvastatin) Using Novel Modified-MOF Nanostructures (UIO-66, UIO-66/Chitosan, and UIO-66/Oxidized Chitosan).

The ubiquitous presence of pharmaceutical pollutants in the environment significantly threatens human health and aquatic ecosystems. Conventional wastewater treatment processes often fall short of effectively removing these emerging contaminants. Therefore, the development of high-performance adsorbents is crucial for environmental remediation. This research utilizes molecular simulation to explore the potential of novel modified metal-organic frameworks (MOFs) in pharmaceutical pollutant removal, paving the way for the design of efficient wastewater treatment strategies. Utilizing UIO-66, a robust MOF, as the base material, we developed UIO-66 functionalized with chitosan (CHI) and oxidized chitosan (OCHI). These modified MOFs' physical and chemical properties were first investigated through various characterization techniques. Subsequently, molecular dynamics simulation (MDS) and Monte Carlo simulation (MCS) were employed to elucidate the adsorption mechanisms of rosuvastatin (ROSU) and simvastatin (SIMV), two prevalent pharmaceutical pollutants, onto these nanostructures. MCS calculations demonstrated a significant enhancement in the adsorption energy by incorporating CHI and OCHI into UIO-66. This increased ROSU from -14,522 to -16,459 kcal/mol and SIMV from -17,652 to -21,207 kcal/mol. Moreover, MDS reveals ROSU rejection rates in neat UIO-66 to be at 40%, rising to 60 and 70% with CHI and OCHI. Accumulation rates increase from 4 Å in UIO-66 to 6 and 9 Å in UIO-CHI and UIO-OCHI. Concentration analysis shows SIMV rejection surges from 50 to 90%, with accumulation rates increasing from 6 to 11 Å with CHI and OCHI in UIO-66. Functionalizing UIO-66 with CHI and OCHI significantly enhanced the adsorption capacity and selectivity for ROSU and SIMV. Abundant hydroxyl and amino groups facilitated strong interactions, improving performance over that of unmodified UIO-66. Surface functionalization plays a vital role in customizing the MOFs for pharmaceutical pollutant removal. These insights guide next-gen adsorbent development, offering high efficiency and selectivity for wastewater treatment.

Recent advancements in bio-based dielectric and piezoelectric polymers and their biomedical applications.

The advent of polymer-based dielectrics marked a significant breakthrough in dielectric materials. However, despite their many advantages, they pose serious environmental threats. Therefore, in recent years, there has been growing interest in bio-based polymers as a sustainable alternative to traditional petroleum-based polymers. Their renewable nature and reduced environmental impact can fulfil the rising demand for eco-friendly substitutes. Beyond their ecological benefits, bio-based polymers also possess distinctive electrical properties that make them extremely attractive in a variety of applications. Considering these, herein, we present recent advancements in bio-based dielectric polymers and nanocomposites. First, the fundamental concepts of dielectric and polymer-based dielectric materials are covered. Then, we will delve into the discussion of recent advancements in the dielectric properties and thermal stability of bio-based polymers, including polylactic acid, polyhydroxyalkanoates, polybutylene succinate, starch, cellulose, chitosan, chitins, and alginates, and their nanocomposites. Other novel bio-based dielectric polymers and their distinct dielectric characteristics have also been pointed out. In an additional section, the piezoelectric properties of these polymers and their recent biomedical applications have been highlighted and discussed thoroughly. In conclusion, this paper thoroughly discusses the recent advances in bio-based dielectric polymers and their potential to revolutionize the biomedical industry while cultivating a more sustainable and greener future.

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites.

This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William's plot, demonstrating a minimal number of outliers (66-71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.

Tackling antibiotic contaminations in wastewater with novel Modified-MOF nanostructures: A study of molecular simulations and DFT calculations.

The contamination of wastewater with antibiotics has emerged as a critical global challenge, with profound implications for environmental integrity and human well-being. Adsorption techniques have been meticulously investigated and developed to mitigate and alleviate their effects. In this study, we have investigated the adsorption behaviour of Erythromycin (ERY), Gentamicin (GEN), Levofloxacin (LEVO), and Metronidazole (MET) antibiotics as pharmaceutical contaminants (PHCs) on amide-functionalized (RC (=O)NH2)/MIL-53 (Al) (AMD/ML53A), using molecular simulations and density functional theory (DFT) calculations. Based on our DFT calculations, it becomes apparent that the adsorption tendencies of antibiotics are predominantly governed by the presence of AMD functional groups on the adsorbent surface. Specifically, hydrogen bonding (HB) and van der Waals (vdW) interactions between antibiotics and AMD groups serve as the primary mechanisms facilitating adsorption. Furthermore, we have observed that the adsorption behaviors of these antibiotics are influenced by their respective functional groups, molecular shapes, and sizes. Our molecular simulations delved into how the AMD/ML53A surfaces interact with antibiotics as PHCs. Moreover, various chemical quantum descriptors based on Frontier Molecular Orbitals (FMO) were explored to elucidate the extent of AMD/ML53A adsorption and to assess potential alterations in their electronic properties throughout the adsorption process. Monte Carlo simulation showed that ERY molecules adsorb stronger to the adsorbent in acidic and basic conditions than other contaminants, with high energies: -404.47 kcal/mol in acidic and -6375.26 kcal/mol in basic environments. Molecular dynamics (MD) simulations revealed parallel orientation for the ERY molecule's adsorption on AMD/ML53A with 80% rejection rate. In conclusion, our study highlighted the importance of modeling in developing practical solutions for removing antibiotics as PHCs from wastewater. The insights gained from our calculations can facilitate the design of more effective adsorption materials, ultimately leading to a more hygienic and sustainable ecosystem.

Environmental pollutants and exosomes: A new paradigm in environmental health and disease.

This study investigates the intricate interplay between environmental pollutants and exosomes, shedding light on a novel paradigm in environmental health and disease. Cellular stress, induced by environmental toxicants or disease, significantly impacts the production and composition of exosomes, crucial mediators of intercellular communication. The heat shock response (HSR) and unfolded protein response (UPR) pathways, activated during cellular stress, profoundly influence exosome generation, cargo sorting, and function, shaping intercellular communication and stress responses. Environmental pollutants, particularly lipophilic ones, directly interact with exosome lipid bilayers, potentially affecting membrane stability, release, and cellular uptake. The study reveals that exposure to environmental contaminants induces significant changes in exosomal proteins, miRNAs, and lipids, impacting cellular function and health. Understanding the impact of environmental pollutants on exosomal cargo holds promise for biomarkers of exposure, enabling non-invasive sample collection and real-time insights into ongoing cellular responses. This research explores the potential of exosomal biomarkers for early detection of health effects, assessing treatment efficacy, and population-wide screening. Overcoming challenges requires advanced isolation techniques, standardized protocols, and machine learning for data analysis. Integration with omics technologies enhances comprehensive molecular analysis, offering a holistic understanding of the complex regulatory network influenced by environmental pollutants. The study underscores the capability of exosomes in circulation as promising biomarkers for assessing environmental exposure and systemic health effects, contributing to advancements in environmental health research and disease prevention.

Tissue engineering in otology: a review of achievements.

Tissue engineering application in otology spans a distance from the pinna to auditory nerve covered with specialized tissues and functions such as sense of hearing and aesthetics. It holds the potential to address the barriers of lack of donor tissue, poor tissue match, and transplant rejection through provision of new and healthy tissues similar to the host and possesses the capacity to renew, to regenerate, and to repair in-vivo and was shown to be a bypasses for any need to immunosuppression. This review aims to investigate the application of tissue engineering in otology and to evaluate the achievements and challenges in external, middle and inner ear sections. Since gaining the recent knowledge and training on use of different scaffolds is essential for otology specialists and who look for the recovery of ear function and aesthetics of patients, it is shown in this review how utilizing tissue engineering and cell transplantation, regenerative medicine can provide advancements in hearing and ear aesthetics to fit different patients' needs.

Regenerative medicine by utilizing tissue engineering and cell transplantation was shown to provide advancements in hearing and ear aesthetics to fit different patients' needs.Gaining the necessary knowledge and training on use of different scaffolds is essential for otology specialists and patients who search for hearing and ear aesthetics.It is crucial that patients are instructed for differences exist between various scaffolds for hearing and ear aesthetics.

Integrative analysis of multi machine learning models for tetracycline photocatalytic degradation with MOFs in wastewater treatment.

This study focuses on the utilization of connectionist models, specifically Independent Component Analysis (ICA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Genetic Algorithm-Particle Swarm Optimization (GAPSO) integrated with a least-squares support vector machine (LSSVM) to forecast the degradation of tetracycline (TC) through photocatalysis using Metal-Organic Frameworks (MOFs). The primary objective of this study was to evaluate the viability and precision of these connectionist models in estimating the efficiency of TC degradation, particularly within the context of wastewater treatment. The input parameters for these models cover essential MOF characteristics, such as pore size and surface area, along with critical operational factors, such as pH, TC concentration, catalyst dosage, and illumination duration, all of which are linked to the photocatalytic performance of MOFs. Sensitivity analysis revealed that the illumination duration is the primary influencer of TC photodegradation with MOF photocatalysts, while the MOFs' surface area is the second crucial parameter shaping the efficiency and dynamics of the TC-MOF photocatalytic system. The developed LSSVM models display impressive predictive capabilities, effectively forecasting the experimental degradation of TC with high accuracy. Among these models, the GAPSO-LSSVM model excels as the top performer, achieving notable evaluation metrics, including STD, RMSE, MSE, MRE, and R2 at values of 3.09, 3.42, 11.71, 5.95, and 0.986, respectively. In comparison, the PSO-LSSVM, ICA-LSSVM, and GA-LSSVM models yield mean relative errors of 6.18%, 7.57%, and 11.37%, respectively. These outcomes highlight the exceptional predictive capabilities of the GAPSO-LSSVM model, solidifying its position as the most accurate and dependable model for predicting TC photodegradation in this study. This study contributes to advancing photocatalytic research and effectively reinforces the importance of leveraging machine learning methodologies for tackling environmental challenges.

Mechanisms and factors affecting the removal of minocycline from aqueous solutions using graphene-modified resorcinol formaldehyde aerogels.

In recent years, concerns about the presence of pharmaceutical compounds in wastewater have increased. Various types of residues of tetracycline family antibiotic compounds, which are widely used, are found in environmental waters in relatively low and persistent concentrations, adversely affecting human health and the environment. In this study, a resorcinol formaldehyde (RF) aerogel was prepared using the sol-gel method at resorcinol/catalyst ratio of 400 and resorcinol/water ratio of 2 and drying at ambient pressure for removing antibiotics like minocycline. Next, RF aerogel was modified with graphene and to increase the specific surface area and porosity of the modified sample and to form the graphene plates without compromising the interconnected porous three-dimensional structure of the aerogel. Also, the pores were designed according to the size of the minocycline particles on the meso- and macro-scale, which bestowed the modified sample the ability to remove a significant amount of the minocycline antibiotic from the aqueous solution. The removal percentage of the antibiotic obtained by UV-vis spectroscopy. Ultimately, the performance of prepared aerogels was investigated under various conditions, including adsorbent doses (4-10 mg), solution pHs (2-12), contact times of the adsorbent with the adsorbate (3-24 h), and initial concentration of antibiotic (40-100 mg/l). The results from the BET test demonstrated that the surface area of the resorcinol formaldehyde aerogel sample, which included 1 wt% graphene (RF-G1), exhibited an augmentation in comparison to the surface area of the pure aerogel. Additionally, it was noted that the removal percentage of minocycline antibiotic for both the unmodified and altered samples was 71.6% and 92.1% at the optimal pH values of 4 and 6, respectively. The adsorption capacity of pure and modified aerogel for the minocycline antibiotic was 358 and 460.5 mg/g, respectively. The adsorption data for the modified aerogel was studied by the pseudo-second-order model and the results obtained from the samples for antibiotic adsorption with this model revealed a favorable fit, which indicated that the chemical adsorption in the rapid adsorption of the antibiotic by the modified aerogel had occurred.

Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents.

In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO2) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO2 solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refined the model, enhancing its performance and generalization capacity. By incorporating experimental CO2 solubility data in varied DES compositions and temperatures, we trained the model to capture the intricate solubility behaviour precisely. The analytical capability of the evolving hybrid model was validated against an extensive dataset of experimental CO2 solubility values, demonstrating its superiority over individual QSPR and GPR models. The model achieves high accuracy, capturing the complex interactions between CO2 and DES components under varying thermodynamic conditions. The versatility of the evolving hybrid model was highlighted by its ability to accommodate new experimental data and adapt to different DES compositions and temperatures. The proposed QSPR-GPR model presented a powerful tool for predicting CO2 solubility in DES, providing valuable insights for designing and optimizing solvent systems in carbon capture technologies. The model's remarkable performance enhances our understanding of CO2 solubility mechanisms and contributes to sustainable solutions for mitigating greenhouse gas emissions. As research in DESs progresses, the evolving hybrid QSPR-GPR model offers a versatile and accurate means for predicting CO2 solubility, supporting advancements in carbon capture and utilization processes towards a greener and more sustainable future.

Insights into the Adsorption Properties of Mixed Matrix Membranes (Pebax 1657-g-Chitosan-PVDF-Bovine Serum Albumin@ZIF-CO3-1) for the Antiviral COVID-19 Treatment Drugs Remdesivir and Nirmatrelvir: An In Silico Study.

The effect of the COVID-19 pandemic on the accumulation of environmental pollutants has been significant. In that way, waste management systems have faced problems, and the amount of hazardous and medical wastes has increased. As pharmaceuticals associated with the treatment of COVID-19 enter the environment, aquatic and terrestrial ecosystems have been negatively impacted, potentially disrupting natural processes and harming aquatic life. This analysis seeks to appraise the potential of mixed matrix membranes (MMMs) composed of Pebax 1657-g-chitosan-polyvinylidene fluoride (PEX-g-CHS-PVDF)-bovine serum albumin (BSA)@ZIF-CO3-1 as adsorbents for removing remdesivir (REMD) and nirmatrelvir (NIRM) from aqueous environments. An in silico study was conducted to explore the adsorption characteristics, physicochemical properties, and structural features of these MMMs, employing quantum mechanical (QM) calculations, molecular dynamics (MD) simulations, and Monte Carlo (MC) simulations as research methodologies. Incorporating BSA@ZIF-CO3-1 into the PEX-g-CHS-PVDF polymer matrix improved the physicochemical properties of MMMs by promoting the compatibility and interfacial adhesion between the two materials, facilitated by electrostatic interactions, van der Waals forces, and hydrogen bonding. Investigation of the interaction mechanism between the title pharmaceutical pollutants and the surfaces of MMMs, along with the description of their adsorption behavior, was also conducted by applying MD and MC approaches. Our observations indicate that the adsorption behavior of REMD and NIRM is influenced by molecular size, shape, and the presence of functional groups. Molecular simulation analysis demonstrated that the MMM membrane is a highly suitable adsorbent for the adsorption of REMD and NIRM drugs, with a higher affinity toward REMD adsorption. Our study emphasizes the significance of computational modeling in developing practical strategies for eliminating COVID-19 drug contaminants from wastewater. The knowledge obtained through our molecular simulations and QM calculations can assist in creating more efficient adsorption materials, resulting in a cleaner and healthier environment.

Microneedle Systems for Vaccine Delivery: the story so far.

INTRODUCTION: Vaccine delivery via a microneedle (MN) system has been identified as a potential alternative to conventional vaccine delivery. MN can be self-administered, is pain-free and is capable of producing superior immunogenicity. Over the last few decades, significant research has been carried out in this area, and this review aims to provide a comprehensive picture on the progress of this delivery platform. AREAS COVERED: This review highlights the potential role of skin as a vaccine delivery route using a microneedle system, examines recent advancements in microneedle fabrication techniques, and provides an update on potential preclinical and clinical studies on vaccine delivery through microneedle systems against various infectious diseases. Articles for the review study were searched electronically in PubMed, Google, Google Scholar, and Science Direct using specific keywords to cover the scope of the article. The advanced search strategy was employed to identify the most relevant articles. EXPERT OPINION: A significant number of MN mediated vaccine candidates have shown promising results in preclinical and clinical trials. The recent emergence of cleanroom free, 3D or additive manufacturing of MN systems and stability, together with the dose-sparing capacity of the Nanopatch® system, have made this platform, commercially, highly lucrative.