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Lung injuries are increasingly prevalent due to various diseases causing alveolar damage, potentially leading to respiratory disorders. This study employed an incubation method to develop nano-encapsulated ibuprofen within a chitosan matrix for targeted pulmonary therapy. The encapsulation was successful without altering the molecular structure of chitosan, and a 500 mg dose was identified as optimal through lung tissue cell viability and histological analysis. The controlled release mechanism of this formulation ensures targeted delivery to the lungs, reducing inflammation and promoting alveolar regeneration. This approach highlights the importance of dose optimization and presents a promising strategy to enhance the efficacy and safety of pulmonary treatments. [ABSTRACT FROM AUTHOR]

In this study, the biosynthesis of polyhydroxyalkanoates (PHAs) was carried out using Pseudomonas putida and Pseudomonas aeruginosa . These PHAs were produced using reagent-grade glycerol and crude glycerol as the carbon sources. The objective was to compare the production of PHAs and to functionalize these polymers with silver nanoparticles to provide antibacterial properties for potential biomedical applications. The findings from the physical and chemical analyses confirmed the successful synthesis and extraction of PHAs, achieving comparable yields using both crude glycerol and reagent-grade glycerol as carbon sources across both strains. Approximately 16% higher PHAs production was obtained using Pseudomonas putida compared to Pseudomonas aeruginosa , and no significant difference was observed in the production rate of PHAs between the two carbon sources used, which means that crude glycerol could be utilized even though it has more impurities. Notably, PHAs functionalized with silver nanoparticles showed improved antibacterial effectiveness, especially those derived from reagent-grade glycerol and the Pseudomonas aeruginosa strain.

Today, smart materials are commonly used in various fields of science and technology, such as medicine, electronics, soft robotics, the chemical industry, the automotive field, and many others. Smart polymeric materials hold good promise for the future due to their endless possibilities. This group of advanced materials can be sensitive to changes or the presence of various chemical, physical, and biological stimuli, e.g., light, temperature, pH, magnetic/electric field, pressure, microorganisms, bacteria, viruses, toxic substances, and many others. This review concerns the newest achievements in the area of smart polymeric materials. The recent advances in the designing of stimuli-responsive polymers are described in this paper. [ABSTRACT FROM AUTHOR]

Biomarkers play a pivotal role in the screening, diagnosis, prevention, and post-treatment follow-up of various malignant tumors. In certain instances, identifying these markers necessitates prior treatment due to the complex nature of the tumor microenvironment. Consequently, advancing techniques that exhibit selectivity, specificity, and enable streamlined analysis hold significant importance. Molecularly imprinted polymers (MIPs) are considered synthetic antibodies because they possess the property of molecular recognition with high selectivity and sensitivity. In recent years, there has been a notable surge in the investigation of these materials, primarily driven by their remarkable adaptability in terms of tailoring them for specific target molecules and integrating them into diverse analytical technologies. This review presents a comprehensive analysis of molecular imprinting techniques, highlighting their application in developing sensors and analytical methods for cancer detection, diagnosis, and monitoring. Therefore, MIPs offer great potential in oncology and show promise for improving the accuracy of cancer screening and diagnosis procedures. [ABSTRACT FROM AUTHOR]

Mesoporous silica nanoparticles (MSNs) exhibit highly beneficial characteristics for devising efficient biosensors for different analytes. Their unique properties, such as capabilities for stable covalent binding to recognition groups (e.g., antibodies or aptamers) and sensing surfaces, open a plethora of opportunities for biosensor construction. In addition, their structured porosity offers capabilities for entrapping signaling molecules (dyes or electroactive species), which could be released efficiently in response to a desired analyte for effective optical or electrochemical detection. This work offers an overview of recent research studies (in the last five years) that contain MSNs in their optical and electrochemical sensing platforms for the detection of cancer biomarkers, classified by cancer type. In addition, this study provides an overview of cancer biomarkers, as well as electrochemical and optical detection methods in general. [ABSTRACT FROM AUTHOR]

Sugar beet molasses is a low-value byproduct from the sugar industry. It contains significant amounts of sucrose (approx. 50% (w/w)), which can be used for many different applications, for example, as feedstock for the production of fuel (as ethanol) and biobased chemicals such as 5-hydoxymethyl furfural (HMF). To produce platform chemicals, sucrose is hydrolyzed into its monomeric C6 sugars: glucose and fructose. When comparing the hydrolysis rates of molasses with a pure sucrose solution, the specific reaction rate is much slower (Qp/x,60min = 93 and 70 gprod L−1 h−1 gcell−1 for pure sucrose and crude molasses, respectively) at the same sucrose concentration (300 g/L) and process conditions. To clarify why molasses inhibits the enzymatic hydrolysis rate, the influence of its viscosity and inorganic and organic composition was investigated. Also, the effects of molasses and treated molasses on pure enzymes, invertase (from Saccharomyces cerevisiae, 0.05 mg/mL), compared with hydrolysis using whole cells of Baker's yeast (3 mg/mL), were tested. The results indicate an inhibitory effect of potassium (Qp/x,60min = 76 gprod L−1 h−1 gcell−1), generally at high salt concentrations (Qp/x,60min = 67 gprod L−1 h−1 gcell−1), which could be correlated to the solution's high salt concentrations and possibly the synergistic effects of different ions when applying concentrations that were four times that in the molasses. Also, the viscosity and sucrose purity seem to have an effect, where pure sucrose solutions and thick juice from the sugar mill yielded higher hydrolysis rates (Qp/x,60min = 97 gprod L−1 h−1 gcell−1) than molasses-type solutions with a higher viscosity (Qp/x,60min = 70–74 gprod L−1 h−1 gcell−1). Attempting to further understand the effects of different components on the invertase activity, an in silico investigation was performed, indicating that high salt concentrations affected the binding of sucrose to the active site of the enzyme, which can result in a lower reaction rate. This knowledge is important for future scale-up of the hydrolysis process, since reduced hydrolysis rates require larger volumes to provide a certain productivity, requiring larger process equipment and thereby higher investment costs. [ABSTRACT FROM AUTHOR]

The use of microfluidic fuel cells (μFCs) as power sources in the development of non-implantable biomedical devices, such as patch sensors, could be broadened with the use of physiological fluids as fuels and the use of low-cost materials. In this work, we report the evaluation of a paper-based photo-μFC working with human blood as fuel, and we implement a nanocomposite as anode that presents photoactivity under visible light. The electrocatalyst was synthesized using Zn(NO3)2 and TiCl4 as precursors of the ZnO and the TiO2, respectively, which were coated with Au nanoparticles. It was demonstrated that ZnO and TiO2 nanoparticles consisted of crystals with wurtzite and anatase phases, with average sizes of 26.6 and 19.5 nm, respectively, and the TiO2/ZnO/Au nanocomposite presents 2.62 eV of band gap energy, demonstrating its photoactivity under visible light irradiation. The photocatalyst was tested as photoanode in half-cell for glucose oxidation in aqueous solutions, under illumination and dark conditions, showing characteristics peaks of glucose oxidation in both cases, but higher current was generated under illumination. Finally, we present the implementation of this anode and the construction of a paper-based photo-μFC using human blood as fuel; its performance was evaluated under visible light irradiations and in the dark. The results show a better performance under illumination with voltage, current, and power density values of 0.95 V, 1.2 mA cm−2, and 0.458 mW/cm2, respectively, compared to the corresponding values of 0.8 V, 0.96 mA cm−2, and 0.274 mW/cm2 in dark conditions. [ABSTRACT FROM AUTHOR]

One of the most important reasons for an increased mortality rate of cancer is late diagnosis. Point-of-care (POC) diagnostic sensors can provide rapid and cost-effective diagnosis and monitoring of cancer biomarkers. Portable, disposable, and sensitive sarcosine solid-contact ion-selective potentiometric sensors (SC-ISEs) were fabricated as POC analyzers for the rapid determination of the prostate cancer biomarker sarcosine. Tungsten trioxide nanoparticles (WO3 NPs), polyaniline nanoparticles (PANI NPs), and PANI-WO3 nanocomposite were used as ion-to-electron transducers on screen-printed sensors. WO3 NPs and PANI-WO3 nanocomposite have not been investigated before as ion-to-electron transducer layers in potentiometric SC sensors. The designated sensors were characterized using SEM, XRD, FTIR, UV-VIS spectroscopy, and EIS. The inclusion of WO3 and PANI in SC sensors enhanced the transduction at the interface between the screen-printed SC and the ion-selective membrane, offering lower potential drift, a longer lifetime, shorter response time, and better sensitivity. The proposed sarcosine sensors exhibited Nernstian slopes over linear response ranges 10−3–10−7 M, 10−3–10−8 M, 10−5–10−9 M, and 10−7–10−12 M for control, WO3 NPs, PANI NPs, and PANI-WO3 nanocomposite-based sensors, respectively. From a comparative point of view between the four sensors, PANI-WO3 nanocomposite inclusion offered the lowest potential drift (0.5 mV h−1), the longest lifetime (4 months), and the best LOD (9.95 × 10−13 M). The proposed sensors were successfully applied to determine sarcosine as a potential prostate cancer biomarker in urine without prior sample treatment steps. The WHO ASSURED criteria for point-of-care diagnostics are met by the proposed sensors. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study evaluates the effectiveness of an artificial intelligence (AI) model for predicting the best experimental conditions to reduce particle size during the synthesis of ZnO nanoparticles. Firstly, an artificial neural networks (ANN) was trained using 52 experimental data from the synthesis of ZnO nanoparticles. The selected input variables were temperature, experimental time, and NaOH concentration, and the output variable was nanoparticle size. The performance of the ANN was measured with the root mean square error and mean absolute percentage error, and the obtained values for the selected ANN were 0.67% and 9.87%, respectively. These values were calculated by using real and predicted values. RESULTS: A genetic algorithm (GA) model was coupled with the ANN to find the best operational conditions for the reduced size of ZnO nanoparticles. According to the AI model, a temperature of 59 °C, an experimental time of 56 min, and a concentration of NaOH of 0.08 should be tested to obtain ZnO nanoparticles with 5.67 nm of diameter. After applying the conditions predicted by the model, ZnO nanoparticles with a mean diameter of 5.3 ± 0.4 nm were obtained. The results were confirmed by using several characterization methods, such as approximation of effective masses (5.3 nm), equation Debye–Scherrer (5.2 nm), and high‐resolution transmission electron microscope (HR‐TEM) selected micrograph (5.5 nm). The photocatalytic activity of the synthesized nanoparticles was evaluated using the synthetic dye thymol blue. The best discoloration efficiency was reached by the synthesized ZnO nanoparticles at 74%, while the commercial ZnO only achieved 58%. CONCLUSION: According to the ANN‐GA model, it was possible to predict the experimental conditions needed to obtain ZnO nanoparticles with reduced sizes and excellent photocatalytic activity. © 2023 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

Neurotransmitters (NTs) play a crucial role in regulating the behavioral and physiological functions of the nervous system. Imbalances in the concentrations of NT have been directly linked to various neurological diseases (e.g., Parkinson's, Huntington's, and Alzheimer's disease), in addition to multiple psychotic disorders such as schizophrenia, depression, dementia, and other neurodegenerative disorders. Hence, the rapid and real-time monitoring of the NTs is of utmost importance in comprehending neurological functions and identifying disorders. Among different sensing techniques, electrochemical biosensors have garnered significant interest due to their ability to deliver fast results, compatibility for miniaturization and portability, high sensitivity, and good controllability. Furthermore, the utilization of enzymes as recognition elements in biosensing design has garnered renewed attention due to their unique advantages of catalytic biorecognition coupled with simultaneous signal amplification. This review paper primarily focuses on covering the recent advances in enzymatic electrochemical biosensors for the detection of NTs, encompassing the importance of electrochemical sensors, electrode materials, and electroanalytical techniques. Moreover, we shed light on the applications of enzyme-based biosensors for NTs detection in complex matrices and in vivo monitoring. Despite the numerous advantages of enzymatic biosensors, there are still challenges that need to be addressed, which are thoroughly discussed in this paper. Finally, this review also presents an outlook on future perspectives and opportunities for the development of enzyme-based electrochemical biosensors for NTs detection. [ABSTRACT FROM AUTHOR]

Hydrogen is a type of clean energy which has the potential to replace the fossil energy for transportation, domestic and industrial applications. To expand the hydrogen production method and reduce the consumption of fossil energy, technologies of using renewable energy to generate hydrogen have been developed widely. Due to the advantages of widespread distribution and various hydrogen production methods, most of the research or review works focus on the solar and biomass energy hydrogen production systems. To achieve a comprehensive acknowledge on the development state of current renewable energy hydrogen production technology, a review on hydrogen production systems driven by solar, wind, biomass, geothermal, ocean and hydropower energy has been presented. The reaction process, energy efficiency, exergy efficiency, hydrogen production rate, economic and environmental performance of these systems have been evaluated. Based on the analysis of these different systems, the challenge and prospects of them are also analyzed. [ABSTRACT FROM AUTHOR]

The use of fossil-fueled power stations to generate electricity has had a damaging effect over the years, necessitating the need for alternative energy sources. Microgrids consisting of renewable energy source concepts have gained a lot of consideration in recent years as an alternative because they use advances in information and communication technology (ICT) to increase the quality and efficiency of services and distributed energy resources (DERs), which are environmentally friendly. Nevertheless, microgrids are constrained by the outbreaks of faults, which have an impact on their performance and necessitate dynamic energy management and optimization strategies. The application of artificial intelligence (AI) is gaining momentum as a vital key at this point. This study focuses on a comprehensive review of applications of artificial intelligence strategies on hybrid renewable microgrids for optimization, power quality enhancement, and analyses of fault outbreaks in microgrids. The use of techniques such as machine learning (ML), genetic algorithms (GA), artificial neural networks (ANN), fuzzy logic (FL), particle swarm optimization (PSO), heuristic optimization, artificial bee colony (ABC), and others is reviewed for various microgrid strategies such as regression and classification in this study. Applications of AI in microgrids are reviewed together with their benefits, drawbacks, and prospects for the future. The coordination and maximum penetration of renewable energy, solar PV, and wind in a hybrid microgrid under fault outbreaks are furthermore reviewed. [ABSTRACT FROM AUTHOR]

Prostate and lung cancers are the most common types of cancer and affect a large part of the population around the world, causing deaths. Therefore, the rapid identification of cancer can profoundly impact reducing cancer-related death rates and protecting human lives. Significant resources have been dedicated to investigating new methods for early disease detection. Cancer biomarkers encompass various biochemical entities, including nucleic acids, proteins, sugars, small metabolites, cytogenetic and cytokinetic parameters, and whole tumor cells in bodily fluids. These tools can be utilized for various purposes, such as risk assessment, diagnosis, prognosis, treatment efficacy, toxicity evaluation, and predicting a return. Due to these versatile and critical purposes, there are widespread studies on the development of new, sensitive, and selective approaches for the determination of cancer biomarkers. This review illustrates the significant lung and prostate cancer biomarkers and their determination utilizing electrochemical sensors, which have the advantage of improved sensitivity, low cost, and simple analysis. Additionally, approaches such as improving sensitivity with nanomaterials and ensuring selectivity with MIPs are used to increase the performance of the sensor. This review aims to overview the most recent electrochemical biosensor applications for determining vital biomarkers of prostate and lung cancers in terms of nanobiosensors and molecularly imprinted polymer (MIP)-based biosensors., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

Heavy metal contamination has increased over the globe, causing significant environmental issues owing to direct and indirect releases into water bodies. As a result, metal removal from water entities must be addressed soon. Various adsorbents such as MOFs and chitosan have demonstrated promising results in water treatment. The present study prepared a composite material (chitosan-UiO-66-glycidyl methacrylate MOF) by a microwave-assisted method. The structure and morphology of the chitosan-MOF composite were studied using FE-SEM, EDX, XRD, BET, FT-IR, and TGA techniques. In addition, the adsorption of Pb(II) from aqueous solution onto the chitosan-MOF composite was analyzed in a batch study concerning pH, contact time, initial metal ion concentration, and adsorbent dosage. The composite has a large surface area of 867 m2/g with a total pore volume of 0.51 cm3/g and thermal stability of up to 400 ∘ C . Following an analysis of the adsorption isotherms, kinetics, and thermodynamics, the Langmuir model showed an excellent fit with the adsorption data (R2 = 0.99) and chi-squared (X2 = 3.609). The adsorption process was a spontaneous exothermic reaction and the pseudo-second-order rate equation fitted the kinetic profile well. Moreover, the composite is recyclable, retaining 83.45% of its removal effectiveness after 5 consecutive cycles, demonstrating it as a sustainable adsorbent for metal recovery. This study introduces a novel synthesized composite with enhanced recyclability and a higher potential for eliminating pollutants from industrial wastewater. [ABSTRACT FROM AUTHOR]

The purpose of this work is to develop a bioanode using the enzymes of glucose oxidase (GOx) and alcohol dehydrogenase (ADH) as catalysts to oxidised glucose and alcohol present in different beverages. The study was carried out using the covalent bonding method for both enzymes via the functionalization of carbon nanofibers for the formation of carboxyl groups that can form bonds with the amine groups of the enzyme, as well as using tetrabutylammonium bromide (TBAB) with Nafion. The optimum operation parameters of both enzymes (pH and temperature) were determined for the later evaluation in a microfluidic fuel cell. In addition, using the scanning electrochemical microscopy technique, a local study of enzymatic processes is used to demonstrate that the enzymes immobilized on the same electrode remain active. The evaluation of the microfluidic fuel cell was carried out using different solutions, 0.01 M glucose, 0.01 M ethanol and a mixture of 0.01 M glucose and 0.01 M ethanol, all in phosphate buffer solutions at pH 7, where it was possible to obtain a maximum performance of 5.07 ± 0.1 mW cm−2, and there was a significant increase in current density compared to non-composite solutions (glucose or ethanol). In addition, different alcoholic beverages were used to evaluate the versatility and adaptability of the bi-enzymatic anode electrode with the perspective use in Lab-on-a-Chip systems. [ABSTRACT FROM AUTHOR]

A deep neural network has multiple layers to learn more complex patterns and is built to simulate the activity of the human brain. Currently, it provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. The present study deals with the topological properties of deep neural networks. The topological index is a numeric quantity associated to the connectivity of the network and is correlated to the efficiency and accuracy of the output of the network. Different degree-related topological indices such as Zagreb index, Randic index, atom-bond connectivity index, geometric-arithmetic index, forgotten index, multiple Zagreb indices, and hyper-Zagreb index of deep neural network with a finite number of hidden layers are computed in this study. [ABSTRACT FROM AUTHOR]

Herein, we report the synthesis of Au nanoparticles (AuNPs) in chitosan (CTS) solution by chemically reducing HAuCl4. CTS was further functionalized with glycidyl methacrylate (chitosan-g-glycidyl methacrylate/AuNP, CTS-g-GMA/AuNP) to improve the mechanical properties for cellular regeneration requirements of CTS-g-GMA/AuNP. Our nanocomposites promote excellent cellular viability and have a positive effect on cytokine regulation in the inflammatory and anti-inflammatory response of skin cells. After 40 days of nanocomposite exposure to a skin wound, we showed that our films have a greater skin wound healing capacity than a commercial film (TheraForm®), and the presence of the collagen allows better cosmetic ave aspects in skin regeneration in comparison with a nanocomposite with an absence of this protein. Electrical percolation phenomena in such nanocomposites were used as guiding tools for the best nanocomposite performance. Our results suggest that chitosan-based Au nanocomposites show great potential for skin wound repair. [ABSTRACT FROM AUTHOR]

The direct integration of paper-based microfluidic fuel cells (μFC's) toward creating autonomous lateral flow assays has attracted attention. Here, we show that an air-breathing paper-based μFC could be used as a power supply in pregnancy tests by oxidizing the human urine used for the diagnosis. We present an air-breathing paper-based μFC connected to a pregnancy test, and for the first time, as far as we know, it is powered by human urine without needing any external electrolyte. It uses TiO 2 -Ni as anode and Pt/C as cathode; the performance shows a maximum value of voltage and current and power densities of ∼0.96 V, 1.00 mA cm − 2 , and 0.23 mW cm − 2 , respectively. Furthermore, we present a simple design of a paper-based μFC's stack powered with urine that shows a maximum voltage and maximum current and power densities of ∼1.89 V, 2.77 mA cm − 2 and 1.38 mW cm − 2 , respectively, which powers the display of a pregnancy test allowing to see the analysis results. [ABSTRACT FROM AUTHOR]

Molecularly imprinted polymers (MIPs) continue to gain increasing attention as functional materials due to their unique characteristics such as higher stability, simple preparation, robustness, better binding capacity, and low cost. In particular, MIP-coated inorganic nanoparticles have emerged as a promising platform for various biomedical applications ranging from drug delivery to bioimaging. The integration of MIPs with inorganic nanomaterials such as silica (SiO2), iron oxide (Fe3O4), gold (Au), silver (Ag), and quantum dots (QDs) combines several attributes from both components to yield highly multifunctional materials. These materials with a multicomponent hierarchical structure composed of an inorganic core and an imprinted polymer shell exhibit enhanced properties and new functionalities. This review aims to provide a general overview of key recent advances in the fabrication of MIPs-coated inorganic nanoparticles and highlight their biomedical applications, including drug delivery, biosensor, bioimaging, and bioseparation. [ABSTRACT FROM AUTHOR]

Metal/metal oxide nanoparticles have gained increasing attention in recent years due to their outstanding features, including optical and catalytic properties, as well as their excellent conductivity. The implementation of metal/metal oxide nanoparticles, combined with molecularly imprinted polymers (MIPs) has paved the way for a new generation of building blocks to engineer and enhance the fascinating features of advanced sensors. This review critically evaluates the impact of combining metal/metal oxide nanoparticles with MIPs in sensors. It covers synthesis strategies, advantages of coupling these materials with MIPs, and addresses questions about the selectivity of these hybrid materials. In the end, the current challenges and future perspectives of this field are discussed, with a particular focus on the potential applications of these hybrid composites in the sensor field. This review highlights the exciting opportunities of using metal/metal oxide nanoparticles along with MIPs for the development of next-generation sensors., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Industry 4.0 has started to transform the manufacturing industries by embracing digitalization, automation, and big data, aiming for interconnected systems, autonomous decisions, and smart factories. Machine learning techniques, such as artificial neural networks (ANN), have emerged as potent tools to address the related computational tasks. These advancements have also reached the pharmaceutical industry, where the Process Analytical Technology (PAT) initiative has already paved the way for the real-time analysis of the processes and the science- and risk-based flexible production. This paper aims to assess the potential of ANNs within the PAT concept to aid the modernization of pharmaceutical manufacturing. The current state of ANNs is systematically reviewed for the most common manufacturing steps of solid pharmaceutical products, and possible research gaps and future directions are identified. In this way, this review could aid the further development of machine learning techniques for pharmaceutical production and eventually contribute to the implementation of intelligent manufacturing lines with automated quality assurance. [ABSTRACT FROM AUTHOR]

The evolution of biosensors and diagnostic devices has been thriving in its ability to provide reliable tools with simplified operation steps. These evolutions have paved the way for further advances in sensing materials, strategies, and device structures. Polymeric composite materials can be formed into nanostructures and networks of different types, including hydrogels, vesicles, dendrimers, molecularly imprinted polymers (MIP), etc. Due to their biocompatibility, flexibility, and low prices, they are promising tools for future lab-on-chip devices as both manufacturing materials and immobilization surfaces. Polymers can also allow the construction of scaffold materials and 3D structures that further elevate the sensing capabilities of traditional 2D biosensors. This review discusses the latest developments in nano-scaled materials and synthesis techniques for polymer structures and their integration into sensing applications by highlighting their various structural advantages in producing highly sensitive tools that rival bench-top instruments. The developments in material design open a new door for decentralized medicine and public protection that allows effective onsite and point-of-care diagnostics. [ABSTRACT FROM AUTHOR]