Present Work Research Articles

Biological Potential and Therapeutic Effectiveness of Hinokiflavone in Medicine: The Effective Components of Herbal Medicines for Treatment of Cancers and Associated Complications

Background: Plants have been providing us medicines and food material for centuries. Traditional system of medicine, including Ayurveda, and Traditional Chinese medicines have been playing important role in health sectors for the treatment of human disorders since very early age. Plant secondary metabolites, including flavonoids, coumarins, saponins, and tannins have significant therapeutic potential in medicine. Biflavonoids are dimers of flavonoids, linked by a C–O–C or C–C bond. Hinokiflavone is an important class of biflavonoids found to be present in Toxicodendron succedaneum, Isophysis tasmanica, Juniperus rigida, Juniperus phoenicea, Platycladi cacumen, Rhus succedanea, Selaginella tamariscina, Platycladus orientalis, Selaginella bryopteris, and Metasequoia glyptostroboides. Methods: Biological potential of hinokiflavone in medicine have been analyzed in the present work through scientific data analysis of various literature work. Scientific database, including Google, Scopus, Science Direct, and PubMed were searched to collect all the scientific information of the present work using terms flavonoid, biflavonoid, herbal medicine, and hinokiflavone. Pharmacological activities of hinokiflavone were analyzed in the present work in very detailed manner. Analytical data of hinokiflavone were collected and analyzed in present work in order to know the biological source of hinokiflavone. Results: Present work signified the biological importance of hinokiflavone against various types of cancerous disorders, including breast cancer, colorectal cancer, esophageal squamous cancer, adenocarcinoma, hepatocellular carcinoma, myeloid leukemia, and melanoma. Further, its antiinflammatory, hepatoprotective, anti-viral, and anti-oxidant potential were also discussed in the present work. Moreover, its biological potential against COVID-19 and hair loss, and procoagulant activity were also summarized in this paper. Analytical data on hinokiflavone signified the importance of various analytical techniques in the extraction, separation, and identification of hinokiflavone with their pharmacokinetic parameters. Conclusion: Present work signified the biological importance and therapeutic potential of hinokiflavone in medicine.

Green synthesis of NiO nanoparticles using a Cd hyperaccumulator (Lactuca sativa L.) and its application as a Pb(II) and Cu(II) adsorbent

The proliferation of heavy metal contamination, namely, lead and copper, has emerged as a pervasive worldwide peril to human existence. One potential solution for addressing this issue is the utilization of nanoparticle adsorption, specifically for nickel oxide nanoparticles. However, to promote environmental sustainability, scholarly studies have identified green synthesis as a prospective methodology. The process of cadmium phytoremediation, although offering a potential remedy for heavy metal contamination, gives rise to apprehensions surrounding the proper disposal of cadmium hyperaccumulator plant biomass. Hence, it is imperative to establish suitable strategies for the disposition or usage of this biomass. The utilization of hyperaccumulator extracts for the eco-friendly production of nickel oxide nanoparticles remains unexplored in the current literature. The main aim of this research is to explore the possible application of Lactuca sativa L. as a plant species with the ability to accumulate high levels of cadmium. The study also seeks to examine the efficacy of utilizing these plants in the fabrication of nickel oxide nanoparticles and evaluate their capacity to absorb lead and copper ions. The characterization of nickel oxide nanoparticles is conducted through a range of techniques, encompassing scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, X-ray fluorescence, Fourier transform infrared spectroscopy, transmission electron microscopy, Brunauer-Emmett-Teller spectroscopy, and vibrating sample magnetometry. The present work achieved successful utilization of Lactuca sativa L. extract for the manufacture of nickel oxide nanoparticles, which showed notable efficacy as an adsorbent for lead and copper. The synthesis of nickel oxide nanoparticles using an extract from Lactuca sativa L. has demonstrated the viability of utilizing hyperaccumulator biomass in the phytoremediation of cadmium. This finding presents a promising avenue for investigating hyperaccumulator plants as potential sources for nanoparticle synthesis in the field of environment-based nanotechnology development.

Bimetallic Ni-Fe Supported by Gadolinium Doped Ceria (GDC) Catalyst for CO2 Methanation

CO2 conversion into fuels and high value-added chemical feedstocks, such as methane, has gained novel interest as a crucial process for further manufacturing multi-carbon products. Methane, CH4, becomes a promising alternative for environmental and energy supply issues. Nickel-based catalysts were found to be very active and selective for CH4 production. The use of promoter and support material to develop high activity, high selectivity, and durable catalysts for CO2 methanation at low temperature is a challenge. Gadolinium-Doped Ceria (GDC) has been known as material for Solid Oxide Fuel Cell (SOFC) and Solid Oxide Electrolysis Cell (SOEC) due to higher ionic conductivity and lower operating temperatures. However, few researches have been done regarding to CO2 methanation over GDC as catalyst support so far. In this present work, CO2 methanation was investigated over bimetallic Ni-Fe catalyst supported by GDC. The results showed that CH4 production rate by using Ni-Fe/GDC catalyst was higher than that of GDC at all reaction temperatures carried on. Ni-Fe/GDC showed remarkable CH4 production rate as of 17.73 mmol.gcat−1.h−1 at 280 °C. No catalytic activity was produced by GDC catalyst only. The highest CO2 conversion (46.50%) was observed at 280 °C, with almost 100% selectivity to CH4. The turnover frequency (TOF) value of Ni-Fe/GDC (4529.32 h−1) was the highest than that of Ni and common CO2 methanation catalyst, Ni/Al2O3 catalysts at 280 °C, further displaying the outstanding low-temperature catalytic activity. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Optimization of the Caustic Treatment of Jet-Fuel to Reduce the Consumption of Caustic Soda in an Oil Refinery

Jet-Fuel desulfurization is one of the most important processes in the petroleum industries due to the high quality required for aviation fuels. In the pre-sent work, the desulfurization of Jet-Fuel with a content of sulfurous compounds was studied using a process assisted with sodium hydroxide. It was considered that the composition of the turbo fuel varies because different blends of crude oil are processed in the refinery under study. The effects of operating conditions, such as reaction time (15 to 30 min) and the amount of caustic soda (NaOH) in its solution (0.024 to 0.2 mol/L) were investigated. The objective functions (responses) were the NaOH consumption and the total cost of production while the acidity and the total sulfur content of Jet-A1 were considered as restrictions. The optimal conditions of the proposed model were obtained using the optimization with the fmincon function of the MATLAB software. These optimal conditions were found to vary according to the content of sulfur compounds present in the process feed. From the optimal points obtained, it was possible to determine the frequency of change of the NaOH solution that allows its maximum use.

Biological Importance and Therapeutic Potential of Calycopterin From Dracocephalum kotschyi: An Overview of Current Scientific Research Work.

Dracocephalum kotschyi Boiss is an important medicinal plant of Asia, Europe and Iran. The therapeutic potential of Dracocephalum kotschyi has been mentioned in traditional as well as in modern medicine, mainly due to its therapeutic effectiveness against different types of human disorders and associated secondary complications. Calycopterin is a flavonoid class phytochemical commonly called 5,4-dihydroxy-3,6,7,8- tetramethoxyflavone, which has been isolated from Dracocephalum kotschyi. It is widely used for the treatment of human disorders and associated secondary complications. Medicinal importance and therapeutic potential of calycopterin was investigated in the present work through scientific data analysis of different research works. Scientific data on calycopterin published in different research articles were collected from PubMed, Google, Google Scholar, Science Direct, SpringerLink, and Scopus and analyzed in the present work. Further, scientific data was collected from books and book chapters. Analytical data on calycopterin was also collected and analyzed in the present work. Scientific data analysis of different research work revealed the biological importance and therapeutic potential of calycopterin. Calycopterin showed therapeutic effectiveness against different types of cancerous disorders, including human prostate cancer, breast cancer, cervical cancer, lymphocyte proliferation and hepatoblastoma cancer. Moreover, the biological effectiveness of calycopterin on neuroprotection and Pseudomonas aeruginosa has also been discussed in the present work with their pharmacokinetic parameters. Nevertheless, more scientific investigations must be conducted to explore the healthbeneficial aspects of calycopterin in medicine. Present work signified the biological importance and therapeutic potential of calycopterin for the treatment of human disorders and complications, including different types of cancerous disorders.

Study of Structural Elucidation, Degradation Kinetics and Antimicrobial Analysis of Copper (II) Sesame-Soap Complexes with Urea and Thiourea Ligand in Non-aqueous Solvents

Abstract: Surfactants have great importance in biological and drug industries and the complexes of metallic soaps with various ligands are used in approximately every region of national economy. Therefore, our keen interest to study of degradation kinetics and biological importance of Cu (II) surfactants in non-aqueous and non-polar solvent benzene. Present research work has been initiated with synthesis, systematic study of structural elucidation, thermal degradation, kinetics and biocidal activities of copper (II) sesame-soap complexes with macrocyclic nitrogen and sulphur containing donar ligands like urea and thiourea. The thermal degradation of copper (II) sesame-soap complexes were carried out for analysis of degradation kinetics and estimation of kinetic and thermodynamic parameters using different methods at heating rate 10oC min-1. Copper (II) sesame-soap complexes of such ligands have also been analysed against Staphylococus aureus. This research work consolidates the synthesis of copper (II) sesame soap-urea and thiourea complexes by conventional methods and the structure of these complexes were assigned according to elemental analysis and molecular weight determinations. IR, NMR and ESR spectral studies have also been done to understand structural aspects. The anti-microbial activities of copper (II) sesame-soap urea and thiourea complexes have been evaluated by testing against Staphylococus aureus. The present research work includes information of thermal analysis using TGA technique to find out their kinetic and thermodynamic parameters by using diverse equations such as Coats-Redfern equation, Horowitz-Metzger equation, Broido equation, Piloyan–Novikova equation. Moreover, the results obtained from anti-microbial screening have been used to analyze the anti-microbial activities of copper (II) sesame-soap urea and thiourea complexes against gram-positive bacteria Staphylococus aureus. These results show that complexes of copper ion co-ordinated with different nitrogen, oxygen and sulphur containing ligands, are very important in the field of pharmaceutical chemistry due to its significant role in the inhibition activity. The study of these complexes concludes that the synthesized complexes were found to possess appreciable bactericidal properties at different concentrations because chelation increases the anti-microbial potency.

Multiple-node model of wind turbine generating system for unbalanced distribution system load flow analysis

This paper discusses a method to integrate a wind turbine generating system (WTGS) into a three-phase unbalanced distribution system load flow (DSLF) analysis. The proposed method is based on the single-phase multiple-node model. In the present work, the single-phase multiple-node model is extended to a three-phase multiple-node model to facilitate the load flow analysis of a three-phase unbalanced power system network. The multiple-node model (i.e., three-node model) will only modify the load flow analysis by introducing two lines and two load buses to the distribution system network where the WTGS is installed. Thus, a standard three-phase load flow program can be employed to compute the unknown quantities in the DSLF problem formulation. The proposed method is verified by incorporating the model into the load flow analysis of three-phase distribution networks. The investigation uses two representative distribution networks (i.e., 19-bus and 25-bus networks). The results of the study confirm the validity of the proposed method.

Metformin, Chlorpropamide, and Glibenclamide: Interactions with Metal Ions and Cyclodextrins

Abstract: In the field of Chemistry, it is essential to study molecules with biological activity on chronic degenerative diseases to design drugs that help to improve the health of people with diseases such as diabetes, arterial hypertension, rheumatoid arthritis, cancer, among others; and that in turn may have fewer side effects. Diabetes mellitus is a serious metabolic disorder that affects millions of people worldwide; due to the illness severe affectations, there is a great interest in improving pharmacological treatments (hypoglycemic drugs) used in this disease. Nowadays, it has been reported that metallodrugs and drug-cyclodextrin inclusion compounds have higher therapeutic activity than free drugs, with fewer side effects. In the present work, we compile, analyze, synthesize and discuss published information on the hypoglycemic drugs Metformin (C4H11N5), Chlorpropamide (C10H13ClN2O3S) and Glibenclamide (C23H28ClN3O5S) complexed with metal ions or included in cyclodextrins in aqueous solution. Nowadays, this type of complex should be widely studied for the treatment of type 2 diabetes mellitus. Physicochemical and thermodynamic properties, synthesis, mechanism of action, as well as analytical studies on the interaction of hypoglycemic agents with different complexing agents, are discussed.

A novel 2D bismuthene-molybdenum disulfide nanocomposite for high energy density supercapacitors and fabrication scaled to pouch cell

Emerging novel 2D materials with unique electrochemical properties generate massive interest among researchers to fabricate the supercapacitors with high energy density without fading actual power density. In present work, a novel 2D Bismuthene-Molybdenum disulfide composite (Biene-MoS2 NC) was synthesized which serves as an effective active material for the fabrication of electrodes for supercapacitors with superior electrochemical characteristics. The synthesized Biene-MoS2 NC electrode provides the improved specific capacity of 195.9 mAh/g at the sweep rate of 10 mV/s together with the total stored capacity, outer surface adsorption capacity, and intercalation capacity of 285, 10.8, and 274.2 mAh/g respectively and their percentage of capacitance and diffusion of 36.3 % and 63.7 % respectively. The pouch type supercapacitor cell was fabricated using Biene-MoS2 NC as positive electrode (cathode) and activated carbon (AC) as negative electrode (anode) which demonstrated high areal capacitance of 38.2 mF/cm2 at the current density of 0.5 mA/cm2 and also it delivered the enhanced areal energy and power densities of 11.94 μWh/cm2 and 1 mW/cm2 respectively.

DyGCN: Efficient Dynamic Graph Embedding With Graph Convolutional Network.

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes in graphs, has received significant attention. In recent years, there has been a surge of efforts, among which graph convolutional networks (GCNs) have emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In the present work, an efficient dynamic graph embedding approach is proposed, called dynamic GCN (DyGCN), which is an extension of the GCN-based methods. The embedding propagation scheme of GCN is naturally generalized to a dynamic setting in an efficient manner, which propagates the change in topological structure and neighborhood embeddings along the graph to update the node embeddings. The most affected nodes are updated first, and then their changes are propagated to further nodes, which in turn are updated. Extensive experiments on various dynamic graphs showed that the proposed model can update the node embeddings in a time-saving and performance-preserving way.

Chemical bath deposition-based synthesis of tin sulfide on silicon substrate and its application as a photodetector

Tin sulfide (SnS) is an attractive compound semiconductor for photovoltaic and sensing applications because of its abundance and non-toxicity. In the present work, the synthesis of SnS thin films on silicon substrates by chemical bath deposition (CBD) technique has been demonstrated. Tin chloride (SnCl2.2H2O) and thiourea were used as the tin and sulfur sources to deposit the SnS films with triethanolamine (TEA) as the complexing agent and sodium hydroxide (NaOH) as alkaline bath. To understand the growth mechanism, a time interrupted growth series was adopted at a temperature of 90 °C for different time durations i.e., 60 min, 120 min, and 180 min. A photosensor was fabricated using the SnS/Si heterojunction and its performance was investigated through electrical (current-voltage) and optical (photo-response) measurements. The I–V characteristics of the developed photodetector were recorded under dark and different light intensities. The device exhibited significant response to light even without a bias voltage.

Insights of effectivity analysis of learning-based approaches towards software defect prediction

Software defect prediction is one of the essential sets of operation towards mitigating issues of risk management in software development known to contribute towards enhancing the quality of software. There is evolution of various methodologies towards resolving this issue while learning-based methodology is witnessed to be the most dominant contributor. The problem identified is that there are yet many unsolved queries associated with practical viability of such learning-based approach adoption in software quality management. Proposed approaches discussed in this paper contributes towards mitigating this challenge by introducing a simplified, compact, and crisp analysis of effectiveness associated with learning-based schemes. The paper presents its major findings of effectivity analysis of machine learning, deep learning, hybrid, and other miscellaneous approaches deployed for fault prediction followed by highlighting research trend. The major findings infer that feature selection, data imbalance, interpretability, and in adequate involvement of context are prime gaps in existing methods. The paper also contributes towards research gap as well as essential learning outcomes of present review work.

The Impact of Polycystic Ovary Syndrome (PCOS) on the Risk of Developing Ovarian Cancer and Thyroid Disorders: A Comprehensive Review.

Polycystic ovary syndrome (PCOS) is a common hormonal disorder that affects women. It is characterized by hyperandrogenism, polycystic ovarian morphology, and other related disorders. It is associated with various health conditions, such as infertility and increased risk of heart problems. Ovarian cancer is also a significant concern, as it is the fifth leading cause of death in women. While there is evidence suggesting a potential association between PCOS and ovarian cancer, the exact nature of this relationship remains unclear. Thyroid disorders, particularly hypothyroidism and Hashimoto's thyroiditis, have also been linked to PCOS. The presence of hypothyroidism can contribute to the development of polycystic ovarian morphology, affecting ovulation and hormone balance. Many works have shown a higher ubiquity of autoimmune thyroid disease in PCOS patients, indicating a potential association between the two conditions. The occurrence of PCOS, hirsutism, and acne underscores the frequency of endocrine disorders in women. This review paper examines the present relevant work on the association between PCOS and ovarian cancer as well as PCOS and thyroid disorders. A systematic literature search was conducted on PubMed, such as PubMed, Scopus, and Google Scholar database, to identify peer-reviewed publications pertaining to PCOS, ovarian cancer, and thyroid disorders. While some studies have delineated a significant link between PCOS and ovarian cancer or thyroid disorders, others have yielded inconclusive results. Further research is necessary to establish a definitive causal relationship between these conditions. Understanding the relationship between PCOS, ovarian cancer, and thyroid disorders is crucial for early detection, accurate diagnosis, and effective management of these conditions. Identifying potential risk factors and developing appropriate screening strategies can improve women's health outcomes and reduce the burden associated with these disorders.

Semantic richness effects in isolated spoken word recognition: Evidence from massive auditory lexical decision.

While known to influence visual lexical processing, the semantic information we associate with words has recently been found to influence auditory lexical processing as well. The present work explored the influence of semantic richness in auditory lexical decision. Study 1 recreated an experiment investigating semantic richness effects in concrete nouns (Goh et al., 2016). In Study 2, we expanded the stimulus set from 442 to 8,626 items, exploring the robustness of effects observed in Study 1 against a larger data set with increased diversity in both word class and other characteristics of interest. We also utilized generalized additive mixed models to investigate potential nonlinear effects. Results indicate that semantic richness effects become more nuanced and detectable when a wider set of items belonging to different parts of speech is examined. Findings are discussed in the context of models of spoken word recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Proposal of a similarity measure for unified modeling language class diagram images using convolutional neural network

The unified modeling language (UML) represents an essential tool for modeling and visualizing software systems. UML diagrams provide a graphical representation of a system's components. Comparing and processing these diagrams, for instance, can be complicated, especially as software projects grow in size and complexity. In such contexts, deep learning techniques have emerged as a promising solution for solving complex problems. One of these crucial problems is the measurement of similarity between images, making it possible to compare and calculate the differences between two given diagrams. The present work intends to build a method for calculating the degree of similarity between two UML class diagrams. With a goal to provide teachers a helpful tool for assessing students' UML class diagrams.

Planar Two-Dimensional Capacitive Displacement Sensor Based on Time Grating

Efforts to meet the demand for planar two-dimensional (2D) displacement measurement systems are limited by the difficulties of manufacturing 2D diffraction gratings with high precision over a large area. The present work addresses this issue by capitalizing on the high-precision of one-dimensional capacitive displacement sensors based on time-grating to develop a planar 2D capacitive displacement sensor technology composed of a large-area array of excitation electrodes positioned on a fixed ruler and a small-area array of induction electrodes assembled on a coplanar moving ruler that travels over the fixed ruler at a fixed separation gap. Firstly, the individual traveling wave signals of the induction electrodes on the moving ruler are analyzed theoretically to develop a method for decoupling information pertaining to the separate displacements in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> directions. Then, the feasibility of the proposed decoupling method is verified by simulations based on a three-dimensional electric field model. Finally, the proposed measurement technology is validated experimentally based on tests conducted using a prototype sensor manufactured using standard printed circuit board technology. The experimental results demonstrate that the prototype sensor achieves measurement errors of ±8.2 μm and ±6.8 μm in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> directions, respectively, over the full 200 mm × 200 mm measurement range of the sensor.

Synthesis and Characterization of Epigallocatechin Gallate-mediated Hydroxyapatite.

Hydroxyapatite is a significant material that finds its application in the field of dental and bone tissue engineering. The formulation of nanohydroxyapatite with the aid of bioactive compounds has gained importance in recent years due to the beneficial activity contributed by them. The present work focuses on the formulation of nanohydroxyapatite synthesis using epigallocatechin gallate, an active biochemical component of green tea. The prepared epigallocatechin gallate-mediated nanohydroxyapatite (epi-HAp) was nanoglobular in shape and composed of calcium, phosphorous, carbon and oxygen, which was confirmed by Scanning electron microscope- energy dispersive X-ray analysis (SEM-EDX). The Attenuated Total Reflection-Infra red spectroscopy (ATR-IR) and X-ray photoelectron spectroscopy (XPS) assured that the reduction and stabilisation of nanohydroxyapatite were mediated by epigallocatechin gallate. The epi-HAp exhibited anti-inflammatory behaviour along with nil effect on cytotoxicity. To be precise, the epi-HAp can be an effective biomaterial in bone and dental applications.

Coordinated scheduling of integrated electricity, heat, and hydrogen systems considering energy storage in heat and hydrogen pipelines

The increasing interdependency of electricity, heat, and hydrogen sectors in recent years provides a growing incentive to make use of the possible synergies obtained when coordinating these energy sectors from the perspective of an integrated energy system (IES). However, coordinated scheduling frameworks remain poorly developed for integrated electricity, heat, and hydrogen systems, and the energy storage potentials in heat and hydrogen pipelines have not been quantitatively analyzed. The present work addresses these issues by separately analyzing the energy storage value provided by district heat systems (DHSs) and hydrogen transmission systems (HTSs). These storage potentials are applied to develop an operation model that considers the delay characteristics of heat pipelines and hydrogen pipeline storage. Then, we develop a coordinated scheduling model for a power-heat-hydrogen IES. The analysis is facilitated by quantifying the value of energy storage in heat pipelines and hydrogen pipelines in terms of operation costs. Finally, a case study is conducted to verify the necessity of accounting for the dynamic energy storage potentials of the DHS and HTS during IES optimization. Numerical results verify the economic value of comprehensive consideration of heat and hydrogen pipeline storage, which results in higher operational flexibility of IES with lower operating cost and reduced wind power curtailment.

High-Fidelity Specular SVBRDF Acquisition From Flash Photographs.

Obtaining accurate SVBRDFs from 2D photographs of shiny, heterogeneous 3D objects is a highly sought-after goal for domains like cultural heritage archiving, where it is critical to document color appearance in high fidelity. In prior work such as the promising framework by Nam et al., the problem is simplified by assuming that specular highlights exhibit symmetry and isotropy about an estimated surface normal. The present work builds on this foundation with several significant modifications. Recognizing the importance of the surface normal as an axis of symmetry, we compare nonlinear optimization for normals with a linear approximation proposed by Nam et al. and find that nonlinear optimization is superior to the linear approximation, while noting that the surface normal estimates generally have a very significant impact on the reconstructed color appearance of the object. We also examine the use of a monotonicity constraint for reflectance and develop a generalization that also enforces continuity and smoothness when optimizing continuous monotonic functions like a microfacet distribution. Finally, we explore the impact of simplifying from an arbitrary 1D basis function to a traditional parametric microfacet distribution (GGX), and we find this to be a reasonable approximation that trades some fidelity for practicality in certain applications. Both representations can be used in existing rendering architectures like game engines or online 3D viewers, while retaining accurate color appearance for fidelity-critical applications like cultural heritage or online sales.

In English

Artificial implants are a favorable environment for bacterial adhesion and subsequent biofilm formation, thereby accelerating the development of infection in the area of implant incorporation. Despite significant progress in the development of various endoprostheses over the past decades, bacterial periprosthetic infection is one of the main factors leading to complications in their use, prolongation of rehabilitation, and significant economic losses. The present work is devoted to the creation of hybrid hydrogel nanocomposites with complex antimicrobial action for endoprosthetics in the maxillofacial region and for filling postoperative cavities (primarily after tumor removal). These nanocomposites were created on the basis of pre-synthesized spongy polyvinylformal with encapsulated gold nanoparticles, the pore space of which was partially filled with pH-sensitive hydrogels based on acrylic acid (or copolymers based on acrylamide and acrylic acid) with sorbed Albucid. The structure of the synthesized hybrid hydrogel materials was confirmed by IR spectroscopy. Studies of the kinetics of hydrogel swelling in buffer solutions with different pH values have shown that the sample filled with a copolymer of acrylamide and acrylic acid with their ratio 95:5 has the optimal properties for preserving the geometric dimensions of the material for endoprosthetics, while in the case of incorporation of 100 % acrylic acid, the degree of swelling of the material (and, respectively, its dimensions) can vary significantly with a change of рН. Antimicrobial effect of the developed hybrid hydrogel materials was investigated using the following bacterial cultures: Escherichia coli ATCC 25922, Enterococcus faecalis ATCC 29213, Staphylococcus aureus ATCC 25923, and Pseudomonas aeruginosa ATCC 27853. The antibacterial effect of polyvinylformal-based composites with incorporated gold nanoparticles that were saturated with Albucid on all test microorganisms was demonstrated (growth inhibition zones ranged from 15 to 35 mm), which will prevent microbial contamination of the developed hybrid hydrogel material when it is used in endoprosthesis.