Cancer is one of the leading causes of death worldwide, accounting for about 10 million deaths a year, or nearly one in six deaths. The most common types of cancer are breast, colorectal, lung, and prostate cancers. Prolonged application of hormone drugs leads to the development of resistance. The development of agents with high activity against resistant cells is a challenge. It is important to create novel targeted compounds and search for active molecules among previously developed. The study aimed to evaluate the sensitivity of 4-hydroxytamoxifen-resistant cells to 5-fluorouracil (5-FU) and analyse the signalling pathways that are regulated by 5-FU in breast cancer cells. Antiproliferative activity was assessed by the MTT assay, immunoblotting was used to evaluate the expression of proteins in breast cancer cells. Activity of 5-FU was evaluated on parental MCF7 cells and a cell subline with resistance to 4-hydroxytamoxifen (HT), named MCF7/HT. The 4-hydroxytamoxifen-resistant cells showed high sensitivity to 5-FU. Expression of estrogen receptor α (a key driver of breast cancer growth) in MCF7 and MCF7/HT cells was not sensitive to 5-FU treatment. In both parental and resistant cells, 5-FU induces changes in the activity of several signalling proteins. 5-FU activated Akt, ERKs 1/2, and cyclin D1. The data suggest that 5-FU should be further investigated as a chemotherapeutic for hormone-resistant cancers; the combination of 5-FU with Akt and ERKs 1/2 inhibitors may be effective.

Ternary intermetallic RTX compounds are composed of R - a rare-earth metal and T, X - different d or р-elements [1]. This family of intermetallics has a wide range of possible applications, such as magnetocaloric cooling, gas liquefaction and others [2-4]. Experimentally and theoretically it was found that T-sublattice doping can significantly improve their magnetic and electronic properties [3], a composition-Induced magnetic transition was revealed in GdMn_1-xTi_xSi for x = 0 - 1 [4]. Therefore, further study of their properties may be useful for various environmentally sustainable applications.

In this work, the series GdFe_1-xNi_xSi compounds was synthesized and investigated for x = 0 - 0.4 due to the solubility limit [5]. The theoretical calculations were carried out for the tetragonal structure for x ranging between 0 and 0.5. The electronic structure, magnetic moments and types of magnetic orderings were investigated using the DFT+U method taking into account strong electron correlations in the 4f Gd shell [5]. In the self-consistent DFT+U calculations, the theoretical total magnetic moment of GdFe_1-xNi_xSi was found to be solely formed by the Gd ion, and Ni, Si are either non-magnetic or have small magnetic moments 0.02 mB at Si and 0.1mB at Ni. In the calculations for x = 0 - 0.25, in GdFe1-xNixSi the ferromagnetic (FM) ordering of the Gd magnetic moments was found as the most stable. For GdFe_0.7Ni_0.3Si and compositions with the larger content of Ni, the antiferromagnetic (AFM) ordering was found to be more preferable in total energy. Several types of AFM orderings were checked. The one with the Gd moments being aligned antiferromagnetically in ‘‘W slabs’’ and ferromagnetically in ‘‘BaAl₄ blocks’’. This type of AFM became more stable than the ferromagnetic one with the differences in total energy equal to 0.09 meV/f.u. (x=0.3) and 0.29 meV/f.u. (x=0.35). From experimental magnetic measurements, the behaviour of the magnetization curves and Curie temperature for GdFe_1-xNi_xSi differs from the one for V, Cr, Ti [4] with the Ni-doping and a decrease in interatomic distances. The magnetocaloric effect (MCE) of the GdFe_1-xNi_xSi systems changes with a change in composition from 3.8 (x=0.1, T_C = 111 K) and 3.3 (x=0.2, T_C = 104 K) J/kgK to 1.4 (x=0.3, T_C = 106 K) J/kgK which can be attributed to the FM-AFM magnetic transition.

Thus, for the GdFe_1-xNi_xSi series, the transition from a ferromagnetic (low Ni content) to an antiferromagnetic ordering (x > 0.25) was identified using our first-principles calculations. The results are supported by the experimental magnetic data. The data obtained may indicate promising prospects of the GdTSi compounds studied in this work and the whole group of ternary intermetalliс compounds with rare earth metals, which will motivate further research.

The study is supported by the Russian Science Foundation (RSF) project No. 18-72-10098.

[1] Gupta, S.; Suresh, K. Review on magnetic and related properties of RTX compounds. J. Alloys Compd. 2015, 618, 562–606. https://doi.org/10.1016/j.jallcom.2014.08.079

[2] Kuchin, A.G.; Platonov, S.P., Mukhachev, R.D.; Lukoyanov, A.V.; Volegov, A.S.; Gaviko, V.S.; Yakovleva, M.Yu. Large magnetic entropy change in GdRuSi optimal for magnetocaloric liquefaction of nitrogen. Metals 2023, 13, 290. https://doi.org/10.3390/met13020290

[3] Kuchin, A.G.; Platonov S.P.; Lukoyanov, A.V.; Volegov, A.S.; Gaviko, V.S.; Mukhachev, R.D.; Yakovleva, M.Yu. Remarkable increase of Curie temperature in doped GdFeSi compound, Intermetallics 2021, 133, 107183. https://doi.org/10.1016/j.intermet.2021.107183

[4] Mukhachev, R.D.; Lukoyanov, A.V. Composition-Induced Magnetic Transition in GdMn1-xTixSi Intermetallic Compounds for x = 0–1. Metals 2021, 11, 1296. https://doi.org/10.3390/met11081296

[5] Kuchin, A.G.; Platonov, S.P.; Mukhachev, R.D.; Lukoyanov, A.V.; Volegov, A.S.; Gaviko, V.S.; Yakovleva, M.Yu. Magnetocaloric effect and magnetic ordering in GdFe1xTxSi, T = Cr, V, Ni. Phys. Chem. Chem. Phys. 2023, 25, 15508. https://doi.org/10.1039/D3CP01088K

Heart and blood vessel disorders are referred to as cardiovascular diseases (CVDs). It is one of the leading global cause of death and consists of many disorders that harm cardiovascular system. The World Health Organization (WHO) estimates that in 2019, 18 million deaths worldwide were caused by CVDs, accounting for about 32% of all deaths. Therefore, early detection and prediction of cardiovascular disease can be beneficial in identifying high-risk individuals and enabling timely interventions to reduce the disease's impact and improve patient outcomes. This research provides a machine learning (ML)-based framework CVD detection to satisfy this criterion. The proposed model includes data preprocessing, hyperparameter optimization using GridSearchCV, and classification by supervised learning approaches such as support vector machine (SVM), K-nearest neighbors (KNN), XGBoost, random forest (RF), LightBoost (LB), and stochastic gradient descent (SGD). All these models are carried out on the publicly accessed database, namely Kaggle. The experimental results demonstrate that the suggested ML technique has attained 92.76% detection rate with the SGD classifier on the 80:20 training/testing ratios which is superior to the well-received approaches.

This paper presents the planning of the potential and feasibility of a complete Solar Solution for the mess at the Shri Mata Vaishno Devi University (SMVDU) campus. Since there is ample space near the mess, solar steam generating plants are proposed on the mess to reduce Liquified Petroleum Gas (LPG) consumption substantially. A total of forty concentrators (sixteen square meters each) are proposed to be installed. The project's life is proposed to be twenty-five years with a capital cost of $19.02 Thousand and additional operation and maintenance costs. The financial analysis shows that the total savings from the project are $172.82 Thousand with a Cost-benefit Ratio of 6.40. The project's break even is approximated to be attained by the fortieth month of operation. Beyond the financial benefit, the project is proposed to have multiple other benefits to the institution. The benefits are that the use of fossil fuels (LPG) for cooking can be avoided by the installation of a thermal cooking system, it shall provide a better sustainability score in various rankings done worldwide for the university, the cost of tender of mess for future can be reduced drastically, the project will be brought up as a project that shall be displayed at every level in the Union Territory so that we shall promote the development of renewable energy uses.

The present study focuses on the electrochemical performance of PEO/PMMA blended nanocomposite solid polymer electrolytes [BNSPE] with Cadmium bromide (CdBr₂) salt and Cerium oxide (CeO₂) nanofiller. Thin films of blended nanocomposite solid polymer electrolytes [BNSPE] were signalised by distinct way of working in characterization studies such as X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), UV Visible Spectroscopy and Scanning Electron Microscopy (SEM). The X-ray diffraction (XRD) have divulge the origination of an amorphous structure in the case of blended nanocomposite solid polymer electrolyte [BNSPE] system and the particle size were premeditated by using Debye-Scherrer equation. The Fourier transform infrared spectroscopy (FTIR) exploration were carried out to diagnosis the molecular interactions taking place within the blended nanocomposite solid polymer electrolyte [BNSPE] system and optical analysis was accomplished by UV spectroscopy whereas Scanning electron microscopy (SEM) investigation has authorised the identification of the interaction of CeO₂ nanofiller amalgamated with blended polymer electrolytes exhibiting an ameliorate amorphicity. The aim of present work is to be aware of the significance of CeO₂ nanofillers with the blended nanocomposite solid polymer electrolyte [BNSPE] system towards recognition of an appreciable ionic conduction.

How to formulate peptides into metered dose inhalers (MDI) is a bottleneck issue hampering the clinical translation of relevant products. In our previous studies, a bottom-up method to prepare peptide-containing particles for MDI was reported. Nevertheless, the formation mechanism of the particles remains unclear. In this work, considering the production workflow, a confinement-segregation theory was put forward as a hypothesis to explain the formation mechanism. Confinement and segregation were two major processes during formation, and the definitions were provided in detail. Based on the theory, some factors influencing particle formation were also discussed, which promoted the future formulation design. It is believed that the proposed theory will provide new insights into the study of peptide-containing MDI, and boost the clinical translation.

Globally, the increase in population density, various epidemics (COVID-19, SARS, and MERS etc.), climate change, global warming, and the reduction of arable land have caused damage to the ecosystem. Quality soil is the most important factor that has a direct impact on safe food and a clean environment. Different pollutant loads, microbiological activities, climatic and topographic conditions and current land use can change the properties of the soil. In recent years, fertile agricultural lands have been used in the construction industry. This situation explains the inadequacy between population growth and food supply. Both polluting parameters and non-purpose uses affect soil quality negatively, and alternative solutions are sought for this. One of these solutions is the application of various additives to the soil. Among these substances, biochar is a widely used additive in agricultural production, soil quality improvement, and pollutant treatment in water and soil environments. Among these substances, biochar has been widely used in agricultural production, soil quality improvement, and pollutant treatment in water and soil environments. It is a carbon-rich product formed by pyrolysis method of biochar, food and agricultural wastes in an oxygen-free environment at ≥250 °C. In this study, current research is examined to explain the interaction of soil quality with biochar. The biochar materials used, the production conditions, the 3-step reaction in the soil were examined. It summarizes the recent developments on soil quality of biochar with porous structure and high specific surface area.

Semiconductor electronics transform the landscape of computing, communication, energy harvesting, automobiles, biotechnology, and other electronic devices. This transformation is brought about by the ability to sense, receive, manipulate, and transmit data from the diverse systems of vertical stacks of semiconductor layers and microdevices. Though the discrete design details of each semiconductor may be extremely complex, the fundamental processing steps of each semiconductor device align well with the photolithography procedure. When these semiconductor layers are stacked using photolithography, the signal noise between the device features and layers is restricted by passivation or dielectric insulation provided by SiO₂ layers. Depending on the type of functionality and data sensing mechanism of semiconductors, SiO₂ layers have an intended fitness for their purpose. The purpose of SiO₂ layers can be segregated as an encapsulation of the semiconductor device, making part of the semiconductor layer inert, i.e., passivated, creating a hard mask to prevent an impact of the subsequent process like ion implantation or diffusion, insulating a part of the layer as in intermetallic dielectric or gate dielectric or to improve adhesion of the subsequent deposition.

The functionality of adhesion of SiO₂ is by far been a less studied area. The adhesive characteristics of SiO₂ for subsequent deposition and the thickness of SiO₂ affect the stress distribution. Stresses due to SiO₂ thin film, which could be a few nanometers to a few microns thick depending on the functionality, are modeled in this research. The stresses in SiO₂ films may cause delamination or discontinuity affecting the performance and reliability of the optical or semiconductor devices they are built into. The classical molecular dynamics (MD) simulation technique is employed to investigate the stress characteristics of deposited films by leveraging the outcomes of atomistic modeling. A cluster made of fused silica is employed as the substrate. For the SiO2 deposition process simulation, silicon atoms with high energies and low-energy oxygen atoms are injected. This model is carefully controlled to ensure the stoichiometry conditions. This analysis uses open-source code LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) and Ovito (Open Visualization) tool. The research in this paper is focused on the SiO₂ thin-film simulation to validate the analytical and experimental stress.

The paper aims to investigate the integration of sustainability management and lean practices in small and medium enterprises (SMEs) and its impact on supply chain performance. The study also explores the mediating role of process innovation in this relationship. Sustainability has gained significant attention in recent years as organizations strive to align their operations with environmental and social responsibility goals. Similarly, lean practices have been widely adopted to streamline processes and eliminate waste in supply chains. However, limited research has focused on the simultaneous integration of sustainability management and lean practices in SMEs and their combined effect on supply chain performance. The research adopts quantitative approach, which involves collecting survey data from a sample of SMEs operating in diverse industries. Statistical analyses, including structural equation modeling, are conducted to examine the direct and indirect relationships among sustainability management, lean practices, process innovation, and supply chain performance.

Through the lens of SMEs, the research examines how the integration of sustainability and Lean practices fosters competitive advantages and sustainable performance outcomes in the supply chain context. The findings contribute to both theoretical and practical domains by shedding light on the mechanisms through which sustainability and Lean practices synergistically influence supply chain performance. For SMEs, the research offers valuable insights into harnessing sustainable and Lean principles to achieve operational excellence and long-term success. Ultimately, this study advocates for a holistic approach to supply chain management that embraces sustainability, Lean thinking, and process innovation to foster enhanced performance and a more sustainable future for enterprises of all scales.

There is a growing number of frail patients whose health conditions require constant monitoring by the physician. Unfortunately, the budget restrictions of hospitals and the concomitant resolution of patients to stay home require that this control is to be carried out remotely. Today, IoT wearables are the most promising technology solution for sensing patients' physiological values h24. Those measurements need to be stored permanently and then processed in order to provide support to physicians in charge of taking in-time clinical decisions consistent with the patient health status. In many studies appeared so far, the processing of the Patient-Generated Health Data (PGHD) is done by Supervised Machine Learning (SML) algorithms. These methods provide optimal solution to the classification and regression problems. On the contrary, SML are not suitable when the objective is to provide physicians with basic descriptive statistics based on the physiological measurements, over a given time interval (e.g., hourly, daily, weekly, and so on). The DataBase Management System is the best software technology that suits such a need.

In the present paper, the PGHD are simulated by means of the ThingsBoard IoT platform, while their storage and processing are done by making recourse to PostgreSQL. In detail, a PostgreSQL relational database collects the PGHD, a set of SQL views implement the classical operators of descriptive statistics. The solution is parametric, so the interval of investigation can be customized according to physician's needs. In addition, an SQL trigger implements an alert each time a potential critical situation in the health status of the patient is identified.