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Escitalopram treatment ameliorates chronic immobilization stress-induced depressive behavior and cognitive deficits by modulating BDNF expression in the hippocampus
Major depressive disorder (MDD) affects 21% of the global population. Chronic exposure to stressful situations may affect the onset, progression, and biochemical alterations underlying MDD and associated cognitive impairments. Patients exhibiting MDD are mainly treated with several antidepressants; one is escitalopram, a selective serotonin reuptake inhibitor. However, whether or not it mitigates chronic stress-induced cognitive deficits is unknown. The present study exposed rats to chronic immobilization stress (CIS) 2 hours/day for 10 days. Then, escitalopram (5 mg and 10 mg/kg i.p.) was administered for 14 days and subjected to the elevated plus maze, open field test, forced swim test, sucrose preference test, and radial arm maze task. A different set of animals were used to assess the vascular endothelial growth factor (VEGF), glial fibrillary acidic protein (GFAP), and brain derived neurotrophic factor (BDNF) levels in the hippocampus, frontal cortex, and amygdale. Our data suggest that escitalopram significantly protected CIS-induced spatial learning and memory deficits, behavioral depression, and anxiety. Furthermore, escitalopram (10 mg/kg) shows a remarkable recovery of dentate gyrus and hippocampal atrophy. In addition, the restoration of molecular markers BDNF, VEGF, and GFAP expression is also implicated in the neuroprotective mechanisms of escitalopram. Our results suggested that esciatlorpam restores cognitive impairments in stressed rats by regulating neurotrophic factors and astrocytic markers. 2024 Shilpa Borehalli Mayegowda et al. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). All Rights Reserved. -
Green-synthesized nanoparticles and their therapeutic applications: A review
Antibiotic-resistant microorganisms are a rising issue when it comes to human health. Microbial pathogens that cause harmful infections are quickly becoming resistant to the antimicrobial action of traditional antibiotics. Nanotechnology, an innovative sector being an indispensable part of healthcare and research, has in-depth and extensive applications. Nano-compounds have been promising antimicrobial agents, anti-cancerous mediators, vehicles for drug delivery, formulations for functional foods, identification of pathogens, food and drug packaging industry, and many more. However, the chemical synthesis of nanoparticles (NPs) has certain drawbacks such as causing toxicity and other adverse effects. For more than a decade, the use of NPs that are conjugated or green-synthesized has gained popularity due to the two-fold action of metallic NPs mixed with biological sources. In contrast, NPs synthesized using plant or microbial extracts, conjugated with biologically active components, appear to be a safe alternative approach as they are environmentally friendly and cost-effective. Such environmentally safe techniques are referred to as "green nanotechnology"or "clean technology"and are feasible alternatives to chemical methods. Furthermore, NPs conjugated with natural biomolecules have improved bioavailability and have minimal side effects, as they are smaller in size and have higher permeability in addition to being reducing and stabilizing agents possessing excellent antioxidant activity. NPs serve as potential antimicrobial agents due to their affinity towards sulphur-rich amino acids, adhere to microbial cell walls by means of electrostatic attraction, and disrupt the cytoplasmic membrane along with the nucleic acid of microbes. They possess anticancer activity owing to oxidative stress, damage to cellular DNA, and lipid peroxidation. The green-synthesized NPs are thus a promising and safe alternative for healthcare therapeutic applications. 2023 the author(s), published by De Gruyter. -
Phytonanotechnology for the Removal of Pollutants from the Contaminated Soil Environment
Over-consumption of chemically synthesized components aids country toward industrial revolution, which symbolizes for economic prosperity. On the other hand, industrial revolution is responsible for soil pollution, due to its toxic effluents. The main source of soil pollutants includes fertilizers, pesticides, untreated wastewater used for irrigation, land application of sewage sludge due to rich organic content, petroleum leakage and leaching from landfills, etc. The crops grown out of this contaminated soil make the plant to changes its nutritional valve, bioaccumulates the chemicals, and also hinder with its vigor. Studies proved that prevent measures should prioritize in minimizing the adverse effect on the environment. Use of Phyto-nanotechnology in wastewater treatment, as nano fertilizer, nanotechnology-based biocontrol agents, and other areas before the hazardous chemicals entering soil. Green synthesized nanoparticles assist as excellent bio remedial agents as they are rich in biomolecules like carbohydrates, proteins, lipids, and several enzymes also deter-mine its efficacy of action. Hence, this chapter highlights the various eco-friendly and inexpensive products or formulation used for removal of toxic and recalcitrant materials which are dreadfully risky to human health. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
"Forgotten Communities" understanding the Anglo Indian community through the monthly magazine " The Review" /
A community has its own traditions and practices that have been followed by ancestors for generations. The Anglo-Indian community in India has a marginal, ethnic and minority existence in the social, cultural and political arena. Fighting for survival since the British era the community has emerged with its own uniqueness while living in India. It is considered as one of the most minority of races in India. -
Mechanical strength and impact resistance of hybrid fiber reinforced concrete with coconut and polypropylene fibers
This experimental study investigates the mechanical properties and resistance to impact of concrete reinforced with coconut fibers (CF) and polypropylene fibers (PPF). The fiber proportions were decided based on the results obtained from the tests on coconut fiber reinforced concrete (CFRC) and polypropylene fiber reinforced concrete (PPFRC), tested individually. PP fibers of 12 mm and 24 mm of 0.1%, 0.2%, and 0.3% of the volume of concrete were used in PPFRC. Coconut fibers having 50 mm and 75 mm of 0.2%, 0.3%, and 0.4% of the volume of concrete were used in CFRC. Based on test results, PPF (12 mm) and CF (50 mm) were selected for hybrid fiber reinforced concrete (HyFRC). By varying both PPF and CF content, three different proportions with a total fiber content of 0.2% and 0.3% of the volume of concrete were selected. The improvement in strength was observed to be maximum when the total fiber content in the hybrid fiber reinforced concrete was 0.3%. The increase in impact resistance of HyFRC was almost double that of individual FRC and three times that of plain concrete. 2022 -
Stability and statistical analysis on melting heat transfer in a hybrid nanofluid with thermal radiation effect
The dual solutions for the stagnation point flow in a cobaltCeO2/kerosene hybrid nanofluid with melting heat transfer and thermal radiation are analyzed. The partial differential equations are solved by the conversion of the partial differential equations into nonlinear ordinary differential equations by utilizing suitable scaling group transformations. Numerical solutions are obtained by employing the built-in function in the MATLAB software (bvp4c). Physically recoverable solutions are found employing stability analysis. The factor variables of interest (melting parameter, the nanoparticle volume fraction of cobalt and CeO2) are then further analyzed by utilizing the sensitivity analysis (based on the response surface methodology model) for heat transfer rate, as well as the skin friction coefficient. It is found that the heat transfer and skin friction tend to be significantly higher in a hybrid nanofluid due to the radiation and melting heat transfer. The lower branch is found to be unstable, whereas the upper branch is found to be stable. Also, the heat transfer rate and skin friction coefficient are found to be negatively sensitive toward the melting parameter. The model in this study can be applied for microscopic propulsion systems and the nano-electromechanical systems integrated with a nano-based system. IMechE 2021. -
Ternary Blended Geo-Polymer Concrete - A Review
The manufacturing of ordinary Portland cement produces carbon di oxide which is responsible for global warming. Geopolymer concrete in the field of construction leads to economic sustainability and reduces adverse effects on environment. Geopolymers are inorganic polymers obtained from chemical reaction between an alkaline activator's solution and an alumina-silicate material without using cement. Alkali activators are Homogeneous mixture consisting of two (NaOH and Na2SO3) or more chemicals in different proportions are highly corrosive and difficult to handle. There are still some limitations with respect to the alkaline activators in geopolymer concrete. To overcome ordinary portland cement, many wastes materials such as Silica-fume, GGBS, fly ash etc. have been used in recent studies to create eco-friendly cements by geo-polymerization reactions. Geopolymers are economic & good alternative construction material in making concrete This review paper briefly explains on previous literatures, properties, materials of geopolymer concrete, testing and practical applications of geopolymer concrete. Published under licence by IOP Publishing Ltd. -
Heat transport of magnetized Newtonian nanoliquids in an annular space between porous vertical cylinders with discrete heat source
A numerical study of MHD natural convection in an upright porous cylindrical annulus filled with magnetized nanomaterial is made by using the specificity of nanoliquids to improve the phenomenon of heat transport. The upper and lower walls are thermally insulated, whereas the outer wall is kept at a lesser temperature. The finite volume method is used to treat the governing equations via computer code with Fortran programming. The results obtained are given for the values of the Rayleigh number between 103 and 106, aspect ratio Ar = 2, radii ratio ? = 2, Hartmann number (0 ? Ha ? 80), Darcy number (10?5 ? Da ? 10?2), porosity ratio (0.1 ? ? ?0.9), and the nanoparticles volume fraction (0 ? ? ? 0.1). The transferred thermal flux, in laminar natural convection, increases with the growth of the nanoparticle concentration, the Darcy number, the porosity, the Rayleigh number and, the length of the source. 2020 Elsevier Ltd -
Magneto-thermal-convection stability in an inclined cylindrical annulus filled with a molten metal
Purpose: Metal-cooled reactors generally use molten metals such as sodium, potassium or a combination of sodium and potassium because of their excellent heat transfer properties so that the reactor can operate at much lower pressures and higher temperatures. The purpose of this paper is to investigate the stability of natural convection in an inclined ring filled with molten potassium under the influence of a radial magnetism. Design/methodology/approach: A numerical simulation of electrically conductive fluid natural convection stability is performed on an inclined cylindrical annulus under the influence of a radial magnetism. The upper and lower walls are adiabatic, while the internal and external cylinders are kept at even temperatures. The equations governing this fluid system are solved numerically using finite volume method. The SIMPLER algorithm is used for pressure-speed coupling in the momentum equation. Findings: Numerical results for various effective parameters that solve the problem in the initial oscillatory state are discussed in terms of isobars, isotherms and flow lines in the annulus for a wide range of Hartmann numbers (0 ? Ha ? 80), inclination angles (0 ? ? ? 90) and radii ratios ? ? 6. The dependency stability diagrams between complicated situations with the critical value of the Rayleigh number RaCr and the corresponding frequency FrCr are established on the basis of the numeric data of this investigation. The angle of inclination and the radii ratio of the annulus have a significant effect on the stabilization of the magneto-convective flux and show that the best stabilization of the natural oscillatory convection is obtained by the intensity of the strongest magnetic field, the high radii ratio and inclination of the annulus at ? = 30. Practical implications: This numerical model is selected for its various applications in technology and industry. Originality/value: To the best of the authors knowledge, the influence of the inclination of the cylindrical annulus (ring), with various radii ratio, on natural oscillatory convection under a radial magnetism has never been investigated. 2020, Emerald Publishing Limited. -
Ge-GaAs-Ge Heterojunction MOSFETs for Mixed-Signal Applications
A lattice matched heterojunction intraband tunnel (HJIBT) FET is proposed. The performance dependence of the device on conduction band (CB) discontinuity at source-channel and drain-channel interface is addressed using numerical simulation. Various mechanisms governing transport phenomena in the HJIBT FET are investigated in detail for different CB offsets (CBOs). For low gate to source voltage ( ${V}_{\text {GS}}$ ), thermionic emission is found to be the most significant transport mechanism. For moderate ${V}_{\text {GS}}$ , intraband tunneling phenomenon dominates over thermionic emission and continues to remain so. At high ${V}_{\text {GS}}$ , band-to-band tunneling occurs in HJIBT FETs. The proposed device shows improved figures of merit such as drain-induced barrier lowering (DIBL), ON-current ( ${I}_{ \mathrm{\scriptscriptstyle ON}}$ ) to OFF-current ( ${I}_{ \mathrm{\scriptscriptstyle OFF}}$ ) ratio ( ${I}_{ \mathrm{\scriptscriptstyle ON}}/{I}_{ \mathrm{\scriptscriptstyle OFF}}$ ), subthreshold slope (SS), gate capacitance ( ${C}_{\text {G}}$ ), ${g}_{m}$ (transconductance), and ${f}_{T}$ (cut-off frequency), with respect to conventional MOSFET. Also, the design of a high-performance hybrid 6T-static random access memory (SRAM) is proposed. 1963-2012 IEEE. -
An Intelligent Recommendation System Using Market Segmentation
Electronic commerce, sometimes known as E-Commerce, is exchanging services and goods over the internet. These E-Commerce systems generate a lot of information. To solve these Data Overload issues, Recommender Systems are deployed. Because of the change to online buying, companies must now accommodate customers needs while also providing more options. The strategies and compromises of common recommender systems will be discussed to assist clients in these situations. Recommendation algorithms generate lists of things that the user have been previously using (content filtering) or develop recommendations and analyzing what items users purchase and identify similar target users (collaborative filtering). To assist clients in these situations, The Apriori algorithm, standard and custom metrics, association rules, aggregation, and pruning are used to improve results after a review of popular recommender system strategies that have been used. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Understanding the developmental relevance of animated cartoons: How people perceive the United Nations productions /
The mass media has emerged as one of the greatest tools and a catalyst in propelling some of the great changes in the society. Animated cartoon is one of the forms of mass media. Although often understated, mildly acknowledged and subtle in its ways, it holds the potential to make positive changes if used optimally. -
Predicting Song Popularity Using Data Analysis
In today's music landscape, predicting a song's success is crucial for musicians, record labels, and streaming platforms. This paper introduces a methodology for estimating popularity using Spotify data, termed the 'Proxy Popularity Score.' Three models - Random Forest, LightGBM Regressor, and XGBoost Regressor - are utilized for prediction. Performance metrics including mean absolute error, mean squared error, root mean squared error, and R-squared error are employed to evaluate model accuracy. Correlation values of 99.85%, 99.87%, and 99.84% are achieved for XGBoost, LightGBM, and Random Forest respectively. The study concludes with a ranking of songs based on predicted popularity scores. 2024 IEEE. -
Novel splitring resonator antennas for biomedical application
Our paper presents the design and development of split ring resonator based metamaterial antenna for biomedical i.e., Industrial, Scientific and Medical(ISM-2.45GHz) applications and also used in biosensors. Now a day the biological changes in the human body such as glucose content in blood, heart rate, respiratory rate, brain tumor are monitored by the use of wireless body area networks. In such networks the main part of the system is antenna with compactness and wider bandwidth. We have designed gain enhanced and wide bandwidth antennas with size reduction of more than 95% compared to the conventional patch antenna. The design methodology is based on Metamaterial which is an emerging technology uses split ring resonators for size reduction. We have designed double square split ring shape superstrate antenna and circular ring resonator antenna with stub for 2.48GHz. Also they have better return loss (>12dB). Our antennas are fed with microstrip feeding and Coplanar Waveguide (CPW) feeding for better impedance matching and easy fabrication. The fabricated antennas are tested using Network analyzer. The measured results are good in agreement with simulated results. 2015, Journal of Pure and Applied Microbiology. All rights reserved. -
Implementing a programmable drop voltage controller vlsi
This study offers a new synchronized practice area door array (FPGAs), to minimize electricity usage. Concurrent bit-serial architecture is shown in the figure to minimize energy consumption and timing synchronization of switching structures. Researchers offer a fine-grained energy control system with each Look-up database to minimize the Static energy by the channel length, which is now equivalent to the dynamical one (LUT). A 90 nm Processor is the planned field-programmable VLSI. Its electricity consumption is 42 percent lower than that of sequential design. 2021, SciTechnol, All Rights Reserved. -
Research Advancements In Autism Spectrum Disorder Using Neuroimaging
Autism Spectrum Disorder (ASD) is a complex neurological condition that manifests as a spectrum of symptoms at varying levels of severity.. Insufficient data and heterogeneous characteristics of ASD are the primary causes of it being a complex, challenging, and intriguing field of research. ASD is declared one of the fastest-growing mental disorders affecting the normal life of subjects at various levels of severity and stages of age. Recent research work observed a significant change in brain structure, functional connectivity, and network using neuroimaging resources. Each autistic brain is as unique as a fingerprint for typically developed subjects. Magnetic Resonance Imaging (MRI) is accepted as an excellent diagnostic technology for numerous disorders with a satisfactory amount of information by medical experts. Cognitive deficits brain MRI modalities contain microscopic information, which is time-consuming and needs experts to interpret. Artificial intelligence (AI) strategies (Machine Learning and Deep Learning) are implemented with various imaging modalities to decrypt the information for diagnosis and to support computer-added solutions for appropriate treatment. The research aims to discover the various evolutionary impacts of artificial intelligence for the diagnosis of Autism syndrome disorder using neuroimaging. To automate the diagnosis using artificial intelligence methodologies, medical imaging has proved to be of immense use. Though neuroimaging and AI produced satisfactory diagnostic solutions for many mental disorders, research is required to explore the autistic brain for more neuroimaging information to be used for further investigation. Some of the Internet of Things (IoT) solutions for detection and training are also invented but not with the use of Neuroimaging. Autism is a neurological condition that affects the brain, and hence more research is advised using imaging and AI techniques to support the community to enjoy a normal life. 2023 American Institute of Physics Inc.. All rights reserved. -
Users Perception and Barriers to Using Self-Driven Rental Bikes
The research study has two objectives. The first objective of this paper was to find users' perception towards self-drive rental bikes. The second objective was to identify the factors that act as barriers to users using self-drive rental bikes. The research was a formal and structured conclusive research type and used quantitative data analysis techniques. The study had a representative sample of 350 respondents. The population selected for this study were people of various demographics in Bangalore. We used judgemental sampling to decide on the right sample. In achieving both objectives, factor analysis was used to arrive at a minimum number of factors or dimensions. The major perception factors are: Economical Choice, Environmental Consciousness, Alternative Source of Transport, Rationality, and Convenience. The major barriers to using self-drive rental bikes are Safety Issues, Conservative Nature of Users, the Expensive Nature of Service, and the Difficulty in Using Mobile Applications. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Solar Mapping of India using Support Vector Machine
Accurate knowledge of global solar radiation (GSR) data is necessary for various solar energy based applications. However, in spite of its importance, the number of solar radiation measuring stations is comparatively rare throughout the world due to financial cost and difficulties in measurement techniques. The objective of this current study is to assess the solar energy potential and to develop solar resource mapping of India without utilizing the direct measurement techniques. GSR is predicted with commonly available meteorological parameters like minimum and maximum temperature as its inputs by using Support Vector Machine (SVM) based solar radiation model. The SVM model is validated with measured data from India Meteorological Department (IMD). This study simplifies the major challenge of preparing GSR data for various solar energy applications in a big country like India. Also the life cycle cost of Solar PV is analyzed in India. The payback period will be around 3 years for an annually solar radiation of range from 3.5 to 6 kWh/m 2 /day. This work eliminates the requirement of costly pyranometer to get GSR data. Solar resource mapping of India is developed without direct measurement technique thus avoids GSR data recording, daily maintenance and subsequently the increasing cost of GSR data collection. 2018 Web Portal IOP. All rights reserved. -
A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification; [Un modelo marco progresivo UNDML para el diagntico y clasificaci del ccer de mama]
According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the diseases fatality rate. Early cancer detection raises the chance of survival by up to 8 %. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy. 2024; Los autores. -
A protoberberine alkaloid based ratiometric pH-responsive probe for the detection of diabetic ketoacidosis
Herein we report a ratiometric naturally occurring fluorescent pH probe, berberrubine (BBn) for the direct detection of diabetic ketoacidosis (DKA) conditions of patients having type I diabetes mellitus. The photophysical properties of the probe during pH titrations showed remarkable changes in absorption spectra where two absorption bands at 377 and 326 nm have disappeared followed by the emergence of an absorption maxima at 346 nm in highly acidic conditions. In addition, a fluorescence enhancement effect was observed in the alkaline pH, with a bathochromic shift of 33 nm. Moreover, the solution switches the color from light yellow to light pink with the change of pH from acidic to basic. A pKa value of 7.57 and a good linearity between pH 5.09.0 indicate that the probe can be used efficiently for the DKA condition, where pH variations are in the range of 67. The excellent water solubility, photostability, reversibility, and selectivity of BBn make it a potential pH sensing agent for acidic microenvironments. The reversible sensing of pH variations during DKA could be effective in primary detection and diagnosis which can assist in avoiding further complications of acidosis. 2021 Elsevier Ltd