Trace elements dyshomeostasis in liver and brain of weanling mice under altered dietary selenium conditions.

BACKGROUND: A balanced diet containing selenium (Se) and other trace elements is essential for normal development and growth. Se has been recognized as an essential trace element; however, its interaction with other elements has not been fully investigated. In the present study, sodium (Na), magnesium (Mg), potassium (K), calcium (Ca), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), copper (Cu), zinc (Zn), Se and rubidium (Rb), were analysed in liver and brain regions under altered dietary Se intake in weanling mice to identify major discriminatory elements. METHODS: The study investigated the effects of different levels of Se intake on the elemental composition in liver and brain tissues of weaned mice. After 24 weeks of feeding with Se adequate, deficient, and excess diets, elemental analysis was performed on the harvested tissues using Inductively coupled plasma mass spectrometry (ICP-MS). Statistical analysis that included analysis of covariance (ANCOVA), correlation coefficient analysis, principal component analysis, and partial least squares discriminant analysis were performed. RESULTS: The ANCOVA showed statistically significant changes and correlations among the analysed elements under altered dietary Se status. The multivariate analysis showed differential changes in elements in liver and brain regions. The results suggest that long-term dietary Se alternations lead to dyshomeostasis in trace elements that are required in higher concentrations compared to Se. It was observed that changes in the Fe, Co, and Rb levels were similar in all the tissues studied, whereas the changes in Mg, Cr, and Mn levels were different among the tissues under altered dietary Se status. Additionally, the changes in Rb levels correlated with the dietary Se intake but had no relation with the tissue Se levels. CONCLUSIONS: The findings suggest interactions between Mg, Cr, Mn, Fe, Co, and Se under altered Se status may impact cellular functions during postnatal development. However, the possible biological significance of alterations in Rb levels under different dietary Se paradigms needs to be further explored.

Improved Wireless Medical Cyber-Physical System (IWMCPS) Based on Machine Learning.

Medical cyber-physical systems (MCPS) represent a platform through which patient health data are acquired by emergent Internet of Things (IoT) sensors, preprocessed locally, and managed through improved machine intelligence algorithms. Wireless medical cyber-physical systems are extensively adopted in the daily practices of medicine, where vast amounts of data are sampled using wireless medical devices and sensors and passed to decision support systems (DSSs). With the development of physical systems incorporating cyber frameworks, cyber threats have far more acute effects, as they are reproduced in the physical environment. Patients' personal information must be shielded against intrusions to preserve their privacy and confidentiality. Therefore, every bit of information stored in the database needs to be kept safe from intrusion attempts. The IWMCPS proposed in this work takes into account all relevant security concerns. This paper summarizes three years of fieldwork by presenting an IWMCPS framework consisting of several components and subsystems. The IWMCPS architecture is developed, as evidenced by a scenario including applications in the medical sector. Cyber-physical systems are essential to the healthcare sector, and life-critical and context-aware health data are vulnerable to information theft and cyber-okayattacks. Reliability, confidence, security, and transparency are some of the issues that must be addressed in the growing field of MCPS research. To overcome the abovementioned problems, we present an improved wireless medical cyber-physical system (IWMCPS) based on machine learning techniques. The heterogeneity of devices included in these systems (such as mobile devices and body sensor nodes) makes them prone to many attacks. This necessitates effective security solutions for these environments based on deep neural networks for attack detection and classification. The three core elements in the proposed IWMCPS are the communication and monitoring core, the computational and safety core, and the real-time planning and administration of resources. In this study, we evaluated our design with actual patient data against various security attacks, including data modification, denial of service (DoS), and data injection. The IWMCPS method is based on a patient-centric architecture that preserves the end-user's smartphone device to control data exchange accessibility. The patient health data used in WMCPSs must be well protected and secure in order to overcome cyber-physical threats. Our experimental findings showed that our model attained a high detection accuracy of 92% and a lower computational time of 13 sec with fewer error analyses.

A study to explore the appropriateness of dosing regimen of vancomycin in critically ill patients in a tertiary care unit of India.

Introduction: Vancomycin is used in proven or suspected MRSA and MRE infections. An AUC/MIC ratio of ≥400 is the current accepted critical PK/PD"efficacy" target of vancomycin activity. The present study was conducted to ascertain the appropriateness of practice of current dosage regimen of vancomycin (1 g BD) based on population pharmacokinetic approach. Methods: A single-center prospective study with the ICU setting of a tertiary care center was conducted. A total of 15 adult patients with sepsis treated with vancomycin were included over 15 months from May 2019 to July 2020. Blood samples were obtained at 5, 10, and 30 minutes and thereafter at 2 and 6 hours following the completion of the vancomycin infusion. The data obtained from HPLC estimation was analyzed using a population pharmacokinetic approach with NLME, Phoenix 8.3.2.166. The pharmacokinetic model was based on covariates such as bodyweight and urinary creatinine clearance to predict drug concentrations. Results: A total of 83 vancomycin blood samples were analyzed. The mean AUC0-last and AUC0-∞ in patients who improved and died were (AUC(0-last)=293 (152.97); AUC(0-∞)=535.14 (353.67) and (AUC(0-last)=137.19 (51.37); AUC(0-∞)=582.12 (1036.09) respectively, the difference between the two outcome groups was not statistically significant (p=0.104). The pharmacokinetic model was best described by a two-compartment linear model. The goodness-of-fit plots showed that the final covariate pharmacokinetic model (having bodyweight and urinary creatinine clearance) adequately described the observed vancomycin concentrations. Conclusions: Based on the finding of the study it was concluded that 1 g BD dosing of vancomycin is inappropriate. Including covariates such as urinary creatinine clearance and weight in the pharmacokinetic model helped predict drug concentrations more accurately. However, further studies are required to demonstrate efficacy regarding applying this strategy.

Numerical and Experimental Analysis of DVA on the Flexible-Rigid Rail Vehicle Carbody Resonant Vibration.

This paper examines the influence of the equipment considered as a DVA (Dynamic Vibration Absorber) upon the mode of vertical vibrations of the car body in high-speed vehicles. The car body is represented as an Euler-Bernoulli beam to minimize flexible vibration. The DVA approach is used to find the appropriate suspension frequencies for various types of equipment. A vertical mathematical model with a flexible car body and equipment is developed to investigate the effect of equipment mass, suspension stiffness, damping, and mounting location on car-body flexible vibrations. A three-dimensional, rigid-flexible coupled vehicle system dynamics model is developed to simulate the car body and equipment's response to track irregularities. The experimental result was considered to verify the theoretical analysis and dynamic simulation. The mathematical analysis demonstrates that the DVA theory can be used to design the suspension parameters of the equipment and that it is suitable and effective in reducing the flexible vibration of the car body in which the vertical bending mode is greatly affected. Heavy equipment should be mounted as close to the car body's center as possible to achieve significant flexible vibration reduction, whereas light equipment contributes very little flexible vibration reduction.

Smart water management framework for irrigation in agriculture.

Global demand and pressure on natural resources is increasing, which is greater on the availability of pure and safe drinking water. The use of new-age technologies including Smart sensors, embedded devices, and Cloud computing can help deliver efficient and safe management for provisioning drinking water for consumers and irrigation for agriculture. The management actions combined with real-time data gathering, monitoring, and alerting with proactive actions, prevent issues from occurring. This research presents a secure and smart research framework to enhance the existing irrigation system. This involves a low-budget irrigation model that can provide automated control and requirements as per the season, climate by using smart device sensors and Cloud communications. The authors presented four unique algorithms and water management processing rules. This also includes alerting scenarios for device and component failures and water leakage by automatically switching to alternative mode and sending alert messages about the faults to resolve the operational failures.The objective of this research is to identify new-age technologies for providing efficient and effective farming methods and investigate Smart IoT-based water management. The highlights of this research are to investigate IoT water management systems using algorithms for irrigation farming, for which this research presents a secure and smart research framework. This involves a low-budget irrigation model that provides automated control and requirements as per the season, climate by using smart device sensors and Cloud communications. Alerts for device and component failures and water leakage are also in-built for switching to alternative mode to resolve the operational failures.

A novel approach on water resource management with Multi-Criteria Optimization and Intelligent Water Demand Forecasting in Saudi Arabia.

Ever-increasing demands for freshwater resources have elevated the likelihood of severe water stress in several places of Saudi Arabia during the last several decades. With effective decision-making processes, development objectives on water resource management emerge. In the following series of research articles, recent innovations in various objective demand forecasting systems are examined and contrasted in terms of their utility in resolving tough challenges in water resource management. Hence, this study proposes a novel approach to water resource management integrating Multi-Criteria Optimization and Intelligent Water Demand Forecasting (MCO-IWDF). This framework addresses the challenges in allocating various water resources to multiple water sectors in a future changing environment. In order to plan for future water needs, water managers use a variety of tools. When forecasting future water demand, the most common method is to estimate current per-capita consumption (gpcd) and multiply this by the expected population growth. Conserving water in the Kingdom of Saudi Arabia to improve irrigation issues. This research analyzes the current situation of available water resources and the water demand in Saudi Arabia. The machine intelligence and big data analytic approach improve the proposed water resource management scheme. The simulation analysis identifies the highest performance in demand prediction accuracy of 98.96% and optimization ratio of 97.87% compared to the existing models. Over time, a mathematical model is used to conduct simulation experiments. Studying the problem, creating a model and collecting data are just some of the steps involved in simulation research. Response analysis and a simulation report are also part of this process. The case study analysis results in a significant satisfactory level of 99.23%.

MXenes based nano-heterojunctions and composites for advanced photocatalytic environmental detoxification and energy conversion: A review.

Extensive research is being done to develop multifunctional advanced new materials for high performance photocatalytic applications in the field of energy production and environmental detoxification, MXenes have emerged as promising materials for enhancing photocatalytic performance owing to their excellent mechanical properties, appropriate Fermi levels, and adjustability of chemical composition. Numerous experimental and theoretical research works implied that the dimensions of MXenes have a significant impact on their performance. For photocatalysis to thrive in the future, we must understand the current state of the art for MXene in different dimensions. Using MXene co-catalysts in widely used in photocatalytic applications such as CO2 reduction, hydrogen production and organic pollutant oxidation, this study focuses on the most recent developments in MXenes based materials, structural modifications, innovations in reaction and material engineering. It has been reported that using 5 mg of CdS-MoS2-MXene researchers were able to generate as high as 9679 µmol/g/h hydrogen under visible light. The MXenes based heterojunction photocatalyst Co3O4/MXene was utilized to degrade 95% bisphenol A micro-pollutant in just 7 min. Numerous novel materials, their preparations and performances have been discussed. Depending upon the nature of MXene-based materials, the synthesis techniques and photocatalytic mechanism of MXenes as co-catalyst are also summarized. Finally, some final thoughts and prospects for developing highly efficient MXene-based photocatalysts are provided which will indeed motivate researchers to design novel hybrid materials based on MXenes for sustainable solutions to energy and pollution issues.

Empirical research on sustainable developmental goals and priorities for water sustainability in Saudi Arabia.

Saudi Arabia is dry and devoid of permanent water sources. Saudi Arabia's desalination plants, which provide drinkable water, rely on its oil reserves. It is one of the world's driest countries, but its population uses fresh water at a third rate. Therefore, the most crucial step is to alter people's behavior by using water more efficiently and intelligently by adopting water conservation techniques. Through adopting a strategic water strategy, the Saudi government has simplified the regulatory system in the water sector. It examines the most effective ways to generate and control water through its water policy. This research aims to forecast the water consumption required for different sectors until 2030 in Saudi Arabia and designed an intelligent Water Resource Demand Forecasting (iWRDF) model. This model used the internet of things (IoT) and big data analytics (BDA) in demand forecasting. The experimental outcomes outperform the proposed model with an accuracy of 96.86% than the existing models. Furthermore, this model helps find sustainable developmental goals and priorities for water sustainability in Saudi Arabia.

Supervise the physicochemical quality of ground water using soft computing technique.

Water resources are essential for human beings and nowadays polluted water jeopardizes the human health. Toxic substances come from houses, industries and farm lands, dust mix with water causes water pollution. This pollution depreciates the quality of water and affects the human life. In this paper, our objective is to evaluate and supervise the physicochemical quality of the ground water, for the safety of human beings. The sample quality of 15 sites was used for measuring important parameters like pH, EC, Ca2+, Mg2+, Na+, K+, Cl-, SO42-, Also, NH4+ and NO3-, Fe2+ and HCO3-12 were considered for performance analysis. A soft computing component fuzzy logic system is used to design an intelligent system. The fuzzy logic system-based model measures groundwater quality status along with its sustainability. The results obtained from the model help the authorities, policy makers to plan proper policies for geochemical operation (water treatment process) and a foundation for observing the physicochemical quality of water in the area.

Machine learned hybrid Gaussian analysis of COVID-19 pandemic in India.

This article discusses short term forecasting of the Novel Corona Virus (COVID -19) data for infected, recovered and active cases using the Machine learned hybrid Gaussian and ARIMA method for the spread in India. The Covid-19 data is obtained from the World meter and MOH (Ministry of Health, India). The data is analyzed for the period from January 30, 2020 (the first case reported) to October 15, 2020. Using ARIMA (2, 1, 0), we obtain the short forecast up to October 31, 2020. The several statistics parameters have tested for the goodness of fit to evaluate the forecasting methods but the results show that ARIMA (2, 1, 0) gives better forecast for the data system. It is observed that COVID 19 data follows quadratic behavior and in long run it spreads with high peak roughly estimated in September 18, 2020. Also, using nonlinear regression it is observed that the trend in long run follows the Gaussian mixture model. It is concluded that COVID 19 will follow secondary shock wave in the month of November 2020. In India we are approaching towards herd immunity. Also, it is observed that the impact of pandemic will be about 441 to 465 days and the pandemic will end in between April-May 2021. It is concluded that primary peak observed in September 2020 and the secondary shock wave to be around November 2020 with sharp peak. Thus, it is concluded that the people should follow precautionary measures and it is better to maintain social distancing with all safety measures as the pandemic situation is not in control due to non-availability of medicines.

IoT-based analysis for controlling & spreading prediction of COVID-19 in Saudi Arabia.

Presently, novel coronavirus outbreak 2019 (COVID-19) is a major threat to public health. Mathematical epidemic models can be utilized to forecast the course of an epidemic and cultivate approaches for controlling it. This paper utilizes the real data of spreading COVID-19 in Saudi Arabia for mathematical modeling and complex analyses. This paper introduces the Susceptible, Exposed, Infectious, Recovered, Undetectable, and Deceased (SEIRUD) and Machine learning algorithm to predict and control COVID-19 in Saudi Arabia.This COVID-19 has initiated many methods, such as cloud computing, edge-computing, IoT, artificial intelligence. The use of sensor devices has increased enormously. Similarly, several developments in solving the COVID-19 crisis have been used by IoT applications. The new technology relies on IoT variables and the roles of symptoms using wearable sensors to forecast cases of COVID-19. The working model involves wearable devices, occupational therapy, condition control, testing of cases, suspicious and IoT elements. Mathematical modeling is useful for understanding the fundamental principle of the transmission of COVID-19 and providing guidance for possible predictions. The method suggested predicts whether COVID-19 would expand or die in the long term in the population. The mathematical study results and related simulation are described here as a way of forecasting the progress and the possible end of the epidemic with three forms of scenarios: 'No Action,' 'Lockdowns and New Medicine.' The lock case slows it down the peak by minimizing infection and impacts area equality of the infected deformation. This study familiarizes the ideal protocol, which can support the Saudi population to breakdown spreading COVID-19 in an accurate and timely way. The simulation findings have been executed, and the suggested model enhances the accuracy ratio of 89.3%, prediction ratio of 88.7%, the precision ratio of 87.7%, recall ratio of 86.4%, and F1 score of 90.9% compared to other existing methods.

Improved water resource management framework for water sustainability and security.

The water resource is an essential field of economic growth, social progress, and environmental integrity. A novel solution is offered to meet water needs, distribution, and IoT-based quality management requirements. With technological growth, this paper presents an IoT-enabled Water Resource Management and Distribution Monitoring System (IWRM-DMS) using sensors, gauge meters, flow meters, ultrasonic sensors, motors to implement in rural cities. Thus, research proposes that the IWRM-DMS establish the rural demand for water and the water supply system to minimize water demand. The system proposed includes different sensors, such as the water flow sensor, the pH sensor, the water pressure valve, the flow meters, and ultrasound sensors. This water system has been developed, which addresses the demand for domestic water in the village. Machine Intelligence has been designed for demand prediction in the decision support system. The simulation results confirm the applicability of the proposed framework in real-time environments. The proposed IWRM-DMS has been proposed to analyse the water quality to ensure water distribution in a rural area to achieve less MAPE (21.41%) and RMSE(15.12%), improve efficiency (96.93%), Reliability (98.24%), enhance prediction (95.29%)), the overall performance (97.34%), moisture content ratio (7.4%), cost-effectiveness ratio (95.7%) when compared to other popular methods.

Nonlinear dynamics for the spread of pathogenesis of COVID-19 pandemic.

Coronaviruses did not invite attention at a global level and responsiveness until the series of 2003-SARS contagion followed by year-2012 MERS plus, most recently, 2019-nCoV eruptions. SARS-CoV &MERS-CoV are painstaking, extremely pathogenic. Also, very evidently, both have been communicated from bats to palm-civets & dromedary camels and further transferred ultimately to humans. No country has been deprived of this viral genomic contamination wherever populaces reside and are interconnected. This study aimed to develop a mathematical model for calculating the transmissibility of this viral genome. The analysis aids the study of the outbreak of this Virus towards the other parts of the continent and the world. The parameters such as population mobility, natural history, epidemiological characteristics, and the transmission mechanism towards viral spread when considered into crowd dynamism result in improved estimation. This article studies the impact of time on the amount of susceptible, exposed, the infected person taking into account asymptomatic and symptomatic ones; recovered i.e., removed from this model and the virus particles existing in the open surfaces. The transition from stable phase to attractor phase happens after 13 days i.e.; it takes nearly a fortnight for the spread to randomize among people. Further, the pandemic transmission remains in the attractor phase for a very long time if no control measures are taken up. The attractor-source phase continues up to 385 days i.e., more than a year, and perhaps stabilizes on 386th day as per the Lyapunov exponent's analysis. The time series helps to know the period of the Virus's survival in the open sources i.e. markets, open spaces and various other carriers of the Virus if not quarantined or sanitized. The Virus cease to exist in around 60 days if it does not find any carrier or infect more places, people etc. The changes in LCEs of all variables as time progresses for around 400 days have been forecasted. It can be observed that phase trajectories indicate how the two variables interact with each other and affect the overall system's dynamics. It has been observed that for exposed and asymptomatically infected (y-z), as exposed ones (y) change from 0 to 100 the value of asymptomatically infected (z) increased upto around 58, at exposed ones (y)=100, asymptomatically infected (z) has two values as 58 and 10 i.e. follows bifurcation and as exposed ones (y) changes values upto 180, the value of asymptomatically infected (z) decreases to 25 so for exposed ones (y) from 100 to 180, asymptomatically infected (z) varies from 58 to 25 to 10 follows bifurcation. Also, phase structures of exposed-symptomatically infected (y-u), exposed-removed (y-v), exposed-virus in the reservoir (y-w), asymptomatically infected-removed (z-v), symptomatically infected-removed (u-v) specifically depict bifurcations in various forms at different points. In case of asymptomatically infected-virus in the reservoir (z-w), at asymptomatically infected (z)=10, the value of viruses in the reservoir (w)=50, then as asymptomatically infected (z) increases to upto around 60. At this point, removed ones (v) increase from 50 to 70 and asymptomatically infected (z) decrease to 20 i.e., crosses the same value twice, which shows its limiting is known as limit cycle behavior and both the values tend to decrease towards zero. It shows a closed-loop limit cycle. Today, there has been no scientific revolution in the development of vaccination, nor has any antiviral treatment been successful, resulting in lack of its medication. Based on the phases, time series, and complexity analysis of the model's various parameters, it is studied to understand the variation in this pandemic's scenario.

Silicate glass matrix@Cu2O/Cu2V2O7 p-n heterojunction for enhanced visible light photo-degradation of sulfamethoxazole: High charge separation and interfacial transfer.

Focusing on the treatment of pharmaceuticals contaminated water by advanced oxidation processes, a novel three dimensional silicate glass matrix (3-DG) coupled Cu2O/Cu2V2O7 p-n heterojunction was constructed by in-situ hydrothermal technique. The optimal Cu2O/Cu2V2O7 with 30 wt % Cu2V2O7 (CV-30) degrades 90.1 % sulfamethoxazole (SMX) in 60 min and nearly 100 % removal in 45 min via coupling with 3-DG. Under natural sunlight ∼ 80 % SMX removal was observed. The internal electric field of the p-n junction facilitates the electron flow via the interface. 3-D silicate glass increases the visible light absorption dramatically via internal reflection which facilitates higher exposure for the junction and shortens the diffusion length of charge carriers. The effect of reaction parameters suggests that HCO3- and CO32- ions substantially escalate the SMX removal rate. Scavenging experiments and ESR probe suggest O2- as the main active species followed by OH radicals. The degradation products were detected by LC-MS analysis and a degradation mechanism was also predicted. The photocatalytic mechanism was explained in terms of the electron transfer facilitated by conventional transfer and Z-scheme. This strategy to construct such highly visible and solar active p-n heterojunctions will pave way for future opportunities for the degradation of recalcitrant pharmaceutical pollutants.

A Study on Non-Linear DPL Model for Describing Heat Transfer in Skin Tissue during Hyperthermia Treatment.

The article studies the simulation-based mathematical modeling of bioheat transfer under the Dirichlet boundary condition. We used complex non-linear dual-phase-lag bioheat transfer (DPLBHT) for analyzing the temperature distribution in skin tissues during hyperthermia treatment of infected cells. The perfusion term, metabolic heat source, and external heat source were the three parts of the volumetric heat source that were used in the model. The non-linear DPLBHT model predicted a more accurate temperature within skin tissues. The finite element Runge-Kutta (4,5) (FERK (4,5)) method, which was based on two techniques, finite difference and Runge-Kutta (4,5), was applied for calculating the result in the case of our typical non-linear problem. The paper studies and presents the non-dimensional unit. Thermal damage of normal tissue was observed near zero during hyperthermia treatment. The effects of the non-dimensional time, non-dimensional space coordinate, location parameter, regional parameter, relaxation and thermalization time, metabolic heat source, associated metabolic heat source parameter, perfusion rate, associated perfusion heat source parameter, and external heat source coefficient on the dimensionless temperature profile were studied in detail during the hyperthermia treatment process.

Transport properties of RuV-based half-Heusler semiconductors for thermoelectric applications: a computational study.

A systematic study of electronic structure, mechanical and transport properties of RuV-based half-Heusler alloys (RuVZ, Z = As, P, Sb) have been presented using ab initio density functional and Boltzmann transport theory. The electronic structures are obtained using generalized gradient approximation with Perdew-Burke-Ernzerhof functional. All the compounds are crystallized in face centered cubic phase with space group #216. Our preliminary electronic structure simulations reveal that all the alloys are non-magnetic semiconductors. Additionally, the phonon dispersion and elastic constants (along with the related elastic moduli) also verify mechanical stability of the alloys. Due to strong dependence on the electronic bandgap in thermoelectric materials, we have estimated bandgap using more accurate hybrid functional i.e. Heyd-Scuseria-Ernzerhof. The transport coefficients (e.g. Seebeck, electrical conductivity, thermal conductivity due to electrons) are calculated by solving the Boltzmann transport equation for charge carriers as implemented in BoltzTraP software under constant relaxation time approximation. The lattice thermal conductivity due to phonons is calculated using more reliable shengBTE code based upon the Boltzmann transport equation for phonons. We have calculated the more reliable value of the thermoelectric figure of merit, ZT (related to the conversion efficiency) for all the compounds. The obtained ZT for RuVAs, RuVP and RuVSb is 0.41(0.32), 0.21(0.16) and 0.70(0.61) for p(n)-type behavior at 900 K. The corresponding carrier concentrations are also predicted. High value of ZT is obtained for RuVSb alloy due to low lattice thermal conductivity. Among these compounds, RuVSb emerged out as a most suitable candidate for thermoelectric power generation device. Minimum lattice thermal conductivity in theoretical limit along with the corresponding maximum value of ZT is also predicted in these alloys.

Delayed Propriospinal Myoclonus Following Dorsal Spinal Cord Surgery.

Cerebral disorders are known to be associated with myoclonus, but spinal pathologies have received little attention as a causative factor in movement disorders. Propriospinal myoclonus (PSM) is a rare hyperkinetic movement disorder caused by activity of a spinal pattern generator localized in a few segments of the spinal cord, spreading to other intraspinal segments via propriospinal pathways. Majority of cases of PSM are reported as functional movement disorders. Structural lesions were found in only a small number of reported cases. We present this rare case report of a patient who developed PSM 2 years following spinal surgery, done 5 years ago for D6-D7 vertebral body collapse. To the best of our knowledge, only few cases of PSM have been reported after spinal surgery and none from India.

Altered dietary selenium influences brain iron content and behavioural outcomes.

Selenium (Se) is an essential micronutrient that provides antioxidant defence through selenoproteins, but at high concentrations, deleterious effects have been reported. The present study examines the antioxidant response in brain regions and behavioural functions in mice under various dietary Se paradigms; Se-deficient, Se-adequate and Se-excess. Se levels were found to be reduced in the cortex and hippocampus of Se-deficient animals, whereas no change was observed in animals on Se-excess diet. In the hippocampus, iron (Fe) levels increased in animals on Se-deficient and Se-excess diets. Moreover, in Se-deficient animals, Fe levels increased in cortex also. Interestingly, Se content in the hair positively correlated with the dietary Se intake. Total and Se-dependent glutathione peroxidase activity decreased in the cortex, hippocampus and cerebellum of animals on Se-deficient diet. On the other hand, the activity of these enzymes decreased in the cortex of animals on Se-excess diet. Further, lipid peroxidation increased in the cortex of animals on Se-deficient diet and in the hippocampus of animals on Se-excess diet. Cognitive functions assessed by Morris water maze and Y-maze tests revealed deficits in Se-deficient state. However, in Se-excess state cognitive deficits were observed only in Y-maze test. These findings suggest that long-term dietary variation in Se influences oxidative stress that impacts cognitive functions. Therefore, it is suggested that maintenance of Se status during postnatal development may be crucial for mental health.

Wide spectral degradation of Norfloxacin by Ag@BiPO4/BiOBr/BiFeO3 nano-assembly: Elucidating the photocatalytic mechanism under different light sources.

Metallic Ag deposited BiPO4/BiOBr/BiFeO3 ternary nano-hetero-structures were rationally designed and synthesized by a simple precipitation-wet impregnation-photo deposition method. The plasmonic junction possesses an excellent wide spectrum photo-response and makes best use of BiPO4 which is otherwise a poor photocatalyst. Ag@BiPO4/BiOBr/BiFeO3 showed superior photocatalytic activity for degradation of norfloxacin (NFN) under visible, ultra-violet, near-infra-red and natural solar light. Especially catalyst APBF-3 (0.3 wt% Ag@BiPO4/BiOBr/BiFeO3) shows 98.1% degradation of NFN (20 mg/L) in 90 min under visible light and 99.1% in less than 45 min under UV exposure. Free radical scavenging experiments and electron spin resonance (ESR) results has been used for explanation of charge transfer, photocatalytic mechanism and role of radicals for binary, ternary and Ag deposited ternary junctions for UV and visible exposure. Metallic Ag in addition to its surface plasmon resonance helps in protection of high conduction band and valence band in the three semiconductors. A dual Z-scheme mechanism has been predicted by comparing with possibilities of double charge and vectorial charge transfer.