Green route to fabrication of Semal-ZnO nanoparticles for efficient solar-driven catalysis of noxious dyes in diverse aquatic environments.

This work successfully demonstrates a sustainable and environmentally friendly approach for synthesizing Semal-ZnO nanoparticles (NPs) using the aqueous leaf extract of Bombax ceiba L. These NPs exhibit an absorption peak at approximately 390 nm in the UV-visible spectrum and an energy gap (Eg) of 3.11 eV. Detailed analyses of the morphology and particle size using various spectroscopic and microscopic techniques, XRD, FE-SEM with EDS, and HR-TEM reveal crystallographic peaks attributable to the hexagonal phase, with an average crystal size of 17 nm. The Semal-ZnO NPs also exhibit a notable photocatalytic efficiency for degrading methylene blue (MB) and methyl orange (MO) under sunlight in different water samples collected from diverse natural sources, indicating that they are promising photocatalysts for environmental remediation. The photocatalytic efficiency of the biofabricated Semal-ZnO NPs is impressive, exhibiting a photodegradation rate of up to 99% for MB and 79% for MO in different water samples under exposure to sunlight. The novel phytofabricated Semal-ZnO NPs are thus a beacon of hope for the environment, with their desirable photocatalytic efficiency, pseudo-first-order kinetics, and ability to break down noxious dye pollutants in various aquatic environments.

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization.

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the "HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques.

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

Fluorescence Study of Pr3+ Doped CdS Nanoparticles and its Applications in Sensors and Detectors.

Fluorescence spectra of Pr3+ doped CdS nanoparticles, synthesized by chemical precipitation method, have been recorded at room temperature. The synthesized particles are nearly spherical shaped and the grain size is decreased with the increase in Pr3+ concentration. The chemical identity of the nanoparticles was confirmed by EDAX spectrum, the absorption peaks was confirmed by FTIR spectrum and then the recorded values were compared with the CIE diagram. The oscillator strengths of the 4f ↔ 4I transitions are parameterized in terms of three phenomenological Judd-Ofelt intensity parameters Ωλ (λ = 2, 4 and 6). Using the fluorescence data and these Ωλ parameters, theoretical and experimental study of various radiative properties viz., spontaneous emission probability (A), radiative life time , fluorescence branching ratio and stimulated emission cross-section were evaluated. The values of these parameters indicate that 3P0→ 3H4 transition can be considered to be good laser transition in the visible colour region. Also, excitation with 493 nm, leads to similar blue regions. The synthesized Pr3+ doped CdS nanomaterials could be useful for sensing and detecting devices, particularly for temperature sensing measurement and bio-sensing detection.

Fluoride release by restorative materials after applying surface coating agents.

The prompt use of an enamel surface covering reagent is advised to safeguard the dental restorative substance from mishaps. Therefore, it is of interest to assess the fluoride emitting capabilities of standard GIC, and Zirconomer cement together with surface coverings and without surface coverings. The conventional GIC cement was part of experimental category A while Zirconomer cement was part of category B. For every experimental categories, a set of sixty brass mould prototypes in the form of disc with dimensions: diameter (6±0.1mm) and thickness (2±0.1 mm) were created and subsequently covered with Teflon strip in accordance with the package recommendations. Also, for both experimental categories, such pellets were randomly allocated to three sub-categories of 20 each. For one category petroleum jelly was administered with a cotton bud and then delicately dried under airflow (A3 subcategory and B3 subcategory); for another sub-category G-Coat was laced through a micro-tip dispenser and light treated for twenty seconds (A2 subcategory and B2 subcategory); the rest 20 specimens were left without any coating (A1 subcategory and B1 subcategory). It was observed that in subcategory A1 and A3 there was continuous decline in emission of fluoride ion as the days progressed. However there was an increase in emission of fluoride in A2 subcategory on moving to day 5 from day 1. However, from day 5 onwards decline in fluoride emission was observed in A2 subcategory. It was concluded that both materials studied (GIC and Zirconomer) exhibited fluoride emission whether or not they were surface-coated for protection.

Developing an SDN security model (EnsureS) based on lightweight service path validation with batch hashing and tag verification.

Software-defined networking (SDN) has significantly transformed the field of network management through the consolidation of control and provision of enhanced adaptability. However, this paradigm shift has concurrently presented novel security concerns. The preservation of service path integrity holds significant importance within SDN environments due to the potential for malevolent entities to exploit network flows, resulting in a range of security breaches. This research paper introduces a model called "EnsureS", which aims to enhance the security of SDN by proposing an efficient and secure service path validation approach. The proposed approach utilizes a Lightweight Service Path Validation using Batch Hashing and Tag Verification, focusing on improving service path validation's efficiency and security in SDN environments. The proposed EnsureS system utilizes two primary techniques in order to validate service pathways efficiently. Firstly, the method utilizes batch hashing in order to minimize computational overhead. The proposed EnsureS algorithm enhances performance by aggregating packets through batches rather than independently; the hashing process takes place on each one in the service pathway. Additionally, the implementation of tag verification enables network devices to efficiently verify the authenticity of packets by leveraging pre-established trust relationships. EnsureS provides a streamlined and effective approach for validating service paths in SDN environments by integrating these methodologies. In order to assess the efficacy of the Proposed EnsureS, a comprehensive series of investigations were conducted within a simulated SDN circumstance. The efficacy of Proposed EnsureS was then compared to that of established methods. The findings of our study indicate that the proposed EnsureS solution effectively minimizes computational overhead without compromising on the established security standards. The implementation successfully reduces the impact of different types of attacks, such as route alteration and packet spoofing, increasing SDN networks' general integrity.

Hyper-parameter tuned deep learning approach for effective human monkeypox disease detection.

Human monkeypox is a very unusual virus that can devastate society. Early identification and diagnosis are essential to treat and manage an illness effectively. Human monkeypox disease detection using deep learning models has attracted increasing attention recently. The virus that causes monkeypox may be passed to people, making it a zoonotic illness. The latest monkeypox epidemic has hit more than 40 nations. Computer-assisted approaches using Deep Learning techniques for automatically identifying skin lesions have shown to be a viable alternative in light of the fast proliferation and ever-growing problems of supplying PCR (Polymerase Chain Reaction) Testing in places with limited availability. In this research, we introduce a deep learning model for detecting human monkeypoxes that is accurate and resilient by tuning its hyper-parameters. We employed a mixture of convolutional neural networks and transfer learning strategies to extract characteristics from medical photos and properly identify them. We also used hyperparameter optimization strategies to fine-tune the Model and get the best possible results. This paper proposes a Yolov5 model-based method for differentiating between chickenpox and Monkeypox lesions on skin pictures. The Roboflow skin lesion picture dataset was subjected to three different hyperparameter tuning strategies: the SDG optimizer, the Bayesian optimizer, and Learning without Forgetting. The proposed Model had the highest classification accuracy (98.18%) when applied to photos of monkeypox skin lesions. Our findings show that the suggested Model surpasses the current best-in-class models and may be used in clinical settings for actual Human Monkeypox disease detection and diagnosis.

Fluoride risk assessment from agricultural soils in India: a study based on vertical, spatial and geochemical distribution.

Fluoride (F) in agricultural soil is increasing continuously due to injudicious application of F-laden fertilizers, causing global concern about fluorosis disease. The objective of the study was to assess F risk in humans due to soil ingestion, dermal contact, and particulate inhalation during various agricultural activities. The study also emphasized chemical fractionation, distribution, and geochemical understanding of high F incidence. Agricultural surface soil was sampled randomly from 5 km × 5 km square grids besides soil profile samples for studying the vertical distribution of F. Various F fractions in soil (1:1 soil:water ratio, calcium chloride extractable F, hot water soluble F, exchangeable F, Fe-Mn oxide bound F, organic matter bound F, residual F, and total F) were estimated using the sequential fractionation method. Multivariate geochemical analysis and soil F risk were also assessed in humans. The water soluble F (F1:1) and CaCl2 extractable F (FCa) varied between 0.11 to 6.73 mg kg-1 and 1.02 to 6.94 mg F kg-1 soil, respectively. Total fluoride (TF) however, ranged between 115 to 456 mg F kg-1. A higher average of FCa/TF moving down the soil profile indicated a propensity for F endemicity. Weathering, ion-exchange, alkalinity, and clay were found to control the soil geochemistry of the area. The F contamination index explained > 82% variance of F contamination, but the hazard quotient of F for an adult was found < 1, indicating no potential fluorosis risk in the area. This study is the first of its kind in India, where ecological risk due to F from agricultural soil was assessed in humans and will be a benchmark for future researchers.

CVD grown carbon nanofibers: an efficient DSPE sorbent for cleanup of multi-class pesticide residue in high fat and low water commodities by QuEChERS using GC-ECD.

An inexpensive, effective, and efficient dispersive solid-phase extraction (DSPE) sorbent was developed as an alternative to traditionally used sorbents (primary secondary amine and C18) for fatty matrices using the QuEChERS method. Catalytic chemical vapor deposition grown carbon nanofibers dispersed on activated carbon fibers (Ni-ACF/CNF) having a BET specific surface area of 738 (m2/g) were for the first time evaluated as a DSPE material for sample cleanup before gas chromatographic analysis. Based on cleanup performance and recoveries, 10 mg of Ni-ACF/CNF was found optimal for the determination of twenty-seven multi-class pesticides in high fat and low water commodities/matrices (peanut, soybean, sesame, and flaxseed). The recoveries obtained for all analytes were in the range ~ 72 to ~ 117%, with relative standard deviation values ≤ 15%. The limits of detection and quantification values were 0.7-4.2 ng/g and 2.3-13.9 ng/g, respectively. The matrix match calibration curve was linear in the range 20-500 ng/g with a correlation coefficient of ≥ 0.993. The results reveal that the Ni-ACF/CNF is a competent DSPE sorbent, similar to primary secondary amines and C18 sorbent materials, for pesticide determination by QuEChERS methods in high fat and low water commodities. Graphical abstract.

Tendon Transfer for Persistent Radial Nerve Palsy Using Single-Split FCU Technique and Re-Routing of Extensor Pollicis Longus: A Prospective Study of 25 Cases.

PURPOSE: Optimal treatment of persistent radial nerve palsy is controversial. However, most authors agree that tendon transfers lead to satisfactory result in majority of patients. Triple tendon transfer using flexor carpi radialis, pronator teres, and palmaris longus is the most preferred tendon transfer. The aim of this study was to evaluate the results of a split flexor carpi ulnaris (FCU) as a single transfer in such patients and compare our results with other transfers from the available literature. MATERIALS AND METHODS: A total of 25 patients (20 males and 5 females: mean age: 30.9 years) were selected for FCU split transfer in persistent radial nerve palsy injury from April 2014 to May 2015. All patients were followed up with a mean followup of 1 year. Clinical outcomes were assessed using Bincaz score. RESULTS: Using Bincaz score, 68% of our patients had good-to-excellent result, while 32% patients had fair and none had bad results. Wrist extension was comparatively less in high radial nerve palsy as compared to low radial nerve palsy patients, but all patients were functionally independent. Power grip was assessed using a jammer dynamometer. All patients had adequate grip strength and good active extension of the wrist and fingers. CONCLUSION: Single transfer using split FCU can be a preferred alternative in patients of low radial nerve palsy, it can be considered for high radial nerve patients in whom multiple donors are absent due to the nature of injury, for example, brachial plexus injury.

Molecular analysis of genetic diversity and population genetic structure in Ephedra foliata: an endemic and threatened plant species of arid and semi-arid regions of India.

Studies on the genetic diversity and structure in endangered and threatened species are of utmost importance to design and promote effective conservation and management programs. Ephedra foliata, an endemic and threatened species growing naturally in arid and semi-arid regions of north western India, was investigated to estimate genetic variability and population structure using inter-simple sequence repeats (ISSR) and directed amplification of mini-satellite DNA (DAMD) markers. Twenty-five (ISSR 15; DAMD 10) markers produced 449 fragments, of which 382 were polymorphic in nature, revealing 84.59% polymorphism. ISSR markers revealed higher levels of polymorphism, polymorphic information content, marker index, diversity index and effective multiplex ratio than DAMD markers. Higher values of polymorphism, genetic diversity and Shannon information index at species level than at population level revealed that E. foliata possess high genetic diversity. AMOVA revealed much higher variance within populations than among the populations. The three clustering approaches viz., UPGMA, PCoA, and STRUCTURE, grouped the eleven investigated populations into two clusters revealing two genetic populations and the patterns of clustering of populations was in accordance with their geographic distribution, suggesting that these populations have evolved in response to their local environments. The high level of genetic differentiation (G ST = 0.31) and moderate gene flow (N m = 1.11) among populations could be due to geographic isolation, regional climatic conditions, over-exploitation and improper seed setting. To the best of our knowledge, this study is the first endeavour to estimate genetic diversity and population structure of E. foliata using molecular markers.

Screening of Indian Lingzhi or Reishi Medicinal Mushroom, Ganoderma lucidum (Agaricomycetes): A UPC2-SQD-MS Approach.

Oriental medicinal mushroom Ganoderma lucidum has been widely used for the promotion of health and longevity owing to its various bioactive constituents. Therefore, comprehending metabolomics of different G. lucidum parts could be of paramount importance for investigating their pharmacological properties. Ultra-performance convergence chromatography (UPC2) along with mass spectrometry (MS) is an emerging technique that has not yet been applied for metabolite profiling of G. lucidum. This study has been undertaken to establish metabolomics of the aqueous extracts of mycelium (GLM), fruiting body (GLF), and their mixture (GLMF) using ultra-performance convergence chromatography single quadrupole mass spectrometry (UPC2-SQD-MS). Aqueous extracts of G. lucidum prepared using an accelerated solvent extraction technique have been characterized for their mycochemical activities in terms of total flavonoid content, 1,1-diphenyl-2-picryl-hydrazyl scavenging activity, and ferric ion reducing antioxidant power. The UPC2-SQD-MS technique has been used for the first time for metabolite profiling of G. lucidum on a Princeton Diol column (4.6 × 250 mm; 5 µm) using supercritical CO2 (solvent) and 20 mM ammonium acetate in methanol (co-solvent). In the present study, UPC2-SQD-MS was found to be a rapid, efficient, and high-throughput analytical technique, whose coupling to principal component analysis (PCA) and phytochemical evaluation could be used as a powerful tool for elucidating metabolite diversity between mycelium and fruiting body of G. lucidum. PCA showed a clear distinction in the metabolite compositions of the samples. Mycochemical studies revealed that overall GLF possessed better antioxidant properties among the aqueous extracts of G. lucidum.

Arsenite stress variably stimulates pro-oxidant enzymes, anatomical deformities, photosynthetic pigment reduction, and antioxidants in arsenic-tolerant and sensitive rice seedlings.

Contamination of arsenic (As) in rice (Oryza sativa L.) paddies and subsequent uptake by rice plants is a serious concern, because rice is a staple crop for millions of people. Identification of As toxicity and detoxification mechanisms in paddy rice cultivars would help to reduce As-associated risk. Arsenic tolerance and susceptibility mechanisms were investigated in 2 differential As-accumulating rice genotypes, Triguna and IET-4786, selected from initial screening of 52 rice cultivars as an As-tolerant and an As-sensitive cultivar, respectively, on the basis of root and shoot length during various arsenite (AsIII) exposures (0-50 µM). Indicators of oxidative stress, such as pro-oxidant enzymes (reduced nicotinamide adenine dinucleotide phosphate [NADPH] oxidase and ascorbate oxidase) and nitric oxide, were more numerous in the sensitive cultivar than in the tolerant cultivar. Arsenic-induced anatomical deformities were frequent in the sensitive cultivar, showing more distorted and flaccid root cells than the tolerant cultivar. Chlorophyll and carotenoid synthesis were inhibited in both cultivars, although the decline was more prominent in the sensitive cultivar at higher doses of As. Furthermore, the tolerant cultivar tolerated As stress by producing more antioxidants, such as proline, sustaining the ratio of ascorbate, dehydroascorbate, and glutathione peroxidase (GPX) activity as well as As detoxifying enzymes arsenate reductase, whereas these respective metabolic activities declined in sensitive cultivar, resulting in greater susceptibility to As toxicity.

Global nucleosome positioning regulates salicylic acid mediated transcription in Arabidopsis thaliana.

BACKGROUND: The nucleosome positioning regulates the gene expression and many other DNA-related processes in eukaryotes. Genome-wide mapping of nucleosome positions and correlation of genome-wide nucleosomal remodeling with the changes in the gene expression can help us understanding gene regulation on genome level. RESULTS: In the present study, we correlate the gene expression and the genomic nucleosomal remodeling in response to salicylic acid (SA) treatment in A. thaliana. We have mapped genome-wide nucleosomes by performing tiling microarray using 146 bp mononucleosomal template DNA. The average nucleosomal coverage is approximately 346 bp per nucleosome both under the control and the SA-treated conditions. The nucleosomal coverage is more in the coding region than in the 5' regulatory regions. We observe approximately 50% nucleosomal remodeling on SA treatment where significant nucleosomal depletion and nucleosomal enrichment around the transcription start site (TSS) occur in SA induced genes and SA repressed genes respectively in response to SA treatment. Especially in the case of the SA-induced group, the nucleosomal remodeling over the minimal promoter in response to SA is especially significant in the Non-expresser of PR1 (NPR1)-dependent genes. A detailed investigation of npr1-1 mutant confirms a distinct role of NPR1 in the nucleosome remodeling over the core promoter. We have also identified several motifs for various hormonal responses; including ABRE elements in the remodeled nucleosomal regions around the promoter region in the SA regulated genes. We have further identified that the W-box and TGACG/C motif, reported to play an important role in SA-mediated induction, are enriched in nucleosome free regions (NFRs) of the promoter region of the SA induced genes. CONCLUSIONS: This is the first study reporting genome-wide effects of SA treatment on the chromatin architecture of A. thaliana. It also reports significant role of NPR1 in genome-wide nucleosomal remodeling in response to SA.

Feasibility Study of Phragmites karka and Christella dentata Grown in West Bengal as Arsenic Accumulator.

A survey was undertaken, in arsenic (As) contaminated area of the Nadia district, West Bengal, India, to find native As accumulator plants. As was determined both in soil and plant parts. The results showed that the mean translocation factor of Pteris vittata L, Phragmites karka (Cav.) Trin. Ex. Steud and Christella dentata Forssk were higher than 1. It thus appeared that these plants can be efficient accumulators of As. Phytoremediation ability of C. dentata and P. karka was evaluated and compared with known As-hyperaccumulators -P. vittata and Adiantum capillus veneris L. Plants were grown in the As spiked soil (25, 50, 75 and 100 mg kg(-1)). As accumulation was found to be highest in P. vittata, 117.18 mg kg(-1) in leaf at 100 mg kg(-1) As treatment, followed by A. capillus veneris, P. karka and C. dentata being 74, 83.87 and 40.36 mg kg(-1), respectively. Lipid peroxidation increased after As exposure in all plants. However, the antioxidant enzyme activity and molecules concentration also increased which helped the plants to overcome As-induced oxidative stress. The study indicates that P. karka and C. dentata could be considered as As-accumulators and find application for As-phytoextraction in field conditions.

Irrational fixed dose combinations & need for intervention: understanding of dental clinicians and residents.

CONTEXT: There is a growing concern about ever-burgeoning list of irrational fixed dose combinations (FDCs) which have flooded pharmaceutical market recently in India. Till date no structured study has evaluated the level of understanding among the dental clinicians and residents about these concepts. The present study is designed to fulfil that lacuna. OBJECTIVE: To evaluate the knowledge, attitude and practice, regarding the use of FDCs by the dental residents and dental clinicians in a tertiary care teaching dental hospital. MATERIALS AND METHODS: The present study was carried out among postgraduate students and dental clinicians working at Rajasthan Dental College, a tertiary care teaching dental hospital, in Jaipur, India. Sixty residents and 77 dental clinicians from the departments of Orthodontics, Prosthodontics, Oral Medicine, Periodontology, Conservative Dentistry & Endodontics, Oral & Maxillofacial Surgery, Pedodontics who gave their informed consent were enrolled. A prevalidated questionnaire regarding knowledge, attitude and prescribing practice of fixed dose combinations was filled up. Data was analysed with suitable statistical tests. RESULTS: Out of the 60 residents and 77 dental clinicians recruited for the study, none of them were aware about all of the advantages and disadvantages of FDCs. On an average, only 47% of residents and 61% dental clinicians were aware of FDC included in WHO Essential Medicines List (EML). Only 47% residents and 58% dental clinicians could recall a single banned FDC in India. Common sources of information about FDCs were Monthly Index of Medical Specialities (MIMS), medical representatives and internet. The most commonly prescribed irrational FDC was diclofenac + paracetamol combination, 42% residents and 41% dental clinicians and residents believed that regular Continuous Medical Education (CMEs) stressing upon rational use of medicine could reduce the magnitude of this problem. CONCLUSION: It is the need of the hour to reduce the magnitude of this problem by sensitizing dental residents and prescribers regarding the efficacy, safety, suitability, rationality and cost benefit of FDCs available for patient use.

Impact of arsenic toxicity on black gram and its amelioration using phosphate.

The toxicity of arsenic in soil and ground water is one of the most important environmental problems particularly in South-East Asia. Arsenic-polluted irrigation water creates hazard in soil environment and also in crop quality. In the present study, response of black gram (Vigna mungo L.) to arsenic with or without phosphate application was investigated. Arsenic-treated plants showed reduction in their growth and pigment content. Arsenic significantly enhanced lipid peroxidation, electrolyte leakage, and level of proline showing oxidative stress. Arsenic toxicity was associated with an increase in the activities of antioxidative enzymes like superoxide dismutase, peroxidase, and ascorbate peroxidase whereas catalase activity decreased at higher arsenic dose. Joint application of phosphate with arsenic resulted in significant alterations in most of the parameters tested under the purview of arsenic treatment alone which lead to better growth in black gram.