A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental dispatch in power systems.

This work implements the recently developed nth state Markovian jumping particle swarm optimisation (PSO) algorithm with local search (NS-MJPSOloc) awareness method to address the economic/environmental dispatch (EED) problem. The proposed approach, known as the Non-dominated Sorting Multi-objective PSO with Local Best (NS-MJPSOloc), aims to enhance the performance of the PSO algorithm in multi-objective optimisation problems. This is achieved by redefining the concept of best local candidates within the search space of multi-objective optimisation. The NS-MJPSOloc algorithm uses an evolutionary factor-based mechanism to identify the optimum compromise solution, a Markov chain state jumping technique to control the Pareto-optimal set size, and a neighbourhood's topology (such as a ring or a star) to determine its size. Economic dispatch refers to the systematic allocation of available power resources in order to fulfill all relevant limitations and effectively meet the demand for electricity at the lowest possible operating cost. As a result of heightened public consciousness regarding environmental pollution and the implementation of clean air amendments, nations worldwide have compelled utilities to adapt their operational practises in order to comply with environmental regulations. The (NS-MJPSOloc) approach has been utilised for resolving the EED problem, including cost and emission objectives that are not commensurable. The findings illustrate the efficacy of the suggested (NS-MJPSOloc) approach in producing a collection of Pareto-optimal solutions that are evenly dispersed within a single iteration. The comparison of several approaches reveals the higher performance of the suggested (NS-MJPSOloc) in terms of the diversity of the Pareto-optimal solutions achieved. In addition, a measure of solution quality based on Pareto optimality has been incorporated. The findings validate the effectiveness of the proposed (NS-MJPSOloc) approach in addressing the multi-objective EED issue and generating a trade-off solution that is both optimal and of high quality. We observed that our approach can reduce ∼ 6.4% of fuel costs and ∼ 9.1% of computational time in comparison to the classical PSO technique. Furthermore, our method can reduce ∼ 9.4% of the emissions measured in tons per hour as compared to the PSO approach.

Are the variations in ECG morphology associated to different blood glucose levels? implications for non-invasive glucose monitoring for T1D paediatric patients.

AIMS: Recent clinical trials and real-world studies highlighted those variations in ECG waveforms and HRV recurrently occurred during hypoglycemic and hyperglycemic events in patients with diabetes. However, while several studies have been carried out for adult age, there is lack of evidence for paediatric patients. The main aim of the study is to identify the correlations of variations in ECG Morphology waveforms with blood glucose levels in a paediatric population. METHODS: T1D paediatric patients who use CGM were enrolled. They wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glucose metrics, ECG parameters and HRV features were collected, and Wilcoxon rank-sum test and Spearman's correlation analysis were used to explore if different levels of blood glucose were associated to ECG morphological changes. RESULTS: Results showed that hypoglycaemic events in paediatric patients with T1D are strongly associated with variations in ECG morphology and HRV. CONCLUSIONS: Results showed the opportunity of using the ECG as a non-invasive adding instrument to monitor the hypoglycaemic events through the integration of the ECG continuous information with CGM data. This innovative approach represents a promising step forward in diabetes management, offering a more comprehensive and effective means of detecting and responding to critical changes in glucose levels.

Evaluation of the photocatalytic degradation mechanism of methylene blue using nascent and Ag+ ions-modified TiO2.

In photocatalytic removal of organic pollutants, adsorption and degradation are two important processes that take place. Various instrumental techniques and trapping experiments have been used to identify the reactive species and the mechanism of photodegradation. The present work focuses on investigating the mechanism of photo-induced degradation from the comparative characterization of fresh and used samples, isotherm models, competitive adsorption, and desorption studies of pure and Ag+-modified TiO2 NPs. The comparative characterizations of fresh and used NPs were carried out with FT-IR, EDX, and XRF analyses after methylene blue (MB) degradation. The Ag+ doped TiO2 used in this study was fabricated using simple impregnation technique. The prepared NPs were characterized using techniques including XPS, XRD, SEM/EDX, XRF, UV-DRS, and pH point-zero charge analyses (pHPZC). The Ag+-modified TiO2 NPs showed improved efficiency compared to pure TiO2 NPs using normal compact fluorescent light (CFL). The Langmuir and Freundlich isotherm models were applied to test the adsorption behavior on the surface photocatalysts. The investigational data finest fitted to the Langmuir isotherms model compared to Freundlich model, suggesting the homogeneous monolayer adsorption followed by degradations. The competitive removal of MB in the presence of a photo-generated electrons trapper (Cd2+) was enhanced almost 3-folds (115 mg/L) compared to the removal from a single MB solution (40 mg/L). The characterization of the used samples as well as adsorption in the dark and negligible desorption of used samples support the involvement of the proposed photo-induced degradation mechanism.

A Huffman code LSB based image steganography technique using multi-level encryption and achromatic component of an image.

In the recent couple of years, due to the accelerated popularity of the internet, various organizations such as government offices, military, private companies, etc. use different transferring methods for exchanging their information. The Internet has various benefits and some demerits, but the primary bad mark is security of information transmission over an unreliable network, and widely uses of images. So, Steganography is the state of the art of implanting a message in the cover objects, that nobody can suspect or identify it. Therefore, in the field of cover steganography, it is very critical to track down a mechanism for concealing data by utilizing different blends of compression strategies. Amplifying the payload limit, and robustness, and working on the visual quality are the vital factors of this research to make a reliable mechanism. Different cover steganography research strategies have been recommended, and each adores its benefits and impediments but there is a need to foster some better cover steganography implements to accomplish dependability between the essential model of cover steganography. To handle these issues, in this paper we proposed a method in view of Huffman code, Least Significant Bits (LSB) based cover steganography utilizing Multi-Level Encryption (MLE) and colorless part (HC-LSBIS-MLE-AC) of the picture. It also used different substitution and flicking concepts, MLE, Magic matrix, and achromatic concepts for proving the proficiency, and significance of the method. The algorithm was also statistically investigated based on some Statistical Assessment Metrics (SAM) such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR), Normalized Cross Correlation (NCC), Structural Similarity Index Metric (SSIM), etc. and different perspectives. The observational outcomes show the likelihood of the proposed algorithm and the capacity to give unwavering quality between security, payload, perception, computation, and temper protection.

Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions.

INTRODUCTION: Hypoglycaemia is a harmful potential complication in people with type 1 diabetes mellitus (T1DM) and can be exacerbated in patients receiving treatment, such as insulin therapies, by the very interventions aiming to achieve optimal blood glucose levels. Symptoms can vary greatly, including, but not limited to, trembling, palpitations, sweating, dry mouth, confusion, seizures, coma, brain damage or even death if untreated. A pilot study with healthy (euglycaemic) participants previously demonstrated that hypoglycaemia can be detected non-invasively with artificial intelligence (AI) using physiological signals obtained from wearable sensors. This protocol provides a methodological description of an observational study for obtaining physiological data from people with T1DM. The aim of this work is to further improve the previously developed AI model and validate its performance for glycaemic event detection in people with T1DM. Such a model could be suitable for integrating into a continuous, non-invasive, glucose monitoring system, contributing towards improving surveillance and management of blood glucose for people with diabetes. METHODS AND ANALYSIS: This observational study aims to recruit 30 patients with T1DM from a diabetes outpatient clinic at the University Hospital Coventry and Warwickshire for a two-phase study. The first phase involves attending an inpatient protocol for up to 36 hours in a calorimetry room under controlled conditions, followed by a phase of free-living, for up to 3 days, in which participants will go about their normal daily activities unrestricted. Throughout the study, the participants will wear wearable sensors to measure and record physiological signals (eg, ECG and continuous glucose monitor). Data collected will be used to develop and validate an AI model using state-of-the-art deep learning methods. ETHICS AND DISSEMINATION: This study has received ethical approval from National Research Ethics Service (ref: 17/NW/0277). The findings will be disseminated via peer-reviewed journals and presented at scientific conferences. TRIAL REGISTRATION NUMBER: NCT05461144.

Novel modeling and optimization framework for Navy Blue adsorption onto eco-friendly magnetic geopolymer composite.

The disproportionate potency of dyes in textile wastewater is a global concern that needs to be contended. The present study comprehensively investigates the adsorption of Navy-Blue dye (NB) onto bentonite clay based geopolymer/Fe3O4 nanocomposite (GFC) using novel statistical and machine learning frameworks in the following steps; (1) synthesis and characterization of GFC, (2) experimental testing and modelling of NB adsorption onto GFC following Box-Behnken design and three response surface prediction models namely stepwise regression analysis (SRA), Support vector regression (SVR) and Kriging (KR), (3) parametric, sensitivity, thermodynamic and kinetic analysis of pH, GFC dose and contact time on adsorption performance, and (4) finding global parametric solution of the process using Latin Hypercube, Sobol and Taguchi orthogonal array sampling and combining SRA-SVR-KR predictions with novel hybrid simulated annealing (SA)-desirability function (DF) approach. Under the given testing range, parametric/sensitivity analysis revealed the critical role of pH over others accounting ∼37% relative effect and primarily derived the NB adsorption. The statistical evaluation of models revealed that all models could be utilized for elucidating and predicting the NB removal using GFC, however, SVR accuracy was better among others for this particular work, as the overall computed root mean squared error was only 0.55 while the error frequency counts remained <1 for 90% predictions. GFC showed 86.29% NB removal for the given experimental matrix which can be elevated to 96.25% under optimum conditions. The NB adsorption was found to be physical, spontaneous, favorable and obeyed pseudo-2nd order kinetics. The results demonstrate the suitability of GFC as the promising cost-effective and efficient alternative for the decolourization of urban and drinking water streams and elucidate the potential of machine learning models for accurate prediction & elevation of adsorption processes with less experimentation in water purification applications.

Power mean based image segmentation in the presence of noise.

In image segmentation and in general in image processing, noise and outliers distort contained information posing in this way a great challenge for accurate image segmentation results. To ensure a correct image segmentation in presence of noise and outliers, it is necessary to identify the outliers and isolate them during a denoising pre-processing or impose suitable constraints into a segmentation framework. In this paper, we impose suitable removing outliers constraints supported by a well-designed theory in a variational framework for accurate image segmentation. We investigate a novel approach based on the power mean function equipped with a well established theoretical base. The power mean function has the capability to distinguishes between true image pixels and outliers and, therefore, is robust against outliers. To deploy the novel image data term and to guaranteed unique segmentation results, a fuzzy-membership function is employed in the proposed energy functional. Based on qualitative and quantitative extensive analysis on various standard data sets, it has been observed that the proposed model works well in images having multi-objects with high noise and in images with intensity inhomogeneity in contrast with the latest and state-of-the-art models.

Pulmonary physical therapy techniques for the management of COVID-19 patients: A systematic review.

OBJECTIVE: To review studies reporting pulmonary physical therapy techniques for the management of coronavirus disease-2019 patients. METHODS: The systematic review was conducted according to Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines Comprised search of MEDLINE via PubMed, Physiotherapy Evidence Database (PEDro), Excerpta Medica Database (EMBASE), Allied and Complementary Medicine Database (AMED) and Cumulated Index to Nursing and Allied Health Literature (CINHAL) for Observational and interventional studies published in English language between December 2019 and January 2022 describing pulmonary physical therapy techniques for the management of coronavirus disease-2019 patients. Google Scholar and reference lists of relevant studies were also searched to identify additional articles. Methodological quality of the included studies was assessed using either the Physiotherapy Evidence Database (PEDro) scale for interventional studies or the National Institutes of Health quality assessment tool for observational studies. RESULTS: Of the 3767 studies found, 17(0.45%) were analysed; 13(76.5%) observational and 4(23.5%) interventional. The most common pulmonary physical therapy techniques used were active cycle of breathing techniques, positive expiratory pressure device, breathing exercises, percussions, and chest abdomen muscle exercises. However, majority of the studies applied prone positioning and suctioning as priority treatment. During mechanical ventilation, mucus clearance and alveolar recruitment manoeuvres were commonly applied. CONCLUSIONS: There was scarcity of high-quality studies regarding the use of different pulmonary physical therapy techniques in coronavirus disease-2019 patients. Based on available literature, different techniques can be used, depending on stage and severity of the disease.

Multi-scale-average-filter-assisted level set segmentation model with local region restoration achievements.

Segmentation of noisy images having light in the background it is a challenging task for the existing segmentation approaches and methods. In this paper, we suggest a novel variational method for joint restoration and segmentation of noisy images which are having intensity and inhomogeneity in the existence of high contrast light in the background. The proposed model combines statistical local region information of circular regions centered at each pixel with a multi-phase segmentation technique enabling inhomogeneous image restoration. The proposed model is written in the fuzzy set framework and resolved through alternating direction minimization approach of multipliers. Through experiments, we have tested the performance of the suggested approach on diverse types of synthetic and real images in the existence of intensity and in-homogeneity; and evaluate the precision, as well as, the robustness of the suggested model. Furthermore, the outcomes are, then, compared with other state-of-the-art models including two-phase and multi-phase approaches and show that our method has superiority for images in the existence of noise and inhomogeneity. Our empirical evaluation and experiments, using real images, evaluate and assess the efficiency of the suggested model against several other closest rivals. We observed that the suggested model can precisely segment all the images having brightness, diffuse edges, high contrast light in the background, and inhomogeneity.

Modeling and verification of authentication threats mitigation in aspect-oriented mal sequence woven model.

The modeling of security threats is equally important as the modeling of functional requirements at the design stage of software engineering. However, unlike functional requirements modeling, the modeling of security threats is neglected, which consequently introduces software defects during the early stages of software engineering. Hence, there is a need to mitigate these threats at the design stage. Security threats, specifically authentication threats, crosscut other functional and non-functional requirements when modeled using the object-oriented paradigm. This not only makes the design complex but also results in tangling and scattering problems. We therefore model authentication threats using the aspect-oriented modeling (AOM) technique since it separates crosscutting concerns and localizes them as separate units called aspects. Our main research aim is to remove scattering and tangling in security threats modeling using all the core features of the aspect-oriented technique. In this paper, we propose a research approach to model security threats and their mitigation in mal sequence diagram. Using this approach, our contribution makes a clear difference from previous work. Our first contribution is the modeling of authentication threats in the mal sequence diagram using the security profile and AOM profile. Our second contribution is the mathematical verification of the aspect-oriented mal sequence woven model in terms of correctness and completeness. Using the proposed approach, the scattering and tangling from the resultant woven model are successfully removed at the design stage. Thus, the complexity of models and the time and effort required for future modifications of design models are reduced.

Probing the chemical constituents of Cassia javanica and its in vitro analyses as a potent drug.

The phytochemical screening of the crude methanolic stem extracts of Cassia javanica plant showed the presence of different classes of organic compounds like alkaloids, tannins, flavonoids, saponins, phlobatanins, steroids, anthraquinone and cardiac glycoside. The starching frequencies of these functional groups were determined from FT-IR spectroscopic data. The crude and their fractions were examined for antibacterial potential against Klebsiella pneumoniae and Proteus mirabilis. The antibacterial assay showed maximum zone of inhibition for ethyl acetate fraction, i.e. 20 mm against Proteus mirabilis and 18 mm against Klebsiella pneumoniae in the comparison with Levofloxacin used as standard (40 mm). Meanwhile for methanolic crude extract, the inhibition zone was recorded 14 mm against Klebsiella pneumoniae and 22 mm against Proteus mirabilis. The minimum inhibitory concentrations and minimum bactericidal concentrations were recorded as 187.5 µg ml-1 against Proteus mirabilis and 93.75 µg ml-1 against Klebsiella pneumoniae. The scavenging free radical assay was noted as 69.61% at the concentration of 100 ppm.

Potential protective role of curcumin powder to regulate arsenic-induced hepatorenal toxicity and hyperlipidemic metabolic dysfunction in rat model.

The present work was conceptualized to determine the potential protective effects of curcumin on arsenic-induced kidney damage in male albino rat model. Thirty six male albino rats were selected, weighed about 175±10g and classified into four groups (9 rats in each group) such as C group (control with basal diet), Cur group (curcumin 200mg/kg body weight), AI group (arsenic-induced 5mg/kg body weight) and AI + Cur group (arsenic 5mg/kg+curcumin 200mg/kg body weight), respectively. Arsenic and curcumin were offered through the gavage method once daily with basal diet. The different analyzed parameters showed that arsenic-induced elevation of aspartate amino transferase, alkaline phosphatase, bilirubin urea, alanine aminotransferase and creatinine significantly decreased with curcumin application in AI + Cur group. Similarly, the statistically significant decline of low-density lipoprotein (LDL), cholesterol, triglyceride and increased in high-density lipoprotein (HDL) was observed in rats of AI + Cur group with curcumin treatment as compared to the rats of AI group. The level of different enzymes of the liver as well as kidney was noted depleted on arsenic exposure whereas increased in level was observed with curcumin application in AI + Cur group. Moreover, pathological histology changes were also recorded. The outcomes suggest that curcumin has a potential effect against arsenic-induced toxicity in biological model.

Synthesis of AgNPs coated with secondary metabolites of Acacia nilotica: An efficient antimicrobial and detoxification agent for environmental toxic organic pollutants.

This study concentrates on biosynthesis of Silver Nanoparticles (AgNPs) from stem extract of Acacia nilotica (A. nilotica). The reaction was completed at a temperature ~40-45 °C and time duration of 5 h. AgNPs were thoroughly investigated via advanced characterization techniques such as UV-Vis spectrophotometry (UV-Vis), Fourier Transform Infrared spectroscopy (FTIR), X-ray Diffractometry (XRD), Field Emission Scanning Electron Microscopy (FESEM), High Resolution Transmission Electron Microscopy (HRTEM), X-ray Photoelectron Spectroscopy (XPS), Thermo Gravimetric Analysis (TGA), Diffuse Reflectance Spectroscopy (DRS), Brunner-Emmett-Teller (BET), Dynamic Light Scattering (DLS), and Zeta potential analysis. AgNPs with average size below 50 nm were revealed by all the measuring techniques. Maximum surface area ~5.69 m2/g was reported for the as synthesized NPs with total pore volume ~0.0191 mL/g and average pore size ~1.13 nm. Physical properties such as size and shape have changed the surface plasmon resonance peak in UV-visible spectrum. Antimicrobial activity was reported due to denaturation of microbial ribosome's sulphur and phosphorus bond by silver ions against bacterium Methicillin Resistant Staphylococcus aureus (MRSA) and fungus Candida Albican (CA). Furthermore, AgNPs degraded toxic pollutants such as 4-nitrophenol (4-NP), 2-nitrophenol (2-NP) and various hazardous dyes such as Congo Red (CR), Methylene Blue (MB) and Methyl Orange (MO) up to 95%. The present work provided low cost, green and an effective way for synthesis of AgNPs which were utilized as potential antimicrobial agents as well as effective catalyst for detoxification of various pollutants and dyes.

Inhibition of hormonal and behavioral effects of stress by tryptophan in rats.

OBJECTIVES: Stress in known to alter hormonal systems. Pharmacological doses of tryptophan, the essential amino acid precursor of serotonin, increase circulating leptin and decrease ghrelin in normal healthy adults. Because systemically injected leptin inhibits stress-induced behavioral deficits and systemically injected serotonin modulates leptin release from the adipocytes, we used tryptophan as a pharmacological tool to modulate hormonal and behavioral responses in unstressed and stressed rats. METHODS: Leptin, ghrelin, serotonin, tryptophan, and behavior were studied in unstressed and stressed rats following oral administration of 0, 100, 200, and 300 mg/kg of tryptophan. RESULTS: Following oral administration of tryptophan at a dose of 300 mg/kg, circulating levels of serotonin and leptin increased and those of ghrelin decreased in unstressed animals. No effect occurred on 24-hours cumulative food intake and elevated plus maze performance. Exposure to 2 hours immobilization stress decreased 24 hours cumulative food intake and impaired performance in elevated plus maze monitored next day. Serum serotonin decreased, leptin increased, and no effect occurred on ghrelin. Stress effects on serotonin, leptin, food intake, and elevated plus maze performance did not occur in tryptophan-pretreated animals. Tryptophan-induced decreases of ghrelin also did not occur in stressed animals. CONCLUSION: The findings show an important role of serum serotonin, leptin, and ghrelin in responses to stress and suggest that the essential amino acid tryptophan can improve therapeutics in stress-induced hormonal and behavioral disorders.

A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis.

This paper proposes a novel Adaptive Region-based Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.

New Bioactive Heteroleptic Copper(II) Carboxylates: Structure, Enzymatic and DNA-Binding Studies.

Two new binuclear O-bridged copper(II) carboxylates with chemical formulas [Cu2(3-ClC6H4CH2COO)4(phen)2] (1) and [Cu2(3-ClC6H4CH2COO)4(bipy)2] (2) where phen = 1,10-phenanthroline and bipy = 2,2'-bipyridine have been synthesized and characterized by FT-IR, UV-Visible spectroscopy, CHN analysis and single crystal XRD. The results revealed distorted square pyramidal geometry around each copper atom of 1 and 2. The DNA interaction studies showed strong binding with Kb = 5.07 × 103 and 4.62 × 103 M-1 for 1 and 2, respectively. Both complexes showed strong enzyme inhibition, i.e., 70% and 90% for α-glucosidase with IC50 = 34.6 and 30.1 µM for 1 and 2, respectively, where acarbose was employed as control. However, both the complexes were found inactive against α-amylase. Using galantamine hydrobromide as control, 1 showed moderate inhibition activity (47%) with IC50 = 179.4 µM for acetylcholine esterase whereas 2 showed strong inhibition activity (76%) with IC50 = 95.8 µM for butyrylcholine esterase. The data reflects active anti-diabetic and anti-Alzheimer's nature of the synthesized complexes.

Fasting leptin and glucose in normal weight, over weight and obese men and women diabetes patients with and without clinical depression.

A large number of diabetes patients suffer from major depression and are at high risk of mortality. In view of a role of leptin in diabetes, depression and energy homeostasis, the present study concerns circulating levels of leptin in different BMI groups of un-depressed and depressed diabetes patients. Six hundred thirty male and female patients with a primary diagnosis of diabetes were grouped according to BMI and with or without clinical symptoms of depression. Age matched healthy, normal weight male and female volunteers without clinical symptoms of depression or diabetes were taken as controls. Blood samples were obtained after an overnight fast of 12 h. Serum was stored for the determination of leptin and glucose. We found that there were more female than male diabetes patients with comorbid depression. Fasting leptin was higher in normal weight non-diabetes women than men; but comparable in normal weight men and women diabetes patients. Fasting glucose levels were higher in diabetes than non diabetes groups; values were comparable in men and women. Depression was associated with a decrease and increase in leptin respectively in normal-overweight and obese men and women diabetes patients. Glucose levels were also higher in obese depressed than un-depressed diabetes patients. The results suggested that the female gender is at greater risk to comorbid diabetes with depression. Adipo-insular axis plays an important role in diabetes, associated depression and in the greater risk of the female gender to comorbid diabetes with depression.

Report: Protective effects of rice bran oil in haloperidol-induced tardive dyskinesia and serotonergic responses in rats.

Effect of administration of Rice bran oil (RBO) was evaluated on haloperidol elicited tardive dyskinesia in rats. Albino Wistar rats treated with haloperidol in drinking water at a dose of 0.2mg/kg/day and RBO by oral tubes at a dose of 0.4 mL/day for 5 weeks. Motor coordination, VCMs and 8-hydroxy-2-(di-n-propylamino) tetraline)[8-OH-DPAT] _syndrome were monitored. Striatal serotonin (5-hydroxytryptamine; 5-HT) and 5-hydroxyindolacetic acid (5-HIAA) levels were determined by high performance liquid chromatography (HPLC-EC). Rats treated with haloperidol orally at a dose of for a period of 5 weeks developed VCMs, which increased progressively as the treatment continued for 5 weeks. Motor coordination impairment started after the 1st week and was maximally impaired after 3 weeks and gradually returned to the 1st week value. Co-administration of RBO prevented haloperidol_induced VCMs as well impairment of motor coordination. The intensity of 8-OH-DPAT_induced syndrome and decreased 5-HT metabolism were greater in water + haloperidol treated animals than RBO + haloperidol treated animals. The present study suggested that involvement of free radical in the development of TD and point to RBO as a possible therapeutic option to treat this hyperkinetic motor disorder.

Regional Image Features Model for Automatic Classification between Normal and Glaucoma in Fundus and Scanning Laser Ophthalmoscopy (SLO) Images.

Glaucoma is one of the leading causes of blindness worldwide. There is no cure for glaucoma but detection at its earliest stage and subsequent treatment can aid patients to prevent blindness. Currently, optic disc and retinal imaging facilitates glaucoma detection but this method requires manual post-imaging modifications that are time-consuming and subjective to image assessment by human observers. Therefore, it is necessary to automate this process. In this work, we have first proposed a novel computer aided approach for automatic glaucoma detection based on Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from all the existing methods, our approach can extract both geometric (e.g. morphometric properties) and non-geometric based properties (e.g. pixel appearance/intensity values, texture) from images and significantly increase the classification performance. Our proposed approach consists of three new major contributions including automatic localisation of optic disc, automatic segmentation of disc, and classification between normal and glaucoma based on geometric and non-geometric properties of different regions of an image. We have compared our method with existing approaches and tested it on both fundus and Scanning laser ophthalmoscopy (SLO) images. The experimental results show that our proposed approach outperforms the state-of-the-art approaches using either geometric or non-geometric properties. The overall glaucoma classification accuracy for fundus images is 94.4% and accuracy of detection of suspicion of glaucoma in SLO images is 93.9 %.

Behavioral, hormonal and central serotonin modulating effects of injected leptin.

Leptin is viewed as an important target for developing novel therapeutics for obesity, depression/anxiety and cognitive dysfunctions. The present study therefore concerns behavioral, hormonal and central serotonin modulating effects of systemically injected leptin. Pharmacological doses (100 and 500 µg/kg) of leptin injected systemically decreased 24h cumulative food intake and body weight in freely feeding rats and improved acquisition and retention of memory in Morris water maze test. Potential anxiety reducing, hormonal and serotonin modulating effects of the peptide hormone were determined in a separate experiment. Animals injected with 100 or 500 µg/kg leptin were tested for anxiety in an elevated plus maze test 1h later. A significant increase in the number of entries and time passed in open arm of the elevated plus maze in leptin injected animals suggested pronounced anxiety reducing effect. Moreover, circulating levels of leptin correlated significantly with anxiety reducing effects of the peptide hormone. Serum serotonin increased and ghrelin decreased in leptin injected animals and correlated, positively and negatively respectively, with circulating leptin. Corticosterone increased at low dose and levels were normal at higher dose. Serotonin metabolism in the hypothalamus and hippocampus decreased only at higher dose of leptin. The results support a role of leptin in the treatment of obesity, anxiety and cognitive dysfunctions. It is suggested that hormonal and serotonin modulating effects of leptin can alter treatment efficacy in particularly comorbid conditions.