Modelling Accessibility based on Urbanization using Artificial Neural Networks (ANN)

The present study involves modelling the accessibility index with respect to the traffic volume, Right of Way width and Population density. It also involves the collection of number of different types of opportunities like schools, hospitals, ATMs, bus-stops and parks to determine the accessibility index. Two different methods are used in the study such as Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) to develop models in order to predict the accessibility index. Based on the R2 value obtained in the models, it is observed that ANN has better prediction capability than MLR model. The study acts as a guide to the urban transportation planners to understand the change in accessibility index when there is a change in urbanization variables

HEPATOPROTECTIVE ACTIVITY OF ALSTONIA SCHOLARIS FRUITS AGAINST CARBON TETRACHLORIDE-INDUCED HEPATOTOXICITY IN RATS

The hepatoprotective activity of ethanolic extract of fruits of Alstonia scholaris was evaluated by carbon tetrachloride (CCL4)-induced hepatotoxicity in rats. The toxicant CCL4 was used to induce hepatotoxicity at a dose of 2 ml/kg as 1:1 mixture with olive oil. The ethanolic extract of fruits of Alstonia scholaris was administered in the dose of 150 and 300 mg/kg/day orally for 5 days. Silymarin (50 mg/kg) was used as standard drug. The hepatoprotective effect of the ethanolic extract was evaluated by the assessment of biochemical parameters such as SGOT, SGPT, SALKP, total bilirubin and histopathological studies of the liver. Treatment of animals with the ethanolic extract significantly reduced the liver damage and the symptoms of liver injury by restoration of architecture of liver as indicated by lower levels of serum bilirubin as compared with the normal and silymarin treated groups

Evaluating the efficacy and safety of simple combination of analgesics with and without low dose opioid for perioperative analgesia, hemodynamic and recovery profile in various surgeries posted under general anaesthesia: a prospective randomised controlled study

Background: Multimodal analgesia is an emerging technique.It has been consistently demonstrated to minimise opioid consumption, related side effects and vital component of enhanced recovery after surgery pathways. The current study presents use of combination of readily available medication as a part of multimodal analgesia. Balanced anaesthesia with multimodal analgesia is harmonious use of combination of agents to produce desired effects with minimal side effects of individual agents. Methods: This study was done in a tertiary health care centre, Government General Hospital, Kakinada over a duration of two months from August 2022 to September 2022. 60 adult patients of either sex of physical status ASA grade 1and 2 undergoing elective surgery under general anaesthesia were randomly allocated into Group A and Group B of 30 patients. Group 1: received Inj. Lignocaine+Inj. Paracetamol+Inj. Magnesium sulphate+Inj. Fentanyl. Group 2:received Inj. Lignocaine+Inj. Paracetamol+Inj. Magnesium sulphate+Normal saline (control group) as premedication for perioperative analgesia. Results: All patients were hemodynamically stable for first 30 minutes period of observation in Group A compared to Group B. There is clinically and statistically significant difference in the duration of analgesia in Group A compared to Group B. There is no statistically significant difference in the Numerical Rating Scale (NRS) for pain in both the groups. Conclusions: This study concluded that simple analgesia combination of multimodal analgesia regimen comprising of Inj. Lignocaine, Inj. Paracetamol and Inj. Magnesium sulphate produces safe and effective analgesia with good recovery profiles and no adverse opioid related side effects for ASA 1 and 2 patients posted under general anaesthesia

PRELIMINARY PHYTOCHEMICAL, PHARMACOGNOSTIC AND PHYSICOCHEMICAL EVALUATION OF ERANTHEMUM NIGRUM LEAF

Objective: To analyze the pharmacognostic characteristics and physiochemical parameters of the leaves of Eranthemum nigrum (E. nigrum). Methods: Microscopic characters and powder analysis had been carried out with the help of a microscope. The physiochemical properties such as loss on drying, total ash value, acid insoluble ash value, water soluble ash value, extractive values and fluorescence of E. nigrum had been performed. Results: Macroscopically, the leaves are simple, elliptical in shape, dull with smooth margins and acute apex. Microscopically, the leaf showed the presence of epidermal cells with uniseriate multicellular covering trichomes and diacytic stomata, followed by 4-6 layered collenchymatous cells and 10-14 numbered conjoint, collateral closed vascular bundles are some of the diagnostic characteristics observed from an anatomical study. Powder microscopy of leaf revealed the presence of uniseriate multicellular covering trichomes, lignified xylem vessels, epidermis with diacytic stomata and parenchyma cells. The investigations also included leaf surface data i.e., quantitative leaf microscopy and fluorescence analysis. Physiochemical parameters such as loss on drying, extractive values and ash values were also determined. Preliminary phytochemical screening showed the presence of steroids, alkaloids, tannins, saponins, carbohydrates, glycosides, amino acids and proteins. Conclusion: The morphological, microscopical and physicochemical parameter results provided in this paper may be utilized as a basis for the preparation of a monograph on E. nigrum leaves

DEVELOPMENT AND VALIDATION OF STABILITY INDICATING REVERSED PHASE HIGHPRESSURE LIQUID CHROMATOGRAPHY METHOD FOR SIMULTANEOUS ESTIMATION OF METFORMIN AND EMPAGLIFLOZIN IN BULK AND TABLET DOSAGE FORM

ABSTRACT Objective: To develop accurate, fast, simple, and precise reversed-phase high-pressure liquid chromatography method for simultaneous determination of the binary mixture of metformin (MET) and empagliflozin (EMPA) in dosage forms. Methods: The method uses a mobile phase consisting of phosphate buffer, acetonitrile, methanol (15:80:5 v/v/v), an octadecyl silica C-18 column (4.6 mm × 250 mm, 5 μ particle size) in isocratic mode, detection wavelength of 227 nm, and a flow rate of 1 mL/minutes. Results:  The  measured  retention  times  for  MET  and  EMPA  and  were  2.528  and  4.140 minutes,  respectively.  The  percentage  recoveries  of  MET  and  EMPA  were 101.12% and 100.55%, respectively. The relative standard deviation for assay of tablets was found to beÂ

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ESTIMATION OF PAROXETINE HYDROCHLORIDE FROM ITS TABLET FORMULATION BY UV SPECTROPHOTOMETRY

A simple, precise and accurate UV Spectrophotometric method was developed for the estimation of Paroxetine hydrochloride. The developed method obeyed Beer-Lambert’s law in the concentration range of 5-30 µg/ml with a correlation coefficient of 0.995. The recovery study was carried out at three different levels and was found to be satisfactory. The percent amount of drug estimated by this method is 100%, found to be in good agreement with label claim of marketed tablet formulation. The validation parameters like linearity, precision, accuracy, robustness and ruggedness were studied and were found to be within limits. The proposed method can be adopted for routine quality control analysis of estimation of Paroxetine hydrochloride in pharmaceutical formulation

Crop Yield Prediction Using Gradient Boosting Neural Network Regression Model

The finest utility sector is agriculture, especially in emerging nations like India. Utilizing historical data in agriculture can change the context of decision-making and increase farmer productivity. Approximately a part of India's population is employed in agriculture, however this sector contributes just 14% of the country's GDP. This can be explained in part by farmers not making sufficient decisions on yield forecast. By examining numerous climatic elements, such as rainfall, and land characteristics, such as soil type and ground water salinity, as well as historical records of crops cultivated, the suggested machine learning technique tries to estimate the agricultural yield for a certain location. Finally, we anticipate that our proposed Machine Learning Gradient Boosting Neural Network Regression (Grow Net) model was predicting the accurate yield. Finally our system is expected to predict the yield based on dataset we have taken. We were compared our proposed algorithm with various Machine Learning algorithms such as Random Forest, Support Vector Machine, KNN, Multi-layer Perceptron Regressor, Gradient Boosting Regressor and results shows that proposed was given best RMSE ,MAE and R2 value

UV SPECTROPHOTOMETRIC METHOD DEVELOPMENT AND VALIDATION FOR SIMULTANEOUS ESTIMATION OF ALPRAZOLAM AND MEBEVERINE HYDROCHLORIDE IN BULK DRUG AND PHARMACEUTICAL FORMULATION

A simple, accurate, precise, sensitive, rapid and economical spectrophotometric method was developed and validated for simultaneous estimation of Alprazolam (ALP) and Mebeverine HCl (MBH) in bulk drug and pharmaceutical formulation. The estimation of these drugs was carried out by using 0.1M HCl as a solvent. The wavelength maxima for Alprazolam and Mebeverine HCl were found to be 262.3 nm and 222.5 nm. The linearity range was observed in the concentration range of 3-15 µg/ml for both drugs and regression equation was found to be for ALP 0.0565x+0.0138 and for MBH 0.049x-0.0126. Percentage recoveries for Alprazolam and Mebeverine HCl were found to be 99.84% and 99.47% respectively. % RSD values for Intra-day precision were found to be for ALP 1.18% and for MBH 0.59%. Inter-day precision %RSD values were found to be for ALP 0.94% and for MBH 0.69%. LOD was found to be for ALP 1.42 (µg/ml) and for MBH 2.1542 (µg/ml). LOQ was found to be for ALP 4.3242 (µg/ml) and for MBH 6.5442 (µg/ml). The %Assay of Alprazolam and Mebeverine HCl were found to be 99.20% and 100.02% respectively. Statistical analysis proved that the developed method can be successfully used for simultaneous analysis of Alprazolam and Mebeverne HCl in pure and tablet dosage forms

PHARMACOGNOSTIC STUDY OF MANSOA ALLIACEA LEAF

Mansoa alliacea Lam. (Family: Bignoniaceae) is a native plant from Amazonian basin in South America. Plant derivatives are used as anti-inflammatory, antioxidant, antiseptic and antibacterial agents. The study was aimed to determine the pharmacognostic and phytochemicals present in Mansoa alliacea. Micro and organoleptic characteristics of fresh and dried leaf samples had been examined. Physicochemical variables had been done by using WHO suggested variables; preliminary phytochemical of leaf sample had been performed to identify the presence of alkaloids, flavonoids, tannins and phenols, and quinones using the ethanolic extract of the leaves of M. alliacea