PROCESS OPTIMIZATION, FORMULATION AND EVALUATION OF HYDROGEL {GUARGUM-G-POLY (ACRYLAMIDE)} BASED DOXOFYLLINE MICROBEADS

Objective: The objective of the present study was to improve the physical and chemical properties of natural polymers and to reduce the cost of product by graft copolymerization techniques using a natural polymer (Guar gum) and a synthetic polymer {poly (acrylamide)}. The optimized formulation of hydrogel was formulated as microbeads and loaded with Doxofylline and characterized with different parameters.Methods: Graft copolymer of guar gum-g-poly (acrylamide) was prepared by free radical polymerization technique in a specially designed jacked reaction vessel under constant flow of nitrogen. To initiate the reaction, Ceric ammonium nitrate (CAN) was used as reaction initiator. The graft co-polymer was characterised by using FTIR, TGA, and SEM. Polymeric blend beads of the grafted copolymer with sodium alginate were prepared by cross linking with calcium chloride in ionic gelation method and used to deliver a model new generation anti asthmatic drug, Doxofylline. Preparation condition of beads was optimized by considering the percentage entrapment efficiency, particle size, swelling capacity of beads in different PH conditions and their release data.Results: The formation of grafted copolymers is confirmed by FTIR studies and TGA studies showed a comparatively higher thermal stability of grafted copolymer. The pAAm-g-GG/sodium alginate microbeads were almost spherical in shape as indicated by the SEM studies. Swelling index was found to be maximum in Phosphate buffer PH 7.4 and minimum in Phosphate buffer PH 9.2. Release of doxofylline was found to be in a controlled manner with increasing polyacrylamide content in the copolymer and sodium alginate content in microbeads and higher release was observed in PH 7.4 medium than that of PH 1.2. In vitro release kinetics of doxofylline from the polymeric beads followed Higuchi kinetics model.Conclusion: Hydrogel based Doxofylline microbeads were successfully developed by using optimized batches of Guar gum-g-poly (acrylamide) and sodium alginate by free radical ionization technique. All the characterization parameters came under acceptance criteria.  Key words: Hydrogel, Microbeads, Guar gum, Acrylamide, Sodium alginat

Pulse oximetry as a screening tool for congenital heart disease in neonates: A diagnostic study

Introduction: Many studies have been done for screening of congenital heart disease (CHD) in the neonatal period utilizing pulse oximetry as a screening tool along with routine clinical assessment, but none of them from our province. Objective: The objective of the study was to find out the diagnostic accuracy of pulse oximeter at three different sites as a screening tool to diagnose CHD among neonates. Methods: A diagnostic study was conducted in neonatal intensive care unit of a tertiary care hospital of Odisha from October 2016 to September 2018 after approval from the Institutional Ethics Committee. Three hundred and seventy-four neonates (both inborn and outborn) with gestational age >34 weeks were included in the study. Oxygen saturation (SpO2) in the right hand (RH), right foot (RF), and left foot (LF) was estimated by pulse oximeter among all participants after 10 min of postnatal life. All the study subjects were evaluated by two-dimensional (2D) echocardiography for the detection of CHDs. All the diagnostic accuracy tests (sensitivity [Sn], specificity [Sp], positive predictive value, negative predictive value, and diagnostic odds ratio) were calculated taking 2D echocardiography as the gold standard with software, and for all statistical purpose, p<0.05 was considered statistically significant. Results: Cutoff value of the RH SpO2 was 90.0% with Sn of 68.80% and Sp of 98.20%; area under curve (AUC) 0.851 (0.766 and 0.914), p<0.001, for the RF, SpO2 was 90.0% with Sn 78.0% and Sp 92.1%; AUC 0.865 (0.782 and 0.925), p<0.001, and for LF, it was 87% with Sn 77.1% and Sp 94.0%; AUC 0.864 (0.781 and 0.924), p<0.001. Conclusion: Along with the clinical skills, pulse oximetry can be used as an early screening tool for the detection of CHD in the neonatal period and of three different sites, RF found to be better

NANO MEDICINE: AN EMERGING TREND IN MOLECULAR DELIVERY

Nanobiopharmaceuticals has involved, understanding issues related to target specific drug delivery. Previously the traditional dosage forms showed a greater difficulty in achieving bioavailability and therapeutic index. So addition of various functionalities to nano size materials interfacing them into various biological molecules has improved the drug delivery. Integration of nanomedicinals with biotechnology has brought a radical change in the research field both in vivo and invitro. It has paved the way to new therapies, surgical interventions and advanced drug delivery systems. Nanoparticles as nanomedicines opens the potential for crossing various biological barriers within the body especially the potential to cross the blood brain barrier may open new ways for drug delivery into the brain. Nanoparticles as drug delivery systems can be designed to improve the pharmacological and therapeutic properties of the drug. This manuscript has emphasized various nanobiopharmaceuticals in the field of cancer therapy, regenerative medicine, drug delivery, diagnostic devices, gene therapy and tissue engineering . Keywords:  Nanobiopharmaceuticals, target specific drug delivery, regenerative medicines, tissue engineering, cancer therapy

Recognition of oral pathology as a subject among dental undergraduates: Addressing their concerns

Oral and Maxillofacial Pathology is a speciality department of dentistry which deals with identification of diseases affecting oral and maxillofacial regions and investigates the cause, processes and effect of these diseases. Books, internet, lectures have made great progress but there is still a gap between students' understanding and interest in the subject. The aim of this study was to evaluate and analyse the challenges faced by undergraduate students in understanding oral pathology theory & practical and modify the teaching process accordingly. A descriptive cross-sectional study of 300 BDS third year students was conducted in our college. The results stated that 83% of the participants attended classes, 90% found the topics covered to be useful, 97% reported that the classes were taken to provide an in depth view of the subject, 71% faced difficulties in understanding the topics in theory classes. 46% attended seminars, 68% viewed more than 4 slides on an average in one practical class, and 80% faced difficulties in identifying the slide. Our data concluded, that if certain measures will be taken so as to engage the students' interests, then this subject would prove to be a much better scope of learning for the budding dentists

An Integrated Statistical-Machine Learning Approach for Runoff Prediction

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.Validerad;2022;Nivå 2;2022-07-05 (sofila);Funder: , G.B. Pant University of Agriculture and Technology, India; Gola Barrage gauge station Haldwani–Kathgodam, India; Portuguese Foundation for Science and Technology (PTDC/CTA-OHR/30561/2017, WinTherface)</p

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique

An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio.Validerad;2024;Nivå 2;2024-04-08 (marisr);Full text license: CC BY 4.0</p

Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity

Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water moves through the soil. On the other hand, its measurement is difficult, time-consuming, and expensive; hence Pedotransfer Functions (PTFs) are commonly used for its estimation. Despite significant development over the years, the PTFs showed poor performance in predicting Ks. Using Genetic Algorithm (GA), two hybrid Machine Learning based PTFs (ML-PTF), i.e. a combination of GA with Multilayer Perceptron (MLP-GA) and Support Vector Machine (SVM-GA), were proposed in this study. We compared the performances of four machine learning algorithms for different sets of predictors. The predictor combination containing sand, clay, Field Capacity, and Wilting Point showed the highest accuracy for all the ML-PTFs. Among the ML-PTFs, the SVM-GA algorithm outperformed the rest of the PTFs. It was noticed that the SVM-GA PTF demonstrated higher efficiency than the MLP-GA algorithm. The reference model for hydraulic conductivity prediction was selected as the SVM-GA PTF paired with the K-5 predictor variables. The proposed PTFs were compared with 160 models from past literature. It was found that the algorithms advocated were an improvement over these PTFs. The current model would help in efficient spatio-temporal measurement of hydraulic conductivity using pre-available databases.Validerad;2022;Nivå 2;2022-05-11 (sofila)</p