Fast Dissolving Sublingual Films of Ondansetron Hydrochloride: Effect of Additives on in vitro Drug Release and Mucosal Permeation

Ondansetron hydrochloride, a 5 HT3 antagonist is a powerful antiemetic drug which has oral bioavailability of 60% due to hepatic first pass metabolism and has a short half-life of 5 h. To overcome the above draw back, the present study was carried out to formulate and evaluate fast dissolving films of ondansetron hydrochloride for sublingual administration. The films were prepared from polymers such as polyvinylalcohol, polyvinyl pyrrolidone, Carbopol 934P in different ratios by solvent casting method. Propylene glycol or PEG 400 as plasticizers and mannitol or sodium saccharin as sweeteners were also included. The IR spectral studies showed no interaction between drug and polymer or with other additives. Satisfactory results were obtained when subjected to physico-chemical tests such as uniformity of weight, thickness, surface pH, folding endurance, uniformity of drug content, swelling index, bioadhesive strength, and tensile strength. Films were also subjected to in vitro drug release studies by using USP dissolution apparatus. Ex vivo drug permeation studies were carried out using porcine membrane model. In vitro release studies indicated 81–96% release within 7 min and 66–80% within 7 min during ex vivo studies. Drug permeation of 66–77% was observed through porcine mucosa within 40 min. Higher percentage of drug release was observed from films containing the sweeteners. The stability studies conducted for a period of 8 weeks showed no appreciable change in drug content, surface pH, and in vitro drug release

Not Available

Not AvailablePrediction of local scale frost events can be helpful for farmers to minimize crop loss due to frost damage. This study aims to detect a temporal trend in the occurrence of frost events and develop frost prediction models using multivariate statistical techniques like logistic regression, artificial neural network model, and thumb rules for two diverse locations of India (Palampur and Ludhiana). In these statistical models, eight daily meteorological parameters viz., maximum temperature (Tmax), minimum temperature (Tmin), wind speed, precipitation, sunshine duration, cumulative pan evaporation, morning relative humidity (RH1), and afternoon relative humidity (RH2) 1 to 5 days preceding the frost events for the period of 2004–2016 and 1982–2013 at Palampur and Ludhiana, respectively were used. Principal Component Analysis was performed to select the weather parameter that has maximum effect on the occurrence of frost event. Ten different skill scores like accuracy, bias, and probability of false detection were used to evaluate the accuracy of frost prediction models. The Mann–Kendall trend test showed a significant increasing annual trend in the number of frost events at Ludhiana, with a remarkable increase in December. The results also showed that lower afternoon relative humidity 1-day preceding the frost event at Palampur and calm wind and lower evaporation 1-day preceding at Ludhiana augmented the occurrence of frost events. Among the techniques for developing frost prediction models, the logistic regression model performed better over artificial neural network and thumb rule-based models. The logistic regression model performed better for the plain region (Ludhiana) than for the hilly area (Palampur). The developed models are most suitable for predicting the radiation frost.Not Availabl

Not Available

Not AvailableThe dry spells and rainfall deficit within crop season, play vital role in determining productivity of rainfed crops. Dry Spell Index (DSI) was formulated to quantify cumulative impact of dry spells during kharif season (Jun-Sep) on major rainfed crops of India. District-wise variability of DSI were analyzed across rainfed regions of India using rainfall data of 1636 stations. Comparison of DSI with Standardized Precipitation Index (SPI), hitherto, a widely used drought index showed that, central and eastern Karnataka, northern Rajasthan and western Gujarat are becoming wetter in terms of total seasonal rainfall as indicated by SPI, and becoming drier in terms of total dry spell duration within the season as per DSI. The impact of DSI on yield of major rainfed crops viz., cotton, groundnut, maize, pearl millet, pigeon pea and sorghum were estimated. The analysis showed that, the impact of dry spells integrated in form of the DSI on yields of six major rainfed crops was higher in comparison to total rainfall indicated by SPI for six major rainfed crops in India. Groundnut and pearl millet crops experienced higher duration of dry spells in comparison to other crops. The productivity of all the crops was significantly influenced by DSI across more than 65% growing regions. The yield loss was about 75–99% in 24% of sorghum, 23% of groundnut and 13% of pearl millet and it was about 50–74% in 44% of cotton, 24% of groundnut, 17% of maize, 16% each of pearl millet & sorghum and 12% of pigeon pea growing regions. We also found that by minimizing the cumulative impact of dry spells, yield can be increased twice in more than 55%, 49% and 42% areas of pearl millet, pigeon pea and groundnut growing regions, respectively. This study will help developing adaptation strategies to sustain crop production in rainfed regions of India.Not Availabl

Not Available

Not AvailableThis study investigates the spatio-temporal changes in maize yield under projected climate and identified the potential adaptation measures to reduce the negative impact. Future climate data derived from 30 general circulation models were used to assess the impact of future climate on yield in 16 major maize growing districts of India. DSSAT model was used to simulate maize yield and evaluate adaptation strategies during mid (2040-69) and end-centuries (2070-99) under RCP 4.5 and 8.5. Genetic coefficients were calibrated and validated for each of the study locations. The projected climate indicated a substantial increase in mean seasonal maximum (0.9–6.0 °C) and minimum temperatures (1.1–6.1 °C) in the future (the range denotes the lowest and highest change during all the four future scenarios). Without adaptation strategies, climate change could reduce maize yield in the range of 16% (Tumkur) to 46% (Jalandhar) under RCP 4.5 and 21% (Tumkur) to 80% (Jalandhar) under RCP 8.5. Only at Dharwad, the yield could remain slightly higher or the same compared to the baseline period (1980–2009). Six adaptation strategies were evaluated (delayed sowing, increase in fertilizer dose, supplemental irrigation, and their combinations) in which a combination of those was found to be effective in majority of the districts. District-specific adaptation strategies were identified for each of the future scenarios. The findings of this study will enable in planning adaptation strategies to minimize the negative impact of projected climate in major maize growing districts of India.Not Availabl

Not Available

file:///C:/Users/NS%20Raju/Downloads/Algorithmsforweather-basedmanagementdecisionsinmajor.pdfCrop weather calendars (CWC) serve as tools for taking crop management decisions. However, CWCs are not dynamic, as they were prepared by assuming normal sowing dates and fixed occurrence as well as duration of phenological stages of rainfed crops. Sowing dates fluctuate due to variability in monsoon onset and phenology varies according to crop duration and stresses encountered. Realizing the disadvantages of CWC for issuing accurate agromet advisories, a protocol of dynamic crop weather calendar (DCWC) was developed by All India Coordinated Research Project on Agrometeorology (AICRPAM). The DCWC intends to automatize agromet advisories using prevailing and forecasted weather. Different modules of DCWC, namely, Sowing & irrigation schedules, crop contingency plans, phenophase-wise crop advisory, and advisory for harvest were prepared using long-term data of ten crops at nine centers of AICRPAM in eight states in India. Modules for predicting sowing dates and phenology were validated for principal crops and varieties at selected locations. The predicted sowing dates of 10 crops pooled over nine centers showed close relationships with observed values (r2 of .93). Predicted phenology showed better agreement with observed in all crops except cotton (Gossypium L.; at Parbhani) and pigeon pea [Cajanus cajan (L.) Millsp.] (at Bangalore). Predicted crop phenology using forecasted and realized weather by DCWC are close to each other, but number of irrigations differed, and it failed for accurate prediction in groundnut at Anantapur in drought year (2014). The DCWCs require further validation for making it operational to issue agromet advisories in all 732 districts of IndiaNot Availabl

Not Available

Not AvailableCrop weather calendars (CWC) serve as tools for taking crop management decisions. However, CWCs are not dynamic, as they were prepared by assuming normal sowing dates and fixed occurrence as well as duration of phenological stages of rainfed crops. Sowing dates fluctuate due to variability in monsoon onset and phenology varies according to crop duration and stresses encountered. Realizing the disadvantages of CWC for issuing accurate agromet advisories, a protocol of dynamic crop weather calendar (DCWC) was developed by All India Coordinated Research Project on Agrometeorology (AICRPAM). The DCWC intends to automatize agromet advisories using prevailing and forecasted weather. Different modules of DCWC, namely, Sowing & irrigation schedules, crop contingency plans, phenophase-wise crop advisory, and advisory for harvest were prepared using long-term data of ten crops at nine centers of AICRPAM in eight states in India. Modules for predicting sowing dates and phenology were validated for principal crops and varieties at selected locations. The predicted sowing dates of 10 crops pooled over nine centers showed close relationships with observed values (r2 of .93). Predicted phenology showed better agreement with observed in all crops except cotton (Gossypium L.; at Parbhani) and pigeon pea [Cajanus cajan (L.) Millsp.] (at Bangalore). Predicted crop phenology using forecasted and realized weather by DCWC are close to each other, but number of irrigations differed, and it failed for accurate prediction in groundnut at Anantapur in drought year (2014). The DCWCs require further validation for making it operational to issue agromet advisories in all 732 districts of India.Not Availabl