Farmakokinetika ceftriaksona u teladi

The pharmacokinetics of ceftriaxone was determined after a single intravenous and intramuscular administration at the dose rate of 10 mg/kg in crossbred cow calves. The drug concentration in plasma was quantified through High Performance Liquid Chromatography with UV detection. Following intravenous administration the drug was rapidly distributed (t1/2α: 0.13 ± 0.01 h; Vd(area); 0.44 ± 0.07 L/kg) and eliminated (t1/2β: 1.58 ± 0.06 h) from the body with a clearance rate of 3.15 ± 0.41 mL/min/kg. Following intramuscular administration, the peak plasma drug concentration (Cmax) was 15.34 ± 2.39 μg/mL at 0.25 hours (Tmax) suggesting very rapid absorption. The drug was extensively distributed (Vd(area): 1.16 ± 0.15 L/kg) and slowly eliminated (t1/2β: 5.02 ± 0.51 hours; Cl(B): 2.71 ± 0.29 mL/min/kg) following intramuscular administration. The absolute bioavailability of ceftriaxone was 47.0 ± 5.0% following intramuscular injection. However, it can be used at a dosage of 10 mg/kg intramuscularly, repeated at twelve-hourly intervals, for the treatment of susceptible bacteria infections in calves.Farmakokinetika ceftriaksona određivana je u križane teladi nakon njegove jednokratne intravenske i intramuskularne primjene u dozi od 10 mg/kg. Koncentracija lijeka u plazmi određivana je tekućinskom kromatografifi jom visokog učinka s UV zrakama. Raspodjela lijeka bila je brza nakon intravenske primjene (t1/2α: 0,13 ± 0,01 h; Vd(area): 0,44 ± 0,07 L/kg), a izlučivanje (t1/2β: 1,58 ± 0,06 h) iz tijela s klirensom od 3,15 ± 0,41 mL/min/kg. Nakon intramuskularne primjene vršna koncentracija u plazmi iznosila je (Cmax) 15,34 ± 2,39 μg/mL tijekom 0,25 sati (Tmax) što upućuje na vrlo brzu apsorpciju. Raspodjela lijeka bila je izrazito dobra (Vd(area) 1,16 ± 0,15 L/kg), a izlučivanje sporo (t1/2β: 5,02 ± 0,51 sati; Cl(B): 2,71 ± 0,29 mL/min/kg) nakon intramuskularne primjene. Apsolutna biološka raspoloživost nakon intramuskularne primjene ceftriaksona iznosila je 47,0 ± 5,0%. Međutim, on se može rabiti u dozi od 10 mg/kg i.m. te ponavljati u razmacima od 12 sati radi liječenja bakterijskih zaraza u teladi

Health profile of adolescent girls visiting obstetrics and gynecology department of tertiary care hospital

Background: Gynecological problems of adolescents occupy a special space in the spectrum of gynecological disorders of all ages. In this study, an attempt has been made to review the health profile of adolescent girls visiting department of Obstetrics and Gynecology of a tertiary care hospital.Methods: This observational study was conducted at a tertiary care teaching hospital during June 2014 to May 2016. Data was collected after due permission.Results: Adolescent girls having gynecological problems were 2.3%. Mean age of menarche was 12.5 years. Anemia was present in 89(62.7%). About 72(50.7%) adolescent girls were having abnormal body mass index (BMI). Majority of girls 136(95.8%) had menstrual problems. Leucorrhoea, Pelvic Inflammatory Disease (PID), ovarian mass, urinary problems, breast problems, injury to genital tract and sexual assault were present in 42(29.6%), 24(16.9%), 20(14.1%), 13(9.2%), 12(8.5%), 4(2.8%) and 1(0.7%) respectively.Conclusions: A very small proportion of adolescent girls came to the hospital for health-related issues. Anemia was present in more than half of adolescent girls and almost half of adolescent girls were having abnormal BMI. Majority of adolescent girls had menstrual problems. Health education regarding normal physiology, various gynecological problems, importance of nutrition and exercise for adolescents is necessary

A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification

Sustainable energy systems rely on energy yield from renewable resources such as solar radiation and wind, which are typically not on-demand and need to be stored or immediately consumed. Solar irradiance is a highly stochastic phenomenon depending on fluctuating atmospheric conditions, in particular clouds and aerosols. The complexity of weather conditions in terms of many variable parameters and their inherent unpredictability limit the performance and accuracy of solar power forecasting models. As renewable power penetration in electricity grids increases due to the rapid increase in the installation of photovoltaics (PV) systems, the resulting challenges are amplified. A regional PV power prediction system is presented and evaluated by providing forecasts up to 72 h ahead with an hourly time resolution. The proposed approach is based on a local radiation forecast model developed by Blue Sky. In this paper, we propose a novel method of deriving forecast equations by using an irradiance classification approach to cluster the dataset. A separate equation is derived using the GEKKO optimization tool, and an algorithm is assigned for each cluster. Several other linear regressions, time series and machine learning (ML) models are applied and compared. A feature selection process is used to select the most important weather parameters for solar power generation. Finally, considering the prediction errors in each cluster, a weighted average and an average ensemble model are also developed. The focus of this paper is the comparison of the capability and performance of statistical and ML methods for producing a reliable hourly day-ahead forecast of PV power by applying different skill scores. The proposed models are evaluated, results are compared for different models and the probabilistic time series forecast is presented. Results show that the irradiance classification approach reduces the forecasting error by a considerable margin, and the proposed GEKKO optimized model outperforms other machine learning and ensemble models. These findings also emphasize the potential of ML-based methods, which perform better in low-power and high-cloud conditions, as well as the need to build an ensemble or hybrid model based on different ML algorithms to achieve improved projections

A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification

Sustainable energy systems rely on energy yield from renewable resources such as solar radiation and wind, which are typically not on-demand and need to be stored or immediately consumed. Solar irradiance is a highly stochastic phenomenon depending on fluctuating atmospheric conditions, in particular clouds and aerosols. The complexity of weather conditions in terms of many variable parameters and their inherent unpredictability limit the performance and accuracy of solar power forecasting models. As renewable power penetration in electricity grids increases due to the rapid increase in the installation of photovoltaics (PV) systems, the resulting challenges are amplified. A regional PV power prediction system is presented and evaluated by providing forecasts up to 72 h ahead with an hourly time resolution. The proposed approach is based on a local radiation forecast model developed by Blue Sky. In this paper, we propose a novel method of deriving forecast equations by using an irradiance classification approach to cluster the dataset. A separate equation is derived using the GEKKO optimization tool, and an algorithm is assigned for each cluster. Several other linear regressions, time series and machine learning (ML) models are applied and compared. A feature selection process is used to select the most important weather parameters for solar power generation. Finally, considering the prediction errors in each cluster, a weighted average and an average ensemble model are also developed. The focus of this paper is the comparison of the capability and performance of statistical and ML methods for producing a reliable hourly day-ahead forecast of PV power by applying different skill scores. The proposed models are evaluated, results are compared for different models and the probabilistic time series forecast is presented. Results show that the irradiance classification approach reduces the forecasting error by a considerable margin, and the proposed GEKKO optimized model outperforms other machine learning and ensemble models. These findings also emphasize the potential of ML-based methods, which perform better in low-power and high-cloud conditions, as well as the need to build an ensemble or hybrid model based on different ML algorithms to achieve improved projections

Evaluation of High Resolution WRF Solar

The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS

Cr (VI) induced changes in the activity of few ion dependent ATPases in three <span style="font-size:14.0pt;line-height:115%;font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman";color:black;mso-ansi-language:EN-IN; mso-fareast-language:EN-IN;mso-bidi-language:HI" lang="EN-IN">vital organs of mudskipper <i>Periophthalmus dipes </i>(Pisces: Gobidae)</span>

45-49Experiments were carried out to assess the dose and duration dependent influence of Cr(VI) to few ion dependent ATPases in liver, brain and muscle of a coastal euryhaline teleost Periophthalmus dipes. Fishes were exposed to three sublethal concentrations (5, 10 and 15 mg/l) of Cr(VI), prepared by using K2CrO4. with normal seawater, for three exposure duration (2, 4 and 6 days). In the present report, effects of Cr(VI) toxicity on total ATPase, (Na+,K+)-ATPase, (Ca++)-ATPase, (Mg++)-ATPase, (Ca++,HCO3-)-ATPase and (Mg++,HCO3-)-ATPase in liver, brain and muscle were evaluated. Though results indicated a general dose and duration-dependent inhibitory trend, exposure duration was predominant over dose in the inhibition of the enzyme activity.</span

Effects of dyeing and printing industry effluent on acid and alkaline phosphatase in few vital organs of a coastal teleost, <i>Periophthalmus dipes</i>

186-190Experiments were carried out to assess the dose and duration dependent effects of dyeing and printing effluent on the lysosomal enzyme, acid phosphatase and a membrane bound enzyme, alkaline phosphatase in five vital tissues viz. gills, intestine, liver, brain and muscle, of a coastal euryhaline teleost Periophthalmus dipes. Fishes were exposed to different effluent dilution viz. 0.1%, 0.5% and 1% for three test periods (2, 4 and 6 days) and the activity of the enzymes was estimated. The results show both significant inhibition at the lower concentrations and stimulation in the higher effluent concentration. Significant dose and duration dependent changes occurred in the gills whereas, predominant duration dependent changes were noticed in other tissues examined