Estimation of Measles Immunization Coverage in Guwahati by Ranked Set Sampling

In order to study the efficacy of the ranked set sampling (RSS), as an alternative procedure, for estimation of the proportion of children aged 12–23 immunized against measles vaccine, a study is conducted in slum and non-slum regions of Guwahati, the capital of Assam, India. The RSS-based approach in the cases of both perfect and imperfect rankings is compared with its counterpart simple random sampling (SRS). The results revealed that estimates based on RSS with set size (4) are very close to Census report for Assam (2012) and has less variability than the SRS estimator. RSS-based estimates for different choices of probability of ranking error (ρ) are not only more accurate but are more precise and efficient than the SRS procedure, and also suggest that a the procedure of RSS better than the classical SRS

Time-to-death approach in revealing chronicity and severity of COVID-19 across the world

Background The outbreak of coronavirus disease, 2019 (COVID-19), which started from Wuhan, China,in late 2019, have spread worldwide. A total of 5,91,971 cases and 2,70,90 deaths were registered till 28th March, 2020. We aimed to predict the impact of duration of exposure to COVID-19 on the mortality rates increment. Methods In the present study, data on COVID-19 infected top seven countries viz., Germany, China, France, United Kingdom, Iran, Italy and Spain, and World as a whole, were used for modeling. The analytical procedure of generalized linear model followed by Gompertz link function was used to predict the impact lethal duration of exposure on the mortality rates. Findings Of the selected countries and World as whole, the projection based on 21st March, 2020 cases, suggest that a total (95% Cl) of 76 (65–151) days of exposure in Germany, mortality rate will increase by 5 times to 1%. In countries like France and United Kingdom, our projection suggests that additional exposure of 48 days and 7 days, respectively, will raise the mortality rates to 10%. Regarding Iran, Italy and Spain, mortality rate will rise to 10% with an additional 3–10 days of exposure. World’s mortality rates will continue increase by 1% in every three weeks. The predicted interval of lethal duration corresponding to each country has found to be consistent with the mortality rates observed on 28th March, 2020. Conclusion The prediction of lethal duration was found to have apparently effective in predicting mortality, and shows concordance with prevailing rates. In absence of any vaccine against COVID-19 infection, the present study adds information about the quantum of the severity and time elapsed to death will help the Government to take necessary and appropriate steps to control this pandemic

Improved performance of immobilized lipase from optimized biosupport material (polyvinyl alcohol/AlgNa) and its characterization

Enzymes play an imminent role as biocatalyst for various biotechnological applications as well as production of biodiesel. Here, we focused on the preparation of biosupport materials (polyvinyl alcohol/AlgNa) and their use in immobilization of lipase from Pseudomonas cepacia. Lipase was successfully immobilized onto polyvinyl alcohol/AlgNa in the form of biosupport materials by entrapment method. The mechanical strength, swelling ratio, thermal properties, optimum temperature and pH, lipase loading, leaching, immobilization yields and activity, characterization of the support materials were performed. The optimized pH and temperature for free lipase were 8.0 and 40°C, respectively, while the best pH and temperature for polyvinyl alcohol/AlgNa immobilized lipase were 8.0 and 50°C. 73.12% of the initial activity was retained for the immobilized biosupport catalyst in 6 cycles. The biosupport catalyst beads showed a fascinating degree of immobilized lipase activity along with high immobilization yield. The highest immobilized lipase activity and loading efficiency found to be 87.28 (U/g) and 55.2%, respectively. SEM analysis confirms the development of macro-porous structure from the combination of alginate. No sharp chemical interaction was observed in the behaviour of the functional groups of polyvinyl alcohol and AlgNa in the polyvinyl alcohol/sodium alginate blends, which were confirmed from Fourier transform infrared (FTIR) spectra. The immobilized biosupport catalysts are easily separable, recyclable and could be frequently used for transesterification

Enhanced analgesic effect of morphine-nimodipine combination after intraspinal administration as compared to systemic administration in mice

Calcium plays an important role in the pathophysiology of pain. A number of studies have investigated the effect of L-type calcium channel blockers on the analgesic response of morphine. However, the results are conflicting. In the present study, the antinociceptive effect of morphine (2-5 Mg) and nimodipine (1 Mg) co-administered intraspinally in mice was observed using the tail flick test. It was compared to the analgesic effect of these drugs (morphine - 250 μg subcutaneously; nimodipine - 100 Mg intraperitoneally) after systemic administration. Nimodipine is highly lipophilic and readily crosses the blood brain barrier. Addition of nimodipine to morphine potentiated the analgesic response of the latter when administered through the intraspinal route but not when administered through systemic route. It may be due to direct inhibitory effect of morphine and nimodipine on neurons of superficial laminae of the spinal cord after binding to M-opioid receptors and L-type calcium channels respectively

Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

Traditional classification algorithms often fail in learning from highly imbalanced datasets because the training involves most of the samples from majority class compared to the other existing minority class. In this paper, a Multiple Learners-based Ensemble SMOTEBagging (ML-ESB) technique is proposed. The ML-ESB algorithm is a modified SMOTEBagging technique in which the ensemble of multiple instances of the single learner is replaced by multiple distinct classifiers. The proposed ML-ESB is designed for handling only the binary class imbalance problem. In ML-ESB the ensembles of multiple distinct classifiers include Naïve Bays, Support Vector Machine, Logistic Regression and Decision Tree (C4.5) is used. The performance of ML-ESB is evaluated based on six binary imbalanced benchmark datasets using evaluation measures such as specificity, sensitivity, and area under receiver operating curve. The obtained results are compared with those of SMOTEBagging, SMOTEBoost, and cost-sensitive MCS algorithms with different imbalance ratios (IR). The ML-ESB algorithm outperformed other existing methods on four datasets with high dimensions and class IR, whereas it showed moderate performance on the remaining two low dimensions and small IR value datasets