Microfluidic-controlled manufacture of liposomes for the solubilisation of a poorly water soluble drug

Besides their well-described use as delivery systems for water-soluble drugs, liposomes have the ability to act as a solubilizing agent for drugs with low aqueous solubility. However, a key limitation in exploiting liposome technology is the availability of scalable, low-cost production methods for the preparation of liposomes. Here we describe a new method, using microfluidics, to prepare liposomal solubilising systems which can incorporate low solubility drugs (in this case propofol). The setup, based on a chaotic advection micromixer, showed high drug loading (41 mol%) of propofol as well as the ability to manufacture vesicles with at prescribed sizes (between 50 and 450 nm) in a high-throughput setting. Our results demonstrate the ability of merging liposome manufacturing and drug encapsulation in a single process step, leading to an overall reduced process time. These studies emphasise the flexibility and ease of applying lab-on-a-chip microfluidics for the solubilisation of poorly water-soluble drugs

Forecasting the Anti-Rabies Vaccine Demand at Jawaharlal Medical College and Hospital, Ajmer, Rajasthan: A Comparative Analysis based on Time Series Model

Background: In India, high mortality and morbidity rates of human rabies is observed. Hence, a structured surveillance system is yet to be put in place for public health discussion. At the tertiary care hospital and all public health centres, requirement of anti-rabies vaccine is needed in advance to predict the upcoming months coverage so that wastage of vaccine is minimum. Objective: To find a suitable model for forecasting the appropriate stock of anti-rabies vaccines to avoid shortage and over-supply at anti rabies clinic. Methods and Material: This was a record based cross sectional study, conducted at anti rabies clinic of Jawaharlal Nehru Medical College and Hospital, Ajmer. Data of used anti rabies vaccine was taken from immunization inventory during the period from 2017 to 2020. Time series analysis based on Holt-Winter and Box-Jenkins methods were carried out to predict the need of vaccine. Results: Study series was not stationary and stationarity was observed by taken difference in the observation between two consequent months. Residuals of the series were normally distributed and independent to each other. ARIMA(0, 1, 1) was the best model in comparison to Holt-Winter model for prediction because of low value of model selection criterion.  The forecasted value for anti-rabies vaccine was done for the year 2021. Conclusions: The following study concluded that time series can be used as a tool to forecast anti-rabies vaccine coverage and will help the policy makers to formulate appropriate plans and strategies and improve the management of vaccination resources and inventory

Larvicidal Activity of Methanol and Chloroform Extract of Swertia celiata against Three Mosquito Vectors

Background: Mosquitoes are an important public health concern as they spread life-threatening diseases such as malaria, filaria, Japanese encephalitis, dengue fever, chikungunya, and yellow fever. In the last decades, synthetic insecticides were extensively used for the control of these vector-borne diseases but it also reported the detrimental side-effects in human beings and pet animals. To overcome the side effects, plants-derived secondary metabolites were screened and tested for insecticidal properties. The present study deals with the insecticidal activity of chloroform and methanol extracts of Swertia celiata leaves against Culex quenquifasciatus, Aedes aegypti, and Anopheles stephensi larvae.Method: The S. celiata leaves were subjected to chloroform and methanol with 1:3 (Weight/ Volume) ratio and the extracted solvent was dried using rotary vacuum evaporator. The larvicidal activity of the extract was tested using WHO method and LC50 and LC90 were evaluated by probit analysis.Results: The LC50 value of chloroform extract of S. celiata was found to be 65.288, 67.406 and 71.608 ppm whereas LC90 was 184.721, 186.582 and 192.497 ppm against C. quinquefasciatus, Ae. aegypti and A. stephensi, respectively. The methanolic extract was also found potent; LC50 was 91.503, 101.574 and 99.104 ppm whereas LC90 was 230.823, 271.927 and 234.257 ppm against C. quinquefasciatus, Ae. aegypti and A. stephensi, respectively. Both chloroform and methanol extract were found significantly lethal tothe tested mosquito vectors.Conclusion: Taken results together, chloroform extract showed higher toxicity as compared to methanolic extract against all the tested species. The study clearly revealed that S. ciliata extract or bioactive compounds can be used as an alternative to synthetic insecticides

A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions

Online portals provide an enormous amount of news articles every day. Over the years, numerous studies have concluded that news events have a significant impact on forecasting and interpreting the movement of stock prices. The creation of a framework for storing news-articles and collecting information for specific domains is an important and untested problem for the Indian stock market. When online news portals produce financial news articles about many subjects simultaneously, finding news articles that are important to the specific domain is nontrivial. A critical component of the aforementioned system should, therefore, include one module for extracting and storing news articles, and another module for classifying these text documents into a specific domain(s). In the current study, we have performed extensive experiments to classify the financial news articles into the predefined four classes Banking, Non-Banking, Governmental, and Global. The idea of multi-class classification was to extract the Banking news and its most correlated news articles from the pool of financial news articles scraped from various web news portals. The news articles divided into the mentioned classes were imbalanced. Imbalance data is a big difficulty with most classifier learning algorithms. However, as recent works suggest, class imbalances are not in themselves a problem, and degradation in performance is often correlated with certain variables relevant to data distribution, such as the existence in noisy and ambiguous instances in the adjacent class boundaries. A variety of solutions to addressing data imbalances have been proposed recently, over-sampling, down-sampling, and ensemble approach. We have presented the various challenges that occur with data imbalances in multiclass classification and solutions in dealing with these challenges. The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques. From the result, it’s clear that the performance of Random Forest classifier with data balances using the over-sampling technique SMOTE is best in terms of precision, recall, F-1, and accuracy. From the ensemble classifiers, the Balanced Bagging classifier has shown similar results as of the Random Forest classifier with SMOTE. Random forest classifier's accuracy, however, was 100% and it was 99% with the Balanced Bagging classifier

Unveiling the Power of Self-Attention for Shipping Cost Prediction: The Rate Card Transformer

Amazon ships billions of packages to its customers annually within the United States. Shipping cost of these packages are used on the day of shipping (day 0) to estimate profitability of sales. Downstream systems utilize these days 0 profitability estimates to make financial decisions, such as pricing strategies and delisting loss-making products. However, obtaining accurate shipping cost estimates on day 0 is complex for reasons like delay in carrier invoicing or fixed cost components getting recorded at monthly cadence. Inaccurate shipping cost estimates can lead to bad decision, such as pricing items too low or high, or promoting the wrong product to the customers. Current solutions for estimating shipping costs on day 0 rely on tree-based models that require extensive manual engineering efforts. In this study, we propose a novel architecture called the Rate Card Transformer (RCT) that uses self-attention to encode all package shipping information such as package attributes, carrier information and route plan. Unlike other transformer-based tabular models, RCT has the ability to encode a variable list of one-to-many relations of a shipment, allowing it to capture more information about a shipment. For example, RCT can encode properties of all products in a package. Our results demonstrate that cost predictions made by the RCT have 28.82% less error compared to tree-based GBDT model. Moreover, the RCT outperforms the state-of-the-art transformer-based tabular model, FTTransformer, by 6.08%. We also illustrate that the RCT learns a generalized manifold of the rate card that can improve the performance of tree-based models

Superconducting fluctuations and characteristic time scales in amorphous WSi

We study magnitudes and temperature dependences of the electron-electron and electron-phonon interaction times which play the dominant role in the formation and relaxation of photon induced hotspot in two dimensional amorphous WSi films. The time constants are obtained through magnetoconductance measurements in perpendicular magnetic field in the superconducting fluctuation regime and through time-resolved photoresponse to optical pulses. The excess magnetoconductivity is interpreted in terms of the weak-localization effect and superconducting fluctuations. Aslamazov-Larkin, and Maki-Thompson superconducting fluctuation alone fail to reproduce the magnetic field dependence in the relatively high magnetic field range when the temperature is rather close to Tc because the suppression of the electronic density of states due to the formation of short lifetime Cooper pairs needs to be considered. The time scale {\tau}_i of inelastic scattering is ascribed to a combination of electron-electron ({\tau}_(e-e)) and electron-phonon ({\tau}_(e-ph)) interaction times, and a characteristic electron-fluctuation time ({\tau}_(e-fl)), which makes it possible to extract their magnitudes and temperature dependences from the measured {\tau}_i. The ratio of phonon-electron ({\tau}_(ph-e)) and electron-phonon interaction times is obtained via measurements of the optical photoresponse of WSi microbridges. Relatively large {\tau}_(e-ph)/{\tau}_(ph-e) and {\tau}_(e-ph)/{\tau}_(e-e) ratios ensure that in WSi the photon energy is more efficiently confined in the electron subsystem than in other materials commonly used in the technology of superconducting nanowire single-photon detectors (SNSPDs). We discuss the impact of interaction times on the hotspot dynamics and compare relevant metrics of SNSPDs from different materials