Novel approach towards antimicrobial chemotherapy optimization in lower respiratory tract infections in children: An observational study.

ABSTRACT: The use of local antibiogram in guiding clinical decisions is an integral part of the antimicrobial stewardship program. Conventional antibiograms are not disease-specific, ignore the distribution of microorganisms, obscure the in-vitro efficacy interrelationships, and have limited use in polymicrobial infections.We aimed to develop an in-house empiric, disease-specific, antimicrobial prescription auxiliary for the treatment of hospitalized pediatric pneumonia patients and to present the methods which help to choose the first and the second line antimicrobial therapy, while accounting for cost and safety aspects.A retrospective single center observational study was conducted on bronchoscopy obtained sputum culture. Analysis of probabilities, variance minimization, Boolean network modeling, and dominance analysis were applied to analyze antibiogram data. The Kirby-Bauer disk diffusion method was used to test the susceptibility of all isolates. Final optimization analysis included local drug acquisition cost (standardized to price per DDD) and safety profile.Data of 145 pediatric patients hospitalized with pneumonia with 218 isolates over 5 years was collected. A combination of statistical methods such as probabilities of drug efficacy, variance minimization, Boolean network modeling, and dominance analysis can help to choose the optimal first-line and the second-line antimicrobial treatment and optimize patient care. This research reveals that ampicillin is the optimal choice as the first-line drug and piperacillin-tazobactam is the second-line antimicrobial drug if the first one is not effective, while accounting for cost and safety aspects.The paper proposes a new methodology to adapt empiric antimicrobial therapy recommendations based on real world data and accout for costs and risk of adverse events.

Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic.

As the COVID-19 pandemic came unexpectedly, many real estate experts claimed that the property values would fall like the 2007 crash. However, this study raises the question of what attributes of an apartment are most likely to influence a price revision during the pandemic. The findings in prior studies have lacked consensus, especially regarding the time-on-the-market variable, which exhibits an omnidirectional effect. However, with the rise of Big Data, this study used a web-scraping algorithm and collected a total of 18,992 property listings in the city of Vilnius during the first wave of the COVID-19 pandemic. Afterwards, 15 different machine learning models were applied to forecast apartment revisions, and the SHAP values for interpretability were used. The findings in this study coincide with the previous literature results, affirming that real estate is quite resilient to pandemics, as the price drops were not as dramatic as first believed. Out of the 15 different models tested, extreme gradient boosting was the most accurate, although the difference was negligible. The retrieved SHAP values conclude that the time-on-the-market variable was by far the most dominant and consistent variable for price revision forecasting. Additionally, the time-on-the-market variable exhibited an inverse U-shaped behaviour.

Stock price reaction to the drug development setbacks in the pharmaceutical industry.

BACKGROUND: Investments in pharmaceutical companies remain challenging due to the inherent uncertainties of risk assessment. OBJECTIVES: Our paper aims to assess the impact of the drug development setbacks (DDS) on the stock price of pharmaceutical companies while taking into account the company's financial situation, pipeline size and trend of the stock price before the DDS. METHODS: The model-based clustering based on finite Gaussian mixture modeling was employed to identify the clusters of pharmaceutical companies with homogenous parameters. An artificial neural network was constructed to aid the prediction of the positive mean rate of return 120 days after the DDS. RESULTS: Our results reveal that a higher pipeline size and a lower rate of return before the DDS, as well as a lower ratio of the market value of the equity and the book value of the total liabilities, are associated with a positive mean rate of return 120 days after the DDS. CONCLUSION: In general, the DDS have a negative impact on the company's stock price, but this risk can be minimized by investors choosing the companies that satisfy certain criteria. Graphical abstract The higher pipeline size(spip) and lower rate of return before (srr) the drug development setback (DDS) and the Market Value of Equity/Book Value of Total Liabilities ratio (sx4) are associated with a positive mean rate of return 120 days after the DDS.