Designing user-centered decision support systems for climate disasters: What information do communities and rescue responders need during floods?

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical. This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens' response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Predicting Pharmacokinetics of Drugs Using Artificial Intelligence Tools: A Systematic Review.

BACKGROUND AND OBJECTIVE: Pharmacokinetic studies encompass the examination of the absorption, distribution, metabolism, and excretion of bioactive compounds. The pharmacokinetics of drugs exert a substantial influence on their efficacy and safety. Consequently, the investigation of pharmacokinetics holds great importance. However, laboratory-based assessment necessitates the use of numerous animals, various materials, and significant time. To mitigate these challenges, alternative methods such as artificial intelligence have emerged as a promising approach. This systematic review aims to review existing studies, focusing on the application of artificial intelligence tools in predicting the pharmacokinetics of drugs. METHODS: A pre-prepared search strategy based on related keywords was used to search different databases (PubMed, Scopus, Web of Science). The process involved combining articles, eliminating duplicates, and screening articles based on their titles, abstracts, and full text. Articles were selected based on inclusion and exclusion criteria. Then, the quality of the included articles was assessed using an appraisal tool. RESULTS: Ultimately, 23 relevant articles were included in this study. The clearance parameter received the highest level of investigation, followed by the  area under the concentration-time curve (AUC) parameter, in pharmacokinetic studies. Among the various models employed in the articles, Random Forest and eXtreme Gradient Boosting (XGBoost) emerged as the most commonly utilized ones. Generalized Linear Models and Elastic Nets (GLMnet) and Random Forest models showed the most performance in predicting clearance. CONCLUSION: Overall, artificial intelligence tools offer a robust, rapid, and precise means of predicting various pharmacokinetic parameters based on a dataset containing information of patients or drugs.

Application of SPRA Technology for Delivery of Erythropoietin: Stability Evaluation of Conjugated Erythropoietin with Adamantane and in SPRA Inclusion Complex.

Background: As a widely used therapeutic protein, recombinant human erythropoietin (rhEPO) is currently one of the most effective biopharmaceuticals on the market for the treatment of anemia in patients with chronic renal disease. Increasing in vivo rhEPO half-life and its bioactivity is a significant challenge. It was hypothesized that the application of self-assembly PEGylation retaining activity, named supramolecular (SPRA) technology, could prolong the protein half-life without a significant loss of bioactivity. Objectives: This study aimed to assess the stability of rhEPO during synthetic reactions, including the conjugation with adamantane and the formation of the SPRA complex. To do this, the secondary structure of the protein was also evaluated. Methods: FTIR, ATR-FTIR, Far-UV-CD, and SDS-PAGE methods were employed. Thermal stability studies of SPRA-rhEPO complex and rhEPO were investigated at 37°C for ten days using a nanodrop spectrophotometer. Results: The secondary structure of lyophilized rhEPO, AD-rhEPO, and rhEPO (pH 8) was compared to rhEPO. Results showed that the secondary structure of the protein was unaffected by lyophilization, pH change, and the formation of covalent bonds in conjugation reaction. SPRA-rhEPO complex was also stable for seven days in phosphate buffer (pH 7.4) at 37°C. Conclusions: It was concluded that the stability of rhEPO could increase by complexation using SPRA technology.

Removal of Cd(ӀӀ) and phenol using novel cross-linked magnetic EDTA/chitosan/TiO2 nanocomposite.

In the present study, a novel cross-linked magnetic EDTA/chitosan/TiO2 (MECT) was prepared as eco-friendly and efficient bioadsorbent for the removal of Cd(II) ions and phenol from aqueous solution. Magnetic chitosan was improved by surface functionalization and cross-linking of it with EDTA and photocatalytic with TiO2. The nanocomposite was characterized by FE-SEM, EDX, FTIR and XRD techniques and Cd(II) ions adsorption and phenol degradation under varied experimental conditions were investigated. Results revealed that MECT nanoparticle with an average diameter of 40nm had the best performance in adsorption of Cd(II) and degradation of phenol at optimum pH values of 5-6. Moreover, the adsorption kinetics proceeded according to the mechanism of the pseudo-second-order model. The maximum adsorption capacity of Cd(II) obtained from Langmuir model was 209.205mgg-1 and phenol degradation efficiency was up to 90%. Reusability of MECT was tested and the adsorption and degradation capacities were not affected after five cycles. The paper suggests that the MECT is a promising recyclable nanocomposite for the removal of hazardous pollutants from contaminated water.