Explore the vast range of titles available.

IntroductionAlthough studies have examined the utility of clinical decision support tools in improving acute kidney injury (AKI) outcomes, no study has evaluated the effect of real-time, personalised AKI recommendations. This study aims to assess the impact of individualised AKI-specific recommendations delivered by trained clinicians and pharmacists immediately after AKI detection in hospitalised patients.Methods and analysisKAT-AKI is a multicentre randomised investigator-blinded trial being conducted across eight hospitals at two major US hospital systems planning to enrol 4000 patients over 3 years (between 1 November 2021 and 1 November 2024). A real-time electronic AKI alert system informs a dedicated team composed of a physician and pharmacist who independently review the chart in real time, screen for eligibility and provide combined recommendations across the following domains: diagnostics, volume, potassium, acid–base and medications. Recommendations are delivered to the primary team in the alert arm or logged for future analysis in the usual care arm. The planned primary outcome is a composite of AKI progression, dialysis and mortality within 14 days from randomisation. A key secondary outcome is the percentage of recommendations implemented by the primary team within 24 hours from randomisation. The study has enrolled 500 individuals over 8.5 months. Two-thirds were on a medical floor at the time of the alert and 17.8% were in an intensive care unit. Virtually all participants were recommended for at least one diagnostic intervention. More than half (51.6%) had recommendations to discontinue or dose-adjust a medication. The median time from AKI alert to randomisation was 28 (IQR 15.8–51.5) min.Ethics and disseminationThe study was approved by the ethics committee of each study site (Yale University and Johns Hopkins institutional review board (IRB) and a central IRB (BRANY, Biomedical Research Alliance of New York). We are committed to open dissemination of the data through clinicaltrials.gov and sharing of data on an open repository as well as publication in a peer-reviewed journal on completion.Trial registration numberNCT04040296.

[...]they reported that ethosomes showed higher drug concentration in the skin as compared to deformable liposomes. The average drug content value was near to 100%, indicated that there is no loss of the material during the preparation and during the storage. [...]the prepared liposomal vesicular dispersions are having enough drug content to produce the effect of the drug. [...]FTIR spectral analysis proved the compatibility of the drug and excipients used in the study. [...]it was concluded that the optimum storage condition for the liposomes was found to be between 2-8°C. Pharmacokinetic study The newly developed formulations for the existing drugs are to be evaluated for their bioavailability because in vitro testing cannot always predict the in vivo performance.

[...]LfNPs represent a superior nano-carrier for the targeted delivery of Lnd in cancer cells intended for the efficient treatment of melanoma though detailed in vivo investigations are warranted. [...]the pellet was dispersed in phosphate buffered saline (PBS) and stored at 4 °C or lyophilized as per the experimental constraint. A high Lnd release at mild acidic pH values, as observed in our studies is favorable for precisely targeting tumor cells and reducing toxicity to normal cells. [...]Lnd-LfNPs exhibited lactoferrin receptor specific cellular uptake, prolonged retention and enhanced cytotoxicity in melanoma cells. [...]our results suggest use of LfNPs as potential delivery vehicle to elevate the anticancer effects of chemotherapeutic drugs such as Lnd.

The structural characterization of the synthesized formulation was done by X-ray diffraction, thermo gravimetric analysis, differential scanning calorimetry, Fourier transform infrared spectroscopy, vibrating sample magnetometer and transmission electron microscopy techniques. Fe304-Chs and Fe304-Lnd Nps are represented in (Figure 2). [...]the 100% peak which was observed at 35.4 2theta corresponding to the (311) plane that perfectly represents pure magnetite plane of standard JCPDS data (Card No.19-0629). Incorporating the parameters like initial amount of drug added and the amount of drug unbound, which were estimated from absorbance studies in equation. 1 the encapsulation efficiency of Lnd was calculated to be 70%. pH dependent drug release In vitro drug release study of the Fe3O4-Lnd was carried out in different pH buffer solutions to evaluate the effect of pH on the cleavage of the imine bond of Fe3O4-Lnd linkage, at room temperature. [...]the results imply that the designed nanoparticle delivery system could ensure the high stability of conjugated drug and be deemed suitable for further application.

In this paper, we present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. In particular, we provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology in order for the latter to execute optimal safe performance which is measured through certain Key Performance Indicators (KPIs). The network consists of a set of fixed Base Stations (BS) which are equipped with antennas, which one can control by adjusting their vertical tilt angle. The aforementioned process is called Remote Electrical Tilt (RET) optimization. Recent research has focused on performing this RET optimization by employing Reinforcement Learning (RL) strategies due to the fact that they have self-learning capabilities to adapt in uncertain environments. The term safety refers to particular constraints bounds of the network KPIs in order to guarantee that when the algorithms are deployed in a live network, the performance is maintained. In our proposed architecture the safety is ensured through model-checking techniques over combined discrete system models (automata) that are abstracted through the learning process. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions, and those that are allowed and blocked according to the safety specification.

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainable AI (XAI) -- arguably one of the biggest concerns today for the AI community. Work on explainable MR as well as on MR approaches to explainability in other areas of AI has continued ever since. It is especially potent in modern MR branches, such as argumentation, constraint and logic programming, planning. We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape. This document reports our work in-progress on MR explainability.