Guiding principles for research advisors in the pharmacy resident research process.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Reconstruction of the Passive Layer of AISI 304 and 316 Steel After Scratching.

Austenitic stainless steels are used widely in many fields due to their good mechanical properties and high resistance to corrosion. This work focuses on the reconstruction of the passive film after scratching. The purpose of the study was to compare changes in the rate of passive layer reconstruction and to discuss the effect of both the type of material and its electrochemical treatment on the reconstruction of the passive layer for two types of stainless steel: 304 and 316.

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

Background: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given the high frequency and time-dependency of their use and other factors affecting FO. We sought to employ unsupervised machine learning methods to uncover medication administration patterns correlating with FO.

Production and properties of particleboard and paper from waste poppy straw.

Due to the scarcity of wood in some countries, it is necessary to replace it with other raw materials and at the same time use the waste material. The aim of this research is to use poppy waste straw for the efficient conversion of possible lignocellulosic materials - pulps and particleboards. Their suitability for the production of composites is assessed on the basis of selected physical or mechanical properties.

Using artificial intelligence and statistics for managing peritoneal metastases from gastrointestinal cancers.

Objective: The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers.

Methods: Relevant keywords and search criteria were comprehensively researched on PubMed and Google Scholar to identify articles and reviews related to the topic. The AI approaches considered were conventional machine learning (ML) and deep learning (DL) models, and the relevant statistical approaches included biostatistics and logistic models.