SUMMARY ANSWER: The inter-observer agreement among embryologists deciding whether to freeze blastocysts of marginal morphology was low and was not improved by a modified grading system.

WHAT IS KNOWN ALREADY: While previous research on inter-observer variability on the decision of which embryo to transfer from a cohort of blastocysts is good, the impact of grading variability regarding decision to freeze borderline blastocysts has not been investigated. Agreement for inner cell mass (ICM) and trophectoderm (TE) grade is only fair, factors which contribute to the grade that influences decision to freeze.

STUDY DESIGN, SIZE, DURATION: This was a prospective study involving 18 embryologists working at four different IVF clinics within a single organisation between January 2019 and July 2019.

PARTICIPANTS/MATERIALS, SETTING, METHODS: All embryologists currently practicing blastocyst grading at a multi-site organisation were invited to participate. The survey was comprised of blastocyst images in three planes and asked (i) the likelihood of freezing and (ii) whether the blastocyst would be frozen based on visual assessment. Blastocysts varied by quality and were categorised as either top (n = 20), borderline (n = 60) or non-viable/degenerate quality (n = 20). A total of 1800 freeze decisions were assessed. To assess the impact of grading criteria on inter-observer agreement for decision to freeze, the survey was taken once when the embryologists used the Gardner criteria and again 6 months after transitioning to a modified Gardner criterion with four grades for ICM and TE. The fourth grade was introduced with the aim to promote higher levels of agreement for the clinical usability decision when the blastocyst was of marginal quality.

METHODS: A bibliometric review was conducted on literature from over 23 years from January 2000 to May 2023. Articles focusing on any type of OSCE research in pharmacy education in both undergraduate and postgraduate sectors were included. Articles were excluded if they were not original articles or not published in English. A summative content analysis was also conducted to identify key topics.

RESULTS: A total of 192 articles were included in the analysis. There were 242 institutions that contributed to the OSCE literature in pharmacy education, with the leading country being Canada. Most OSCE research came from developed countries and were descriptive studies based on single institution data. The top themes emerging from content analysis were student perceptions on OSCE station styles (n = 98), staff perception (n = 19), grade assessment of OSCEs (n = 145), interprofessional education (n = 11), standardized patients (n = 12), and rubric development and standard setting (n = 8).

METHODS: A search of four databases was conducted: Web of Science, PubMed, Dimensions, and Scopus for research papers dated between January 2016 and September 2021. The search keywords are 'data mining', 'machine learning' in combination with 'suicidal behaviour', 'suicide', 'suicide attempt', 'suicidal ideation', 'suicide plan' and 'self-harm'. The studies that used machine learning techniques were synthesized according to the countries of the articles, sample description, sample size, classification tasks, number of features used to develop the models, types of machine learning techniques, and evaluation of performance metrics.

RESULTS: Thirty-five empirical articles met the criteria to be included in the current review. We provide a general overview of machine learning techniques, examine the feature categories, describe methodological challenges, and suggest areas for improvement and research directions. Ensemble prediction models have been shown to be more accurate and useful than single prediction models.

OBJECTIVES: This study aimed to examine two key drug repurposing methodologies in general diseases and specifically in AD, which are artificial intelligent (AI) approach and molecular docking approach. In addition, the hybrid approach that integrates AI with molecular docking techniques will be explored too.

METHODOLOGY: This study systematically compiled a comprehensive collection of relevant academic articles, scientific papers, and research studies which were published up until November 2024 (as of the writing of this review paper). The final selection of papers was filtered to include studies related to Alzheimer's disease and general diseases, and then categorized into three groups: AI articles, molecular docking articles, and hybrid articles.

RESULTS: As a result, 331 papers were identified that employed AI for drug repurposing in general diseases, and 58 papers focused specifically in AD. For molecular docking in drug repurposing, 588 papers addressed general diseases, while 46 papers were dedicated to AD. The hybrid approach combining AI and molecular docking in drug repurposing has 52 papers for general diseases and 9 for AD. A comparative review was done across the methods, results, strengths, and limitations in those studies. Challenges of drug repurposing in AD are explored and future prospects are proposed.