Key Pitfalls Research Articles

The future of multimodal artificial intelligence models for integrating imaging and clinical metadata: a narrative review.

With the ongoing revolution of artificial intelligence (AI) in medicine, the impact of AI in radiology is more pronounced than ever. An increasing number of technical and clinical AI-focused studies are published each day. As these tools inevitably affect patient care and physician practices, it is crucial that radiologists become more familiar with the leading strategies and underlying principles of AI. Multimodal AI models can combine both imaging and clinical metadata and are quickly becoming a popular approach that is being integrated into the medical ecosystem. This narrative review covers major concepts of multimodal AI through the lens of recent literature. We discuss emerging frameworks, including graph neural networks, which allow for explicit learning from non-Euclidean relationships, and transformers, which allow for parallel computation that scales, highlighting existing literature and advocating for a focus on emerging architectures. We also identify key pitfalls in current studies, including issues with taxonomy, data scarcity, and bias. By informing radiologists and biomedical AI experts about existing practices and challenges, we hope to guide the next wave of imaging-based multimodal AI research.

Nanomedicine Tumor Targeting.

Nanomedicines are extensively explored for cancer therapy. By delivering drug molecules more efficiently to pathological sites and by attenuating their accumulation in healthy organs and tissues, nanomedicine formulations aim to improve the balance between drug efficacy and toxicity. More than 20 cancer nanomedicines are approved for clinical use, and hundreds of formulations are in (pre)clinical development. Over the years, several key pitfalls have been identified as bottlenecks in nanomedicine tumor targeting and translation. These go beyond materials- and production-related issues, and particularly also encompass biological barriers and pathophysiological heterogeneity. In this manuscript, the author describes the most important principles, progress, and products in nanomedicine tumor targeting, delineates key current problems and challenges, and discusses the most promising future prospects to create clinical impact.

Opportunities and challenges for school food programs in Canada

As Canada works towards developing a national school food program, it is timely to examine the lessons learned from the programs of other countries. Analyzing these insights can help Canada avoid key pitfalls and replicate promising practices in program design and implementation. The Government of Canada has the advantage of learning from one of the longest standing national school food programs and our southern neighbour: the United States (U.S.). This paper distills vital lessons from the U.S. school food programs, with a focus on addressing four critical aspects: access, emphasis on health and education, funding, and program implementation. First, the U.S. experience demonstrates the significance of universal free school meals. The historical inadequacies of means-tested programs result in inefficiencies, stigma, and exclusion of students in need. Second, the paper argues for an emphasis on health and education benefits. Third, it underscores the necessity of adequate funding. Inadequate reimbursements in the U.S. have compromised meal quality and led to the food industry’s capitalization on school meals, with negative implications for children’s health. Lastly, harnessing the power of procurement and employment can stimulate local economies, create good jobs, and foster a healthier food environment. As Canada tailors its national school food program to its diverse regions and communities, it has an extraordinary opportunity to avoid the policy and program implementation errors revealed by the U.S. experience.

Opportunities and challenges for school food programs in Canada

As Canada works towards developing a national school food program, it is timely to examine the lessons learned from the programs of other countries. Analyzing these insights can help Canada avoid key pitfalls and replicate promising practices in program design and implementation. The Government of Canada has the advantage of learning from one of the longest standing national school food programs and our southern neighbour: the United States (U.S.). This paper distills vital lessons from the U.S. school food programs, with a focus on addressing four critical aspects: access, emphasis on health and education, funding, and program implementation. First, the U.S. experience demonstrates the significance of universal free school meals. The historical inadequacies of means-tested programs result in inefficiencies, stigma, and exclusion of students in need. Second, the paper argues for an emphasis on health and education benefits. Third, it underscores the necessity of adequate funding. Inadequate reimbursements in the U.S. have compromised meal quality and led to the food industry’s capitalization on school meals, with negative implications for children’s health. Lastly, harnessing the power of procurement and employment can stimulate local economies, create good jobs, and foster a healthier food environment. As Canada tailors its national school food program to its diverse regions and communities, it has an extraordinary opportunity to avoid the policy and program implementation errors revealed by the U.S. experience.

Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development of surgical best practices, identifying key pitfalls, enhancing situational awareness, and disseminating that information via real-time, intraoperative decision-making. The objective of the present study was to examine the feasibility and accuracy of a novel computer vision algorithm for hysterectomy surgical step identification. This was a retrospective study conducted on surgical videos of laparoscopic hysterectomies performed in 277 patients in five medical centers. We used a surgical intelligence platform (Theator Inc.) that employs advanced computer vision and AI technology to automatically capture video data during surgery, deidentify, and upload procedures to a secure cloud infrastructure. Videos were manually annotated with sequential steps of surgery by a team of annotation specialists. Subsequently, a computer vision system was trained to perform automated step detection in hysterectomy. Analyzing automated video annotations in comparison to manual human annotations was used to determine accuracy. The mean duration of the videos was 103 ± 43 min. Accuracy between AI-based predictions and manual human annotations was 93.1% on average. Accuracy was highest for the dissection and mobilization step (96.9%) and lowest for the adhesiolysis step (70.3%). The results of the present study demonstrate that a novel AI-based model achieves high accuracy for automated steps identification in hysterectomy. This lays the foundations for the next phase of AI, focused on real-time clinical decision support and prediction of outcome measures, to optimize surgeon workflow and elevate patient care.

Effect of ursodiol on alginate/PLL nanoparticles with non-ionic surfactant for gene delivery

Hearing loss is a widespread condition, affecting people from a range of demographics. Gene therapy is an emerging method for the amelioration of this condition. Challenges associated with the delivery of genes to various sites within the ear remain a significant challenge. In the present work, a novel polymer nanoparticle delivery system was developed, incorporating a bile acid excipient. Bile acids have previously been shown to improve drug delivery through their permeation enhancing properties; however, few studies report their use in gene delivery systems. Nanoparticles were developed with sodium alginate and poly-L-lysine through an ionotropic gelation method. Various surfactants including Tween-80 and poly-ethylene glycol 6000 were incorporated to both improve the solubility of the bile acid, ursodiol, and to modify nanoparticle properties. The evaluation of the nanoparticle’s safety profiles was the primary outcome of this study. The secondary aims were to perform genetic studies, such as transfection efficiency. The nanoparticles generated in this study demonstrated formulation-dependent variability in particle size ranging from 30 to 300 nm. Several of the developed formulations demonstrated suitable safety profiles; further, the introduction of bile acid helped to reduce toxicity. Transfection efficiency for all formulations remained low, potentially due to poor plasmid release inside the cell. Poor transfection efficiency is one of the key pitfalls associated with polymer nanoparticles Overall the present study developed nanoparticles with suitable safety profiles but limited efficacy. The use of modified polymers, additional excipients, and cell-targeting peptides are potential methods that may be explored in future studies to help further improve gene delivery.

Development of an iron overload HepG2 cell model using ferrous ammonium citrate

Cell-based iron overload models provide tremendous utility for the investigations into the pathogenesis of different diseases as well as assessing efficacy of various therapeutic strategies. In the literature, establishing such models vary widely with regards to cell lines, iron source, iron treatment conditions and duration. Due to this diversity, researchers reported significant differences in the measured outcomes, either in cellular function or response to a stimulus. Herein, we report the process required to establish an iron overload HepG2 cell model to achieve a consistent and reproducible results such that the literature can strive towards a consensus. Iron loading in cells was achieved with 50 μM of iron every 24 h for 2 days, followed by an additional 24 h of maintenance in fresh media. We demonstrated that iron overloaded cells had significantly increased ROS generation, labile and total iron whilst having various cellular functions resemble cells without iron overload. The present report addresses key pitfalls with regards to the lack of consensus currently present in the literature.

MP26-07 ARTIFICIAL INTELLIGENCE-ENABLED AUTOMATED IDENTIFICATION OF KEY STEPS IN ROBOTIC-ASSISTED RADICAL PROSTATECTOMY

You have accessJournal of UrologyCME1 Apr 2023MP26-07 ARTIFICIAL INTELLIGENCE-ENABLED AUTOMATED IDENTIFICATION OF KEY STEPS IN ROBOTIC-ASSISTED RADICAL PROSTATECTOMY Abhinav Khanna, Alenka Antolin, Maya Zohar, Figure Bar, Danielle Ben-Ayoun, Stephen A. Boorjian, Igor Frank, Paras Shah, Vidit Sharma, R. Houston Thompson, Tamir Wolf, Dotan Asselmann, and Matthew Tollefson Abhinav KhannaAbhinav Khanna More articles by this author , Alenka AntolinAlenka Antolin More articles by this author , Maya ZoharMaya Zohar More articles by this author , Figure BarFigure Bar More articles by this author , Danielle Ben-AyounDanielle Ben-Ayoun More articles by this author , Stephen A. BoorjianStephen A. Boorjian More articles by this author , Igor FrankIgor Frank More articles by this author , Paras ShahParas Shah More articles by this author , Vidit SharmaVidit Sharma More articles by this author , R. Houston ThompsonR. Houston Thompson More articles by this author , Tamir WolfTamir Wolf More articles by this author , Dotan AsselmannDotan Asselmann More articles by this author , and Matthew TollefsonMatthew Tollefson More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003254.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Analysis of surgical video allows surgeons to gain valuable insights into their practice. Opportunities include developing best practices, identifying key pitfalls, facilitating situational awareness, and revisiting intraoperative decision-making. However, surgical video review is laborious and time-intensive, thus limiting its routine and widespread use. We aim to develop a computer vision algorithm for automated identification of key surgical steps during robotic-assisted radical prostatectomy (RARP). METHODS: Retrospective surgical videos from RARP performed at a tertiary-care academic referral center were manually annotated by a team of medical image annotators under the supervision of two fellowship-trained urologic oncologists. Full-length surgical videos were annotated with the following steps of surgery: preparation, adhesiolysis, lymph node dissection, Retzius space dissection, anterior bladder neck transection, posterior bladder neck transection, seminal vesicle and posterior dissection, lateral (including neurovascular bundle) and apical dissection, urethral transection, urethrovesical anastomosis, specimen retrieval and final inspection. Manually annotated videos were then utilized to train a computer vision algorithm to perform automated video annotation of RARP surgical video. Accuracy of automated video annotation was determined by comparing to manual human annotations as the reference standard. RESULTS: A total of 107 full-length RARP videos [average 117±40 minutes] were manually annotated with sequential steps of surgery. Of these, 70 cases served as a training dataset for algorithm development, 14 cases were used for internal validation, and 23 were used as a separate testing cohort for evaluating algorithm accuracy. Concordance between AI-enabled automated video analysis and manual human video annotation was 87.6%. Algorithm accuracy was highest for the vesicourethral anastomosis step (98.6%) and lowest for the final inspection and extraction step (63.0%). CONCLUSIONS: We present results of an AI-enabled computer vision algorithm for automated annotation of full-length RARP surgical video. Automated surgical video analysis has practical applications in retrospective video review by surgeons, surgical training, quality assessment, and for the development of future algorithms to associate perioperative and long-term outcomes with intraoperative surgical events. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e354 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Abhinav Khanna More articles by this author Alenka Antolin More articles by this author Maya Zohar More articles by this author Figure Bar More articles by this author Danielle Ben-Ayoun More articles by this author Stephen A. Boorjian More articles by this author Igor Frank More articles by this author Paras Shah More articles by this author Vidit Sharma More articles by this author R. Houston Thompson More articles by this author Tamir Wolf More articles by this author Dotan Asselmann More articles by this author Matthew Tollefson More articles by this author Expand All Advertisement PDF downloadLoading ...

Fine-Grained Performance and Cost Modeling and Optimization for FaaS Applications

Function-as-a-Service (FaaS) has become a mainstream cloud computing paradigm for developers to build cloud-native applications in recent years. By taking advantage of serverless architecture, FaaS applications bring many desirable benefits, including built-in scalability, high availability, and improved cost-effectiveness. However, predictability and trade-off of performance and cost are still key pitfalls for FaaS applications due to poor infrastructure transparency and lack of performance and cost models that fit the new paradigm. In this study, we therefore fill this gap by proposing formal performance and cost modeling and optimization algorithms, which enable accurate prediction and fine-grained control over the performance and cost of FaaS applications. The proposed model and algorithms provide better predictability and trade-off of performance and cost for FaaS applications, which help developers to make informed decisions on cost reduction, performance improvement, and configuration optimization. We validate the proposed model and algorithms via extensive experiments on AWS. We show that the modeling algorithms can accurately estimate critical metrics, including response time, cost, exit status, and their distributions, regardless of the complexity and scale of the application workflow. Also, the depth-first bottleneck alleviation algorithm for trade-off analysis can effectively solve two optimization problems with fine-grained constraints.

Tocqueville and the Ostroms

Although it is commonplace for political scientists to draw upon historical thinkers and the ‘great books’ of the past, the practice of using historical works as reference points for contemporary issues remains under-investigated. To address this practice, this article is positioned at the crossroads of social science and intellectual history. By examining the relationship of political economists Elinor and Vincent Ostrom with Alexis de Tocqueville, the article demonstrates some of the potential risks incurred by social scientists drawing on historical thinkers. After exploring the similarities between the Ostroms and Tocqueville, it identifies three key pitfalls of the Ostroms’ ‘Tocquevillian’ rhetoric. These pitfalls result in obscuring meaning and overlooking philosophical insights, both of which detract from the Ostroms’ project. Insights from the field of intellectual history are offered. A final section presents a key example of Vincent Ostrom overlooking Tocqueville's thought where Tocqueville's insights were directly applicable to his work. The article parses political science's relationship with the past and offers a critique that is applicable beyond Tocqueville and the Ostroms.

Citizen science and the potential for mobility policy – Introducing the Bike Barometer

In this paper, we report on a citizen science pilot project involving adolescents who digitize and assess their daily home-to-school routes in different school neighborhoods in Flanders (Belgium). As part of this pilot project, a web-based platform, called the “Bike Barometer” (“Fietsbarometer” in Dutch) was developed. We introduce the tool in this paper and summarize the insights gained from the pilot. From the official launch of the platform in March until the end of the pilot in June 2020, 1,256 adolescents from 31 schools digitized 5657 km of roads, of which 3,750 km were evaluated for cycling friendliness and safety. The added value and potential of citizen science in general and the platform in particular are illustrated. The results offer detailed (spatial) insights into local safety conditions for Flanders and for specific school neighborhoods. The potential for mobility policy is twofold: (i) the cycling friendliness and traffic flows in school environments can be monitored over time and (ii) the platform has the potential to create local ecosystems of adolescents and teachers (both considered citizen scientists here) and policymakers. Two key pitfalls are identified as well: the need for a critical mass of citizen scientists and a minimum level of commitment required from local policymakers. By illustrating the untapped potential of citizen science, we argue that the intersection between citizen science and local policymaking in the domain of mobility deserves much more attention.

CyTOF® for the Masses.

Mass cytometry has revolutionized immunophenotyping, particularly in exploratory settings where simultaneous breadth and depth of characterization of immune populations is needed with limited samples such as in preclinical and clinical tumor immunotherapy. Mass cytometry is also a powerful tool for single-cell immunological assays, especially for complex and simultaneous characterization of diverse intratumoral immune subsets or immunotherapeutic cell populations. Through the elimination of spectral overlap seen in optical flow cytometry by replacement of fluorescent labels with metal isotopes, mass cytometry allows, on average, robust analysis of 60 individual parameters simultaneously. This is, however, associated with significantly increased complexity in the design, execution, and interpretation of mass cytometry experiments. To address the key pitfalls associated with the fragmentation, complexity, and analysis of data in mass cytometry for immunologists who are novices to these techniques, we have developed a comprehensive resource guide. Included in this review are experiment and panel design, antibody conjugations, sample staining, sample acquisition, and data pre-processing and analysis. Where feasible multiple resources for the same process are compared, allowing researchers experienced in flow cytometry but with minimal mass cytometry expertise to develop a data-driven and streamlined project workflow. It is our hope that this manuscript will prove a useful resource for both beginning and advanced users of mass cytometry.

Intravascular Large B-Cell Lymphoma

Intravascular large B-cell lymphoma (IVLBCL) is a rare hematopathologic entity, posing both a clinical and histologic challenge for diagnosis. Numerous pitfalls can hinder making the diagnosis. To summarize recent developments in literature pertaining to IVLBCL and point out key pitfalls pathologists should be prepared to encounter. Literature review via PubMed search and hospital (Darnall Medical Library) resources. The 3 primary pitfalls of IVLBCL include masking of IVLBCL, mimicry by IVLBCL, and mimicry of IVLBCL. These scenarios illustrate the importance of histologic pattern recognition and subsequent usage of immunohistochemistry, especially in context of a clinical history that may be noncharacteristic.

Cooperation for the implementation of digital applications in rural cultural tourism marketing

PurposeThis study aims to identify pitfalls in the use of cooperation for the implementation of digital applications in rural cultural tourism marketing and derive practical implications for avoiding them.Design/methodology/approachA total of 14 semi-structured in-depth interviews were conducted with the participants of the cooperation project around the Web app “The Legend of the Call of the Mountain” in the Zugspitz Region in Germany.FindingsThe study has identified four key pitfalls: challenges in the long-term financing, destination-specific characteristics of rural areas, doubts about the use of evaluations and the limited horizon of many cultural institutions.Research limitations/implicationsBecause of the qualitative approach and the single case study, the results may lack generalisability. Therefore, future research should place the results to a larger scale, for example, with a mixed methods approach.Practical implicationsPractical implications can be derived from the study for avoiding pitfalls and thus for a successful use of such cooperation projects. The implications draw attention to the different possibilities of long-term financing, the important role of a central tourism organisation, the special possibilities of evaluations and the important role of general awareness raising for the added values of such cooperation.Originality/valueBy examining an exemplary cooperation project, the paper fills an existing research gap, as there has been little knowledge in empirical cultural tourism research about the special conditions for using cooperation to implement digital applications in rural cultural tourism marketing.

Harnessing cultural heritage for sustainable development: an analysis of three internationally funded projects in MENA Countries

ABSTRACT The importance of cultural heritage as an enabler of sustainable development is widely acknowledged. However, its role has remained marginalised in past and current international development agendas. This paper provides an original interdisciplinary study interrogating the potential of harnessing cultural heritage (both tangible and intangible) for sustainable development. It also reflects on how international policies and narratives on cultural heritage for development have been implemented on the ground. In particular, it critically examines the contribution of cultural heritage to worldwide developmental issues, with an approach that traverses the three sustainable development dimensions. It investigates how cultural heritage has been used to tackle global challenges, such as poverty alleviation in marginalised groups (economic dimension); gender equality and the empowerment of women (social dimension); and environmental sustainability (environmental dimension). The analysis focuses on three heritage for development projects funded through the ‘Millennium Development Goals Achievement Fund’, jointly implemented by UNESCO in partnership with other United Nations organisations and local partners in the Middle East and North Africa between 2008 and 2013. The article sheds light on multifaceted aspects of cultural heritage for sustainable development by discussing key achievements and common pitfalls.

A brief introduction to the analysis of time-series data from biologging studies.

Recent advances in tagging and biologging technology have yielded unprecedented insights into wild animal physiology. However, time-series data from such wild tracking studies present numerous analytical challenges owing to their unique nature, often exhibiting strong autocorrelation within and among samples, low samples sizes and complicated random effect structures. Gleaning robust quantitative estimates from these physiological data, and, therefore, accurate insights into the life histories of the animals they pertain to, requires careful and thoughtful application of existing statistical tools. Using a combination of both simulated and real datasets, I highlight the key pitfalls associated with analysing physiological data from wild monitoring studies, and investigate issues of optimal study design, statistical power, and model precision and accuracy. I also recommend best practice approaches for dealing with their inherent limitations. This work will provide a concise, accessible roadmap for researchers looking to maximize the yield of information from complex and hard-won biologging datasets. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.

Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing

Automated sound recognition tools can be a useful complement to d/Deaf and hard of hearing (DHH) people's typical communication and environmental awareness strategies. Pre-trained sound recognition models, however, may not meet the diverse needs of individual DHH users. While approaches from human-centered machine learning can enable non-expert users to build their own automated systems, end-user ML solutions that augment human sensory abilities present a unique challenge for users who have sensory disabilities: how can a DHH user, who has difficulty hearing a sound themselves, effectively record samples to train an ML system to recognize that sound? To better understand how DHH users can drive personalization of their own assistive sound recognition tools, we conducted a three-part study with 14 DHH participants: (1) an initial interview and demo of a personalizable sound recognizer, (2) a week-long field study of in situ recording, and (3) a follow-up interview and ideation session. Our results highlight a positive subjective experience when recording and interpreting training data in situ, but we uncover several key pitfalls unique to DHH users---such as inhibited judgement of representative samples due to limited audiological experience. We share implications of these results for the design of recording interfaces and human-the-the-loop systems that can support DHH users to build sound recognizers for their personal needs.

Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study

BackgroundDue to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012.MethodsA meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012.ResultsOur search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls.ConclusionThe majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings.

The child with an acute limp

<h2>Abstract</h2> The limping child is a common presentation to the emergency department. When confronted with a crying child who cannot walk or explain why, a structured approach based on an appreciation of the underlying physiology can help identify those children with (sometimes subtle) red flags, avoid unnecessary interventions and hospital stays. Here we discuss the differential diagnosis of an acute atraumatic limp, and suggest an approach to examination and investigation, including some key pitfalls and signs to look out for.

How to be successful in an academic interview in pediatric oncology: A survey of Children's Oncology Group (COG) and International Society of Paediatric Oncology (SIOP) mentors.

A successful academic interview has been reported as the most important factor contributing to ranking of candidates for residency. However, little published guidance exists to help a prospective oncologist or researcher give such an interview. The International Society of Paediatric Oncology (SIOP) Young Investigator (YI) Network and Children's Oncology Group (COG) YI group thus cosponsored a survey of senior investigators seeking their advice. An electronic survey covering aspects of the academic interview of both trainees and faculty were sent to all current/past mentors serving in the COG YI mentorship program and those registered as mentors in the SIOP YI mentorship program. The responses were quantitatively and qualitatively analyzed. The response rate was 43.7% (118/270) from 25 countries. Majority of United States (US) interviewers (86.8%) conducted interviews individually, while 74% of non-US interviewers conducted panel interviews or both types equally (P<.001). Majority of interviewers (83.4%) at least occasionally contacted colleagues for off the record opinions on candidates, and 40.9% conducted an internet or social media search. Enthusiasm for the job (97.2%) and being a team player (95.3%) were the qualities most rated as at least moderately important, while a priority for work-life balance (45.4%) and having interests/hobbies outside of medicine (29.2%) were considered less important. Interviewers provided interview questions, tips for candidates, and key pitfalls to avoid. Candidates should prepare for their academic interviews in advance, be enthusiastic and honest when giving responses. Detailed guidance for those applying at different career stages and in different countries are provided.