Combining population genomics and biophysical modelling to assess connectivity patterns in an Antarctic fish.

Connectivity is a fundamental process of population dynamics in marine ecosystems. In the last decade, with the emergence of new methods, combining different approaches to understand the patterns of connectivity among populations and their regulation has become increasingly feasible. The Western Antarctic Peninsula (WAP) is characterized by complex oceanographic dynamics, where local conditions could act as barriers to population connectivity. Here, the notothenioid fish Harpagifer antarcticus, a demersal species with a complex life cycle (adults with poor swim capabilities and pelagic larvae), was used to assess connectivity along the WAP by combining biophysical modelling and population genomics methods. Both approaches showed congruent patterns. Areas of larvae retention and low potential connectivity, observed in the biophysical model output, coincide with four genetic groups within the WAP: (1) South Shetland Islands, (2) Bransfield Strait, (3) the central and (4) the southern area of WAP (Marguerite Bay). These genetic groups exhibited limited gene flow between them, consistent with local oceanographic conditions, which would represent barriers to larval dispersal. The joint effect of geographic distance and larval dispersal by ocean currents had a greater influence on the observed population structure than each variable evaluated separately. The combined effect of geographic distance and a complex oceanographic dynamic would be generating limited levels of population connectivity in the fish H. antarcticus along the WAP. Based on this, population connectivity estimations and priority areas for conservation were discussed, considering the marine protected area proposed for this threatened region of the Southern Ocean.

Morphology and diet are decoupled in nearshore notothenoids from King George Island, West Antarctica.

Antarctic notothenioid fishes show wide adaptive morphological radiation, linked to habitat preferences and food composition. However, direct comparisons of phenotypic variability and feeding habits are still lacking, particularly in stages inhabiting nearshore areas. To assess these relationships, we collected juveniles and adults of the most common benthic species inhabiting shallow waters off the South Shetland Islands within a similar size range, the plunderfish Harpagifer antarcticus, the black rockcod Notothenia coriiceps, and the marbled rockcod Notothenia rossii. Individual size ranges varied from 44.0 to 98.9 mm standard length (LS) (H. antarcticus), from 95.8 to 109.3 mm LS (N. coriiceps), and from 63.0 to 113.0 mm LS (N. rossii). Notothenioid fish showed different morphospace variability, being larger for H. antarcticus than the other Notothenia species and associated with the position of the posterior end of the operculum, along with the location and relative size of the eye. The evolutionary allometry was low, but the static allometry was much higher, especially for H. antarcticus and N. rossii. The diet was mainly carnivorous, consisting of amphipods and euphausiids. Macroalgae were scarce or totally absent in the gut contents of all species. Only H. antarcticus showed an increase in the prey number and ingested prey volume with fish size. Finally, there was a significant covariation between shape changes and LS in all species (allometric effects), however, not with prey composition, probably due to the small size range or ontogenetic stage and the relative similarity (or lack of contrast) in the benthic environment that they utilized.

Taking advantage of asynchronous digital feedback: development of an at-home basic suture skills training program for undergraduate medical students that facilitates skills retention.

Purpose: To date, there are no training programs for basic suturing that allow remote deliberate practice. This study seeks to evaluate the effectiveness of a basic suture skills training program and its 6-month skill retention applying unsupervised practice and remote digital feedback. Methods: Fourth-year medical-student trainees reviewed instructional videos from a digital platform and performed unsupervised practice as needed at their homes. When they felt competent, trainees uploaded a video of themselves practicing the skill. In < 72 h, they received expert asynchronous digital feedback. The course had two theoretical stages and five video-based assessments, where trainees performed different suturing exercises. For the assessment, a global (GRS) and specific rating scale (SRS) were used, with a passing score of 20 points (max:25) and 15 (max:20), respectively. Results were compared to previously published work with in-person expert feedback (EF) and video-guided learning without feedback (VGL). A subgroup of trainees underwent a 6-month skills retention assessment. Results: Two-hundred and forty-three trainees underwent the course between March and December 2021. A median GRS of 24 points was achieved in the final assessment, showing significantly higher scores (p < 0.001) than EF and VGL (20.5 and 15.5, respectively). Thirty-seven trainees underwent a 6-month skills retention assessment, improving in GRS (23.38 vs 24.03, p value = 0.06) and SRS (18.59 vs 19, p value = 0.07). Conclusion: It is feasible to teach basic suture skills to undergraduate medical students using an unsupervised training course with remote and asynchronous feedback through a digital platform. This methodology allows continuous training with the repetition of quality practice, personalized feedback, and skills retention at 6 months.

Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM).

The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical training, particularly the use of large language models (LLMs), in enhancing communication, personalizing feedback, and promoting skill development. We discuss the advancements in simulation-based training, AI-driven assessment tools, video-based assessment systems, virtual reality (VR) and augmented reality (AR) platforms, and the potential role of LLMs in the transcription, translation, and summarization of feedback. Despite the promising opportunities presented by AI integration, several challenges must be addressed, including accuracy and reliability, ethical and privacy concerns, bias in AI models, integration with existing training systems, and training and adoption of AI-assisted tools. By proactively addressing these challenges and harnessing the potential of AI, the future of surgical training may be reshaped to provide a more comprehensive, safe, and effective learning experience for trainees, ultimately leading to better patient outcomes. .

Remote and asynchronous training network: from a SAGES grant to an eight-country remote laparoscopic simulation training program.

BACKGROUND: Limitations in surgical simulation training include lack of access to validated training programs with continuous year-round training and lack of experts' ongoing availability for feedback. A model of simulation training was developed to address these limitations. It incorporated basic and advanced laparoscopic skills curricula from a previously validated program and provided instruction through a digital platform. The platform allowed for remote and asynchronous feedback from a few trained instructors. The instructors were continuously available and provided personalized feedback using a variety of different media. We describe the upscaling of this model to teach trainees at fourteen centers in eight countries. METHODS: Institutions with surgical programs lacking robust simulation curricula and needing instructors for ongoing education were identified. The simulation centers ("skills labs") at these sites were equipped with necessary simulation training hardware. A remote training-the-administrators (TTA) program was developed where personnel were trained in how to manage the skills lab, schedule trainees, set up training stations, and use the platform. A train-the-trainers (TTT) program was created to establish a network of trained instructors, who provided objective feedback through the platform remotely and asynchronously. RESULTS: Between 2019 and 2022, seven institutions in Chile and one in each of the USA, Bolivia, Brazil, Ecuador, El Salvador, México, and Perú implemented a digital platform-based remote simulation curriculum. Most administrators were not physicians (19/33). Eight Instructors were trained with the TTT program and became active proctors. The platform has been used by 369 learners, of whom 57% were general surgeons and general surgery residents. A total of 6729 videos, 28,711 feedback inputs, and 233.7 and 510.2 training hours in the basic and advanced programs, respectively, were registered. CONCLUSION: A remote and asynchronous method of giving instruction and feedback through a digital platform has been effectively employed in the creation of a robust network of continuous year-round simulation-based training in laparoscopy. Training centers were successfully run only with trained administrators to assist in logistics and setup, and no on-site instructors were necessary.

Artificial intelligence in laparoscopic simulation: a promising future for large-scale automated evaluations.

INTRODUCTION: A limitation to expanding laparoscopic simulation training programs is the scarcity of expert evaluators. In 2019, a new digital platform for remote and asynchronous laparoscopic simulation training was validated. Through this platform, 369 trainees have been trained in 14 institutions across Latin America, collecting 6729 videos of laparoscopic training exercises. The use of artificial intelligence (AI) has recently emerged in surgical simulation, showing usefulness in training assessment, virtual reality scenarios, and laparoscopic virtual reality simulation. An AI algorithm to assess basic laparoscopic simulation training exercises was developed. This study aimed to analyze the agreement between this AI algorithm and expert evaluators in assessing basic laparoscopic-simulated training exercises. METHODS: The AI algorithm was trained using 400-bean drop (BD) and 480-peg transfer (PT) videos and tested using 64-BD and 43-PT randomly selected videos, not previously used to train the algorithm. The agreement between AI and expert evaluators from the digital platform (EE) was then analyzed. The exercises being assessed involve using laparoscopic graspers to move objects across an acrylic board without dropping any objects in a determined time (BD < 24 s, PT < 55 s). The AI algorithm can detect object movement, identify if objects have fallen, track grasper clamps location, and measure exercise time. Cohen's Kappa test was used to evaluate the agreement between AI assessments and those performed by EE, using a pass/fail nomenclature based on the time to complete the exercise. RESULTS: After the algorithm was trained, 79.69% and 93.02% agreement were observed in BD and PT, respectively. The Kappa coefficients test observed for BD and PT were 0.59 (moderate agreement) and 0.86 (almost perfect agreement), respectively. CONCLUSION: This first approach of AI use in basic laparoscopic skills simulated training assessment shows promising results, providing a preliminary framework to expand the use of AI to other basic laparoscopic skills exercises.

Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM) / Inovações no treinamento cirúrgico: explorando o papel da inteligência artificial e dos grandes modelos de linguagem (LLM)

ABSTRACT The landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and its integration into education and simulation. This manuscript aims to explore the potential applications and benefits of AI-assisted surgical training, particularly the use of large language models (LLMs), in enhancing communication, personalizing feedback, and promoting skill development. We discuss the advancements in simulation-based training, AI-driven assessment tools, video-based assessment systems, virtual reality (VR) and augmented reality (AR) platforms, and the potential role of LLMs in the transcription, translation, and summarization of feedback. Despite the promising opportunities presented by AI integration, several challenges must be addressed, including accuracy and reliability, ethical and privacy concerns, bias in AI models, integration with existing training systems, and training and adoption of AI-assisted tools. By proactively addressing these challenges and harnessing the potential of AI, the future of surgical training may be reshaped to provide a more comprehensive, safe, and effective learning experience for trainees, ultimately leading to better patient outcomes. .

RESUMO O cenário do treinamento cirúrgico está evoluindo rapidamente com o surgimento da inteligência artificial (IA) e sua integração na educação e simulação. Este artigo explora as aplicações e benefícios potenciais do treinamento cirúrgico assistido por IA, em particular o uso de modelos de linguagem avançados (MLAs), para aprimorar a comunicação, personalizar o feedback e promover o desenvolvimento de habilidades. Discutimos os avanços no treinamento baseado em simulação, ferramentas de avaliação impulsionadas por IA, sistemas de avaliação baseados em vídeo, plataformas de realidade virtual (RV) e realidade aumentada (RA), e o papel potencial dos MLAs na transcrição, tradução e resumo do feedback. Apesar das oportunidades promissoras apresentadas pela integração da IA, vários desafios devem ser abordados, incluindo precisão e confiabilidade, preocupações éticas e de privacidade, viés nos modelos de IA, integração com os sistemas de treinamento existentes, e treinamento e adoção de ferramentas assistidas por IA. Ao abordar proativamente esses desafios e aproveitar o potencial da IA, o futuro do treinamento cirúrgico pode ser remodelado para proporcionar uma experiência de aprendizado mais abrangente, segura e eficaz para os aprendizes, resultando em melhores resultados para os pacientes.