Novices' learning curve in single-port surgery using three surgical training programs

BACKGROUND

The objective of this study was to evaluate the novices' learning curves and proficiency level reached in laparoendoscopic single-site (LESS) surgery using three surgical training programs.

MATERIAL AND METHODS

Participants were randomly divided into three groups, who trained in a specific practice regimen for 12 days using a laparoscopic box simulator and three tasks. Group A trained in three stages using conventional laparoscopic surgery (CLS) with straight instruments, and LESS with straight and articulating instruments for four days each. Group B trained in two stages in LESS with straight and articulating instruments for six days each. Group C trained only in LESS with articulating instruments exclusively for all 12 days. Performance was registered daily during the 12 days to evaluate the participants' progress.

RESULTS

Pre- and post-training analysis of the three groups showed significant differences in performance, denoting the significant improvement in their LESS skills, with no difference between the groups. Group C reached a high level of technical competence with their specific training program in LESS, obtaining a lower asymptote and slow learning rate.

CONCLUSION

Specific training programs in LESS settings using articulated instruments showed a slower learning rate than the other programs but better proficiency in the technique with the best surgical performance.

Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field

Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence. The implementation of machine learning/AI in protein science gives rise to a world of knowledge adventures in the workhorse of the cell and proteome homeostasis, which are essential for making life possible. This opens up epistemic horizons thanks to a coupling of human tacit–explicit knowledge with machine learning power, the benefits of which are already tangible, such as important advances in protein structure prediction. Moreover, the driving force behind the protein processes of self-organization, adjustment, and fitness requires a space corresponding to gigabytes of life data in its order of magnitude. There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and proteostasis networks that are currently unexplored or unrevealed. In this systematic review and biochemical meta-analysis, we aim to contribute to bridging the gap between what we call binomial artificial intelligence (AI) and protein science (PS), a growing research enterprise with exciting and promising biotechnological and biomedical applications. We undertake our task by exploring “the state of the art” in AI and machine learning (ML) applications to protein science in the scientific literature to address some critical research questions in this domain, including What kind of tasks are already explored by ML approaches to protein sciences? What are the most common ML algorithms and databases used? What is the situational diagnostic of the AI–PS inter-field? What do ML processing steps have in common? We also formulate novel questions such as Is it possible to discover what the rules of protein evolution are with the binomial AI–PS? How do protein folding pathways evolve? What are the rules that dictate the folds? What are the minimal nuclear protein structures? How do protein aggregates form and why do they exhibit different toxicities? What are the structural properties of amyloid proteins? How can we design an effective proteostasis network to deal with misfolded proteins? We are a cross-functional group of scientists from several academic disciplines, and we have conducted the systematic review using a variant of the PICO and PRISMA approaches. The search was carried out in four databases (PubMed, Bireme, OVID, and EBSCO Web of Science), resulting in 144 research articles. After three rounds of quality screening, 93 articles were finally selected for further analysis. A summary of our findings is as follows: regarding AI applications, there are mainly four types: 1) genomics, 2) protein structure and function, 3) protein design and evolution, and 4) drug design. In terms of the ML algorithms and databases used, supervised learning was the most common approach (85%). As for the databases used for the ML models, PDB and UniprotKB/Swissprot were the most common ones (21 and 8%, respectively). Moreover, we identified that approximately 63% of the articles organized their results into three steps, which we labeled pre-process, process, and post-process. A few studies combined data from several databases or created their own databases after the pre-process. Our main finding is that, as of today, there are no research road maps serving as guides to address gaps in our knowledge of the AI–PS binomial. All research efforts to collect, integrate multidimensional data features, and then analyze and validate them are, so far, uncoordinated and scattered throughout the scientific literature without a clear epistemic goal or connection between the studies. Therefore, our main contribution to the scientific literature is to offer a road map to help solve problems in drug design, protein structures, design, and function prediction while also presenting the “state of the art” on research in the AI–PS binomial until February 2021. Thus, we pave the way toward future advances in the synthetic redesign of novel proteins and protein networks and artificial metabolic pathways, learning lessons from nature for the welfare of humankind. Many of the novel proteins and metabolic pathways are currently non-existent in nature, nor are they used in the chemical industry or biomedical field.

Steerable Surgical Instrument for Conventional and Single-Site Minimally Invasive Surgery

Background. This article aims to present an innovative design of a steerable surgical instrument for conventional and single-site minimally invasive surgery (MIS), which improves the dexterity and maneuverability of the surgeon while offering a solution to the limitations of current tools. Methods. The steerable MIS instrument consists of a deflection structure with a curved sliding joints design that articulates the distal tip in two additional degrees of freedom (DoFs), relative to the instrument shaft, using transmission by cables. A passive ball-joint mechanism articulates the handle relative to the instrument shaft, improves wrist posture, and prevents collision of instrument handles during single-site MIS procedures. The two additional DoFs of the articulating tip are activated by a thumb-controlled device, using a joystick design mounted on the handle. This steerable MIS instrument was developed by additive manufacturing in a 3D printer using PLA polymer. Results. Prototype testing showed a maximum tip deflection of 60° in the left and right directions, with a total deflection of 120°. With the passive ball-joint fully offset, the steerable tip achieved a deflection of 90° for the right and 40° for the left direction, with a total deflection of 130°. Furthermore, the passive ball-joint mechanism in the handle obtained a maximum range of motion of 60°. Conclusions. This steerable MIS instrument concept offers an alternative to enhance the application fields of conventional and single-site MIS, increasing manual dexterity of the surgeon and the ability to reach narrow anatomies from other directions.