Explore the vast range of titles available.

Topological superconductivity can be induced in an s-wave superconductor by an adjacent magnetic layer with a non-collinear spin structure. Good candidates are atomic-scale spin textures with large tilting angles between neighboring spins which exhibit intriguing properties such as topological orbital moments and topological Hall conductivity. Here we investigate the coupling of such non-coplanar spin structures to an adjacent superconducting layer based on a tight-binding model. We consider spin structures recently observed in ultrathin Fe and Mn-based films on the Re(0001) surface such as the triple-Q state, atomic- and nano-scale skyrmion lattices, and study the effect of a continuous distortion of the spin state. We characterize the topology of the spin texture via the scalar spin chirality and the topology of the superconductor by its Chern number. We find that a non-zero total scalar spin chirality, leads to a gapped topological superconducting phase while only trivial superconducting phases can appear for a vanishing value. Our study shows that the size of the superconducting gap in the topological phase rises with the total scalar spin chirality. For an atomic-scale skyrmion lattice, we predict the existence of chiral edge modes on a realistic island structure detectable by the local density of states or supercurrents.

IntroductionThe most common way of assessing surgical performance is by expert raters to view a surgical task and rate a trainee’s performance. However, there is huge potential for automated skill assessment and workflow analysis using modern technology. The aim of the present study was to evaluate machine learning (ML) algorithms using the data of a Myo armband as a sensor device for skills level assessment and phase detection in laparoscopic training.Materials and methodsParticipants of three experience levels in laparoscopy performed a suturing and knot tying task on silicon models. Experts rated performance using Objective Structured Assessment of Surgical Skills (OSATS). Participants wore Myo armbands (Thalmic Labs™, Ontario, Canada) to record acceleration, angular velocity, orientation, and Euler orientation. ML algorithms (decision forest, neural networks, boosted decision tree) were compared for skill level assessment and phase detection.Results28 participants (8 beginner, 10 intermediate, 10 expert) were included, and 99 knots were available for analysis. A neural network regression model had the lowest mean absolute error in predicting OSATS score (3.7 ± 0.6 points, r2 = 0.03 ± 0.81; OSATS min.-max.: 4–37 points). An ensemble of binary-class neural networks yielded the highest accuracy in predicting skill level (beginners: 82.2% correctly identified, intermediate: 3.0%, experts: 79.5%) whereas standard statistical analysis failed to discriminate between skill levels. Phase detection on raw data showed the best results with a multi-class decision jungle (average 16% correctly identified), but improved to 43% average accuracy with two-class boosted decision trees after Dynamic time warping (DTW) application.ConclusionModern machine learning algorithms aid in interpreting complex surgical motion data, even when standard analysis fails. Dynamic time warping offers the potential to process and compare surgical motion data in order to allow automated surgical workflow detection. However, further research is needed to interpret and standardize available data and improve sensor accuracy.

At the beginning of the COVID-19 pandemic, it was assumed that SARS-CoV-2 could be transmitted through surgical smoke generated by electrocauterization. Minimally invasive surgery (MIS) was targeted due to potentially higher concentrations of the SARS-CoV-2 particles in the pneumoperitoneum. Some surgical societies even recommended open surgery instead of MIS to prevent the potential spread of SARS-CoV-2 from the pneumoperitoneum. This study aimed to detect SARS-CoV-2 in surgical smoke during open and MIS. Patients with SARS-CoV-2 infection who underwent open surgery or MIS at Heidelberg University Hospital were included in the study. A control group of patients without SARS-CoV-2 infection undergoing MIS or open surgery was included for comparison. The trial was approved by the Ethics Committee of Heidelberg University Medical School (S-098/2021). The following samples were collected: nasopharyngeal and intraabdominal swabs, blood, urine, surgical smoke, and air samples from the operating room. An SKC BioSampler was used to sample the surgical smoke from the pneumoperitoneum during MIS and the approximate surgical field during open surgery in 15 ml of sterilized phosphate-buffered saline. An RT-PCR test was performed on all collected samples to detect SARS-CoV-2 viral particles. Twelve patients with proven SARS-CoV-2 infection underwent open abdominal surgery. Two SARS-CoV-2-positive patients underwent an MIS procedure. The control group included 24 patients: 12 underwent open surgery and 12 MIS. One intraabdominal swab in a patient with SARS-CoV-2 infection was positive for SARS-CoV-2. However, during both open surgery and MIS, none of the surgical smoke samples showed any detectable viral particles of SARS-CoV-2. The air samples collected at the end of the surgical procedure showed no viral particles of SARS-CoV-2. Major complications (CD ≥ IIIa) were more often observed in SARS-CoV-2 positive patients (10 vs. 4, p  = 0.001). This study showed no detectable viral particles of SARS-CoV-2 in surgical smoke sampled during MIS and open surgery. Thus, the discussed risk of transmission of SARS-CoV-2 via surgical smoke could not be confirmed in the present study.

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method’s current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

The rapid adoption of robotic surgical systems across Europe has led to a critical gap in training and credentialing for gastrointestinal (GI) surgeons. Currently, there is no existing standardised curriculum to guide robotic training, assessment and certification for GI trainees. This manuscript describes the protocol to achieve a pan-European consensus on the essential components of a comprehensive training programme for GI robotic surgery through a five-stage process. In Stage 1, a Steering Committee, consisting of international experts, trainees and educationalists, has been established to lead and coordinate the consensus development process. In Stage 2, a systematic review of existing multi-specialty robotic training curricula will be performed to inform the formulation of key position statements. In Stage 3, a comprehensive survey will be disseminated across Europe to capture the current state of robotic training and identify potential challenges and opportunities for improvement. In Stage 4, an international panel of GI surgeons, trainees, and robotic theatre staff will participate in a three-round Delphi process, seeking ≥ 70% agreement on crucial aspects of the training curriculum. Industry and patient representatives will be involved as external advisors throughout this process. In Stage 5, the robotic training curriculum for GI trainees will be finalised in a dedicated consensus meeting, culminating in the production of an Explanation and Elaboration (E&E) document. The study protocol has been registered on the Open Science Framework (https://osf.io/br87d/).

Image-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019. Measurement(s) colorectum Technology Type(s) Laparoscopy Factor Type(s) surgery type Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14178773

BackgroundMinimally invasive total gastrectomy (MITG) is a mainstay for curative treatment of patients with gastric cancer. To define and standardize optimal surgical techniques and further improve clinical outcomes through the enhanced MITG surgical quality, there must be consensus on the key technical steps of lymphadenectomy and anastomosis creation, which is currently lacking. This study aimed to determine an expert consensus from an international panel regarding the technical aspects of the performance of MITG for oncological indications using the Delphi method.MethodsA 100-point scoping survey was created based on the deconstruction of MITG into its key technical steps through local and international expert opinion and literature evidence. An international expert panel comprising upper gastrointestinal and general surgeons participated in multiple rounds of a Delphi consensus. The panelists voted on the issues concerning importance, difficulty, or agreement using an online questionnaire. A priori consensus standard was set at > 80% for agreement to a statement. Internal consistency and reliability were evaluated using Cronbach's α.ResultsThirty expert upper gastrointestinal and general surgeons participated in three online Delphi rounds, generating a final consensus of 41 statements regarding MITG for gastric cancer. The consensus was gained from 22, 12, and 7 questions from Delphi rounds 1, 2, and 3, which were rephrased into the 41 statetments respectively. For lymphadenectomy and aspects of anastomosis creation, Cronbach’s α for round 1 was 0.896 and 0.886, and for round 2 was 0.848 and 0.779, regarding difficulty or importance.ConclusionsThe Delphi consensus defined 41 steps as crucial for performing a high-quality MITG for oncological indications based on the standards of an international panel. The results of this consensus provide a platform for creating and validating surgical quality assessment tools designed to improve clinical outcomes and standardize surgical quality in MITG.

This article investigates an actor’s perspective on digital capitalism. We study software workers’ orientations towards their work by focusing on the social use value they attribute to it. The concept of use value allows us to examine the contradictions software workers might experience in digital capitalism. Drawing on the literature on the control of software workers and the New Spirit of Digital Capitalism, we identify hindrances to the workers’ claims of a social use value and explore the imaginaries of technology which might form the basis for a critique or legitimation of digital capitalism. We find that software workers hold strong claims of a societal use value towards their work. While their ethos of good technology forms a strong foundation to critique hindrances they perceive in creating useful technology, imaginaries of technology as an autonomous force might delegitimise the workers’ claims.

Topological orbital moments can arise in non-coplanar spin structures even in the absence of spin-orbit coupling and a net topological orbital magnetization occurs for the triple-Q state and for isolated skyrmions. For atomic-scale skyrmion lattices, a significant effect can also be expected, however, no studies have been reported yet. Here, we observe via spin-polarized scanning tunneling microscopy (SP-STM) a non-coplanar atomic-scale spin structure with a roughly square magnetic unit cell for a pseudomorphic Fe monolayer on three atomic Ir layers on the Re(0001) surface. Employing density functional theory (DFT) calculations we consider different skyrmionic lattices as potential magnetic ground states which are found to be energetically favored with respect to any spin spiral state. Comparison of simulated and experimental SP-STM images provides strong evidence for an atomic-scale skyrmion lattice. By mapping the DFT total energies to an atomistic spin model we demonstrate that these spin textures are stabilized by the interplay of the Dzyaloshinskii-Moriya and four-spin interactions. We evaluate the emerging phenomena of the different non-coplanar magnetic states and find significant local topological orbital moments oriented perpendicular to the surface, which order in an antiferromagnetic fashion.

Exocrine and endocrine pancreas are interconnected anatomically and functionally, with vasculature facilitating bidirectional communication. Our understanding of this network remains limited, largely due to two-dimensional histology and missing combination with three-dimensional imaging. In this study, a multiscale 3D-imaging process was used to analyze a porcine pancreas. Clinical computed tomography, digital volume tomography, micro-computed tomography and Synchrotron-based propagation-based imaging were applied consecutively. Fields of view correlated inversely with attainable resolution from a whole organism level down to capillary structures with a voxel edge length of 2.0 µm. Segmented vascular networks from 3D-imaging data were correlated with tissue sections stained by immunohistochemistry and revealed highly vascularized regions to be intra-islet capillaries of islets of Langerhans. Generated 3D-datasets allowed for three-dimensional qualitative and quantitative organ and vessel structure analysis. Beyond this study, the method shows potential for application across a wide range of patho-morphology analyses and might possibly provide microstructural blueprints for biotissue engineering.