Hasil untuk "Surgery"

M. Fehlings, A. Vaccaro, Jefferson R. Wilson et al.

Background There is convincing preclinical evidence that early decompression in the setting of spinal cord injury (SCI) improves neurologic outcomes. However, the effect of early surgical decompression in patients with acute SCI remains uncertain. Our objective was to evaluate the relative effectiveness of early (<24 hours after injury) versus late (≥24 hours after injury) decompressive surgery after traumatic cervical SCI. Methods We performed a multicenter, international, prospective cohort study (Surgical Timing in Acute Spinal Cord Injury Study: STASCIS) in adults aged 16–80 with cervical SCI. Enrolment occurred between 2002 and 2009 at 6 North American centers. The primary outcome was ordinal change in ASIA Impairment Scale (AIS) grade at 6 months follow-up. Secondary outcomes included assessments of complications rates and mortality. Findings A total of 313 patients with acute cervical SCI were enrolled. Of these, 182 underwent early surgery, at a mean of 14.2(±5.4) hours, with the remaining 131 having late surgery, at a mean of 48.3(±29.3) hours. Of the 222 patients with follow-up available at 6 months post injury, 19.8% of patients undergoing early surgery showed a ≥2 grade improvement in AIS compared to 8.8% in the late decompression group (OR = 2.57, 95% CI:1.11,5.97). In the multivariate analysis, adjusted for preoperative neurological status and steroid administration, the odds of at least a 2 grade AIS improvement were 2.8 times higher amongst those who underwent early surgery as compared to those who underwent late surgery (OR = 2.83, 95% CI:1.10,7.28). During the 30 day post injury period, there was 1 mortality in both of the surgical groups. Complications occurred in 24.2% of early surgery patients and 30.5% of late surgery patients (p = 0.21). Conclusion Decompression prior to 24 hours after SCI can be performed safely and is associated with improved neurologic outcome, defined as at least a 2 grade AIS improvement at 6 months follow-up.

G. M. Dam, G. Themelis, L. M. Crane et al.

F. McDermott, A. Heeney, M. Kelly et al.

B. Nordlinger, M. Guiguet, J. Vaillant et al.

P. Rutgeerts, K. Geboes, G. Vantrappen et al.

P. Rothwell, M. Eliasziw, S. Gutnikov et al.

Nicholas Meyer

Suppose that $W$ and $W'$ are smooth, compact, and oriented $4$-manifolds that are either diffeomorphic to $S^1$ times the exterior $E_Y(K)$ of a fibered knot $K$ in a closed, connected, orientable $3$-manifold $Y$, or are diffeomorphic to $Σ_{g,1}$ bundles over the $2$-torus with monodromy fixing the boundary of the fiber pointwise. If $f: \partial W' \to \partial W$ is an orientation-preserving diffeomorphism of the $3$-torus boundaries, we have that $X = W \cup_f W'$ is a closed, oriented $4$-manifold that fibers over $S^1$. In particular, if $W' = T^2 \times D^2$ and $W = S^1 \times E_Y(K)$, then our result shows that the result of doing torus surgery in $S^1\times Y$ along $S^1 \times K$ is a $4$-manifold that fibers over $S^1$. Furthermore, we extend work of Zentner by showing that the result of torus surgery along $S^1$ times the unknot $\mathcal{U}$ in $S^1 \times S^3$ is diffeomorphic to $S^1$ times a lens space.

Guankun Wang, Rui Tang, Mengya Xu et al.

Endoscopic surgery is the gold standard for robotic-assisted minimally invasive surgery, offering significant advantages in early disease detection and precise interventions. However, the complexity of surgical scenes, characterized by high variability in different surgical activity scenarios and confused image features between targets and the background, presents challenges for surgical environment understanding. Traditional deep learning models often struggle with cross-activity interference, leading to suboptimal performance in each downstream task. To address this limitation, we explore multi-task learning, which utilizes the interrelated features between tasks to enhance overall task performance. In this paper, we propose EndoARSS, a novel multi-task learning framework specifically designed for endoscopy surgery activity recognition and semantic segmentation. Built upon the DINOv2 foundation model, our approach integrates Low-Rank Adaptation to facilitate efficient fine-tuning while incorporating Task Efficient Shared Low-Rank Adapters to mitigate gradient conflicts across diverse tasks. Additionally, we introduce the Spatially-Aware Multi-Scale Attention that enhances feature representation discrimination by enabling cross-spatial learning of global information. In order to evaluate the effectiveness of our framework, we present three novel datasets, MTLESD, MTLEndovis and MTLEndovis-Gen, tailored for endoscopic surgery scenarios with detailed annotations for both activity recognition and semantic segmentation tasks. Extensive experiments demonstrate that EndoARSS achieves remarkable performance across multiple benchmarks, significantly improving both accuracy and robustness in comparison to existing models. These results underscore the potential of EndoARSS to advance AI-driven endoscopic surgical systems, offering valuable insights for enhancing surgical safety and efficiency.

Tian Hao, Tong Lu, Che Chan

Robotic assistance in minimally invasive surgery can greatly enhance surgical precision and reduce surgeon fatigue. This paper presents a focused investigation on the kinematic and ergonomic design principles for a laparoscopic surgical robotic arm aimed at high-precision tasks. We propose a 7-degree-of-freedom (7-DOF) robotic arm system that incorporates a remote center of motion (RCM) at the instrument insertion point and ergonomic considerations to improve surgeon interaction. The design is implemented on a general-purpose robotic platform, and a series of simulated surgical tasks were performed to evaluate targeting accuracy, task efficiency, and surgeon comfort compared to conventional manual laparoscopy. Experimental results demonstrate that the optimized robotic design achieves significantly improved targeting accuracy (error reduced by over 50%) and shorter task completion times, while substantially lowering operator muscle strain and discomfort. These findings validate the importance of kinematic optimization (such as added articulations and tremor filtering) and human-centered ergonomic design in enhancing the performance of robot-assisted surgery. The insights from this work can guide the development of next-generation surgical robots that improve surgical outcomes and ergonomics for the operating team.

Amir-Ala Mahmoud, Dominick J. Falcon, Valeria P. Bustos et al.

Background The purpose of this review is to characterize themes among the five reconstructive microsurgery articles achieving the highest Relative Citation Ratios (RCRs) published in the past 20 years in the top journals. In doing so, researchers may be better informed on how to propose salient research questions to impact the field and understand future directions in plastic surgery.

John T. Paige, Laura Bonanno

J V Ramana Reddy, Toshitaka Watanabe, Taro Hayashi et al.

Background: For acute type-A aortic dissection (ATAAD) surgery, early post-surgery assessment is crucially important for effective treatment plans, underscoring the need for a framework to identify the risk level of aortic dissection cases. We examined true-lumen narrowing during follow-up examinations, collected morphological data 14 days (early stages) after surgery, and assessed patient risk levels over 2.8 years. Purpose: To establish an implementable framework supported by mathematical techniques to predict the risk of aortic dissection patients experiencing true-lumen narrowing after ATAAD surgery. Materials and Methods: This retrospective study analyzed CT data from 21 ATAAD patients. Forty uniformly distributed cross-sectional shapes (CSSs) are derived from each lumen to account for gradual changes in shape. We introduced the form factor (FF) to assess CSS morphology. Linear discriminant analysis (LDA) is used for the risk classification of aortic dissection patients. Leave-one-patient-out cross-validation (LOPO-CV) is used for risk prediction. Results: For this investigation, we examined data of 21 ATAAD patients categorized into high-risk, medium-risk, and low-risk cases based on clinical observations of the range of true-lumen narrowing. Our risk classification machine-learning (ML) model preserving the model's generalizability. The model's predictions reliably identified low-risk patients, thereby potentially reducing hospital visits. It also demonstrated proficiency in accurately predicting the risk for all high-risk patients. Conclusion: The suggested method anticipates the risk linked to aortic enlargement in patients with a narrowing true lumen in the early stage following ATAAD surgery, thereby aiding follow-up doctors in enhancing patient care.

Michele Laurelli

As large-scale AI systems proliferate, ensuring compliance with data privacy laws such as the General Data Protection Regulation (GDPR) has become critical. This paper introduces Brain Surgery, a transformative methodology for making every local AI model GDPR-ready by enabling real-time privacy management and targeted unlearning. Building on advanced techniques such as Embedding-Corrupted Prompts (ECO Prompts), blockchain-based privacy management, and privacy-aware continual learning, Brain Surgery provides a modular solution that can be deployed across various AI architectures. This tool not only ensures compliance with privacy regulations but also empowers users to define their own privacy limits, creating a new paradigm in AI ethics and governance.

Ryo Fujii, Hideo Saito, Hiroki Kajita

Surgical tool detection is a fundamental task for understanding egocentric open surgery videos. However, detecting surgical tools presents significant challenges due to their highly imbalanced class distribution, similar shapes and similar textures, and heavy occlusion. The lack of a comprehensive large-scale dataset compounds these challenges. In this paper, we introduce EgoSurgery-Tool, an extension of the existing EgoSurgery-Phase dataset, which contains real open surgery videos captured using an egocentric camera attached to the surgeon's head, along with phase annotations. EgoSurgery-Tool has been densely annotated with surgical tools and comprises over 49K surgical tool bounding boxes across 15 categories, constituting a large-scale surgical tool detection dataset. EgoSurgery-Tool also provides annotations for hand detection with over 46K hand-bounding boxes, capturing hand-object interactions that are crucial for understanding activities in egocentric open surgery. EgoSurgery-Tool is superior to existing datasets due to its larger scale, greater variety of surgical tools, more annotations, and denser scenes. We conduct a comprehensive analysis of EgoSurgery-Tool using nine popular object detectors to assess their effectiveness in both surgical tool and hand detection. The dataset will be released at https://github.com/Fujiry0/EgoSurgery.

Kohei Ando, Satoshi Miyahara, Shuhei Hanada et al.

ABSTRACT Bone and soft tissue infections caused by biofilm-forming bacteria, such as methicillin-resistant Staphylococcus aureus (MRSA), remain a significant clinical challenge. While the control of local infection is necessary, systemic treatment is also required, and biofilm eradication is a critical target for successful management. Topical antibiotic treatments, such as antibiotic-loaded bone cement (ALBC), have been used for some time, and continuous local antibiotic perfusion therapy, a less invasive method, has been developed by our group. However, the optimal antibiotics and concentrations for biofilms of clinical isolates are still not well understood. We examined the efficacy of high concentrations of gentamicin against MRSA biofilms and the role of gentamicin resistance genes in biofilm eradication. We collected 101 MRSA samples from a hospital in Japan and analyzed their gene properties, including methicillin and gentamicin resistance, and their minimum biofilm eradication concentration (MBEC) values. Our results showed that high concentrations of gentamicin are effective against MRSA biofilms and that even concentrations lower than the MBEC value could eliminate biofilms after prolonged exposure. We also identified three aminoglycoside/gentamicin resistance genes [aac(6′)-aph(2″), aph(3′)-III, and ant(4′)-IA] and found that the presence or absence of these genes may inform the selection of treatments. It was also found that possession of the aac(6′)-aph(2″) gene correlated with the minimum inhibitory concentration/MBEC values of gentamicin. Although this study provides insight into the efficacy of gentamicin against MRSA biofilms and the role of gentamicin resistance genes, careful selection of the optimal treatment strategy is needed for clinical application.IMPORTANCEOur analysis of 101 MRSA clinical isolates has provided valuable insights that could enhance treatment selection for biofilm infections in orthopedics. We found that high concentrations of gentamicin were effective against MRSA biofilms, and even prolonged exposure to concentrations lower than the minimum biofilm eradication concentration (MBEC) value could eliminate biofilms. The presence of the aac(6′)-aph(2″) gene, an aminoglycoside resistance gene, was found to correlate with the minimum inhibitory concentration (MIC) and MBEC values of gentamicin, providing a potential predictive tool for treatment susceptibility. These results suggest that extended high concentrations of local gentamicin treatment could effectively eliminate MRSA biofilms in orthopedic infections. Furthermore, testing for gentamicin MIC or the possession of the aac(6′)-aph(2″) gene could help select treatment, including topical gentamicin administration and surgical debridement.

Xufeng Lu, Wenhai Deng, Shuaibin Wang et al.

Nanozymes constitute a promising treatment strategy for antitumor therapy. However, the catalytic function of metal‒organic framework (MOF)-based nanozymes during cuproptosis remains unclear. In this study, a Cu(Ⅱ)-based MOF nanocomposite loaded with the copper ionophore elesclomol and surface modified with polyethylene glycol polymer (PEG) was developed (ES@Cu(Ⅱ)-MOF) for effective cuproptosis induction. The peroxidase (POD)-like activity of ES@Cu(Ⅱ)-MOF generated an abundance of hydroxyl radicals (•OH) via a Fenton-like reaction, and its glutathione peroxidase (GSH-Px)-like activity converted Cu2+ into more toxic Cu+ ions to efficiently consume endogenous GSH. Notably, the rapid accumulation of Cu+ and ES in tumor cells induced the aggregation of lipoylated dihydrolipoamide S-acetyltransferase (DLAT) and the downregulation of Fe‒S cluster proteins, ultimately leading to cuproptosis. ES@Cu(Ⅱ)-MOF exhibited extraordinary cytotoxicity against breast cancer cells in vitro and significantly suppressed 4T1 breast tumor growth in vivo. Moreover, ES@Cu(Ⅱ)-MOF induced immunogenic cell death (ICD) to increase the antitumor immune response. Furthermore, combining ES@Cu(Ⅱ)-MOF with an anti-programmed cell death-ligand 1 (PD-L1) antibody converted the immunosuppressive tumor microenvironment to an immunogenic microenvironment, thus effectively inhibiting breast tumor growth. Overall, this work provides an innovative approach utilizing nanozymes to facilitate cuproptosis for cancer treatment, which potentially enhances the effectiveness of immune checkpoint inhibitor-based immunotherapy.

Zhiwei Zhang, MD, Xiaoting Li, MD, Jiawei Zhuang, MD et al.

Summary: Recent evidence demonstrates that low engraftment rates limit the efficacy of human induced pluripotent stem cell–derived cardiomyocytes (hiPSC-CMs) for cardiac repair after myocardial infarction. In this study, we attempted to overcome this limitation by enhancing the proliferative capacity of transplanted hiPSC-CMs. We found that miR-590-3p overexpression increased the proliferative capacity of hiPSC-CMs. miR-590-3p overexpression increased the number of engrafted cells and had a higher efficacy for myocardial repair than control cells. Moreover, we confirmed the safety of using miR-590-3p-overexpressing hiPSC-CMs in pig hearts. These results indicated that miR-590-3p overexpression stimulated hiPSC-CM cell cycle re-entry to induce cell proliferation and increased the therapeutic efficacy in MI.

F. Rehbein

Xu Xu, Chao Zheng

Inversive distance circle packings introduced by Bowers-Stephenson are natural generalizations of Thurston's circle packings on surfaces. To find piecewise Euclidean metrics on surfaces with prescribed combinatorial curvatures, we introduce the combinatorial Calabi flow, the fractional combinatorial Calabi flow and the combinatorial $p$-th Calabi flow for the Euclidean inversive distance circle packings. Due to the singularities possibly developed by these combinatorial curvature flows, the longtime existence and convergence of these combinatorial curvature flows have been a difficult problem for a long time. To handle the potential singularities along these combinatorial curvature flows, we do surgery along these flows by edge flipping under the weighted Delaunay condition. Using the discrete conformal theory recently established by Bobenko-Lutz for decorated piecewise Euclidean metrics on surfaces, we prove the longtime existence and global convergence for the solutions of these combinatorial curvature flows with surgery. This provides effective algorithms for finding piecewise Euclidean metrics on surfaces with prescribed combinatorial curvatures.

Jehan Ghafuri, Sabah Jassim

This paper aims to develop a simple procedure to reduce and control the condition number of random matrices, and investigate the effect on the persistent homology (PH) of point clouds of well- and ill-conditioned matrices. For a square matrix generated randomly using Gaussian/Uniform distribution, the SVD-Surgery procedure works by: (1) computing its singular value decomposition (SVD), (2) replacing the diagonal factor by changing a list of the smaller singular values by a convex linear combination of the entries in the list, and (3) compute the new matrix by reversing the SVD. Applying SVD-Surgery on a matrix often results in having different diagonal factor to those of the input matrix. The spatial distribution of random square matrices are known to be correlated to the distribution of their condition numbers. The persistent homology (PH) investigations, therefore, are focused on comparing the effect of SVD-Surgery on point clouds of large datasets of randomly generated well-conditioned and ill-conditioned matrices, as well as that of the point clouds formed by their inverses. This work is motivated by the desire to stabilise the impact of Deep Learning (DL) training on medical images in terms of the condition numbers of their sets of convolution filters as a mean of reducing overfitting and improving robustness against tolerable amounts of image noise. When applied to convolution filters during training, the SVD-Surgery acts as a spectral regularisation of the DL model without the need for learning extra parameters. We shall demonstrate that for several point clouds of sufficiently large convolution filters our simple strategy preserve filters norm and reduces the norm of its inverse depending on the chosen linear combination parameters. Moreover, our approach showed significant improvements towards the well-conditioning of matrices and stable topological behaviour.