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Robotic-assisted surgery has evolved over the past two decades from mechanically assisted laparoscopy into an increasingly intelligent, biologically responsive therapeutic modality. While early robotic systems primarily enhanced dexterity and visualization, contemporary platforms now integrate artificial intelligence, advanced imaging, real-time biosensing, and pharmacological interfaces, extending surgical capability beyond physical manipulation alone. Robotic-assisted surgery is thus emerging at the intersection of digital intelligence and molecular medicine, with the potential to sense, interpret, and modulate tumor biology during operative intervention. However, current literature remains largely procedure-centered and mechanically focused, offering limited synthesis of how robotic systems converge with pharmacology, artificial intelligence, and systems medicine to actively regulate oncologic processes. A cohesive, oncology-oriented framework integrating intelligent robotics with intraoperative drug delivery and real-time biological feedback is notably lacking. This narrative review critically consolidates recent advances in pharmacologically enabled robotic platforms, AI-driven intraoperative cognition, and emerging closed-loop surgical-pharmacological architectures. It examines next-generation systems incorporating localized drug delivery, molecular diagnostics, and real-time pharmacodynamic monitoring, while discussing translational pathways and regulatory considerations shaping semi-autonomous oncologic ecosystems. By reframing robotic-assisted surgery as a dynamic therapeutic science rather than a purely mechanical tool, this review highlights a new frontier in precision oncology that has the potential to enable individualized, adaptive, and predictive cancer interventions.
Over the past four decades, shoulder arthroscopy has advanced from diagnostic exploration to sophisticated therapeutic procedures. However, current techniques remain limited in addressing complex pathologies. We propose the concept of the Era of Shoulder Arthroscopy 2.0 (ESA2.0)-defined by surgical precision, biological integration, and digital innovation. Technologies such as three-dimensional (3D) imaging, augmented reality, and robotics are being explored to enhance accuracy and reproducibility. Concurrently, biological strategies, including platelet-rich plasma, stem cells, and scaffold-based techniques, may improve healing. Smart systems, including AI-assisted diagnosis and wearable rehabilitation tools, support more personalized treatment and optimize outcomes. This paradigm may define a future standard in arthroscopic care, offering solutions to challenges previously deemed intractable.
Dinoflagellates, a group of marine unicellular algae, are known for the fascinating glowing effects in coastal waters. While this natural mechanoluminescent phenomenon has been explored in pressure sensors and optical transducers, technologies to shape dinoflagellate-containing materials into more complex, engineering-relevant geometries remain limited. Here, we report a three-dimensional printing strategy to manufacture complex-shaped mechanoluminescent objects using dinoflagellates embedded in biocompatible hydrogels. The growth and mechanoluminescence of the entrapped dinoflagellates were investigated by optical microscopy, emission spectroscopy, and mechanical testing of cell-laden gels. Dinoflagellate-laden gels showed strong bioluminescence when compressed at sufficiently high strain and strain rates. By incorporating the dinoflagellates into a photo-curable hydrogel, we shaped such living material into complex geometries using a widely available light-based printing technique. The ability to print dinoflagellate-laden gels into intricate shapes broadens the design space available for the creation of mechanoluminescent living objects for applications in soft robotics, self-powered sensing, and optical transduction.
High inorganic particle loadings in soft gels typically compromise mechanical compliance, making the integration of high filling and large deformability of the composite gel a longstanding challenge. Here, we report a physically crosslinked poly(hydroxyethyl methacrylate-co-N-vinylformamide)/polyvinylpyrrolidone (P(HEMA-co-NVF)/PVP) polymer matrix that enables a record-high ZnO loading of 80 wt.% while achieving an ultrahigh fracture elongation of 650%. Through coordination and hydrogen bonding interactions between inorganic particles and the polymer network, ZnO particles are uniformly dispersed, ensuring structural integrity and broadly tunable mechanical properties: tensile strength could be precisely regulated from 63 to 682 kPa and toughness from 0.03 to 2.04 MJ/m3 by varying monomer composition. This developed strategy is generalizable to other fillers-including Al2O3, Fe3O4, and graphene-enabling diverse functional robust composite inorganic particle gels, showing improved thermal conductivity, electrical conductivity, or magnetic responsiveness. These high-filling, highly stretchable composite gels offer promising applications in flexible sensors, soft actuators, and robotics.
Soft robotics holds large promises for precision agriculture, as it has the potential to overcome challenges associated with subsoil exploration, including precision maneuvering, and minimizing soil disturbance. While prior studies have evaluated soft robots in tubes, glass beads, or sand, we perform empirical measurements and validations in real soil environments and show how the diameter-to-length (D/L) ratio affects the penetration performance, taking inspiration from earthworms' D/L ratios. Through a series of experiments, this study investigated the locomotion capabilities and the behavior of various robot designs when submerged within soil. We fabricated five pneumatically-actuated soft robots with different D/L ratios, measured their vertical burrowing depth, their axial force generation, and characterized the total drag force versus depth. We introduced a capability ratio (CR) comparing available axial force to experienced total drag at depth, giving an estimate of the theoretical depth that each design could reach before stalling. We evaluated buckling forces using Euler limits under mixed end conditions representing soil confinement and available lateral support. Results show that designs with D/L ratios similar to that of an earthworm exhibit lower drag forces, but D/L is not the only parameter influencing performance. The largest-diameter design generated the highest axial force but underperformed due to buckling, while the mid-range geometry achieved the greatest depth through a balanced combination of axial force generation, drag, and stability. These findings highlight the importance of tailored design parameters for efficient subsoil exploration, the relevance of mimicking natural proportions, and demonstrate that force-based metrics can provide practical performance estimates for soft burrowing robots.
Mechanical pop-up systems, which reconfigure from two-dimensional (2D) into complex three-dimensional (3D) shapes, are promising for advanced manufacturing and deployable devices. Multistable pop-up kirigami systems are especially advantageous because their properties decouple the simple, 2D fabricated state from the complex, 3D operational state, which greatly simplifies manufacturing. We identify that these pop-up systems are designed by the multiloop coupling strategy, which, however, has traditionally been limited by the geometric symmetry. In this work, we extend this strategy by removing symmetry constraints to create a generalized pop-up kirigami platform that enables programmable multistability with controlled, asymmetric pop-up and twisting motions. Using this platform, we demonstrate previously inaccessible formations, including tristable units with two programmable spatial states and large-scale tessellations of interconnected units. We showcase the versatility of this platform through applications in reconfigurable metamaterials, deployable arrays, soft robotics, and flexible electronics.
Helical structures provide critical functions in structural stability, locomotion, and mechanical flexibility. Among the helical structures, the dynamic coiled tendril formation in climbing plants upon contact with support structures inspires the development of numerous helix-based actuators and soft robotics. However, achieving precise spatiotemporal control over helix formation and actuation at the microscale remains a challenge. We introduce a materials system in which the spatial location and dynamics of helix formation are governed by the intrinsic bending resulting from the differential swelling of polyacrylic acid copolymer hydrogels, with electric fields serving as the primary control for electroosmotic flow-induced swelling/deswelling phase transitions. By manipulating electric field polarity and using patterned substrates, we achieve reversible spatiotemporal control over helix formation and actuation. The swelling/deswelling mechanism enables the applications of rotary actuation and controlled microsphere capture-release. Our approach represents a notable advancement in the precise dynamical control of helix formation, opening avenues for the development of sophisticated microactuators and artificial muscle systems.
Robotic-assisted aortic valve replacement (RAVR) has emerged as a novel minimally invasive approach to surgically manage aortic valve disease. A 74-year-old man presented with several months of worsening dyspnea and chest pain on exertion. Preoperative work-up identified severe aortic stenosis along with coronary artery disease. Despite being at low operative risk, the patient had a small aortic root, coronary disease not amenable to surgical revascularization, and wanted to avoid sternotomy. The decision was made to perform RAVR with percutaneous coronary intervention in a hybrid approach to manage his concomitant pathologies. We demonstrate the safety and feasibility of performing RAVR using a sutureless aortic valve prosthesis to optimize hemodynamics in a patient with a small aortic root. We also describe the combination of RAVR and percutaneous coronary intervention as part of a hybrid treatment paradigm to manage concomitant aortic stenosis and coronary artery disease.
Three-dimensional (3D) reconstruction model is an emerging technology that significantly enhanced perioperative metrics. This work aimed to delineate the contribution of preoperative 3D reconstruction model to robot-assisted radical nephrectomy (RARN) and IVC thrombectomy. A retrospective cohort study was conducted on consecutive patients with RCC and IVC tumor thrombus (Mayo level 1-3) who underwent robotic surgery by a single surgeon (January 2023-January 2026). Preoperative computed tomography urography (CTU) images were used to generate 3D reconstruction models, enabling detailed visualization of tumor-vessel relationships and quantitative measurement of tumor parameters. The primary oncological endpoint was recurrence-free survival (RFS); overall survival (OS) was also evaluated as a secondary endpoint. Postoperative complications were graded using Clavien-Dindo (≥III defined as major), and renal function was assessed by eGFR. A two-sided p value < 0.05 was considered significant. Among 71 patients, those with preoperative 3D reconstruction (n = 27) versus without (n = 44) showed significantly shorter postoperative hospital stay (8.0 vs 10.0 days, P = 0.04) and higher postoperative estimated glomerular filtration rate (eGFR) (76.0 vs 60.0, P = 0.01). Other perioperative benefits (blood transfusion, major complications) were not statistically significant. RFS and OS did not differ significantly between groups. Preoperative 3D reconstruction helps facilitate RARN and IVC thrombectomy and improves perioperative outcomes. This technique holds promise for broader application in complex urological surgery.
The modified MacIntosh anterior cruciate ligament reconstruction (ACLR) consists of a combined intra- and extra-articular construct using iliotibial band autograft. It is used in skeletally immature patients to avoid physeal injury. However, its effect on articular cartilage contact mechanics, rotational laxity, and graft loading is poorly understood. Relative to the intact knee, a modified MacIntosh ACLR would (1) increase and anteriorize lateral tibiofemoral contact stress, (2) decrease internal rotation (IR) and varus laxity, and (3) offload the intra-articular graft relative to the native ACL in response to simulated pivot-shift loads. Controlled laboratory study. Eight cadaveric knees (mean age 56 ± 7 years; 50% male) were tested on a robotic manipulator in 3 states: intact, ACL-sectioned, and modified MacIntosh ACLR. Peak contact stress and the anterior-posterior position of the center of contact stress (CCS) in the lateral compartment were calculated during simulated pivot shifts. Laxity was assessed for 3 uniplanar loading conditions (134 N anterior, 8 N·m varus, and 5 N·m IR) at knee flexion angles from 0° to 90°. Tissue forces were determined via the principle of superposition. After modified MacIntosh ACLR, peak stress increased by 0.31 to 0.35 MPa (P < .03) and the CCS shifted anteriorly by 1.3 to 3.3 mm (P < .05) relative to the intact knee. ACLR decreased IR and varus laxity by 18% to 24% (P≤ .01) and 13% to 24% (P < .02), respectively, compared with the intact state. During simulated pivot shift, in situ force in the intra-articular graft component (27 to 36 N) was 51% to 65% less than native ACL force, whereas the extra-articular component carried 25 to 30 N. In a cadaveric model of a simulated pivot shift, a modified MacIntosh ACLR anteriorized and mildly increased lateral compartment contact stress compared to the native knee, while decreasing lateral compartment contact stress relative to the ACL-deficient knee. The extraarticular limb also increased constraint to rotational loads, partially offloading the intra-articular graft. Partial intraarticular graft offloading with clinical pivoting maneuvers provides a biomechanical rationale for the low number of graft failures after modified MacIntosh ACLR. The ability of a modified MacIntosh ACLR to decrease and anteriorize contact stress in the posterior aspect of the lateral compartment offloads the region most affected by ACL injury and the pivot shift contact. Long-term clinical studies are needed to determine whether altered contact mechanics after modified MacIntosh ACLR influence long-term cartilage health.
Robot-assisted upper-limb rehabilitation is widely used after stroke, but links to neuroplasticity biomarkers remain uncertain. To synthesize biomarker evidence after robot-assisted upper-limb training and quantify post-intervention effects versus non-robot comparators. We searched PubMed, Web of Science, Embase, Cochrane Library, EBSCOhost, and Scopus from inception to December 31, 2025. Randomized controlled trials (RCTs) and nonrandomized intervention studies enrolling adults with stroke were eligible if they reported at least one neuroplasticity biomarker. Prespecified comparison classes were used; only the primary contrast (robot-assisted training vs. non-robot control) was pooled at post using DerSimonian-Laird random-effects models. Effects were expressed as standardized mean differences (SMDs; Hedges' g) or mean differences (MDs) with 95% confidence intervals (CIs). Fifty-five studies, including 24 RCTs, were included. Four RCTs contributed to the primary quantitative synthesis, and each pooled endpoint included two or three trials. Robot-assisted training was associated with improved ipsilesional resting motor threshold (RMT; SMD 1.77, 95% CI: 0.46-3.08; k = 2) and upper-limb impairment (Fugl-Meyer assessment-upper extremity [FMA-UE]; MD 4.48 points, 95% CI: 0.33-8.62; k = 3). Effects were uncertain for ipsilesional motor-evoked potential (MEP) amplitude (SMD 0.52, 95% CI: -0.31-1.34; k = 2) and activities of daily living (ADL). Heterogeneity was moderate to substantial, prediction intervals crossed the null, and certainty was low or very low. Robot-assisted training may improve impairment and corticospinal excitability, but randomized biomarker evidence remains sparse and heterogeneous. Biomarkers should be interpreted as exploratory observations, not validated surrogates or causal evidence of cortical restitution.
A novel series of Schiff base metal complexes Cu(II), Ni(II), Co(II), and Zn(II) were produced from 4-dimethyl amino benzaldehyde and L-leucine. These generated compounds were investigated for their biological activities comprising of anticancer, DNA binding, antioxidant, anti-diabetic, and anti-inflammatory assay. The outcomes demonstrate that the coordinated metal ion enact a substantial aspect in modulating bioactivity. Among the complexes, Cu(II) revealed the highest cytotoxicity against HeLa cells, exhibited a concentration-dependent decrease in viability, while free ligand remained largely inactive. DNA binding studies revealed moderate to strong interactions with CT-DNA, predominantly through intercalation, following the series: Cu(II) > Ni(II) > Co(II) > Zn(II), correlating by their cytotoxic effects. In antioxidant and anti-inflammatory assays, Cu(II) showed the highest activity, whereas Zn(II) displayed superior α-glucosidase inhibition, indicating promising anti-diabetic potential. Molecular docking further supported these findings, highlighting strong binding interactions of Cu(II) with biomolecular targets and favorable enzyme binding for Zn(II). The study establishes a clear structure-activity relationship governed by the metal center. The Cu(II) complex emerges as a promising multifunctional candidate with notable anticancer and biomolecular interactions, while the Zn(II) complex shows potential for anti-diabetic applications. These findings provide useful insights for the design of metal-based therapeutic agents.
Carinal reconstruction is the primary surgical intervention for tracheal tumors involving the tracheal carina. However, the complexity of airway management and the challenges associated with invasive carina reconstruction significantly increase its operative difficulty. A 48-year-old male patient presented with a 6-month history of persistent cough. Cervical and thoracic computed tomography (CT) imaging, along with bronchoscopic biopsy, confirmed a diagnosis of tracheal adenoid cystic carcinoma (TACC), with the tumor extending to the tracheal carina and left main bronchus. The patient subsequently underwent robot-assisted carinal resection and reconstruction using a three-port approach under extracorporeal membrane oxygenation (ECMO) support. Intraoperative oxygen saturation remained stable, and the postoperative course was uneventful. This case suggests that that the combination of ECMO support and robotic assistance facilitates adequate oxygenation and enables a minimally invasive, safe and efficient approach to carinal resection and reconstruction. Further studies and broader clinical experience across multiple centers are required to validate the safety and practicality of this technique.
Induced glutamatergic neurons (iGluNeurons) generated by Neurogenin-2 (NGN2) overexpression in human pluripotent stem cells are a powerful model for studying human neuronal maturation and function; however, NGN2-based protocols still lack standardized culture conditions that critically affect neuronal development and function. Three key factors have been identified by previous literature, namely the composition of extracellular matrix coating, the initial plating density, and the choice of culture medium, but the differential effects of their combination have not been thoroughly analyzed. Here, we investigated the combinatorial effects of these three variables, testing eight distinct culture conditions resulting from the combinations of two coatings (poly-L-ornithine and polyethyleneimine), two media (BrainPhys and Neurobasal), and two cell densities (4800 and 1200 cells/mm²). We assessed electrophysiological properties at the single-cell and network levels, characterized morphofunctional and proteomic features across multiple developmental stages. Electrophysiological data indicate that medium composition and plating density, rather than substrate coating, determine neuronal maturation dynamics, with BrainPhys and high density promoting rapid but transient maturation while Neurobasal and low density supporting gradual and sustained network development. Morphofunctional analyzes of synapses and the axon initial segment, together with neuronal maturation markers, support an early BrainPhys-driven acceleration of development that is later exceeded by Neurobasal. To enable accurate proteome profiling of the iGluNeuron system-comprising human neurons and rat astrocytes-we developed a robust taxonomic filtering algorithm that selectively identifies human-specific proteins. This approach confirmed the presence of a conserved core of NGN2-driven differentiation pathways across all settings, in addition to condition-specific signatures. Finally, in the optimal conditions identified through our experimental analyzes, robust spontaneous and evoked synaptic activity was observed. These results provide a framework for optimizing iGluNeuron cultures, balancing rapid maturation and long-term functional stability, and establishing a benchmark for human neuronal models in disease research and drug screening.
For clinical nurses, manually entering information into hospital information systems (HISs) remains time-consuming and prone to omissions. Although speech recognition can reduce the need for manual entry, its use in clinical settings has historically been limited by code-switching, medical terminology, and noisy ward environments. Recent advances in customized automatic speech recognition (ASR) and large language models (LLMs) now make speech-based, structured documentation aligned with nursing frameworks such as DART (data, action, response, and teaching) increasingly feasible. This study developed and evaluated an integrated ASR and LLM system that transforms spoken nursing input into structured DART notes and evaluated its accuracy, usability, and clinical feasibility within HIS workflows. A code-switching nursing speech corpus from emergency and ward settings was used to fine-tune the Whisper large-v2 model with parameter-efficient adaptation. The LLM generated schema-constrained DART records from ASR transcripts, which were verified by nurses before being uploaded to the corresponding HIS fields. Evaluation included mixed error rate for ASR accuracy, F1-scores, and agreement statistics for DART classification, hallucination assessments based on factual correctness, and analysis of nurse feedback on system use. The fine-tuned ASR model reduced the mixed error rate from 44.79% to 6.67%. DART generation achieved a macroaveraged F1-score of 0.82 (95% CI 0.80-0.84) and met the noninferiority margin relative to human transcripts (δ=-0.04). The hallucination rate was 2.51%. During deployment, the monthly volume of valid nursing notes generated through voluntary use of the ASR system increased from 32,724 to 65,417, where each note represented a single documentation entry generated per patient care episode. Among 120 participating nurses, 91 (75.8%) reported reduced workload and improved completeness. The integrated ASR and LLM system was feasible and showed strong performance, with good acceptance among clinical nurses. It reduced the manual documentation burden, improved record completeness, and demonstrated the value of an ASR- and LLM-supported workflow for nursing documentation.
Robotic cardiac surgery has emerged as an alternative to conventional sternotomy for adult congenital heart defect repairs. However, comparative data between robotic and open surgical approaches for right-sided partial anomalous pulmonary venous drainage repair in adults are lacking. This study aimed to compare perioperative outcomes between these two surgical modalities. This retrospective single-center cohort study included 129 consecutive adult patients who underwent surgical repair for partial anomalous pulmonary venous drainage between April 2012 and June 2025. Patients were divided into robotic (n=62) and open surgery (n=67) groups. The primary endpoint was hospital stay. Secondary endpoints included blood product utilization, postoperative complications, and 30-day mortality. Multivariable regression analyses were performed to identify independent predictors of outcomes. Hospital length of stay was significantly shorter in the robotic group (4.7±1.8 vs 6.0±3.7 days, P=.002). After multivariable adjustment, robotic surgery remained independently associated with reduced hospital stay (β=-.121, P=.029). Blood product utilization was significantly lower in the robotic group (1.3±1.6 vs 2.0±2.0 units, P=.04). In multivariable negative binomial regression, robotic surgery was associated with a 62.5% reduction in transfusion requirements (incidence rate ratio=.375, P<.001). Despite longer cardiopulmonary bypass durations in the robotic group (138.6±37.2 vs 74.1±33.1 minutes, P<.001), complication rates were comparable (22.6% vs 26.9%, P=.72). Thirty-day mortality was similar between groups (1.6% vs 1.5%, P=.73). Robotic surgery for right-sided partial anomalous pulmonary venous drainage repair in adults is associated with shorter hospital length of stay and reduced blood product utilization compared to conventional open surgery, with equivalent safety outcomes.
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Accurate digital twin reconstruction of articulated objects is essential for graphics-driven simulation, visualization, and embodied applications. However, existing 3D Gaussian Splatting (3DGS) approaches primarily target static scenes and fail to capture the structural dynamics of multi-joint objects, leading to geometric inconsistencies and limited realism. We present a structure-aware reconstruction framework that attaches articulated Gaussian primitives to the kinematic hierarchy, enabling dynamic and editable digital twin reconstruction from multi-view observations via differentiable rendering. By explicitly modeling link-level geometry and motion coupling, the proposed method produces temporally consistent reconstructions that generalize to novel viewpoints and unseen joint configurations. Extensive experiments demonstrate improved visual fidelity, geometric accuracy, and cross-view consistency on articulated robotic systems. Furthermore, the reconstructed digital twins facilitate scalable simulation and enhance sim-to-real policy transfer, highlighting the importance of dynamic Gaussian-based modeling for high-fidelity digital twin applications.
Flexible needles are prone to bending-induced deflection during insertion into soft tissue, which compromises targeting accuracy and increases the difficulty of closed-loop control. Existing physics-based models often exhibit limited generalization capability, whereas purely data-driven methods generally lack physical interpretability. This study aims to develop a flexible needle tip deflection prediction method that combines physical modeling and data-driven correction to improve prediction accuracy, robustness, and generalization performance. A mechanics-guided prediction framework integrating self-supervised consistency learning and supervised residual correction is proposed. First, a needle bending model is established using the Rayleigh-Ritz method to provide physically informed baseline predictions. Then, a self-supervised consistency constraint is introduced to capture the intrinsic relationships among samples at different insertion depths. In addition, a supervised residual branch is incorporated to compensate for system errors that are difficult to describe explicitly in complex insertion environments. Experimental results demonstrate that the proposed method remains stable across different network scales. Under the optimal configuration, it achieves a mean absolute error of 0.36 mm, a root mean square error of 0.51 mm, and a maximum error of 1.45 mm. The method also maintains favorable generalization performance in tests beyond the training depth range. In static target puncture experiments and straight-line trajectory tracking experiments, average errors of 0.95±0.16 mm and 0.28±0.22 mm are achieved, respectively. The proposed method improves the prediction accuracy of flexible needle tip deflection while preserving physical interpretability through mechanics-based guidance. The resulting predictive framework provides stable and reliable prior information for autonomous closed-loop control and offers an effective route toward high-accuracy and robust needle insertion in soft tissue.