Frontiers in medical technology最新文献

Minimally invasive endoluminal interventions increasingly rely on magnetic actuation to navigate narrow lumens while limiting wall loads. Here we present a contact-aware control framework for steering a deformable, silicone-based soft microrobot with embedded magnetic particles using an externally positioned permanent magnet. We develop a dynamic model capturing viscous drag, nonlinear frictional loads, and viscoelastic wall contact, and implement a closed-loop architecture that combines vision-based state estimation with model-based force inference while optimizing magnet orientation to regulate the force vector and normal reaction. Performance is evaluated in simulation and on a benchtop testbed across three control modes. In the nominal-case benchmark, force-plus-angle control reduced the root-mean-square tracking error from 4.8 to 2.1 mm (-56%), decreased peak tracking error from 14.6 to 8.0 mm (-45%), lowered the integrated performance index from 4.4 × 10^-¹⁰ to 1.6 × 10^-¹⁰ (-64%), and attenuated peak normal reaction force from 2.0 × 10^{- 6} to 0.8 × 10^{- 6} N (-60%) compared with operation without force regulation. To assess robustness, we further performed a simulation-based Monte Carlo analysis (n = 500 trials per mode) under parametric uncertainty and measurement noise, confirming that the contact-aware modes preserve their performance advantage; non-parametric tests indicated statistically significant inter-mode differences with moderate-to-large effect sizes. A trade-off analysis in the {tracking error, peak normal load} plane showed that, in the explored regime, improved tracking does not inherently require higher peak contact forces. Finally, a first-order shear-thinning surrogate suggested low sensitivity of the relative conclusions to moderate non-Newtonian effects. Overall, the results identify force-aware magnet orientation as a safety-relevant control degree of freedom for endoluminal navigation and provide a transferable control methodology for future magnetic microrobotic platforms.

Introduction: Encapsulating herbal extracts with wound-healing properties in liposomes may enhance their stability and delivery performance. This study evaluated the biological efficacy of a liposome-encapsulated ethanolic extract of Centella asiatica (LEC) using in vitro and in vivo wound-healing models.

Methods: The ethanolic extract was incorporated into liposomes using the thin-film hydration method. Anti-inflammatory activity was assessed in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages. Cell viability and migration were evaluated in normal human dermal fibroblasts (NHDFs). In vivo wound-healing efficacy was examined using a rat excision wound model with daily topical application of LEC.

Results: LEC significantly reduced TNF-α and IL-1β production in a dose-dependent manner and enhanced fibroblast viability and migratory capacity compared with the crude extract and vitamin E controls. In vivo, topical LEC markedly accelerated wound contraction, achieving 99.9 ± 0.1% closure by Day 12, which was significantly greater than the normal saline-treated control (p < 0.05) and higher than the blank liposome group, while demonstrating comparable efficacy to vitamin E. Histological analysis revealed enhanced re-epithelialization, increased collagen deposition, and reduced inflammatory cell infiltration in LEC-treated wounds.

Conclusion: These findings indicate that liposomal encapsulation enhances the bioactivity of C. asiatica extract during the inflammatory and proliferative phases of wound repair, supporting further development of LEC as a topical wound-healing formulation.

Chronic diseases such as diabetes and cardiovascular disease require frequent monitoring and timely clinical feedback to prevent complications. Internet of Medical Things (IoMT) systems increasingly combine near-patient sensing with Fog and Cloud computing so that time-critical preprocessing and inference can run close to the patient while compute-intensive training and population-level analytics remain in the Cloud. This review synthesizes primary studies published between 2020 and 2025 that implement AI-enabled IoMT, with an emphasis on systems that report both diagnostic performance and network quality-of-service (QoS). Following PRISMA 2020, we screened database records and included 14 primary studies; we focus the joint performance-QoS synthesis on six IoMT-Fog-Cloud frameworks for diabetes and cardiovascular disease and compare them with two recent multi-disease AI-IoMT models (DACL and TasLA). Diabetes-oriented implementations commonly report accuracy around 95%-96% using explainable or ensemble deep learning, whereas some cardiovascular frameworks report >99% accuracy in controlled settings; we therefore discuss plausible sources of optimistic performance, including small datasets, class imbalance, curated benchmarks, and potential leakage/overfitting in simulation-based evaluations. Across IoMT-Fog-Cloud studies, placing preprocessing and/or inference at the Fog layer repeatedly reduces end-to-end latency for streaming biosignals, but multi-Fog provisioning can increase energy and power demands. To support more reproducible comparisons, we organize 14 extracted metrics into (i) diagnostic performance (accuracy, precision, recall, F1-score, sensitivity, specificity) and (ii) system/network QoS (latency, jitter, throughput, bandwidth utilization, processing/execution time, network usage, energy consumption, power consumption), and we translate the evidence into study-linked design recommendations for future deployments.

Objectives: To evaluate the accuracy of heart rate and respiratory rate acquired by commercially-available device cameras and software.

Methods: One-hundred and eleven subjects were enrolled at an urban academic teaching hospital in Texas. Heart rate (HR) and respiratory rate (RR) measurements were obtained using three commercially-available cameras and processed using software from Presage Technologies. These values were compared to manual counts of a three-lead ECG and end-tidal CO2 from a patient monitor.

Results: The cameras were able to capture HR in 83% of the measurements and RR in 94% of the RR measurements. Camera-acquired HR showed an extremely high correlation with R∼0.99 and a root-mean-square error (RMSE) of 1.62. Respiratory rate showed a high correlation with R∼0.91 and an RMSE of 1.71.

Conclusions: Heart rate and respiratory rate can be accurately acquired using commercially-available camera devices and software for signal processing.

[This corrects the article DOI: 10.3389/fmedt.2025.1567537.].

Background: Ischaemic mitral regurgitation (IMR) is a significant complication of myocardial infarction associated with increased morbidity and mortality. Large animal models are essential for testing novel mitral valve therapies, yet no consensus exists on the optimal infarction strategy to induce IMR.

Methods: A systematic review and meta-analysis were conducted to evaluate infarction strategies used to induce IMR in large animal models. Studies were identified through database searches and screened according to predefined inclusion criteria. Subgroups were stratified by infarction strategy. Proportions of IMR development and severity were analysed using a random-effects model, and reporting quality was assessed across studies.

Results: Forty-four studies met the inclusion criteria, comprising 869 animals across 52 subgroups. Ethanol injection in select obtuse marginal arteries (EtOH-OMx strategy) yielded the highest rate of IMR development (87%, 95% CI: 79%-96%) with the lowest associated mortality. Ligation of obtuse marginal arteries under cardiopulmonary bypass (CPB-OM2, OM3 and CPB-OMx) demonstrated high mortality and inconsistent IMR severity. Reporting quality was variable, with frequent omissions regarding sex, randomisation, and adverse event documentation.

Conclusions: This review identifies the EtOH-OMx strategy as a promising method for inducing IMR in large animal models, demonstrating favourable performance within the limitations of available data.

Background: Ventricular assist devices (VADs) are an effective treatment for end-stage heart failure and can significantly improve patients' quality of life. However, when the rotational speed of the VAD does not match the intraventricular blood volume, ventricular suction may occur. Severe suction can lead to ventricular collapse, making accurate and real-time suction detection critically important.

Methods: Two statistical features and two frequency-domain features were extracted from the pump flow signal to build a classification and regression tree (CART) model. Additionally, a secondary decision-making process was applied using a time-domain threshold.

Results: The proposed method was validated using both in vivo and in vitro experimental data. Experimental results show that, compared to existing suction detection techniques, the proposed approach not only reduces computational complexity but also achieves higher detection accuracy and enhanced algorithmic stability.

Conclusions: The proposed method provides a more efficient and reliable solution for real-time ventricular suction detection, which is crucial for the safe operation of VADs in clinical settings.

In 2025, three U.S. agencies within the Department of Health and Human Services (FDA, NIH, CDC) alongside EPA and the Departments of the Navy and Veterans Affairs, began substituting animal testing applications with reliable, human-relevant methods. The impact of this shift in public policy taking place at agencies historically bullish on animal testing is still reverberating in the United States and around the world. Here, we examine the circumstances that enabled such momentous reforms, including the role of the FDA Modernization Act 2.0 in advancing new alternative methods, collectively referred to as NAMs. We explain how animal testing, despite its poor value in predicting the safety and efficacy of drugs in humans, came to dominate drug discovery, basic sciences, and environmental toxicity assessments since the inception of the Federal Food, Drug, and Cosmetic Act (Federal FD&C Act) in 1938. Specifically, we identify critical junctures, including catastrophic government decisions, that made the overall research enterprise acutely dependent on animals, leading to the existing predicament-an indefensible 92% failure rate in translating drugs from preclinical studies to actual therapies. Notably, our analysis chronicles events through the lens of "path dependency," a social sciences phenomenon that occurs when faulty past decisions lock-in future action. Finally, we recognize that our narrative is a departure from the medical establishment account or the talking points of powerful interest groups, including some in the academic elite who continue to shore up animal-centric paradigms in drug development for reasons we also outline.

Introduction: Phantoms and reference structures are essential tools for calibration and correlative imaging in pre-clinical and research applications of X-ray-based imaging. They serve as reference standards, ensuring consistency and accuracy in imaging results. However, generating individual phantoms often involves a complex creation process, high production costs, and significant time investment.

Material and methods: Conic reference structures were 3D printed using a mixture of UV-curable resin and X-ray contrast agents. These structures were then embedded together with lung specimens of SARS-CoV-2-infected rhesus macaques in a methyl methacrylate-based solution. The polymerized blocks were scanned using propagation-based phase-contrast microCT, a method chosen for its superior ability to enhance contrast, especially in low-absorbing biological samples. Utilizing the conic reference structures, subsequently performed histological sections were co-registered into the 3D context of the microCT datasets.

Results: The produced 3D printed models were highly visible in terms of contrast and detail in both imaging methods, allowing for a precise co-registration of microCT and histological imaging.

Conclusions: The novel methodology of using contrast agents and resin in 3D printing enables the generation of customizable, contrast-specific phantoms and reference structures. These can be straightforwardly segmented from the embedding material, significantly simplifying and enhancing the workflow of multimodal imaging processes. In this study, 3D printed conic reference structures were effectively used to automate and streamline the precise multimodal fusion of microCT and histological imaging.

Objective: This study aims to evaluate the clinical efficacy of laparoscopic hiatal hernia repair (LHHR) in treating gastroesophageal reflux disease (GERD) and to through the therapeutic effect of total (360°) and partial (270°) laparoscopic fundoplication.

Methods: This retrospective observational study enrolled 100 patients, both with and without documented extra-oesophageal symptoms of GERD. Data were extracted from medical records, covering basic information, symptoms, treatments, and follow-up. Symptom relief and quality of life were assessed via GERD-Q score, Reflux Symptom Index (RSI), and EORTC QLQ-C30 scale, offering a foundation for comprehensive GERD patient management and treatment evaluation in clinical practice.

Results: The DeMeester index significantly decreased postoperatively in both the laparoscopic Nissen fundoplication (LNF) group (from 55.23 ± 25.12 to 11.45 ± 10.20, p < 0.05) and the laparoscopic Toupet fundoplication (LTF) group (from 60.51 ± 28.40 to 11.70 ± 9.65, p < 0.05). The RSI scores improved at 12 months postoperatively in both groups: LNF group (from 23.1 ± 15.4 to 13.7 ± 9.6, p < 0.05) and LTF group (from 21.9 ± 15.8 to 12.8 ± 8.2, p < 0.05). The GERD scores also improved postoperatively: LNF group (from 13 ± 5.0 to 10 ± 4.4, p < 0.05) and LTF group (from 10 ± 4.7 to 7.5 ± 4.5, p < 0.05).

Conclusion: Our report demonstrates that LHHR significantly improved GERD regarding symptom frequency, acid reflux occurrences and DeMeester score. Both LNF and LTF provide good results.