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In the research field of multi-robot cooperation, reliable and low-cost motion capture is crucial for system development and validation. To address the high costs of traditional motion capture systems, this study proposes a real-time 6D pose estimation and tracking method for multi-robot systems based on YolPnP-FT. Using only an Intel RealSense D435i depth camera, the system achieves simultaneous robot classification, 6D pose estimation, and multi-target tracking in real-world environments. The YolPnP-FT pipeline introduces a keypoint confidence filtering strategy (PnP-FT) at the output of the YOLOv8 detection head and employs Gaussian-penalized Soft-NMS to enhance robustness under partial occlusion. Based on these detection results, a linearly weighted combination of Mahalanobis distance and cosine distance enables stable ID assignment in visually similar multi-robot scenarios. Experimental results show that, at a camera height below 2.5 m, the system achieves an average position error of less than 0.009 m and an average angular error of less than 4.2°, with a stable tracking frame rate of 19.8 FPS at 1920 × 1080 resolution. Furthermore, the perception outputs are validated in a CoppeliaSim-based simulation environment, confirming their utility for downstream coordination tasks. These results demonstrate that the proposed method provides a low-cost, real-time, and deployable perception solution for multi-robot systems.

Nanozymes offer diverse therapeutic potentials for cancer treatment which is dependent on the development of nanomaterials. Quasi-metal-organic framework is a class of metal-organic framework-derived nanomaterials with a transition state from metal-organic frameworks towards metal oxide featuring porous structure and high activity. Herein an iron-based quasi-metal-organic framework nanozyme Q-MIL-53(Fe) is reported via a controlled deligandation strategy, exhibiting enhanced peroxidase-/catalase-mimic activity and glutathione depletion capacity, whose underlying mechanisms are studied via density functional theory calculations. Q-MIL-53(Fe) demonstrates biocompatibility and superior antitumor efficacy compared to pristine MIL-53(Fe). It can activate antitumor immune response by inducing ferroptosis and immunogenic cell death, promoting dendritic cell maturation and T lymphocytes infiltration. Furthermore, a combination of Q-MIL-53(Fe) and programmed cell death protein 1 antibody amplifies cancer immunotherapy. This study validates the antitumor activity of quasi-metal-organic frameworks and its immunotherapy induction potential. It would broaden the application of quasi-metal-organic frameworks and open avenues for developing antitumor nanozymes. Nanozymes offer diverse therapeutic avenues for cancer treatment. In this work, the authors report an iron-based quasi-metal organic framework nanozyme Q-MIL-53(Fe) with enhanced peroxidase and catalase-mimicking activity and glutathione depletion capacity and use it for tumor immunotherapy via inducing ferroptosis and immunogenic cell death.

Faced with the increasingly severe global aging population with fewer children, the research, development, and application of elderly-care robots are expected to provide some technical means to solve the problems of elderly care, disability and semi-disability nursing, and rehabilitation. Elderly-care robots involve biomechanics, computer science, automatic control, ethics, and other fields of knowledge, which is one of the most challenging and most concerned research fields of robotics. Unlike other robots, elderly-care robots work for the frail elderly. There is information exchange and energy exchange between people and robots, and the safe human-robot interaction methods are the research core and key technology. The states of the art of elderly-care robots and their various nursing modes and safe interaction methods are introduced and discussed in this paper. To conclude, considering the disparity between current elderly care robots and their anticipated objectives, we offer a comprehensive overview of the critical technologies and research trends that impact and enhance the feasibility and acceptance of elderly care robots. These areas encompass the collaborative assistance of diverse assistive robots, the establishment of a novel smart home care model for elderly individuals using sensor networks, the optimization of robot design for improved flexibility, and the enhancement of robot acceptability.

Cyclic loads caused by natural factors such as strong winds are common in plant growth environments. Prolonged exposure to such loads can compromise the anchorage performance of plants. This study examines how cyclic loading influences the root anchorage of Betula platyphylla, a prominent tree species in northern China. A series of pull-out tests were performed on soil-embedded roots, including monotonic pull-out tests and 100 cycles of loading and unloading. The research results show that under different cyclic load amplitudes, the peak bearing capacity is negatively correlated with the load amplitude. Energy dissipation in the root system increases with higher load amplitudes but decreases as the number of cycles increases. From the initial cycle to the 25th cycle, energy dissipation decreased substantially, with no further significant reduction observed between the 25th and 100th cycles. To more effectively capture the nonlinear hysteretic behavior of roots, an enhanced Bouc-Wen model was developed and successfully fitted to the force-displacement curves. The model accurately replicated the hysteresis loops and characterized the damage progression in root anchorage under cyclic loading. These findings offer valuable insights into the mechanical stability of plant roots under repeated environmental stresses and provide a robust framework for modeling root anchorage performance in natural settings.

Physiotherapy robots offer a feasible and promising solution for achieving safe and efficient treatment. Among these, acupoint recognition is the core component that ensures the precision of physiotherapy robots. Although the research on the acupoint recognition such as hand and ear has been extensive, the accurate location of acupoints on the back of the human body still faces great challenges due to the lack of significant external features. This paper designs a two-stage acupoint recognition method, which is achieved through the cooperation of two detection networks. First, a lightweight RTMDet network is used to extract the effective back range from the image, and then the acupoint coordinates are inferred from the extracted back range, reducing the inference consumption caused by invalid information. In addition, the RTMPose network based on the SimCC framework converts the acupoint coordinate regression problem into a classification problem of sub-pixel block subregions on the X and Y axes by performing sub-pixel-level segmentation of images, significantly improving detection speed and accuracy. Meanwhile, the multi-layer feature fusion of CSPNeXt enhances feature extraction capabilities. Then, we designed a physiotherapy interaction interface. Through the three-dimensional coordinates of the acupoints, we independently planned the physiotherapy task path of the physiotherapy robot. We conducted performance tests on the acupoint recognition system and physiotherapy task planning in the physiotherapy robot system. The experiments have proven our effectiveness, achieving a recall of 90.17% on human datasets, with a detection error of around 5.78 mm. At the same time, it can accurately identify different back postures and achieve an inference speed of 30 FPS on a 4070Ti GPU. Finally, we conducted continuous physiotherapy tasks on multiple acupoints for the user. The experimental results demonstrate the significant advantages and broad application potential of this method in improving the accuracy and reliability of autonomous acupoint recognition by physiotherapy robots.

Background The prevalence of ischaemic heart failure (HF) continues to increase. Diabetes mellitus (DM) concomitant with ischaemic HF increases the risk of major adverse cardiovascular events (MACEs). As a promising predictor for cardiovascular diseases, the predictive value of the monocyte to high-density lipoprotein cholesterol ratio (MHR) for MACE in the ischaemic HF with DM cohort has never been investigated before. Objective We aimed to investigate the MHR as a predictor for MACE in ischaemic HF patients with DM who underwent percutaneous coronary intervention (PCI). Methods This observational study enrolled 1049 patients with ischaemic HF and DM undergoing PCI from June 2017 to June 2019. The baseline data were collected. MACEs, including all-cause mortality, nonfatal myocardial infarction, and any revascularization, were recorded within the 36-month follow-up. The characteristics and incidence of MACE were analysed in four groups stratified by the quartiles of MHR. The hazard ratio for MACE was analysed with Cox regression models. The incidence of MACE in the four groups was evaluated by Kaplan‒Meier survival analysis. Restricted cubic spline analysis was performed to determine the nonlinear correlation between the MHR and MACE. Results After the 36-month follow-up, 407 patients (38.8%) experienced MACEs. The incidence of MACE was significantly higher among patients in the upper MHR quartile than among those in the lower MHR quartiles (23.4% vs. 36.0% vs. 41.4% and 54.6%; P  < 0.001, respectively), which was consistent with the Kaplan‒Meier survival analyses ( P  < 0.0001). A multivariate Cox regression model showed that the MHR was an independent risk factor for MACE after variables were adjusted (adjusted HR: 2.11; 95% CI 1.47–3.03; P  < 0.001). Its predictive effects on MACE showed no interaction with hypercholesterolemia ( P  > 0.05). Conclusion The MHR was a significant and independent predictor of MACEs in ischaemic HF patients with DM undergoing PCI.

ObjectiveTo determine the association of D-dimer to albumin ratio (DAR) with major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) in ischaemic heart failure patients with diabetes mellitus.DesignA retrospective observational cohort study.SettingSingle centre in Beijing, China, conducted at one of the largest cardiology centres in China.ParticipantsFrom June 2017 to June 2019, 3707 patients with heart failure and concomitant multiple vessel disease undergoing elective PCI were screened. A total 1021 of patients were enrolled after exclusion and the follow-up period was up to 36 months.Primary and secondary outcome measuresThe MACE was the primary measured outcome. The secondary outcomes were all-cause mortality, non-fatal myocardial infarction and any revascularisation.MethodsThese participants were grouped according to DAR tertiles. The cumulative incidence functions, Cox regression, restricted cubic spline and receiver operating characteristic curves were used to determine the association between DAR and outcomes. The subgroup analysis was also performed.ResultsAfter follow-up, MACE occurred in 404 (39.6%) participants. The cumulative hazards curve manifested significant differences in MACE, all-cause mortality and any revascularisation (log-rank test: all p<0.001). In adjusted models, DAR was an independent risk factor of MACE (tertile 2: HR 1.82, 95% CI 1.37 to 2.42; tertile 3: HR 1.74, 95% CI 1.28 to 2.36) and all-cause mortality (tertile 2: HR 2.04, 95% CI 1.35 to 3.11; tertile 3: HR 1.89, 95% CI 1.20 to 2.98). The optimal cut-off of DAR was 1.2. In the stratified analysis, sex, age, hypertension, hypercholesterolaemia, total revascularisation and any interfered vessel did not affect the independent predictive ability.ConclusionHigher DAR was independently associated with MACE and all-cause mortality after PCI in ischaemic heart failure patients with diabetes mellitus.

Background:The incidence of unstable angina (UA), a type of cardiovascular disease (CVD), has increased in recent years. Meanwhile, timely percutaneous coronary intervention (PCI) or percutaneous transluminal coronary angioplasty (PTCA) procedures are crucial for patients with UA who also have diabetes mellitus (DM). Additionally, exploring other factors that may influence the prognosis of these patients could provide long-term benefits. The systemic immune-inflammation index (SII), a novel marker for assessing inflammation levels, has been shown to correlate with the long-term prognosis of various diseases. Thus, this study aimed to investigate the predictive value of the SII for the long-term prognosis of patients with UA and DM after revascularization.Methods:A total of 937 UA patients who underwent revascularization, of which 359 also had DM, were included in this study. Patients were divided into two groups: the low SII group (<622.675 × 109/L; n = 219, 61.0%) and the high SII group (≥622.675 × 109/L; n = 140, 39.0%). The primary outcome was the frequency of major adverse cardiovascular and cerebrovascular events (MACCEs). The secondary outcome was the incidence of all-cause death.Results:Of the 359 patients who visited our institution between January 2018 and January 2020, 23 patients (10.5%) in the low SII group experienced MACCEs, whereas 34 cases (24.3%) in the high SII group experienced MACCEs, showing a statistically significant difference (p < 0.001). After conducting univariate and multivariate regression analyses on the endpoint events, we identified several risk factors for MACCEs. These risk factors included high SII levels, a history of myocardial infarction (MI), prior PCI or coronary artery bypass grafting (CABG), elevated brain natriuretic peptide (BNP), and the lack of angiotensin-converting enzyme inhibitors (ACEI) or statin use. Upon adjusting for covariates including age, sex, body mass index (BMI), BNP, smoking, hypertension, PCI or CABG history, MI history, statin use, ACEI use, and the presence of three-vessel coronary disease, only high SII levels remained a risk factor for MACCEs (HR: 0.155, 95% CI: 0.063–0.382; p = 0.001). However, high SII levels were not identified as a risk factor for other individual endpoint events, including non-fatal stroke, cardiovascular death, non-fatal MI, or cardiac rehospitalization.Conclusion:Elevated SII levels following percutaneous intervention are associated with poor outcomes in patients with UA and DM. Therefore, regular monitoring and controlling inflammation levels may help improve long-term outcomes.

Babesiosis, a zoonotic parasitic disease caused by Babesia protozoa, poses significant infection risks across mammalian species. Clinical manifestations in vertebrate hosts range from spontaneous abortion to fatal outcomes, with immunocompromised individuals potentially transmitting the pathogen through blood products or transplanted organs, thereby amplifying epidemiological risks. Effective disease management carries substantial public health implications for livestock production, companion animal welfare, and food safety in endemic regions. In global endemic zones, conventional diagnostic approaches combine morphological identification of Babesia spp. with complementary serological assays. Contemporary molecular diagnostics, particularly nucleic acid amplification techniques, have emerged as valuable adjunctive tools. A critical challenge in veterinary practice involves persistent subclinical carriers among treated livestock populations, necessitating precise parasite speciation for effective transmission control. This review synthesizes recent advancements in babesiosis detection methodologies, with particular emphasis on their implementation in clinical microbiology laboratories. This article introduces the latest progress in Babesiosis detection technology and its application in clinical microbiology laboratories, to provide a theoretical and practical basis for the comprehensive prevention and control of Babesiosis.

As science and technology continue to advance, the use of flow cytometry is becoming more widespread. It can provide important information about cells in the body by detecting and analysing them, thereby providing a reliable basis for disease diagnosis. In the diagnosis of bovine epidemic diseases, flow cytometry can be used to detect bovine viral diarrhoea, bovine leukaemia, bovine brucellosis, bovine tuberculosis, and other diseases. This paper describes the structure of a flow cytometer (liquid flow system, optical detection system, data storage and analysis system) and its working principles for rapid quantitative analysis and sorting of single cells or biological particles. Additionally, the research progress of flow cytometry in the diagnosis of bovine epidemic diseases was reviewed in order to provide a reference for future research and application of flow cytometry in the diagnosis of bovine epidemic diseases.