Relationship between Burden of Cerebral Small Vessel Disease on Imaging and Cognitive Impairment of COPD Patients.

OBJECTIVE: This study aims to explore the relationship between the burden of cerebral small vessel disease (CSVD) on imaging and cognitive impairment (CI) in patients with chronic obstructive pulmonary disease (COPD). METHODS: The study included 118 COPD patients admitted to Changxing People's Hospital between July 2020 and July 2023. All patients received a 1.5T MRI of the brain and pulmonary function tests. A cognitive function assessment was conducted via the Montreal Cognitive Assessment (MoCA) scale, and patients were divided into two groups. The relationship between the MoCA and CSVD burden score was analyzed by Pearson correlation, and to identify risk factors, multiple logistic regression analysis was performed. RESULTS: The study showed a negative correlation between the MoCA and CSVD burden score in COPD patients (r=-0.479, P<0.001). Multiple logistic regression analysis found that age (OR=2.264, 95% CI: 1.426-3.596, P<0.001), COPD grade (OR=3.139, 95% CI: 2.012-4.898, P<0.001), as well as CSVD burden score (OR=5.336, 95% CI: 1.191-23.900, P<0.001) were the independent risk factors for CI in COPD patients (P<0.05). CONCLUSION: When screening for cognitive impairment in COPD patients, the CSVD burden score can be used in conjunction with cognitive assessment scales to make judgments.

Predefined-time distributed optimization and anti-disturbance control for nonlinear multi-agent system with neural network estimator: A hierarchical framework.

This paper addresses the predefined-time distributed optimization of nonlinear multi-agent system using a hierarchical control approach. Considering unknown nonlinear functions and external disturbances, we propose a two-layer hierarchical control framework. At the first layer, a predefined-time distributed estimator is employed to produce optimal consensus trajectories. At the second layer, a neural-network-based predefined-time disturbance observer is introduced to estimate the disturbance, with neural networks used to approximate the unknown nonlinear functions. A neural-network-based anti-disturbance sliding mode control mechanism is presented to ensure that the system trajectories can track the optimal trajectories within a predefined time. The feasibility of this hierarchical control framework is verified by utilizing the Lyapunov method. Numerical simulations are conducted separately using models of robotic arms and mobile robots to validate the effectiveness of the proposed method.

Radiogenomics uncovers an interplay between angiogenesis and clinical outcomes in bladder cancer.

BACKGROUND: Precision medicine has become a promising clinical treatment strategy for various cancers, including bladder cancer, where angiogenesis plays a critical role in cancer progression. However, the relationship between angiogenesis, immune cell infiltration, clinical outcomes, chemotherapy, and targeted therapy remains unclear. METHODS: We conducted a comprehensive evaluation of angiogenesis-related genes (ARGs) to identify their association with immune cell infiltration, transcription patterns, and clinical outcomes in bladder cancer. An ARG score was constructed to identify angiogenic subgroups in each sample and we evaluated their predictive performance for overall survival rate and treatment response. In addition, we optimized existing clinical detection protocols by performing image data processing. RESULTS: Our study revealed the genomic-level mutant landscape and expression patterns of ARGs in bladder cancer specimens. Using analysis, we identified three molecular subgroups where ARG mutations correlated with patients' pathological features, clinical outcomes, and immune cell infiltration. To facilitate clinical applicability, we constructed a precise nomogram based on the ARG score, which significantly correlated with stem cell index and drug sensitivity. Finally, we proposed the radiogenomics model, which combines the precision of genomics with the convenience of radiomics. CONCLUSION: Our study sheds light on the prognostic characteristics of ARGs in bladder cancer and provides insights into the tumor environment's characteristics to explore more effective immunotherapy strategies. The findings have significant implications for the development of personalized treatment approaches in bladder cancer and pave the way for future studies in this field.

An inertial neural network approach for loco-manipulation trajectory tracking of mobile robot with redundant manipulator.

This paper proposes a novel constrained optimization model to address the loco-manipulation problem of mobile robot with redundant manipulator for trajectory tracking. To alleviate the accumulative error of the end-effector's position, a new control law is designed to eliminate the negative effect from the deviation of the initial position, leading to better performance than existing ones. To deal with the locomotion constraints in the loco-manipulation problem, the optimization model is converted to an augmented Lagrangian primal-dual problem. Furthermore, an inertial neural network approach is used to solve the problem and the corresponding Lyapunov proof guarantees the convergence of variables. The numerical simulations show that the proposed approach is more suitable for application since the model is more effective and the algorithm has better convergence rate.

An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization.

This paper presents an inertial neural network to solve the source localization optimization problem with l1-norm objective function based on the time of arrival (TOA) localization technique. The convergence and stability of the inertial neural network are analyzed by the Lyapunov function method. An inertial neural network iterative approach is further used to find a better solution among the solutions with different inertial parameters. Furthermore, the clock asynchronization is considered in the TOA l1-norm model for more general real applications, and the corresponding inertial neural network iterative approach is addressed. The numerical simulations and real data are both considered in the experiments. In the simulation experiments, the noise contains uncorrelated zero-mean Gaussian noise and uniform distributed outliers. In the real experiments, the data is obtained by using the ultra wide band (UWB) technology hardware modules. Whether or not there is clock asynchronization, the results show that the proposed approach always can find a more accurate source position compared with some of the existing algorithms, which implies that the proposed approach is more effective than the compared ones.

Positron emission tomography/computed tomography using 2-deoxy-2-fluoro-18-fluoro-D-glucose peri-tumoral halo uptake layer method outperforms magnetic resonance imaging and ultrasound methods in tumor size measurement of breast cancer.

As a non-invasive method, positron emission tomography (PET)/computed tomography (CT) using 2-deoxy-2-fluoro-18-fluoro-D-glucose (18F-FDG) is applied as a useful modality in the diagnosis of breast cancer. By evaluating glucose metabolism, this method can also be used in staging, restaging and post-therapeutic response evaluation. To evaluate the reliability of the 18F-FDG PET/CT-based peri-tumoral halo uptake layer (PHL) method for assessing tumor size, a total of 79 female patients with breast cancer who underwent 18F-FDG PET/CT, breast ultrasound and magnetic resonance imaging (MRI) evaluations were included in the present study. Upon examination by two independent nuclear medicine radiologists, tumor sizes were estimated by 18F-FDG PET/CT using margins defined as the inner line of the PHL. Pathological tumor sizes were evaluated on the direction of largest diameter indicated by previous imaging examination, which were also utilized as final standards. Statistical analysis of the results suggested that 18F-FDG PET/CT had a more linear correlation with pathology compared with breast ultrasound (r2=0.89 vs. 0.73) and MRI (r2=0.89 vs. 0.69) in terms of tumor size estimation, including a significantly lower bias in size difference relative to pathology. 18F-FDG PET/CT also exhibited improved performance compared with breast ultrasound and MRI in T stage assessment. These results indicated that the 18F-FDG PET/CT-based PHL method was superior to breast ultrasound and MRI, and that it provides sufficient reliability and high accuracy for measuring tumor size in patients with breast cancer.