[Preliminary Study on Current Research Status and Clinical Application of Lower Limb Rehabilitation Robot Mechanisms].

As an assistive device to restore lower limb athletic ability, human lower limb rehabilitation robots are becoming increasingly important in the field of rehabilitation and clinical applications. With the advancement of science and technology, both domestic and foreign research in this field has been developed. This study provides a detailed overview of the development of lower limb rehabilitation robots and reviews the current status of clinical applications, with a focus on mechanism research, from the perspectives of degrees of freedom, workspace, singularity, gait simulation, kinematic simulation and human-robot interaction, etc. The results show that domestic research on lower limb rehabilitation robots focus on the design and optimization of the mechanism configuration, while foreign research focus on the improvement and innovation of the control system and training mode based on human-computer interaction. Finally, the current state of research is combined with an outlook on future trends.

One Stone, Two Birds: A Peptide-Au(I) Infinite Coordination Supermolecule for the Confederate Physical and Biological Radiosensitization in Cancer Radiation Therapy.

Over half of cancer patients are subjected to radiotherapy, but owing to the deficient amount of reactive oxygen radicals (ROS) and DNA double-strand breaks (DSBs), a fair number of them suffer from radiotherapy resistance and the subsequent short-term survival opportunity. To overcome it, many successes have been achieved in radiosensitizer discovery using physical strategy and/or biological strategy, but significant challenges remain regarding developing clinically translational radiosensitizers. Herein, a peptide-Au(I) infinite coordination supermolecule termed PAICS is developed that combined both physical and biological radiosensitization and possessed pharmaceutical characteristics including adequate circulatory stability, controllable drug release, tumor-prioritized accumulation, and the favorable body eliminability. As expected, monovalent gold ion endowed this supermolecule with high X-ray absorption and the subsequent radiosensitization. Furthermore, a peptide targeting CRM1, is assembled into the supermolecule, which successfully activates p53 and apoptosis pathway, thereby further sensitizing radiotherapy. As a result, PAICS showed superior ability for radiotherapy sensitization in vivo and maintained a favorable safety profile. Thus, the PAICS reported here will offer a feasible solution to simultaneously overcome both the pharmaceutical obstacles of physical and biological radiosensitizers and will enable the development of a class of nanomedicines for tumor radiotherapy sensitization.

SODL-IR-FISTA: sparse online dictionary learning with iterative reduction FISTA for cone-beam X-ray luminescence computed tomography.

Cone beam X-ray luminescence computed tomography (CB-XLCT) is an emerging imaging technique with potential for early 3D tumor detection. However, the reconstruction challenge due to low light absorption and high scattering in tissues makes it a difficult inverse problem. In this study, the online dictionary learning (ODL) method, combined with iterative reduction FISTA (IR-FISTA), has been utilized to achieve high-quality reconstruction. Our method integrates IR-FISTA for efficient and accurate sparse coding, followed by an online stochastic approximation for dictionary updates, effectively capturing the sparse features inherent to the problem. Additionally, a re-sparse step is introduced to enhance the sparsity of the solution, making it better suited for CB-XLCT reconstruction. Numerical simulations and in vivo experiments were conducted to assess the performance of the method. The SODL-IR-FISTA achieved the smallest location error of 0.325 mm in in vivo experiments, which is 58% and 45% of the IVTCG-L 1 (0.562 mm) and OMP-L 0 (0.721 mm), respectively. Additionally, it has the highest DICE similarity coefficient, which is 0.748. The results demonstrate that our approach outperforms traditional methods in terms of localization precision, shape restoration, robustness, and practicality in live subjects.

Dictionary Learning Method Based on K-Sparse Approximation and Orthogonal Procrustes Analysis for Reconstruction in Bioluminescence Tomography.

Bioluminescence tomography (BLT) is one kind of noninvasive optical molecular imaging technology, widely used to study molecular activities and disease progression inside live animals. By combining the optical propagation model and inversion algorithm, BLT enables three-dimensional imaging and quantitative analysis of light sources within organisms. However, challenges like light scattering and absorption in tissues, and the complexity of biological structures, significantly impact the accuracy of BLT reconstructions. Here, we propose a dictionary learning method based on K-sparse approximation and Orthogonal Procrustes analysis (KSAOPA). KSAOPA uses an iterative alternating optimization strategy, enhancing solution sparsity with k-coefficients Lipschitzian mappings for sparsity(K-LIMAPS) in the sparse coding stage, and reducing errors with Orthogonal Procrustes analysis in the dictionary update stage, leading to stable and precise reconstructions. We assessed the method performance through simulations and in vivo experiments, which showed that KSAOPA excels in localization accuracy, morphological recovery, and in vivo applicability compared to other methods.

SBL-LCGL: sparse Bayesian learning based on Laplace distribution for robust cone-beam x-ray luminescence computed tomography.

Objective. To address the quality and accuracy issues in the distribution of nanophosphors (NPs) using Cone-beam x-ray luminescence computed tomography (CB-XLCT) by proposing a novel reconstruction strategy.Approach. This paper introduces a sparse Bayesian learning reconstruction method termed SBL-LCGL, which is grounded in the Lipschitz continuous gradient condition and the Laplace prior to overcome the ill-posed inverse problem inherent in CB-XLCT.Main results. The SBL-LCGL method has demonstrated its effectiveness in capturing the sparse features of NPs and mitigating the computational complexity associated with matrix inversion. Both numerical simulation andin vivoexperiments confirm that the method yields satisfactory imaging results regarding the position and shape of the targets.Significance. The advancements presented in this work are expected to enhance the clinical applicability of CB-XLCT, contributing to its broader adoption in medical imaging and diagnostics.

An Apoptosis-Related Gene Prognostic Index for Colon Cancer.

Purpose: To construct an apoptosis-related gene prognostic index (ARGPI) for colon cancer, and clarify the molecular and immune characteristics of the risk subgroup as defined by the prognostic index and the benefits of adjuvant chemotherapy. Integrating the prognostic index and clinicopathological risk factors to better evaluate the prognosis of patients with colon cancer. Methods: Based on the colon adenocarcinoma data in the TCGA database, 20 apoptosis-related hub genes were screened by weighted gene co-expression network analysis (WGCNA). Five genes constituting the prognosis model were determined by Cox regression and verified by the Gene Expression Omnibus (GEO) dataset. Then the molecular and immune characteristics of risk subgroups defined by the prognostic index and the benefits of adjuvant chemotherapy were analyzed. Finally, nomograms integrating ARGPI and four clinicopathological risk factors were used to evaluate the prognosis of patients with colon cancer. Results: The ARGPI was constructed based on the FAS, VWA5A, SPTBN2, PCK1, and TIMP1 genes. In the TCGA cohort, patients in the low-risk subgroup had a longer progression-free interval (PFI) than patients in the high-risk subgroup, which coincided with the results of the GEO cohort. The comprehensive results showed that the high-risk score was related to the enrichment of the cell cycle pathway, high mutation rate of TP53 and KRAS, high infiltration of T regulatory cells (Tregs), immunosuppressive state, and less chemotherapeutic benefit. However, low-risk scores are related to drug metabolism-related pathways, low TP53 and KRAS mutation rates, high infiltration of plasma cells, more resting CD4 memory cells and eosinophils, active immune function, and better chemotherapeutic benefits. Receiver operating characteristic curve of two-year progress prediction evaluation showed that the ARGPI had higher prognostic accuracy than TNM staging. Nomograms integrating ARGPI and clinicopathological risk factors can better evaluate the prognosis of patients with colon cancer. Conclusions: The ARGPI is a promising biomarker for determining risk of colon cancer progression, molecular and immune characteristics, and chemotherapeutic benefit. This is a reliable method to predict the prognosis of colon cancer patients. It also can assist doctors in formulating more effective treatment strategies.