BACKGROUND: Robot-assisted gait training is incorporated into guidelines for stroke rehabilitation. It is a promising tool combined with conventional therapy for low ambulatory patients. The heavy weight and bulky appearance of a robotic exoskeleton limits its practicality. On the other hand, soft robotic exosuit (SRE) based on its light weight and inconspicuous property, is better tolerated by patients in daily life. The aim of this study is to review the efficacy of the SRE with regard to walking ability and biomechanical properties in stroke patients. METHODS: Electronic searches were carried out in PubMed, Embase, Cochrane Library, Web of Science, and the Physiotherapy Evidence Database. Clinical trials that investigated the effectiveness of SREs on ambulation ability in patients with post-stroke hemiparesis were eligible. Qualitative data synthesis was subsequently performed. RESULTS: Nine studies were identified as relevant, involving a total of 83 patients. For the assessment of SRE efficacy, outcome measures were walking ability and biomechanical properties. In terms of both immediate effect and training effect, SREs improved the walking speed, walking distance, peak ankle dorsiflexion angle during swing phase, peak paretic propulsion, stride length and compensated gait in stroke patients. CONCLUSIONS: SRE improved the ambulation ability of stroke patients in terms of walking ability and biomechanical properties. The small number of studies limits the generalizability of interpretation. More controlled studies with better quality are required to reach a more solid conclusion on this issue.

BACKGROUND: Running with the appropriate intensity may produce a positive influence on the mechanical properties of cortical bone structure. However, few studies have discussed the effects of different running intensities on the mechanical properties at different levels, especially at the micro-level, because the micromechanical parameters are difficult to measure experimentally. METHODS: An approach that combines finite element analysis and experimental data was proposed to predict a micromechanical parameter in the rat femoral cortical bone structure, namely, the micro-level failure strain. Based on the previous three-point bending experimental information, fracture simulations were performed on the femur finite element models to predict their failure process under the same bending load, and the micro-level failure strains in tension and compression of these models were back-calculated by fitting the experimental load-displacement curves. Then, the effects of different running intensities on the micro-level failure strain of rat femoral cortical bone structure were investigated. RESULTS: The micro-level failure strains of the cortical bone structures expressed statistical variations under different running intensities, which indicated that different mechanical stimuli of running had significant influences on the micromechanical properties. The greatest failure strain occurred in the cortical bone structure under low-intensity running, and the lowest failure strain occurred in the structure under high-intensity running. CONCLUSIONS: Moderate and low-intensity running were effective in enhancing the micromechanical properties, whereas high-intensity running led to the weakening of the micromechanical properties of cortical bone. Based on these, the changing trends in the micromechanical properties were exhibited, and the effects of different running intensities on the fracture performance of rat cortical bone structures could be discussed in combination with the known mechanical parameters at the macro- and nano-levels, which provided the theoretical basis for reducing fracture incidence through running exercise.

Modern omics technologies can generate massive amounts of biomedical data, providing unprecedented opportunities for individualized precision medicine. However, traditional statistical methods cannot effectively process and utilize such big data. To meet this new challenge, machine learning algorithms have been developed and applied rapidly in recent years, which are capable of reducing dimensionality, extracting features, organizing data and forming automatable data-driven clinical decision systems. Data-driven clinical decision-making have promising applications in precision medicine and has been studied in digestive diseases, including early diagnosis and screening, molecular typing, staging and stratification of digestive malignancies, as well as precise diagnosis of Crohn's disease, auxiliary diagnosis of imaging and endoscopy, differential diagnosis of cystic lesions, etiology discrimination of acute abdominal pain, stratification of upper gastrointestinal bleeding (UGIB), and real-time diagnosis of esophageal motility function, showing good application prospects. Herein, we reviewed the recent progress of data-driven clinical decision making in precision diagnosis of digestive diseases and discussed the limitations of data-driven decision making after a brief introduction of methods for data-driven decision making.