Dynamic biomechanical effect of lower body positive pressure treadmill training for hemiplegic gait rehabilitation after stroke: A case report.

BACKGROUND: Lower body positive pressure (LBPP) treadmill has potential applications for improving the gait of patients after stroke, but the related mechanism remains unclear. CASE SUMMARY: A 62-year-old male patient suffered from ischemic stroke with hemiplegic gait. He was referred to our hospital because of a complaint of left limb weakness for 2 years. The LBPP training was performed one session per day and six times per week for 2 wk. The dynamic plantar pressure analysis was taken every 2 d. Meanwhile, three-digital gait analysis and synchronous electromyography as well as clinical assessments were taken before and after LBPP intervention and at the 4-wk follow-up. During LBPP training, our patient not only improved his lower limb muscle strength and walking speed, but more importantly, the symmetry index of various biomechanical indicators improved. Moreover, the patient's planter pressure transferring from the heel area to toe area among the LBPP training process and the symmetry of lower body biomechanical parameters improved. CONCLUSION: In this study, we documented a dynamic improvement of gait performance in a stroke patient under LBPP training, which included lower limb muscle strength, walking speed, and symmetry of lower limb biomechanics. Our study provides some crucial clues about the potential dynamic mechanism for LBPP training on gait and balance improvement, which is related to rebuilding foot pressure distribution and remodeling symmetry of biomechanics of the lower limb.

Current Application of Digital Diagnosing Systems for Retinopathy of Prematurity.

BACKGROUND AND OBJECTIVE: Retinopathy of prematurity (ROP), a proliferative vascular eye disease, is one of the leading causes of blindness in childhood and prevails in premature infants with low-birth-weight. The recent progress in digital image analysis offers novel strategies for ROP diagnosis. This paper provides a comprehensive review on the development of digital diagnosing systems for ROP to software researchers. It may also be adopted as a guide to ophthalmologists for selecting the most suitable diagnostic software in the clinical setting, particularly for the remote ophthalmic support. METHODS: We review the latest literatures concerning the application of digital diagnosing systems for ROP. The diagnosing systems are analyzed and categorized. Articles published between 1998 and 2020 were screened with the two searching engines Pubmed and Google Scholar. RESULTS: Telemedicine is a method of remote image interpretation that can provide medical service to remote regions, and yet requires training to local operators. On the basis of image collection in telemedicine, computer-based image analytical systems for ROP were later developed. So far, the aforementioned systems have been mainly developed by virtue of classic machine learning, deep learning (DL) and multiple machine learning. During the past two decades, various computer-aided systems for ROP based on classic machine learning (e.g. RISA, ROPtool, CAIER) became available and have achieved satisfactory performance. Further, automated systems for ROP diagnosis based on DL are developed for clinical applications and exhibit high accuracy. Moreover, multiple instance learning is another method to establish an automated system for ROP detection besides DL, which, however, warrants further investigation in future. CONCLUSION: At present, the incorporation of computer-based image analysis with telemedicine potentially enables the detection, supervision and in-time treatment of ROP for the preterm babies.

Risk factors for pulmonary embolism in patients with COVID-19: a systemic review and meta-analysis.

PURPOSE: To detect the risk factors for pulmonary embolism (PE) in patients with COVID-19. METHODS: Studies were searched for in PubMed, Cochrane Library, Web of Science, and EMBASE. Two authors independently screened articles and extracted data. The data were pooled by meta-analysis and three subgroup analyses were performed. RESULTS: Of the 2210 articles identified, 27 studies were included. Pooled analysis suggested that males (odds ratio (OR) 1.49, 95% confidence interval (CI) 1.26-1.75, P = 0.000), obesity (OR 1.37, 95% CI 1.03-1.82, P = 0.033), mechanical ventilation (OR 3.34, 95% CI 1.90-5.86, P = 0.000), severe parenchymal abnormalities (OR 1.92, 95% CI 1.43-2.58, P = 0.000), ICU admission (OR 2.44, 95% CI 1.48-4.03, P = 0.000), and elevated D-dimer and white blood cell values (at two time points: hospital admission or closest to computed tomography pulmonary angiography) (P = 0.000) correlated with a risk for PE occurrence in COVID-19 patients. However, age and common comorbidities had no association with PE occurrence. Computed tomography pulmonary angiography, unclear-ratio/low-ratio, and hospitalization subgroups had consistent risk factors with all studies; however, other subgroups had fewer risk factors for PE. CONCLUSIONS: Risk factors for PE in COVID-19 were different from the classic risk factors for PE and are likely to differ in diverse study populations.