Los sujetos de ambos grupos tienden a corregir inmediatamente los errores de activación de las articulaciones, para corregir estos errores estos cambian la dirección del movimiento y activan la articulación que les permita posicionarse en la ubicación inmediatamente anterior a la ejecución del error.

Anaplastic thyroid cancer (ATC) comprises approximately 2% of all thyroid cancers, and its median survival rate remains poor because of its resistance to conventional therapy. Vascular endothelial growth factor receptor (VEGFR)-targeted therapeutics-loaded mesoporous silica nanoparticles represent a major advance for angiogenesis imaging and inhibition in lethal cancers. In the present study, we aimed to assess whether 131I-labeled anti-VEGFR2 targeted mesoporous silica nanoparticles would have antitumor efficacy in an ATC tumor-bearing nude mouse model. Using in vitro and in vivo studies, we investigated the increased targeting ability and retention time in the anti-VEGFR2 targeted group using confocal microscopy and a γ counter. The tumor tissue radioactivity of the anti-VEGFR2 targeted group at 24 and 72 h after intratumoral injection was significantly higher than that of the non-targeted groups (all P < 0.05). Moreover, we found that radioactive accumulation was obvious even at 3 week post-injection in the anti-VEGFR2 targeted group via single-photon emission computed tomography/computed tomography, which was not seen at 3 day post-injection in the Na131I group. Meanwhile, compared with the non-targeted group, tumor growth in the targeted group was significantly inhibited, without causing apparent systemic toxic effects. Additionally, the median survival time in the targeted group (41 days) was significantly prolonged compared with that in the non-targeted (34 days) or Na131I (25 days) groups (both P < 0.01). Our data support the view that the as-developed 131I-labeled anti-VEGFR2 targeted mesoporous silica nanoparticles showed promising results in ATC tumor-bearing mouse model and such an approach might represent a novel therapeutic option for ATC.

Background The number of deaths worldwide caused by coronavirus disease (COVID-19) is increasing rapidly. Information about the clinical characteristics of patients with COVID-19 who were not admitted to hospital is limited. Some risk factors of mortality associated with COVID-19 are controversial (eg, smoking). Moreover, the impact of city closure on mortality and admission rates is unknown. Objective The aim of this study was to explore the risk factors of mortality associated with COVID-19 infection among a sample of patients in Wuhan whose conditions were reported on social media. Methods We enrolled 599 patients with COVID-19 from 67 hospitals in Wuhan in the study; 117 of the participants (19.5%) were not admitted to hospital. The demographic, epidemiological, clinical, and radiological features of the patients were extracted from their social media posts and coded. Telephone follow-up was conducted 1 month later (between March 15 and 23, 2020) to check the clinical outcomes of the patients and acquire other relevant information. Results The median age of patients with COVID-19 who died (72 years, IQR 66.5-82.0) was significantly higher than that of patients who recovered (61 years, IQR 53-69, P <.001). We found that lack of admission to hospital (odds ratio [OR] 5.82, 95% CI 3.36-10.1; P <.001), older age (OR 1.08, 95% CI 1.06-1.1; P <.001), diffuse distribution (OR 11.09, 95% CI 0.93-132.9; P =.058), and hypoxemia (odds ratio 2.94, 95% CI 1.32-6.6; P =.009) were associated with increasing odds of death. Smoking was not significantly associated with mortality risk (OR 0.9, 95% CI 0.44-1.85; P =.78). Conclusions Older age, diffuse distribution, and hypoxemia are factors that can help clinicians identify patients with COVID-19 who have poor prognosis. Our study suggests that aggregated data from social media can also be comprehensive, immediate, and informative in disease prognosis.

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Aiming at stroke patients’ hand rehabilitation training, we present a hand exoskeleton with both active and passive control modes for neural rehabilitation. The exoskeleton control system is designed as a human–robot interaction control system based on field-programmable gate array (FPGA) and Android mobile terminal with good portability and openness. Passive rehabilitation pattern based on proportional derivative (PD) inverse dynamic control method and active rehabilitation pattern based on impedance method, are established respectively. By the comparison of the threshold value and the force on the fingertip of the exoskeleton from the sensor, the automatic switch between active and passive rehabilitation mode is accomplished. The hand model is built in Android environment that can synchronize the movement of the hand. It can also induce patients to participate in rehabilitation training actively. To verify the proposed control approach, we set up and conduct an experiment to do the passive rehabilitation mode, active rehabilitation mode, and active plus passive mode experimental researches. The experiment results effectively verify the feasibility of the exoskeleton system fulfilling the proposed control strategy.