Analysis of the Influence of High-Dose rhGH Therapy on Serum Vitamin D and IGF-1 Levels in School-Age Children with Idiopathic Short Stature.

OBJECTIVE: To discuss the influence of high-dose recombinant human growth hormone (rhGH) therapy on serum vitamin D and insulin-like growth factor-1 (IGF-1) levels in school-age children with idiopathic short stature (ISS). METHOD: A total of 103 school-age children with ISS were selected from June 2016 to June 2020 in our hospital. The enrolled cases were divided into the low-dose group (n = 59) and high-dose group (n = 44) according to the treatment dose of rhGH. After the treatment, the height (Ht), height standard deviation score (Ht SDS), growth velocity (GV), and other indicators were recorded. The serum 25-hydroxy vitamin D [25-(OH)D] and IGF-1 levels of the two groups were tested, and the occurrence of adverse reactions was recorded. RESULTS: After treatment, the high-dose group outperformed the low-dose group in various growth effect indicators such as Ht, Ht SDS, and GV (P < 0.05). After treatment, the serum 25-(OH)D of children with ISS in the two groups increased significantly, but there was no significant difference between the two groups (P > 0.05). After treatment, the serum IGF-1 of children with ISS in the two groups increased significantly, but there was no significant difference between the two groups (P > 0.05). For children with ISS, adverse reactions induced by rhGH therapy were very rare. There was no significant difference in the incidence of adverse reactions induced by different doses of rhGH in the treatment of ISS (P > 0.05). CONCLUSION: rhGH has definite efficacy in the treatment of ISS children, for it can significantly increase the annual growth rate of ISS children in a dose-dependent manner. High-dose rhGH for ISS has a better therapeutic effect. At the same time, regardless of the dose level of rhGH, serum 25-(OH)D and IGF-1 levels in children with ISS were increased, with less adverse reactions and higher safety.

Monolithic hydrogel nanowells-in-microwells enabling simultaneous single cell secretion and phenotype analysis.

Cytokine secretion is a form of cellular communication that regulates a wide range of biological processes. A common approach for measuring cytokine secretion from single cells is to confine individual cells in arrays of nanoliter wells (nanowells) fabricated using polydimethylsiloxane. However, this approach cannot be easily integrated in standard microwell plates in order to take advantage of high-throughput infrastructure for automated and multiplexed analysis. Here, we used laser micropatterning to fabricate monolithic hydrogel nanowells inside wells in a microwell plate (microwells) using polyethylene glycol diacrylate (PEGDA). This approach produces high-aspect ratio nanowells that retain cells and beads during reagent exchange, enabling simultaneous profiling of single cell secretion and phenotyping via immunostaining. To limit contamination between nanowells, we used methylcellulose as a media additive to reduce diffusion distance. Patterning nanowells monolithically in microwells also dramatically increases density, providing ∼1200 nanowells per microwell in a microwell plate. Using this approach, we profiled IL-8 secretion from single MDA-MB-231 cells, which showed significant heterogeneity. We further profiled the polarization of THP-1 cells into M1 and M2 macrophages, along with their associated IL-1ß and CCL-22 secretion profiles. These results demonstrate the potential to use this approach for high-throughput secretion and phenotype analysis on single cells.

Towards the Development of a Learning-Based Intention Classification Framework for Pushrim-Activated Power-Assisted Wheelchairs.

There has been a growth in the design and use of power assist devices for manual wheelchairs (MWCs) to alleviate the physical load of MWC use. A pushrim-activated power-assisted wheel (PAPAW) is an example of a power assist device that replaces the conventional wheel of a MWC. Although the use of PAPAWs provides some benefits to MWC users, it can also cause difficulties in maneuvering the wheelchair. In this research, we examined the characteristics of wheelchair propulsion when using manual and powered wheels. We used the left and right wheels' angular velocity to calculate the linear and angular velocity of the wheelchair. Results of this analysis revealed that the powered wheel's controller is not optimally designed to reflect the intentions of a wheelchair user. To address some of the challenges with coordinating the pushes on PAPAWs, we proposed the design of a user-intention detection framework. We used the kinematic data of MWC experiments and tested six supervised learning algorithms to classify one of four movements: "not moving", "moving straight forward", "turning left", and "turning right". We found that all the classification algorithms determined the type of movement with high accuracy and low computation time. The proposed intention detection framework can be used in the design of learning-based controllers for PAPAWs that take into account the individualized characteristics of wheelchair users. Such a system may improve the experience of PAPAW users.