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Objective: To investigate the relationship between Interleukin-1beta (IL-1beta) and diabetic peripheral neuropathy (DPN) using animal models. Materials: The rat model of diabetic neuropathy was induced by intraperitoneal injection of a single dose of streptozotocin (STZ) at 65mg/kg. Diabetic rats were randomly divided into two groups (10 each), one treated with 0.9% saline (DMS group) and the other with interleukin-1 receptor antagonist (IL-1RA) at 50mg/kg (DMI group) twice a day for 5 weeks. Ten normal rats matched for weight, age and sex served as normal controls (Con group) and were treated with saline. Morphologic studies of sciatic nerves were achieved using light and transmission electron microscopy. Results: Transmission electron microscopy of the sciatic nerve showed the ultrastructure of myelin and the axon in the IL-1RA group was highly protected compared to diabetic controls. Conclusions: High levels of circulating IL-1beta may be associated with the risk of DPN and anti-IL-1 treatment may provide a potential strategy for the prevention of diabetic neuropathy.

This series of papers examines aspects of aging cognition in both healthy older adults and those diagnosed with dementias, and although few in number, gives an important overview of some of the key issues in examining the relationships between aging and psychosocial variables, including social cognition. The special issue includes five papers which cover: goal related decision making (Ito et al.); trust in cooperative behavior (Telga and Lupianez); impact of anxiety on self-perceived cognitive difficulties (Spalding et al.); factors associated with depression in Wuhan, China (Liu et al.); and neurophysiological functional connectivity related to apathy in people living with dementia (Altunkaya et al.).

Aims To evaluate the effects of a novel, low-volume, high-intensity Progressive Resistance Training (PRT) technique on blood glucose control in elderly Chinese patients with Type 2 Diabetes. Materials and methods The PReTTy2 trial enrolled 300 male and female patients with Type 2 Diabetes in a randomized resistance training program with the bioDensity™ technique. 100 were control patients with no training intervention and 200 had resistance training. Anthropometry, biochemical parameters, HbA1c and fasting plasma glucose (FPG) were measured at baseline, 3-month and 6-month intervals. Results 265 patients completed the study with no adverse events. There were no statistically significant differences in HbA1c for all patients, control and PRT groups, at baseline (p = 0.60), 3 months (p = 0.42) and 6-months (p = 0.45). Subgroup analysis with baseline HbA1c > 7.5% (58 mmol/mol), showed statistically significant differences in HbA1c and FPG between groups at 6 months (p Conclusions PRT improves glycemic indices in elderly patients with Type 2 Diabetes with poor glucose control as an adjunct to diet and medication. Progressive Resistance Training with bioDensity™ is feasible, safe and effective in elderly patients with Type 2 Diabetes.

Additional file 10: Supplementary Table S3. Substitution rates in the chloroplast protein coding genes of Oedogoniales species.

Additional file 7: Supplementary Fig. S7. Phylogenetic tree of the Oedogoniales algae based on 18S rDNA sequences. Numbers at the branches represent Bayesian posterior probabilities (≥0.5). Branch lengths are proportional to the genetic distances, which are indicated by the scale bar (Bold species are the newly included in this study).

Additional file 3: Supplementary Fig. S3. Gene map of Oe. sp. (FACHB-3311) chloroplast genomes.

Additional file 6: Supplementary Fig. S6. Heat map of ANI values of the nine Oedogoniales cp genomes.

Additional file 5: Supplementary Fig. S5. Gene map of Oe. sp. (FACHB-3313) chloroplast genomes. Arrows show the direction of transcription. The same colour block shows the functional gene group (legend at bottom left). Transfer RNAs are represented by their one-letter amino acid code. The grey circle on the inside shows a graph of the GC content.

Additional file 8: Supplementary Table S1. Insertion sites of group I introns of the nine Oedogoniaes cp genomes [81].

Glucocorticoids (GCs) can effectively control airway inflammation, but can also cause airway epithelial injury. Tectorigenin, a type of isoflavone isolated from various medicinal plants, has hypolipidemic activity, hepatoprotective, and antioxidant effects. We aimed to investigate whether Tectorigenin can repair GCs-induced airway epithelial injury. Airway epithelial cell line (9HTE cells) were treated with dexamethasone (Dex), Tectorigenin, or further transfected, then cell viability, migration, and invasion were examined by Cell Counting Kit (CCK-8), wound healing, and Transwell assays. The expressions of potential miRNAs related to the effect of Tectorigenin were detected by quantitative polymerase chain reaction (qPCR). Expressions of poptosis-related proteins Bcl-2-associated protein-X (Bax), B-cell lymphoma-2 (Bcl-2), Cleaved Caspase-3, and related to Mitorgen-activated protein kinase (MAPK) signaling pathway serine/threonine kinase (Raf1), extracellular signal-regulated kinase kinase 1/2 (MEK1/2), and extracellular signal-regulated kinase 1/2 (ERK1/2) were detected by Western blot. Dex inhibited the cell viability, migration and invasion by promoting Bax and Cleaved Caspase-3 expressions (p

In recent years, deep learning has gained unprecedented success in various domains, the key of the success is the larger and deeper deep neural networks (DNNs) that achieved very high accuracy. On the other side, since GPU global memory is a scarce resource, large models also pose a significant challenge due to memory requirement in the training process. This restriction limits the DNN architecture exploration flexibility. In this paper, we propose Capuchin, a tensor-based GPU memory management module that reduces the memory footprint via tensor eviction/prefetching and recomputation. The key feature of Capuchin is that it makes memory management decisions based on dynamic tensor access pattern tracked at runtime. This design is motivated by the observation that the access pattern to tensors is regular during training iterations. Based on the identified patterns, one can exploit the total memory optimization space and offer the fine-grain and flexible control of when and how to perform memory optimization techniques. We deploy Capuchin in a widely-used deep learning framework, Tensorflow, and show that Capuchin can reduce the memory footprint by up to 85% among 6 state-of-the-art DNNs compared to the original Tensorflow. Especially, for the NLP task BERT, the maximum batch size that Capuchin can outperforms Tensorflow and gradient-checkpointing by 7x and 2.1x, respectively. We also show that Capuchin outperforms vDNN and gradient-checkpointing by up to 286% and 55% under the same memory oversubscription.

Additional file 2.