No abstract

Early brain injury is considered to be a major risk that is related to the prognosis of subarachnoid hemorrhage (SAH). In SAH model rats, brain edema and apoptosis have been closely related with death rate and neurological function. Sirtuin 1 (SIRT1) was reported to be involved in apoptosis in cerebral ischemia and brain tumor formation through p53 deacetylation. The present study aimed to evaluate the role of SIRT1 in a rat endovascular perforation model of SAH. The SIRT1 activator resveratrol (RES) was administered 48 h prior to SAH induction and the SIRT1 inhibitor Sirtinol (SIR) was used to reverse the effects of RES on SIRT1 expression. Mortality rate, neurological function and brain water content were measured 24 h post‑SAH induction. Proteins associated with the blood brain barrier (BBB), apoptosis and SIRT1 in the cortex, such as zona occludens 1 (ZO‑1), occludin, claudin‑5, SIRT1, p53 and cleaved caspase3 were investigated. mRNA expression of the p53 downstream molecules including Bcl‑associated X protein, P53 upregulated modulator of apoptosis, Noxa and BH3 interacting‑domain death agonist were also investigated. Neuronal apoptosis was also investigated by immunofluorescence. RES pretreatment reduced the mortality rate and improved neurological function, which was consistent with reduced brain water content and neuronal apoptosis; these effects were partially reversed by co‑treatment with SIR. SIRT1 may reduce the brain water content by improvement of dysfunctional BBB permeability, and protein analysis revealed that both ZO‑1, occludin and claudin‑5 may be involved, and these effects were reversed by SIRT1 inhibition. SIRT1 may also affect apoptosis post‑SAH through p53 deacetylation, and the analysis of p53 related downstream pro‑apoptotic molecules supported this hypothesis. Localization of neuron specific apoptosis revealed that SIRT1 may regulate neuronal apoptosis following SAH. SIRT1 may also ease brain edema and neuronal protection through BBB improvement and p53 deacetylation. SIRT1 activators such as RES may have the potential to improve the prognosis of patients with SAH and clinical research should be investigated further.

Early brain injury (EBI) is the primary cause of poor outcome in subarachnoid hemorrhage (SAH) patients. Rolipram, a specific phosphodiesterase-4 inhibitor which is traditionally used as an anti-depressant drug, has been recently proven to exert neuroprotective effects in several central nervous system insults. However, the role of rolipram in SAH remains uncertain. The current study was aimed to investigate the role of rolipram in EBI after SAH and explore the potential mechanism. Adult male Sprague-Dawley rats were subjected to an endovascular perforation process to produce an SAH model. Rolipram was injected intraperitoneally at 2 h after SAH with a dose of 10 mg/kg. We found that rolipram significantly ameliorated brain edema and alleviated neurological dysfunction after SAH. Rolipram treatment remarkably promoted the expression of Sirtuin 1 (SIRT1) while inhibited NF-κB activation. Moreover, rolipram significantly inhibited the activation of microglia as well as down-regulated the expression of pro-inflammatory cytokines TNF-α, IL-1ß, and IL-6. In addition, rolipram increased the expression of protective cytokine IL-10. Furthermore, rolipram significantly alleviated neuronal death after SAH. In conclusion, these data suggested that rolipram exerts neuroprotective effects against EBI after SAH via suppressing neuroinflammation and reducing neuronal loss. The neuroprotective effects of rolipram were associated with regulating the SIRT1/NF-κB pathway. Rolipram could be a novel and promising therapeutic agent for SAH treatment.

No abstract

Piezoresistive pressure sensors exhibit inherent nonlinearity and sensitivity to ambient temperature, requiring multidimensional compensation to achieve accurate measurements. However, recent studies on software compensation mainly focused on developing advanced and intricate algorithms while neglecting the importance of calibration data and the limitation of computing resources. This paper aims to present a novel compensation method which generates more data by learning the calibration process of pressure sensors and uses a larger dataset instead of more complex models to improve the compensation effect. This method is performed by the proposed aquila optimizer optimized mixed polynomial kernel extreme learning machine (AO-MPKELM) algorithm. We conducted a detailed calibration experiment to assess the quality of the generated data and evaluate the performance of the proposed method through ablation analysis. The results demonstrate a high level of consistency between the generated and real data, with a maximum voltage deviation of only 0.71 millivolts. When using a bilinear interpolation algorithm for compensation, extra generated data can help reduce measurement errors by 78.95%, ultimately achieving 0.03% full-scale (FS) accuracy. These findings prove the proposed method is valid for high-accuracy measurements and has superior engineering applicability.