The miR-9-5p/CXCL11 pathway is a key target of hydrogen sulfide-mediated inhibition of neuroinflammation in hypoxic ischemic brain injury

We previously showed that hydrogen sulfide(H2S)has a neuroprotective effect in the context of hypoxic ischemic brain injury in neonatal mice.However,the precise mechanism underlying the role of H2S in this situation remains unclear.In this study,we used a neonatal mouse model of hypoxic ischemic brain injury and a lipopolysaccharide-stimulated BV2 cell model and found that treatment with L-cysteine,a H2S precursor,attenuated the cerebral infarction and cerebral atrophy induced by hypoxia and ischemia and increased the expression of miR-9-5p and cystathionineβsynthase(a major H2S synthetase in the brain)in the prefrontal cortex.We also found that an miR-9-5p inhibitor blocked the expression of cystathionineβsynthase in the prefrontal cortex in mice with brain injury caused by hypoxia and ischemia.Furthermore,miR-9-5p overexpression increased cystathionine-β-synthase and H2S expression in the injured prefrontal cortex of mice with hypoxic ischemic brain injury.L-cysteine decreased the expression of CXCL11,an miR-9-5p target gene,in the prefrontal cortex of the mouse model and in lipopolysaccharide-stimulated BV-2 cells and increased the levels of proinflammatory cytokines BNIP3,FSTL1,SOCS2 and SOCS5,while treatment with an miR-9-5p inhibitor reversed these changes.These findings suggest that H2S can reduce neuroinflammation in a neonatal mouse model of hypoxic ischemic brain injury through regulating the miR-9-5p/CXCL11 axis and restoringβ-synthase expression,thereby playing a role in reducing neuroinflammation in hypoxic ischemic brain injury.

Novel Sc-doped Bi_(3)TiNbO_(9) ferroelectric nanofibers prepared by electrospinning for visible-light photocatalysis

In this work,a novel Sc-doped Bi_(3)TiNbO_(9) ferroelectric nanofiber was first prepared by electrospinning.The nanofibers are approximately 100 nm level in diameter.The photocatalytic activity analysis reveals that the Bi_(3)TiNbO_(9) nanofibers synthesized by electrospinning are superior to powders prepared by the solid-state method,which is mainly attributed to the high specific surface area and high surface activity of the nanofibers.In addition,doping Sc can reduce the band gap of Bi_(3)TiNbO_(9) and further improve the photocatalytic efficiency.Bi_(3)TiNbO_(9) nanofibers containing 5 mol%Sc have the highest photocatalytic activity,which can degrade 98.55%of RhB under 405 nm light irradiation for 120 min.In addition,the catalytic mechanism of the catalyst was obtained by scavenger tests,active species capture tests and band structure analyses.After three cycles of photocatalytic experiment,the degradation rate is decreased by only 2.42%,proving that the nanofibers catalyst has excellent stability.This work provides good prospects for the practical application of electrospinning technology in the field of photocatalysis.

Development of one-class classification method for identifying healthy T.granosa from those contaminated with uncertain heavy metals by LIBS

Laser-induced breakdown spectroscopy(LIBS)can be used for the rapid detection of heavy metal contamination of Tegillarca granosa(T.granosa),but an appropriate classification model needs to be constructed.In the one-class classification method,only target samples are needed in training process to achieve the recognition of abnormal samples,which is suitable for rapid identification of healthy T.granosa from those contaminated with uncertain heavy metals.The construction of a one-class classification model for heavy metal detection in T.granosa by LIBS has faced the problem of high-dimension and small samples.To solve this problem,a novel one-class classification method was proposed in this study.Here,the principal component scores and the intensity of the residual spectrum were combined as extracted features.Then,a one-class classifier based on Mahalanobis distance using the extracted features was constructed and its threshold was set by leave-one-out crossvalidation.The sensitivity,specificity and accuracy of the proposed method were reached to 1,0.9333 and 0.9667 respectively,which are superior to the previously reported methods.

DynIceData:a gridded ice-water classification dataset at short-time intervals based on observations from multiple satellites over the marginal ice zone

High-resolution observations of short-term changes in sea ice are critical to understanding ice dynamics and also provide important information used in advice to shipping,especially in the Arctic.Although individual satellite sensors provide periodic sea ice obser-vations with spatial resolutions of tens of meters,information regarding changes that occur over short time intervals of minutes or hours is limited.In this study,a gridded ice-water classification dataset with a high temporal resolution was developed based on observations acquired by multiple satellite sensors in the Marginal Ice Zone(MIZ).This dataset-DynIceData-which combines Sentinel-1 Synthetic Aperture Radar(SAR)data with Gaofen-3(GF-3)SAR and SDGSAT-1 thermal infrared imagery was used to obtain observations of the MIZ with a range of temporal resolutions ran-ging from minutes to tens of hours.The areas of the Arctic covered include the Kara Sea,Beaufort Sea,and Greenland Sea during the period from August 2021 to August 2022.Object-oriented segmen-tation and thresholding were used to obtain the ice-water classifi-cation map from Sentinel-1 and GF-3 SAR image pairs and Sentinel-1 SAR and SDGSAT-1 thermal image pairs.The time interval between the images in each pair ranged from 1 minute to 68 hours.Ten-kilometer grid sample granules with a spatial resolution of 25 m for the GF-3 SAR data and 30 m for the SDGSAT-1 thermal data were used.The classification was verified as having an overall accuracy of at least 95.58%.The DynIceData dataset consists of 7338 samples,which could be used as reference data for further research on rapid changes in sea ice patterns at different short time scales and provide support for research on thermodynamic and dynamic models of sea ice in combination with other environmen-tal data,thus potentially improving the accuracy of sea ice forecast-ing using Artificial Intelligence.The dataset can be accessed at https://doi.org/10.57760/sciencedb.j00001.00784.