ObjectivesWe used two automated software commonly employed in clinical practice—Olea Sphere (Olea) and Shukun-PerfusionGo (PerfusionGo)—to compare the diagnostic utility and volumetric agreement of computed tomography perfusion (CTP)-predicted final infarct volume (FIV) with true FIV in patients with anterior-circulation acute ischemic stroke (AIS).MethodsIn all, 122 patients with anterior-circulation AIS who met the inclusion and exclusion criteria were retrospectively enrolled and divided into two groups: intervention group (n = 52) and conservative group (n = 70), according to recanalization of blood vessels and clinical outcome (NIHSS) after different treatments. Patients in both groups underwent one-stop 4D-CT angiography (CTA)/CTP, and the raw CTP data were processed on a workstation using Olea and PerfusionGo post-processing software, to calculate and obtain the ischemic core (IC) and hypoperfusion (IC plus penumbra) volumes, hypoperfusion in the conservative group and IC in the intervention group were used to define the predicted FIV. The ITK-SNAP software was used to manually outline and measure true FIV on the follow-up non-enhanced CT or MRI-DWI images. Intraclass correlation coefficients (ICC), Bland–Altman, and Kappa analysis were used to compare the differences in IC and penumbra volumes calculated by the Olea and PerfusionGo software to investigate the relationship between their predicted FIV and true FIV.ResultsThe IC and penumbra difference between Olea and PerfusionGo within the same group (p < 0.001) was statistically significant. Olea obtained larger IC and smaller penumbra than PerfusionGo. Both software partially overestimated the infarct volume, but Olea significantly overestimated it by a larger percentage. ICC analysis showed that Olea performed better than PerfusionGo (intervention-Olea: ICC 0.633, 95%CI 0.439–0.771; intervention-PerfusionGo: ICC 0.526, 95%CI 0.299–0.696; conservative-Olea: ICC 0.623, 95%CI 0.457–0.747; conservative-PerfusionGo: ICC 0.507, 95%CI 0.312–0.662). Olea and PerfusionGo had the same capacity in accurately diagnosing and classifying patients with infarct volume <70 ml.ConclusionBoth software had differences in the evaluation of the IC and penumbra. Olea’s predicted FIV was more closely correlated with the true FIV than PerfusionGo’s prediction. Accurate assessment of infarction on CTP post-processing software remains challenging. Our results may have important practice implications for the clinical use of perfusion post-processing software.

IntroductionSudden sensorineural hearing loss (SSHL) is a critical otologic emergency characterized by a rapid decline of at least 30 dB across three consecutive frequencies in the pure-tone audiogram within a 72-hour period. This audiological condition has been associated with alterations in brain cortical and subcortical structures, as well as changes in brain functional activities involving multiple networks. However, the extent of cerebral intrinsic brain activity disruption in SSHL remains poorly understood. The aimed of this study is to investigate intrinsic brain activity alterations in SSHL using static and dynamic fractional amplitude of low-frequency fluctuation (fALFF) analysis.MethodsResting-state functional magnetic resonance imaging (fMRI) data were acquired from a cohort of SSHL patients (unilateral, n = 102) and healthy controls (n = 73). Static and dynamic fALFF methods were employed to analyze the acquired fMRI data, enabling a comprehensive examination of intrinsic brain activity changes in SSHL.ResultsOur analysis revealed significant differences in static fALFF patterns between SSHL patients and healthy controls. SSHL patients exhibited decreased fALFF in the left fusiform gyrus, left precentral gyrus, and right inferior frontal gyrus, alongside increased fALFF in the left inferior frontal gyrus, left superior frontal gyrus, and right middle temporal gyrus. Additionally, dynamic fALFF analysis demonstrated elevated fALFF in the right superior frontal gyrus and right middle frontal gyrus among SSHL patients. Intriguingly, we observed a positive correlation between static fALFF in the left fusiform gyrus and the duration of hearing loss, shedding light on potential temporal dynamics associated with intrinsic brain activity changes.DiscussionThe observed disruptions in intrinsic brain activity and temporal dynamics among SSHL patients provide valuable insights into the functional reorganization and potential compensatory mechanisms linked to hearing loss. These findings underscore the importance of understanding the underlying neural alterations in SSHL, which could pave the way for the development of targeted interventions and rehabilitation strategies aimed at optimizing SSHL management.

The diagnosis and management of sleep problems depend heavily on sleep staging. For autonomous sleep staging, many data-driven deep learning models have been presented by trying to construct a large-labeled auxiliary sleep dataset and test it by electroencephalograms on different subjects. These approaches suffer a significant setback cause it assumes the training and test data come from the same or similar distribution. However, this is almost impossible in scenario cross-dataset due to inherent domain shift between domains. Unsupervised domain adaption was recently created to address the domain shift issue. However, only a few customized UDA solutions for sleep staging due to two limitations in previous UDA methods. First, the domain classifier does not consider boundaries between classes. Second, they depend on a shared model to align the domain that could miss the information of domains when extracting features. Given those restrictions, we present a novel UDA approach that combines category decision boundaries and domain discriminator to align the distributions of source and target domains. Also, to keep the domain-specific features, we create an unshared attention method. In addition, we investigated effective data augmentation in cross-dataset sleep scenarios. The experimental results on three datasets validate the efficacy of our approach and show that the proposed method is superior to state-of-the-art UDA methods on accuracy and MF1-Score.

The mechanism of brain aging is not fully understood. Few studies have attempted to identify molecular changes using bioinformatics at the subregional level in the aging brain. This study aimed to identify the molecular signatures and key genes involved in aging, depending on the brain region. Differentially expressed genes (DEGs) associated with aging of the cerebral cortex (CX), hippocampus (HC), and cerebellum (CB) were identified based on five datasets from the Gene Expression Omnibus (GEO). The molecular signatures of aging were explored using functional and pathway analyses. Hub genes of each brain region were determined by protein–protein interaction network analysis, and commonly expressed DEGs (co-DEGs) were also found. Gene–microRNAs (miRNAs) and gene–disease interactions were constructed using online databases. The expression levels and regional specificity of the hub genes and co-DEGs were validated using animal experiments. In total, 32, 293, and 141 DEGs were identified in aging CX, HC, and CB, respectively. Enrichment analysis indicated molecular changes related to leukocyte invasion, abnormal neurotransmission, and impaired neurogenesis due to inflammation as the major signatures of the CX, HC, and CB. Itgax is a hub gene of cortical aging. Zfp51 and Zfp62 were identified as hub genes involved in hippocampal aging. Itgax and Cxcl10 were identified as hub genes involved in cerebellar aging. S100a8 was the only co-DEG in all three regions. In addition, a series of molecular changes associated with inflammation was observed in all three brain regions. Several miRNAs interact with hub genes and S100a8. The change in gene levels was further validated in an animal experiment. Only the upregulation of Zfp51 and Zfp62 was restricted to the HC. The molecular signatures of aging exhibit regional differences in the brain and seem to be closely related to neuroinflammation. Itgax, Zfp51, Zfp62, Cxcl10, and S100a8 may be key genes and potential targets for the prevention of brain aging.

Previous studies have shown that short-term monocular pattern deprivation can shift perceptual dominance in favor of the deprived eye. However, little is known about the effect of monocular pattern deprivation on contrast sensitivity (CS) and its corresponding mechanisms. Here, contrast sensitivity function (CSF) in the nondominant eye of normal subjects was evaluated before and after 150 min of monocular pattern deprivation. To obtain a CSF with high precision and efficiency before deprivation effect washout, a quick CSF (qCSF) method was used to assess CS over a wide range of spatial frequencies and at two external noise levels. We found that (1) monocular pattern deprivation effectively improved the CS of the deprived eye with larger effect on high spatial frequencies, (2) CS improvement only occurred when external noise was absent and its amount was spatial frequency dependent, and (3) a perceptual template model (PTM) revealed that decreased internal additive noise accounted for the mechanism of the monocular pattern derivation effect. These findings help us better understand the features of short-term monocular pattern deprivation and shed light on the treatment of amblyopia.