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Background Obstructive sleep apnea (OSA) is mainly characterized by sleep fragmentation and chronic intermittent hypoxia (CIH), the latter one being associated with multiple organ injury. Recently, OSA-induced cognition dysfunction has received extensive attention from scholars. Astrocytes are essential in neurocognitive deficits via A1/A2 phenotypic changes. Nucleotide oligomerization domain (NOD)-like receptor protein 3 (NLRP3) inflammasome is considered the most important factor inducing and maintaining neuroinflammation. However, whether the NLRP3 regulates the A1/A2 transformation of astrocytes in CIH-related brain injury remains unclear. Methods We constructed an OSA-related CIH animal model and assessed the rats' learning ability in the Morris water maze; the histopathological assessment was performed by HE and Nissl staining. The expression of GFAP (astrocyte marker), C3d (A1-type astrocyte marker), and S100a10 (A2-type astrocyte marker) were detected by immunohistochemistry and immunofluorescence. Western blotting and RT-qPCR were used to evaluate the changes of A1/A2 astrocyte-related protein and NLRP3/Caspase-1/ASC/IL-1β. Results The learning ability of rats decreased under CIH. Further pathological examination revealed that the neurocyte in the hippocampus were damaged. The cell nuclei were fragmented and dissolved, and Nissl bodies were reduced. Immunohistochemistry showed that astrocytes were activated, and morphology and number of astrocytes changed. Immunofluorescence, Western blotting and RT-qPCR showed that the expression of C3d was increased while S100a10 was decreased. Also, the expression of the inflammasome (NLRP3/Caspase-1/ASC/IL-1β) was increased. After treatment of MCC950 (a small molecule inhibitor of NLRP3), the damage of nerve cells was alleviated, the Nissl bodies increased, the activation of astrocytes was reduced, and the expression of A2-type astrocytes was increased. In contrast, A1-type astrocytes decreased, and the expression of inflammasome NLRP3/Caspase-1/ASC/IL-1β pathway-related proteins decreased. Conclusion The NLRP3 inflammasome could regulate the A1/A2 transformation of astrocytes in brain injury induced by CIH

Background In children, obstructive sleep apnea (OSA) can cause cognitive dysfunctions. Amyloid-beta and tau are elevated in OSA. Neurofilament light (NfL) is a marker of neuro-axonal damage, but there are no reports of NfL for OSA. The objective was to investigate the serum levels of NfL and tau in children with or without OSA and explore their relationship with cognitive dysfunctions caused by OSA. Methods This retrospective case–control study included children diagnosed with adenoid tonsil hypertrophy from July 2017 to September 2019 at the Second Affiliated Hospital of Xi’an Jiaotong University. Correlations between cognitive scores and tau and NfL were examined. Results Fifty-six OSA and 49 non-OSA children were included. The serum NfL levels were higher in the OSA group (31.68 (27.29–36.07) pg/ml) than in the non-OSA group (19.13 (17.32–20.95) pg/ml) (P < 0.001). Moreover, NfL was correlated with the course of the disease, apnea–hypopnea index (AHI), obstructive apnea index (OAI), obstructive apnea–hypopnea index (OAHI), average oxygen saturation (SaO2), respiratory arousal index (RAI), and cognitive dysfunctions evaluated by the Chinese Wechsler Intelligence Scale for Children (C-WISC) (all P < 0.05). The area under the receiver operating characteristics curve (AUC) of NfL was 0.816 (95%CI: 0.736–0.897). Multiple regression analysis revealed that NfL was significantly associated with verbal intelligence quotient (VIQ), performance intelligence quotient (PIQ) and full-scale intelligence quotient (FIQ) (P < 0.001, respectively). Conclusions Serum NfL levels are associated with the severity of cognitive dysfunctions in children diagnosed with adenoid tonsil hypertrophy and might be a candidate noninvasive, objective marker to identify cognitive dysfunctions in children with OSA.

AimTo explore the role of smell and taste changes in preventing and controlling the COVID-19 pandemic, we aimed to build a forecast model for trends in COVID-19 prediction based on Google Trends data for smell and taste loss.MethodsData on confirmed COVID-19 cases from 6 January 2020 to 26 December 2021 were collected from the World Health Organization (WHO) website. The keywords “loss of smell” and “loss of taste” were used to search the Google Trends platform. We constructed a transfer function model for multivariate time-series analysis and to forecast confirmed cases.ResultsFrom 6 January 2020 to 28 November 2021, a total of 99 weeks of data were analyzed. When the delay period was set from 1 to 3 weeks, the input sequence (Google Trends of loss of smell and taste data) and response sequence (number of new confirmed COVID-19 cases per week) were significantly correlated (P &lt; 0.01). The transfer function model showed that worldwide and in India, the absolute error of the model in predicting the number of newly diagnosed COVID-19 cases in the following 3 weeks ranged from 0.08 to 3.10 (maximum value 100; the same below). In the United States, the absolute error of forecasts for the following 3 weeks ranged from 9.19 to 16.99, and the forecast effect was relatively accurate. For global data, the results showed that when the last point of the response sequence was at the midpoint of the uptrend or downtrend (25 July 2021; 21 November 2021; 23 May 2021; and 12 September 2021), the absolute error of the model forecast value for the following 4 weeks ranged from 0.15 to 5.77. When the last point of the response sequence was at the extreme point (2 May 2021; 29 August 2021; 20 June 2021; and 17 October 2021), the model could accurately forecast the trend in the number of confirmed cases after the extreme points. Our developed model could successfully predict the development trends of COVID-19.ConclusionGoogle Trends for loss of smell and taste could be used to accurately forecast the development trend of COVID-19 cases 1–3 weeks in advance.