Aims: Cigarette smoking is a modifiable risk factor for Alzheimer's disease (AD), and controlling risk factors may curb the progression of AD. However, the underlying neural mechanisms of the effects of smoking on cognition remain largely unclear.Therefore, we aimed to explore the interaction effects of smoking × cognitive status on cortico-striatal circuits, which play a crucial role in addiction and cognition, in individuals without dementia. Methods:We enrolled 304 cognitively normal (CN) non-smokers, 44 CN smokers, 130 mild cognitive impairment (MCI) non-smokers, and 33 MCI smokers. The mixedeffect analysis was performed to explore the interaction effects between smoking and cognitive status (CN vs. MCI) based on functional connectivity (FC) of the striatal subregions (caudate, putamen, and nucleus accumbens [NAc]). Results:The significant interaction effects of smoking × cognitive status on FC of the striatal subregions were detected in the left inferior parietal lobule (IPL), bilateral cuneus, and bilateral anterior cingulate cortex (ACC). Specifically, increased FC of right caudate to left IPL was found in CN smokers compared with non-smokers. The MCI smokers showed decreased FC of right caudate to left IPL and of right putamen to

Background: Mild cognitive impairment (MCI) is the prodromal phase of Alzheimer’s disease (AD) and has a high risk of progression to AD. Cigarette smoking is one of the important modifiable risk factors in AD progression. Cholinergic dysfunction, especially the nucleus basalis of Meynert (NBM), is the converging target connecting smoking and AD. However, how cigarette smoking affects NBM connectivity in MCI remains unclear.Objective: This study aimed to evaluate the interaction effects of condition (non-smoking vs. smoking) and diagnosis [cognitively normal (CN) vs. MCI] based on the resting-state functional connectivity (rsFC) of the NBM.Methods: After propensity score matching, we included 86 non-smoking CN, 44 smoking CN, 62 non-smoking MCI, and 32 smoking MCI. All subjects underwent structural and functional magnetic resonance imaging scans and neuropsychological tests. The seed-based rsFC of the NBM with the whole-brain voxel was calculated. Furthermore, the mixed effect analysis was performed to explore the interaction effects between condition and diagnosis on rsFC of the NBM.Results: The interaction effects of condition × diagnosis on rsFC of the NBM were observed in the bilateral prefrontal cortex (PFC), bilateral supplementary motor area (SMA), and right precuneus/middle occipital gyrus (MOG). Specifically, the smoking CN showed decreased rsFC between left NBM and PFC and increased rsFC between left NBM and SMA compared with non-smoking CN and smoking MCI. The smoking MCI showed reduced rsFC between right NBM and precuneus/MOG compared with non-smoking MCI. Additionally, rsFC between the NBM and SMA showed a significant negative correlation with Wechsler Memory Scale-Logical Memory (WMS-LM) immediate recall in smoking CN (r = −0.321, p = 0.041).Conclusion: Our findings indicate that chronic nicotine exposure through smoking may lead to functional connectivity disruption between the NBM and precuneus in MCI patients. The distinct alteration patterns on NBM connectivity in CN smokers and MCI smokers suggest that cigarette smoking has different influences on normal and impaired cognition.

Background. Although obesity affects human health and cognitive function, the influence of abdominal obesity on cognitive function is still unclear. Methods. The MoCA scale was used to evaluate the overall cognitive function and the function of each subitem of 196 subjects, as well as the SDMT and TMT-A scales for evaluating the attention and information processing speed. In addition, radioimmunoassay was used to detect the serum levels of Aβ40, Aβ42, and tau protein in 45 subjects. Subjects were divided into abdominal and nonabdominal obesity groups. Before and after correcting confounding factors, the differences in cognitive scale evaluation indexes and three protein levels between the two groups were compared. We also explore further the correlation between various cognitive abilities and the waist circumference/levels of the three proteins. Linear regression was used to identify the independent influencing factors of various cognitive functions and three protein levels. Results. After correcting for multiple factors, we observed the lower scores of visuospatial function, execution, and language in the MoCA scale, as well as higher levels of Aβ40 and tau protein in the abdominal obesity group, supported by the results of correlation analysis. Abdominal obesity was identified as an independent negative influencing factor of MoCA visual space, executive power, and language scores and an independent positive influencing factor of Aβ40, Aβ42, and tau protein levels. Conclusion. Abdominal obesity may play a negative role in visuospatial, executive ability, and language function and a positive role in the Aβ40, Aβ42, and tau protein serum levels.

Background: LncRNA plays a vital role in tumor proliferation, migration, and treatment. Since it is difficult for the gene expression levels detected by different platforms to reach the same standard, the signatures composed of many immune-related single lncRNAs are still inaccurate. Using two immune-related lncRNAs to form a gene pair and cleverly assigning values can effectively meet the demand for a higher-accuracy dual biomarker combination. Methods: Co-expression and differential expression analysis were performed on immune genes and lncRNAs data from The Cancer Genome Atlas and the ImmPort database to obtain differentially expressed immune-related lncRNAs for pairwise pairing. The prognostic-related differentially expressed immune-related lncRNAs (PR-DE-irlncRNAs) pairs were then identified by univariate cox regression and used for lasso regression to create a prognostic model. Various methods were used to validate the predictive prognostic performance of the model. We also explored the potential guiding value of the model in immunotherapy and chemotherapy and constructed a nomogram suitable for efficient prognosis prediction. Mechanistic exploration of anti-tumor immunity and mutational perspectives are also included. We also analyzed the correlation between the model and immune checkpoint inhibitors (ICIs)-related, m6a-related, and multidrug resistance genes. Results: We used a total of 20 PR-DE-irlncRNAs pairs to create a prognosis model. Various methods have verified the model's excellent performance in predicting the prognosis of patients. We reasoned that lncRNAs/ TP53 mutation might play a positive/ negative anti-tumor role through the immune system through multi-perspective analysis. Finally, the prognostic model has been found to be closely related to immunotherapy and chemotherapy and the expression of ICIs/M6A/multidrug resistance-related genes.Conclusion: The prognostic model has an excellent performance in predicting the prognosis of patients, as well as the potential value of practical guidance for treatment.

To explore the interaction effects of smoking status (non-smoking vs. smoking) and disease (cognitively normal (CN) vs. MCI) based on resting-state functional connectivity (rsFC) of the corticostriatal circuits. We included 304 CN non-smokers, 44 CN smokers, 130 MCI non-smokers, and 33 MCI smokers. The seed-based rsFC of striatal subregions (caudate, putamen, and nucleus accumbens [NAc]) with the whole-brain voxel was calculated. Furthermore, we performed mixed effect analysis to explore the interaction effects between smoking status and disease. Significant interaction effects were detected between: (1) right caudate and left inferior parietal lobule (IPL); (2) right putamen and bilateral cuneus; (3) bilateral NAc and bilateral anterior cingulate cortex (ACC). The post-hoc analyses revealed that the CN smokers showed increased rsFC between right caudate and left IPL compared to non-smokers; while the MCI smokers demonstrated decreased rsFC between right putamen and cuneus, and increased rsFC between bilateral NAc and ACC compared to non-smokers. In MCI smokers, the rsFC value between left NAc and ACC was positively correlated with Semantic Verbal Fluency (SVF, r = 0.387, p = 0.026), and the rsFC value between right NAc and ACC was positively correlated with SVF (r = 0.390, p = 0.025), Wechsler memory scale-logical memory (WMS-LM) immediate recall (r = 0.378, p = 0.03), and WMS-LM delayed recall (r = 0.367, p = 0.036). Our findings suggest that chronic nicotine exposure may lead to functional connectivity alterations of corticostriatal circuits in MCI patients, and the pattern is different from CN smokers.

Recent studies have shown that in the preclinical phase of Alzheimer's disease (AD), subtle cognitive changes can be detected using sensitive neuropsychological measures, and have proposed the concept of objectively-defined subtle cognitive decline (Obj-SCD). We aimed to assess the functional alteration of hippocampal subfields in individuals with Obj-SCD and its association with cognition and pathological biomarkers. Forty-two participants with cognitively normal (CN), 29 with Obj-SCD, and 55 with mild cognitive impairment (MCI) were retrospectively collected from the ADNI database. Neuropsychological performance, functional MRI, and cerebrospinal fluid (CSF) data were obtained. We calculated the seed-based functional connectivity (FC) of hippocampal subfields (cornu ammonis1 [CA1], CA2/3/dentate gyrus [DG], and subiculum) with whole-brain voxels. Additionally, we analyzed the correlation between FC values of significantly altered regions and neuropsychological performance and CSF biomarkers. The Obj-SCD group showed lower FC between left CA1-CA2/3/DG and right thalamus and higher FC between right subiculum and right superior parietal gyrus (SPG) compared with the CN and MCI groups. In the Obj-SCD group, FC values between left CA2/3/DG and right Tiantian Qiu and Qingze Zeng have contributed equally to this work and share first authorship. Shouping Dai and Fei Xie have contributed equally to this work and share senior authorship.

Background: Ferroptosis is an iron-dependent, new type of programmed cell death different from apoptosis, necrosis, and autophagy. At present, ferroptosis has been confirmed to be closely related to the prognosis and treatment of cancer. However, the relationship between ferroptosis and the progression and prognosis of different types of thyroid cancer (THCA) is unclear.Methods: First, we performed differential expression analysis on the data of the two databases to obtain differentially expressed ferroptosis-related genes (DE-FRGs). Through differential expression, univariate Cox and lasso regression analysis, we identified 14 prognostic-related differently expressed ferroptosis-related genes (PR-DE-FRGs) for building risk assessment models. Subsequently, various validation methods are used to test the performance of the model. Then, we explored the mechanism of ferroptosis in the development and prognosis of THCA from the aspects of gene set enrichment analysis (GSEA), tumor microenvironment (TME), high frequency gene mutation, cell stemness and AL928654.4/miR-1287-5p/GPX4 regulatory axis. Finally, we verified the promising clinical application of the model.Results: ANGPTL7, DRD4, SRXN1, TXNRD1, CDKN2A, MIOX, PGD and TFRC (HR>1) were identified as prognostic risk factors, whereas CAPG, GPX4, ARNTL, ISCU, BID and DPP4 (HR<1) were the opposite. Immunohistochemical (IHC) images and Gene Expression Omnibus (GEO) data validated differential expression of 7 and 12 PR-DE-FRGs, respectively. Through the study of the tumor microenvironment, we obtained the immune landscape of THCA and revealed to us that ferroptosis may influence the prognosis of THCA patients by affecting anti-tumor immunity. Subsequently, we found that the predicted and validated AL928654.4/miR-1287-5p/GPX4 regulatory axis may play an important role in regulating cancer cell apoptosis and ferroptosis. Finally, immunophenoscore, chemotherapeutic drug sensitivity analysis, ICIs related genes, and Nomogram were performed to prove the good clinical application prospects of the risk assessment model.Conclusion: The risk assessment model can effectively predict the treatment effect and prognosis of THCA patients, and achieve the effect that can guide clinical treatment. Ferroptosis may be involved.