Association between dietary inflammatory index and Parkinson’s disease from National Health and Nutrition Examination Survey (2003–2018): a cross-sectional study

ObjectedTo explore the association between Parkinson’s disease (PD) and dietary inflammatory index (DII) scores in adults over 40 years old in the US.MethodData were collected from the National Health and Nutrition Examination Survey (NHANES) conducted from 2003 to 2018. A total of 21,994 participants were included in the study. A weighted univariate and multivariable logistic regression analysis was performed to investigate the association between the DII and PD, in which continuous variables or categorical variables grouped by tertiles was used. The relationship between DII and PD has been further investigated using propensity score matching (PSM) and a subgroup analysis stratified based on DII and PD characteristics. Moreover, restricted cubic spline (RCS) analysis was conducted to examine whether there was a nonlinear association between DII and PD.ResultsA total of 21,994 participants were obtained for statistical analysis, made up of 263 patients with PD and 21,731 participants without PD. Univariate and multivariable logistics regression analysis showed DII to be positively associated with PD before and after matching. Subgroup analysis revealed a statistical difference in non-Hispanic whites, but RCS analysis suggested that there was no nonlinear relationship between the DII and PD.ConclusionFor participants over 40 years of age, higher DII scores were positively correlated with PD. In addition, these results support the ability of diet to be used as an intervention strategy for managing PD

Relevance Assessments for Web Search Evaluation: Should We Randomise or Prioritise the Pooled Documents? (CORRECTED VERSION)

In the context of depth-$k$ pooling for constructing web search test collections, we compare two approaches to ordering pooled documents for relevance assessors: the prioritisation strategy (PRI) used widely at NTCIR, and the simple randomisation strategy (RND). In order to address research questions regarding PRI and RND, we have constructed and released the WWW3E8 data set, which contains eight independent relevance labels for 32,375 topic-document pairs, i.e., a total of 259,000 labels. Four of the eight relevance labels were obtained from PRI-based pools; the other four were obtained from RND-based pools. Using WWW3E8, we compare PRI and RND in terms of inter-assessor agreement, system ranking agreement, and robustness to new systems that did not contribute to the pools. We also utilise an assessor activity log we obtained as a byproduct of WWW3E8 to compare the two strategies in terms of assessment efficiency.Comment: 30 pages. This is a corrected version of an open-access TOIS paper ( https://dl.acm.org/doi/pdf/10.1145/3494833

人間らしい対話システムの構築に向けた研究

早大学位記番号:新8727早稲田大

Diagnostic value and mechanism of plasma S100A1 protein in acute ischemic stroke: a prospective and observational study

Background Plasma S100A1 protein is a novel inflammatory biomarker associated with acute myocardial infarction and neurodegenerative disease’s pathophysiological mechanisms. This study aimed to determine the levels of this protein in patients with acute ischemic stroke early in the disease progression and to investigate its role in the pathogenesis of acute ischemic stroke. Methods A total of 192 participants from hospital stroke centers were collected for the study. Clinically pertinent data were recorded. The volume of the cerebral infarction was calculated according to the Pullicino formula. Multivariate logistic regression analysis was used to select independent influences. ROC curve was used to analyze the diagnostic value of AIS and TIA. The correlation between S100A1, NF-κB p65, and IL-6 levels and cerebral infarction volume was detected by Pearson correlation analysis. Results There were statistically significant differences in S100A1, NF-κB p65, and IL-6 among the AIS,TIA, and PE groups (S100A1, [230.96 ± 39.37] vs [185.85 ± 43.24] vs [181.47 ± 27.39], P < 0.001; NF-κB p65, [3.99 ± 0.65] vs [3.58 ± 0.74] vs [3.51 ± 0.99], P = 0.001; IL-6, [13.32 ± 1.57] vs [11.61 ± 1.67] vs [11.42 ± 2.34], P < 0.001). Multivariate logistic regression analysis showed that S100A1 might be an independent predictive factor for the diagnosis of disease (P < 0.001). The AUC of S100A1 for diagnosis of AIS was 0.818 (P < 0.001, 95% CI [0.749–0.887], cut off 181.03, Jmax 0.578, Se 95.0%, Sp 62.7%). The AUC of S100A1 for diagnosis of TIA was 0.720 (P = 0.001, 95% CI [0.592–0.848], cut off 150.14, Jmax 0.442, Se 50.0%, Sp 94.2%). There were statistically significant differences in S100A1, NF-κB p65, and IL-6 among the SCI,MCI, and LCI groups (S100A1, [223.98 ± 40.21] vs [225.42 ± 30.92] vs [254.25 ± 37.07], P = 0.001; NF-κB p65, [3.88 ± 0.66] vs [3.85 ± 0.64] vs [4.41 ± 0.45], P < 0.001; IL-6, [13.27 ± 1.65] vs [12.77 ± 1.31] vs [14.00 ± 1.40], P = 0.007). Plasma S100A1, NF-κB p65, and IL-6 were significantly different from cerebral infarction volume (S100A1, r = 0.259, P = 0.002; NF-κB p65, r = 0.316, P < 0.001; IL-6, r = 0.177, P = 0.036). There was a positive correlation between plasma S100A1 and IL-6 with statistical significance (R = 0.353, P < 0.001). There was no significant positive correlation between plasma S100A1 and NF-κB p65 (R < 0.3), but there was statistical significance (R = 0.290, P < 0.001). There was a positive correlation between IL-6 and NF-κB p65 with statistical significance (R = 0.313, P < 0.001). Conclusion S100A1 might have a better diagnostic efficacy for AIS and TIA. S100A1 was associated with infarct volume in AIS, and its level reflected the severity of acute cerebral infarction to a certain extent. There was a correlation between S100A1 and IL-6 and NF-κB p65, and it was reasonable to speculate that this protein might mediate the inflammatory response through the NF-κB pathway during the pathophysiology of AIS