Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

PURPOSE: Although there are significant differences between China and the United States (US) in trauma medical services, there has been no direct comparative research on the epidemiological data of trauma centers between the 2 countries. This study aims to fill this research gap by directly comparing trauma centers in China and the US, providing valuable data and insights for the development of trauma centers in both countries, promoting academic exchange and cooperation internationally, and enhancing the level of global trauma medical care. METHODS: This is a multicenter retrospective descriptive study. Data were collected for trauma patients with an injury severity score ≥16 treated from September 2013 to September 2019 at 2 hospital trauma centers in these 2 countries. Detailed clinical data (including injury mechanism, age, injury site, injury severity score, pre-hospital transport time, whether blood transfusion was performed, whether resuscitative thoracotomy was conducted, hospital and intensive care unit stay duration, the number of organ donor patients, mortality rates, and costs) were meticulously compiled and retrospectively analyzed to identify differences between the 2 trauma centers. The comparison was conducted using SPSS 23 software. Continuous variables are reported as median (Q₁, Q₃), and Mann Whitney U test is used to compare the median of continuous variables. Use clinically relevant critical points to classify continuous variables, with categorical variables represented as n (%), and comparisons were made between the 2 groups using the χ² test or Fisher's exact test. Statistical significance was defined as a 2-sided p < 0.05. RESULTS: These results point to significant differences in trauma center capacity, pre-hospital transport times, treatment procedures, hospital stay duration, mortality rates, and costs between the 2 centers. The volume of patients in trauma centers is less in China (2465 vs. 5288). Pre-hospital transport time was notably longer in China (180 min vs. 14 min), and the rate of emergency blood transfusions was lower in China (18.4% vs. 50.6%), Emergency thoracotomy was not performed in China but was conducted in 9.8% of cases in the US. Hospitalization costs were significantly lower in China than in the US ($5847 vs. $75,671). CONCLUSION There are clear differences in trauma center capacity (number of patients treated), pre-hospital transport time, age distribution of injured patients, injury mechanisms, injury sites, whether emergency thoracotomy is performed, hospital costs, and length of stay between the 2 trauma centers in China and America. Understanding these differences can help us further recognize the characteristics of Eastern and Western trauma patients.
Humans ; China/epidemiology* ; Trauma Centers/statistics & numerical data* ; Retrospective Studies ; United States/epidemiology* ; Male ; Female ; Wounds and Injuries/therapy* ; Middle Aged ; Adult ; Injury Severity Score ; Length of Stay/statistics & numerical data* ; Treatment Outcome

The concept and harms of hypothermia were introduced.The research progress of the intelligent warning equipment and model for hypothermia was reviewed,and the advantages and problems in practical application were analyzed.It's pointed out the intelligent warning equipment had to be improved in environmental adaptability,operational convenience and functio-nal stability and the model be enhanced in robustness,large-scale clinical validation and warning parameter accessibility.[Chinese Medical Equipment Journal,2024,45(5):86-94]

Jumonji domain-containing protein D3(JMJD3)is a 2-oxoglutarate-dependent dioxygenase that specif-ically removes transcriptional repression marks di-and tri-methylated groups from lysine 27 on histone 3(H3K27me2/3).The erasure of these marks leads to the activation of some associated genes,thereby influencing various biological processes,such as development,differentiation,and immune response.However,comprehensive descriptions regarding the relationship between JMJD3 and inflammation are lacking.Here,we provide a comprehensive overview of JMJD3,including its structure,functions,and involvement in inflammatory pathways.In addition,we summarize the evidence supporting JMJD3's role in several inflammatory diseases,as well as the potential therapeutic applications of JMJD3 inhibitors.Additionally,we also discuss the challenges and opportunities associated with investigating the functions of JMJD3 and developing targeted inhibitors and propose feasible solutions to provide valuable insights into the functional exploration and discovery of potential drugs targeting JMJD3 for inflammatory diseases.

Objective To analyze the core functional component groups(CFCG)in Yinchenhao Decoction(YCHD)and their possible pathways for treating hepatic fibrosis based on network pharmacology.Methods PPI data were extracted from DisGeNET,Genecards,CMGRN and PTHGRN to construct a weighted network using Cytoscape 3.9.1.The data of the chemical components in YCHD were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP),and the potential active components and targets were selected using PreADMET Web server and SwissTargetPrediction.A fusion model was constructed to obtain the functional effect space and evaluate the effective proteins to identify the CFCG followed by GO and KEGG pathway enrichment analyses for all the targets.In cultured human hepatic stellate cells(LX-2 cells),the cytotoxicity of different compounds in YCHD was tested using CCK-8 assay;the effects of these compounds on collagen α1(Col1a1)mRNA expression and the pathways in 20 ng/mL TGF-β1-stimulated cells were analyzed using RT-qPCR and Western blotting.Results A total of 1005 pathogenic genes,226 potential active components and 1529 potential targets in YCHD and 52 potential targets of CFCG were obtained.Benzyl acetate,vanillic acid,clorius,polydatin,lauric acid and ferulic acid were selected for CCK-8 verification,and they all showed minimal cytotoxicity below the concentration of 200 μmol/L.Clorius,polydatin,lauric acid and ferulic acid all effectively inhibited TGF-β1-induced LX-2 cell activation.At the concentration of 200 μmol/L,all these 4 components inhibited PI3K,p-PI3K,AKT,p-AKT,ERK,p-ERK,P38 MAPK and p-P38 MAPK expressions in TGF-β1-induced LX-2 cells.Conclusion The therapeutic effect of YCHD on hepatic fibrosis is probably mediated by its core functional components including benzyl acetate,vanillic acid,clorius,polydatin,lauric acid and ferulic acid,which inhibit the PI3K-AKT and MAPK pathways in hepatic stellate cells.