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Clinical value of serum Klotho protein in patients with acute traumatic brain injury complicated by acute kidney injury
Abstract Traumatic Brain Injury (TBI) is a major cause of mortality and morbidity, with acute kidney injury (AKI) as a common complication. This study aimed to investigate the potential of serum Klotho protein levels as a diagnostic and prognostic marker for AKI in TBI patients.The results showed that serum Klotho level 177.3 (165.43,195.07) (pg/ml) was elevated in patients with traumatic brain injury as compared to normal group 134.8(125.6,138.9) (pg/ml) . We conducted univariate analyses of all potential factors, resulting in four variables for inclusion in the multivariate analysis. Among them, the HR value of Klotho protein was 2.076. Comparing the predictive efficacy of serum creatinine and Klotho protein, the ROC value of Klotho protein was 0.832 (95% CI: 0.709–0.898), suggesting higher predictive ability than serum creatinine. Modestly elevated serum Klotho protein levels were associated with a higher risk of AKI and a poor long-term prognosis.These findings suggested that serum Klotho protein levels may serve as an early diagnostic indicator and predictor of outcomes in TBI patients with AKI, providing insights for potential therapeutic interventions. Further research is needed to validate these findings and explore the clinical utility of targeting the Klotho pathway in TBI-associated AKI.
Differences of the respiratory microbiota between children suffering from community acquired pneumonia with presence or absence of asthma
Mechanical behavior and energy absorption capability of trigonometric function curved rod cell-based lattice structures under compressive loading
The effect of 8-week combined balance and plyometric training on change of direction performance of young badminton players
Genetic and clinical distinction between aggressive NK-cell leukemia and extranodal NK/T-cell lymphoma with bone marrow involvement
The common yet enigmatic activity of histone tail clipping
Semantic knowledge graph fusion for fake news detection: Unifying content-based features and evidence-based analysis in the COVID-19 infodemic
In the era of digital communication, the rapid spread of information has brought both benefits and challenges. While it has democratized access to knowledge, it has also led to an increase in fake news, with significant societal repercussions. The COVID-19 pandemic has exacerbated this issue, resulting in what the World Health Organization has termed an “infodemic." In light of this, developing effective methods for detecting fake news is of paramount importance. In this paper, we introduce a novel approach that integrates knowledge graphs and Named Entity Recognition (NER) based on a biomedical language model to address the challenge of fake news detection. Our method aims to enhance detection accuracy by combining content analysis with entity-level insights. Our approach involves three key components. First, content analysis uses a contextual language model to capture the semantic context of the content, enabling the extraction of meaningful insights essential for identifying fake news. Second, the NER component, built on a biomedical language model, precisely identifies and categorizes entities within the content, offering a deeper understanding crucial for detecting misinformation in the biomedical domain. Finally, entity integration employs knowledge graph embeddings to transform identified entities into a format that facilitates enhanced processing and detection. By blending these components, our method creates a unified representation of the content, incorporating both semantic context and entity-based insights. This comprehensive approach significantly improves the accuracy of fake news detection. Our extensive experiments demonstrate the effectiveness of this method, particularly in the early identification of false information. The results underscore the potential of our approach as a powerful tool in combating misinformation, particularly in critical areas such as public health.
Earthquake rupture variability along the central seismic gap segment (78°– 82°E) of the Himalayan Frontal Thrust, Western and Central Himalaya
Effects of in ovo stimulation with potential epigenetic modulators on immune system phenotype across three generations in a chicken model
Genetic effect on working memory activation pattern and structural properties of cortical gray matter
Unraveling the role of HIF2α in melanoma progression and epithelial–mesenchymal transition
The relationship of the ratio of platelet distribution width to serum albumin with kidney disease progression in patients with hypertension
Abstract Platelet distribution width (PDW), which represents the heterogeneity of platelet size, can predict a poor prognosis in various populations. However, the PDW-to-serum albumin ratio (PAR) has not been evaluated in any disease population, and whether the PAR could be a prognostic marker in hypertension remains unknown. The relationship between the PAR and adverse outcomes was examined retrospectively using longitudinal data of 1,578 patients with hypertension from the Fukushima Cohort Study. Participants were categorized into tertiles by baseline PAR. The primary endpoint of the present study was a kidney event, defined as a combination of a 50% decline in eGFR from baseline and end-stage kidney disease requiring kidney replacement therapy. During the median follow-up period of 5.4 years, 146 patients had kidney events. The higher PAR group (tertile 3) showed an increased incidence of kidney events on Kaplan–Meier curve analysis. Compared with the lowest PAR tertile, the highest PAR tertile (tertile 3) showed a significantly higher risk of kidney events (adjusted hazard ratio 3.74, 95% confidence interval (CI) 1.65–8.48). Similar relationships were observed for risks of all-cause death and cardiovascular events. The predictive value of the PAR for kidney events was superior to that of PDW alone. The areas under the curves for PDW and the PAR were 0.61 (95% CI 0.56–0.66) and 0.77 (95% CI 0.74–0.81), respectively (P < 0.001). The PAR could be a useful predictive marker of adverse outcomes in this population.
Aged Turkey manure shapes microbial diversity and antibiotic resistance genes in soil and plants under fertilization
Deep learning based knowledge tracing in intelligent tutoring systems
Cohort study on Medical-Integrated holistic nursing’s impact on intensive care unit patients’ outcomes, complications, and comprehensive health care
Structure–function analysis defines the minimal functional C-terminal domain of the variant surface glycoprotein of Trypanosoma brucei
Scientific and technological innovation cooperation network of the Greater Bay Area in South China: A social network analysis
Regional innovation cooperation focused upon either regions within one nation or transnational regions. Different from the discussion in the existing literature, the Guangdong- Hong Kong-Macao Greater Bay Area exists as an exceptional cross-border city-to-city cooperation under “one country, two systems”. Based on the social network analysis on co- patents in the Greater Bay Area, this paper aims to investigate the models and features of the innovation cooperation network of science and technology in the Greater Bay Area and the different characteristics of collaboration policy on cross-border knowledge flow before and after 2015. The results show that the innovation network of science and technology in the Greater Bay Area has a loosely structured pattern, with Hong Kong-Shenzhen-Guangzhou as a prominent hub. This structure portrays multiple centers that radiate out to its peripheries. The national policy launched in 2015 reduces the cooperation barriers and promotes the cross-border collaboration. The universities and research institutes possess significant intermediary roles and innovation autonomy in the scientific and technological innovation cooperation network of the Greater Bay Area.