Enhancing Dental Material Performance: Tung Oil-Infused Polyurea Microcapsule Coatings for Self-Healing and Antimicrobial Applications.

Within the realm of dental material innovation, this study pioneers the incorporation of tung oil into polyurea coatings, setting a new precedent for enhancing self-healing functionality and durability. Originating from an ancient practice, tung oil is distinguished by its outstanding water resistance and microbial barrier efficacy. By synergizing it with polyurea, we developed coatings that unite mechanical strength with biological compatibility. The study notably quantifies self-healing efficiency, highlighting the coatings' exceptional capacity to mend physical damages and thwart microbial incursions. Findings confirm that tung oil markedly enhances the self-repair capabilities of polyurea, leading to improved wear resistance and the inhibition of microbial growth, particularly against Streptococcus mutans, a principal dental caries pathogen. These advancements not only signify a leap forward in dental material science but also suggest a potential redefinition of dental restorative practices aimed at prolonging the lifespan of restorations and optimizing patient outcomes. Although this study lays a substantial foundation for the utilization of natural oils in the development of medical-grade materials, it also identifies the critical need for comprehensive cytotoxicity assays. Such evaluations are essential to thoroughly assess the biocompatibility and the safety profile of these innovative materials for clinical application. Future research will concentrate on this aspect, ensuring that the safety and efficacy of the materials align with clinical expectations for dental restorations.

Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.

AIMS: In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully sufficient to promptly recognize patients with early-stage hypertrophic cardiomyopathy (HCM) without symptomatic and instrumental features. Considering the sudden death of HCM, developing a novel diagnostic model to clarify the patients with early-stage HCM and the immunological characteristics can avoid misdiagnosis and attenuate disease progression. METHODS AND RESULTS: Three hundred eighty-five samples from four independent cohorts were systematically retrieved. The weighted gene co-expression network analysis, differential expression analysis (|log2(foldchange)| > 0.5 and adjusted P < 0.05), and protein-protein interaction network were sequentially performed to identify HCM-related hub genes. With a machine learning algorithm, the least absolute shrinkage and selection operator regression algorithm, a stable diagnostic model was developed. The immune-cell infiltration and biological functions of HCM were also explored to characterize its underlying pathogenic mechanisms and the immune signature. Two key modules were screened based on weighted gene co-expression network analysis. Pathogenic mechanisms relevant to extracellular matrix and immune pathways have been discovered. Twenty-seven co-regulated genes were recognized as HCM-related hub genes. Based on the least absolute shrinkage and selection operator algorithm, a stable HCM diagnostic model was constructed, which was further validated in the remaining three cohorts (n = 385). Considering the tight association between HCM and immune-related functions, we assessed the infiltrating abundance of various immune cells and stromal cells based on the xCell algorithm, and certain immune cells were significantly different between high-risk and low-risk groups. CONCLUSIONS: Our study revealed a number of hub genes and novel pathways to provide potential targets for the treatment of HCM. A stable model was developed, providing an efficient tool for the diagnosis of HCM.

TYG Index as a Novel Predictor of Clinical Outcomes in Advanced Chronic Heart Failure with Renal Dysfunction Patients.

Background: The triglyceride-glucose (TYG) index is a novel and reliable marker reflecting insulin resistance. Its predictive ability for cardiovascular disease onset and prognosis has been confirmed. However, for advanced chronic heart failure (acHF) patients, the prognostic value of TYG is challenged due to the often accompanying renal dysfunction (RD). Therefore, this study focuses on patients with aHF accompanied by RD to investigate the predictive value of the TYG index for their prognosis. Methods and Results: 717 acHF with RD patients were included. The acHF diagnosis was based on the 2021 ESC criteria for acHF. RD was defined as the eGFR < 90 mL/(min/1.73 m2). Patients were divided into two groups based on their TYG index values. The primary endpoint was major adverse cardiovascular events (MACEs), and the secondary endpoints is all-cause mortality (ACM). The follow-up duration was 21.58 (17.98-25.39) months. The optimal cutoff values for predicting MACEs and ACM were determined using ROC curves. Hazard factors for MACEs and ACM were revealed through univariate and multivariate COX regression analyses. According to the univariate COX regression analysis, high TyG index was identified as a risk factor for MACEs (hazard ratio = 5.198; 95% confidence interval [CI], 3.702-7.298; P < 0.001) and ACM (hazard ratio = 4.461; 95% CI, 2.962-6.718; P < 0.001). The multivariate COX regression analysis showed that patients in the high TyG group experienced 440.2% MACEs risk increase (95% CI, 3.771-7.739; P < 0.001) and 406.2% ACM risk increase (95% CI, 3.268-7.839; P < 0.001). Kaplan-Meier survival analysis revealed that patients with high TyG index levels had an elevated risk of experiencing MACEs and ACM within 30 months. Conclusion: This study found that patients with high TYG index had an increased risk of MACEs and ACM, and the TYG index can serve as an independent predictor for prognosis.

The impact of university STEM assets: A systematic review of the empirical evidence.

BACKGROUND: Innovation ecosystems are an important driver of regional economic growth and development. STEM assets connected to universities may play an important role in such ecosystems. OBJECTIVE: To systematically review the literature relating to the effect of university STEM assets on regional economies and innovation ecosystems, providing a better understanding of how the impact is generated and constrained, as well as identifying any gaps in knowledge. METHODS: Keyword and text word searches using the Web of Science Core Collection (Clarivate), Econlit (EBSCO) and ERIC (EBSCO) were performed in July 2021 and February 2023. Papers were double screened on abstract and title, and were included if there was consensus that they fulfilled the inclusion criteria of: (i) relating to an OECD country; (ii) having been published between 1 January 2010 and 28 February 2023; and (iii) concerning the impact of STEM assets. Data extraction was undertaken for each article by a single reviewer and checked by a second reviewer. Due to the heterogeneity of the study designs and outcome measures used, it was not possible to perform a quantitative synthesis of results. A narrative synthesis was subsequently undertaken. RESULTS: Of the 162 articles identified for detailed review, 34 were accepted as being sufficiently relevant to the study to be included for final analysis. Three important features identified were that the literature: i) is predominately concerned with supporting new businesses; ii) describes a high level of involvement with a university in providing that support; and iii studies economic impacts at local, regional and national levels. DISCUSSION: The evidence points to a gap in the literature relating to looking at the broader impact of STEM assets and any corresponding transformational, system-level effects that go beyond narrowly defined, short to medium-term outcomes. The main limitation of this review is that information on STEM assets in the non-academic literature is not captured.

Milk-Derived Proteins and Peptides in Head and Neck Carcinoma Treatment.

Research investigating milk-derived proteins has brought to light the potential for their use as novel anticancer agents. This paper aims to systematically review studies examining the effectiveness of milk-derived proteins in the treatment of head and neck cancer. A systematic literature search of Medline, Evidence-Based Medicine, and Web of Science databases including papers published from all dates was completed. Inter-rater reliability was high during the title, abstract, and full-text screening phases. Inclusion criteria, exclusion criteria, and data extraction were based on the PICOS tool and research questions. Reporting followed the Preferred Reporting Items for Systematic Review and Meta-Analysis criteria. Eligible in vitro and in vivo studies (n = 8/658) evaluated lactoferrin, α-lactalbumin, and its complexes, such as HAMLET, BAMLET and lactalbumin-oleic acid complexes, as well as lactoperoxidase, whey, and casein. Their effectiveness in the treatment of head and neck cancer cells lines found that these compounds can inhibit tumour growth modulate cancer gene expression, and have cytotoxic effects on cancer cells. However, the exact mechanisms by which these effects are achieved are not well understood. Systematically designed, large, optimally controlled, collaborative studies, both in vitro and in vivo, will be required to gain a better understanding of their potential role in the treatment of head and neck cancer.