Search

Background: Fine particulate matter (PM2.5) induces ocular surface toxicity through pyroptosis, oxidative stress, autophagy, and inflammatory responses. However, the precise molecular pathways through which PM2.5 causes corneal damage remain unclear. This study aims to investigate the underlying mechanisms by exposing human corneal epithelial cells (HCECs) to PM2.5. Methods: After the morphology and chemical composition analysis of the PM samples, we conducted both in vivo and in vitro experiments to investigate PM2.5-induced corneal epithelial damage. We assessed corneal barrier function in HCECs using transepithelial electrical resistance (TEER) assays. To explore the molecular mechanisms of PM2.5-induced corneal epithelial damage, we performed whole-transcriptome resequencing, quantitative RT-PCR, and western blotting in vitro. In addition, we analyzed mouse corneas exposed to concentrated ambient PM2.5 through immunofluorescence staining to observe the resulting changes in corneal epithelial protein expression in vivo. Results: Our results showed significant impairment of corneal epithelial barrier function in PM2.5-treated HCECs, as indicated by decreased TEER values. The expression of thrombospondin-1 (THBS1) and claudin-1, both key factors for maintaining corneal epithelial barrier integrity, was markedly reduced at the gene and protein levels in both in vitro and in vivo PM2.5 exposure models. Moreover, the levels of tight junction-associated proteins, including occludin, zonula occludens-1 (ZO-1) and ZO-2, essential components of the corneal epithelial barrier, were significantly diminished in PM2.5-treated HCECs. Conclusion: PM2.5 exposure leads to corneal epithelium damage by disrupting tight junction proteins and THBS1 expression. These findings provide insight into potential pathways for PM2.5-induced ocular toxicity and underscore the need for protective strategies against such environmental pollutants.

The twist-bend nematic (NTB) phase of achiral liquid crystals (LCs) manifests a unique self-assembled heliconical structure with nanometer-scale pitch length, mirroring the chiral symmetry-breaking phenomena in nature, thus sparking widespread research interest. However, the ingenious NTB phase is only stable at high temperatures within a very limited temperature interval, often undergoing inevitable crystallization at low temperatures. Herein, room temperature supercooled NTB material systems composed of meticulously designed LC dimer mixtures with varying molecular curvatures and central flexibility were developed, resulting in complete resistance to crystallization even after 1 year of storage. Furthermore, the proposed NTB material systems demonstrated exceptional compatibility with common nematic LCs, facilitating the tailoring of overall physical parameters, particularly to achieve a sufficiently low bend elastic constant with excellent stability. This work represents a paradigmatic advancement forward in realizing stable NTB phase materials with a broad temperature range and resistance to crystallization, thereby tackling the enduring and seemingly insurmountable challenge while providing impetus for further exploration of their applications in soft matter, crystallography, and advanced photonics.

ZnO nanorod nonwoven fabrics (ZNRN) were developed through hydrothermal synthesis to facilitate the prevention of the transmission of respiratory pathogens. The superhydrophobicity and antibacterial properties of ZNRN were improved through the response surface methodology. The synthesized material exhibited significant water repellency, indicated by a water contact angle of 163.9°, and thus demonstrated antibacterial rates of 91.8% for Escherichia coli (E. coli) and 79.75% for Staphylococcus aureus (S. aureus). This indicated that E. coli with thinner peptidoglycan may be more easily killed than S. aureus. This study identified significant effects of synthesis conditions on the antibacterial effectiveness, with comprehensive multivariate analyses elucidating the underlying correlations. In addition, the ZnO nanorod structure of ZNRN was characterized through SEM and XRD analyses. It endows the properties of superhydrophobicity (thus preventing bacteria from adhering to the ZNRN surface) and antibacterial capacity (thus damaging cells through the puncturing of these nanorods). Consequently, the alignment of two such features is desired to help support the development of personal protective equipment, which assists in avoiding the spread of respiratory infections.

Frequency responsiveness within a broad dynamic range in adaptive systems while also reducing high-frequency induced heating remains a challenge for advanced photonics. Here, authors report a frequency-actuated heliconical soft architecture with reversible modulation of the photonic bandgap in a wide spectral range.

With the development of modern science and technology, more and more high-dimensional data appear in the application fields. Since the high dimension can potentially increase the complexity of the covariance structure, comparing the covariance matrices among populations is strongly motivated in high-dimensional data analysis. In this article, we consider the proportionality test of two high-dimensional covariance matrices, where the data dimension is potentially much larger than the sample sizes, or even larger than the squares of the sample sizes. We devise a novel high-dimensional spatial rank test that has much-improved power than many existing popular tests, especially for the data generated from some heavy-tailed distributions. The asymptotic normality of the proposed test statistics is established under the family of elliptically symmetric distributions, which is a more general distribution family than the normal distribution family, including numerous commonly used heavy-tailed distributions. Extensive numerical experiments demonstrate the superiority of the proposed test in terms of both empirical size and power. Then, a real data analysis demonstrates the practicability of the proposed test for high-dimensional gene expression data.

Skin aging is a physiological issue that is still relatively poorly understood. Studies have demonstrated that the dermal extracellular matrix (ECM) plays important roles in skin aging. However, the roles of the changes in ECM characteristics and the molecules that are secreted to the extracellular space and are involved in the formation of the dermal matrix from birth to old age remain unclear. To explore the way in which the ECM microenvironment supports the functions of skin development across different age groups is also poorly understood, we used a decellularization method and matrisome analysis to compare the composition, expression, and function of the dermal ECM in toddler, teenager, adult, and elderly skin. We found that the collagens, glycoproteins, proteoglycans, and regulatory factors that support skin development and interact with these core ECM proteins were differentially expressed at different ages. ECM expression markers occurring during the process of skin development were identified. In addition, our results elucidated the characteristics of ECM synthesis, response to skin development, and the features of the ECM that support epidermal stem cell growth via the basement membrane during skin aging.

Volatility monitoring of a tiny amount of organic solvent, which reflects the profound understanding of the micro‐environmental chemical physics effect, plays a significant role in chemical reactions. Common approaches, such as microfluidic technology, undeniably encounter the issue of complicated fabrication or high cost, impeding their large‐scale application for monitoring the processes of chemical physics in micro‐reactions. Herein, a helical polymer film (HPF) molded by a cholesteric liquid crystal (CLC) is reported to record the helical arrangement. By infiltrating less than 1.0 μL volatile solvent into the HPF and monitoring the corresponding reflection spectral behaviors, an intrinsic dynamic chemical physics process, solvent properties, and molecular interactions between the solvent and the polymer network are quantitatively evaluated. Furthermore, to further explore the underlying interaction mechanism that results in spectral band shift, a dynamical model is established and elucidated, providing remarkable information and understanding of the dynamic chemical physics processes and interfacial molecular interactions between the solvent and the polymer. This work has tied a close bond among chemical engineering, photonics, and soft condensed physics, which can provide an avenue for the new field of application of LC photonics beyond display and optics.

Accumulating evidence suggests that long noncoding RNA (lncRNA) functions as a critical regulator in cancer biology. Here, we characterized the role of lncRNA PCED1B antisense RNA 1 (PCED1B-AS1) in glioblastoma (GBM). PCED1B-AS1 was notably upregulated in GBM tissues and cell lines and closely associated with larger tumor size and higher grade. Patients with high PCED1B-AS1 had shorter survival time than those with low PCED1B-AS1. Functional experiments showed that depletion of PCED1B-AS1 significantly inhibited, while overexpression of PCED1B-AS1 promoted cell proliferation, glucose uptake, and lactate release. Mechanistically, PCED1B-AS1 was able to directly bind to the 5′-UTR of HIF-1α mRNA and potentiate HIF-1α translation, leading to increased HIF-1α protein level, thereby promoting the Warburg effect and tumorigenesis. Importantly, PCED1B-AS1 lost the carcinogenic properties in the absence of HIF-1α. In addition, we also confirmed the existence of the PCED1B-AS1/HIF-1α regulatory axis in vivo. Taken together, our findings demonstrate that PCED1B-AS1 is a novel oncogenic lncRNA in GBM and functions in a HIF-1α-dependent manner, which provides a promising prognostic biomarker and druggable target for GBM.

Extracellular matrix (ECM) proteins play an essential role in various biological processes in multicellular organisms, and their abnormal regulation can lead to many diseases. For large-scale ECM protein identification, especially through proteomic-based techniques, a theoretical reference database of ECM proteins is required. In this study, based on the experimentally verified ECM datasets and by the integration of protein domain features and a machine learning model, we developed ECMPride, a flexible and scalable tool for predicting ECM proteins. ECMPride achieved excellent performance in predicting ECM proteins, with appropriate balanced accuracy and sensitivity, and the performance of ECMPride was shown to be superior to the previously developed tool. A new theoretical dataset of human ECM components was also established by applying ECMPride to all human entries in the SwissProt database, containing a significant number of putative ECM proteins as well as the abundant biological annotations. This dataset might serve as a valuable reference resource for ECM protein identification.

ABSTRACT: Purpose: The endovascular treatment of intracranial aneurysms was proven safe and effective compared to the alternative method of surgical clipping, despite the high recurrence rate. Follow-up of embolized intracranial aneurysms is mandatory for the early detection of recurrence and improved outcomes. DSA is used as the reference standard for this assessment. To determine the effectiveness of MRA in follow-up evaluations of intracranial aneurysms after embolization by comparing DSA, CE-MRA, and TOF-MRA.Materials and Methods: Sixty-eight consecutive patients undergoing DSA, TOF-MRA, and CE-MRA during an interval of

Communities are often associated with important structural characteristics of a complex network system, therefore detecting communities is considered to be a fundamental problem in network analysis. With the development of data collection technology and platform, more and more sources of network data are acquired, which makes the form of network as well as the related data more complex. To achieve integrative community detection of a multi-layer attributed network that involves multiple network layers together with their attribute data, effectively utilizing the information from the multiple networks and the attributes may greatly enhance the accuracy of community detection. To this end, in this article, we study the integrative community detection problem of a multi-layer attributed network from the perspective of matrix factorization, and propose a penalized alternative factorization (PAF) algorithm to resolve the corresponding optimization problem, followed by the convergence analysis of the PAF algorithm. Results of the numerical study, as well as an empirical analysis, demonstrate the advantages of the PAF algorithm in community discovery accuracy and compatibility with multiple types of network-related data.

Hepatitis B virus (HBV) DNA is prone to mutations because of the proofreading deficiencies of HBV polymerase. We have identified hepatocellular carcinoma (HCC) survival-associated HBV mutations in the X protein region of HBV DNA. In the present study, we extend our research to assess HCC survival-associated HBV mutations in the HBV precore/core (PreC/C) region. The PreC/C region was amplified and sequenced and the HBV mutations were identified according to the NCBI database (http://www.ncbi.nlm.nih.gov/genome/5536). The relationships between the mutations in the PreC/C region and HCC survival were analyzed. Survival curves were generated using the Kaplan-Meier method, and comparisons between the curves were made using the log-rank test. Multivariate survival analysis was performed using a Cox proportional hazards model. After adjusting for clinical characteristics, the 1915, 2134, 2221, 2245 and 2288 mutational sites were identified as statistically significant independent predictors of HCC survival by multivariate survival analysis using a Cox proportional hazards model. In addition, the mutational site of 1896 was identified for its association with survival at a borderline significance level. A total of five mutations in the precore/core region were identified as independent predictors of postoperative survival in HCC patients. The analysis of HBV DNA mutations may help identify patient subgroups with poor prognosis and may help refine therapeutic decisions regarding HCC patients.