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Diabetic retinopathy (DR) is a common microvascular complication of diabetes mellitus. Optical coherence tomography angiography (OCTA) has been developed to visualize the retinal microvasculature and choriocapillaris based on the motion contrast of circulating blood cells. Depth-resolved ability and non-invasive nature of OCTA allow for repeated examinations and visualization of microvasculature at the retinal capillary plexuses and choriocapillaris. OCTA enables quantification of microvascular alterations in the retinal capillary network, in addition to the detection of classical features associated with DR, including microaneurysms, intraretinal microvascular abnormalities, and neovascularization. OCTA has a promising role as an objective tool for quantifying extent of microvascular damage and identify eyes with diabetic macular ischaemia contributed to visual loss. Furthermore, OCTA can identify preclinical microvascular abnormalities preceding the onset of clinically detectable DR. In this review, we focused on the applications of OCTA derived quantitative metrics that are relevant to early detection, staging and progression of DR. Advancement of OCTA technology in clinical research will ultimately lead to enhancement of individualised management of DR and prevention of visual impairment in patients with diabetes.

Nowadays, with the rapid growth of the internet of things (IoT), massive amounts of time series data are being generated. Time series data play an important role in scientific and technological research for conducting experiments and studies to obtain solid and convincing results. However, due to privacy restrictions, limited access to time series data is always an obstacle. Moreover, the limited available open source data are often not suitable because of a small quantity and insufficient dimensionality and complexity. Therefore, time series data generation has become an imperative and promising solution. In this paper, we provide an overview of classical and state-of-the-art time series data generation methods in IoT. We classify the time series data generation methods into four major categories: rule-based methods, simulation-model-based methods, traditional machine-learning-based methods, and deep-learning-based methods. For each category, we first illustrate its characteristics and then describe the principles and mechanisms of the methods. Finally, we summarize the challenges and future directions of time series data generation in IoT. The systematic classification and evaluation will be a valuable reference for researchers in the time series data generation field.

Initially reported as pneumonia of unknown origin, COVID-19 is increasingly being recognized for its impact on the nervous system, despite nervous system invasions being extremely rare. As a result, numerous studies have been conducted to elucidate the mechanisms of nervous system damage and propose appropriate coping strategies. This review summarizes the mechanisms by which SARS-CoV-2 invades and damages the central nervous system, with a specific focus on aspects apart from the immune response and inflammatory storm. The latest research findings on these mechanisms are presented, providing new insights for further in-depth research.

This study investigates the immunomodulatory effects of Lonicera japonica Thunb. extract, a perennial semi-evergreen vine of the Caprifoliaceae family, on lipopolysaccharide (LPS)-induced immune responses in loach ( Misgurnus anguillicaudatus ) through miRNA regulatory mechanisms. Small RNA libraries constructed from hepatic tissues of LPS-challenged (CK) and Lonicera japonica-treated (LJ) groups yielded 139.6 million clean reads with characteristic 21–23 nucleotide length distribution. Abundance analysis revealed significant differential expression patterns within the let-7 family: miR-let-7-9, miR-let-7-6, and miR-let-7–18 exhibited higher abundance in the infection group, whereas miR-let-7-1, miR-let-7-17, and miR-let-7–16 showed elevated abundance in the treatment group. Comparative profiling identified 55 differentially expressed miRNAs (41 upregulated, 14 downregulated), with animal-undef-351, animal-mir-21-6, and animal-undef-603 demonstrating the most significant expression differences (P<0.01). KEGG enrichment analysis highlighted predominant involvement in sphingolipid signaling pathway, glycerophospholipid metabolism, T cell receptor signaling pathway, and TNF signaling pathway. GO analysis revealed significant enrichment in biological processes related to glycosylation, cellular components of transcription regulator complexes, and molecular functions associated with double-stranded DNA binding. These findings demonstrate that L. japonica alleviates LPS-induced inflammation by modulating miRNA expression networks, particularly through sphingolipid metabolism and TNF signaling pathways, providing novel molecular evolutionary insights into the immunoregulatory mechanisms of traditional Chinese medicine.

Wall-thickness deformation is a critical indicator of fatigue risk in flexible risers exposed to vortex-induced vibration (VIV), especially under combined internal and external flow conditions. This study examines the spanwise evolution and distribution of wall-thickness deformation in a riser traversing air and water. The effects of external flow velocity, internal flow velocity, and internal fluid density on in-line (IL) and cross-flow (CF) wall deformation are systematically analyzed at characteristic positions. The results show that wall deformation exhibits strong spatial variability and media property dependence: IL deformation in the air-exposed segment is amplified under lock-in conditions due to lower damping, while the submerged segment experiences consistently larger deformation driven by added-mass effects. Internal flow influences wall-thickness response in a non-monotonic manner, and increased internal fluid density suppresses deformation while shifting the dominant frequency. These findings demonstrate that wall-thickness deformation is a sensitive and integrative response to fluid–structure interaction, offering a direct basis for identifying high-risk zones and improving fatigue-resistant design in deep-sea riser systems.

Purpose: To systematically review and evaluate predictive models for assessing the risk of lung infection in intensive care unit (ICU) patients. Methods: A comprehensive computerized search was conducted across multiple databases, including CNKI, Wanfang, VIP, SinoMed, PubMed, Web of Science, Embase, and the Cochrane Library, covering literature published up to March 2, 2024. The PRISMA guidelines were followed for data synthesis, and data extraction was performed according to the CHARMS checklist. The PROBAST tool was used to evaluate the risk of bias and the applicability of the included studies. Results: Fourteen studies encompassing 20 predictive models were included. The area under the curve (AUC) values of these models ranged from 0.722 to 0.936. Although the models demonstrated good applicability, the risk of bias in the included studies was high. Common predictors across the models included age, length of hospital stay, mechanical ventilation, use of antimicrobial drugs or glucocorticoids, invasive procedures, and assisted ventilation. Conclusion: Current predictive models for lung infection risk in ICU patients exhibit strong predictive performance. However, the high risk of bias highlights the need for further improvement. The main sources of bias include the neglect of handling missing data in the research, use of univariate analysis to select candidate predictors, lack of assessment of model performance, and failure to address overfitting. Future studies should expand the sample size based on the characteristics of the data and specific problems, conduct prospective studies, flexibly apply traditional regression models and machine learning, effectively combine the two, and give full play to their advantages in developing prediction models with better predictive performance and more convenient operation.

Previous researches have confirmed that modified nanoscale carbon black (MCB) can decrease the bioavailability of heavy metals in soil and accumulation in plant tissues, resulting in the increase of biomass of plant. However, as a nanoparticle, the effects of MCB on plant cell morphology and microbial communities in Cd-contaminated soil are poorly understood. This study, through greenhouse experiments, investigated the effects of MCB as an amendment for 5 mg·kg -1 Cd-contaminated soil on plant growth, plant cellular morphogenesis, and microbial communities. Two types of plants, metal-tolerant plant ryegrass ( Lolium multiflorum ), and hyperaccumulator plant chard ( Beta vulgaris L. var. cicla) were selected. The results indicated that adding MCB to Cd-contaminated soil, the dry biomass of shoot ryegrass and chard increased by 1.07 and 1.05 times, respectively, comparing with control group (the treatment without MCB). Meanwhile, the physiological characteristics of plant root denoted that adding MCB reduced the damage caused by Cd to plants. The acid phosphatase activity of soils treated with MBC was higher and the dehydrogenase activity was lower than control group during whole 50 days of incubation, while the urease and catalase activity of soils treated with MBC were higher than control group after 25 days of incubation. When compared with the treatment without MCB, the abundances of nitrogen-functional bacteria ( Rhodospirillum and Nitrospira ) and phosphorus-functional bacteria ( Bradyrhizobium and Flavobacterium ) increased but that of nitrogen-functional bacteria, Nitrososphaera , declined. The presence of MCB resulted in increased microbial community abundance by reducing the bioavailability of heavy metals in soil, while increasing the abundance of plants by increasing the amount of available nitrogen in soil. The result of this study suggests that MCB could be applied to the in-situ immobilization of heavy metal in contaminated soils because of its beneficial effects on plants growth, root cellular morphogenesis, and microbial community.

Surface subsidence caused by high-intensity coal mining in the western mining area will have a negative impact on the environment. Mining subsidence has the characteristics of large scope, long duration, and strong destructiveness. In order to deeply understand the law of surface movement and deformation under the high-intensity mining of coal mines in western China, taking the Caojiatan 122,106 working face as an example, this study was conducted to obtain the surface movement characteristics and law by the method of surface rock movement measurement. The results showed that the surface subsidence in this study is mainly divided into three stages: start-up stage, active stage, and recession stage, with the active stage characterized by abrupt and intensive settlement. The maximum measured subsidence reached 4.173 m along the strike and 3.350 m along the dip. Numerical simulations further demonstrated strong vertical connectivity within the overburden, with surface subsidence area covering approximately 2/3 of the direct roof area. The predicted maximum subsidence values from simulation were 4.21 m (strike) and 3.36 m (dip), closely aligning with field data. A probability integral model was calibrated using observed data, yielding key parameters: subsidence coefficient = 0.537, main influence angle tangent = 4.435, horizontal movement coefficient = 0.20, inflection point offset = 76.90 m, and propagation angle = 86.2°. This study provides a validated methodology for predicting surface deformation in western mining areas and offers practical insights for subsidence mitigation and land restoration.

Curcuma wenyujin is recognized as one of the traditional Chinese medicinal herbs classified under the ‘Eight Flavors of Zhejiang’. This herb is characterized by its high content of volatile oils and curcumin, with sesquiterpenes being the primary component. It exhibits a range of pharmacological activities, including anti-tumor, anti-inflammatory, and antiviral effects. The quality and yield of C. wenyujin are significantly influenced by abiotic stresses, with salt-alkali stress being one of the key factors among them. In this study, C. wenyujin was exposed to salt-alkali stress, with its leaves serving as the primary research object. Transcriptome and metabolome sequencing analyses were performed to explore the associated pathways and key genes in C. wenyujin that respond to salt-alkali stress. The results of transcriptome sequencing revealed a total of 438 differentially expressed genes significantly enriched in 12 pathways, including Biosynthesis of secondary metabolites, Metabolic pathways and Zeatin biosynthesis. Through comprehensive analysis, we have identified two key candidate genes, CwPER5 and CwBGLU32 , which are highly Likely to be involved in regulating metabolite synthesis under stress conditions. Additionally, non-targeted metabolomics sequencing identified a total of 781 metabolites, among which 166 were found to be significantly differentially accumulated. The most annotated metabolites were linked to the Biosynthesis of plant secondary metabolites pathway, followed by the ABC transporters pathway and the biosynthesis of alkaloids derived from ornithine, lysine and nicotinic acid pathway. This research offers a scientific foundation for the development and utilization of traditional Chinese medicine resources, specifically C. wenyujin , as well as a theoretical basis for breeding new varieties of C. wenyujin that exhibit resistance to salt-alkaline stress.

High intensity focused ultrasound (HIFU) has demonstrated its safety, efficacy and noninvasiveness in the ablation of solid tumor. However, its further application is limited by its inherent deficiencies, such as postoperative recurrence caused by incomplete ablation and excessive intensity affecting surrounding healthy tissues. Recent research has indicated that the integration of nanomaterials with HIFU exhibits a promising synergistic effect in tumor ablation. The concurrent utilization of nanomaterials with HIFU can help overcome the limitations of HIFU by improving targeting and ablation efficiency, expanding operation area, increasing operation accuracy, enhancing stability and bio-safety during the process. It also provides a platform for multi-therapy and multi-mode imaging guidance. The present review comprehensively expounds upon the synergistic mechanism between nanomaterials and HIFU, summarizes the research progress of nanomaterials as cavitation nuclei and drug carriers in combination with HIFU for tumor ablation. Furthermore, this review highlights the potential for further exploration in the development of novel nanomaterials that enhance the synergistic effect with HIFU on tumor ablation.