Exposure to Chinese famine and the risk of hyperuricemia in later life: a population-based cross-sectional study

BackgroundLimited studies have investigated the relationship between famine exposure and the risk of hyperuricemia in later life. Consequently, the primary purpose of the current study was to examine the potential association between exposure to Chinese famine and hyperuricemia, as well as any gender disparities in this relationship.MethodThe data were obtained from the China PEACE (China Patient-Centered Evaluative Assessment of Cardiac Events) Million Persons Project in Rongchang. The study participants were enrolled into different cohorts based on their birthdates: the fetal-exposed cohort (born between 1959 and 1962), the childhood-exposed cohort (born between 1949 and 1958), the adolescence-exposed cohort (born between 1941 and 1948), and the non-exposed cohorts (born between 1963 and 1974). The potential association between famine exposure and hyperuricemia was assessed using binary logistic regression models.ResultsA total of 6,916 individuals were enrolled in the current study with an average age of 60.11 ± 9.22 years, out of which 3,544 were women. After adjusting for confounding factors, fetal (OR = 0.530, 95% CI: 0.411–0.0.683), childhood (OR = 0.642, 95% CI: 0.494–0.833) exposure to the Chinese famine for men was negatively associated with hyperuricemia. Conversely, exposure to the Chinese famine during fetal (OR = 2.144, 95% CI: 1.622–2.834), childhood (OR = 1.485, 95% CI: 1.105–1.997), and adolescence (OR = 1.967, 95% CI: 1.465–2.641) for women was positively associated with hyperuricemia. Furthermore, the impact of famine on hyperuricemia that has been observed in exposed women might be intensified by the presence of dyslipidemia, abdominal obesity, and overweight/obesity.ConclusionWomen exposed to the Chinese famine during fetal, childhood, and adolescence were positively associated with hyperuricemia, while men exhibited a negative association during fetal and childhood. Additionally, the effect of famine on hyperuricemia in exposed women appears to be intensified by the presence of dyslipidemia, abdominal obesity, and overweight/obesity

Neuro-Fuzzy Based High-Voltage DC Model to Optimize Frequency Stability of an Offshore Wind Farm

Lack of synchronization between high voltage DC systems linking offshore wind farms and the onshore grid is a natural consequence owing to the stochastic nature of wind energy. The poor synchronization results in increased system disturbances, grid contingencies, power loss, and frequency instability. Emphasizing frequency stability analysis, this research investigates a dynamic coordination control technique for a Double Fed Induction Generator (DFIG) consisting of OWFs integrated with a hybrid multi-terminal HVDC (MTDC) system. Line commutated converters (LCC) and voltage source converters (VSC) are used in the suggested control method in order to ensure frequency stability. The adaptive neuro-fuzzy inference approach is used to accurately predict wind speed in order to further improve frequency stability. The proposed HVDC system can integrate multiple distributed OWFs with the onshore grid system, and the control strategy is designed based on this concept. In order to ensure the transient stability of the HVDC system, the DFIG-based OWF is regulated by a rotor side controller (RSC) and a grid side controller (GSC) at the grid side using a STATCOM. The devised HVDC (MTDC) is simulated in MATLAB/SIMULINK, and the performance is evaluated in terms of different parameters, such as frequency, wind power, rotor and stator side current, torque, speed, and power. Experimental results are compared to a conventional optimal power flow (OPF) model to validate the performance.© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Investigation of glucose-modified liposomes using polyethylene glycols with different chain lengths as the linkers for brain targeting

Background: An intimidating challenge to transporting drugs into the brain parenchyma is the presence of the blood-brain barrier (BBB). Glucose is an essential nutritional substance for brain function sustenance, which cannot be synthesized by the brain. Its transport primarily depends on the glucose transporters on the brain capillary endothelial cells. In this paper, the brain-targeted properties of glucose-modified liposomes using polyethylene glycols with different chain lengths as the linkers were compared and evaluated to establish an optimized drug-delivery system. Methods: Coumarin 6-loaded liposomes (GLU200-LIP, GLU400-LIP, GLU1000-LIP, and GLU2000-LIP) composed of phospholipids and glucose-derived cholesterols were prepared by thin-film dispersion-ultrasound method. The BBB model in vitro was developed to evaluate the transendothelial ability of the different liposomes crossing the BBB. The biodistribution of liposomes in the mice brains was identified by in vivo and ex vivo nearinfrared fluorescence imaging and confocal laser scanning microscopy and further analyzed quantitatively by high-performance liquid chromatography. Results: Glucose-derived cholesterols were synthesized and identified, and coumarin 6-loaded liposomes were prepared successfully. The particle sizes of the four types of glucose-modified liposomes were around or smaller than 100 nm with a polydispersity index less than 0.300. GLU400-LIP, GLU1000-LIP, and GLU2000-LIP achieved higher cumulative cleared volumes on BBB model in vitro after 6 hours compared with GLU200-LIP (P < 0.05) and were significantly higher than that of the conventional liposome (P < 0.001). The qualitative and quantitative biodistribution results in the mice showed that the accumulation of GLU1000-LIP in the brain was the highest among all the groups (P < 0.01 versus LIP). Conclusion: The data indicated that GLU400-LIP, GLU1000-LIP, and GLU2000-LIP all possess the potential of brain targeting, among which GLU1000-LIP, as a promising drug-delivery system, exhibited the strongest brain delivery capacity.Nanoscience & NanotechnologyPharmacology & PharmacySCI(E)0ARTICLE163-175

MicroRNA-1224 Inhibits Tumor Metastasis in Intestinal-Type Gastric Cancer by Directly Targeting FAK

Intestinal-type gastric cancer (GC) of the Lauren classification system has specific epidemiological characteristics and carcinogenesis patterns. MicroRNAs (miRNAs) have prognostic significance, and some can be used as prognostic biomarkers in GC. In this study, we identified miR-1224 as a potential survival-related miRNA in intestinal-type GC patients by The Cancer Genome Atlas (TCGA) analysis. Using quantitative real-time PCR (qRT-PCR), we showed that the relative expression of miR-1224 was significantly decreased in intestinal-type GC tissues compared to matched adjacent normal mucosa tissues (p < 0.01). We found that high miR-1224 expression was associated with no lymph-node metastasis (p < 0.05) and good prognosis (p = 0.028) in 90 intestinal-type GC tissues. Transfection of intestinal-type GC cells with miR-1224 mimics showed that miR-1224 suppressed cell migration in vitro (wound healing assay and Transwell migration assay), whereas the transfection of cells with miR-1224 inhibitor promoted cell migration in vitro. miR-1224 also suppressed intestinal-type GC cell metastasis in a xenograft mouse model. Furthermore, bioinformatics, luciferase reporter, Western blotting, and immunohistochemistry (IHC) studies demonstrated that miR-1224 directly bound to the focal adhesion kinase (FAK) gene, and downregulated its expression, which decreased STAT3 and NF-κB signaling and subsequent the epithelial-to-mesenchymal transition (EMT). Repression of FAK is required for the miR-1224-mediated inhibition of cell migration in intestinal-type GC. The present study demonstrated that miR-1224 is downregulated in intestinal-type GC. miR-1224 inhibits the metastasis of intestinal-type GC by suppressing FAK-mediated activation of the STAT3 and NF-κB pathways, and subsequent EMT. miR-1224 could represent an important prognostic factor in intestinal-type GC

MIBiG 3.0 : a community-driven effort to annotate experimentally validated biosynthetic gene clusters

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/

A Linguistic Hierarchy Model with Self-Confidence Preference Relations and Its Application in Co-Regulation of Food Safety in China

Linguistic preference relations are widely used by decision makers to elicit their preferences over alternatives in the Group Decision Making (GDM) process. Recent studies have shown that self-confidence, as an important human psychological behavior, has an important influence on decision-making results. However, multiple self-confidence levels of decision makers are seldom considered in the linguistic preference relation. Meanwhile many real-word decision-making problems are analyzed in a hierarchical structure, in which a complicated problem can be divided into several easier comprehended sub-problems. Hence, this paper aims at designing a linguistic hierarchy model with self-confidence preference relation (LHM-SCPR) to discuss complex GDM problems in a hierarchical structure. In the SC-LPR, each element contains two components, the first one is the preference value between pairs of alternatives, and the second one that is defined on a linguistic term set represents decision maker’s self-confidence level associated to the first component. Meanwhile, a nonlinear programming model is proposed to derive individual preference vector from SC-LPR. Then, we apply LHM-SCPR in co-regulation of food safety to present the validity of this method, and find that improving the participation skills regarding co-regulation of food safety is the most pressing task. Finally, detailed comparative analysis and discussion are presented to verify the validity of the proposal

An Optimization-Based Approach to Social Network Group Decision Making with an Application to Earthquake Shelter-Site Selection

The social network has emerged as an essential component in group decision making (GDM) problems. Thus, this paper investigates the social network GDM (SNGDM) problem and assumes that decision makers offer their preferences utilizing additive preference relations (also called fuzzy preference relations). An optimization-based approach is devised to generate the weights of decision makers by combining two reliable resources: in-degree centrality indexes and consistency indexes. Based on the obtained weights of decision makers, the individual additive preference relations are aggregated into a collective additive preference relation. Further, the alternatives are ranked from best to worst according to the obtained collective additive preference relation. Moreover, earthquakes have occurred frequently around the world in recent years, causing great loss of life and property. Earthquake shelters offer safety, security, climate protection, and resistance to disease and ill health and are thus vital for disaster-affected people. Selection of a suitable site for locating shelters from potential alternatives is of critical importance, which can be seen as a GDM problem. When selecting a suitable earthquake shelter-site, the social trust relationships among disaster management experts should not be ignored. To this end, the proposed SNGDM model is applied to evaluate and select earthquake shelter-sites to show its effectiveness. In summary, this paper constructs a novel GDM framework by taking the social trust relationship into account, which can provide a scientific basis for public emergency management in the major disasters field

Layer-Aware Unequal Error Protection for Scalable H.264 Video Robust Transmission over Packet Lossy Networks

The Scalable Video Coding (SVC) amendment of the H.264/AVC standard is an up-to-date video compression standard. The various scalable layers have different contribution to the quality of the reconstructed video sequence due to the use of hierarchical prediction and the drift propagation. This paper proposes a novel trapezoidal-unequal error protection (UEP) scheme which significantly reduces the redundancy but rarely decreases the performance by taking into account the characteristics of the video coding and the adoptive forward error correction (FEC) sufficiently. In order to optimally distribute FEC codes, the paper then proposes a layer-aware distortion model to accurately estimate the decrement of video quality caused by the loss of quality enhancement layers, drift propagation and error concealment in the scalable H.264/AVC video. Experimental results show that the proposed trapezoidal UEP scheme has better robustness and in the meanwhile reduces the coding redundancy greatly in different channel circumstance compared with the traditional UEP scheme

Effects of Pause Design on the Decline in Pulling Effort and the Evaluation of Perceived Effort in Pulling Tasks

Pulling is one of the manual material handling activities that could lead to work-related musculoskeletal disorders. The objectives of this study were to explore the development of muscular fatigue when performing intermittent pulling tasks and to establish models to predict the pull strength decrease due to performing the tasks. A simulated truck pulling experiment was conducted. Eleven healthy male adults participated. The participants pulled a handle with a load of 40 kg, which resulted in a pulling force of approximately 123 N. The pulling tasks lasted for 9 or 12 min with one, two, or three pauses embedded. The total time period of the embedded pauses was 3 min. The pull strength after each pull and rest was measured. Ratings of the perceived exertion on body parts after each pull were also recorded. The results showed insignificant differences regarding the development of muscular fatigue related to rest frequency. We found that the development of muscular fatigue for pulling tasks with embedded pauses was significantly slower than that for continuous pulls. The forearm had a higher CR-10 score than the other body parts indicating that the forearm was the body part suffering early muscle fatigue. An exponential model was developed to predict the pull strength of the pulling tasks with embedded pauses. This model may be used to assess the developing of muscular fatigue for pulling tasks