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The association between the ratio of uric acid relative to high-density lipoprotein cholesterol (UHR) and abdominal aortic calcification (AAC), and the mediating effect of diabetes, is not fully understood. The ultimate assessment encompassed 2,731 participants (average age: 58.64 years; 51.59% women) from the 2013–2014 National Health and Nutrition Examination Survey. The presence of abdominal aortic calcification (AAC) was evaluated employing Kauppila’s (1997) semi-quantitative scoring system, with the results expressed in Kauppila semi-quantitative units. In the analysis, AAC scores were treated as continuous variables. AAC was also categorised as a binary variable, with non-zero scores assigned a value of 1 and zero scores assigned a value of 0. Similarly, SAAC was categorised as a binary variable, with scores greater than to 6 assigned a value of 1 and scores less than or equal to 6 assigned a value of 0. The present study employed weighted multiple logistic regression and linear regression analysis to investigate the association between UHR and AAC scores, as well as between AAC and SAAC. Subgroup and interaction analyses were conducted in order to investigate whether these associations varied by different confounders. The present study employed causal mediation analysis to assess the mediating effect of diabetes between UHR and abdominal aortic calcification (AAC). Sensitivity analysis was performed to determine the robustness of the association results between UHR and AAC. A one-unit rise in the log2-transformed UHR led to a 0.53 increase in the AAC scores [ β(95% confidence interval , CI): 0.53 (0.31 , 0.75) ] and a 43% higher risk of AAC [ odds ratio (OR) (95% CI): 1.43 (1.22 , 1.67) ], and the risk of SAAC increased by 60% [ OR (95% CI): 1.60 (1.21 , 2.12) ]. The findings of this study indicate that diabetes mediated 7.5% of the association between UHR and AAC scores, and 14% of the association between UHR and SAAC risk. This study found significant positive correlations between UHR and AAC scores, and between UHR and the risk of AAC and SAAC. It also found that diabetes partially mediated the association between UHR and AAC scores, as well as between UHR and SAAC. These results imply that UHR could be a useful clinical biomarker for predicting AAC risk and identifying AAC and SAAC, while diabetes partly explains this association.

Prior studies have validated a novel index, designated as the cholesterol-HDL-glucose (CHG) index, which has emerged as a promising biological marker for abnormalities in lipid metabolism and insulin resistance. At present, however, there is an absence of data demonstrating its capacity to predict the risk of CMD. The objective of this study is to evaluate the comparative efficacy of the CHG index and the triglyceride-glucose (TyG) index in predicting cardiovascular metabolic disease (CMD) risk. This study was conducted on a cohort of 6471 participants from CHARLS. A binary logistic regression analysis was performed using R software, utilizing restricted cubic spline techniques to evaluate the dose–response relationship. The evaluation of predictive performance was carried out through the use of receiver operating characteristic curves. To quantify the improvements in predictive capability, two important indices were calculated: Net Reclassification Improvement and Integrated Discrimination Improvement were used to assess the enhancements in our predictive models. Finally, a sensitivity analysis was conducted. An increase in each unit of CHG and TyG was associated with a 83% and 46% rise in the risk of CMD, respectively. The occurrence of CMD in the highest quartile for the CHG index (OR = 1.69, 95% CI 1.42–2.00) increased by 69%, while the TyG index (OR = 1.61, 95% CI 1.36–1.92) exhibited an increase of 61%. A linear correlation was identified between the two indices and the risk of CMD. The predictive capabilities and incremental predictive value of both indices were found to be analogous. The CHG index exhibited a substantial linear positive correlation with CMD, demonstrating assessment capabilities for CMD risk that were analogous to those of the TyG index.

High temperatures are known to reduce anthocyanin accumulation in a number of diverse plant species. In potato (Solanum tuberosum L.), high temperature significantly reduces tuber anthocyanin pigment content. However, the mechanism of anthocyanin biosynthesis in potato tuber under heat stress remains unknown. Here we show that high temperature causes reduction of anthocyanin biosynthesis in both potato tuber skin and flesh, with white areas forming between the vasculature and periderm. Heat stress reduced the expression of the R2R3 MYB transcription factors (TFs) StAN1 and StbHLH1, members of the transcriptional complex responsible for coordinated regulation of the skin and flesh pigmentation, as well as anthocyanin biosynthetic pathway genes in white regions. However, the core phenylpropanoid pathway, lignin, and chlorogenic acid (CGA) pathway genes were up-regulated in white areas, suggesting that suppression of the anthocyanin branch may result in re-routing phenylpropanoid flux into the CGA or lignin biosynthesis branches. Two R2R3 MYB TFs, StMYB44-1 and StMYB44-2, were highly expressed in white regions under high temperature. In transient assays, StMYB44 represses anthocyanin accumulation in leaves of Nicotiana tabacum and N. benthamiana by directly suppressing the activity of the dihydroflavonol reductase (DFR) promoter. StMYB44-1 showed stronger repressive capacity than StMYB44-2, with both predicted proteins containing the repression-associated EAR motif with some variation. StMYB44-1 conferred repression without a requirement for a basic helix–loop–helix (bHLH) partner, suggesting a different repression mechanism from that of reported anthocyanin repressors. We propose that temperature-induced reduction of anthocyanin accumulation in potato flesh is caused by down-regulation of the activating anthocyanin regulatory complex, by enhancing the expression of fleshspecific StMYB44 and alteration of phenylpropanoid flux.

Colorectal cancer (CRC) is one of the leading causes of cancer-related death, with most of the people who have the disease developing numerous liver metastases. Sixty percent of colon cancer patients have liver metastases. Only 25% of those with resectable hepatic metastases are alive, and recurrence occurs in nearly half of these cases. Regardless of the fact that left-sided cancer has a higher rate of liver metastases, past study reveals that left- and right-sided liver metastatic colon cancer patients have different survival rates. Hepatic artery infusion (HAI) combined with systemic chemotherapy is a treatment option for patients with unresectable liver-only or liver-dominant colon liver metastases. Although HAI has only been performed in a few locations previously, this study used randomized trials of floxuridine (FUDR) to characterize patient selection and first perioperative results during the deployment of a new HAI program. In this research, we also looked at the technical aspects of placing implantable pumps and catheters for HAI chemotherapy, as well as the efficacy, morbidity, and outcomes of this therapy in colon cancer patients with numerous liver metastases. The parameters like toxicity, overall survival rate, response rate, and progression-free response for the suggested therapy are also analyzed. These findings have important implications for colon cancer adjuvant HAI chemotherapy.

Background The prognostic nutritional index (PNI) serves as an indicator of systemic inflammation, immunological function, and nutritional condition in individuals. The aim of this study was to investigate the potential association between PNI and severe abdominal aortic calcification (SAAC) in middle-aged adults. Methods The study included 1,436 subjects aged 40–60 years (average age 49.72 years, with females representing 50.97%) from the 2013–2014 NHANES. AAC scores greater than 6 is defined as SAAC. PNI was defined as 5× Lymphocyte Count (10 ^ 9/L) + Serum Albumin (g/L). To investigate the independent association of PNI with SAAC, we performed weighted multivariable logistic regression analyses using NHANES sampling weights to account for the complex survey design. We constructed three progressively adjusted models: Crude model, Model 1 (age/sex/race-adjusted), and Model 3 (fully adjusted for all covariates). Additional stratification analyses and interaction examinations were conducted to evaluate potential modifying effects of confounding variables. For nonlinear analyses, we employed restricted cubic splines (RCS) with 4 knots at the 5th, 35th, 65th, and 95th percentiles to flexibly model the dose-response relationship between PNI and SAAC probability. The nonlinearity was tested using likelihood ratio tests comparing the linear and spline models. We further assessed the association between PNI and SAAC in hypertensive subgroups using generalized additive models and smoothed curves. Sensitivity analyses to determine the robustness of the PNI and SAAC association results. Results In fully adjusted models, each unit increase in PNI was also associated with a 11% increase in the risk of SAAC occurrence [ OR (95% CI): 1.11 (1.07 , 1.16) ]. Subgroup analyses showed that the relationship between PNI and SAAC was modified by hypertension ( P -interaction < 0.0001). In the nonhypertensive population, the association between PNI and SAAC was significantly positive. Conclusions We found that nutritional status combined with inflammation did not have a protective effect on SAAC. Conversely, in middle-aged individuals (especially those without hypertension), higher PNI scores were associated with a higher risk of SAAC, which was unexpected.

Although numerous effective Simultaneous Localization and Mapping (SLAM) systems have been developed, complex dynamic environments continue to present challenges, such as managing moving objects and enabling robots to comprehend environments. This paper focuses on a visual SLAM method specifically designed for complex dynamic environments. Our approach proposes a dynamic feature removal module based on the tight coupling of instance segmentation and multi-view geometric constraints (TSG). This method seamlessly integrates semantic information with geometric constraint data, using the fundamental matrix as a connecting element. In particular, instance segmentation is performed on frames to eliminate all dynamic and potentially dynamic features, retaining only reliable static features for sequential feature matching and acquiring a dependable fundamental matrix. Subsequently, based on this matrix, true dynamic features are identified and removed by capitalizing on multi-view geometry constraints while preserving reliable static features for further tracking and mapping. An instance-level semantic map of the global scenario is constructed to enhance the perception and understanding of complex dynamic environments. The proposed method is assessed on TUM datasets and in real-world scenarios, demonstrating that TSG-SLAM exhibits superior performance in detecting and eliminating dynamic feature points and obtains good localization accuracy in dynamic environments.

Background The current genome-wide association study (GWAS) of Lewy body dementia (LBD) suffers from low power due to a limited sample size. In addition, the genetic determinants underlying LBD and the shared genetic etiology with Alzheimer’s disease (AD) and Parkinson’s disease (PD) remain poorly understood. Methods Using the largest GWAS summary statistics of LBD to date (2591 cases and 4027 controls), late-onset AD (86,531 cases and 676,386 controls), and PD (33,674 cases and 449,056 controls), we comprehensively investigated the genetic basis of LBD and shared genetic etiology among LBD, AD, and PD. We first conducted genetic correlation analysis using linkage disequilibrium score regression (LDSC), followed by multi-trait analysis of GWAS (MTAG) and association analysis based on SubSETs (ASSET) to identify the trait-specific SNPs. We then performed SNP-level functional annotation to identify significant genomic risk loci paired with Bayesian fine-mapping and colocalization analysis to identify potential causal variants. Parallel gene-level analysis including GCTA-fastBAT and transcriptome-wide association analysis (TWAS) was implemented to explore novel LBD-associated genes, followed by pathway enrichment analysis to understand underlying biological mechanisms. Results Pairwise LDSC analysis found positive genome-wide genetic correlations between LBD and AD (rg = 0.6603, se = 0.2001; P = 0.0010), between LBD and PD (rg = 0.6352, se = 0.1880; P = 0.0007), and between AD and PD (rg = 0.2136, se = 0.0860; P = 0.0130). We identified 13 significant loci for LBD, including 5 previously reported loci (1q22, 2q14.3, 4p16.3, 4q22.1, and 19q13.32) and 8 novel biologically plausible genetic associations (5q12.1, 5q33.3, 6p21.1, 8p23.1, 8p21.1, 16p11.2, 17p12, and 17q21.31), among which APOC1 (19q13.32), SNCA (4q22.1), TMEM175 (4p16.3), CLU (8p21.1), MAPT (17q21.31), and FBXL19 (16p11.2) were also validated by gene-level analysis. Pathway enrichment analysis of 40 common genes identified by GCTA-fastBAT and TWAS implicated significant role of neurofibrillary tangle assembly (GO:1902988, adjusted P = 1.55 × 10 −2 ). Conclusions Our findings provide novel insights into the genetic determinants of LBD and the shared genetic etiology and biological mechanisms of LBD, AD, and PD, which could benefit the understanding of the co-pathology as well as the potential treatment of these diseases simultaneously.

In recent years, the development of polymerase chain reaction (PCR) technology has focused on digital PCR, which depends on the microfluidics. Based on continuous-flow microfluidic technology, this paper designed a miniaturized digital PCR amplification system, and greatly reduced the area required for microdroplet generation and reaction. The core rod. made of polydimethylsiloxane (PDMS), was combined with the Teflon tube to form 3D microfluidics, which requires only one heating source to form the temperature difference required for gene amplification. Only two 34 g needles can form and transmit micro-droplets in a 4-fold tapered Teflon tube, which is the simplest method to generate digital PCR droplets as far as we know, which allows the microdroplet generation device to be free from dependence on expensive chips. A complementary metal oxide semiconductor (CMOS) camera was used as a detection tool to obtain fluorescence video for the entire loop area or a specified loop area. In addition, we developed a homebrew for automatic image acquisition and processing to realize the function of digital PCR. This technique realizes the analysis of clinical serum samples of hepatitis B virus (HBV) and obtained the same results as real-time quantitative PCR. This system has greatly reduced the size and cost of the entire system, while maintaining a stable response.

Background Drug‐eluting beads bronchial arterial chemoembolization (DEB‐BACE) can embolize the tumor‐feeding artery and also be loaded with antitumor drugs, which can be released slowly into the local tumor environment. The effect of DEB‐BACE in patients with lung cancer remains unclear. We evaluated the efficacy and safety of DEB‐BACE with gemcitabine‐loaded CalliSpheres beads in patients with non‐small cell lung cancer (NSCLC). Methods From May 2017 to December 2018, six patients with NSCLC who were ineligible or refused to receive standard treatment underwent DEB‐BACE with gemcitabine‐loaded CalliSpheres beads. The primary endpoint was the objective response rate (ORR). The secondary endpoints were progression‐free survival (PFS), overall survival (OS), and quality of life. Safety was evaluated by the occurrences of adverse events and serious adverse events. Results All patients were treated with DEB‐BACE loaded with gemcitabine (800 mg) using CalliSpheres beads. Five patients also received transarterial infusion with nedaplatin (80–100 mg). Of the six patients, five underwent a second session of DEB‐BACE, with intervals of one month between the first and second session. The median follow‐up time was 16.5 months (7.0–23.0 months). ORR and disease control rate were 50.0% and 100.0%, 50.0% and 83.3%, 50.0% and 66.7% respectively at 2, 4, and 6 months after DEB‐BACE. One patient maintained a partial response and the other five had progressive disease, of whom two patients died and the other three remained alive receiving targeted therapy, radiotherapy, transarterial infusion or thermal ablation. The median PFS was 8.0 months (4–23 months), and the 6‐ and 12 month PFS rates were 66.7% and 16.7%, respectively. The median OS was 16.5 months (7–23 months), and the six and 12 month OS rates were 100.0% and 66.7%, respectively. Hemoptysis, cough and dyspnea disappeared after DEB‐BACE in four patients. Global quality of life, physical and emotional functioning were all significantly improved at two months (P < 0.05). There were no serious adverse events. Conclusions DEB‐BACE with gemcitabine‐loaded CalliSpheres beads is a feasible and well‐tolerated treatment for patients with NSCLC who are ineligible or refuse to receive standard treatment.

Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a sufficient volume of data, the data augmentation technique as a data-space solution is widely utilized. In this paper, we propose a Generative Adversarial Networks (GANs) based model that uses conditional knowledge to generate high-quality seismic waveforms. Unlike the existing method of generating samples directly from noise, the proposed method generates synthetic samples based on the statistical characteristics of real seismic waveforms in embedding space. Moreover, a content loss is added to relate high-level features extracted by a pre-trained model to the objective function to enhance the quality of the synthetic data. The classification accuracy is increased from 96.84% to 97.92% after mixing a certain amount of synthetic seismic waveforms, and results of the quality of seismic characteristics derived from the representative experiment show that the proposed model provides an effective structure for generating high-quality synthetic seismic waveforms. Thus, the proposed model is experimentally validated as a promising approach to realistic high-quality seismic waveform data augmentation.