Power-law behavior of transcriptional bursting regulated by enhancer-promoter communication.

Revealing how transcriptional bursting kinetics are genomically encoded is challenging because genome structures are stochastic at the organization level and are suggestively linked to gene transcription. To address this challenge, we develop a generic theoretical framework that integrates chromatin dynamics, enhancer-promoter (E-P) communication, and gene-state switching to study transcriptional bursting. The theory predicts that power law can be a general rule to quantitatively describe bursting modulations by E-P spatial communication. Specifically, burst frequency and burst size are up-regulated by E-P communication strength, following power laws with positive exponents. Analysis of the scaling exponents further reveals that burst frequency is preferentially regulated. Bursting kinetics are down-regulated by E-P genomic distance with negative power-law exponents, and this negative modulation desensitizes at large distances. The mutual information between burst frequency (or burst size) and E-P spatial distance further reveals essential characteristics of the information transfer from E-P communication to transcriptional bursting kinetics. These findings, which are in agreement with experimental observations, not only reveal fundamental principles of E-P communication in transcriptional bursting but also are essential for understanding cellular decision-making.

Serine protease Rv2569c facilitates transmission of Mycobacterium tuberculosis via disrupting the epithelial barrier by cleaving E-cadherin.

Epithelial cells function as the primary line of defense against invading pathogens. However, bacterial pathogens possess the ability to compromise this barrier and facilitate the transmigration of bacteria. Nonetheless, the specific molecular mechanism employed by Mycobacterium tuberculosis (M.tb) in this process is not fully understood. Here, we investigated the role of Rv2569c in M.tb translocation by assessing its ability to cleave E-cadherin, a crucial component of cell-cell adhesion junctions that are disrupted during bacterial invasion. By utilizing recombinant Rv2569c expressed in Escherichia coli and subsequently purified through affinity chromatography, we demonstrated that Rv2569c exhibited cell wall-associated serine protease activity. Furthermore, Rv2569c was capable of degrading a range of protein substrates, including casein, fibrinogen, fibronectin, and E-cadherin. We also determined that the optimal conditions for the protease activity of Rv2569c occurred at a temperature of 37°C and a pH of 9.0, in the presence of MgCl2. To investigate the function of Rv2569c in M.tb, a deletion mutant of Rv2569c and its complemented strains were generated and used to infect A549 cells and mice. The results of the A549-cell infection experiments revealed that Rv2569c had the ability to cleave E-cadherin and facilitate the transmigration of M.tb through polarized A549 epithelial cell layers. Furthermore, in vivo infection assays demonstrated that Rv2569c could disrupt E-cadherin, enhance the colonization of M.tb, and induce pathological damage in the lungs of C57BL/6 mice. Collectively, these results strongly suggest that M.tb employs the serine protease Rv2569c to disrupt epithelial defenses and facilitate its systemic dissemination by crossing the epithelial barrier.

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics.

Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

An analysis of AURKB's prognostic and immunological roles across various cancers.

Aurora kinase B (AURKB), an essential regulator in the process of mitosis, has been revealed through various studies to have a significant role in cancer development and progression. However, the specific mechanisms remain poorly understood. This study, therefore, seeks to elucidate the multifaceted role of AURKB in diverse cancer types. This study utilized bioinformatics techniques to examine the transcript, protein, promoter methylation and mutation levels of AURKB. The study further analysed associations between AURKB and factors such as prognosis, pathological stage, biological function, immune infiltration, tumour mutational burden (TMB) and microsatellite instability (MSI). In addition, immunohistochemical staining data of 50 cases of renal clear cell carcinoma and its adjacent normal tissues were collected to verify the difference in protein expression of AURKB in the two tissues. The results show that AURKB is highly expressed in most cancers, and the protein level of AURKB and the methylation level of its promoter vary among cancer types. Survival analysis showed that AURKB was associated with overall survival in 12 cancer types and progression-free survival in 11 cancer types. Elevated levels of AURKB were detected in the advanced stages of 10 different cancers. AURKB has a potential impact on cancer progression through its effects on cell cycle regulation as well as inflammatory and immune-related pathways. We observed a strong association between AURKB and immune cell infiltration, immunomodulatory factors, TMB and MSI. Importantly, we confirmed that the AURKB protein is highly expressed in kidney renal clear cell carcinoma (KIRC). Our study reveals that AURKB may be a potential biomarker for pan-cancer and KIRC.

Copper-Catalyzed sp2 C-H Arylation and Alkynylation of Allenes via Hydrogen Atom Abstraction.

Development of methods for the sp2 C-H transformations of allenes has received much attention, and it presents a powerful tool for the synthesis of complicated allene-containing bioactive molecules. With a copper-catalyzed radical relay, sp2 allenic C-H arylation and alkynylation were established herein, using various aryl boronic acids and trimethoxysilyl-substituted alkynes as carbon nucleophiles and using electrophilic N-F reagents as nitrogen-centered radical precursors. These methods featured excellent site selectivity to deliver fully substituted allenes efficiently. Moreover, with silyl-substituted allenes as substrates, a subsequent dual sp2 C-H functionalization process was established as well, which allowed for the divergent synthesis of multifunctionalized allenes, significantly expanding their chemical spaces.

Swift Covalent Gelation Coupled with Robust Wet Adhesive Powder: A Novel Approach for Acute Massive Hemorrhage Control in Dynamic and High-Pressure Wound Environments.

The quest for efficient hemostatic agents in emergency medicine is critical, particularly for managing massive hemorrhages in dynamic and high-pressure wound environments. Traditional self-gelling powders, while beneficial due to their ease of application and rapid action, fall short in such challenging conditions. To bridge this gap, the research introduces a novel self-gelling powder that combines ultrafast covalent gelation and robust wet adhesion, presenting a significant advancement in acute hemorrhage control. This ternary system comprises ε-polylysine (ε-PLL) and 4-arm polyethylene glycol succinyl succinate (4-arm-PEG-NHS) forming the hydrogel framework. Na2HPO4 functions as the "H+ sucker" to expedite the amidation reaction, slashing gelation time to under 10 s, crucial for immediate blood loss restriction. Moreover, PEG chains' hydrophilicity facilitates efficient absorption of interfacial blood, increasing the generated hydrogel's cross-linking density and strengthens its tissue bonding, thereby resulting in excellent mechanical and wet adhesion properties. In vitro experiments reveal the optimized formulation's exceptional tissue compliance, procoagulant activity, biocompatibility and antibacterial efficacy. In porcine models of heart injuries and arterial punctures, it outperforms commercial hemostatic agent Celox, confirming its rapid and effective hemostasis. Conclusively, this study presents a transformative approach to hemostasis, offering a reliable and potent solution for the emergency management of massive hemorrhage.

The cumulative antitumor effects of regorafenib and radiotherapy in hepatocellular carcinoma.

Regorafenib is a second-line standard treatment for hepatocellular carcinoma (HCC). However, the efficacy of regorafenib is often limited due to drug resistance, individual differences among patients, and irrational drug use. Radiotherapy (RT) is an important method of localized HCC treatment, and combining RT with other therapies may exert a synergetic antitumor effect. Platelet-derived growth factor receptor-like (PDGFRL) is a tumor suppressor in various solid tumors. However, the function of PDGFRL in HCC is still unknown. In this study, we explored whether regorafenib and RT exert a synergetic effect on the treatment of HCC. The antitumor effect and mechanisms of the combination of regorafenib and RT were verified in a xenograft mouse model in vivo and in HCC cells in vitro. The combination treatment significantly inhibited cell proliferation and promoted apoptosis both in vitro and in vivo. PDGFRL, a potential target of regorafenib, was increased after cumulative treatment in HCC cells, and PDGFRL suppressed HCC cell proliferation and promoted apoptosis by inhibiting STAT3 pathway activation. Furthermore, the cumulative antitumor effect was dependent on the upregulated expression of PDGFRL and inhibition of STAT3 signaling pathway activation in HCC cells. This study increased the understanding of the molecular mechanism underlying the effect of regorafenib plus RT on HCC and provided a theoretical basis for the clinical practice of HCC.

Prognostic and predictive value of super-enhancer-derived signatures for survival and lung metastasis in osteosarcoma.

BACKGROUND: Risk stratification and personalized care are crucial in managing osteosarcoma due to its complexity and heterogeneity. However, current prognostic prediction using clinical variables has limited accuracy. Thus, this study aimed to explore potential molecular biomarkers to improve prognostic assessment. METHODS: High-throughput inhibitor screening of 150 compounds with broad targeting properties was performed and indicated a direction towards super-enhancers (SEs). Bulk RNA-seq, scRNA-seq, and immunohistochemistry (IHC) were used to investigate SE-associated gene expression profiles in osteosarcoma cells and patient tissue specimens. Data of 212 osteosarcoma patients who received standard treatment were collected and randomized into training and validation groups for retrospective analysis. Prognostic signatures and nomograms for overall survival (OS) and lung metastasis-free survival (LMFS) were developed using Cox regression analyses. The discriminatory power, calibration, and clinical value of nomograms were evaluated. RESULTS: High-throughput inhibitor screening showed that SEs significantly contribute to the oncogenic transcriptional output in osteosarcoma. Based on this finding, focus was given to 10 SE-associated genes with distinct characteristics and potential oncogenic function. With multi-omics approaches, the hyperexpression of these genes was observed in tumor cell subclusters of patient specimens, which were consistently correlated with poor outcomes and rapid metastasis, and the majority of these identified SE-associated genes were confirmed as independent risk factors for poor outcomes. Two molecular signatures were then developed to predict survival and occurrence of lung metastasis: the SE-derived OS-signature (comprising LACTB, CEP55, SRSF3, TCF7L2, and FOXP1) and the SE-derived LMFS-signature (comprising SRSF3, TCF7L2, FOXP1, and APOLD1). Both signatures significantly improved prognostic accuracy beyond conventional clinical factors. CONCLUSIONS: Oncogenic transcription driven by SEs exhibit strong associations with osteosarcoma outcomes. The SE-derived signatures developed in this study hold promise as prognostic biomarkers for predicting OS and LMFS in patients undergoing standard treatments. Integrative prognostic models that combine conventional clinical factors with these SE-derived signatures demonstrate substantially improved accuracy, and have the potential to facilitate patient counseling and individualized management.

Autoimmune diseases and the risk of bladder cancer: A Mendelian randomization analysis.

OBJECTIVE: To investigate the association between autoimmune diseases (AIDs) and bladder cancer (BC) at the genetic level using Mendelian randomization (MR). METHODS: Single nucleotide polymorphisms (SNPs) associated with the seven AIDs were extracted from the IEU GWAS database, and the SNPs were quality-controlled using strict screening criteria. The association between AIDs and BC risk was assessed by inverse-variance weighted (IVW), MR-Egger regression and Weighted median method. The heterogeneity of SNPs was evaluated by Cochran Q test. MR-Egger intercept test and MR-PRESSO global test were used to test the horizontal pleiotropy of SNPs. Both sides with potential causal associations were validated using the validation set. RESULTS: Our result showed that genetically predicted RA was significantly associated with an increased risk of BC (IVW OR = 1.214, 95 % CI = 1.062-1.388, P = 0.005). MS nominally increased the risk of BC (IVW OR = 1.095, 95 % CI = 1.005-1.193, P = 0.037), consistent with the results of the MR analysis of the BC validation cohort. However SLE, T1D, UC, CD, and MG were not causally associated with BC risk (P > 0.05). The sensitivity analyses showed that there was no heterogeneity or horizontal pleiotropy in our findings. CONCLUSION: This study provides evidence of a causal relationship between AIDs and BC risk at the genetic level, confirming a causal relationship between RA and MS in increasing the risk of BC.

Distribution and prognostic value of high-sensitivity cardiac troponin T and I across glycemic status: a population-based study.

BACKGROUND: Whether distributions and prognostic values of high-sensitivity cardiac troponin (hs-cTn) T and I are different across normoglycemic, prediabetic, and diabetic populations is unknown. METHODS: 10127 adult participants from the National Health and Nutrition Examination Survey 1999-2004 with determined glycemic status and measurement of at least one of hs-cTn assays were included, from whom healthy participants and presumably healthy diabetic and prediabetic participants were selected to investigate pure impacts of glycemic status on distributions of hs-cTn. The nonparametric method and bootstrapping were used to derive the 99th upper reference limits of hs-cTn and 95% CI. Participants with available follow-up and hs-cTn concentrations of all 4 assays were included in prognostic analyses. Associations of hs-cTn with all-cause and cardiac-specific mortality were modeled by Cox proportional hazard regression under the complex survey design. The incremental value of hs-cTn to an established risk score in predicting cardiac-specific mortality was assessed by the 10-year area under time-dependent receiver operating characteristic curve (AUC) using the Fine-Grey competing risk model. RESULTS: Among 9714 participants included in prognostic analyses, 5946 (61.2%) were normoglycemic, 2172 (22.4%) prediabetic, and 1596 (16.4%) diabetic. Hyperglycemic populations were older than the normoglycemic population but sex and race/ethnicity were similar. During the median follow-up of 16.8 years, hs-cTnT and hs-cTnI were independently associated with all-cause and cardiac-specific mortality across glycemic status. In the diabetic population, adjusted hazard ratios per 1-standard deviation increase of log-transformed hs-cTnT and hs-cTnI (Abbott) concentrations were 1.77 (95% CI 1.48-2.12; P < .001) and 1.83 (95% CI 1.33-2.53; P < .001), respectively, regarding cardiac-specific mortality. In the diabetic but not the normoglycemic population, adding either hs-cTnT (difference in AUC: 0.062; 95% CI 0.038-0.086; P < 0.001) or hs-cTnI (Abbott) (difference in AUC: 0.071; 95% CI 0.046-0.097; P < 0.001) would significantly increase the discriminative ability of the risk score; AUC of the score combined with hs-cTnT would be further improved by incorporating hs-cTnI (0.018; 95%CI 0.006-0.029; P = 0.002). The 99th percentile of hs-cTnT of the presumably healthy diabetic population was higher than the healthy population and had no overlap in 95% CIs, however, for hs-cTnI 99th percentiles of the two populations were very close and 95% CIs extensively overlapped. CONCLUSIONS: Hs-cTnT and hs-cTnI demonstrated consistent prognostic associations across glycemic status but incremental predictive values in hyperglycemic populations only. The susceptibility of hs-cTnT 99th percentiles to diabetes plus the additive value of hs-cTnI to hs-cTnT in diabetic cardiovascular risk stratification suggested hs-cTnI and hs-cTnT may be differentially associated with glycemic status, but further research is needed to illustrate the interaction between hyperglycemia and hs-cTn.

Unravelling immune microenvironment features underlying tumor progression in the single-cell era.

The relationship between the immune cell and tumor occurrence and progression remains unclear. Profiling alterations in the tumor immune microenvironment (TIME) at high resolution is crucial to identify factors influencing cancer progression and enhance the effectiveness of immunotherapy. However, traditional sequencing methods, including bulk RNA sequencing, exhibit varying degrees of masking the cellular heterogeneity and immunophenotypic changes observed in early and late-stage tumors. Single-cell RNA sequencing (scRNA-seq) has provided significant and precise TIME landscapes. Consequently, this review has highlighted TIME cellular and molecular changes in tumorigenesis and progression elucidated through recent scRNA-seq studies. Specifically, we have summarized the cellular heterogeneity of TIME at different stages, including early, late, and metastatic stages. Moreover, we have outlined the related variations that may promote tumor occurrence and metastasis in the single-cell era. The widespread applications of scRNA-seq in TIME will comprehensively redefine the understanding of tumor biology and furnish more effective immunotherapy strategies.

Frailty as an Effect Modifier in Randomized Controlled Trials: A Systematic Review.

BACKGROUND: The effect of clinical interventions may vary by patients' frailty status. Understanding treatment effect heterogeneity by frailty could lead to frailty-guided treatment strategies and reduce overtreatment and undertreatment. This systematic review aimed to examine the effect modification by frailty in randomized controlled trials (RCTs) that evaluate pharmacological, non-pharmacological, and multicomponent interventions. METHODS: We searched PubMed, Web of Science, EMBASE, and ClinicalTrial.gov, from their inception to 8 December 2023. Two reviewers independently extracted trial data and examined the study quality with senior authors. RESULTS: Sixty-one RCTs that evaluated the interaction between frailty and treatment effects in older adults were included. Frailty was evaluated using different tools such as the deficit accumulation frailty index, frailty phenotype, and other methods. The effect of several pharmacological interventions (e.g., edoxaban, sacubitril/valsartan, prasugrel, and chemotherapy) varied according to the degree of frailty, whereas other treatments (e.g., antihypertensives, vaccinations, osteoporosis medications, and androgen medications) demonstrated consistent benefits across different frailty levels. Some non-pharmacological interventions had greater benefits in patients with higher (e.g., chair yoga, functional walking, physical rehabilitation, and higher dose exercise program) or lower (e.g., intensive lifestyle intervention, psychosocial intervention) levels of frailty, while others (e.g., resistance-type exercise training, moderate-intensive physical activity, walking and nutrition or walking) produced similar intervention effects. Specific combined interventions (e.g., hospital-based disease management programs) demonstrated inconsistent effects across different frailty levels. DISCUSSION: The efficacy of clinical interventions often varied by frailty levels, suggesting that frailty is an important factor to consider in recommending clinical interventions in older adults. REGISTRATION: PROSPERO registration number CRD42021283051.

4D nucleome equation predicts gene expression controlled by long-range enhancer-promoter interaction.

Recent experimental evidence strongly supports that three-dimensional (3D) long-range enhancer-promoter (E-P) interactions have important influences on gene-expression dynamics, but it is unclear how the interaction information is translated into gene expression over time (4D). To address this question, we developed a general theoretical framework (named as a 4D nucleome equation), which integrates E-P interactions on chromatin and biochemical reactions of gene transcription. With this equation, we first present the distribution of mRNA counts as a function of the E-P genomic distance and then reveal a power-law scaling of the expression level in this distance. Interestingly, we find that long-range E-P interactions can induce bimodal and trimodal mRNA distributions. The 4D nucleome equation also allows for model selection and parameter inference. When this equation is applied to the mouse embryonic stem cell smRNA-FISH data and the E-P genomic-distance data, the predicted E-P contact probability and mRNA distribution are in good agreement with experimental results. Further statistical inference indicates that the E-P interactions prefer to modulate the mRNA level by controlling promoter activation and transcription initiation rates. Our model and results provide quantitative insights into both spatiotemporal gene-expression determinants (i.e., long-range E-P interactions) and cellular fates during development.

Trends and disparities in the prevalence of physical activity among US adults with epilepsy, 2010-2022.

BACKGROUND: Physical activity may be associated with health benefits for people with epilepsy. It remains unclear how the prevalence of physical activity has changed at a national level over the years and whether this prevalence varies between subgroups. METHODS: The National Health and Interview Survey, which was conducted from 2010 to 2017 and again in 2022, was used for our nationally representative study. This study explored the trends and disparities in meeting physical activity guidelines among US individuals with epilepsy and non-epilepsy adults. RESULTS: The prevalence of adults with epilepsy meeting physical activity guidelines was consistently lower and remained unchanged compared to those without epilepsy. Among the population with epilepsy, the prevalence of aerobic physical activity was 38.1 % (95 % CI, 32.6 %-43.5 %) in 2010 and 39.0 % (95 % CI, 33.4 %-44.7 %) in 2017 (P for trend = 0.84), and remained unchanged in 2022 (39.1 %). For muscle-strength training, the prevalence was 17.5 % (95 % CI, 13.3 %-21.7 %) in 2010 and 18.8 % (95 % CI, 14.8 %-22.8 %) in 2017 (P for trend = 0.82). The prevalence for both activities combined was 12.4 % (95 % CI, 8.7 %-16.2 %) in 2010 and 16.6 % (95 % CI, 12.8 %-20.5 %) in 2017 (P for trend = 0.26). The prevalence of aerobic physical activity varied by educational attainment, body mass index, comorbid conditions, alcohol-drinking status, and epilepsy status. CONCLUSION: This study suggests that the adherence rate to meeting physical activity guidelines among US adults with epilepsy was at a low level and had not improved over time. This finding highlights the need for additional nationwide efforts to promote physical activity in the US population with epilepsy.

Inhibition of valve mesenchymal stromal cell calcium deposition by bFGF through alternative polyadenylation regulation of the CAT gene.

OBJECTIVE: Calcific aortic valve disease (CAVD) is the leading cause of angina, heart failure, and death from aortic stenosis. However, the molecular mechanisms of its progression, especially the complex disease-related transcriptional regulatory mechanisms, remain to be further elucidated. METHODS: This study used porcine valvular interstitial cells (PVIC) as a model. We used osteogenic induced medium (OIM) to induce calcium deposition in PVICs to calcify them, followed by basic fibroblast growth factor (bFGF) treatment to inhibit calcium deposition. Transcriptome sequencing was used to study the mRNA expression profile of PVICs and its related transcriptional regulation. We used DaPars to further examine alternative polyadenylation (APA) between different treatment groups. RESULTS: We successfully induced calcium deposition of PVICs through OIM. Subsequently, mRNA-seq was used to identify differentially expressed mRNAs for three different treatments: control, OIM-induced and OIM-induced bFGF treatment. Global APA events were identified in the OIM and bFGF treatment groups by bioinformatics analysis. Finally, it was discovered and proven that catalase (CAT) is one of the potential targets of bFGF-induced APA regulation. CONCLUSION: We described a global APA change in a calcium deposition model related to CAVD. We revealed that transcriptional regulation of the CAT gene may contribute to bFGF-induced calcium deposition inhibition.

Association between phthalates exposure and myocardial damage in the general population: A cross-sectional study.

BACKGROUND: Cardiovascular consequences of phthalates exposure have been given increasing attention, but the association of phthalates with subclinical cardiovascular disease (CVD) was unknown. Accordingly, this study aimed to investigate the association between phthalates exposure and high-sensitivity cardiac troponin I (hs-cTnI), a marker of myocardial injury, which was detectable in the subclinical stage of CVD. METHODS: Participants aged 6 years or older with available urinary phthalates metabolites and serum hs-cTnI concentrations were included in the National Health and Nutrition Examination Survey 2003-2004 cycle. Multivariable linear regression and weighted quantiles sum (WQS) regression were used to assess the association of hs-cTnI with individual phthalates and their co-exposure. Di-2-ethylhexylphthalate (ΣDEHP), high-molecular-weight phthalate (ΣHMWP), and low-molecular-weight phthalate (ΣLMWP) were defined as the molecular sum of phthalates metabolites in urine. RESULTS: 2241 participants were finally included. The percent change of serum hs-cTnI concentrations related to per 1-standard deviation increase of logarithmic urinary phthalates concentrations was 3.4% (0.1-6.7, P = 0.04) for ΣDEHP, 3.6% (0.3-6.9, P = 0.03) for ΣHMWP, and 3.5% (0.2-6.8, P = 0.04) for ΣLMWP. Co-exposure to phthalates metabolites expressed as the WQS index also demonstrated a positive association with hs-cTnI. A similar association pattern was found in the population with no prior CVD. CONCLUSIONS: This study indicated the potential of phthalates to myocardial injury which may occur even before clinically apparent CVD was identified, emphasizing the significance of reducing phthalates in the prevention of CVD.

Uric acid levels and heart failure: A mendelian randomization study.

BACKGROUND AND AIMS: Uric acid, the end-product of purine metabolism within the human body, has been the subject of studies exploring its potential association with cardiovascular and cerebrovascular diseases. However, the precise relationship between uric acid levels and heart failure remains elusive. METHODS AND RESULTS: In this particular study, aggregated data from genome-wide association studies on uric acid and heart failure were utilized to perform a two-sample Mendelian randomization (MR) analysis utilizing R software. The aim was to uncover any causal link between these variables. The primary outcome was assessed using inverse variance weighted (IVW) methodology, while sensitivity analyses employed MR-Egger, weighted median (WME), and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) techniques. IVW results revealed a possible causal relationship between elevated uric acid levels and an increased risk of heart failure (OR: 1.09, 95 % CI: 1.01-1.17, P < 0.05). Encouragingly, the directions provided by MR-Egger and WME aligned with IVW findings, and no anomalies were detected in the remaining sensitivity analyses. CONCLUSION: These outcomes indicate the stability of the results of the study, thereby suggesting that heightened uric acid levels may contribute to an augmented risk of heart failure.

Determinants of Maximum Magnetic Anomaly Detection Distance.

The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are established, calibrated and analyzed according to the experiment configuration. A maximum detectable distance map as a function of target size and measurement resolution is then obtained from parametric sweeping on an experimentally calibrated finite element analysis model. We find that the logarithm of detectable distance is positively proportional to the logarithm of object size while negatively proportional to the logarithm of resolution, within the ranges of 1 m~500 m and 1 pT~1 µT, respectively. A three-parameter empirical formula (namely distance-size-resolution logarithmic relationship) is firstly developed to determine the most economic sensor configuration for a given detection task, to estimate the maximum detection distance for a given magnetic sensor and object, or to evaluate minimum detectable object size at a given magnetic anomaly detection scenario.