Adenosine deaminase acting on RNA 1 (ADAR1) as crucial regulators in cardiovascular diseases: structures, pathogenesis, and potential therapeutic approach

Cardiovascular diseases (CVDs) are a group of diseases that have a major impact on global health and are the leading cause of death. A large number of chemical base modifications in ribonucleic acid (RNA) are associated with cardiovascular diseases. A variety of ribonucleic acid modifications exist in cells, among which adenosine deaminase-dependent modification is one of the most common ribonucleic acid modifications. Adenosine deaminase acting on ribonucleic acid 1 (Adenosine deaminase acting on RNA 1) is a widely expressed double-stranded ribonucleic acid adenosine deaminase that forms inosine (A-to-I) by catalyzing the deamination of adenosine at specific sites of the target ribonucleic acid. In this review, we provide a comprehensive overview of the structure of Adenosine deaminase acting on RNA 1 and summarize the regulatory mechanisms of ADAR1-mediated ribonucleic acid editing in cardiovascular diseases, indicating Adenosine deaminase acting on RNA 1 as a promising therapeutic target in cardiovascular diseases

Blood Pressure Changes in Relation to Arsenic Exposure in a U.S. Pregnancy Cohort

Background: Inorganic arsenic exposure has been related to the risk of increased blood pressure based largely on cross-sectional studies conducted in highly exposed populations. Pregnancy is a period of particular vulnerability to environmental insults. However, little is known about the cardiovascular impacts of arsenic exposure during pregnancy. Objectives: We evaluated the association between prenatal arsenic exposure and maternal blood pressure over the course of pregnancy in a U.S. population. Methods: The New Hampshire Birth Cohort Study is an ongoing prospective cohort study in which \u3e 10% of participant household wells exceed the arsenic maximum contaminant level of 10 μg/L established by the U.S. EPA. Total urinary arsenic measured at 24–28 weeks gestation was measured and used as a biomarker of exposure during pregnancy in 514 pregnant women, 18–45 years of age, who used a private well in their household. Outcomes were repeated blood pressure measurements (systolic, diastolic, and pulse pressure) recorded during pregnancy. Results: Using linear mixed effects models, we estimated that, on average, each 5-μg/L increase in urinary arsenic was associated with a 0.15-mmHg (95% CI: 0.02, 0.29; p = 0.022) increase in systolic blood pressure per month and a 0.14-mmHg (95% CI: 0.02, 0.25; p = 0.021) increase in pulse pressure per month over the course of pregnancy. Conclusions: In our U.S. cohort of pregnant women, arsenic exposure was associated with greater increases in blood pressure over the course of pregnancy. These findings may have important implications because even modest increases in blood pressure impact cardiovascular disease risk

A Longitudinal Analysis about the Effect of Air Pollution on Astigmatism for Children and Young Adults

Purpose: This study aimed to investigate the correlation between air pollution and astigmatism, considering the detrimental effects of air pollution on respiratory, cardiovascular, and eye health. Methods: A longitudinal study was conducted with 127,709 individuals aged 4-27 years from 9 cities in Guangdong Province, China, spanning from 2019 to 2021. Astigmatism was measured using cylinder values. Multiple measurements were taken at intervals of at least 1 year. Various exposure windows were used to assess the lagged impacts of air pollution on astigmatism. A panel data model with random effects was constructed to analyze the relationship between pollutant exposure and astigmatism. Results: The study revealed significant associations between astigmatism and exposure to carbon monoxide (CO), nitrogen dioxide (NO2), and particulate matter (PM2.5) over time. A 10 {\mu}g/m3 increase in a 3-year exposure window of NO2 and PM2.5 was associated with a decrease in cylinder value of -0.045 diopters and -0.017 diopters, respectively. A 0.1 mg/m3 increase in CO concentration within a 2-year exposure window correlated with a decrease in cylinder value of -0.009 diopters. No significant relationships were found between PM10 exposure and astigmatism. Conclusion: This study concluded that greater exposure to NO2 and PM2.5 over longer periods aggravates astigmatism. The negative effect of CO on astigmatism peaks in the exposure window of 2 years prior to examination and diminishes afterward. No significant association was found between PM10 exposure and astigmatism, suggesting that gaseous and smaller particulate pollutants have easier access to human eyes, causing heterogeneous morphological changes to the eyeball

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient’s quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients.Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach.Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis.Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC

Acupuncture treatment for post-stroke depression: Intestinal microbiota and its role

Stroke-induced depression is a common complication and an important risk factor for disability. Besides psychiatric symptoms, depressed patients may also exhibit a variety of gastrointestinal symptoms, and even take gastrointestinal symptoms as the primary reason for medical treatment. It is well documented that stress may disrupt the balance of the gut microbiome in patients suffering from post-stroke depression (PSD), and that disruption of the gut microbiome is closely related to the severity of the condition in depressed patients. Therefore, maintaining the balance of intestinal microbiota can be the focus of research on the mechanism of acupuncture in the treatment of PSD. Furthermore, stroke can be effectively treated with acupuncture at all stages and it may act as a special microecological regulator by regulating intestinal microbiota as well. In this article, we reviewed the studies on changing intestinal microbiota after acupuncture treatment and examined the existing problems and development prospects of acupuncture, microbiome, and poststroke depression, in order to provide new ideas for future acupuncture research

Application of diffusion tensor imaging in the diagnosis of post-stroke aphasia: a meta-analysis and systematic review

IntroductionDiffusion Tensor Imaging (DTI) indicators of different white matter (WM) fibers and brain region lesions for post-stroke aphasia (PSA) are inconsistent in existing studies. Our study examines the consistency and differences between PSA tests performed with DTI. In addition, obtaining consistent and independent conclusions between studies was made possible by utilizing DTI in PSA assessment.MethodsIn order to gather relevant studies using DTI for diagnosing PSA, we searched the Web of Science, PubMed, Embase, and CNKI databases. Based on the screening and evaluation of the included studies, the meta-analysis was used to conduct a quantitative analysis. Narrative descriptions were provided for studies that met the inclusion criteria but lacked data.ResultsFirst, we reported on the left hemisphere. The meta-analysis showed that fractional anisotropy (FA) of the arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF), inferior frontal-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), and uncinate fasciculus (UF) were decreased in the PSA group in comparison with the healthy controls (p < 0.00001). However, in the comparison of axial diffusivity (AD), there was no statistically significant difference in white matter fiber tracts in the dual-stream language model of the PSA group. Elevated radial diffusivity (RD) was seen only in the IFOF and ILF (PIFOF = 0.01; PILF = 0.05). In the classic Broca’s area, the FA of the PSA group was decreased (p < 0.00001) while the apparent diffusion coefficient was elevated (p = 0.03). Secondly, we evaluated the white matter fiber tracts in the dual-stream language model of the right hemisphere. The FA of the PSA group was decreased only in the IFOF (p = 0.001). AD was elevated in the AF and UF (PAF < 0.00001; PUF = 0.009). RD was elevated in the AF and UF (PAF = 0.01; PUF = 0.003). The other fiber tracts did not undergo similar alterations.ConclusionIn conclusion, DTI is vital for diagnosing PSA because it detects WM changes effectively, but it still has some limitations. Due to a lack of relevant language scales and clinical manifestations, diagnosing and differentiating PSA independently remain challenging.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=365897

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

BackgroundPancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.MethodsIn this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.ResultsOur analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.ConclusionOur study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease

Acupuncture for insomnia symptoms in hypertensive patients: a systematic review and meta-analysis

PurposeIn the realm of pain management, traditional Chinese medicine, specifically acupuncture, has garnered increasing attention. This meta-analysis pioneers the evaluation of acupuncture’s effectiveness in treating insomnia among hypertensive patients.MethodsWe conducted a comprehensive search across several databases—PubMed, Web of Science, Cochrane Library, WANFANG, China National Knowledge Infrastructure (CNKI), Sinomed, and the Chinese Journal of Science and Technology (VIP). Additionally, forward and backward articles of studies published from the inception of these databases until 10 September 2023, were reviewed. This systematic review and meta-analysis included all randomized controlled trials (RCTs) focusing on acupuncture for insomnia in hypertensive patients, without imposing language or date restrictions. We rigorously assessed all outcome measures reported in these trials. The evidence was synthesized by calculating the difference between mean differences (MD) in symptom change. The quality of the evidence was determined using the Cochrane Risk of Bias tool. This study is registered with PROSPERO under number CRD42023461760.ResultsOur analysis included 16 RCTs, comprising 1,309 patients. The findings revealed that acupuncture was significantly more effective than the control group in reducing insomnia symptoms, as indicated by a greater decrease in the PSQI score (MD = −3.1, 95% CI [−3.77 to −2.62], p < 0.00001). Additionally, improvements in both systolic and diastolic blood pressure were more pronounced in the acupuncture group compared to the control group (SBP: MD = −10.31, 95% CI [−16.98 to −3.64], p = 0.002; DBP: MD = −5.71, 95% CI [−8.19 to −3.23], p < 0.00001). These results suggest that acupuncture not only improves sleep quality but also lowers blood pressure in patients suffering from hypertension and insomnia. Further research is warranted to elucidate optimal acupuncture points and the duration of treatment for maximized therapeutic effect.Systematic review registration:https://www.crd.york.ac.uk/prospero, CRD42023461760

Association between Arsenic Exposure from Drinking Water and Longitudinal Change in Blood Pressure among HEALS Cohort Participants

Background: Cross-sectional studies have shown associations between arsenic exposure and prevalence of high blood pressure; however, studies examining the relationship of arsenic exposure with longitudinal changes in blood pressure are lacking. Method: We evaluated associations of arsenic exposure in relation to longitudinal change in blood pressure in 10,853 participants in the Health Effects of Arsenic Longitudinal Study (HEALS). Arsenic was measured in well water and in urine samples at baseline and in urine samples every 2 years after baseline. Mixed-effect models were used to estimate the association of baseline well and urinary creatinine-adjusted arsenic with annual change in blood pressure during follow-up (median, 6.7 years). Result: In the HEALS population, the median water arsenic concentration at baseline was 62 μg/L. Individuals in the highest quartile of baseline water arsenic or urinary creatinine-adjusted arsenic had a greater annual increase in systolic blood pressure compared with those in the reference group (β = 0.48 mmHg/year; 95% CI: 0.35, 0.61, and β = 0.43 mmHg/year; 95% CI: 0.29, 0.56 for water arsenic and urinary creatinine-adjusted arsenic, respectively) in fully adjusted models. Likewise, individuals in the highest quartile of baseline arsenic exposure had a greater annual increase in diastolic blood pressure for water arsenic and urinary creatinine-adjusted arsenic, (β = 0.39 mmHg/year; 95% CI: 0.30, 0.49, and β = 0.45 mmHg/year; 95% CI: 0.36, 0.55, respectively) compared with those in the lowest quartile. Conclusion: Our findings suggest that long-term arsenic exposure may accelerate age-related increases in blood pressure. These findings may help explain associations between arsenic exposure and cardiovascular disease

Experimental study on LBL beams

Six specimens were made and tested to study the mechanical properties of LBL beams. The mean ultimate loading value is 68.39 MPa with a standard deviation of 6.37 MPa, giving a characteristic strength (expected to be exceeded by 95% of specimens) of 57.91 MPa, and the mean ultimate deflection is 53.3 mm with a standard deviation of 5.5 mm, giving the characteristic elastic modulus of 44.3 mm. The mean ultimate bending moment is 20.18 kN.m with a standard deviation of 1.88 kN.m, giving the characteristic elastic modulus of 17.08 kN.m. The mean elastic modulus is 9688 MPa with a standard deviation of 1765 MPa, giving the characteristic elastic modulus of 6785 MPa, and the mean modulus of rupture is 93.3 MPa with a standard deviation of 8.6 MPa, giving the characteristic elastic modulus of 79.2 MPa. The strain across the cross-section for all LBL beams is basically linear throughout the loading process, following standard beam theory