Insight into a single-chamber air-cathode microbial fuel cell for nitrate removal and ecological roles.

Bioelectrochemical systems are sustainable and potential technology systems in wastewater treatment for nitrogen removal. The present study fabricated an air-cathode denitrifying microbial fuel cell (DNMFC) with a revisable modular design and investigated metabolic processes using nutrients together with the spatiotemporal distribution characteristics of dominated microorganisms. Based on the detection of organics and solvable nitrogen concentrations as well as electron generations in DNMFCs under different conditions, the distribution pattern of nutrients could be quantified. By calculation, it was found that heterotrophic denitrification performed in DNMFCs using 56.6% COD decreased the Coulombic efficiency from 38.0% to 16.5% at a COD/NO3 --N ratio of 7. Furthermore, biological denitrification removed 92.3% of the nitrate, while the residual was reduced via electrochemical denitrification in the cathode. Correspondingly, nitrate as the electron acceptor consumed 16.7% of all the generated electrons, and the residual electrons were accepted by oxygen. Microbial community analysis revealed that bifunctional bacteria of electroactive denitrifying bacteria distributed all over the reactor determined the DNMFC performance; meanwhile, electroactive bacteria were mainly distributed in the anode biofilm, anaerobic denitrifying bacteria adhered to the wall, and facultative anaerobic denitrifying bacteria were distributed in the wall and cathode. Characterizing the contribution of specific microorganisms in DNMFCs comprehensively revealed the significant role of electroactive denitrifying bacteria and their cooperative relationship with other functional bacteria.

An inverse association of dietary choline with atherosclerotic cardiovascular disease among US adults: a cross-sectional NHANES analysis.

BACKGROUND: The role of diet choline in atherosclerotic cardiovascular disease (ASCVD) is uncertain. Findings from animal experiments are contradictory while there is a lack of clinical investigations. This study aimed to investigate the association between choline intake and ASCVD based on individuals from the National Health and Nutrition Examination Survey (NHANES) database. METHODS: This cross-sectional study was conducted in 5525 individuals from the NHANES between 2011 and 2018. Participants were categorized into the ASCVD (n = 5015) and non-ASCVD (n = 510) groups. Univariable and multivariable-adjusted regression analyses were employed to investigate the relationship between diet choline and pertinent covariates. Logistic regression analysis and restricted cubic spline analysis were used to evaluate the association between choline intake and ASCVD. RESULTS: ASCVD participants had higher choline intake compared to those without ASCVD. In the higher tertiles of choline intake, there was a greater proportion of males, married individuals, highly educated individuals, and those with increased physical activity, but a lower proportion of smokers and drinkers. In the higher tertiles of choline intake, a lower proportion of individuals had a history of congestive heart failure and stroke. After adjusting for age, gender, race, ethnicity, and physical activity, an inverse association between choline intake and heart disease, stroke, and ASCVD was found. A restricted cubic spline analysis showed a mirrored J-shaped relationship between choline and ASCVD, stroke and congestive heart failure in males. There was no association between dietary choline and metabolic syndrome. CONCLUSION: An inverse association was observed between choline intake and ASVCD among U.S. adults. Further large longitudinal studies are needed to test the causal relationship of choline and ASVCD.

Opioids-Induced Long QT Syndrome: A Challenge to Cardiac Health.

The challenge posed by opioid overdose has become a significant concern for health systems due to the complexities associated with drug prohibition, widespread clinical use, and potential abuse. In response, healthcare professionals have primarily concentrated on mitigating the hallucinogenic and respiratory depressant consequences of opioid overdose to minimize associated risks. However, it is crucial to acknowledge that most opioids possess the capacity to prolong the QT interval, particularly in cases of overdose, thereby potentially resulting in severe ventricular arrhythmias and even sudden death if timely intervention is not implemented. Consequently, alongside addressing the typical adverse effects of opioids, it is imperative to consider their cardiotoxicity. To enhance comprehension of the correlation between opioids and arrhythmias, identify potential targets for prompt intervention, and mitigate the hazards associated with clinical utilization, an exploration of the interaction between drugs and ion channels, as well as their underlying mechanisms, becomes indispensable. This review primarily concentrates on elucidating the impact of opioid drugs on diverse ion channels, investigating recent advancements in this domain, and attaining a deeper understanding of the mechanisms underlying the prolongation of the QT interval by opioid drugs, along with potential interventions.

Detecting early-warning biomarkers associated with heart-exosome genetic-signature for acute myocardial infarction: A source-tracking study of exosome.

The genetic information of plasma total-exosomes originating from tissues have already proven useful to assess the severity of coronary artery diseases (CAD). However, plasma total-exosomes include multiple sub-populations secreted by various tissues. Only analysing the genetic information of plasma total-exosomes is perturbed by exosomes derived from other organs except the heart. We aim to detect early-warning biomarkers associated with heart-exosome genetic-signatures for acute myocardial infarction (AMI) by a source-tracking analysis of plasma exosome. The source-tracking of AMI plasma total-exosomes was implemented by deconvolution algorithm. The final early-warning biomarkers associated with heart-exosome genetic-signatures for AMI was identified by integration with single-cell sequencing, weighted gene correction network and machine learning analyses. The correlation between biomarkers and clinical indicators was validated in impatient cohort. A nomogram was generated using early-warning biomarkers for predicting the CAD progression. The molecular subtypes landscape of AMI was detected by consensus clustering. A higher fraction of exosomes derived from spleen and blood cells was revealed in plasma exosomes, while a lower fraction of heart-exosomes was detected. The gene ontology revealed that heart-exosomes genetic-signatures was associated with the heart development, cardiac function and cardiac response to stress. We ultimately identified three genes associated with heart-exosomes defining early-warning biomarkers for AMI. The early-warning biomarkers mediated molecular clusters presented heterogeneous metabolism preference in AMI. Our study introduced three early-warning biomarkers associated with heart-exosome genetic-signatures, which reflected the genetic information of heart-exosomes carrying AMI signals and provided new insights for exosomes research in CAD progression and prevention.

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks.

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image-classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf. RESULTS: Weed-detection NNs, including DenseNet, GoogLeNet and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k-fold cross-validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing data sets for estimating weed coverage. The inference speed of ResNet was similar to that of GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network for weed detection and coverage estimation. CONCLUSION: These results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing calculation of the herbicide requirements for variable-rate herbicide applications. The proposed method can be employed in a machine vision-based autonomous site-specific spraying system of smart sprayers. © 2024 Society of Chemical Industry.

Strategies for decellularization, re-cellularIzation and crosslinking in liver bioengineering.

Liver transplantation is the only definitive treatment for end-stage liver disease and its availability is restricted by organ donor shortages. The development of liver bioengineering provides the probability to create a functional alternative to reduce the gap in organ demand and supply. Decellularized liver scaffolds have been widely applied in bioengineering because they can mimic the native liver microenvironment and retain extracellular matrix (ECM) components. Multiple approaches including chemical, physical and biological methods have been developed for liver decellularization in current studies, but a full set of unified criteria has not yet been established. Each method has its advantages and drawbacks that influence the microstructure and ligand landscape of decellularized liver scaffolds. Optimizing a decellularization method to eliminate cell material while retaining as much of the ECM intact as possible is therefore important for biological scaffold applications. Furthermore, crosslinking strategies can improve the biological performance of scaffolds, including reinforcing biomechanics, delaying degradation in vivo and reducing immune rejection, which can better promote the integration of re-cellularized scaffolds with host tissue and influence the reconstruction process. In this review, we aim to present the different liver decellularization techniques, the crosslinking methods to improve scaffold characteristics with crosslinking and the preparation of soluble ECM.

Comprehensive analysis of clinical prognosis and biological significance of CNIH4 in cervical cancer.

BACKGROUND: Cornichon homolog 4 (CNIH4) belongs to the CNIH family. It functions as an oncogene in many tumors. However, CNIH4's significance in the immune landscape and its predictive potential in cervical cancer (CESC) is unexplored. METHODS: CNIH4 levels and its effect on the survival of patients with CESC were evaluated using data retrieved from The Cancer Genome Atlas (TCGA). The oncogenic effect of CNIH4 in CESC was determined using small interfering RNA-mediated transfected cell lines and tumorigenesis experiments in animal models. RESULTS: Higher expression of CNIH4 was found in advanced tumor and pathological stages, as well as lymph node metastasis. CNIH4 expression correlated positively with the infiltration of macrophages M2 and resting dendritic cells into the affected tissue. Additionally, functional enrichment of RNA-sequencing of CNIH4-knocked down CESC cell lines showed the association of CNIH4 to the PI3K-Akt signaling pathway. Single-sample gene set enrichment analysis highlighted several immune pathways that were elevated in the CESC samples with enhanced levels of CNIH4, including Type-I and Type-II IFN-response pathways. The impact of CNIH4 on drug sensitivity was further assessed using the GDSC database. As CNIH4 is linked to the immune landscape in CESC, this study determined a four-gene risk prediction signature utilizing CNIH4-related immunomodulators. The risk score quantified from the prediction signature was an independent predictive indicator in CESC. Receiver operating characteristic curve analysis verified the good predictive ability of the four-gene signature in TCGA-CESC cohort. Thus, the CNIH4-related model showed potential as an auxiliary TNM staging system tool. CONCLUSION: CNIH4 may be an effective predictive biomarker for patients with cervical cancer, thus providing new ideas and research directions for CESC.

FOXD1 expression-based prognostic model for uveal melanoma.

FOXD1, a new member of the FOX transcription factor family, serves as a mediator and biomarker for cell reprogramming. But its contribution to prognosis of uveal melanoma (UVM) is unclear. This study demonstrated that FOXD1 might promote tumor growth and invasion, because FOXD1 expression was negatively correlated with overall survival, progression-free survival, and disease-specific survival in UVM patients. This conjecture was verified in cell culture with human uveal melanoma cell line (MUM2B) as model cells. Additionally, the biological mechanisms of FOXD1 based on FOXD1-related genomic spectrum, molecular pathways, tumor microenvironment, and drug treatment sensitivity were examined using The Cancer Genome Atlas (TCGA) database, aiming to reasonably explain why FOXD1 leads to poor prognosis of UVM. On these bases, a novel tumor prognostic model was established using the FOXD1-related immunomodulators TMEM173, TNFRSF4, TNFSF13, and ULBP1, which will enable the stratification of disease seriousness and clinical treatment for patients.

Risk factors analysis of surgical site infections in postoperative colorectal cancer: a nine-year retrospective study.

BACKGROUND: Colorectal cancer (CRC) patients undergoing surgery are at a high risk of developing surgical site infections (SSIs), which contribute to increased morbidity, prolonged hospitalization, and escalated healthcare costs. Understanding the incidence, risk factors, and impact of SSIs is crucial for effective preventive strategies and improved patient outcomes. METHODS: This retrospective study analyzed data from 431 CRC patients who underwent surgery at Huangshan Shoukang Hospital between 2014 and 2022. The clinical characteristics and demographic information were collected. The incidence and impact of SSIs were evaluated, and independent risk factors associated with SSIs were identified using multivariable logistic regresison. A nomogram plot was constructed to predict the likelihood of SSIs occurrence. RESULTS: The overall incidence rate of SSIs was 7.65% (33/431). Patients with SSIs had significantly longer hospital stays and higher healthcare costs. Risk factors for SSIs included elevated Body Mass Index (BMI) levels (odds ratio, 1.12; 95% CI, 1.02-1.23; P = 0.017), the presence of diabetes (odds ratio, 3.88; 95% CI, 1.42 - 9.48; P = 0.01), as well as specific surgical factors such as open surgical procedures (odds ratio, 2.39; 95% CI [1.09; 5.02]; P = 0.031), longer surgical duration (odds ratio, 1.36; 95% CI [1.01; 1.84]; P = 0.046), and the presence of a colostomy/ileostomy (odds ratio, 3.17; 95% CI [1.53; 6.62]; P = 0.002). Utilizing multivariable regression analysis, which encompassed factors such as open surgical procedures, the presence of diabetes and colostomy/ileostom, the nomogram plot functions as a visual aid in estimating the individual risk of SSIs for patients. CONCLUSIONS: Risk factors for SSIs included higher BMI levels, the presence of diabetes, open surgical procedures, longer surgical duration, and the presence of colostomy/ileostomy. The nomogram plot serves as a valuable tool for risk assessment and clinical decision-making.

Precision Joint RF Measurement of Inter-Satellite Range and Time Difference and Scalable Clock Synchronization for Multi-Microsatellite Formations.

The rapid development of multi-satellite formations requires inter-satellite radio frequency (RF) measurement to be both precise and scalable. The navigation estimation of multi-satellite formations using a unified time reference demands the simultaneous RF measurement of the inter-satellite range and time difference. However, high-precision inter-satellite RF ranging and time difference measurements are investigated separately in existing studies. Different from the conventional two-way ranging (TWR) method, which is limited by its reliance on a high-performance atomic clock and navigation ephemeris, asymmetric double-sided two-way ranging (ADS-TWR)-based inter-satellite measurement schemes can eliminate such reliance while ensuring measurement precision and scalability. However, ADS-TWR was originally proposed for ranging-only applications. In this study, by fully exploiting the time-division non-coherent measurement characteristic of ADS-TWR, a joint RF measurement method is proposed to obtain the inter-satellite range and time difference simultaneously. Moreover, a multi-satellite clock synchronization scheme is proposed based on the joint measurement method. The experimental results show that when inter-satellite ranges are hundreds of kilometers, the joint measurement system has a centimeter-level accuracy for ranging and a hundred-picosecond-level accuracy for time difference measurement, and the maximum clock synchronization error was only about 1 ns.