The association between adipokines and pulmonary diseases: a mendelian randomization study.

BACKGROUND: The role of adipokines in the development of lung diseases is significant, yet their specific relationship with different lung diseases remains unclear. METHODS: In our research, we analyzed genetic variations associated with adipokines and various lung conditions such as interstitial lung disease, chronic obstructive pulmonary disease, asthma, lung cancer, sleep apnea, pneumonia, and tuberculosis, using data from public genome-wide studies. We employed Mendelian randomization techniques, including inverse variance weighting, weighted median, and MR-Egger regression methods, and conducted sensitivity checks to validate our findings. RESULTS: A study using the FinnGen database, which included 198,955 participants, identified 13 SNPs associated with adiponectin. Notably, adiponectin was found to significantly reduce the risk of interstitial lung disease and idiopathic pulmonary fibrosis. However, little evidence was found to establish a direct cause-effect relationship between the six adipokines and several other lung conditions, including sarcoidosis, asthma, chronic obstructive pulmonary disease, lung cancer, tuberculosis, pneumonia, and sleep apnea syndrome. CONCLUSION: This study reveals a reverse link between adiponectin levels and the likelihood of interstitial lung disease, including idiopathic pulmonary fibrosis.

Unveiling alkali metal poisoning of CrMn catalyst for selective catalytic reduction of NOx with NH3: An experimental and theoretical study.

Alkali metal poisoning has been an intricate and unsolved issue confining the catalytic activity of NH3-SCR catalysts up to now. Herein, the effect of NaCl and KCl on catalytic activity of CrMn catalyst for NH3-SCR of NOx was systematically investigated to clarify the alkali metal poisoning by combined experiments and theoretical calculations. It unveiled that NaCl/KCl could deactivate CrMn catalyst due to the decrease in specific surface area, electron transfer (Cr5++Mn3+↔Cr3++Mn4+), redox ability and oxygen vacancy and NH3/NO adsorption. In addition, NaCl cut off E-R mechanism reactions by inactivating surface Brønsted/Lewis acid sites. DFT calculations revealed that (1) Na and K could weaken MnO bond, (2) competitive adsorption between Cl and NH3 was a main reason weakening Lewis acid, (3) Cl adsorption was also a major cause diminishing Brønsted acid and oxygen vacancy, (4) Both Na and K seriously impeded NO adsorption/activation, (5) NaCl/KCl increased the reaction heat of H2O desorption (rate-determining step) in E-R mechanism reactions and KCl elevated its energy barrier in L-H mechanism reactions. Thus, this study provides the deep understanding of alkali metal poisoning and a well strategy to synthesize NH3-SCR catalysts with outstanding alkali metal resistance.

Elucidating the facet-dependent reactivity of CrMn catalyst for selective catalytic reduction of NOx with NH3.

The facet-dependent reactivity of CrMn catalysts was still unclear, hindering the further enhancement of their low-temperature SCR performance. Herein, the facet-dependent reactivity of CrMn1.5O4 catalyst for NH3-SCR of NOx was innovatively illustrated by numerous characterizations and density functional theory (DFT) calculations. Exposed (100) facet of CrMn1.5O4 catalyst exhibited best low-temperature SCR activity with ≥90 % NO conversion within 148-296 °C and 2.86 × 10-3 mol/(g·s) reaction rate within 160-240 °C. The characterizations revealed that (100) facet could induce the increase of BET specific area, electron transfer, concentration of Mn4+ and Oα, surface acidity, redox ability, NH3 and NOx adsorption/activation capacity. Subsequently, DFT calculations demonstrated that (100) facet exhibited the strongest affinity for NH3 and NO due to its unique 3O3c-Mn5c-2O4c bond and abundant charges transfer near the active adsorption sites, and Brønsted acid and oxygen vacancies were most easily formed on (100) facet. Furthermore, H2O formation as the rate determining step easily occurred on (100) facet. Eventually, we successfully improved the low-temperature SCR activity of CrMn1.5O4 catalyst by further tailoring highly active (100) facet from 0.754 to 0.865. This work provides the deeper understanding of facet-dependent reactivity and a good strategy to improve the catalytic activity of the catalysts.

Changes in the composition of bacterial communities and pathogen levels during wastewater treatment.

Wastewater treatment plants have been described as a potential source of spreading pathogens to the receiving water. However, few studies are reporting the presence and concentration changes of pathogens in these matrices. High-throughput sequencing provides new insights into understanding the changes of bacterial communities throughout wastewater treatment plants (WWTPs). In this study, the changes in microbial community composition and the levels of representative pathogens of effluents during the wastewater treatment process in two municipal WWTPs (A and B) were analyzed using Illumina NovaSeq sequencing and qPCR. Proteobacteria was the most abundant phylum in all samples, accounting for 45.0-75.2% of the bacterial community, followed by Firmicutes, Bacteroidetes, Actinobacteria, and Nitrospirae. A slight difference was observed between the bacterial community compositions of WWTPs A and B. However, a significant difference in the community compositions of effluent samples at different treatment stages was observed. Nutrients had a more substantial impact on bacterial community composition than physicochemical factors. Most human-associated Bacteroides and Mycobacterium were eliminated during the wastewater treatment process in both WWTPs. The bacterial community richness in WWTP A was significantly higher than that in WWTP B. The results of this study will provide insights into the potential problems that exist in WWTPs. In turn, these insights can enable the efficient and stable operation of WWTPs and help prevent the spread of pathogens.

Genes related to N6-methyladenosine in the diagnosis and prognosis of idiopathic pulmonary fibrosis.

Introduction: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive pulmonary fibrotic disease with unknown etiology and poor outcomes. It severely affects the quality of life. In this study, we comprehensively analyzed the expression of N6-methyladenosine (m6A) RNA methylation regulators using gene expression data from various tissue sources in IPF patients and healthy volunteers. Methods: The gene expression matrix and clinical characteristics of IPF patients were retrieved from the Gene Expression Omnibus database. A random forest model was used to construct diagnosis signature m6A regulators. Regression analysis and correlation analysis were used to identify prognosis m6A regulators. Consensus cluster analysis was used to construct different m6A prognosis risk groups, then functional enrichment, immune infiltration and drug sensitivity analysis were performed. Result: Five candidate m6A genes from lung tissue were used to predict the incidence, and the incidence was validated using datasets from bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells. Subsequently, the BALF dataset containing outcomes data was used for the prognosis analysis of m6A regulators. METTL14, G3BP2, and ZC3H13 were independent protective factors. Using correlation analysis with lung function in the lung tissue-derived dataset, METTL14 was a protective factor in IPF. Based on METTL14 and G3BP2, a consensus cluster analysis was applied to distinguish the prognostic m6A regulation patterns. The low-risk group's prognosis was significantly better than the high-risk group. Biological processes regulated by various risk groups included fibrogenesis and cell adhesion. Analysis of immune cell infiltration showed upregulation of neutrophils in the m6A high-risk group. Subsequently, five m6A high-risk group sensitive drugs and one m6A low-risk group sensitive drug were identified. Discussion: These findings suggest that m6A regulators are involved in the diagnosis and prognosis of IPF, and m6A patterns are a method to identify IPF outcomes.

A study on the nitrogen removal efficacy of bacterium Acinetobacter tandoii MZ-5 from a contaminated river of Shenzhen, Guangdong Province, China.

Heterotrophic nitrification-aerobic denitrification (HN-AD) has advantages over the traditional nitrogen removal process when removing multiple types of nitrogen in wastewater treatment. Acinetobacter tandoii MZ-5, which is capable of HN-AD, was isolated from the sediment of a polluted river for the first time. It used NH4+-N, NO2--N and NO3--N as sole nitrogen sources with maximum removal rates of 2.28, 1.18 and 1.04 mg L-1h-1, respectively. Simultaneous nitrification and denitrification were observed when using mixed N sources and NH4+-N was preferentially utilized. High nitrogen removal efficiencies (>90%) were achieved under the following conditions: C/N ratio 11-18, pH 6-8, 25-30 °C and dissolved oxygen 7.35-7.66 mg L-1. Strain MZ-5 was effective at treating wastewater from landfill leachate treatment plants, with NH4+-N, NO3--N and total nitrogen (TN) removal efficiencies of 99.28%, 44.85% and 45.31%, respectively. Thus, strain MZ-5 may be a good candidate for wastewater treatment.

A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN.

BACKGROUND AND OBJECTIVES: Mammography analysis is an effective technology for early detection of breast cancer. Micro-calcification clusters (MCs) are a vital indicator of breast cancer, so detection of MCs plays an important role in computer aided detection (CAD) system, this paper proposes a new hybrid method to improve MCs detection rate in mammograms. METHODS: The proposed method comprises three main steps: firstly, remove label and pectoral muscle adopting the largest connected region marking and region growing method, and enhance MCs using the combination of double top-hat transform and grayscale-adjustment function; secondly, remove noise and other interference information, and retain the significant information by modifying the contourlet coefficients using nonlinear function; thirdly, we use the non-linking simplified pulse-coupled neural network to detect MCs. RESULTS: In our work, we choose 118 mammograms including 38 mammograms with micro-calcification clusters and 80 mammograms without micro-calcification to demonstrate our algorithm separately from two open and common database including the MIAS and JSMIT; and we achieve the higher specificity of 94.7%, sensitivity of 96.3%, AUC of 97.0%, accuracy of 95.8%, MCC of 90.4%, MCC-PS of 61.3% and CEI of 53.5%, these promising results clearly demonstrate that the proposed approach outperforms the current state-of-the-art algorithms. In addition, this method is verified on the 20 mammograms from the People's Hospital of Gansu Province, the detection results reveal that our method can accurately detect the calcifications in clinical application. CONCLUSIONS: This proposed method is simple and fast, furthermore it can achieve high detection rate, it could be considered used in CAD systems to assist the physicians for breast cancer diagnosis in the future.

An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.

Breast cancer is becoming a leading death of women all over the world; clinical experiments demonstrate that early detection and accurate diagnosis can increase the potential of treatment. In order to improve the breast cancer diagnosis precision, this paper presents a novel automated segmentation and classification method for mammograms. We conduct the experiment on both DDSM database and MIAS database, firstly extract the region of interests (ROIs) with chain codes and using the rough set (RS) method to enhance the ROIs, secondly segment the mass region from the location ROIs with an improved vector field convolution (VFC) snake and following extract features from the mass region and its surroundings, and then establish features database with 32 dimensions; finally, these features are used as input to several classification techniques. In our work, the random forest is used and compared with support vector machine (SVM), genetic algorithm support vector machine (GA-SVM), particle swarm optimization support vector machine (PSO-SVM), and decision tree. The effectiveness of our method is evaluated by a comprehensive and objective evaluation system; also, Matthew's correlation coefficient (MCC) indicator is used. Among the state-of-the-art classifiers, our method achieves the best performance with best accuracy of 97.73%, and the MCC value reaches 0.8668 and 0.8652 in unique DDSM database and both two databases, respectively. Experimental results prove that the proposed method outperforms the other methods; it could consider applying in CAD systems to assist the physicians for breast cancer diagnosis.