[Applicability of H2FPEF and HFA-PEFF Scores in Chinese Patients Suffering From Heart Failure With Preserved Ejection Fraction and Heart Failure With Preserved Ejection Fraction Complicated With Atrial Fibrillation].

Objective To analyze the diagnostic values of H2FPEF and HFA-PEFF scores for heart failure with preserved ejection fraction (HFpEF) and HFpEF complicated with atrial fibrillation (HFpEF-AF) in Chinese patients and explore the related factors. Methods A cross-sectional study was conducted.A total of 835 consecutive HFpEF patients treated in the Department of Geriatric Cardiology,the First Hospital of Lanzhou University from 2009 to 2020 were selected and assigned to a HFpEF-AF group (n=267) and a HFpEF group (n=568) according to the presence of AF or not.HFA-PEFF and H2FPEF scores were used for retrospective diagnosis and the diagnostic consistency of the two scores was assessed.One hundred and thirty-six healthy volunteers with age and sex matching the patients during the same period were selected as healthy controls.The receiver operating characteristic (ROC) curves were established for H2FPEF and HFA-PEFF scores in diagnosing HFpEF-AF and HFpEF,on the basis of which the diagnostic performance of the two scores was evaluated. Results There was no difference in the HFA-PEFF score between the two groups (P=0.070).However,the HFpEF-AF group had higher mean H2FPEF score and higher proportion of patients with the score no less than 6 than the HFpEF group (P<0.001).According to the ROC curves,HFA-PEFF and H2FPEF scores demonstrated high performance in diagnosing all HFpEF patients,with the area under the curve (AUC) of 0.892 and 0.922 and the optimal cut-offs of 4 and 4,respectively.The HFA-PEFF score showed similar performance in diagnosing HFpEF and HFpEF-AF,with the AUC of 0.899 and 0.911,respectively.The H2FPEF score had higher performance in diagnosing HFpEF-AF (AUC of approximately 1.000) and low performance in diagnosing HFpEF (AUC of 0.885). Conclusions The HFA-PEFF score is applicable in the diagnosis of both HFpEF and HFpEF-AF.The H2FPEF score may underestimate HFpEF in Chinese patients,and its applicability in the Chinese patients with HFpEF alone remains to be investigated.

The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis.

INTRODUCTION: Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to systematically evaluate the performance and value of ML models for predicting HF prognosis. METHODS: PubMed, Web of Science, Scopus, and Embase online databases were searched up to April 30, 2023, to identify studies on the use of ML models to predict HF prognosis. HF prognosis primarily encompasses readmission and mortality. The meta-analysis was conducted by MedCalc software. Subgroup analyses include grouping based on types of ML models, time intervals, sample sizes, the number of predictive variables, validation methods, whether to conduct hyperparameter optimization and calibration, data set partitioning methods. RESULTS: A total of 31 studies were included. The most common ML models were random forest, boosting, support vector machine, neural network. The area under the receiver operating characteristic curve (AUC) for predicting HF readmission was 0.675 (95% CI: 0.651-0.699, p < 0.001), and the AUC for predicting HF mortality was 0.790 (95% CI: 0.765-0.816, p < 0.001). Subgroup analyses revealed that models with the prediction time interval of 1 year, sample sizes ≥10,000, the number of predictive variables ≥100, external validation, hyperparameter tuning, calibration adjustment, and data set partitioning using 10-fold cross-validation exhibited favorable performance within their respective subgroups. CONCLUSION: The performance of ML models in predicting HF readmission is relatively poor, while its performance in predicting HF mortality is moderate. The quality of the relevant studies is generally low, it is essential to enhance the predictive capabilities of ML models through targeted improvements in practical applications.

Heart failure potentially affects the cortical structure of the brain.

BACKGROUND: Heart failure (HF) has been reported to affect cerebral cortex structure, but the underlying cause has not been determined. This study used Mendelian randomization (MR) to reveal the causal relationship between HF and structural changes in the cerebral cortex. METHODS: HF was defined as the exposure variable, and cerebral cortex structure was defined as the outcome variable. Inverse-variance weighted (IVW), MR-Egger regression and weighted median (WME) were performed for MR analysis; MR-PRESSO and Egger's intercept was used to test horizontal pleiotropy; and "leave-one-out" was used for sensitivity analysis. RESULTS: Fifty-two single nucleotide polymorphisms (SNPs) were defined as instrumental variables (IVs), and there was no horizontal pleiotropy in the IVs. According to the IVW analysis, the OR and 95% CI of cerebral cortex thickness were 0.9932 (0.9868-1.00) (P=0.0402), and the MR-Egger intercept was -15.6× 10-5 (P = 0.7974) and the Global test pval was 0.078. The P-value of the cerebral cortex surface was 0.2205, and the MR-Egger intercept was -34.69052 (P= 0.6984) and the Global Test pval was 0.045. HF had a causal effect on the surface area of the caudal middle frontal lobule (P=0.009), insula lobule (P=0.01), precuneus lobule (P=0.049) and superior parietal lobule (P=0.044). CONCLUSIONS: HF was potentially associated with changes in cortical thickness and in the surface area of the caudal middle frontal lobule, insula lobule, precuneus lobule and superior parietal lobule.

Qifu Yixin Formula Improves Heart Failure by Enhancing ß-Arrestin2 Mediated the SUMOylation of SERCA2a.

Purpose: This study aimed to elucidate the protective mechanism of Traditional Chinese Medicine (TCM) Qifu Yixin formula (QFYXF) to improve heart failure (HF) by promoting ß-arrestin2 (ß-arr2)-mediated SERCA2a SUMOylation. Materials and Methods: The transverse aortic constriction (TAC)-induced HF mice were treated with QFYXF or carvedilol for 8 weeks. ß-arr2-KO mice and their littermate wild-type (WT) mice were used as controls. Neonatal rat cardiomyocytes (NRCMs) were used in vitro. Cardiac function was evaluated by echocardiography and serum NT-proBNP. Myocardial hypertrophy and myocardial fibrosis were assessed by histological staining. ß-arr2, SERCA2a, SUMO1, PLB and p-PLB expressions were detected by Western blotting, immunofluorescence and immunohistochemistry. SERCA2a SUMOylation was detected by Co-IP. The molecular docking method was used to predict the binding ability of the main active components of QFYXF to ß-arr2, SERCA2a, and SUMO1, and the binding degree of SERCA2a to SUMO1 protein. Results: The HF model was constructed 8 weeks after TAC. QFYXF ameliorated cardiac function, inhibiting myocardial hypertrophy and fibrosis. QFYXF promoted SERCA2a expression and SERCA2a SUMOylation. Further investigation showed that QFYXF promoted ß-arr2 expression, whereas Barbadin (ß-arr2 inhibitor) or ß-arr2-KO reduced SERCA2a SUMOylation and attenuated the protective effect of QFYXF improved HF. Molecular docking showed that the main active components of QFYXF had good binding activities with ß-arr2, SERCA2a, and SUMO1, and SERCA2a had a high binding degree with SUMO1 protein. Conclusion: QFYXF improves HF by promoting ß-arr2 mediated SERCA2a SUMOylation and increasing SERCA2a expression.

Development of a prognostic model based on different disulfidptosis related genes typing for kidney renal clear cell carcinoma.

Background: Kidney renal clear cell carcinoma (KIRC) is a common and clinically significant subtype of kidney cancer. A potential therapeutic target in KIRC is disulfidptosis, a novel mode of cell death induced by disulfide stress. The aim of this study was to develop a prognostic model to explore the clinical significance of different disulfidptosis gene typings from KIRC. Methods: A comprehensive analysis of the chromosomal localization, expression patterns, mutational landscape, copy number variations, and prognostic significance of 10 disulfide death genes was conducted. Patients were categorized into distinct subtypes using the Non-negative Matrix Factorization (NMF) typing method based on disulfidptosis gene expression patterns. Weighted Gene Co-expression Network Analysis (WGCNA) was used on the KIRC dataset to identify differentially expressed genes between subtype clusters. A risk signature was created using LASSO-Cox regression and validated by survival analysis. An interaction between risk score and immune cell infiltration, tumor microenvironment characteristics and pathway enrichment analysis were investigated. Results: Initial findings highlight the differential expression of specific DRGs in KIRC, with genomic instability and somatic mutation analysis revealing key insights into their role in cancer progression. NMF clustering differentiates KIRC patients into subgroups with distinct survival outcomes and immune profiles, and hierarchical clustering identifies gene modules associated with key biological and clinical parameters, leading to the development of a risk stratification model (LRP8, RNASE2, CLIP4, HAS2, SLC22A11, and KCTD12) validated by survival analysis and predictive of immune infiltration and drug sensitivity. Pathway enrichment analysis further delineates the differential molecular pathways between high-risk and low-risk patients, offering potential targets for personalized treatment. Lastly, differential expression analysis of model genes between normal and KIRC cells provides insights into the molecular mechanisms underlying KIRC, highlighting potential biomarkers and therapeutic targets. Conclusion: This study contributes to the understanding of KIRC and provides a potential prognostic model using disulfidptosis gene for personalized management in KIRC patients. The risk signature shows clinical applicability and sheds light on the biological mechanisms associated with disulfide-induced cell death.

Investigating the Mechanism of Chinese Medicine Formula AACO Against Chronic Heart Failure by Network Pharmacology Analysis and Experimental Validation.

Background: A traditional Chinese medicine (TCM) formula, containing Astragalus membranaceus (Fisch.) Bunge, Aconitum wilsonii Stapf ex Veitch, Curcuma longa L., and Radix ophiopogonis (AACO), has therapeutic value for the treatment of chronic heart failure (CHF). Objective: This study intends to explore the pharmacological mechanism underlying the activity of the AACO formula against CHF. Materials and Methods: Using the TCM Systems Pharmacology database and Bioinformatics Analysis Tool for Molecular Mechanism of TCM, the active ingredients contained in the herbs of the AACO formula were screened. Meanwhile, the target genes related to these active ingredients were identified and genes correlated with CHF were screened. Protein-protein interaction networks were built to elucidate the relationships between the AACO formula and CHF. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analysis were carried out using the DAVID database. A "drug-component-target-disease" network was constructed with Cytoscape 3.7.0. The therapeutic effect of the AACO formula was proven by hemodynamic study, echocardiography evaluation, and histological analysis in transverse aortic constriction-induced CHF mice and was validated in vitro. Results: A total of 105 active ingredients and 1026 related targets were screened and identified, and 240 related targets overlapping with CHF were selected. According to GO analysis, the enriched genes participated in gene expression and cardiac contraction regulation by Ca2+ regulation. From KEGG analysis, the calcium axis was identified as one of the main mechanisms through which the AACO formula exerts an anti-CHF effect. AACO was validated to significantly improve cardiac diastolic and systolic functions in vivo via an increase in the rate of Ca2+ reuptake of the myocardial sarcoplasmic reticulum and improved myocardial contractility in vitro. Conclusions: Network pharmacology is a convenient method to study the complex pharmacological mechanisms of TCM. The calcium axis likely participates in the anti-CHF mechanism of AACO.

A comprehensive review on Gossypium hirsutum resistance against cotton leaf curl virus.

Cotton (Gossypium hirsutum L.) is a significant fiber crop. Being a major contributor to the textile industry requires continuous care and attention. Cotton is subjected to various biotic and abiotic constraints. Among these, biotic factors including cotton leaf curl virus (CLCuV) are dominant. CLCuV is a notorious disease of cotton and is acquired, carried, and transmitted by the whitefly (Bemisia tabaci). A cotton plant affected with CLCuV may show a wide range of symptoms such as yellowing of leaves, thickening of veins, upward or downward curling, formation of enations, and stunted growth. Though there are many efforts to protect the crop from CLCuV, long-term results are not yet obtained as CLCuV strains are capable of mutating and overcoming plant resistance. However, systemic-induced resistance using a gene-based approach remained effective until new virulent strains of CLCuV (like Cotton Leaf Curl Burewala Virus and others) came into existence. Disease control by biological means and the development of CLCuV-resistant cotton varieties are in progress. In this review, we first discussed in detail the evolution of cotton and CLCuV strains, the transmission mechanism of CLCuV, the genetic architecture of CLCuV vectors, and the use of pathogen and nonpathogen-based approaches to control CLCuD. Next, we delineate the uses of cutting-edge technologies like genome editing (with a special focus on CRISPR-Cas), next-generation technologies, and their application in cotton genomics and speed breeding to develop CLCuD resistant cotton germplasm in a short time. Finally, we delve into the current obstacles related to cotton genome editing and explore forthcoming pathways for enhancing precision in genome editing through the utilization of advanced genome editing technologies. These endeavors aim to enhance cotton's resilience against CLCuD.

Interaction between systemic iron parameters and left ventricular structure and function in the preserved ejection fraction population: a two-sample bidirectional Mendelian randomization study.

BACKGROUND: Left ventricular (LV) remodeling and diastolic function in people with heart failure (HF) are correlated with iron status; however, the causality is uncertain. This Mendelian randomization (MR) study investigated the bidirectional causal relationship between systemic iron parameters and LV structure and function in a preserved ejection fraction population. METHODS: Transferrin saturation (TSAT), total iron binding capacity (TIBC), and serum iron and ferritin levels were extracted as instrumental variables for iron parameters from meta-analyses of public genome-wide association studies. Individuals without myocardial infarction history, HF, or LV ejection fraction (LVEF) < 50% (n = 16,923) in the UK Biobank Cardiovascular Magnetic Resonance Imaging Study constituted the outcome dataset. The dataset included LV end-diastolic volume, LV end-systolic volume, LV mass (LVM), and LVM-to-end-diastolic volume ratio (LVMVR). We used a two-sample bidirectional MR study with inverse variance weighting (IVW) as the primary analysis method and estimation methods using different algorithms to improve the robustness of the results. RESULTS: In the IVW analysis, one standard deviation (SD) increased in TSAT significantly correlated with decreased LVMVR (ß = -0.1365; 95% confidence interval [CI]: -0.2092 to -0.0638; P = 0.0002) after Bonferroni adjustment. Conversely, no significant relationships were observed between other iron and LV parameters. After Bonferroni correction, reverse MR analysis showed that one SD increase in LVEF significantly correlated with decreased TSAT (ß = -0.0699; 95% CI: -0.1087 to -0.0311; P = 0.0004). No heterogeneity or pleiotropic effects evidence was observed in the analysis. CONCLUSIONS: We demonstrated a causal relationship between TSAT and LV remodeling and function in a preserved ejection fraction population.

Hydrochemical characteristics and genetic mechanism of porous sandstone geothermal water in northern Jinan, Shandong, China.

Porous sandstone geothermal water is an important geothermal resource, which is a low-carbon and clean resource, but lacks systematic research on a regional scale. The northern part of Jinan City is rich in geothermal resources, specifically porous sandstone thermal reservoirs. However, there is still incomplete research on the mechanism of geothermal genesis and the hydrochemical characteristics of geothermal water in porous sandstone. This study aims to address this gap by collecting 21 groundwater samples from northern Jinan and comparing their conventional ion and isotope characteristics to investigate the hydrochemical characteristics during the formation of geothermal water and uncover the genesis mechanism of porous sandstone geothermal water. The results indicate that the geothermal water is classified as Na-Cl type and Na-SO4-Cl type. The hydrochemical characteristics of geothermal water are primarily influenced by water-rock interaction and groundwater mixing. The water source primarily comes from the atmospheric precipitation in the Taiyi mountains, with an altitude of 910.75-1542.2 m.s.a.l.. The estimated temperature of the thermal reservoir ranges from 51 to 78 °C, and the depth of geothermal water circulation is estimated to be between 1316 and 2216 m. Based on the characteristics of the geothermal field, including the "cap rock, water source, heat source, reservoir, and channel," a conceptual model of the porous sandstone geothermal water flow system is proposed. This model offers novel insights into the genesis mechanism of geothermal water under similar geological conditions.

A modified shuffled frog leaping algorithm with inertia weight.

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

Causal associations between liver traits and Colorectal cancer: a Mendelian randomization study.

OBJECTIVE: This study aimed to investigate the causal associations between several liver traits (liver iron content, percent liver fat, alanine transaminase levels, and liver volume) and colorectal cancer (CRC) risk using a Mendelian randomization (MR) approach to improve our understanding of the disease and its management. METHODS: Genetic variants were used as instrumental variables, extracted from genome-wide association studies (GWAS) datasets of liver traits and CRC. The Two-Sample MR package in R was used to conduct inverse variance weighted (IVW), MR Egger, Maximum likelihood, Weighted median, and Inverse variance weighted (multiplicative random effects) MR approaches to generate overall estimates of the effect. MR analysis was conducted with Benjamini-Hochberg method-corrected P values to account for multiple testing (P < 0.013). MR-PRESSO was used to identify and remove outlier genetic variants in Mendelian randomization (MR) analysis. The MR Steiger test was used to assess the validity of the assumption that exposure causes outcomes. Leave-one-out validation, pleiotropy, and heterogeneity testing were also conducted to ensure the reliability of the results. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. RESULTS: The MR analysis suggested a causal effect between liver volume and a reduced risk of CRC (OR 0.60; 95% CI, 0.44-0.82; P = 0.0010) but did not provide evidence for causal effects of liver iron content, percent liver fat, or liver alanine transaminase levels. The MR-PRESSO method did not identify any outliers, and the MR Steiger test confirmed that the causal direction of the analysis results was correct in the Mendelian randomization analysis. MR results were consistent with heterogeneity and pleiotropy analyses, and leave-one-out analysis demonstrated the overall values obtained were consistent with estimates obtained when all available SNPs were included in the analysis. Multivariable MR was utilized for validation of our findings using the IVW method while also adjusting for potential confounding or pleiotropy bias. CONCLUSION: The study provides tentative evidence for a causal role of liver volume in CRC, while genetically predicted levels of liver iron content, percent liver fat, and liver alanine transaminase levels were not associated with CRC risk. The findings may inform the development of targeted therapeutic interventions for colorectal liver metastasis (CRLM) patients, and the study highlights the importance of MR as a powerful epidemiological tool for investigating causal associations between exposures and outcomes.

Effect of thyroid hormone replacement treatment on cardiac diastolic function in adult patients with subclinical hypothyroidism: a meta-analysis.

Background: Although subclinical hypothyroidism (SCH) is related to abnormalities in left ventricular diastolic function, the use of levothyroxine as a regular treatment remains debatable. This meta-analysis aimed to determine whether thyroid hormone replacement therapy affects cardiac diastolic function in patients with SCH as measured by echocardiography. Methods: This meta-analysis included a search of the EMBASE, PubMed, Web of Science, and Cochrane Library databases from their inception to May 18, 2023, for studies analyzing cardiac morphology and functional changes in patients with SCH before and after thyroid hormone replacement. The outcome measures were cardiac morphology and diastolic and overall cardiac function, as assessed using ultrasound parameters (including ventricular wall thickness, chamber size, mitral wave flow, tissue Doppler, and speckle tracking). The quality of the studies was assessed using the Newcastle-Ottawa Scale. The standard mean differences (MDs) and 95% confidence intervals (CI) were calculated using fixed- or random-effects models. Results: Seventeen studies met the inclusion criteria. A total of 568 patients participated and completed the follow-up. All studies specifically stated that serum thyrotropin levels returned to normal by the end of the study period. Compared with baseline levels, no significant morphological changes were observed in the heart. In terms of diastolic function, we discovered that the ratios of E-velocity to A-velocity (E/A) had greatly improved after thyroid hormone replacement therapy, whereas the ratios of the mitral inflow E wave to the tissue Doppler e' wave (E/e') had not. Global longitudinal strain (GLS) increased significantly after treatment with levothyroxine. Conclusion: In adult patients with SCH, thyroid hormone supplementation can partially but not completely improve parameters of diastolic function during the observation period. This meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement, an updated guideline for reporting systematic reviews (11) and was registered with INPLASY (INPLASY202320083). Systematic review registration: https://inplasy.com/inplasy-2023-2-0083.

Fine mapping and candidate gene analysis of a novel fertility restorer gene for C-type cytoplasmic male sterility in maize (Zea mays L.).

KEY MESSAGE: A novel strong fertility restorer gene Rf12 for C-type cytoplasmic male sterility of maize was finely mapped on chromosome 2. Its best candidate gene Zm00001d007531 is predicted to encode a p-type PPR protein. The lack of strong restorer gene of maize CMS-C greatly limits its application in hybrid seed production. Therefore, the cloning of maize CMS-C novel strong restorer genes is necessary. In this study, a strong restorer line ZH91 for maize CMS-C was found, and the novel restorer gene named Rf12 in ZH91 had been mapped in a 146 kb physical interval on maize chromosome 2. Using the third-generation high-throughput sequencing (ONT), the whole genome sequence of ZH91 was got, and with integrating the annotation information of the reference genome B73_RefGen_v4 and B73_RefGen_v5, four candidate genes were predicted in ZH91 within the mapping region. Then using gene cloning, stranded specific RNA sequencing, qRT-PCR analysis and subcellular localization, Zm00001d007531 was identified as the most likely candidate gene of Rf12. Zm00001d007531 encodes a p-type PPR protein with 19 PPR motifs and targets mitochondria and chloroplast. Stranded specific RNA sequencing and qRT-PCR results both show that the expression of Zm00001d007531 between anthers of near-isogenic lines C478Rf12Rf12 and C478rf12rf12 was significantly difference in pollen mother cell stage. And the result of sequence alignment for Zm00001d007531 gene in 60 materials showed that there are twelve SNPs in CDS region of Zm00001d007531 were tightly linked to the fertility. The finding of a novel strong restorer germplasm resource ZH91 for maize CMS-C can greatly promote the application of maize CMS-C line in maize hybrid seeds production, and the identification of candidate gene Zm00001d007531 can accelerate the backcrossing process of maize CMS-C strong restorer gene Rf12 to some extent.

Transcriptome Sequencing of lncRNAs, circRNAs, miRNAs, mRNAs, and Interaction Network Constructing in Acute Exacerbation of Idiopathic Pulmonary Fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) patients who suffer from acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) are at increased risk of respiratory deterioration and death. Non-coding RNAs (ncRNAs) play a vital role in AE-IPF, but studies of crosstalk between transcripts of IPF based on Traditional Chinese Medicine (TCM) syndrome type are relatively few. The construction of long non-coding RNAs (lncRNA)/circular RNAs (circRNA)-microRNAs (miRNA)-mRNA interaction networks can promote understanding RNA interaction in different syndrome types of AE-IPF. The study aimed to identify the difference in RNA transcription expression between IPF patients with "lung heat and collateral stasis (LHCS)" and "lung deficiency with collateral stasis (LDCS)" syndromes, further to construct the potential RNA networks. METHODS: Five IPF patients with LHCS and five IPF patients with LDCS were recruited in this study to perform RNA sequencing and miRNA sequencing. Further analysis was carried out on the differential expression profiles of lncRNAs, circRNAs, miRNAs, and mRNAs among patients with LHCS and LDCS. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The lncRNA/circRNA-miRNA-mRNA competing endogenous RNAs (ceRNAs) network was constructed, and the key regulatory molecules were analyzed. RESULTS: For LHCS and LDCS, we identified 69 lncRNAs, 150 circRNAs, 27 miRNAs, and 56 mRNAs. Differential expression analysis through GO and KEGG highlights that differentially expressed mRNAs have significant associations with pathways such as tight junction and Hepatitis C. Within the ceRNA network, all nodes have a direct or indirect association with LHCS progression. The hsa-miR-150-5p core sub-network is composed of 1 lncRNA, 6 circRNAs, 1 miRNA, and 5 mRNAs. From the ceRNA sub-network analysis, NR_120628/hsa-miR-150-5p/E2F3 and hsa-circ-0053515/hsa-miR-150-5p/E2F3 emerged as the pivotal ceRNA pairs. CONCLUSIONS: This study highlights that the NR_120628/hsa-miR-150-5p/E2F3 and hsa-circ-0053515/hsa-miR-150-5p/E2F3 axes could be central in the regulation of LHCS, providing valuable insights into potential directions for subsequent research on LHCS. TRIAL REGISTRATION: Chinese clinical trial registry (CHiCTR23007405). Registered on July 27, 2023. https://www.chictr.org.cn/.

Deriving particulate backscattering coefficient at 400†nm from small-scale optically shallow waters using Landsat-8 data: a case study at Luhuitou Peninsula, Sanya.

The particulate backscattering coefficient (bbp) plays an important role in the growth of coral reefs by influencing the light field conditions. Small-scale optically shallow waters are commonly found in coastal fringing reefs, making it challenging to monitor the spatial and temporal patterns accurately using Aqua satellites with a low spatial resolution. In this study, six existing optimization-based algorithms for deriving bbp at 400 nm (bbp(400)) were evaluated with three simulated Landsat-8 (spatial resolution = 30 m) data sets and in situ data from the Luhuitou Peninsula, Sanya. The comparison results indicated that the HOPE (hyperspectral optimization process exemplar) (Fix-H-error or Fix-H-error-free) algorithm which sets an input value of the water depth alone outperformed other algorithms. However, the estimated bbp(400) from all the algorithms tended to be either overestimated and underestimated due to the improper the spectral shape value of the backscattering coefficient. The HOPE (Fix-H-error) algorithm estimated-bbp(400) from in situ reflectance also had a good correlation with the in situ total suspended particle concentrations data derived-bbp(400), with a correlation coefficient of 0.83. Therefore, the HOPE (Fix-H-error) algorithm was selected to estimate the bbp(400) from satellite-based Landsat-8 data of the Luhuitou Peninsula, Sanya. Time-series (2014-2021) results from these Landsat-8 images reveal the seasonal variation of bbp(400). The bbp(400) was low from May to September every year. From October to December or January, bbp(400) had an increasing trend, and then it decreased until May. Spatial analysis indicated that bbp(400) decreased with increasing water depth. The spatial and temporal patterns of bbp(400) were consistent with in situ observations reported in the literature. This study preliminarily showed the efficiency of an optimization-based algorithm in deriving bbp(400) in small-scale optically shallow water region using Landsat-8 data.

Neural optimization machine: a neural network approach for optimization and its application in additive manufacturing with physics-guided learning.

Neural networks (NNs) are increasingly used in design to construct the objective functions and constraints, which leads to the needs of optimization of NN models with respect to design variables. A Neural Optimization Machine (NOM) is proposed for constrained single/multi-objective optimization by appropriately designing the NN architecture, activation function and loss function. The NN's built-in backpropagation algorithm conducts the optimization and is seamlessly integrated with the additive manufacturing (AM) process-property model. The NOM is tested using several numerical optimization problems. It is shown that the increase in the dimension of design variables does not increase the computational cost significantly. Next, a brief review of the physics-guided machine learning model for fatigue performance prediction of AM components is given. Finally, the NOM is applied to design processing parameters in AM to optimize the mechanical fatigue properties through the physics-guided NN under uncertainties. One novel contribution of the proposed methodology is that the constrained process optimization is integrated with physics/knowledge and the data-driven AM process-property model. Thus, a physics-compatible process design can be achieved. Another significant benefit is that the training and optimization are achieved in a unified NN model, and no separate process optimization is needed. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Mechanism of new coronavirus pneumonia agreement prescription on 2019 novel coronavirus-infected pneumonia based on network pharmacology analysis and the validation.

PURPOSE: To investigate the mechanism of action underlying the effective treatment of New Coronavirus Pneumonia Agreement Prescription (NCPAP) on 2019 Novel Coronavirus-Infected Pneumonia (2019-NCIP) using network pharmacology. METHODS: In this retrospective study, 50 patients with 2019-NCIP were recruited, including 16 who received symptomatic treatment and 34 that received NCPAP formula treatment on the basis of symptomatic treatment. Hospitalization and lymphocyte percentages were served as efficacy evaluation indicators. Moreover, pharmacological analysis was performed to identify the target disease of NCPAP. Active ingredients in herbs were screened using the Traditional Chinese Medications Systems Pharmacology (TCMSP) database, and related target genes were identified. We then queried therapeutic target data for coronavirus-associated genes. The protein-protein interaction network was constructed to examine the relationships between these targets. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) network enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. RESULTS: NCPAP significantly reduced hospitalization time and increased both the absolute value and percentage of lymphocytes. Bioinformatics and cytokine analysis suggested that preventing cytokine storm syndrome and regulating immune response are the key mechanisms of NCPAP in treating 2019-NCIP. CONCLUSIONS: The possible mechanisms of NCPAP in the treatment of 2019-NCIP are reduction of cytokine storms and regulation of the immune response.

Mitochondria-Related Gene MAOB is a Key Biomarker of Osteoarthritis and Inhibition of Its Expression Reduces LPS-induced Chondrocyte Damage.

The mitochondria are an important organelle in cells responsible for producing energy, and its abnormal function is closely related to the occurrence and development of osteoarthritis. Finding key genes associated with mitochondrial dysfunction in osteoarthritis can provide new ideas for the study of its pathogenesis. Firstly, 371 differential expressed genes (DEGs) were obtained through bioinformatics analysis of the GSE12021 and GSE55235 datasets in the GEO database, and 24 mitochondria-related DEGs (Mito-DEGs) were obtained by crossing differential genes with mitochondrial related genes. Next, KEGG and GO analysis of Mito-DEGs showed that upregulated Mito-DEGs were mainly enriched in small molecule catabolic process and tryptophan metabolism, while downregulated Mito-DEGs were mainly enriched in acetyl-CoA metabolic process and fatty acid biosynthesis. Furthermore, the key genes ME2 and MAOB were obtained through protein-protein interaction network analysis and lasso cox analysis of the 24 Mito-DEGs. In addition, the comparison results of immune cell scores showed differences between T cells CD4 memory resting, T cells regulatory (Tregs), Mast cells resting, and Mast cells activated in the OA group and the control group. More importantly, the potential regulatory mechanisms of key genes were studied through GSEA analysis and their correlation with immune infiltrating cells, immune checkpoints, m6A, and ferroptosis. Finally, in LPS-induced C28/I2 cells, silencing MAOB reduced inflammation injury and inhibited mitochondrial damage. Our research findings suggest that MAOB may hold potential as a target for the diagnosis and treatment of osteoarthritis.

Circulating interleukins and risk of colorectal cancer: a Mendelian randomization study.

BACKGROUND: Recent studies have suggested a potential causal association between Interleukins (ILs) and Colorectal Cancer (CRC), and thus, it is important to examine the causal relationship between them using a Mendelian randomization (MR) approach. METHODS: The instrumental variables were extracted for IL-1ra, IL-6, IL-6ra, IL-8, IL-16, IL-18, IL-27 from genome-wide association studies of European ancestry. Summary statistics of CRC were also retrieved. An inverse variance-weighted MR approach was implemented as the primary method to compute overall effects from multiple instruments. Additional MR approaches and sensitivity and heterogeneity pleiotropy analyses were also conducted respectively. RESULTS: Our analysis suggested a causal effect between an increase of IL-8 and a reduced risk of CRC (odds ratio 0.65; 95% confidence interval, 0.43-0.98; p = 0.041) and did not provide evidence for causal effects of IL-1ra, IL-6, IL-6ra, IL-16, IL-18, IL-27. Sensitivity analyses suggested the robustness of MR results and that they were unlikely to be affected by unbalanced pleiotropy or significant heterogeneity. CONCLUSIONS: This study investigated the role of ILs in the development of CRC and we found a causal effect between an increase of IL-8 and a reduced risk of CRC but not found evidence for causal effects of IL-1ra, IL-6, IL-6ra, IL-16, IL-18, IL-27. Sensitivity analyses suggested the robustness of MR results and that they were unlikely to be affected by unbalanced pleiotropy or significant heterogeneity.