A glutathione S-transferase regulates lignin biosynthesis and enhances salt tolerance in tomato.

Salt stress adversely affects the growth and yield of crops. Glutathione S-transferases (GSTs) are involved in plant growth and responses to biotic and abiotic stresses. In this study, 400 mM NaCl stress significantly induced the expression of Glutathione S-transferase U43 (SlGSTU43) in the roots of the wild-type tomato (Solanum lycopersicum L.) plants. Overexpressing SlGSTU43 enhanced the ability of scavenging reactive oxygen species (ROS) in tomato leaves and roots under NaCl stress, while SlGSTU43 knock-out mutants showed the opposite performance. RNA sequencing analysis revealed that overexpressing SlGSTU43 affected the expression of genes related to lignin biosynthesis. We demonstrated that SlGSTU43 can regulate the lignin content in tomato through its interaction with SlCOMT2, a key enzyme involved in lignin biosynthesis, and promote the growth of tomato plants under NaCl stress. In addition, SlMYB71 and SlWRKY8 interact each other, and can directly bind to the promoter of SlGSTU43 to transcriptionally activate its expression separately or in combination. When SlMYB71 and SlWRKY8 were silenced in tomato plants individually or collectively, the plants were sensitive to NaCl stress, and their GST activities and lignin contents decreased. Our research indicates that SlGSTU43 can enhance salt stress tolerance in tomato by regulating lignin biosynthesis, which is regulated by interacting with SlCOMT2, as well as SlMYB71 and SlWRKY8. This finding broadens our understanding of GST functions.

Deubiquitinase USP10 promotes osteosarcoma autophagy and progression through regulating GSK3ß-ULK1 axis.

BACKGROUND: Deubiquitinating enzymes (DUBs) are pivotal in maintaining cell homeostasis by regulating substrate protein ubiquitination in both healthy and cancer cells. Ubiquitin-specific protease 10 (USP10) belongs to the DUB family. In this study, we investigated the clinical and pathological significance of USP10 and Unc-51-like autophagy activating kinase 1 (ULK1) in osteosarcoma (OS), as well as the mechanism of USP10 action in ULK1-mediated autophagy and disease progression. RESULTS: The analysis of OS and adjacent normal tissues demonstrated that USP10 and ULK1 were significantly overexpressed in OS, and a positive association between their expression and malignant properties was observed. USP10 knockdown in OS cells reduced ULK1 mRNA and protein expression, whereas USP10 overexpression increased ULK1 mRNA and protein expression. In vitro experiments showed that USP10 induced autophagy, cell proliferation, and invasion by enhancing ULK1 expression in OS cell lines. Furthermore, we found that the regulation of ULK1-mediated autophagy, cell proliferation, and invasion in OS by USP10 was dependent on glycogen synthase kinase 3ß (GSK3ß) activity. Mechanistically, USP10 promoted ULK1 transcription by interacting with and stabilising GSK3ß through deubiquitination, which, in turn, increased the activity of the ULK1 promoter, thereby accelerating OS progression. Using a xenograft mouse model, we showed that Spautin-1, a small-molecule inhibitor targeting USP10, significantly reduced OS development, with its anti-tumour activity significantly enhanced when combined with the chemotherapeutic agent cisplatin. CONCLUSION: Collectively, we demonstrated that the USP10-GSK3ß-ULK1 axis promoted autophagy, cell proliferation, and invasion in OS. The findings imply that targeting USP10 may offer a promising therapeutic avenue for treating OS.

Corrigendum: Long non-coding RNA AL139385.1 as a novel prognostic biomarker in lung adenocarcinoma.

[This corrects the article DOI: 10.3389/fonc.2022.905871.].

Identification of STAM-binding protein as a target for the treatment of gemcitabine resistance pancreatic cancer in a nutrient-poor microenvironment.

Pancreatic cancer (PC) is a highly malignant solid tumor whose resistance to gemcitabine (GEM) chemotherapy is a major cause of poor patient prognosis. Although PC is known to thrive on malnutrition, the mechanism underlying its chemotherapy resistance remains unclear. The current study analyzed clinical tissue sample databases using bioinformatics tools and observed significantly upregulated expression of the deubiquitinase STAMBP in PC tissues. Functional experiments revealed that STAMBP knockdown remarkably increases GEM sensitivity in PC cells. Multiple omics analyses suggested that STAMBP enhances aerobic glycolysis and suppresses mitochondrial respiration to increase GEM resistance in PC both in vitro and in vivo. STAMBP knockdown decreased PDK1 levels, an essential regulator of the aerobic glycolytic process, in several cancers. Mechanistically, STAMBP promoted the PDK1-mediated Warburg effect and chemotherapy resistance by modulating E2F1 via direct binding to E2F1 and suppressing its degradation and ubiquitination. High-throughput compound library screening using three-dimensional protein structure analysis and drug screening identified the FDA drug entrectinib as a potent GEM sensitizer and STAMBP inhibitor, augmenting the antitumor effect of GEM in a patient-derived xenograft (PDX) model. Overall, we established a novel mechanism, via the STAMBP-E2F1-PDK1 axis, by which PC cells become chemoresistant in a nutrient-poor tumor microenvironment.

Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein.

INTRODUCTION AND OBJECTIVES: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels. PATIENTS AND METHODS: A total of 6,980 patients treated between January 2012 and December 2018 were included. Pre-treatment laboratory tests and clinical data were obtained. The significant risk factors for HCC were identified, and the relative risk of each variable affecting its diagnosis was calculated using ML and univariate regression analysis. The data set was then randomly partitioned into validation (20 %) and training sets (80 %) to develop the ML models. RESULTS: Twelve independent risk factors for HCC were identified using Gaussian naïve Bayes, extreme gradient boosting (XGBoost), random forest, and least absolute shrinkage and selection operation regression models. Multivariate analysis revealed that male sex, age >60 years, alkaline phosphate >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen >5 ng/mL, and fibrinogen >4 g/L were the risk factors, whereas hypertension, calcium <2.25 mmol/L, potassium ≤3.5 mmol/L, direct bilirubin >6.8 µmol/L, hemoglobin <110 g/L, and glutamic-pyruvic transaminase >40 U/L were the protective factors in HCC patients. Based on these factors, a nomogram was constructed, showing an area under the curve (AUC) of 0.746 (sensitivity = 0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity = 0.462, specificity=0.766). Compared with several ML algorithms, the XGBoost model had an AUC of 0.832 (sensitivity = 0.745, specificity=0.766) and an independent validation AUC of 0.829 (sensitivity = 0.766, specificity = 0.737), making it the top-performing model in both sets. The external validation results have proven the accuracy of the XGBoost model. CONCLUSIONS: The proposed XGBoost demonstrated a promising ability for individualized prediction of HCC in HBV-related cirrhosis patients with low-level AFP.

Tetradentate Pt(II) complexes based on xylenylamino linked dual pyrazolate chelates for organic light emitting diodes.

In this work, we report the syntheses of three Pt(II) emitters, namely, Pt4N1, Pt4N2, and Pt4N3, to which their tetradentate chelates were assembled by linking two pyrazolate chelates with a single xylenylamino entity. Functionalization of Pt4N1 was achieved upon addition of electronegative CF3 substituent on pyridinyl groups and switching to more electron deficient pyrazinyl groups in giving Pt4N2 and Pt4N3, respectively. The vertical arranged xylenylamino entity has effectively suppressed the inter-molecular π-π stacking and Pt···Pt interaction, as shown by the single crystal X-ray structural analyses. Upon fabrication of OLED devices, Pt4N2 and Pt4N3 based devices delivered efficient cyan and green emission, with an EQEmax of 15.2% and 11.2%, respectively, affirming the successfulness of the tetradentate chelating strategy.

A Comparative Study on the Muscle and Gut Microbiota of Opsariichthys bidens from Rice Field and Pond Culture Breeding Modes.

To investigate difference in the quality of the different parts (back, tail muscles, and fish skin) of Opsariichthys bidens from pond and rice field cultures, a comparative study was conducted in terms of nutritional composition, volatile flavor profiles and gut microbiota. In detail, the texture, free amino acids, fatty acids were further assessed. The results suggested that the moisture content, crude protein and crude fat content in the skin of O. bidens are higher than those in the back and tail muscles, regardless of breeding modes. The fish cultured in the rice field had a higher protein content than those from the pond culture, while the fat content of the rice field-cultured fish was significantly low compared to the fish from the pond culture, especially in the back and tail parts. A total of 43 volatile components were detected by Gas Chromatography-Mass Spectrometry (GC-MS), with a maximum of 18 types of aldehydes and the highest concentration being nonanal. Compared to pond cultures, the fish from the rice field cultures showed more abundant flavor composition and odor-active compounds. The total content of DHA (Docosahexaenoic Acid) and EPA (Eicosapentaenoic Acid) in the rice field-cultured fish was higher than that of the pond group, while no significant disparity in amino acid composition was observed (p > 0.05). Comparative and clustering analyses of gut microbiota revealed notable discrepancies in the gut microbiota of O. bidens from two aquaculture systems. However, an inherent correlation between the gut microbiome and meat quality would be further emphasized in further studies. This study can offer a theoretical reference for the development of high-quality aquatic products by selecting the appropriate aquaculture models.

Spectacle correction may affect refractive progression in children with unilateral myopic anisometropia: A retrospective study.

OBJECTIVE: To investigate the effect of spectacle correction on refractive progression in children with unilateral myopic anisometropia (UMA). METHODS: In this retrospective study, 153 children with UMA (aged 8-12 years) were recruited and classified into an uncorrected (UC) group (n = 47) and a spectacle (SP) group (n = 106). The spherical equivalent refraction (SER) of the myopic eyes ranged from -0.75 to -4.00 D; the SER of the emmetropic eyes ranged from +1.00 to -0.25 D; anisometropia was ≥1.00 D and the follow-up duration was 1 year. Nineteen subjects from the SP group with follow-up records spanning at least 6 months before and after wearing spectacles were selected as a subgroup. Changes in the SER and axial length (AL), the degree of anisometropia and interocular AL differences of the two groups and the subgroup were analysed. RESULTS: During the 1-year follow-up period, AL and SER changes in myopic eyes were significantly greater than those in emmetropic eyes in the UC group (p < 0.001). For the UC group, the degree of anisometropia and AL change increased (all p < 0.001). For the SP group, there were no significant differences in the degree of anisometropia or AL change (all p > 0.05). When comparing the groups, AL elongation of the myopic eyes in the UC group occurred significantly faster than in the SP group (p = 0.02), and AL elongation for the emmetropic eyes in the UC group occurred significantly slower than in the SP group (p = 0.04). For the subgroup, the AL and SER changes in the myopic eyes 6 months before wearing spectacles occurred significantly faster than those after correction (p < 0.001). CONCLUSIONS: Spectacle correction could prevent increased anisometropia in uncorrected children with UMA by slowing myopia progression in the myopic eyes and accelerating the myopic shift in the contralateral eye.

Joint use of population pharmacokinetics and machine learning for prediction of valproic acid plasma concentration in elderly epileptic patients.

BACKGROUND: Valproic acid (VPA) is a commonly used broad-spectrum antiepileptic drug. For elderly epileptic patients, VPA plasma concentrations have a considerable variation. We aim to establish a prediction model via a combination of machine learning and population pharmacokinetics (PPK) for VPA plasma concentration. METHODS: A retrospective study was performed incorporating 43 variables, including PPK parameters. Recursive Feature Elimination with Cross-Validation was used for feature selection. Multiple algorithms were employed for ensemble model, and the model was interpreted by Shapley Additive exPlanations. RESULTS: The inclusion of PPK parameters significantly enhances the performance of individual algorithm model. The composition of categorical boosting, light gradient boosting machine, and random forest (7:2:1) with the highest R2 (0.74) was determined as the ensemble model. The model included 11 variables after feature selection, of which the predictive performance was comparable to the model that incorporated all variables. CONCLUSIONS: Our model was specifically tailored for elderly epileptic patients, providing an efficient and cost-effective approach to predict VPA plasma concentration. The model combined classical PPK with machine learning, and underwent optimization through feature selection and algorithm integration. Our model can serve as a fundamental tool for clinicians in determining VPA plasma concentration and individualized dosing regimens accordingly.

Impact of Pre-Existing Immunity and Age on Antibody Responses to Live Attenuated Influenza Vaccine.

Live attenuated influenza vaccines (LAIV) typically induce a poor hemagglutination inhibition (HI) response, which is the standard correlate of protection for inactivated influenza vaccines. The significance of the HI response is complicated because the LAIV vaccine primarily induces the local mucosal immune system, while the HI assay measures the circulating serum antibody response. However, age and pre-existing immunity have been identified as important factors affecting LAIV immunogenicity. This study aimed to extend our understanding of LAIV-induced immunity, particularly, the impact age and pre-existing immunity have on eliciting functional and neutralising antibody responses in paediatric and adult populations vaccinated with LAIV. Thirty-one children and 26 adults were immunized with the trivalent LAIV during the 2013-2014 influenza season in Norway. Children under 9 years received a second dose of LAIV 28 days after the first dose. Blood samples were collected pre- and post-vaccination. HI, microneutralization (MN) and enzyme-linked lectin assay for neuraminidase (NA) antibodies were measured against the vaccine strains. IgG antibody avidity against hemagglutinin (HA) and NA proteins from the vaccine strains was also assessed. Significant correlations were observed between HI and MN responses to A/California/7/2009 (A/H1N1)pdm09-like strain and B/Massachusetts/2/2012-like strain, suggesting that MN is a potential immunological correlate for LAIV. However, the relationship between recipient age (or priming status) and serological response varied between vaccine strains. There was a notable increase in HI and MN responses in all cohorts except naive children against the H1N1 strain, where most recipients had responses below the protective antibody threshold. NAI responses were generally weak in naive children against all vaccine strains compared with adults or antigen-primed children. Post-vaccination antibody avidity increased only in primed children below nine years of age against the A/H1N1 strain. Overall, our findings indicate that LAIV elicits functional and neutralizing antibody responses in both naive and antigen experienced cohorts, however, the magnitude and kinetics of the response varies between vaccine strains.

U-shaped association between triglyceride glucose-body mass index with all-cause and cardiovascular mortality in US adults with osteoarthritis: evidence from NHANES 1999-2020.

The association between insulin resistance (IR) and the risk of all-cause mortality and cardiovascular mortality among osteoarthritis (OA) patients remains uncertain. This study aims to clarify the correlation between a novel marker of IR, the triglyceride glucose-body mass index (TyG-BMI), and the risk of all-cause mortality and cardiovascular mortality in OA patients. Data from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2020 were analyzed. Multivariable Cox proportional hazards regression analysis and restricted cubic spline plots were employed to elucidate the association between the TyG-BMI index and the risk of all-cause mortality or cardiovascular mortality in OA patients. Additionally, subgroup analysis was conducted to explore potential interactions and identify populations at elevated risk of mortality. The study cohort comprised 4097 OA patients who were followed up for a period of 20 years, during which 1197 cases of all-cause mortality and 329 cases of mortality attributed to cardiovascular disease were recorded. Our findings revealed a U-shaped nonlinear relationship between the TyG-BMI index and the risk of all-cause mortality or cardiovascular mortality in OA patients, with the lowest mortality risk thresholds identified at 282 and 270, respectively. Moreover, surpassing these thresholds was associated with a 3% increase in the risk of all-cause mortality and a 5% increase in the risk of cardiovascular mortality for every 10-unit increment in TyG-BMI level. Among American OA patients, a U-shaped nonlinear relationship exists between the TyG-BMI index and the risk of all-cause mortality or cardiovascular mortality. These findings underscore the significant role of IR in the progression of OA.

A Multi-Strategy Hybrid Sparse Reconstruction Method Based on Spatial-Temporal Sparse Wave Number Analysis for Enhancing Pipe Ultrasonic-Guided Wave Anomaly Imaging.

Ultrasonic-guided waves (UGWs) in defective pipes are subject to severe coherent noise caused by imperfect detection conditions, mode conversion, and intrinsic characteristics (dispersion and multiple modes), inducing the limited performance of anomaly imaging. To achieve the high resolution and accuracy of anomaly imaging, a multi-strategy hybrid sparse reconstruction (MHSR) method based on spatial-temporal sparse wavenumber analysis (ST-SWA) is proposed. MHSR leverages the capability of ST-SWA to extract the wavenumber dispersion curves, thereby providing a more refined and precise search space for MHSR. Furthermore, it mitigates the impact of coherent noise by conducting dispersion compensation on the reconstructed signal. The sparse compensated signals through MHSR are employed for sparse reconstruction imaging. To validate the efficacy of the proposed method, UGW testing is performed on the defective steel pipe, and the results demonstrate the significant enhancement of anomaly imaging in defect resolution and positioning accuracy. The lowest estimated errors for axial and circumferential defect positions are 10 mm and 4 mm, respectively.

Exploring brain asymmetry in early-stage Parkinson's disease through functional and structural MRI.

OBJECTIVE: This study explores the correlation between asymmetrical brain functional activity, gray matter asymmetry, and the severity of early-stage Parkinson's disease (PD). METHODS: Ninety-three early-stage PD patients (ePD, H-Y stages 1-2.5) were recruited, divided into 47 mild (ePD-mild, H-Y stages 1-1.5) and 46 moderate (ePD-moderate, H-Y stages 2-2.5) cases, alongside 43 matched healthy controls (HCs). The study employed the Hoehn and Yahr (H-Y) staging system for disease severity assessment and utilized voxel-mirrored homotopic connectivity (VMHC) for analyzing brain functional activity asymmetry. Asymmetry voxel-based morphometry analysis (VBM) was applied to evaluate gray matter asymmetry. RESULTS: The study found that, relative to HCs, both PD subgroups demonstrated reduced VMHC values in regions including the amygdala, putamen, inferior and middle temporal gyrus, and cerebellum Crus I. The ePD-moderate group also showed decreased VMHC in additional regions such as the postcentral gyrus, lingual gyrus, and superior frontal gyrus, with notably lower VMHC in the superior frontal gyrus compared to the ePD-mild group. A negative correlation was observed between the mean VMHC values in the superior frontal gyrus and H-Y stages, UPDRS, and UPDRS-III scores. No significant asymmetry in gray matter was detected. CONCLUSIONS: Asymmetrical brain functional activity is a significant characteristic of PD, which exacerbates as the disease severity increases, resembling the dissemination of Lewy bodies across the PD neurological framework. VMHC emerges as a potent tool for characterizing disease severity in early-stage PD.

Automated segmentation of brain metastases with deep learning: a multi-center, randomized crossover, multi-reader evaluation study.

BACKGROUND: Artificial intelligence has been proposed for brain metastasis (BM) segmentation but it has not been fully clinically validated. The aim of this study was to develop and evaluate a system for BM segmentation. METHODS: A deep-learning-based BM segmentation system (BMSS) was developed using contrast-enhanced MR images from 488 patients with 10,338 brain metastases. A randomized crossover, multi-reader study was then conducted to evaluate the performance of the BMSS for BM segmentation using data prospectively collected from 50 patients with 203 metastases at five centers. Five radiology residents and five attending radiologists were randomly assigned to contour the same prospective set in assisted and unassisted modes. Aided and unaided Dice similarity coefficients (DSCs) and contouring times per lesion were compared. RESULTS: The BMSS alone yielded a median DSC of 0.91 (95% confidence interval, 0.90-0.92) in the multi-center set and showed comparable performance between the internal and external sets (p = 0.67). With BMSS assistance, the readers increased the median DSC from 0.87 (0.87-0.88) to 0.92 (0.92-0.92) (p < 0.001) with a median time saving of 42% (40-45%) per lesion. Resident readers showed a greater improvement than attending readers in contouring accuracy (improved median DSC, 0.05 [0.05-0.05] vs. 0.03 [0.03-0.03]; p < 0.001), but a similar time reduction (reduced median time, 44% [40-47%] vs. 40% [37-44%]; p = 0.92) with BMSS assistance. CONCLUSIONS: The BMSS can be optimally applied to improve the efficiency of brain metastasis delineation in clinical practice.

Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization.

Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.

Asian Screening Array and Next-Generation Sequencing Based Panels Applied to Preimplantation Genetic Testing for Monogenic Disorders Preclinical Workup in 294 Families: A Retrospective Analysis.

OBJECTIVE: Currently, the most commonly used methods for linkage analysis of pre-implantation genetic testing for monogenic disorders (PGT-M) are next generation sequencing (NGS) and SNP array. We aim to investigate whether the application efficacy of Asian screening array (ASA) in PGT-M preclinical workup for the Chinese population is superior to NGS based single nucleotide polymorphism (SNP) panels. METHODS: We conducted a retrospective analysis by reviewing 294 couples from a single center over the past 4 years and compared the detection results between NGS-based SNP panels and ASA. Using the numbers of informative SNPs upstream and downstream flanking of variants, we assessed the detection efficiency of both methods in monogenic diseases, chromosomal microdeletion syndrome and males with de novo variants, among other scenarios. RESULTS: Results indicate that ASA offers a greater number of informative SNPs compared with NGS-based SNP panels. Additionally, data analysis for ASA is generally more straightforward and may require less computational resources. While ASA can address most PGT-M challenges, we have also identified certain genes in previous tests that are not suitable for PGT-M using ASA. CONCLUSION: The application of ASA in PGT-M preclinical workup for Chinese populations has good practical value as it can perform linkage analysis for most genetic variants. However, for certain variants, NGS or other testing methods, such as mutated allele revealed by sequencing with aneuploidy and linkage analysis (MARSALA), may still be necessary for completion.

Fully Flexible Molecular Alignment Enables Accurate Ligand Structure Modeling.

Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates. However, molecular similarity is not consistently high because molecules often require changes to achieve specific purposes, leading to reduced alignment precision. To address this issue, we propose a new alignment method─Z-align. This method uses topological structural information as a criterion for evaluating similarity, reducing the reliance on molecular fingerprint similarity. Our method has achieved success rates significantly higher than those of other methods at moderate levels of similarity. Additionally, our approach can comprehensively and flexibly optimize bond lengths and angles of molecules, maintaining a high accuracy even when dealing with larger molecules. Consequently, our proposed solution helps in achieving more accurate binding poses in protein-ligand docking problems, facilitating the development of small molecule drugs. Z-align is freely available as a web server at https://cloud.zelixir.com/zalign/home.

Corrigendum: Cortical gyrification pattern of depression in Parkinson's disease: a neuroimaging marker for disease severity?

[This corrects the article DOI: 10.3389/fnagi.2023.1241516.].

Serum metabolomics analysis of patients with chronic obstructive pulmonary disease and 'frequent exacerbator' phenotype.

Chronic obstructive pulmonary disease (COPD) exacerbations accelerate loss of lung function and increased mortality. The complex nature of COPD presents challenges in accurately predicting and understanding frequent exacerbations. The present study aimed to assess the metabolic characteristics of the frequent exacerbation of COPD (COPD­FE) phenotype, identify potential metabolic biomarkers associated with COPD­FE risk and evaluate the underlying pathogenic mechanisms. An internal cohort of 30 stable patients with COPD was recruited. A widely targeted metabolomics approach was used to detect and compare serum metabolite expression profiles between patients with COPD­FE and patients with non­frequent exacerbation of COPD (COPD­NE). Bioinformatics analysis was used for pathway enrichment analysis of the identified metabolites. Spearman's correlation analysis assessed the associations between metabolites and clinical indicators, while receiver operating characteristic (ROC) analysis evaluated the ability of metabolites to distinguish between two groups. An external cohort of 20 patients with COPD validated findings from the internal cohort. Out of the 484 detected metabolites, 25 exhibited significant differences between COPD­FE and COPD­NE. Metabolomic analysis revealed differences in lipid, energy, amino acid and immunity pathways. Spearman's correlation analysis demonstrated associations between metabolites and clinical indicators of acute exacerbation risk. ROC analysis demonstrated that the area under the curve (AUC) values for D­fructose 1,6­bisphosphate (AUC=0.871), arginine (AUC=0.836), L­2­hydroxyglutarate (L­2HG; AUC=0.849), diacylglycerol (DG) (16:0/20:5) (AUC=0.827), DG (16:0/20:4) (AUC=0.818) and carnitine­C18:2 (AUC=0.804) were >0.8, highlighting their discriminative capacity between the two groups. External validation results demonstrated that DG (16:0/20:5), DG (16:0/20:4), carnitine­C18:2 and L­2HG were significantly different between patients with COPD­FE and those with COPD­NE. In conclusion, the present study offers insights into early identification, mechanistic understanding and personalized management of the COPD­FE phenotype.

Advancing Real-World Image Dehazing: Perspective, Modules, and Training.

Restoring high-quality images from degraded hazy observations is a fundamental and essential task in the field of computer vision. While deep models have achieved significant success with synthetic data, their effectiveness in real-world scenarios remains uncertain. To improve adaptability in real-world environments, we construct an entirely new computational framework by making efforts from three key aspects: imaging perspective, structural modules, and training strategies. To simulate the often-overlooked multiple degradation attributes found in real-world hazy images, we develop a new hazy imaging model that encapsulates multiple degraded factors, assisting in bridging the domain gap between synthetic and real-world image spaces. In contrast to existing approaches that primarily address the inverse imaging process, we design a new dehazing network following the "localization-and-removal" pipeline. The degradation localization module aims to assist in network capture discriminative haze-related feature information, and the degradation removal module focuses on eliminating dependencies between features by learning a weighting matrix of training samples, thereby avoiding spurious correlations of extracted features in existing deep methods. We also define a new Gaussian perceptual contrastive loss to further constrain the network to update in the direction of the natural dehazing. Regarding multiple full/no-reference image quality indicators and subjective visual effects on challenging RTTS, URHI, and Fattal real hazy datasets, the proposed method has superior performance and is better than the current state-of-the-art methods. See more results: https://github.com/fyxnl/KA Net.