Bongkrekic acid alleviates airway inflammation via breaking the mPTP/mtDAMPs/RAGE feedback loop in a steroid-insensitive asthma model.

Mitochondrial dysfunction is critical in the pathogenesis of asthma. Mitochondrial permeability transition pore (mPTP) regulates the release of mitochondrial damage-associated molecular patterns (mtDAMPs) to maintain mitochondrial homeostasis. Bongkrekic acid (BKA) is a highly selective inhibitor of mPTP opening, participates the progression of various diseases. This research investigated the exact roles of BKA and mPTP in the pathogenesis of asthma and elucidated its underlying mechanisms. In the present study, cytochrome c, one of the mtDAMPs, levels were elevated in asthmatic patients, and associated to airway inflammation and airway obstruction. BKA, the inhibitor of mPTP markedly reversed TDI-induced airway hyperresponsiveness, airway inflammation, and mitochondrial dysfunction. Pretreatment with mitochondrial precipitation, to simulate the release of mtDAMPs, further increased TDI-induced airway inflammation and the expression of RAGE in mice. Administration of the inhibitor of RAGE, FPS-ZM1, alleviated the airway inflammation, the abnormal open of mPTP and mitochondrial dysfunction induced by mtDAMPs and TDI. Furthermore, stimulation with different mtDAMPs activated RAGE signaling in human bronchial epithelial cells. Accordingly, our study indicated that mPTP was important and BKA was efficient in alleviating inflammation in TDI-induced asthma. A positive feedback loop involving mPTP, mtDAMPs and RAGE was present in TDI-induced asthma, indicating that mPTP might serve as a potential therapeutic target for asthma.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. CLINICAL RELEVANCE STATEMENT: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. KEY POINTS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

Shear-wave elastography in renal stiffness in children with hematuria and/or proteinuria.

BACKGROUND: We sought to evaluate renal stiffness in children with hematuria and/or proteinuria using shear wave elastography (SWE) and to investigate the clinical value of renal stiffness in children with hematuria and/or proteinuria. METHODS: According to the results of urinary occult blood and urinary protein tests, 349 pediatric patients were categorized into one of four groups: pure hematuria (HU), pure proteinuria (PU), concomitant hematuria and proteinuria (HUPU), or control (non-HUPU). Patient demographic data, laboratory test results, and renal ultrasound data were collected. RESULTS: There were significant differences in cortical/medullary elasticity among the four groups (the most sensitive cutoff value between HU and PU was 1.72) (P < 0.05). We found that hematuria and proteinuria interacted with renal cortical elasticity (P < 0.05) but that hematuria and proteinuria did not interact with renal medullary elasticity or cortical/medullary elasticity (P > 0.05). Renal elasticity values correlated with sex, age, body surface area, body mass index, qualitative urinary protein, urine N-acetyl-ß-D-glucosaminidase, 24-hour urinary protein quantity, renal volume, and renal cortical thickness (P < 0.05). CONCLUSIONS: SWE can be used to detect changes in renal stiffness in children with hematuria and/or proteinuria. SWE is beneficial for the early detection of glomerular disease in children with abnormal urine test results. IMPACT: This study evaluated the utility of shear wave elastography for the assessment of renal elasticity in pediatric patients presenting with hematuria and/or proteinuria. Children with pure proteinuria had significantly higher renal cortical/medullary elasticity values than those with pure hematuria. An interaction effect between hematuria and proteinuria on renal cortical stiffness was observed. Shear wave elastography can be used as a tool to assess early renal injury in children with urinalysis abnormalities.

Comparison of Reduced and Full Field of View in Diffusion-Weighted MRI on Image Quality: A Meta-Analysis.

BACKGROUND: Reduced field of view (rFOV) diffusion-weighted imaging (DWI) in MRI shows potential for enhanced image quality compared with traditional full field of view (fFOV) DWI. Evaluating rFOV DWI's impact on image quality is important for clinical adoption. OBJECTIVE: To assess the efficacy of rFOV DWI in improving image quality, focusing on artifact reduction, signal-to-noise ratio (SNR) improvement, and lesion detectability. STUDY TYPE: Meta-analysis. POPULATION: Systematic literature search was conducted in PubMed, Embase, the Cochrane Library, and Web of Science ending in January 2024. Thirteen studies with 765 participants focusing on DWI quality using rFOV was analyzed. FIELD STRENGTH/SEQUENCE: SS-EPI, Rtr-SS-EPI, 2D-SS-EPI at 3.0 T. ASSESSMENT: Two investigators performed the data extraction. QUADAS-2 assessed bias. The image quality assessment of rFOV and fFOV DWI were compared. STATISTICAL TESTS: Standardized mean difference (SMD) was utilized to evaluate and standardize MRI image quality. Heterogeneity was assessed using the I2 statistic and publication bias was evaluated with Egger's test. RESULTS: The QUADAS-2 analysis revealed that most studies exhibited a low risk of bias and minimal concerns regarding applicability. Statistical analysis indicated that rFOV DWI yielded higher subjective image quality scores (SMD = 0.535, 95% CI: 0.339, 0.731, I2 = 45.7%) compared with fFOV DWI and was more effective in reducing artifacts (SMD = 0.44, 95% CI: 0.209, 0.672, I2 = 42.3%) than fFOV DWI. However, a decrease in SNR was noted with rFOV DWI (SMD = -0.670, 95% CI: -1.187 to -0.152, I2 = 87.9%). Additionally, rFOV DWI demonstrated enhancements in lesion visibility (SMD = 0.432, 95% CI: -1.187, -0.152, I2 = 53.1%) and anatomical details (SMD = 0.598, 95% CI: 0.121, 1.075, I2 = 90.8%). DATA CONCLUSION: rFOV DWI enhances MRI image quality by reducing artifacts and improving lesion visibility with a SNR trade-off. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic.

BACKGROUND AND PURPOSE: The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features. METHODS: A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley's Additive interpretation (SHAP) illustrated the importance of CT features. RESULTS: A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group's radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models. CONCLUSION: Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes.

Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION: CRD42022375712.

Comparative Genome-Wide Alternative Splicing Analysis between Preadipocytes and Adipocytes.

Alternative splicing is a ubiquitous regulatory mechanism in gene expression that allows a single gene to generate multiple messenger RNAs (mRNAs). Adipocyte development is regulated by many processes, and recent studies have found that splicing factors also play an important role in adipogenic development. In the present study, we further investigated the differences in selective shearing during different periods of adipocyte differentiation. We identified five alternative splicing types including skipped exon, mutually exclusive exon, Alternative 5' splice site, Alternative 3' splice site, and Retained intron, with skipped exons being the most abundant type of selective shearing. In total, 641 differentially expressed selective shearing genes were obtained, enriched in 279 pathways, from which we selected and verified the accuracy of the sequencing results. Overall, RNA-seq revealed changes in the splicing and expression levels of these new candidate genes between precursor adipocytes and adipocytes, suggesting that they may be involved in adipocyte generation and differentiation.

Feasibility and reproducibility of cardiovascular magnetic resonance-feature tracking for quantitative right atrial function in dilated cardiomyopathy patients.

Background: The importance of right heart assessment in dilated cardiomyopathy (DCM) is increasingly recognized. The development of cardiovascular magnetic resonance-feature tracking (CMR-FT) has provided a novel approach to quantify myocardial deformation and evaluate cardiac function. In this study, we aimed to evaluate the feasibility and reproducibility of CMR-FT for the quantitative derivation of right atrial (RA) strain and strain rate (SR) in patients with DCM. Methods: A total of 68 DCM patients (84% male; aged 50.6±13.2 years) and 58 healthy controls (81% male; aged 48.4±11.2 years) were retrospectively enrolled from September 2018 to August 2022 at the First Affiliated Hospital of Zhejiang Chinese Medical University and Shenzhen Clinical Medical College of Guangzhou University of Chinese Medicine. RA reservoir, conduit, and booster strain (εs, εe, and εa) and peak positive, peak early negative, and peak late negative SR (SRs, SRe, and SRa) were measured using CMR-FT and compared between 2 groups using Student's t-test. Intra- and inter-observer reproducibility was evaluated using intraclass correlation coefficients (ICC) and Bland-Altman plots. Results: Compared to healthy controls, DCM patients showed significantly lower RA strain (εs: 19.7%±9.0% vs. 44.4%±9.7%; εe: 7.9%±5.3% vs. 25.8%±8.6%; εa: 11.8%±6.2% vs. 18.6%±5.1%, all P<0.001) and SR (SRs: 1.17±0.48 vs. 1.92±0.62 s-1; SRe: -0.85±0.56 vs. -1.94±0.63 s-1; SRa: -1.39±0.71 vs. -2.01±0.65 s-1, all P<0.001). There was no significant difference in RA maximum volume index between the 2 groups. Simple linear regression analysis demonstrated a significant correlation between N-terminal B-type natriuretic peptide (NT-proBNP), RA emptying fraction passive (RAEF passive), and RA εe [(NT-proBNP and εe): r=-0.48, P<0.001, 95% confidence interval (CI): -0.64 to -0.26; and (RAEF passive and εe): r=0.41, P=0.001, 95% CI: 0.22 to 0.56, respectively] in DCM patients. Intra- and inter-observer reproducibility was excellent (all ICCs >0.85) for RA deformation measurements. Conclusions: CMR-FT is a promising, noninvasive approach for the quantitative assessment of RA phasic function in patients with DCM. DCM patients exhibit impaired RA reservoir, conduit, and booster pump function prior to visible RA enlargement.

Morphological and regional spontaneous functional aberrations in the brain associated with Crohn's disease: a systematic review and coordinate-based meta-analyses.

Crohn's disease is an acknowledged "brain-gut" disorder with unclear physiopathology. This study aims to identify potential neuroimaging biomarkers of Crohn's disease. Gray matter volume, cortical thickness, amplitude of low-frequency fluctuations, and regional homogeneity were selected as indices of interest and subjected to analyses using both activation likelihood estimation and seed-based d mapping with permutation of subject images. In comparison to healthy controls, Crohn's disease patients in remission exhibited decreased gray matter volume in the medial frontal gyrus and concurrently increased regional homogeneity. Furthermore, gray matter volume reduction in the medial superior frontal gyrus and anterior cingulate/paracingulate gyri, decreased regional homogeneity in the median cingulate/paracingulate gyri, superior frontal gyrus, paracentral lobule, and insula were observed. The gray matter changes of medial frontal gyrus were confirmed through both methods: decreased gray matter volume of medial frontal gyrus and medial superior frontal gyrus were identified by activation likelihood estimation and seed-based d mapping with permutation of subject images, respectively. The meta-regression analyses showed a positive correlation between regional homogeneity alterations and patient age in the supplementary motor area and a negative correlation between gray matter volume changes and patients' anxiety scores in the medial superior frontal gyrus. These anomalies may be associated with clinical manifestations including abdominal pain, psychiatric disorders, and possibly reflective of compensatory mechanisms.

Convolutional neural network applied to preoperative venous-phase CT images predicts risk category in patients with gastric gastrointestinal stromal tumors.

OBJECTIVE: The risk category of gastric gastrointestinal stromal tumors (GISTs) are closely related to the surgical method, the scope of resection, and the need for preoperative chemotherapy. We aimed to develop and validate convolutional neural network (CNN) models based on preoperative venous-phase CT images to predict the risk category of gastric GISTs. METHOD: A total of 425 patients pathologically diagnosed with gastric GISTs at the authors' medical centers between January 2012 and July 2021 were split into a training set (154, 84, and 59 with very low/low, intermediate, and high-risk, respectively) and a validation set (67, 35, and 26, respectively). Three CNN models were constructed by obtaining the upper and lower 1, 4, and 7 layers of the maximum tumour mask slice based on venous-phase CT Images and models of CNN_layer3, CNN_layer9, and CNN_layer15 established, respectively. The area under the receiver operating characteristics curve (AUROC) and the Obuchowski index were calculated to compare the diagnostic performance of the CNN models. RESULTS: In the validation set, CNN_layer3, CNN_layer9, and CNN_layer15 had AUROCs of 0.89, 0.90, and 0.90, respectively, for low-risk gastric GISTs; 0.82, 0.83, and 0.83 for intermediate-risk gastric GISTs; and 0.86, 0.86, and 0.85 for high-risk gastric GISTs. In the validation dataset, CNN_layer3 (Obuchowski index, 0.871) provided similar performance than CNN_layer9 and CNN_layer15 (Obuchowski index, 0.875 and 0.873, respectively) in prediction of the gastric GIST risk category (All P >.05). CONCLUSIONS: The CNN based on preoperative venous-phase CT images showed good performance for predicting the risk category of gastric GISTs.

Assessment of Right Atrial Function Measured with Cardiac MRI Feature Tracking for Predicting Outcomes in Patients with Dilated Cardiomyopathy.

Background Right atrial (RA) function strain is increasingly acknowledged as an important predictor of adverse events in patients with diverse cardiovascular conditions. However, the prognostic value of RA strain in patients with dilated cardiomyopathy (DCM) remains uncertain. Purpose To evaluate the prognostic value of RA strain derived from cardiac MRI (CMR) feature tracking (FT) in patients with DCM. Materials and Methods This multicenter, retrospective study included consecutive adult patients with DCM who underwent CMR between June 2010 and May 2022. RA strain parameters were obtained using CMR FT. The primary end points were sudden or cardiac death or heart transplant. Cox regression analysis was used to determine the association of variables with outcomes. Incremental prognostic value was evaluated using C indexes and likelihood ratio tests. Results A total of 526 patients with DCM (mean age, 51 years ± 15 [SD]; 381 male) were included. During a median follow-up of 41 months, 79 patients with DCM reached the primary end points. At univariable analysis, RA conduit strain was associated with the primary end points (hazard ratio [HR], 0.82 [95% CI: 0.76, 0.87]; P < .001). In multivariable Cox analysis, RA conduit strain was an independent predictor for the primary end points (HR, 0.83 [95% CI: 0.77, 0.90]; P < .001). A model combining RA conduit strain with other clinical and conventional imaging risk factors (C statistic, 0.80; likelihood ratio, 92.54) showed improved discrimination and calibration for the primary end points compared with models with clinical variables (C statistic, 0.71; likelihood ratio, 37.12; both P < .001) or clinical and imaging variables (C statistic, 0.75; likelihood ratio, 64.69; both P < .001). Conclusion CMR FT-derived RA conduit strain was an independent predictor of adverse outcomes among patients with DCM, providing incremental prognostic value when combined in a model with clinical and conventional CMR risk factors. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Age- and Sex-Specific MR-Feature Tracking Reference Values of Right Atrial Deformation in Healthy Adults.

BACKGROUND: Although right atrial (RA) myocardial deformation has important implications for patient diagnosis, prognosis, and risk stratification, its implementation in clinical practice has been hampered by limited normal reference values, especially in Asian populations. PURPOSE: To establish age- and sex-specific reference values for RA strain, strain rate (SR), and displacement based on a large sample of healthy Chinese adults using MR-feature tracking (MR-FT). STUDY TYPE: Retrospective. POPULATION: 524 healthy Chinese adults (287 male; mean age 43.7 ± 11.9 years). FIELD STRENGTH/SEQUENCE: 1.5T/balanced steady-state free precession. ASSESSMENT: RA deformation parameters, including reservoir, conduit, and booster strain (εs, εe, and εa), peak positive, early negative, and late negative SR (SRs, SRe, and SRa), and total, passive, and active displacement (Ds, De, and Da), were assessed using MR-FT. STATISTICAL TESTS: Student's t-test, one-way ANOVA, coefficients of determination (r2 ), intraclass correlation coefficients (ICC), and Bland-Altman plots. A P value <0.05 was considered significant. RESULTS: Women demonstrated significantly greater magnitudes of RA deformation parameters than men: εs (57.4% ± 15.1% vs. 44.3% ± 12.6%), εe (37.5% ± 13.4% vs. 27.4% ± 10.9%), εa (19.9% ± 5.7% vs. 16.9% ± 5.0%), SRs (2.62 ± 0.88 sec-1 vs. 2.00 ± 0.63 sec-1 ), SRe (-2.98 ± 1.26 sec-1 vs. -2.16 ± 0.92 sec-1 ), SRa (-2.28 ± 0.75 sec-1 vs. -1.84 ± 0.62 sec-1 ), Ds (-7.80 ± 1.90 mm vs. -7.46 ± 1.70 mm), and De (-4.84 ± 1.31 mm vs. -4.49 ± 1.21 mm). For both sexes, aging was significantly associated with decreased RA reservoir and conduit function (εs, SRs, Ds, εe, SRe, and De), and with increased εa and Da. RA deformation measurements had good to excellent intraobserver and interobserver reproducibility, with ICCs ranging from to 0.790 to 0.972. DATA CONCLUSION: This study provides age- and sex-specific reference values of RA strain, SR, and displacement based on a large cohort of healthy Chinese adults using MR-FT. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer.

This study aims to develop an MRI-based radiomics model to assess the likelihood of recurrence in luminal B breast cancer. The study analyzed medical images and clinical data from 244 patients with luminal B breast cancer. Of 244 patients, 35 had experienced recurrence and 209 had not. The patients were randomly divided into the training set (51.5 ± 12.5 years old; n = 171) and the test set (51.7 ± 11.3 years old; n = 73) in a ratio of 7:3. The study employed univariate and multivariate Cox regression along with the least absolute shrinkage and selection operator (LASSO) regression methods to select radiomics features and calculate a risk score. A combined model was constructed by integrating the risk score with the clinical and pathological characteristics. The study identified two radiomics features (GLSZM and GLRLM) from DCE-MRI that were used to calculate a risk score. The AUCs were 0.860 and 0.868 in the training set and 0.816 and 0.714 in the testing set for 3- and 5-year recurrence risk, respectively. The combined model incorporating the risk score, pN, and endocrine therapy showed improved predictive power, with AUCs of 0.857 and 0.912 in the training set and 0.943 and 0.945 in the testing set for 3- and 5-year recurrence risk, respectively. The calibration curve of the combined model showed good consistency between predicted and measured values. Our study developed an MRI-based radiomics model that integrates clinical and radiomics features to assess the likelihood of recurrence in luminal B breast cancer. The model shows promise for improving clinical risk stratification and treatment decision-making.

The value of bronchodilator response in FEV1 and FeNO for differentiating between chronic respiratory diseases: an observational study.

BACKGROUND: There is no uniform standard for a strongly positive bronchodilation test (BDT) result. In addition, the role of bronchodilator response in differentiating between asthma, chronic obstructive pulmonary disease (COPD), and asthma-COPD overlap (ACO) in patients with a positive BDT result is unclear. We explored a simplified standard of a strongly positive BDT result and whether bronchodilator response combined with fractional exhaled nitric oxide (FeNO) can differentiate between asthma, COPD, and ACO in patients with a positive BDT result. METHODS: Three standards of a strongly positive BDT result, which were, respectively, defined as post-bronchodilator forced expiratory volume in 1-s responses (ΔFEV1) increasing by at least 400 mL + 15% (standard I), 400 mL (standard II), or 15% (standard III), were analyzed in asthma, COPD, and ACO patients with a positive BDT result. Receiver operating characteristic curves were used to determine the optimal values of ΔFEV1 and FeNO. Finally, the accuracy of prediction was verified by a validation study. RESULTS: The rates of a strongly positive BDT result and the characteristics between standards I and II were consistent; however, those for standard III was different. ΔFEV1 ≥ 345 mL could predict ACO diagnosis in COPD patients with a positive BDT result (area under the curve [AUC]: 0.881; 95% confidence interval [CI] 0.83-0.94), with a sensitivity and specificity of 90.0% and 91.2%, respectively, in the validation study. When ΔFEV1 was < 315 mL combined with FeNO < 28.5 parts per billion, patients with a positive BDT result were more likely to have pure COPD (AUC: 0.774; 95% CI 0.72-0.83). CONCLUSION: The simplified standard II can replace standard I. ΔFEV1 and FeNO are helpful in differentiating between asthma, COPD, and ACO in patients with a positive BDT result.

Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment.

PURPOSE: The tumor immune microenvironment is a valuable source of information for predicting prognosis in breast cancer (BRCA) patients. To identify immune cells associated with BRCA patient prognosis from the Cancer Genetic Atlas (TCGA), we established an MRI-based radiomics model for evaluating the degree of immune cell infiltration in breast cancer patients. METHODS: CIBERSORT was utilized to evaluate the degree of infiltration of 22 immune cell types in breast cancer patients from the TCGA database, and both univariate and multivariate Cox regressions were employed to determine the prognostic significance of immune cell infiltration levels in BRCA patients. We identified independent prognostic factors for BRCA patients. Additionally, we obtained imaging features from the Cancer Imaging Archive (TCIA) database for 73 patients who underwent preoperative MRI procedures, and used the Least Absolute Shrinkage and Selection Operator (LASSO) to select the best imaging features for constructing an MRI-based radiomics model for evaluating immune cell infiltration levels in breast cancer patients. RESULTS: According to the results of Cox regression analysis, M2 macrophages were identified as an independent prognostic factor for BRCA patients (HR = 32.288, 95% CI: 3.100-357.478). A total of nine significant features were selected to calculate the radiomics-based score. We established an intratumoral model with AUCs (95% CI) of 0.662 (0.495-0.802) and 0.678 (0.438-0.901) in the training and testing cohorts, respectively. Additionally, a peritumoral model was created with AUCs (95% CI) of 0.826 (0.710-0.924) and 0.752 (0.525-0.957), and a combined model was established with AUCs (95% CI) of 0.843 (0.723-0.938) and 0.744 (0.491-0.965). The peritumoral model demonstrated the highest diagnostic efficacy, with an accuracy, sensitivity, and specificity of 0.773, 0.727, and 0.818, respectively, in its testing cohort. CONCLUSION: The MRI-based radiomics model has the potential to evaluate the degree of immune cell infiltration in breast cancer patients, offering a non-invasive imaging biomarker for assessing the tumor microenvironment in this disease.

Inhibition of xanthine oxidase by allopurinol suppresses HMGB1 secretion and ameliorates experimental asthma.

BACKGROUND: Extracellular high mobility group box 1 (HMGB1) is a key mediator in driving allergic airway inflammation and contributes to asthma. Yet, mechanism of HMGB1 secretion in asthma is poorly defined. Pulmonary metabolic dysfunction is recently recognized as a driver of respiratory pathology. However, the altered metabolic signatures and the roles of metabolic to allergic airway inflammation remain unclear. METHODS: Male C57BL/6 J mice were sensitized and challenged with toluene diisocyanate (TDI) to generate a chemically induced asthma model. Pulmonary untargeted metabolomics was employed. According to results, mice were orally administered allopurinol, a xanthine oxidase (XO) inhibitor. Human bronchial epithelial cells (16HBE) were stimulated by TDI-human serum albumin (HSA). RESULTS: We identified the purine metabolism was the most enriched pathway in TDI-exposed lungs, corresponding to the increase of xanthine and uric acid, products of purine degradation mediated by XO. Inhibition of XO by allopurinol ameliorates TDI-induced oxidative stress and DNA damage, mixed granulocytic airway inflammation and Th1, Th2 and Th17 immunology as well as HMGB1 acetylation and secretion. Mechanistically, HMGB1 acetylation was caused by decreased activation of the NAD+-sirtuin 1 (SIRT1) axis triggered by hyperactivation of the DNA damage sensor poly (ADP-ribose)-polymerase 1 (PARP-1). This was rescued by allopurinol, PARP-1 inhibitor or supplementation with NAD+ precursor in a SIRT1-dependent manner. Meanwhile, allopurinol attenuated Nrf2 defect due to SIRT1 inactivation to help ROS scavenge. CONCLUSIONS: We demonstrated a novel regulation of HMGB1 acetylation and secretion by purine metabolism that is critical for asthma onset. Allopurinol may have therapeutic potential in patients with asthma.

A Low Eosinophil to Platelet Ratio as a Worse Prognostic Index for Emergency Department Attendance in Acute Exacerbation of COPD.

Purpose: Identifying prognosis for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is challenging. Eosinophils and platelet are involved in the development of COPD, which may predict adverse events. The objective of this study was to determine the effect of the eosinophil to platelet ratio (EPR) in predicting adverse events in patients with AECOPD who visited the emergency department. Patients and Methods: The records of patients with AECOPD treated at Dalian Municipal Friendship Hospital from January 2018 to December 2020 were retrospectively reviewed. The relationship between the clinical characteristics and EPR, as cut-off value of 0.755, was evaluated. Results: A total of 508 patients with an AECOPD (316 male, 192 female) were included. An optimal AUC cutoff of 0.755 for the EPR segregated the patients into 2 groups with significantly different mortality (25.3% vs 5.5%, P < 0.001). The same mortality risk with lower EPR was observed among the patients with emergency room attendance (35.6% vs 11.1%, P < 0.001). A model including EPR <0.755, exacerbation history, PaO2 <60mmHg, PaCO2 >50 mm Hg, hypoalbuminemia and age ≥80 was developed to predict death risk and showed good performance. Conclusion: During severe COPD exacerbation, an EPR < 0.755 preceding therapy can predict worse outcomes in patients with an AECOPD.

Choline metabolism in regulating inflammatory bowel disease-linked anxiety disorders: A multi-omics exploration of the gut-brain axis.

Anxiety and depression caused by inflammatory bowel disease (IBD) negatively affect the mental health of patients. Emerging studies have demonstrated that the gut-brain axis (GBA) mediates IBD-induced mood disorders, but the underlying mechanisms of these findings remain unknown. Therefore, it's vital to conduct comprehensive research on the GBA in IBD. Multi-omics studies can provide an understanding of the pathological mechanisms of the GBA in the development of IBD, helping to uncover the mechanisms underlying the onset and progression of the disease. Thus, we analyzed the prefrontal cortex (PFC) of Dextran Sulfate Sodium Salt (DSS)-induced IBD mice using transcriptomics and metabolomics. We observed increased mRNA related to acetylcholine synthesis and secretion, along with decreased phosphatidylcholine (PC) levels in the PFC of DSS group compared to the control group. Fecal metagenomics also revealed abnormalities in the microbiome and lipid metabolism in the DSS group. Since both acetylcholine and PC are choline metabolites, we posited that the DSS group may experience choline deficiency and choline metabolism disorders. Subsequently, when we supplemented CDP-choline, IBD mice exhibited improvements, including decreased anxiety-like behaviors, reduced PC degradation, and increased acetylcholine synthesis in the PFC. In addition, administration of CDP-choline can restore imbalances in the gut microbiome and disruptions in lipid metabolism caused by DSS treatment. This study provides compelling evidence to suggest that choline metabolism plays a crucial role in the development and treatment of mood disorders in IBD. Choline and its metabolites appear to have a significant role in maintaining the stability of the GBA.