Aurantiamide mitigates acute kidney injury by suppressing renal necroptosis and inflammation via GRPR-dependent mechanism.

Acute kidney injury (AKI) manifests as a clinical syndrome characterised by the rapid accumulation of metabolic wastes, such as blood creatinine and urea nitrogen, leading to a sudden decline in renal function. Currently, there is a lack of specific therapeutic drugs for AKI. Previously, we identified gastrin-releasing peptide receptor (GRPR) as a pathogenic factor in AKI. In this study, we investigated the therapeutic potential of a novel Chinese medicine monomer, aurantiamide (AA), which exhibits structural similarities to our previously reported GRPR antagonist, RH-1402. We compared the therapeutic efficacy of AA with RH-1402 both in vitro and in vivo using various AKI models. Our results demonstrated that, in vitro, AA attenuated injury, necroptosis, and inflammatory responses in human renal tubular epithelial cells subjected to repeated hypoxia/reoxygenation and lipopolysaccharide stimulation. In vivo, AA ameliorated renal tubular injury and inflammation in mouse models of ischemia/reperfusion and cecum ligation puncture-induced AKI, surpassing the efficacy of RH-1402. Furthermore, molecular docking and cellular thermal shift assay confirmed GRPR as a direct target of AA, which was further validated in primary cells. Notably, in GRPR-silenced HK-2 cells and GRPR systemic knockout mice, AA failed to mitigate renal inflammation and injury, underscoring the importance of GRPR in AA's mechanism of action. In conclusion, our study has demonstrated that AA serve as a novel antagonist of GRPR and a promising clinical candidate for AKI treatment.

Application of Optogenetics in Neurodegenerative Diseases.

Optogenetics, a revolutionary technique integrating optical and genetic methodologies, offers unparalleled precision in spatial targeting and temporal resolution for cellular control. This approach enables the selective manipulation of specific neuronal populations, inducing subtle electrical changes that significantly impact complex neural circuitry. As optogenetics precisely targets and modulates neuronal activity, it holds the potential for significant breakthroughs in understanding and potentially altering the course of neurodegenerative diseases, characterized by selective neuronal loss leading to functional deficits within the nervous system. The integration of optogenetics into neurodegenerative disease research has significantly advanced in the field, offering new insights and paving the way for innovative treatment strategies. Its application in clinical settings, although still in the nascent stages, suggests a promising future for addressing some of the most challenging aspects of neurodegenerative disorders. In this review, we provide a comprehensive overview of these research undertakings.

CCDC59 alleviates bleomycin-induced inflammation and pulmonary fibrosis by increasing SP-B and SP-C expression in mice.

BACKGROUND: Pulmonary fibrosis is a progressive disease with high incidence and poor prognosis. It is urgent to explore new therapeutic methods for pulmonary fibrosis. As a new treatment method, gene therapy has attracted more and more attention. CCDC59 is a transcriptional coactivator of SP-B and SP-C. Our study mainly aims to explore the effect of overexpression of CCDC59 gene in pulmonary fibrosis of mice. METHODS: CCDC59 overexpressing lentivirus was constructed and then concentrated. RT-qPCR, Western blotting, and immunofluorescence assays were used to detect the expression of CCDC59, SP-B and SP-C protein in cell line and lung tissues after infected with lentivirus. Immunohistochemical staining and hematoxylin-eosin staining assays were used to assess the degree of fibrosis and ELISA assay was used to detect the concentrations of inflammatory factors, SP-B, and SP-C in bronchoalveolar lavage fluid of mice. Dynamic changes of mice lung function at various time points were assessed by lung function test assay. HIPPO pathway and proliferation capacity of alveolar type II epithelial cells were evaluated by immunofluorescence staining and Western blotting. RESULTS: Results showed that endotracheal instillation of CCDC59 overexpressed lentivirus significantly alleviated bleomycin-induced inflammation and pulmonary fibrosis in mice. Overexpression of CCDC59 protein in type II alveolar epithelial cells can enhance the expression of SP-B and SP-C. Overexpression of CCDC59 protein significantly protected against pulmonary inflammatory response and improved lung function of mice. Overexpression of CCDC59 protein significantly alleviated the hyperactivation of HIPPO pathway and increased the proliferative capacity of type II alveolar epithelial cells in lung. CONCLUSION: CCDC59 can alleviate inflammation and pulmonary fibrosis in mice by upregulating the expression of SP-B and SP-C in type II alveolar epithelial cells and alleviating the hyperactivation of HIPPO pathway. Our study offers a new potential treatment for pulmonary fibrosis.

Communication between alveolar macrophages and fibroblasts via the TNFSF12-TNFRSF12A pathway promotes pulmonary fibrosis in severe COVID-19 patients.

BACKGROUND: Severe COVID-19 infection has been associated with the development of pulmonary fibrosis, a condition that significantly affects patient prognosis. Understanding the underlying cellular communication mechanisms contributing to this fibrotic process is crucial. OBJECTIVE: In this study, we aimed to investigate the role of the TNFSF12-TNFRSF12A pathway in mediating communication between alveolar macrophages and fibroblasts, and its implications for the development of pulmonary fibrosis in severe COVID-19 patients. METHODS: We conducted single-cell RNA sequencing (scRNA-seq) analysis using lung tissue samples from severe COVID-19 patients and healthy controls. The data was processed, analyzed, and cell types were annotated. We focused on the communication between alveolar macrophages and fibroblasts and identified key signaling pathways. In vitro experiments were performed to validate our findings, including the impact of TNFRSF12A silencing on fibrosis reversal. RESULTS: Our analysis revealed that in severe COVID-19 patients, alveolar macrophages communicate with fibroblasts primarily through the TNFSF12-TNFRSF12A pathway. This communication pathway promotes fibroblast proliferation and expression of fibrotic factors. Importantly, silencing TNFRSF12A effectively reversed the pro-proliferative and pro-fibrotic effects of alveolar macrophages. CONCLUSION: The TNFSF12-TNFRSF12A pathway plays a central role in alveolar macrophage-fibroblast communication and contributes to pulmonary fibrosis in severe COVID-19 patients. Silencing TNFRSF12A represents a potential therapeutic strategy for mitigating fibrosis in severe COVID-19 lung disease.

Identification of symptom clusters and change trajectories in patients with acute exacerbation of chronic obstructive pulmonary disease.

Aim and objectives: This study aimed to identify symptom cluster (SC) patterns and change trajectories in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), the correlation of the SCs with laboratory and imaging indicators, and the intrinsic association of the SCs with prognostic outcomes and disease burden. Method: Symptom information was collected using a digital evaluation scoring system at the time of admission, on the third day after admission, and upon discharge. Laboratory and imaging examination data were compiled simultaneously. Exploratory factor analysis was used to identify the AECOPD SCs. The number of factors (clusters) was determined by examining factors with eigenvalues ≥1.0, using 0.50 for factor loadings as the minimum cut-off value. Spearman's correlation analysis was used to explore the link between the SCs and laboratory and imaging indicators, as well as the relationship between the severity of the symptoms in different clusters, prognostic outcomes, and disease burden. Results: This study included 148 patients. Three SCs were identified: activity-nutrition SC, breath-sleep SC and respiratory SC. Correlation analysis indicated a connection between the activity-nutrition SC and the white blood cell count, and serum sodium and potassium levels, whereas the breath-sleep SC was correlated with white blood cells and eosinophil counts, serum potassium level, and pleural effusion. Additionally, the respiratory SC was associated with serum calcium and magnesium levels, the partial pressure of carbon dioxide, and C-reactive protein (CRP) level. There was a positive correlation between the activity-nutrition SC and hospitalization cost, as well as between the breath-sleep SC and both the hospitalization length and cost. Conclusion: Patients with AECOPD presented three SCs that affected the length and cost of hospitalization. Concurrently, the severity of the symptoms in the clusters was related to white blood cell and eosinophil counts; serum sodium, potassium, calcium, and magnesium levels; CRP level; the partial pressure of carbon dioxide; and pleural effusion, indicating that the symptoms in each clusters may share related physiological mechanisms. An in-depth exploration of the pathogenesis and intervention paths of health problems is of great significance for promoting precision nursing.

Synthesis of Controllable Superparamagnetic Nano Fe3O4 Based on Reduction Method for Colloidal Clusters of Magnetically Responsive Photonic Crystals.

In this paper, we designed and investigated a reduction-based method to synthesize controllably monodisperse superparamagnetic nano Fe3O4 colloidal clusters for magnetically responsive photonic crystals. It was shown that the addition of ascorbic acid (VC) to the system could synthesize monodisperse superparamagnetic nano Fe3O4 and avoided the generation of γ-Fe2O3 impurities, while the particle size and saturation magnetization intensity of nano Fe3O4 gradually decreased with the increase of VC dosage. Nano Fe3O4 could be rapidly assembled into photonic crystal dot matrix structures under a magnetic field, demonstrating tunability to various diffraction wavelengths. The nano Fe3O4 modified by polyvinylpyrrolidone (PVP) and silicon coated could be stably dispersed in a variety of organic solvents and thus diffracted different wavelengths under a magnetic field. This is expected to be applied in various scenarios in the field of optical color development.

Quantitative Pattern of hPTMs by Mass Spectrometry-Based Proteomics with Implications for Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is known for its aggressive nature, and TNBC management is currently challenging due to the lack of effective targets. Despite the importance of histone post-translational modifications (hPTMs) in breast cancer, their associations with molecular subtypes of breast cancer, especially TNBC, are poorly understood. In this study, a combination of untargeted and targeted proteomics approaches, supplemented by a derivatization method, was applied to breast cancer cells and tissue samples. Untargeted proteomics of eight breast cancer cell lines belonging to different molecular subtypes revealed 36 modified peptides with 12 lysine modification sites in histone H3, and the most frequently reported top 5 histone H3 methylation and acetylation sites were covered. Then, targeted proteomics was carried out to quantify the total 20 target hPTMs at the covered modification sites (i.e., mono-, di-, trimethylation, and acetylation for each site), indicating the difficulty in distinguishing TNBC cells from normal cells. Subsequently, the analysis in TNBC patients revealed significant expression differences in 4 specific hPTMs (H3K14ac, H3K27me1, H3K36me2, and H3K36me3) between TNBC and adjacent normal tissue samples. These unique hPTM patterns allowed for the differentiation of TNBC from normal cases. This finding provides promising implications for advancing targeted treatment strategies for TNBC in the future.

Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction.

PURPOSE: To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC. METHODS: This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM). RESULTS: T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively. CONCLUSION: A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.

Real-time detection of 20 amino acids and discrimination of pathologically relevant peptides with functionalized nanopore.

Precise identification and quantification of amino acids is crucial for many biological applications. Here we report a copper(II)-functionalized Mycobacterium smegmatis porin A (MspA) nanopore with the N91H substitution, which enables direct identification of all 20 proteinogenic amino acids when combined with a machine-learning algorithm. The validation accuracy reaches 99.1%, with 30.9% signal recovery. The feasibility of ultrasensitive quantification of amino acids was also demonstrated at the nanomolar range. Furthermore, the capability of this system for real-time analyses of two representative post-translational modifications (PTMs), one unnatural amino acid and ten synthetic peptides using exopeptidases, including clinically relevant peptides associated with Alzheimer's disease and cancer neoantigens, was demonstrated. Notably, our strategy successfully distinguishes peptides with only one amino acid difference from the hydrolysate and provides the possibility to infer the peptide sequence.

Chitosan-based fluorescent probe for the detection of Fe3+ in real water and food samples.

Iron ions play a crucial role in the environment and the human body. Therefore, developing an effective detection method is crucial. In this paper, we report CNS2, a chitosan-based fluorescent probe utilizing naphthalimide as a fluorophore. CNS2 is designed to "quench" its own yellow fluorescence through the specific binding of compounds containing enol structures to Fe3+. Studying the fluorescence lifetime of CNS2 in the presence or absence of Fe3+ reveals that the quenching mechanism is static. The presence of multiple recognition sites on the chitosan chain bound to Fe3+ gave CNS2 rapid recognition (1 min) and high sensitivity, with a detection limit as low as 0.211 µM. Moreover, the recognition of Fe3+ by CNS2 had a good specificity and was not affected by interferences. More importantly, in this study, CNS2 was successfully utilised to prepare fluorescent composite membranes and to detect Fe3+ in real water samples and a variety of food samples. The results show that the complex sample environment still does not affect the recognition of Fe3+ by CNS2. All the above experiments obtained more satisfactory results, which provide strong support for the detection of Fe3+ by the probe CNS2 in practical applications.

The awareness and determinants of diabetic foot ulcer prevention among diabetic patients: Insights from NHANES (2011-2018).

The prevention of diabetic foot ulcers (DFU) precedes treatment, in that early prevention significantly reduces the incidence of foot ulcers. The main objectives of this study were to examine the current prevalence of proactive foot ulcer examinations among diabetic patients and analyze influencing factors, in order to provide a scientific reference for the prevention of DFU in diabetic patients. The National Health and Nutrition Examination Survey (NHANES) 2011-2018 (n = 1278) data were utilized in this cross-sectional study. The dependent variable was whether patients underwent self-initiated foot ulcer inspections; risk factors that may lead to foot ulcers were included as independent variables. To explore the connection between the patient's subjective motivation to inspect foot ulcers and risk variables, the weighted logistic regression model was further carried out. Among all risk factors, race, body mass index (BMI) and hypertension were statistically significant between whether patients were examined for foot ulcers or not. In the fully adjusted logistic regression model, only hypertension was positively correlated with diabetic patient-initiated examination for foot ulcers. This study suggests that there is still room for improvement in the knowledge and behavior of diabetic patients to be proactive in preventing DFU. Health care and community workers should conduct targeted training on diabetic foot prevention to reduce and prevent DFU by reinforcing knowledge to build positive attitudes and drive preventive behavior change.

Novel Prognostic Nomogram to Predict Progression-Free Survival of Patients with Hepatocellular Carcinoma After Transarterial Chemoembolization.

Purpose: A retrospective analysis of hepatocellular carcinoma (HCC) patients treated with transarterial chemoembolization (TACE) to identify risk factors was conducted, and a novel predictive nomogram model was constructed. Patients and Methods: A total of 346 HCC patients who underwent TACE as initial treatment were retrospectively included, of which 208 were randomly allocated to the derivation cohort and 138 were allocated to the validation cohort. Progression-free survival (PFS) was used as the follow-up endpoint according to mRECIST. Kaplan‒Meier analysis and the Cox regression model screened out some indicators associated with short-term prognosis, and R language was further used to construct a nomogram model. The nomogram was compared with the classical BCLC staging system. Results: The independent predictors affecting PFS in HCC patients undergoing TACE included the following: 1. Baseline indicators: age (P=0.013), albumin-bilirubin (ALBI) grade (grade 2 vs grade 1, P=0.029; grade 3 vs grade 1, P<0.001), and portal vein tumour thrombus (PVTT, P<0.001); 2. Indicators at the 1-month follow-up: Neutrophil To Lymphocyte Ratio (NLR, P=0.032) and changes in alpha-fetoprotein (AFP, P<0.05) and des-γ-carboxy prothrombin (DCP, P<0.001); and 3. Cumulative treatment numbers of TACE in 6 months (P=0.007). In the derivation cohort, the calibration curve of the nomogram showed a high consistency between the predicted and actual PFS probability, and the nomogram outperformed the BCLC staging system (P=0.004). This result was also confirmed in the validation cohort (P=0.012). Conclusion: The constructed nomogram was suggested to have good predictive efficacy and could be used as a complementary assessment to predict the survival and prognosis of HCC patients treated with TACE.

A Multi-Label Detection Deep Learning Model with Attention-Guided Image Enhancement for Retinal Images.

At present, multi-disease fundus image classification tasks still have the problems of small data volumes, uneven distributions, and low classification accuracy. In order to solve the problem of large data demand of deep learning models, a multi-disease fundus image classification ensemble model based on gradient-weighted class activation mapping (Grad-CAM) is proposed. The model uses VGG19 and ResNet50 as the classification networks. Grad-CAM is a data augmentation module used to obtain a network convolutional layer output activation map. Both the augmented and the original data are used as the input of the model to achieve the classification goal. The data augmentation module can guide the model to learn the feature differences of lesions in the fundus and enhance the robustness of the classification model. Model fine tuning and transfer learning are used to improve the accuracy of multiple classifiers. The proposed method is based on the RFMiD (Retinal Fundus Multi-Disease Image Dataset) dataset, and an ablation experiment was performed. Compared with other methods, the accuracy, precision, and recall of this model are 97%, 92%, and 81%, respectively. The resulting activation graph shows the areas of interest for model classification, making it easier to understand the classification network.

The impact of pharmacist intervention on the intravenous-to-oral switch therapy of proton pump inhibitors in cardiovascular surgery.

BACKGROUND: The prescriptions of proton pump inhibitors (PPIs) have been widely concerned due to both huge increase in medical costs and possible long-term adverse events (AEs) caused by the improper route of drug administration. The aim of this study was to assess the effectiveness of pharmacist interventions on the clinical outcome and safety of switching from intravenous (IV) to oral PPIs therapy. PATIENTS AND METHODS: A retrospective, single-center, pre- intervention (early -stage)- and intervention (later -stage) study was performed in a Chinese hospital. RESULTS: A total of 1736 patients were included in the study. After 12 months of interventions, significant improvements in the number of rational IV to oral switch in patients with oral switch indications were found. The median duration of oral therapy was increased, while the duration of PPIs therapy was decreased. Pharmacist interventions led to significant reductions in mean PPI costs, mean total drug costs, mean hospitalization costs, and the risk for long-term adverse events. CONCLUSION: This study provides important evidence on the beneficial effect of pharmacist interventions on promoting an optimal IV to oral switch of PPIs and substantial cost saving by shortening the duration of IV PPIs therapy and reducing the risk for long-term AEs.

Bacterial cellulose flakes loaded with Bi2MoO6 nanoparticles and quantum dots for the photodegradation of antibiotic and dye pollutants.

Effective strategies to improve charge separation in semiconductor particles are critical for improving the photodegradation of organic pollutants at levels sufficient for environmental applications. Herein, Bi2MoO6 (BMOMOF), comprising both nanoparticles (NPs) and quantum dots (QDs), was synthesized from a bismuth-based metal-organic framework (Bi-MOF) precursor. Surface defects on BMOMOF, the combination of NPs and QDs, and modified energy band edges improved photogenerated charge separation and facilitated redox reactions. When compared to BMO derived from uncoordinated Bi, the BMOMOF photocatalyst (PC) was more efficient at photodegrading tetracycline hydrochloride (TCH) and ciprofloxacin (CIP), two widely-used antibiotics ubiquitous in wastewater, as well as the carcinogenic pollutant rhodamine B (RhB). BMOMOF was then loaded on the biopolymer bacterial cellulose (BC) to further enhance photocatalytic performance and facilitate the recovery of the PC after water treatment processes. The novel BMOMOF/BC photocatalytic flakes were significantly larger than pure BMOMOF, and thus easier to recuperate. Furthermore, anchoring BMOMOF on BC flakes augmented significantly the photodegradation of TCH, CIP, and RhB, mainly because hydroxyl groups in BC act as hole traps facilitating photogenerated electron-hole separation. Results obtained with BMOMOF/BC highlight promising approaches to develop optimal PCs for aqueous pollutants degradation.

Versatile iodine-doped BiOCl with abundant oxygen vacancies and (110) crystal planes for enhanced pollutant photodegradation.

Crystal plane regulation, defect engineering, and element doping can effectively solve the problems of large band gaps, poor light absorption, and fast recombination of BiOCl. In this work, iodine-doped BiOCl (I/BiOCl) nanowafers with abundant (110) crystal planes and oxygen vacancies (OV) were prepared by a simple hydrothermal method and assessed for pollutant photodegradation. I/BiOCl with a molar ratio of I to Cl of 0.6 (I0.6/BiOCl) degraded under visible light 95.8% of the toxic dye rhodamine B and 85.1% of the persistent antibiotic tetracycline in 5 and 10 min, respectively. In comparison, unmodified BiOCl photodegraded only between 42.0% and 48.2% of these critical water pollutants. Furthermore, I0.6/BiOCl was highly stable with most of its photocatalytic activity remaining after 4 cycles. Three reasons explain the excellent photodegradation properties of I0.6/BiOCl. First, the doped photocatalyst grew abundant (110) crystal planes, which inhibits the recombination of photogenerated electron-hole pairs. Second, the large quantity of OV present in I0.6/BiOCl increased active sites for reactive oxygen species generation, improved photogenerated charge separation, and pollutants adsorption. Lastly, I0.6/BiOCl had a modified electronic band structure enhancing light absorption. Overall, these results describe a promising photocatalyst capable of degrading efficiently major pollutants with different structures.

Application of preoperative CT texture analysis in papillary gastric adenocarcinoma.

BACKGROUND: This study aimed to analyze the ability of computed tomography (CT) texture analysis to discriminate papillary gastric adenocarcinoma (PGC) and to explore the diagnostic efficacy of multivariate models integrating clinical information and CT texture parameters for discriminating PGCs. METHODS: This retrospective study included 20 patients with PGC and 80 patients with tubular adenocarcinoma (TAC). The clinical data and CT texture parameters based on the arterial phase (AP) and venous phase (VP) of all patients were collected and analyzed. Two CT signatures based on the AP and VP were built with the optimum features selected by the least absolute shrinkage and selection operator method. The performance of CT signatures was tested by regression analysis. Multivariate models based on regression analysis and the support vector machine (SVM) algorithm were established. The diagnostic performance of the established nomogram based on regression analysis was evaluated by receiver operating characteristic curve analysis. RESULTS: Thirty-two and fifteen CT texture parameters extracted from AP and VP CT images, respectively, differed significantly between PGCs and TACs (all p < 0.05). The diagnostic performance of CT signatures based on the AP and VP achieved AUCs of 0.873 and 0.859 in distinguishing PGCs. Multivariate models that integrated two CT signatures and age based on regression analysis and the SVM algorithm showed favorable performance in preoperatively predicting PGCs (AUC = 0.922 and 0.914, respectively). CONCLUSION: CT texture analysis based multivariate models could preoperatively predict PGCs with satisfactory diagnostic efficacy.

Improving Image Quality and Reducing Scan Time for Synthetic MRI of Breast by Using Deep Learning Reconstruction.

Objectives: To investigate a deep learning reconstruction algorithm to reduce the time of synthetic MRI (SynMRI) scanning on the breast and improve the image quality. Materials and Methods: A total of 192 healthy female volunteers (mean age: 48.1 years) underwent the breast MR examination at 3.0 T from September 2020 to June 2021. Standard SynMRI and fast SynMRI scans were collected simultaneously on the same volunteer. Deep learning technology with a generative adversarial network (GAN) was used to generate high-quality fast SynMRI images by end-to-end training. Peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM) were used to compare the image quality of generated images from fast SynMRI by deep learning algorithms. Results: Fast SynMRI acquisition time is half of the standard SynMRI scan, and the generated images of the GAN model show that PSNR and SSIM are improved and MSE is reduced. Conclusion: The application of deep learning algorithms with GAN model in breast MAGiC MRI improves the image quality and reduces the scanning time.

Development and validation of multivariate models integrating preoperative clinicopathological and radiographic findings to predict HER2 status in gastric cancer.

The combination of trastuzumab and chemotherapy is recommended as first-line therapy for patients with human epidermal growth factor receptor 2 (HER2) positive advanced gastric cancers (GCs). Successful trastuzumab-induced targeted therapy should be based on the assessment of HER2 overexpression. This study aimed to evaluate the feasibility of multivariate models based on hematological parameters, endoscopic biopsy, and computed tomography (CT) findings for assessing HER2 overexpression in GC. This retrospective study included 183 patients with GC, and they were divided into primary (n = 137) and validation (n = 46) cohorts at a ratio of 3:1. Hematological parameters, endoscopic biopsy, CT morphological characteristics, and CT value-related and texture parameters of all patients were collected and analyzed. The mean corpuscular hemoglobin concentration value, morphological type, 3 CT value-related parameters, and 22 texture parameters in three contrast-enhanced phases differed significantly between the two groups (all p < 0.05). Multivariate models based on the regression analysis and support vector machine algorithm achieved areas under the curve of 0.818 and 0.879 in the primary cohort, respectively. The combination of hematological parameters, CT morphological characteristics, CT value-related and texture parameters could predict HER2 overexpression in GCs with satisfactory diagnostic efficiency. The decision curve analysis confirmed the clinical utility.

Clinicopathological features and CT findings of papillary gastric adenocarcinoma.

PURPOSE: This study aimed to analyze the clinicopathological and computed tomography (CT) findings of papillary gastric adenocarcinoma and to evaluate the feasibility of the multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas. METHODS: This retrospective study included 22 patients with papillary gastric adenocarcinoma and 88 patients with tubular adenocarcinoma. The demographic data, tumor markers, histopathological information, CT morphological characteristics, and CT value-related parameters of all patients were collected and analyzed. The multivariate model based on regression analysis was performed to improve the diagnostic efficacy for discriminating papillary gastric adenocarcinomas preoperatively. The diagnostic performance of the established nomogram was evaluated by receiver operating characteristic curve analysis. RESULTS: The distribution of age, carcinoembryonic antigen, differentiation degree, neural invasion, human epidermal growth factor receptor 2 overexpression, P53 mutation status, 4 CT morphological characteristics, and 10 CT valued-related parameters differed significantly between papillary gastric adenocarcinoma and tubular adenocarcinoma groups (all p < 0.05). The established multivariate model based on clinical information and CT findings for discriminating papillary gastric adenocarcinomas preoperatively achieved the area under the curve of 0.920. CONCLUSION: There existed differences in clinicopathological features and CT findings between papillary gastric adenocarcinomas and tubular adenocarcinomas. The combination of demographic data, tumor markers, CT morphological characteristics, and CT value-related parameters could discriminate papillary gastric adenocarcinomas preoperatively with satisfactory diagnostic efficiency.