Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

ObjectiveTraditional Chinese medicine (TCM) constitutes a valuable cultural heritage and an important source of antitumor compounds. Poria (Poria cocos (Schw.) Wolf), the dried sclerotium of a polyporaceae fungus, was first documented in Shennong’s Classic of Materia Medica and has been used therapeutically and dietarily in China for millennia. Traditionally recognized for its diuretic, spleen-tonifying, and sedative properties, modern pharmacological studies confirm that Poria exhibits antioxidant, anti-inflammatory, antibacterial, and antitumor activities. Pachymic acid (PA; a triterpenoid with the chemical structure 3β-acetyloxy-16α-hydroxy-lanosta-8,24(31)-dien-21-oic acid), isolated from Poria, is a principal bioactive constituent. Emerging evidence indicates PA exerts antitumor effects through multiple mechanisms, though these remain incompletely characterized. Neuroblastoma (NB), a highly malignant pediatric extracranial solid tumor accounting for 15% of childhood cancer deaths, urgently requires safer therapeutics due to the limitations of current treatments. Although PA shows multi-mechanistic antitumor potential, its efficacy against NB remains uncharacterized. This study systematically investigated the potential molecular targets and mechanisms underlying the anti-NB effects of PA by integrating network pharmacology-based target prediction with experimental validation of multi-target interactions through molecular docking, dynamic simulations, and in vitro assays, aimed to establish a novel perspective on PA’s antitumor activity and explore its potential clinical implications for NB treatment by integrating computational predictions with biological assays. MethodsThis study employed network pharmacology to identify potential targets of PA in NB, followed by validation using molecular docking, molecular dynamics (MD) simulations, MM/PBSA free energy analysis, RT-qPCR and Western blot experiments. Network pharmacology analysis included target screening via TCMSP, GeneCards, DisGeNET, SwissTargetPrediction, SuperPred, and PharmMapper. Subsequently, potential targets were predicted by intersecting the results from these databases via Venn analysis. Following target prediction, topological analysis was performed to identify key targets using Cytoscape software. Molecular docking was conducted using AutoDock Vina, with the binding pocket defined based on crystal structures. MD simulations were performed for 100 ns using GROMACS, and RMSD, RMSF, SASA, and hydrogen bonding dynamics were analyzed. MM/PBSA calculations were carried out to estimate the binding free energy of each protein-ligand complex. In vitro validation included RT-qPCR and Western blot, with GAPDH used as an internal control. ResultsThe CCK-8 assay demonstrated a concentration-dependent inhibitory effect of PA on NB cell viability. GO analysis suggested that the anti-NB activity of PA might involve cellular response to chemical stress, vesicle lumen, and protein tyrosine kinase activity. KEGG pathway enrichment analysis suggested that the anti-NB activity of PA might involve the PI3K/AKT, MAPK, and Ras signaling pathways. Molecular docking and MD simulations revealed stable binding interactions between PA and the core target proteins AKT1, EGFR, SRC, and HSP90AA1. RT-qPCR and Western blot analyses further confirmed that PA treatment significantly decreased the mRNA and protein expression of AKT1, EGFR, and SRC while increasing the HSP90AA1 mRNA and protein levels. ConclusionIt was suggested that PA may exert its anti-NB effects by inhibiting AKT1, EGFR, and SRC expression, potentially modulating the PI3K/AKT signaling pathway. These findings provide crucial evidence supporting PA’s development as a therapeutic candidate for NB.

Aim To study the mechanism of quereetin (Que) inhibiting mitochondrial damage induced by Aβ

ObjectiveTo prepare a rat model of heart failure after myocardial infarction by ligation of the anterior descending branch of the left coronary artery， and to observe the effect of Shenfu Yixin granules on the mitochondrial dynamics of rats with heart failure. MethodFifty SD male rats were randomly taken ten as the sham operation group and the rest as modeling group. The rat model of heart failure after myocardial infarction was prepared by ligation of anterior descending branch of left coronary artery. According to the left ventricular ejection fraction（LVEF） on the 28^th day after operation， the model rats were randomly divided into the model group， Shenfu Yixin granule low-dose and high-dose groups（3.011， 15.055 g·kg^-1） and sacubitril valsartan sodium group（20.83 mg·kg^-1）. Each administration group was gavaged daily with the corresponding dose of drug solution， while the sham operation group and model group were given the same amount of normal saline once a day for 28 days， with 6 rats in each group. Ultrasound was used to detect the cardiac function parameters， rat heart mass and body mass were weighed to calculate the cardiac mass index， enzyme linked immunosorbent assay（ELISA） was used to detect serum brain natriuretic peptide（BNP） and soluble growth stimulation expressed gene 2 protein（sST2） levels. Hematoxylin-eosin（HE） staining was used to observe the pathological morphology of the myocardium. Real-time fluorescence quantitative polymerase chain reaction（Real-time PCR） and Western blot were used to detect the mRNA and protein expression of mitochondrial fusion protein 1/2（Mfn1/2）， optic atrophy protein 1（Opa1）， dynamin-related protein 1（Drp1） and fission protein 1（Fis1）. ResultCompared with the sham operation group， the mRNA and protein expression of LVEF， Mfn1， Mfn2， Opal in the model group decreased（P<0.05）， while BNP， sST2， cardiac mass index， Drp1， Fis1 mRNA and protein levels increased（P<0.05）. Compared with the model group， the expression of LVEF， Mfn1， Mfn2， Opal mRNA and protein increased in Shenfu Yixin granule high-dose and sacubitril valsartan sodium groups（P<0.05）， while BNP， sST2， cardiac mass index， Drp1， Fis1 mRNA and protein levels decreased（P<0.05）. Pathological observation showed that compared with the sham operation group， the model group had disordered arrangement of myocardial cells， inflammatory cell infiltration and myocardial fibrosis. Compared with the model group， the degree of inflammatory cell infiltration， myocardial or interstitial fibrosis was improved and alleviated in all administered groups. ConclusionShenfu Yixin granules can resist heart failure， reduce cardiac mass index， decrease BNP and sST2 contents， and improve cardiac function. Its mechanism may be related to the adjustment of mitochondrial dynamics.

Objective:To investigate the value of multi-parametric analysis based on dual-layer detector spectral CT (DLCT) in predicting the initial recurrence risk for papillary thyroid carcinoma (PTC).Methods:From November 2021 to October 2022, 102 PTC patients confirmed by pathology were retrospectively collected at the First Affiliated Hospital of Nanjing Medical University in this cross-sectional study. There were 25 males and 77 females, with an age of (42±13) years old. The initial recurrence risk assessment for PTC patients was categorized into a low-risk group (75 cases) and an intermediate-high-risk group (27 cases). Clinical data, including age, gender, body mass index, history of nodular goiter, history of Hashimoto thyroiditis, and preoperative thyroid function, were collected. Tumor morphological features, including size, location, shape, aspect ratio, the degree of thyroid capsule contact, calcification, and cystic change, were evaluated. Quantitative DLCT parameters, including iodine concentration (IC), standardized iodine concentration (NIC), effective atomic number (Z eff), standardized effective atomic number (NZ eff), electronic density (ED), CT values under different energy levels (40-200 keV, 30 keV intervals) and slope of energy spectrum curve (λ HU) both in the arterial and venous phase were measured. The differences in clinical, morphological features, and spectral CT quantitative parameters between the two groups were compared using independent sample ttest, Mann-Whitney U test, or χ2 test. Multivariate logistic regression analyses were used to construct three models based on clinical and morphological features, quantitative DLCT parameters and their combination, respectively. The receiver operating characteristic curve was used to evaluate the predictive performance of these models for the initial recurrence risk of PTC patients, and the area under the curve (AUC) was compared using the DeLong test. Results:Significant differences were found in gender, lesion long diameter, lesion short diameter and calcification between the low-risk group and intermediate-high-risk groups ( P<0.05). The arterial phase IC, arterial phase Z eff, arterial phase λ HU, arterial phase CT 40 keV, venous phase NIC and venous phase NZ eff in intermediate-high-risk group were significantly lower than those in the low-risk group ( P<0.05). The logistic regression analysis revealed that the clinical model included gender ( OR=2.895, 95% CI 1.047-8.002, P=0.040) and lesion long diameter ( OR=1.142, 95% CI 1.042-1.251, P=0.004), with an AUC of 0.720, sensitivity of 63.0%, and specificity of 78.7% in predicting the initial recurrence risk of PTC patients. The DLCT quantitative parameter model included arterial phase IC ( OR=0.580, 95% CI 0.370-0.908, P=0.017), venous phase NIC ( OR=0.077, 95% CI 0.011-0.536, P=0.010), and venous phase NZ eff ( OR=0.002, 95% CI 0.001-0.103, P=0.009), with an AUC of 0.774, sensitivity of 71.9%, and specificity of 70.0%. The AUC of the combined model was 0.857, with a sensitivity of 74.1%, and specificity of 88.0%, outperforming the clinical model ( Z=2.92, P=0.004) and the DLCT quantitative parameter model ( Z=2.07, P=0.046). Conclusion:Multi-parametric analysis based on DLCT can help predict the initial recurrence risk for PTC, and combining it with clinical and morphological features, the predictive accuracy can be improved.

Pain is one of the most common symptoms in pancreatic cancer patients. Pancreatic cancer-related pain has various sources and complex mechanisms, which seriously affects patients' quality of life and adversely affects their prognosis. Effective pain management may prolong the survival of pancreatic cancer patients. Since the pathogenesis of pancreatic cancer-related pain has not been elucidated, part of the pain management may be based on the mechanism, and part of the pain management may be based on physician's experience only. This article discusses both the pathophysiological classification and treatment strategies of pancreatic cancer-related pain, with the aim of providing reference for clinical analgesic practice in pancreatic cancer.