Diabetic nephropathy (DN) is a critical microvascular complication of diabetes. Increasing evidence suggests that dysregulation of glutamine metabolism contributes to DN pathogenesis. This study aimed to explore alterations in glutamine metabolism-related genes (GMRGs) in DN. Nine differentially expressed GMRGs (DE-GMRGs) were identified by intersecting 103 GMRGs with 2,281 DEGs from the GSE142153 dataset comparing normal and DN groups. Notably, DE-GMRGs located on autosomes were significantly enriched in pathways related to glutamine metabolism and the metabolism of alanine, aspartate, and glutamate. The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methods were employed to pinpoint key genes, SLC7A5 and SLC25A12. SLC7A5 was found to be upregulated in DN, whereas SLC25A12 showed decreased expression. The diagnostic potential of these genes was further validated by assessing the area under the receiver operating characteristic (ROC) curve. Correlation analysis revealed strong associations between these key genes and clinical markers such as glomerular filtration rate (GFR), serum creatinine, and immune cells, including mast cells and effector memory CD8 T cells. Drug prediction and molecular docking analyses indicated that valproic acid might serve as an effective therapeutic agent targeting these genes. Glutamine metabolism-related genes SLC7A5 and SLC25A12 were identified as potential diagnostic and therapeutic targets for DN. These findings offer valuable clinical insights for the diagnosis and management of DN.

To explore the relationship between the triglyceride-glucose index (TyG), the monocyte to high-density lipoprotein cholesterol ratio (MHR) and the severity of coronary artery disease (CAD) under different glucose metabolism states.

A retrospective analysis was conducted on 526 patients who underwent coronary angiography (CAG) for the first time in the Affiliated Hospital of Xuzhou Medical University from January 2024 to January 2025. Among them, there were 122 patients in the non-CAD group and 404 patients in the CAD group. According to the Gensini score, the CAD group was further divided into a mild group (n = 147) and a moderate-to-severe group (n = 257). Meanwhile, they were divided into normal glucose regulation (NGR), prediabetes (Pre-DM), and diabetes mellitus (DM) groups according to the glucose metabolism state. Multivariate Logistic regression, restricted cubic spline (RCS), and receiver operating characteristic (ROC) curve analyses were used.

Both the TyG index and MHR were independent risk factors for the occurrence and severity of CAD (P<0.05). In the DM group, the TyG index was significantly associated with the severity of CAD (OR=4.30, 95% CI: 1.48-12.49, P<0.01); in the NGR group, MHR was significantly associated with the severity of CAD (OR=436.1, 95% CI: 15.4-12342, P<0.001). RCS analysis suggested a significant linear positive correlation between the TyG index and the severity of CAD (P-overall=0.006, P-non-linear=0.917), while there was a non-linear relationship between MHR and the severity of CAD (P-overall=0.007, P-non-linear=0.033). ROC analysis showed that the area under the curve (AUC) of the combined prediction was 0.655, higher than that of the TyG index (0.618) and MHR (0.631).

TyG index and MHR can serve as independent biomarkers of new-onset CAD severity. In DM patients, TyG offers greater predictive value, while MHR is more predictive in NGR individuals.

This study aimed to develop and validate a Deep Learning(DL)-based model for detecting Prostate cancer (PCa) in patients with gray-zone PSA levels using multiparametric Magnetic Resonance Imaging (mp-MRI).

This retrospective multi-center study initially reviewed data from 1778 patients with suspected prostate lesions who presented at multiple hospitals between December 2015 and October 2023. The DL algorithm was trained on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) values. The performance of the algorithm was evaluated using the area under the receiver operating characteristic (ROC) curve and compared to the diagnostic performance of radiologists with varying levels of expertise.

The final study cohort comprised 274 patients, who were divided into training, validation, and test sets in a 6:2:2 ratio, corresponding to 162, 56, and 56 patients, respectively. The mean age of the patients was 71.8 years, and the cohort included 90 patients with prostate cancer and 184 without. The multi-attribute Convolutional Neural Networks (CNNs) model achieved an area under the curve (AUC) of 0.84, surpassing individual ADC and T2WI models. It also outperformed a transformer-based model (AUC 0.79). When compared to radiologists, the DL model performed similarly to experienced radiologists (AUC 0.86) and outperformed less experienced radiologists (AUC 0.71).

The multimodal CNNs, integrating ADC and T2WI data, demonstrated high accuracy for PCa detection in patients with gray-zone PSA levels, comparable to senior radiologists. This model can aid clinical decision-making, assist in challenging cases, and help reduce unnecessary biopsies.

Inflammatory myofibroblastoma tumor (IMT) is rare neoplasm that has high recurrence rate and low likelihood of metastasis. Here, we report the efficacy and safety of microwave ablation (MWA) combined with transarterial embolization in a patient with unresectable retroperitoneal IMT measuring 89 mm in maximum diameter. Multiple courses of MWA, the last of which was performed under magnetic resonance imaging guidance, and one course of transarterial embolization resulted in complete response without complications. After more than 1.5 years of the last MWA treatment, follow-up showed sustained complete response. This case suggested that the combination of MWA and transarterial embolization is a new option for retroperitoneal IMT. A multicenter and large-sample clinical trial is required to confirm the efficacy and safety of MWA combined with transarterial embolization for retroperitoneal IMT.

To explore a noninvasive predicting model for identifying patients with stage I lung invasive adenocarcinoma (IAC).

This study enrolled 289 patients from two medical centers, with 227 and 62 patients in the training and validation sets, respectively. Patients' chest computed tomography (CT) images were used. The K-means cluster algorithm was employed to group patients into new clusters based on radiomics features. In addition, logistic regression was used to develop prediction models. Diagnostic efficiency was assessed using the area under the receiver operating characteristic curve, along with calibration and decision curve analysis.

The K-means cluster algorithm classified patients into cluster 1 (training: 143; validation: 35) and cluster 2 (training: 84; validation: 27). Cluster 2 had a higher proportion of patients with IAC. The optimal model incorporating tumor diameter, tumor type, and cluster labels achieved the best discriminatory performance, with area under the receiver operating characteristic curve values of 0.848 (95% confidence interval: 0.799-0.898) in the training set and 0.744 (95% confidence interval: 0.583-0.905) in the validation set.

This study proposes a radiomics model that accurately identifies patients with IAC. This prediction tool could aid in personalized risk classification and treatment planning.

To explore the evaluation value of different reconstruction methods of enhanced computed tomography (CT) images on the cardiac structure dose in patients undergoing radiotherapy for breast cancer.

A retrospective analysis of the cardiac structure data of 200 female patients with left-sided breast cancer, who underwent enhanced CT scans was conducted. Image reconstruction was performed with a time interval of 5% of the cardiac cycle (0%-95%), obtaining maximum intensity projection (MIP), minimum intensity projection (MinIP), and average intensity projection (AIP) images. The left ventricle (LV) and the left anterior descending artery (LAD) were delineated on the four types of images.

The MinIP, MIP, and AIP images showed larger LAD volumes than enhanced CT images (P < 0.05). Compared with the dose sum, the changes in V5Gy, V30Gy, V40Gy, Dmax, and Dmean for LV on MIP images were all <5% (4.02%, 2.17%, 1.39%, 2.99%, and 1.93%, respectively). The Dmax and Dmean change rates for LV on MinIP images were both <5%, at 2.00% and 4.73%, respectively; the Dmax and Dmean change rates for LV on AIP images were also both <5%, at 3.01% and 3.99%. Compared with the dose sum, only the Dmax change rates for LAD on MIP, MinIP, and AIP images were <5% (all P > 0.05), at 4.96%, 4.01%, and 4.07%, respectively.

In LV dose-volume assessment, the differences in assessment metrics between MIP images and enhanced CT images are small, indicating that MIP imaging can serve as a supplementary method to enhanced CT imaging for this form of assessment.

This study developed a nomogram using computed tomography (CT)-based delta-radiomics features and clinicopathological factors to predict lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC) receiving neoadjuvant chemoradiotherapy (nCRT).

This study retrospectively enrolled 170 patients with ESCC receiving nCRT. The delta-radiomics signature model was constructed utilizing least absolute shrinkage and selection operator regression, and the radiomics score (radScore) was determined for each patient. A combined nomogram was established using the radScore and independent influencing factors obtained through univariate and multivariate analyses. The consistency and predictive ability of the nomogram were assessed using the calibration curve and the area under the receiver operating factor curve (AUC). The clinical benefits were assessed using decision curve analysis (DCA).

Two predictive models were constructed. The AUC values for the delta-radiomics signature model were 0.881 [95% confidence interval (CI): 0.827-0.935]. According to the univariate and multivariate analyses, the tumor length, tumor differentiation, and radScore were independent factors influencing LNM (P < 0.05). A combined nomogram was constructed from these factors, and the AUC reached 0.938 (95% CI: 0.898-0.979). DCA demonstrated that the clinical benefits of the nomogram for patients across an extensive range were more significant than the radiomics model alone.

This CT-based delta-radiomics nomogram model could benefit LNM in patients with ESCC following nCRT.

Chondrosarcoma is the second most common malignant primary bone tumor. Given the poorer prognosis of high-grade chondrosarcomas and the lack of prognostic tools, this study aimed to develop a nomogram for better prognosis evaluation.

A comprehensive approach was adopted, involving Kaplan-Meier analysis for survival curve generation, log-rank test for comparing survival differences, and multivariate Cox regression analysis for identifying independent prognostic determinants. Utilizing the "rms" package, nomograms were constructed to predict overall survival (OS) and cancer-specific survival (CSS) of patients with high-grade chondrosarcoma. Nomogram reliability was validated through the concordance index (C-index) and calibration curves, which quantitatively and graphically evaluated the predictive accuracy.

A total of 1,198 high-grade chondrosarcoma cases retrieved from the SEER database were retrospectively analyzed. Following Kaplan-Meier survival analysis, several variables (age, sex, tumor size, stage, metastasis, surgery, chemotherapy, and radiation for OS) were incorporated into the multivariate Cox regression models for OS and CSS. Nomograms for OS and CSS were established based on the derived independent prognostic factors. The C-indices of the training and validation cohorts were 0.8117 and 0.7642 for the OS and 0.8475 and 0.8173 for the CSS analysis, respectively. The calibration plots further corroborated the nomograms' accurate predictive capacity for both OS and CSS.

Nomograms capable of precisely estimating OS and CSS in high-grade chondrosarcoma were successfully developed. These nomograms offer clinicians a valuable tool for more accurate survival prediction of patients with high-risk chondrosarcoma, which optimizes postoperative treatment strategies and improves patient management and outcomes.

This systematic review and meta-analysis compared fibroblast activation protein inhibitor (FAPI) and 2-[18F]-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) regarding diagnostic efficacy in primary liver cancers, focusing on sensitivity, specificity, and clinical applicability.

PubMed was searched (up to July 31, 2024) for studies evaluating FAPI and FDG PET/CT for hepatocellular carcinoma and intrahepatic cholangiocarcinoma. A total of 9 studies involving 214 patients and 416 lesions were analyzed. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Diagnostic parameters were synthesized using R Studio's "Mada" package with a random-effects model. Heterogeneity was assessed via I² statistics.

FAPI PET/CT exhibited superior pooled sensitivity (0.918, 95% CI: 0.862-0.953) to FDG PET/CT (0.472, 95% confidence interval [CI]: 0.309-0.642). FAPI demonstrated lower specificity than FDG (0.464, 95% CI: 0.281-0.647, vs. 0.678, 95% CI: 0.505-0.851). Furthermore, it obtained a higher area under the curve (0.846 vs. 0.627), indicating high overall diagnostic accuracy. Moreover, FAPI demonstrated superior performance in detecting small lesions (≤1 cm) and FDG-negative tumors, particularly in cirrhotic livers. Contrarily, FDG showed better specificity for benign lesions. Across studies, heterogeneity was mainly attributed to lesion size, cirrhosis prevalence, and tracer subtypes.

FAPI PET/CT achieved higher sensitivity and diagnostic accuracy than FDG in primary liver cancers, particularly in early-stage and metabolically heterogeneous tumors. FAPI offers transformative potential for clinical use despite its lower specificity in patients with cirrhosis. To optimize integration into diagnostic pathways, standardized protocols and large-scale validation are needed.

No report is available on the treatment of primary colorectal cancer (CRC) by transarterial chemotherapy combined with lipiodol chemoembolization in tumor-feeding arteries.

To determine the safety and efficacy of transarterial infusion chemotherapy (TAI) and lipiodol chemoembolization for the treatment of primary CRC.

Thirty-seven patients with advanced CRC received TAI and lipiodol chemoembolization once a month, 1-3 times in all. Clinical efficacy, complications, and effectiveness were evaluated 1 month after each session.

All 37 patients were successfully treated. The most common complications were abdominal discomfort, nausea, tenesmus, and myelosuppression. The overall disease control and objective response rates were 97.3% and 67.6%, respectively. Twenty-four patients survived and 13 died; the median survival duration was 21.1 months. Age was a significant influencing factor of overall survival.

Transarterial chemotherapy combined with lipiodol chemoembolization appears to be safe and effective for treating advanced CRC.