Renal fat deposition measured on dixon-based MRI is significantly associated with early kidney damage in obesity.

PURPOSE: To investigate the renal fat deposition on Dixon-based magnetic resonance imaging (MRI) and to explore the predictive value of renal fat biomarkers of magnetic resonance (MR-RFBs) for early kidney damage in obesity. METHODS: This prospective study included 56 obese volunteers and 47 non-obese healthy volunteers. All volunteers underwent renal magnetic resonance examinations. The differences in MR-RFBs [including renal proton density fat fraction (PDFF), renal sinus fat volume (RSFV), and perirenal fat thickness (PRFT)] measured on Dixon-based MRI between the obese and non-obese volunteers were analyzed using a general linear model, taking sex, age, diabetes, and hypertension as covariates. The relationship between estimated glomerular filtration rate (eGFR) and demographic, laboratory, and imaging parameters in obese volunteers was examined by correlation analysis. RESULTS: Obese volunteers had higher MR-RFBs than non-obese volunteers after controlling for confounders (all p < 0.001). Renal PDFF (r = - 0.383; p = 0.004), RSFV (r = - 0.368; p = 0.005), and PRFT (r = - 0.451; p < 0.001) were significantly negatively correlated with eGFR in obesity. After adjusting for age, sex, body mass index, diabetes, hypertension, visceral adipose tissue, subcutaneous adipose tissue, renal PDFF, and RSFV, PRFT remained independently negatively associated with eGFR (ß = - 0.587; p = 0.003). CONCLUSIONS: All MR-RFBs are negatively correlated with eGFR in obesity. The MR-RFBs, especially PRFT, may have predictive value for early kidney damage in obesity.

Preoperative CT-based radiomics and deep learning model for predicting risk stratification of gastric gastrointestinal stromal tumors.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively. PURPOSE: To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively. METHODS: The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre-trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre-trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single-phase and dual-phase images. The arterial phase, venous phase and dual-phase machine learning models were built, respectively, and the morphological features were added to the dual-phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed. RESULTS: The area under the curve (AUC) of the dual-phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887-0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357-0.891). CONCLUSION: In this study, we established a combined model that incorporated dual-phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs.

A case study of a liver transplant-treated patient with glycogen storage disease type Ia presenting with multiple inflammatory hepatic adenomas: an analysis of clinicopathologic and genetic data.

BACKGROUND: Glycogen storage disease (GSD) is a disease caused by excessive deposition of glycogen in tissues due to genetic disorders in glycogen metabolism. Glycogen storage disease type I (GSD-I) is also known as VonGeirk disease and glucose-6-phosphatase deficiency. This disease is inherited in an autosomal recessive manner, and both sexes can be affected. The main symptoms include hypoglycaemia, hepatomegaly, acidosis, hyperlipidaemia, hyperuricaemia, hyperlactataemia, coagulopathy and developmental delay. CASE PRESENTATION: Here, we present the case of a 13-year-old female patient with GSD Ia complicated with multiple inflammatory hepatic adenomas. She presented to the hospital with hepatomegaly, hypoglycaemia, and epistaxis. By clinical manifestations and imaging and laboratory examinations, we suspected that the patient suffered from GSD I. Finally, the diagnosis was confirmed by liver pathology and whole-exome sequencing (WES). WES revealed a synonymous mutation, c.648 G > T (p.L216 = , NM_000151.4), in exon 5 and a frameshift mutation, c.262delG (p.Val88Phefs*14, NM_000151.4), in exon 2 of the G6PC gene. According to the pedigree analysis results of first-generation sequencing, heterozygous mutations of c.648 G > T and c.262delG were obtained from the patient's father and mother. Liver pathology revealed that the solid nodules were hepatocellular hyperplastic lesions, and immunohistochemical (IHC) results revealed positive expression of CD34 (incomplete vascularization), liver fatty acid binding protein (L-FABP) and C-reactive protein (CRP) in nodule hepatocytes and negative expression of ß-catenin and glutamine synthetase (GS). These findings suggest multiple inflammatory hepatocellular adenomas. PAS-stained peripheral hepatocytes that were mostly digested by PAS-D were strongly positive. This patient was finally diagnosed with GSD-Ia complicated with multiple inflammatory hepatic adenomas, briefly treated with nutritional therapy after diagnosis and then underwent living-donor liver allotransplantation. After 14 months of follow-up, the patient recovered well, liver function and blood glucose levels remained normal, and no complications occurred. CONCLUSION: The patient was diagnosed with GSD-Ia combined with multiple inflammatory hepatic adenomas and received liver transplant treatment. For childhood patients who present with hepatomegaly, growth retardation, and laboratory test abnormalities, including hypoglycaemia, hyperuricaemia, and hyperlipidaemia, a diagnosis of GSD should be considered. Gene sequencing and liver pathology play important roles in the diagnosis and typing of GSD.

Analysis of Risk Factors for Intraoperative Bleeding in the Surgical Treatment of Cesarean Scar Pregnancy and Development of Predictive Models.

Objective: The objective of this study was to investigate the risk factors associated with cesarean scar pregnancy (CSP) and to develop a model for predicting intraoperative bleeding risk. Methods: We retrospectively analyzed the clinical data of 208 patients with CSP who were admitted to the People's Hospital of Leshan between January 2018 and December 2022. Based on whether intraoperative bleeding was ≥ 200 mL, we categorized them into two groups for comparative analysis: the excessive bleeding group (n = 27) and the control group (n = 181). Identifying relevant factors, we constructed a prediction model and created a nomogram. Results: We observed that there were significant differences between the two groups in several parameters. These included the time of menstrual cessation (P = 0.002), maximum diameter of the gestational sac (P < 0.001), thickness of the myometrium at the uterine scar (P = 0.001), pre-treatment blood HCG levels (P = 0.016), and the grade of blood flow signals (P < 0.001). We consolidated the above data and constructed a clinical prediction model. The model exhibited favorable results in terms of predictive efficacy, discriminative ability (C-index = 0.894, specificity = 0.834, sensitivity = 0.852), calibration precision (mean absolute error = 0.018), and clinical decision-making utility, indicating its effectiveness. Conclusion: The clinical prediction model related to the risk of hemorrhage that we developed in this experiment can assist in the development of appropriate interventions and effectively improve patient prognosis.

m6A-induced TRIB3 regulates Hippo pathway through interacting with LATS1 to promote the progression of lung adenocarcinoma.

Recent studies have indicated that dysregulation of the Hippo/Yes-associated protein (YAP) axis is associated with tumor progression and therapy resistance in various cancer types, including lung adenocarcinoma (LUAD). Understanding the regulation of Hippo signaling in LUAD is of great significance. Elevated levels of TRIB3, a pseudo kinase, have been observed in certain lung malignancies and are associated with an unfavorable prognosis. Our research aims to investigate whether increased TRIB3 levels enhance the malignant characteristics of LUAD cells and tumor progression through its interaction with the Hippo signaling pathway. In this study, we reported a positive correlation between elevated expression of TRIB3 and LUAD progression. Additionally, TRIB3 has the ability to enhance TEAD luciferase function and suppress Hippo pathway activity. Moreover, TRIB3 increases total YAP protein levels and promotes YAP nuclear localization. Mechanistic experiments revealed that TRIB3 directly interacts with large tumor suppressor kinase 1 (LATS1), thereby suppressing Hippo signaling. Moreover, the decrease in METTL3-mediated N6-methyladenosine modification of TRIB3 results in a substantial elevation of its expression levels in LUAD cells. Collectively, our research unveils a novel discovery that TRIB3 enhances the growth and invasion of LUAD cells by interacting with LATS1 and inhibiting the Hippo signaling pathway. TRIB3 may serve as a potential biomarker for an unfavorable prognosis and a target for novel treatments in YAP-driven lung cancer.

Activation of CHPF by transcription factor NFIC promotes NLRP3 activation during the progression of colorectal cancer.

Given the role of chondroitin polymerizing factor (CHPF) in several cancers, we investigated its role in the progression of colorectal cancer (CRC) and its association with NLRP3 inflammasome activation. High expression of CHPF in CRC predicted poor patient prognosis. Using colony formation, EdU staining, wound healing, Transwell invasion, and flow cytometry assays, we revealed that the downregulation of CHPF inhibited the malignant behavior of CRC cells. CHPF promoted NLRP3 inflammasome activation by inducing the MAPK signaling pathway, as evidenced by enhanced expression of Phos-ERK1/2, Phos-MEK1, Phos-MEK2, and NLRP3. Additionally, nuclear factor 1 C-type (NFIC) was revealed as a potential upstream transcription factor of CHPF in the modulation of CRC, and the anti-tumor effects elicited through its knockdown were compromised by CHPF in vitro and in vivo. In summary, we demonstrated that NFIC promoted NLRP3 activation to support CRC development via the CHPF-mediated MAPK signaling.