A new notable compression source of left renal vein entrapment: right renal artery.

PURPOSE: To estimate the incidences of left renal vein (LRV) entrapment by right renal artery (RRA), a phenomenon primarily reported as case reports. METHODS: The cross-sectional study consecutively screened renal vessel CT data of 38 (Renal) patients with nephropathy and 305 (Non-renal) patients with peripheral arterial diseases in a teaching hospital in northeast China between November 2018 and March 2023. The LRV compression by adjacent anatomical structures, including but not limited to RRA and multiple compression-related parameters, were investigated through multiplanar analysis of the CT data. RESULTS: The overall LRV entrapment rates by adjacent structures were 41.93% (12/31) and 24.00% (6/25), the rates of RRA-sourced LRV compression 22.58% (7/31) and 20.00% (5/25), and the rates of compression by superior mesenteric artery (SMA) 16.13% (5/31) and 4.00% (1/25) in the Renal and Non-renal groups, respectively, with no significance. The venous segments distal to the RRA-compressed site had a significantly larger transectional lumen area than those of the non-compressed veins in both groups (3.09 ± 1.29 vs. 1.82 ± 0.23, p < 0.001 and 4.30 ± 2.65 vs. 2.12 ± 0.55, p = 0.006; maximum-to-minimum area ratios in Renal and Non-renal groups, respectively). Nearly 80% of RRAs were found arising anteriorly rightwards instead of passing straight to the right. CONCLUSION: RRA-sourced LRV compression was not rare, and its incidence was higher than that of the compression by SMA in both patient cohorts. RRA could be a more common compression source than SMA concerning LRV entrapment. Further investigations involving different populations, including healthy individuals, are needed.

Chronic arsenic exposure-provoked biotoxicity involved in liver-microbiota-gut axis disruption in chickens based on multi-omics technologies.

INTRODUCTION: Arsenic has been ranked as the most hazardous substance by the U.S. Agency for Toxic Substances and Disease Registry. Environmental arsenic exposure-evoked health risks have become a vital public health concern worldwide owing to the widespread existence of arsenic. Multi-omics is a revolutionary technique to data analysis providing an integrated view of bioinformation for comprehensively and systematically understanding the elaborate mechanism of diseases. OBJECTIVES: This study aimed at uncovering the potential contribution of liver-microbiota-gut axis in chronic inorganic arsenic exposure-triggered biotoxicity in chickens based on multi-omics technologies. METHODS: Forty Hy-Line W-80 laying hens were chronically exposed to sodium arsenite with a dose-dependent manner (administered with drinking water containing 10, 20, or 30 mg/L arsenic, respectively) for 42 d, followed by transcriptomics, serum non-targeted metabolome, and 16S ribosomal RNA gene sequencing accordingly. RESULTS: Arsenic intervention induced a serious of chicken liver dysfunction, especially severe liver fibrosis, simultaneously altered ileal microbiota populations, impaired chicken intestinal barrier, further drove enterogenous lipopolysaccharides translocation via portal vein circulation aggravating liver damage. Furtherly, the injured liver disturbed bile acids (BAs) homoeostasis through strongly up-regulating the BAs synthesis key rate-limiting enzyme CYP7A1, inducing excessive serum total BAs accumulation, accompanied by the massive synthesis of primary BA-chenodeoxycholic acid. Moreover, the concentrations of secondary BAs-ursodeoxycholic acid and lithocholic acid were markedly repressed, which might involve in the repressed dehydroxylation of Ruminococcaceae and Lachnospiraceae families. Abnormal BAs metabolism in turn promoted intestinal injury, ultimately perpetuating pernicious circle in chickens. Notably, obvious depletion in the abundance of four profitable microbiota, Christensenellaceae, Ruminococcaceae, Muribaculaceae, and Faecalibacterium, were correlated tightly with this hepato-intestinal circulation process in chickens exposed to arsenic. CONCLUSION: Our study demonstrates that chronic inorganic arsenic exposure evokes liver-microbiota-gut axis disruption in chickens and establishes a scientific basis for evaluating health risk induced by environmental pollutant arsenic.

A radiomics nomogram based on MSCT and clinical factors can stratify fibrosis in inflammatory bowel disease.

Intestinal fibrosis is one of the major complications of inflammatory bowel disease (IBD) and a pathological process that significantly impacts patient prognosis and treatment selection. Although current imaging assessment and clinical markers are widely used for the diagnosis and stratification of fibrosis, these methods suffer from subjectivity and limitations. In this study, we aim to develop a radiomics diagnostic model based on multi-slice computed tomography (MSCT) and clinical factors. MSCT images and relevant clinical data were collected from 218 IBD patients, and a large number of quantitative image features were extracted. Using these features, we constructed a radiomics model and transformed it into a user-friendly diagnostic nomogram. A nomogram was developed to predict fibrosis in IBD by integrating multiple factors. The nomogram exhibited favorable discriminative ability, with an AUC of 0.865 in the validation sets, surpassing both the logistic regression (LR) model (AUC = 0.821) and the clinical model (AUC = 0.602) in the test set. In the train set, the LR model achieved an AUC of 0.975, while the clinical model had an AUC of 0.735. The nomogram demonstrated superior performance with an AUC of 0.971, suggesting its potential as a valuable tool for predicting fibrosis in IBD and improving clinical decision-making. The radiomics nomogram, incorporating MSCT and clinical factors, demonstrates promise in stratifying fibrosis in IBD. The nomogram outperforms traditional clinical models and offers personalized risk assessment. However, further validation and addressing identified limitations are necessary to enhance its applicability.

MRI-based radiomics model for preoperative prediction of extramural venous invasion of rectal adenocarcinoma.

BACKGROUND: Extramural venous invasion (EMVI) is an important prognostic factor of rectal adenocarcinoma. However, accurate preoperative assessment of EMVI remains difficult. PURPOSE: To assess EMVI preoperatively through radiomics technology, and use different algorithms combined with clinical factors to establish a variety of models in order to make the most accurate judgments before surgery. MATERIAL AND METHODS: A total of 212 patients with rectal adenocarcinoma between September 2012 and July 2019 were included and distributed to training and validation datasets. Radiomics features were extracted from pretreatment T2-weighted images. Different prediction models (clinical model, logistic regression [LR], random forest [RF], support vector machine [SVM], clinical-LR model, clinical-RF model, and clinical-SVM model) were constructed on the basis of radiomics features and clinical factors, respectively. The area under the curve (AUC) and accuracy were used to assess the predictive efficacy of different models. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were also calculated. RESULTS: The clinical-LR model exhibited the best diagnostic efficiency with an AUC of 0.962 (95% confidence interval [CI] = 0.936-0.988) and 0.865 (95% CI = 0.770-0.959), accuracy of 0.899 and 0.828, sensitivity of 0.867 and 0.818, specificity of 0.913 and 0.833, PPV of 0.813 and 0.720, and NPV of 0.940 and 0.897 for the training and validation datasets, respectively. CONCLUSION: The radiomics-based prediction model is a valuable tool in EMVI detection and can assist decision-making in clinical practice.

Radiomic Nomogram for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer.

RATIONALE AND OBJECTIVES: The detection of axillary lymph node metastasis (ALNM) in patients with breast cancer is a crucial determinant in the decision-making process for axillary surgery and potential therapies. The objective of this study was to develop and validate a radiomics nomogram that integrates radiomics features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) with clinical factors to predict ALNM in patients with breast cancer. MATERIALS AND METHODS: A total of 177 patients with breast cancer were randomly divided into a training set (n = 123) and a validation set (n = 54) using a 7:3 ratio. From the DCE-MRI images, 2818 radiomics features were extracted from the primary tumor and axillary lymph node (ALN). Subsequently, optimal features were selected through the least absolute shrinkage and selection operator algorithm to construct the Radscore. Clinical factors were identified using univariate logistic regression analysis and included in a multivariate logistic regression analysis. Using the Radscore and clinical factors, a radiomics nomogram was developed using the Support Vector Machine method. The predicting efficacy of our model was visually appraised utilizing a receiver operator characteristic (ROC) curve, while its clinical application and predictive accuracy were assessed through decision curve analysis (DCA) and calibration curves, respectively. RESULTS: The results revealed Ki67, multifocality, and MRI-reported ALN status as independent risk factors for ALNM. The radiomics nomogram demonstrated good calibration and discrimination with areas under the ROC curve of 0.92 (95% confidence interval [CI], 0.88-0.97) in the training set and 0.90 (95% CI, 0.72-0.90) in the validation set. DCA revealed the clinical usefulness of the radiomics nomogram. CONCLUSION: The DCE-MRI-based radiomics nomogram is a reliable tool for assessing ALNM in patients with breast cancer.

A Mammography-Based Radiomic Nomogram for Predicting Malignancy in Breast Suspicious Microcalcifications.

RATIONALE AND OBJECTIVES: Preoperative accurate identification of benign and malignant breast lesions is vital for patients to achieve individualized treatment. This study aimed to develop and validate a mammography-based radiomic nomogram for predicting malignant risk of breast suspicious microcalcifications (MCs). MATERIALS AND METHODS: 496 patients with histologically confirmed breast suspicious MCs were randomly divided into the training set (n = 346) and validation set (n = 150). Radiomics features was extracted from the craniocaudal and mediolateral oblique images. Least absolute shrinkage and selection operator algorithm were used to select radiomics features, then radiomics score (Rad-score) was calculated. Univariate analysis was used to identify malignant MCs-related clinical independent risk factors. Multivariate logistic regression was used to establish a clinical-radiomics model by incorporating Rad-score and clinic factors. A nomogram was developed to visualize the clinical-radiomics model. The receiver operating characteristic curve, calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. RESULTS: The Rad-score was consisted of 29 optimal radiomics features. We developed a nomogram by incorporating Rad-score, menopause status, MCs morphology and distribution, the area under the curve value of the combined model was 0.926(95% confidence interval [CI]: 0.878-0.975) for the validation set. The calibration curves and DCA indicated the combined model had favorable calibration and clinical utility. CONCLUSION: The combined model could be considered as a potential imaging marker to predict malignant risk of breast suspicious MCs.

Small arteriole sign: an imaging feature for staging T4a colon cancer.

OBJECTIVES: By analyzing the distribution of existing and newly proposed staging imaging features in pT1-3 and pT4a tumors, we searched for a salient feature and validated its diagnostic performance. METHODS: Preoperative multiphase contrast-enhanced CT images of the training cohort were retrospectively collected at three centers from January 2016 to December 2017. We used the chi-square test to analyze the distribution of several stage-related imaging features in pT1-3 and pT4a tumors, including small arteriole sign (SAS), outer edge of the intestine, tumor invasion range, and peritumoral adipose tissue. Preoperative multiphase contrast-enhanced CT images of the validation cohort were retrospectively collected at Beijing Cancer Hospital from January 2018 to December 2018. The diagnostic performance of the selected imaging feature, including accuracy, sensitivity, and specificity, was validated and compared with the conventional clinical tumor stage (cT) by the McNemar test. RESULTS: In the training cohort, a total of 268 patients were enrolled, and only SAS was significantly different between pT1-3 and pT4a tumors. The accuracy, sensitivity, and specificity of the SAS and conventional cT in differentiating T1-3 and T4a tumors were 94.4%, 81.6%, and 97.3% and 53.7%, 32.7%, and 58.4%, respectively (all p < 0.001). In the validation cohort, a total of 135 patients were collected. The accuracy, sensitivity, and specificity of the SAS and the conventional cT were 93.3%, 76.2%, and 96.5% and 62.2%, 38.1%, and 66.7%, respectively (p < 0.001, p = 0.021, p < 0.001). CONCLUSION: Small arteriole sign positivity, an indirect imaging feature of serosa invasion, may improve the accuracy of identifying T4a colon cancer. CLINICAL RELEVANCE STATEMENT: Small arteriole sign helps to distinguish T1-3 and T4a colon cancer and further improves the accuracy of preoperative CT staging of colon cancer. KEY POINTS: • The accuracy of preoperative CT staging of colon cancer is not ideal, especially for T4a tumors. • Small arteriole sign (SAS) is a newly defined imaging feature that shows the appearance of tumor-supplying arterioles at the site where they penetrate the intestine wall. • SAS is an indirect imaging marker of tumor invasion into the serosa with a great value in distinguishing between T1-3 and T4a colon cancer.

Novel findings from arsenic­lead combined exposure in mouse testicular TM4 Sertoli cells based on transcriptomics.

Arsenic (As) and lead (Pb) exist widespread in daily life, and they are common harmful substances in the environment. As and Pb pollute the environment more often in combination than in isolation. The TM4 Sertoli cell line is one of the most common normal mouse testicular Sertoli cell lines. In vitro, we found that the type of combined action of As and Pb on TM4 Sertoli cells was additive action by using the isobologram analysis. To further investigate the combined toxicity of As and Pb, we performed mRNA and miRNA sequencing on TM4 Sertoli cells exposed to As alone (4 µM NaAsO2) and AsPb combined (4 µM NaAsO2 and 150 µM PbAc), respectively. Compared with the control group, 1391 differentially expressed genes (DEGs) and 6 differentially expressed miRNAs (DEMs) were identified in the As group. Compared with the control group, 2384 DEGs and 44 DEMs were identified in the AsPb group. Compared with the As group, 387 DEGs and 4 DEMs were identified in the AsPb group. Through data analysis, we discovered for the first time that As caused the dysfunction of cholesterol synthesis and energy metabolism, and disrupted cyclic adenosine monophosphate signaling pathway and wingless/integrated (Wnt) signaling pathway in TM4 Sertoli cells. In addition to affecting cholesterol synthesis and energy metabolism, AsPb combined exposure also up-regulated the antioxidant reaction level of TM4 Sertoli cells. Meanwhile, the Wnt signaling pathway of TM4 Sertoli cells was relatively normal when exposed to AsPb. In conclusion, at the transcription level, the combined action of AsPb is not merely additive effect, but involves synergistic and antagonistic effects. The new discovery of the joint toxic mechanism of As and Pb breaks the stereotype of the combined action and provides a good theoretical basis and research clue for future study of the combined-exposure of harmful materials.

Radiomic features of the hippocampal based on magnetic resonance imaging in the menopausal mouse model linked to neuronal damage and cognitive deficits.

Estrogen deficiency in the early postmenopausal phase is associated with an increased long-term risk of cognitive decline or dementia. Non-invasive characterization of the pathological features of the pathological hallmarks in the brain associated with postmenopausal women (PMW) could enhance patient management and the development of therapeutic strategies. Radiomics is a means to quantify the radiographic phenotype of a diseased tissue via the high-throughput extraction and mining of quantitative features from images acquired from modalities such as CT and magnetic resonance imaging (MRI). This study set out to explore the correlation between radiomics features based on MRI and pathological features of the hippocampus and cognitive function in the PMW mouse model. Ovariectomized (OVX) mice were used as PWM models. MRI scans were performed two months after surgery. The brain's hippocampal region was manually annotated, and the radiomic features were extracted with PyRadiomics. Chemiluminescence was used to evaluate the peripheral blood estrogen level of mice, and the Morris water maze test was used to evaluate the cognitive ability of mice. Nissl staining and immunofluorescence were used to quantify neuronal damage and COX1 expression in brain sections of mice. The OVX mice exhibited marked cognitive decline, brain neuronal damage, and increased expression of mitochondrial complex IV subunit COX1, which are pathological phenomena commonly observed in the brains of AD patients, and these phenotypes were significantly correlated with radiomics features (p < 0.05, |r|>0.5), including Original_firstorder_Interquartile Range, Original_glcm_Difference Average, Original_glcm_Difference Average and Wavelet-LHH_glszm_Small Area Emphasis. Meanwhile, the above radiomics features were significantly different between the sham-operated and OVX groups (p < 0.01) and were associated with decreased serum estrogen levels (p < 0.05, |r|>0.5). This initial study indicates that the above radiomics features may have a role in the assessment of the pathology of brain damage caused by estrogen deficiency using routinely acquired structural MR images.

Sensory nerves directly promote osteoclastogenesis by secreting peptidyl-prolyl cis-trans isomerase D (Cyp40).

Given afferent functions, sensory nerves have recently been found to exert efferent effects and directly alter organ physiology. Additionally, several studies have highlighted the indirect but crucial role of sensory nerves in the regulation of the physiological function of osteoclasts. Nonetheless, evidence regarding the direct sensory nerve efferent influence on osteoclasts is lacking. In the current study, we found that high levels of efferent signals were transported directly from the sensory nerves into osteoclasts. Furthermore, sensory hypersensitivity significantly increased osteoclastic bone resorption, and sensory neurons (SNs) directly promoted osteoclastogenesis in an in vitro coculture system. Moreover, we screened a novel neuropeptide, Cyp40, using an isobaric tag for relative and absolute quantitation (iTRAQ). We observed that Cyp40 is the efferent signal from sensory nerves, and it plays a critical role in osteoclastogenesis via the aryl hydrocarbon receptor (AhR)-Ras/Raf-p-Erk-NFATc1 pathway. These findings revealed a novel mechanism regarding the influence of sensory nerves on bone regulation, i.e., a direct promoting effect on osteoclastogenesis by the secretion of Cyp40. Therefore, inhibiting Cyp40 could serve as a strategy to improve bone quality in osteoporosis and promote bone repair after bone injury.

FOXG1 regulates the proliferation and apoptosis of human lung cancer cells.

FOXG1, a transcriptional factor belonging to the Forkhead Box (Fox) superfamily, is highly expressed in the brain tissue during brain development and plays an important role in cellular proliferation. Recently, FOXG1 was reported to play important roles in oncogenesis, wherein its abnormal expression regulates tumor cell proliferation. However, the expression and role of FOXG1 in lung cancer remain largely unknown. This study investigated the clinical significance, expression, and role of FOXG1 in lung cancer. We found that FOXG1 was highly expressed in lung cancer tissues. MTT, CCK-8 and colony formation assays showed that FOXG1 overexpression could enhance the proliferation of A549 lung cancer cells. Flow cytometry analysis revealed that FOXG1 promoted the cell cycle and suppressed cell apoptosis. Additionally, the expression levels of PTEN, phosphorylated AKT, mTOR, p53, and Bax were significantly altered in response to changes in FOXG1 expression, indicating that FOXG1 regulated the PI3K pathway. Furthermore, in the xenograft mouse model, the upregulated FOXG1 expression strongly promoted tumor growth. In conclusion, these results suggested that FOXG1 was a critical regulator of the proliferation of lung cancer cells and enhanced tumor growth in vivo.

A radiomics signature associated with underlying gene expression pattern for the prediction of prognosis and treatment response in hepatocellular carcinoma.

PURPOSE: Identifying robust prognosis and treatment efficiency predictive biomarkers of hepatocellular carcinoma (HCC) is challenging. The purpose of this study is to develop a radiomics approach for predicting the overall survival (OS) based on pretreatment CT images and to explore the radiomic-associated key genes. METHODS: Patients with pathologically or clinically proven HCC from three data sets were retrospectively included in this study. The institute internal data that received transarterial chemoembolization (TACE) treatment was used as the training set to construct the radiomics signature to predict OS by the least absolute shrinkage and selection operator COX (LASSO-COX) regression algorithms. The model was externally tested in 41 patients from The Cancer Genome Atlas (TCGA) with available CT images. Area under the receiver operating characteristics curve (AUC) and the log-rank test were used for survival analysis based on high versus low radiomics score. RNA sequencing data of TCGA and Gene Expression Omnibus (GEO) public database were used for gene expression analysis. RESULTS: A total of 752 patients were divided into the Radiomics cohort (n = 267), the TCGA cohort (n = 338) and GEO cohort (n = 147). The rad-score divided patients into high and low risk groups, with significant survival differences (P < 0.0001 and P = 0.0055) in the training and external test set. The AUC for 5 years' OS were 0.730 and 0.695, respectively. Seven OS-related genes (SPP1, GJA5, GJA4, INMT, PDZD4, ALDOA and MAFG) were identified, all of which were related with TACE efficiency, except for MAFG (P greater than 0.05). CONCLUSIONS: CT-radiomics signature could effectively predict the prognosis and treatment response of HCC, which were also associated with the tumor microenvironment heterogeneity.

Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma.

BACKGROUND: According to clinical practice guidelines, transarterial chemoembolization (TACE) is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma (HCC). Early prediction of treatment response can help patients choose a reasonable treatment plan. This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival. METHODS: A total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed. The tumor response was assessed by modified response evaluation criteria in solid tumors (mRECIST), and the response of the first TACE to each session and its correlation with overall survival were evaluated. The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator (LASSO), and four machine learning models were built with different types of regions of interest (ROIs) (tumor and corresponding tissues) and the model with the best performance was selected. The predictive performance was assessed with receiver operating characteristic (ROC) curves and calibration curves. RESULTS: Of all the models, the random forest (RF) model with peritumor (+10 mm) radiomic signatures had the best performance [area under ROC curve (AUC) = 0.964 in the training cohort, AUC = 0.949 in the validation cohort]. The RF model was used to calculate the radiomic score (Rad-score), and the optimal cutoff value (0.34) was calculated according to the Youden's index. Patients were then divided into a high-risk group (Rad-score > 0.34) and a low-risk group (Rad-score ≤ 0.34), and a nomogram model was successfully established to predict treatment response. The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves. Multivariate Cox regression identified six independent prognostic factors for overall survival, including male [hazard ratio (HR) = 0.500, 95% confidence interval (CI): 0.260-0.962, P = 0.038], alpha-fetoprotein (HR = 1.003, 95% CI: 1.002-1.004, P < 0.001), alanine aminotransferase (HR = 1.003, 95% CI: 1.001-1.005, P = 0.025), performance status (HR = 2.400, 95% CI: 1.200-4.800, P = 0.013), the number of TACE sessions (HR = 0.870, 95% CI: 0.780-0.970, P = 0.012) and Rad-score (HR = 3.480, 95% CI: 1.416-8.552, P = 0.007). CONCLUSIONS: The radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.

A Comprehensive Prediction Model Based on MRI Radiomics and Clinical Factors to Predict Tumor Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer.

RATIONALE AND OBJECTIVES: To establish a prediction model for the efficacy of neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), using pretreatment magnetic resonance imaging (MRI) multisequence image features and clinical parameters. MATERIALS AND METHODS: Patients with clinicopathologically confirmed LARC were included (training and validation datasets, n = 100 and 27, respectively). Clinical data of patients were collected retrospectively. We analyzed MRI multisequence imaging features. The tumor regression grading (TRG) system proposed by Mandard et al was adopted. Grade 1-2 of TRG was a good response group, and grade 3-5 of TRG was a poor response group. In this study, a clinical model, a single sequence imaging model, and a comprehensive model combined with clinical imaging were constructed, respectively. The area under the subject operating characteristic curve (AUC) was used to evaluate the predictive efficacy of clinical, imaging, and comprehensive models. The decision curve analysis method evaluated the clinical benefit of several models, and the nomogram of efficacy prediction was constructed. RESULTS: The AUC value of the comprehensive prediction model is 0.99 in the training data set and 0.94 in the test data set, which is significantly higher than other models. Radiomic Nomo charts were developed using Rad scores obtained from the integrated image omics model, circumferential resection margin(CRM), DoTD, and carcinoembryonic antigen(CEA). Nomo charts showed good resolution. The calibrating and discriminating ability of the synthetic prediction model is better than that of the single clinical model and the single sequence clinical image omics fusion model. CONCLUSION: Nomograph, based on pretreatment MRI characteristics and clinical risk factors, has the potential to be used as a noninvasive tool to predict outcomes in patients with LARC after nCRT.

Efficacy and Interpretability Analysis of Noninvasive Imaging Based on Computed Tomography in Patients with Hepatocellular Carcinoma After Initial Transarterial Chemoembolization.

RATIONALE AND OBJECTIVES: The objective of this study is to accurately and timely assess the efficacy of patients with hepatocellular carcinoma (HCC) after the initial transarterial chemoembolization (TACE). MATERIALS AND METHODS: This retrospective study consisted of 279 patients with HCC in Center 1, who were split into training and validation cohorts in the ratio of 4:1, and 72 patients in Center 2 as an external testing cohort. Radiomics signatures both in the arterial phase and venous phase of contrast-enhanced computed tomography images were selected by univariate analysis, correlation analysis, and least absolute shrinkage and selection operator regression to build the predicting models. The clinical model and combined model were constructed by independent risk factors after univariate and multivariate logistic regression analysis. The biological interpretability of radiomics signatures correlating transcriptome sequencing data was explored using publicly available data sets. RESULTS: A total of 31 radiomics signatures in the arterial phase and 13 radiomics signatures in the venous phase were selected to construct Radscore_arterial and Radscore_venous, respectively, which were independent risk factors. After constructing the combined model, the area under the receiver operating characteristic curve in three cohorts was 0.865, 0.800, and 0.745, respectively. Through correlation analysis, 11 radiomics signatures in the arterial phase and 4 radiomics signatures in the venous phase were associated with 8 and 5 gene modules, respectively (All P < .05), which enriched some pathways closely related to tumor development and proliferation. CONCLUSION: Noninvasive imaging has considerable value in predicting the efficacy of patients with HCC after initial TACE. The biological interpretability of the radiological signatures can be mapped at the micro level.

From animal to cell model: Pyroptosis targeted-fibrosis is a novel mechanism of lead-induced testicular toxicity.

Lead (Pb) exists widely in soil and seriously threatens agricultural soil and food crops. Pb can cause serious damage to organs. In this study, the animal model of Pb-induced rat testicular injury and the cell model of Pb-induced TM4 Sertoli cell injury were established to verify whether the testicular toxicity of Pb was related to pyroptosis-mediated fibrosis. The results of experiment in vivo showed that Pb could cause oxidative stress and up-regulated the expression levels of inflammation, pyroptosis, and fibrosis-related proteins in the testis of rats. The results of experiments in vitro showed that Pb induced the cell damage, enhanced the reactive oxygen species level in the TM4 Sertoli cells. After using nuclear factor-kappa B inhibitor and Caspase-1 inhibitor, the elevation of TM4 Sertoli cell inflammation, pyroptosis, and fibrosis-related proteins induced by Pb exposure was significantly decreased. Taken together, Pb can cause pyroptosis-targeted fibrosis and ultimately issues in testicular damage.

Chronic arsenic exposure induces ferroptosis via enhancing ferritinophagy in chicken livers.

Arsenic (As) is a well-known pollutant in the environment, whose contamination in groundwater is a serious threat to animals and humans. Ferroptosis, a form of cell death caused by iron-dependent lipid peroxidation, is involved in various pathological processes. Ferritinophagy is the selective autophagy of ferritin and a crucial step in the induction of ferroptosis. However, the mechanism of ferritinophagy in poultry livers exposed to As remains unexplored. In this study, we investigated whether As-induced chicken liver injury is related to ferritinophagy-mediated ferroptosis at the cellular and animal levels. Our results showed that As exposure via drinking water induced hepatotoxicity in chickens, characterized by abnormal liver morphology and elevated liver function markers. Our data suggested chronic As exposure led to mitochondrial dysfunction, oxidative stress, and impaired cellular processes in chicken livers and LMH cells. Our results also showed that As exposure activated the AMPK/mTOR/ULK1 signaling pathway and significantly changed the levels of ferroptosis and autophagy-related proteins in chicken livers and LMH cells. Moreover, As exposure induced iron overload and lipid peroxidation in chicken livers and LMH cells. Interestingly, pretreatment with ferrostatin-1, chloroquine (CQ), and deferiprone alleviated these aberrant effects. Using CQ, we found that As-induced ferroptosis is autophagy-dependent. Our findings further suggested chronic As exposure induced chicken liver injury by promoting ferritinophagy-mediated ferroptosis, as evidence by activated autophagy, decreased mRNA expression of FTH1, increased intracellular iron content, and alleviation of ferroptosis through pretreatment with CQ. In conclusion, ferritinophagy-mediated ferroptosis is one of the critical mechanisms of As-induced chicken liver injury. Inhibiting ferroptosis may provide new insights for preventing and treating liver injury induced by environmental As exposure in livestock and poultry.

A robust and automatic CT-3D ultrasound registration method based on segmentation, context, and edge hybrid metric.

BACKGROUND: The fusion of computed tomography (CT) and ultrasound (US) image can enhance lesion detection ability and improve the success rate of liver interventional radiology. The image-based fusion methods encounter the challenge of registration initialization due to the random scanning pose and limited field of view of US. Existing automatic methods those used vessel geometric information and intensity-based metric are sensitive to parameters and have low success rate. The learning-based methods require a large number of registered datasets for training. PURPOSE: The aim of this study is to provide a fully automatic and robust US-3D CT registration method without registered training data and user-specified parameters assisted by the revolutionary deep learning-based segmentation, which can further be used for preparing training samples for the study of learning-based methods. METHODS: We propose a fully automatic CT-3D US registration method by two improved registration metrics. We propose to use 3D U-Net-based multi-organ segmentation of US and CT to assist the conventional registration. The rigid transform is searched in the space of any paired vessel bifurcation planes where the best transform is decided by a segmentation overlap metric, which is more related to the segmentation precision than Dice coefficient. In nonrigid registration phase, we propose a hybrid context and edge based image similarity metric with a simple mask that can remove most noisy US voxels to guide the B-spline transform registration. We evaluate our method on 42 paired CT-3D US datasets scanned with two different US devices from two hospitals. We compared our methods with other exsiting methods with both quantitative measures of target registration error (TRE) and the Jacobian determinent with paired t-test and qualitative registration imaging results. RESULTS: The results show that our method achieves fully automatic rigid registration TRE of 4.895 mm, deformable registration TRE of 2.995 mm in average, which outperforms state-of-the-art automatic linear methods and nonlinear registration metrics with paired t-test's p value less than 0.05. The proposed overlap metric achieves better results than self similarity description (SSD), edge matching (EM), and block matching (BM) with p values of 1.624E-10, 4.235E-9, and 0.002, respectively. The proposed hybrid edge and context-based metric outperforms context-only, edge-only, and intensity statistics-only-based metrics with p values of 0.023, 3.81E-5, and 1.38E-15, respectively. The 3D US segmentation has achieved mean Dice similarity coefficient (DSC) of 0.799, 0.724, 0.788, and precision of 0.871, 0.769, 0.862 for gallbladder, vessel, and branch vessel, respectively. CONCLUSIONS: The deep learning-based US segmentation can achieve satisfied result to assist robust conventional rigid registration. The Dice similarity coefficient-based metrics, hybrid context, and edge image similarity metric contribute to robust and accurate registration.