LncRNA DANCR promotes macrophage lipid accumulation through modulation of membrane cholesterol transporters.

The progression of atherosclerosis (AS), the pathological foundation of coronary artery disease (CAD), is featured by massive lipid deposition in the vessel wall. LncRNAs are implicated in lipid disorder and AS, whereas the specific role of lncRNA DANCR in atherogenesis remains unknown. Here, we demonstrated that DANCR promotes macrophage lipid accumulation by regulating the expression of membrane cholesterol transport proteins. qPCR showed that compared to control groups, CAD patients and atherosclerotic mice had higher DANCR levels. Treating human THP-1 macrophages and mouse RAW264.7 macrophages with ox-LDL significantly upregulated the expression levels of DANCR. Oil Red O staining showed that the silence of DANCR robustly reduced, while overexpression of DANCR significantly increased the numbers and size of lipid droplets in ox-LDL-treated THP-1 macrophages. In contrast, the opposite phenomena were observed in DANCR overexpressing cells. The expression of ABCA1, ABCG1, SR-BI, and NBD-cholesterol efflux was increased obviously by DANCR inhibition and decreased by DANCR overexpression, respectively. Furthermore, transfection with DANCR siRNA induced a robust decrease in the levels of CD36, SR-A, and Dil-ox-LDL uptake, while DANCR overexpression amplified the expression of CD36, SR-A and the uptake of Dil-ox-LDL in lipid-laden macrophages. Lastly, we found that the effects of DANCR on macrophage lipid accumulation and the expression of membrane cholesterol transport proteins were not likely related to miR-33a. The present study unraveled the adverse role of DANCR in foam cell formation and its relationship with cholesterol transport proteins. However, the competing endogenous RNA network underlying these phenomena warrants further exploration.

Functional near-infrared spectroscopy-based neurofeedback training regulates time-on-task effects and enhances sustained cognitive performance.

Time-on-task effect is a common consequence of long-term cognitive demand work, which reflects reduced behavioral performance and increases the risk of accidents. Neurofeedback is a neuromodulation method that can guide individuals to regulate their brain activity and manifest as changes in related symptoms and cognitive behaviors. This study aimed to examine the effects of functional near-infrared spectroscopy-based neurofeedback training on time-on-task effects and sustained cognitive performance. A randomized, single-blind, sham-controlled study was performed: 17 participants received feedback signals of their own dorsolateral prefrontal cortex activity (neurofeedback group), and 16 participants received feedback signals of dorsolateral prefrontal cortex activity from the neurofeedback group (sham-neurofeedback group). All participants received 5 neurofeedback training sessions and completed 2 sustained cognitive tasks, including a 2-back task and a psychomotor vigilance task, to evaluate behavioral performance changes following neurofeedback training. Results showed that neurofeedback relative to the sham-neurofeedback group exhibited increased dorsolateral prefrontal cortex activation, increased accuracy in the 2-back task, and decreased mean response time in the psychomotor vigilance task after neurofeedback training. In addition, the neurofeedback group showed slower decline performance during the sustained 2-back task after neurofeedback training compared with sham-neurofeedback group. These findings demonstrate that neurofeedback training could regulate time-on-task effects on difficult task and enhance performance on sustained cognitive tasks by increasing dorsolateral prefrontal cortex activity.

Profiles of Family and School Experiences and Adjustment of Adolescents During the Transition to High School.

Although family and school experiences play an important role in adolescents' adjustment during the transition to high school, most prior studies investigated the effects of these experiences in isolation; their joint implications for both adolescents' concurrent and long-term adjustment outcomes are less clear, and the potential role of individual characteristics within such associations remains understudied. Based on 525 10th graders (Mage = 15.48, SDage = 0.71, 43.6% boys) who participated in a longitudinal study, the present research aimed to identify distinct family and school experience profiles among first-year high school students and examine their associations with adolescents' internalizing problems and externalizing problems, both concurrently and 18 months later. Latent profile analysis revealed four distinctive profiles: thriving, low resources-moderate family risk, developmental stress-high parental conflicts, and developmental stress-high peer victimization profiles. The other three profiles (vs. the thriving profile) reported significantly higher levels of concurrent internalizing problems; while these differences diminished after 18 months. However, the enduring impacts of these profiles on internalizing problems persisted among adolescents with higher levels of environmental sensitivity. Additionally, adolescents characterized by two developmental stress profiles (vs. the thriving profile) exhibited significantly higher levels of externalizing problems both currently and longitudinally. Findings underscore the importance of identifying at-risk populations among adolescents during the transition to high school by including both family and school experiences when examining environmental influence on their adjustment, as well as the necessity to take individual environmental sensitivity into account when examining these associations.

Endoscopic and Surgical Treatment of Gastrointestinal Neuroendocrine Neoplasms: A Population-based Comparative Study.

BACKGROUND: Controversy surrounds the indications for endoscopic treatment (ET) versus surgery in addressing gastrointestinal neuroendocrine neoplasms (GI-NENs). This paper aims to compare the long-term survival prognosis between ET and surgery for patients with GI-NENs. METHODS: A retrospective analysis of GI-NEN patients diagnosed between 2000 and 2020 was conducted using the SEER database. Overall survival (OS) and cancer-specific survival (CSS) were estimated using the Kaplan-Meier method. Prognostic factors were assessed through univariate and multivariate Cox regression analyses. Propensity score matching (PSM) was employed to adjust for clinical variables. RESULTS: This study encompassed 12,016 patients with GI-NENs, with 3732 patients (31.1%) undergoing ET and 8284 patients (68.9%) opting for surgery. The rectum was the most frequent location for ET, while the small bowel was the predominant site for surgery. Both the ET and surgical groups exhibited similar overall survival risk and cancer-specific survival risk before and after matching. No significant differences in the 1-year, 3-year, 5-year, and 10-year OS and CSS rates were observed between the ET and surgery groups after PSM. Nevertheless, subgroup analysis revealed a significantly better CSS in the ET group than the surgery group, particularly in stage I and tumors sized <10mm (P<0.01). In the colon subgroup, the OS and CSS of the ET group were superior to those of the surgery group (P<0.05). CONCLUSION: Endoscopic treatment and surgery demonstrate comparable long-term survival prognoses for treating GI-NENs. ET emerges as a viable option for patients averse to surgical interventions.

Automated machine learning for predicting liver metastasis in patients with gastrointestinal stromal tumor: a SEER-based analysis.

Gastrointestinal stromal tumors (GISTs) are a rare type of tumor that can develop liver metastasis (LIM), significantly impacting the patient's prognosis. This study aimed to predict LIM in GIST patients by constructing machine learning (ML) algorithms to assist clinicians in the decision-making process for treatment. Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases from 2010 to 2015 were assigned to the developing sets, while cases from 2016 to 2017 were assigned to the testing set. Missing values were addressed using the multiple imputation technique. Four algorithms were utilized to construct the models, comprising traditional logistic regression (LR) and automated machine learning (AutoML) analysis such as gradient boost machine (GBM), deep neural net (DL), and generalized linear model (GLM). We evaluated the models' performance using LR-based metrics, including the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA), as well as AutoML-based metrics, such as feature importance, SHapley Additive exPlanation (SHAP) Plots, and Local Interpretable Model Agnostic Explanation (LIME). A total of 6207 patients were included in this study, with 2683, 1780, and 1744 patients allocated to the training, validation, and test sets, respectively. Among the different models evaluated, the GBM model demonstrated the highest performance in the training, validation, and test cohorts, with respective AUC values of 0.805, 0.780, and 0.795. Furthermore, the GBM model outperformed other AutoML models in terms of accuracy, achieving 0.747, 0.700, and 0.706 in the training, validation, and test cohorts, respectively. Additionally, the study revealed that tumor size and tumor location were the most significant predictors influencing the AutoML model's ability to accurately predict LIM. The AutoML model utilizing the GBM algorithm for GIST patients can effectively predict the risk of LIM and provide clinicians with a reference for developing individualized treatment plans.

The influence of lymphadenectomy on the prognosis of small intestinal stromal tumors: a study based on the SEER database.

BACKGROUND: Small intestinal stromal tumors (SISTs) typically require surgical treatment. However, the impact of lymphadenectomy (LA) on long-term prognosis in patients remains unclear. Therefore, we plan to analyze the effect of LA on the prognosis of patients with SISTs using the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: Data on SISTs patients between 2000 and 2019 were obtained from the SEER database. Multiple imputation (MI) was employed to handle missing data, while propensity score matching (PSM) was conducted to mitigate selection bias in the comparative assessments between the LA group and the No-LA group. Kaplan-Meier analyses and multivariate Cox proportional hazards models were utilized to evaluate both overall survival (OS) and cancer-specific survival (CSS). RESULTS: A total of 2412 patients diagnosed with SISTs were included in the study, with 879 undergoing LA and 1533 not undergoing LA. There were no significant differences observed between the two cohorts concerning long-term OS (hazard ratio [HR] 0.97, 95% confidence interval [CI] 0.84-1.13, P = 0.720) and CSS (HR 1.05, 95% CI 0.86-1.29, P = 0.622). After PSM, 1596 patients (798 in the LA group and 789 in the No-LA group) were matched for comparison. There was also no difference in long-term OS and CSS between the two groups. Subgroup analysis revealed that in the age group > 60 years, the CSS in the No-LA group was superior to that in the LA group. Multivariate Cox regression analysis revealed that age, M stage, marital status, and mitotic rate are significant risk factors influencing OS and CSS. CONCLUSIONS: Conducting LA in patients with SISTs does not enhance long-term prognosis. For patients aged over 60 years, it may be more advisable to refrain from performing LA.

Prognostic factors for liver metastasis in patients with small intestinal stromal tumor: A retrospective analysis of surveillance, epidemiology, and end results.

BACKGROUND: Liver metastasis (LIM) is the most common distant site of metastasis in small intestinal stromal tumors (SISTs). The aim of this study was to determine the risk and prognostic factors associated with LIM in patients with SISTs. METHODS: Patients diagnosed with gastrointestinal stromal tumors between 2010 and 2019 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models, as well as a Cox regression model were used to explore the risk factors associated with the development and prognosis of LIM. Additionally, the overall survival (OS) of patients with LIM was analyzed using the Kaplan-Meier method. Furthermore, a predictive nomogram was constructed, and the model's performance was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). RESULTS: A total of 1582 eligible patients with SISTs were included, among whom 146 (9.2%) were diagnosed with LIM. Poor tumor grade, absence of surgery, later T-stage, and no chemotherapy were associated with an increased risk of developing LIM. The nomogram prediction model achieved an AUC of 0.810, 95% Confidence Interval (CI) 0.773-0.846, indicating good performance, and the calibration curve showed excellent accuracy in predicting LIM. The OS rate of patients with LIM was significantly lower than that of patients without LIM (p < 0.001). CONCLUSIONS: Patients with SISTs who are at high risk of developing LIM deserve more attention during follow-up, as LIM can significantly affect patient prognosis. The nomogram demonstrated good calibration and discrimination for predicting LIM.

Metabolic parameters of pretreatment 2-[18F]fluoro-D-glucose positron emission tomography for prognosis in patients with gallbladder adenocarcinoma: a cohort study.

Background: The incidence of gallbladder adenocarcinoma (GBA) is relatively low, yet it exhibits a high degree of malignancy and a significantly low 5-year survival rate. The aim of this study was to investigate the prognostic value of pretreatment 2-[18F]fluoro-D-glucose positron emission tomography {2-[18F]FDG PET} parameters in predicting outcomes for patients with GBA. Methods: In total, 67 patients with GBA who underwent 2-[18F]FDG PET/computed tomography (CT) before treatment were retrospectively analyzed at Chinese PLA General Hospital from January 2012 to June 2022. All patients were diagnosed by pathology, and their baseline characteristics and clinical data were collected. The metabolic PET parameters of the primary and metastatic lesions were measured, including the maximum and average standardized uptake values (SUVs), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The prognostic significance of metabolic parameters and other clinical variables was assessed using Cox proportional hazards regression models. Differences in progression-free survival (PFS) and overall survival (OS) in relation to metabolic parameters were examined using the Kaplan-Meier method. Results: During a median follow-up period of 14.2 months, 43 patients (64.2%) experienced tumor recurrence or progression, and 38 patients (56.7%) died of cancer. In the univariate Cox regression analysis, liver parenchymal invasion (P=0.001), lymph node metastasis (P=0.007), distant metastases (P=0.049), tumor differentiation (P=0.028), surgery (P=0.014), carcinoembryonic antigen (CEA) level (P=0.030), carbohydrate antigen 19-9 (CA19-9) level (P=0.003), TLG (P=0.005), MTV (P<0.001), sum of the TLGs of the primary and metastatic lesions (total TLG, tTLG) (P=0.001), and sum of the MTVs of the primary and metastatic lesions (total MTV, tMTV) (P<0.001) were significant predictors of PFS. In multivariate analysis, MTV was an independent predictor of PFS [hazard ratio (HR) =2.785; 95% confidence interval (CI): 1.204-6.441; P=0.017]. In the univariate Cox regression analysis, liver parenchymal invasion (P=0.001), lymph node metastasis (P=0.027), distant metastases (P=0.036), tumor differentiation (P=0.047), surgery (P=0.002), neutrophil-to-lymphocyte ratio (NLR) (P=0.011), CEA level (P=0.036), CA19-9 level (P<0.001), TLG (P=0.007), MTV (P<0.001), tTLG (P=0.003), and tMTV (P<0.001) were significant predictors of OS. In the multivariate analysis, higher CA19-9 levels >37 U/mL and a greater tMTV (HR =2.961; 95% CI: 1.092-8.024; P=0.033) were predictive of OS. Conclusions: Our study results suggest that pretreatment 2-[18F]FDG PET parameters can not only assist in the diagnosis of patients with GBA but may also serve as predictive factors for the prognosis of these patients and should thus be applied in their treatment.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

OBJECTIVE: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model ß was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS: A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model ß: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION: The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.

BACKGROUND: Acute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating both mortality rates and the financial burden of hospitalization. The aim of our study is to construct a predictive model utilizing automated machine learning (AutoML) algorithms for the early prediction of AKI in patients with AP. METHODS: We retrospectively analyzed patients who were diagnosed with AP in our hospital from January 2017 to December 2021. These patients were randomly allocated into a training set and a validation set at a ratio of 7:3. To develop predictive models for each set, we employed the least absolute shrinkage and selection operator (LASSO) algorithm along with AutoML. A nomogram was developed based on multivariate logistic regression analysis outcomes. The model's efficacy was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Additionally, the performance of the model constructed via AutoML was evaluated using decision curve analysis (DCA), feature importance, SHapley Additive exPlanations (SHAP) plots, and locally interpretable model-agnostic explanations (LIME). RESULTS: This study incorporated a total of 437 patients who met the inclusion criteria. Out of these, 313 were assigned to the training cohort and 124 to the validation cohort. In the training and validation cohorts, AKI occurred in 68 (21.7%) and 29(23.4%) patients, respectively. Comparative analysis revealed that the AutoML models exhibited enhanced performance over traditional logistic regression (LR). Furthermore, the deep learning (DL) model demonstrated superior predictive accuracy, evidenced by an area under the ROC curve of 0.963 in the training set and 0.830 in the validation set, surpassing other comparative models. The key variables identified as significant in the DL model within the training dataset included creatinine (Cr), urea (Urea), international normalized ratio (INR), etiology, smoking, alanine aminotransferase (ALT), hypertension, prothrombin time (PT), lactate dehydrogenase (LDH), and diabetes. CONCLUSION: The AutoML model, utilizing DL algorithm, offers considerable clinical significance in the early detection of AKI among patients with AP.

The mechanisms of ferroptosis and its role in atherosclerosis.

Ferroptosis is a newly identified form of non-apoptotic programmed cell death, characterized by the iron-dependent accumulation of lethal lipid reactive oxygen species (ROS) and peroxidation of membrane polyunsaturated fatty acid phospholipids (PUFA-PLs). Ferroptosis is unique among other cell death modalities in many aspects. It is initiated by excessive oxidative damage due to iron overload and lipid peroxidation and compromised antioxidant defense systems, including the system Xc-/ glutathione (GSH)/glutathione peroxidase 4 (GPX4) pathway and the GPX4-independent pathways. In the past ten years, ferroptosis was reported to play a critical role in the pathogenesis of various cardiovascular diseases, e.g., atherosclerosis (AS), arrhythmia, heart failure, diabetic cardiomyopathy, and myocardial ischemia-reperfusion injury. Studies have identified dysfunctional iron metabolism and abnormal expression profiles of ferroptosis-related factors, including iron, GSH, GPX4, ferroportin (FPN), and SLC7A11 (xCT), as critical indicators for atherogenesis. Moreover, ferroptosis in plaque cells, i.e., vascular endothelial cell (VEC), macrophage, and vascular smooth muscle cell (VSMC), positively correlate with atherosclerotic plaque development. Many macromolecules, drugs, Chinese herbs, and food extracts can inhibit the atherogenic process by suppressing the ferroptosis of plaque cells. In contrast, some ferroptosis inducers have significant pro-atherogenic effects. However, the mechanisms through which ferroptosis affects the progression of AS still need to be well-known. This review summarizes the molecular mechanisms of ferroptosis and their emerging role in AS, aimed at providing novel, promising druggable targets for anti-AS therapy.

RUNX2 prompts triple negative breast cancer drug resistance through TGF-ß pathway regulating breast cancer stem cells.

Triple-negative breast cancer (TNBC) stands out as the most aggressive subtype within the spectrum of breast cancer. The current clinical guidelines propose treatment strategies involving cytotoxic agents like epirubicin or paclitaxel. However, the emergence of acquired resistance frequently precipitates secondary tumor recurrence or the spread of metastasis. In recent times, significant attention has been directed toward the transcription factor RUNX2, due to its pivotal role in both tumorigenesis and the progression of cancer. Previous researches suggest that RUNX2 might be intricately linked to the development of resistance against chemotherapy, with its mechanism of action possibly intertwined with the signaling of TGF-ß. Nevertheless, the precise interplay between their effects and the exact molecular mechanisms underpinning chemoresistance in TNBC remain elusive. Therefore, we have taken a multifaceted approach from in vitro and in vivo experiments to validate the relationship between RUNX2 and TGF-ß and to search for their pathogenic mechanisms in chemoresistance. In conclusion, we found that RUNX2 affects chemoresistance by regulating cancer cell stemness through direct binding to TGF-ß, and that TGF-ß dually regulates RUNX2 expression. The important finding will provide a new reference for clinical reversal of the development of chemoresistance in breast cancer.

Protective role of resilience on the associations between childhood maltreatment and internalising and externalising problems.

Different types of childhood maltreatment have negative effects on individual mental and behavioural outcomes. However, most of previous studies investigated their effects separately. Little is known about the effects of co-occurring maltreatment profiles on adolescents' developmental outcomes and the potential protective factor. The current study sought to identify distinct profiles of childhood maltreatment and examine the effects of profiles of childhood maltreatment on internalising and externalising problems and the protective role of resilience based on two-wave longitudinal data, which was collected from a sample of 670 Chinese adolescents (Mage = 15.50, SDage = 0.75, 48.4% boys). Four profiles of childhood maltreatment, that is, No maltreatment (67.9%), High neglect (23.0%), High abuse and neglect/Low sexual abuse (5.0%), and Multi-maltreatment (4.1%), were identified. Adolescents in High neglect, High abuse and neglect/Low sexual abuse, and Multi-maltreatment profiles were more likely to report internalising and externalising problems. Further, significant moderating effects of resilience only emerged for the association between the High neglect profile and internalising problems, such that high levels of resilience may weaken the association between the High neglect profile and internalising problems. Our findings revealed the importance and utility of identifying maltreatment profiles to tailor treatment based on specific maltreatment experiences. Resilience-oriented intervention could be considered for Chinese adolescents who have experienced high neglect.

Clinical characteristics and prognosis of early-onset cholangiocarcinoma: a population-based study.

BACKGROUND: Little is known about the disease of early-onset cholangiocarcinoma (EOC). The primary objective of this study was to compare EOC with later-onset cholangiocarcinoma (LOC) concerning clinical features and survival prognosis. METHODS: 19325 cholangiocarcinoma patients were extracted from 1975 to 2020 in the SEER database. Cox regression analysis and Kaplan-Meier survival curves were used for the evaluation of cause-specific survival (CSS) and overall survival (OS). To reduce confounding, we compared survival differences between the EOC and LOC groups using propensity score matching (PSM). RESULTS: 4037 cholangiocarcinoma patients were included in the study, of which 274 were EOC and 3763 were LOC. Early-onset patients were more likely to be non-white, and intrahepatic cholangiocarcinoma. At diagnosis, patients had advanced AJCC stage, lymph node metastase and distant metastase. The EOC patients were more likely to receive surgery, radiotherapy, and chemotherapy than later-onset patients. Multifactorial COX analysis indicated that EOC patients had lower mortality risk than later-onset patients, and similar results were obtained after PSM; Kaplan-Meier survival curves corroborated that early-onset patients exhibited better OS than later-onset patients, and this survival advantage persisted after PSM. Further subgroup analysis following matching demonstrated that early-onset patients had better OS than later-onset patients in the surgical subgroup, while there were no statistically significant differences in the radiotherapy and chemotherapy subgroups. CONCLUSION: The EOC patients typically exhibit an intrahepatic presentation and generally experience a more favorable prognosis. Surgery emerged as a critical treatment modality significantly influencing the overall prognosis of EOC.

Reciprocal Links Between Friendship Quality and Peer Victimization Among Middle Adolescents.

There is a robust association between friendship quality and victimization in adolescence; yet, it remains unclear whether friendship quality may be linked causally with different forms of victimization in middle adolescence. To fill this gap, this study examines the bidirectional associations of friendship quality and relational/verbal victimization with data collected at two time points, 6 months apart, in a sample of 671 middle Chinese adolescents (Mage = 15.63, SDage = 0.73, 49% males). Cross-lagged panel analyses revealed a two-way relationship between friendship quality and victimization, which existed in both verbal and relational victimization. Multi-group panel analyses observed that the cross-lagged associations between friendship quality and relational/verbal victimization were only found for males, but not for females. This result suggests that adolescent males' victimization is both affected by and a predictor of friendship quality, with implications for youth prevention programs.

Diagnostic and prognostic value of the gut microbiota and its metabolite butyrate in children with biliary atresia.

PURPOSE: To determine the prevalent microbiological profile of biliary atresia (BA) patients at the time of its occurrence by studying their intestinal flora. METHODS: A total of 118 gut microbiota samples from three groups of 43 BA patients, 33 disease controls (DC) with other cholestatic diseases and 42 healthy controls (HC), were analyzed by deep mining of public data. Subsequently, a total of 23 fecal samples from three groups of clinically collected patients (11 BA, 6 DC and 6 HC) were sequenced for 16S rRNA gene amplification and analyzed for serum butyrate (BU) level by liquid chromatography. RESULTS: Taxonomic analysis revealed significant differences in the composition of the intestinal microbiota between BA patients and controls, with a reduction in diversity and a higher abundance of Proteobacteria, Streptococcus and Lactobacillus in the BA group. Database and clinical data analyses concluded that Streptococcus/Bacteroides (AUC = 0.9035, 95% CI 0.8347-0.9722, P < 0.0001) or Streptococcus/Eggerthella (AUC = 0.8333, 95% CI 0.6340-1.000, P = 0.027) was the best microbiota to differentiate between BA and DC. Serum butyrate levels were low in the BA and DC groups and differed from the HC group (P = 0.01, P = 0.04). Butyrate levels in BA were negatively correlated with jaundice clearance and cholangitis, but not statistically significant. CONCLUSIONS: Our study reveals changes in the composition of the gut microbiota in BA, especially the butyrate-producing microbiota, and suggests the potential for using gut microbiota as a noninvasive diagnostic benefit for BA. Low levels of serum butyrate in BA may indicate a poor prognosis.

Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT.

PURPOSE: To investigate the clinical value of radiomic analysis on [18F]FDG and [18F]FLT PET on the differentiation of [18F]FDG-avid benign and malignant pulmonary nodules (PNs). METHODS: Data of 113 patients with inconclusive PNs based on preoperative [18F]FDG PET/CT who underwent additional [18F]FLT PET/CT scans within a week were retrospectively analyzed in the present study. Three methods of analysis including visual analysis, radiomic analysis based on [18F]FDG PET/CT images alone, and radiomic analysis based on dual-tracer PET/CT images were evaluated for differential diagnostic value of benign and malignant PNs. RESULTS: A total of 678 radiomic features were extracted from volumes of interest (VOIs) of 123 PNs. Fourteen valuable features were thereafter selected. Based on a visual analysis of [18F]FDG PET/CT images, the diagnostic accuracy, sensitivity, and specificity were 61.6%, 90%, and 28.8%, respectively. For the test set, the area under the curve (AUC), sensitivity, and specificity of the radiomic models based on [18F]FDG PET/CT plus [18F]FLT signature were equal or better than radiomics based on [18F]FDG PET/CT only (0.838 vs 0.810, 0.778 vs 0.778, 0.750 vs 0.688, respectively). CONCLUSION: Radiomic analysis based on dual-tracer PET/CT images is clinically promising and feasible for the differentiation between benign and malignant PNs. CLINICAL RELEVANCE STATEMENT: Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT. KEY POINTS: • Radiomics brings new insights into the differentiation of benign and malignant pulmonary nodules beyond the naked eyes. • Dual-tracer imaging shows the biological behaviors of cancerous cells from different aspects. • Radiomics helps us get to the histological view in a non-invasive approach.