Analysis of high-risk human papillomavirus infections and cervical intraepithelial neoplasia: factors influencing awareness among women of childbearing age in southwest China.

BACKGROUND: High-risk Human Papillomavirus (HPV) infections are a leading cause of cervical diseases among Han Chinese women of reproductive age. Despite studies like Mai et al. (2021) addressing HPV prevalence in Southern China, awareness remains low, especially in Southwest China. Our study addresses this gap. OBJECTIVE: This hospital-based, retrospective study analyzes the prevalence of high-risk HPV and its association with cervical intraepithelial neoplasia (CIN) among Han Chinese women of reproductive age in Southwest China. METHODS: Data were collected from 724 women undergoing routine health exams from December 2022 to April 2023. A total of 102 women with high-risk HPV infections were identified. A survey assessed HPV awareness, CIN incidence, and socio-demographic factors influencing awareness. RESULTS: Of the 724 women, 102 (14.1%) were diagnosed with high-risk HPV, with HPV-16 being the most common subtype (22.5%). Awareness was significantly lower among unmarried women (OR: 6.632, p = 0.047), those with high school education or less (OR: 20.571, p = 0.003), and rural residents (OR: 19.483, p = 0.020). HPV-16 was detected in 54.55% of women with high-grade CIN. CONCLUSION: There is an urgent need for targeted education and HPV vaccination in Southwest China, particularly for women with lower education, rural residents, and older individuals. Subtype-specific strategies are essential for preventing and managing CIN.

MR radiomics unveils neoadjuvant chemo-responsiveness with insights into selective treatment de-intensification in HPV-positive oropharyngeal carcinoma.

BACKGROUND: Accurate prediction of neoadjuvant chemotherapy (NAC) response allows for NAC-guided personalized treatment de-intensification in HPV-positive oropharyngeal squamous cell carcinoma (OPSCC). In this study, we aimed to apply baseline MR radiomic features to predict NAC response to help select NAC-guided de-intensification candidates, and to explore biological underpinnings of response-oriented radiomics. METHODS: Pre-treatment MR images and clinical data of 131 patients with HPV-positive OPSCC were retrieved from Fudan University Shanghai Cancer Center. Patients were divided into training cohort (n = 47), validation cohort 1 (n = 49) from NAC response-adapted de-intensification trial (IChoice-01, NCT04012502) and real-world validation cohort 2 (n = 35). NAC prediction model using linear support vector machine (SVM) was built and validated. Subsequent nomograms combined radiomics and clinical characteristics were established to predict survival outcomes. RNA-seq and proteomic data were compared to interpret the molecular features underlying radiomic signatures with differential NAC response. FINDINGS: For NAC response prediction, the fusion model with both oropharyngeal and nodal signatures achieved encouraging performance to predict good responders in the training cohort (AUC 0·89, 95% CI, 0·79-0·95) and validation cohort 1 (AUC 0·71, 95% CI, 0·59-0·83). For prognosis prediction, radiomics-based nomograms exhibited satisfactory discriminative ability between low-risk and high-risk patients (PFS, C-index 0·85, 0·76 and 0·83; OS, C-index 0·79, 0·76 and 0·87, respectively) in three cohorts. Expression analysis unveiled NAC poor responders had predominantly enhanced keratinization while good responders were featured by upregulated immune response and oxidative stress. INTERPRETATION: The MR-based radiomic models and prognostic models efficiently discriminate among patients with different NAC response and survival risk, which help candidate selection in HPV-positive OPSCC with regard to personalized treatment de-intensification.

Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.

Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.

Magnetic resonance imaging-based prognostic model for subsequent distant metastasis in patients with ipsilateral breast tumor recurrence following breast-conserving surgery.

Background: Ipsilateral breast tumor recurrence (IBTR) following breast-conserving surgery (BCS) has been considered a risk factor for distant metastasis (DM). Limited data are available regarding the subsequent outcomes after IBTR. Therefore, this study aimed to determine the clinical course after IBTR and develop a magnetic resonance imaging (MRI)-based predictive model for subsequent DM. Methods: We retrospectively extracted quantitative features from MRI to construct a radiomics cohort, with all eligible patients undergoing preoperative MRI at time of primary tumor and IBTR between 2010 and 2018. Multivariate Cox analysis was performed to identify factors associated with DM. Three models were constructed using different sets of clinicopathological, qualitative, and quantitative MRI features and compared. Additionally, Kaplan-Meier analysis was performed to assess the prognostic value of the optimal model. Results: Among the 183 patients who experienced IBTR, 47 who underwent MRI for both primary and recurrent tumors were enrolled. Multivariate analysis demonstrated that the independent prognostic factors were human epidermal growth factor receptor 2 (HER2) status [hazard ratio (HR) =5.40] and background parenchymal enhancement (BPE) (HR =7.94) (all P values <0.01). Furthermore, four quantitative MRI features of recurrent tumors were selected through the least absolute shrinkage and selection operator (LASSO) method. The combined model exhibited superior performance [concordance index (C-index) 0.77] compared to the clinicoradiological model (C-index 0.71; P=0.006) and radiomics model (C-index 0.70; and P=0.01). Furthermore, the combined model successfully categorized patients into low- and high-risk subgroups with distinct prognoses (P<0.001). Conclusions: The clinicopathological and MRI features of IBTR were associated with secondary events following surgery. Additionally, the MRI-based combined model exhibited the highest predictive efficacy. These findings could be helpful in risk stratification and tailoring follow-up strategies in patients with IBTR.

Magnetic resonance imaging-based radiomics analysis for prediction of treatment response to neoadjuvant chemoradiotherapy and clinical outcome in patients with locally advanced rectal cancer: A large multicentric and validated study.

Our study investigated whether magnetic resonance imaging (MRI)-based radiomics features could predict good response (GR) to neoadjuvant chemoradiotherapy (nCRT) and clinical outcome in patients with locally advanced rectal cancer (LARC). Radiomics features were extracted from the T2 weighted (T2W) and Apparent diffusion coefficient (ADC) images of 1070 LARC patients retrospectively and prospectively recruited from three hospitals. To create radiomic models for GR prediction, three classifications were utilized. The radiomic model with the best performance was integrated with important clinical MRI features to create the combined model. Finally, two clinical MRI features and ten radiomic features were chosen for GR prediction. The combined model, constructed with the tumor size, MR-detected extramural venous invasion, and radiomic signature generated by Support Vector Machine (SVM), showed promising discrimination of GR, with area under the curves of 0.799 (95% CI, 0.760-0.838), 0.797 (95% CI, 0.733-0.860), 0.754 (95% CI, 0.678-0.829), and 0.727 (95% CI, 0.641-0.813) in the training and three validation datasets, respectively. Decision curve analysis verified the clinical usefulness. Furthermore, according to Kaplan-Meier curves, patients with a high likelihood of GR as determined by the combined model had better disease-free survival than those with a low probability. This radiomics model was developed based on large-sample size, multicenter datasets, and prospective validation with high radiomics quality score, and also had clinical utility.

Predicting Regional Recurrence and Prognosis in Stereotactic Body Radiation Therapy-Treated Clinical Stage I Non-small Cell Lung Cancer Using a Radiomics Model Constructed With Surgical Data.

PURPOSE: Risk stratification of regional recurrence (RR) is clinically important in the design of adjuvant treatment and surveillance strategies in patients with clinical stage I non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). This study aimed to develop a radiomics model predicting occult lymph node metastasis (OLNM) using surgical data and apply it to the prediction of RR in SBRT-treated early-stage NSCLC patients. METHODS AND MATERIALS: Patients with clinical stage I NSCLC who underwent curative surgery with systematic lymph node dissection from January 2013 to December 2018 (the training cohort) and from January 2019 to December 2020 (the validation cohort) were included. A preoperative computed tomography-based radiomics model, a clinical feature model, and a fusion model predicting OLNM were constructed. The performance of the 3 models was quantified and compared in the training and validation cohorts. Subsequently, the radiomics model was used to predict RR in a cohort of consecutive SBRT-treated early-stage NSCLC patients from 2 academic medical centers. RESULTS: A total of 769 patients were included. Eight computed tomography features were identified in the radiomics model, achieving areas under the curves of 0.85 (95% CI, 0.81-0.89) and 0.83 (95% CI, 0.80-0.88) in the training and validation cohorts, respectively. Nevertheless, adding clinical features did not improve the performance of the radiomics model. With a median follow-up of 40.0 (95% CI, 35.2-44.8) months, 32 of the 213 patients in the SBRT cohort developed RR and those in the high-risk group based on the radiomics model had a higher cumulative incidence of RR (P < .001) and shorter regional recurrence-free survival (P = .02), progression-free survival (P = .004) and overall survival (P = .006) than those in the low-risk group. CONCLUSIONS: The radiomics model based on pathologically confirmed data effectively identified patients with OLNM, which may be useful in the risk stratification among SBRT-treated patients with clinical stage I NSCLC.

Predicting Prognosis of Phyllodes Tumors Using a Mammography- and Magnetic Resonance Imaging-Based Radiomics Model: A Preliminary Study.

PURPOSE: To investigate whether a radiomics model based on mammography (MG) and magnetic resonance imaging (MRI) can be used to predict disease-free survival (DFS) after phyllodes tumor (PT) surgery. METHOD: About 131 PT patients who underwent MG and MRI before surgery between January 2010 and December 2020 were retrospectively enrolled, including 15 patients with recurrence and metastasis and 116 without recurrence. 884 and 3138 radiomic features were extracted from MG and MR images, respectively. Then, multiple radiomics models were established to predict the recurrence risk of the patients by applying a support vector machine classifier. The area under the ROC curve (AUC) was calculated to evaluate model performance. After dividing the patients into high- and low-risk groups based on the predicted radiomics scores, survival analysis was conducted to compare differences between the groups. RESULTS: In total, 3 MG-related and 5 MRI-related radiomic models were established; the prediction performance of the T1WI feature fusion model was the best, with an AUC value of 0.93. After combining the features of MG and MRI, the AUC increased to 0.95. Furthermore, the MG, MRI and all-image radiomic models had statistically significant differences in survival between the high- and low-risk groups (P < .001). All-image radiomics model showed higher survival performance than the MG and MRI radiomics models alone. CONCLUSIONS: Radiomics features based on preoperative MG and MR images can predict DFS after PT surgery, and the prediction score of the image radiomics model can be used as a potential indicator of recurrence risk.

Parabacteroides distasonis-Derived Outer Membrane Vesicles Enhance Antitumor Immunity Against Colon Tumors by Modulating CXCL10 and CD8+ T Cells.

Purpose: Given the potent immunostimulatory effects of bacterial outer membrane vesicles (OMVs) and the significant anti-colon tumor properties of Parabacteroides distasonis (Pd), this study aimed to elucidate the role and potential mechanisms of Pd-derived OMVs (Pd-OMVs) against colon cancer. Methods: This study isolated and purified Pd-OMVs from Pd cultures and assessed their characteristics. The effects of Pd-OMVs on CT26 cell uptake, proliferation, and invasion were investigated in vitro. In vivo, a CT26 colon tumor model was used to investigate the anti-colon tumor effects and underlying mechanisms of Pd-OMVs. Finally, we evaluated the biosafety of Pd-OMVs. Results: Purified Pd-OMVs had a uniform cup-shaped structure with an average size of 165.5 nm and a zeta potential of approximately -9.56 mV, and their proteins were associated with pathways related to immunity and apoptosis. In vitro experiments demonstrated that CT26 cells internalized the Pd-OMVs, resulting in a significant decrease in their proliferation and invasion abilities. Further in vivo studies confirmed the accumulation of Pd-OMVs in tumor tissues, which significantly inhibited the growth of colon tumors. Mechanistically, Pd-OMVs increased the expression of CXCL10, promoting infiltration of CD8+ T cells into tumor tissues and expression of pro-inflammatory factors TNF-α, IL-1ß, and IL-6. Notably, Pd-OMVs demonstrated a high level of biosafety. Conclusion: This paper elucidates that Pd-OMVs can exert significant anti-colon tumor effects by upregulating the expression of the chemokine CXCL10, thereby increasing the infiltration of CD8+ T cells into tumors and enhancing antitumor immune responses. This suggests that Pd-OMVs may be developed as a novel nanoscale potent immunostimulant with great potential for application in tumor immunotherapy. As well as developed as a novel nano-delivery carrier for combination with other antitumor drugs.

Res-TransNet: A Hybrid deep Learning Network for Predicting Pathological Subtypes of lung Adenocarcinoma in CT Images.

This study aims to develop a CT-based hybrid deep learning network to predict pathological subtypes of early-stage lung adenocarcinoma by integrating residual network (ResNet) with Vision Transformer (ViT). A total of 1411 pathologically confirmed ground-glass nodules (GGNs) retrospectively collected from two centers were used as internal and external validation sets for model development. 3D ResNet and ViT were applied to investigate two deep learning frameworks to classify three subtypes of lung adenocarcinoma namely invasive adenocarcinoma (IAC), minimally invasive adenocarcinoma and adenocarcinoma in situ, respectively. To further improve the model performance, four Res-TransNet based models were proposed by integrating ResNet and ViT with different ensemble learning strategies. Two classification tasks involving predicting IAC from Non-IAC (Task1) and classifying three subtypes (Task2) were designed and conducted in this study. For Task 1, the optimal Res-TransNet model yielded area under the receiver operating characteristic curve (AUC) values of 0.986 and 0.933 on internal and external validation sets, which were significantly higher than that of ResNet and ViT models (p < 0.05). For Task 2, the optimal fusion model generated the accuracy and weighted F1 score of 68.3% and 66.1% on the external validation set. The experimental results demonstrate that Res-TransNet can significantly increase the classification performance compared with the two basic models and have the potential to assist radiologists in precision diagnosis.

Development and validation of peritumoral vascular and intratumoral radiomics to predict pathologic complete responses to neoadjuvant chemotherapy in patients with triple-negative breast cancer.

BACKGROUND: To develop and validate a peritumoral vascular and intratumoral radiomics model to improve pretreatment predictions for pathologic complete responses (pCRs) to neoadjuvant chemoradiotherapy (NAC) in patients with triple-negative breast cancer (TNBC). METHODS: A total of 282 TNBC patients (93 in the primary cohort, 113 in the validation cohort, and 76 in The Cancer Imaging Archive [TCIA] cohort) were retrospectively included. The peritumoral vasculature on the maximum intensity projection (MIP) from pretreatment DCE-MRI was segmented by a Hessian matrix-based filter and then edited by a radiologist. Radiomics features were extracted from the tumor and peritumoral vasculature of the MIP images. The LASSO method was used for feature selection, and the k-nearest neighbor (k-NN) classifier was trained and validated to build a predictive model. The diagnostic performance was assessed using the ROC analysis. RESULTS: One hundred of the 282 patient (35.5%) with TNBC achieved pCRs after NAC. In predicting pCRs, the combined peritumoral vascular and intratumoral model (fusion model) yields a maximum AUC of 0.82 (95% confidence interval [CI]: 0.75, 0.88) in the primary cohort, a maximum AUC of 0.67 (95% CI: 0.57, 0.76) in the internal validation cohort, and a maximum AUC of 0.65 (95% CI: 0.52, 0.78) in TCIA cohort. The fusion model showed improved performance over the intratumoral model and the peritumoral vascular model, but not significantly (p > 0.05). CONCLUSION: This study suggested that combined peritumoral vascular and intratumoral radiomics model could provide a non-invasive tool to enable prediction of pCR in TNBC patients treated with NAC.

Identification of Chronic Pancreatitis Associated microRNAs and Genes for the Diagnosis of Pancreatic Cancer.

OBJECTIVE: The timely identification of both malignant and nonmalignant pancreatic lesions has the potential to significantly enhance prognosis and implement risk management strategies across various levels. microRNAs (miRs) and their corresponding targets play a crucial role in the development of pancreatic lesions and can serve as valuable diagnostic and therapeutic targets. The objective of our study was to investigate potential diagnostic markers that can effectively differentiate between malignant and nonmalignant pancreatic lesions. METHODS: Gene Expression Omnibus (GEO) database with GSE24279 dataset was utilized to screen differentially expressed miRNAs (DEMs). We utilized the TargetScanHuman database to predict the target genes associated with hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p. Furthermore, a cohort comprising healthy individuals (n = 52), chronic pancreatitis (CP; n = 34), and pancreatic adenocarcinoma (PAAD; n = 53) patients was recruited to ascertain the levels of plasma markers. RESULTS: We identified 3 miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and 2 proteins (PCDH1 and AMN) as potential diagnostic markers for distinguishing between CP and PAAD. The area under the curve (AUC) values for all markers exceeded .800. Notably, a combination of plasma PCDH1 and AMN demonstrated excellent diagnostic performance (AUC = .921; 95% CI: .866-.977; sensitivity = .792; specificity = .941) in discriminating between CP and PAAD. In addition, the model of hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p yielded an AUC of .928, sensitivity of .830, and specificity of .912, respectively. CONCLUSION: Plasma levels of miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and their corresponding targets (PCDH1 and AMN) hold promise as potential biomarkers for predicting PAAD in patients with CP.

Small Molecules Promote the Rapid Generation of Dental Epithelial Cells from Human-Induced Pluripotent Stem Cells.

Human-induced pluripotent stem cells (hiPSCs) offer a promising source for generating dental epithelial (DE) cells. Whereas the existing differentiation protocols were time-consuming and relied heavily on growth factors, herein, we developed a three-step protocol to convert hiPSCs into DE cells in 8 days. In the first phase, hiPSCs were differentiated into non-neural ectoderm using SU5402 (an FGF signaling inhibitor). The second phase involved differentiating non-neural ectoderm into pan-placodal ectoderm and simultaneously inducing the formation of oral ectoderm (OE) using LDN193189 (a BMP signaling inhibitor) and purmorphamine (a SHH signaling activator). In the final phase, OE cells were differentiated into DE through the application of Purmorphamine, XAV939 (a WNT signaling inhibitor), and BMP4. qRT-PCR and immunostaining were performed to examine the expression of lineage-specific markers. ARS staining was performed to evaluate the formation of the mineralization nodule. The expression of PITX2, SP6, and AMBN, the emergence of mineralization nodules, and the enhanced expression of AMBN and AMELX in spheroid culture implied the generation of DE cells. This study delineates the developmental signaling pathways and uses small molecules to streamline the induction of hiPSCs into DE cells. Our findings present a simplified and quicker method for generating DE cells, contributing valuable insights for dental regeneration and dental disease research.

Comparison and Evaluation of the Accuracy for Thoracic and Lumbar Pedicle Screw Fixation in Early-Onset Congenital Scoliosis Children.

BACKGROUND: Compared to adult scoliosis, correcting scoliosis in children often presents greater challenges. This is attributed to two key factors. Firstly, it involves accounting for the growth potential of children. Secondly, the thinner pedicles in children can complicate screw insertion, particularly when dealing with existing deformities. The utilization of intraoperative navigation technology offers a modest improvement in the precision of screw placement but does come with the drawback of increased radiation exposure. The aim of this study is to investigate and assess the accuracy of manually inserting pedicle screws in the thoracic and lumbar spine to rectify deformities in children with early-onset congenital scoliosis. METHODS: In this retrospective study, 26 hospitalized patients diagnosed with early-onset congenital scoliosis between December 2014 and December 2019 were selected. The cohort comprised 16 boys and 10 girls, aged between 2 and 10 years, with an average age of 4.68 ± 2.42 years. Pedicle screw fixation was applied in the segment spanning from T1 to L5. Pedicle screws were inserted manually, guided by the positioning of the C-arm and anatomical markers. The assessment of pedicle screw placement was based on the distance of penetration into the medial, lateral, or anterior bone cortex of the vertebral body, including the pedicle, categorized into three grades: Grade 1 (placement <2 mm), Grade 2 (placement between 2-4 mm), and Grade 3 (placement >4 mm). Grade 1 indicates accurate pedicle screw placement, while Grades 2 and 3 signify abnormal pedicle screw placement. Complications related to pedicle screw insertion were also recorded, both during and after the surgical procedure. RESULTS: A total of 173 pedicle screws were inserted in this study, with an average of 6.65 screws per patient. Accurate screw placement was achieved in 143 cases (82.7%), while 30 pedicle screws were found to be abnormal. Among the abnormal screws, 24 were categorized as Grade 2 (13.9%), and 6 as Grade 3 (3.5%). Grade 2 abnormalities were distributed across 20 thoracic vertebrae and 4 lumbar vertebrae, while Grade 3 abnormalities affected 5 thoracic vertebrae and 1 lumbar vertebra. When comparing the lumbar and thoracic vertebral regions, a significant difference in the rate of abnormal screw placement was observed (χ2 = 5.801, p < 0.05). The rate of abnormal screw placement was higher in the thoracic vertebral region with abnormal vertebral bodies than in the lumbar vertebral regions. Furthermore, a statistically significant difference in the rate of abnormal screw placement was found between the concave and convex sides (χ2 = 23.047, p < 0.05). The concave side of the abnormal vertebral body had a higher rate of abnormal screw placement (55.6%, 15/27) compared to the convex side (20.1%, 7/34), and this difference was statistically significant (p < 0.05). Throughout the intraoperative and postoperative follow-up period, spanning from 12 to 56 months, only one patient experienced issues with wound healing, and no complications related to pedicle screw placement occurred, such as hemopneumothorax, pedicle fracture, nerve root injury, aortic injury, screw loosening, pullout or breakage, or spinal cord injury. CONCLUSIONS: In children under 10 years of age with early-onset congenital scoliosis, the freehand placement of thoracic and lumbar pedicle screws demonstrates a high level of accuracy. Moreover, complications associated with pedicle screw insertion are infrequent following surgery. It is advisable to exercise caution when placing pedicle screws in thoracic vertebral bodies and morphologically abnormal vertebral bodies, with particular attention to the concave side when screw placement is required in these regions.

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model. METHODS: This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model. RESULTS: The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05). CONCLUSIONS: Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.

Integrating serum pharmacochemistry and network pharmacology to reveal the mechanism of chickpea in improving insulin resistance.

Although chickpea have great potential in the treatment of obesity and diabetes, the bioactive components and therapeutic targets of chickpea to prevent insulin resistance (IR) are still unclear. The purpose of this study was to investigate the chemical and pharmacological characteristics of chickpea on IR through serum pharmacochemistry and network pharmacology. The results revealed that compared with other polar fractions, the ethyl acetate extract of chickpea (CE) had the definitive performance on enhancing the capacities of glucose consumption and glycogen synthesis. In addition, we analyzed the components of CE in vivo and in vitro based on UPLC-Q-Orbitrap HRMS technology. There were 28 kinds of in vitro chemical components, among which the isoflavones included biochanin A, formononetin, ononin, sissotrin, and astragalin, etc. Concerningly, the chief prototype components of CE absorbed into the blood were biochanin A, formononetin, loliolide, and lenticin, etc. Furthermore, a total of 209 common targets between IR and active components of CE were screened out by network pharmacology, among which the key targets involved PI3K p85, NF-κB p65 and estrogen receptor 1, etc. Specifically, KEGG pathway analysis indicated that PI3K-AKT signaling pathway, HIF-1 signaling pathway, and AGE-RAGE signaling pathway may play critical roles in the IR remission by CE. Finally, the in vitro validation experiments disclosed that CE significantly balanced the oxidative stress state of IR-HepG2 cells and inhibited expressions of inflammatory cytokines. In conclusion, the present study will be an important reference for clarifying the pharmacodynamic substance basis and underlying mechanism of chickpea to alleviate IR.

Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer.

PURPOSE: To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. METHODS: A total of 178 patients (mean age: 59.35, range 20-85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients' pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23-74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan-Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). RESULTS: Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077-2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281-4.396). CONCLUSIONS: We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.

6-Gingerol regulates triglyceride and cholesterol biosynthesis to improve hepatic steatosis in MAFLD by activating the AMPK-SREBPs signaling pathway.

Excessive synthesis of triglycerides and cholesterol accelerates the progression of hepatic steatosis in metabolic-associated fatty liver disease (MAFLD). However, the precise mechanism by which 6-gingerol mitigates hepatic steatosis in MAFLD model mice has yet to be fully understood. The present study observed that 6-gingerol administration exhibited significant protective effects against obesity, insulin resistance, and hepatic steatosis in mice subjected to a high-fat diet (HFD), and mitigated lipid accumulation in HepG2 cells treated with palmitate (PA). Following the hepatic lipidomic analysis, we confirmed that the AMPK-SREBPs signaling pathway as the underlying molecular mechanism by which 6-gingerol inhibited triglyceride and cholesterol biosynthesis, both in vivo and in vitro, through Western blot and immunofluorescence assay. Additionally, the application of an AMPK agonist/inhibitor further validated that 6-gingerol promoted AMPK activation by increasing the phosphorylation level of AMPK in vitro. Notably, the inhibitory effect of 6-gingerol on cholesterol biosynthesis, rather than triglyceride biosynthesis, was significantly diminished after silencing SREBP2 using a lentiviral plasmid shRNA in HepG2 cells. Our study demonstrates that 6-gingerol mitigates hepatic triglyceride and cholesterol biosynthesis to alleviate hepatic steatosis by activating the AMPK-SREBPs signaling pathway, indicating that 6-gingerol may be a potential candidate in the therapy of MAFLD.

MRI characteristics of H3 G34-mutant diffuse hemispheric gliomas and possible differentiation from IDH-wild-type glioblastomas in adolescents and young adults.

OBJECTIVE: H3 G34-mutant diffuse hemispheric gliomas (G34m-DHGs) are rare and constitute a new infiltrating brain tumor entity whose characteristics require elucidation, and their difference from isocitrate dehydrogenase-wild-type glioblastomas (IDH-WT-GBMs) needs to be clarified. In this study, the authors report the demographic, clinical, and neuroradiological features of G34m-DHG and investigate the capability of quantitative MRI features in differentiating them. METHODS: Twenty-three patients with G34m-DHG and 30 patients with IDH-WT-GBM were included in this retrospective study. The authors reviewed the clinical, radiological, and molecular data of G34m-DHGs and compared their neuroimaging features with those of IDH-WT-GBMs in adolescents and young adults. Visually Accessible Rembrandt Images (VASARI) features were extracted, and the Kruskal-Wallis test was performed. A logistic regression model was constructed to evaluate the diagnostic performance for differentiating between G34m-DHG and IDH-WT-GBM. Subsequently, FeAture Explorer (FAE) was used to generate the machine learning pipeline and select important radiomics features that had been extracted with PyRadiomics. Estimates of the performance were supplied by metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC). RESULTS: The mean age of the 23 patients with G34m-DHG was 23.7 years (range 11-45 years), younger than the mean age of patients with IDH-WT-GBM (30.96 years, range 5-43 years). All tumors were hemispheric. Most cases were immunonegative for ATRX (95%) and Olig2 (100%), were immunopositive for p53 (95%), and exhibited MGMT promoter methylation (81%). The radiological presentations of G34m-DHG were different from those of IDH-WT-GBM. The majority of the G34m-DHGs were in the frontal, parietal, and temporal lobes and demonstrated no or only faint contrast enhancement (74%), while IDH-WT-GBMs were mostly seen in the frontal lobe and showed marked contrast enhancement in 83% of cases. The FAE-generated model, based on radiomics features (AUC 0.925) of conventional MR images, had better discriminatory performance between G34m-DHG and IDH-WT-GBM than VASARI feature analysis (AUC 0.843). CONCLUSIONS: G34m-DHGs most frequently occur in the frontal, parietal, and temporal lobes in adolescent and young adults and are associated with radiological characteristics distinct from those of IDH-WT-GBMs. Successful identification can be achieved by using either VASARI features or radiomics signatures, which may contribute to prognostic evaluation and assist in clinical settings.

Extracellular vesicles from organoid-derived human retinal progenitor cells prevent lipid overload-induced retinal pigment epithelium injury by regulating fatty acid metabolism.

Retinal degeneration (RD), a group of diseases leading to irreversible vision loss, is characterised by retinal pigment epithelium (RPE) or retinal neuron damage and loss. With fewer risks of immune rejection and tumorigenesis, stem cell-secreted extracellular vesicles (EVs) offer a new cell-free therapeutic paradigm for RD, which remains to be investigated. Human retinal organoid-derived retinal progenitor cells (hERO-RPCs) are an easily accessible and advanced cell source for RD treatment. However, hERO-RPCs-derived EVs require further characterisation. Here, we compared the characteristics of EVs from hERO-RPCs (hRPC-EVs) with those of human embryonic stem cell (hESC)-derived EVs (hESC-EVs) as controls. Based on in-depth proteomic analysis, we revealed remarkable differences between hRPC-EVs and hESC-EVs. A comparison between EVs and their respective cells of origin demonstrated that the protein loading of hRPC-EVs was more selective than that of hESC-EVs. In particular, hESC-EVs were enriched with proteins related to angiogenesis and cell cycle, whereas hRPC-EVs were enriched with proteins associated with immune modulation and retinal development. More importantly, compared with that of hESC-EVs, hRPC-EVs exhibited a lower correlation with cell proliferation and a unique capacity to regulate lipid metabolism. It was further confirmed that hRPC-EVs potentially eliminated lipid deposits, inhibited lipotoxicity and oxidative stress, and enhanced phagocytosis and survival of oleic acid-treated ARPE-19 cells. Mechanistically, hRPC-EVs are integrated into the mitochondrial network of oleic acid-treated ARPE-19 cells, and increased the level of mitochondrial fatty acid ß-oxidation-related proteins. Thus, organoid-derived hRPC-EVs represent a promising source of cell-free therapy for RD, especially for blinding diseases related to abnormal lipid metabolism in RPE cells.

Radiomics for predicting survival in patients with locally advanced rectal cancer: a systematic review and meta-analysis.

Background: Radiomics has recently received considerable research attention for providing potential prognostic biomarkers for locally advanced rectal cancer (LARC). We aimed to comprehensively evaluate the methodological quality and prognostic prediction value of radiomic studies for predicting survival outcomes in patients with LARC. Methods: The Cochrane, Embase, Medline, and Web of Science databases were searched. The radiomics quality score (RQS), Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist, the Image Biomarkers Standardization Initiative (IBSI) guideline, and the Prediction Model Risk of Bias Assessment Tool were used to assess the quality of the selected studies. A further meta-analysis of hazard ratio (HR) regarding disease-free survival (DFS) and overall survival (OS) was performed. Results: Among the 358 studies reported, 15 studies were selected for our review. The mean RQS score was 7.73±4.61 (21.5% of the ideal score of 36). The overall TRIPOD adherence rate was 64.4% (251/390). Most of the included studies (60%) were assessed as having a high risk of bias (ROB) overall. The pooled estimates of the HRs were 3.14 [95% confidence interval (CI): 2.12-4.64, P<0.01] for DFS and 3.36 (95% CI: 1.74-6.49, P<0.01) for OS. Conclusions: Radiomics has potential to noninvasively predict outcome in patients with LARC. However, the overall methodological quality of radiomics studies was low, and the adherence to the TRIPOD statement was moderate. Future radiomics research should put a greater focus on enhancing the methodological quality and considering the influence of higher-order features on reproducibility in radiomics.