Iron metabolism: backfire of cancer cell stemness and therapeutic modalities.

Cancer stem cells (CSCs), with their ability of self-renewal, unlimited proliferation, and multi-directional differentiation, contribute to tumorigenesis, metastasis, recurrence, and resistance to conventional therapy and immunotherapy. Eliminating CSCs has long been thought to prevent tumorigenesis. Although known to negatively impact tumor prognosis, research revealed the unexpected role of iron metabolism as a key regulator of CSCs. This review explores recent advances in iron metabolism in CSCs, conventional cancer therapies targeting iron biochemistry, therapeutic resistance in these cells, and potential treatment options that could overcome them. These findings provide important insights into therapeutic modalities against intractable cancers.

The efficacy and safety of tislelizumab combined with gemcitabine plus cisplatin in the treatment of postoperative patients with muscle-invasive upper tract urothelial carcinoma.

BACKGROUND: A combination of immune checkpoint inhibitors (ICIs) and chemotherapy has demonstrated excellent clinical efficacy and safety in treating a variety of cancers, including urothelial carcinoma (UC). However, its efficacy and safety in patients with muscle-invasive upper tract urothelial carcinoma (UTUC) who are undergoing radical surgery remain uncertain. The purpose of this retrospective study was to examine the effectiveness and safety of tislelizumab combined with gemcitabine plus cisplatin (TGC) as a first-line postoperative adjuvant treatment in this population. METHODS: This single-center, real-world study retrospectively analyzed the data from 71 patients with muscle-invasive UTUC who had radical nephroureterectomy (RNU) at the Affiliated Hospital of Xuzhou Medical University between November 1, 2020, and November 1, 2023. Among the 71 patients, 30 received adjuvant therapy of TGC within 90 days after RNU and 41 underwent surveillance. No patients receive preoperative neoadjuvant therapy. The TGC therapy group received adjuvant therapy every 3 weeks postoperatively until the first recurrence, first metastasis, or death due to any reason, whichever occurred first. The patients were followed up telephonically and through outpatient visits to record and evaluate their disease-free survival (DFS) and treatment-related adverse events (TRAEs). RESULTS: This study assessed the DFS of 41 and 30 patients in the surveillance group and TGC therapy group, respectively. The median DFS of the surveillance group was 16.5 [95% confidence interval (CI), 14.7-18.3] months, while the median DFS of the TGC group has not yet reached [hazard ratio (HR) 0.367 (95% CI, 0.169-0.796); p = 0.008], with 21 patients still undergoing follow-up. Compared with the surveillance group, the TGC therapy group had dramatically improved DFS after RNU and reduced risk by 63.3%. Of the 30 patients receiving combination therapy, 28 experienced TRAEs; all TRAEs were consistent with the frequently reported events in the chemotherapy-alone regimens, and there were no treatment-related deaths. CONCLUSION: This study demonstrates that TGC therapy exhibits excellent clinical efficacy in patients undergoing radical surgery, significantly improving DFS and displaying great safety.

Inflammatory markers predict survival in patients with postoperative urothelial carcinoma receiving tislelizumab (PD-1 inhibitor) adjuvant therapy.

BACKGROUND: In the management of urothelial carcinoma, patient selection for immunotherapy, particularly with immune checkpoint inhibitors such as PD-1 (programmed cell death protein 1), is important for treatment efficacy. Inflammatory markers are useful for predicting treatment outcomes and immune-related adverse events (irAEs). This study aims to retrospectively explore the associations between inflammatory markers and outcomes in patients with postoperative urothelial carcinoma undergoing tislelizumab (PD-1 inhibitor) adjuvant therapy. METHODS: A retrospective analysis was conducted on 133 patients with postoperative urothelial carcinoma who received tislelizumab adjuvant therapy at the Affiliated Hospital of Xuzhou Medical University from April 2020 to August 2023. The prognostic effects of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) on disease-free survival (DFS) and overall survival (OS) were assessed using Cox regression models. The correlation between inflammatory markers and the onset of irAEs was analyzed using logistic regression models. RESULTS: NLR < 5 and MLR < 0.31 were significantly associated with better outcomes compared to NLR >5 and MLR >0.31, respectively. Multivariate analysis revealed that an NLR < 5 was independently associated with better DFS and OS. However, there was no significant effect on the DFS and OS between PLR < 135 and PLR >135. Patients who experienced irAEs had longer DFS and OS. Multivariate analysis demonstrated that irAEs were an independent prognostic risk factor for DFS and OS. There was no significant difference in the occurrence of irAEs among different NLR, PLR, and MLR groups. CONCLUSION: In patients with postoperative urothelial carcinoma receiving tislelizumab adjuvant therapy, the assessment of NLR and MLR before treatment may serve as valuable predictive markers of clinical outcome.

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. METHODS: A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. RESULTS: At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). CONCLUSIONS: The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

Donafenib inhibits PARP1 expression and induces DNA damage, in combination with PARP1 inhibitors promotes apoptosis in liver cancer cells.

Liver cancer is a prevalent malignant tumor globally. The newly approved first-line drug, donafenib, is a novel oral small molecule multi-tyrosine kinase inhibitor that has significant antitumor effects on liver cancer. This study aims to investigate the antitumor effects of donafenib on liver cancer and to explore its potential mechanisms. Donafenib significantly inhibited the viability of Huh-7 and HCCLM3 cells, inhibited malignant cell proliferation, and promoted cell apoptosis, as demonstrated by CCK-8, EdU, and Calcein/PI (propidium iodide) staining experiments. The results of DNA damage detection experiments and western blot analysis indicate that donafenib caused considerable DNA damage in liver cancer cells. The analysis of poly (ADP-ribose) polymerase 1 (PARP1) in liver cancer patients using online bioinformatics data websites such as TIMER2.0, GEPIA, UALCAN, cBioPortal, Kaplan-Meier Plotter, and HPA revealed a high expression of PARP1, which is associated with poor prognosis. Molecular docking and western blot analysis demonstrated that donafenib can directly target and downregulate the protein expression of PARP1, a DNA damage repair protein, thereby promoting DNA damage in liver cancer cells. Western blot and immunofluorescence detection showed that the group treated with donafenib combined with PARP1 inhibitor had significantly higher expression of γ-H2AX and 8-OHdG compared to the groups treated with donafenib or PARP1 inhibitors alone, the combined treatment suppresses the expression of the antiapoptotic protein Bcl2 and enhances the protein expression level of the proapoptotic protein Bcl-2-associated X protein (BAX). These data suggest that the combination of donafenib and a PARP1 inhibitor results in more significant DNA damage in cells and promotes cell apoptosis. Thus, the combination of donafenib and PARP1 inhibitors has the potential to be a treatment option for liver cancer.

Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: a multicenter study.

OBJECTIVES: To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI. METHODS: Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2-5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models. RESULTS: Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively. CONCLUSION: The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs. CLINICAL RELEVANCE STATEMENT: Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs. KEY POINTS: The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.

Redox Relay-Induced C-S Radical Cross-Coupling Strategy: Application in Nontraditional Site-Selective Thiocyanation of Quinoxalinones.

Oxidative cross-coupling is a powerful strategy to form C-heteroatom bonds. However, oxidative cross-coupling for constructing C-S bond is still a challenge due to sulfur overoxidation and poisoning transition-metal catalysts. Now, electrochemical redox relay using sulfur radicals formed in situ from inorganic sulfur source offers a solution to this problem. Herein, electrochemical redox relay-induced C-S radical cross-coupling of quinoxalinones and ammonium thiocyanate with bromine anion as mediator is presented. The electrochemical redox relay comprised initially the formation of sulfur radical via indirect electrochemical oxidation, simultaneous electrochemical reduction of the imine bond, electro-oxidation-triggered radical coupling involving dearomatization-rearomatization, and the reformation of the imine bond through anodic oxidation. Applying this strategy, various quinoxalinones bearing multifarious electron-deficient/-rich substituents at different positions were well compatible with moderate to excellent yields and good steric hindrance compatibility under constant current conditions in an undivided cell without transition-metal catalysts and additional redox reagents. Synthetic applications of this methodology were demonstrated through gram-scale preparation and follow-up transformation. Notably, such a unique strategy may offer new opportunities for the development of new quinoxalinone-core leads.

Brønsted Acid-Catalyzed Intramolecular Tandem Double Cyclization of γ-Hydroxy Acetylenic Ketones with Alkynes into Naphtho[1,2-b]furan-3-ones.

A metal-free and atom-economic route for the synthesis of naphtho[1,2-b]furan-3-ones has been realized via p-TsOH·H2O-catalyzed intramolecular tandem double cyclization of γ-hydroxy acetylenic ketones with alkynes in formic acid. The benzene-linked furanonyl-ynes are the key intermediates obtained by the scission/recombination of C-O double bonds. Further, the structural modifications of the representative product were implemented by reduction, demethylation, substitution, and [5 + 2]-cycloaddition.

Structural and functional changes in the default mode network in drug-resistant epilepsy.

PURPOSE: To investigate brain network properties and connectivity abnormalities of the default mode network (DMN) in drug-resistant epilepsy (DRE). The study was based on probabilistic fiber tracking and functional connectivity (FC) analysis, to explore the structural and functional connectivity patterns change between frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE). METHODS: A total of 33 DRE patients (18 TLE and 15 FLE) and 30 healthy controls (HCs) were recruited. The volume fraction of the septal brain region of the DMN in DRE was calculated using FreeSurfer. The FC analysis was performed using Data Processing and Analysis for Brain Imaging in MATLAB. The structural connections between brain regions of the DMN were calculated based on probabilistic fiber tracking. RESULTS: The left precuneus (PCUN) volumes in epilepsy groups were lower than that in HCs. Compared with FLE, TLE showed reduced FC between the left hippocampus (HIP) and PCUN/medial frontal gyrus, and between the right inferior parietal lobule (IPL) and right superior temporal gyrus. Compared with HCs, FLE showed increased FCs between the right IPL and occipital lobe, and between the left superior frontal gyrus (SFG) and bilateral superior temporal gyrus. In terms of structural connectivity, TLE exhibited increased connectivity strength between the left SFG and left PCUN, and showed reduced connection strength between the left HIP and left posterior cingulate gyrus/left PCUN, when compared with the FLE. CONCLUSIONS: TLE and FLE patients showed structural and functional changes in the DMN. Compared with FLE patients, the TLE patients showed reduced structural and functional connection strengths between the left HIP and PCUN. These alterations in connection strengths holds promise for the identification of TLE and FLE.

CT Radiomics for Predicting Pathological Complete Response of Axillary Lymph Nodes in Breast Cancer After Neoadjuvant Chemotherapy: A Prospective Study.

BACKGROUND: The diagnostic effectiveness of traditional imaging techniques is insufficient to assess the response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC), especially for pathological complete response (pCR). A radiomics model based on computed tomography (CT) could be helpful. PATIENTS AND METHODS: Prospective consecutive breast cancer patients with positive axillary LNs initially were enrolled, who received NAC prior to surgery. Chest contrast-enhanced thin-slice CT scan was performed both before and after the NAC (recorded as the first and the second CT respectively), and on both of them, the target metastatic axillary LN was identified and demarcated layer by layer. Using pyradiomics-based software that was independently created, radiomics features were retrieved. A pairwise machine learning workflow based on Sklearn (https://scikit-learn.org/) and FeAture Explorer was created to increase diagnostic effectiveness. An effective pairwise auto encoder model was developed by the improvement of data normalization, dimensionality reduction, and features screening scheme as well as the comparison of the prediction effectiveness of the various classifiers. RESULTS: A total of 138 patients were enrolled, and 77 (58.7%) in the overall group achieved pCR of LN after NAC. Nine radiomics features were finally chosen for modeling. The AUCs of the training group, validation group, and test group were 0.944 (0.919-0.965), 0.962 (0.937-0.985), and 1.000 (1.000-1.000), respectively, and the corresponding accuracies were 0.891, 0.912, and 1.000. CONCLUSION: The pCR of axillary LNs in breast cancer following NAC can be precisely predicted using thin-sliced enhanced chest CT-based radiomics.

GS6.1 controls kernel size and plant architecture in rice.

MAIN CONCLUSION: A novel QTL GS6.1 increases yield per plant by controlling kernel size, plant architecture, and kernel filling in rice. Kernel size and plant architecture are critical agronomic traits that greatly influence kernel yield in rice. Using the single-segment substitution lines (SSSLs) with an indica cultivar Huajingxian74 as a recipient parent and American Jasmine as a donor parent, we identified a novel quantitative trait locus (QTL), named GS6.1. Near isogenic line-GS6.1 (NIL-GS6.1) produces long and narrow kernels by regulating cell length and width in the spikelet hulls, thus increasing the 1000-kernel weight. Compared with the control, the plant height, panicles per plant, panicle length, kernels per plant, secondary branches per panicle, and yield per plant of NIL-GS6.1 are increased. In addition, GS6.1 regulates the kernel filling rate. GS6.1 controls kernel size by modulating the transcription levels of part of EXPANSINs, kernel filling-related genes, and kernel size-related genes. These results indicate that GS6.1 might be beneficial for improving kernel yield and plant architecture in rice breeding by molecular design.

CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy.

OBJECTIVE: To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. RESULTS: The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. CONCLUSIONS: A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.

qGLF5 from Oryza rufipogon Griff. improves kernel shape, plant architecture, and yield in rice.

KEY MESSAGE: A novel QTL qGLF5 from Oryza rufipogon Griff. improves yield per plant and plant architecture in rice. Kernel size and plant architecture are critical agronomic traits that are key targets for improving crop yield. From the single-segment substitution lines of Oryza rufipogon Griff. in the indica cultivar Huajingxian74 (HJX74) background, we identified a novel quantitative trait locus (QTL), named qGLF5, which improves kernel shape, plant architecture, and yield per plant in rice. Compared with the control HJX74, the plant height, panicles per plant, panicle length, primary branches per panicle, secondary branches per panicle, and kernels per plant of the near-isogenic line-qGLF5 (NIL-qGLF5) are significantly increased. NIL-qGLF5 has long and narrow kernels by regulating cell number, cell length and width in the spikelet hulls. Yield per plant of NIL-qGLF5 is increased by 35.02% compared with that of HJX74. In addition, qGLF5 significantly improves yield per plant and plant architecture of NIL-gw5 and NIL-GW7. These results indicate that qGLF5 might be beneficial for improving plant architecture and kernel yield in rice breeding by molecular design.

Dual Proton/Silver-Catalyzed Serial (5 + 2)-Cycloaddition and Nazarov Cyclization of (E)-2-Arylidene-3-hydroxyindanones with Conjugated Eneynes: Synthesis of Indanone-Fused Benzo[cd]azulenes.

A one-pot step-economic tandem process involving (5 + 2)-cycloaddition and Nazarov cyclization reactions has been reported for the facile synthesis of indanone-fused benzo[cd]azulenes from (E)-2-arylidene-3-hydroxyindanones and conjugated eneynes. This highly regio- and stereoselective bisannulation reaction is enabled by dual silver and Brønsted acid catalysis and opens up a new avenue for the construction of important bicyclo[5.3.0]decane skeletons.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

Neurovascular coupling changes in patients with magnetic resonance imaging negative focal epilepsy.

Brain neuron activity is closely related to cerebral blood flow (CBF) changes. Alterations in the regional homogeneity (ReHo) and CBF occur in patients with magnetic resonance imaging negative focal epilepsy (FEP-MRI-). However, the coupling alterations of ReHo and CBF in FEP-MRI- remain unclear. The study aims to explore neurovascular coupling alterations and their clinical implication in FEP-MRI-. We collected resting-state magnetic resonance imaging (MRI) data from 31 healthy controls (HCs) and 48 patients with FEP-MRI-,including three-dimensional (3D) T1-weighted imaging, 3D arterial spin labeling (ASL) imaging,and resting-state functional MRI (rs-fMRI). The CBF and ReHo values were calculated from the ASL and rs-fMRI data, respectively. The CBF/ReHo ratio per voxel and whole-brain CBF-ReHo coupling were compared between the two groups. Correlation analysis involved the CBF/ReHo ratio and clinical indicators in FEP-MRI-. Patients with FEP-MRI- showed significantly increased cross-subject CBF-ReHo and global cross-voxel CBF-ReHo coupling. The CBF/ReHo ratio was higher in the bilateral orbitofrontal gyrus, right parietal lobe, and right middle frontal gyrus of patients with FEP-MRI-. Nevertheless, this ratio was lower in the bilateral supplementary motor areas, the left middle and posterior cingulate gyrus, and the right central sulcus cover. The CBF/ReHo ratio was markedly correlated with cognitive function, memory, intelligence, and epilepsy duration in the above abnormal brain regions. CBF/ReHo ratio may be useful as an indicator of neuropathological mechanisms. These results support the hypothesis that CBF/ReHo ratio relates to the neuropathological mechanisms of FEP-MRI-. Furthermore, it offers new perspectives for studying the mechanisms of MRI-negative epilepsy.

Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography-Based Radiomics Analysis.

OBJECTIVE: The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs). METHODS: Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC). RESULTS: In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621). CONCLUSIONS: The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.

Mesoporous nanodrug delivery system: a powerful tool for a new paradigm of remodeling of the tumor microenvironment.

Tumor microenvironment (TME) plays an important role in tumor progression, metastasis and therapy resistance. Remodeling the TME has recently been deemed an attractive tumor therapeutic strategy. Due to its complexity and heterogeneity, remodeling the TME still faces great challenges. With the great advantage of drug loading ability, tumor accumulation, multifactor controllability, and persistent guest molecule release ability, mesoporous nanodrug delivery systems (MNDDSs) have been widely used as effective antitumor drug delivery tools as well as remolding TME. This review summarizes the components and characteristics of the TME, as well as the crosstalk between the TME and cancer cells and focuses on the important role of drug delivery strategies based on MNDDSs in targeted remodeling TME metabolic and synergistic anticancer therapy.

Iron metabolism: State of the art in hypoxic cancer cell biology.

The tumor microenvironment (TME) promotes the malignant transformation of cancer cells, mainly through metabolic reprogramming. As one of the most prominent features of the TME, hypoxia contributes to cancer cell death resistance, invasion, metastasis, and therapy-resistant phenotypes. As an important cofactor for various enzymes, iron is essential for ATP generation, antioxidant protein function, and DNA-damage repair in hypoxic cancer cells. Iron metabolism, as a promoter of aggressive hypoxic cancer cell biology, has attracted an increasing amount of attention. Iron utilization, storage, and efflux are enhanced in hypoxic cancer cells, which further contributes to cancer cell proliferation, metastasis, ferroptosis resistance, and immune escape. This review describes the relationship between iron metabolism and proliferation, metastasis, and ferroptosis of hypoxic cancer cells, as well as several iron-targeted cancer therapy strategies. Understanding the hypoxia-specific regulatory mechanism of iron metabolism could aid the development of targeted therapy against refractory hypoxic cancer cells.

The Conversion of MRI Data With Multiple b-Values into Signature-Like Pictures to Predict Treatment Response for Rectal Cancer.

BACKGROUND: Diffusion weighted imaging (DWI) at multiple b-values has been used to predict the pathological complete response (pCR) to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Non-Gaussian models fit the signal decay of diffusion by several physical values from different approaches of approximation. PURPOSE: To develop a deep learning method to analyze DWI data scanned at multiple b-values independent on Gaussian or non-Gaussian models and to apply to a rectal cancer neoadjuvant chemoradiotherapy model. STUDY TYPE: Retrospective. POPULATION: A total of 472 participants (age: 56.6 ± 10.5 years; 298 males and 174 females) with locally advanced adenocarcinoma were enrolled and chronologically divided into a training group (n = 200; 42 pCR/158 non-pCR), a validation group (n = 72; 11 pCR/61 non-pCR) and a test group (n = 200; 44 pCR/156 non-pCR). FIELD STRENGTH/SEQUENCE: A 3.0 T MRI scanner. DWI with a single-shot spin echo-planar imaging pulse sequence at 12 b-values (0, 20, 50, 100, 200, 400, 600, 800, 1000, 1200, 1400, and 1600 sec/mm2 ). ASSESSMENT: DWI signals from manually delineated tumor region were converted into a signature-like picture by concatenating all histograms from different b-values. Pathological results (pCR/non-pCR) were used as the ground truth for deep learning. Gaussian and non-Gaussian methods were used for comparison. STATISTICAL TESTS: Analysis of variance for age; Chi-square for gender and pCR/non-pCR; area under the receiver operating characteristic (ROC) curve (AUC); DeLong test for AUC. P < 0.05 for significant difference. RESULTS: The AUC in the test group is 0.924 (95% CI: 0.866-0.983) for the signature-like pictures converted from 35 bins, and it is 0.931 (95% CI: 0.884-0.979) for the signature-like pictures converted from 70 bins, which is significantly (Z = 3.258, P < 0.05) larger than Dapp , the best predictor in non-Gaussian methods with AUC = 0.773 (95% CI: 0.682-0.865). DATA CONCLUSION: The proposed signature-like pictures provide more accurate pretreatment prediction of the response to neoadjuvant chemoradiotherapy than the fitted methods for locally advanced rectal cancer. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.