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For locally advanced rectal cancer (LARC) patients who receive neoadjuvant chemoradiotherapy (nCRT), there are no reliable indicators to accurately predict pathological complete response (pCR) before surgery. For patients with clinical complete response (cCR), a \"Watch and Wait\" (W&W) approach can be adopted to improve quality of life. However, W&W approach may increase the recurrence risk in patients who are judged to be cCR but have minimal residual disease (MRD). Magnetic resonance imaging (MRI) is a major tool to evaluate response to nCRT; however, its ability to predict pCR needs to be improved. In this prospective cohort study, we explored the value of circulating tumor DNA (ctDNA) in combination with MRI in the prediction of pCR before surgery and investigated the utility of ctDNA in risk stratification and prognostic prediction for patients undergoing nCRT and total mesorectal excision (TME). We recruited 119 Chinese LARC patients (cT3-4/N0-2/M0; median age of 57; 85 males) who were treated with nCRT plus TME at Fudan University Shanghai Cancer Center (China) from February 7, 2016 to October 31, 2017. Plasma samples at baseline, during nCRT, and after surgery were collected. A total of 531 plasma samples were collected and subjected to deep targeted panel sequencing of 422 cancer-related genes. The association among ctDNA status, treatment response, and prognosis was analyzed. The performance of ctDNA alone, MRI alone, and combining ctDNA with MRI was evaluated for their ability to predict pCR/non-pCR. The model combining ctDNA and MRI improved the predictive performance compared with the models derived from individual information, and combining ctDNA with HR_feature can stratify patients with a high risk of recurrence. Therefore, ctDNA can supplement MRI to better predict nCRT response, and it could potentially help patient selection for nonoperative management and guide the treatment strategy for those with different recurrence risks.

Selective paraffin capture from olefin/paraffin mixtures could afford high-purity olefins directly, but suffers from the issues of low separation selectivity and olefin productivity. Herein, we report an ultramicroporous material (PCP-IPA) with parallel-aligned linearly extending isophthalic acid units along the one-dimensional channel, realizing the efficient production of ultra-high purity C 2 H 4 and C 3 H 6 (99.99%). The periodically expanded and parallel-aligned aromatic-based units served as a paraffin nano-trap to contact with the exposed hydrogen atoms of both C 2 H 6 and C 3 H 8 , as demonstrated by the simulation studies. PCP-IPA exhibits record separation selectivity of 2.48 and separation potential of 1.20 mol/L for C 3 H 8 /C 3 H 6 (50/50) mixture, meanwhile the excellent C 2 H 6 /C 2 H 4 mixture separation performance. Ultra-high purity C 3 H 6 (99.99%) and C 2 H 4 (99.99%) can be directly obtained through fixed-bed column from C 3 H 8 /C 3 H 6 and C 2 H 6 /C 2 H 4 mixtures, respectively. The record C 3 H 6 productivity is up to 15.23 L/kg from the equimolar of C 3 H 8 /C 3 H 6 , which is 3.85 times of the previous benchmark material, demonstrating its great potential for those important industrial separations. Selective paraffin capture to afford high-purity olefins directly is critical but still challenging. Herein, the authors discover a distinctive ultramicroporous material featuring closely packed and linearly-extended isophthalic acid units and thus serving as a paraffin nano-trap for the efficient production of ultra-high purity olefins from paraffins.

ObjectivesThe aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC).MethodsOne training cohort of 239 and two validation datasets of 80 and 52 NSCLC patients were enrolled in this study. Nine hundred seventy-five radiomics features were extracted from each patient’s 2D and 3D CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate a radiomics signature. Cox hazard survival analysis and Kaplan-Meier were performed in both cohorts. The radiomics nomogram was developed by integrating the optimized radiomics signature and clinical predictors, its calibration and discrimination were evaluated.ResultsThe radiomics signatures were significantly associated with NSCLC patients’ survival time. The signature derived from the combined 2D and 3D features showed a better prognostic performance than those from 2D or 3D alone. Our radiomics nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of patients’ survival compared with clinical predictors alone in the validation cohort. The calibration curve showed predicted survival time was very close to the actual one.ConclusionsThe radiomics signature from the combined 2D and 3D features further improved the predicted accuracy of survival prognosis for the patients with NSCLC. Combination of the optimal radiomics signature and clinical predictors performed better for individualied survival prognosis estimation in patients with NSCLC. These findings might affect trearment strategies and enable a step forward for precise medicine.Key Points• We found both 2D and 3D radiomics signature have favorable prognosis, but 3D signature had a better performance.• The radiomics signature generated from the combined 2D and 3D features had a better predictive performance than those from 2D or 3D features.• Integrating the optimal radiomics signature with clinical predictors significantly improved the predictive power in patients’ survival compared with clinical TNM staging alone.

Extensive efforts have been made to improve the separation selectivity of hydrocarbon isomers with nearly distinguishable boiling points; however, how to balance the high regeneration energy consumption remains a daunting challenge. Here we describe the efficient separation of hexane isomers by constructing and exploiting the rotational freedom of organic linkers and inorganic SnF 6 2− anions within adaptive frameworks, and reveal the nature of flexible host-guest interactions to maximize the gas-framework interactions while achieving potential energy storage. This approach enables the discrimination of hexane isomers according to the degree of branching along with high capacity and record mono-/di-branched selectivity (6.97), di-branched isomers selectivity (22.16), and upgrades the gasoline to a maximum RON (Research Octane Number) of 105. Benefitting from the energy regulation of the flexible pore space, the material can be easily regenerated only through a simple vacuum treatment for 15 minutes at 25 °C with no temperature fluctuation, saving almost 45% energy compared to the commercialized zeolite 5 A. This approach could potentially revolutionize the whole scenario of alkane isomer separation processes. Balancing the separation selectivity and regeneration energy of alkane isomers is a daunting challenge. Here the authors utilize flexible host-guest interactions to maximize the gas-framework interactions while achieving potential energy storage to set new benchmarks for alkane isomer separation.

Refineries generally employ multiple energy-intensive distillation/adsorption columns to separate and purify complicated chemical mixtures. Materials such as multi-functional molecular separators integrating various modules capable of separating molecules according to their shape and chemical properties simultaneously may represent an alternative. Herein, we address this challenge in the context of one-step removal of alkynes and propadiene from cracking gases (up to 10 components) using a multi-functional and responsive material ZU-33 through a guest/temperature dual-response regulation strategy. The responsive and guest-adaptive properties of ZU-33 provide the optimized binding energy for alkynes and propadiene, and avoid the competitive adsorption of olefins and paraffins, which is verified by breakthrough tests, single-crystal X-ray diffraction experiments, and simulation studies. The responsive properties to different stimuli endow materials with multiple regulation methods and broaden the boundaries of the applicability of porous materials to challenging separations. Separating mixtures of hydrocarbons of low molecular weight is desirable but challenging. Here, the authors report a porous material with responsive and self-adaptive properties that enables one-step removal of alkynes and propadiene from cracking gases.

The separation of xylene isomers ( para -, meta -, orth -) remains a great challenge in the petrochemical industry due to their similar molecular structure and physical properties. Porous materials with sensitive nanospace and selective binding sites for discriminating the subtle structural difference of isomers are urgently needed. Here, we demonstrate the adaptively molecular discrimination of xylene isomers by employing a NbOF 5 2− -pillared metal–organic framework (NbOFFIVE-bpy-Ni, also referred to as ZU-61) with rotational anionic sites. Single crystal X-ray diffraction studies indicate that ZU-61 with guest-responsive nanospace/sites can adapt the shape of specific isomers through geometric deformation and/or the rotation of fluorine atoms in anionic sites, thereby enabling ZU-61 to effectively differentiate xylene isomers through multiple C–H···F interactions. ZU-61 exhibited both high meta -xylene uptake capacity (3.4 mmol g −1 ) and meta -xylene/ para -xylene separation selectivity (2.9, obtained from breakthrough curves), as well as a favorable separation sequence as confirmed by breakthrough experiments: para -xylene elute first with high-purity (≥99.9%), then meta -xylene, and orth -xylene. Such a remarkable performance of ZU-61 can be attributed to the type anionic binding sites together with its guest-response properties. The separation of xylene isomers remains a great challenge in industry due to their similar molecular structure and physical properties. Here the authors demonstrate adaptively molecular discrimination of xylene isomers by employing a NbOF 5 2− -pillared metal–organic framework with rotational anionic sites.

Isotope tracing has helped to determine the metabolic activities of organs. Methods to probe metabolic heterogeneity within organs are less developed. We couple stable-isotope-labeled nutrient infusion to matrix-assisted laser desorption ionization imaging mass spectrometry (iso-imaging) to quantitate metabolic activity in mammalian tissues in a spatially resolved manner. In the kidney, we visualize gluconeogenic flux and glycolytic flux in the cortex and medulla, respectively. Tricarboxylic acid cycle substrate usage differs across kidney regions; glutamine and citrate are used preferentially in the cortex and fatty acids are used in the medulla. In the brain, we observe spatial gradations in carbon inputs to the tricarboxylic acid cycle and glutamate under a ketogenic diet. In a carbohydrate-rich diet, glucose predominates throughout but in a ketogenic diet, 3-hydroxybutyrate contributes most strongly in the hippocampus and least in the midbrain. Brain nitrogen sources also vary spatially; branched-chain amino acids contribute most in the midbrain, whereas ammonia contributes in the thalamus. Thus, iso-imaging can reveal the spatial organization of metabolic activity. Iso-imaging integrates stable-isotope infusions with imaging mass spectrometry to enable quantitative analysis of metabolic activity in mammalian tissues with spatial resolution. IsoScope software facilitates analysis of iso-imaging data.

Physisorption relying on crystalline porous materials offers prospective avenues for sustainable separation processes, greenhouse gas capture, and energy storage. However, the lack of end-to-end deep learning model for adsorption prediction confines the rapid and precise screen of crystalline porous materials. Here, we present DeepSorption, a spatial atom interaction learning network that realizes accurate, fast, and direct structure-adsorption prediction with only information of atomic coordinate and chemical element types. The breakthrough in prediction is attributed to the awareness of global structure and local spatial atom interactions endowed by the developed Matformer, which provides the intuitive visualization of atomic-level thinking and executing trajectory in crystalline porous materials prediction. Complete adsorption curves prediction could be performed using DeepSorption with a higher accuracy than Grand canonical Monte Carlo simulation and other machine learning models, a 20-35% decline in the mean absolute error compared to graph neural network CGCNN and machine learning models based on descriptors. Since the established direct associations between raw structure and target functions are based on the understanding of the fundamental chemistry of interatomic interactions, the deep learning network is rationally universal in predicting the different physicochemical properties of various crystalline materials. Accurate end-to-end deep learning models for adsorption prediction in porous materials would help its discovery. Here, the authors present DeepSorption, a spatial atom interaction learning network to predict structure-adsorption from atomic coordinates and chemical element types.

Cancer-associated fibroblasts (CAFs) are a major cellular component of tumor microenvironment in most solid cancers. Altered cellular metabolism is a hallmark of cancer, and much of the published literature has focused on neoplastic cell-autonomous processes for these adaptations. We demonstrate that exosomes secreted by patient-derived CAFs can strikingly reprogram the metabolic machinery following their uptake by cancer cells. We find that CAF-derived exosomes (CDEs) inhibit mitochondrial oxidative phosphorylation, thereby increasing glycolysis and glutamine-dependent reductive carboxylation in cancer cells. Through 13C-labeled isotope labeling experiments we elucidate that exosomes supply amino acids to nutrient-deprived cancer cells in a mechanism similar to macropinocytosis, albeit without the previously described dependence on oncogenic-Kras signaling. Using intra-exosomal metabolomics, we provide compelling evidence that CDEs contain intact metabolites, including amino acids, lipids, and TCA-cycle intermediates that are avidly utilized by cancer cells for central carbon metabolism and promoting tumor growth under nutrient deprivation or nutrient stressed conditions.

Background Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients. Methods Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman’s correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively. Results Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers ( AQP9 , HLA-DQA1 , MCEMP1 , NKG7 , and S100A12 ) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target ( S100A12 ) had a high binding affinity (docking score = -10 kcal/mol). Conclusion AQP9 , HLA-DQA1 , MCEMP1 , NKG7 , and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.