A Multicenter Cohort Study on DNA Methylation for Endometrial Cancer Detection in Cervical Scrapings.

BACKGROUND: The increasing incidence of endometrial cancer (EC) has highlighted the need for improved early detection methods. This study aimed to develop and validate a novel DNA methylation classifier, EMPap, for EC detection using cervical scrapings. METHODS: EMPap incorporated the methylation status of BHLHE22 and CDO1, along with age and body mass index (BMI), into a logistic regression model to calculate the endometrial cancer methylation (EM) score for identifying EC in cervical scrapings. We enrolled 1297 patients with highly suspected EC, including 196 confirmed EC cases, and assessed the EMPap performance in detecting EC. RESULTS: EMPap demonstrated robust diagnostic accuracy, with an area under the curve of 0.93, sensitivity of 90.3%, and specificity of 89.3%. It effectively detected EC across various disease stages, grades, and histological subtypes, and consistently performed well across patient demographics and symptoms. EMPap correctly identified 87.5% of the type II ECs and 53.8% of premalignant lesions. Notably, compared with transvaginal ultrasonography (TVS) in patients with postmenopausal bleeding, EMPap exhibited superior sensitivity (100% vs. 82.0%) and specificity (85.2% vs. 38.5%). In asymptomatic postmenopausal women, EMPap maintained high sensitivity (89.5%) and negative predictive value (NPV) (98.3%). CONCLUSIONS: This study demonstrated the potential of EMPap as an effective tool for EC detection. Despite the limited sample size, EMPap showed promise for identifying type II EC and detecting over 50% of premalignant lesions. As a DNA methylation classifier, EMPap can reduce unnecessary uterine interventions and improve diagnosis and outcomes.

Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.

BACKGROUND: Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). METHODS: Among 713 consecutive patients, with 275 in training cohort, 222 patients in internal validation cohort, and 216 patients in external validation cohort, we developed and validated a multitask machine learning model for simultaneously predicting peritoneal recurrence (TACSPR) and disease-free survival (TACSDFS). The accuracy of the model for prediction of peritoneal recurrence and prognosis as well as its association with adjuvant chemotherapy were evaluated. RESULTS: The TACSPR and TACSDFS were independently associated with peritoneal recurrence and disease-free survival in three cohorts, respectively (all P < 0.001). The TACSPR demonstrated a favorable performance for peritoneal recurrence in all three cohorts. In addition, the TACSDFS also showed a satisfactory accuracy for disease-free survival among included patients. For stage II and III diseases, adjuvant chemotherapy improved the survival of patients with low TACSPR and low TACSDFS, or high TACSPR and low TACSDFS, or low TACSPR and high TACSDFS, but had no impact on patients with high TACSPR and high TACSDFS. CONCLUSIONS: The multitask machine learning model allows accurate prediction of peritoneal recurrence and survival for GC and could distinguish patients who might benefit from adjuvant chemotherapy.

Cluster-Level Heterostructure of PMo12/Cu for Efficient and Selective Electrocatalytic Hydrogenation of High-Concentration 5-Hydroxymethylfurfural.

Electrochemical hydrogenation of aldehyde molecules, exemplified by 5-hydroxymethylfurfural (HMF), offers a sustainable approach for synthesizing higher value-added alcohols. However, severe coupling side reactions impede its practical implementation at high concentrations. In this work, a cluster-level heterostructure of a PMo12/Cu catalyst is synthesized by loading Keggin-type phosphomolybdic acid (H3PMo12O40, PMo12) onto Cu nanowires. The catalyst exhibits high selectivity in electrocatalytic hydrogenation (ECH) of HMF to 2,5-bishydroxymethylfuran (BHMF) under an unprecedentedly high substrate concentration of 1.0 M. Under -0.3 V (vs RHE) with 1.0 M HMF, PMo12/Cu shows a Faradaic efficiency as high as 98% with an excellent productivity of 4.35 mmol cm-2 h-1 toward BHMF, much higher than those on the pristine Cu nanowires. Mechanism studies and density functional theory calculations demonstrate that the heterostructural interface of PMo12/Cu serves as an active reaction center for the ECH. The unique electronic properties and geometric structure promote the dissociative reduction of water molecules to generate H* and reduce HMF with a decreased reaction energy barrier, which is responsible for exceptional reactivity and selectivity.

A high-performance watermelon skin ion-solvating membrane for electrochemical CO2 reduction.

Ion-solvating membranes have been gaining increasing attention as core components of electrochemical energy conversion and storage devices. However, the development of ion-solvating membranes with low ion resistance and high ion selectivity still poses challenges. In order to propose an effective strategy for high-performance ion-solvating membranes, this study conducted a comprehensive investigation on watermelon skin membranes through a combination of experimental research and molecular dynamics simulation. The micropores and continuous hydrogen-bonding networks constructed by the synergistic effect of cellulose fiber and pectin enable the hypodermis of watermelon skin membranes to have a high ion conductivity of 282.3 mS cm-1 (room temperature, saturated with 1 M KOH). The negatively charged groups and hydroxyl groups on the microporous channels increase the formate penetration resistance of watermelon skin membranes in contrast to commercially available membranes, and this is crucial for CO2 electroreduction. Therefore, the confinement of proton donors and negatively charged groups within three-dimensional microporous polymers gives inspiration for the design of high-performance ion-solvating membranes.

Endothelial progenitor cell-derived extracellular vesicles: the world of potential prospects for the treatment of cardiovascular diseases.

Cardiovascular diseases (CVDs) have emerged as a predominant threat to human health, surpassing the incidence and mortality rates of neoplastic diseases. Extracellular vesicles (EVs) serve as vital mediators in intercellular communication and material exchange. Endothelial progenitor cells (EPCs), recognized as precursors of vascular endothelial cells (ECs), have garnered considerable attention in recent years due to the potential therapeutic value of their derived extracellular vesicles (EPC-EVs) in the context of CVDs. This comprehensive review systematically explores the origins, characteristics, and functions of EPCs, alongside the classification, properties, biogenesis, and extraction techniques of EVs, with particular emphasis on their protective roles in CVDs. Additionally, we delve into the essential bioactive components of EPC-EVs, including microRNAs, long non-coding RNAs, and proteins, analyzing their beneficial effects in promoting angiogenesis, anti-inflammatory and anti-oxidant activities, anti-fibrosis, anti-apoptosis, and myocardial regeneration. Furthermore, this review comprehensively investigates the therapeutic potential of EPC-EVs across various CVDs, encompassing acute myocardial infarction, myocardial ischemia-reperfusion injury, atherosclerosis, non-ischemic cardiomyopathies, and diabetic cardiovascular disease. Lastly, we summarize the potential challenges associated with the clinical application of EPC-EVs and outline future directions, aiming to offer a valuable resource for both theoretical insights and practical applications of EPC-EVs in managing CVDs.

Y4 RNA fragments from cardiosphere-derived cells ameliorate diabetic myocardial ischemia‒reperfusion injury by inhibiting protein kinase C ß-mediated macrophage polarization.

The specific pathophysiological pathways through which diabetes exacerbates myocardial ischemia/reperfusion (I/R) injury remain unclear; however, dysregulation of immune and inflammatory cells, potentially driven by abnormalities in their number and function due to diabetes, may play a significant role. In the present investigation, we simulated myocardial I/R injury by inducing ischemia through ligation of the left anterior descending coronary artery in mice for 40 min, followed by reperfusion for 24 h. Previous studies have indicated that protein kinase Cß (PKCß) is upregulated under hyperglycemic conditions and is implicated in the development of various diabetic complications. The Y4 RNA fragment is identified as the predominant small RNA component present in the extracellular vesicles of cardio sphere-derived cells (CDCs), exhibiting notable anti-inflammatory properties in the contexts of myocardial infarction and cardiac hypertrophy. Our investigation revealed that the administration of Y4 RNA into the ventricular cavity of db/db mice following myocardial I/R injury markedly enhanced cardiac function. Furthermore, Y4 RNA was observed to facilitate M2 macrophage polarization and interleukin-10 secretion through the suppression of PKCß activation. The mechanism by which Y4 RNA affects PKCß by regulating macrophage activation within the inflammatory environment involves the inhibition of ERK1/2 phosphorylation In our study, the role of PKCß in regulating macrophage polarization during myocardial I/R injury was investigated through the use of PKCß knockout mice. Our findings indicate that PKCß plays a crucial role in modulating the inflammatory response associated with macrophage activation in db/db mice experiencing myocardial I/R, with a notable exacerbation of this response observed upon significant upregulation of PKCß expression. In vitro studies further elucidated the protective mechanism by which Y4 RNA modulates the PKCß/ERK1/2 signaling pathway to induce M2 macrophage activation. Overall, our findings suggest that Y4 RNA plays an anti-inflammatory role in diabetic I/R injury, suggesting a novel therapeutic approach for managing myocardial I/R injury in diabetic individuals.

The optimal dosage of aspirin for preventing preeclampsia in high-risk pregnant women: A network meta-analysis of 23 randomized controlled trials.

This study aimed to assess the effectiveness and optimal dosage of aspirin in preventing preeclampsia in high-risk pregnant women. Traditional and network meta-analyses were conducted on data from 23 randomized controlled trials involving 10 547 pregnant women. The findings demonstrated that aspirin significantly reduced the incidence of preeclampsia (OR = 0.66, 95%CI [0.58, 0.75]), with the best preventive effect observed at a dosage of 80-100 mg/day (OR = 0.51, 95%CI [0.36, 0.72]). No significant differences were found in the occurrence of postpartum hemorrhage (OR = 1.03, 95%CI [0.79, 1.33]), small for gestational age (OR = 0.83, 95%CI [0.50, 1.35]), placental abruption (OR = 0.96, 95%CI [0.53, 1.73]), and intrauterine growth restriction (OR = 0.63, 95%CI [0.45, 1.86]) between women taking aspirin and those taking placebos. Different doses of aspirin showed a reduction in preeclampsia incidence, but there was no significant difference in efficacy between the dosage groups. Side effects did not significantly differ between placebo and different aspirin dosage groups. SUCRA analysis suggested that 80-100 mg/day may be the optimal dosage, prioritizing both effectiveness and minimizing side effects. Sensitivity analysis confirmed the robustness of the findings. However, improvements are needed in addressing issues like loss to follow-up, reporting bias, and publication bias. In conclusion, a dosage of 80-100 mg/day is recommended for preventing preeclampsia in high-risk pregnant women, although individual circumstances should be considered for optimizing the balance between effectiveness and safety.

A nomogram for prediction of ERCP success in patients with bile duct leaks: a multicenter study.

BACKGROUND: Bile duct leaks (BDLs) are serious complications that occurs after hepatobiliary surgery and trauma, leading to rapid clinical deterioration. Endoscopic retrograde cholangiopancreatography (ERCP) is the first-line treatment for BDLs, but it is not clear which patients will respond to this therapy and which patients will require additional surgical intervention. The aim of our study was to explore the predictors of successful ERCP for BDLs. METHODS: A retrospective analysis was conducted using data from six centers' databases. All consecutive patients who were clinically confirmed as BDLs were included in the study. Collected data were demographics, disease severity, and ERCP procedure characteristics. Univariate and multivariate analysis were used to select independent predictive factors that affect the outcome of ERCP for BDLs, and a nomogram was established. Calibration and ROC curves were used to evaluate the models. RESULTS: Four hundred and forty-eight consecutive patients were clinically confirmed as BDLs and 347 were excluded. In the 101 patients included patients, clinical success was achieved in 78 patients (77.2%). In logistic multivariable regression, two independent factors were negatively associated with the success of ERCP: SIRS (OR, 0.183; 95% CI 0.039-0.864; P = 0.032) and high-grade leak (OR 0.073; 95% CI 0.010-0.539; P = 0.010). Two independent factors were positively associated with the success of ERCP: leak-bridging drainage (OR 4.792; 95% CI 1.08-21.21; P = 0.039) and cystic duct leak (OR 6.193; 95% CI 1.03-37.17; P = 0.046). The prediction model with these four factors was evaluated using a receiver-operating characteristic (ROC) curve, which demonstrated an area under the curve of 0.9351. The calibration curve showed that the model had good predictive accuracy. CONCLUSION: Leak-bridging drainage and cystic duct leak are positive predictors for the success of ERCP, while SIRS and high-grade leak are negative predictors. This prediction model with nomogram has good predictive ability and practical clinical value, and may be helpful in clinical decision-making and prognostication.

Pathomics Signature for Prognosis and Chemotherapy Benefits in Stage III Colon Cancer.

Importance: The current TNM staging system may not provide adequate information for prognostic purposes and to assess the potential benefits of chemotherapy for patients with stage III colon cancer. Objective: To develop and validate a pathomics signature to estimate prognosis and benefit from chemotherapy using hematoxylin-eosin (H-E)-stained slides. Design, Setting, and Participants: This retrospective prognostic study used data from consecutive patients with histologically confirmed stage III colon cancer at 2 medical centers between January 2012 and December 2015. A total of 114 pathomics features were extracted from digital H-E-stained images from Nanfang Hospital of Southern Medical University, Guangzhou, China, and a pathomics signature was constructed using a least absolute shrinkage and selection operator Cox regression model in the training cohort. The associations of the pathomics signature with disease-free survival (DFS) and overall survival (OS) were evaluated. Patients at the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, formed the validation cohort. Data analysis was conducted from September 2022 to March 2023. Main Outcomes and Measures: The prognostic accuracy of the pathomics signature as well as its association with chemotherapy response were evaluated. Results: This study included 785 patients (mean [SD] age, 62.7 [11.1] years; 437 [55.7%] male). A pathomics signature was constructed based on 4 features. Multivariable analysis revealed that the pathomics signature was an independent factor associated with DFS (hazard ratio [HR], 2.46 [95% CI, 2.89-4.13]; P < .001) and OS (HR, 2.78 [95% CI, 2.34-3.31]; P < .001) in the training cohort. Incorporating the pathomics signature into pathomics nomograms resulted in better performance for the estimation of prognosis than the traditional model in a concordance index comparison in the training cohort (DFS: HR, 0.88 [95% CI, 0.86-0.89] vs HR, 0.73 [95% CI, 0.71-0.75]; P < .001; OS: HR, 0.85 [95% CI, 0.84-0.86] vs HR, 0.74 [95% CI, 0.72-0.76]; P < .001) and validation cohort (DFS: HR, 0.83 [95% CI, 0.82-0.85] vs HR, 0.70 [95% CI, 0.67-0.72]; P < .001; OS: HR, 0.80 [95% CI, 0.78-0.82] vs HR, 0.69 [0.67-0.72]; P < .001). Further analysis revealed that patients with a low pathomics signature were more likely to benefit from chemotherapy (eg, combined cohort: DFS: HR, 0.44 [95% CI, 0.28-0.69]; P = .001; OS: HR, 0.43 [95% CI, 0.29-0.64]; P < .001). Conclusions and Relevance: These findings suggest that a pathomics signature could help identify patients most likely to benefit from chemotherapy in stage III colon cancer.

Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study.

BACKGROUND: Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM. METHODS: Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts. RESULTS: By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations. CONCLUSIONS: This study proposed that PCS integrating pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.

Potential Added Value of 18F-FDG PET Metabolic Parameters in Predicting Disease Relapse in Type 1 Autoimmune Pancreatitis.

BACKGROUND: The predictive value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic parameters for predicting AIP relapse is currently unknown. This study firstly explored the value of 18F-FDG PET/CT parameters as predictors of type 1 AIP relapse. METHODS: This multicenter retrospective cohort study analyzed 51 patients who received 18F-FDG PET/CT prior to treatment and did not receive maintenance therapy after remission. The study collected baseline characteristics and clinical data and conducted qualitative and semi-quantitative analysis of pancreatic lesions and extrapancreatic organs. The study used three thresholds to select the boundaries of pancreatic lesions to evaluate metabolic parameters, including the maximum standard uptake value (SUVmax), mean standard uptake value (SUVmean), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and tumor-to-normal liver standard uptake value ratio (SUVR). Univariate and multivariate analyses were performed to identify independent predictors and build a recurrence prediction model. The model was internally validated using the bootstrap method and a nomogram was created for clinical application. RESULTS: In the univariable analysis, the relapsed group showed higher levels of SUVmax (6.0 ± 1.6 vs. 5.2 ± 1.1; P = 0.047), SUVR (2.3 [2.0-3.0] vs. 2.0 [1.6-2.4]; P = 0.026), and TLG2.5 (234.5 ± 149.1 vs. 139.6 ± 102.5; P = 0.020) among the 18F-FDG PET metabolic parameters compared to the non-relapsed group. In the multivariable analysis, serum IgG4 (OR, 1.001; 95% CI, 1.000-1.002; P = 0.014) and TLG2.5 (OR, 1.007; 95% CI, 1.002-1.013; P = 0.012) were independent predictors associated with relapse of type 1 AIP. A receiver-operating characteristic curve of the predictive model with these two predictors demonstrated an area under the curve of 0.806. CONCLUSION: 18F-FDG PET/CT metabolic parameters, particularly TLG2.5, are potential predictors for relapse in patients with type 1 AIP. A multiparameter model that includes IgG4 and TLG2.5 can enhance the ability to predict AIP relapse.

STIC-HD live flow technology in the antenatal diagnosis of scimitar syndrome: A case report.

Scimitar syndrome (SS) is a rare entity with an incidence of approximately 1-3 in 200 000 people. It is typically characterized by complete or partial anomalous pulmonary venous drainage from the right lung into the systemic venous circulation, most commonly the inferior vena cava (IVC). For the first time, we report the diagnosis of SS in a fetus in utero using four-dimensional (4D) spatiotemporal image correlation combined with high-definition live flow rendering mode (STIC-HD live flow).

Use of patient-derived tumor organoid platform to predict the benefit of postoperative adjuvant chemotherapy for poor responders to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Postoperative adjuvant chemotherapy (AC) for poor responders to neoadjuvant chemoradiotherapy (nCRT) remains debatable among patients with locally advanced rectal cancer (LARC), necessitating biomarkers to accurately predict the benefits of AC. This study aimed to develop a patient-derived tumor organoid (PDTO) platform to predict the benefit of AC in LARC patients showing poor nCRT response. PDTOs were established using irradiated rectal cancer specimens with poor nCRT responses, and their sensitivity to chemotherapy regimens was tested. The half-maximal inhibitory concentration (IC50) value for the PDTO drug test was defined based on the clinical outcomes, and the accuracy of the PDTO prognostic predictions was calculated. Predictive models were developed and validated using the PDTO drug test results. Between October 2018 and December 2021, 86 PDTOs were successfully constructed from 138 specimens (success rate 62.3%). The optimal IC50 cut-off value for the organoid drug test was 39.31 µmol/L, with a sensitivity of 84.75%, a specificity of 85.19%, and an accuracy of 84.88%. Multivariate Cox regression analysis revealed that the PDTO drug test was an independent predictor of prognosis. A nomogram based on the PDTO drug test was developed, showing good prognostic ability in predicting the 2-year and 3-year disease-free survivals (AUC of 0.826 [95% CI, 0.721-0.931] and 0.902 [95% CI, 0.823-0.982], respectively) and overall survivals (AUC of 0.859 [95% CI, 0.745-0.973] and 0.885 [95% CI, 0.792-0.978], respectively). The PDTO drug test can predict the benefit of postoperative AC in poor responders with LARC to nCRT.

Selected Bacteria Are Critical for Karst River Carbon Sequestration via Integrating Multi-omics and Hydrochemistry Data.

Recalcitrant dissolved organic carbon (RDOC) produced by microbial carbon pumps (MCPs) in the ocean is crucial for carbon sequestration and regulating climate change in the history of Earth. However, the importance of microbes on RDOC formation in terrestrial aquatic systems, such as rivers and lakes, remains to be determined. By integrating metagenomic (MG) and metatranscriptomic (MT) sequencing, we defined the microbial communities and their transcriptional activities in both water and silt of a typical karst river, the Lijiang River, in Southwest China. Betaproteobacteria predominated in water, serving as the most prevalent population remodeling components of dissolved organic carbon (DOC). Binning method recovered 45 metagenome-assembled genomes (MAGs) from water and silt. Functional annotation of MAGs showed Proteobacteria was less versatile in degrading complex carbon, though cellulose and chitin utilization genes were widespread in this phylum, whereas Bacteroidetes had high potential for the utilization of macro-molecular organic carbon. Metabolic remodeling revealed that increased shared metabolites within the bacterial community are associated with increased concentration of DOC, highlighting the significance of microbial cooperation during producing and remodeling of carbon components. Beta-oxidation, leucine degradation, and mevalonate (MVA) modules were significantly positively correlated with the concentration of RDOC. Blockage of the leucine degradation pathway in Limnohabitans and UBA4660-related MAGs were associated with decreased RDOC in the karst river, while the Fluviicola-related MAG containing a complete leucine degradation pathway was positively correlated with RDOC concentration. Collectively, our study revealed the linkage between bacteria metabolic processes and carbon sequestration. This provided novel insights into the microbial roles in karst-rivers carbon sink.

A nomogram based on collagen signature for predicting the immunoscore in colorectal cancer.

Objectives: The Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore. Methods: A total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method. Results: The collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (P<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients. Conclusions: The collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.

Large common bile duct stones in high-risk elderly patients: Immediate endoscopic stone removal or elective stone removal? A single-center retrospective study.

BACKGROUND AND OBJECTIVE: For high-risk elderly patients with chronic diseases, endoscopic stone removal for large common bile duct stones is associated with a high risk of adverse events and incomplete stone removal. The aim of this study was to investigate whether the treatment strategy of short-term biliary plastic stent placement followed by elective endoscopic stone removal is more effective and safer than immediate endoscopic stone removal. METHODS: The data of 262 high-risk elderly patients who received endoscopic retrograde cholangiopancreatography (ERCP) for large common bile duct (CBD) stones from 2017 to 2022 were retrospectively analyzed. The patients were divided into group A (immediate stone removal) and group B (stent drainage + elective stone removal). The baseline data of the 2 groups were matched 1:1 by propensity score matching. The stone clearance rate, ERCP procedure time, total hospital stay, and procedure-related adverse events were compared between the matched groups. In group B, stone size before and after stent placement, hospital stay, procedure time and adverse events of two ERCPs were compared. RESULTS: A total of 57 pairs of patients were successfully matched between the 2 groups. The stone clearance rate in group B was higher than that in group A (89.5% vs. 75.3, P = 0.049). The total hospital stay in group B was longer than that in group A (11.86 ± 3.912 d vs. 19.14 ± 3.176 d, P<0.001). The total adverse event rate in group A was higher than that in group B (29.8% vs. 12.3%, P = 0.005). The incidence of cholangitis/cholecystitis after ERCP was significantly higher in group A than in group B (7.0% vs. 0.9% P = 0.029). There was no significant difference in the incidence of post-ERCP pancreatitis, bleeding, pneumonia, and cardio-cerebrovascular events between the 2 groups. There were no perforation cases in either group. After plastic biliary stent placement in group B, the stone size was significantly smaller than before stent placement (1.59 ± 0.544 cm vs. 1.95 ± 0.543 cm, P < 0.001), and there was no significant difference in the total adverse event incidence between the two ERCP procedures (18.8% vs. 10.9%, P = 0.214). CONCLUSION: For high-risk elderly patients with large CBD stones, the treatment strategy involving temporary placement of plastic stent and elective endoscopic stone removal is safer and more effective than immediate stone removal.

Highly Efficient Biomass Upgrading by a Ni-Cu Electrocatalyst Featuring Passivation of Water Oxidation Activity.

Electricity-driven organo-oxidations have shown an increasing potential recently. However, oxygen evolution reaction (OER) is the primary competitive reaction, especially under high current densities, which leads to low Faradaic efficiency (FE) of the product and catalyst detachment from the electrode. Here, we report a bimetallic Ni-Cu electrocatalyst supported on Ni foam (Ni-Cu/NF) to passivate the OER process while the oxidation of 5-hydroxymethylfurfural (HMF) is significantly enhanced. A current density of 1000 mA cm-2 can be achieved at 1.50 V vs. reversible hydrogen electrode, and both FE and yield keep close to 100 % over a wide range of potentials. Both experimental results and theoretical calculations reveal that Cu doping impedes the OH* deprotonation to O* and hereby OER process is greatly passivated. Those instructive results provide a new approach to realizing highly efficient biomass upgrading by regulating the OER activity.

Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer.

The current tumor-node-metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II-III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50-layer residual network model for predicting disease-free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P < 0.001). The collagenDL nomogram, integrating the collagenDL classifier and three clinicopathologic predictors, improved the prediction performance, which showed satisfactory discrimination and calibration. These results were independently validated in the internal and external validation cohorts. In addition, high-risk stage II and III CC patients with high-collagenDL classifier, rather than low-collagenDL classifier, exhibited a favorable response to adjuvant chemotherapy. In conclusion, the collagenDL classifier could predict prognosis and adjuvant chemotherapy benefits in stage II-III CC patients.