Immunoassays for Extracellular Vesicle Detection via Transmembrane Proteins Using Surface Plasmon Resonance Biosensors.

Extracellular vesicles (EVs) are preeminent carriers of biomarkers and have become the subject of intense biomedical research for medical diagnostics using biosensors. To create effective EV-based immunoassays, it is imperative to develop surface chemistry approaches with optimal EV detection targeting transmembrane protein biomarkers that are not affected by cell-to-cell variability. Here, we developed a series of immunoassays for the detection of EVs derived from mouse monocyte cells using surface plasmon resonance (SPR) biosensors. We chemically immobilized antibodies onto mixed self-assembled monolayers of oligo ethylene glycol (OEG) alkanethiolates with carboxylic and hydroxylic terminal groups. The effects of antibody clonality (monoclonal vs polyclonal) and antibody surface coverage in targeting EVs via CD81 tetraspanins were investigated. We determined binding kinetic parameters, establishing trends from steric hindrance effects and epitope recognition properties of antibodies. Our results indicate that a 40% surface coverage of polyclonal antibodies covalently linked onto a mixed SAM with 10% of terminated -COOH groups yields a promising approach for EV detection with a linear range of 1.9 × 108-1.9 × 109 EVs/mL and a limit of detection of 5.9 × 106 EVs/mL. This optimal immunoassay exhibits a 1.92 nM equilibrium dissociation constant for bound EVs, suggesting a high binding affinity when CD81 is targeted. Our study provides important insights into surface chemistry development for EV detection targeted via transmembrane protein biomarkers using antibodies, which has promising applications for disease diagnostics.

Unlocking the crucial role of cancer-associated fibroblasts in tumor metastasis: Mechanisms and therapeutic prospects.

BACKGROUND: Tumor metastasis represents a stepwise progression and stands as a principal determinant of unfavorable prognoses among cancer patients. Consequently, an in-depth exploration of its mechanisms holds paramount clinical significance. Cancer-associated fibroblasts (CAFs), constituting the most abundant stromal cell population within the tumor microenvironment (TME), have garnered robust evidence support for their pivotal regulatory roles in tumor metastasis. AIM OF REVIEW: This review systematically explores the roles of CAFs at eight critical stages of tumorigenic dissemination: 1) extracellular matrix (ECM) remodeling, 2) epithelial-mesenchymal transition (EMT), 3) angiogenesis, 4) tumor metabolism, 5) perivascular migration, 6) immune escape, 7) dormancy, and 8) premetastatic niche (PMN) formation. Additionally, we provide a compendium of extant strategies aimed at targeting CAFs in cancer therapy. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review delineates a structured framework for the interplay between CAFs and tumor metastasis while furnishing insights for the potential therapeutic developments. It contributes to a deeper understanding of cancer metastasis within the TME, facilitating the utilization of CAF-targeting therapies in anti-metastatic approaches.

Preoperative prediction of lymph node metastasis in colorectal cancer using 18F-FDG PET/CT peritumoral radiomics analysis.

BACKGROUND: Radiomics has been used in the diagnosis of tumor lymph node metastasis (LNM). However, to date, most studies have been based on intratumoral radiomics. Few studies have focused on the use of 18F-fluorodeoxyglucose positron emission computed tomography (18F-FDG PET/CT) peritumoral radiomics for the diagnosis of LNM in colorectal cancer (CRC). PURPOSE: Determining the value of radiomics features extracted from 18F-FDG PET/CT images of the peritumoral region in predicting LNM in patients with CRC. METHODS: The clinical data and preoperative 18F-FDG PET/CT images of 244 CRC patients were retrospectively analyzed. Intratumoral and peritumoral radiomics features were screened using the mutual information method, and least absolute shrinkage and selection operator regression. Based on the selected radiomics features, a radiomics score (Rad-score) was calculated, and independent risk factors obtained from univariate and multivariate logistic regression analyses were used to construct clinical and combined (Radiomics + Clinical) models. The performance of these models was evaluated using the DeLong test, while their clinical utility was assessed by decision curve analysis. Finally, a nomogram was constructed to visualize the predictive model. RESULTS: The most optimal set of features retained by the feature filtering process were all peritumoral radiomic features. Carcinoembryonic antigen levels, PET/CT-reported lymph node status and Rad-score were found to be independent risk factors for LNM. All three LNM risk assessment models exhibited good predictive performance, with the combined model showing the best classification results, with areas under the curve of 0.85 and 0.76 in the training and validation groups, respectively. The DeLong test revealed that the performance of the combined model was superior to that of the clinical and radiomics models in both the training and validation groups, although this difference was only statistically significant in the training group. DCA indicated that the combined model displayed better clinical utility. CONCLUSIONS: 18F-FDG PET/CT peritumoral radiomics is uniquely suited to predict the presence of LNM in patients with CRC. In particular, the predictive efficacy of LNM for precision therapy and individualized patient management can be improved by using a combination of clinical risk factors.

Internet of things-based management versus standard management of home noninvasive ventilation in COPD patients with hypercapnic chronic respiratory failure: a multicentre randomized controlled non-inferiority trial.

Background: Effective monitoring and management are crucial during long-term home noninvasive positive pressure ventilation (NPPV) in patients with hypercapnic chronic obstructive pulmonary disease (COPD). This study investigated the benefit of Internet of Things (IOT)-based management of home NPPV. Methods: This multicenter, prospective, parallel-group, randomized controlled non-inferiority trial enrolled patients requiring long-term home NPPV for hypercapnic COPD. Patients were randomly assigned (1:1), via a computer-generated randomization sequence, to standard home management or IOT management based on telemonitoring of clinical and ventilator parameters over 12 months. The intervention was unblinded, but outcome assessment was blinded to management assignment. The primary outcome was the between-group comparison of the change in health-related quality of life, based on severe respiratory insufficiency questionnaire scores with a non-inferiority margin of -5. This study is registered with Chinese Clinical Trials Registry (No. ChiCTR1800019536). Findings: Overall, 148 patients (age: 72.7 ± 6.8 years; male: 85.8%; forced expiratory volume in 1 s: 0.7 ± 0.3 L; PaCO2: 66.4 ± 12.0 mmHg), recruited from 11 Chinese hospitals between January 24, 2019, and June 28, 2021, were randomly allocated to the intervention group (n = 73) or the control group (n = 75). At 12 months, the mean severe respiratory insufficiency questionnaire score was 56.5 in the intervention group and 50.0 in the control group (adjusted between-group difference: 6.26 [95% CI, 3.71-8.80]; P < 0.001), satisfying the hypothesis of non-inferiority. The 12-month risk of readmission was 34.3% in intervention group compared with 56.0% in the control group, adjusted hazard ratio of 0.56 (95% CI, 0.34-0.92; P = 0.023). No severe adverse events were reported. Interpretation: Among stable patients with hypercapnic COPD, using IOT-based management for home NPPV improved health-related quality of life and prolonged the time to readmission. Funding: Air Liquide Healthcare (Beijing) Co., Ltd.

Enhancing the prediction of symptomatic radiation pneumonitis for locally advanced non-small-cell lung cancer by combining 3D deep learning-derived imaging features with dose-volume metrics: a two-center study.

OBJECTIVE: This study aims to examine the ability of deep learning (DL)-derived imaging features for the prediction of radiation pneumonitis (RP) in locally advanced non-small-cell lung cancer (LA-NSCLC) patients. MATERIALS AND METHODS: The study cohort consisted of 90 patients from the Fudan University Shanghai Cancer Center and 59 patients from the Affiliated Hospital of Jiangnan University. Occurrences of RP were used as the endpoint event. A total of 512 3D DL-derived features were extracted from two regions of interest (lung-PTV and PTV-GTV) delineated on the pre-radiotherapy planning CT. Feature selection was done using LASSO regression, and the classification models were built using the multilayered perceptron method. Performances of the developed models were evaluated by receiver operating characteristic curve analysis. In addition, the developed models were supplemented with clinical variables and dose-volume metrics of relevance to search for increased predictive value. RESULTS: The predictive model using DL features derived from lung-PTV outperformed the one based on features extracted from PTV-GTV, with AUCs of 0.921 and 0.892, respectively, in the internal test dataset. Furthermore, incorporating the dose-volume metric V30Gy into the predictive model using features from lung-PTV resulted in an improvement of AUCs from 0.835 to 0.881 for the training data and from 0.690 to 0.746 for the validation data, respectively (DeLong p < 0.05). CONCLUSION: Imaging features extracted from pre-radiotherapy planning CT using 3D DL networks could predict radiation pneumonitis and may be of clinical value for risk stratification and toxicity management in LA-NSCLC patients. CLINICAL RELEVANCE STATEMENT: Integrating DL-derived features with dose-volume metrics provides a promising noninvasive method to predict radiation pneumonitis in LA-NSCLC lung cancer radiotherapy, thus improving individualized treatment and patient outcomes.

Using tumor habitat-derived radiomic analysis during pretreatment 18F-FDG PET for predicting KRAS/NRAS/BRAF mutations in colorectal cancer.

BACKGROUND: To investigate the association between Kirsten rat sarcoma viral oncogene homolog (KRAS) / neuroblastoma rat sarcoma viral oncogene homolog (NRAS) /v-raf murine sarcoma viral oncogene homolog B (BRAF) mutations and the tumor habitat-derived radiomic features obtained during pretreatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in patients with colorectal cancer (CRC). METHODS: We retrospectively enrolled 62 patients with CRC who had undergone 18F-FDG PET/computed tomography from January 2017 to July 2022 before the initiation of therapy. The patients were randomly split into training and validation cohorts with a ratio of 6:4. The whole tumor region radiomic features, habitat-derived radiomic features, and metabolic parameters were extracted from 18F-FDG PET images. After reducing the feature dimension and selecting meaningful features, we constructed a hierarchical model of KRAS/NRAS/BRAF mutations by using the support vector machine. The convergence of the model was evaluated by using learning curve, and its performance was assessed based on the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The SHapley Additive exPlanation was used to interpret the contributions of various features to predictions of the model. RESULTS: The model constructed by using habitat-derived radiomic features had adequate predictive power with respect to KRAS/NRAS/BRAF mutations, with an AUC of 0.759 (95% CI: 0.585-0.909) on the training cohort and that of 0.701 (95% CI: 0.468-0.916) on the validation cohort. The model exhibited good convergence, suitable calibration, and clinical application value. The results of the SHapley Additive explanation showed that the peritumoral habitat and a high_metabolism habitat had the greatest impact on predictions of the model. No meaningful whole tumor region radiomic features or metabolic parameters were retained during feature selection. CONCLUSION: The habitat-derived radiomic features were found to be helpful in stratifying the status of KRAS/NRAS/BRAF in CRC patients. The approach proposed here has significant implications for adjuvant treatment decisions in patients with CRC, and needs to be further validated on a larger prospective cohort.

Extracellular vesicles incorporating retrovirus-like capsids for the enhanced packaging and systemic delivery of mRNA into neurons.

The blood-brain barrier (BBB) restricts the systemic delivery of messenger RNAs (mRNAs) into diseased neurons. Although leucocyte-derived extracellular vesicles (EVs) can cross the BBB at inflammatory sites, it is difficult to efficiently load long mRNAs into the EVs and to enhance their neuronal uptake. Here we show that the packaging of mRNA into leucocyte-derived EVs and the endocytosis of the EVs by neurons can be enhanced by engineering leucocytes to produce EVs that incorporate retrovirus-like mRNA-packaging capsids. We transfected immortalized and primary bone-marrow-derived leucocytes with DNA or RNA encoding the capsid-forming activity-regulated cytoskeleton-associated (Arc) protein as well as capsid-stabilizing Arc 5'-untranslated-region RNA elements. These engineered EVs inherit endothelial adhesion molecules from donor leukocytes, recruit endogenous enveloping proteins to their surface, cross the BBB, and enter the neurons in neuro-inflammatory sites. Produced from self-derived donor leukocytes, the EVs are immunologically inert, and enhanced the neuronal uptake of the packaged mRNA in a mouse model of low-grade chronic neuro-inflammation.

MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas.

The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.

Evaluation of bone marrow invasion on the machine learning of 18 F-FDG PET texture analysis in lower gingival squamous cell carcinoma.

OBJECTIVES: Lower gingival squamous cell carcinoma (LGSCC) has the potential to invade the alveolar bone. Traditionally, the diagnosis of LGSCC relied on morphological imaging, but inconsistencies between these assessments and surgical findings have been observed. This study aimed to assess the correlation between LGSCC bone marrow invasion and PET texture features and to enhance diagnostic accuracy by using machine learning. METHODS: A retrospective analysis of 159 LGSCC patients with pretreatment 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) examination from 2009 to 2017 was performed. We extracted radiomic features from the PET images, focusing on pathologic bone marrow invasion detection. Extracted features underwent the least absolute shrinkage and selection operator algorithm-based selection and were then used for machine learning via the XGBoost package to distinguish bone marrow invasion presence. Receiver operating characteristic curve analysis was performed. RESULTS: From the 159 patients, 88 qualified for further analysis (59 men; average age, 69.2 years), and pathologic bone marrow invasion was identified in 69 (78%) of these patients. Three significant radiological features were identified: Gray level co-occurrence matrix_Correlation, INTENSITY-BASED_IntensityInterquartileRange, and MORPHOLOGICAL_SurfaceToVolumeRatio. An XGBoost machine-learning model, using PET radiomic features to detect bone marrow invasion, yielded an area under the curve value of 0.83. CONCLUSION: Our findings highlighted the potential of 18 F-FDG PET radiomic features, combined with machine learning, as a promising avenue for improving LGSCC diagnosis and treatment. Using 18 F-FDG PET texture features may provide a robust and accurate method for determining the presence or absence of bone marrow invasion in LGSCC patients.

HOXC6: A promising biomarker linked to an immunoevasive microenvironment in colorectal cancer based on TCGA analysis and cohort validation.

HOXC6 plays an essential part of the carcinogenesis of solid tumors, but its functional relevance within the immune contexture in patients with colorectal cancer (CRC) is still uncertain. We intended to investigate the predictive value of HOXC6 expression for survival outcomes and its correlation with immune contexture in CRC patients by utilizing the Cancer Genome Atlas database (n = 619). Validation was performed in cohorts from Zhongshan Hospital (n = 200) and Shanghai Cancer Center (n = 300). Immunohistochemical (IHC) staining was utilized to compare the levels of immunocytes infiltrating the tumor between the groups with high and low expression of HOXC6. Elevated levels of HOXC6 expression in CRC tissues were linked to malignant progression and poor prognosis. HOXC6 as a risk factor for survival of CRC patients was confirmed. Receiver operating characteristic analysis confirmed its diagnostic value, and a reliable prognostic nomogram was constructed. KEGG analysis and GSEA showed that HOXC6 participated in immune regulation, and its expression was tightly linked to the abundance of infiltrating immunocytes. HOXC6 was upregulated in patients diagnosed with CRC within the two cohorts, and high HOXC6 levels were correlated with a worse prognosis. The high-HOXC6 expression group showed increased infiltration of Treg cells, CD68+ macrophages, CD66b+ neutrophils, and CD8+ T-cells and elevated levels of PD-L1 and PD-1, but decreased levels of granzyme B and perforin. These findings suggest that HOXC6 abundance in patients with CRC determines a poor prognosis, promotes an immunoevasive environment, and directs CD8+ T-cell dysfunction. HOXC6 is expected to become a prospective biomarker for the outcome of CRC.

Suppression of long noncoding RNA SNHG6 alleviates cigarette smoke-induced lung inflammation by modulating NF-κB signaling.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a widespread inflammatory disease with a high mortality rate. Long noncoding RNAs play important roles in pulmonary diseases and are potential targets for inflammation intervention. METHODS: The expression of small nucleolar RNA host gene 6 (SNHG6) in mouse lung epithelial cell line MLE12 with or without cigarette smoke extract (CSE) treatment was first detected using quantitative reverse-transcription PCR. ELISA was used to evaluate the release of inflammatory cytokines (TNF-α, IL-1ß, and IL-6). The binding site of miR-182-5p with SNHG6 was predicted by using miRanda, which was verified by double luciferase reporter assay. RESULTS: Here, we revealed that SNHG6 was upregulated in CS-exposed MLE12 alveolar epithelial cells and lungs from COPD-model mice. SNHG6 silencing weakened CS-induced inflammation in MLE12 cells and mouse lungs. Mechanistic investigations revealed that SNHG6 could upregulate IκBα kinase through sponging the microRNA miR-182-5p, followed by activated NF-κB signaling. The suppressive effects of SNHG6 silencing on CS-induced inflammation were blocked by an miR-182-5p inhibitor. CONCLUSION: Overall, our findings suggested that SNHG6 regulates CS-induced inflammation in COPD by activating NF-κB signaling, thereby offering a novel potential target for COPD treatment.

Colourimetric and SERS dual-mode aptasensor using Au@Ag and magnetic nanoparticles for the detection of Campylobacter jejuni.

A dual-mode aptasensor has been developed for the effective detection of Campylobacter jejuni (C. jejuni), a major cause of gastrointestinal disease worldwide. The aptasensor utilizes nanoparticles, specifically a core-shell structure consisting of gold and silver (Au@Ag NPs), along with magnetic nanoparticles (MNPs). When Campylobacter jejuni is introduced, "Au@Ag NPs-Aptamer-Campylobacter jejuni-Aptamer-MNPs" sandwich complexes are formed due to the high affinity of the aptamer for the bacterial surface membrane proteins. The dual-mode aptasensor can magnetically enrich the sample in just 15 min, and the presence of Campylobacter jejuni is determined by observing a color change. Additionally, the concentration of Campylobacter jejuni can be quantified using surface-enhanced Raman spectroscopy (SERS) and standard curves. This results in a wider linear range (1.8 × 101-108 CFU/mL) under optimal conditions, a lower limit of detection (6 CFU/mL), and a higher selectivity for the detection of bacteria compared to previously reported sensors. Compared with traditional microbial culture counting methods, the dual-mode aptasensor does not require Raman reporters. The physical action of magnetic enrichment, along with the application of Au@Ag NPs, improves the accuracy of the dual-mode aptasensor, offering the advantages of convenience and high sensitivity. Moreover, by utilizing different types of aptamers, this aptasensor can be modified to detect a wider range of harmful pathogens in various environments.

A novel dual-mode aptasensor based colorimetry and electrochemical detection of norovirus in fecal sample.

Norovirus is a leading cause of acute gastroenteritis in humans. This paper presents the development of a novel dual-mode aptasensor for detecting norovirus using colorimetry and electrochemical methods. The initial colorimetric method utilizes gold nanoparticles (AuNPs) and sodium chloride to establish a positive correlation between the concentration of norovirus in a solution and the absorbance ratio A650/A520. The naked eye can detect concentrations as low as 0.1 µg/mL, corresponding to a Ct value of 33 (2.2 copies/µL, CT = 34.102-3.2185·lgX), allowing for qualitative and semi-quantitative analysis. For more accurate trace analysis, a gold electrode is modified with a thiol-modified aptamer and closed with 6-Mercapto-1-hexanol. After incubation with norovirus, the virus specifically binds to the aptamer, causing changes in its spatial structure and distance from the electrode surface. These changes can then be detected using electrochemical square wave voltammetry (SWV). Under optimal reaction conditions, the peak current from SWV exhibits a strong linear relationship with the logarithm of norovirus concentrations between 10-9 µg/mL and 10-2 µg/mL. The regression equation Y = 14.76789 + 1.03983·lgX, with an R2 value of 0.987, accurately represents this relationship. The limit of detection was determined to be 1.365 × 10-10 µg/mL. Furthermore, the aptasensor demonstrated high specificity for norovirus in fecal samples, making it a promising tool for detecting norovirus in various sample types.

Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study.

Background: Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs. Methods: Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC). Findings: The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91). Interpretation: Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies. Funding: This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).

Analysis of the heavy metal contents' effect on steroidal saponins and the anti-breast cancer activity of Paris polyphylla var. yunnanensis.

Background: P. polyphylla var. yunnanensis, as a near-threatened and ethnic medicine in China, used to be a key ingredient in traditional Chinese medicine in treatment of traumatic injuries, sore throat, snakebites, and convulsions for thousands of years. However, there were no reports on the inverse relationship between the contents of heavy metals and saponins and its anti-breast cancer pharmacological activity in P. polyphylla var. yunnanensis. Methods: The present study aimed to reveal the characteristics of heavy metal contents and saponins and its anti-breast cancer pharmacological activity and their interrelationships in P. polyphylla var. yunnanensis from different production areas. The contents of heavy metal and steroidal saponins in P. polyphylla var. yunnanensis were analyzed by inductively coupled plasma mass spectrometry (ICP-MS) and the high-performance liquid chromatography technique, respectively. The Pearson correlation was used to study the correlation between saponins and heavy metals. 4T1 mouse mammary tumor cells were selected and cultivated for antitumor studies in vitro. Cell Counting Kit-8 (CCK-8) assay, Hoechst staining, and flow cytometry analysis were used for the examination of the proliferation and apoptosis of 4T1 tumor cells. Mouse breast cancer 4T1 cells were subcutaneously injected into BALB/c mice to construct a tumor model to explore the in vivo inhibitory effect on breast cancer. TUNEL assay and immunohistochemistry were used for the examination of the effect of P. polyphylla var. yunnanensis from different origins on cancer cell proliferation and apoptosis induction in 4T1 tumor mice. Results: Heavy metal contents were highly correlated with the content of steroidal saponins. The overall content of 10 metals in the three producing origins was of the order C3 >C2 >C1. The total content of eight steroidal saponins in the extracts of P. polyphylla var. yunnanensis from three different origins was C1 >C2 >C3. The Pearson correlation study showed that in all of the heavy metals, the contents of Cd and Ba were positively correlated with the main steroidal saponins in P. polyphylla var. yunnanensis, while Al, Cr, Cu, Fe, Zn, As, Hg, and Pb showed a negative correlation. In vitro experiments showed that the extracts of P. polyphylla var. yunnanensis from three origins could inhibit the proliferation and induce cell apoptosis of 4T1 cells in a concentration- and time-dependent manner, especially in the C1 origin. In vivo experiments showed that the extract of P. polyphylla var. yunnanensis from the three origins could inhibit the growth of tumors and induce the apoptosis of tumor cells. In the three origins, C1 origin had the lowest total heavy metal level but the highest total steroidal saponin level. Therefore, it showed a better effect in reducing the expression of the human epidermal growth factor receptor 2 (HER2) and Kiel 67 (Ki67) and increasing the expression of p53 in tumor tissues compared to the other origins. In conclusion, in the three origins, C1 origin exhibits antitumor pharmacological effects in vivo and in vitro which are better than those in the other origins. Conclusion: In this study, we found that with the increase of the heavy metal content, the content of steroid saponins and anti-breast cancer activity decreased. The results showed that the high content of the total heavy metals may not be conducive to the accumulation of steroidal saponins in P. polyphylla var. yunnanensis and lead to the low anti-breast cancer activity. The results of this study suggest that the content of heavy metals should be controlled in the artificial cultivation process of P. polyphylla var. yunnanensis.

Single-cell dissection, hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis.

Ulcerative colitis (UC) develops as a result of complex interactions between various cell types in the mucosal microenvironment. In this study, we aim to elucidate the pathogenesis of ulcerative colitis at the single-cell level and unveil its clinical significance. Using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis, we identify a subpopulation of plasma cells (PCs) with significantly increased infiltration in UC colonic mucosa, characterized by pronounced oxidative stress. Combining 10 machine learning approaches, we find that the PC oxidative stress genes accurately distinguish diseased mucosa from normal mucosa (independent external testing AUC=0.991, sensitivity=0.986, specificity=0.909). Using MCPcounter and non-negative matrix factorization, we identify the association between PC oxidative stress genes and immune cell infiltration as well as patient heterogeneity. Spatial transcriptome data is used to verify the infiltration of oxidatively stressed PCs in colitis. Finally, we develop a gene-immune convolutional neural network deep learning model to diagnose UC mucosa in different cohorts (independent external testing AUC=0.984, sensitivity=95.9%, specificity=100%). Our work sheds light on the key pathogenic cell subpopulations in UC and is essential for the development of future clinical disease diagnostic tools.

Loss of HES1 expression is associated with extracellular matrix remodeling and tumor immune suppression in KRAS mutant colon adenocarcinomas.

The loss of HES1, a canonical Notch signaling target, may cooperate with KRAS mutations to remodel the extracellular matrix and to suppress the anti-tumor immune response. While HES1 expression is normal in benign hyperplastic polyps and normal colon tissue, HES1 expression is often lost in sessile serrated adenomas/polyps (SSAs/SSPs) and colorectal cancers (CRCs) such as those right-sided CRCs that commonly harbor BRAF or KRAS mutations. To develop a deeper understanding of interaction between KRAS and HES1 in colorectal carcinogenesis, we selected microsatellite stable (MSS) and KRAS mutant or KRAS wild type CRCs that show aberrant expression of HES1 by immunohistochemistry. By comparing the transcriptional landscapes of microsatellite stable (MSS) CRCs with or without nuclear HES1 expression, we investigated differentially expressed genes and activated pathways. We identified pathways and markers in the extracellular matrix and immune microenvironment that are associated with mutations in KRAS. We found that loss of HES1 expression positively correlated with matrix remodeling and epithelial-mesenchymal transition but negatively correlated with tumor cell proliferation. Furthermore, loss of HES1 expression in KRAS mutant CRCs correlates with a higher M2 macrophage polarization and activation of IL6 and IL10 immunosuppressive signature. Identifying these HES1-related markers may be useful for prognosis stratification and developing treatment for KRAS-mutant CRCs.

CT imaging-based radiomics signatures improve prognosis prediction in postoperative colorectal cancer.

OBJECTIVE: To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients. METHODS: A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and 19 patient images retrieved from The Cancer Imaging Archive (TCIA) as the external validation set. In training, radiomics features were extracted from the preoperative NCE/CE-CT, then selected through 5-fold cross validation LASSO Cox method and used to construct Rad-scores. Models derived from Rad-scores and clinical factors were constructed and compared. Kaplan-Meier analyses were also used to compare the survival probability between the high- and low-risk Rad-score groups. Finally, a nomogram was developed to predict the OS. RESULTS: In training, a clinical model achieved a C-index of 0.796 (95% CI: 0.722-0.870), while clinical and two Rad-scores combined model performed the best, achieving a C-index of 0.821 (95% CI: 0.743-0.899). Furthermore, the models with the CE-CT Rad-score yielded slightly better performance than that of NCE-CT in training. For the combined model with CE-CT Rad-scores, a C-index of 0.818 (95% CI: 0.742-0.894) and 0.774 (95% CI: 0.556-0.992) were achieved in both the training and validation sets. Kaplan-Meier analysis demonstrated a significant difference in survival probability between the high- and low-risk groups. Finally, the areas under the receiver operating characteristics (ROC) curves for the model were 0.904, 0.777, and 0.843 for 1, 3, and 5-year survival, respectively. CONCLUSION: NCE-CT or CE-CT radiomics and clinical combined models can predict the OS for CRC patients, and both Rad-scores are recommended to be included when available.

Combined Doxorubicin Mesoporous Carbon Nanospheres for Effective Tumor Lymphatic Metastasis by Multi-Modal Chemo-Photothermal Treatment in vivo.

Introduction: Sentinel lymph node (SLN) is the first regional lymph node where tumor cells metastasize, and its identification and treatment are of great significance for the prevention of tumor metastasis. However, the current clinical modalities for identification and treatment of SLN are still far from satisfactory owing to their high cost, invasiveness and low accuracy. We aim to design a novel nanomedicine system for SLN imaging and treatment with high efficacy. Methods: We designed and prepared hollow mesoporous carbon spheres (HMCS) and loaded with the chemotherapeutic drug doxorubicin (DOX), which is then modified with polyvinyl pyrrolidone (PVP) to obtain nanomedicine: HMCS-PVP-DOX. Results: HMCS-PVP with a size of about 150 nm could retain in the lymph nodes for a long time and stain the lymph nodes, which could be easily observed by the naked eye. At the same time, HMCS-PVP exhibited excellent photoacoustic and photothermal imaging capabilities, realizing multimodal imaging to locate lymph nodes precisely. Due to its high specific surface area, HMCS could be largely loaded with the chemotherapeutic drug doxorubicin (DOX). HMCS-PVP-DOX displayed highly efficient synergistic chemotherapy-photothermal therapy for lymphatic metastases in both cellular and animal experiments due to its significant photothermal effect under 1064 nm laser irradiation. HMCS-PVP-DOX also displayed great stability and biosafety. Discussion: Multifunctional nanomedicine HMCS-PVP-DOX is expected to provide a novel paradigm for designing nanomedicine to the diagnosis and treatment of lymphatic metastases because of its good stability and safety.

Identification of antigen-presentation related B cells as a key player in Crohn's disease using single-cell dissecting, hdWGCNA, and deep learning.

Crohn's disease (CD) arises from intricate intercellular interactions within the intestinal lamina propria. Our objective was to use single-cell RNA sequencing to investigate CD pathogenesis and explore its clinical significance. We identified a distinct subset of B cells, highly infiltrated in the CD lamina propria, that expressed genes related to antigen presentation. Using high-dimensional weighted gene co-expression network analysis and nine machine learning techniques, we demonstrated that the antigen-presenting CD-specific B cell signature effectively differentiated diseased mucosa from normal mucosa (Independent external testing AUC = 0.963). Additionally, using MCPcounter and non-negative matrix factorization, we established a relationship between the antigen-presenting CD-specific B cell signature and immune cell infiltration and patient heterogeneity. Finally, we developed a gene-immune convolutional neural network deep learning model that accurately diagnosed CD mucosa in diverse cohorts (Independent external testing AUC = 0.963). Our research has revealed a population of B cells with a potential promoting role in CD pathogenesis and represents a fundamental step in the development of future clinical diagnostic tools for the disease.