A Two-color Single-molecule Sequencing Platform and Its Clinical Applications.

DNA sequencers have become increasingly important research and diagnostic tools over the past 20 years. In this study, we developed a single-molecule desktop sequencer, GenoCare 1600 (GenoCare), which utilizes amplification-free library preparation and two-color sequencing-by-synthesis chemistry, making it more user-friendly compared with previous single-molecule sequencing platforms for clinical use. Using the GenoCare platform, we sequenced an Escherichia coli standard sample and achieved a consensus accuracy exceeding 99.99%. We also evaluated the sequencing performance of this platform in microbial mixtures and coronavirus disease 2019 (COVID-19) samples from throat swabs. Our findings indicate that the GenoCare platform allows for microbial quantitation, sensitive identification of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and accurate detection of virus mutations, as confirmed by Sanger sequencing, demonstrating its remarkable potential in clinical application.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). MATERIALS AND METHODS: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. CONCLUSION: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

OBJECTIVES: Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. METHODS: A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. RESULTS: Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. CONCLUSIONS: The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.

Genome-wide analysis of FRF gene family and functional identification of HvFRF9 under drought stress in barley.

FHY3 and its homologous protein FAR1 are the founding members of FRS family. They exhibited diverse and powerful physiological functions during evolution, and participated in the response to multiple abiotic stresses. FRF genes are considered to be truncated FRS family proteins. They competed with FRS for DNA binding sites to regulate gene expression. However, only few studies are available on FRF genes in plants participating in the regulation of abiotic stress. With wide adaptability and high stress-resistance, barley is an excellent candidate for the identification of stress-resistance-related genes. In this study, 22 HvFRFs were detected in barley using bioinformatic analysis from whole genome. According to evolution and conserved motif analysis, the 22 HvFRFs could be divided into subfamilies I and II. Most promoters of subfamily I members contained abscisic acid and methyl jasmonate response elements; however, a large number promoters of subfamily II contained gibberellin and salicylic acid response elements. HvFRF9, one of the members of subfamily II, exhibited a expression advantage in different tissues, and it was most significantly upregulated under drought stress. In-situ PCR revealed that HvFRF9 is mainly expressed in the root epidermal cells, as well as xylem and phloem of roots and leaves, indicating that HvFRF9 may be related to absorption and transportation of water and nutrients. The results of subcellular localization indicated that HvFRF9 was mainly expressed in the nuclei of tobacco epidermal cells and protoplast of arabidopsis. Further, transgenic arabidopsis plants with HvFRF9 overexpression were generated to verify the role of HvFRF9 in drought resistance. Under drought stress, leaf chlorosis and wilting, MDA and O2 - contents were significantly lower, meanwhile, fresh weight, root length, PRO content, and SOD, CAT and POD activities were significantly higher in HvFRF9-overexpressing arabidopsis plants than in wild-type plants. Therefore, overexpression of HvFRF9 could significantly enhance the drought resistance in arabidopsis. These results suggested that HvFRF9 may play a key role in drought resistance in barley by increasing the absorption and transportation of water and the activity of antioxidant enzymes. This study provided a theoretical basis for drought resistance in barley and provided new genes for drought resistance breeding.

Development and Validation of a Dual-Energy CT-Based Model for Predicting the Number of Central Lymph Node Metastases in Clinically Node-Negative Papillary Thyroid Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate a dual-energy CT (DECT)-based model for preoperative prediction of the number of central lymph node metastases (CLNMs) in clinically node-negative (cN0) papillary thyroid carcinoma (PTC) patients. MATERIALS AND METHODS: Between January 2016 and January 2021, 490 patients who underwent lobectomy or thyroidectomy, CLN dissection, and preoperative DECT examinations were enrolled and randomly allocated into the training (N = 345) and validation cohorts (N = 145). The patients' clinical characteristics and quantitative DECT parameters obtained on primary tumors were collected. Independent predictors of> 5 CLNMs were identified and integrated to construct a DECT-based prediction model, for which the area under the curve (AUC), calibration, and clinical usefulness were assessed. Risk group stratification was performed to distinguish patients with different recurrence risks. RESULTS: More than 5 CLNMs were found in 75 (15.3%) cN0 PTC patients. Age, tumor size, normalized iodine concentration (NIC), normalized effective atomic number (nZeff) and the slope of the spectral Hounsfield unit curve (λHu) in the arterial phase were independently associated with> 5 CLNMs. The DECT-based nomogram that incorporated predictors demonstrated favorable performance in both cohorts (AUC: 0.842 and 0.848) and significantly outperformed the clinical model (AUC: 0.688 and 0.694). The nomogram showed good calibration and added clinical benefit for predicting> 5 CLNMs. The KaplanMeier curves for recurrence-free survival showed that the high- and low-risk groups stratified by the nomogram were significantly different. CONCLUSION: The nomogram based on DECT parameters and clinical factors could facilitate preoperative prediction of the number of CLNMs in cN0 PTC patients.

Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study.

Background: For patients with sentinel lymph node (SLN) metastasis and low risk of residual non-SLN (NSLN) metastasis, axillary lymph node (ALN) dissection could lead to overtreatment. This study aimed to develop and validate an automated preoperative deep learning-based tool to predict the risk of SLN and NSLN metastasis in patients with breast cancer (BC) using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images. Methods: In this machine learning study, we retrospectively enrolled 988 women with BC from three hospitals in Zhejiang, China between June 1, 2013 to December 31, 2021, June 1, 2017 to December 31, 2021, and January 1, 2019 to June 30, 2023, respectively. Patients were divided into the training set (n = 519), internal validation set (n = 129), external test set 1 (n = 296), and external test set 2 (n = 44). A convolutional neural network (CNN) model was proposed to predict the SLN and NSLN metastasis and was compared with clinical and radiomics approaches. The performance of different models to detect ALN metastasis was measured by the area under the curve (AUC), accuracy, sensitivity, and specificity. This study is registered at ChiCTR, ChiCTR2300070740. Findings: For SLN prediction, the top-performing model (i.e., the CNN algorithm) achieved encouraging predictive performance in the internal validation set (AUC 0.899, 95% CI, 0.887-0.911), external test set 1 (AUC 0.885, 95% CI, 0.867-0.903), and external test set 2 (AUC 0.768, 95% CI, 0.738-0.798). For NSLN prediction, the CNN-based model also exhibited satisfactory performance in the internal validation set (AUC 0.800, 95% CI, 0.783-0.817), external test set 1 (AUC 0.763, 95% CI, 0.732-0.794), and external test set 2 (AUC 0.728, 95% CI, 0.719-0.738). Based on the subgroup analysis, the CNN model performed well in tumour group smaller than 2.0 cm, with the AUC of 0.801 (internal validation set) and 0.823 (external test set 1). Of 469 patients with BC, the false positive rate of SLN prediction declined from 77.9% to 32.9% using CNN model. Interpretation: The CNN model can predict the SLN status of any detectable lesion size and condition of NSLN in patients with BC. Overall, the CNN model, employing ready DCE-MRI images could serve as a potential technique to assist surgeons in the personalized axillary treatment of in patients with BC non-invasively. Funding: National Key Research and Development projects intergovernmental cooperation in science and technology of China, National Natural Science Foundation of China, Natural Science Foundation of Zhejiang Province, and Zhejiang Medical and Health Science Project.

Jasmonates regulate apical hook development by repressing brassinosteroid biosynthesis and signaling.

An apical hook is a special structure formed during skotomorphogenesis in dicotyledonous plant species. It is critical for protecting the shoot apical meristem from mechanical damage during seed germination and hypocotyl elongation in soil. Brassinosteroid (BR) and jasmonate (JA) phytohormones antagonistically regulate apical hook formation. However, the interrelationship between BRs and JAs in this process has not been well elucidated. Here, we reveal that JAs repress BRs to regulate apical hook development in Arabidopsis (Arabidopsis thaliana). Exogenous application of methyl jasmonate (MeJA) repressed the expression of the rate-limiting BR biosynthetic gene DWARF4 (DWF4) in a process relying on 3 key JA-dependent transcription factors, MYC2, MYC3, and MYC4. We demonstrated that MYC2 interacts with the critical BR-activated transcription factor BRASSINAZOLE RESISTANT 1 (BZR1), disrupting the association of BZR1 with its partner transcription factors, such as those of the PHYTOCHROME INTERACTING FACTOR (PIF) family and downregulating the expression of their target genes, such as WAVY ROOT GROWTH 2 (WAG2), encoding a protein kinase essential for apical hook development. Our results indicate that JAs not only repress the expression of BR biosynthetic gene DWF4 but, more importantly, attenuate BR signaling by inhibiting the transcriptional activation of BZR1 by MYC2 during apical hook development.

Development of a model based on the age-adjusted Charlson comorbidity index to predict survival for resected perihilar cholangiocarcinoma.

BACKGROUND: Perihilar cholangiocarcinoma (pCCA) has a poor prognosis and urgently needs a better predictive method. The predictive value of the age-adjusted Charlson comorbidity index (ACCI) for the long-term prognosis of patients with multiple malignancies was recently reported. However, pCCA is one of the most surgically difficult gastrointestinal tumors with the poorest prognosis, and the value of the ACCI for the prognosis of pCCA patients after curative resection is unclear. AIM: To evaluate the prognostic value of the ACCI and to design an online clinical model for pCCA patients. METHODS: Consecutive pCCA patients after curative resection between 2010 and 2019 were enrolled from a multicenter database. The patients were randomly assigned 3:1 to training and validation cohorts. In the training and validation cohorts, all patients were divided into low-, moderate-, and high-ACCI groups. Kaplan-Meier curves were used to determine the impact of the ACCI on overall survival (OS) for pCCA patients, and multivariate Cox regression analysis was used to determine the independent risk factors affecting OS. An online clinical model based on the ACCI was developed and validated. The concordance index (C-index), calibration curve, and receiver operating characteristic (ROC) curve were used to evaluate the predictive performance and fit of this model. RESULTS: A total of 325 patients were included. There were 244 patients in the training cohort and 81 patients in the validation cohort. In the training cohort, 116, 91 and 37 patients were classified into the low-, moderate- and high-ACCI groups. The Kaplan-Meier curves showed that patients in the moderate- and high-ACCI groups had worse survival rates than those in the low-ACCI group. Multivariable analysis revealed that moderate and high ACCI scores were independently associated with OS in pCCA patients after curative resection. In addition, an online clinical model was developed that had ideal C-indexes of 0.725 and 0.675 for predicting OS in the training and validation cohorts. The calibration curve and ROC curve indicated that the model had a good fit and prediction performance. CONCLUSION: A high ACCI score may predict poor long-term survival in pCCA patients after curative resection. High-risk patients screened by the ACCI-based model should be given more clinical attention in terms of the management of comorbidities and postoperative follow-up.

Textbook outcomes in liver surgery for gallbladder cancer patients treated with curative-intent resection: a multicenter observational study.

BACKGROUND: Cholecystectomy, hepatectomy, and lymphadenectomy are recommended as the curative treatment for resectable gallbladder cancer (GBC). Textbook outcomes in liver surgery (TOLS) is a novel composite measure that has been defined by expert consensus to represent the optimal postoperative course after hepatectomy. This study aimed to determine the incidence of TOLS and the independent predictors associated with TOLS after curative-intent resection in GBC patients. METHODS: All consecutive GBC patients who underwent curative-intent resection between 2014 and 2020 were enrolled from a multicenter database from 11 hospitals as the training and the internal testing cohorts, and Southwest Hospital as the external testing cohort. TOLS was defined as no intraoperative grade greater than or equal to 2 incidents, no grade B/C postoperative bile leaks, no postoperative grade B/C liver failure, no 90-day postoperative major morbidity, no 90-day readmission, no 90-day mortality after hospital discharge, and R0 resection. Independent predictors of TOLS were identified using logistic regression and were used to construct the nomogram. The predictive performance was assessed using the area under the curve and calibration curves. RESULTS: TOLS was achieved in 168 patients (54.4%) and 74 patients (57.8%) from the training and internal testing cohorts, and the external testing cohort, respectively. On multivariate analyses, age less than or equal to 70 years, absence of preoperative jaundice (total bilirubin≤3 mg/dl), T1 stage, N0 stage, wedge hepatectomy, and no neoadjuvant therapy were independently associated with TOLS. The nomogram that incorporated these predictors demonstrated excellent calibration and good performance in both the training and external testing cohorts (area under the curve: 0.741 and 0.726). CONCLUSIONS: TOLS was only achieved in approximately half of GBC patients treated with curative-intent resection, and the constructed nomogram predicted TOLS accurately.

Machine Learning Classifier for Preoperative Prediction of Early Recurrence After Bronchial Arterial Chemoembolization Treatment in Lung Cancer Patients.

RATIONALE AND OBJECTIVES: Bronchial arterial chemoembolization (BACE) was deemed as an effective and safe approach for advanced standard treatment-ineligible/rejected lung cancer patients. However, the therapeutic outcome of BACE varies greatly and there is no reliable prognostic tool in clinical practice. This study aimed to investigate the effectiveness of radiomics features in predicting tumor recurrence after BACE treatment in lung cancer patients. MATERIALS AND METHODS: A total of 116 patients with pathologically confirmed lung cancer who received BACE treatment were retrospectively recruited. All patients underwent contrast-enhanced CT within 2 weeks before BACE treatment and were followed up for more than 6 months. We conducted a machine learning-based characterization of each lesion on the preoperative contrast-enhanced CT images. In the training cohort, recurrence-related radiomics features were screened by least absolute shrinkage and selection operator (LASSO) regression. Three predictive radiomics signatures were built with linear discriminant analysis (LDA), support vector machine (SVM) and logistic regression (LR) algorithms, respectively. Univariate and multivariate LR analyses were performed to select the independent clinical predictors for recurrence. The radiomics signature with best predictive performance was integrated with the clinical predictors to form a combined model, which was visualized as a nomogram. The performance of the combined model was assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: Nine recurrence-related radiomics features were screened out, and three radiomics signatures (RadscoreLDA, RadscoreSVM and RadscoreLR) were built based on these features. Patients were classified into the low-risk and high-risk groups based on the optimal threshold of three signatures. Progression-free survival (PFS) analysis showed that patients of low-risk group achieved longer PFS than patients of high-risk group (P < 0.05). The combined model including RadscoreLDA and independent clinical predictors (tumor size, carcinoembryonic antigen and pro-gastrin releasing peptide) achieved the best predictive performance for recurrence after BACE treatment. It yields AUCs of 0.865 and 0.867 in the training and validation cohorts, with accuracy (ACC) of 0.804 and 0.750, respectively. Calibration curves indicated that the probability of recurrence predicted by the model fits well with the actual recurrence probability. DCA showed that the radiomics nomogram was clinically useful. CONCLUSION: The radiomics and clinical predictors-based nomogram can predict tumor recurrence after BACE treatment effectively, which allowing oncologists to identify potential recurrence and enable better patient management and clinical decision-making.

Multi-algorithms analysis for pre-treatment prediction of response to transarterial chemoembolization in hepatocellular carcinoma on multiphase MRI.

OBJECTIVES: This study compared the accuracy of predicting transarterial chemoembolization (TACE) outcomes for hepatocellular carcinoma (HCC) patients in the four different classifiers, and comprehensive models were constructed to improve predictive performance. METHODS: The subjects recruited for this study were HCC patients who had received TACE treatment from April 2016 to June 2021. All participants underwent enhanced MRI scans before and after intervention, and pertinent clinical information was collected. Registry data for the 144 patients were randomly assigned to training and test datasets. The robustness of the trained models was verified by another independent external validation set of 28 HCC patients. The following classifiers were employed in the radiomics experiment: machine learning classifiers k-nearest neighbor (KNN), support vector machine (SVM), the least absolute shrinkage and selection operator (Lasso), and deep learning classifier deep neural network (DNN). RESULTS: DNN and Lasso models were comparable in the training set, while DNN performed better in the test set and the external validation set. The CD model (Clinical & DNN merged model) achieved an AUC of 0.974 (95% CI: 0.951-0.998) in the training set, superior to other models whose AUCs varied from 0.637 to 0.943 (p < 0.05). The CD model generalized well on the test set (AUC = 0.831) and external validation set (AUC = 0.735). CONCLUSIONS: DNN model performs better than other classifiers in predicting TACE response. Integrating with clinically significant factors, the CD model may be valuable in pre-treatment counseling of HCC patients who may benefit the most from TACE intervention.

Prediction of placenta accreta spectrum with nomogram combining radiomic and clinical factors: A novel developed and validated integrative model.

OBJECTIVE: To develop and validate a clinicoradiomic nomogram based on sagittal T2WI images to predict placenta accreta spectrum (PAS). METHODS: Between October 2016 and April 2022, women suspected of PAS by ultrasound were enrolled. After taking into account exclusion criteria, 132 women were retrospectively included in the study. The variance threshold SelectKBest and the least absolute shrinkage and selection operator were applied to select radiomic features, which was further used to calculate the Rad-score. Multivariable logistic regression was used to screen clinical factor. RESULTS: Based on 13 radiomic features, five radiomic models were constructed. A clinical factor of intraplacental T2-hypointense bands was obtained by multivariate logistic regression. The area under the curve (AUC) value of the stochastic gradient descent (SGD) radiomic model was 0.82 in the training cohort and 0.78 in the test cohort. After adding clinical factors to the SGD radiomic model, the AUC value of the clinicoradiomic model was significantly increased from 0.82 and 0.78 to 0.84 in both the training and test cohorts. The nomogram of the clinicoradiomic model was constructed, which had good performance verified by calibration and a decision curve. CONCLUSION: The presented nomogram could be useful for predicting PAS.

Proteomic analysis of DZIP3 interactome and its role in proliferation and metastasis in gastric cancer cells.

Gastric cancer is a serious malignant tumor in the world, accounting for the third cause of cancer death worldwide. The pathogenesis of gastric cancer is very complex, in which epigenetic inheritance plays an important role. In our study, we found that DZIP3 was significantly up-regulated in gastric cancer tissues as compared to adjacent normal tissue, which suggested it may be play a crucial part in gastric cancer. To clarify the mechanism of it, we further analyzed the interacting proteome and transcriptome of DZIP3. An association between DZIP3 and some epigenetic regulators, such as CUL4B complex, was verified. We also present the first proteomic characterization of the protein-protein interaction (PPI) network of DZIP3. Then, the transcriptome analysis of DZIP3 demonstrated that knockdown DZIP3 increased a cohort of genes, including SETD7 and ZBTB4, which have essential role in tumors. We also revealed that DZIP3 promotes proliferation and metastasis of gastric cancer cells. And the higher expression of DZIP3 is positively associated with the poor prognosis of several cancers. In summary, our study revealed a mechanistic role of DZIP3 in promoting proliferation and metastasis in gastric cancer, supporting the pursuit of DZIP3 as a potential target for gastric cancer therapy.

The role of metabolic reprogramming of oxygen-induced macrophages in the dynamic changes of atherosclerotic plaques.

Atherosclerosis (As) is a chronic vascular inflammatory disease. Macrophages are the most important immune cells in atherosclerotic plaques, and the phenotype of plaque macrophages shifts dynamically to adapt to changes in the plaque microenvironment. The aerobic microenvironment of early atherosclerotic plaques promotes the transformation of M2/alternatively activated macrophages mainly through oxidative phosphorylation; the anoxic microenvironment of advanced atherosclerotic plaques mainly promotes the formation of M1/classically activated macrophages through anaerobic glycolysis; and the adventitia angiogenesis of aged atherosclerotic plaques leads to an increase in the proportion of M2/M1 macrophages. Therefore, this review deeply elucidates the dynamic change mechanism of plaque macrophages and the regulation of plaque oxygen content and immune metabolism to find new targets for the treatment of As.

Inactivation of KDM5A suppresses growth and enhances chemosensitivity in liver cancer by modulating ROCK1/PTEN/AKT pathway.

Liver cancer is a kind of malignant tumor with poor sensitivity to chemotherapy. It is urgent to investigate approaches to improve the outcome of chemotherapy. KDM5A has been reported to be an oncogene in various cancers and is associated with drug resistance. However, the functions of KDM5A in chemotherapeutic sensitivity of liver cancer not been well illustrated. In this study, we found that KDM5A was upregulated in liver cancer tissue and cell lines. KDM5A knockdown using a gene interference strategy suppressed the growth of liver cancer in vitro and in vivo. CPI-455, a pharmacological inactivation of KDM5A enhanced the cytotoxicity of cisplatin (CDDP) in liver cells. CPI-455 and CDDP cotreatment resulted in apoptosis and mitochondrial dysfunction. We also found that knockdown or inactivation of KDM5A resulted in the downregulation of ROCK1, an oncogene regulating the activation of the PTEN/AKT signaling pathway. In particular, overexpression of ROCK1 or SF1670, a pharmacological inhibitor of PTEN, alleviated the cytotoxicity of CPI-455 and CDDP cotreatment. In HCCLM3 xenografts, CPI-455 and CDDP cotreatment dramatically inhibited the growth of xenograft tumor compared to CPI-455 or CDDP treatment alone. In conclusion, this study suggested that targeting the inactivation of KDM5A is an efficient strategy to enhance the chemosensitivity of liver cancer cells to CDDP by modulating the ROCK1/PTEN/AKT signaling pathway.

Brassinosteroid signals cooperate with katanin-mediated microtubule severing to control stamen filament elongation.

Proper stamen filament elongation is essential for pollination and plant reproduction. Plant hormones are extensively involved in every stage of stamen development; however, the cellular mechanisms by which phytohormone signals couple with microtubule dynamics to control filament elongation remain unclear. Here, we screened a series of Arabidopsis thaliana mutants showing different microtubule defects and revealed that only those unable to sever microtubules, lue1 and ktn80.1234, displayed differential floral organ elongation with less elongated stamen filaments. Prompted by short stamen filaments and severe decrease in KTN1 and KTN80s expression in qui-2 lacking five BZR1-family transcription factors (BFTFs), we investigated the crosstalk between microtubule severing and brassinosteroid (BR) signaling. The BFTFs transcriptionally activate katanin-encoding genes, and the microtubule-severing frequency was severely reduced in qui-2. Taken together, our findings reveal how BRs can regulate cytoskeletal dynamics to coordinate the proper development of reproductive organs.

Impact of perioperative blood transfusion on long-term survival in patients with different stages of perihilar cholangiocarcinoma treated with curative resection: A multicentre propensity score matching study.

Background & aim: The association of perioperative blood transfusion (PBT) with long-term survival in perihilar cholangiocarcinoma (pCCA) patients after surgical resection with curative intent is controversial and may differ among different stages of the disease. This study aimed to investigate the impact of PBT on long-term survival of patients with different stages of pCCA. Methods: Consecutive pCCA patients from three hospitals treated with curative resection from 2012 to 2019 were enrolled and divided into the PBT and non-PBT groups. Propensity score matching (PSM) was used to balance differences in baseline characteristics between the PBT and non-PBT groups. Kaplan-Meier curves and log-rank test were used to compare overall survival (OS) and recurrence-free survival (RFS) between patients with all tumor stages, early stage (8th AJCC stage I), and non-early stage (8th AJCC stage II-IV) pCCA in the PBT and non-PBT groups. Cox regression analysis was used to determine the impact of PBT on OS and RFS of these patients. Results: 302 pCCA patients treated with curative resection were enrolled into this study. Before PSM, 68 patients (22 patients in the PBT group) were in the early stage and 234 patients (108 patients in the PBT group) were in the non-early stage. Patients with early stage pCCA in the PBT group had significantly lower OS and RFS rates than those in the non-PBT group. However, there were with no significant differences between the 2 groups with all tumor stages and non-early stage pCCA. After PSM, there were 18 matched pairs of patients with early stage and 72 matched pairs of patients with non-early stage. Similar results were obtained in the pre- and post-PSM cohorts: patients with early stage pCCA in the PBT group showed significantly lower OS and RFS rates than those in the non-PBT group, but there were no significant differences between the 2 groups for patients with all tumor stages and non-early stage pCCA. Cox regression analysis demonstrated that PBT was independently associated with worse OS and RFS for patients with early stage pCCA. Conclusions: PBT had a negative impact on long-term survival in patients with early stage pCCA after curative resection, but not in patients with non-early stage pCCA.

Multiple primary malignancies - hepatocellular carcinoma combined with splenic lymphoma: A case report.

BACKGROUND: Primary liver cancer is one of the most common malignant tumours, while primary splenic lymphoma is a rare malignancy. Thus, cases of hepatocellular carcinoma (HCC) combined with splenic lymphoma are extremely rare. CASE SUMMARY: We present a 62-year-old woman who was admitted to the Interventional Radiology Department with a lump in the spleen and liver as well as multiple enlarged lymph nodes visible by ultrasound. Contrast-enhanced computed of the abdomen revealed a circular, low-density, shallow mass (approximately 2.6 cm in diameter) in the left intrahepatic lobe and multiple round, low-density shadows in the spleen with clear boundaries (maximum diameter 7.6 cm). Based on the characteristic clinical symptoms and explicit radiological findings, the clinical diagnosis was HCC with metastasis to the liver portal, retroperitoneal lymph nodes, and spleen. After transcatheter arterial chemoembolization and sequential radiofrequency ablation, the -fetoprotein level returned to the normal range, and the hepatitis B cirrhosis improved. In addition, splenic tumour biopsy confirmed the diagnosis of primary malignant lymphoma, which went into remission after chemotherapy. CONCLUSION: HCC with primary splenic non-Hodgkin lymphoma is extremely rare and easily misdiagnosed. Better understanding would facilitate early diagnosis, treatment and prognosis.

Alpha-fetoprotein, protein induced by vitamin K absence or antagonist-II, lens culinaris agglutinin-reactive fraction of alpha-fetoprotein alone and in combination for early detection of hepatocellular carcinoma from nonalcoholic fatty liver disease: A multicenter analysis.

BACKGROUND: Current surveillance strategies for hepatocellular carcinoma (HCC) among patients with nonalcoholic fatty liver disease (NAFLD) are insufficient. This study aimed to investigate the diagnostic performance of alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II (PIVKA-II), lens culinaris agglutinin-reactive fraction of AFP (AFP-L3), and their combinations in HCC underlying NAFLD patients. METHODS: Serologic AFP, AFP-L3, and PIVKA-II levels in NAFLD patients with and without HCC were measured. By receiver operating characteristic (ROC) analyses, the area under the curve (AUC), sensitivity, and specificity were obtained to evaluate the diagnostic accuracy of each biomarker and their combinations. RESULTS: This study was conducted on 139 patients with NAFLD-HCC and 345 NAFLD controls. The elevation of these three biomarkers was observed in patients with NAFLD-HCC compared to those in NAFLD controls (all P < 0.001). When they were analyzed individually, PIVKA-II showed the best performance in diagnosing any-stage HCC with an AUC of 0.869, followed by AFP (0.763; vs. PIVKA-II, P < 0.001) and AFP-L3 (0.689; vs. PIVKA-II, P < 0.001). When they were analyzed in combination, AFP + PIVKA-II yielded the highest AUC (0.906), followed by AFP + PIVKA-II + AFP-L3 (0.904; vs. AFP + PIVKA-II, P = 0.086), PIVKA-II + AFP-L3 (0.881; vs. AFP + PIVKA-II, P < 0.001), and AFP + AFP-L3 (0.759; vs. AFP + PIVKA-II, P < 0.001). Similar findings were obtained in the subgroup with early-stage NAFLD-HCC, as well as the non-cirrhotic subgroup. CONCLUSIONS: These data validated the better diagnostic ability of PIVKA-II than AFP or AFP-L3 alone for diagnosing any-stage HCC among patients with NAFLD, and the combination of AFP + PIVKA-II significantly improved the diagnostic accuracy of NAFLD-HCC.