Advances in the differentiation of pluripotent stem cells into vascular cells.

Blood vessels constitute a closed pipe system distributed throughout the body, transporting blood from the heart to other organs and delivering metabolic waste products back to the lungs and kidneys. Changes in blood vessels are related to many disorders like stroke, myocardial infarction, aneurysm, and diabetes, which are important causes of death worldwide. Translational research for new approaches to disease modeling and effective treatment is needed due to the huge socio-economic burden on healthcare systems. Although mice or rats have been widely used, applying data from animal studies to human-specific vascular physiology and pathology is difficult. The rise of induced pluripotent stem cells (iPSCs) provides a reliable in vitro resource for disease modeling, regenerative medicine, and drug discovery because they carry all human genetic information and have the ability to directionally differentiate into any type of human cells. This review summarizes the latest progress from the establishment of iPSCs, the strategies for differentiating iPSCs into vascular cells, and the in vivo transplantation of these vascular derivatives. It also introduces the application of these technologies in disease modeling, drug screening, and regenerative medicine. Additionally, the application of high-tech tools, such as omics analysis and high-throughput sequencing, in this field is reviewed.

The FLRT3-UNC5B checkpoint pathway inhibits T cell-based cancer immunotherapies.

Cancers exploit coinhibitory receptors on T cells to escape tumor immunity, and targeting such mechanisms has shown remarkable clinical benefit, but in a limited subset of patients. We hypothesized that cancer cells mimic noncanonical mechanisms of early development such as axon guidance pathways to evade T cell immunity. Using gain-of-function genetic screens, we profiled axon guidance proteins on human T cells and their cognate ligands and identified fibronectin leucine-rich transmembrane protein 3 (FLRT3) as a ligand that inhibits T cell activity. We demonstrated that FLRT3 inhibits T cells through UNC5B, an axon guidance receptor that is up-regulated on activated human T cells. FLRT3 expressed in human cancers favored tumor growth and inhibited CAR-T and BiTE + T cell killing and infiltration in humanized cancer models. An FLRT3 monoclonal antibody that blocked FLRT3-UNC5B interactions reversed these effects in an immune-dependent manner. This study supports the concept that axon guidance proteins mimic T cell checkpoints and can be targeted for cancer immunotherapy.

Neutralizing antibodies to block viral entry and for identification of entry inhibitors.

Neutralizing antibodies (NAbs) are naturally produced by our immune system to combat viral infections. Clinically, neutralizing antibodies with potent efficacy and high specificity have been extensively used to prevent and treat a wide variety of viral infections, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Human Immunodeficiency Virus (HIV), Dengue Virus (DENV) and Hepatitis B Virus (HBV). An overwhelmingly large subset of clinically effective NAbs operates by targeting viral envelope proteins to inhibit viral entry into the host cell. Binding of viral envelope protein to the host receptor is a critical rate limiting step triggering a cascade of downstream events, including endocytosis, membrane fusion and pore formation to allow viral entry. In recent years, improved structural knowledge on these processes have allowed researchers to also leverage NAbs as an indispensable tool in guiding discovery of novel antiviral entry inhibitors, providing drug candidates with high efficacy and pan-genus specificity. This review will summarize the latest progresses on the applications of NAbs as effective entry inhibitors and as important tools to develop antiviral therapeutics by high-throughput drug screenings, rational design of peptidic entry inhibitor mimicking NAbs and in silico computational modeling approaches.

LI-RADS category 3, 4, and M observations: a multiple parameters diagnostic model for hepatocellular carcinoma.

BACKGROUND: Hepatic lesions categorized as LR-3, LR-4, and LR-M are challenging to accurately assess and diagnose. PURPOSE: To combine potential clinical and/or magnetic resonance imaging (MRI) features for a more comprehensive hepatocellular carcinoma (HCC) versus non-HCC diagnosis for patients with LR-3, LR-4, and LR-M graded lesions. METHODS: Data were consecutively retrieved from 82 at-risk patients with LR-3 (n = 43), LR-4 (n = 20), and LR-M (n = 23) lesions. Significant findings for the differentiation of HCC and non-HCC, including MRI features and clinical factors, were identified with univariable and multivariable analyses. The variables for a prediction model were selected through stepwise use of Akaike's Information Criterion (AIC) to build multivariable logistic regression model. RESULTS: Serum alpha-fetoprotein (AFP) >16.2 ng/mL (odds ratio [OR] = 22.4; P = 0.006), septum (OR = 52.1; P = 0.011), and hepatobiliary phase (HBP) hypointensity (OR = 40.2; P = 0.001) were confirmed as independent predictors of HCC. When combining the three predictors and mild-moderate T2 hyperintensity, the model (AIC = 50.91) showed good accuracy with a C-index of 0.948. CONCLUSION: In at-risk patients with LR-3, LR-4, or LR-M lesions, integrating AFP, septum, HBP hypointensity, and mild-moderate T2 hyperintensity achieved high diagnostic performance for the diagnosis of HCC.

Delta-radiomics models based on multi-phase contrast-enhanced magnetic resonance imaging can preoperatively predict glypican-3-positive hepatocellular carcinoma.

Objectives: The aim of this study is to investigate the value of multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) based on the delta radiomics model for identifying glypican-3 (GPC3)-positive hepatocellular carcinoma (HCC). Methods: One hundred and twenty-six patients with pathologically confirmed HCC (training cohort: n = 88 and validation cohort: n = 38) were retrospectively recruited. Basic information was obtained from medical records. Preoperative multi-phase CE-MRI images were reviewed, and the 3D volumes of interest (VOIs) of the whole tumor were delineated on non-contrast T1-weighted imaging (T1), arterial phase (AP), portal venous phase (PVP), delayed phase (DP), and hepatobiliary phase (HBP). One hundred and seven original radiomics features were extracted from each phase, and delta-radiomics features were calculated. After a two-step feature selection strategy, radiomics models were built using two classification algorithms. A nomogram was constructed by combining the best radiomics model and clinical risk factors. Results: Serum alpha-fetoprotein (AFP) (p = 0.013) was significantly related to GPC3-positive HCC. The optimal radiomics model is composed of eight delta-radiomics features with the AUC of 0.805 and 0.857 in the training and validation cohorts, respectively. The nomogram integrated the radiomics score, and AFP performed excellently (training cohort: AUC = 0.844 and validation cohort: AUC = 0.862). The calibration curve showed good agreement between the nomogram-predicted probabilities and GPC3 actual expression in both training and validation cohorts. Decision curve analysis further demonstrates the clinical practicality of the nomogram. Conclusion: Multi-phase CE-MRI based on the delta-radiomics model can non-invasively predict GPC3-positive HCC and can be a useful method for individualized diagnosis and treatment.

Potential for NPY receptor-related therapies for polycystic ovary syndrome: an updated review.

Polycystic ovary syndrome (PCOS) is a complex endocrine disease that can cause female infertility and bring economic burden to families and to society. The clinical and/or biochemical manifestations include hyperandrogenism, persistent anovulation, and polycystic ovarian changes, often accompanied by insulin resistance and obesity. Although its pathogenesis is unclear, PCOS involves the abnormal regulation of the hypothalamic-pituitary-ovarian axis and the abnormal activation of GnRH neurons. Neuropeptide Y (NPY) is widely distributed in the arcuate nucleus of the hypothalamus and functions as the physiological integrator of two neuroendocrine systems, one governing feeding and the other controlling reproduction. In recent years, an increasing number of studies have focused on the improvement of the reproductive and metabolic status of PCOS through the therapeutic application of NPY and its receptors. In this review, we summarize the central and peripheral regulation of NPY and its receptors in the development of PCOS and discuss the potential for NPY receptor-related therapies for PCOS.

Monitoring AKT activity and targeting in live tissue and disease contexts using a real-time Akt-FRET biosensor mouse.

Aberrant AKT activation occurs in a number of cancers, metabolic syndrome, and immune disorders, making it an important target for the treatment of many diseases. To monitor spatial and temporal AKT activity in a live setting, we generated an Akt-FRET biosensor mouse that allows longitudinal assessment of AKT activity using intravital imaging in conjunction with image stabilization and optical window technology. We demonstrate the sensitivity of the Akt-FRET biosensor mouse using various cancer models and verify its suitability to monitor response to drug targeting in spheroid and organotypic models. We also show that the dynamics of AKT activation can be monitored in real time in diverse tissues, including in individual islets of the pancreas, in the brown and white adipose tissue, and in the skeletal muscle. Thus, the Akt-FRET biosensor mouse provides an important tool to study AKT dynamics in live tissue contexts and has broad preclinical applications.

Diffusion-Weighted Imaging as a Quantitative Imaging Biomarker for Predicting Proliferation Rate in Hepatocellular Carcinoma: Developing a Radiomics Nomogram.

PURPOSE: This study aimed to explore the predictive performance of diffusion-weighted imaging with apparent diffusion coefficient map in predicting the proliferation rate of hepatocellular carcinoma and to develop a radiomics-based nomogram. METHODS: This was a single-center retrospective study. A total of 110 patients were enrolled. The sample included 38 patients with low Ki67 expression (Ki67 ≤10%) and 72 with high Ki67 expression (Ki67 >10%) as demonstrated by surgical pathology. Patients were randomly divided into either a training (n = 77) or validation (n = 33) cohort. Diffusion-weighted imaging with apparent diffusion coefficient maps was used to extract radiomic features and the signal intensity values of tumor (SI tumor ), normal liver (SI liver ), and background noise (SI background ) from all samples. Subsequently, the clinical model, radiomic model, and fusion model (with clinical data and radiomic signature) were developed and validated. RESULTS: The area under the curve (AUC) of the clinical model for predicting the Ki67 expression including serum α-fetoprotein level ( P = 0.010), age ( P = 0.015), and signal noise ratio ( P = 0.026) was 0.799 and 0.715 in training and validation cohorts, respectively. The AUC of the radiomic model constructed by 9 selected radiomic features was 0.833 and 0.772 in training and validation cohorts, respectively. The AUC of the fusion model containing serum α-fetoprotein level ( P = 0.011), age ( P = 0.019), and rad score ( P < 0.001) was 0.901 and 0.781 in training and validation cohorts, respectively. CONCLUSIONS: Diffusion-weighted imaging as a quantitative imaging biomarker can predict Ki67 expression level in hepatocellular carcinoma across various models.

Recent progress on the role of non-coding RNA in postoperative cognitive dysfunction.

Postoperative cognitive dysfunction (POCD), especially in elderly patients, is a serious complication characterized by impairment of cognitive and sensory modalities after surgery. The pathogenesis of POCD mainly includes neuroinflammation, neuronal apoptosis, oxidative stress, accumulation of Aß, and tau hyperphosphorylation; however, the exact mechanism remains unclear. Non-coding RNA (ncRNA) may play an important role in POCD. Some evidence suggests that microRNA, long ncRNA, and circular RNA can regulate POCD-related processes, making them promising biomarkers in POCD diagnosis, treatment, and prognosis. This article reviews the crosstalk between ncRNAs and POCD, and systematically discusses the role of ncRNAs in the pathogenesis and diagnosis of POCD. Additionally, we explored the possible mechanisms of ncRNA-associated POCD, providing new knowledge for developing ncRNA-based treatments for POCD.

Radiomic analysis based on multi-phase magnetic resonance imaging to predict preoperatively microvascular invasion in hepatocellular carcinoma.

BACKGROUND: The prognosis of hepatocellular carcinoma (HCC) remains poor and relapse occurs in more than half of patients within 2 years after hepatectomy. In terms of recent studies, microvascular invasion (MVI) is one of the potential predictors of recurrence. Accurate preoperative prediction of MVI is potentially beneficial to the optimization of treatment planning. AIM: To develop a radiomic analysis model based on pre-operative magnetic resonance imaging (MRI) data to predict MVI in HCC. METHODS: A total of 113 patients recruited to this study have been diagnosed as having HCC with histological confirmation, among whom 73 were found to have MVI and 40 were not. All the patients received preoperative examination by Gd-enhanced MRI and then curative hepatectomy. We manually delineated the tumor lesion on the largest cross-sectional area of the tumor and the adjacent two images on MRI, namely, the regions of interest. Quantitative analyses included most discriminant factors (MDFs) developed using linear discriminant analysis algorithm and histogram analysis with MaZda software. Independent significant variables of clinical and radiological features and MDFs for the prediction of MVI were estimated and a discriminant model was established by univariate and multivariate logistic regression analysis. Prediction ability of the above-mentioned parameters or model was then evaluated by receiver operating characteristic (ROC) curve analysis. Five-fold cross-validation was also applied via R software. RESULTS: The area under the ROC curve (AUC) of the MDF (0.77-0.85) outperformed that of histogram parameters (0.51-0.74). After multivariate analysis, MDF values of the arterial and portal venous phase, and peritumoral hypointensity in the hepatobiliary phase were identified to be independent predictors of MVI (P < 0.05). The AUC value of the model was 0.939 [95% confidence interval (CI): 0.893-0.984, standard error: 0.023]. The result of internal five-fold cross-validation (AUC: 0.912, 95%CI: 0.841-0.959, standard error: 0.0298) also showed favorable predictive efficacy. CONCLUSION: Noninvasive MRI radiomic model based on MDF values and imaging biomarkers may be useful to make preoperative prediction of MVI in patients with primary HCC.

Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma.

Rhabdomyosarcoma (RMS) is a common childhood cancer that shares features with developing skeletal muscle. Yet, the conservation of cellular hierarchy with human muscle development and the identification of molecularly defined tumor-propagating cells has not been reported. Using single-cell RNA-sequencing, DNA-barcode cell fate mapping and functional stem cell assays, we uncovered shared tumor cell hierarchies in RMS and human muscle development. We also identified common developmental stages at which tumor cells become arrested. Fusion-negative RMS cells resemble early myogenic cells found in embryonic and fetal development, while fusion-positive RMS cells express a highly specific gene program found in muscle cells transiting from embryonic to fetal development at 7-7.75 weeks of age. Fusion-positive RMS cells also have neural pathway-enriched states, suggesting less-rigid adherence to muscle-lineage hierarchies. Finally, we identified a molecularly defined tumor-propagating subpopulation in fusion-negative RMS that shares remarkable similarity to bi-potent, muscle mesenchyme progenitors that can make both muscle and osteogenic cells.

Optimization of the cut-offs in acetylcholine receptor antibodies and diagnostic performance in myasthenia gravis patients.

OBJECTIVE: This study aims to establish an optimization procedure to define the cut-offs of quantitative assays for acetylcholine receptor antibody (AChRAb), evaluate their diagnostic performance in myasthenia gravis (MG), and explore the association with clinical features. METHODS: Samples from a representative cohort of 77 MG patients, 80 healthy controls (HC) and 80 other autoimmune diseases (OAD) patients were tested using competitive inhibition ELISA and RIA. Raw values (OD and cpm) and processed values (inhibition rate, binding rate and concentration) were used to define the cut-offs with statistical methods, a rough method, and receiver operating characteristic (ROC) curve. Optimal cut-offs were selected by comparing false positive rates in HC and OAD individuals. The diagnostic performance was evaluated in whole MG cohort and subgroups. Agreement between ELISA and RIA for AChRAb positivity were examined with Kappa test and McNemar test. Clinical association with AChRAb was explored by comparison among subgroups and with Spearman rank correlation. RESULTS: The optimal cut-offs for AChRAb positivity were determined as OD ≤ 1.79 for ELISA and cpm ≥ 1234.12 for RIA, which derived from statistical method and performed better than those derived from ROC curves. The sensitivity and specificity were 74.03%, 100% for ELISA, and 74.03%, 99.37% for RIA. There was good agreement between ELISA and RIA for AChRAb positivity in whole cohort and subgroups (weighted к ≥ 0.71, p < 0.01; McNemar test, p > 0.05). Levels of AChRAb were different in MG subgroups (p < 0.01). Correlation between Quantitative Myasthenia Gravis scores and AChRAb levels was moderate for ELISA and RIA (rs = -0.60 and 0.57, p < 0.01). CONCLUSION: The raw testing values of ELISA and RIA were found as optimal quantitative measures of AChRAb levels. There are good agreements on diagnostic performance between two assays. Quantitative values are more informative than positivity in association with clinical features.