Identification of a fundamental cryoinjury mechanism in MSCs and its mitigation through cell-cycle synchronization prior to freezing.

Clinical development of cellular therapies, including mesenchymal stem/stromal cell (MSC) treatments, has been hindered by ineffective cryopreservation methods that result in substantial loss of post-thaw cell viability and function. Proposed solutions to generate high potency MSC for clinical testing include priming cells with potent cytokines such as interferon gamma (IFNγ) prior to cryopreservation, which has been shown to enhance post-thaw function, or briefly culturing to allow recovery from cryopreservation injury prior to administering to patients. However, both solutions have disadvantages: cryorecovery increases the complexity of manufacturing and distribution logistics, while the pleiotropic effects of IFNγ may have uncharacterized and unintended consequences on MSC function. To determine specific cellular functions impacted by cryoinjury, we first evaluated cell cycle status. It was discovered that S phase MSC are exquisitely sensitive to cryoinjury, demonstrating heightened levels of delayed apoptosis post-thaw and reduced immunomodulatory function. Blocking cell cycle progression at G0/G1 by growth factor deprivation (commonly known as serum starvation) greatly reduced post-thaw dysfunction of MSC by preventing apoptosis induced by double-stranded breaks in labile replicating DNA that form during the cryopreservation and thawing processes. Viability, clonal growth and T cell suppression function were preserved at pre-cryopreservation levels and were no different than cells prior to freezing or frozen after priming with IFNγ. Thus, we have developed a robust and effective strategy to enhance post-thaw recovery of therapeutic MSC.

Activation of the Hedgehog signaling pathway leads to fibrosis in aortic valves.

BACKGROUND: Fibrosis is a pathological wound healing process characterized by excessive extracellular matrix deposition, which interferes with normal organ function and contributes to ~ 45% of human mortality. Fibrosis develops in response to chronic injury in nearly all organs, but the a cascade of events leading to fibrosis remains unclear. While hedgehog (Hh) signaling activation has been associated with fibrosis in the lung, kidney, and skin, it is unknown whether hedgehog signaling activation is the cause or the consequence of fibrosis. We hypothesize that activation of hedgehog signaling is sufficient to drive fibrosis in mouse models. RESULTS: In this study, we provide direct evidence to show that activation of Hh signaling via expression of activated smoothened, SmoM2, is sufficient to induce fibrosis in the vasculature and aortic valves. We showed that activated SmoM2 -induced fibrosis is associated with abnormal function of aortic valves and heart. The relevance of this mouse model to human health is reflected in our findings that elevated GLI expression is detected in 6 out of 11 aortic valves from patients with fibrotic aortic valves. CONCLUSIONS: Our data show that activating hedgehog signaling is sufficient to drive fibrosis in mice, and this mouse model is relevant to human aortic valve stenosis.

A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma.

BACKGROUND: Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management. PURPOSE: This study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy. METHODS: In this dual-centre study, we retrospectively enrolled 384 HCC patients with complete clinical, pathological and radiological data. Aiming to synthesize radiological information derived from both tumour parenchyma and peritumoral microenvironment regions, a modelling strategy based on a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) was proposed to predict histopathological grading using preoperative contrast enhanced computed tomography (CT) images. Multi-scale inputs were defined as three-scale enlargement of an original minimum bounding box in width and height by given pixels, which correspondingly contained more peritumoral analysis areas with the enlargement. Multi-region inputs were defined as three regions of interest (ROIs) including a squared ROI, a precisely delineated tumour ROI, and a peritumoral tissue ROI. The DenseCNN structure was designed to consist of a shallow feature extraction layer, dense block module, and transition and attention module. The proposed MSMR-DenseCNN was pretrained by the ImageNet dataset to capture basic graphic characteristics from the images and was retrained by the collected retrospective CT images. The predictive ability of the MSMR-DenseCNN models on triphasic images was compared with a conventional radiomics model, radiological model and clinical model. RESULTS: MSMR-DenseCNN applied to the delayed phase (DP) achieved the highest area under the curve (AUC) of 0.867 in the validation cohort for grading prediction, outperforming those on the arterial phase (AP) and portal venous phase (PVP). Fusion of the results on triphasic images did not increase the predictive ability, which underscored the role of DP for grading prediction. Compared with a single-scale and single-region network, the DP-phase based MSMR-DenseCNN model remarkably raised sensitivity from 67.4% to 75.5% with comparable specificity of 78.6%. MSMR-DenseCNN on DP defeated conventional radiomics, radiological and clinical models, where the AUCs were correspondingly 0.765, 0.695 and 0.612 in the validation cohort. CONCLUSIONS: The MSMR-DenseCNN modelling strategy increased the accuracy for preoperative prediction of grading in HCC, and enlightens similar radiological analysis pipelines in a variety of clinical scenarios in HCC management.

Characterization and Function of Cryopreserved Bone Marrow from Deceased Organ Donors: A Potential Viable Alternative Graft Source.

Despite the readily available graft sources for allogeneic hematopoietic cell transplantation (alloHCT), a significant unmet need remains in the timely provision of suitable unrelated donor grafts. This shortage is related to the rarity of certain HLA alleles in the donor pool, nonclearance of donors owing to infectious disease or general health status, and prolonged graft procurement and processing times. An alternative hematopoietic progenitor cell (HPC) graft source obtained from the vertebral bodies (VBs) of deceased organ donors could alleviate many of the obstacles associated with using grafts from healthy living donors or umbilical cord blood (UCB). Deceased organ donor-derived bone marrow (BM) can be preemptively screened, cryogenically banked for on-demand use, and made available in adequate cell doses for HCT. We have developed a good manufacturing practice (GMP)-compliant process to recover and cryogenically bank VB-derived HPCs from deceased organ donor (OD) BM. Here we present results from an analysis of HPCs from BM obtained from 250 deceased donors to identify any substantial difference in composition or quality compared with HPCs from BM aspirated from the iliac crests of healthy living donors. BM from deceased donor VBs was processed in a central GMP facility and packaged for cryopreservation in 5% DMSO/2.5% human serum albumin. BM aspirated from living donor iliac crests was obtained and used for comparison. A portion of each specimen was analyzed before and after cryopreservation by flow cytometry and colony-forming unit potential. Bone marrow chimerism potential was assessed in irradiated immunocompromised NSG mice. Analysis of variance with Bonferroni correction for multiple comparisons was used to determine how cryopreservation affects BM cells and to evaluate indicators of successful engraftment of BM cells into irradiated murine models. The t test (with 95% confidence intervals [CIs]) was used to compare cells from deceased donors and living donors. A final dataset of complete clinical and matched laboratory data from 226 cryopreserved samples was used in linear regressions to predict outcomes of BM HPC processing. When compared before and after cryopreservation, OD-derived BM HPCs were found to be stable, with CD34+ cells maintaining high viability and function after thawing. The yield from a single donor is sufficient for transplantation of an average of 1.6 patients (range, 1.2 to 7.5). CD34+ cells from OD-derived HPCs from BM productively engrafted sublethally irradiated immunocompromised mouse BM (>44% and >67% chimerism at 8 and 16 weeks, respectively). Flow cytometry and secondary transplantation confirmed that OD HPCs from BM is composed of long-term engrafting CD34+CD38-CD45RA-CD90+CD49f+ HSCs. Linear regression identified no meaningful predictive associations between selected donor-related characteristics and OD BM HPC quality or yield. Collectively, these data demonstrate that cryopreserved BM HPCs from deceased organ donors is potent and functionally equivalent to living donor BM HPCs and is a viable on-demand graft source for clinical HCT. Prospective clinical trials will soon commence in collaboration with the Center for International Blood and Marrow Research to assess the feasibility, safety, and efficacy of Ossium HPCs from BM (ClinicalTrials.gov identifier NCT05068401).

Prognostic Impact of Serum CRP Level in Head and Neck Squamous Cell Carcinoma.

Objective: This study evaluated the association of pretreatment serum C-reactive protein (CRP) level with prognosis in patients with head and neck squamous cell carcinoma (HNSCC). Methods: Within a single-center retrospective study, HNSCC patients receiving treatment between 2014 and 2016 were analyzed regarding the prognostic value of CRP serum levels. X-Tile software was used to determine the optimal cutoff value of serum CRP level. The log-rank test and Kaplan-Meier method were used to assess the effects of CRP level on prognosis in patients with HNSCC. Univariate and multivariate analyses (enter method) using a Cox proportional hazards model were utilized to identify prognostic indicators of progression-free survival (PFS) as the primary outcome and overall survival (OS) as the secondary outcome. Results: A total of 221 patients with HNSCC were assessed for eligibility, and 208 cases were included in the analysis. The HNSCC patients in the low-group (CRP ≤11.3 mg/L) showed better survival than those in the high-group (CRP > 11.3 mg/L). The univariate and multivariate analyses showed that N1-3 stage and a high serum CRP level (>11.3 mg/L) were unfavorable prognostic factors for PFS and OS in patients with HNSCC. Conclusion: Serum CRP level is an independent prognostic marker for patients with HNSCC. CRP level could be regarded as a novel prognostic factor for HNSCC patients.

Head and Neck Squamous Cell Carcinoma: NT5E Could Be a Prognostic Biomarker.

Head and neck squamous cell carcinoma (HNSCC) is a type of tumour with a relatively poor prognosis. In recent years, immune checkpoint inhibitors, such as CTLA-4 and PD-1/PDL-1 inhibitors, have improved the treatment status of advanced tumours. However, the emergence of drug resistance has brought difficulties to clinical treatment, and new immune checkpoint research is imminent. The hypoxia-adenosine pathway, in which CD73 encoded by the NT5E gene is a key enzyme for adenosine production, has been identified as an immune checkpoint of great potential. Therefore, NT5E may play an important role in HNSCC. We performed a detailed bioinformatics analysis of NT5E in HNSCC, and the results showed that the overexpression of NT5E in HNSCC was associated with poor prognosis. Our further investigation of the coexpression pattern of HNSCC could provide a reference for drug resistance and immunotherapy studies.

Radioanatomical Study of the Skull Base and Septum in Chinese: Implications for Using the HBF for Endoscopic Skull Base Reconstruction.

Objective: Radioanatomy provides surgeons with different choices to prevent the failure of reconstruction caused by improper flap selection and the occurrence of CSF leakage or other severe complications. To establish a radioanatomical model, this study radioanatomically investigated the use of the Hadad-Bassagasteguy nasoseptal flap (HBF) in skull base reconstruction performed via the transethmoidal, transsphenoidal, and transclival approaches to provide preoperative guidance for the selection of approaches for skull base reconstruction and preparation of the HBF. Methods: The computed tomography images of 40 Chinese adults were selected for the radioanatomical measurement of data related to the HBF and skull base reconstruction via the transethmoidal, transsphenoidal, and transclival approaches. The results were analyzed using radioanatomy combined with SPSS-based analysis. Results: In the 40 patients, the area of the HBF exceeded that of skull base defects reconstructed via the transethmoidal approach by 10.21 ± 1.97 cm2, and the anterior margin width, posterior margin width, upper margin length, and lower margin lengths of the HBF all exceeded the corresponding values of skull base defects requiring reconstruction by at least 8.4 mm. The area of the HBF exceeded that of reconstructed skull base defects by an average of 10.72 ± 2.04 cm2. The area of the HBF exceeded that of skull base defects reconstructed via the transclival approach by 9.01 ± 2.87 cm2. The difference between the anterior margin width of the HBF and the middle width of skull base defects reconstructed via the transclival approach did not exceed 6 mm in only one case (5.4 mm). Conclusion: In Chinese adults, the HBF can cover skull base defects reconstructed via the transethmoidal, transsphenoidal, and transclival approaches, permitting its use in skull base reconstruction performed via all three approaches. Radioanatomy can be used for preoperative guidance to plan surgery via the transethmoidal, transsphenoidal, and transclival approaches.

LncRNA NCK1-AS1 Promotes Malignant Cellular Phenotypes of Laryngeal Squamous Cell Carcinoma via miR-137/NCK1 Axis.

Increasing evidence demonstrates that many long noncoding RNAs (lncRNAs) are implicated with the development of laryngeal squamous cell carcinoma (LSCC). As shown by bioinformatics analysis, lncRNA non-catalytic region of tyrosine kinase adaptor protein 1-antisense 1 (NCK1-AS1) is upregulated in tissues of head and neck squamous cell carcinoma. The study aimed to explore the role and mechanism of NCK1-AS1 in LSCC. NCK1-AS1 expression in LSCC cells was evaluated by reverse transcription qPCR. The viability, proliferation, invasion, migration, and apoptosis of LSCC cells with indicated transfection were evaluated by CCK-8 assays, Ethynyl deoxyuridine incorporation assays, Transwell assays, wound healing assays, and TUNEL assays, respectively. Subcellular fractionation assays were performed to evaluate the cellular distribution of NCK1-AS1 and NCK1. NCK1 protein level in LSCC cells with indicated transfection was quantified by western blotting. The binding relation between miR-137 and NCK1-AS1 (or NCK1) were determined using RNA immunoprecipitation assays and luciferase reporter assays. NCK1-AS1 was highly expressed in LSCC cell lines. NCK1-AS1 depletion suppressed LSCC cell viability, proliferation, invasion, and migration while enhancing cell apoptosis. NCK1, an adjacent gene of NCK1-AS1, is also highly expressed in LSCC cells and was positively regulated by NCK1-AS1. Moreover, NCK1-AS1 interact with miR-137 to upregulate NCK1 expression. NCK1 was the downstream target of miR-137 and was negatively correlated to miR-137. In addition, overexpressed NCK1 reversed the suppressive impact of NCK1-AS1 depletion on malignant behaviors of LSCC cells. NCK1-AS1 contributes to LSCC cellular behaviors by upregulating NCK1 via interaction with miR-137.

LncRNA SNHG16 promotes proliferation and migration in laryngeal squamous cell carcinoma via the miR-140-5p/NFAT5/Wnt/ß-catenin pathway axis.

BACKGROUND: Recent studies demonstrate that long noncoding RNAs (lncRNAs) are involved in the development of various cancers. Many lncRNAs were reported to abnormally express in laryngeal squamous cell carcinoma (LSCC) and play pivotal roles in its development. LncRNA small nucleolar RNA host gene 16 (SNHG16) was previously validated as an oncogene in hepatocellular carcinoma. Nevertheless, the biological role of SNHG16 in LSCC still needs more explorations. The goal of this assay is to explore the function and molecular mechanism of lncRNA SNHG16 in the development of LSCC. METHODS AND RESULTS: First, RT-qPCR demonstrated the upregulation of SNHG16 in LSCC cells and tissues. Loss-of-function assays determined the inhibitive influence of SNHG16 downregulation on cell viability, growth, and migration in LSCC. Furthermore, SNHG16 bound with miR-140-5p in LSCC. MiR-140-5p overexpression suppressed LSCC cell proliferation and migration. NFAT5 was identified as a direct target of miR-140-5p. Through rescue experiments, overexpression of NFAT5 reversed SNHG16 knockdown-mediated suppression on cell viability, growth, and migration in LSCC. Additionally, NFAT5 overexpression activated while NFAT5 downregulation inhibited the Wnt/ß-catenin signaling pathway. CONCLUSION: LncRNA SNHG16 is upregulated in LSCC and contributes to the development of LSCC via regulating the miR-140-5p/NFAT5/Wnt/ß-catenin pathway axis. The SNHG16/miR-140-5p/NFAT5/Wnt/ß-catenin pathway axis might provide a novel strategy for LSCC treatment.

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study.

BACKGROUND: Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) patients at diagnosis or during follow-up, which causes severe disease deterioration, and limits the possibility of surgical approaches. This study aimed to investigate whether computed tomography (CT)-based radiomics analysis could help predict development of MaVI in HCC. METHODS: A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups. CT-based radiomics signature was built via multi-strategy machine learning methods. Afterwards, MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model (CRIM, clinical-radiomics integrated model) via random forest modeling. Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development. Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development, progression-free survival (PFS), and overall survival (OS) based on the selected risk factors. RESULTS: The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors (P < 0.001). CRIM could predict MaVI with satisfactory areas under the curve (AUC) of 0.986 and 0.979 in the training (n = 154) and external validation (n = 72) datasets, respectively. CRIM presented with excellent generalization with AUC of 0.956, 1.000, and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory. Peel9_fos_InterquartileRange [hazard ratio (HR) = 1.98; P < 0.001] was selected as the independent risk factor. The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development (P < 0.001), PFS (P < 0.001) and OS (P = 0.002). CONCLUSIONS: The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Multi-scale patches convolutional neural network predicting the histological grade of hepatocellular carcinoma.

Preoperative predicting histological grade of hepatocellular carcinoma (HCC) is a crucial issue for the evaluation of patient prognosis and determining clinical treatment strategies. Previous studies have shown the potential of preoperative medical imaging in HCC grading diagnosis, however, there still remain challenges. In this work, we proposed a multi-scale 2D dense connected convolutional neural network (MS-DenseNet) for the classification of grade. This architecture consisted of three CNN branches to extract features of CT image patches in different scale. Then the outputs for each CNN branch were concatenated to the final fully connected layer. Our network was developed and evaluated on 455 HCC patients from two different centers. For data augmentation, more than 2000 patches for each scale were cropped from transverse section 2D region of interest on these patients. Besides, three-channel inputs including original CT image, tumor region and peritumoral component provided complementary knowledge. Experimental results demonstrated that the proposed method achieved encouraging prediction performance with AUC of 0.798 in testing dataset.Clinical Relevance-The proposed MS-DenseNet yielded an encouraging prediction performance for HCC histological grade and might assist the clinical diagnosis and decision making of HCC patients.

Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study.

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management. In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction using two imaging modalities-contrast-enhanced computed tomography (CE-CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI). A total of 750 HCCs were enrolled from five Chinese tertiary hospitals. Retrospective CE-CT (n = 306, collected between March, 2013 and July, 2019) and EOB-MRI (n = 329, collected between March, 2012 and March, 2019) data were used to train two DL models, respectively. Prospective external validation (n = 115, collected between July, 2015 and February, 2018) was performed to assess the developed models. Furthermore, DL-based attention maps were utilized to visualize high-risk MVI regions. Our findings revealed that the EOB-MRI-based DL model achieved superior prediction outcome to the CE-CT-based DL model (area under receiver operating characteristics curve (AUC): 0.812 vs. 0.736, p = 0.038; sensitivity: 70.4% vs. 57.4%, p = 0.015; specificity: 80.3% vs. 86.9%, p = 0.052). DL attention maps could visualize peritumoral high-risk areas with genuine histopathologic confirmation. Both DL models could stratify high and low-risk groups regarding progression free survival and overall survival (p < 0.05). Thus, DL can be an efficient tool for MVI prediction, and EOB-MRI was proven to be the modality with advantage for MVI assessment than CE-CT.

A critical role of AREG for bleomycin-induced skin fibrosis.

We report our discovery of an important player in the development of skin fibrosis, a hallmark of scleroderma. Scleroderma is a fibrotic disease, affecting 70,000 to 150,000 Americans. Fibrosis is a pathological wound healing process that produces an excessive extracellular matrix to interfere with normal organ function. Fibrosis contributes to nearly half of human mortality. Scleroderma has heterogeneous phenotypes, unpredictable outcomes, no validated biomarkers, and no effective treatment. Thus, strategies to slow down scleroderma progression represent an urgent medical need. While a pathological wound healing process like fibrosis leaves scars and weakens organ function, oral mucosa wound healing is a scarless process. After re-analyses of gene expression datasets from oral mucosa wound healing and skin fibrosis, we discovered that several pathways constitutively activated in skin fibrosis are transiently induced during oral mucosa wound healing process, particularly the amphiregulin (Areg) gene. Areg expression is upregulated ~ 10 folds 24hrs after oral mucosa wound but reduced to the basal level 3 days later. During bleomycin-induced skin fibrosis, a commonly used mouse model for skin fibrosis, Areg is up-regulated throughout the fibrogenesis and is associated with elevated cell proliferation in the dermis. To demonstrate the role of Areg for skin fibrosis, we used mice with Areg knockout, and found that Areg deficiency essentially prevents bleomycin-induced skin fibrosis. We further determined that bleomycin-induced cell proliferation in the dermis was not observed in the Areg null mice. Furthermore, we found that inhibiting MEK, a downstream signaling effector of Areg, by selumetinib also effectively blocked bleomycin-based skin fibrosis model. Based on these results, we concluded that the Areg-EGFR-MEK signaling axis is critical for skin fibrosis development. Blocking this signaling axis may be effective in treating scleroderma.

Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.

PURPOSE: The purpose of this study was to develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM). METHODS: In this retrospective study, we enrolled 192 patients diagnosed with CRLM who received first-line chemotherapy and were followed by response assessment. Tumor response was identified according to the Response Evaluation Criteria in Solid Tumors (RECIST). Contrast-enhanced multidetector computed tomography (MDCT) images were fed as inputs of the ResNet10-based DL radiomics model, and the possibility of response was predicted as the output. The final combined DL radiomics model was constructed by integrating the response-related clinical factors and the developed DL radiomics signature. A time-independent validation cohort (n = 48) was extracted from the 192 patients to evaluate the DL model with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity. Meanwhile, a traditional radiomics model was constructed using least absolute shrinkage and selection operator (lasso) as comparisons with the DL-based model. RESULTS: According to RECIST criteria, 131 patients were identified as responders with complete response, partial response, and stable disease, while 61 patients were nonresponders with progression disease. The selected predictive clinical factor turned out to be the carcinoembryonic antigen (CEA) level with AUC of 0.489 (95% confidence interval [CI], 0.380-0.599) and 0.558 (95% CI, 0.374-0.741) in the training and validation cohorts, respectively. The DL-based model provided better performance than the traditional classifier-based radiomics model with significantly higher AUC (training: 0.903 [95% CI, 0.851-0.955] vs 0.745 [95% CI, 0.659-0.831]; validation: 0.820 [95% CI, 0.681-0.959] vs 0.598 [95% CI, 0.422-0.774]). The combination of DL-based model with the CEA level provided slightly increased performance with AUC of 0.935 [95% CI, 0.897-0.973] in the training cohort and 0.830 [95% CI, 0.688-0.973] in the validation cohort. CONCLUSIONS: The developed DL-based radiomics model could improve the efficiency to predict the response to chemotherapy in CRLM, which may assist in subsequent personalized treatment decision-making in CRLM management.

Identification and characterization of a large source of primary mesenchymal stem cells tightly adhered to bone surfaces of human vertebral body marrow cavities.

BACKGROUND: Therapeutic allogeneic mesenchymal stromal cells (MSCs) are currently in clinical trials to evaluate their effectiveness in treating many different disease indications. Eventual commercialization for broad distribution will require further improvements in manufacturing processes to economically manufacture MSCs at scales sufficient to satisfy projected demands. A key contributor to the present high cost of goods sold for MSC manufacturing is the need to create master cell banks from multiple donors, which leads to variability in large-scale manufacturing runs. Therefore, the availability of large single donor depots of primary MSCs would greatly benefit the cell therapy market by reducing costs associated with manufacturing. METHODS: We have discovered that an abundant population of cells possessing all the hallmarks of MSCs is tightly associated with the vertebral body (VB) bone matrix and only liberated by proteolytic digestion. Here we demonstrate that these vertebral bone-adherent (vBA) MSCs possess all the International Society of Cell and Gene Therapy-defined characteristics (e.g., plastic adherence, surface marker expression and trilineage differentiation) of MSCs, and we have therefore termed them vBA-MSCs to distinguish this population from loosely associated MSCs recovered through aspiration or rinsing of the bone marrow compartment. RESULTS: Pilot banking and expansion were performed with vBA-MSCs obtained from 3 deceased donors, and it was demonstrated that bank sizes averaging 2.9 × 108 ± 1.35 × 108 vBA-MSCs at passage 1 were obtainable from only 5 g of digested VB bone fragments. Each bank of cells demonstrated robust proliferation through a total of 9 passages, without significant reduction in population doubling times. The theoretical total cell yield from the entire amount of bone fragments (approximately 300 g) from each donor with limited expansion through 4 passages is 100 trillion (1 × 1014) vBA-MSCs, equating to over 105 doses at 10 × 106 cells/kg for an average 70-kg recipient. DISCUSSION: Thus, we have established a novel and plentiful source of MSCs that will benefit the cell therapy market by overcoming manufacturing and regulatory inefficiencies due to donor-to-donor variability.

Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis.

Purpose: Developing an MRI-based radiomics model to effectively and accurately predict the predominant histopathologic growth patterns (HGPs) of colorectal liver metastases (CRLMs). Materials and Methods: In this study, 182 resected and histopathological proven CRLMs of chemotherapy-naive patients from two institutions, including 123 replacement CRLMs and 59 desmoplastic CRLMs, were retrospectively analyzed. Radiomics analysis was performed on two regions of interest (ROI), the tumor zone and the tumor-liver interface (TLI) zone. Decision tree (DT) algorithm was used for radiomics modeling on each MR sequence, and fused radiomics model was constructed by combining the radiomics signature of each sequence. The clinical and combination models were developed through multivariate logistic regression method. The performance of the developed models was assessed by receiver operating characteristic (ROC) curves with indicators of area under curve (AUC), accuracy, sensitivity, and specificity. A nomogram was constructed to evaluate the discrimination, calibration, and usefulness. Results: The fused radiomicstumor and radiomicsTLI models showed better performance than any single sequence and clinical model. In addition, the radiomicsTLI model exhibited better performance than radiomicstumor model (AUC of 0.912 vs. 0.879) in internal validation cohort. The combination model showed good discrimination, and the AUC of nomogram was 0.971, 0.909, and 0.905 in the training, internal validation, and external validation cohorts, respectively. Conclusion: MRI-based radiomics method has high potential in predicting the predominant HGPs of CRLM. Preoperative non-invasive identification of predominant HGPs could further explore the ability of HGPs as a potential biomarker for clinical treatment strategy, reflecting different biological pathways.

MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma.

BACKGROUND: Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients. PURPOSE: To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC. STUDY TYPE: Retrospective. POPULATION: An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts. FIELD STRENGTH/SEQUENCES: Contrast-enhanced T1 -weight MRI was performed with a 1.5T scanner. ASSESSMENT: A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling. STATISTICAL TESTS: The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness. RESULTS: The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively. DATA CONCLUSION: The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC. J. MAGN. RESON. IMAGING 2020;52:1679-1687.

Accurate Preoperative Distinction of Intracranial Hemangiopericytoma From Meningioma Using a Multihabitat and Multisequence-Based Radiomics Diagnostic Technique.

Background: Intracranial hemangiopericytoma (IHPC) and meningioma are both meningeal neoplasms, but they have extremely different malignancy and outcomes. Because of their similar radiological characteristics, they are difficult to distinguish prior to surgery, leading to a high rate of misdiagnosis. Methods: We enrolled 292 patients (IHPC, 155; meningiomas, 137) with complete clinic-radiological and histopathological data, from a 10-year database established at Tiantan hospital. Radiomics analysis of tumor and peritumoral edema was performed on multisequence magnetic resonance images, and a fusion radiomics signature was generated using a machine-learning strategy. By combining clinic-radiological data with the fusion radiomics signature, we developed an integrated diagnostic approach that we named the IHPC and Meningioma Diagnostic Tool (HMDT). Results: The HMDT displayed remarkable diagnostic ability, with areas under the curve (AUCs) of 0.985 and 0.917 in the training and validation cohorts, respectively. The calibration curve showed excellent agreement between the diagnosis predicted by HMDT and the histological outcome, with p-values of 0.801 and 0.622 for the training and the validation cohorts, respectively. Cross-validation showed no statistical difference across three divisions of the cohort, with average AUCs of 0.980 and 0.941 for the training and validation cohorts, respectively. Stratification analysis showed consistent performance of the HMDT in distinguishing IHPC from highly misdiagnosed subgroups of grade I meningioma and angiomatous meningioma (AM) with AUCs of 0.913 and 0.914 in the validation cohorts for the two subgroups. Conclusions: By integrating clinic-radiological information with radiomics signature, the proposed HMDT could assist in preoperative diagnosis to distinguish IHPC from meningioma, providing the basis for strategic decisions regarding surgery.