An epigenetic circuit controls neurogenic programs during neocortex development.

The production and expansion of intermediate progenitors (IPs) are essential for neocortical neurogenesis during development and over evolution. Here, we have characterized an epigenetic circuit that precisely controls neurogenic programs, particularly properties of IPs, during neocortical development. The circuit comprises a long non-coding RNA (LncBAR) and the BAF (SWI/SNF) chromatin-remodeling complex, which transcriptionally maintains the expression of Zbtb20. LncBAR knockout neocortex contains more deep-layer but fewer upper-layer projection neurons. Intriguingly, loss of LncBAR promotes IP production, but paradoxically prolongs the duration of the cell cycle of IPs during mid-later neocortical neurogenesis. Moreover, in LncBAR knockout mice, depletion of the neural progenitor pool at embryonic stage results in fewer adult neural progenitor cells in the subventricular zone of lateral ventricles, leading to a failure in adult neurogenesis to replenish the olfactory bulb. LncBAR binds to BRG1, the core enzymatic component of the BAF chromatin-remodeling complex. LncBAR depletion enhances association of BRG1 with the genomic locus of, and suppresses the expression of, Zbtb20, a transcription factor gene known to regulate both embryonic and adult neurogenesis. ZBTB20 overexpression in LncBAR-knockout neural precursors reverses compromised cell cycle progressions of IPs.

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules.

OBJECTIVES: The purpose of this study is to inquire about the potential association between radiomics features and the pathological nature of thyroid nodules (TNs), and to propose an interpretable radiomics-based model for predicting the risk of malignant TN. METHODS: In this retrospective study, computed tomography (CT) imaging and pathological data from 141 patients with TN were collected. The data were randomly stratified into a training group (n = 112) and a validation group (n = 29) at a ratio of 4:1. A total of 1316 radiomics features were extracted by using the pyradiomics tool. The redundant features were removed through correlation testing, and the least absolute shrinkage and selection operator (LASSO) or the minimum redundancy maximum relevance standard was used to select features. Finally, 4 different machine learning models (RF Hybrid Feature, SVM Hybrid Feature, RF, and LASSO) were constructed. The performance of the 4 models was evaluated using the receiver operating characteristic curve. The calibration curve, decision curve analysis, and SHapley Additive exPlanations method were used to evaluate or explain the best radiomics machine learning model. RESULTS: The optimal radiomics model (RF Hybrid Feature model) demonstrated a relatively high degree of discrimination with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% CI, 0.70-0.97; P < 0.001) for the validation cohort. Compared with the commonly used LASSO model (AUC, 0.78; 95% CI, 0.60-0.91; P < 0.01), there is a significant improvement in AUC in the validation set, net reclassification improvement, 0.79 (95% CI, 0.13-1.46; P < 0.05), and integrated discrimination improvement, 0. 20 (95% CI, 0.10-0.30; P < 0.001). CONCLUSION: The interpretable radiomics model based on CT performs well in predicting benign and malignant TNs by using quantitative radiomics features of the unilateral total thyroid. In addition, the data preprocessing method incorporating different layers of features has achieved excellent experimental results. CLINICAL RELEVANCE STATEMENT: As the detection rate of TNs continues to increase, so does the diagnostic burden on radiologists. This study establishes a noninvasive, interpretable and accurate machine learning model to rapidly identify the nature of TN found in CT.

Prenatal exposure of diabetes and progestin-mediated autistic biomarker in peripheral blood mononuclear cells.

Despite the importance of early diagnosis and intervention, the diagnosis of autism spectrum disorders (ASDs) remains delayed as it is mostly based on clinical symptoms and abnormal behaviours appearing after 2 years of age. Identification of autistic markers remains a top priority in achieving an early and effective ASD diagnosis. We have previously reported that prenatal exposure of hormones or diabetes triggers epigenetic changes and oxidative stress, resulting in gene suppression with autism-like behaviours in offspring. Here, a potential biomarker for ASD diagnosis was established through gene analysis in peripheral blood mononuclear cells (PBMCs). The study from in vivo mouse showed that prenatal hormone exposure or maternal diabetes suppresses mRNA expression of estrogen-related receptor α (ERRα), superoxide dismutase 2 (SOD2), G protein-coupled estrogen receptor (GPER) and retinoic acid-related orphan receptor α (RORA) in the brain as well as oxidative stress and mitochondrial dysfunction, subsequently triggering autism-like behaviour in mouse offspring. Also, similar gene suppression was found in hematopoietic stem cells (HSCs) and PBMC, with inherited epigenetic changes being identified on the related promoters. The human case-control study found that mRNA levels of ERRα, SOD2, GPER and RORA were significantly reduced in PBMC from ASD subjects (n = 132) compared with typically developing (n = 135) group. The receiver operating characteristic curve showed a .869 ± .021 of area under the curve for ASD subjects with 95% confidence interval of .829-.909, together with 1.000 of sensitivity and .856 of specificity. In conclusion, the combined mRNA expression in PBMC based on prenatal factor exposure-mediated gene suppression could be a potential biomarker for ASD diagnosis.

Transcript levels of 4 genes in umbilical cord blood are predictive of later autism development: a longitudinal follow-up study.

BACKGROUND: Over recent decades, autism spectrum disorder (ASD) has been of increasing epidemiological importance, given the substantial increase in its prevalence; at present, clinical diagnosis is possible only after 2 years of age. In this study, we sought to develop a potential predictive model for ASD screening. METHODS: We conducted a longitudinal follow-up study of newborns over 3 years. We measured transcript levels of 4 genes (superoxide dismutase-2 [SOD2], retinoic acid-related orphan receptor-α [RORA], G protein-coupled estrogen receptor-1 [GPER], progesterone receptor [PGR]), 2 oxidative stress markers and epigenetic marks at the RORA promoter in case-control umbilical cord blood mononuclear cell (UCBMC) samples. RESULTS: We followed 2623 newborns; we identified 41 children with ASD, 63 with delayed development and 2519 typically developing children. We matched the 41 children with ASD to 41 typically developing children for UCBMC measurements. Our results showed that children with ASD had significantly higher levels of H3K9me3 histone modifications at the RORA promoter and oxidative stress in UCBMC than typically developing children; children with delayed development showed no significant differences. Children with ASD had significantly lower expression of SOD2, RORA and GPER, but higher PGR expression than typically developing children. We established a model based on these 4 candidate genes, and achieved an area under the curve of 87.0% (standard deviation 3.9%) with a sensitivity of 1.000 and specificity of 0.854 to predict ASD in UCBMC. LIMITATIONS: Although the gene combinations produced a good pass/fail cut-off value for ASD evaluation, relatively few children in our study sample had ASD. CONCLUSION: The altered gene expression in UCBMC can predict later autism development, possibly providing a predictive model for ASD screening immediately after birth.

[Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials].

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.

Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface.

Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel-Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.

Baseline apparent diffusion coefficients: Validation study of new predictor of survival in patients with unresectable hepatocellular carcinoma following chemoembolization.

OBJECTIVES: To investigate whether the baseline apparent diffusion coefficient (ADC) can predict survival in the hepatocellular carcinoma (HCC) patients receiving chemoembolization. MATERIALS AND METHODS: Diffusion-weighted MR imaging of HCC patients is performed within 2 weeks before chemoembolization. The ADC of the largest index lesion is recorded. Responses are assessed by mRECIST after the start of the second course of chemoembolization. Receiver operating characteristic (ROC) curve analysis is performed to evaluate the diagnostic performance and determine optimal cut-off values. Cox regression and Kaplan-Meier survival analyses are used to explore the differences in overall survival (OS) between the responders and non-responders. RESULTS: The difference is statistically significant in the baseline ADC between the responders and non-responders (P < 0.001). ROC analyses indicate that the baseline ADC value is a good predictor of response to treatment with an area under the ROC curve (AUC) of 0.744 and the optimal cut-off value of 1.22×10-3 mm2/s. The Cox regression model shows that the baseline ADC is an independent predictor of OS, with a 57.2% reduction in risk. CONCLUSION: An optimal baseline ADC value is a functional imaging response biomarker that has higher discriminatory power to predict tumor response and prolonged survival following chemoembolization in HCC patients.

[Progress of classification algorithms for motor imagery electroencephalogram signals].

Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.

Antitumor Immunity Augmented by Combining Radiofrequency Ablation with Anti-CTLA-4 Therapy in a Subcutaneous Murine Hepatoma Model.

PURPOSE: To evaluate whether antitumor immunity is enhanced by combining radiofrequency (RF) ablation and anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) therapy and to evaluate its effect on untreated tumors. MATERIALS AND METHODS: First, 40 mice with tumors established in the bilateral flanks were randomly divided into 4 groups: the control group, the RF ablation-alone group, the anti-CTLA-4-alone group, and the RF ablation + anti-CTLA-4 group. In each group, 8 mice were used for untreated tumor evaluation and survival observation, and another 2 mice were killed for histopathologic study. Then, a rechallenge test was performed in another 32 mice to determine whether systemic antitumor immunity was established. RESULTS: Although the volume of the untreated tumors continued to increase until the end of the observation in all groups, tumor growth rates in the RF ablation + anti-CTLA-4 group were significantly smaller than tumor growth rates in the other 3 groups (all P < .05). The overall survival time of mice in the RF ablation + anti-CTLA-4 group was significantly longer than that of mice in the other 3 groups (all P < .05). Histopathologic studies of the untreated tumors showed more CD4-and CD8+ lymphocyte infiltration in mice from the RF ablation + anti-CTLA-4 group than in mice from the other 3 groups (all P < .05). After a tumor rechallenge, tumor rejection was apparent in 75% of the mice in the RF ablation + anti-CTLA-4 group, in 25% of the mice in the RF ablation group, and in 0% of the mice in the control and anti-CTLA-4 groups. CONCLUSIONS: This study demonstrated that RF ablation-induced systemic antitumor immunity was enhanced by the combined use of anti-CTLA-4 therapy in a multi-subcutaneous murine hepatoma model.

Study of enhanced radiofrequency heating by pre-freezing tissue.

In our previous animal model study, we found that radiofrequency (RF) ablation of pre-frozen tumor resulted in improved therapeutic effects. To understand the underlying mechanisms and optimize the treatment protocol, the RF heating pattern in pre-frozen tissue was studied in this paper. Both ex vivo and in vivo experiments were conducted to compare the temperature profiles of RF heating with or without pre-freezing. Results showed that the heating rate of in vivo tissues was significantly higher with pre-freezing. However, little difference was observed in the heating rate of ex vivo tissues with or without pre-freezing. In the histopathologic analysis of in vivo tissues, both a larger ablation area and a wider transitional zone were found in the tissue with pre-freezing. To investigate the cause for the enhancement in RF heating, the parameters affecting the tissue temperature rise were studied. It was found that the electrical conductivity of in vivo tissue with pre-freezing was much higher at low frequencies, but little difference was found at the 460 kHz frequency commonly used in clinical applications. A finite element model for RF heating was developed and validated to fit the thermal conductivity of in vivo tissue including effects of pre-freezing and the associated blood perfusion rate. Results showed that the enhancement of the heating rate was primarily attributed to the decreased blood perfusion rate in the tissue with vascular damage caused by pre-freezing. The ablation volume was increased by 104% due to the reduced heat dissipation.

Diffusion-Weighted MR Imaging of Hepatocellular Carcinoma: Current Value in Clinical Evaluation of Tumor Response to Locoregional Treatment.

The established size-based image biomarkers for tumor burden measurement continue to be applied to solid tumors, as size measurement can easily be used in clinical practice. However, in the setting of novel targeted therapies and liver-directed locoregional treatments for hepatocellular carcinoma (HCC), simple tumor anatomic changes can be less informative and usually appear later than biologic changes. Functional magnetic resonance (MR) imaging has the potential to be a promising technique for assessment of HCC response to therapy. Diffusion-weighted MR imaging is now widely used as a standard imaging modality to evaluate the liver. This review discusses the current clinical value of diffusion-weighted MR imaging in the evaluation of tumor response after nonsurgical locoregional treatment of HCC.

Thiophene functionalized silicon-containing aggregation-induced emission enhancement materials: applications as fluorescent probes for the detection of nitroaromatic explosives in aqueous-based solutions.

Two novel aggregation-induced emission enhancement (AIEE) molecules, namely, 3,4-diphenyl-2,5-di(2-thienyl)phenyltrimethylsilane (DPTB-TMS) and bis[3,4-diphenyl- 2,5-di(2-thienyl)phenyl]methylphenylsilane (DPTB-TMS) were designed and synthesized. The optical properties of the two silanes were completely opposite to the traditional luminescent materials. Unlike the aggregation caused quenching, they all emit faint fluorescence in the dispersed state, while emission intensity increased sharply in aggregate states. Fluorescence spectra showed that the two compounds exhibited AIEE properties and that is due to the weak π-π stacking caused by the restriction of intramolecular rotations of dye segments, particularly the -SiMe3 and thienyl groups in the aggregate state. As fluorescent (FL) probes, the fluorescence quenching behavior was further investigated. Thanks to the richer-electron thiophene groups, both compounds showed good performance in detecting nitroaromatics, especially picric acid (PA). The two AIEE FL probes exhibited better quenching efficiency in aqueous-based than in organic-based solutions. For DPTB-MPS, the addition of 80 µM nitrobenzene, 60 µM m-nitrobenzene and 40 µM PA resulted in about 50% quenching in aqueous solutions. The quenching mechanism would be electron transfer from silanes to nitroaromatics. This work provides a basis for designing organic-silanes with "abnormal" but useful optical properties and FL probes with AIEE properties for the detection of nitroaromatics.

Local Tumor Progression Predictive Model Based on MRI for Colorectal Cancer Liver Metastases after Radiofrequency Ablation.

PURPOSE: To investigate the post-radiofrequency ablation (RFA) magnetic resonance imaging (MRI) characteristics in patients with liver metastases from colorectal cancer and to build a predictive model for local tumor progression based on these imaging markers. MATERIALS AND METHODS: A cohort of 73 patients with 110 colorectal cancer liver metastases (CRCLM) who underwent RFA and MRI one month post-ablation was included in image signs analysis and predictive model training. Using a newly developed MRI appearance scoring criteria, MR Image Appearance Scoring at One Month after RFA (MRIAS 1MO), the semi-quantitative analysis of MRI findings within the ablation zone were conducted independently by two radiologists. The intraclass correlation coefficient (ICC) was calculated to evaluate measurement reliability. Differences in MRIAS 1MO scores were compared using Mann-Whitney U test, focusing on local tumor response outcomes. Using local tumor progression (LTP) as the primary end point, MRIAS 1MO scores and other lesion morphological and clinical characteristics were included to establish predictive model. Predication accuracy was subsequently evaluated using calibration curve, time-dependent concordance index (C index) curve, and LTP-free survival (LTPFS) curve. Another cohort comprising 60 patients with 76 CRCLMs provided additional MRIAS 1MO scores and clinical data associated with LTP. We evaluated the performance of the established predictive model using calibration curve, time-dependent C index curve, and LTPFS curve. RESULTS: The MRIAS 1MO criteria exhibited strong measurement reliability. The ICC values of T1S (scores from T1WI), T2S (scores form T2WI) and NCES (scores by adding T1S to T2S) MRIS (the overall scores) were 0.825, 0.779, 0.826 and 0.873, respectively. Lesions with LTP showed significantly higher median values for the overall MRIAS 1MO score (MRIS) compared to lesions without LTP (16 vs. 12, p < 0.001). MRIS and lesion diameter were independent prognostic factors of LTP and were included in predictive model (hazard ratio: MRIS over 13.5:4.275, lesion diameter larger than 30 mm: 2.056). The predictive model demonstrated an overall C index of 0.721 and risk stratification using the predictive model resulted in significantly different LPTFS times. In the validation cohort, the C index were 0.825, 0.794 and 0.764 at six, twelve and twenty-four months, respectively. Patients classified as high-risk in the validation cohort had a median LTPFS time of 10.0 months, while the median LTPFS time was not reached in the low-risk group. CONCLUSIONS: The semi-quantitative MRIAS 1MO criteria, used for post-RFA MRI appearance analysis, exhibited strong measurement reliability. Prediction models established based on overall MRIAS 1MO score (MRIS) and lesion diameter had good predictive performance for LTP in patients undergoing RFA for CRCLM treatment.

Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation.

PURPOSE: To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA). METHODS: Between August 2016 and June 2019, we retrospectively included 515 lung metastases in 233 CRC patients who received RFA (412 in the training group and 103 in the test group). Multivariable analysis was performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering and dilated with 5 mm and 10 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from intraoperative CT data. The performance of these signatures was primarily evaluated using the area under the receiver operating characteristics curve (AUC) via the DeLong test, calibration curves through the Hosmer-Lemeshow test, and decision curve analysis. RESULTS: A total of 412 out of 515 metastases (80%) achieved complete response. Four clinical variables (cancer antigen 19-9, simultaneous systemic treatment, site of lung metastases, and electrode type) were utilized to construct the clinical model. The Habitat signature was combined with the Peri-5 signature, which achieved a higher AUC than the Peri-10 signature in the test set (0.825 vs. 0.816). The Habitat+Peri-5 signature notably surpassed the clinical and intratumor radiomics signatures (AUC: 0.870 in the test set; both, p < 0.05), displaying improved calibration and clinical practicality. CONCLUSIONS: The habitat-based radiomics signature can offer precise predictions and valuable assistance to physicians in developing personalized treatment strategies.

Histone methyltransferase SETD2 is required for proper hippocampal lamination and neuronal maturation.

Proper formation of the hippocampus is crucial for the brain to execute memory and learning functions. However, many questions remain regarding how pyramidal neurons (PNs) of the hippocampus mature and precisely position. Here we revealed that Setd2, the methyltransferase for histone 3 lysine 36 trimethylation (H3K36me3), is essential for the precise localization and maturation of PNs in the hippocampal CA1. The ablation of Setd2 in neural progenitors leads to irregular lamination of the CA1 and increased numbers of PNs in the stratum oriens. Setd2 deletion in postmitotic neurons causes mislocalization and immaturity of CA1 PNs. Transcriptome analyses revealed that SETD2 maintains the expressions of clustered protocadherin (cPcdh) genes. Together, Setd2 is required for proper hippocampal lamination and maturation of CA1 PNs.

MetaBCI: An open-source platform for brain-computer interfaces.

BACKGROUND: Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain. METHOD: This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases. RESULTS: The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities. CONCLUSIONS: MetaBCI can greatly lower the BCI's technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.

Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals.

Objective.Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography signals, limiting their practical application.Approach.We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015).Main results.Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. Compared to traditional algorithms that involved a large number of training samples, the RAVE algorithm achieved similar or even better classification performance on the datasets (Pan2023, BNCI001-2014, BNCI001-2015), even when it did not use or only used a small number of within-session training samples.Significance.These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.

A CT-based radiomics approach to predict immediate response of radiofrequency ablation in colorectal cancer lung metastases.

Objectives: To objectively and accurately assess the immediate efficacy of radiofrequency ablation (RFA) on colorectal cancer (CRC) lung metastases, the novel multimodal data fusion model based on radiomics features and clinical variables was developed. Methods: This case-control single-center retrospective study included 479 lung metastases treated with RFA in 198 CRC patients. Clinical and radiological data before and intraoperative computed tomography (CT) scans were retrieved. The relative radiomics features were extracted from pre- and immediate post-RFA CT scans by maximum relevance and minimum redundancy algorithm (MRMRA). The Gaussian mixture model (GMM) was used to divide the data of the training dataset and testing dataset. In the process of modeling in the training set, radiomics model, clinical model and fusion model were built based on a random forest classifier. Finally, verification was carried out on an independent test dataset. The receiver operating characteristic curves (ROC) were drawn based on the obtained predicted scores, and the corresponding area under ROC curve (AUC), accuracy, sensitivity, and specificity were calculated and compared. Results: Among the 479 pulmonary metastases, 379 had complete response (CR) ablation and 100 had incomplete response ablation. Three hundred eighty-six lesions were selected to construct a training dataset and 93 lesions to construct a testing dataset. The multivariate logistic regression analysis revealed cancer antigen 19-9 (CA19-9, p<0.001) and the location of the metastases (p< 0.05) as independent risk factors. Significant correlations were observed between complete ablation and 9 radiomics features. The best prediction performance was achieved with the proposed multimodal data fusion model integrating radiomic features and clinical variables with the highest accuracy (82.6%), AUC value (0.921), sensitivity (80.3%), and specificity (81.4%). Conclusion: This novel multimodal data fusion model was demonstrated efficient for immediate efficacy evaluation after RFA for CRC lung metastases, which could benefit necessary complementary treatment.

Exploration of the impact of multimode thermal therapy versus radiofrequency ablation on CD8+ T effector cells of liver malignancies based on single cell transcriptomics.

Introduction: Multimode thermal therapy (MTT) is an innovative interventional therapy developed for the treatment of liver malignancies. When compared to the conventional radiofrequency ablation (RFA), MTT typically offers improved prognosis for patients. However, the effect of MTT on the peripheral immune environment and the mechanisms underlying the enhanced prognosis have yet to be explored. The aim of this study was to further investigate the mechanisms responsible for the difference in prognosis between the two therapies. Methods: In this study, peripheral blood samples were collected from four patients treated with MTT and two patients treated with RFA for liver malignancies at different time points before and after the treatment. Single cell sequencing was performed on the blood samples to compare and analyze the activation pathways of peripheral immune cells following the MTT and RFA treatment. Results: There was no significant effect of either therapy on the composition of immune cells in peripheral blood. However, the differential gene expression and pathway enrichment analysis demonstrated enhanced activation of T cells in the MTT group compared to the RFA group. In particular, there was a remarkable increase in TNF-α signaling via NF-κB, as well as the expression of IFN-α and IFN-γ in the CD8+ effector T (CD8+ Teff) cells subpopulation, when compared to the RFA group. This may be related to the upregulation of PI3KR1 expression after MTT, which promotes the activation of PI3K-AKT-mTOR pathway. Conclusion: This study confirmed that MTT could more effectively activate peripheral CD8+ Teff cells in patients compared with RFA and promote the effector function, thus resulting in a better prognosis. These results provide a theoretical basis for the clinical application of MTT therapy.

Resolving the lineage relationship between malignant cells and vascular cells in glioblastomas.

Glioblastoma multiforme (GBM), a highly malignant and heterogeneous brain tumor, contains various types of tumor and non-tumor cells. Whether GBM cells can trans-differentiate into non-neural cell types, including mural cells or endothelial cells (ECs), to support tumor growth and invasion remains controversial. Here we generated two genetic GBM models de novo in immunocompetent mouse brains, mimicking essential pathological and molecular features of human GBMs. Lineage-tracing and transplantation studies demonstrated that, although blood vessels in GBM brains underwent drastic remodeling, evidence of trans-differentiation of GBM cells into vascular cells was barely detected. Intriguingly, GBM cells could promiscuously express markers for mural cells during gliomagenesis. Furthermore, single-cell RNA sequencing showed that patterns of copy number variations (CNVs) of mural cells and ECs were distinct from those of GBM cells, indicating discrete origins of GBM cells and vascular components. Importantly, single-cell CNV analysis of human GBM specimens also suggested that GBM cells and vascular cells are likely separate lineages. Rather than expansion owing to trans-differentiation, vascular cell expanded by proliferation during tumorigenesis. Therefore, cross-lineage trans-differentiation of GBM cells is very unlikely to occur during gliomagenesis. Our findings advance understanding of cell lineage dynamics during gliomagenesis, and have implications for targeted treatment of GBMs.