OIP5-AS1 enhances the malignant characteristics and resistance to chemotherapy of pancreatic cancer cells by targeting miR-30d-5p/MARCH8.

MARCH8, an E3 ubiquitin ligase, plays a crucial role in regulating various cellular processes such as protein degradation and signaling pathways and is implicated in the development and spread of pancreatic cancer. Analysis of pancreatic cancer tissues compared to adjacent normal tissues showed a decrease in miRNA-30d-5p levels and an increase in OIP5-AS1 and MARCH8 levels, as confirmed by qRT-PCR and Western blot analysis. The dual-luciferase reporter assay demonstrated a binding relationship between OIP5-AS1 and miRNA-30d-5p, as well as between miRNA-30d-5p and MARCH8 in PACN-1 cells, derived from a human pancreatic carcinoma specimen. Further investigations utilizing various assays revealed that OIP5-AS1 inhibited apoptosis and promoted cell proliferation, invasion, and migration in PACN-1 cells via the miRNA-30d-5p/MARCH8 axis in vitro. Tumor experiments in nude mice confirmed that OIP5-AS1 enhanced PACN-1 cell growth in vivo through the miRNA-30d-5p/MARCH8 axis. Additionally, OIP5-AS1 was found to activate downstream genes of the JAK-STAT pathway, namely IFNAR2, SOCS3, and JAK1, in PACN-1 cells. Furthermore, OIP5-AS1 increased the IC50 values for doxorubicin, gemcitabine, and cisplatin in PACN-1 cells, as determined by the Cell Counting Kit-8 assay. Overall, OIP5-AS1 was shown to promote aggressive traits and resistance to chemotherapy in PACN-1 cells through the miRNA-30d-5p/MARCH8 axis.

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.

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.

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.

Histone methylation mediated by NSD1 is required for the establishment and maintenance of neuronal identities.

Appropriate histone modifications emerge as essential cell fate regulators of neuronal identities across neocortical areas and layers. Here we showed that NSD1, the methyltransferase for di-methylated lysine 36 of histone H3 (H3K36me2), controls both area and layer identities of the neocortex. Nsd1-ablated neocortex showed an area shift of all four primary functional regions and aberrant wiring of cortico-thalamic-cortical projections. Nsd1 conditional knockout mice displayed defects in spatial memory, motor learning, and coordination, resembling patients with the Sotos syndrome carrying NSD1 mutations. On Nsd1 loss, superficial-layer pyramidal neurons (PNs) progressively mis-expressed markers for deep-layer PNs, and PNs remained immature both morphologically and electrophysiologically. Loss of Nsd1 in postmitotic PNs causes genome-wide loss of H3K36me2 and re-distribution of DNA methylation, which accounts for diminished expression of neocortical layer specifiers but ectopic expression of non-neural genes. Together, H3K36me2 mediated by NSD1 is required for the establishment and maintenance of region- and layer-specific neocortical identities.

E3 Ubiquitin Ligase MARCH8 Promotes Pancreatic Cancer Growth and Metastasis by Activating STAT3 via Degradation of PTPN4.

OBJECTIVE: The role E3 ubiquitin ligase membrane-associated RING-CH 8 (MARCH8) has not been studied in pancreatic cancer. METHOD: Pancreatic cancer cell lines and the normal pancreatic cells were tested in vitro studies and male athymic nude mice were tested in vivo studies. Measuring cell viability by Cell Counting Kit-8 assay (CCK8), 5-ethynyl-2'- deoxyuridine (Edu) staining, and colony formation assay. Wound healing assay was implemented for cell migration and Transwell assay was performed for cell invasion to evaluate the histological status by hematoxylin and eosin staining and to detect the protein ubiquitination by ubiquitination assay. The protein expression was determined by immunohistochemistry staining and western blotting, and mRNA expression was measured by quantitative reverse transcription polymerase chain reaction. RESULT: The expression of MARCH8 was increased whereas PTPN4 was decreased in pancreatic cancer cells. Overexpression of MARCH8 promoted the growth, migration, and invasion of cells, and knockdown of PTPN4 had the similar effects both in vitro and in vivo. MARCH8 promoted PTPN4 protein degradation through ubiquitination. Moreover, PTPN4 suppressed the transcription activities of STAT3 by impairing the level of pSTAT3 (705), while inhibition of PTPN4 activated phosphorylation of STAT3. CONCLUSIONS: MARCH8 promoted pancreatic cancer growth and invasion through mediating the degradation of PTPN4 and activated the phosphorylation of STAT3.

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.

Expert consensus on the clinical application of totally implantable venous access devices in the upper arm (2022 Edition).

With the widespread adoption of ultrasound guidance, Seldinger puncture techniques, and intracardiac electrical positioning technology for the placement of peripherally inserted central catheters in recent years, an increasing number of medical staff and patients now accept peripheral placement of totally implantable venous access devices (TIVADs) in the upper arm. This approach has the advantage of completely avoiding the risks of hemothorax, pneumothorax, and neck and chest scarring. Medical specialties presently engaged in this study in China include internal medicine, surgery, anesthesiology, and interventional departments. However, command over implantation techniques, treatment of complications, and proper use and maintenance of TIVAD remain uneven among different medical units. Moreover, currently, there are no established quality control standards for implantation techniques or specifications for handling complications. Thus, this expert consensus is proposed to improve the success rate of TIVAD implantation via the upper-arm approach, reduce complication rates, and ensure patient safety. This consensus elaborates on the technical indications and contraindications, procedures and technical points, treatment of complications, and the use and maintenance of upper-arm TIVAD, thus providing a practical reference for medical staff.

[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.

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.

Targeted therapy for intractable cancer on the basis of molecular profiles: An open-label, phase II basket trial (Long March Pathway).

Purpose: We evaluated he effects of molecular guided-targeted therapy for intractable cancer. Also, the epidemiology of druggable gene alterations in Chinese population was investigated. Materials and methods: The Long March Pathway (ClinicalTrials.gov identifier: NCT03239015) is a non-randomized, open-label, phase II trial consisting of several basket studies examining the molecular profiles of intractable cancers in the Chinese population. The trial aimed to 1) evaluate the efficacy of targeted therapy for intractable cancer and 2) identify the molecular epidemiology of the tier II gene alterations among Chinese pan-cancer patients. Results: In the first stage, molecular profiles of 520 intractable pan-cancer patients were identified, and 115 patients were identified to have tier II gene alterations. Then, 27 of these 115 patients received targeted therapy based on molecular profiles. The overall response rate (ORR) was 29.6% (8/27), and the disease control rate (DCR) was 44.4% (12/27). The median duration of response (DOR) was 4.80 months (95% CI, 3.33-27.2), and median progression-free survival (PFS) was 4.67 months (95% CI, 2.33-9.50). In the second stage, molecular epidemiology of 17,841 Chinese pan-cancer patients demonstrated that the frequency of tier II gene alterations across cancer types is 17.7%. Bladder cancer had the most tier-II alterations (26.1%), followed by breast cancer (22.4%), and non-small cell lung cancer (NSCLC; 20.2%). Conclusion: The Long March Pathway trial demonstrated a significant clinical benefit for intractable cancer from molecular-guided targeted therapy in the Chinese population. The frequency of tier II gene alterations across cancer types supports the feasibility of molecular-guided targeted therapy under basket trials.

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.

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.

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.

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.

Fixed template network and dynamic template network: novel network designs for decoding steady-state visual evoked potentials.

Objective. Decomposition methods are efficient to decode steady-state visual evoked potentials (SSVEPs). In recent years, the brain-computer interface community has also been developing deep learning networks for decoding SSVEPs. However, there is no clear evidence that current deep learning models outperform decomposition methods on the SSVEP decoding tasks. Many studies lacked the comparison with state-of-the-art decomposition methods in a fair environment.Approach. This study proposed a novel network design motivated by the works of decomposition methods. Fixed template network (FTN) and dynamic template network (DTN) are two novel networks combining the advantages of fixed templates and subject-specific templates. This study also proposed a data augmentation method for SSVEPs. This study compared the intra-subject classification performance of DTN and FTN with that of state-of-the-art decomposition methods on three public SSVEP datasets.Main results. The results show that both FTN and DTN achieved the suboptimal classification performance compared with state-of-the-art decomposition methods.Significance. Both network designs could enhance the decoding performance of SSVEPs, making them promising networks for improving the practicality of SSVEP-based applications.

Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.

PURPOSE: To develop a novel multimodal data fusion model by incorporating computed tomography (CT) images and clinical variables based on deep learning for predicting the invasiveness risk of stage I lung adenocarcinoma that manifests as ground-glass nodules (GGNs) and compare the diagnostic performance of it with that of radiologists. METHODS: A total of 1946 patients with solitary and histopathologically confirmed GGNs with maximum diameter less than 3 cm were retrospectively enrolled. The training dataset containing 1704 GGNs was augmented by resampling, scaling, random cropping, and so forth, to generate new training data. A multimodal data fusion model based on residual learning architecture and two multilayer perceptron with attention mechanism combining CT images with patient general data and serum tumor markers was built. The distance-based confidence scores (DCS) were calculated and compared among multimodal data models with different combinations. An observer study was conducted and the prediction performance of the fusion algorithms was compared with that of the two radiologists by an independent testing dataset with 242 GGNs. RESULTS: Among the whole GGNs, 606 GGNs are confirmed as invasive adenocarcinoma (IA) and 1340 are non-IA. The proposed novel multimodal data fusion model combining CT images, patient general data, and serum tumor markers achieved the highest accuracy (88.5%), area under a ROC curve (0.957), F1 (81.5%), F1weighted (81.9%), and Matthews correlation coefficient (73.2%) for classifying between IA and non-IA GGNs, which was even better than the senior radiologist's performance (accuracy, 86.1%). In addition, the DCSs for multimodal data suggested that CT image had a stronger influence (0.9540) quantitatively than general data (0.6726) or tumor marker (0.6971). CONCLUSION: This study demonstrated that the feasibility of integrating different types of data including CT images and clinical variables, and the multimodal data fusion model yielded higher performance for distinguishing IA from non-IA GGNs.