Use of reconstruction plates prebent on three-dimensional models to reduce the complications of mandibular reconstruction.

Background/purpose: Though the gold standard method for mandible reconstruction of the defect from segmental mandibulectomy is by osseous flap or graft, using reconstruction plates is still indicated in some cases. Traditionally, the plate is bended immediately after the segmental mandibulectomy by freehand. However, it's difficult to fit well to the original position of mandible, which may result in more complications. This study therefore aimed to investigate whether using prebent plates on computer-aided 3D printing models could reduce the complication rate. Materials and methods: Patients who received mandible reconstruction by reconstruction plate from 2018 to 2022 were enrolled and evaluated in this study. The data, including demographics, indications for surgery, pre-existed preoperative and postoperative therapies, classification of defects, and postoperative outcomes were collected and analyzed. Results: A total of 52 patients were enrolled in our study. The prebent group exhibited a significantly lower complication rate than that of the immediately bent group (P = 0.012). Other risk factors of plate complications included postoperative adjuvant radiotherapy (P = 0.017) and previous surgery (P = 0.047). The complication-free survival rate was also better in the prebent group in a 3-year follow-up period (P = 0.012). Conclusion: Prebent plates on computer-aided printing models proved to be an effective approach to reduce the complications for mandibular reconstruction in segmental mandibulectomy.

Coarse-to-fine interaction on perceived depth in compound grating.

To encode binocular disparity, the visual system uses a pair of left eye and right eye bandpass filters with either a position or a phase offset between them. Such pairs are considered to exit at multiple scales to encode a wide range of disparity. However, local disparity measurements by bandpass mechanisms can be ambiguous, particularly when the actual disparity is larger than a half-cycle of the preferred spatial frequency of the filter, which often occurs in fine scales. In this study, we investigated whether the visual system uses a coarse-to-fine interaction to resolve this ambiguity at finer scales for depth estimation from disparity. The stimuli were stereo grating patches composed of a target and comparison patterns. The target patterns contained spatial frequencies of 1 and 4 cycles per degree (cpd). The phase disparity of the low-frequency component was 0° (at the horopter), -90° (uncrossed), or 90° (crossed), and that of the high-frequency components was changed independent of the low-frequency disparity, in the range between -90° (uncrossed) and 90° (crossed). The observers' task was to indicate whether the target appeared closer to the comparison pattern, which always shared the disparity with the low-frequency component of the target. Regardless of whether the comparison pattern was a 1-cpd + 4-cpd compound or a 1-cpd simple grating, the perceived depth order of the target and the comparison varied in accordance with the phase disparity of the high-frequency component of the target. This effect occurred not only when the low-frequency component was at the horopter, but also when it contained a large disparity corresponding to one cycle of the high-frequency component (±90°). Our findings suggest a coarse-to-fine interaction in multiscale disparity processing, in which the depth interpretation of the high-frequency changes based on the disparity of the low-frequency component.

Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.

Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793-0.852 and accuracies of 0.786-0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.

EEG-Based Motor Imagery Recognition Framework via Multisubject Dynamic Transfer and Iterative Self-Training.

A robust decoding model that can efficiently deal with the subject and period variation is urgently needed to apply the brain-computer interface (BCI) system. The performance of most electroencephalogram (EEG) decoding models depends on the characteristics of specific subjects and periods, which require calibration and training with annotated data prior to application. However, this situation will become unacceptable as it would be difficult for subjects to collect data for an extended period, especially in the rehabilitation process of disability based on motor imagery (MI). To address this issue, we propose an unsupervised domain adaptation framework called iterative self-training multisubject domain adaptation (ISMDA) that focuses on the offline MI task. First, the feature extractor is purposefully designed to map the EEG to a latent space of discriminative representations. Second, the attention module based on dynamic transfer matches the source domain and target domain samples with a higher coincidence degree in latent space. Then, an independent classifier oriented to the target domain is employed in the first stage of the iterative training process to cluster the samples of the target domain through similarity. Finally, a pseudolabel algorithm based on certainty and confidence is employed in the second stage of the iterative training process to adequately calibrate the error between prediction and empirical probabilities. To evaluate the effectiveness of the model, extensive testing has been performed on three publicly available MI datasets, the BCI IV IIa, the High gamma dataset, and Kwon et al. datasets. The proposed method achieved 69.51%, 82.38%, and 90.98% cross-subject classification accuracy on the three datasets, which outperforms the current state-of-the-art offline algorithms. Meanwhile, all results demonstrated that the proposed method could address the main challenges of the offline MI paradigm.

A Hardware-Friendlyand High-Efficiency H.265/HEVC Encoder for Visual Sensor Networks.

Visual sensor networks (VSNs) have numerous applications in fields such as wildlife observation, object recognition, and smart homes. However, visual sensors generate vastly more data than scalar sensors. Storing and transmitting these data is challenging. High-efficiency video coding (HEVC/H.265) is a widely used video compression standard. Compare to H.264/AVC, HEVC reduces approximately 50% of the bit rate at the same video quality, which can compress the visual data with a high compression ratio but results in high computational complexity. In this study, we propose a hardware-friendly and high-efficiency H.265/HEVC accelerating algorithm to overcome this complexity for visual sensor networks. The proposed method leverages texture direction and complexity to skip redundant processing in CU partition and accelerate intra prediction for intra-frame encoding. Experimental results revealed that the proposed method could reduce encoding time by 45.33% and increase the Bjontegaard delta bit rate (BDBR) by only 1.07% as compared to HM16.22 under all-intra configuration. Moreover, the proposed method reduced the encoding time for six visual sensor video sequences by 53.72%. These results confirm that the proposed method achieves high efficiency and a favorable balance between the BDBR and encoding time reduction.

Pre-operative MRI features predict early post-operative recurrence of hepatocellular carcinoma with different degrees of pathological differentiation.

PURPOSE: To investigate the value of pre-operative gadoxetate disodium (Gd-EOB-DTPA) enhanced MRI predicting early post-operative recurrence (< 2 years) of hepatocellular carcinoma (HCC) with different degrees of pathological differentiation. METHODS: Retrospective analysis of pre-operative MR imaging features of 177 patients diagnosed as suffering from HCC and that underwent radical resection. Multivariate logistic regression assessment was adopted to assess predictors for HCC recurrence with different degrees of pathological differentiation. The area under the curve (AUC) of receiver operating characteristics (ROC) was utilized to assess the diagnostic efficacy of the predictors. RESULTS: Among the 177 patients, 155 (87.5%) were males, 22 (12.5%) were females; the mean age was 49.97 ± 10.71 years. Among the predictors of early post-operative recurrence of highly-differentiated HCC were an unsmooth tumor margin and an incomplete/without tumor capsule (p = 0.037 and 0.033, respectively) whereas those of early post-operative recurrence of moderately-differentiated HCC were incomplete/without tumor capsule, peritumoral enhancement along with peritumoral hypointensity (p = 0.006, 0.046 and 0.004, respectively). The predictors of early post-operative recurrence of poorly-differentiated HCC were peritumoral enhancement, peritumoral hypointensity, and tumor thrombosis (p = 0.033, 0.006 and 0.021, respectively). The AUCs of the multi-predictor diagnosis of early post-operative recurrence of highly-, moderately-, and poorly-differentiated HCC were 0.841, 0.873, and 0.875, respectively. The AUCs of the multi-predictor diagnosis were each higher than for those predicted separately. CONCLUSIONS: The imaging parameters for predicting early post-operative recurrence of HCC with different degrees of pathological differentiation were different and combining these predictors can improve the diagnostic efficacy of early post-operative HCC recurrence.

Is OLP potentially malignant? A clue from ZNF582 methylation.

OBJECTIVE: Whether oral lichen planus (OLP) was potentially malignant remains controversial. Here, we examined associations of ZNF582 methylation (ZNF582m ) with OLP lesions, dysplastic features and squamous cell carcinoma (OSCC). MATERIALS AND METHODS: This is a case-control study. ZNF582m was evaluated in both lesion and adjacent normal sites of 42 dysplasia, 90 OSCC and 43 OLP patients, whereas ZNF582m was evaluated only in one mucosal site of 45 normal controls. High-risk habits affecting ZNF582m such as betel nut chewing and cigarette smoking were also compared in those groups. RESULTS: OLP lesions showed significantly lower ZNF582m than those of dysplasia and OSCC. At adjacent normal mucosa, ZNF582m increased from patients of OLP, dysplasia, to OSCC. In addition, ZNF582m at adjacent normal sites in OLP patients was comparable to normal mucosa in control group. Dysplasia/OSCC patients with high-risk habits exhibited significantly higher ZNF582m than those without high-risk habits. However, ZNF582m in OLP patients was not affected by those high-risk habits. CONCLUSIONS: OLP is unlikely to be potentially malignant based on ZNF582m levels. ZNF582m may also be a potential biomarker for distinguishing OLP from true dysplastic features and OSCC, and for monitoring the malignant transformation of OLP, potentially malignant disorders with dysplastic features and OSCC.

Transfer Learning With Optimal Transportation and Frequency Mixup for EEG-Based Motor Imagery Recognition.

Electroencephalography-based Brain Computer Interfaces (BCIs) invariably have a degenerate performance due to the considerable individual variability. To address this problem, we develop a novel domain adaptation method with optimal transport and frequency mixup for cross-subject transfer learning in motor imagery BCIs. Specifically, the preprocessed EEG signals from source and target domain are mapped into latent space with an embedding module, where the representation distributions and label distributions across domains have a large discrepancy. We assume that there exists a non-linear coupling matrix between both domains, which can be utilized to estimate the distance of joint distributions for different domains. Depending on the optimal transport, the Wasserstein distance between source and target domains is minimized, yielding the alignment of joint distributions. Moreover, a new mixup strategy is also introduced to generalize the model, where the inputs trials are mixed in frequency domain rather than in raw space. The extensive experiments on three evaluation benchmarks are conducted to validate the proposed framework. All the results demonstrate that our method achieves a superior performance than previous state-of-the-art domain adaptation approaches.

Improving the Diagnostic Performance by Adding Methylation Marker to Conventional Visual Examination in Identifying Oral Cancer.

BACKGROUND: Visual oral examination (VOE) is a conventional oral cancer screening method. This study aimed to evaluate the value of methylation marker to assist VOE in identifying oral epithelial dysplasia and oral squamous cell carcinoma (OED/OSCC) from non-cancerous lesions in a real-world situation. METHODS: 201 patients with high-risk personal habits who self-perceived oral anomaly were VOE examined, ZNF582 methylation (ZNF582m) tested, and histologically diagnosed. RESULTS: Among them, 132 patients (65.7%) were histologically diagnosed OED/OSCC. Using VOE, 56.1% OED/OSCC patients had possible oral cancer, whereas 37.7% non-OED/OSCC patients had leukoplakia. ZNF582m-positive was detected in 90.2% OED/OSCC patients and 44.9% non-OED/OSCC patients. Various logistic regression models were postulated to evaluate the diagnostic performance of conventional VOE and new strategies using ZNF582m. ROC analysis and its corresponding C-index demonstrated that either triage or co-testing models of VOE and ZNF582m could improve diagnostic performance and discriminative abilities compared with the VOE only approach. CONCLUSIONS: In conclusion, methylation marker test shows equivalent performance to an experienced judgment by oral maxillofacial surgeons and plays a significantly supplementary role in increasing the efficacy in identifying oral malignant lesions. ZNF582m may be an especially important tool for family physicians or general dentists to properly diagnose suspicious oral lesions.

Butylidenephthalide Abrogates the Snail-Induced Cancer Stemness in Oral Carcinomas.

Oral cancer is one of the most common cancers worldwide, especially in South Central Asia. It has been suggested that cancer stem cells (CSC) play crucial roles in tumor relapse and metastasis, and approaches to target CSC may lead to promising results. Here, aldehyde dehydrogenase 1 (ALDH1) and CD44 were utilized to isolate CSCs of oral cancer. Butylidenephthalide, a bioactive phthalide compound from Angelica sinensis, was tested for its anti-CSC effects. MTT assay showed that a lower concentration of butylidenephthalide was sufficient to inhibit the proliferation of patient-derived ALDH1+/CD44+ cells without affecting normal cells. Administration of butylidenephthalide not only reduced ALDH1 activity and CD44 expression, it also suppressed the migration, invasion, and colony formation abilities of ALDH1+/CD44+ cells using a transwell system and clonogenic assay. A patient-derived xenograft mouse model supported our in vitro findings that butylidenephthalide possessed the capacity to retard tumor development. We found that butylidenephthalide dose-dependently downregulated the gene and protein expression of Sox2 and Snail. Our results demonstrated that overexpression of Snail in ALDH1-/CD44- (non-CSCs) cells induced the CSC phenotypes, whereas butylidenephthalide treatment successfully diminished the enhanced self-renewal and propagating properties. In summary, this study showed that butylidenephthalide may serve as an adjunctive for oral cancer therapy.

A gradient-based automatic optimization CNN framework for EEG state recognition.

Objective. The electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variety of analysis methods, deep learning, especially convolutional neural network (CNN), has achieved remarkable results in recent years as a method to effectively extract features from EEG signals. Although deep learning has the advantages of automatic feature extraction and effective classification, it also faces difficulties in network structure design and requires an army of prior knowledge. Automating the design of these hyperparameters can therefore save experts' time and manpower. Neural architecture search techniques have thus emerged.Approach. In this paper, based on an existing gradient-based neural architecture search (NAS) algorithm, partially-connected differentiable architecture search (PC-DARTS), with targeted improvements and optimizations for the characteristics of EEG signals. Specifically, we establish the model architecture step by step based on the manually designed deep learning models for EEG discrimination by retaining the framework of the search algorithm and performing targeted optimization of the model search space. Corresponding features are extracted separately according to the frequency domain, time domain characteristics of the EEG signal and the spatial position of the EEG electrode. The architecture was applied to EEG-based emotion recognition and driver drowsiness assessment tasks.Main results. The results illustrate that compared with the existing methods, the model architecture obtained in this paper can achieve competitive overall accuracy and better standard deviation in both tasks.Significance. Therefore, this approach is an effective migration of NAS technology into the field of EEG analysis and has great potential to provide high-performance results for other types of classification and prediction tasks. This can effectively reduce the time cost for researchers and facilitate the application of CNN in more areas.

Honokiol inhibits arecoline-induced oral fibrogenesis through transforming growth factor-ß/Smad2/3 signaling inhibition.

BACKGROUND/PURPOSE: The habit of areca nut chewing has been regarded as an etiological factor of precancerous oral submucous fibrosis (OSF). In the present study, we aimed to evaluate the anti-fibrosis effect of honokiol, a polyphenolic component derived from Magnolia officinalis. METHODS: The cytotoxicity of honokiol was tested using normal and fibrotic buccal mucosal fibroblasts (fBMFs) derived from OSF tissues. Collagen gel contraction, Transwell migration, invasion, and wound healing capacities were examined. Besides, the expression of TGF-ß/Smad2 signaling as well as α-SMA and type I collagen were measured as well. RESULTS: Honokiol exerted higher cytotoxicity of fBMFs compared to normal cells. The arecoline-induced myofibroblast activities, including collagen gel contractility, cell motility and wound healing capacities were all suppressed by honokiol treatment. In addition, the expression of the TGF-ß/Smad2 pathway was downregulated along with a lower expression of α-SMA and type I collagen in honokiol-receiving cells. CONCLUSION: Our data suggest that honokiol may be a promising compound to alleviate the progression of oral fibrogenesis and prevent the transformation of OSF oral epithelium into cancer.

A gain-control disparity energy model for perceived depth from disparity.

Luminance contrast is one of the key factors in the visibility of objects in the world around us. Previous work has shown that the perceived depth from binocular disparity depends profoundly on the luminance contrast of the image. This dependence cannot be explained by existing disparity models, such as the well-established disparity energy model, because they predict no effect of luminance contrast on depth perception. Here, we develop a model for disparity processing that incorporates contrast normalization of the neural response into the disparity energy model to account for the contrast dependence of perceived depth from disparity. Our model contains an array of disparity channels, each with a different disparity selectivity. The binocular images are first processed by the left- and right-eye receptive fields of each channel. The outputs of the two receptive fields are combined linearly as the excitatory disparity sensitivity and then fed into a nonlinear contrast gain control mechanism. The perceived depth is determined by the weighted average of all the disparity channels that respond to the binocular images. This model provides the first analytic account of how luminance contrast affects perceived depth from disparity.

Dynamic Joint Domain Adaptation Network for Motor Imagery Classification.

Electroencephalogram (EEG) has been widely used in brain computer interface (BCI) due to its convenience and reliability. The EEG-based BCI applications are majorly limited by the time-consuming calibration procedure for discriminative feature representation and classification. Existing EEG classification methods either heavily depend on the handcrafted features or require adequate annotated samples at each session for calibration. To address these issues, we propose a novel dynamic joint domain adaptation network based on adversarial learning strategy to learn domain-invariant feature representation, and thus improve EEG classification performance in the target domain by leveraging useful information from the source session. Specifically, we explore the global discriminator to align the marginal distribution across domains, and the local discriminator to reduce the conditional distribution discrepancy between sub-domains via conditioning on deep representation as well as the predicted labels from the classifier. In addition, we further investigate a dynamic adversarial factor to adaptively estimate the relative importance of alignment between the marginal and conditional distributions. To evaluate the efficacy of our method, extensive experiments are conducted on two public EEG datasets, namely, Datasets IIa and IIb of BCI Competition IV. The experimental results demonstrate that the proposed method achieves superior performance compared with the state-of-the-art methods.

LncRNA MEG3 inhibits self-renewal and invasion abilities of oral cancer stem cells by sponging miR-421.

BACKGROUND/PURPOSE: Oral cancer stem cells (CSCs) have been considered as the key cells that are implicated in tumor recurrence and metastasis. In recent years, great attention has been paid to the significance of various non-coding RNAs due to their regulatory roles in oral CSCs. Although the function of long non-coding RNA MEG3 in various cancers has been investigated, its effects on the features of oral CSCs remained to be determined. METHODS: The expression levels of MEG3 in tongue squamous cell carcinomas and prognostic effect have been evaluated. We assessed the expression of MEG3 in sphere cells (oral CSCs) using qRT-PCR. Secondary sphere formation and invasion assays were conducted to evaluate the self-renewal and metastatic abilities, respectively. Bioinformatics software and luciferase reporter assay were used to predict and verify the relationship between MEG3 and miR-421. RESULTS: MEG3 was downregulated in the tissues of oral cancer and associated with a poor prognosis. In oral CSCs, the expression of MEG3 was repressed and overexpression of MEG3 resulted in suppression of self-renewal and invasion abilities. Luciferase reporter assay showed that miR-421 directly interacted with MEG3, and our subsequent experiment demonstrated that elevation of miR-421 reversed the MEG3-inhibited characteristics of oral CSCs. CONCLUSION: Our findings suggest that MEG3 can serve as a tumor suppressor in oral CSCs by impeding the action of miR-421. Moreover, targeting MEG3-miR-421 axis has the potential to mitigate the tumor recurrence and metastasis.

Oral microbial dysbiosis and its performance in predicting oral cancer.

Dysbiosis of oral microbiome may dictate the progression of oral squamous cell carcinoma (OSCC). Yet, the composition of oral microbiome fluctuates by saliva and distinct sites of oral cavity and is affected by risky behaviors (smoking, drinking and betel quid chewing) and individuals' oral health condition. To characterize the disturbances in the oral microbial population mainly due to oral tumorigenicity, we profiled the bacteria within the surface of OSCC lesion and its contralateral normal tissue from discovery (n = 74) and validation (n = 42) cohorts of male patients with cancers of the buccal mucosa. Significant alterations in the bacterial diversity and relative abundance of specific oral microbiota (most profoundly, an enrichment for genus Fusobacterium and the loss of genus Streptococcus in the tumor sites) were identified. Functional prediction of oral microbiome shown that microbial genes related to the metabolism of terpenoids and polyketides were differentially enriched between the control and tumor groups, indicating a functional role of oral microbiome in formulating a tumor microenvironment via attenuated biosynthesis of secondary metabolites with anti-cancer effects. Furthermore, the vast majority of microbial signatures detected in the discovery cohort was generalized well to the independent validation cohort, and the clinical validity of these OSCC-associated microbes was observed and successfully replicated. Overall, our analyses reveal signatures (a profusion of Fusobacterium nucleatum CTI-2 and a decrease in Streptococcus pneumoniae) and functions (decreased production of tumor-suppressive metabolites) of oral microbiota related to oral cancer.

Antimetastatic Effects of Sesamin on Human Head and Neck Squamous Cell Carcinoma through Regulation of Matrix Metalloproteinase-2.

BACKGROUND: Sesamin is a lignin present in sesame oil from the bark of Zanthoxylum spp. Sesamin reportedly has anticarcinogenic potential and exerts anti-inflammatory effects on several tumors. Hypothesis/Purpose: However, the effect of sesamin on metastatic progression in human head and neck squamous carcinoma (HNSCC) remains unknown in vitro and in vivo; hence, we investigated the effect of sesamin on HNSCC cells in vitro. METHODS AND RESULTS: Sesamin-treated human oral cancer cell lines FaDu, HSC-3, and Ca9-22 were subjected to a wound-healing assay. Furthermore, Western blotting was performed to assess the effect of sesamin on the expression levels of matrix metalloproteinase (MMP)-2 and proteins of the MAPK signaling pathway, including p-ERK1/2, P-p38, and p-JNK1/2. In addition, we investigated the association between MMP-2 expression and the MAPK pathway in sesamin-treated oral cancer cells. Sesamin inhibited cell migration and invasion in FaDu, Ca9-22, and HSC-3 cells and suppressed MMP-2 at noncytotoxic concentrations (0 to 40 µM). Furthermore, sesamin significantly reduced p38 MAPK and JNK phosphorylation in a dose-dependent manner in FaDu and HSC-3 cells. CONCLUSIONS: These results indicate that sesamin suppresses the migration and invasion of HNSCC cells by regulating MMP-2 and is thus a potential antimetastatic agent for treating HNSCC.