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Background: Benign paroxysmal positional vertigo (BPPV) is a common neurological disorder with a high recurrence rate. Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for BPPV recurrence. However, the genomic association between T2DM and BPPV recurrence remains understudied. Methods: Differential gene expression analysis and weighted gene co-expression network analysis were used to identify shared genes between BPPV recurrence and T2DM. The MCC algorithm was employed to select hub genes from the protein-protein interaction network of the shared genes. The predictive efficacy of hub genes for BPPV recurrence and T2DM was assessed using ROC curve analysis. Genemania database was used to identify downstream targets of hub genes. The immune infiltration landscape of BPPV and T2DM was characterized using the CIBERSORT algorithm. Correlation analysis was performed to explore the relationship between hub genes and immune cells. The expression levels of hub genes in patient blood samples were validated using qPCR. Results: Thirteen shared genes were identified and a protein-protein interaction network was constructed for BPPV recurrence and T2DM. Subsequently, four hub genes were selected, and their expression levels effectively predicted the occurrence of BPPV recurrence and T2DM. These hub genes were highly correlated with immune cell infiltration, indicating a common mechanism underlying recurrent BPPV and T2DM. Finally, the upregulation of hub genes in patients with T2DM comorbid with BPPV recurrence was confirmed in blood samples. These hub genes may serve as predictive biomarkers for assessing the recurrence rate in BPPV patients with comorbid T2DM. Conclusion: We proposed shared gene characteristics between BPPV recurrence and T2DM, revealing an immune-mediated inflammatory regulation as a common pathway and identifying four immune-related biomarkers and potential therapeutic targets for T2DM comorbid with recurrent BPPV.
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OBJECTIVE: This study aims to clarify the clinical characteristics of pediatric voice disorders. METHODS: The clinical data of 1782 pediatric patients presenting with voice disorders were retrospectively analyzed. These cases were categorized into four age-based cohorts: 0-3, 4-7, 8-11, and 12-15years. Variables such as gender disparities, laryngoscopic manifestation, disease types, and acoustic parameters were thoroughly examined. RESULTS: A total of 1782 children with acoustic hoarseness were included in this study, comprising 1325 males and 457 females. When the sex ratio among the children in each group was compared, males were found to outnumber females. A notable male predominance was observed across all age groups. Laryngoscopic results revealed that the most prevalent condition was vocal cord nodules (1363 cases, 76.48%), followed by vocal cord polyps (271 cases, 15.20%). Other diseases included laryngeal papillomas, vocal fold movement impairment, vocal cord cysts, functional dysphonia, leukoplakia of the vocal cords, and benign laryngeal tumors such as laryngeal amyloidosis and subglottic granular cell tumors. Among these, adenoid hypertrophy was presented in 382 children (21.44%). Additionally, the Reflux Finding Score (RFS) was conducted, and 799 cases (44.83%) were found to have a score above 7. The distribution of various diseases across different age groups indicated that children with vocal cord nodules (637 cases, 46.74%), vocal cord polyps (109 cases, 40.22%), and laryngeal papillomas (35, 36.84%) were predominantly found in the 4-7 years age group. Pediatric acute laryngitis (three cases, 75%) and vocal fold movement impairment (eight cases, 36.36%) were more common in the 0-3 years age group. Functional dysphonia (four cases, 66.67%) and vocal cord leukoplakia (four cases, 80%) were mainly observed in the 12-15 years age group, while vocal cord cysts were predominantly seen in the 8-11 years age group (four cases, 57.14%). A comparative analysis of acoustic parameters among 153 children showed statistically significant differences in jitter, fundamental frequency (F0), voice handicap index (VHI), reflux symptom index (RSI), and RFS across different pathologies. CONCLUSION: This study highlighted that vocal cord nodules, vocal cord polyps, and laryngeal papillomas were the primary causes of pediatric hoarseness, although the possibility of tumors and rare diseases cannot be disregarded. There was a noticeable gender bias towards males, and functional dysphonia was significantly more prevalent in older children.
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Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.
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Ni60-WC coatings with different WC contents on the bucket tooth substrates were pre- pared using laser cladding technology. Their abrasive wear properties were assessed using the dry sand rubber wheel test system. The substrate and the hard-facing layer were tested for comparison. The results showed that the hardness of the Ni60-WC coatings increased with the increase in WC content. The wear resistance of the bucket tooth substrate was greatly improved by hard-facing and laser cladding Ni60-WC coatings. The wear rate of the hard-facing layer was reduced to 1/6 of that of the tooth substrate. The wear rate of the laser cladding coatings with 20-40 wt.% WC was similar to that of the hard-facing layer. It is worth mentioning that the wear rate of the coatings with 60-80 wt.% WC was only 1/4 of that of the hard-facing layer. Micro-cutting with surface plastic deformation was the main wear mechanism of the substrate to form narrow and deep furrows. The wear mechanism of the hard-facing layer was mainly plastic deformation with a wide groove, and the surface cracks promoted the removal of the material. The removal of the binder phase caused by micro-cutting was the main wear mechanism of the laser cladding Ni60-WC coatings. However, the hard phase of WC hinders micro-cutting and plastic deformation, which improves the wear resistance of the coating.
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BACKGROUND & AIMS: NOTCH signaling in liver sinusoidal endothelial cells (LSECs) regulates liver fibrosis, a pathological feature of chronic liver diseases. POFUT1 is an essential regulator of NOTCH signaling. Here, we investigated the role of LSEC-expressed POFUT1 in liver fibrosis. METHODS: Endothelial-specific Pofut1 knockout mice were generated and experimental liver fibrosis was induced by chronic carbon tetrachloride exposure or common bile duct ligation. Liver samples were assessed by ELISA, histology, electron microscopy, immunostaining and RNA in situ hybridization. LSECs and hepatic stellate cells (HSCs) were isolated for gene expression analysis by RNA sequencing, qPCR, and western blotting. Signaling crosstalk between LSECs and HSCs was investigated by treating HSCs with supernatant from LSEC cultures. Liver single-cell RNA sequencing datasets from patients with cirrhosis and healthy individuals were analyzed to evaluate the clinical relevance of gene expression changes observed in mouse studies. RESULTS: POFUT1 loss promoted injury-induced LSEC capillarization and HSC activation, leading to aggravated liver fibrosis. RNA sequencing analysis revealed that POFUT1 deficiency upregulated fibrinogen expression in LSECs. Consistently, fibrinogen was elevated in LSECs of patients with cirrhosis. HSCs treated with supernatant from LSECs of Pofut1 null mice showed exacerbated activation compared to those treated with supernatant from control LSECs, and this effect was attenuated by knockdown of fibrinogen or by pharmacological inhibition of fibrinogen receptor signaling, altogether suggesting that LSEC-derived fibrinogen induced the activation of HSCs. Mechanistically, POFUT1 loss augmented fibrinogen expression by enhancing NOTCH/HES1/STAT3 signaling. CONCLUSIONS: Endothelial POFUT1 prevents injury-induced liver fibrosis by repressing the expression of fibrinogen, which functions as a profibrotic paracrine signal to activate HSCs. Therapies targeting the POFUT1/fibrinogen axis offer a promising strategy for the prevention and treatment of fibrotic liver diseases. IMPACT AND IMPLICATIONS: Paracrine signals produced by liver vasculature play a major role in the development of liver fibrosis, which is a pathological hallmark of most liver diseases. Identifying those paracrine signals is clinically relevant in that they may serve as therapeutic targets. In this study, we discovered that genetic deletion of Pofut1 aggravated experimental liver fibrosis in mouse models. Moreover, fibrinogen was identified as a downstream target repressed by Pofut1 in liver endothelial cells and functioned as a novel paracrine signal that drove liver fibrosis. In addition, fibrinogen was found to be relevant to cirrhosis and may serve as a potential therapeutic target for this devastating human disease.
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Células Endoteliales , Fibrinógeno , Células Estrelladas Hepáticas , Cirrosis Hepática , Ratones Noqueados , Animales , Humanos , Masculino , Ratones , Tetracloruro de Carbono/toxicidad , Tetracloruro de Carbono/efectos adversos , Modelos Animales de Enfermedad , Células Endoteliales/metabolismo , Fibrinógeno/metabolismo , Fibrinógeno/biosíntesis , Fibrinógeno/genética , Células Estrelladas Hepáticas/metabolismo , Hígado/metabolismo , Hígado/patología , Cirrosis Hepática/metabolismo , Cirrosis Hepática/patología , Cirrosis Hepática/genética , Receptores Notch/metabolismo , Receptores Notch/fisiología , Transducción de SeñalRESUMEN
OBJECTIVES: To explore the value of the synthetic MRI (SyMRI), combined with amide proton transfer-weighted (APTw) MRI for quantitative and morphologic assessment of sinonasal lesions, which could provide relative scale for the quantitative assessment of tissue properties. METHODS: A total of 80 patients (31 malignant and 49 benign) with sinonasal lesions, who underwent the SyMRI and APTw examination, were retrospectively analyzed. Quantitative parameters (T1, T2, proton density (PD)) and APT % were obtained through outlining the region of interest (ROI) and comparing the two groups utilizing independent Student t test or a Wilcoxon test. Receiver operating characteristic curve (ROC), Delong test, and logistic regression analysis were performed to assess the diagnostic efficiency of one-parameter and multiparametric models. RESULTS: SyMRI-derived mean T1, T2, and PD were significantly higher and APT % was relatively lower in benign compared to malignant sinonasal lesions (p < 0.05). The ROC analysis showed that the AUCs of the SyMRI-derived quantitative (T1, T2, PD) values and APT % ranged from 0.677 to 0.781 for differential diagnosis between benign and malignant sinonasal lesions. The T2 values showed the best diagnostic performance among all single parameters for differentiating these two masses. The AUCs of combined SyMRI-derived multiple parameters with APT % (AUC = 0.866) were the highest than that of any single parameter, which was significantly improved (p < 0.05). CONCLUSION: The combination of SyMRI and APTw imaging has the potential to reflect intrinsic tissue characteristics useful for differentiating benign from malignant sinonasal lesions. CLINICAL RELEVANCE STATEMENT: Combining synthetic MRI with amide proton transfer-weighted imaging could function as a quantitative and contrast-free approach, significantly enhancing the differentiation of benign and malignant sinonasal lesions and overcoming the limitations associated with the superficial nature of endoscopic nasal sampling. KEY POINTS: ⢠Synthetic MRI and amide proton transfer-weighted MRI could differentiate benign from malignant sinonasal lesions based on quantitative parameters. ⢠The diagnostic efficiency could be significantly improved through synthetic MRI + amide proton transfer-weighted imaging. ⢠The combination of synthetic MRI and amide proton transfer-weighted MRI is a noninvasive method to evaluate sinonasal lesions.
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Objective: To assess the extent of recurrent laryngeal nerve (RLN) and superior laryngeal nerve (SLN) damage in patients with idiopathic vocal cord paralysis (IVCP) exhibiting different paralytic sides. Methods: A total of 84 IVCP cases were evaluated using stroboscopic laryngoscopy, voice analysis, and laryngeal electromyography (LEMG). The results were compared between patients with left-sided paralysis and right-sided paralysis based on different disease courses (less than or more than 3 months). Results: Initially, the average age and disease progression of IVCP patients were found to be similar regardless of the side of paralysis (p > .05). Additionally, there were no significant variations in voice indicators, such as MPT, DSI, and VHI, between IVCP patients with left and right vocal cord paralysis (p > .05). Furthermore, no disparities were detected in the latencies and amplitudes of the paralyzed RLN and SLN, as well as the durations and amplitudes of the action potentials in the paralyzed TM and PCM, among IVCP patients with left and right vocal cord paralysis (p > .05). Notably, the amplitudes of the left paralytic CM were significantly lower than those of the right paralytic CM (0.45 vs. 0.53, Z = -2.013, p = .044). In addition, no disparities were observed in APDs and amplitudes between the ipsilateral PCM and TM, either for patients with left or right vocal fold paralysis (p > .05). Finally, all the IVCP patients were subdivided into two subgroups according to different disease course (less than or more than 3 months), and in each subgroup, the comparison of voice indicators and LEMG results in IVCP patients with left or right vocal fold paralysis were similar with the above findings (p > .05). Conclusion: Overall, the degree of RLN and SLN damage appeared to be similar in IVCP patients with left and right vocal cord paralysis, provided that the disease course was comparable. Level of Evidence: 4.
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BACKGROUND: There is a lack of effective treatment for idiopathic unilateral vocal fold paralysis (IUVFP). A better phonation was reported by patients after laryngeal nerve stimulation during our clinical examination. OBJECTIVES: This study aims to investigate immediate effect of recurrent laryngeal nerve (RLN) stimulation on phonation in patients with IUVFP. MATERIAL AND METHODS: Sixty-two patients with clinically identified IUVFP underwent RLN stimulation with needle electrodes. Laryngoscopy, acoustic analysis, and voice perception assessment were performed for quantitative comparison of vocal function and voice quality before and after the intervention. RESULTS: Laryngoscopic images showed a larger motion range of the paralyzed vocal fold (p < .01) and better glottal closure (p < .01) after RLN stimulation. Acoustic analysis revealed that the dysphonia severity index increased significantly (p < .01) while the jitter and shimmer decreased after the intervention (p < .05). According to perceptual evaluation, RLN stimulation significantly increased RBH grades in patients with IUVFP (p < .01). Furthermore, the improvement in voice perception had a moderate positive correlation with the decrease in the glottal closure. CONCLUSIONS AND SIGNIFICANCE: This study shows a short-term improvement of phonation in IUVFP patients after RLN stimulation, which provides proof-of-concept for trialing a controlled delivery of RLN stimulation and assessing durability of any observed responses.
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Parálisis de los Pliegues Vocales , Voz , Humanos , Nervio Laríngeo Recurrente , Pliegues Vocales , Parálisis de los Pliegues Vocales/terapia , Voz/fisiología , Fonación/fisiologíaRESUMEN
OBJECTIVE: Accurate vocal cord leukoplakia classification is instructive for clinical diagnosis and surgical treatment. This article introduces a reliable very deep Siamese network for accurate vocal cord leukoplakia classification. STUDY DESIGN: A study of a classification network based on a retrospective database. SETTING: Academic university and hospital. METHODS: The white light image datasets of vocal cord leukoplakia used in this article were classified into 6 classes: normal tissues, inflammatory keratosis, mild dysplasia, moderate dysplasia, severe dysplasia, and squamous cell carcinoma. The classification performance was assessed by comparing it with 6 classical deep learning models, including AlexNet, VGG Net, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: Experiments show the superior classification performance of our proposed network compared to state-of-the-art methods. The overall accuracy is 0.9756. The values of sensitivity and specificity are very high as well. The confusion matrix provides information for the 6-class classification task and demonstrates the superiority of our proposed network. CONCLUSION: Our very deep Siamese network can provide accurate classification results of vocal cord leukoplakia, which facilitates early detection, clinical diagnosis, and surgical treatment. The excellent performance obtained in white light images can reduce the cost for patients, especially those living in developing countries.
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Enfermedades de la Laringe , Pliegues Vocales , Humanos , Pliegues Vocales/diagnóstico por imagen , Pliegues Vocales/patología , Estudios Retrospectivos , Imagen de Banda Estrecha/métodos , Enfermedades de la Laringe/patología , Endoscopía , Leucoplasia/patología , Hiperplasia/patologíaRESUMEN
Sprouting of mossy fibers, one of the most consistent findings in tissue from patients with mesial temporal lobe epilepsy, exhibits several uncommon axonal growth features and has been considered a paradigmatic example of circuit plasticity that occurs in the adult brain. Clarifying the mechanisms responsible may provide new insight into epileptogenesis as well as axon misguidance in the central nervous system. Methyl-CpG-binding protein 2 (MeCP2) binds to methylated genomic DNA to regulate a range of physiological functions implicated in neuronal development and adult synaptic plasticity. However, exploring the potential role of MeCP2 in the documented misguidance of axons in the dentate gyrus has not yet been attempted. In this study, a status epilepticus-induced decrease of neuronal MeCP2 was observed in the dentate gyrus (DG). An essential regulatory role of MeCP2 in the development of functional mossy fiber sprouting (MFS) was confirmed through stereotaxic injection of a recombinant adeno-associated virus (AAV) to up- or down-regulate MeCP2 in the dentate neurons. Chromatin immunoprecipitation sequencing (ChIP-seq) was performed to identify the binding profile of native MeCP2 using micro-dissected dentate tissues. In both dentate tissues and HT22 cell lines, we demonstrated that MeCP2 could act as a transcription repressor on miR-682 with the involvement of the DNA methylation mechanism. Further, we found that miR-682 could bind to mRNA of phosphatase and tensin homolog (PTEN) in a sequence specific manner, thus leading to the suppression of PTEN and excessive activation of mTOR. This study therefore presents a novel epigenetic mechanism by identifying MeCP2/miR-682/PTEN/mTOR as an essential signal pathway in regulating the formation of MFS in the temporal lobe epileptic (TLE) mice. SIGNIFICANCE STATEMENT: Understanding the mechanisms that regulate axon guidance is important for a better comprehension of neural disorders. Sprouting of mossy fibers, one of the most consistent findings in patients with mesial temporal lobe epilepsy, has been considered a paradigmatic example of circuit plasticity in the adult brain. Although abnormal regulation of DNA methylation has been observed in both experimental rodents and humans with epilepsy, the potential role of DNA methylation in this well-documented example of sprouting of dentate axon remains elusive. This study demonstrates an essential role of methyl-CpG-binding protein 2 in the formation of mossy fiber sprouting. The underlying signal pathway has been also identified. The data hence provide new insight into epileptogenesis as well as axon misguidance in the central nervous system.
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Epilepsia del Lóbulo Temporal , Epilepsia , MicroARNs , Animales , Humanos , Ratones , Giro Dentado/metabolismo , Epilepsia del Lóbulo Temporal/metabolismo , Proteína 2 de Unión a Metil-CpG/genética , Proteína 2 de Unión a Metil-CpG/metabolismo , MicroARNs/metabolismo , Fibras Musgosas del Hipocampo , Serina-Treonina Quinasas TOR/metabolismoRESUMEN
BACKGROUND: The establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) is involved in the development of multiple malignancies. However, its role in hypopharyngeal carcinoma (HPC) progression remains uncharacterized. METHODS: This study employed bioinformatics to determine the ESCO2 expression in head and neck squamous cell carcinoma (HNSC) and normal tissues. In vitro cell proliferation, migration, apoptosis, and/or cell cycle distribution assays were used to determine the function of ESCO2 and its relationship with STAT1. Xenograft models were established in nude mice to determine ESCO2 in HPC growth in vivo. Co-immunoprecipitation/mass spectrometry (Co-IP/MS) was conducted to identify the potential ESCO2 binding partners. RESULTS: We found that ESCO2 expression was elevated in HNSC tissues, and ESCO2 depletion suppressed tumor cell migration in vitro and inhibited tumor growth in vitro and in vivo. Co-IP/MS and immunoblotting assays revealed the interaction between ESCO2 and STAT1 in HPC cells. STAT1-overexpression compromised ESCO2-mediated suppressive effects on HPC cell proliferation, viability, and migration. CONCLUSIONS: These findings suggest that ESCO2 is crucial in promoting HPC malignant progression through the STAT1 pathway and provides novel therapeutic targets for HPC treatment.
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Segregación Cromosómica , Neoplasias de Cabeza y Cuello , Animales , Ratones , Humanos , Ratones Desnudos , Proliferación Celular , Carcinoma de Células Escamosas de Cabeza y Cuello/genética , Línea Celular Tumoral , Factor de Transcripción STAT1/genética , Factor de Transcripción STAT1/metabolismo , Acetiltransferasas/genética , Proteínas Cromosómicas no Histona/genéticaRESUMEN
OBJECTIVE: This retrospective study analyzed breathing patterns and age subgroups effect on cortical bone quality of the mandible in growing subjects, aiming to explore the application value of facial skeletal pattern combined with cortical bone density detection in early screening diagnosis of mouth breathing. METHODS: One hundred twenty-six participants were divided into four groups: mouth breathing group (7-9, 10-12 years old) and nasal breathing group (7-9, 10-12 years old). The mandibular anterior, middle, and posterior cortical bone mineral density (CBMD), cortical bone width (MCW), ANB, and FMA values were measured. Independent T-test and Mann-Whitney U test were used to compare the measured values. Binary logistic regression was employed to analyze the correlation between measured variables and the children's breathing patterns. ROC analysis was used to determine the ability of the cortical bone density measurements in early screening diagnosis of MB. RESULTS: Mouth breathing had a negative impact on CBMD and MCW of the pre-mandibular (Pog) in subjects aged 7-9 years and also impacted the development of (Pog) and submandibular (Me) sites in subjects aged 10-12 years. Older children in the nasal breathing group have higher CBMD, MCW, and SNB values and lower FMA values. Single-factor and multiple-factor logistic binary regression analysis showed that FMA, MSPogCBMD, MSPogMCW, and ANB are correlated factors for children at risk of mouth breathing. CONCLUSION: Mouth breathing pattern is closely associated with decreased mandibular CBMD and MCW values in children aged 7-12, where the anterior (Pog) and inferior (Me) sites of anterior mandible are more significantly affected. Furthermore, in combination with facial skeletal pattern, it provides a basis for the early warning diagnosis of mouth breathing.
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Mandíbula , Respiración por la Boca , Humanos , Niño , Adolescente , Estudios Retrospectivos , Radiografía Panorámica , Mandíbula/diagnóstico por imagen , Densidad Ósea , Hueso Cortical , RespiraciónRESUMEN
BACKGROUND: Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification. METHODS: We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS: GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values. CONCLUSION: GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.
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Aprendizaje Profundo , Neoplasias Laríngeas , Humanos , Pliegues Vocales/diagnóstico por imagen , Pliegues Vocales/patología , Imagen de Banda Estrecha/métodos , Endoscopía , Neoplasias Laríngeas/patología , Endoscopía Gastrointestinal , Leucoplasia/diagnóstico por imagen , Leucoplasia/patología , Hiperplasia/patologíaRESUMEN
BACKGROUND: Obstructive sleep apnea (OSA) is a globally prevalent disease with a complex diagnostic method. Severe OSA is associated with multi-system dysfunction. We aimed to develop an interpretable machine learning (ML) model for predicting the risk of severe OSA and analyzing the risk factors based on clinical characteristics and questionnaires. METHODS: This was a retrospective study comprising 1656 subjects who presented and underwent polysomnography (PSG) between 2018 and 2021. A total of 23 variables were included, and after univariate analysis, 15 variables were selected for further preprocessing. Six types of classification models were used to evaluate the ability to predict severe OSA, namely logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and multilayer perceptron (MLP). All models used the area under the receiver operating characteristic curve (AUC) was calculated as the performance metric. We also drew SHapley Additive exPlanations (SHAP) plots to interpret predictive results and to analyze the relative importance of risk factors. An online calculator was developed to estimate the risk of severe OSA in individuals. RESULTS: Among the enrolled subjects, 61.47% (1018/1656) were diagnosed with severe OSA. Multivariate LR analysis showed that 10 of 23 variables were independent risk factors for severe OSA. The GBM model showed the best performance (AUC = 0.857, accuracy = 0.766, sensitivity = 0.798, specificity = 0.734). An online calculator was developed to estimate the risk of severe OSA based on the GBM model. Finally, waist circumference, neck circumference, the Epworth Sleepiness Scale, age, and the Berlin questionnaire were revealed by the SHAP plot as the top five critical variables contributing to the diagnosis of severe OSA. Additionally, two typical cases were analyzed to interpret the contribution of each variable to the outcome prediction in a single patient. CONCLUSIONS: We established six risk prediction models for severe OSA using ML algorithms. Among them, the GBM model performed best. The model facilitates individualized assessment and further clinical strategies for patients with suspected severe OSA. This will help to identify patients with severe OSA as early as possible and ensure their timely treatment. TRIAL REGISTRATION: Retrospectively registered.
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Apnea Obstructiva del Sueño , Humanos , Adulto , Estudios Retrospectivos , Apnea Obstructiva del Sueño/diagnóstico , Apnea Obstructiva del Sueño/epidemiología , Curva ROC , Factores de Riesgo , Aprendizaje AutomáticoRESUMEN
BACKGROUND: TP53, the most mutated gene in solid cancers, has a profound impact on most hallmarks of cancer. Somatic TP53 mutations occur in high frequencies in head and neck cancers, including oral squamous cell carcinoma (OSCC). Our study aims to understand the role of TP53 gain-of-function mutation in modulating the tumor immune microenvironment (TIME) in OSCC. METHODS: Short hairpin RNA knockdown of mutant p53R172H in syngeneic oral tumors demonstrated changes in tumor growth between immunocompetent and immunodeficient mice. HTG EdgeSeq targeted messenger RNA sequencing was used to analyze cytokine and immune cell markers in tumors with inactivated mutant p53R172H. Flow cytometry and multiplex immunofluorescence (mIF) confirmed the role of mutant p53R172H in the TIME. The gene expression of patients with OSCC was analyzed by CIBERSORT and mIF was used to validate the immune landscape at the protein level. RESULTS: Mutant p53R172H contributes to a cytokine transcriptome network that inhibits the infiltration of cytotoxic CD8+ T cells and promotes intratumoral recruitment of regulatory T cells and M2 macrophages. Moreover, p53R172H also regulates the spatial distribution of immunocyte populations, and their distribution between central and peripheral intratumoral locations. Interestingly, p53R172H-mutated tumors are infiltrated with CD8+ and CD4+ T cells expressing programmed cell death protein 1, and these tumors responded to immune checkpoint inhibitor and stimulator of interferon gene 1 agonist therapy. CIBERSORT analysis of human OSCC samples revealed associations between immune cell populations and the TP53R175H mutation, which paralleled the findings from our syngeneic mouse tumor model. CONCLUSIONS: These findings demonstrate that syngeneic tumors bearing the TP53R172H gain-of-function mutation modulate the TIME to evade tumor immunity, leading to tumor progression and decreased survival.
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Carcinoma de Células Escamosas , Neoplasias de Cabeza y Cuello , Neoplasias de la Boca , Microambiente Tumoral , Proteína p53 Supresora de Tumor , Animales , Humanos , Ratones , Carcinoma de Células Escamosas/genética , Linfocitos T CD8-positivos , Citocinas , Modelos Animales de Enfermedad , Mutación con Ganancia de Función , Neoplasias de la Boca/genética , Mutación , Carcinoma de Células Escamosas de Cabeza y Cuello/genética , Proteína p53 Supresora de Tumor/genéticaRESUMEN
BACKGROUND: To investigate the relationship between tongue fat content and severity of obstructive sleep apnea (OSA) and its effects on the efficacy of uvulopalatopharyngoplasty (UPPP) in the Chinese group. METHOD: Fifty-two participants concluded to this study were diagnosed as OSA by performing polysomnography (PSG) then they were divided into moderate group and severe group according to apnea hypopnea index (AHI). All of them were also collected a series of data including age, BMI, height, weight, neck circumference, abdominal circumference, magnetic resonance imaging (MRI) of upper airway and the score of Epworth Sleepiness Scale (ESS) on the morning after they completed PSG. The relationship between tongue fat content and severity of OSA as well as the association between tongue fat content in pre-operation and surgical efficacy were analyzed.Participants underwent UPPP and followed up at 3rd month after surgery, and they were divided into two groups according to the surgical efficacy. RESULTS: There were 7 patients in the moderate OSA group and 45 patients in the severe OSA group. The tongue volume was significantly larger in the severe OSA group than that in the moderate OSA group. There was no difference in tongue fat volume and tongue fat rate between the two groups. There was no association among tongue fat content, AHI, obstructive apnea hypopnea index, obstructive apnea index and Epworth sleepiness scale (all P > 0.05), but tongue fat content was related to the lowest oxygen saturation (r=-0.335, P < 0.05). There was no significantly difference in pre-operative tongue fat content in two different surgical efficacy groups. CONCLUSIONS: This study didn't show an association between tongue fat content and the severity of OSA in the Chinese group, but it suggested a negative correlation between tongue fat content and the lowest oxygen saturation (LSaO2). Tongue fat content didn't influence surgical efficacy of UPPP in Chinese OSA patients. TRIAL REGISTRATION: This study didn't report on a clinical trial, it was retrospectively registered.
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Adiposidad , Pueblos del Este de Asia , Procedimientos Quirúrgicos Otorrinolaringológicos , Apnea Obstructiva del Sueño , Lengua , Humanos , Pueblo Asiatico , Polisomnografía , Apnea Obstructiva del Sueño/diagnóstico , Apnea Obstructiva del Sueño/cirugía , Somnolencia , Lengua/anatomía & histología , Lengua/cirugíaRESUMEN
BACKGROUND: Motor imagery brain-computer interfaces (BCIs) is a classic and potential BCI technology achieving brain computer integration. In motor imagery BCI, the operational frequency band of the EEG greatly affects the performance of motor imagery EEG recognition model. However, as most algorithms used a broad frequency band, the discrimination from multiple sub-bands were not fully utilized. Thus, using convolutional neural network (CNNs) to extract discriminative features from EEG signals of different frequency components is a promising method in multisubject EEG recognition. METHODS: This paper presents a novel overlapping filter bank CNN to incorporate discriminative information from multiple frequency components in multisubject motor imagery recognition. Specifically, two overlapping filter banks with fixed low-cut frequency or sliding low-cut frequency are employed to obtain multiple frequency component representations of EEG signals. Then, multiple CNN models are trained separately. Finally, the output probabilities of multiple CNN models are integrated to determine the predicted EEG label. RESULTS: Experiments were conducted based on four popular CNN backbone models and three public datasets. And the results showed that the overlapping filter bank CNN was efficient and universal in improving multisubject motor imagery BCI performance. Specifically, compared with the original backbone model, the proposed method can improve the average accuracy by 3.69 percentage points, F1 score by 0.04, and AUC by 0.03. In addition, the proposed method performed best among the comparison with the state-of-the-art methods. CONCLUSION: The proposed overlapping filter bank CNN framework with fixed low-cut frequency is an efficient and universal method to improve the performance of multisubject motor imagery BCI.
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OBJECTIVE: Motor imagery BCI plays an increasingly important role in motor disorders rehabilitation. However, the position and duration of the discriminative segment in an EEG trial vary from subject to subject and even trial to trial, and this leads to poor performance of subject-independent motor imagery classification. Thus, determining how to detect and utilize the discriminative signal segments is crucial for improving the performance of subject-independent motor imagery BCI. APPROACH: In this paper, a shallow mirror transformer is proposed for subject-independent motor imagery EEG classification. Specifically, a multihead self-attention layer with a global receptive field is employed to detect and utilize the discriminative segment from the entire input EEG trial. Furthermore, the mirror EEG signal and the mirror network structure are constructed to improve the classification precision based on ensemble learning. Finally, the subject-independent setup was used to evaluate the shallow mirror transformer on motor imagery EEG signals from subjects existing in the training set and new subjects. MAIN RESULTS: The experiments results on BCI Competition IV datasets 2a and 2b and the OpenBMI dataset demonstrated the promising effectiveness of the proposed shallow mirror transformer. The shallow mirror transformer obtained average accuracies of 74.48% and 76.1% for new subjects and existing subjects, respectively, which were highest among the compared state-of-the-art methods. In addition, visualization of the attention score showed the ability of discriminative EEG segment detection. This paper demonstrated that multihead self-attention is effective in capturing global EEG signal information in motor imagery classification. SIGNIFICANCE: This study provides an effective model based on a multihead self-attention layer for subject-independent motor imagery-based BCIs. To the best of our knowledge, this is the shallowest transformer model available, in which a small number of parameters promotes the performance in motor imagery EEG classification for such a small sample problem.