Search Results (46)

Background/Objectives: Obstructive sleep apnea (OSA) is increasingly observed in patients undergoing supracricoid laryngeal surgery; however, the impact of postoperative anatomical changes on sleep-disordered breathing remains insufficiently characterized. This pilot study aimed to assess the incidence and severity of OSA after Open Partial Horizontal Laryngectomy type II (OPHL II) and to evaluate correlations between polysomnographic parameters and radiologic measurements of the neolarynx. Methods: A prospective observational cohort study was conducted on ten patients who underwent OPHL II between 2019 and 2024 and were evaluated at least one year postoperatively. The sample size was determined using a conservative estimate appropriate for a pilot prospective study, which required a long postoperative follow-up period of at least one year. All patients completed Stop-Bang, Berlin, and Epworth questionnaires and underwent overnight polysomnography. Cervical CT scans were used to measure airway length to the vocal cords (ALVC), supralaryngeal tract horizontal (SVTH) and vertical (SVTV) segments, and the base-of-tongue–to–cervical-body distance (BTCB). Results: OSA was detected in all patients: 40% mild, 30% moderate, and 30% severe. Mean AHI was 25.5 ± 18.9 events/h, and OSA severity strongly correlated with AHI (rho = 0.94; p < 0.0001). Among radiologic parameters, SVTV showed a positive correlation with OSA severity (rho = 0.82; p = 0.0035), while BTCB demonstrated a significant negative correlation (rho = −0.71; p = 0.0207). No significant associations were found for ALVC or SVTH. Conclusions: Supracricoid laryngectomy produces anatomical changes that predispose patients to OSA. Radiologic metrics—particularly SVTV and BTCB—appear to be meaningful predictors of OSA severity. A multidisciplinary approach is essential for early diagnosis and management. Due to the small number of patients enrolled larger multicenter studies are needed to confirm these findings and define radiologic criteria associated with postoperative OSA. Full article

Objective: To investigate the integrated relationship between Cerebral Small Vessel Disease (CSVD) markers and quantitative facial soft-tissue measurements in Alzheimer’s disease (AD) continuum, utilizing peripheral muscle health as a potential biomarker for systemic frailty and neurodegeneration. Methods: Retrospective analysis of 3T brain MRI data from 67 patients (AD, N = 45; Mild Cognitive Impairment [MCI], N = 22). CSVD markers were assessed using STRIVE and standardized scales (Fazekas, Potter). Facial soft-tissue metrics, including masseter and tongue volume, temporal muscle thickness (TMT), and fat infiltration (Mercuri Scale), were quantified via semi-automatic segmentation on T1-weighted sequences. Group comparisons (AD vs. MCI) used regression models adjusted for age and sex. The overall central–peripheral relationship was explored via Canonical Correlation Analysis (CCA). Results: The AD group showed a highly significant cognitive decline (MMSE: 23.2 ± 4.1 vs. 28.2 ± 1.4, p < 0.0001). Centrally, the presence of PVSs in the mesencephalic region was the most robust predictor for AD (p = 0.003). Peripherally, average masseter muscle volume was significantly lower in the AD group (p = 0.0273), and masseter fat infiltration was significantly higher (p = 0.025), supporting localized sarcopenia. The CCA demonstrated a statistically significant positive multivariate relationship (r = 0.51, Roy’s Largest Root p = 0.015) between a higher combined CSVD burden and a worse soft tissue profile across the cohort. Conclusions: Quantitative indices of facial soft tissues, particularly masseter muscle volume and quality, reflect systemic frailty and cognitive deterioration along the AD continuum. The strong central–peripheral correlation suggests that sarcopenia and CSVD are interconnected manifestations of a shared pathobiological process. These easily measurable facial markers could serve as valuable, non-invasive peripheral biomarkers, complementing traditional neuroimaging risk stratification in AD. Full article

Background: Bulbar dysfunction is a major complication of amyotrophic lateral sclerosis (ALS). This study aimed to develop and validate a simple, smartphone-based task for the objective assessment of tongue movements and to examine their association with clinical variables. Methods: 37 ALS patients and 20 age- and sex-matched controls performed a tongue lateralization task, recorded with a smartphone. A deep-learning U-Net++-based model was used for segmentation and feature extraction. The frequency and maximum amplitude of tongue movements were quantified. Clinical measures included the ALS Functional Rating Scale-revised (ALSFRS-r) bulbar sub-scores, tongue fasciculations, jaw jerk, and tongue “spasticity”. Between-group differences and associations between tongue metrics and clinical features were assessed. Results: The U-Net++-based model achieved robust segmentation performance. Patients showed lower tongue movement frequency than controls (0.14 vs. 0.40, t = −9.58, p < 0.001). Normalized frequency was associated with dysarthria (t = −3.13, p = 0.003) but not dysphagia (t = −1.05, p = 0.30). Normalized frequency (t = 2.77, p = 0.009) and tongue “spasticity” (t = −2.57, p = 0.015) were both associated with speech performance in a multiple-regression model (R = 0.51, adjusted R² = 0.43). Conclusions: Our method provides an objective, minimally invasive measure of bulbar function in ALS, which correlates with clinical ratings and may detect subtle impairments not captured by standard assessments. This approach offers a promising tool for remote monitoring and may support more effective disease management. Full article

This study presents a zooarchaeological and taphonomic analysis of the previously unstudied component of the Mousterian faunal assemblage from Unit A9 at Grotta di Fumane (northeastern Italy), offering refined insights into Neanderthal subsistence behaviour during Marine Isotope Stage 3. Building on the previously published analysis of the principal portion of the assemblage, the new data reaffirm a subsistence strategy focused on selective transport and intensive on-site processing of high-utility carcass components. The ungulate assemblage—dominated by Cervus elaphus and Capreolus capreolus, with additional contributions from Rupicapra rupicapra and Capra ibex—is characterised by the dominance of hindlimb elements, moderate cranial representation, and a pronounced scarcity of axial remains. These patterns indicate that carcass reduction commenced at kill sites, where low-yield trunk segments were removed, while high-nutritional-value limb portions were preferentially transported to the cave for secondary processing. Taphonomic indicators, including abundant cut marks, percussion notches, and extensive bone fragmentation, demonstrate systematic defleshing, marrow extraction, and possible grease rendering within the cave, activities that were spatially associated with combustion features. Occasional cranial transport suggests targeted acquisition of high-fat tissues such as brains and tongue, behaviour consistent with cold-climate optimisation strategies documented in both ethnographic and experimental contexts. Collectively, the evidence indicates that Unit A9 served as a residential locus embedded within a logistically organised mobility system, where carcass processing, resource exploitation, and lithic activities were closely integrated. These findings reinforce the broader picture of late Neanderthals as adaptable and behaviourally sophisticated foragers capable of strategic planning and efficient exploitation of ungulate prey within the dynamic environments of northern Italy. Full article

This study proposes an approach for detecting alcohol intoxication from speech based on a combination of audio segmentation and a hybrid neural network architecture that integrates convolution neural network (CNN) and long-short term memory (LSTM) layers. The proposed design enables effective modeling of both local spectral patterns and long-term temporal dependencies in speech signals. By operating on relatively long audio segments, the approach allows the simultaneous analysis of complex speech constructions and pause patterns, which are known to be sensitive to alcohol-induced speech impairments. Each audio signal was divided into two equal-duration segments that are processed sequentially by the model, which helps reduce the impact of asymmetrical distribution of intoxication-related speech artifacts. The approach was evaluated using the GradusSpeech-v1 corpus, which contains more than 1300 recordings of Russian tongue twisters collected from 31 speakers under controlled conditions in both sober and intoxicated states. Experimental results demonstrate that the proposed method achieves high performance. When full recordings are analyzed using median aggregation of segment-level predictions, the model reaches Accuracy, Recall, and F1-score values close to 0.93, indicating the effectiveness of the approach for alcohol intoxication detection in speech. Full article

Introduction: Accurate knowledge of the external carotid artery (ECA) anatomy is essential for head and neck surgery, interventional procedures, and imaging interpretation. Although its branching pattern is classically described as relatively constant, clinically relevant anatomical variations are frequently encountered. Cadaveric dissection remains fundamental for identifying rare vascular configurations. Materials and Methods: During an anatomical teaching dissection of a 72-year-old male cadaver, a right-sided lateral cervicotomy was performed to expose the carotid sheath. After mobilisation of the sternocleidomastoid muscle, the ECA and its proximal branches were skeletonised, allowing detailed three-dimensional assessment of their origin, calibre, and neurovascular relationships. Results: The superior thyroid artery originated from the proximal segment of the external carotid artery, in close proximity to the carotid bifurcation. The main anatomical finding was a lingual artery of relatively small initial calibre exhibiting an atypical superior trajectory: after its origin, it crossed superior to the hypoglossal nerve before continuing toward the tongue. This configuration differs from classical descriptions and modified the anatomical arrangement of Beclard’s and Pirogoff’s triangles, creating a potential site of close neurovascular contact. Conclusions: This cadaveric study describes a rare trajectory-based variant of the external carotid artery characterised by a lingual artery crossing superior to the hypoglossal nerve. Awareness of such rare patterns is essential for improving anatomical interpretation and enhancing surgical safety in the head and neck region. Full article

Stropharia rugosoannulata is a widely cultivated edible mushroom known for its nutritional value and umami flavour. Electronic tongue technology and metabolomics revealed that glutamic acid (Glu) and aspartic acid (Asp) levels were positively correlated with umami in the fruiting body developmental stages. Subsequent investigations found that overexpression of SrCS within the TCA cycle resulted in decreased levels of Glu and Asp. Integrating TF-gene-metabolite network modelling with experiments identified SrELT1 as a transcriptional regulator of SrCS. Different temperatures, cultivation substrates and genetics significantly impacted SrELT1 and SrCS expression, thereby affecting Glu and Asp synthesis. The findings suggest that increased Citrate synthase (CS) activity channelled citrate into glycolysis and oxidative phosphorylation without excessive accumulation; in contrast, decreased CS activity shifted metabolism toward the production of metabolites like Glu and Asp. This study provides insights for enhancing the umami of S. rugosoannulata, thereby substantially increasing its market competitiveness in the premium food segment. Full article

Electroencephalography (EEG)-based brain–computer interface (BCI) mimics the brain’s intrinsic information-processing mechanisms by translating neural oscillations into actionable commands. In motor imagery (MI) BCI, imagined movements evoke characteristic patterns over the sensorimotor cortex, forming a biomimetic channel through which internal motor intentions are decoded. However, this biomimetic interaction is highly vulnerable to signal degradation, particularly in mobile or low-resource environments where low sampling frequencies obscure these MI-related oscillations. To address this limitation, we propose a robust MI classification framework that integrates spatial, spectral, and temporal dynamics through a filter bank common spatial pattern with time segmentation (FBCSP-TS). This framework classifies motor imagery tasks into four classes (left hand, right hand, foot, and tongue), segments EEG signals into overlapping time domains, and extracts frequency-specific spatial features across multiple subbands. Segment-level predictions are combined via soft voting, reflecting the brain’s distributed integration of information and enhancing resilience to transient noise and localized artifacts. Experiments performed on BCI Competition IV datasets 2a (250 Hz) and 1 (100 Hz) demonstrate that FBCSP-TS outperforms CSP and FBCSP. A paired t-test confirms that accuracy at 110 Hz is not significantly different from that at 250 Hz (p < 0.05), supporting the robustness of the proposed framework. Optimal temporal parameters (window length = 3.5 s, moving length = 0.5 s) further stabilize transient-signal capture and improve SNR. External validation yielded a mean accuracy of 0.809 ± 0.092 and Cohen’s kappa of 0.619 ± 0.184, confirming strong generalizability. By preserving MI-relevant neural patterns under degraded conditions, this framework advances practical, biomimetic BCI suitable for wearable and real-world deployment. Full article

This study investigated the mechanical properties of a boltless hexagonal segment lining structure in TBM tunnels through a 1:10 scale similarity model test. The analysis considered the effects of burial depth and lateral pressure coefficient. A gypsum-diatomite composite simulated C50 concrete segments, and a custom loading system applied equivalent soil-water loads. The tests examined variations in bending moment, axial force and displacement. The results demonstrate that: (1) The tongue-and-groove joints behave like hinges, effectively reducing joint bending moments. (2) The unique staggered interlocking structure induces significantly higher axial forces at the joints than traditional rectangular segments, increasing susceptibility to stress concentration. (3) Increased burial depth has the most significant impact on the tunnel crown, where the bending moment, axial force, and displacement change most notably. (4) The lateral pressure coefficient (λ) alters the joint load transfer mechanism by modifying the structure’s triaxial stress state. An optimal λ of 0.6 maximizes axial force transfer efficiency, while excessively high values impair horizontal load-bearing capacity. (5) Structural failure was ductile, with a final ovality slightly exceeding 10‰. The findings of this study can provide a reference for the design and application of similar boltless hexagonal segment tunnels. Full article

Speech segmentation is one of the first tasks infants face when learning their mother tongue. It has been argued that statistical learning could function as a gateway to speech segmentation in the absence of pre-existing knowledge about the language to be acquired. However, infants also segment speech with prosodic cues, such as lexical stress. Here, we review recent evidence from studies that look at how infants weigh statistical and prosodic information when segmenting continuous speech. We argue that the idea that statistical regularities have a main role in early speech segmentation, as evidenced in English-learning infants, is not found with German-learning infants. With more natural speech stimuli, German-learning infants only become sensitive to statistical regularities in the speech signal by their first birthday. We provide further support for this hypothesis by showing that there are cross-linguistic differences in how statistical models segment child-directed speech (CDS) and that CDS changes as infants grow. This suggests that speech input to younger infants is not tailored for speech segmentation with statistical cues, but that it is subject to cross-linguistic differences like prosody. Full article

Tongue diagnosis is a crucial method in traditional Chinese medicine (TCM) for obtaining information about a patient’s health condition. In this study, we propose a tongue image segmentation method based on deep learning and a pixel-level tongue color classification method utilizing machine learning techniques such as support vector machine (SVM) and ridge regression. These two approaches together form a comprehensive framework that spans from tongue image acquisition to segmentation and analysis. This framework provides an objective and visualized representation of pixel-wise classification and proportion distribution within tongue images, effectively assisting TCM practitioners in diagnosing tongue conditions. It mitigates the reliance on subjective observations in traditional tongue diagnosis, reducing human bias and enhancing the objectivity of TCM diagnosis. The proposed framework consists of three main components: tongue image segmentation, pixel-wise classification, and tongue color classification. In the segmentation stage, we integrate the Segment Anything Model (SAM) into the overall segmentation network. This approach not only achieves an intersection over union (IoU) score above 0.95 across three tongue image datasets but also significantly reduces the labor-intensive annotation process required for training traditional segmentation models while improving the generalization capability of the segmentation model. For pixel-wise classification, we propose a lightweight pixel classification model based on SVM, achieving a classification accuracy of 92%. In the tongue color classification stage, we introduce a ridge regression model that classifies tongue color based on the proportion of different pixel categories. Using this method, the classification accuracy reaches 91.80%. The proposed approach enables accurate and efficient tongue image segmentation, provides an intuitive visualization of tongue color distribution, and objectively analyzes and quantifies the proportion of different tongue color categories. In the future, this framework holds potential for validation and optimization in clinical practice. Full article

Active and passive surveillance, followed by gene sequencing, continue to be used to identify a diverse range of novel bacteria, viruses, and other microorganisms in ticks with the potential to cause disease in vertebrate hosts following tick bite. In this study, we describe the isolation and characterization of a novel virus from Bothriocroton hydrosauri ticks collected from a blotched blue-tongue, Tiliqua nigrolutea. In an attempt to isolate rickettsia, the inoculation of Vero cell cultures with tick extracts led to the isolation of a virus, identified as a novel tick Orthomyxovirus by electron microscopy and gene sequencing. Transmission electron microscopic analysis revealed that B. hydrosauri tick virus-1 (BHTV-1) is a spherical orthomyxovirus, 85 nm in size. Multiple developmental stages of the virus were evident in vitro. Analysis of putative BHTV-1 amino acid sequences derived from a genomic analysis of virus-infected host cell extracts revealed the presence of six putative RNA segments encoding genes, sharing the closest sequence similarity to viral sequences belonging to the arthropod-borne Thogotovirus genus within the Orthomyxoviridae. Thogotoviruses are an emerging cause of disease in humans and animals following tick bite. The detection of this new thogotovirus, BHTV-1, in B. hydrosauri, a competent vector for human tick-borne infectious diseases, warrants follow-up investigation to determine its prevalence, host range, and pathogenic potential. Full article

Traditional Chinese medicine (TCM) gathers patient information through inspection, olfaction, inquiry, and palpation, analyzing and interpreting the data to make a diagnosis and offer appropriate treatment. Traditionally, the interpretation of this information relies heavily on the physician’s personal knowledge and experience. However, diagnostic outcomes can vary depending on the physician’s clinical experience and subjective judgment. This study employs AI methods to focus on localized tongue assessment, developing an automatic tongue body segmentation using the deep learning network “U-Net” through a series of optimization processes applied to tongue surface images. Furthermore, “ResNet34” is utilized for the identification of “cold”, “neutral”, and “hot” constitutions, creating a system that enhances the consistency and reliability of diagnostic results related to the tongue. The final results demonstrate that the AI interpretation accuracy of this system reaches the diagnostic level of junior TCM practitioners (those who have passed the TCM practitioner assessment with ≤5 years of experience). The framework and findings of this study can serve as (1) a foundational step for the future integration of pulse information and electronic medical records, (2) a tool for personalized preventive medicine, and (3) a training resource for TCM students learning to diagnose tongue constitutions such as “cold”, “neutral”, and “hot”. Full article

This paper describes a multistage framework for face image analysis in computer-aided speech diagnosis and therapy. Multimodal data processing frameworks have become a significant factor in supporting speech disorders’ treatment. Synchronous and asynchronous remote speech therapy approaches can use audio and video analysis of articulation to deliver robust indicators of disordered speech. Accurate segmentation of articulators in video frames is a vital step in this agenda. We use a dedicated data acquisition system to capture the stereovision stream during speech therapy examination in children. Our goal is to detect and accurately segment four objects in the mouth area (lips, teeth, tongue, and whole mouth) during relaxed speech and speech therapy exercises. Our database contains 17,913 frames from 76 preschool children. We apply a sequence of procedures employing artificial intelligence. For detection, we train the YOLOv6 (you only look once) model to catch each of the three objects under consideration. Then, we prepare the DeepLab v3+ segmentation model in a semi-supervised training mode. As preparation of reliable expert annotations is exhausting in video labeling, we first train the network using weak labels produced by initial segmentation based on the distance-regularized level set evolution over fuzzified images. Next, we fine-tune the model using a portion of manual ground-truth delineations. Each stage is thoroughly assessed using the independent test subset. The lips are detected almost perfectly (average precision and F1 score of 0.999), whereas the segmentation Dice index exceeds 0.83 in each articulator, with a top result of 0.95 in the whole mouth. Full article

Optical coherence tomography is a noninvasive imaging technique that provides three-dimensional visualization of subsurface tissue structures. OCT has been proposed and explored in the literature as a tool to assess oral cancer status, select biopsy sites, or identify surgical margins. Our endoscopic OCT device can generate widefield (centimeters long) imaging of lesions at any location in the oral cavity—but it is challenging for raters to quantitatively assess and score large volumes of data. Leveraging a previously developed epithelial segmentation network, this work develops quantifiable biomarkers that provide direct measurements of tissue properties in three dimensions. We hypothesize that features related to morphology, tissue attenuation, and contrast between tissue layers will be able to provide a quantitative assessment of disease status (dysplasia through carcinoma). This work retrospectively assesses seven biomarkers on a lesion-contralateral matched OCT dataset of the lateral and ventral tongue (40 patients, 70 sites). Epithelial depth and loss of epithelial–stromal boundary visualization provide the strongest discrimination between disease states. The stroma optical attenuation coefficient provides a distinction between benign lesions from dysplasia and carcinoma. The stratification biomarkers visualize subsurface changes, which provides potential for future utility in biopsy site selection or treatment margin delineation. Full article