Search Results (468)

As the demand for chitin grows, new chitin sources with unique physicochemical properties are required. Abundant biofouling species, such as amphipods and hydroids, have chitinous skeletal systems that can be utilized for chitin production. However, little is known about these chitin sources. This study investigated the viability of amphipods (Jassa sp.) and hydroids (Coryne sp.) obtained from aquaculture biofouling assemblages as novel sources of chitin. Chitin was extracted from these sources and characterized in terms of its degree of acetylation (DA), crystallinity index (CrI), molecular weight (MW), thermal stability, and surface morphology. Physiochemical characteristics where then compared against commercially available shrimp chitin. Results show that a 32.75% chitin yield can be obtained from hydroids. The percentage DA for amphipod (AC) and hydroid (HC) chitin is 58.4–59.2% and 64.8–66.7%, respectively. AC is characterized as α-chitin with a low molecular weight (MW), while HC is medium-MW β-chitin. This finding is significant because it shows hydroids to be a new source of rare β-chitin. In addition, AC has higher thermal stability than HC. AC and HC greatly differ in terms of surface morphology. Therefore, the chitin biomaterials extracted from amphipods and hydroids have different but favorable properties that can be used for diverse applications. Full article

Background/Objectives: Sarcopenia, defined as the progressive loss of muscle mass and function, is increasingly recognized in pediatric cancer patients as a significant clinical and prognostic factor. Sarcopenia in children arises from malignancy-related inflammation, malnutrition, and treatment toxicity, negatively affecting treatment response, recovery, and quality of life. Methods: We searched MEDLINE and Scopus for English-written articles published over the last five years using synonyms for the terms “sarcopenia” and “pediatric cancer”. Screening and data extraction were performed in a duplicate-blinded method. We qualitatively synthesized eligible articles. Results: Recent studies identify pre-treatment sarcopenia as a marker of poor prognosis, especially in hepatoblastoma and neuroblastoma. Total psoas muscle area (tax) and skeletal muscle index (SMI) are emerging diagnostic tools, though standardized methods remain lacking. Sarcopenia’s etiology is multifactorial, involving impaired mitochondrial metabolism, chemotherapy-induced appetite loss, and systemic inflammation. Sarcopenic obesity is common, particularly among leukemia survivors, often masked by normal BMI. Survivors also face reduced bone density, impaired immunity, and persistent muscle loss, linked to prior therapies such as radiotherapy and hematopoietic stem cell transplantation. Increase in muscle mass post-treatment correlates with better survival outcomes. Conclusions: Early detection of sarcopenia can support timely interventions such as nutritional support and physical activity. Yet, significant diagnostic heterogeneity across existing studies hampers definitive conclusions regarding its true prevalence and the optimal assessment method. Standardized diagnostic criteria are urgently needed to enable more reliable prevalence estimates and evidence-based clinical strategies. Full article

Background: Class II malocclusion is one of the most common and challenging orthodontic problems, often requiring complex, lengthy treatment and sometimes involving extractions or surgery. While conventional fixed appliances have been the gold standard, the increasing demand for aesthetic and comfortable treatment alternatives has made clear aligners a prevalent choice. Understanding the specific biomechanics, limitations, and successful clinical strategies for using aligners—especially in managing vertical dimension and achieving skeletal correction (mandibular advancement)—is crucial for expanding non-invasive treatment options and improving outcomes for a broad range of Class II patients. Objective: The objective of this review is to examine the effectiveness and clinical approaches of clear aligners in Class II correction across different age groups, with particular attention to vertical control, mandibular advancement methods, and the predictability of tooth movements in both growing and fully mature patients. Materials and Methods: This review narratively discusses the most relevant clinical findings and practical strategies for managing Class II malocclusions with clear aligners. Particular attention is given to the integration of auxiliary devices, such as elastics, attachments, and temporary anchorage devices (TADs), which can enhance biomechanical control. Results: The combination of aligners with mini-implants and attachments resulted in a consequent decrease in excessive overjet, improvement in facial profile, and long-term stability supported by fixed retention. In growing patients, correction benefited from mandibular advancement protocols and control of molar extrusion, allowing for preservation of the mandibular plane angle. Movement predictability showed higher reliability in anterior torque movements, whereas maxillary incisor intrusion remained less predictable. Conclusions: Clear aligners, especially when supported by auxiliary device, such as mini-implants and attachments, offer a reliable and aesthetic alternative to conventional orthodontic treatment for Class II malocclusions. However, certain tooth movements may still be less predictable, highlighting the need for careful planning, individualized biomechanics, and ongoing technological improvements. Full article

Clear aligners are increasingly used as an alternative to fixed appliances in orthognathic surgery, particularly for skeletal Class III malocclusions. This scoping review aimed to evaluate the effectiveness of clear aligners in the pre- and postoperative phases of surgical treatment and was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Electronic searches were conducted in PubMed, Scopus, Embase, Web of Science, Cochrane Library, and OpenGrey. Data extraction considered study design, country, sample characteristics, surgical protocol, orthodontic biomechanics, use of auxiliaries, and cephalometric outcomes. Seven studies published between 2020 and 2024 were included. They involved 120 adult patients treated with Invisalign^® combined with Le Fort I osteotomy and bilateral sagittal split osteotomy. All studies reported skeletal improvements, particularly in ANB angle and Wits appraisal, with maintenance of vertical dimensions. Clear aligners facilitated presurgical dental decompensation, torque control, and postsurgical occlusal refinement, with auxiliaries and digital tools enhancing predictability. Despite variability in protocols and limited long-term follow-up, outcomes were comparable to those achieved with fixed appliances. Current evidence supports the clinical viability of integrating clear aligners into orthognathic surgery, although standardized protocols and further high-quality prospective studies are needed to confirm long-term stability. Full article

Radio frequency (RF)-based human activity sensing is an essential component of ubiquitous computing, with WiFi sensing providing a practical and low-cost solution for gesture and activity recognition. However, challenges such as manual data collection, multipath interference, and poor cross-domain generalization hinder real-world deployment. Existing data augmentation approaches often neglect the biomechanical structure underlying RF signals. To address these limitations, we present CM-GR, a cross-modal gesture recognition framework that integrates semantic learning with generative modeling. CM-GR leverages 3D skeletal points extracted from vision data as semantic priors to guide the synthesis of realistic WiFi signals, thereby incorporating biomechanical constraints without requiring extensive manual labeling. In addition, dynamic conditional vectors are constructed from inter-subject skeletal differences, enabling user-specific WiFi data generation without the need for dedicated data collection and annotation for each new user. Extensive experiments on the public MM-Fi dataset and our SelfSet dataset demonstrate that CM-GR substantially improves the cross-subject gesture recognition accuracy, achieving gains of up to 10.26% and 9.5%, respectively. These results confirm the effectiveness of CM-GR in synthesizing personalized WiFi data and highlight its potential for robust and scalable gesture recognition in practical settings. Full article

Skeleton-based action recognition networks have widely adopted the approach of Graph Convolutional Networks (GCN) due to their superior capabilities in modeling data topology, but several key issues still require further investigation. Firstly, the graph convolutional network extracts action features by applying temporal convolution to each key point, which causes the model to ignore the temporal connections between different important points. Secondly, the local receptive field of graph convolutional networks limits their ability to capture correlations between non-adjacent joints. Motivated by the State Space Model (SSM), we propose an Action Spatio-temporal Aggregation Network, named ActionMamba. Specifically, we introduce a novel embedding module called the Action Characteristic Encoder (ACE), which enhances the coupling of temporal and spatial information in skeletal features by combining intrinsic spatio-temporal encoding with extrinsic space encoding. Additionally, we design an Action Perception Model (APM) based on Mamba and GCN. By effectively combining the excellent feature processing capabilities of GCN with the outstanding global information modeling capabilities of Mamba, APM is able to comprehend the hidden features between different joints and selectively filter information from various joints. Extensive experimental results demonstrate that ActionMamba achieves highly competitive performance on three challenging benchmark datasets: NTU-RGB+D 60, NTU-RGB+D 120, and UAV–Human. Full article

Sensation seeking represents a significant risk factor for various mental health disorders and maladaptive behaviors, highlighting the need for objective assessment methods that circumvent the limitations of traditional self-report measures. This study introduces an innovative digital phenotyping approach that combines computational gait analysis with machine learning (ML) to quantify sensation-seeking traits and examines its validity. Natural gait sequences (using a Sony camera at 25 FPS) and self-report measures (Brief Sensation-Seeking Scale for Chinese, BSSS-C) were collected from 233 healthy adults. Computer vision processing through OpenPose extracted 25 skeletal keypoints, which were subsequently transformed into a hip-centered coordinate system and denoised using Gaussian filtering. From these kinematic data, 300 temporospatial gait features capturing various aspects of movement dynamics were derived. Using a supervised ML approach with feature selection, three ML models (SMO Regression, Multilayer Perceptron, and Bagging) were developed and compared through 10-fold cross-validation. The SMO Regression model demonstrated superior performance (r = 0.60, MAE = 3.50, RMSE = 4.59, R² = 0.26), outperforming the other approaches. These results establish proof-of-concept for gait-based digital phenotyping of sensation seeking, offering a scalable, objective assessment paradigm with potential applications in clinical screening and behavioral research. The methodological framework presented here advances the field of behavioral biometrics by demonstrating how computer vision and ML can transform basic movement patterns into meaningful psychological indicators. Full article

Background: Free fatal falls (FFF) represent a distinct form of blunt force trauma (BFT) that is frequently encountered in forensic practice. Distinguishing FFF injuries from other forms of BFT, such as motor vehicle accidents (MVAs), can pose challenges. Despite its growing usage, the role of postmortem computed tomography (PMCT) in diagnosing FFF and its comparison with autopsy remains underexplored. Purpose: This review synthesizes fracture patterns in FFF, examining both extrinsic and intrinsic variables that influence skeletal injuries. It also compares PMCT and autopsy findings to establish a replicable database for forensic analysis. Methods: PubMed and Google Scholar were systematically searched by three independent reviewers. The inclusion criteria required studies to be published in English, report at least 10 cases, focus on fatal falls, and provide precise data on skeletal injuries. Studies lacking detailed descriptions, focusing on survivors, or involving non-free falls were excluded. Data extraction tables facilitated synthesis and analysis. Key Findings: FFF are characterized mainly by axial skeletal fractures, particularly of the chest, skull, and pelvis. A history of intoxication and psychiatric disorders often correlates with the manner of death. Fracture patterns vary by fall height, impact surface, and cause: accidental falls show greater chest and skull involvement, whereas suicidal falls present more pelvic and skull fractures. PMCT detects fractures more frequently than traditional autopsy. Conclusions: Distinct fracture patterns aid in differentiating suicidal from accidental FFF, shaped by extrinsic and intrinsic factors. Given its superior fracture detection capabilities, PMCT should be integrated into forensic protocols for FFF investigations. Full article

Introduction: The elbow is a rare site for bone tumors, and for this reason, the literature provides little data on the epidemiology of metastatic lesions involving the distal humerus, proximal ulna, and radius. Before performing surgery of the metastatic bone, it is first necessary to consider both patients’ and metastatic lesions’ features in order to better choose the best possible treatment. This systematic review aims to collect data on elbow metastases, delineate primary tumors leading to such metastases, guide surgical treatment decisions, and evaluate reconstructive techniques and associated complications. Material and Methods: A systematic literature review was conducted in April 2024, searching the PubMed, MEDLINE, and Cochrane Library databases using specific search terms related to elbow metastases. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) was followed. Eligible studies reported at least one patient with metastatic bone disease involving the elbow region and specified the undertaken treatment. For studies reporting multiple skeletal sites, only elbow-specific data were extracted. We excluded recurrences of primary elbow tumors. The methodological quality of included studies was assessed with the modified Coleman Methodology Score (mCMS). Results: In total, 28 articles (103 patients) were included. The studies were predominantly case reports (68%), with a mean mCMS of 31. Gender was reported for only 41 patients: 71% were male and 29% female. The mean age at diagnosis of elbow metastatic lesion was 55 years old. Renal cell carcinoma was the most common primary tumor (28%), followed by breast (9%) and lung cancer (6%). The distal humerus was the most frequently affected site (85%). A surgical approach was adopted in 90% of cases, whereas 10% of patients were managed conservatively. Forty-five patients underwent wide tumor resection followed by reconstructive surgery while forty-eight patients received a surgical treatment for either pathological fractures or impending fractures. Conclusions: When treating elbow metastasis, a thorough evaluation of the patient is crucial, considering the patient’s functional status, pain management needs, and overall prognosis; all these features influence the treatment of choice. The selected treatment should aim to provide optimal functional outcomes and minimize complications. For patients with pathological or impending fractures, single or double plate fixation is typically the preferred approach. For patients with severe, symptomatic lesions unresponsive to conservative therapy, resection followed by the implantation of a modular prosthesis usually offers the best clinical and functional outcomes. Full article

Micro-expressions, characterized by brief and subtle facial muscle movements, are essential for conveying nuanced emotions in digital humans, yet existing rendering techniques often produce rigid or emotionally monotonous animations due to the inadequate modeling of temporal dynamics and action unit interdependencies. This paper proposes a graph-driven framework for micro-expression rendering that generates emotionally diverse and lifelike expressions. We employ a 3D-ResNet-18 backbone network to perform joint spatio-temporal feature extraction from facial video sequences, enhancing sensitivity to transient motion cues. Action units (AUs) are modeled as nodes in a symmetric graph, with edge weights derived from empirical co-occurrence probabilities and processed via a graph convolutional network to capture structural dependencies and symmetric interactions. This symmetry is justified by the inherent bilateral nature of human facial anatomy, where AU relationships are based on co-occurrence and facial anatomy analysis (as per the FACS), which are typically undirected and symmetric. Human faces are symmetric, and such relationships align with the design of classic spectral GCNs for undirected graphs, assuming that adjacency matrices are symmetric to model non-directional co-occurrences effectively. Predicted AU activations and timestamps are interpolated into continuous motion curves using B-spline functions and mapped to skeletal controls within a real-time animation pipeline (Unreal Engine). Experiments on the CASME II dataset demonstrate superior performance, achieving an F1-score of 77.93% and an accuracy of 84.80% (k-fold cross-validation, k = 5), outperforming baselines in temporal segmentation. Subjective evaluations confirm that the rendered digital human exhibits improvements in perceptual clarity, naturalness, and realism. This approach bridges micro-expression recognition and high-fidelity facial animation, enabling more expressive virtual interactions through curve extraction from AU values and timestamps. Full article

Background/Objectives: Sarcopenia is characterized by the progressive loss of skeletal muscle mass and function. Little is known about the dietary patterns and sarcopenia association. The aim of this systematic review and meta-analysis was to evaluate the association between dietary patterns and the risk of sarcopenia in adults over 50 years old, using the European Working Group on Sarcopenia in Older People, EWGSOP1 and EWGSOP2 criteria. Methods: This review followed PRISMA guidelines and was registered in PROSPERO (CRD42024423323). A systematic search was conducted in PubMed, Web of Science, and Cochrane Library (July 2024–February 2025). Observational studies in adults aged 50–85 years assessing a priori or a posteriori dietary patterns were included. Odds ratios (ORs) and 95% confidence intervals (CIs) were extracted. A random-effects model was used for meta-analysis. I² and meta-regression was performed to explore heterogeneity sources. Methodological quality was assessed with the Joanna Briggs Institute (JBI) checklist. Results: Eleven studies were included (n = 257–3432). The pooled analysis showed a significant 24% risk reduction in sarcopenia with healthy dietary patterns (OR = 0.76, 95% CI: 0.63–0.92, I² = 56.2). Unhealthy dietary patterns did not show a significant association (OR = 1.04, 95% CI: 0.66–1.63). Mediterranean (MD) pattern yielded the strongest effect (OR = 0.62, 95% CI: 0.40–0.95). Meta-regression analyses did not identify significant variables. Conclusions: Healthy dietary patterns, particularly the MD, are associated with a lower risk of sarcopenia in adults over 50 years old and represent a promising nutritional strategy for sarcopenia prevention. Full article

Background: Spinal metastases can cause significant impairment of neurological function and quality of life. Hence, personalized clinical decision-making based on prognosis and likely outcome is desirable. The effectiveness of AI in predicting complications and treatment outcomes for patients with spinal metastases is assessed. Methods: A thorough search was carried out through the PubMed, Scopus, Web of Science, Embase, and Cochrane databases up until 27 January 2025. Included were studies that used AI-based models to predict outcomes for adult patients with spinal metastases. Three reviewers independently extracted the data, and screening was conducted in accordance with PRISMA principles. AUC results were pooled using a random-effects model, and the PROBAST program was used to evaluate the study’s quality. Results: Included were 47 articles totaling 25,790 patients. For training, internal validation, and external validation, the weighted average AUCs were 0.762, 0.876, and 0.810, respectively. The Skeletal Oncology Research Group machine learning algorithms (SORG-MLAs) were the ones externally validated the most, continuously producing AUCs > 0.84 for 90-day and 1-year mortality. Models based on radiomics showed promise in preoperative planning, especially for outcomes of radiation and concealed blood loss. Most research concentrated on breast, lung, and prostate malignancies, which limited its applicability to less common tumors. Conclusions: AI models have shown reasonable accuracy in predicting mortality, ambulatory status, blood loss, and surgical complications in patients with spinal metastases. Wider implementation necessitates additional validation, data standardization, and ethical and regulatory framework evaluation. Future work should concentrate on creating multimodal, hybrid models and assessing their practical applications. Full article

Background/Objectives: Pompe disease (PD) is a rare autosomal recessive disorder caused by a deficiency of the lysosomal acid α-1,4-glucosidase (GAA) encoded by the GAA gene, leading to muscular dysfunctions due to pathological accumulation of glycogen in skeletal and cardiac muscles. PD has been reported in several animals and Japanese quails (JQ; Coturnix japonica), but a causative mutation has yet to be found in JQs with PD. Here, we aimed to identify a pathogenic mutation in JQs associated with PD. Methods: Paraffin-embedded skeletal muscle blocks from four JQs stored since the 1970s were used in this study. After confirming the histopathological phenotypes of PD, Sanger sequencing was performed to identify a pathological mutation in the GAA I gene of JQs. A genotyping survey was conducted using a real-time polymerase chain reaction assay targeting a candidate mutation using DNA samples extracted from 70 new-hatched JQs and 10 eggs from commercial farms. Results: Microscopic analysis confirmed the presence of the PD phenotype in three affected JQs based on abnormal histopathological changes and accumulated glycogen in the affected muscles, while one JQ was unaffected and served as a control. Sanger sequencing revealed that the three affected JQs were homozygous for the deletion of guanine at position 1096 in the open reading frame (c.1096delG). A genotyping survey of 70 JQs and 10 eggs from commercial farms showed that none carried this deletion mutation. Conclusions: This study identified c.1096delG as the pathogenic mutation for PD in JQs. This mutation induces a frameshift and substitution of amino acids at position 366 (alanine to histidine), resulting in premature termination at the 23rd codon (p.A366Hfs*23). This suggests that this mutation causes the deficient activity of GAA in JQs with PD. The identification of the c.1096delG mutation enabled the systematic maintenance of the flock colony in the PD model. Furthermore, this PD model can be used to clarify unknown aspects of PD pathogenesis and develop therapeutic strategies. Full article

Operatic singing, traditionally taught through empirical and subjective methods, demands innovative approaches to enhance its pedagogical effectiveness today. This paper introduces a novel integration of advanced skeletal tracking technology into a prototype framework for operatic singing pedagogy research. Using the Microsoft Kinect Azure DK sensor, this prototype extracts detailed data on spinal, cervical, and shoulder alignment and movement data, with the aim of quantifying biomechanical movements during vocal performance. Preliminary results confirmed high face validity and biomechanical relevance. The incorporation of skeletal-tracking technology into vocal pedagogy research could help clarify certain technical aspects of singing and enhance sensorimotor feedback for the training of operatic singers. Full article

We present mmHSE, a two-stage framework for human skeleton estimation using dual millimeter-Wave (mmWave) Frequency-Modulated Continuous-Wave (FMCW) radar signals. To enable data-driven model design and evaluation, we collect and process over 30,000 range–angle maps from 12 users across three representative indoor environments using a dual-node radar acquisition platform. Leveraging the collected data, we develop a two-stage neural architecture for human skeleton estimation. The first stage employs a dual-branch network with depthwise separable convolutions and self-attention to extract multi-scale spatiotemporal features from dual-view radar inputs. A cross-modal attention fusion module is then used to generate initial estimates of 21 skeletal keypoints. The second stage refines these estimates using a skeletal topology module based on graph convolutional networks, which captures spatial dependencies among joints to enhance localization accuracy. Experiments show that mmHSE achieves a Mean Absolute Error (MAE) of 2.78 cm. In cross-domain evaluations, the MAE remains at 3.14 cm, demonstrating the method’s generalization ability and robustness for non-intrusive human pose estimation from mmWave FMCW radar signals. Full article