Search Results (6,645)

Background: Airway management in patients with cervical infections remains a major challenge because progressive swelling and distortion of the upper airway may rapidly lead to life-threatening airway obstruction. Despite advances in imaging and advanced intubation techniques, the optimal management strategy remains controversial. This scoping review aimed to evaluate the current evidence on airway assessment and management of adult patients with cervical infections. Methods: A scoping review was conducted according to the PRISMA-ScR recommendations. PubMed/MEDLINE, Scopus, and Embase were systematically searched for English-language studies published between January 2006 and May 2026. Studies evaluating airway management in adult patients with deep neck infections or acute infectious epiglottitis/supraglottitis were included. Data regarding study characteristics, airway management strategies, predictors of airway intervention, and clinical outcomes were extracted and synthesized descriptively. Results: Fourteen studies involving 3297 patients met the inclusion criteria. Endotracheal intubation was the most frequently reported airway management strategy, while tracheostomy remained essential in selected patients with extensive multispace infections, descending necrotizing mediastinitis, severe airway alteration, or anticipated prolonged airway protection. Clinical signs of respiratory compromise, multispace cervical involvement, mediastinal extension, and significant supraglottic edema were the most consistently reported predictors of airway intervention. Across the included studies, airway management strategies varied considerably according to disease severity, anatomical findings, institutional protocols, and operator experience. Conclusions: Current evidence suggests an individualized, multidisciplinary approach to airway management in adults with life-threatening cervical infections. Timely airway assessment integrating clinical evaluation, flexible endoscopy, and contrast-enhanced computed tomography is essential to guide appropriate airway intervention. Full article

Background/Objectives: Rotigotine is a conformationally flexible chiral dopamine agonist, but its solution-phase chiroptical behavior and conformational heterogeneity have not been systematically assessed. This study characterized its electronic circular dichroism (ECD), optical rotatory dispersion (ORD), and vibrational circular dichroism (VCD) responses and evaluated their consistency with its absolute configuration. Methods: ECD and ORD data were acquired for rotigotine ((S)-1) and its simplified analog (S)-2 in acetonitrile and methanol, and VCD spectra were recorded in appropriate deuterated solvents. Conformational searches, Boltzmann weighting, and density functional theory/time-dependent density functional theory calculations were performed. A principal-component-analysis/torsion (PCA-Tor) workflow selected a compact but structurally representative conformer ensemble for (S)-1. Results: In acetonitrile, (S)-1 exhibited a positive Cotton effect (CE) at 208.5 nm and a negative CE at 194.0 nm. The Boltzmann-weighted ECD spectrum reproduced the intense negative band below 200 nm and the positive band near 210 nm. The calculated VCD spectrum agreed with the signs and positions of the main experimental bands in the 1500–1100 cm⁻¹ region. Experimental ORD curves of both compounds were negative in acetonitrile and methanol. The calculations reproduced the sign and overall trend, although their magnitudes were protocol- and population-sensitive. Ring puckering strongly affected the calculated ECD and ORD data, whereas side-chain flexibility expanded the accessible conformational space. Conclusions: The combined results support the known S configuration of rotigotine and demonstrate the value of representative conformational sampling for flexible chiral pharmaceuticals. This study also defines the practical utility and limitations of simplified molecular models in chiroptical analysis. Full article

Background and Clinical Significance: Developmental dyslexia is diagnosed through behavioral neuropsychological and language assessment, which depends on the child’s attention, motivation, and cooperation during testing. Auditory-evoked potentials provide objective, non-behavioral indices of neural encoding along the auditory pathway, but they are usually examined in group-level designs and in separate cohorts, so that intra-familial variability, and the level of the pathway at which atypical encoding emerges, remain poorly described. Characterizing siblings who share genetic background, family environment and schooling, using a single protocol that spans the brainstem, subcortical and cortical levels, is therefore clinically informative. Case Presentation: Two non-twin brothers (aged 13 and 11) with a clinical diagnosis of familial dyslexia and normal peripheral hearing underwent independent neuropsychological, speech–language and electrophysiological evaluations, comprising the click auditory brainstem response (ABR), the frequency-following response (FFR) to /da/ (80 dB SPL) in quiet and in ipsilateral and contralateral noise (masker 70 dB SPL; SNR ≈ +10 dB), and the P300 cognitive potential (auditory oddball). Click-ABR latencies and interpeak intervals were normal in both siblings, with an isolated, age-inappropriate V/I amplitude-ratio reduction in one. The FFR showed a condition-dependent, sibling-specific pattern of degradation, ipsilateral in one brother and contralateral in the other, with selective F0 collapse. The P300 was altered in both, with prolonged or absent components and low target-detection accuracy. All comparisons are descriptive. Conclusions: Preserved brainstem transmission contrasted with vulnerable subcortical speech encoding and altered cortical processing, revealing a central and biologically heterogeneous profile even within a family, and supporting the combined use of ABR, FFR and P300 as a complementary objective characterization of auditory processing in dyslexia. Full article

The evolution of modern enterprise architecture has been strongly influenced by distributed web services, especially protocol-based standards such as Simple Object Access Protocol (SOAP) and resource-oriented architectural styles such as Representational State Transfer (REST). Cloud-native ecosystems, microservice architectures, and public API management commonly favor the lightweight, JSON-compatible, and horizontally scalable characteristics of RESTful services, whereas legacy configurations and highly regulated environments continue to use SOAP because of its formal contracts and compatibility with WS-* specifications for message-level security, reliable messaging, and transaction coordination. This paper presents a structured literature review that evaluates the architectural trade-offs, performance patterns, security boundaries, reliability considerations, and enterprise use cases of REST and SOAP. The review follows PRISMA-informed reporting practices and software-engineering review guidance, but it is not presented as an exhaustive systematic review because the original search strategy required REST and SOAP terms to appear together. IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus were searched for studies published between 2021 and 2026, resulting in 32 selected studies. The selected literature contains different evidence roles, including direct REST-SOAP empirical comparisons, REST-only and SOAP-only empirical studies, implementation studies, analytical papers, surveys, reviews, and contextual technical sources. The synthesis therefore separates direct empirical evidence from contextual and secondary evidence. The findings indicate that RESTful APIs generally show lower latency, smaller payloads, simpler parsing, and better horizontal scalability in the reported benchmark and web-facing settings, while SOAP remains relevant where formal service contracts, message-level protection, reliable messaging patterns, and transaction coordination are required. The paper identifies gaps in production-representative stress testing, empirical security comparison, reference-level traceability, and independent validation of hybrid REST-SOAP decision models. The resulting decision framework is presented as a provisional evidence-informed decision aid, not as an empirically validated tool. Full article

Background/Objectives: Cardiovascular diseases remain a leading cause of death worldwide, and early diagnosis is essential. Phonocardiogram (PCG) signals provide a non-invasive and inexpensive means of cardiac assessment. However, accurate interpretation of PCG signals requires clinical expertise. In this paper, we propose TriFusion-PCGNet, a hybrid multi-branch deep learning framework for automated heart sound classification. Methods: The proposed method was evaluated on the PhysioNet/Computing in Cardiology Challenge 2016 dataset. Three complementary PCG representations—the raw signal, band-pass filtered signal, and Hilbert envelope signal—were processed through dedicated feature extraction branches using residual convolutional networks, dilated residual convolutions, and a BiGRU with a temporal attention mechanism. The extracted features were fused and classified using fully connected layers. Performance was evaluated using the original train–validation split and stratified 10-fold cross-validation. Results: Under a strictly separated record-level 10-fold cross-validation protocol, TriFusion-PCGNet achieved 90.87% mean accuracy and 95.34% AUC, with outer test folds isolated from training and model selection. Subject-level analysis showed substantially lower performance, emphasizing the importance of subject-disjoint evaluation for patient-level generalization. Conclusions: TriFusion-PCGNet integrates complementary PCG representations for automated normal/abnormal heart sound classification. However, the substantially lower subject-disjoint performance indicates that the record-level results should not be interpreted as evidence of patient-independent generalization, and further validation on independent subject-annotated datasets is required. Full article

Hierarchical retail forecasting requires predictions that are both accurate and coherent across multiple planning levels, from total demand to individual product–store series. Because these forecasts guide inventory, replenishment, storage, and distribution decisions, improving their coherence and reliability can support more efficient resource use, reduce avoidable overstock and product waste, and limit the need for emergency logistics. This study investigates how global deep-learning architectures interact with post hoc reconciliation in point and probabilistic forecasting. Using an M5-derived hierarchical and grouped structure comprising 42,840 series, we compare three MLP-oriented models, MLP, N-BEATS, and N-HiTS, with five transformer-based models, Transformer, Temporal Fusion Transformer, Informer, PatchTST, and Autoformer. All models are evaluated under a common 28-day forecasting horizon, temporal partition, Optuna-based tuning protocol, and three complete seeded runs. Coherence is imposed using Bottom-Up reconciliation and four MinTrace variants, while probabilistic forecasts are generated through residual-block bootstrap reconciliation. Point and probabilistic performance are assessed level-wise and globally using MASE and scaled CRPS, respectively. The results show that the strongest transformer-based combination outperforms the strongest MLP-based combination at every hierarchy level. PatchTST combined with MinTrace-WLS-struct is particularly effective at aggregate and intermediate levels, achieving a Total-level MASE of 0.537 and sCRPS of 0.037. At the Product–Store level, Bottom-Up reconciliation becomes preferable, with the Transformer attaining the lowest MASE of 1.367 and sCRPS of 0.912. Because granular series dominate the hierarchy-wide average, the lowest overall MASE is obtained by TFT with Bottom-Up reconciliation (1.428), whereas the lowest overall sCRPS is shared by the Transformer and Informer with Bottom-Up reconciliation (0.813). These findings demonstrate that neither the forecasting architecture nor the reconciliation method should be selected independently of hierarchy depth and forecasting objective. Strategic and tactical levels benefit primarily from PatchTST with MinTrace reconciliation, whereas highly granular operational forecasting favors Bottom-Up reconciliation with transformer-based models. From a sustainability perspective, this level-aware framework provides a basis for aligning forecasting decisions with resource efficiency, waste reduction, service reliability, and greater resilience across the retail supply chain. Full article

Foot muscles contribute to impact attenuation and propulsion during running, yet the effects of repeated toe-flexor contractions and associated strength loss on running biomechanics remain poorly understood. The primary objective of this randomized crossover study was to investigate the acute effects of repeated concentric and isometric toe-flexor contractions on hallux flexion strength, used as an index of fatigue; secondary analyses examined running ground-reaction-force outcomes. Nineteen recreationally active adults completed two interventions one week apart: concentric toe-flexor contractions and isometric toe-flexor contractions combined with electrical stimulation of the medial longitudinal arch. Hallux flexion strength and ground reaction forces during treadmill running at 9 and 12 km·h⁻¹ were assessed before and after each intervention. The concentric protocol reduced hallux flexion strength by 20%, whereas the isometric protocol did not. None of the hypothesis-driven running biomechanical outcomes, including contact duration, impact peak force, active peak force, and vertical stiffness, changed significantly after either protocol. Exploratory secondary analyses suggested changes in selected leg stiffness and impulse variables. In recreationally active adults, repeated concentric toe-flexor contractions induced measurable neuromuscular fatigue, whereas the isometric protocol did not significantly reduce hallux flexion strength; biomechanical changes were limited to exploratory outcomes. Full article

The identification of visible submerged hazards, including shallow-water shipwrecks and associated debris, is important for maritime safety, coastal management, environmental monitoring, and marine archeology. The scope is restricted to wrecks that remain optically visible from above in shallow or intertidal water. This study presents Submerged Hazard Identification and Processing using Augmented Image-based Detection (SHIP-AID), a modular GeoAI evaluation framework for high-resolution RGB imagery. Following site-level quality control, the independent source dataset contains 695 images from 403 wreck sites. The dedicated group-disjoint holdout contains 150 images from 88 sites and 184 annotated wreck objects. Five detector backbones and one task-aware underwater-enhancement baseline were evaluated using ten matched training seeds. Under the standard benchmark evaluation protocol, in which precision and recall are reported at the internally determined maximum-F1 point of the confidence sweep, the best configuration achieved precision 0.896, recall 0.861, mAP@50 0.927, and mAP@50–95 0.668 on the locked holdout. At the fixed, validation-selected operating threshold of 0.45, the primary detector produced threshold-specific precision 0.922 and recall 0.837. Site-clustered bootstrap intervals were 0.895–0.951 for mAP@50 and 0.625–0.704 for mAP@50–95. Physically informed attenuation and backscatter augmentation improved stricter-IoU performance relative to generic augmentation, whereas global Otsu thresholding reduced recall and localization accuracy. Performance remained stable under mild degradation, declined under moderate and strong degradation, and became unreliable under severe low visibility. SHIP-AID is therefore positioned as a decision-support framework for prioritizing optically visible shallow-water sites, with sonar, diving, hydrographic, or archeological evidence retained as the confirmation standard. Full article

Soccer requires rapid tactical decision-making, perceptual-cognitive processing, and coordinated physical execution, making it a relevant application domain for virtual reality (VR). VR systems integrate head-mounted displays, inertial measurement units, eye-tracking sensors, and electroencephalographic interfaces to support training, assessment, rehabilitation, and engagement. However, evidence remains fragmented across populations, study designs, sensing configurations, and research purposes, limiting conclusions about effectiveness and real-world application. Following PRISMA 2020, this systematic review synthesized 20 empirical studies published between January 2020 and 16 August 2026 involving school, academy, collegiate, and selected indirect adult soccer populations. Studies were classified by sensor modality, tracking configuration, evidence purpose, and outcome domain. Controlled intervention studies reported possible short-term improvements in selected sensorimotor, tactical, perceptual-cognitive, technical, and motivational outcomes. In contrast, cross-sectional and profiling studies showed that some VR tasks distinguished players by expertise, age, or competitive level; these findings support assessment or discriminative validity but do not demonstrate that VR training improves performance. Rehabilitation and injury-related evidence was limited and partly derived from adult or clinical populations, whereas engagement studies suggested potential benefits for motivation, attention, and participation. Six-degrees-of-freedom HMDs, 360° projection systems, eye tracking, and EEG generated different types of evidence, but no study directly compared hardware configurations within the same sample. The main contribution of this review is an integrated evidence-purpose and sensor-based synthesis that distinguishes training effectiveness from assessment validity across student-soccer applications. Overall, evidence remains promising but preliminary because of small and mixed populations, methodological heterogeneity, incomplete technical reporting, possible publication and language bias, short follow-up, and limited transfer to full-match performance. Standardized sensor reporting, controlled interventions, and longitudinal transfer assessments are required. Protocol registration: OSF. Full article

Life cycle background databases (LCBDs) determine the credibility and reproducibility of life cycle assessment (LCA) results. However, insufficient transparency in databases has long been a critical issue affecting the reliability of LCA applications. Here, the study proposes a user-centric transparency assessment framework covering database configuration, model traceability and data quality assessment and documentation to evaluate four mainstream databases (ecoinvent, GaBi, USLCI, CLCD × WebLCA). At the database level, only six countries hold over 1000 datasets and large dataset volumes stem mainly from repetitive regional/technical variants rather than new unit processes, causing severe combinatorial redundancy. Three quantitative indicators are therefore proposed to distinguish genuine product diversity from redundant data. At the model level, a five-tier hierarchical traceability chain is innovatively constructed from the end-user perspective to enable full backward tracing. Most databases lack reverse traceability from results to raw data, resulting in poor model reproducibility, reflecting a deficiency in basic standards. At the data quality level, current data quality assessments suffer from undisclosed weighting mechanisms and inadequate uncertainty quantification, impeding the identification of quality weaknesses. This user-centric framework assesses the transparency of LCBDs. By detecting the transparency deficiencies, the framework offers references for developers to standardize disclosure protocols and assists users in selecting appropriate databases, while enhanced database transparency further strengthens the scientific credibility of LCA results and the robustness of sustainability assessments. Full article

Background/Objectives: Immune-mediated inflammatory skin diseases (IMSDs), including psoriasis, atopic dermatitis, and systemic sclerosis, are chronic relapsing disorders that impose substantial physical, psychological, and economic burdens. Current assessment relies largely on subjective clinical scoring and conventional imaging modalities that provide limited functional information. This structured narrative review evaluates photoacoustic imaging (PAI) as a non-invasive optical-ultrasound technique for morphological and functional assessment in IMSDs. Methods: We searched PubMed studies published from January 2017 to December 2025 using terms related to photoacoustic imaging, optoacoustic mesoscopy, psoriasis, atopic dermatitis, systemic sclerosis, contact dermatitis, and immune-mediated inflammatory skin disease. Studies were narratively synthesized by disease, PAI modality, comparator, outcome, and translational limitation. Main Findings: PAI can map vascular and pigment-related optical absorption and estimate oxygenation, blood volume, and selected structural biomarkers. In psoriasis, atopic dermatitis, and systemic sclerosis, PAI-derived measures have been associated with clinical severity, subclinical vascular or epidermal changes, and treatment response. However, most available studies are small, single-center, heterogeneous, or based on prototype systems, and several biomarkers still require histopathological and external validation. Conclusions: Current evidence supports PAI as a feasible research and potential adjunctive assessment tool for IMSDs rather than an established replacement for standard clinical, histopathological, or imaging methods. Standardized protocols, validation across skin phototypes, cost-effective devices, and prospective multicenter studies are required before routine clinical adoption. Full article

After major disasters, news media selectively construct the public record of what has been lost. This study examines that pattern by comparing an independently derived satellite-based inventory of damaged cultural heritage in Hatay, Türkiye, with a corpus of 79 Turkish-language online news reports published between February 2023 and February 2024. Under the primary retrieval protocol, only 16 of the 86 ARIA-export assets appeared directly in the coverage; with six further off-export or proximity-based cases, 22 reported assets entered the news in total. A targeted asset-name search then located 15 further reports, containing 17 additional direct references to damaged assets. Even on the most inclusive reading, in which every one of these references is treated as a previously uncounted and non-overlapping asset, at least 53 of the 86 assets (61.6%) remained outside the news record. The analysis also covers attribute salience, temporal patterns, and outlet concentration in the coverage. Drawing on first- and second-level agenda-setting theory, and treating gatekeeping and media-system conditions as contextual mechanisms, it defines visibility deficit as the measurable gap between independently documented damage and the damage that enters a defined news corpus. This gap need not imply deliberate suppression. It may follow from editorial priorities, limited access to sites, the availability of visual material, or the limited capacity of newsrooms to work with geospatial data. By attending to what stays outside the news agenda rather than only to differences in salience among covered items, the study identifies a structural boundary of agenda setting and extends agenda-setting research into measurable non-coverage after disaster. Full article

Deploying prohibited-item detectors on resource-constrained X-ray security inspection terminals is not equivalent to selecting the smallest available YOLO scale: excessive compression can reduce feature capacity, whereas medium-scale detectors may retain avoidable computational redundancy. This study therefore investigates a deployment-oriented operating point through coordinated backbone compression, feature compensation, and class-sensitive optimization. YOLO-SMV and YOLO-EMV are developed from YOLOv8m and YOLO11m by combining MobileNetV3-Small backbone reconstruction, SE/ECA-based channel recalibration, and a VF-BCE objective for difficult and underrepresented categories. A three-seed full-factorial study on YOLO11m shows that VF-BCE provides the largest individual accuracy gain and that ECA repeatedly recovers part of the performance lost in the compressed VF-BCE pathway. The accuracy-oriented ECA+VF-BCE configuration reaches $0.93842\pm 0.00720$ mAP50, whereas the deployment-oriented YOLO-EMV reaches $0.92744\pm 0.00768$; the latter trades $1.098\pm 0.061$ percentage points of mAP50 for a reduction from 20.03 M to 12.04 M parameters and from 67.9 G to 28.8 G FLOPs. Under the common seed-41 SIXray protocol, YOLO-EMV also achieves higher mAP50 than standard YOLO11n, YOLO11s, and YOLO11m, demonstrating that the selected operating point is not reproduced simply by choosing a smaller baseline. Published SIXray results are reported separately as protocol-aware literature context rather than as a cross-paper ranking. Additional OPIXray and PIDray experiments provide multi-benchmark evidence for the component interactions under heavy occlusion and long-tailed class distributions. The deployment-oriented YOLO-EMV model has also been integrated into customs security inspection equipment. Full article

Background/Objectives: Vacuum-assisted breast biopsy (VABB) is widely used for the diagnosis of nonpalpable breast cancer but is not considered definitive treatment because microscopic residual disease may persist within or around the biopsy cavity. The ARTHE phase I trial evaluated a cavity-targeted radionuclide strategy based on same-session sequential intralesional administration of avidin followed by [⁹⁰Y]Y-DOTA-biotin after VABB. Methods: Eighteen women with nonpalpable breast cancer measuring ≤15 mm and a skin-to-cavity distance ≥ 13 mm were treated in three sequential activity-escalation cohorts. The primary objective was to assess acute local and systemic safety, including dose-limiting toxicity. The protocol-specified co-primary objective was to evaluate preliminary antitumor activity, assessed as breast-only pathologic complete response (pCR) at surgery. Given the phase I single-arm design, pCR was analyzed descriptively. The protocol-specified secondary objective was patient-specific dosimetry. Additional exploratory assessments included post-injection biodistribution and integration with subsequent breast-conserving surgery. Results: No dose-limiting toxicities, grade ≥ 3 adverse events, clinically relevant hematologic toxicity, treatment discontinuations, hospitalizations, or treatment-related surgical delays were observed. Local toxicity was limited to grade 1 injection-site pain and/or erythema. Post-injection imaging consistently demonstrated focal intralesional localization without clinically relevant extra-lesional uptake. All patients underwent breast-conserving surgery 4–7 weeks after treatment. No residual tumor cellularity (RTC), from either invasive or in situ carcinoma, was identified in the breast surgical specimen in 5/18 patients (27.8%). However, because diagnostic VABB may have removed part or all of the malignant lesion, the absence of residual carcinoma at surgery cannot be attributed specifically to the radionuclide treatment. Conclusions: The intralesional avidin-mediated local trapping strategy using [⁹⁰Y]Y-DOTA-biotin after VABB was feasible, showed favorable acute and short-term tolerability, was associated with early focal localization on post-injection imaging, and was compatible with standard breast-conserving surgery. Controlled studies are warranted to determine the therapeutic contribution of this post-biopsy cavity-targeted strategy, optimize administered activity, and refine patient selection. Full article

Background: Recent advancements in enabling technologies, including artificial intelligence and telemedicine, alongside robust clinical study outcomes, have led to significant progress in upper limb and hand exoskeletons utilised for post-stroke rehabilitation. Objectives: This review aims to synthesize the recent scientific literature (2010–2025) on post-stroke upper-limb and hand exoskeletons, with particular attention to the sensing architectures—sensing modalities, signal processing, sensor fusion, and sensor-driven control—that integrate technical and biomedical domains to examine device architecture, clinical context, and outcome selection. Methods: A search of PubMed and Scopus was conducted on 10 November 2025, cross-checked against IEEE Xplore, Web of Science, Embase, and ACM Digital Library. We included studies evaluating wearable exoskeletons or robotic orthoses for the upper limb/hand in post-stroke rehabilitation. Two independent reviewers screened records and extracted data, with disagreements resolved by consensus. Data were synthesised using a predefined label-based taxonomy. The review protocol was not registered. Results: From 1889 identified records, 219 studies met the inclusion criteria. The synthesis reveals a transition from rigid, laboratory-centered systems to lighter, soft, and home-oriented solutions. Available evidence suggests potential impairment-level benefits, particularly for proximal motor control, but certainty remains limited due to heterogeneity, small samples, blinding limitations, inconsistent dosing, and limited long-term follow-up; gains in hand/finger dexterity appear even more variable. Discussion: While exoskeleton-assisted therapy appears associated with impairment-level gains, transfer to activities of daily living (ADLs) and real-world function remains inconsistently documented and insufficiently powered to support firm conclusions. Full article