Abstract: Effective canine gastrointestinal health depends on suppression of enteric pathogens and maintenance of epithelial barrier integrity. Limosilactobacillus reuteri ATCC PTA 6127 (Lr6127) is a dog‑derived probiotic, but evidence supporting its protective capacity remains limited. Here, we evaluated the antimicrobial and epithelial‑supportive effects of Lr6127 using a canine epithelial cell model. Cell‑free supernatant (CFS) from Lr6127 significantly inhibited the growth of canine‑relevant pathogens, including Escherichia coli (52.0 ± 1.3%), Clostridium perfringens (54.0 ± 2.7%), and Salmonella typhimurium (48.6 ± 1.2%), compared with the medium control (P <  0.0001). Pathogen inhibition increased in a dose‑dependent manner with increasing CFS concentrations. Untargeted metabolomic analysis revealed enrichment of multiple antimicrobial‑associated metabolites, indicating multi‑component pathogen suppression, with genomic analysis supporting the aromatic amino acid–derived metabolite findings. In addition, viable Lr6127 significantly reduced epithelial adhesion of all tested pathogens (P <  0.01). Beyond direct antimicrobial effects, Lr6127 CFS promoted epithelial wound healing at later time points, accompanied by coordinated modulation of proteins associated with cytoskeletal remodeling and barrier repair. Collectively, these findings suggest that Lr6127 is associated with pathogen suppression, reduced adhesion, and epithelial wound repair mechanisms.

Abstract: Background: Maxillary hypoplasia and skeletal Class III malocclusion are deeply intertwined with upper airway constriction and paranasal sinus dysfunction. Conventional orthopedic interventions often struggle to achieve true 3D skeletal translation without inducing undesirable rotational side effects. The Right Angle Maxillary Protraction Appliance (RAMPA) therapy offers a biomimetic and mechanotherapeutic approach, focusing on anterosuperior protraction to restore both structural harmony and respiratory function. Methods: This feature paper systematically reviews the multi-disciplinary evidence supporting RAMPA therapy, synthesizing findings from recent computational and clinical studies. We examine Finite Element Method (FEM) simulations detailing sutural mechanotransduction and osteogenic "BMP-2 Trigger Zones", Computational Fluid Dynamics (CFD) utilizing shear-thinning rheological models for two-phase air-mucus interactions, and large-cohort CBCT and Coben analyses quantifying longitudinal growth. Results: FEM studies confirm that RAMPA, especially when combined with intraoral devices (e.g., gHu-1, VomPress, Hybrid), achieves predictable anterosuperior displacement and concentrates tensile stress to trigger molecular bone remodeling. CFD simulations reveal that this precise skeletal remodeling optimizes wall shear stress (WSS) and actively facilitates paranasal mucus clearance via enhanced suction and shear-thinning effects. Clinically, RAMPA induces a 1.2-fold acceleration in natural sinonasal growth velocity. Furthermore, volumetric gains are distinctively pronounced in patients with pre-existing empyema (61.2% increase) compared to those with clear sinuses (18% increase), indicating rapid pathophysiological obstruction relief. Conclusions: By integrating controlled biomechanical forces with fluid-dynamic airway optimization, RAMPA therapy acts as a mechanotherapeutic modulator. It bridges the gap between mechanical intervention, molecular signaling, and physiological homeostasis, offering a comprehensive paradigm for pediatric craniofacial and respiratory restoration.

Abstract: Half-cell testing has long served as a convenient and informative platform for screening electrode materials in lithium-ion and sodium-ion batteries. However, the electrochemical performance obtained under such simplified conditions often fails to predict the behavior of practical full cells, where electrode balancing, mass loading, areal capacity, electrolyte amount, pressure, and interfacial instability impose much stricter constraints. In this review, we examine the limitations of half-cell-based assessment and discuss why moving beyond idealized configurations is essential for the realistic evaluation of advanced battery materials. Particular attention is given to the dynamic nature of interfacial chemistry, including the formation and evolution of the solid electrolyte interphase and cathode electrolyte interphase, as well as to the role of electrolyte decomposition, additives, binders, and electrode formulation in determining cell performance. We further analyze how operando and in situ characterization techniques, including X-ray-based methods, vibrational spectroscopies, microscopy, and electrochemical impedance analysis, are reshaping the understanding of structural evolution, interphase development, and degradation processes under realistic operating conditions. Major failure pathways in practical cells, such as capacity fade, impedance growth, mechanical degradation, electrolyte consumption, gas evolution, transition-metal dissolution, and surface reconstruction, are critically discussed for both lithium-ion and sodium-ion systems. Representative electrode chemistries are considered to illustrate how promising material-level properties do not always translate into practical-cell success. Finally, we address the metrics that matter for practical relevance, summarize current mitigation strategies, and highlight validation criteria and testing workflows that can better connect academic materials research with realistic battery development. By integrating interfacial chemistry, operando insight, and practical performance criteria, this review aims to provide a more translational framework for the design and assessment of next-generation lithium-ion and sodium-ion batteries.

Abstract: The increasing adoption of cloud computing has expanded organizational attack surfaces and increased exposure to credential compromise, ransomware, and cloud misconfiguration. Conventional security information and event management (SIEM) systems, based primarily on static correlation rules and signature-based detection, often struggle to process heterogeneous cloud telemetry and prioritize high-severity incidents in real time. This study evaluates the operational impact of an artificial intelligence (AI)-augmented SIEM and Extended Detection and Response (XDR) architecture for cloud threat detection and automated incident response. A mixed-methods comparative case study was conducted across two enterprise-style security environments: an AI-augmented cloud-native SIEM/XDR architecture and a conventional baseline environment based on manual triage and signature-based controls. Three attack scenarios were analyzed: phishing-led account takeover, multi-stage ransomware, and shadow-IT data exfiltration. The AI-augmented environment reduced mean time to triage from 17.4 hours in the conventional baseline to 10.7 minutes and enabled ransomware containment in under five minutes through automated response playbooks. The results also showed improved prioritization of high-severity incidents, reduced analyst workload, and a high automated closure rate. However, limitations were observed in the calibration of behavioral models, vendor dependency, and detection gaps involving legitimate third-party services and password-protected content. The findings should be interpreted as operational evidence at an architectural level, not as an isolated evaluation of individual AI models.

Abstract: Census-style records contain sensitive personal information that must be transformed into structured fields before it can support record linkage, geocoding, duplicate detection, identity verification, and statistical processing. This task is challenging because addresses appear in many forms, including standard street addresses, apartment and unit addresses, university addresses, military APO/FPO/DPO addresses, rural routes, highway addresses, and attention-line records. Rule-based parsers are efficient and transparent but often fail on non-standard formats. Single-prompt Large Language Model (LLM) approaches improve generalization but can suffer from record skipping, field conflation, and long-context degradation when processing heterogeneous documents. In addition, privacy and governance requirements limit the use of cloud-hosted models for sensitive census-style data. This paper presents a three-stage hierarchical multi-agent architecture for structured knowledge extraction from census-style records using a locally deployed open-weight LLM. A Planner Agent analyzes the input document and formulates an extraction strategy. A Manager Agent converts this strategy into a dependency-aware task graph. A fleet of eight specialized Worker Agents performs extraction, validation, and formatting, while a bounded feedback loop supports limited autonomous recovery from extraction failures. The system runs on gpt-oss-20b, a 21-billion-parameter open-weight model deployed on local infrastructure, so input records do not need to be transmitted to an external model provider. The system is evaluated on 700 synthetically generated records across seven address categories. It achieves 95.7% component-level exact match accuracy, compared with 52.3% for a rule-based baseline and 80.9% for a single-prompt LLM baseline using the same model. The largest improvements occur on challenging non-standard categories, including highway addresses (93% vs. 11% rule-based), military addresses (91% vs. 28%), and attention-line records (90% vs. 22%). The results suggest that multi-agent decomposition can improve the robustness and completeness of open-weight LLM extraction while preserving the privacy advantages of on-premise deployment. The study should be interpreted as a prototype evaluation on synthetic data rather than a production-readiness claim.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human malignancies, characterized by late diagnosis, aggressive metastatic behavior, and profound resistance to current therapies. Ion channels are increasingly recognized as active participants in oncogenesis, challenging the historic view of cancer as solely a genetic and biochemical disease. Emerging evidence has established that potassium (K+) channels, a structurally and functionally diverse superfamily of ion-conducting proteins, are systematically dysregulated in PDAC and play active roles in virtually every hallmark of this cancer, including uncontrolled proliferation, resistance to apoptosis, enhanced cell migration and invasion, metabolic reprogramming, and immunosuppressive tumor microenvironment remodeling. Potassium channels deregulation form part of a major deregulation frame, that is the electrochemical network deregulation (ECND). ECND is a neglected player in PDAC that is just recently being recognized as an interesting opportunity for therapeutic interventions. Potassium channels play an important role in ECND because they participate directly or indirectly in all the main manifestations of ECND, that is intracellular alkalosis, extracellular acidosis, plasmatic and mitochondrial membranes voltage, ROS homeostasis, calcium ion signaling, and pseudosynapsis. The lack of success of all pharmacological treatments attempted so far compels us to explore new therapeutic avenues. The deregulation of the electrochemical network in cancer, and in particular of the potassium channels that are an integral part of this network, are poorly recognized and investigated potential targeting areas. Furthermore, there are existing and developing drugs in this regard. This review provides a comprehensive analysis of the major K+ channel families involved in PDAC: voltage-gated (Kv) channels, inward rectifier (Kir) channels, two-pore domain (K2P) channels, and calcium-activated (KCa) channels. We examine the molecular mechanisms by which each channel subfamily contributes to oncogenic signaling, discuss their crosstalk with key PDAC driver pathways including KRAS, PI3K/AKT, and Wnt/β-catenin, and evaluate their potential as diagnostic biomarkers and therapeutic targets. Special attention is paid to the pharmacological landscape, including repurposed drugs and novel channel-targeting strategies. Collectively, the literature supports a model in which K+ channel dysregulation is not an epiphenomenon but a mechanistically integral feature of PDAC physiopathology, warranting prioritized investigation in preclinical and clinical settings.

Abstract: Objective: To evaluate the efficacy of a black raspberry extract-containing composite gel in treating persistent cervical high-risk human papillomavirus (hrHPV) infection and to analyze the clinical factors influencing treatment outcomes, with the goal of informing precision treatment strategies for this population.Methods: This was a prospective cohort study that enrolled 161 patients with persistent hrHPV infection. Participants received vaginal applications of a black raspberry gel every other day (discontinued during menstruation) for three consecutive months, followed by a three-month off-treatment period before re-examination. The primary efficacy endpoint was the viral clearance rate, defined as the clearance of any high-risk HPV subtype that was positive at baseline. Univariate analysis was performed to evaluate the correlation between clinical characteristics and efficacy. Additionally, data from 121 contemporaneous patients who received other treatments were retrospectively collected, and after propensity score matching, the efficacy was compared between the two groups.Results: The response rate among 161 patients who received black raspberry gel intervention was 50.9% (82/161). Univariate analysis showed that significantly higher response rates were observed in younger patients (P=0.011) and those who were premenopausal (P=0.003). Patients with multiple HPV infections had a significantly higher response rate compared to those with single infections (P=0.043). The clearance rates for HPV16 and HPV18 were 66.7% and 70.0%, respectively. No serious adverse events were reported during the intervention period. After propensity score matching, 99 patients were matched in the black raspberry gel group and 99 in the concurrent alternative treatment group. The response rate in the black raspberry gel group (57.6%, 57/99) was non-inferior to that in the concurrent alternative treatment group (55.6%, 55/99). Subtype analysis revealed that the black raspberry gel achieved a significantly higher clearance rate for hrHPV types 16, 18, 33, 39, 51, and 59 compared to the concurrent alternative treatment group (61.5% vs. 31.3%, P=0.011). For hrHPV types 52, 53, 58, and 68, the overall clearance rate was comparable between the black raspberry gel group and the concurrent alternative treatment group (51.0% vs. 50.0%, P=1.000). However, for hrHPV types 31, 35, 56, 66, and 73, the overall clearance rate was significantly lower in the black raspberry gel group than in the concurrent alternative treatment group (39.1% vs. 69.6%, P=0.038).Conclusion: The black raspberry extract-containing composite gel demonstrated a favorable safety profile and certain clinical efficacy in patients with persistent hrHPV infection, particularly showing enhanced effectiveness in younger, premenopausal women and exhibiting potential advantages in clearing high-risk subtypes such as HPV16/18. This study provides new real-world evidence for topical pharmacotherapy in persistent hrHPV infection and offers a preliminary theoretical basis for developing individualized, subtype-specific precision intervention strategies in the future.

Abstract: This study investigates the provenance, weathering history, and tectono-sedimentary evolution of Lower Ediacaran siliciclastic rocks of the Imiter Formation (Saghro Group, Imiter Sub-inlier, Anti-Atlas, Morocco) deposited along the northern margin of Gondwana. An integrated approach combining petrography, whole-rock major and trace element geochemistry, rare earth elements (REE), Sm–Nd isotopes, and organic geochemistry (TOC and δ¹³Cₒᵣg) was used to constrain sediment sources and deposi-tional conditions. Geochemical proxies, including Th/Sc, La/Sc, and Zr/Sc ratios, to-gether with REE distribution patterns, indicate that the sediments were mainly derived from felsic to intermediate rocks of the upper continental crust, with only minor sedi-ment recycling. The negative εNd(t) values (−8.5 to −6.2) and Paleoproterozoic Nd model ages (1.6–2.1 Ga) further suggest erosion of evolved crustal sources related to the West African Craton. Weathering indices (CIA, CIW, PIA) suggest weak to moder-ate chemical weathering under predominantly arid conditions. Redox-sensitive proxies (V–Ni, V/Cr, V/(V+Ni)) and low TOC contents (0.1–0.3 wt.%) indicate deposition under mainly oxic to dysoxic conditions with only transient reducing episodes. Tectonic dis-crimination diagrams, supported by regional magmatism, point to sedimentation within an extensional basin evolving from active margin to continental rift conditions during the late Pan-African orogeny. The Imiter Formation records a system dominat-ed by crustal recycling, syn-rift tectonics, and dynamic redox conditions in a shallow marine environment.

Abstract: Cloud computing and mobile edge computing address the growing demand for computing power driven by the rise in data-intensive applications, but they are prone to creating computing silos, resulting in unbalanced resource utilization. To address this issue, the Computing Power Network (CPN) has been introduced to enable the centralized management and scheduling of resources across the entire network. However, task scheduling in the CPN requires joint selection of computation nodes and routing paths, which greatly increases the complexity of scheduling problem. In existing studies, heuristic methods are difficult to satisfy real-time requirements, whereas deep reinforcement learning methods ignore the collaborative optimization of network resources, making them difficult to adapt to complex CPN scenarios. To this end, we propose a task scheduling method for the CPN, called TS-DQNF. First, the method uses the Deep Q-Network (DQN) to determine the computation node for computation task. Then, it introduces a dynamic congestion-aware mechanism to determine the shortest routing path. Finally, it gradually obtains the optimal task scheduling scheme through multiple rounds of alternating iterations. Simulation results show that the TS-DQNF achieves good performance and good convergence performance under different scenarios and scales.

Abstract: Background: Registered nurses (RNs) working in Primary Health Care (PHC) settings in South Africa (SA) are experiencing burnout, and it is a growing concern. Early recognition of signs and support methods is vital for effective prevention strategies. If burnout is left unattended, it will negatively impact the well-being of RNs, and patient care will be compromised. Aim: The study aims to explore early detection of burnout and the available support systems for RNs working in PHC settings in a district based in Gauteng Province, South Africa.Objectives: To identify signs and symptoms of burnout among RNs, examine contributing organisational stressors, assess awareness and recognition of burnout, and explore existing mitigation and support strategies. Methods: The study followed an exploratory qualitative design approach. In-depth face-to-face semi-structured interviews were conducted with 17 participants, with data saturation reached after the 12 interviews. The data was coded and analyzed using thematic content analysis, which included five interrelated steps. Results: The results revealed that RNs are experiencing burnout. Four main themes emerged: manifestations of burnout among registered nurses, organizational stressors, poor recognition and awareness of burnout, and mitigation and organizational support. Conclusion: Burnout is a reality among RNs in a District in Gauteng Province, South Africa, and it is often neglected until symptoms worsen. Promoting early detection through screening RNs and providing burnout education and awareness, coupled with organizational changes, will benefit RNs' well-being and the quality of care for patients.

Abstract: We investigate a possible connection between early central activity in galaxies and present-day large-scale stellar kinematics. Recent observations from the James Webb Space Telescope have revealed both a population of apparently massive galaxies at high redshift and compact luminous sources (“little red dots”, LRDs), challenging standard hierarchical formation scenarios. While the former suggest rapid early mass assembly, the latter are commonly interpreted as systems dominated by intense nuclear activity. At the same time, analysis of Gaia DR3 data reveals non-zero Galactocentric radial velocity components in the Milky Way, indicating large-scale non-equilibrium motions. We develop a phenomenological framework linking AGN-driven outflows, star formation, and stellar kinematics, in which stars form in dense clumps within multiphase outflows and inherit a small fraction of the outward velocity. Using characteristic values v_out∼10³kms⁻¹ and v_R∼1–10kms⁻¹, we derive a coupling factor f∼10⁻³–10⁻², consistent with Gaia DR3 observations. The model predicts star formation rates of 1–100M_⊙yr⁻¹ and growth timescales of 10⁷–10⁹yr, consistent with JWST constraints. Within this framework, LRDs may represent an early phase of centrally driven evolution, while present-day radial motions may reflect a long-term kinematic imprint of similar processes.

Abstract: Since 2020, the Swiss Personalized Health Network has adopted Semantic Web technologies to standardize health-related data for research in Switzerland. The SPHN Semantic Interoperability Framework promotes semantic interoperability, following the FAIR principles. Within this framework, the SPHN RDF Schema has evolved over five years to define more than 200 concepts across domains such as patient demographics, diagnoses, and laboratory results, enabling the representation of structured and machine-interpretable datasets. This study evaluates the evolution of schema versions from 2021 to 2025 and their adoption, examining structural and semantic changes, and analyzing quantitative metadata from projects in the SPHN Metadata Catalog. Results show consistent reuse of core concepts, especially demographics, diagnoses, and laboratory-related concepts, with 67% of SPHN concepts used in projects. The SPHN framework has proven to be a viable national standard for FAIR health data representation. Nonetheless, semantic modeling alone does not guarantee full interoperability. Future efforts must enhance data structuring and quality at the source, promote RDF adoption in research workflows, and develop user-friendly tools for querying and visualizing data.

Abstract: Background: The Toscana Virus is a little-known virus, present in Italy, transmitted by sandflies and associated with cases of meningitis and meningoencephalitis in humans. this study compared three statistical models (SARIMA, Poisson, and Negative Binomial) to forecast monthly Toscana virus (TOSV) cases in Italy for the period 2023–2024. Materials and Methods: data were extracted from the epidemiological bulletins of the Italian National Institute of Health for the period January 2016–December 2024. The 2016–2022 training set was used to estimate the models, while the 2023–2024 test set validated the predictions. Results: in the model comparison, SARIMA showed the best predictive ability, with the lowest MAE (3.46) and RMSE (5.05), demonstrating that seasonality and temporal de-pendence were well captured. The Poisson and Negative Binomial models, although use-ful, showed lower performance in terms of accuracy (higher RMSE). Conclusions: the results indicate that the SARIMA model is the best suited for forecasting monthly TOSV cases, but it is not perfect, highlighting the need for more complex ap-proaches that also integrate exogenous variables to improve forecast quality.

Abstract: The digitalisation of Indigenous medicinal knowledge constitutes a qualitatively new form of bioprospecting — one that operates through artificial intelligence, open-access repositories, genomic databases, and state-led data infrastructure rather than field collection alone. This article examines this transformation through the Rakhain community of Bangladesh, whose plant-based and non-plant healing heritage occupies a structurally vulnerable position at the intersection of biodiversity law, data governance, and digital capitalism. Drawing on long-term ethnographic engagement with the Rakhain community (2004–2024), Indigenous Gnoseology (Chowdhury, 2026a), reciprocal methodology (Chowdhury, 2026b), and the CARE Principles for Indigenous Data Governance (Carroll et al., 2020), the article argues that existing legal regimes are insufficient without community-centred digital sovereignty. A Rakhain Digital Healing Sovereignty Framework is proposed, grounded in collective consent, tiered knowledge classification, sacred secrecy, reciprocal benefit sharing, and post-research accountability.

Abstract: Tear film instability and dry eye disease (DED) are among the most common postoper-ative complaints after anterior segment surgery. Cataract surgery, corneal refractive procedures, keratoplasty, glaucoma filtration surgery, and pterygium excision can all alter ocular surface homeostasis through mechanisms including corneal denervation, inflammation, loss of goblet cells, and meibomian gland dysfunction. However, the severity and duration of postoperative dry eye vary significantly depending on the procedure performed. This comprehensive review of the literature summarises current evidence on pathophysiology, clinical manifestations, objective tear film changes, and recovery patterns following major anterior segment interventions. Particular emphasis is placed on standardised, non-invasive assessment methods, including tear breakup time, tear meniscus height, meibography, and validated symptom questionnaires. Proce-dure-specific recovery trajectories are compared, distinguishing transient postoperative tear film instability from persistent chronic dry eye disease. Evidence-based management strategies including preoperative risk stratification, intraoperative optimisation, and multimodal postoperative therapy are also reviewed. Understanding these distinct re-covery patterns allows for better surgical planning, improved patient counselling, and earlier intervention to reduce chronic postoperative dry eye and improve visual outcomes and patient satisfaction.

Abstract: The building renovation sector is under growing pressure to balance environmental responsibility, economic efficiency, and occupant well-being simultaneously. Existing evaluation approaches remain predominantly finance-driven, marginalising ecological and social dimensions. This study develops and validates a parametric multi-criteria assessment framework for building renovation elements, structured around the three pillars of sustainability: environmental, economic, and social. A dataset of 33 renovation elements — encompassing green façade systems, extensive and intensive green roofs, interior wall, floor, and ceiling solutions, and exterior envelope and site components — was compiled and digitised as BIM objects in ArchiCAD 26, enriched with non-graphic parameters including cost, lifespan, recyclability, eco-index, maintenance effort, and qualitative social descriptors. Parameters were aggregated using type-specific logic: additive for economic indicators, minimum-value for lifespan, arithmetic mean for environmental indicators, and descriptive aggregation for social attributes. Five renovation scenarios (A–E), each composed of nine elements, were evaluated to demonstrate how the sustainability profile changes with selection priorities. Scenarios A, B, and C confirmed single-dimension dominance (environmental, economic, and social, respectively), Scenario D achieved a balanced three-pillar profile, and Scenario E revealed a latent economic bias in an apparently random element selection. The framework is scalable, extensible, and designed for future integration with BIM environments, mixed reality platforms, and AI-driven decision-support tools.

Abstract: In oral surgery, the classification of the proximity of the mandibular third molar to the mandibular canal, typically performed on panoramic radiographs, is essential for surgical planning. While artificial intelligence (AI) tools have been explored for this task, their performance is limited due to data scarcity and class imbalance. In this work, we study the potential of synthetic data generation for this task using Denoising Diffusion Probabilistic Models (DDPMs) and Generative Adversarial Networks (GANs), both unconditional and conditioned to the tooth-canal relationship. We used public datasets to create and label a training dataset of 5416 images. The results show the lowest Fréchet inception distance (FID) / second-highest Inception Score (IS) for the unconditional GAN (32.48 / 2.14). The unconditional DDPM showed an FID of 34.28 and IS of 1.95. Conditional models showed similar IS but a worse overall FID of 68.19 and 219.11 for DDPM and GAN, respectively. In a paired observer study between the two unconditional models, clinical observers found the DDPM image to be more realistic in 69.6% of cases. Future work should investigate downstream effects of GANs and DDPMs used in data augmentation for the training of an AI classifier.

Abstract: Background/Objectives: This study aimed to examine the associations between diet-related quality of life (DRQOL) and psychological distress, autonomic dysfunction, and migraine in patients with dizziness and balance disorders. Methods: In this retrospective cross-sectional study, 122 patients (56 men, 66 women; mean age 40.4 ± 12.8 years, minimum 14, maximum 65) from the vertigo outpatient clinic at JR Tokyo General Hospital completed self-reported questionnaires. These included the DRQOL scale, Dizziness Handicap Inventory (DHI), Hospital Anxiety and Depression Scale (HADS), Self-rating Depression Scale (SDS), Orthostatic Dysregulation (OD) checklist, and migraine assessments (POUNDing [Pulsating, duration of 4–72 h, Unilateral, Nausea, Disabling], MIDAS, migraine screener). Correlational analyses, group comparisons, and receiver operating characteristic (ROC) analyses were conducted. Results: DRQOL scores showed positive correlations with psychological distress (SDS: ρ = 0.58; HADS-A: ρ = 0.50; HADS-D: ρ = 0.52; all p <  0.001) and OD severity (ρ = 0.48, p <  0.001), but not with age, DHI, or individual migraine indices. Migraine screener-positive patients had significantly higher DRQOL scores (p <  0.01). DRQOL alone modestly discriminated positive migraine screener (AUC = 0.65), improving to AUC = 0.77 in a multivariable model including age and sex. Conclusions: DRQOL can capture psychological and autonomic symptom burden rather than vestibular or headache severity, suggesting that it may serve as a complementary, patient-centered metric in the holistic assessment of dizziness patients.

Abstract: Primary hepatic masses in dogs represent a heterogeneous group of lesions with variable biological behavior and challenging preoperative characterization. The objective of this retrospective study was to describe the clinical presentation, diagnostic findings, surgical management, and outcome of dogs with primary hepatic masses treated surgically. Ten dogs with resectable hepatic lesions and no evidence of extrahepatic metastasis were included. Clinical records, imaging findings, histopathological diagnoses, treatment, and follow-up data were reviewed. Histopathological diagnoses included hepatocellular carcinoma (n=3), nodular hyperplasia (n=2), lobular hyperplasia (n=1), hepatocellular adenoma (n=1), undifferentiated sarcoma (n=1), osteosarcoma (n=1), and one case without definitive histological diagnosis. Tumor size ranged from 3.3 to 18 cm and was not associated with biological behavior. Preoperative cytology showed poor concordance with final histopathological diagnosis in all sampled cases. Abdominal ultrasound identified solitary lesions in all evaluated dogs, although surgery revealed previously undetected multifocal disease in two cases. Most lesions were located in the right hepatic lobes, differing from the predominance of left-sided lesions commonly reported in the literature. All dogs underwent surgical resection. Two perioperative deaths occurred secondary to postoperative renal failure. In the remaining dogs, surgery resulted in complete remission of clinical signs and prolonged survival, including dogs with malignant tumors. Four dogs remained alive and disease-free at the end of the follow-up period (>730 days). These findings highlight the limitations of preoperative diagnostic techniques for predicting the biological behavior and extent of canine hepatic masses. Surgical resection provided substantial clinical benefit and prolonged survival in most cases, supporting its consideration whenever complete excision is technically feasible.

Abstract: The objective of this study was to evaluate the effects of partially replacing wheat bran with poplar wood composite fiber (PWCF) on growth performance, immune status, apparent total tract digestibility (ATTD), and gut microbial composition in growing pigs. A total of 140 healthy crossbred (Duroc × Landrace × Yorkshire) growing pigs with an initial body weight of 47.25 ± 0.49 kg were randomly assigned to two dietary treatments, with five replicates per treatment and fourteen pigs per replicate. The control (CT) group was fed a corn–soybean meal–based diet, whereas the experimental group re-ceived the same diet in which 2% wheat bran was replaced by PWCF. The experiment lasted for 60 days. Compared with the CT group, replacing wheat bran with PWCF did not affect body weight, average daily feed intake, feed conversion ratio, or average daily gain on days 30 or 60 (P > 0.05). In addition, no negative effects were observed on ATTD of nutrients and serum immunoglobulin A (IgA), IgG, and IgM levels at either time point, indicating that PWCF can serve as a suitable partial substitute for wheat bran in growing pig diets. However, it could regulate nitrogen metabolism by reducing blood urea nitrogen (BUN) concentration and the BUN/creatinine ratio, as well as decreasing total free amino acids in serum (P < 0.05). In addition, the antioxidant capacity can be improved by increasing catalase activity. Gut microbiota analysis showed that the re-placement significantly increased the relative abundances of Treponema, Lachnospi-raceae_XPB1014_group, Prevotellaceae_UCG-001, Prevotellaceae_UCG-003, Prevotel-laceae_UCG-004, and norank_f_Oscillospiraceae (P < 0.05). These changes suggest that PWCF modulates gut microbiota and enriches fiber-degrading bacterial populations. Overall, substituting wheat bran with PWCF did not impair growth performance, im-munity, or digestibility, while altering microbial community composition. These find-ings support the potential application of PWCF as an alternative fiber source, contrib-uting to greater diversity in feed formulation.