Abstract: BackgroundPediatric adenotonsillectomy is commonly performed for infectious and obstructive indications, but postoperative hemorrhage remains a concern. This study describes outcomes from a high-volume territorial network in southern Modena province, Italy.Methods: Retrospective observational study of 10,753 pediatric patients (aged 3–18 years) undergoing adenotonsillectomy at Sassuolo Hospital and affiliates (Vignola, Pavullo) from 2005–2024. Indications included recurrent tonsillitis (Paradise criteria), OSA (polysomnography-confirmed or clinical), and recurrent otitis media or otitis media with effusion (OME). Surgical techniques included curettage adenoidectomy and Colorado microdissection needle tonsillectomy. Primary outcomes were postoperative hemorrhage (overall and requiring revision), stratified by indication, age, and technique, compared descriptively with literature ranges. Secondary outcomes included pain (VAS scores), infection rates, and tissue regrowth. Data completeness was verified via electronic records (95.6%). Statistical analyses used descriptive statistics with 95% confidence intervals (95% CI) and χ² tests. Results: A total of 10,753 procedures were analyzed (4,325 tonsillectomies, 3,942 adenotonsillectomies, 2,486 adenoidectomies). Postoperative hemorrhage occurred in 202 patients (1.88%; 95% CI 1.64–2.15%); surgical revision was required in 75 (0.70%; 95% CI 0.56–0.87%), with multifactorial stratification showing higher risk for infectious indications (OR 1.41 vs OSA), younger age <5 years (OR 2.1), and tonsillectomy origin (OR 8.25 vs adenoidectomy); all rates at the lower end of literature ranges (2–5% and 0.9–2.5%, respectively; both p < 0.001 vs. literature means, χ² test). Mean VAS pain scores decreased from 3.2 (day 1) to 1.1 (day 7). No significant infections occurred; tissue regrowth rates aligned with literature (adenoidal 6–26%, tonsillar 5–10%). Conclusions: Sassuolo Hospital's experience highlights favorable postoperative outcomes and low complication rates in adenotonsillar surgery. Limitations include retrospective design and potential selection bias. Prospective studies are needed to confirm these findings.

Abstract: Background: Kawasaki disease (KD) is a systemic vasculitis, of unknown etiology, that usually occurs in children between the ages of six months and five years. Patients at the extremes of ages rarely meet all the clinical criteria required for the diagnosis of KD. Atypical or incomplete presentation can lead to delayed diagnosis and treatment, resulting in a higher incidence of cardiac complications. Case Presentation: We describe the case of a 2-month-old female infant who was admitted to our clinic with persistent fever, generalized maculopapular rash and bilateral conjunctivitis. During hospitalization, she developed oral mucosa and extremity changes. On the 7th day from the onset of fever, the diagnosis of KD was established, and she received intravenous immunoglobulin therapy. The patient responded well to the treatment, presenting no cardiac complications. Conclusions: The presented case underscores that even very young infants can develop complete Kawasaki disease. It also highlights the importance of early identification and appropriate treatment in preventing coronary artery lesions.

Abstract: Objectives: Using high-performance liquid chromatography (HPLC) we developed and validated an in vitro assay for the quantitative determination of beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) activity, supplementing limited current methodologies to assess the efficacy of BACE1 inhibitor compounds. A hexa-histidine tagged peptide substrate of BACE1 was used as the analyte for the determination of in vitro BACE1 activity; it was validated according to ICH guidelines. Methods: The HPLC analysis was performed on the Agilent 1290 Series Infinity II UHPLC System equipped with a Phenomenex Kinetex EVO C18 (100 × 3 mm) 5 µm column. The method was developed using a gradient program comprising of 10 % aqueous acetonitrile (0.02 M TFA) to 30% aqueous acetonitrile (0.02 M TFA) for 5 minutes at a flow rate of 0.6 ml/min. Results: The method showed linearity over the range of 14.92 to 72 µM with R^2=0.9997. The accuracy of the method in terms of mean recovery ranged between 96.62 to 98.38 %. The %RSD for intra- and inter-day precision were less than 5 %. Two commercial inhibitors, AZD3839 and OM99-2, were used to evaluate the performance of the method at their respective IC50, resulting in inhibition of 53.46 and 50.74 % respectively. The described method addresses the void for a practical and cheap alternative to quantitatively determine the activity of BACE1 compared to current commercially available detection assays. Conclusions: We have successfully developed a HPLC method to measure the inhibitory function of two commercial inhibitors of BACE1, indicating suitability of the method for the identification and characterisation of novel BACE1 inhibitors.

Abstract: This study presents a large-scale empirical comparison of operational efficiency metrics derived from the IMO Data Collection System (DCS) and the EU Monitoring, Reporting and Verification (MRV) framework. Using a matched dataset of 15,755 dual-reported vessels and over 50,000 ship-year observations from 2019 to 2024, paired non-parametric tests, effect size estimation, and agreement diagnostics were applied to assess consistency across monitoring systems. Results indicate that although statistically significant differences are detected (p < 0.001), practical differences are negligible (Cohen’s d < 0.025), with MRV-based values averaging approximately 1.4% lower Annual Efficiency Ratio (AER) and fuel intensity than DCS values. Distributional analysis confirms substantial overlap between datasets, and temporal trends show progressive convergence following the implementation of the Carbon Intensity Indicator (CII) regulation. However, pronounced vessel-type heterogeneity is observed. Flexible cargo vessels exhibit consistent efficiency improvements in EU-related voyages, whereas container ships show minimal variation and LNG carriers demonstrate indicator-dependent patterns. Overall, the findings indicate that DCS and MRV provide broadly comparable representations of operational efficiency, with observed differences primarily reflecting vessel-type-specific operational characteristics rather than structural inconsistencies in reporting systems. The study contributes a scalable statistical validation framework for cross-regulatory monitoring assessment.

Abstract: Background/Objectives: Primary biliary cholangitis (PBC) is a chronic immune-mediated cholestatic liver disease with increasing global prevalence. However, the data from Central Asia are lacking. We aimed to describe the clinical, serological, and treatment characteristics of PBC patients in Kazakhstan. Methods: This was a multicenter retrospective observational study across seven hepatology centers in Kazakhstan, including adults diagnosed with PBC between 2014 and 2022. Clinical presentation, laboratory parameters, autoimmune comorbidities, liver disease severity, and ursodeoxycholic acid (UDCA) treatment response were assessed. Biochemical response at 1 year was evaluated using Paris-1 and Barcelona criteria. Results: A total of 230 patients were included; 93.9% were female and 91.3% were of Asian ethnicity, with a median age at diagnosis of 53 years. Cirrhosis was present in 50.2% at diagnosis. PBC with AIH features was identified in 56.1% of patients and was associated with higher rates of cirrhosis, portal hypertension complications, ANA positivity, and higher elastography indices compared with isolated PBC. Overall, approximately 55% of patients achieved a biochemical response to UDCA at 1 year, with similar response rates between PBC and PBC with AIH features groups. Conclusions: This first comprehensive study of PBC in Kazakhstan demonstrates late disease presentation with a high burden of cirrhosis and frequent AIH features. Despite advanced disease, about half of patients achieved biochemical remission on UDCA. These findings underscore the need for earlier diagnosis and optimized management strategies for PBC in Kazakhstan and similar settings in Central Asia.

Abstract: Background: Informal sewing workers are widely exposed to ergonomic and work-load-related risks but remain largely excluded from formal occupational health protection, particularly in low- and middle-income countries. This study aimed to assess ergonomic risk factors, mental workload, and work-related musculoskeletal disorders (WMSDs) among informal sewing workers and to develop preventive guidelines based on the Hier-archy of Ergonomic Controls (HEC). Methods: A mixed-methods study was conducted among 150 informal sewing workers in Ubon Ratchathani Province, Thailand. Quantita-tive data were collected using a structured questionnaire, the Rapid Upper Limb Assess-ment (RULA), the Nordic Musculoskeletal Questionnaire (NMQ), and the NASA Task Load Index (NASA-TLX). Associations between sociodemographic characteristics, ergo-nomic risks, and WMSDs were analyzed using chi-square tests and correlation analysis. Qualitative data were obtained through a focus group discussion with key stakeholders to develop ergonomic control strategies guided by the HEC framework. Results: The majority of participants were female and middle-aged, with widespread exposure to high-risk er-gonomic conditions, including prolonged sitting, repetitive tasks, and awkward postures. A high prevalence of WMSDs was observed, particularly in the neck, shoulders, and back. Younger workers and those with lower educational attainment experienced significantly higher ergonomic risk exposure and WMSD prevalence. NASA-TLX results indicated that physical demand and performance pressure were the main contributors to overall work-load. Application of the HEC framework showed that elimination and substitution con-trols were the most effective strategies for reducing ergonomic risks, followed by engi-neering controls, while administrative measures and personal protective equipment were less effective. Conclusions: Informal sewing workers face substantial ergonomic and mental workload risks that contribute to a high burden of WMSDs. Prioritizing high-er-order ergonomic controls, integrating workload management, and implementing community-based ergonomic interventions are essential to improving occupational health and reducing inequities among informal workers.

Abstract: The livestock sector underpins food security, employment, and rural livelihoods across the Southern African Development Community (SADC), contributing up to 50 % of agricultural GDP and supporting more than 60 % of rural households. Yet, climate change poses escalating threats through heat stress, declining pasture productivity, water scarcity, and vector-borne diseases that compromise productivity and economic resilience. This review identifies and locates effective climate change mitigation strategies along the livestock value chain, spanning production, processing, transport, and consumption, to promote sustainable, low-emission, and inclusive growth in the SADC region. A broad review of 46 peer-reviewed and institutional sources (2000 – 2024) was undertaken, focusing on livestock-related mitigation within SADC and comparable agro-ecological systems. Strategies were thematically categorized by value-chain stage and assessed for their emission-reduction and livelihood-enhancement potential. Located strategies include genetic improvement for low-methane and heat-tolerant breeds, adaptive rangeland and feed management, renewable-energy adoption in processing, climate-resilient transport infrastructure, and consumer awareness of low-emission products. Evidence suggests potential GHG-emission reductions of 18–30 %, coupled with productivity gains and improved smallholder incomes. Coordinated implementation through the SADC Regional Agricultural Investment Plan (2021–2030) and national policies can transform the livestock sector into a climate-resilient driver of inclusive growth. Further research should quantify the socio-economic feasibility and scaling potential of these strategies across production systems. Successful integration of climate change mitigation imperatives must be tailored to local biophysical conditions (e.g., rainfall, soil type) and socio-economic contexts (e.g., market access, cultural practices).

Abstract: In recommendation systems, representing user-item interactions as a bipartite network is a fundamental approach that provides a structured way to model relationships between users and items, allowing for efficient predictions via network science. Collaborative filtering is one of the most widely used and actively researched techniques for recommendation systems, its rationale is to predict user preferences based on shared patterns in user interactions, and vice versa. Memory-based collaborative filtering relies on directly analyzing user-item interactions to provide recommendations using similarity measures, and differs from model-based collaborative filtering which builds a predictive model using machine learning techniques such as neural networks. With the rise of machine learning, memory-based collaborative filtering has often been overshadowed by model-based approaches. However, the recent success of SSCF, a newly proposed memory-based method, has renewed interest in the potential of memory-based approaches. In this paper, we propose Network Shape Automata (NSA), a memory-based collaborative filtering method grounded in the connectivity shape of the bipartite network topology. NSA leverages the Cannistraci-Hebb theory proposed in network science to define brain-inspired network automata, using this paradigm as the foundation for its similarity measure. We evaluate NSA against a range of advanced collaborative filtering methods, both memory-based and model-based, across 16 bipartite network datasets spanning complex systems domains such as social networks and biological networks. Results show that NSA consistently achieves strong performance across diverse datasets and evaluation metrics, ranking most often first on average. Notably, NSA demonstrates strong robustness to network sparsity, while preserving the simplicity, interpretability, and training-free nature of memory-based methods. As a pioneering effort to bridge link prediction and recommendation tasks, NSA not only highlights the untapped potential of memory-based collaborative filtering but also demonstrates the effectiveness of the Cannistraci-Hebb theory in modeling network evolution within recommendation systems.

Abstract: With the Sustainable Development Goals (SDGs) as a reference, this study systematically examines the evolution, characteristics, achievements, and challenges of China-Africa agricultural cooperation. The study elaborates on how China-Africa agricultural cooperation has transitioned from a politically-driven aid model to a comprehensive framework integrating aid, investment, trade, and technology transfer under the guidance of the Forum on China-Africa Cooperation (FOCAC). Despite remarkable achievements between China and Africa in food security, infrastructure construction, and technology transfer, the analysis identifies persistent dilemmas. These include limited impact on comprehensive regional development, scrutiny over trade imbalances and potential resource exploitation, and ineffective utilization of Africa's diverse agricultural resources. To address these issues, the paper proposes future pathways such as maximizing the potential of Agricultural Technology Demonstration Centers (ATDCs), supporting the development of the entire agricultural value chain, and effectively leveraging digital technology. This study argues that it is necessary to adopt a more comprehensive, integrated, and sustainable approach to improve the China-Africa agricultural cooperation model and promote Africa's achievement of S SDGs.

Abstract: Background/Objectives: The rapid advancement of artificial intelligence (AI) has had a notable impact in the healthcare field, particularly in the realm of assessment and diagnosis. One specific area where the integration of AI technologies shows promise is the evaluation of progressive neurological disorders (PNDs). PNDs are characterized by a progressive decline in neurological function, resulting in changes in cognition, movement, and communication. PNDs pose significant challenges in terms of early detection and categorization. Speech and voice changes are important clinical markers in many PNDs. Therefore, the utilization of AI applications for the analysis and classification of speech and voice samples could prove beneficial for streamlining the diagnostic process. This systematic review aimed to investigate the current utilization of AI in the assessment and diagnosis of PNDs through speech signal analysis over the past decade. Methods: In adherence to PRISMA guidelines, Scopus, PubMed, and Web of Science were searched for studies related to machine learning (ML) and deep learning (DL) for speech and voice assessment in people with PNDs. Results: A total of 102 studies were identified for inclusion between 2013 and 2023. The reviewed studies demonstrated a wide range of accuracy, with reported values ranging from 67.43% to 99%. Support Vector Machines (SVMs) were the most frequently used ML models across studies, demonstrating reliable performance in both speech and voice data analysis. Conclusions: AI-based analysis of speech and voice shows strong potential as a non-invasive tool for supporting the assessment and diagnosis of PNDs. The high accuracy reported across studies highlights the promise of these approaches, although methodological variability underscores the need for greater standardization and clinical validation.

Abstract: In this paper, we move beyond the classical setting by redefining the Chebyshev functional in the context of q-circles situated within Minkowski space, rather than the standard Euclidean circles in R². This approach introduces a new theoretical framework suitable for non-Euclidean geometries. We derive sharp estimates for the functional when applied to functions on q-circles that adhere to Hölder-type continuity conditions.

Abstract: Background/Objectives: Glioblastoma (GB) remains the most prevalent primary malignant brain tumor in adults, characterized by its aggressive nature and poor prognosis. The present study endeavored to contribute to the development of advanced computational tools for neuro-oncology by integrating artificial intelligence (AI)-based segmentation and multi-model machine learning (ML) approaches. Methods: A retrospective analysis was conducted on patients with GB. AI-driven algorithms were utilized to perform volumetric segmentation of GB. These quantitative metrics were subsequently integrated into a multi-model ML framework to analyze correlations with patient survival and evaluate the predictive accuracy of the resulting models. Results: A total of 79 patients were ultimately included in the study after meeting all eligibility criteria. The results showed that larger GB tumors were associated with shorter post-treatment survival. Necrotic patterns within GB tumors impacted patient survival rates and response to therapy. Quantitative volumetric analysis of tumor enhancement, shape features, and morphological metrics were associated with patient outcomes. The Neural Network remained the top ML model performer overall for discrimination, but the Random Forest model also showed strong practical performance. Conclusions: As a summary, our study contributes to the development of advanced computational tools for neuro-oncology by integrating AI-based segmentation and multi-model ML approaches, and the results highlight the importance of imaging biomarkers in understanding GB prognosis.

Abstract: Large language models (LLMs) are increasingly deployed as decision-support tools in organizational contexts, yet their susceptibility to contextual framing remains poorly understood. This preregistered experimental study systematically examines how six framing dimensions—procedural justice, outcome severity, stakeholder power, resource scarcity, temporal urgency, and transparency requirements—influence ethical recommendations from three frontier models: Claude 3.5 Sonnet, GPT-4o, and Gemini 1.5 Pro. We developed 5,000 unique organizational vignettes using a fractional factorial experimental design with balanced industry representation, generating 15,000 total model responses. After excluding responses without clear recommendations (n=694, 4.6%), we analyzed 14,306 responses using logistic regression with robust and clustered standard errors. We find that resource scarcity increases endorsement probability by 12.0 percentage points (pp) (OR = 1.67, 95% CI [1.45, 1.93], p < .001), while outcome severity reduces it by 11.3pp (OR = 0.62, 95% CI [0.54, 0.71], p < .001), and procedural justice reduces it by 10.1pp (OR = 0.66, 95% CI [0.57, 0.76], p < .001). These effect sizes are comparable to classical framing research (Tversky & Kahneman: 22pp; McNeil et al.: 18pp) and represent substantial shifts in organizational decision contexts. When multiple framing dimensions align in ethically unfavorable directions, cumulative effects reach approximately 27pp from baseline (range: 25-28pp depending on interaction assumptions), with maximum-to-minimum framing creating a 54-percentage-point total range approaching complete recommendation reversals. Effects appear consistently across all three models, with no significant Dimension × Model interactions, suggesting fundamental architectural properties rather than implementation-specific artifacts. Topic modeling of justification text from the 14,306 analyzed responses reveals systematic "adaptive rationalization"—models invoke utilitarian reasoning when contexts emphasize constraints (+6.7pp in high resource scarcity), deontological reasoning when contexts emphasize high stakes (+2.4pp in high outcome severity), and virtue/justice ethics when contexts emphasize fair processes (+4.4pp in high procedural justice). This suggests models select ethical frameworks to justify contextually appropriate conclusions rather than applying consistent principles across situations. Human validation confirms these patterns reflect genuine framing sensitivity rather than measurement artifacts. Crowdworker validation (n=7,500 responses, one rater each) achieved substantial agreement (Fleiss' κ = 0.71) and 81.3% concordance with expert codings. Subject matter expert evaluation (n=24 experts, 100 vignette pairs each including 20 control pairs, 2,400 total comparisons) detected framing-driven differences in 48.9% of pairs (net of 18.3% baseline false-positive rate), but correctly attributed differences to manipulated dimensions in only 41.3% of cases. Most detected differences (58.7%) were judged problematic for AI advisory systems. These findings raise fundamental questions about deploying LLMs for consequential organizational decisions where surface features may inappropriately influence outcomes. We discuss implications for AI governance, organizational ethics, and the design of more robust decision-support systems.

Abstract: As Australia advances toward a net zero economy, system-wide transformations in the energy sector are becoming increasingly necessary. This transition entails the electrification of key sectors, the integration of renewable energy sources, and the decommissioning of aging infrastructure. However, alongside technological change, there is a growing need to manage emerging forms of waste such as solar panels and batteries and to embed circular economy principles into the transition framework. This paper presents findings from a qualitative study conducted to understand key stakeholder perspectives on policy coherence between net zero and circular economy policies in Australia. The study reveals that there is significant gap in conceptual understanding of both circular economy and net zero transitions and a lack of clear definitions within these policies leading to two classical systems traps: policy resistance and seeking the wrong goal. The focus on recycling and operational emissions within CE and net zero policies respectively, typically lead to suboptimal outcomes being pursued for both policies. These findings underscore the critical need for capacity building, clearer policy articulation, and targeted educational strategies to foster a socially informed, circular approach to decarbonization. By integrating the clean energy transition within broader social and institutional contexts, this paper contributes to a more inclusive and systemic understanding of Australia's net zero future.

Abstract: The STEAM-CT approach integrates Science, Technology, Engineering, Arts, and Mathematics with Computational Thinking (CT) to help students learn how to think, design, and solve problems. It gives students hands-on, interdisciplinary experiences where they apply logic and creativity through real-world applications. The purpose of this study is to foster the development of computational thinking among Deaf students by embedding Artificial Intelligence (AI) learning within a STEAM-CT approach. This learning program consisted of three main phases: (1) exploring AI processes and tools, (2) constructing an AI system, and (3) designing AI-driven innovations. Thirty-six Deaf students from seven Deaf schools participated in this program, which aims to enhance their CT abilities and cultivate their capacity to create AI-based solutions. Students’ progress was measured using a CT framework encompassing knowledge of concepts, applied practices and perspectives. Assessments included multiple-choice tests for CT concepts, task-based rubrics for CT practices, and interviews for CT perspectives. The results showed that Deaf students gained a better understanding of CT concepts, demonstrated advanced CT practices, and exhibited strong CT perspectives. These findings suggest that AI learning through a STEAM-CT approach can effectively promote Deaf students’ computational thinking abilities.

Abstract: In many cases, the pieces of information at our disposal come from a recommender source, that can be either an official news system, a large language model or simply a social network. Often, also, these messages are build so to promote their active spreading, which, on the other hand, has a positive effect on one’s own popularity. However, the content of the message can be false, giving origin to a phenomenon analogous to the spreading of a disease. In principle, there is always the possibility of checking the correctness of the message by “investing” some time, so we can say that this checking has a cost. We develop a simple model based on the mechanism of “risk perception” (propensity of checking the falseness of a message) and mutual trustability, based on the average number of fake messages received and checked. On the other side, the probability of emitting a fake message is inversely proportional to risk perception and the affinity (trustability) among agents is also exploited by the recommender system. This model represents an integration of cognitive psychology with computational agent-based modeling.

Abstract: The gut–skin axis is increasingly implicated in psoriasis pathogenesis, yet the cross-compartment convergence of molecular programs remains incompletely defined. We constructed a conceptual “Triple-Hit” multi-omics framework by integrating five independent public datasets spanning gut microbial functional remodeling (shotgun metagenomics), systemic immune-cell methylomes (PBMC and CD8+ T-cell EPIC 850K), and lesional skin regulatory layers (miRNA and bulk RNA-seq). In the gut compartment, functional profiles exhibited a selective reduction in microbial lipid catabolic potential, including decreased fatty acid degradation and a lowered composite lipid degradation score, alongside heterogeneous shifts across SCFA-associated metabolic pathways. Systemically, PBMC methylomes revealed widespread regional remodeling (45,396 DMRs) enriched for membrane-proximal signaling and cytoskeletal programs, while CD8+ T cells showed specific epigenetic alterations in lipid- and glycosphingolipid-associated loci, suggesting a systemic metabolic–epigenetic alignment. In the skin, we identified a compact miRNA signature (168 DE-miRNAs) and a mechanistically interpretable, directionality-constrained miRNA–mRNA bridge that aligns with an AMP-dominant inflammatory transcriptome, consistent with reduced post-transcriptional restraint. Collectively, these findings support a convergent multi-omics framework linking putative microbial metabolic remodeling, systemic immune priming, and cutaneous effector programs. This study provides a systems-level perspective on psoriasis pathogenesis, highlighting the metabolic–epigenetic–transcriptional convergence as a potential avenue for therapeutic intervention.

Abstract: Demographic decline in many OECD countries is widely indorsed as the principal source of hurling public pension disbursements, whilst trade unions are often blames for staunch antagonism to any transformations that might alleviate the fiscal encumbrance. Building on the premise that financialization is state-acquiesced, with the state reckoned fundamental for market integration, and social regulation of markets against market failures. How then inter-generational equity should be addressed? This work tests the hypothesis that deindustrialization (measured as the tumbling proportion of manufacturing employment) and lower trade-union density are quintessential channels through which demographic change transmutes into ascending pension outlays. Using OECD data from 1960 to 2023, the study utilises longitudinal and panel quantile statistical methods to dissect these links across assorted pension-system clusters (total, mandatory private, mandatory public, mandatory public + voluntary, and mandatory public + private). The study highlights the mediating role of labour market structure to pension financing.

Abstract: Understanding the interactions of nanomaterials with complex tumour models is essential for advancing their use in nanomedicine. Calcium fluoride nanoparticles doped with neodymium and yttrium (CaF₂:Nd³⁺, Y³⁺) exhibit promising properties for biomedical applications, particularly for optical sensing and tagging. This study investigates their interaction with 3D cell spheroids derived from breast cancer (MCF-7) and brain cancer (U-87 MG) cell lines as tumour models. Specific protocols have been developed in Total-reflection X-Ray Fluorescence (TXRF) to evaluate nanoparticles’ internalisation and diffusion within spheroids by quantifying the concentrations of Ca, Nd, and Y taken up by the cells. Minimal background interference enabled precise multi-element detection in low-volume biological samples, yielding very low detection limits and minimal uncertainties. The study demonstrates the effectiveness of TXRF for quantifying rare-earth-doped nanoparticles in 3D cancer models and reveals that, although both cell lines permit nanoparticle diffusion into cells, higher accumulation is observed in glioblastoma cell spheroids. A Weibull diffusion model was applied to help understand the observed internalisation kinetics of nanoparticles into U-87 MG and MCF-7 spheroids. The relevant differences suggest cell-line-dependent uptake behaviour, potentially influenced by differences in cellular architecture, the porosity of the generated spheroid, and its intercellular 3D microstructure. These findings highlight the importance of tumour-specific interactions in the investigation of nanoparticle systems for targeted cancer diagnostics and therapeutics.

Abstract: Background: Severe traumatic brain injury (TBI) remains associated with high in-hospital mortality. Although classical clinical predictors are widely used, additional biomarkers reflecting systemic dysfunction may improve prognostic assessment. Small intestinal bacterial overgrowth (SIBO) may represent a late marker of critical illness. This study evaluated the prognostic value of SIBO compared with traditional predic-tors in severe TBI. Methods: In this retrospective cohort study, 174 patients with severe TBI (Glasgow Coma Scale ≤ 8) were included. Baseline clinical parameters were rec-orded at admission. Quantitative cultures of small intestinal aspirates were obtained at admission (colony-forming units per milliliter, CFU/mL; CFU1) and on days 12–14 (CFU14). Multivariable logistic regression, receiver operating characteristic (ROC), and landmark analyses were performed. Results: Age (OR 1.06 per year, 95% CI 1.03–1.10, p < 0.001), lower GCS (OR 0.48 per point, 95% CI 0.28–0.83, p = 0.008), and res-piratory dysfunction reflected by lower PaO₂/FiO₂ values independently predicted mortality. Late bacterial load >10⁵ CFU/mL showed a strong association with death (OR 5.15, 95% CI 2.15–12.34, p < 0.001). Baseline CFU1 was not significant. The model demonstrated good discrimination (AUC = 0.84). Landmark analysis confirmed higher post-day-14 mortality and delayed discharge with elevated CFU14. Conclusions: Late intestinal bacterial overgrowth is independently associated with mortality and may complement traditional predictors for risk stratification in severe TBI.