Abstract: Companion dogs are increasingly recognized as translational models for studying human physiology and disease. Unlike conventional or genetically engineered laboratory models, dogs are outbred, immunocompetent animals that spontaneously develop complex diseases whose pathogenesis and environmental exposures commonly overlap with those of humans. These distinctive features create opportunities to study mechanisms of disease, progression, and therapeutic responses under conditions that more closely resemble clinical reality. This review highlights evidence for the translational relevance of canine models across multiple therapeutic areas. We further discuss how advances in genomics, transcriptomics, spatial biology, in vitro, and in silico model systems are expanding the translational utility of canine models for applications in human medicine. Although important species differences must be carefully weighed, dogs represent a uniquely valuable comparative model for elucidating disease mechanisms, informing drug development, and accelerating the translation of scientific discoveries to human medicine.

Abstract: Oleoresins are complex natural lipophilic matrices traditionally analyzed using chromatographic techniques that require extensive sample preparation, derivatization, and authentic standards. Amazonian oleoresins from Copaifera and Eperua species (Fabaceae) represent valuable bioresources with recognized pharmacological potential, largely attributed to diterpenoids such as copalic and hardwickiic acids, as well as bioactive sesquiterpenes, including the cannabinoid b-caryophyllene. In this study, we present a proof-of-concept application of Direct Analysis in Real Time coupled with High-Resolution Mass Spectrometry (DART-HRMS) as a rapid, direct, and environmentally friendly approach for chemical fingerprinting and semi-targeted screening of the two most important amazonian oleoresins from these two genera: Eperua oleifera and Copaifera multijuga. Analyses were performed using a Q Exactive Orbitrap coupled to a DART ion source under after conditions optimization. Hardwickiic acid was used as a model compound for method optimization, with optimal performance achieved at 200 °C and 100 V, yielding stable signal intensities (CV < 10%) and high mass accuracy (< 1 ppm). The method enabled reproducible detection of diterpenic acids in both oleoresins, allowing differentiation of their chemical profiles and assessment of short-term stability under ambient conditions. In addition to diterpenes, free fatty acids were also detected, expanding the compositional characterization of these matrices. Compound annotation was performed based on accurate mass measurements and literature comparison, corresponding to Level 5 confidence according to established metabolomics criteria. Although the absence of chromatographic separation limits isomer discrimination and absolute quantification, DART-HRMS provides a rapid and solvent-free strategy for chemical fingerprinting and preliminary characterization of oleoresins. This approach aligns with Green Chemistry principles and shows strong potential as a screening and triage tool for quality control, chemotaxonomic studies, and sustainable valorization of Amazonian natural products.

Abstract: Ear biometrics has emerged as a promising alternative in biometric recognition systems, offering robustness in unconstrained environments where traditional modalities such as face recognition may fail on its own, but can be enhanced by ear. Ear segmentation, in particular, plays a crucial role in downstream recognition by isolating discriminative ear regions and reducing background interference. However, existing approaches to ear detection and segmentation are commonly susceptible to severe occlusions, ear accessories, and variable illumination, and their performance deteriorates on images captured in the wild. To address these limitations, we introduce a tailored ear-segmentation architecture based on a U-Net with a ResNet-50 encoder. Trained and validated on the Annotated Web Ears (AWE) dataset, our method achieves a mean Intersection over Union (IoU) of 77.1% and a pixel-wise accuracy of 99.7%, outperforming the Convolutional Encoder--Decoder (CED) baseline. We further evaluate on the EarSegDB-25 dataset, where our approach attains a test-set IoU of 94.76%, significantly surpassing previous ear segmentation methods based on the original U-Net architecture. High pixel-wise accuracy across methods is largely attributable to background dominance; in contrast, the improved IoU achieved by our approach more accurately reflects gains in ear region segmentation performance. Leveraging a ResNet-50 encoder, our model demonstrates robust performance under occlusion and illumination challenges, achieving state-of-the-art results on AWE and EarSegDB-25 and showing strong potential for biometric applications in unconstrained environments.

Abstract: Chili pepper (Capsicum spp.) plays a crucial cultural, nutritional, and economic role in Senegal, supporting smallholder livelihoods, domestic food systems, and regional export markets. However, post-harvest losses associated with traditional open-air drying and storage remain high, particularly under coastal conditions characterized by high relative humidity and variable weather. Improving drying assessment and storage practices is therefore essential to enhance chili quality, safety, and marketability. This study evaluated smallholder chili drying and storage practices in Senegal, with a particular focus on processor dryness judgment, drying surfaces, and storage materials, and assessed the potential role of a simple equilibrium relative humidity (ERH)-based decision-support tool. A participatory, on-farm study was conducted with six chili processors in the Niayes agroecological zone of western Senegal. Freshly harvested chilies were dried concurrently using black plastic sheeting and processors’ customary drying materials under farmer-managed conditions. Processor assessments of storage readiness were compared with ERH-based classifications using the DryCard tool. Drying duration and dry matter content were recorded for each treatment. Dried chilies were subsequently stored in either airtight glass jars or traditional gunny bags, and weight changes were monitored at 25, 45, and 70 days after storage. Data were analysed descriptively using within-processor comparisons to identify consistent patterns across sites. After 15 days of drying, all chili samples were classified as not dried for storage, with DryCard readings of approximately 75% ERH across sites. Final processor-determined drying durations ranged from 29 to 42 days. DryCard ERH values at this stage ranged from 30% to 70%, with three instances where processor judgement indicated storage readiness despite ERH values exceeding the 65% threshold. Dry matter content varied widely across treatments and sites, ranging from 7.4% to 18.3%. Chilies dried on black plastic sheeting showed higher and more consistent dry matter content (median ≈ 11.5%) compared with chillies dried on processor-preferred materials (median ≈ 9.1%). During storage, chilies kept in airtight glass jars maintained stable weights close to the initial 200 g over 70 days (199–201 g). In contrast, those stored in gunny bags showed progressive weight increases, reaching median values of approximately 208.5 g after 70 days. The study demonstrates that while experiential knowledge remains central to smallholder chili processing, it can be strengthened with simple, objective tools that support more reliable drying and storage decisions. Incremental improvements in drying surface selection, combined with ERH-based assessment and moisture-limiting storage, offer practical pathways for reducing post-harvest losses and quality risks. These context-appropriate interventions can enhance the safety, stability, and economic value of dried chilies without requiring costly infrastructure, making them well-suited to smallholder systems in humid environments.

Abstract: Side-scan sonar is a critical instrument for underwater cultural heritage preservation, as it allows large-scale detection of shipwrecks in turbid waters where optical methods fail. However, the automated segmentation of these targets remains a significant challenge, as severe speckle noise and complex seabed reverberations often obscure the distinct geometric features of submerge structures. To address this challenge, this paper proposes SW-Net, which utilizes a multi-scale input strategy and a novel Directional Filter Bank to inject physical priors into the feature extraction process. Furthermore, by coupling this with a Directional Attention Mechanism, the network dynamically modulates structural features to accurately segment targets despite intensity inversions and speckle noise. As demonstrated by the experimental results on the AI4Shipwrecks dataset, the SW-Net outperforms five state-of-the-art architectures, achieving the highest intersection over union of 39.26% and F1-score of 56.38%. In addition, the model exhibits superior robustness against complex seabed interference while maintaining the lowest computational complexity of 4.01 million parameters among the evaluated methods. Taken together, the SW-Net is proposed to offer a practical solution for shipwreck detection on resource-constrained autonomous underwater vehicles.

Abstract: The persistence of pharmaceutical contaminants such as carbamazepine (CBZ) in aquatic environments presents a growing challenge for conventional wastewater treatment processes. In this study, potato peel waste was valorised into carbonaceous adsorbents via hydrothermal carbonization (HTC) and conventional pyrolysis, and their performance for CBZ removal from water was systematically compared. Hydrochars were prepared at 200 °C under varying residence times and biomass-to-water ratios, while biochars were produced at 400 °C using KOH activation under different reaction times and impregnation ratios. The materials were characterised using BET surface area analysis, CHNS elemental analysis, and FTIR spectroscopy. Adsorption experiments revealed that HTC-derived hydrochars achieved outstanding CBZ removal efficiencies (up to ~100%) and high uptake capacities (~50 mg g⁻¹) within one minute of contact, despite relatively low surface areas (< 2 m² g⁻¹). In contrast, pyrolysis biochars exhibited significantly lower removal efficiencies (7–55%) and slower, less stable adsorption behaviour. Correlation analysis demonstrated that CBZ removal was strongly associated with surface chemistry—particularly carbon, hydrogen, and nitrogen content and N/C ratio—rather than BET surface area or pore diameter. FTIR analysis indicated that π–π interactions, hydrogen bonding, and pore filling collectively govern CBZ adsorption, with oxygen- and nitrogen-containing functional groups playing a dominant role in rapid uptake. These findings highlight hydrothermal carbonization as an effective, low-severity route for producing high-performance adsorbents from food waste and demonstrate the potential of potato peel–derived hydrochars for rapid pharmaceutical remediation in water treatment applications.

Abstract: Cell-derived microparticles with an equivalent diameter below ~1 μm have been observed in a wide spectrum of chronic inflammatory diseases, many of which also present with overlapping comorbidities. These same disorders consistently show microcirculatory disturbances—detectable by nailfold capillaroscopy, laser Doppler techniques, laser speckle imaging or thermal perfusion mapping, and, where measured, the presence of amyloid-containing fibrinaloid microclots ranging from 1–200 μm. Although both microparticles and fibrinaloid microclots have been independently described in numerous conditions, their systematic correspondence has not previously been synthesised. In this commentary, we highlight how the co-occurrence of microparticles and fibrinaloid microclots reflects a shared pathophysiological axis involving endothelial injury, thrombo-inflammation and persistent protein misfolding. We outline the conceptual links underlying this pattern and argue that combined measurement of microparticles and fibrinaloid microclots could offer new diagnostic and mechanistic insights across chronic inflammatory diseases.

Abstract: A comprehensive thermodynamic and molecular-level investigation of adsorption on MgY and NH₄Y zeolites is presented using inverse gas chromatography at infinite dilution combined with a Hamaker-based formalism and an extended five-parameter Lewis acid–base model. The study establishes a unified framework that integrates dispersive, polar, and donor–acceptor interactions while explicitly accounting for temperature-dependent intermolecular geometry. The results demonstrate that adsorption is governed by a dynamic interplay between London dispersion forces, specific acid–base interactions, and thermal effects, rather than by static or purely additive contributions. The London dispersive surface energy decreases linearly with temperature, reflecting the progressive weakening of electronic correlation forces, while the inter-molecular separation distance exhibits a well-defined linear expansion, enabling the determination of intrinsic contact distances at 0 K. A major finding of this work is that the molecular surface area of adsorbed probes is not a constant geometric property but a thermodynamic quantity that follows a quadratic temperature dependence, revealing the adaptive nature of adsorption geometry. The comparison between MgY and NH₄Y highlights two distinct adsorption regimes: MgY is characterized by a structured and strongly dispersive interaction field associated with Mg²⁺ cations, whereas NH₄Y exhibits enhanced polarity, stronger specific interactions, and increased molecular flexibility driven by hydrogen bonding and protonic effects. The thermodynamic analysis of Lewis acid–base interactions shows that classical linear models are insufficient to describe adsorption on zeolite surfaces. A rigorous statistical evaluation demonstrates that the Hamieh five-parameter model provides the most accurate and physically meaningful description, capturing nonlinear donor–acceptor interactions and amphoteric coupling effects. Overall, this work introduces a novel thermodynamic methodology that links macroscopic surface energetics to microscopic interaction parameters, providing new insights into adsorption mechanisms and offering a powerful tool for the rational design of porous materials in catalysis, separation, and energy-related applications.

Abstract: Based on outer raceway control theory and considering the effects of elastic deformation, centrifugal force, and gyroscopic moment between the rolling elements and raceways, a geometric and force analysis of angular contact ball bearings is conducted. A five-degree-of-freedom theoretical model capable of accounting for the combined action of radial force and moment is established. The accuracy of the model is verified through numerical calculations and experimental results from existing literature. Upon validation of the theoretical model, a modified Archard model is employed to develop a wear volume model for the bearing raceways. The influence of both single and combined loads on sliding wear in the bearing raceways is systematically analyzed.

Abstract: Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through the multi-criteria analysis using GIS and hydrogeological field exploration, water balance modelling. The suitability testing was preliminarily performed in the Google Earth Engine environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests, produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could be recharged into the suitable sites is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that there are recharge rates of between 174 and 5,282 m³/day, with a high degree of spatial variability which is caused by local hydrogeological circumstances. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies.

Abstract: Objectives: The study aim was to investigate the relationship between oral hygiene behaviors, periodontal awareness, and self-reported periodontal symptoms among adults using a questionnaire and an internally consistent awareness scoring system. Materials and Methods: A cross-sectional questionnaire study was conducted for adults aged 18-65 years. The dataset comprised 412 respondents, of whom 386 questionnaires were complete and eligible for analysis. The survey included sociodemographic variables, toothbrushing frequency and duration, interdental cleaning, mouthrinse use, smoking, dental attendance patterns, previous periodontal treatment history, awareness of common periodontal signs, and self-reported symptoms. Awareness scores were calculated on a 0-20 scale. Descriptive statistics, chi-square analysis, one-way ANOVA, post hoc Tukey tests, and multivariable logistic regression were used with p< 0.05. Results: Mean age was 31.8 +- 9.6 years; 58.0% of participants were women. Twice-daily toothbrushing was reported by 56.0%, while 38.6% used interdental cleaning aids. The mean periodontal awareness score was 12.7 +- 3.8. Higher awareness scores were observed among respondents with university education, regular dental attendance, and interdental cleaning habits (p< 0.001). Gingival bleeding was the most frequently reported symptom (47.4%), followed by dentin hypersensitivity (33.9%) and halitosis (29.8%). In multivariable analysis, irregular dental attendance, current smoking, and absence of interdental cleaning independently increased the odds of reporting at least one periodontal symptom. Conclusions: Within the limits of this survey-based study, better oral hygiene behaviors and regular preventive dental attendance were associated with stronger periodontal awareness and a lower burden of self-reported periodontal symptoms. Public-health strategies in periodontology should address not only knowledge deficits but also the translation of knowledge into daily plaque-control behavior.

Abstract: Artificial intelligence (AI) is rapidly transforming healthcare, yet its integration into medical education remains inconsistent and lacks standardization across training levels. This article synthesizes current research on AI applications within medical education, highlighting significant gaps between the growing clinical role of AI and its representation in undergraduate and postgraduate curricula. The article identifies disparities in AI awareness and utilization, with postgraduate trainees demonstrating greater familiarity than undergraduates, and notes that most existing educational efforts are concentrated in specialty training and continuing education, particularly in fields such as radiology, pathology, surgery, cardiology, and dentistry. While medical trainees generally express positive attitudes toward acquiring AI competencies, barriers such as the absence of standardized frameworks and AI taxonomy, limited faculty expertise, curricular constraints, and ethical considerations impede broader adoption. Drawing on examples of pioneering programs and a systematic analysis of curricular approaches, we propose a novel, tiered framework for comprehensive AI integration across the medical education continuum. This framework emphasizes universal AI literacy, critical evaluation skills, ethical awareness, and experiential learning at the undergraduate level, with extensions for specialty-specific training and advanced technical or leadership tracks. Recommendations include phased implementation strategies, faculty development initiatives, including “teach the teacher”, and competency-based assessment methods. The article concludes that adequate preparation of future physicians requires a shift from isolated AI initiatives to coordinated, longitudinal integration efforts supported by collaboration among educational institutions, professional societies, and technology experts. Future research should focus on evaluating educational outcomes, developing robust assessment tools for AI competencies, and examining the long-term clinical impact of AI training.

Abstract: Lipedema is a chronic and progressive adipose tissue disorder characterized by disproportionate fat accumulation, microvascular dysfunction, chronic inflammation, and progressive fibrosis. Despite its prevalence and significant impact on quality of life, current therapeutic approaches remain largely symptomatic and fail to address the underlying biological mechanisms of the disease. Emerging evidence suggests that lipedema should be understood as a multifactorial condition involving genetic susceptibility, endothelial alterations, immune dysregulation, and extracellular matrix remodeling. In this context, pharmacological strategies targeting these pathways have gained increasing attention. Metformin, through activation of AMP-activated protein kinase (AMPK), exerts antifibrotic and immunometabolic effects, including inhibition of TGF-β signaling, reduction of extracellular matrix deposition, and modulation of adipose tissue inflammation. In parallel, incretin-based therapies, particularly glucagon-like peptide-1 (GLP-1) receptor agonists and dual GLP-1/GIP agonists such as tirzepatide, have demonstrated pleiotropic effects that extend beyond weight reduction, including improvements in metabolic homeostasis, reduction of systemic inflammation, and enhancement of endothelial function. These therapies appear to act through complementary mechanisms, with metformin primarily targeting tissue remodeling and fibrosis, and incretin-based therapies exerting broader systemic effects on metabolism, inflammation, and vascular integrity. This review proposes a hypothesis-generating mechanistic framework, supporting a shift from weight-centric and symptomatic approaches toward disease-modifying strategies. Although current evidence in lipedema is largely indirect, the convergence of experimental and clinical data provides a strong rationale for further investigation. Future studies should focus on evaluating combined therapeutic approaches and identifying biomarkers that reflect fibrosis, inflammation, and microvascular dysfunction, with the aim of developing targeted and personalized treatments for this complex disorder.

Abstract: Background/Objectives: Several novel biologics and small molecule therapies have emerged for the treatment of antihistamine-refractory chronic spontaneous urticaria (CSU), yet no study has directly compared their speed of response. This study aims to provide indirect evidence on the relative time to meaningful clinical response across approved and investigational therapies using a Bayesian network meta-analysis. Methods: Phase 2 and phase 3 randomized controlled trials reporting UAS7 scores in graphical format for antihistamine-refractory CSU were included. The primary outcome was mean time in weeks to minimal clinically important difference (MCID), defined as a UAS7 reduction of 10 points. Data were extracted using WebPlotDigitizer (v4.7) and analyzed via Bayesian random-effects network meta-analysis in MetaInsight (v6.4.0), with placebo as the reference node. Results: All drugs except rilzabrutinib 400mg daily demonstrated faster mean time to MCID than placebo. Fenebrutinib had the fastest mean time to MCID (0.67–0.76 weeks) and tezepelumab the slowest (5.41–5.65 weeks). Only omalizumab 300mg every 4 weeks, dupilumab 300mg every 2 weeks and ligelizumab 72mg and 120mg every 4 weeks achieved statistically significant reductions compared with placebo. All treatments had wide credible intervals reflecting limited direct comparisons. Conclusions: This is the first network meta-analysis comparing time to meaningful symptom control across therapies for antihistamine-refractory CSU. Omalizumab, dupilumab, and ligelizumab demonstrated statistically significant reductions in time to MCID compared with placebo. Head-to-head trials with standardized outcome reporting would enable more definitive comparative conclusions.

Abstract: Background: Generative artificial intelligence is entering higher education faster than many universities have been able to govern it, particularly in African contexts where policy ambition, institutional capacity, digital infrastructure, and pedagogical practice do not always advance at the same pace. Purpose: This study examines how generative artificial intelligence integration is publicly documented, governed, and framed at two universities in Rwanda: the African Leadership University and the Adventist University of Central Africa. Design: Guided by an integrated framework combining institutional readiness, Diffusion of Innovations, and a rights-based governance lens, the study adopts an interpretivist comparative multiple-case design based on document analysis and secondary analysis. The corpus comprises publicly retrievable institutional webpages, policy documents, academic regulations, handbooks, e-learning materials, research manuals, national policy texts, and recent peer-reviewed scholarship published or available between 2021 and April 2026. Findings: Public evidence indicates visible AI engagement at both universities, but in materially different forms. ALU appears more innovation-signalling, foregrounding AI research, student bootcamps, and academic-support programming. AUCA appears more governance-dense, with stronger public visibility of academic regulations, academic-integrity language, ICT and online-learning policies, plagiarism infrastructure, and AI-and-big-data institutional positioning. However, neither institution publicly presents a fully specified generative AI acceptable-use regime aligned with Rwanda’s evolving national and sectoral AI governance expectations. The findings therefore suggest that visible experimentation is advancing faster than visible rule specificity. Originality/value: The study contributes rare comparative African evidence on university AI governance and introduces a useful analytical distinction between innovation signalling and governance readiness. Practical implications: The central challenge is no longer whether universities will adopt AI, but whether they can align policy clarity, academic-integrity architecture, digital capacity, and educational purpose in institutionally credible ways. The study also identifies concrete priorities for later primary research on implementation, stakeholder interpretation, and assessment design.

Abstract: Individual tree classification has a long history of diverse development, with recent trends focusing on the adoption of machine learning and deep learning approaches. A simple and powerful approach that lets the model auto-pilot, but weakens the need for physical characteristic understanding. Over more than a decade of our research, we have focused on establishing a direct representation of individual trees that bridges 2D top-down imagery and true 3D models. In this study, we investigated the fundamental question of the influence of the input data on these ML/DL models. In 2024, we introduced a novel data transformation method, the Pseudo Tree Crown (PTC), which provides a pseudo-3D pixel-value perspective that enhances the informational richness of images and significantly improves classification performance. Our original implementation was successfully tested on urban and deciduous trees in 2024 and was later extended to Canadian natural conifer species under snow conditions in 2025. However, the original PTC relied on the green band, limiting its applicability to green-leaf species. In this study, we analyzed and compared the performance of different data variations and transformations, such as the Green–Red Vegetation Index (GRVI) and Principal Component Analysis (PCA), as direct input and used their PTC forms. Classifications were conducted using Random Forest, ResNet50, and YOLOv10. The results confirmed the effectiveness of the PTC, which consistently improves classification accuracy by at least 7% without introducing additional computational time or complexity. Furthermore, PTC exhibits robust, consistent behaviour across all data forms, demonstrating its strong resilience and reliability.

Abstract: Background and Clinical Significance: Glioblastoma is the most common and aggressive primary malignant brain tumour in adults. Maximal safe surgical resection remains the cornerstone of treatment; however, tumour vascularisation may increase the risk of in-traoperative bleeding and complicate surgical management. Preoperative endovascular embolisation is commonly used for highly vascular intracranial tumours such as men-ingiomas, whereas its role in glioblastoma remains poorly defined. A focused literature review using the search string (((preoperative) AND (endovascular)) AND (embolization)) AND (glioblastoma) identified only two relevant publications, highlighting the scarcity of available evidence. In this context, we report a case series of three patients with intra-cranial lesions suspected to be high-grade gliomas who underwent preoperative angi-ographic evaluation and, when feasible, endovascular embolisation prior to surgical resection. Case Presentation: Three patients presenting with large intracranial lesions suggestive of high-grade glioma underwent preoperative digital subtraction angi-ography to assess tumour vascular supply (histological analysis confirmed the diagnosis of glioblastoma). In a 61-years-old woman with a right frontal tumour, selective catheteri-sation of a frontal branch of the right anterior cerebral artery enabled embolisation with coils, achieving partial tumour devascularisation before surgery. A second patient, a 53-year-old man with a large left temporo-fronto-insular mass extending to the corpus callosum, underwent embolisation of tumour feeders arising from the anterior choroidal artery using N-butyl cyanoacrylate and Lipiodol prior to resection. In a third case, a 77-year-old man with a left temporo-parietal lesion underwent preoperative angiography that demonstrated tumour capillary blush but no catheterisable feeding arteries, and embolisation was therefore not feasible. All patients subsequently underwent surgical resection without perioperative complications or new neurological deficits. Conclusions: Preoperative angiographic evaluation may help characterise tumour vascular supply in selected glioblastoma cases. When identifiable arterial feeders are present, endovascular embolisation may represent a feasible adjunct to facilitate surgical management. Further studies are required to better define the indications, safety profile, and potential benefits of this approach.

Abstract: Background: Liver abscesses represent an atypical yet potentially life-threatening complication of bacterial, fungal, protozoal, and helminthic infections. Frequently, the clinical findings associated with liver abscesses are nonspecific, necessitating a reliance on imaging for diagnosis. It is uncommon for a liver abscess to radiographically resemble a malignant liver tumor such as hepatocellular carcinoma (HCC). Here, we present the case of a 45-year-old male who was initially diagnosed with HCC (BCLC C) but was subse-quently found to have a liver abscess following biopsy. Case Presentation: A male patient, 45, presented with stiffness and pain in the right upper abdomen. He complained of nausea and vomiting since 10 days before admission as well. All supportive imaging suggested a diagnosis of HCC. A liver abscess was detected during a biopsy. A liver ultrasound-guided FNAB showcased chronic, suppurative in-flammation with negative acid-fast bacilli on Ziehl-Neelsen staining. The patient sub-sequently developed a complication of middle hepatic artery bleeding and underwent immediate embolization. Discussion: In fact, a liver abscess can be the initial manifestation of HCC. Patients tend to have a poorer prognosis because the diagnosis of a liver abscess often delays the discovery of the underlying HCC. Radiographically, liver abscesses range from well-circumscribed cystic lesions with an enhancing rim to heterogeneously enhancing mass-like lesions, which are sometimes indistinguishable from liver neoplasms. However, it is so scarce that a liver abscess may radiographically mimic HCC. Conclusion: Assessing liver abscess is somewhat complicated since the symptoms vary a lot. Therefore, a correct and exact diagnosis entail a combination of more comprehensive clinical and supporting examinations.

Abstract: Introduction Liver transplantation is currently an increasingly popular treatment method for patients with liver failure, both in adults and children. Antibody-mediated rejection (AMR), which is a very rare and poorly understood phenomenon, can lead to deterioration of graft function. The aim of the study was to analyze the clinical and histopathological manifestation of AMR in pediatric patients. Material and methods Sixty-two liver core biopsies from forty-two pediatric patients were included in this retrospective study. AMR was diagnosed in 7 children (in 10 biopsies), 35 demonstrated features of acute T-cell mediated rejection (TCMR) in 52 biopsies. C4d binding assay was performed in all biopsies using the immunohistochemical (IHC) method. The specimens were re-evaluated for signs of acute and chronic rejection, bilirubinostasis, and steatosis. Fibrosis was evaluated using a 6-grade Ishak scale. The Banff classification was used to assess TCMR activity. Evaluation of AMR was performed according to a newly developed original histopathological grading. Relationship between histopathological grading and morphological, as well as laboratory parameters was determined in each group depending on type of rejection. Statistical analysis was performed according to standard indications. Results The median age of patients (months) at the time of biopsy was 47.6 (15.03 – 98.83) and the median time from transplantation (months) was 0.9 (0.3 – 7.6). Results of the study brought evidence that histopathological lesions were the least specific manifestation suggesting AMR. Positive result of C4d staining with or without associated morphological abnormalities statistically increases the likelihood of AMR diagnosis. No statistically significant correlation was found between the type of rejection and laboratory tests. Conclusions: Diagnosis of AMR in transplanted liver is complicated and need to be complex. However, the proposed histopathological grading may be a helpful method for selecting patients who should be assessed for Donor-specific antibodies (DSAs) or in whom AMR should be suspected when DSA cannot be determined.

Abstract: Continuous monitoring of microclimatic variables is essential for precision viticulture and data-driven decision support systems. However, agricultural sensor networks are frequently affected by missing data due to hardware failures, communication issues, or maintenance interruptions. In this work, we propose a spatio-temporal graph-based autoencoder for reconstructing missing temperature and relative humidity time series collected from a five-node vineyard sensor network over a two-year period. The model combines a GRU-D-based temporal encoder with a GraphSAGE spatial module, enabling the joint exploitation of temporal dynamics and inter-node spatial correlations. Experimental results on real-world data show that the proposed approach achieves accurate reconstruction under realistic missing-data conditions. For moderate corruption levels (p=0.3), the model attains reconstruction losses of 0.003 for temperature and 0.005 for humidity using short temporal windows (L=36∼3h), corresponding to MAE values below 0.03∘C and 0.1%, respectively. Even at higher corruption levels (p=0.7), performance remains stable, with losses below 0.008 and 0.011, and MAE values within 0.05∘C and 0.17%. The results highlight a trade-off between temporal context and reconstruction stability: shorter windows yield better performance under moderate corruption, while longer windows (L=144∼12h) improve robustness under extreme data loss (p=0.9), reducing temperature reconstruction loss from 0.027 to 0.021 and MAE from 0.133∘ to 0.226∘. Additionally, temperature is consistently reconstructed more accurately than humidity, reflecting its smoother dynamics and stronger spatial coherence.