Abstract: Patient manual handling during positioning is widely recognised to have low evidence-based practices, which exposes healthcare practitioners (HCPs) to a high risk of work-related musculoskeletal disorders (WRMSDs). This study assessed experts’ opinions regarding challenges and best practices during manual handling for patient positioning in long-term care settings. A semi-structured interview was conducted with purposively recruited subject experts in the UK (n=9; aged 30-62 years). Interviews focused on challenges in patient manual handling, experts’ ideas about best practices, and suggested solutions to persistent challenges, and data were analysed thematically. Major gaps in training and in key aspects of positioning were evident, including patient bed mobility, postural management, and turning patients into side-lying. Experts asserted that realistic and comprehensive training structured on optimised use of low-tech equipment such as wedges, breathable pillows, sliding systems, and sleep systems may be more effectively implemented for safer patient handling, even for single-handed care settings. This study provided a novel model and recommendations to optimise practices in patient bed mobility, posture care, repositioning and turning into side-lying, aimed at improving patient outcomes and mitigating occupational risks.

Abstract: The exceptionally strong geomagnetic storm of 10-11 May 2024 injected new energetic protons and electrons in the terrestrial radiation belts, creating extraordinary conditions to study the loss mechanisms scattering these particles into the atmosphere after the storm. For the first time, four electron belts were observed during several weeks. We show that this structure was due to electron loss highly depending on specific positions. Using the proton and electron fluxes measured by the Energetic Particle Telescope EPT on board PROBA-V, we determine the lifetimes of these populations depending on their energy ranges and positions. We show that the lifetimes are much longer for protons than for electrons, which allows us to determine their time variations independently. For electrons, the wave-particle loss mechanisms depend on the background ionosphere-plasmasphere density. The lifetimes determined after the May 2024 and 10 October 2024 big events are compared with average ones to understand their unusual specificity for the formation of four and three belts, respectively. For the injected protons of 9.5 to 13 MeV, the lifetime is minimum at L~1.9 where the fluxes are maximum, showing a lifetime depending on the flux intensity. Loss is due to pitch angle diffusion and collisions with electrons and nuclei in the ambient plasma and neutral atmosphere. At the outer edge of the proton belt, the flux is depleted at all energies after the geomagnetic perturbation, and we determine that the progressive time of refilling after the storm reaches generally more than 40 days. There is an excellent discrimination between the different populations of energetic electrons (0.5-8 MeV) and the injected protons (9.5-13 MeV) that are still observed several months after the event. Such results contribute to advancing understanding of the interactions between the terrestrial atmosphere and space radiation.

Abstract: Sample-wise detection of P-, R-, and T-peaks in electrocardiograms (ECGs) is challenging because each peak type is sparsely represented (≈1:500 samples in a typical 10-s, 500-Hz ECG at 60 bpm), such that even a small number of false-positives (FPs) can markedly degrade positive predictive value (PPV) and limit the practicality of classifier-only approaches. This study proposes a lightweight ECG peak detection framework that combines binary classifiers with physiological temporal constraints (PTC) to address extreme sample-level class imbalance. Local morphological features are first evaluated using lightweight machine-learning models, among which XGBoost (XGB) exhibited the most stable score-ranking performance. Rather than directly thresholding classifier outputs, prediction scores are interpreted within the framework, which encodes physiological timing relationships. R-peaks are detected using score ranking combined with a refractory-period constraint, and the detected R-peaks serve as temporal landmarks for subsequent P- and T-peak detection within physiologically plausible time windows reflecting the P–QRS–T sequence. Quantitative evaluation was conducted using the Lobachevsky University Electrocardiography Database, hereafter referred to as LUDB. With a temporal tolerance of ±20 ms, the XGB-based system achieved an F1-score of 0.87 for R-peak detection (sensitivity 0.96, PPV 0.79), corresponding to approximately 9–10 true R-peaks with only 2–3 FP samples per 10-s segment. For P- and T-peaks, F1-scores of 0.70 and 0.69 were obtained, respectively. Additional evaluation on arrhythmic LUDB records demonstrated robust R-peak detection across rhythm types. In AF-related rhythms, where organized P waves are physiologically absent, the framework appropriately suppressed P-peak detections, with false-positive rates remaining below 0.31%. Qualitative application to ECG recordings from the PTB-XL database further demonstrated physiologically consistent behavior. These results indicate that reliable and interpretable ECG peak detection under extreme class imbalance can be achieved by integrating lightweight classifiers within the proposed framework, without reliance on complex deep learning architectures.

Abstract: Upper Tropsphere (UT) humidity records are crucial for climat studies.  Pseudo-monthly averaging is applied to maximize temporal representativeness, and enhance the lidar signal allowing to provide WVMR profiles up to 16 km. This study evaluates 11 years (2013–2023) of water vapor mixing ratio (WVMR) profiles from a UV Raman lidar (Lid1200) at the Reunion Island against MLS-Aura satellite retrieval, ERA5 reanalysis, and GRUAN-processed M10 radiosondes.  Results show a systematic dry bias in MLS of up to 30% above 12 km, particularly during the wet season. Lidar exhibits a small underestimates of WVMR around 5% drier than ERA5 all over the UT, with the largest deviations above 14 km, and larger variability during the wet season, Lidar calibration-related challenges during the dry season results in drier than ERA5 WVMR profiles (up to 10%). Aditionnaly, comparisons with GRUAN-processed radiosonde reveal a substantial lidar dry bias, exceeding 100% above 12 km.  Both lidar dry biases might be linked to the GNSS-based lidar calibration. Applying an alternative calibration method produces higher WVMR values, reducing the lidar dry bias w.r.t GRUAN by about 50% at upper-tropospheric levels, improving it's agreement with radiosondes, and revealing ERA5 dry bias increasing with altitude at the UT up to 25%. These efforts complement the global interest in the monitoring and validation of subtropical upper-tropospheric humidity.

Abstract: The paper presents an analysis of the risk of failure of port structures in a modern seaport due to vessel impacts. The analysis addresses potential damage related to port maneuvers of self-maneuvering vessels and possible risk reduction options that can be applied to enhance port resilience. The proposed system model—including ship, port infrastructure and environment—enabled the observation of both implemented and anticipated future risk reduction measures. The analysis was carried out using the ferry terminal in the large Polish Port of Gdynia as a case study. A Bayesian influence diagram—including decisions related to the implementation of risk reduction options—was used to determine the total risk associated with ro-pax ferry port calls. Sustainable risk management led to the implementation of a cloud-based monitoring system and, subsequently, to the design of a new terminal in line with the green port concept. A comparative risk assessment for the two locations demonstrated improved safety and reduced environmental pollution in the new Public Ferry Terminal, primarily due to reduced spatial risk and the implementation of cold-ironing technology in the new terminal. The potential future implementation of an automated mooring system was also discussed.

Abstract: Type 2 diabetes mellitus is marked by chronic hyperglycemia and insulin resistance, leading to progressive tissue damage. Flavonoids such as astragalin have emerged as promising antidiabetic compounds. This study investigated the effects of astragalin on glucose uptake, insulin secretion and ionic mechanisms in pancreatic β-cells (MIN6 and INS-1). Glucose uptake and insulin secretion were quantified by bioluminescence and ELISA, respectively, while ionic currents were assessed by whole-cell patch clamp using selective pharmacological blockers. Astragalin progressively enhanced glucose uptake, reaching a plateau between 3 and 5 h, suggesting improved mitochondrial function and modulation of calcium- and AMPK-dependent signaling pathways. Insulin secretion was significantly stimulated after 1 h of treatment with 100 µM astragalin, involving ATP-sensitive K⁺ channels, voltage-dependent K⁺ channels, and L-type Ca²⁺ channels. Electrophysiological patch-clamp studies showed that astragalin reduces potassium channel currents, indicating partial channel closure and consequent membrane depolarization as corroborated by calcium involvement by using verapamil, an ionic environment associated with insulin exocytosis. These findings suggest that astragalin acts as a metabolic modulator and secretagogue in β-cells coupling insulin-stimulus secretion, representing a potential candidate for antidiabetic therapeutic strategies.

Abstract: Tea tree essential oil (TTO), extracted from Melaleuca alternifolia leaves, is increasingly recognized as a powerful natural antimicrobial for modern food safety applications due to its terpene-rich composition and broad biological activity. Dominant constituents such as terpinen-4-ol, γ-terpinene, and α-terpinene contribute to strong antibacterial, antifungal, and antibiofilm effects, positioning TTO as a clean-label alternative to synthetic preservatives. This review synthesizes current knowledge on the physicochemical properties of TTO, including chemotype variability, hydrophobicity and solubility constraints, oxidative instability, and interactions with food components that influence its functionality. The antimicrobial mechanisms of TTO against major foodborne pathogens and spoilage fungi are examined, emphasizing membrane disruption, disturbance of cellular homeostasis, oxidative stress induction, and quorum-sensing interference. Recent advances such as nanoemulsions, encapsulation, and polymer-based delivery systems have improved TTO stability, reduced volatility, and enabled controlled release, supporting its incorporation into edible coatings, active packaging, and sanitation formulations. These innovations enhance microbial reduction in fresh produce, meat, dairy, and minimally processed foods. Remaining challenges include sensory impacts, volatility losses, regulatory limitations, and concentration-dependent toxicity. Overall, current evidence underscores TTO’s potential as a versatile, sustainable antimicrobial for next-generation food protection strategies.

Abstract: Background Lipophilic environmental contaminants—including persistent organic pollutants (POPs), PFAS, PCBs, and PAHs - exert a long-term biological influence that cannot be explained by acute toxicity alone. Their extreme hydrophobicity drives high-affinity sequestration within lipid-rich tissues, such as adipose depots, myelin sheaths, and endocrine glands, creating "internal reservoirs" with biological half-lives measured in decades. These reservoirs fuel continuous, low-grade endogenous exposure and sequential absorption of hydrophilic species, persisting regardless of ongoing environmental contact. Scope of Review This article integrates toxicokinetic modeling with modern multi-omics evidence to update Zeliger’s model of lipophilicity-driven chronic disease. We examine how these diverse compounds activate a conserved set of biological injury pathways, regardless of their specific chemical structure. Specifically, we analyze the convergence of nuclear receptor disruption, mitochondrial dysfunction (amplified ROS production), calcium dysregulation, neuroimmune activation, and persistent epigenetic remodeling. Major Conclusions Lipophilic pollutants function as a unified category of systemic toxicants that reorganize cellular and metabolic systems. The identified mechanistic signatures provide a systems-level explanation for the epidemiological links between pollutant burdens and metabolic syndrome, cardiovascular morbidity, neurodegeneration, and cross-generational epigenetic effects. These findings validate the use of "total oxidative stress" as a predictor for non-communicable disease onset and support a paradigm shift toward mixture-based regulation and exposomic biomarkers for early detection.

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.