Abstract: Background: Healthcare systems are organized around traditional levels of care (primary, secondary, tertiary), activated only when a patient enters the formal system. Translational medicine typically moves from bench to bedside. Here we propose a reverse translational step: patient-led self-management as a pre-clinical level of care – LINE ZERO. Objective: To introduce and test LINE ZERO – self-management, prevention, and self-care – as an extension of the traditional care model. Methods: An 11-month longitudinal self-experiment (n=1) in a 55-65 year old man with adjustment disorder. Interventions included breathing exercises, light therapy, music therapy, gentle stretching, moderate dietary changes, and self-monitoring of blood pressure, deep sleep, and blood glucose. Results: Deep sleep increased 3-fold (p < 0.001). Blood pressure stabilized. Glycemic variability decreased after an acute stress-related peak. A subsequent work stress episode caused a 30% drop in deep sleep, followed by rapid recovery within 48 hours. Conclusion: LINE ZERO extends the traditional care model by adding a pre-clinical self-management space. It aligns with Future Medicine (predictive, preventive, personalized, participatory) and represents a reverse translational contribution – from patient observation to a conceptual framework applicable to health systems.

Abstract: Special Economic Zones (SEZs) are widely promoted as catalysts for industrialization and export growth in developing countries, yet their capacity to generate sustainable and inclusive regional development remains debated, particularly in sub-Saharan Africa. This study investigates the impact of the Nkok SEZ in Gabon on the forestry sector—a novel case study—by analyzing the resulting economic and spatial disparities between the SEZ (homogeneous space) and its periphery (heterogeneous space). Combining robust econometric methods (Bias-Corrected Fixed Effects, OLS) and principal component analysis (PCA) on time-series data (2014–2022), we show that while the SEZ has significantly boosted export revenues (84%–97% growth) and industrial production through agglomeration and scale economies, these benefits remain largely concentrated. The periphery experiences weaker growth, reinforcing center-periphery dependencies and extractive specialization. Export revenues from the homogeneous space exhibit strong autoregressive effects (77%–94%) but limited macroeconomic diffusion (6%–25%), whereas the heterogeneous space shows lower autoregressive growth but a stronger historical influence on national aggregates, highlighting a structural polarization trap. To address these persistent imbalances, this paper introduces the SEMD model (Segmentation, Evaluation, and Multi-level Disparities Management). This operational framework proposes a six-fold territorial typology (from SEZs to informal circuits), hybrid quantitative-qualitative indicators, and proactive rebalancing mechanisms (vertical and horizontal channels) to institutionalize the diffusion of growth. The SEMD model offers a strategic tool for policymakers in the Global South to reconcile industrial performance with territorial cohesion, moving beyond the mere diagnosis of inequalities toward adaptive, real-time management of polarized development dynamics.

Abstract: For a fixed integer \( n\ge2 \), let \( \Delta_{n-1} \) denote the standard probability simplex in $\( \mathbb R^n \)$. We introduce and analyze the self-power escort transformation \( T_n:\Delta_{n-1}\to\Delta_{n-1} \) defined by \( \begin{equation*}T_n(p)_i=\frac{p_i^{p_i}}{\sum_{j=1}^n p_j^{p_j}},\qquad i=1,\ldots,n,\end{equation*} \) with the continuous boundary convention \( 0^0=1 \). Unlike the usual power escort transformation, whose exponent is an external parameter, the exponent here is the coordinate itself; equivalently, each coordinate is reweighted by \( \exp(p_i\log p_i) \). The paper proves that this elementary self-feedback rule has a rigid global dynamics. Every boundary point is immediately activated, every orbit enters an explicitly described compact core after one step, the uniform distribution is the unique fixed point, and the Hilbert log-diameter contracts exponentially after core entrance. Consequently every orbit converges exponentially to the uniform distribution, and no nontrivial periodic orbit exists. We then compute the complete linear spectrum and second-order normal form at the uniform state, obtaining a sign transition between the binary and higher-dimensional systems. For \( n\ge4 \), the post-core Hilbert contraction constant is sharpened to a two-variable min--max problem involving only the smallest and largest coordinates. We also classify the entire one-step uniformization fiber \( T_n^{-1}(\nu_n) \): besides the vertices and the uniform point, each dimension \( n\ge3 \) has exactly one nonuniform interior orbit type, up to permutation. Finally, in the binary case we prove global contraction in logit coordinates and an arithmetic escape phenomenon: algebraic irrational inputs become transcendental after one step, while rational nonuniform inputs become transcendental after two steps.

Abstract: Around the half of the population in the world are affected by oral diseases, making it one of the most common health conditions. Quantum implementation in medical domain has revealed its potential and versatile applicability especially in medical imaging. This paper explores oral disease identification using hybrid quantum-classical neural networks (HQCNN) and quantum convolution neural networks (QCNN). Our work investigates the possibilities of quantum machine learning in processing complicated dental image data and the contributions it can make in oral healthcare. We implemented a hybrid and a pure QNN leveraging Qiskit framework and a whole dataset of annotated oral disease dataset. Our 8 qubit structured QCNN model and 2 qubit architecture of HQCNN model extract the image features by encoding the features into quantum circuits enabling more expressive demonstration employing fewer parameters. The final result showcases that QCNN and HQCNN perform better than CNNs in disease classification and promise better accuracy, generalization and computational efficiency. This experiment highlights a pioneering step in applying quantum inspired models for oral diagnostics, identifying promising avenues for improving oral healthcare worldwide.

Abstract: The QBist and Relational Quantum Mechanics (RQM) informational readings of quantum theory have been developed across two decades without a clear position on what kind of physical system qualifies as an "agent" or "observer" for the formalism. Fuchs, Mermin, and Schack write as if the agent is a human physicist or a generic Bayesian; Rovelli writes as if any physical system can play the relational-observer role; Healey deflates the agent into an abstract Bayesian without specifying its substrate; the recent Khrennikov-Schack-Zwirn intersubjectivity exchange sharpens the question without resolving it. This paper argues that the QBism/RQM informational reading is substrate-flexible: any physical system whose input-output statistics admit characterization through quantum-probability structure with non-trivial Contextuality-by-Default (CbD) signatures resistant to simplex-embeddable ontological models is a candidate epistemic agent for the formalism. Substrate flexibility is the most coherent reading of the shared formal commitments of QBism and RQM once the agent role is separated from historically human-centered examples; the non-triviality requirement is necessary but not sufficient for agency, which additionally requires an input-output architecture capable of state-sensitive updating across measurement contexts. The thesis preserves the QBist objection to view-from-nowhere framings while removing the requirement that agents be human or conscious; it disciplines Rovelli's "any physical system" claim by indexing it to the non-triviality requirement; and it specifies what would count as evidence for or against. Engineered cortical wetware preparations (Cortical Labs CL1, DishBrain) provide a non-human, non-conscious-in-any-unambiguous-sense, controllable testbed on which the question can be empirically pursued; nothing in the argument requires attributing phenomenal consciousness or quantum-coherent biological dynamics to such systems. The paper distinguishes substrate flexibility from Pienaar's prior extension of the QBist agent (which extends the agent's senses, not its substrate), engages the neo-Everettian opposition (Wallace 2012, 2023) directly, and rejects the recent attempts (Edwards 2024, 2025) to fold classical large language models into a QBism-grounded formalism. Classical AI architectures admit simplex-embeddable models for their token-generation processes and therefore fail the non-triviality requirement; substrate flexibility is narrower, not wider, than such proposals.

Abstract: Photoreceptor (PR) degeneration is a shared pathological feature of multiple blinding retinal diseases. This review examines the mechanisms underlying PR vulnerability to lipid-peroxidation-driven injury, with emphasis on three interconnected features: the marked enrichment of docosahexaenoic acid (DHA) and other polyunsaturated fatty acids (PUFAs) in PR outer-segment disc membranes; the chronically high metabolic demand of PRs and the specialized spatial organization of their mitochondria; and retinal pigment epithelium (RPE)–PR metabolic coupling, including outer-segment renewal and phagocytic turnover, glucose transport and lactate shuttling, and the visual cycle. We also summarize antioxidant defense systems centered on the cystine/glutamate antiporter (xCT)–glutathione (GSH)–glutathione peroxidase 4 (GPX4) axis and mitochondrial GPX4 (mtGPX4), which restrict iron-dependent lipid peroxidation in PRs. We propose that highly oxidizable membrane lipid substrates, mitochondrial homeostatic imbalance, and impaired RPE–PR metabolic coupling may collectively shape PR susceptibility to ferroptosis-associated injury. From a therapeutic perspective, this framework supports multitarget strategies designed to interrupt lipid-peroxidation propagation, stabilize mitochondrial redox homeostasis and quality-control mechanisms, and restore RPE–PR metabolic support and local iron-buffering capacity.

Abstract: This study analyses the application of games and gamification to increase student motivation and ensure an effective and sustainable learning process in the modern sustainable environment of digitalisation and e-learning. Recent changes have revealed that traditional teaching methods do not always sufficiently engage students, increasing the risk of decreased motivation to learn and poorer achievement. Gamification, which is based on the integration of game design elements – points, levels, badges, challenges – into learning activities, can help reduce these risks by promoting student autonomy, a sense of competence and social engagement. The study presents the application of a gamification system based on case analysis using an educational platform that allows the creation of educational games and the assessment of student engagement in real time. The results show that gamification methods significantly increase student activity and motivation to learn, while integrated game elements improve student’s motivation and learning experience. Based on the analysis, the paper presents conclusions and recommendations for the effective, high-quality, and sustainable organisation of the modern education process based on gamification.

Abstract: Background/Objectives: Adults with single ventricle physiology (SVP) represent a growing population with complex cardiovascular conditions and an increasing need for noncardiac surgery (NCS). However, perioperative outcomes in this group remain poorly characterized. This study aimed to evaluate perioperative complications and mortality in adults with SVP undergoing NCS. Methods: We conducted a retrospective cohort study including all adult patients (≥18 years) with SVP who underwent NCS requiring anesthesia or sedation at a tertiary university hospital between 1 January 1995 and 30 November 2023. Demographic data, comorbidities, type of procedure and anesthetic technique were collected. Complications were defined as intraoperative or postoperative adverse events requiring intervention or associated with hemodynamic, respiratory, or cardiovascular instability. Primary outcomes were perioperative complications and all-cause mortality at 24 hours, 30 days, and one year, analyzed per procedure. Results: A total of 114 procedures were performed in 67 patients (mean age 32.3 ± 10.8 years). Most procedures were elective (78.9%) and minimally invasive, frequently performed under sedation (67.6%). Common comorbidities included arrhythmias (56.1%), liver disease (52.6%), and heart failure (20.2%). The overall complication rate was 6.1% (2.6% intraoperative, 3.5% postoperative). Mortality was 0.9% at 24 hours, 1.8% at 30 days and 3.5% at one year. Adverse outcomes were more frequent in patients with earlier-stage palliation, advanced functional limitation or multiple comorbidities. Conclusions: Perioperative outcomes in adults with SVP undergoing NCS are acceptable when procedures are elective and managed in specialized settings. Risk remains heterogeneous and appears to be influenced by physiological status and stage of palliation.

Abstract: Increasing regulatory demands for high-quality plastic recycling create a strong need for novel tracer systems that enable reliable polymer identification and sorting. This feasibility study evaluates germanium nanocrystals (GeNCs) as Raman-detectable tracer materials in polypropylene (PP). The synthesis of GeNC/PP composite materials possessing various GeNC contents via a solvent-based intercalation process followed by compounding and injection molding is reported. Hydride-terminated GeNCs were synthesized and subse-quently functionalized with dodecyl ligands to ensure chemical stability, compatibility with the polymer matrix, and processability under conventional melt-processing condi-tions. The dodecyl-functionalized GeNCs were successfully stabilized and homogeneous-ly integrated into the PP matrix. Raman spectroscopy demonstrates the clear detection of GeNCs within the composites through a characteristic Ge–Ge optical phonon mode at 296 cm⁻¹, which is well separated from the intrinsic Raman bands of polypropylene. The Ra-man signal intensity increases systematically with increasing GeNC concentration. Ra-man mapping reveals an overall homogeneous distribution of the nanocrystals within the polymer, while a slight tendency toward agglomeration is observed at higher loadings. These results demonstrate that GeNCs are well suited as optically detectable tracers for polypropylene and can be reliably identified using Raman spectroscopy, highlighting their potential for tracer-based sorting concepts in advanced recycling and digital material passport applications.

Abstract: Background/Objectives: Dolutegravir is an integrase strand transfer inhibitor widely used in HIV therapy due to its high resistance barrier and favourable safety profile. As pharmacopoeial coverage continues to evolve and local formulation efforts increase, relia-ble analytical methods are required to ensure drug quality, safety, and efficacy. This study aimed to develop and validate two simple, sensitive, and cost-effective high-performance liquid chromatography with UV detection (HPLC-UV) methods for quantifying dolute-gravir in bulk form and human plasma. Methods: Reverse-phase HPLC methods were developed and validated according to ICH Q2(R2) guidelines. Hydrochlorothiazide and carbamazepine were used as internal standards for bulk and plasma analysis, respective-ly. Chromatographic separation was achieved on C18 columns. Key validation parameters included linearity, accuracy, precision, limit of detection (LOD), limit of quantitation (LOQ), robustness, and system suitability. Plasma samples were prepared using liquid–liquid extraction with diethyl ether. Results: The bulk method showed excellent linearity over 0.5–100 µg/mL with an R2 of 0.9997, while the plasma method was linear over 0.8–10 µg/mL with an R2 of 0.9956. Accuracy ranged from 95.2% to 104.4% for bulk and showed acceptable recoveries for plasma samples. Precision was satisfactory with %RSD values below 6% across all methods. LOD and LOQ were 0.42 µg/mL and 1.26 µg/mL for bulk analysis, and 0.55 µg/ml and 1.68 µg/ml, for plasma analysis respectively. Robustness and system suitability tests confirmed method reliability. Conclusions: The developed HPLC-UV methods are simple, reproducible, and suitable for routine quality control of dolutegravir in pharmaceutical formulations and for its quantification in human plasma for pharmacokinetic applications.

Abstract: We present a rigorous and comprehensive development of the Vertex Shift Method(VSM), a derivative-based polynomial normalization technique extending Loh’s quadraticmidpoint insight [1] to arbitrary-degree polynomials and time-dependent dynamicalsystems. Given a polynomial P(x) of degree n ≥2, VSM identifies a critical point φsatisfying P′(φ) = 0 and applies the shift x= y+ φ, producing a normalized form Q(y)with no linear term.We prove: (i) the linear-term elimination theorem—the shifted polynomial has zerolinear coefficient by construction; (ii) the spectral translation theorem µi = λi−φ, all rootsshift rigidly by exactly φ; (iii) companion-matrix similarity via S(φ) = exp(φD); (iv) theCurved Shift Theorem for time-varying polynomials via the Implicit Function Theorem;and (v) explicit conditioning reduction bounds. A central contribution is the identificationof VSM as a structure-aware preconditioning operator in coefficient space:eliminating a1 reduces coefficient imbalance—the principal driver of ill-conditioning incompanion matrices.Numerical experiments on eight polynomial families demonstrate conditioning improve-ments of 30×to 2.3 ×105×, 15–57% QR iteration reductions, and 3–20×root-accuracyimprovements. All results are fully reproducible via the companion Jupyter notebook.

Abstract: Microtubules contain ordered aromatic amino-acid networks whose optical excitations have been proposed to support non-trivial energy-transfer dynamics. Here, we examined whether bound tryptamine ligands can perturb the excitonic structure of the tubulin tryptophan network. A virtual screen of 294 tryptamines was performed across seven known binding regions of the tubulin heterodimer using AutoDock Vina. From this screen, top-ranked tryptamine ligands were carried forward for excited-state analysis. Geometry optimization and time-dependent density functional theory (TD-DFT) calculations were used to obtain vertical excitation energies and transition dipole moments for the ligand-bound states in the ultraviolet range. These ligand properties were then incorporated into a tight-binding Hamiltonian describing the tubulin tryptophan excitation network in order to evaluate changes in exciton energies and eigenvector delocalization. The calculations indicate that tryptamine binding can modify the excitonic landscape of tubulin in a ligand-dependent manner, with the magnitude of the perturbation governed by excitation wavelength, transition dipole strength, and spatial orientation relative to the intrinsic tryptophan network. These results support the possibility that aromatic ligands may provide a chemically tunable route to altering the optical response of tubulin and motivate future experimental tests of ligand-dependent modulation of microtubule photophysics.

Abstract: Mitochondria play essential roles in cellular metabolism and signaling, regulating biosynthetic pathways, calcium homeostasis, redox balance, and cell fate beyond ATP production. Their continual remodeling through fusion, fission, and mitophagy maintains mitochondrial quality control and adapts organelle function to cellular demands. Here, we review how mitochondrial dynamics, fusion, fission, and mitophagy modulate metabolic reprogramming and signaling to drive cancer progression and therapy resistance. Emerging evidence indicates that in cancer, mitochondrial fusion enhances respiratory efficiency and oxidative phosphorylation, whereas fission promotes glycolytic adaptation, rapid biomass accumulation, and stress tolerance. Mitophagy further refines metabolic fitness by eliminating damaged mitochondria and sustaining redox homeostasis. Together, these processes underscore that dysregulation of mitochondrial dynamics is a hallmark of cancer and a key driver of metabolic reprogramming and therapeutic resistance. In this review, we summarize how mitochondrial fusion, fission, and mitophagy govern metabolic circuitry in cancer development and therapy resistance. We highlight their functional impact on tumor progression and discuss emerging therapeutic strategies targeting mitochondrial dynamics and associated machinery. Understanding this dynamic metabolic crosstalk may reveal new vulnerabilities and guide the development of mitochondria-targeted cancer therapies.

Abstract: This paper presents a laboratory-validated integrated assessment framework combining Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) for the non-destructive evaluation of reinforced concrete (RC) structures. A single RC beam specimen (3000 × 300 × 200 mm; C30/37, w/c = 0.50, CEM I 42.5N; 3 × T12 at 35 mm soffit cover) was subjected to four precisely controlled deterioration states: intact dry (Model A), water-filled crack (Model B), fully saturated (Model C), and chloride-induced active corrosion (Model D). Seven ERT datasets were acquired using a Wenner-Schlumberger array at electrode spacings of a = 7, 15, and 30 mm, and three 800 MHz GPR profiles were recorded for Models A, B, and C/D respectively. The ERT results demonstrate a systematic three-orders-of-magnitude decrease in median resistivity from the intact state (ρ₀ = 558 Ω·m) to the corroded condition (ρ = 10.6 Ω·m), with a depth-preferential low-resistivity distribution at rebar depth (z ≈ 24–48 mm; ρ < 10 Ω·m) providing partial discrimination between active corrosion and bulk moisture saturation. GPR analysis at 800 MHz confirms a reference wave velocity of v = 0.096 ± 0.008 m/ns by hyperbola fitting, localised 25–35% amplitude attenuation at the water-filled crack position, and pervasive 50–65% attenuation under saturated and corroded conditions. A four-stage integrated interpretation framework is validated against known ground-truth conditions: Stage 1 establishes local reference baselines (ρ₀, A₀); Stage 2 identifies anomalies against threshold criteria; Stage 3 cross-validates co-located GPR and ERT signatures; Stage 4 assigns risk categories 1–4. Explicit failure mode analysis identifies six conditions under which the framework is unreliable, most critically the moisture–corrosion ambiguity and the invisibility of dry cracks. The framework correctly classifies all four beam conditions and provides higher diagnostic confidence than either method applied independently.

Abstract: This study characterized the cultivable oral microbiota of domestic dogs and evaluated antimicrobial susceptibility patterns and resistance accumulation in a veterinary context. A cross-sectional analytical design was conducted including 100 domestic dogs attended in urban veterinary clinics in southern Ecuador, from which 139 bacterial isolates were obtained through oral swabbing and conventional microbiological identification. Antimicrobial susceptibility was assessed using the disk diffusion method according to CLSI guidelines. Resistance accumulation was defined as the number of antimicrobial classes to which each isolate exhibited resistance, and multidrug resistance as resistance to three or more classes. A predominance of Gram-negative bacteria was observed (65.5%), with Pseudomonas (27.3%), Klebsiella (20.9%), and Enterobacter (7.9%) as the most frequent genera, while Staphylococcus aureus represented 34.5% of isolates. Resistance to at least one antimicrobial was detected in 71.2% of isolates, and multidrug resistance in 9.4% of the total dataset, exclusively among Gram-negative bacteria, corresponding to 14.3% within this group. Resistance to two or more antimicrobial classes was observed in 42.9% of Gram-negative isolates. Multivariable logistic regression showed that bacterial genus was the only factor significantly associated with resistance accumulation, with Enterobacter presenting a higher odds ratio compared to Pseudomonas (adjusted OR = 16.30; 95% CI: 1.69–157.14; p = 0.016), while host-related variables were not significant (p > 0.05). These results indicate that antimicrobial resistance in the canine oral microbiota is primarily structured by bacterial identity rather than host factors, highlighting the role of the oral cavity as a reservoir of resistant bacteria with implications for veterinary clinical practice and epidemiological surveillance.

Abstract: Background: Symbiotic fungi play essential roles throughout the entire cycle of orchid plants, including seed germination, seedling development, and maturation. Dendrobium officinale Kimura & Migo (Orchidaceae) (D. officinale) is a rare and highly valued traditional Chinese medicinal herb. Currently, artificial breeding using tissue culture technology is widely adopted and essential in the Dendrobium industry; however, this approach may impair or disrupt the plant’s ability to establish and maintain symbiotic relationships with mycorrhizal fungi; Methods: In this study, the fungal endophyte community (FEC) in the roots of D. officinale cultivated under four different modes was analyzed using high-throughput sequencing. Correlation analyses were also carried out to examine the relationships between bioactive compounds and the FEC; Results: 1) the FEC in D. officinale roots was dominated by Ascomycota and Basidiomycota, with significant differences in abundance, diversity, and community structure among cultivation modes; 2) the FEC under greenhouse cultivation mode differed significantly from those under tree epiphytic cultivation in terms of fungal nutritional types and dominant taxa; 3) six major mycorrhizal fungal taxa were identified in Dendrobium roots, although non-mycorrhizal fungi accounted for approximately 97% of the community; and 4) the main bioactive compounds were positively correlated with variations in the FEC; Conclusions: this study provides a foundation for understating the growth of D. officinale under different cultivation modes and highlights the relationship between bioactive compound accumulation and mycorrhizal fungal communities.

Abstract: In this study, the traffic operational effects of a pacemaker system (PMS) on the traffic operation in the Geumnam Tunnel on the Seoul–Yangyang Expressway was evaluated herein using a before–after analysis based on long-term vehicle detection system (VDS) data. Changes in spatiotemporal traffic flow and traffic capacity, and speed improvement under different levels of service (LOS) were analyzed using data from five VDS detectors installed upstream and downstream of the tunnel. After PMS installation, (i) increased average and 25th-percentile speeds at most detector locations and decreased standard deviation of speed were observed both near the tunnel exit and the downstream sections, (ii) maximum traffic volume was increased from 1661 to 1765 veh/h/lane (~6.3% increase), (iii) LOS-based speed improvement analysis showed that mean speed and 25th-percentile speed increased by ~6.5%, indicating the alleviation of speed reduction among low-speed vehicles due to PMS. These results prove that PMS increases vehicle speed, reduces speed variability, and enhances traffic flow stability and processing capability. These findings provide empirical evidence supporting the operational effectiveness of a PMS as a practical tool for mitigating phantom congestion in highway tunnel sections and reducing the speed differences between vehicles and improve traffic stream stability.

Abstract: Climate change is a major hazard to the agricultural systems of the world as it is changing the temperature regimes, precipitation patterns, and soil dynamics, which are weakening crop production and the stability of the ecosystems. The proposed research is a hybrid modeling framework that combines Multiple Linear Regression (MLR) with a deep learning architecture (PatchTST) based on the Transformer to quantify and predict the effect of climate variability on the productivity of agriculture. Multi-source data such as global weather data, crop data, and ISRIC-WISE soil data were harmonized with stringent preprocessing that included imputation, normalization, and spatial-temporal alignment. The regression analysis reveals a statistically significant negative impact of temperature on crop yield, while precipitation and soil fertility exhibit positive contributions. To capture complex non-linear dependencies and long-term temporal patterns, the PatchTST model was trained using time-series inputs enriched with satellite-derived vegetation indices. The proposed model significantly outperforms conventional deep learning approaches, achieving an R2 of 0.98, RMSE of 0.0172, and MAE of 0.0134. Attention-based interpretability highlights soil moisture and NDVI as dominant predictors, reinforcing the model’s physical and agronomic relevance. The findings instruct that integrating interpretable statistical models with advanced deep learning enhances predictive accuracy while addressing the transparency limitations of black-box approaches. This framework provides a robust decision-support tool for empathetic climate variability impacts on agricultural productivity.

Abstract:

Glioblastoma multiforme (GBM) is the most aggressive primary brain malignancy with limited treatment options and poor clinical outcomes. There is growing interest in using Zika virus as a treatment for GBM due to its selectivity in finding and killing rapidly proliferating neural cells. Several studies reproducibly show that Zika can effectively kill GBM cells. We sought to uncover the molecular mechanisms driving this cytotoxic effect by performing a meta-analysis of transcriptomic studies in which Zika virus was used to kill GBM cells. We integrated four datasets from studies on GBM and added neuroblastoma (NBM) studies as an outgroup comparator. Our analysis identified a shared molecular signature of the Zika-infected GBM cell. Interestingly, GBM cells killed by the Zika virus showed dysregulation of pathways commonly implicated in proliferation and metastasis, including TNF, NF-κB, and p53 signaling. Using a hypothesis-free design, we found several long non-coding RNAs (lncRNAs) that were consistently dysregulated in Zika-infected GBMs, many of which have previously unrecognized roles in cancer cell death. Among this group, we validated four lncRNAs for a role in Zika-mediated oncolysis. Experimental testing of MELTF-AS1, TIPARP-AS1, NR2F1-AS1, and SLC9A3-AS1 in adult GBM cell lines confirmed pronounced differential gene expression. Silencing of MELTF-AS1 augmented Zika-induced cell death, while knockdown of TIPARP-AS1, NR2F1-AS1, and SLC9A3-AS1 attenuated oncolysis, identifying a novel class of pro-oncolytic lncRNAs that critically contribute to ZIKV-mediated cytotoxicity. These findings elucidate Zika’s oncolytic mechanisms, highlight novel lncRNA targets, and support further exploration of lncRNA modulation as a strategy to enhance oncolytic virotherapy for GBM and related malignancies.

Abstract: Background/Objectives: Immunonutrition uses dietary bioactive compounds to support immune function while preserving systemic physiological balance. Donkey milk, bovine colostrum, and royal jelly contain complementary antimicrobial, immunoglobulin-rich, and immunoregulatory components, but their combined effects remain insufficiently characterized. Methods: A 6-week controlled study was conducted in female rabbits assigned to four groups (n = 15/group): vaccinated only (G1), immunonutraceutical only (G2), vaccination plus immunonutraceutical (G3), and pre-conditioned immunonutraceutical followed by vaccination and continued supplementation (G4). Serum total immunoglobulins and lysozyme were measured longitudinally. Biochemical indices were monitored throughout the study, and hematological parameters were evaluated at the final time point. Mixed-effects models, generalized estimating equations, principal component analysis, and correlation-based systems analyses were applied. Results: Supplementation significantly modulated both humoral and innate immune responses. The strongest terminal immunoglobulin response was observed in G4 (26.00 ± 5.80 mg/mL), whereas sustained lysozyme elevation was most pronounced in supplemented groups, particularly G3 (3.13 ± 0.44 ng/mL). Within-subject analysis demonstrated significant innate–adaptive immune coherence (p = 0.000006). Biochemical analyses showed coordinated metabolic adaptation without evidence of organ toxicity, and hematological findings indicated preserved inflammatory and hematopoietic stability. Conclusions: Multi-component immunonutraceutical supplementation enhanced humoral and innate immune dynamics in a timing-dependent manner while maintaining biochemical and hematological safety. These findings support the potential of combined donkey milk, bovine colostrum, and royal jelly as functional ingredients for coordinated immune support.