Abstract: Background/Objectives: Human milk composition is shaped by gestational age at de-livery and stage of lactation; however, proteomic differences between milk from mothers of preterm and term infants and their temporal patterns remain incompletely characterised. Methods: This prospective study enrolled 40 lactating mothers: 20 who delivered preterm infants (< 32 weeks’ gestation) and 20 who delivered at term (37–42 weeks). Each provided milk samples during first 10 days postpartum and at the fifth week. Milk serum was analysed using quantitative data-independent acquisition mass spectrometry. Differential protein abundance was assessed separately at each time point; functional annotation was performed using Gene Ontology biological process analysis. Results: Eighty samples were analysed. During early lactation, 10 proteins differed significantly, most showing lower abundance in preterm milk. At week five, 19 pro-teins were differentially abundant, predominantly higher in preterm samples. Im-mune-related proteins constituted the largest functional category at both stages. Im-munoglobulin heavy constant gamma 4 remained consistently downregulated in pre-term milk (fold change −1.6). Ferritin heavy chain (1.5) and HLA class II histocompati-bility antigen gamma chain (1.8) were elevated only early, whereas calprotectin subu-nits S100A8 (5.6) and S100A9 (5.2) were markedly upregulated later. Conclusions: Proteomic differences vary across lactation stages, identifying lactation timing as a critical contextual factor in comparative human milk proteome studies.

Abstract: Background: Accurate diagnosis of periprosthetic joint infection (PJI) remains challenging, particularly in culture-negative and borderline cases where current practices lead to high diagnostic uncertainty. SynTuition™, a machine-learning–based probability score integrating preoperative biomarkers, was developed to support clinical decision-making. This study compared its diagnostic performance and economic impact with standard physician practice. Methods: A total of 12 physicians provided diagnoses of 274 clinical vignettes representing suspected PJI cases. SynTuition probabilities were converted to binary Diagnostic classifications using a validated threshold. Diagnostic accuracy, agreement, indecision rates, decision-curve analysis, and misdiagnosis-related costs were evaluated. Results: SynTuition achieved an overall percent agreement of 96.0% when compared against the expert adjudicated clinical reference, outperforming the pooled physician group at 90.8%. Physicians showed high indecision (38–48%) in inconclusive 2018 ICM cases, whereas SynTuition generated a definitive diagnosis with an 86.7% agreement against expert adjudication. Decision curve analysis demonstrated higher net benefit for SynTuition across a broad range of thresholds, reducing projected unnecessary revision by up to 5.8%. Economic modeling showed a reduction in misdiagnosis-related costs from $6.9 million to $2.9 million per 1,000 suspected PJI cases, yielding estimated savings of $4,000 per suspected case. Conclusions: SynTuition demonstrated high diagnostic accuracy, lower uncertainty, and significant clinical and economic advantages over routine physician practice, supporting its integration into clinical decision-making for suspected PJI, particularly in diagnostically ambiguous cases.

Abstract: Waste-to-energy (WtW) systems constitute a complex thermochemical interface between energy production and waste management. This can be done by generating CO2 streams of mixed biogenic and fossil origin. Net-negative emissions can be achieved by integrating carbon capture and storage (CCS) into WtE plants. However, the physical chemistry of the capturing process under heterogeneous conditions is not yet fully understood. This review analyzes the molecular and thermodynamic foundations of CO2 capture in WtE contexts and emphasizes solvent-solute interactions, reaction equilibria, and energy landscapes governing sorption and regeneration. Moreover, the chemistry of amine-based systems, ionic liquids, and solid sorbents will be examined, with respect to flue gas composition, impurity tolerance and degradation pathways, as well as the thermodynamic and kinetic frameworks for CO2 compression, phase behavior and geochemical storage reactions. The present review presents WtE–CCS as a particular field where the principles of physical chemistry contribute substantially to the development of sustainable approaches to environmental management.

Abstract: Previous research has found association between PM_2.5 exposure and the worsening of depression. Nevertheless, studies specifically examining the harmful effect of components of PM_2.5 were relatively limited.A national survey enrolled individuals aged 45 and older, gathering personal data and assessing depression in mainland China. Monthly exposure to PM_2.5 and its seven components, black carbon (BC), organic matter (OM), nitrate (NO^3-), sulfate (SO₄^2-), ammonium (NH⁴⁺), soil particles (SOIL) and sea salt (SS) were matched via each participant's residence. Linear mixed effect models (LME) to assess the association between single pollutants with depression score, and weighted quantile sum (WQS) regression was used to investigate the effect of mixed exposure and identify contribution of each component. The modified effects of social activity and green space were evaluated. 9,725 participants for depression were included in this analysis, respectively. In single exposure model, per interquartile range (IQR) rise in PM_2.5 (29.18μg/m³), BC (2.25μg/m³), OM (7.18μg/m³), SOIL (6.04μg/m³) and SS (0.14μg/m³) were significantly associated with increase of 0.90 (95%CI: 0.59, 1.20), 0.71[0.42, 1.09], 0.94[0.61, 1.26]), 0.51[0.38, 0.63]), 0.53[0.33, 0.73] point in depression score. In mixed exposure models, for each IQR increase in the mixture of all components, depression score increased by 1.104 (95%CI: 0.901, 1.307), and BC held the largest index weight (33.6%), followed by SOIL (28.59%) and SS (25.05%). The harmful effect of PM_2.5 and specific components on depression were lower in those participating in social activity or in higher level green space (p< 0.05).The harmful effects of PM_2.5 on depression may be influenced by its components. Social activity and green space could reduce the risk of depression related to PM_2.5 and its components.

Abstract: Type Ia supernovae (SNe Ia) are luminous thermonuclear transients whose peak luminosities can be standardized, enabling measurements of luminosity distance over cosmological redshifts and an empirical Hubble diagram of distance modulus versus redshift that constrains the distance–redshift relation. Direct empirical tests in which redshift-dependent scalings of fundamental constants are applied to SN Ia distances remain scarce relative to fixed-constant interpretations. The aim is to determine whether a one-parameter unified-flow scaling of the distance scale can reproduce the Pantheon+SH0ES SN Ia Hubble diagram without introducing an explicit dark-energy term, and to quantify the resulting constraint on the scaling exponent. The model treats redshift evolution as a single coherent scaling that links the effective gravitational coupling and the light-propagation scale in a reciprocal manner, yielding an analytic luminosity-distance prediction under a matter-closure expansion law. The scaling exponent is estimated from Pantheon+SH0ES (1701 SNe Ia spanning redshifts 0.00122 to 2.26137) using the full statistical and systematic covariance matrix and an exact analytic profiling of the distance-modulus offset. The best fit is an exponent of negative 0.4975 (68% profile interval from negative 0.5165 to negative 0.4785) with a minimum chi-squared of 1751.82 for 1699 degrees of freedom; the fixed-constants matter-only baseline is disfavored by a chi-squared difference of 640.20, while a supernova-only flat Lambda CDM benchmark gives a matter density parameter of 0.3612 with an uncertainty of 0.0187 and a minimum chi-squared of 1752.51. After profiling the distance-modulus offset, the unified-flow and Lambda CDM distance laws differ by about 0.259 magnitudes at redshift 2.26137 and separate further at higher redshift. These results provide an empirically constrained, covariance-respecting phenomenological distance law consistent with current SN Ia distances and yield a falsifiable prediction for future higher-redshift standard candles or standard sirens.

Abstract:

Autophagy is a tightly regulated catabolic process essential for cellular homeostasis, stress adaptation, and regeneration. In the nematode Caenorhabditis elegans, with its short lifespan, transparent body, and well-defined genetics, the process can be investigated at tissue- and age-specific manner, making it an excellent model to study the connection between autophagy and longevity. While autophagy is indispensable for development and homeostasis, recent studies have revealed that its role in aging is more complex than previously thought. During post-reproductive life, autophagic flux and the degradative capacity of lysosomes decline, resulting in the accumulation of undegraded material and cellular stress. Several studies have demonstrated that the experimental modulation of core autophagy in aged or post-reproductive C. elegans, particularly in neurons, can improve proteostasis, preserve tissue integrity, and extend lifespan. Here we review the current results obtained using the genetic model system Caenorhabditis elegans that link autophagy to lifespan regulation. We focus on studies that investigate unexpected, context-dependent, or deleterious effects of inhibiting autophagy-related genes during aging. We also discuss how age- and tissue-specific modulation of autophagy could define the most effective strategies for promoting healthy aging. This could provide relevant insights for the therapeutic targeting of autophagy in humans.

Abstract: This manuscript advances a formal nutritional hypothesis proposing that acidic phytocannabinoids (e.g., THCA, CBDA, CBGA) may function as conditional endocannabinoid system (ECS)–supportive micronutrients, provisionally termed “ECS vitamers.” The endocannabinoid system plays a central role in maintaining physiological homeostasis, and age- and stress-associated declines in ECS tone have been reported. Acidic phytocannabinoids exhibit structural relatedness, lipophilicity, metabolic transformation, antioxidant capacity, and biological activity in ECS-related pathways—characteristics that parallel certain properties of established vitamin families. These similarities do not establish essentiality. Rather, they provide a rationale for structured investigation. This manuscript formalizes a testable framework outlining the criteria, experimental designs, and clinical validation steps required to determine whether acidic phytocannabinoids meet accepted definitions of micronutrients or vitamers.

Abstract: We propose a thermodynamic variational framework in which quantum mechanics, classical dynamics, and gravitation emerge as equilibrium regimes of a single free-energy functional defined on probability distributions rather than on trajectories, wavefunctions, or spacetime metrics. The functional balances Fisher information, potential energy, and Shannon entropy, encoding an exploration–exploitation trade-off uniquely fixed by information-theoretic considerations. Matching the Fisher term to the quantum kinetic energy fixes its coefficient without free parameters. Extremization of the functional yields the continuity equation and the quantum Hamilton–Jacobi equation, and thus reproduces the Schrödinger equation as a thermodynamic equilibrium condition. At mesoscopic scales, competition between Fisher information and entropy introduces a characteristic quantum–classical crossover length that provides a thermodynamic perspective on decoherence. Measurement is interpreted as an irreversible thermodynamic transition, with energetic costs bounded by Landauer's principle. In the macroscopic regime, we show that requiring thermodynamic stability and local boundary response selects area-law entropy scaling as the leading contribution under stated assumptions. Given an area-law entropy, standard local arguments recover Einstein's field equations. The framework yields falsifiable predictions across quantum, mesoscopic, and gravitational regimes.

Abstract: Interferon-induced transmembrane proteins (IFITMs) are broad-spectrum antiviral factors that restrict the entry of many enveloped viruses, including HIV-1, by modifying host membrane properties and trapping fusion at the hemifusion stage. Beyond blocking entry in target cells, IFITMs also reduce the infectivity of virions produced from IFITM-expressing cells, a phenomenon termed “negative imprinting”. Conserved motifs, such as the amphipathic helix and oligomerization motifs, have been reported to be essential for IFITM-mediated protection of target cells from viral infection. Yet, the impact of IFITM incorporation on progeny virion infectivity remains poorly defined. Here, we show that IFITM3 mutants defective in target cell protection activity still markedly impair HIV-1 fusion/infection upon incorporating into virions, without affecting viral maturation or Env incorporation. Immunofluorescence studies suggest mislocalization of the IFITM3 mutants as the reason for the lack of antiviral activity in target cells. Testing the antiviral activity of chimeras between antiviral and non-antiviral IFITM orthologs failed to clearly identify a domain responsible for reduction of HIV-1 infectivity, suggesting that multiple domains may be required for negative imprinting. Interestingly, co-incorporation of non-antiviral dog IFITM1 with human IFITM3 did not interfere with IFITM3’s negative imprinting activity, despite forming mixed hetero-oligomers. This finding implies a dominant, oligomerization-independent antiviral phenotype of IFITM3 in virions. Our findings suggest that IFITMs may protect target cells and negatively imprint progeny virions through distinct mechanisms, underscoring the need to further characterize the molecular basis for the reduced fusion competence of IFITM-containing HIV-1 particles.

Abstract: The increasing vulnerability of global food systems – exacerbated by the pandemic, climate change, and disruptions to international supply chains – has highlighted the importance of local food production for sustainability, food security, and rural resilience. At the same time, the LOHAS (Lifestyles of Health and Sustainability) value system is gaining prominence, shaping consumer demand for locally produced, environmentally responsible, and health-oriented products. While the existing literature predominantly addresses LOHAS consumers and local food systems as separate research domains, limited empirical attention has been paid to the value-based alignment between LOHAS principles and local food producers, particularly from a territorial and place-based perspective. This study seeks to address this gap by examining how LO-HAS value dimensions are reflected in the self-identification and operational practices of local food producers, and by analysing how such value alignment contributes to the sustainability and resilience of territorially embedded rural production systems. The empirical analysis draws on an online survey conducted in the second quarter of 2024 among 73 local producers operating in Zala and Vas counties in Western Hungary. Factor analysis and cluster analysis were applied to identify underlying value structures and producer typologies. The results reveal two distinct producer clusters, one of which exhibits a strong alignment with LOHAS values. Producers within this cluster place particular emphasis on sustainability, environmental responsibility, health consciousness, and authenticity, alongside a pronounced commitment to local embeddedness and community-oriented practices. Overall, the findings demonstrate that LOHAS-related values are not confined to the consumer side but are increasingly embedded in territorially grounded local production models. This value alignment may play a significant role in strengthening short food supply chains rooted in specific geographical contexts, thereby contributing to the long-term socio-economic and environmental sustainability of rural regions.

Abstract: Background/Objectives: This article is a narrative review that examines the development of attachment from intrauterine life to the first thousand days of a child's life, integrating psychoanalytic, neuroscientific, genetic, and cross-cultural perspectives. Biological, relational, neurological, and cultural factors interact and determine individual differences in socio-emotional functioning. This paper aims to propose a reinterpretation of early attachment, describing it as both a clinical and relational phenomenon and an adaptive process inscribed in human evolutionary history, according to the described Four-Domain Integrative Framework.. Methods: The review examined three main areas of evidence: early attachment characteristics, cross-cultural caregiving variations, and genetic and epigenetic mechanisms underlying environmental sensitivity. Results: The first identified seven characteristics of early attachment (proximity seeking, emotional attunement, intrauterine experiences, maternal holding, security patterns, brain plasticity, and maternal stress) which represent developmental mechanisms that generate individual differences in trust, self-regulation, resilience, and psychopathological vulnerability. Second, cross-cultural variations in six distinct caregiving contexts were examined, demonstrating that secure attachment emerges through culturally specific pathways, differentially influencing motor development, sleep patterns, hypothalamic-pituitary-adrenal axis maturation axis maturation, and social skills. Finally, the differential susceptibility model was provided through the analysis of five genetic and epigenetic systems (oxytocin receptor gene, serotonin transporter gene, dopamine receptor gene, glucocorticoid receptor methylation, and fetal programming) that modulate environmental sensitivity. Conclusions: Biological, relational, neurological, and cultural factors interact and determine individual differences in socio-emotional functioning.

Abstract: Doxorubicin causes dose-dependent cardiotoxicity linked to epigenetic dysregulation, especially DNA methylation. Metformin shows cardioprotective effects through metabolic and epigenetic mechanisms. This study examined the role of metformin in counteracting doxorubicin-induced DNA methylation changes. H9c2 cardiomyoblasts were treated with doxorubicin with or without metformin (0.7–2.8 mM). Cell viability and IC₅₀ were determined by MTT assay. Genome-wide DNA methylation was analysed by whole-genome bisulfite sequencing, followed by PCA and differential methylation analysis with FDR correction. Doxorubicin reduced cell viability (IC₅₀ = 0.164 µM), while metformin increased IC₅₀ values. PCA showed clear group separation. Numerous DMRs were associated with oxidative stress, mitochondrial function, apoptosis, and chromatin regulation. Metformin induced dose-dependent genome-wide methylation changes in cardiac cells, supporting a direct epigenetic cardioprotective effect.

Abstract: CDKL5 Deficiency Disorder (CDD) is a severe neurodevelopmental encephalopathy char-acterized by early disruptions of synaptic maturation and network stability, leading to persistent motor, cognitive, and behavioral impairments. Given the role of the endocan-nabinoid system in synaptic development, neuroinflammation, and neuronal resilience, we investigated whether chronic enhancement of endogenous 2-arachidonoylglycerol (2-AG) signaling via monoacylglycerol lipase (MAGL) inhibition could mitigate key pathological features in Cdkl5 knockout (KO) mice. Using an intermittent 6-week treat-ment, the MAGL inhibitor JZL184 robustly increased plasma 2-AG levels, inhibited MAGL activity, and activated CB1-AKT signaling without evidence of receptor desensiti-zation. Despite this clear pharmacodynamic efficacy, behavioral improvement was lim-ited: neither dose rescued core behavioral deficits, although the higher dose selectively re-duced stereotypic jumping and modestly improved cue-dependent associative memory. At the cellular level, JZL184 induced biologically meaningful effects, partially restoring dendritic spine maturation and increasing neuronal survival in the vulnerable CA1 hip-pocampal region. In contrast, microglial responses were dose-dependent and divergent, with the lower dose exerting anti-inflammatory effects, while the higher dose increased cortical microglial density and AIF-1 expression, suggesting engagement of compensatory or off-target mechanisms. Overall, these findings show that MAGL inhibition activates neuroprotective pathways and ameliorates select structural deficits in Cdkl5 KO mice, but is insufficient to reverse established behavioral abnormalities, highlighting the limits of endocannabinoid enhancement and the need for developmentally timed or multimodal therapeutic strategies in CDD.

Abstract: Computational thinking (CT) has become a cross-curriculum priority in many educational jurisdictions, yet research consistently reports uneven integration in initial teacher education (ITE), limited preservice teacher confidence, and persistent misconceptions that equate CT with coding. Concurrently, generative artificial intelligence (GenAI) has rapidly entered university programmes, offering new possibilities for modelling problem solving, generating multiple representations, and supporting iterative design. However, the psychological dimensions of engagement with CT and emerging technologies, including self-efficacy beliefs, affective responses such as anxiety and curiosity, cognitive load management, and the formation of professional identity, remain under-theorised in the teacher education literature. This thematic literature review synthesises 54 sources across three intersecting domains: CT frameworks and their pedagogical implications, CT integration in preservice teacher preparation, and GenAI in teacher education and learning design. Drawing on Bandura's social cognitive theory, cognitive load theory, and research on technology-related affect, the review foregrounds the affective, cognitive, and cultural dimensions of preservice teachers' engagement with CT and GenAI. The review proposes the GenAI-Enabled Computational Thinking for Preservice Teachers (GECT-P) model, which integrates CT dimensions with GenAI-supported learning cycles, psychological mediators, and teacher education outcomes. The model positions prompting as an epistemic and pedagogical practice that can make CT visible, supports cycles of decomposition, abstraction, pattern recognition, and algorithmic design, and embeds critical AI literacy, ethics, affective scaffolding, and classroom enactment. Design principles and practical pathways are offered for teacher educators seeking to prepare graduates who can develop CT with and beyond GenAI across diverse curriculum areas.

Abstract: We discuss gravitational concepts and candidate specifications for dark matter that, together, can help explain known ratios of dark-matter effects to ordinary-matter effects and can help explain eras in the rate of expansion of the universe. The ratios pertain to galaxies and galaxy evolution, galaxy clusters, and densities of the universe. The candidate specifications for dark matter reuse, with variations, a set of known elementary particles. Regarding galaxy evolution and the rate of expansion of the universe, we deploy multipole-expansion methods that combine Newtonian gravity, aspects of motions of sub-objects of gravitationally interacting objects, and Lorentz invariance. One outgrowth from our work suggests relationships among some physics constants. Another outgrowth from our work suggests a basis for a candidate specification for quantum gravity.

Abstract: We propose a phenomenological model in which the vacuum energy relevant for black hole interiors is bounded by QCD-scale physics and by thermal effects. In this framework, vacuum fluctuations are effectively limited between a hadronic upper scale and a lower, thermally controlled scale. We explore how such a QCDbounded vacuum structure can modify the interior region of black holes, leading to non-singular cores while preserving standard general relativity in the exterior. The analysis is qualitative in nature and does not rely on a full underlying quantum field theoretic derivation. Instead, it aims to capture the main physical ingredients that may connect hadronic physics, vacuum structure and black hole geometry. We discuss the conditions under which singularities can be avoided, and we outline possible implications for the relation between black hole interiors and the cosmological vacuum energy. The model is intended as a conceptual framework that can be further tested and refined in more complete theoretical settings.

Abstract: This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotion recognition from audio (speech), visual (face), and integrated multimodal audio-visual data. The developed framework, named eXCube2, uses a three-dimensional SNN that is spatially structured according to a human brain template. The BIAI models developed in eXCube2 are trainable on spatio- and spectro-temporal data using brain-inspired learning rules. Such models are explainable in terms of revealing patterns in data and are adaptable to new data. The eXCube2 models are implemented as software systems and tested on speech and video data of subjects expressing emotional states. The use of a brain template for the SNN structure enables brain-inspired tonotopic and stereo mapping of audio inputs, topographic mapping of visual data, and the combined use of both modalities. This novel approach not only brings AI-based emotion recognition closer to human perception, but also results in higher accuracy and better explainability than existing AI systems. This is demonstrated through experiments on benchmark datasets, achieving classification accuracy above 80% on single-modality data and 90% when multimodal audio-visual data are used and a “don’t know” output is introduced. The paper further discusses possible applications of the proposed eXCube2 framework to other audio, visual, and audio-visual data for solving challenging problems, such as recognizing emotional states of people from different origins; brain state diagnosis (e.g., Parkinson’s disease, Alzheimer’s disease, ADHD, dementia); measuring response to treatment over time; evaluating satisfaction responses from online clients; human–robot interaction; chatbots; and interactive computer games. The SNN-based implementation of BIAI also enables the use of neuromorphic chips and platforms, leading to reduced power consumption, smaller device size, higher performance accuracy, and improved adaptability and explainability.

Abstract: One of the most important early causes of death among polytraumatized patients is represented by coagulopathy as a result of alteration of the coagulation pathway with a result that varies from harmful haemorrhagic diathesis to thrombosis. The poly-traumatized patient is characterized by acute posttraumatic coagulopathy resulted by the alteration of tissue integrity and hypo perfusion of peripheral tissues. Consumptive thrombocytopenia together with platelet dysfunction, coagulation factor deficiency, and hyperfibrinolysis, with iatrogenic treatment (massive transfusion, hypothermia, and excess volume repletion solutions) are part of the pathogenesis of posttraumatic coagulopathy. Because of the substantial impact of alteration of the fluid-coagulant status on therapeutic management, it is natural that an early diagnosis will help to improve medical care procedures. This is possible with the help of ROTEM (rotational thromboelastometry) which indicates the type of coagulopathy early on. This method provides information about clot formation, formation kinetics, clot firmness, and about the reverse process fibrinolysis through physical analysis methods of its viscoelastic property. It provides in-depth information compared to the "usual" coagulogram, as the ROTEM method targets the coagulation process as an assembly of the blood in its entirety. Following the review of the currently available data on mortality and morbidity of patients whose treatment plan was guided in real time by dynamic methods of assessing the coagulation status, it can be said that individualized therapy, applied based on successive data, had much better results in terms of the quality of health care, costs, and most importantly in the survival of patients with minimal side effects. Therefore, there is a strong recommendation to approach these tests from the emergency room units and as soon as the patient is admitted to intensive care.

Abstract:

Breast cancer–associated malignant pleural effusion (MPE) is a common and debilitating manifestation of advanced disease, yet current management is largely limited to indwelling pleural catheters and chemical pleurodesis and offers only transient palliation without addressing the underlying tumor biology. We propose that integrating patient-derived organoid modeling of pleural tumor cells with characterization via technologies like next-generation sequencing could shift MPE care from symptom management toward precision intervention. Organoid-based drug testing enables ex vivo evaluation of local therapeutic agents, including intrapleural chemotherapy, immune modulators, and bispecific antibodies, while paired genomic profiling may reveal actionable resistance pathways unique to pleural metastases. Together, these approaches could identify rational, localized combination therapies that improve local control, reduce effusion recurrence, and ultimately extend survival. By coupling functional and molecular analyses directly to the pleural compartment, we envision a translational framework that redefines breast MPE from a purely palliative condition to one amenable to mechanism-driven, patient-tailored therapy.

Abstract: The Deep Image Prior(DIP) suggests that it is possible to train a randomly initialized network with a suitable architecture to solve the inverse imaging problem by simply optimizing its parameters to reconstruct a single degraded image. However, the learning effect it seeks is often achieved with the most naive local convolution, which inevitably leads to the inverse imaging problem being limited by the model’s generative ability. Furthermore, image info is often not related to surrounding pixels but to overall color and spatial info. Simple local convolution in inverse imaging can’t capture precise details. Moreover, DIP is an unsupervised process but requires iterations to learn inverse imaging, consuming computational power and limiting adaption of global attention. To solve these problems, this article explores the possibility of globalizing the DIP task’s learning and introducing tri-directional multi-head self-attention to optimize the computation consumption brought by pixel-level attention. Our observations found that global learning can effectively enhance the detail information of edge pixels, making images more vivid and textures clearer. In addition, tri-directional multi-head self-attention can efficiently replace the global perception ability of pixel-level self-attention. Finally, we demonstrate that global learning can effectively improve the imaging effect of inverse imaging problems and enhance the information of texture edge pixels. Moreover, tri-directional multi-head self-attention can effectively alleviate the computation redundancy of pixel-level self-attention, thus achieving efficient and high-quality inverse imaging tasks. The principles of this approach—global feature capture and efficient attention modeling—extend its potential applicability beyond imaging to domains such as software security. For instance, it can enhance tasks like vulnerability analysis by reconstructing obscured code patterns and improve threat modeling through efficient correlation of multi-dimensional attack vectors, balancing detail fidelity with computational practicality.