INTRODUCTION Schizophrenia is a complex psychiatric disorder with a partially understood pathophysiology involving multiple neurotransmitter systems and receptor families in its onset and progression. Modern therapeutic approaches for effective schizophrenia treatment require multi-target drug actions that exert therapeutic effects through interactions with various neurotransmitter systems. AREAS COVERED This review provides an updated overview of schizophrenia pathophysiology, focusing on current dopaminergic, serotonergic, and glutamatergic hypotheses, with particular attention to multi-target antipsychotics and the rationale for their development. The literature was searched using relevant online scientific journal databases. For the overview of recent advances and novel multi-target ligands, studies published primarily between 2020 and 2026 were included. State-of-the-art strategies for designing multi-target ligands, including molecular modeling and computer-aided drug design approaches, are discussed, along with the opportunities and limitations associated with multifunctional compounds. EXPERT OPINION Advancing schizophrenia treatment depends on the ongoing development of well-balanced multi-target ligands, which require careful adjustment of affinities across multiple neurotransmitter systems and receptor families while avoiding off-target interactions and maintaining optimal physicochemical and pharmacokinetic properties. Nevertheless, multi-target antipsychotics offer substantial advantages in therapy, positioning them as a transformative direction in antipsychotic drug discovery.

Researchers in Bosnia and Herzegovina face systemic barriers to publishing, including scarce funding and weak institutional support. Limited mentorship and low research literacy increase vulnerability to predatory journals and conferences. Fragmented academic and healthcare systems hinder collaboration and international visibility. Financial constraints and APC costs divert high‐quality local research away from mainstream journals. Practical reforms should focus on research support offices, regional collaborations, and improved promotion criteria.

Spiking neural networks can exhibit complex emergent dynamics, but the credibility of spatially explicit agent-based implementations depends on systematic verification and validation (V&V). This study introduces LANA (Local Adaptive Neural Agents), an agent-based spiking neural network in which neurons, propagating signals, directed synapses, and a diffusive environmental field are represented as distinct interacting components. We present a five-level V&V framework spanning operator-level tests, single-neuron mechanisms, propagation behavior, network-level dynamics, and sensitivity/robustness analysis. Across 13 predefined tests and approximately 2000 simulation runs, the model satisfied all prespecified pass criteria: synaptic delays reproduced the expected propagation law exactly, environmental decay and diffusion matched analytical expectations, threshold and refractory mechanisms behaved as predicted, inhibition suppressed firing monotonically, and environmental coupling induced a transition toward higher variability and oscillatory-like activity. Matched-seed comparisons further showed that explicit signal transport and environmental feedback substantially amplify activity relative to a neuron-only baseline while leaving synaptic delay propagation unchanged. Additional regime and lesion experiments demonstrated distinct resting, hyperexcitable, and focal-lesion states, with the lesion condition producing an acute decline followed by only partial recovery. Together, these results provide a transparent V&V baseline for LANA and illustrate how agent-based spiking models can be tested and interpreted across multiple scales.

The rapid integration of artificial intelligence into private and public-sector decision-making has outpaced the development of standards governing the interaction between human judgment and machine intelligence. Existing frameworks—the EU AI Act Regulation, the NIST AI Risk Management Framework, and ISO/IEC 42001—regulate AI systems as discrete technical artifacts but do not standardize the hybrid intelligence configurations in which human cognition and algorithmic outputs jointly produce governance decisions. This paper proposes a three-layer standards framework comprising technical interoperability standards governing how AI outputs are communicated to human decision-makers, procedural standards governing human-AI task allocation and escalation protocols, and accountability standards governing responsibility attribution in distributed decision configurations. The framework is grounded in the Quadruple Bottom Line (QBL), which adds governance as a fourth sustainability dimension. To move beyond a purely conceptual contribution, the paper provides operationalization tools—including a role allocation matrix, confidence calibration thresholds, an accountability mapping template, and a domain classification schema—and proposes a three-tier conformity assessment methodology for evaluating framework implementation. By establishing the hybrid human–AI decision configuration as the unit of standardization, the paper introduces a governance architecture that enables operational, auditable, and comparable hybrid intelligence systems.

The open science paradigm, digitalisation, interdisciplinarity and internationalisation have significantly changed the research process, collaboration, dissemination and impact of scholarly work in the 21 st century. Research impact assessment should include new metrics based on Web 2.0 channels suffering from the following issues: data quality (i.e. accuracy, coverage, comprehensiveness), heterogeneity of data sources and APIs and potential manipulation (i.e. metrics gaming). Although the Findable, Accessible, Interoperable and Reusable (FAIR) principles were designed for research data, they can also be applied to research impact metrics to increase their discoverability and reusability. The main aim of this European Cooperation in Science and Technology (COST) Action is to remove barriers for the wider adoption and reusability of metrics, based on Web 2.0 technologies, which are a significant and vital part of research ecosystems. These metrics can serve as the basis for enhanced research impact assessment and, thus, improve recognition of excellence and foster the further development of science and society. Although Scientometrics, based on Web 2.0, is a paradigm that is over 10 years old, it has not yet been widely adopted. Therefore, a plan or roadmap for transition to Scientometrics 2.0 is needed. This should include recommendations for overcoming the challenges associated with new research impact metrics, as well as frameworks for the evaluation of new metrics and data sources. These challenges include the heterogeneity and comprehensiveness of metrics data sources, the varying quality of metrics data, metrics data gaming etc. Due to the multifaceted nature of these challenges, the Action proposes to create synergies between all interested actors: researchers, research software engineers, librarians, representatives of metrics data providers and policy-makers. This article presents an edited version of the original funding proposal submitted to the COST Open Call 2021.

Background: Negative public attitudes promote undesirable stereotypes and stigma in stutterers. Method: To mitigate negative attitudes, 403 respondents combined from 16 international samples filled out the Public Opinion Survey of Human Attributes–Stuttering (POSHA–S) before and after interventions to improve attitudes and were compared to 249 respondents from seven control groups. Investigators aimed (a) to replicate an extreme case of regression to the mean (i.e., “crossover” effect) reported earlier in larger combined samples in which respondents with high pre-scores ended with low post-scores, respondents with low pre-scores finished with high post-scores, and intermediate scorers were unchanged; and (b) to identify individual POSHA–S items related to overall attitude change and among the high and low scorers. Results: As in previous studies, stuttering attitudes improved in the intervention group but not in the control group. Intervention and control respondents demonstrated “crossover” but less than the earlier samples due to lower pre–post correlations. Item contributions to pre–post change and differences among the three change groups were inconsistent; however, high agreement items by respondents were less likely to vary than low agreement items. Conclusion: The “crossover” effect was replicated, and future research should explore its presence in other measures or conditions.

Climate change significantly affects human physiology and contributes to increased morbidity and mortality, with heat stress representing one of the most severe consequences of thermal imbalance. The aim of this study was to analyze morphological changes to leukocytes on the peripheral blood smears of Wistar rats exposed to hyperthermia using the geometric morphometrics method. A total of forty Wistar albino rats were divided into three experimental groups according to water temperature exposure (37 °C, 41 °C, and 44 °C). Peripheral blood smears were prepared, stained, and digitally recorded using Motic Images Plus 2.0 software, after which selected images were analyzed using geometric morphometric programs (tpsDig, tpsUtil, and MorphoJ) to evaluate leukocyte shape variations. Comparative analysis demonstrated statistically significant morphological changes in polymorphonuclear cell shapes between the control group (37 °C) and rats exposed to 41 °C (p = 0.009). Significant differences were also identified in mononuclear cell morphology between the antemortem and postmortem groups (p = 0.00307). The findings indicate that exposure to elevated temperatures induces measurable alterations in white blood cell morphology, confirming that hyperthermia produces significant structural changes in polymorphonuclear cells and mononuclear cells detectable through geometric morphometric analysis.

The present study investigates who was considered authoritative in matters of language in the 16th and 17th centuries as well as how grammar authors position themselves with respect to these authorities. It evaluates whether a shift in referencing norms may already be observed from the 16th to the 17th century in these regards. As it is claimed that in England the 16th century marks the beginning of English grammar writing ( McCarthy 2020 : 19–20) and the 17th century saw a shift in favour of English being recognized as a separate academic discipline ( Beal 2004 : 102), one can ask if and how onomastic — that is, name-based — references, appear in those grammars and how they can be categorized. One of the findings of this study is that the onomastic references found in the 16th- and 17th-century grammars can be categorized along the six semantic categories suggested in previous work for 19th-century grammars ( Busse et al. 2020 : 11–12). These include, for example, quotations, opinions, or mere mentions. To account for a possible shift in reference strategies over time, these semantic categories were reevaluated by means of inter-rater reliability (IRR) in the 16th- and 17th-century context. Our main findings show that while some 16th-century authors put emphasis on Latinate authors, others embrace moving away from the Latinate approach by not referring to the established Latinate authorities at all or only rarely. A significant shift in onomastic referencing can be observed from the 16th to the 17th century, however the Latinate authorities still held significant ground in the 18th century grammar texts.

UK summer heatwaves are dictated by the polar jet stream position and sea surface temperature (SST) variability, affecting the Summer North Atlantic Oscillation (SNAO) index. The SNAO can determine and influence the Central England Precipitation (CEP) and Central England Temperature (CET). A strong and significant negative correlation ( r  = −0.63) is found between the SNAO and CEP, but a weaker correlation for the CET. Summers with highly positive SNAO (i.e. 1976 and 2018) are among the driest and warmest on record. In this study, we highlight the roles of large‐scale atmospheric circulations and use of Met Office‐defined weather regimes in understanding UK heatwave characteristics.

Biological invasions are a major driver of biodiversity loss, yet inconspicuous or “cryptic” species often escape detection and public awareness, limiting management responses. We investigated the freshwater jellyfish Craspedacusta sowerbii, likely native to China and now present on six continents, through a 22‐month multilingual online survey across 17 European countries (n = 1388), to assess public knowledge, perceptions and views on its management. Results revealed low public awareness: over 80% of respondents did not know the scientific name, 49% misidentified its freshwater habitat as marine and only 33% recognized it as non‐native. Aesthetic perceptions were largely positive, but mass occurrences triggered caution and direct contact was rare and usually harmless. Bayesian Network analysis showed that direct observation—rather than demographic factors or scientific knowledge—was the strongest predictor of concern and support for public discussion. While C. sowerbii was not widely perceived as a local threat, many respondents acknowledged its potential ecological implications and supported greater public awareness and management actions. Our findings highlight how visual subtlety and limited media coverage reduce detection and prioritization of cryptic invasive alien species (IAS), hindering early warning systems and policy development. Leveraging citizen science and experiential engagement can improve recognition and reporting, complement formal monitoring and support more inclusive management strategies. We recommend integrating cryptic invasions like that of C. sowerbii into IAS frameworks and policies, emphasizing public participation and awareness to address overlooked, but potentially impactful species.

Very thin ZnO films were fabricated on p-type Si (100) substrates using two different solvents, methanol (M) and 2-methoxyethanol (2M), via a simple spin-coating method. Five-layer ZnO films were characterized structurally using X-ray diffraction (XRD), with detailed analysis performed using the Williamson–Hall (W-H) method. Surface morphology was examined by scanning electron microscopy (SEM) and atomic force microscopy (AFM), while ultraviolet–visible (UV–Vis) spectroscopy provided optical properties, including the optical band gap determined via the Kubelka–Munk theory. The crystallite sizes calculated from W-H models were larger than those estimated by the Debye–Scherrer method, indicating that peak broadening is influenced by both crystallite size and lattice strain. Strain, stress, and deformation energy density values, derived from the Uniform Deformation Model (UDM), Uniform Stress Deformation Model (USDM), and Uniform Deformation Energy Density Model (UDEDM), respectively, were higher in films prepared with 2-methoxyethanol than methanol. ZnO films from 2-methoxyethanol exhibited greater homogeneity and lower surface roughness, while their optical band gap (~3.3 eV) was slightly higher than that of methanol-derived films (~3.02 eV) but still below bulk ZnO (3.37 eV). These results show that ZnO thin films prepared with 2-methoxyethanol exhibit superior microstructural, mechanical, and optical properties, producing smoother and more uniform films with enhanced optical characteristics, and are therefore more commonly preferred for optoelectronic and photovoltaic applications due to their improved crystallinity, low surface roughness, and suitable band gap.  

Objective Description of the clinical presentation and tumor behavior, its general and pathological characteristics, diagnostic and therapeutic approaches, as well as treatment outcomes in children with neuroblastoma (NB) at a single-center. Special focus was placed on the duration of the diagnostic process and the correlation between disease stage, presence of metastases, and treatment outcomes. Given the small cohort, this report is presented as a descriptive case series complemented by a focused review of the literature NB. Material and Methods A retrospective study was conducted for the time period from January 1st, 2010, to December 31st, 2024. The study included 13 patients (7 males, 6 females; median age: 44 months; range, 0-114) diagnosed with NB who underwent surgery and were hospitalized at the Department of Pediatric Surgery, Clinical Center of the University of Sarajevo. Demographic, clinical, biochemical, radiological, and treatment data were extracted from medical records. A narrative review of major published NB cohorts was conducted to compare our observations with existing evidence. Results The median age at diagnosis was 44 months. The largest number of patients were in the age group of 2-4 years, and most presented with stage four disease (according to International Neuroblastoma Staging System classification). The tumor was located in the adrenal gland in 92.3% of cases. The median survival time was 36 months. Patients had elevated serum neuron-specific enolase, urinary vanillylmandelic acid, as well as blood ferritin and lactate dehydrogenase levels. The most common symptoms were loss of appetite, presence of a palpable mass, and abdominal pain. Metastases were present in 76.9% of patients, and stage four was associated with aggressive metastatic dissemination. A multimodal approach was used in treatment. The median time from symptom onset to primary care physician consultation was 12 days, and the median time from physician consultation to diagnosis was 14 days. Relapse occurred in 46.2% of cases, most frequently in patients with stage four disease. A total of 7 patients (46.2%) survived. Conclusion Advanced-stage disease at diagnosis, metastatic burden, and unfavorable outcomes, are dominating factors in our single-center experience. When compared with larger published cohorts, our findings align with global patterns. Due to inherent sample size limitations, these observations are hypothesis-generating and the need for larger, multicenter studies remains.

Model quantization has become essential for efficient large language model deployment, yet existing approaches involve clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques preserve fidelity but typically do not accelerate inference. This paper explores the middle ground of statistically-lossless compression through three complementary notions of losslessness for quantized LLMs. First, task-lossless compression preserves zero-shot benchmark accuracy within natural sampling variance and remains achievable at aggressive bitwidths. Second, we formalize the stricter notion of distribution-lossless compression, requiring the quantized model's next-token distribution to be practically indistinguishable from the original, and propose the Expected Acceptance Rate (EAR), the maximum token-agreement probability under optimal coupling, as a directly interpretable fidelity metric (for example, EAR>= 0.99 indicates 99% agreement). Third, we prove a gamma-squared variance law showing that symmetric quantization inflates noise variance by gamma squared relative to asymmetric quantization, making asymmetry necessary for distribution-lossless fidelity but not for task-level preservation. Using SLQ, a layer-wise non-uniform method with asymmetric quantization and wide bitwidth search, we achieve task-lossless compression at well below 4 bits per parameter (as low as 3.3 bits depending on the model), distribution-lossless compression at 5 to 6 bits per parameter on average, and inference speedups of 1.7 to 3.6x relative to FP16 with optimized kernels. Source code is available at https://github.com/IST-DASLab/SLQ.

Deploying post-quantum cryptography on highly constrained devices remains challenging due to the large key sizes and substantial storage and memory-traffic demands of leading lattice-based schemes. Although constructions such as Kyber, Dilithium, and NTRU offer strong resistance against quantum adversaries, their multi-kilobyte public keys and intensive memory access patterns limit practical adoption in microcontrollers, smart cards, and low-power edge environments. This work proposes a hybrid key-encapsulation mechanism that integrates a compact, seed-generated Module-LWE structure with a quantum-secure hash-based authentication layer. The design employs a small public seed to instantiate lattice matrices on demand via a lightweight pseudorandom generator and incorporates a Merkle-tree commitment to represent compressed auxiliary error information. Additional design considerations—including sparsity-aware secret keys, SIMD-friendly polynomial operations, and cache-efficient decryption paths—are intended to reduce runtime memory usage and computational overhead. The security of the proposed construction is analysed under both Module-LWE and hash-based one-way assumptions, with further consideration of constant-time execution and cache-line alignment to mitigate side-channel risks. This hybrid approach outlines a design pathway toward post-quantum key-encapsulation mechanisms suitable for deployment on memory-limited and energy-constrained platforms.

Metal additive manufacturing (AM) relies on alloy feedstock powders that may come into contact with the workers’ skin during handling, yet skin-relevant data on metal release and biological reactivity remain limited. Here, we assessed the cutaneous bioactivity of the fine particle fraction of four gas-atomized Fe-based AM powders (316L stainless steel, Fe-powder A, and tooling steels B and C). Powders were sieved to <10 μm and characterized by scanning electron microscopy and X-ray photoelectron spectroscopy before and after incubation in artificial sweat (ASW). Metal biodissolution was quantified in ASW and keratinocyte culture medium using atomic absorption spectrophotometry. Cellular responses were evaluated in HaCaT keratinocytes using Cell Painting-based phenomics and multiplex cytokine/chemokine profiling and in an ex vivo full-thickness human skin explant model, including superficial barrier disruption, IL-8/CXCL8 quantification, and histological assessment. ASW exposure induced marked shifts in the outermost surface composition across powders, indicating sweat-driven surface transformation. Biodissolution was low and medium-dependent, with Fe dominating the release in ASW, and with an overall metal release remaining limited in cell culture medium. In HaCaT cells, MCP-1/CCL2, IL-6, and IL-8/CXCL8 were quantifiable but showed no significant changes following powder exposure. Cell Painting revealed subtle, shared phenotypic signatures, primarily involving mitochondrial-associated features, without evidence of broad cellular stress. In the ex vivo skin model, AM powders did not increase IL-8/CXCL8 secretion, the particles remained localized to the skin surface without detectable penetration, and coexposure with Staphylococcus epidermidis did not enhance bacterial colonization or induce inflammation. To the best of our knowledge, this is the first study that applies a human skin explant model to evaluate dermal responses to metal AM powders. Overall, the tested AM powders showed low short-term cutaneous reactivity under skin-relevant conditions, providing human-relevant evidence to inform occupational risk assessment in AM environments.