Background/Objectives: In many low- and middle-income countries (LMICs), including Bosnia and Herzegovina, oncology services are constrained by a limited number of specialists and uneven access to evidence-based care. Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, may provide clinical decision support to help standardize treatment and assist clinicians where oncology expertise is scarce. This study aimed to evaluate the concordance, safety, and clinical appropriateness of ChatGPT-generated treatment recommendations compared to decisions made by a multidisciplinary team (MDT) in the management of newly diagnosed breast cancer patients. Methods: This retrospective study included 91 patients with newly diagnosed, treatment-naïve breast cancer, presented to an MDT in Bosnia and Herzegovina in 2023. Patient data were entered into ChatGPT-4.0 to generate treatment recommendations. Four board-certified oncologists, two internal and two external, evaluated ChatGPT’s suggestions against MDT decisions using a 4-point Likert scale. Agreement was analyzed using descriptive statistics, Cronbach’s alpha, and Fleiss’ kappa. Results: The mean agreement score between ChatGPT and MDT decisions was 3.31 (SD = 0.10), with high consistency across oncologist ratings (Cronbach’s alpha = 0.86). Fleiss’ kappa indicated moderate inter-rater reliability (κ = 0.31, p < 0.001). Higher agreement was observed in patients with hormone receptor-negative tumors and those treated with standard chemotherapy regimens. Lower agreement occurred in cases requiring individualized decisions, such as low-grade tumors or uncertain indications for surgery or endocrine therapy. Conclusions: ChatGPT showed high concordance with MDT treatment plans, especially in standardized clinical scenarios. In resource-limited settings, AI tools may support oncology decision-making and help bridge gaps in clinical expertise. However, careful validation and expert oversight remain essential for safe and effective use in practice.

The scarcity of high-quality wood encouraged the development of various technological processes for joining wood. The finger joint is one of the most widespread technological processes for wood joining. This study aimed to determine the effect of steaming and heat modification of beech wood, as well as the type of adhesive, on the mechanical characteristics of finger joints. Samples made from un-modified beech, steamed-treated, and heat-treated beech wood were bonded with polyvinyl acetate (PVAC), non-structural, and structural polyurethane (PUR) adhesives. Compressive tests on wood materials were used to evaluate their mechanical performance. The finger joint samples were tested for their bending performance. Modulus of rupture, modulus of elasticity, and compressive strength were calculated. An analysis of variance (ANOVA) was conducted to evaluate the impact of wood modification type and adhesive used on the mechanical characteristics of the finger joints. According to the results of this study, it can be concluded that the steaming process does not influence changes in the mechanical characteristics of the finger joints. Heat treatment of beech and the type of adhesive used significantly influence the tested mechanical characteristics of the finger joints and beech wood. Heat-treated beech had lower values of modulus of rupture (70 MPa) and density (690 kg/m3) and higher values of compression strength (59 MPa) in relation to un-modified (780 kg/m3) and steamed-treated (800 kg/m3) beech wood.

Acute mesenteric ischemia (AMI) is a life-threatening condition characterised by oxidative stress, inflammation, apoptosis, and necrosis of intestinal epithelial cells. Different drugs with vasoactive, antioxidant, and anti-inflammatory properties have been used to treat AMI. Levosimendan is a drug with proven anti-ischemic effects used in the management of acute congestive heart failure. This study evaluated the protective effects of levosimendan pretreatment on intestinal, as well as lung, heart, and kidney tissue in a rat model of mesenteric artery ischemia/reperfusion (I/R) injury. Male Wistar rats (N = 24) were divided into four groups: control, I/R, levosimendan (LS) 1 mg/kg i.p, and LS + I/R (1 mg/kg i.p. 30 min before injury). I/R by itself caused elevation of oxidative markers (thyobarbituric acid reactive species (TBARS), hydrogen peroxide (H2O2), super oxide anjon radical (O2−), and nitrogen dioxide (NO2−)), induced inflammation (macrophage infiltration and Interleukin-6 (IL-6) production), and apoptosis (nuclear factor kappa light-chain enhancer of activated B cells (NF-κB), cleaved caspase-3 (CC3), and terminal deoxy-nucleotidyl transferase (TdT)-mediated dUTP nick end labelling (TUNEL)). Levosimendan pretreatment significantly reduced oxidative stress markers and enhanced antioxidant defences (catalase (CAT), reduced glutathione (GSH), and superoxide dismutase (SOD)). Histological analysis revealed reduced mucosal damage and preserved goblet cells in intestinal tissue. Similar protective effects of levosimendan were observed in other organs such as lung, heart, and kidney. Immunohistochemistry showed reduced epithelial apoptosis and upregulation of antioxidant and anti-inflammatory proteins. These findings highlight levosimendan’s ability to protect mesenteric I/R tissue injury and multi-organ damage by suppressing oxidative stress, inflammation, and apoptosis, emphasising its therapeutic potential in clinical settings.

Background/Objectives: Renal failure (RF) and systolic heart failure (sHF) are very often associated with each other, and their synergistic influence can affect the prognosis of acute pulmonary embolism (aPE) patients. The aim of this study is to evaluate the associations between RF, sHF, and in-hospital mortality in patients with normotensive aPE. Methods: We analyzed data from the Regional PE Registry (REPER), and 1968 patients with CT pulmonary angiography-confirmed aPE who had a systolic blood pressure of 100 mmHg and higher, and for whom creatinine blood levels and left ventricular ejection fraction (LVEF) were measured at admission to hospital were enrolled. The patients were divided into four groups: the first group comprised patients without renal and systolic heart failure, the second those with RF (creatinine clearance less than 60 mL/min), the third those with sHF (LVEF less than 50%), and the fourth those with both RF and sHF. The primary endpoint of this study was in-hospital all-cause mortality. Results: There are significant differences between in-hospital mortality among the groups: 38/1247 (3.0%) vs. 63/514 (12.9%) vs. 10/99 (10.1%) vs. 20/108 (18.5%) (p < 0.001). In the multivariable regression model adjusted for age, right ventricular dysfunction, and troponin levels, the presence of renal failure, sHF, and both were independently associated with in-hospital all-cause mortality with ORs of 3.59 (95%CI 2.04–6.30, p < 0.001) vs. 3.97 (1.71–9.25, p = 0.001) vs. 6.39 (3.15–12.99, p < 0.001), respectively. Conclusions: The association of renal failure and systolic heart failure has a deleterious prognosis in patients with normotensive aPE.

Abstract This study employs EEG and eye-tracking to assess how brand equity, creative complexity, and spatial layout influence implicit consumer responses to point-of-sale (POS) beer advertisements. Through the theoretical lens of predictive coding and processing fluency, laboratory testing with Serbian beer consumers (N = 20) revealed that simpler designs yielded superior attention performance across TFD and TTFF (d up to 2.62), independent of brand strength. Spatial repositioning reduced packshot detection time by 0.89s (p<0.001, d=1.78) in horizontal versus vertical layouts. EEG showed no significant brand differences (valence d=0.07, p=0.765), offering a theoretical interpretation consistent with predictive coding, wherein expected stimuli elicit reduced neural activation, with brand strength operating solely through attentional pathways. Eye-tracking revealed strong brands’ automatic attentional capture of iconic elements (e.g., letter ‘J’; TTFF=0.47s), theoretically reconciled via processing fluency as effortless decoding. We derive actionable POS benchmarks: packshot detection < 0.5s, slogan engagement > 1.0s, emotional valence > 5.0, cognitive load < 5.0. This advances GDPR/NDA-compliant methodology while offering practical guidelines grounded in neurocognitive theory.

Diffusion-triggered convection can occur in a gravitationally stable system of two superimposed mixtures. As instability develops, two distinct patterns may emerge: double-diffusive (DD) or diffusion-layer convection (DLC). Traditionally, nonsymmetric patterns above and below the interface were thought to require chemical reaction. We show that symmetry can be broken by composition-dependent diffusion, with or without cross diffusion. Furthermore, only the composition-dependent cross diffusion can lead to a range of coexisting patterns and provide new insights into staircase instability.

A Boolean-algebraic framework for maximal-degree U-k-seminets is presented, unifying combinatorial and algebraic properties. This work extends Aczel’s quasigroup theory and Belousov’s k-net constructions by introducing a computational framework for U-k-seminets of maximal degree µ. Key results include: (1) explicit bounds for µ in terms of set cardinality t and t-order d (µ = t−d+2), (2) existence conditions for nonequipotent sets, and (3) inequalities governing µ and t ((t+2)/2 < µ ≤ t). Theorems are validated via tabulated solutions for m = t−d, demonstrating scalable applications in finite geometry and network design. The framework bridges partial quasigroups and block designs, offering algorithmic tools for seminets with maximal degree constraints.

Abstract Despite striking successes in identifying novel biomarkers for improved patient stratification and predicting disease progression, numerous challenges remain in the effective integration and exploitation of multiomic data in biomedical applications beyond cancer, for which most bioinformatics strategies are developed and validated. That focus on cancer severely limits the effective development and advancement of algorithms in machine learning and artificial intelligence that do not suffer degraded out-of-domain performance. Generalizability and interpretability of models, however, are also required for robust insights that may translate into clinical practice. Work across different independent datasets is critical for establishing models robust towards unwanted variation in assays, protocols, and cohort populations. Disease-specific context like ethnicity, socioeconomic background, sex, lifestyle, disease phase, and tissue type also strongly affect molecular profiles. We here discuss atherosclerotic cardiovascular disease (ASCVD) as a high-impact non-cancer use case for the challenges remaining in the development and application of the latest bioinformatics approaches to multiomics data integration. ASCVD remains the leading cause of death globally. Disease aetiology, progression, and therapy outcome depend on a complex interplay of genetic, environmental, and lifestyle factors. Integrating these diverse data types effectively remains a challenge but holds transformative potential for personalized medicine. Discovery and access to data of sufficient diversity and extent form key bottlenecks. We here compile a first comprehensive overview of key data sets in ASCVD to complement the established cancer-focused resources as a foundation for future effective development and application of state-of-the-art bioinformatics tools for multiomic data integration.