Abstract: Spent mushroom substrate (SMS), the main by-product of mushroom production, is rich in valuable compounds that could be recovered by ultrasound-assisted extraction (UAE) and exploited as fat-mimetic functional ingredients in food formulations. In this study, low-fat cookies prototypes were developed by incorporating a dietary fiber extract obtained from SMS using UAE. The extraction process was optimized following a Box–Behnken experimental design, identifying optimal conditions at a specific energy input of 200 J/mL, a particle size of 2 mm, and a solute-to-solvent ratio of 1:27, yielding a dietary fiber recovery of 30.82%. The optimized SMS extract exhibited high oil-holding capacity (1.39 g/g), emulsion stability (80%), and foaming capacity (83.55%). Four cookie formulations were evaluated, among which G1 (50% fat replacement) showed the best balance between consumer acceptability and an improved nutritional profile, characterized by higher protein (8.4 g/100 g), total dietary fiber (7.10 g/100 g), and mineral contents. Notably, G1 cookies displayed a significant reduction in predicted glycemic index, decreasing from 83.84 in the control to 69.65. Overall, these results demonstrate that optimized SMS-derived dietary fiber is an effective functional ingredient for the development of low-fat, high-fiber, and reduced-glycemic cookies, contributing to the valorization of agro-industrial by-products within a circular economy framework.

Abstract: Depression, anxiety, and stress-related disorders continue to rise globally, with South Africa’s burden intensified by structural inequalities and a 91% mental health treatment gap. Accessible complementary interventions are urgently needed. This exploratory mixed-methods pilot study examined the feasibility, acceptability, and preliminary effectiveness of astronomy-based mental health support grounded in Attention Restoration Theory and awe research. Two retreats combined guided naked-eye and telescope-based stargazing with nature immersion: a proof-of-concept peer camp (n=19, Glencairn) and a family-focused retreat (n=27, Sutherland). Quantitative outcomes using the Depression Anxiety Stress Scale (DASS-21) were collected in the Glencairn cohort, alongside qualitative data from Most Significant Change focus groups and asynchronous text-based interviews. Significant reductions in depression, anxiety, and stress were observed in the Glencairn cohort, while qualitative findings across both settings indicated experiences of calm, perspective shifts, and relational connection. However, increased environmental novelty and family dynamics introduced competing cognitive demands in the Sutherland setting. These findings provide preliminary evidence that astronomy-based interventions may support short-term psychological well-being, while highlighting key design considerations, including cognitive spaciousness, contextual onboarding, and relational facilitation, for implementation in diverse African contexts.

Abstract: Today, the aviation industry is transitioning from fossil fuel to renewable energy. Re-newable energy systems have advantages, such as cleanliness and reduced emissions, but also face limitations in battery energy density and aerodynamic performance dur-ing operation. Therefore, electric ducted propulsion fans (eDPFs) are a promising so-lution that uses duct components to enhance aerodynamic efficiency and operational safety. This study utilizes average Navier-Stokes analysis, incorporating Reynolds numbers and a k-ω SST turbulence model, to examine eDPF configurations both with and without a secondary air intake channel, concentrating on internal flow dynamics and aerodynamic efficiency. The air intake channel, which is located close to the tip of the rotor blade, helps the eDPF move more mass and create more thrust. Several dif-ferent configurations of the secondary air intake channel were tested by varying the intake channel position, curvature, and size of the inlet and outlet ports under static conditions at 6000 rpm. The best design improved thrust by an additional 2.2% com-pared to the baseline case without the auxiliary intake port

Abstract: Background/Objectives: Obesity and menopause are major determinants of skeletal deterioration; however, their combined effects on bone remodeling and associated cellular bioenergetics remain incompletely understood. This study aimed to determine whether obesity induces osteoporotic alterations under both estrogen-replete and estrogen-deficient conditions and to evaluate the therapeutic potential of dental tissue–derived mesenchymal stem cells (D-MSCs). Methods: Female mice were subjected to ovariectomy (OVX) and/or high-fat diet (HFD) feeding for 16 weeks to establish obesity-associated osteoporosis models. D-MSCs were administered intraperitoneally at defined intervals. Body weight and serum leptin levels were measured to assess metabolic status. Femoral tissues were analyzed by quantitative real-time PCR for estrogen receptors (ERα, ERβ), inflammatory markers (Il-1β, Tnf-α), mitochondrial regulators (Pgc1α, Pgc1β), and the OPG/RANKL ratio. Histological analysis was performed to evaluate bone marrow adiposity. Results: HFD significantly increased body weight and serum leptin levels in both intact and OVX mice. Obesity was associated with reduced expression of ERα and ERβ, decreased Pgc1α levels, and a lower OPG/RANKL ratio, accompanied by increased Il-1β, Tnf-α, and Pgc1β expression. D-MSC administration attenuated body weight gain and reduced leptin levels, particularly in OVX mice. In femoral tissue, D-MSC treatment restored estrogen receptor expression, increased Pgc1α, decreased Pgc1β, and normalized the OPG/RANKL ratio. In addition, inflammatory marker expression and bone marrow adiposity were reduced following MSC administration. Conclusions: Obesity induces bone remodeling dysregulation under both intact and estrogen-deficient conditions, characterized by altered estrogen signaling, inflammatory activation, and mitochondrial imbalance. D-MSC administration was associated with partial restoration of these alterations, suggesting a potential role in modulating metabolic and skeletal homeostasis in obesity-associated bone loss.

Abstract: Sustainable urban transportation is fundamentally linked to public health outcomes, specifically the mitigation of fatal traffic risks under environmental stress. While stressors like adverse weather affect entire cities, traditional road safety models often assume uniform risk, thereby masking the spatial inequalities inherent in the urban fabric. This study addresses this gap by investigating the geographically heterogeneous impact of environmental stressors—including rainfall, surface moisture, and lighting conditions—on the conditional probability of fatal crash outcomes in Melbourne, Australia. Analyzing 43,075 severe crashes through a multi-stage geospatial framework (Getis-Ord Gi* and Geographically Weighted Logistic Regression), this research diagnoses how varying urban development patterns mediate the lethality of these stressors. The findings unmask a critical “threshold-crossing” effect for wet surfaces, where risk transitions from protective to hazardous based on local infrastructure form and street geometry. Significant spatial inequalities are identified: high-density inner-urban cores and adjacent coastal corridors exhibit a heightened sensitivity to visibility failures and moisture, whereas newer industrial peripheries show stronger protective “risk compensation” effects. These results reveal a systemic mismatch between historical urban form and contemporary climate-driven public health risks. By identifying localized “lethality thresholds”, this study provides a robust evidence base for integrated planning and equitable resource allocation. It enables urban planners to move beyond generalized safety warnings toward targeted structural interventions, ensuring that sustainable transportation networks prioritize safety equity for all citizens regardless of their location within the urban environment.

Abstract: Triangular mesh is one of the most widely used representations for 3D surfaces. However, high-resolution mesh models often contain a large number of triangles, leading to significant burdens in storage, transmission, and real-time rendering. Mesh simplification aims to reduce model complexity while preserving geometric fidelity and structural features. Classical methods, such as quadric error metrics (QEM), rely solely on local geometric errors, making them difficult to distinguish between redundant regions and structurally important features, often resulting in feature loss and topological degradation. To address these limitations, this study proposes a structure-aware triangular mesh simplification framework based on graph neural networks (GNNs)-guided QEM. GNNs are employed as a structural importance estimator to predict geometric saliencies of mesh edges. The predicted importances are incorporated into the classical QEM edge collapse cost through a soft modulation mechanism. Furthermore, a geometry-saliency driven dynamic cost modulation strategy is designed, enabling the simplification process to prioritize critical features in early stages and gradually transition to global error minimization in later stages, without compromising the geometric optimality of QEM. In terms of model design, hybrid structural representation GNNs are constructed by integrating spectral geometry and a dual-branch architecture. Laplacian positional encoding is introduced to capture global topological information, while 1-hop and 2-hop message passing branches enable multi-scale representation of complex geometric structures. In addition, a staged inference strategy is adopted to dynamically update graph structural features during simplification, effectively mitigating topological drift. Experimental results on the TOSCA dataset demonstrate that the proposed method achieves stable performance across various simplification ratios. It consistently outperforms FQMS and remains comparable to classical QEM in terms of geometric error (P_CD) and normal consistency (P_NE). For structural preservation (P_LE), the method shows clear advantages, with win-rates generally exceeding 70%. Moreover, it significantly improves the preservation of local geometric details at low to moderate simplification ratios. In summary, the proposed method effectively enhances local structural preservation while maintaining global geometric topology, providing an interpretable and practical solution for integrating learning-based structural awareness with classical geometric optimization in mesh simplification.

Abstract: This systematic review provides a comprehensive and quantitatively grounded synthesis of machine learning (ML) approaches for electrocardiography (ECG)-based detection of dysglycemia, with a specific focus on translational readiness for clinical screening. A structured literature search across PubMed, Scopus, Web of Science, and IEEE Xplore (February 2025) identified 183 records, of which 17 studies met predefined inclusion criteria following PRISMA-guided screening. The included studies demonstrate substantial heterogeneity in dataset size (ranging from <50 to >25,000 subjects), ECG acquisition modalities (single-lead, 12-lead, wearable), feature representations (raw signals, heart rate variability, engineered features), and ML strategies (classical algorithms, deep learning, and multimodal models). Reported model performance is generally high, with accuracy values frequently exceeding 0.85 and area under the curve (AUC) ranging from 0.78 to 0.99. Smaller experimental studies often report inflated performance (up to 96–99% accuracy), whereas large-scale population-based investigations demonstrate more moderate but clinically plausible results (AUC ≈ 0.80–0.85). External validation, a key requirement for clinical applicability, was performed in only a limited subset of studies (approximately 12%). From a physiological perspective, ML models exploit ECG alterations associated with dysglycemia, including reduced heart rate variability, QT interval prolongation, and changes in ventricular depolarization and repolarization dynamics. However, the relationship between metabolic dysfunction and ECG signals remains indirect. A key finding of this review is the mismatch between reported predictive performance and model maturity. The majority of studies (≈65–70%) are classified as early-stage (Level 1–2 or 2–3), relying on small, single-center datasets and internal validation. Only a minority of studies achieve near-translational maturity (Level 4), characterized by large-scale datasets and external validation. ECG-based dysglycemia detection represents a promising non-invasive and scalable screening paradigm. However, its clinical translation is constrained by the lack of standardized ECG acquisition protocols, limited dataset diversity, insufficient external validation, and fragmented methodological frameworks. Future research should prioritize large multi-center datasets, standardized feature extraction pipelines, hybrid interpretable models, and prospective validation to enable robust, generalizable, and clinically deployable screening systems.

Abstract: One-dimensional fibrous scaffolds with tunable bioactivity offer promise for bone tissue regeneration, yet optimal calcium phosphate phases for enhancing osteogenic perfor-mance remain underexplored. This study aimed to evaluate the impact of monetite, brushite, and cerium-doped phosphates deposition on electrospun nylon nanofibres func-tionalized via matrix-assisted pulsed laser evaporation (MAPLE). Six nylon fibre composi-tions were synthesized, coated with three calcium phosphate phases, calcined at varying temperatures (500–800 °C) before laser deposition. Physicochemical properties were as-sessed using energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), and fibre diameter measurements. Biocompatibility assays following MC3T3 pre-osteoblast seeding and incubation evaluated biological performance. EDX confirmed homogeneous phase deposition; SEM showed phase- and temperature-dependent mor-phology, with monetite yielding uniform granular structures and cerium-doped phos-phate at 800 °C forming dense aggregates. Brushite-coated fibres exhibited superior preos-teoblast metabolic activity versus monetite variants, indicating phase-specific stimulation of bone cells growth. These phosphate-functionalized nylon fibres retain structural integ-rity, hierarchical porosity, and enhanced bioactivity, providing a versatile electrospin-ning-MAPLE platform for customizable bone grafts with clinical potential.

Abstract: Reliable photovoltaic (PV) power forecasting based on deep learning typically requires large historical datasets to capture the high temporal and spatial variability of solar irradiance. However, in many real-world applications, data availability is limited to short observation periods, hindering the effective training of deep learning models. This paper investigates how sky image data augmentation techniques can improve the generalization capability of Convolutional Neural Networks (CNNs) trained under data scarcity. Three augmentation-based oversampling methods—SMOTE, Mixup-kNN, and Mixup-RP—are evaluated, along with two novel hybrid strategies that combine them in-parallel and in-series configurations. The proposed framework is validated on two distinct PV power nowcasting case studies, in which the original sky image training datasets span less than one month. Experimental results show average performance improvements of up to 50% on external validation data when training the CNN on the augmented datasets compared to the original base datasets, demonstrating that accurate PV power nowcasting is feasible even under data-scarce conditions typical of newly installed PV systems, and highlighting the potential of data-efficient learning approaches for renewable energy applications.

Abstract: It is shown that one of the promising areas in the design GaAs of high-temperature operational amplifiers (Op-Amps) are circuit solutions based on an "folded" stage. 12 (Op-Amp) new circuits are considered, which are implemented on GaAs nJFET and GaAs bipolar p-n-p transistors. Due to the original circuit design solutions in the proposed Op-Amp, the systematic components of the zero offset voltage are minimized due to the influence of the GaAs p-n-p BJT base currents and the asymmetry of the gate-drain JFET transistors of the input differential pair. Mathematical constraints on the static mode of the Op-Amp have been obtained, in which the total zero offset voltage takes on minimal values. As an example, a computer simulation of one of the Op-Amp circuits of the class in question was performed in the LTspice environment. The proposed GaAs Op-Amp family is recommended for use in analog automation devices operating at elevated temperatures. (up to 250-300°C).

Abstract: Metabolic dysfunction is increasingly recognized as a risk factor for poor outcomes in breast cancer, but whether incretin-based therapies confer survival benefit beyond weight loss remains unresolved. Using a federated electronic health record platform spanning nearly 29 million patients, we evaluated breast cancer survival after semaglutide and tirzepatide initiation in routine care. In 1:1 propensity-matched pooled-comparator analyses, semaglutide was associated with improved overall survival versus metformin, sodium–glucose cotransporter 2 (SGLT2) inhibitor, and dipeptidyl peptidase 4 (DPP4) inhibitor users, with 54 deaths among 2,433 semaglutide users (2.2%) versus 395 deaths among 2,433 comparators (16.2%) over 24 months (log-rank P < 0.001). Tirzepatide showed a favorable survival association relative to pooled anti-diabetic comparators that did not meet statistical significance (P = 0.24), with 3 deaths among 220 users (1.4%) versus 64 deaths among 220 comparators (29.1%). In a head-to-head propensity-score–matched comparison, overall survival did not differ significantly between semaglutide- and tirzepatide-treated patients with pre-existing breast cancer (2,117 per arm; P = 0.12). In semaglutide-treated patients alive and observable at the 1-year landmark, higher maximum dose achieved was significantly associated with lower post-landmark mortality (P = 0.034), with an event rate of approximately 1.0% in the high-dose group (≥1.7 mg) versus approximately 4.5% in the low-dose group (0.25–1.0 mg). Despite a linear dose–weight loss relationship for semaglutide, however, weight-loss strata did not separate survival outcomes (global P = 0.22). In tirzepatide-treated patients alive and observable at the same landmark, neither maximum dose achieved nor weight-loss strata separated post-landmark survival (P = 0.98 and P = 0.50, respectively). Structured EHR and AI-based clinical-note analyses further showed significantly lower frequency of documented metastatic disease in semaglutide-treated patients relative to pooled anti-diabetic comparators, including any metastasis (7.0% versus 15.0%, rate ratio 0.5, P < 0.001), bone metastasis (1.0% versus 5.2%, rate ratio 0.2, P < 0.001), and liver, lung, or brain metastases (all P < 0.001). LLM-derived cause-of-death extraction further showed a 60% lower relative proportion of cancer-associated deaths in semaglutide-treated patients (19% of ascertainable deaths) than in matched pooled anti-diabetic comparators (47% of ascertainable deaths), with comparator deaths more often attributed to cancer progression involving metastatic breast cancer, leptomeningeal carcinomatosis, and cancer-driven organ failure. Overall, this study demonstrates that semaglutide use in patients with pre-existing breast cancer is associated with a dose-correlated but weight-loss independent improvement in overall survival. These findings motivate prospective trials of GLP-1 receptor agonists in breast cancer across various stages and treatment settings.

Abstract: Background: Neuropathic pain (NP), a debilitating condition from nervous system le-sions, is poorly managed by current therapies. The cingulate cortex is crucial for affec-tive pain processing, yet a comprehensive spatiotemporal understanding of its molec-ular changes in NP is lacking. Methods: This study performed RNA sequencing to pro-file transcriptomic alterations in the anterior cingulate (ACC) and midcingulate (MCC) cortices of mice at two and four weeks after spared nerve injury. Bioinformatics anal-yses, including differential expression, functional enrichment, weighted gene co-expression network analysis, and protein-protein interaction (PPI) network con-struction, were employed. Results: We identified widespread, time-dependent tran-scriptional dysregulation in both regions, with differentially expressed genes increas-ing over time. Analyses confirmed central roles for synaptic plasticity and neuroin-flammatory pathways. Importantly, we uncovered significant dysregulation in prote-ostasis and mitochondrial function pathways, mechanisms shared with neurodegen-erative diseases. PPI analysis identified stage-specific hub genes (e.g., early interfer-on-stimulated genes and late ribosomal proteins in ACC; persistent extracellular ma-trix components in MCC). Conclusions: This study provides a detailed transcriptomic atlas of the cingulate cortex in NP, reinforcing known mechanisms while elucidating novel dysregulation in protein homeostasis and mitochondrial pathways. The findings highlight convergent pathophysiology with neurodegeneration and offer a new theo-retical framework with potential therapeutic targets for chronic NP.

Abstract: In the Republic of Kazakhstan, more than 300 million tons of ash are stored in dumps, containing substantial quantities of valuable metals. The aim of this work is to achieve waste-free and carbon-neutral processing of Ekibastuz coal. The innovations include: a smelter using new "ideal mixing–ideal displacement" method, lowering energy consumption by two to three times compared to current models; extraction degree exceeding 70% for germanium and zinc, and successful production of stone-cast products; a distiller based on the "counter colliding jets" method, with tenfold reduction in energy consumption, compared to a traditional one; a zinc-method for hydrogen production, reducing electricity to one-third of that required by electrolysis; carbon reduction from its dioxide by hydrogen. The analysis suggests that reaching carbon neutrality by 2060 for a boiler with a power rating of 125 MW will necessitate increasing its power output by approximately 2.58 times. A comprehensive assessment of the economic efficiency related to the processing of Ekibastuz coal, including 15% gas decarbonization, suggests that the system could realize a payback period of around eight years.

Abstract: Background: Breast cancer and gynecological malignancies, including cervical, ovarian, and endometrial cancers, remain leading causes of cancer incidence and mortality among women worldwide. This study investigated hereditary predisposition rates in women diagnosed with breast or gynecological cancer, focusing on the effect of age on pathogenic/likely pathogenic (P/LP) variant detection. We sought to determine whether younger age at diagnosis should be used as a criterion for patient selection for genetic testing. Methods: A total of 9084 consecutive females with breast cancer or gynecological tumors underwent NGS-based genetic testing (53 cancer-relevant genes) at Genekor laboratory from 2020-2026. Multivariable logistic regression evaluated factors associated with P/LP variant detection, adjusting for tumor type and family history. Results: Overall P/LP prevalence was approximately 20% (one in five patients), with tumor-specific rates of 19.24% in breast cancer, 27.59% in ovarian cancer, and 26.67% in endometrial cancer. P/LP prevalence declined significantly with age from 24.37% in patients <40 years to 15.90% in those ≥70 years, while VUS remained stable (40-43%). P/LP patients had earlier diagnosis (median 45 vs 46 years, p<0.001), driven predominantly by high-risk genes (13.87% in <40y vs 7.11% in ≥70y). BRCA1 showed stronger age enrichment than BRCA2 (8.14% vs 3.16% in <40y; median diagnosis 43 vs 45 years). Age remained independently associated with P/LP detection in multivariable analysis, with an 18% reduction in odds per 10-year increase for any P/LP (OR 0.82, 95% CI 0.78-0.86) and a stronger 28% reduction for high-risk variants (OR 0.72, 95% CI 0.67-0.78). Family history also independently predicted P/LP detection (OR 1.40, 95% CI 1.19-1.66). Conclusions: While younger patients have a higher prevalence of high-risk variants, clinically actionable findings were identified across all age groups, including patients ≥70 years These findings indicate that focusing on age to determine eligibility for genetic testing is insufficient. Expanding testing accessibility to individuals regardless of their age at diagnosis may enhance the identification of genetic cancer risk and optimize patient management.

Abstract: To screen high-quality porcine-derived lactic acid bacteria for swine production, this study compared growth performance, acid production, acid and bile salt tolerance, and genome characteristics of Lactiplantibacillus plantarum (MRS002), Lactobacillus amylovorus (MRS003), and Ligilactobacillus salivarius (MRS004). All three strains showed typical anaerobic growth. L. amylovorus had a longer growth cycle and higher biomass, while L. plantarum and L. salivarius grew faster and produced more acid, with pH values reaching 4.2 and 4.3 at 24 h. L. plantarum and L. salivarius also exhibited higher survival rates under 0.3% bile salt and pH 2.0 stress. Genome annotation revealed that more than 50% of genes were related to metabolism in all strains. L. plantarum possessed the most comprehensive metabolic and stress-resistance gene networks; L. amylovorus was enriched in starch-degradation pathways; and L. salivarius showed unique advantages in aromatic amino acid metabolism. In summary, L. salivarius MRS004 displays superior probiotic traits, L. plantarum MRS002 has broad adaptability, and L. amylovorus MRS003 is suitable for high-starch feed fermentation. This study provides theoretical support and strain resources for probiotic development and antibiotic-free breeding.

Abstract: Understanding climate-streamflow dependencies is crucial for evaluating reservoir impacts and adaptive water management. This study analyzed streamflow in two key Brazilian reservoirs, Três Marias (São Francisco Basin) and Serra da Mesa (Tocantins Basin), using monthly records from 1979 to 2020. A 12-month moving average temporal filter enhanced low-frequency climate signals to assess hydrological variability and memory. Temporal smoothing substantially clarified climate–streamflow dependencies, with correlation gains reaching 106% for PDO, 204% for ENSO, and more than 4,200% for the Antarctic Oscillation (AAO) in Três Marias. The filtered analysis revealed contrasting hydrological memory structures: Três Marias exhibited multi-year memory with maximum correlations at approximately 22–27 months, while Serra da Mesa showed faster response times of 4–12 months. To evaluate predictive implications, streamflow forecasting was performed using two deep learning architectures: LSTM (recurrent neural network baseline) and TCN (temporal convolutional network). TCN substantially outperformed LSTM in Três Marias (R² = 0.95 vs. 0.05), demonstrating that convolutional architectures effectively exploit low-frequency persistence when scale-aware preprocessing reveals it. These findings show that temporal filtering provides an effective framework for detecting climate–streamflow dependencies and hydrological memory, with direct implications for seasonal-to-decadal forecasting and climate-informed reservoir management under changing conditions.

Abstract: Background: Postoperative hyperbilirubinemia is a serious complication after cardiac surgery and has been associated with increased perioperative morbidity and mortality. However, data specifically addressing patients undergoing redo valve surgery remain limited. This study aimed to determine the incidence and risk factors of postoperative hyperbilirubinemia after redo valve surgery, and evaluate its association with early postoperative outcomes. Methods: We retrospectively reviewed 259 adult patients who underwent elective redo valve surgery under cardiopulmonary bypass (CPB) between March 2018 and July 2024. Postoperative hyperbilirubinemia was defined as a serum total bilirubin level > 3 mg/dL at any time after surgery. Patients were divided into a hyperbilirubinemia group and a non-hyperbilirubinemia group. Perioperative variables were compared between groups. Univariable and multivariable logistic regression analyses were performed to identify risk factors for postoperative hyperbilirubinemia. Postoperative complications and in-hospital mortality were also compared. Results: Postoperative hyperbilirubinemia occurred in 101 of 259 patients (39.0%). Compared with patients without hyperbilirubinemia, those with hyperbilirubinemia had longer mechanical ventilation and intensive care unit stay, and higher rates of pneumonia, reintubation, tracheostomy, continuous renal replacement therapy, and in-hospital mortality. Univariable logistic regression showed that higher EuroSCORE II, higher preoperative total bilirubin and direct bilirubin levels, lower hemoglobin and platelet count, pulmonary hypertension, anemia, longer operative time, CPB duration, and aortic cross-clamp time, lower nasopharyngeal temperature, greater intraoperative blood loss, larger red blood cell and plasma transfusion volumes, and concomitant surgery on all three valves were associated with postoperative hyperbilirubinemia. Multivariable analysis identified elevated preoperative direct bilirubin, prolonged CPB duration, and more plasma transfusion as independent risk factors. Receiver operating characteristic analysis showed that peak postoperative total bilirubin was associated with in-hospital mortality, with an optimal cut-off value of 3.95 mg/dL (AUC 0.756, sensitivity 66.7%, specificity 80.2%, p = 0.003). Conclusions: Postoperative hyperbilirubinemia is common after redo valve surgery and is associated with worse early postoperative outcomes and higher in-hospital mortality. Elevated preoperative direct bilirubin, prolonged CPB duration, and more plasma transfusion are independent risk factors for postoperative hyperbilirubinemia in this high-risk population.

Abstract: Autocatalytic sets are chemical reaction networks in which the molecules mutually catalyze each other's formation supported by an ambient food set. They are believed to have played an important role in the origin of metabolism and life, and have been studied extensively both theoretically and experimentally. Autocatalytic sets often consist of a hierarchical structure of smaller and smaller autocatalytic subsets. Of particular interest are irreducible autocatalytic sets and closed autocatalytic sets. Previously, it has been shown that finding {\it all} such autocatalytic subsets is, in principle, intractable. Here, several algorithms are presented to enumerate irreducible and closed autocatalytic sets, either exhaustively (but only practical in limited cases) or in the form of a random sample. Their implementation in a C++ program, made available as a GitHub repository, is then tested on instances of a computational model of chemical reaction networks known as the binary polymer model.

Abstract: Containerized ornamental plant production requires efficient irrigation strategies to balance plant quality with water and nutrient conservation. This study evaluated the effects of different leaching fraction (LF) levels (0%, 20%, 40%, and 60%) on plant growth, ornamental quality, water use, and macronutrient leaching in off-season potted Curcuma cv. ‘Jasmine Pink’. Irrigation volumes were determined using crop coefficient (Kc)-based estimates derived from evaporation pan measurements. The results showed that the highest LF level (60%) significantly improved several ornamental quality traits, including flower number per cluster, leaf greenness, specific leaf area, and compactness index, while also increasing aerial dry weight and photosynthetic performance during the flowering stage. These improvements were associated with reduced substrate electrical conductivity, indicating that higher LF might effectively mitigated root-zone salt accumulation and promoted favorable physiological conditions for plant growth. However, increasing LF also resulted in greater irrigation water consumption and higher macronutrient losses through leachate, particularly for potassium. In contrast, lower LF treatments (0–20%) improved water use efficiency and reduced nutrient losses but showed moderate salt accumulation in the root zone, which slightly limited photosynthetic performance and ornamental development. Overall, the results indicate that a higher LF (60%) provides optimal plant growth and ornamental quality for off-season potted Curcuma production, although integrated strategies may be required to reduce water and nutrient losses. These findings provide practical insights for optimizing irrigation management in container-grown ornamental crops.

Abstract: Background. Alzheimer's disease (AD) is a progressive neurodegenerative disorder with a complex etiology, often diagnosed late in its course. Early detection of AD biomarkers could aid in timely intervention and management. Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to unravel cellular heterogeneity and identify molecular signatures associated with disease states. Here, we employ scRNA-seq on peripheral blood samples to investigate potential predictive biomarkers for AD. Methods. We analyzed the publicly available scRNA-seq dataset GSE181279, comprising peripheral blood cells from three individuals with Alzheimer’s disease and two healthy controls. Single-cell RNA sequencing was performed on these samples to profile the transcriptomic landscape of individual cells. Bioinformatics analyses were employed to identify differentially expressed genes and cellular subtypes associated with AD pathology. Machine learning algorithms were utilized to develop predictive models based on gene expression patterns, aiming to discriminate between AD patients and healthy controls. Results. Our scRNA-seq (GSE181279) analysis revealed distinct gene expression profiles and cellular subtypes in peripheral blood samples from AD patients compared to healthy controls. We identified several dysregulated genes and cell populations associated with AD pathology, including immune cell activation and neuroinflammatory processes. Differential-expression and enrichment analyses identified candidate genes and pathways associated with immune activation, stress-response signaling, and altered cellular homeostasis in AD. In an exploratory leave-one-out analysis, a two-gene model incorporating BTG1 and DUSP1 separated AD from healthy controls within this very small dataset; these findings require validation in larger independent cohorts. Conclusions. This exploratory analysis suggests that peripheral-blood scRNA-seq may help identify candidate biomarkers associated with AD. The identified gene expression signatures and cellular subtypes associated with AD pathology provide valuable insights into the underlying molecular mechanisms of the disease. Furthermore, the development of accurate predictive models based on scRNA-seq data suggests a promising avenue for early diagnosis and intervention in AD. Further validation and prospective studies are warranted to assess the clinical utility and generalizability of these findings in larger cohorts.