Search Results (2,143)

Background and Objectives: Periodontitis is a chronic multifactorial inflammatory disease characterized by progressive destruction of the tooth-supporting tissues. Although periodontal diagnosis is primarily based on clinical and radiographic parameters, salivary biomarkers may provide additional biological information on inflammatory activity and host-response regulation. Matrix metalloproteinase-9 (MMP-9) is involved in extracellular matrix degradation and periodontal tissue destruction, whereas melatonin is a circadian-related molecule with antioxidant, anti-inflammatory, and immunomodulatory properties. However, evidence regarding the simultaneous assessment of salivary MMP-9 and melatonin using standardized pre-sleep saliva collection remains limited. This pilot study aimed to evaluate salivary melatonin and MMP-9 concentrations in periodontal health and stage III periodontitis, to assess their interrelationship, and to explore their preliminary in-sample ability to distinguish periodontal health from stage III periodontitis. Materials and Methods: This cross-sectional pilot study included 18 systemically healthy non-smoking adults: 9 periodontally healthy participants and 9 patients with stage III periodontitis. Pre-sleep unstimulated saliva was collected between 23:00 and 24:00 h or immediately before sleep, following a standardized protocol. Salivary MMP-9 and melatonin concentrations were determined using enzyme-linked immunosorbent assay. Periodontal diagnosis was established according to the 2017 World Workshop classification. Group comparisons, correlation analyses, and receiver operating characteristic curve analyses were performed as exploratory analyses. Results: Salivary melatonin concentrations were significantly lower in patients with stage III periodontitis than in periodontally healthy participants (p = 0.019), with a large effect size (Hedges’ g = 1.261). Salivary MMP-9 concentrations were higher in the periodontitis group, but the difference was not statistically significant (p = 0.465). No significant correlation was found between salivary melatonin and MMP-9 levels. Receiver operating characteristic analysis suggested preliminary in-sample discriminatory ability for melatonin (AUC = 0.827; p = 0.019), whereas MMP-9 showed limited and non-significant discriminatory ability (AUC = 0.593; p = 0.508). Conclusions: In this pilot sample, lower pre-sleep salivary melatonin levels were associated with stage III periodontitis, whereas MMP-9 showed limited standalone discriminatory ability. These findings should be interpreted as preliminary and hypothesis-generating. The present study does not establish a clinically applicable diagnostic cut-off or validated diagnostic utility for salivary melatonin. Larger, independently validated studies are needed to confirm these observations. Full article

This study addresses the role of solar energy technologies in supporting sustainable and low-carbon residential energy systems through a comparative assessment of selected system configurations for a single-family house located in Lublin, Poland: photovoltaic–thermal (PVT) collectors, a hybrid system combining photovoltaic (PV) panels and solar thermal (ST) collectors, and a standalone PV installation. The analysis was carried out under the primary assumption of limited rooftop area available for renewable energy systems. The operational performance of each configuration was simulated using POLYSUN v. 2025.1 software, while the environmental impacts over a 25-year lifetime were evaluated using life cycle assessment (LCA) in SimaPro v. 10.3.0.1, including IPCC 2021 Global Warming Potential (GWP100) and ReCiPe 2016 Endpoint indicators. The results indicate that the PV + ST configuration achieved the best energy and environmental performance, providing the highest solar contribution (57.8%), the lowest total energy consumption (5136 kWh/year), and the lowest environmental impacts in both impact assessment methods (64.3 tCO₂ eq. and 3487 Pt). Under the adopted design assumptions, the PVT system exhibited intermediate overall performance between the PV + ST and standalone PV systems. The study demonstrates that combining energy performance analysis with LCA provides a more comprehensive basis for selecting sustainable solar technologies for low-carbon residential buildings than energy indicators alone. Full article

Soilborne diseases caused by Rhizoctonia solani, Fusarium oxysporum f. sp. betae, Aphanomyces cochlioides, and Pythium spp. constrain sugar-beet production worldwide, with yield losses exceeding 50% in severely affected fields. These pathogens frequently co-occur, yet most biological control and microbiome studies address them individually, and recommendations from model crops often fail to translate to sugar beet’s distinctive root biology and rotation systems. This critical review synthesises evidence on the rhizosphere microbiome as a plant protection resource, organising the literature by agronomic applicability: crop rotation, organic amendments, soil physicochemical management, microbial inoculants and synthetic communities, and microbiome-informed breeding and diagnostics. For each strategy, we state the evidence directness (direct sugar-beet field trials, greenhouse data, cross-crop extrapolation, or mechanistic inference) and evaluate the gap between experimental efficacy and field-ready recommendations. Direct field evidence remains limited: the best-characterised example is Rhizoctonia-suppressive soil linked to non-ribosomal peptide synthetase (NRPS)-producing Pseudomonadaceae and Burkholderiaceae, with 2,4-DAPG-producing Pseudomonas populations also being correlated with disease suppression in sugar-beet seedlings, plus one integrated fungicide-biocontrol field trial. We identify multi-pathogen challenge experiments, multi-site field validation of synthetic communities, and microbiome-informed breeding as priority research gaps. Rhizosphere microbiome management should be integrated into existing disease programmes rather than deployed as a stand-alone replacement. Full article

Background: The C-reactive protein-to-albumin (CRP/Alb) ratio integrates systemic inflammation and nutritional status and has emerged as a promising prognostic biomarker in hospitalized patients. However, because chronological age is itself a major determinant of mortality, it remains unclear whether the prognostic performance of the CRP/Alb ratio is influenced by age. Objectives: This study evaluated the prognostic performance of the CRP/Alb ratio for 30-day and 7-day in-hospital mortality, assessing the incremental contribution of chronological age and whether the association and discriminative performance of the CRP/Alb ratio differed across age groups. Methods: We retrospectively analyzed 3791 consecutive adult patients admitted to the Acute Care Unit of Clinical Medicine (formerly Internal and Aging Medicine) of the Policlinico Riuniti University Hospital, Foggia, Italy, between 2019 and 2025. The primary outcome was 30-day in-hospital mortality, whereas 7-day in-hospital mortality was considered a secondary outcome. Prognostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, multivariable logistic regression, age-adjusted models, age-stratified ROC comparisons, and interaction analyses. Results: Among 3791 patients, 542 (14.3%) died within 30 days of hospitalization and 240 (6.3%) within 7 days. For 30-day mortality, the CRP/Alb ratio showed an AUC of 0.733 (95% CI 0.712–0.754), which increased to 0.759 (95% CI 0.739–0.779) after incorporating chronological age into the model (p < 0.001). For 7-day mortality, the corresponding AUCs were 0.752 (95% CI 0.724–0.781) and 0.768 (95% CI 0.740–0.796), respectively (p = 0.005). In multivariable logistic regression, each doubling of the CRP/Alb ratio was associated with higher odds of both 30-day mortality (OR = 1.484, 95% CI 1.409–1.563) and 7-day mortality (OR = 1.578, 95% CI 1.457–1.710), independently of chronological age. No significant interaction between ln(CRP/Alb ratio) and age was observed for either 30-day (p = 0.322) or 7-day mortality (p = 0.491), and age-stratified ROC curves did not differ significantly across age groups. Conclusions: The CRP/Alb ratio was independently associated with both 30-day and 7-day in-hospital mortality, with discriminative performance largely preserved across age groups. Chronological age provided a modest but statistically significant improvement in discrimination but did not significantly modify the association between the CRP/Alb ratio and mortality. These findings support the CRP/Alb ratio as a readily available marker for short-term risk stratification in hospitalized adults, while external validation is required before its use as a stand-alone clinical prediction tool. Full article

Flood prediction remains a critical challenge in environmental risk management and disaster preparedness. Accurate river-level forecasting is essential for the development of reliable early warning systems and the mitigation of flood-related risks. However, conventional ensemble approaches, such as averaging and majority voting, often exhibit limited adaptability when individual models respond differently to anomalies or incomplete data. To address this limitation, this study proposes a graph-based synchronization framework that integrates XGBoost and Random Forest models using a Graph Convolutional Network (GCN). The proposed framework represents the outputs of the base prediction models as graph nodes and employs graph message passing to learn context-dependent relationships between their predictions. The framework is evaluated using real-world hydrological observations from the Lepenec River Basin in North Macedonia together with meteorological data obtained from the OpenWeatherMap API. Experimental results demonstrate that the proposed GCN-based synchronization framework outperforms both the standalone prediction models and the previously proposed linear synchronization method, achieving an R² value of 0.91 and a Mean Absolute Error (MAE) of 0.21. The obtained results indicate that graph-based synchronization provides an adaptive approach for integrating heterogeneous machine-learning models and has the potential to support future flood early-warning systems and intelligent environmental monitoring applications. Full article

Emergency Medical Services (EMS) personnel require exceptional spatial awareness and rapid decision-making within the confined environment of an ambulance. While Virtual Reality (VR) offers a safe alternative to traditional training, the lack of high-fidelity, regionally accurate, and VR-optimized 3D ambulance models limits its application. This study presents a scenario-driven methodology for developing a VR-ready 3D ambulance environment prototype tailored for Emergency Medical Technician (EMT) training. Utilizing reality-capture techniques, terrestrial laser scanning was performed to accurately document the interior of a standard Hungarian ambulance simulator. The resulting point cloud underwent systematic processing, manual retopology, PBR shading, and the implementation of a custom dual-rigging animation system to optimize complex mechanical movements—such as stretcher operations—for standalone VR platforms. The workflow successfully reduced the vertex count to 25,373 while maintaining millimeter-level spatial fidelity. Technical evaluation confirmed that geometrical, functional, and material objectives were fulfilled, whereas pedagogical implementation remains incomplete. Structural accuracy and animation readiness were verified through preliminary inspection within Blender’s VR viewport inspector. However, interactive game-engine integration remains future work, and educational effectiveness has not yet been tested with EMT learners. Overall, this workflow delivers a 3D asset foundation that establishes the necessary technical basis for subsequent software implementation and clinical evaluation. Full article

Newborn screening (NBS) in Pakistan remains limited by fragmented service delivery, uneven provincial capacity, and the absence of an implementation pathway that links policy intent to operational planning. This study aimed to co-design a provincial NBS implementation framework for Punjab. To do so, we used a sequential blended workshop model supported by visual planning methods. We conducted a qualitative, three-phase co-design process with 19 participants from laboratory medicine, pediatrics, obstetrics, public health, academia, and policy. Phase I consisted of pre-work and an online workshop to establish a shared knowledge base and identify implementation barriers and enablers. Phase II used an in-person, visually facilitated planning workshop to translate these findings into a provincial NBS implementation framework. Phase III involved a asynchronous email-based review to refine the framework and reach consensus. Content analysis from Phase I identified five implementation domains: physical and technical infrastructure, operational systems, clinical integration, workforce and engagement, and governance and policy. In Phase II, these domains were translated into a visual implementation framework that specified priorities, stakeholders, risks, enablers, and action areas for phased rollout. Congenital hypothyroidism was identified as the most feasible entry-point condition. The final framework positioned NBS not as a stand-alone pilot, but as a coordinated provincial system requiring governance, financing, referral pathways, quality assurance, and workforce development. This study contributes an implementation-oriented model for moving from fragmented NBS initiatives to a provincial implementation framework in a low- and middle-income setting. It also offers a practical foundation for broader national scale-up. Full article

Background/Objectives: The C-reactive protein–albumin–lymphocyte (CALLY) index integrates systemic inflammation, nutritional status, and host immunity. We evaluated its association with postoperative pathological axillary nodal status and its incremental discrimination beyond routinely recorded preoperative clinical factors. Methods: This retrospective single-center diagnostic-accuracy study screened 380 patients evaluated between July 2019 and January 2026. The primary cohort included 247 unilateral M0 patients undergoing upfront surgery; 80 neoadjuvant-treated M0 patients were analyzed separately. CALLY was calculated from laboratory values obtained within 7 days before surgery. Associations and discrimination were assessed using logistic regression, Firth penalized logistic regression when clinical nodal status caused separation, paired bootstrap AUC comparisons, restricted cubic splines, and prespecified sensitivity analyses for BMI and available inflammatory/comorbidity covariates. Results: In the primary cohort, 75 patients (30.4%) were pN-positive. Median CALLY was 2.629 in pN-positive and 3.586 in pN0 patients (p = 0.058), and standalone discrimination was weak (AUC = 0.576; 95% CI, 0.496–0.651). In the age- and cT-adjusted model, the OR per CALLY doubling was 0.89 (95% CI, 0.72–1.10; p = 0.271). Preoperative cN status nearly separated the outcome; in a Firth model including age, cT, cN, and CALLY, the CALLY OR was 0.78 (profile 95% CI, 0.51–1.21; p = 0.250). Adding CALLY to age, cT, and cN changed the AUC from 0.988 to 0.988 (ΔAUC = −0.0001; 95% CI, −0.0013 to 0.0041; p = 0.551). No nonlinearity was detected (p = 0.352), and BMI/confounder sensitivities were concordant. Secondary neoadjuvant and observed-burden analyses remained exploratory and imprecise. Conclusions: In this single-center upfront-surgery cohort, preoperative CALLY was not independently associated with postoperative axillary nodal positivity and provided no convincing incremental discrimination beyond the available clinical assessment. The data do not support using CALLY alone for axillary staging or surgical decisions; limited high-burden event counts preclude a definitive exclusion of smaller associations. Full article

This study proposes EQAS (Empowered Question-Answering System), a hybrid framework designed to support context-aware question-answering and intelligent feedback generation in domain-specific knowledge environments. EQAS integrates fine-tuned transformer-based models, instruction-guided large language models, domain-specific knowledge graphs, and LangChain-based vector retrieval to improve contextual relevance, response quality, and feedback consistency. To evaluate the proposed framework, a benchmark dataset consisting of 10,000 real-world question–answer pairs was constructed from authentic user interactions and domain-related information resources. Experimental evaluation across established transformer-based architectures and recent instruction-tuned large language models showed that the Llama-3.3-70B-Instruct baseline achieved the highest standalone QA performance (F1: 77.42; EM: 44.60), while the EQAS transformer-based configuration achieved an F1 score of 75.48 and an Exact Match score of 41.80. A controlled ablation analysis using Llama-3.3-70B-Instruct as the fixed QA backbone further showed that incorporating knowledge graph enhancement increased the F1 score from 77.42 to 79.93 and the Exact Match score from 44.60 to 46.82, corresponding to absolute improvements of 2.51 and 2.22 points, respectively. A complementary human-centered evaluation of 1000 generative responses by three NLP researchers yielded an overall quality score of 4.34/5 across correctness, clarity, sufficiency, and helpfulness, with an overall Krippendorff’s $\alpha$ of 0.80. Furthermore, the framework provides context-sensitive explanatory feedback that can support user understanding and knowledge acquisition during information-seeking interactions. The findings suggest that EQAS offers a scalable solution for intelligent question-answering, feedback support, and knowledge assistance in complex information environments. The proposed framework highlights the potential of combining large language models with structured knowledge representations to support context-aware question-answering and explanatory feedback generation in domain-specific information environments. Full article

There is a growing need for high-efficiency power electronic converters that can effectively convert energy, regulate voltages, and enhance power quality in standalone power generators, as the use of renewable energy sources and battery energy storage devices increases. The quasi-impedance-source inverter (qZSI) has attracted significant interest due to its single-stage buck-boost operation, continuous input current, reduced reliance on passive elements, and increased reliability. In this paper, the control strategy and implementation of the qZSI with battery assistance for standalone photovoltaic energy generation are discussed. To analyze the operational characteristics and design the control strategy of the qZSI, the system equations are linearized around the nominal operating point to develop a small-signal model, from which the direct current (DC) side and alternative current (AC) side transfer functions are derived and used as the basis for controller design. Using the proposed model, hybrid controllers are designed to control the shoot-through duty cycle, maintain DC link voltage stability, and battery charging to achieve stable power generation. Furthermore, the SPWM technique is applied to produce AC power with minimal harmonic content and higher efficiency. Application results show stable dynamic behavior, effective battery energy management, improved voltage regulation, and reduced harmonic distortion in the output waveform. The main contribution is a low-complexity coordinated PI and PR control framework for standalone battery-assisted qZSI operation, experimentally validated under DC- and AC-side disturbances without requiring an additional battery-side power-conversion stage. Full article

Fungal biomarkers may support antifungal stewardship in critically ill adults, but their clinical effect depends on the decision rule linking test results to treatment. We systematically reviewed ICU or ICU-relevant adult studies in which fungal biomarkers or rapid fungal diagnostics explicitly informed antifungal management. Searches were updated through 24 July 2026 (PROSPERO CRD420261432481), and clinically compatible Candida randomised outcomes were synthesised by rule direction. Twenty-one reports represented 19 independent studies. Five core Candida trials randomised 677 participants (661 analysed). Rule-in assignment increased systemic antifungal receipt (two trials; RR 2.06, 95% CI 1.58–2.67) without demonstrated patient benefit. In three rule-out trials, mortality was 34/126 versus 38/132 (RR 0.94, 95% CI 0.63–1.40), while only 16 post-randomisation invasive Candida events informed highly uncertain fungal-safety estimates (RR 1.85, 95% CI 0.51–6.65). The evidence is therefore insufficient to establish a clinically acceptable fungal-safety margin for biomarker-guided discontinuation. Biomarkers should therefore support, not replace, risk-based reassessment: timely negative results may justify supervised discontinuation, whereas isolated positivity should not be a stand-alone treatment trigger. Full article

The increasing deployment of renewable energy sources and energy storage technologies has stimulated growing interest in their contribution to energy security and energy independence. This study examines the structure, conceptual organization, and evolution of the emerging research domain linking renewable energy, energy storage, energy security, and energy independence. This domain is referred to here as Renewables-Storage-Security at the Country Level (RSSC). A bibliometric analysis of 468 Scopus-indexed publications was conducted using performance analysis, keyword co-occurrence analysis, overlay visualization, thematic evolution analysis, and thematic mapping. The results indicate that the RSSC analytical domain has evolved from a predominantly techno-economic focus on renewable energy and energy storage integration toward broader system-oriented themes related to energy transition, energy security, resilience, long-duration storage, hydrogen technologies, Power-to-X, and energy planning. Within the analyzed Scopus-indexed corpus, concepts such as energy security, energy independence, resilience, and energy self-sufficiency exhibited differentiated network positions and thematic associations. Energy independence was weakly represented as a standalone keyword theme and was more often associated with broader themes of energy-system transformation. This study contributes to a better understanding of the evolving role of storage-supported renewable power systems in contemporary energy-security research. Full article

The increasing demand for sustainable outdoor lighting has accelerated the development of solar-powered lighting systems. However, conventional solar lamps typically employ fixed illumination levels and simple day–night switching mechanisms, resulting in inefficient battery utilization and limited adaptability to changing environmental conditions. This study proposes a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, adaptive pulse-width modulation (PWM)-based illumination control, ultrasonic sensing, wireless communication, and embedded control into a unified standalone platform. The TRIZ contradiction matrix was employed during the conceptual design stage to systematically resolve key engineering contradictions involving illumination performance, energy efficiency, hardware complexity, battery lifetime, and user convenience. The proposed prototype was developed using an AT89S51 microcontroller to coordinate battery charging protection, environmental sensing, adaptive brightness regulation, and manual wireless operation. Experimental validation demonstrated stable photovoltaic charging with a regulated battery charging voltage of 14.4 V, reliable execution of embedded control functions, seamless transition between manual and autonomous operating modes, and adaptive LED brightness regulation according to real-time environmental conditions. The integrated PWM control strategy reduced unnecessary energy consumption by dynamically adjusting illumination intensity based on object detection rather than maintaining constant full-power operation. The experimental results further verified the feasibility of combining software-driven adaptive control with renewable energy harvesting to achieve intelligent energy management without increasing hardware complexity. Overall, the proposed system demonstrates that the integration of TRIZ-based systematic innovation with embedded intelligent control provides a practical, energy-efficient, and cost-effective solution for autonomous outdoor lighting. The proposed architecture offers valuable engineering insights for future smart lighting applications in off-grid infrastructure, sustainable communities, and smart city environments. Full article

Background: Accurate breast lesion classification and localization from mammograms remain challenging because lesions vary in size, morphology, density, contrast, and boundary clarity. This study proposes MSF-Swin-ICFNet, an image-derived Multi-Scale Swin Transformer framework with Image-Derived Cross-Scale Fusion (ICF) for joint benign–malignant classification and lightweight lesion localization. Methods: The proposed framework extracts hierarchical representations from four Swin Transformer stages and aligns, adaptively recalibrates, and fuses these multi-scale features into a shared representation for classification and lesion-mask prediction. The model was evaluated on CBIS-DDSM, comprising 891 patients and 1592 mammograms, and RTM, comprising 573 patients and 10,070 mammograms. Patient-level stratified partitioning was used to ensure that mammograms and associated annotations from the same patient remained within a single data partition. Classification was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC, while lesion localization was assessed using the Dice coefficient. Results: MSF-Swin-ICFNet achieved strong mammogram-level classification performance on the patient-disjoint test cohorts. On CBIS-DDSM, the model achieved 0.949 accuracy, 0.931 F1-score, and 0.985 AUC-ROC. On RTM, it achieved 0.968 accuracy, 0.956 F1-score, and 0.992 AUC-ROC. The corresponding Dice coefficients for lesion localization were 0.786 on CBIS-DDSM and 0.878 on RTM. Ablation experiments further supported the contribution of cross-scale fusion, scale recalibration, feature refinement, and the boundary-aware localization head. Conclusions: MSF-Swin-ICFNet demonstrates promising retrospective performance for image-based breast lesion classification with complementary lesion localization. The results support the technical potential of adaptive hierarchical feature fusion for mammographic analysis. However, the model is not intended as a standalone diagnostic system, and independent multicentre validation and radiologist reader studies are required before clinical deployment. Full article

The deep sea harbors a substantial proportion of the ocean’s unexplored biological and chemical diversity, while hydrothermal vent ecosystems expose resident organisms to unusual combinations of hydrostatic pressure, steep chemical gradients, reduced compounds, and elevated metal concentrations. This Review examines the deep-sea vent mussel Bathymodiolus azoricus, a dominant species at Mid-Atlantic Ridge hydrothermal fields, as a source of biological mechanisms and molecular systems with potential biomedical relevance. We integrate three areas that have largely developed separately in the literature: innate immunity and host–symbiont interactions, mussel-derived wet adhesion and byssal structural proteins, and shell biomineralization and repair. These published observations are complemented by targeted reanalyses of legacy and more recent B. azoricus transcriptomic resources, used here as supporting transcriptomic evidence for molecular families relevant to these themes rather than as standalone genome-scale transcriptomic studies. Particular attention is given to mussel foot proteins and byssal collagens as candidate templates for wet-tissue adhesives and structural biomaterials, and to shell-derived calcium carbonate as a potential precursor for calcium-phosphate-based materials. We further advance a specific, testable hypothesis—long-term exposure to the metal-rich hydrothermal vent environment may have influenced the metal-binding chemistry of B. azoricus adhesive and structural proteins, potentially generating functional properties distinct from those of shallow-water mytilids. This possibility is biologically plausible in light of established DOPA–metal coordination mechanisms in mussel adhesion, but no direct comparative measurements of Fe³⁺-binding affinity, metal-mediated cross-linking, or adhesive performance currently demonstrate such an advantage in B. azoricus. The species should therefore be regarded not as a proven source of superior vent-adapted biomaterials, but as a well-suited experimental system in which immunity, bioadhesion, biomineralization, and environmental adaptation converge to generate specific hypotheses for biomedical discovery. Comparative functional studies, protein-level validation of transcript-derived candidates, and improved molecular characterization of foot and mantle tissues will be required to test these possibilities. Full article