Search Results (1,950)

Timely identification of infectious disease threats is essential for public health readiness. Conventional indicator-based surveillance systems, while dependable for tracking established pathogens, frequently lack the agility and sensitivity to detect new infections promptly. Syndromic surveillance, which examines pre-diagnostic and non-specific health indicators from many data sources in near real time, serves as a significant complementary method that improves early warning and situational awareness. This narrative study analysed selected experiences with syndromic surveillance, utilising peer-reviewed literature and institutional records. Four primary data streams were examined: emergency department and hospital records, pharmacy and over the counter (OTC) sales, participative citizen-generated data, and hybrid multi-source systems. Syndromic indicators were reported to identify outbreaks two to fourteen days before standard laboratory reporting across many trials. Data from the emergency department exhibited the highest sensitivity and specificity (47.34% and 91.95%, respectively), whereas pharmacy and participative data offered early indicators at the community level. Integrated systems like ESSENCE (Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA) and SurSaUD^® (Saint-Maurice cedex, Paris, France) attained enhanced accuracy yet necessitated significant data integration and governance capabilities. Syndromic surveillance enhances epidemic preparation by detecting atypical health-seeking behaviours and variations from baseline patterns prior to formal diagnosis. Nonetheless, its efficacy is contingent upon data quality, interoperability, and contextual adaptation. Countries like Bulgaria could improve national early-warning capabilities and overall health security through the gradual adoption of pilot projects and integration with existing surveillance networks. Full article

Background: Inflammatory bowel disease (IBD) is associated with chronic pain and reduced quality of life, even in the absence of active intestinal inflammation. International studies suggest that fibromyalgia (FM), a chronic pain disorder characterized by widespread musculoskeletal pain, fatigue, sleep disturbance, and multiple somatic symptoms, is more prevalent among patients with IBD than among the general population. However, data from Saudi Arabia are limited. Methods: This cross-sectional study was conducted at King Abdulaziz University Hospital in Jeddah, Saudi Arabia, during July and August of 2024. Patients with biopsy-confirmed IBD were identified from hospital records and contacted by phone to screen for FM using a validated Arabic version of the Fibromyalgia Rapid Screening Tool. Demographic data, comorbidities, medication exposure, IBD characteristics, disease activity, and laboratory parameters were extracted from the medical records and compared between patients with and without FM. Results: Of 274 patients with IBD (mean age 30.9 ± 9.2 years; 56.9% male), 51 (18.6%; 95% CI 14.2–23.7) met criteria for FM. Patients with FM tended to be older than those without and were more likely to have comorbidities, particularly thyroid disorders, as well as low Vitamin D levels. Prior 5-aminosalicylic acid use was also more common among patients with FM. Inflammatory markers, hematological indices, IBD phenotypes, and disease activity were similar between the groups. Conclusions: Saudi patients with IBD often have comorbid FM. Routine FM screening in IBD clinics may help avoid misattributing central pain to active inflammation and unnecessary treatment escalation. Full article

Accurate early prognostic assessment is a central aspect of clinical decision-making in horses presenting with colic. Despite the availability of multiple clinical and laboratory parameters, reliable biomarkers that provide useful information at the time of admission remain limited. The erythrocyte sedimentation rate is a non-specific indicator of systemic inflammation that has recently been validated for automated point-of-care use in horses. Its behaviour in equine colic has not been previously characterised. This study included 85 horses admitted for colic at two university referral hospitals. The erythrocyte sedimentation rate was measured at admission in all horses and again approximately 24 h after surgery in a subset of surgical cases. Horses were classified as medically managed, surgical survivors or surgical non-survivors. Group comparisons were performed using non-parametric statistical methods, and associations with survival were evaluated. The admission erythrocyte sedimentation rate was significantly lower in surgical non-survivors compared to the other groups (p < 0.05). Erythrocyte sedimentation rate rose in surviving horses within 24 h, while it barely changed among non-survivors, and there were no significant changes among surgical values. Actually, these observations suggest that low erythrocyte sedimentation rates on admission are associated with low chances of survival for horses undergoing colic surgery and thus make these rates a feasible additional prognostic indicator for equine colic. Full article

The COVID-19 pandemic underscored the urgent need for rapid, reliable, and safe laboratory workflows that ensure both diagnostic accuracy and biosafety for laboratory personnel. We developed a comprehensive approach for SARS-CoV-2 detection using saliva-based RT-qPCR that spans the entire process from sample transfer to final disposal. This workflow integrates biosafety principles with efficient diagnostic procedures, ensuring safe handling, minimized exposure risks, and reliable molecular testing. Critical components included biosecurity, standardized protocols for sample receipt, secure transfer, safe processing, and environmentally responsible disposal. By applying a holistic safety framework, we not only protected laboratory staff during the pandemic but also established a model that can inform preparedness for future emerging infectious disease threats. This approach demonstrates how laboratory safety and diagnostic efficiency can be simultaneously achieved, offering a reference for institutions seeking to balance biosafety and public health needs in outbreak situations. Full article

This dataset presents a real-world lipidomics resource for developing and benchmarking quality control methods, batch effect detection algorithms, and data validation workflows. The data originates from a cross-sectional clinical study of psoriatic arthritis (PsA) patients (n = 81) and healthy controls (n = 26), matched for age, sex, and body mass index, which was collected at a tertiary university rheumatology center. Subtle laboratory irregularities were detected only through advanced unsupervised analysis, after passing conventional quality control and standard analytical methods. Blood samples were processed using standardized protocols and analyzed using high-resolution and tandem mass spectrometry platforms. Both targeted and untargeted lipid assays captured lipids of several classes (including carnitines, ceramides, glycerophospholipids, sphingolipids, glycerolipids, fatty acids, sterols and esters, endocannabinoids). The dataset is organized into four comma-separated value (CSV) files: (1) Box–Cox-transformed and imputed lipidomics values; (2) outlier-cleaned and imputed values on the original scale; (3) metadata including clinical classifications, biological sex, and batch information for all assay types and control sample processing dates; and (4) a variable-level description file (readme.csv). The 292 lipid variables are named according to LIPID MAPS classification and standardized nomenclature. Complete batch documentation and FAIR-compliant data structure make this dataset valuable for testing the robustness of analytical pipelines and quality control in lipidomics and related omics fields. This unique dataset does not compete with larger lipidomics quality control datasets for comparisons of results but provides a unique, real-life lipidomics dataset displaying traces of the laboratory sample processing schedule, which can be used to challenge quality control frameworks. Full article

Indoor environmental comfort plays a central role in occupants’ well-being, learning outcomes, and productivity, especially in educational buildings characterized by high occupancy variability and diverse activities. This paper presents a real-world dataset collected at the Cesena Campus of the University of Bologna, aimed at supporting occupant-centric comfort analysis and prediction in classrooms and laboratories. The dataset integrates continuous environmental measurements, such as temperature, humidity, noise, air pressure, and CO₂ concentration, with subjective comfort feedback gathered from students during regular lectures. Data were collected using permanently installed ceiling sensors and additional control sensors placed near occupants, enabling both longitudinal monitoring and validation analyses. Furthermore, the dataset includes both repeated comfort perception reports and a one-time comfort definition phase capturing individual relevance weights for different comfort dimensions. By combining objective and subjective data in realistic academic settings, the dataset provides a valuable resource for developing, benchmarking, and validating data-driven models for smart campus applications, indoor comfort prediction, and human-centered building analytics. Full article

The development of an immersive virtual reality laboratory in the facilities of a private Mexican university is presented. This laboratory contemplates the use of different disciplines and different student profiles, for which it was developed considering technological, ergonomic, educational, and disciplinary requirements. A primary assessment of a selected group of students was developed to find out the initial level of satisfaction with the user experience in the laboratory and the improvements to be proposed for future adaptations. Full article

In order to solve the challenges of laboratory safety management in universities, such as insufficient supervision of high-frequency risk behaviors in responsibility traceability, a laboratory safety hazard perception and responsibility traceability system based on deep learning is proposed. Based on the YOLOv5s object detection model, the channel attention mechanism SE and NWD loss functions are introduced, with DeepSORT tracking to realize multi-target tracking and hidden danger perception in laboratory scenarios. Then, the responsibility matching algorithm and visual traceability mechanism are proposed to build a full-chain management system of “risk perception, analysis and tracking, and responsibility traceability”. Experiments show that the mean average precision (mAP) of YOLO-lab in the laboratory scene is 87.8%. Taking the experimenter not wearing a lab coat as an example, through the test of the laboratory scene, the multi-target tracking effect is excellent and the responsibility traceability report is generated, which solves the problem of “visible and uncontrollable behavior, traceable and unproven” in traditional supervision, and provides an intelligent technical path for laboratory safety governance. Full article

Classical earth pressure theories often struggle to account for the complex coupling effects of wall displacement and spatial non-uniformity under non-limit states. This study presents an interpretable machine learning framework designed to extract universal mechanical laws from heterogeneous experimental datasets. Using a multi-source database of rigid retaining walls with sandy backfill, a three-stage feature refinement strategy is proposed that incorporates Recursive Feature Elimination, Collinearity Analysis, and Interpretability Comparison to identify a parsimonious set of five fundamental physical parameters. A SHapley Additive exPlanations-Categorical Boosting (CatBoost-SHAP) framework is established to predict the active earth pressure coefficient (K) and interpret the underlying mechanisms across various movement modes (RB, RT, and T). Results demonstrate that the model effectively captures the progressive evolution of shear bands and the soil arching effect. Specifically, a critical displacement threshold of Δ/H ≈ 0.006 is identified, marking the transition from mode-dominated stress non-uniformity to magnitude-driven limit states. Leave-One-Dataset-Out Cross-Validation (LODOCV) confirms the model’s ability to maintain physical consistency over purely statistical fitting despite significant inter-literature heterogeneity. Finally, a Graphical User Interface (GUI) is developed to facilitate rapid, displacement-based design in engineering practice. This research bridges the gap between empirical laboratory observations and generalized mechanical logic, providing a data-driven foundation for refined geotechnical design. Full article

Background/Objectives: While COVID-19 severity is strongly associated with older age, younger adults may also develop complications. This study investigated factors associated with transfer to ICU in hospitalized younger adults during May–December 2021. Materials and methods: Eighty-six consecutive patients aged ≥18 and <50 years were admitted to the University Multi-profile Hospital for Active Treatment of Infectious and Parasitic Diseases “Prof. Ivan Kirov”, Sofia, Bulgaria between 12 May 2021, and 7 December 2021. Results: Among 86 patients (median age 40 years; 55.8% female), common symptoms included fever (89.5%), cough (91.9%), headache (76.7%), and gastrointestinal symptoms (83.7%). Laboratory abnormalities were frequent: lymphopenia (median 0.94 × 10⁹/L), elevated CRP (28.9 mg/L), LDH (333 U/L), and ferritin (198.5 µg/L). Severe andcritical disease occurred in 17.5% of cases, with 43% requiring oxygen therapy. In descriptive/univariate analyses, ICU transfer was more frequent among patients with hypoxemia (SaO₂ < 90%) and higher CRP, D-dimer, vomiting and ferritin. In a Firth penalized logistic regression model (rare-event setting), endocrine/metabolic comorbidity showed a directionally consistent association with ICU transfer; gastrointestinal comorbidity signals were not interpretable because of extremely small cell counts. Conclusions: Younger adults usually present with mild to moderate COVID-19; however, a substantial minority develop severe illness. Hypoxemia and elevated inflammatory and coagulation markers were consistently associated with worse outcomes. Endocrine/metabolic comorbidity showed the most consistent association with transfer to ICU in this cohort, although all comorbidity estimates should be interpreted cautiously, given the small number of events. Full article

This paper presents the results of research on an innovative, integrated IACMS (Intelligent Integrated Automation, Control, and Monitoring System), developed for energy-efficient operation of auxiliary machinery in ship engine rooms. The system, validated both in the laboratory and during full-scale operation on the MF Skania Ro-Pax ferry, integrates process monitoring, diagnostics, predictive maintenance, and intelligent energy optimization within a unified control architecture. This approach enables a significant reduction in electricity consumption while maintaining thermal safety and operational reliability. Laboratory tests focused on a pump cooling system with PLC and frequency converter control, achieving a 90.5% reduction in energy consumption compared to conventional constant-speed operation. During full-scale validation, the IACMS managed the seawater pump via adaptive frequency control (30–60 Hz). Two consecutive voyages demonstrated energy savings of 84.6% and 86.0%, with a daily energy reduction of 0.84 MWh, resulting in a decrease of approximately 0.5 tons of CO₂ emissions per day. Additionally, an observed reduction of about 6–7% in daily generator-set energy was recorded during the analyzed period; this vessel-level value is indicative, as the generator supplies multiple onboard consumers. All trials confirmed stable cooling system temperatures, and comprehensive diagnostics revealed no negative impact of inverter control on the technical condition of equipment. The findings indicate that IACMS is a universal and scalable tool for improving energy efficiency and enabling predictive maintenance in ship engine room auxiliary systems. The system was positively validated in commercial operation and certified by the Polish Register of Shipping, confirming its technological maturity and readiness for widespread adoption in the maritime industry. The results pave the way for further deployments of intelligent energy management solutions in shipping, supporting maritime decarbonization goals. Full article

Introduction: Patients with extremely elevated IL-6 levels remain poorly characterized, and no specific plasma concentration has been established to reliably predict mortality or guide immunomodulatory interventions. We hypothesized that extreme hypercytokinemia is associated with increased mortality in sepsis. The primary objective was to identify, in patients with hyperinflammatory endotype, an IL-6 threshold associated with a significantly elevated risk of death. Methods: We conducted a retrospective, single-center observational study based on a historical cohort of adult patients with consecutive activation of the in-hospital sepsis code, a prospective and standardized institutional care pathway, at Vall d’Hebron University Hospital between July 2018 and December 2024. Patients fulfilling Sepsis-2 diagnostic criteria and criteria for severe sepsis or septic shock were eligible. Plasma interleukin-6 (IL-6) levels were routinely determined in all patients. The analysis included patients with complete clinical and laboratory data available in the study database. To identify the IL-6 threshold associated with critical risk of death, a cumulative conditional relative frequency analysis was performed. A quantile-based analysis was conducted using predefined intervals of 1000 pg/mL and 15,000 pg/mL. A multivariable logistic regression analysis was conducted to identify clinical and laboratory parameters independently associated with IL-6 > 15,000 pg/mL and outcome. Results are presented as odds ratios (ORs). Survival differences were assessed using Kaplan–Meier analysis. Results: Overall mortality was 31% in the 1669 patients analyzed. Median IL-6 concentration was 772 pg/mL (IQR: 164–8750 pg/mL) with significantly higher levels in non-survivors (2137 pg/mL, IQR: 267–34,758). A critical IL-6 cutoff of 14,930 pg/mL was identified, which was rounded to 15,000 pg/mL for clinical applicability. IL-6 > 15,000 pg/mL was associated with increased mortality (OR 2.22, 95% CI: 1.12–5.36). Kaplan–Meier analysis revealed significantly reduced survival in patients above this IL-6 threshold (p < 0.0001). Conclusions: In this cohort of patients with severe sepsis or septic shock, plasma IL-6 levels > 15,000 pg/mL defined a critical threshold beyond which mortality risk exceeded survival probability. Critical hypercytokinemia may serve as a clinically relevant biomarker to identify patients with sepsis and multiorgan dysfunction who could benefit from precision immunomodulatory therapies. Full article

Background: Community-acquired pneumonia (CAP) in children may be complicated by necrotizing pneumonia (NP), complicated parapneumonic effusion (CPPE), and lung abscess. These complications prolong hospitalization and require medical and surgical intervention. Objectives. To describe clinical course, diagnostic workup, and management of cCAP (complicated CAP) in children admitted to the Women’s and Children’s Health Department, Padua University Hospital, between January 2022 and September 2024. To identify factors associated with disease severity and evaluate outcomes. Methods: All children hospitalized for cCAP during the study period were included. Data collected comprised clinical features, laboratory and imaging findings, medical and surgical management, and outcomes. Results: Forty patients (mean age 4.4 y; 13.15% of pneumonia admission) were included: 67.5% had NP with CPPE, 22.5% isolated effusion, 10% NP without effusion. All patients were febrile at onset, 62.2% had cough, 32.5% abdominal pain, 30% rhinitis. NP was confirmed by contrast-enhanced chest CT. Thirty patients (75%) had positive microbiological testing, mainly Streptococcus pneumoniae and Streptococcus pyogenes. 77.5% required oxygen therapy (five invasive ventilation and one with ECMO). Median fever duration 18 days (IQR 15–27) with elevated CRP (median peak 300 mg/L). Pleural drainage was performed in 66.7%, fibrinolytics in 17.5%, thoracoscopic decortication in 12.5%, and lobectomy in one patient. Radiological resolution occurred at a median of 31 days post-discharge, with normal pulmonary function at a median of 15 months. Conclusions: Despite pediatric cCAP severity, short- and long-term outcomes are favorable. Early recognition and integrated management are crucial, and further prospective studies are warranted to optimize care and identify severity predictors. Full article

Background and Objectives: Acute pancreatitis (AP) is an acute inflammatory disease ranging from mild, self-limiting forms to severe presentations associated with high morbidity and mortality. Early prognostic assessment is crucial for guiding clinical management. This study aimed to evaluate the prognostic value of the red cell distribution width-to-albumin ratio (RDW/Alb, RAR) in relation to clinically relevant outcomes, including intensive care unit (ICU) admission and in-hospital mortality, in patients with AP. Materials and Methods: This retrospective study included 282 patients diagnosed with AP who were hospitalized at Mersin University Hospital between January 2019 and February 2024. Clinical, laboratory, and radiological data were retrospectively analyzed. The predictive performance of RAR was evaluated and compared with established clinical scoring systems, including bedside index for severity in acute pancreatitis (BISAP), systemic inflammatory response syndrome (SIRS), harmless acute pancreatitis score (HAPS), and pancreatitis activity scoring system (PASS). Results: The median RDW-to-albumin ratio (RAR) was 3.9 (range: 2.6–36.7). Receiver operating characteristic (ROC) curve analysis demonstrated that RAR showed good predictive performance for ICU admission (Area Under the Curve (AUC): 0.781; p < 0.001; optimal cut-off: 4.15) and high predictive performance for in-hospital mortality (AUC: 0.927; p < 0.001; optimal cut-off: 5.26). RAR exhibited limited but statistically significant discriminatory performance when compared with the BISAP score (AUC: 0.591; p = 0.017), whereas no significant predictive performance was observed in relation to PASS, HAPS, or SIRS scores. Conclusions: Within the context of this retrospective cohort, RAR is a simple, inexpensive, and readily available biomarker that may be associated with ICU admission and in-hospital mortality in patients with AP. Given the absence of standard severity endpoints such as persistent organ failure or pancreatic necrosis, these findings should not be interpreted as evidence of conventional disease severity prediction but rather as hypothesis-generating observations that warrant validation in larger prospective studies. Full article

Corporate Social Responsibility (CSR) has evolved into a strategic governance framework through which organisations address environmental sustainability, stakeholder expectations, and long-term institutional viability. In knowledge-intensive organisations such as universities, Green Artificial Intelligence (GAI) is increasingly recognised as an internal CSR agenda. GAI can reduce digital and energy-related environmental impacts while enhancing educational and operational performance. This study examines how higher education leaders, as organisational decision-makers, form intentions to adopt GAI within institutional CSR and digital sustainability strategies. It focuses specifically on leadership intentions to implement key GAI practices, including Smart Energy Management Systems, Energy-Efficient Machine Learning models, Virtual and Remote Laboratories, and AI-powered sustainability dashboards. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates how performance expectancy, effort expectancy, social influence, and facilitating conditions shape behavioural intentions to adopt GAI. Survey data were collected from higher education leaders across Saudi universities, representing diverse national and cultural backgrounds within a shared institutional context. The findings indicate that facilitating conditions, performance expectancy, and social influence significantly influence adoption intentions, whereas effort expectancy does not. Gender and cultural context also moderate several adoption pathways. Generally, the results demonstrate that adopting GAI in universities constitutes a governance-level CSR decision rather than a purely technical choice. This study advances CSR and digital sustainability research by positioning GAI as a strategic tool for responsible digital transformation and by offering actionable insights for higher education leaders and policymakers. Full article