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Free flavin adenine dinucleotide (FAD) is metabolized to flavin mononucleotide (FMN) and adenine monophosphate (AMP) by hydrolases and to 4′,5′-cyclic phosphoriboflavin (cFMN) and AMP by the triose kinase FMN cyclase (TKFC). Yet, the lack of analytical standards for cFMN might have resulted in the incidence of cFMN in biological specimens being underreported. To address this shortcoming, cFMN was synthesized from either FMN or FAD. The optimization of the FAD to cFMN reaction conditions revealed that an equimolar ratio of ZnSO₄ and FAD yielded pure cFMN upon the precipitation of AMP-Zn salts. cFMN is stable to aqueous acidic and basic conditions and is readily extracted from biological samples for detection by liquid chromatography coupled with mass spectrometry. Although cFMN is hydrolyzed by liver tissue extracts to FMN and riboflavin, the mechanisms for this conversion remain elusive. Full article

(This article belongs to the Special Issue Feature Papers in the Natural and Bio-Derived Molecules Section)

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34 pages, 11603 KB

Open AccessArticle

Mapping Co-Creation and Co-Production in Public Administration: A Scientometric Study

by Rok Hržica

Adm. Sci. 2026, 16(1), 55; https://doi.org/10.3390/admsci16010055 (registering DOI) - 21 Jan 2026

Abstract

This study presents a comprehensive bibliometric and science mapping analysis of research on co-creation and co-production in public administration, based on 819 publications indexed in the Web of Science (WoS). The analysis of scientific production in this field shows sparse early contributions before 2005, followed by steady growth after 2010 and accelerated expansion from 2016 onward, driven primarily by European and United States research communities. In terms of scholarly influence, the results identify a stable core of highly productive and influential authors, institutions, and countries, with strong concentration in Northern and Western Europe and Anglo-Saxon contexts. To address the intellectual structure of the field, science mapping identifies four dominant thematic clusters: (1) co-production and value creation, (2) participation and public engagement, (3) governance and policy, and (4) knowledge development, lessons learned, and evaluative insights. Examining thematic and keyword evolution over time, the findings indicate a shift from early conceptual and normative discussions toward more applied and implementation-oriented research, with increasing attention to barriers, challenges, and enabling conditions in recent years. Overall, the findings show that research on co-creation and co-production has evolved from conceptual fragmentation toward greater thematic consolidation and analytical maturity, while persistent implementation challenges remain. By systematically mapping these developments, the study provides a structured overview that supports future conceptual integration and informs both research agendas and practice-oriented discussions on co-creation and co-production in public administration. Full article

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10 pages, 1857 KB

Open AccessArticle

Algorithm for Reporting Free Hemoglobin in ECMO Patients: Need for a Multidisciplinary Approach

by Ivana Baršić Lapić, Ljiljana Zaninović, Daniel Lovrić, Ana Lončar Vrančić, Dora Rebrek and Dunja Rogić

J. Clin. Med. 2026, 15(2), 867; https://doi.org/10.3390/jcm15020867 (registering DOI) - 21 Jan 2026

Abstract

Background: Intravascular hemolysis is a common complication in patients undergoing extracorporeal membrane oxygenation (ECMO), with plasma free hemoglobin (pfHb) serving as a biomarker for detection. Without standardized protocols, laboratories face challenges in interpreting and reporting results. Hemolysis indices may enhance reporting accuracy. Methods: This retrospective observational study at University Hospital Center Zagreb included 61 lithium heparin plasma samples from ECMO patients. pfHb was measured using the Harboe method (fHb) and estimated from hemolysis indices on Abbott Alinity c analyzer (efHb). Total and conjugated bilirubin, hemolysis, icterus, and lipemia indices (HIL) were recorded. Method comparison used Passing-Bablok regression and Bland–Altman analysis. An algorithm for pfHb reporting accounting for HIL interferences was developed. Results: Significant differences were observed between methods, with Harboe yielding higher median fHb (261 mg/L) versus efHb (58 mg/L). Regression analysis showed constant negative bias of −91 mg/L (95% CI: −143 to −16) for efHb relative to fHb. Bland–Altman analysis demonstrated wide limits of agreement. Correlation between fHb and efHb was moderate (Spearman’s rho = 0.618, p < 0.001). The delta between methods increased with higher bilirubin concentrations. An algorithm integrating HIL indices with the Harboe method was developed to guide result validation and reporting. Conclusions: Accurate hemolysis assessment in ECMO patients requires careful interpretation, appropriate method selection, and laboratory–clinician collaboration. The proposed algorithm improves the clinical utility of pfHb testing by accounting for analytical interferences and supporting informed decision-making. Full article

(This article belongs to the Special Issue Clinical Guidelines in Critical Care Medicine)

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24 pages, 2006 KB

Open AccessArticle

HiRo-SLAM: A High-Accuracy and Robust Visual-Inertial SLAM System with Precise Camera Projection Modeling and Adaptive Feature Selection

by Yujuan Deng, Liang Tian, Xiaohui Hou, Xin Liu, Yonggang Wang, Xingchao Liu and Chunyuan Liao

Sensors 2026, 26(2), 711; https://doi.org/10.3390/s26020711 (registering DOI) - 21 Jan 2026

Abstract

HiRo-SLAM is a visual-inertial SLAM system developed to achieve high accuracy and enhanced robustness. To address critical limitations of conventional methods, including systematic biases from imperfect camera models, uneven spatial feature distribution, and the impact of outliers, we propose a unified optimization framework that integrates four key innovations. First, Precise Camera Projection Modeling (PCPM) embeds a fully differentiable camera model in nonlinear optimization, ensuring accurate handling of camera intrinsics and distortion to prevent error accumulation. Second, Visibility Pyramid-based Adaptive Non-Maximum Suppression (P-ANMS) quantifies feature point contribution through a multi-scale pyramid, providing uniform visual constraints in weakly textured or repetitive regions. Third, Robust Optimization Using Graduated Non-Convexity (GNC) suppresses outliers through dynamic weighting, preventing convergence to local minima. Finally, the Point-Line Feature Fusion Frontend combines XFeat point features with SOLD2 line features, leveraging multiple geometric primitives to improve perception in challenging environments, such as those with weak textures or repetitive structures. Comprehensive evaluations on the EuRoC MAV, TUM-VI, and OIVIO benchmarks show that HiRo-SLAM outperforms state-of-the-art visual-inertial SLAM methods. On the EuRoC MAV dataset, HiRo-SLAM achieves a 30.0% reduction in absolute trajectory error compared to strong baselines and attains millimeter-level accuracy on specific sequences under controlled conditions. However, while HiRo-SLAM demonstrates state-of-the-art performance in scenarios with moderate texture and minimal motion blur, its effectiveness may be reduced in highly dynamic environments with severe motion blur or extreme lighting conditions. Full article

(This article belongs to the Section Navigation and Positioning)

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23 pages, 4678 KB

Open AccessArticle

RP-DAD-HPLC Method for Quantitative Analysis of Clofazimine and Pyrazinamide for Inclusion in Fixed-Dose Combination Topical Drug Delivery System

by Marius Brits, Francelle Bouwer and Joe M. Viljoen

Methods Protoc. 2026, 9(1), 16; https://doi.org/10.3390/mps9010016 - 21 Jan 2026

Abstract

Reversed-phase high-performance liquid chromatography (RP-HPLC) remains one of the most widely applied analytical techniques in the development and quality control testing of finished pharmaceutical products. The combination of gradient chromatographic methods with diode-array detection (DAD) enhances selectivity, ensuring accuracy and reliability when testing drugs with diverse chemical properties in a single dosage form (i.e., fixed-dose combination (FDC) products). In this study, an RP-DAD-HPLC method was developed for the quantitative analysis of clofazimine (CFZ) and pyrazinamide (PZA) for inclusion in an FDC topical drug delivery system. Chromatographic separation was achieved using a C18 column (4.6 mm × 150 mm, 5 µm particle size) with gradient elution at 1 mL/min, employing 0.1% aqueous formic acid and acetonitrile (mobile phases). PZA and CFZ were detected at 254 nm and 284 nm, respectively. The method was validated in accordance with ICH Q2 guidelines, assessing specificity (considering interference from solvents, product matrix, and degradation products), linearity (7.8–500.0 µg/mL, r² = 0.9999), system repeatability (%RSD ≤ 2.7%), and intermediate precision (25–500 µg/mL, %RSD ≤ 0.85%). Method robustness was evaluated using a three-level Box–Behnken design (BBD) with response surface methodology (RSM) to assess the effects of variations in detection wavelength, mobile phase flow rate, and column temperature. Full article

(This article belongs to the Section Biochemical and Chemical Analysis & Synthesis)

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18 pages, 1540 KB

Open AccessArticle

Analysis-Based Dynamic Response of Possible Self-Excited Oscillation in a Pumped-Storage Power Station

by Yutong Mao, Jianxu Zhou, Qing Zhang, Wenchao Cheng and Luyun Huang

Appl. Sci. 2026, 16(2), 1074; https://doi.org/10.3390/app16021074 - 21 Jan 2026

Abstract

Pumped-storage power stations (PSPSs) are vital for grid stability, yet pump-turbines (PTs) operating in the S-shaped region often induce severe hydraulic instability. To reveal the mechanism of these self-excited oscillations, this study establishes a nonlinear mathematical model based on rigid water column theory and a cubic polynomial approximation of the PT’s nonlinear characteristics. Both analytical derivations and numerical simulations were conducted. Analytical results indicate that, in the absence of surge tanks, self-excited oscillations occur when the PT’s negative hydraulic impedance modulus exceeds the pipeline impedance. With a single surge tank, the system behaves analogously to the Van der Pol oscillator, exhibiting oscillations that converge to a stable limit cycle governed by system parameters. Numerical simulations for a dual-surge-tank system further reveal that, due to initial negative damping, the PT transitions to alternative stable equilibria. Crucially, the transition direction is governed by the polarity of the initial disturbance: negative perturbations lead to the regular turbine region, while positive ones lead to the reverse pump region. Additionally, pipe friction causes the steady-state discharge to deviate slightly from the theoretical static value, with deviations remaining below 2.96%. This work provides a theoretical basis for stability prediction in PSPSs. Full article

(This article belongs to the Section Energy Science and Technology)

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27 pages, 3891 KB

Open AccessArticle

Multi-Frequency Time-Reversal and Topological Derivative Fusion Imaging of Steel Pipe Defects via Sparse Bayesian Learning

by Xinyu Zhang, Changzhi He, Zhen Li and Shaofeng Wang

Appl. Sci. 2026, 16(2), 1084; https://doi.org/10.3390/app16021084 - 21 Jan 2026

Abstract

Steel pipes play a vital role in energy and industrial transportation systems, where undetected defects such as cracks and wall thinning may lead to severe safety hazards. Although ultrasonic guided waves enable long-range inspection, their defect imaging performance is often limited by dispersion, multimode interference, and strong noise. In this work, a multi-frequency fusion imaging method integrating time-reversal, topological derivative, and sparse Bayesian learning is proposed for guided wave-based defect detection in steel pipes. Multi-frequency guided waves are employed to enhance defect sensitivity and suppress frequency-dependent ambiguity. Time-reversal focusing is used to concentrate scattered energy at defect locations, while the topological derivative provides a global sensitivity map as physics-guided prior information. These results are further fused within a sparse Bayesian learning framework to achieve probabilistic defect imaging and uncertainty quantification. Dispersion compensation based on the semi-analytical finite element method is introduced to ensure accurate wavefield reconstruction at different frequencies. Domain randomization is also incorporated to improve robustness against uncertainties in material properties, temperature, and measurement noise. Numerical simulation results verify that the proposed method achieves high localization accuracy and significantly outperforms conventional TR-based imaging in terms of resolution, false alarm suppression, and stability. The proposed approach provides a reliable and robust solution for guided wave inspection of steel pipelines and offers strong potential for engineering applications in nondestructive evaluation and structural health monitoring. Full article

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33 pages, 2502 KB

Open AccessReview

A Review of Heat Wave Impacts on the Food–Energy–Water Nexus and Policy Response

by Manman Wang, Sze Yui Lu, Hairong Xin, Yuxuan Fan, Hao Zhang, Sujata Saunik and Rajib Shaw

Climate 2026, 14(1), 27; https://doi.org/10.3390/cli14010027 - 21 Jan 2026

Abstract

Heat waves have emerged as an escalating climate threat, triggering cascading disruptions across food, energy, and water systems, thereby undermining resilience and sustainability. However, reviews addressing heat wave impacts on the food–energy–water (FEW) nexus remain scarce, resulting in a fragmented understanding of cross-system interactions and limiting the ability to assess cascading risks under extreme heat. This critical issue is examined through bibliometric analysis, scoping review, and policy analysis. A total of 103 publications from 2015 to 2024 were retrieved from Web of Science and Scopus, and 63 policy documents from the United States, the European Union, Japan, China, and India were collected for policy analysis. Bibliometric analysis was conducted to identify the most influential articles, journals, countries, and research themes in this field. The scoping review indicates that agricultural losses are most frequently reported (32), followed by multiple impacts (19) and cross-sectoral disruptions (18). The use of spatial datasets and high-frequency temporal data remains limited, and community-scale studies and cross-regional comparisons are uncommon. Mechanism synthesis reveals key pathways, including direct system-specific stress on food production, water availability, and energy supply; indirect pressures arising from rising demand and constrained supply across interconnected systems; cascading disruptions mediated by infrastructure and system dependencies; and maladaptation risks associated with uncoordinated sectoral responses. Policy analysis reveals that most countries adopt sector-based adaptation approaches with limited across-system integration, and insufficient data and monitoring infrastructures. Overall, this study proposes an integrated analytical framework for understanding heat wave impacts on the FEW nexus, identifies critical research and governance gaps, and provides conceptual and practical guidance for advancing future research and strengthening coordinated adaptation across food, energy, and water sectors. Full article

(This article belongs to the Special Issue Climate Change and Food Sustainability: A Critical Nexus)

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23 pages, 2515 KB

Open AccessReview

Platelet-Rich Plasma from the Research to the Clinical Arena: A Journey Toward the Precision Regenerative Medicine

by Elisabetta Mormone, Vittoria D’Esposito, Paola De Luca, Fulvio E. O. Ferrara, Francesca P. Bellotti, Pietro Formisano and Eugenio Caradonna

Int. J. Mol. Sci. 2026, 27(2), 1058; https://doi.org/10.3390/ijms27021058 - 21 Jan 2026

Abstract

Platelet-rich plasma (PRP) is a cornerstone of regenerative medicine, offering therapeutic potential across numerous clinical disciplines. Its efficacy relies on concentrated platelets and plasma components that release growth factors, cytokines, and extracellular vesicles to orchestrate tissue repair, immunomodulation, and angiogenesis. Recent findings have uncovered novel mechanisms, such as mitochondrial transfer from platelets to target cells and the delivery of bioactive microRNAs that regulate inflammation and metabolic reprogramming. However, despite its potential, PRP therapy is often limited by inconsistent results. In this review, we examine how patient-specific factors—including age, comorbidities, and lifestyle—and technical variables in preparation and storage, influence the biological quality of the final product. Therefore, standardizing protocols and accounting for individual biological variability are essential for achieving reproducible outcomes. In conclusion, PRP is a complex therapeutic agent whose success depends on both intrinsic bioactive content and extrinsic processing factors. Integrating these molecular insights with personalized patient assessment is crucial to optimizing PRP treatment procedures. Future research should focus on refining standardization to fully establish PRP as a precision medicine tool in regenerative therapy. Full article

(This article belongs to the Special Issue Advancements in Regenerative Medicine Research)

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22 pages, 1714 KB

Open AccessArticle

Integrating Machine-Learning Methods with Importance–Performance Maps to Evaluate Drivers for the Acceptance of New Vaccines: Application to AstraZeneca COVID-19 Vaccine

by Jorge de Andrés-Sánchez, Mar Souto-Romero and Mario Arias-Oliva

AI 2026, 7(1), 34; https://doi.org/10.3390/ai7010034 - 21 Jan 2026

Abstract

Background: The acceptance of new vaccines under uncertainty—such as during the COVID-19 pandemic—poses a major public health challenge because efficacy and safety information is still evolving. Methods: We propose an integrative analytical framework that combines a theory-based model of vaccine acceptance—the cognitive–affective–normative (CAN) model—with machine-learning techniques (decision tree regression, random forest, and Extreme Gradient Boosting) and SHapley Additive exPlanations (SHAP) integrated into an importance–performance map (IPM) to prioritize determinants of vaccination intention. Using survey data collected in Spain in September 2020 (N = 600), when the AstraZeneca vaccine had not yet been approved, we examine the roles of perceived efficacy (EF), fear of COVID-19 (FC), fear of the vaccine (FV), and social influence (SI). Results: EF and SI consistently emerged as the most influential determinants across modelling approaches. Ensemble learners (random forest and Extreme Gradient Boosting) achieved stronger out-of-sample predictive performance than the single decision tree, while decision tree regression provided an interpretable, rule-based representation of the main decision pathways. Exploiting the local nature of SHAP values, we also constructed SHAP-based IPMs for the full sample and for the low-acceptance segment, enhancing the policy relevance of the prioritization exercise. Conclusions: By combining theory-driven structural modelling with predictive and explainable machine learning, the proposed framework offers a transparent and replicable tool to support the design of vaccination communication strategies and can be transferred to other settings involving emerging health technologies. Full article

(This article belongs to the Special Issue Explainable and Trustworthy AI in Health and Biology: Enabling Transparent and Actionable Decision-Making)

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8 pages, 178 KB

Open AccessProceeding Paper

FIWARE-Powered Smart Farming: Integrating Sensor Networks for Sustainable Soil Management

by Christos Hitiris, Cleopatra Gkola, Dimitrios J. Vergados, Vasiliki Karamerou and Angelos Michalas

Proceedings 2026, 134(1), 58; https://doi.org/10.3390/proceedings2026134058 - 21 Jan 2026

Abstract

Digital transformation in agriculture addresses key challenges such as climate change, water shortages, and sustainable production. Precision agriculture technologies rely on the Internet of Things (IoT) sensor networks, analytics, and automated systems to manage resources efficiently and increase productivity. Fragmented infrastructures and vendor-specific platforms lead to unintegrated data silos that obstruct regional solutions. This paper will emphasize FIWARE, an open-source, standard-based platform that can be integrated with existing agricultural sensors in municipalities or regions. FIWARE takes all these disparate sensors (soil probes, weather stations, and irrigation meters) and integrates them into a single real-time information system, providing a set of decision support tools to the user to facilitate adaptive irrigation. Case studies show the benefits of FIWARE, including water savings, reduced runoff, better decision-making, and improved climate resilience. Full article

(This article belongs to the Proceedings of The 18th International Conference of the Hellenic Association of Agricultural Economists)

13 pages, 2357 KB

Open AccessArticle

A Prevention-Focused Geospatial Epidemiology Framework for Identifying Multilevel Vulnerability Across Diverse Settings

by Cindy Ogolla Jean-Baptiste

Healthcare 2026, 14(2), 261; https://doi.org/10.3390/healthcare14020261 - 21 Jan 2026

Abstract

Background/Objectives: Geographic Information Systems (GIS) offer essential capabilities for identifying spatial concentrations of vulnerability and strengthening context-aware prevention strategies. This manuscript describes a geospatial architecture designed to generate anticipatory, place-based risk identification applicable across diverse community and institutional environments. Interpersonal Violence (IPV), one of several preventable harms that benefit from this spatially informed analysis, remains a critical public health challenge shaped by structural, ecological, and situational factors. Methods: The conceptual framework presented integrates de-identified surveillance data, ecological indicators, environmental and temporal dynamics into a unified spatial epidemiological model. Multilevel data layers are geocoded, spatially matched, and analyzed using clustering (e.g., Getis-Ord Gi*), spatial dependence metrics (e.g., Moran’s I), and contextual modeling to support anticipatory identification of elevated vulnerability. Framework Outputs: The model is designed to identify spatial clustering, mobility-linked risk patterns, and emerging escalation zones using neighborhood disadvantage, built-environment factors, and situational markers. Outputs are intended to support both clinical decision-making (e.g., geocoded trauma screening, and context-aware discharge planning), and community-level prevention (e.g., targeted environmental interventions and cross-sector resource coordination). Conclusions: This framework synthesizes behavioral theory, spatial epidemiology, and prevention science into an integrative architecture for coordinated public health response. As a conceptual foundation for future empirical research, it advances the development of more dynamic, spatially informed, and equity-focused prevention systems. Full article

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Graphical abstract

11 pages, 879 KB

Open AccessCommunication

Extraction of pH-Dependent DNA-Binding Anti-Tumoral Peptides from Saccharomyces cerevisiae

by Francesco Ragonese and Loretta Mancinelli

Pharmaceuticals 2026, 19(1), 184; https://doi.org/10.3390/ph19010184 - 21 Jan 2026

Abstract

Cancer remains a significant challenge in the field of medicine, primarily due to its inherent plasticity and the development of resistance to conventional therapeutic interventions. Genomic mutations and the activation of oncogenes enable cancer cells to resist senescence and apoptosis, leading to uncontrolled growth with harmful consequences. Small peptides are molecules with interesting anti-tumour properties and represent a valid alternative to conventional treatments. Our group has previously identified a class of small peptides bound to the DNA that can be extracted from the chromatin of various tissues, including wheat germ and trout. These peptide pools have been shown to possess interesting antiproliferative and apoptotic properties, and they are associated with cell cycle regulation. However, given the complexity of the extraction process, it is necessary to identify a substrate that will enable a more efficient extraction of these peptides, while also ensuring a composition that is simple to investigate. The present study developed a method for the extraction of this group of peptides from yeast, and the extract was then tested on cancer cells in order to confirm its anti-tumoral properties. The peptides were obtained from chromatin extracted from Saccharomyces cerevisiae cells through alkalisation and purification by gel filtration chromatography. The extract was tested on HeLa cells to verify its effects on vitality and the cell cycle. The data demonstrate that the chromatographic profile of this peptide extract indicates a more basic composition than the pool extracted from other tissues and exhibits comparable antiproliferative properties. The ability to rapidly obtain a biologically active, analytically accessible, and adequately purified fraction from the widely available substrate Saccharomyces cerevisiae represents a significant advance in the study of these DNA-binding peptides. Full article

(This article belongs to the Topic Peptoids and Peptide Based Drugs)

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Graphical abstract

18 pages, 635 KB

Open AccessArticle

A Federated Deep Learning Framework for Sleep-Stage Monitoring Using the ISRUC-Sleep Dataset

by Alba Amato

Appl. Sci. 2026, 16(2), 1073; https://doi.org/10.3390/app16021073 - 21 Jan 2026

Abstract

Automatic sleep-stage classification is a key component of long-term sleep monitoring and digital health applications. Although deep learning models trained on centralized datasets have achieved strong performance, their deployment in real-world healthcare settings is constrained by privacy, data-governance, and regulatory requirements. Federated learning (FL) addresses these issues by enabling decentralized training in which raw data remain local and only model parameters are exchanged; however, its effectiveness under realistic physiological heterogeneity remains insufficiently understood. In this work, we investigate a subject-level federated deep learning framework for sleep-stage classification using polysomnography data from the ISRUC-Sleep dataset. We adopt a realistic one subject = one client setting spanning three clinically distinct subgroups and evaluate a lightweight one-dimensional convolutional neural network (1D-CNN) under four training regimes: a centralized baseline and three federated strategies (FedAvg, FedProx, and FedBN), all sharing identical architecture and preprocessing. The centralized model, trained on a cohort with regular sleep architecture, achieves stable performance (accuracy 69.65%, macro-F1 0.6537). In contrast, naive FedAvg fails to converge under subject-level non-IID data (accuracy 14.21%, macro-F1 0.0601), with minority stages such as N1 and REM largely lost. FedProx yields only marginal improvement, while FedBN—by preserving client-specific batch-normalization statistics—achieves the best federated performance (accuracy 26.04%, macro-F1 0.1732) and greater stability across clients. These findings indicate that the main limitation of FL for sleep staging lies in physiological heterogeneity rather than model capacity, highlighting the need for heterogeneity-aware strategies in privacy-preserving sleep analytics. Full article

(This article belongs to the Special Issue Recent Advances in Deep Learning and Machine Learning in Information Systems)

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10 pages, 4700 KB

Open AccessCase Report

Carbon Monoxide Poisoning in Putrefied Corpses: A Difficult Diagnosis

by Francesco Gabrielli, Francesco Calabrò, Lorenzo Franceschetti, Silvio Chericoni and Valentina Bugelli

Forensic Sci. 2026, 6(1), 5; https://doi.org/10.3390/forensicsci6010005 - 21 Jan 2026

Abstract

Background. Determining the cause and manner of death in scenes involving multiple and putrified bodies found in the same environment is a real challenge for forensic pathologists. While common scenarios include fires, vehicle crashes, and natural disasters, one of the most common causes is drug intoxication or poisoning, and the scene must be carefully evaluated based on circumstantial evidence. Carbon monoxide (CO) (also called “the silent killer”) remains one of the leading agents capable of producing simultaneous fatalities. In multi-body scenes, distinguishing between homicide–suicide, double suicide, and accidental deaths adds further complexity. The aim of this study is to highlight the limitations of toxicological and pathological investigations in advanced putrefaction and to emphasize the role of scene investigation in the interpretation of suspected CO-related deaths. Methods. The authors report a case of suspected CO intoxication involving two bodies in an advanced stage of decomposition recovered from the same room. The scene investigation, coupled with the presence of a malfunctioning combustion source, raised suspicion of CO exposure; however, analytical interpretation was severely constrained by the altered condition of biological samples. Results. Advanced decomposition magnifies these challenges. Putrefactive changes can mimic traumatic injuries, hide hypostasis, and compromise both macroscopic and microscopic evaluations due to autolysis and gas formation. Toxicological investigations are frequently hindered by the degradation or absence of key biological matrices such as blood, cavity fluids, or vitreous humor, rendering carboxyhaemoglobin quantification unreliable or impossible. These limitations may lead to incorrect medico-legal conclusions. Conclusions. Determining the cause and manner of death in complex multi-body scenes requires careful evaluation of circumstantial evidence and scene investigation, particularly when advanced decomposition compromises biological analyses and toxicological interpretation. Full article

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