Search Results (176)

Background/Objectives: Large-scale (epi)genomic studies have substantially advanced our understanding of the molecular landscape of meningiomas, most recently embedded in the cIMPACT-NOW update 8. As a result, molecular data are increasingly integrated into risk-adapted treatment algorithms. However, it remains uncertain to what extent non-invasive MRI can capture underlying molecular variation and risk. Methods: We assembled a large, single-institution cohort of 225 newly diagnosed meningiomas (WHO grades 1–3) with available preoperative MRI, as well as comprehensive epigenome-wide methylation and copy-number profiling. Tumors were segmented into core and edema regions using a state-of-the-art automated pipeline from the BraTS challenge. Radiomic features were extracted and used to train Random Forest classifiers to predict WHO grade, molecular risk, and specific alterations such as 1p loss in a hold-out test set. Results: Our models achieved accuracy above 91% for integrated molecular risk classification, 87.5% for 1p chromosomal status, and 76.8% for WHO grade prediction, with corresponding AUCs of 0.91, 0.90, and 0.89, underscoring the robustness of radiomic features in capturing histopathological and, especially, molecular characteristics. Conclusions: Preoperative MRI effectively captures the underlying molecular biology of meningiomas and may enable rapid molecular assessment to inform decision-making and prioritization of confirmatory testing. However, it is not yet ready for clinical use, showing lower accuracy for current WHO grade classification. Full article

Background: Lowering lipid levels after an acute coronary syndrome is critical for preventing recurrent adverse cardiovascular events. Multiple medications are now available, but there is limited evidence comparing how frequently they lead to the achievement of guideline-recommended lipid targets. Methods and Results: This observational study evaluated the impact of novel lipid-lowering therapies (alirocumab, evolocumab, inclisiran, and bempedoic acid) in patients with a history of atherosclerotic cardiovascular disease or familial hypercholesterolaemia treated with maximum-tolerated doses of high-intensity statin therapy with or without ezetimibe. Our primary assessment was the achievement of LDL-C below 1.4 mmol/L as per the European Society of Cardiology guidelines. The study comprised of 256 patients. Reduction in LDL-C was greatest with alirocumab and evolocumab, achieving a reduction of 62% (95% confidence interval [CI], 51 to 93; p < 0.001) and 58% (95% CI, 47 to 88; p < 0.001) after 12 months, respectively. This was followed by inclisiran with a reduction of 47% (95% CI, 37 to 78; p < 0.001) and bempedoic acid with a reduction of 36% (95% CI, 22 to 69; p < 0.001). Patients treated with alirocumab and evolocumab started from a higher baseline LDL-C than inclisiran, due to the higher LDL threshold required for initiation of alirocumab and evolocumab in the UK. Despite this, inclisiran, evolocumab, and alirocumab were all associated with similar proportions of patients achieving LDL targets: 35%, 42%, and 37% of patients achieved a guideline-recommended LDL-C target of <1.4 mmol/L. Patients with a baseline LDL-C > 4 mmol/L were more likely to reach the ESC target when treated with alirocumab or evolocumab compared to inclisiran, with results of 33.3% vs. 24.1% (p = 0.016) and 35.7% vs. 24.1% (p = 0.05). Conclusions: Alirocumab and evolocumab were associated with the greatest reductions in LDL-C, followed by inclisiran and bempedoic acid. Overall, alirocumab, evolocumab, and inclisiran led to approximately 40% of patients reaching ESC targets for LDL-C. In patients with a baseline LDL-C > 4 mmol/L, significantly more patients achieved LDL-C targets when treated with alirocumab or evolocumab compared to inclisiran. Strength and limitations: This was the first study to comprehensively compare the efficacy of novel lipid-lowering therapies in achieving guideline-recommended LDL targets within a high-risk cardiovascular population. The sample size was relatively small, especially for patients treated with bempedoic acid. Full article

Akkermansia muciniphila—a mucus-resident commensal—has emerged as a promising target at the interface of metabolism, barrier function, and immunity. Observational human studies link higher intestinal abundance of A. muciniphila with healthier adiposity and glycemic profiles, while preclinical experiments demonstrate causal benefits on adiposity, insulin resistance, gut-barrier integrity, and inflammatory tone. These effects are attributed to mucus-layer reinforcement, reduced intestinal permeability and endotoxemia, production of short-chain fatty acids, and host signaling by defined bacterial components. In a randomized proof-of-concept trial in overweight/obese insulin-resistant adults, pasteurized A. muciniphila was safe and well-tolerated and improved insulin sensitivity and total cholesterol versus placebo; live cells showed directionally favorable but non-significant trends. A separate multicenter randomized trial of a five-strain consortium that included A. muciniphila improved post-prandial glucose and HbA1c in type 2 diabetes, supporting translational potential while underscoring the need for strain-resolved studies. Evidence for liver and cardiovascular benefits is strong in animals (e.g., MASLD and atherosclerosis models) but remains preliminary in humans. Inter-individual response heterogeneity—potentially influenced by baseline Akkermansia levels and gut-barrier status—highlights the value of personalized, microbiome-guided approaches. Larger, longer clinical studies are now warranted to define optimal dosing and formulation (live vs. pasteurized), durability, safety across populations, and impacts on hard outcomes (clinically meaningful weight change, glycemic endpoints, and cardiometabolic events). Overall, A. muciniphila represents a promising microbial adjunct for metabolic health with a plausible path from postbiotic concepts to clinical application, pending confirmatory trials. Full article

Bangladesh is among the world’s most climate-vulnerable countries, facing recurrent hazards that disrupt lives and livelihoods. Among these, heatwaves and cold snaps strongly affect electricity consumption, representing a key socio-economic impact of climate extremes. In this study, we used meteorological and electricity data from six sub-regions of Bangladesh to examine long-term changes in extreme temperature days and their effects on electricity usage. Results showed that western inland stations (Chuadanga, Jashore) experienced hotter summers and colder winters, whereas coastal sites (Barishal, Patuakhali) were moderated by maritime influences. Trend analysis revealed significant increases in hot-day frequency since 1961 (up to 1.8 days yr⁻¹ at coastal areas, while cold-day frequencies generally declined but with regional variability. Electricity demand followed a clear pattern, being highest on hot days, lowest on cold days, and intermediate on normal days. Among the regions, Khulna consistently recorded the greatest demand (up to 161 MWh), while Patuakhali remained the lowest (~19–32 MWh). Regression analysis further showed that demand rises with maximum temperature, with slopes up to 5.7 MWh °C⁻¹ and moderate correlations (r = 0.27–0.47). Importantly, the temperature–demand relationship has strengthened in recent years, as similar climatic conditions now correspond to higher electricity use, reflecting both climatic pressures and socio-economic growth. These findings highlight the challenge of temperature extremes for electricity demand and the need to integrate climate–energy linkages into adaptation planning. Full article

In the context of mounting climate impacts and growing urgency to meet the Paris Agreement goals, the ocean is now increasingly being recognised not just as a victim of climate change, but as an indispensable part of the solution. Research has demonstrated that readily actionable ocean-based climate solutions can help close the emissions gap (the difference between the greenhouse gas emission reductions needed to limit global warming to 1.5 °C, and projected global emissions considering current national pledges and policies) by providing approximately a third of the mitigation needed to keep the Paris Agreement’s 1.5 °C goal within reach. This mitigation potential (of fully actioning these solutions) is unequally divided across seven key ocean-based action areas (listed in decreasing order of magnitude): phasing out offshore oil and gas; deploying offshore renewable energy infrastructure; decarbonising maritime transport and associated infrastructure; decarbonising ocean and aquatic food value chains; carbon capture and storage; marine and coastal conservation and restoration; and decarbonising coastal tourism. We argue that achieving the full potential of ocean climate solutions will require smart governance, drastically increased financial investment, and international cooperation. Accomplishing this, however, will bring strong co-benefits for biodiversity, food systems, and coastal resilience. The Third United Nations Ocean Conference and 30th United Nations Climate Change Conference of the Parties (COP 30) present rare opportunities to mainstream the ocean into global climate strategies. Full article

Urbanization is a dominant force reshaping human settlements, driving socio-economic development while also causing significant environmental challenges. With over 56% of the world’s population now residing in urban areas—a figure expected to rise to two-thirds by 2050—land use changes are accelerating rapidly. The conversion of natural landscapes into impervious surfaces such as concrete and asphalt intensifies the Urban Heat Island (UHI) effect, raises urban temperatures, and strains local ecosystems. This study investigates land use and landscape changes in Ceredigion County, UK, utilizing remote sensing and GIS techniques to analyze urbanization impacts over two decades (2003–2023). Results indicate significant urban expansion of approximately 122 km², predominantly at the expense of agricultural and forested areas, leading to vegetation loss and changes in water availability. County-wide mean land surface temperature (LST) increased from 21.4 °C in 2003 to 23.65 °C in 2023, with urban areas recording higher values around 27.1 °C, reflecting a strong UHI effect. Spectral indices (NDVI, NDWI, NDBI, and NDBaI) reveal that urban sprawl adversely affects vegetation health, water resources, and land surfaces. The Urban Thermal Field Variance Index (UTFVI) further highlights areas experiencing thermal discomfort. Additionally, machine learning models, including Linear Regression and Random Forest, were employed to forecast future LST trends, projecting urban LST values to potentially reach approximately 27.4 °C by 2030. These findings underscore the urgent need for sustainable urban planning, reforestation, and climate adaptation strategies to mitigate the environmental impacts of rapid urban growth and ensure the resilience of both human and ecological systems. Full article

Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO₂ below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO₂ near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article

Climate change has intensified extreme weather events and accelerated soil salinization, posing serious threats to crop yield and quality. Salinity stress, now affecting about 20% of irrigated lands, is expected to worsen due to rising temperatures and sea levels. At the same time, the global population is projected to exceed 9 billion by 2050, demanding a 70% increase in food production (UN, 2019; FAO). Agriculture, responsible for 34% of global greenhouse gas emissions, urgently needs sustainable solutions. Microbial inoculants, known as “plant probiotics,” offer a promising eco-friendly alternative by enhancing crop resilience and reducing environmental impact. In this study, we evaluated the plant growth-promoting (PGP) traits and melatonin-producing capacity of Bacillus aerius EH2-5. To assess its efficacy under salt stress, soybean seedlings at the VC stage were inoculated with EH2-5 and subsequently subjected to salinity stress using 150 mM and 100 mM NaCl treatments. Plant growth parameters, the expression levels of salinity-related genes, and the activities of antioxidant enzymes were measured to determine the microbe’s role in promoting plant growth and mitigating salt-induced oxidative stress. Here, our study shows that the melatonin-synthesizing Bacillus aerius EH2-5 (7.48 ng/mL at 24 h after inoculation in Trp spiked LB media) significantly improved host plant (Glycine max L.) growth, biomass, and photosynthesis and reduced oxidative stress during salinity stress conditions than the non-inculcated control. Whole genome sequencing of Bacillus aerius EH2-5 identified key plant growth-promoting and salinity stress-related genes, including znuA, znuB, znuC, and zur (zinc uptake); ptsN, aspA, and nrgB (nitrogen metabolism); and phoH and pstS (phosphate transport). Genes involved in tryptophan biosynthesis and transport, such as trpA, trpB, trpP, and tspO, along with siderophore-related genes yusV, yfhA, and yfiY, were also detected. The presence of multiple stress-responsive genes, including dnaK, dps, treA, cspB, srkA, and copZ, suggests EH2-5′s genomic potential to enhance plant tolerance to salinity and other abiotic stresses. Inoculation with Bacillus aerius EH2-5 significantly enhanced soybean growth and reduced salt-induced damage, as evidenced by increased shoot biomass (29%, 41%), leaf numbers (12% and 13%), and chlorophyll content (40%, 21%) under 100 mM and 150 mM NaCl compared to non-inoculated plants. These results indicate EH2-5′s strong potential as a plant growth-promoting and salinity stress-alleviating rhizobacterium. The EH2-5 symbiosis significantly enhanced a key ABA biosynthesis enzyme-related gene NCED3, dehydration responsive transcription factors DREB2A and NAC29 salinity stresses (100 mM and 150 mM). Moreover, the reduced expression of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT) by 16%, 29%, and 24%, respectively, and decreased levels of malondialdehyde (MDA) and hydroxy peroxidase (H₂O₂) by 12% and 23% were observed under 100 mM NaCl compared to non-inoculated plants. This study demonstrated that Bacillus aerius EH2-5, a melatonin-producing strain, not only functions effectively as a biofertilizer but also alleviates plant stress in a manner comparable to the application of exogenous melatonin. These findings highlight the potential of utilizing melatonin-producing microbes as a viable alternative to chemical treatments. Therefore, further research should focus on enhancing the melatonin biosynthetic capacity of EH2-5, improving its colonization efficiency in plants, and developing synergistic microbial consortia (SynComs) with melatonin-producing capabilities. Such efforts will contribute to the development and field application of EH2-5 as a promising plant biostimulant for sustainable agriculture. Full article

Climbing vines have recently induced increasing threats to forest growth under favourable environmental changes. In mangrove forests, the native vine Derris trifoliata became invasive and is now one of the main threats. Yet current management relies on manual removal with low efficiency. Exploring an alternative, cost-effective method is required. To assess the potential of a proposed biological control method, this study performed a pot-plant experiment using Cuscuta japonica to infect D. trifoliata and three common mangrove species in Beihai, China. Results showed that D. trifoliata had a higher infection rate and high host mortality (90%) than mangrove (0%). It also had significantly decreased moisture by 4%, nitrogen by 14%, phosphorus by 27%, potassium by 49% and increased soluble sugar by 49% and protein by 20%, whereas only moisture (2% reduction) and one or two minerals of Excoecaria agallocha and Aegiceras corniculatum were influenced. Only Kandelia obovata had neither effective haustoria nor any nutrients impact from the infection. This study indicated that C. japonica can cause more damage to D. trifoliata than to mangrove species and has the potential to be used as a biological control agent for the threatened mangrove forests of A. corniculatum and K. obovata with monitoring and control. Further field tests are required to bring this method into practice. Full article

Background: Homocysteine (Hcy) is a sulfur-containing amino acid crucial for various physiological processes, with elevated levels linked to cardiovascular and neurological adverse conditions. Various factors contribute to high Hcy, and past studies of impact factors relied on traditional statistical methods. Recently, machine learning (ML) techniques have greatly improved and are now widely applied in medical research. This study used four ML methods to identify key factors influencing Hcy in healthy elderly Taiwanese men, comparing their accuracy using multiple linear regression (MLR). The study seeks to improve Hcy prediction accuracy and provide insights into relevant impact factors. Methods: A total of 468 healthy elderly men were studied in terms of 33 parameters using four ML methods: random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and elastic net (EN). MLR served as a benchmark. Model performance was assessed using SMAPE, RAE, RRSE, and RMSE. Results: All ML methods demonstrated lower prediction errors than MLR, indicating higher accuracy. By averaging the importance scores from the four ML models, C-reactive protein (CRP) emerged as the leading impact factor for Hcy, followed by GPT, WBC, LDH, eGFR, and sport volume (SV). Conclusions: Machine learning methods outperformed MLR in predicting Hcy levels in healthy elderly Taiwanese men. CRP was identified as the most crucial factor, followed by GPT/ALT, WBC, LDH, and eGFR. Full article

Backgrounds/Objective: The impact of metabolic disturbances induced by an unbalanced diet on cognitive decline in mid-life is now widely observed, although the mechanisms are not well identified. Here we report that glutamatergic vasoactive pathways are a key feature of high-fat-diet (HFD)-induced neurogliovascular uncoupling in mice. Methods: C57Bl6/J mice are fed either with normal diet (ND) or high-fat diet (HFD) during 6 or 12 months and characterized for metabolic status. Cerebral vascular tree from pial to intraparenchymal arteries, is investigated with Halpern’s arteriography and with differential interference contrast infrared imaging of brain slices. Results: A 70% alteration in the myogenic tone of the basilar artery is observed as early as 6 months (M6) after the HFD. Infrared imaging revealed a 77% reduction in the glutamate-induced vasodilation of intraparenchymal arterioles appearing after 12 months (M12) of the HFD. The respective contributions of enzymes involved in glutamatergic pathways were altered as a function of HFD and time. The decrease in astrocytic COX I observed at M6 was followed by a loss of neuronal COX II and a compensatory action of NOS at M12. Conclusions: This HFD-induced neurogliovascular uncoupling pathway offers therapeutic targets to consider for improving cerebral vasoactive functions while preventing peripheral metabolic disturbances. Full article

Background: Polysubstance use, particularly combining opioids with stimulants such as cocaine, is rising among individuals with substance use disorders. This practice aims to balance cocaine’s stimulant effects with opioids’ sedative effect, potentially decreasing adverse outcomes. We hypothesized that concurrent exposure to cocaine and opioids would reduce the risk of neonatal opioid withdrawal syndrome (NOWS) compared to opioid use alone. Methods: This analysis draws from an ongoing prospective study of maternal substance use (SUD) at Regional One Health’s perinatal center in Memphis, TN, and included mothers and their infants born between 2018 and 2022. Maternal SUD was identified via screening questionnaires, urine toxicology, or umbilical cord tissue analysis. Participants were grouped into using (a) opioids with cocaine (OwC) and (b) opioids without cocaine (OwoC). Univariate and regression analyses were conducted to assess the risk of NOWS. Results: A total of 353 infants were born to 342 mothers, with 31% (110/353) of the infants born to women who used cocaine along with opioids. While maternal demographics were similar, the OwC group had significantly lower rates of prenatal care, chronic pain history, and MOUD enrollment (p = 0.03). Infants in the OwC group had significantly higher rates of NOWS (p < 0.01), longer hospital stays (p < 0.01), and 6.5 times greater odds of developing NOWS (p < 0.001). NOWS was associated with an average 15-day increase in the length of stay for term infants (95% CI: 11.2, 18.8; p < 0.001). Conclusions: Contrary to our hypothesis, our study highlights the significant impact of maternal cocaine use on the increased likelihood of NOWS and extended hospital stays for affected infants. Full article

USP (usual temperature pitch)-modified asphalt optimizes its rheological properties through reactions between the modifier and the asphalt. This significantly enhances the high- and low-temperature adaptability and environmental friendliness of asphalt. It has now become an important research direction in the field of highway engineering. This article systematically investigates the impact of different dosages of USP warm mix modifier on asphalt binders through rheological and microstructural analysis. Base asphalt and SBS-modified asphalt were blended with USP at varying ratios. Conventional tests (penetration, softening point, ductility) were combined with dynamic shear rheometry (DSR, AASHTO T315) and bending beam rheometry (BBR, AASHTO T313) to characterize temperature/frequency-dependent viscoelasticity. High-temperature performance was quantified via multiple stress creep recovery (MSCR, ASTM D7405), while fluorescence microscopy and FTIR spectroscopy elucidated modification mechanisms. Key findings reveal that (1) optimal USP thresholds exist at 4.0% for base asphalt and 4.5% for SBS modified asphalt, beyond which the rutting resistance factor (G*/sin δ) decreases by 20–31% due to plasticization effects; (2) USP significantly improves low-temperature flexibility, reducing creep stiffness at −12 °C by 38% (USP-modified) and 35% (USP/SBS composite) versus controls; (3) infrared spectroscopy displays that no new characteristic peaks appeared in the functional group region of 4000–1300 cm⁻¹ for the two types of modified asphalt after the incorporation of USP, indicating that no chemical changes occurred in the asphalt; and (4) fluorescence imaging confirmed that the incorporation of USP led to disintegration of the spatial network structure of the control asphalt, explaining the reason for the deterioration of high-temperature performance. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

With the increasing occurrence of bacterial resistance, it is now essential to look for new alternatives to protect the curative utilization of antibiotics within the One Health concept. Here, we adapt and optimize a broth microdilution method and compare it against the broth macrodilution method for evaluating the antibacterial activity of a complex essential oils mix (EO mix) against four livestock pathogens: Escherichia coli, Bacillus cereus, Pseudomonas aeruginosa, and Staphylococcus aureus. Microdilution method performance (final volume well: 300 µL; inoculum: 1.0 × 10⁶ CFU/mL) was evaluated following CLSI recommendations, by comparing the MIC of each of the four strains with the MICs obtained with the macrodilution method (final volume tube: 2 mL; inoculum 1.0 × 10⁶ CFU/mL). Microdilution analysis was performed with an automated plate reader (Bioscreen C), and three bacterial growth parameters (OD max, lag phase, and growth rate) were calculated (DMFit curve-fitting software (v2.1; courtesy of the Institute of Food Research, Norwich, UK)). EO mix MICs were determined for E. coli, S. aureus, and B. cereus. Our results emphasize the importance of ensuring the accuracy of MIC results by performing three technical and three biological replicates, and combining OD max, lag phase, and growth rate to assess the impact of an EO mix at sub-MIC levels. Full article