Abstract: Triply Periodic Minimal Surface (TPMS)-based Interpenetrating Phase Composites (IPCs) combine two co-continuous solid phases across a smooth, doubly curved interface, providing a multifunctional design space for tailoring mechanical and thermal properties. This study investigates the effective thermo-elastic and thermal-transport behavior of TPMS-based IPCs using two homogenization approaches: Finite Element (FE)-based Representative Unit Cell (RUC) homogenization and Mechanics of Structure Genome (MSG)-based homogenization. Four TPMS topologies – gyroid, diamond (Schwarz D), PMY, and F-Rhombic Dodecahedron (F-RD) – are considered over relative densities ranging from 0.1 to 0.5. Conforming-mesh RUC models are generated using Microgen and analyzed in Abaqus for FE simulations with periodic boundary conditions, and in the in-house CmbsFE code for MSG-based homogenization. The two approaches are assessed for predicting effective elastic properties, coefficients of thermal expansion (CTEs), and thermal conductivities. The study further examines the influence of TPMS topology and relative density on the multifunctional response of two-phase IPCs, supporting the design and optimization of lightweight structural–thermal materials.

Abstract: A substantial minority of patients do not recover fully after mild traumatic brain injury. Persistent post-concussion syndrome includes both cognitive and non-cognitive manifestations, yet DSM-5 and ICD-11 withdrew the syndrome as a distinct diagnosis and reorganized its persistent sequelae exclusively within neurocognitive categories. While this reorganization addresses the cognitive component of the syndrome, it fails to account for its non-cognitive consequences, including vestibular, oculomotor, headache-related, autonomic, sleep, and pain-related manifestations. In some patients, these may be the most disabling consequences of the injury. This narrative review examines the resulting diagnostic gap and other continuing disagreements. Published criteria differ in symptom count, duration, and other requirements, yielding an unacceptably wide range of prevalence estimates, from 11.4% to 38.7%. We review the current status of this syndrome and propose an approach that separates diagnosis from attribution. Finally, we discuss the interactions between cognitive and non-cognitive consequences through shared neural substrates, so abnormalities in one domain may influence function in another. Because cognitive and non-cognitive manifestations may dissociate, a patient may have normal or minimal findings in one domain and functionally significant abnormalities in the other; therefore, evaluation confined to one domain alone (as reflected in the new classifications) cannot exclude the syndrome.

Abstract: Background: Cholestasis liver diseases involve impaired bile flow and the buildup of potentially harmful bile acids in liver, which damages hepatocytes and cholangiocytes. Ongoing, cholestasis triggers inflammation, oxidative stress, mitochondrial dysfunction, apoptosis and progressive liver fibrosis. Although oxidative stress is now acknowledged as a key factor in cholestatic liver injury, its connection to metabolic and inflammatory pathways and its therapeutic potential still need further investigation. Main body: This review explores the relationship between obstructive liver injury and oxidative stress, focusing on the molecular mechanisms involved and potential treatments. The buildup of bile acids leads to increased production of reactive oxygen species, lipid peroxidation, oxidative DNA damage, and mitochondrial dysfunction, which worsens cell damage and inflam-matory signals. Mitochondrial impairment disrupts energy metabolism and redox balance, while changes in fatty acid oxidation and increased glycolysis contribute to metabolic reprogramming during cholestatis. Neutrophils and other inflammatory cells can further exacerbate oxidative damage by producing reactive oxygen and nitrogen species. These processes lead to apoptosis of hepatocytes and cholangiocytes, activation of hepatic stellate cells, deposition of extracellular ma-trix, and advancement to fibrosis. The review compiles current evidence from primary sclerosing cholangitis, primary biliary cholangitis, intrahepatic cholestasis, and experimental models of ob-structive and drug- induced cholestatic liver injury. Special focus is placed on antioxidant defenses, protecting mitochondria, regulating bile acid balance, and pharmacological approaches to reduce oxidative and inflammatory damage. New treatments that target redox imbalance and enhance bile flow may offer ways to slow disease progression, especially for patients who do not respond well to standard therapies. Conclusion: Oxidative stress is central mechanistic link between bile acid ac-cumulation, inflammation, mitochondrial dysfunction, and fibrosis in cholestatic liver disease. A deeper insight into this interconnected network may aid in developing targeted antioxidant and mitochondrial therapies,improving outcomes in obstructive liver injury.

Abstract: Wetlands are important for water control, carbon storage, biodiversity and climate resilience, but they are difficult to map because their edges are often gradual and mixed with forest, shrubland or upland. The main problem is that many wetland maps use government polygon inventories as if every 10 m pixel inside or outside a polygon were perfectly correct. In practice, pixels near polygon boundaries can be uncertain, and this can affect both model training and accuracy assessment. It should be noted that another practical problem is data preparation: conventional wetland mapping often needs long satellite time series, cloud masking and many processing steps before a model can be trained. In this study, we address these two problems by using a boundary aware 10 m wetland mapping workflow for the 2024 GeoNB LiDAR covered part of New Brunswick, Canada. We used 64 AlphaEarth satellite embedding bands as the main predictor group, together with LiDAR terrain variables and Sentinel 2 spectral bands. We also removed a 20 m zone around wetland polygon edges from training and testing so that uncertain boundary pixels did not control the results. Two models were tested: an ExtraTrees classifier and an Attention U Net segmentation model. The ExtraTrees model reached a wetland F1 score of 75.17% and an overall accuracy of 96.51%. The Attention U Net model reached a wetland F1 score of 75.78% and an overall accuracy of 96.92%. These scores should be read within the evaluation setup used for each model, since the ExtraTrees metrics were computed on the full mapped domain and the Attention U Net metrics were computed on the held out test area. AlphaEarth variables appeared in 19 of the 20 most important ExtraTrees predictors, although these latent variables should not be read as direct physical measurements. The AlphaEarth only Attention U Net result was also close to the LiDAR only result. Both final maps were conservative in area: ExtraTrees mapped 6.75% wetland and Attention U Net mapped 6.38%, compared with the 7.29% buffered reference. On the basis of these results, AlphaEarth embeddings appear to carry useful wetland information while reducing the need for long satellite image collection, cloud masking and compositing. The study also shows that removing uncertain polygon edge pixels gives a clearer and more defensible way to evaluate wetland maps made from government inventory data.

Abstract: Background: Patients with dementia frequently present with multiple chronic conditions (MCCs). Growing evidence indicates these conditions do not co-occur by chance but may interact in ways that accelerate cognitive decline. In this study, we presented a machine-learning framework to identify these non-random, personalized MCC patterns associated with dementia, based on patients’ pre-existing conditions. Furthermore, we demonstrated a patient example illustrating how this approach enables the early detection of emerging MCC trajectories. Methods: To address this challenge, we introduced a lazy decision tree (lazyDT-MCCPath) framework using National Alzheimer’s Coordinating Center data. For each patient, lazyDT-MCCPath dynamically fine-tunes its training distribution based on pre-existing conditions from the preceding 3-5 years, while remaining MCCs were used to predict dementia risk. The decision tree provides a transparent pathway showing which emerging MCCs were associated with higher dementia risk. Results: Unlike traditional eager decision trees (eagerDT), which lacked sufficient training data for 17%-33% of patient profiles, lazyDT-MCCPath successfully modeled all profiles and achieved a 0.17 improvement in AUROC (0.66 vs. 0.83). Conclusions: lazyDT-MCCPath effectively translates pre-existing MCC profiles into personalized dementia risk probabilities for emerging conditions, which may help clinicians prioritize MCCs to monitor more closely.

Abstract: Judge scores increasingly control downstream actions in AI systems, yet a threshold calibrated at one operatingpoint can retain authority after the judge or workload changes. The threshold remains fixed while its relationship tocorrectness, cost, and accepted-cohort performance can drift. Here, we introduce the stale gate, a versioned operationalcontract that links score-based routing to an explicit decision cost and two monitored promises: accepted outcomesshould remain consistent with the judge’s stated probabilities, and the accepted cohort should retain a declared referenceperformance. For calibrated scores, we derive the cost-minimizing threshold and use predeclared restart banks ofanytime-valid e-processes to test whether either contract has expired. In controlled experiments, we show that additiveoverconfidence of 0.15 increased accepted failure from 0.241 to 0.320 and expected cost by 0.327 per task. We findthat the restart bank alarmed in 16 of 5,000 no-drift deployments, tracked first-order delay within 1–4% over fiveshifts, and reduced mean epoch-8 detection delay from 585 to 156 audited labels. Across 80 prospective API-serveddeployments, we observe that the same judge edit produced a 0.424 claim gap with onset-epoch alarms in 5/5 GSM8Kdeployments but a 0.068 gap with no transition in 10/10 MBPP deployments; an honestly reported harder arithmeticworkload changed routing without triggering a claim-contract alarm. These findings show that score authority shouldbe treated as versioned evidence: drift can be priced, failed contracts distinguished, and gates recalibrated when theevidence supporting their decisions no longer applies. Together, these results provide a basis for managing the validityof judge-gated decisions over time, while studies across broader models and long-horizon settings can determine howthese controls generalize to evolving AI systems.

Abstract: In this study, the numerical comparison is performed in solving first-order neutrosophic initial value problems employing two algorithms, namely, the Euler and fourth-order Runge-Kutta (RK4) methods in the neutrosophic environment. As an example of such kind of problem, the model y^' (t)=0.5∙y(t) on [0,2] is chosen, but in the case of the additional neutrosophic initial condition y(0)=⟨(0.8,1.0,1.3)⟩. This problem is represented as crisp-endpoint systems by means of ( α,β,γ )-cut approach, and then, it is solved numerically at each cut level while using exact solution as a reference. In accordance with the iteration results, the maximum absolute error of the Euler algorithm at h=0.2 is 1.619×10^(-1) at point t=2, whereas for the RK4 approach, this value is 2.7×10^(-6), meaning that the improvement degree reaches about five orders of magnitude. The analysis of step refinement confirms the convergence orders of the methods that are 1.00 and 4.00 for the Euler and RK4 approaches, respectively.

Abstract: Agent evaluations can compress stochastic generation, execution, and model-based judging into a single score,obscuring whether a measured difference reflects a change in the system or variability in the evaluation itself. Here,we introduce the determinism ladder, a measurement framework that compares fresh generation, temperature-zerogeneration, and content-addressed replay through executable verification, together with a frozen-candidate experimentthat distinguishes variability in judge verdicts from disagreement with executed outcomes. Across HumanEval+ andMBPP+ in two hosted model-and-verifier stacks, we found that replay nearly eliminated the observed within-taskvariation. In the primary stack, live variances of 0.0301 and 0.0142 fell to 0 and 0.0010 under replay, while a secondstack reproduced the same ordering. Temperature zero also reduced generation variance in both stacks, althoughthe remaining variation was stack-specific. On 200 frozen MBPP+ candidates, a stochastic judge introduced verdictvariance of 0.0073. Setting the judge temperature to zero removed the observed verdict variation, yet majority verdictsstill disagreed with executed outcomes on 23.5% of tasks, demonstrating that repeatability and reference agreementare distinct properties of an evaluation. For an illustrative fixed-task difference of 0.02, the measured variancescorresponded to point budgets of 3–8 runs per arm under live evaluation and one under valid replay, subject to the statedplanning assumptions. Together, these results show that evaluation variability can be measured under stage-specificcontrols rather than absorbed into a single score, providing a basis for matching replication and evaluation design tothe claim an agent benchmark is intended to support.

Abstract: This paper studies the existence and uniqueness of solutions for an irregular fractional boundary value problem (IFBVP) of order \(q\in(3/2,2]\). In order to apply Rus's fixed point theorem with the maximum and \(L^2\)metrics, we derive new estimates for the associated Green's function. We evaluate the squared Green’s function in a double integral to obtain the constant in the \(L^2\)estimate. Evaluating the mixed terms of this integral makes the appearance of the hypergeometric function ₃F₂(1), in which we apply classical transformation formulas to obtain finite sum evaluations of these terms, yielding the explicit constant \(\mathcal{K}_2(q,\rho,\theta)\). Using these estimates, we establish existence and uniqueness of solutions to the IFBVP under a Lipschitz condition on the nonlinear function \(f\)with constant \(L>0\), provided that \(L\sqrt{\mathcal{K}_2(q,\rho,\theta)}<1\). Numerical comparisons with an existing condition involving \(\Lambda(q,\rho,\theta)\)show that our condition is less restrictive for some parameter values, while the existing condition is less restrictive for others.

Abstract: The arc-PVD Ti–Cr–Mo–Ni–N coatings on HG20, HS123, HV420 carbide alloys and C80W1 tool steel, while having approximately the same elemental and phase compositions, differ in morphology, macrostructure, and level of macrostresses. When deposited on steel, the coating exhibits macrolayering, a more extended coating–substrate transition zone, and develops tensile macrostresses, in contrast to the coatings on carbide alloys, which develop compressive macrostresses. The behaviour can be explained by the overheating of the coating during deposition, which results from ion bombardment and is more pronounced on steel due to its poorer thermal diffusivity relative to carbide alloys. The CLTE difference between steel and the coating is significantly larger than that between the carbides and the coating, further contributing to the effect. It was found that, considering these features, the Ti–Cr–Mo–Ni–N coating on steel exhibits lower hardness (~20.3GPa), lower adhesion strength to the substrate (Lc3≈30N, the indenter load at scratch testing corresponding to the onset of coating wear), and a higher wear intensity at room temperature and 500°C (49.80 and 74.20·10−6 mm3·N−1·m−1, respectively), as compared with the hardness values of 25.1–21.7GPa, Lc3≈100N, and wear intensity (corresponding to the absence of a visible wear crater) for the Ti–Cr–Mo–Ni–N coatings on the aforementioned carbide alloys.

Abstract: Adaptive AI systems change after evaluation through updates to models, memory, prompts, and tools. Yet, an earlierscore may remain numerically correct while no longer describing the current system. Reuse of evaluation outcomesfor optimization can also compromise the independence of that evidence. Here, we introduce the ContaminationClock, a runtime protocol that links scores to system snapshots and evaluation histories. We isolate measurement fromupdates and record provenance and reuse to assess historical validity, current applicability, and permission for reuseseparately. In three prospective studies, we tested whether memory containing evaluation outcomes preferentiallyimproved performance on reused tasks. In the first study, we found greater gains on reused than on sealed tasks in allten evaluation episodes after adjustment for matched unrelated memory (mean adjusted contrast 0.469; 95% interval[0.377, 0.560]). We reproduced this advantage in another model class, while unrelated-memory shams yielded anear-zero mean contrast. By varying exposure, we further showed that the mean contrast rose from 0.119 to 0.453 asexact traces were supplied for 25% to 100% of reused tasks. We also found that token reordering preserved the meancontrast, whereas removing the verified decision from each trace reduced it to−0.013. By comparison, a leak-freefine-tuning update did not trigger the contamination alert (differential point estimate 0.069; frozen bounds ±0.12). Inthis controlled test, we nevertheless treated the earlier claim as superseded because the model had changed. This workprovides a protocol for making reevaluation an explicit, auditable part of agent development while preserving validhistorical evidence. Future studies with larger models and semantic memory could test whether the protocol keepsperformance claims reliable across successive updates.

Abstract: The adult configuration of the aortic arch complex (AoAC) results from transformation of its embryonic precursor. Concepts of the embryology of variants and malformations of the AoAC traditionally focus on transformation processes that are driven by regression and persistence of embryonic vascular segments. However, such concepts cannot explain every variant or malformation since they neglect other morphogenetic processes involved in the transformation of the embryonic AoAC, such as blood vessel “migration”, and septation of the aortic sac (AoS) and distal outflow tract (OFT) of the embryonic heart. This article focuses on the hitherto neglected role of septation of the AoS and embryonic OFT in normal and abnormal patterning of the AoAC. It is shown (1) how phylogenetic “deviations” from the mammalian “standard” of AoS septation lead to the AoAC branching patterns found in non-mammalian vertebrates; and (2) how ontogenetic deviations from the human “standard” can lead to AoAC anomalies, such as “bovine arch”; subclavian artery as first branch of the aortic arch; anomalous origin of supra-aortic branches from the proximal ascending aorta; anomalous origin of one pulmonary artery from the proximal ascending aorta; anomalous origin of one common carotid artery from the proximal pulmonary trunk; and tritruncal hearts.

Abstract: The rapid advancement of digital technologies has transformed the operational and strategic landscape of Small and Medium-Sized Enterprises (SMEs), creating opportunities to improve organisational performance through digitally enabled Strategic Management Accounting (SMA). While traditional management accounting primarily supports operational control and historical financial reporting, SMA extends this role by integrating financial and non-financial information to facilitate strategic planning, competitive positioning, and long-term decision-making. The emergence of cloud computing, artificial intelligence, enterprise resource planning systems, big data analytics, blockchain, and mobile accounting has significantly enhanced the ability of SMEs to generate timely, accurate, and strategic information. Despite these opportunities, the adoption of digitally enabled SMA remains uneven, particularly among township-based SMEs operating in resource-constrained environments such as Soweto, South Africa. Existing literature indicates that organisational limitations, technological barriers, infrastructural deficiencies, regulatory uncertainty, and inadequate managerial capabilities continue to constrain digital transformation within SMEs. Furthermore, much of the existing research has focused on large organisations and developed economies, resulting in limited understanding of how digital transformation influences SMA adoption within township businesses. This literature review critically synthesises current scholarly evidence regarding Strategic Management Accounting, digital transformation, theoretical explanations for technology adoption, and the organisational factors influencing digitally enabled SMA implementation among SMEs. The review further identifies major research gaps and proposes future research directions that contribute to both theory and practice by providing a context-specific understanding of digital transformation and strategic accounting within South African township economies.

Abstract: Development of opioid vaccines to prevent overdose is an active area of research. The protective efficacy of these vaccines depends on vaccine-induced antibodies (Ab) that bind to opioids with high affinity and targeted specificity. Because Ab performance is ultimately constrained by the in vivo behavior of the drug, and clinically relevant peak blood concentrations Cmax associated with lethal opioid overdose are undefined, a clear understanding of opioid pharmacokinetics in humans is essential for rational vaccine design, preclinical modeling, and interpretation of clinical results. The purpose of this review is to summarize available data on key PK parameters of opioid analogues in humans to a) facilitate establishment of Ab correlates of protection during opioid vaccine development and testing and b) assess Ab cross-reactivity to off-target opioid agonists and antagonists. Lastly, we discuss their utility in informing design and interpretation of preclinical and clinical studies of opioid vaccines and protective therapeutics.

Abstract: 5G Advanced and 6G V2X Environments require intelligence that is real-time, resource-light and robust across high-density vehicle mobility, non-stationary channels and hard ware constrained edge nodes. Existing AI-driven resource man agement approaches — including CNNs, LSTMs, GNNs, Deep Reinforcement Learning, Transformers, and Federated Learning — have limitations, such as high computational overhead, large data requirements, poor generalization under topology churn, and catastrophic forgetting under continuous retraining. These limitations render them ill-suited for deployment, especially at the level of radio units. To address this issue, this paper proposes NeuroChaos-V2X, a chaos-inspired edge intelligence framework that integrates vehicle mobility, wireless channel, and edge computing states into a unified multi-domain state vector. A Neu roChaos Feature Transformation module, employing GLS and Logistic-map neurons, converts raw network measurements into chaos-derived descriptors — firing time, firing rate, energy, and entropy — without requiring deep architectures or large training datasets. These features drive a unified NeuroChaos Decision Engine supporting edge server selection, task offloading, resource prioritization, and handover management simultaneously. The framework’s low-sample learning capability, CPU-only footprint, and sensitivity to small state perturbations make it inherently suited to resource-constrained V2X edge tiers. This position paper establishes the conceptual foundation and motivates experimental validation against GNN, DRL, and Transformer baselines in future 6G-V2X simulation environments.

Abstract: Fossil physeteroids exhibited substantially greater diversity in body size and feeding ecology than their extant relatives, yet body-length estimates for many taxa remain dependent on incomplete skeletons and proportional scaling from a limited number of reference species. This study reassesses body-size estimates in fossil physeteroids using a cranial proportion-based framework centered on bizygomatic width (BZW) and condylobasal length (CBL). The reference body lengths of Zygophyseter varolai and Brygmophyseter shigensis were reevaluated using published vertebral measurements, preserved skeletal length, and scale-bar-based skeletal data, and were conservatively set at approximately 6.5 m and 6.0 m, respectively. These revised reference values were then used to generate updated body-length estimates for a broad sample of extinct physeteroids. Revised estimates indicate that large-bodied physeteroids were relatively uncommon. Among the non-kogiid genera examined, only seven reached or exceeded 6 m in estimated total length, whereas Livyatan and Eophyseter were the only fossil genera estimated to exceed 9.5 m. Macroraptorial physeteroids ranged from approximately 4.0–4.3 m in Acrophyseter spp. to approximately 15–16 m in the giant Livyatan melvillei. The results further suggest that large body size evolved under multiple ecological regimes. Some large-bodied physeteroids were macroraptorial predators, whereas others show adaptations consistent with suction feeding or predation on smaller prey. The temporal distribution of giant forms is therefore more consistent with repeated, independent increases in body size than with a single evolutionary trend toward gigantism. However, incomplete preservation and uncertainty in the body lengths of reference taxa remain important limitations. The framework presented here provides a more consistent basis for reassessing body-size diversity in fossil physeteroids and can be refined as more complete cranial and postcranial material becomes available.

Abstract: Sustainability in the textile supply chain involves complex trade-offs across economic, environmental and social aspects, driven by the variability in materials, manufacturing processes and technologies, industrial management, geographical locations, working conditions, logistics, and product use. While multi-criteria decision-making (MCDM) methods have been increasingly applied to navigate these trade-offs, existing reviews have narrowly focused on bibliometric trends, offering limited insight into the contextual application and sustainability criteria used in these assessments. This study addresses this gap through a systematic literature review by analysing 96 studies that investigate the application of MCDM methods in sustainability management across the textile supply chain, with specific attention to methodological rigor and contextual relevance. The review identifies two key research dimensions: (1) the MCDM methods employed and their application contexts in respective scope of studies, and (2) the applied and prioritized criteria for sustainability management in textile industries. Results show that the identification of influential factors for sustainability implementation is the most common scope of study, with DEMATEL being the dominant MCDM tool and identifying government policies as the most influential factor. This is followed by sustainable supplier selection, with frequent use of hybrid AHP-TOPSIS methods, often in fuzzy settings, and a predominant focus on economic criteria, particularly cost and financial flexibility. The majority of corporate sustainability assessments rely on quantitative environmental data derived from corporate reports, and public data and use single-method MCDM models without explicit weighting schemes. Product-level assessments commonly employ distance-based MCDM methods to support the interpretation of Life Cycle Assessment results or to expand the sustainability perspective by incorporating economic and social dimensions. This review advances the understanding of how MCDM methods support sustainability decision-making in different contexts and settings of textile supply chain and highlights key criteria shaping such evaluations.

Abstract: Constrained-layer damping in sandwich floors and wall panels is usually analysed with uniform faces and a uniform core, while phononic treatments of periodicity address stop bands rather than the first few forced resonances of a finite plate. A seven-degree-of-freedom rectangular finite element is formulated here for a three-layer sandwich plate whose face thicknesses and core shear moduli vary in the plane as separable cosines. The kinematics follow Mead–Markus: Kirchhoff faces, first-order shear in a soft Kelvin–Voigt core, and a single transverse deflection. Membrane interpolation is bilinear; bending uses the Adini–Clough–Melosh incomplete quartic. A Navier–Galerkin sine series on two single-mass plates verifies the element to within one frequency step. On the modulated plate the Galerkin first peak is f₁ = 8.93 Hz and the finite-element first peak is f₁ = 9.57 Hz; on the uniform plate both methods give f₁ = 7.66 Hz. These three frequencies belong to the materials and planform of those two plates. Three archives then isolate core periodicity, face periodicity, and the layout of a fixed total attached mass. The first resonance of the core-campaign reference stays near 12 Hz; its height follows the area-averaged storage modulus. The two mean face thicknesses c_1m and c_3m dominate the higher peaks; at fixed sum an unequal split is stiffer than two equal faces. Relocating the attached masses onto midlines or a diagonal rearranges the centre-point frequency-response function more than reweighting the 3 × 3 lattice. The spectra contain no phononic stop band.

Abstract: This study examines the link between sustainable corporate Innovation and digital business resilience in the face of global market disruptions from 2021 to 2026, and how managers respond. While previous studies have focused on the elements of sustainability and digital transformation or resilience, there is a lack of focus on the dynamic interplay between these elements within sectors and over time. This study draws on a framework of dynamic capabilities and organizational learning and suggests a proposed relationship between sustainable innovation and resilience outcomes that is mediated by managerial responses. A multiple-case study design is followed, with secondary data that are gathered from corporate reports, industry publications, and academic case studies in the manufacturing, services, and technology industries. Patterns around capability development and adaptation are identified through thematic analysis and cross-case synthesis. The results show that the managerial response is key to sustainable Innovation being realized as resilience, and that there are differences among sectors and a clear timeline of reactive to proactive and AI-based. The study is of theoretical significance because of the integration of sustainability, Innovation, and resilience.

Abstract: The metabolism of drugs takes place for the most part in the liver. Next to the liver, however, microbiota in the gut also play an active role in the metabolism of drugs, either in generating active compounds or deactivation of the drug. In vivo, the gastrointestinal tract is populated by a complex, and highly individual microbiota. Gut organoids are powerful ex vivo tools for studying drug metabolism. Organoids also allow for studying the role of microbiota in drug metabolism. The technical challenges in co-culturing eukaryotic organoids with prokaryotic bacteria can be overcome. Next steps will be to mimic the complex composition of the gut microbiome. This manuscript provides a state of the art overview of current concepts and of most promising avenues for future research and applications in drug development.