Altered intra- and inter-network connectivity in autism spectrum disorder.

OBJECTIVE: A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored. METHODS: We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC. RESULTS: Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity. CONCLUSIONS: ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.

The Effects of Assisted Freezing with Different Ultrasound Power Rates on the Quality and Flavor of Braised Beef.

This study investigated the effects of ultrasound-assisted immersion freezing (UIF) at different power rates (0, 200, 400, and 600 W) on the changes in beef quality and flavor after braising. The results demonstrated that UIF treatment at 400 W significantly reduced the juice loss (cooking loss decreased from 49.04% to 39.74%) and fat oxidation (TBARS value decreased from 0.32 mg/kg to 0.20 mg/kg) of braised beef. In addition, the tenderness (hardness value decreased from 5601.50 g to 2849.46 g) and color stability of braised beef were improved after UIF treatment. The flavor characteristics of braised beef were characterized using an electronic nose and an electronic tongue. The PCA analysis data showed that the cumulative contribution rates of the first and second principal components were 85% and 93.2%, respectively, with the first principal component accounting for a higher proportion. The UIF-400 W group had the highest concentration for the first principal component, and the differentiation was not significant compared to the control group. The total amino acid values of different power UIF treatment groups were improved compared to the AF treatment group, indicating that UIF can effectively reduce the losses caused by freezing. The results demonstrate that ultrasound-assisted freezing treatment is beneficial in enhancing the tenderness and flavor attributes of beef after braising, providing new insights into the processing of meat products with desirable quality characteristics.

The Methodology for Evaluating the Operating State of SF6 HVCBs Based on IDDA.

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.

In situ characterization of heterogeneous surface wetting in porous materials.

The performance of nano- and micro-porous materials in capturing and releasing fluids, such as during CO2 geo-storage and water/gas removal in fuel cells and electrolyzers, is determined by their wettability in contact with the solid. However, accurately characterizing wettability is challenging due to spatial variations in dynamic forces, chemical heterogeneity, and surface roughness. In situ measurements can potentially measure wettability locally as a contact angle - the angle a denser phase (e.g water) contacts solid in the presence of a second phase (e.g. hydrogen, air, CO2) - but suffer from difficulties in accurately capturing curvatures, contact areas, and contact loops of multiphase fluids. We introduce a novel extended topological method for in situ contact angle measurement and provide a comparative review of current geometric and topological methods, assessing their accuracy on ideal surfaces, porous rocks containing CO2, and water in gas diffusion layers. The new method demonstrates higher accuracy and reliability of in situ measurements for uniformly wetting systems compared to previous topological approaches, while geometric measurements perform best for mixed-wetting domains. This study further provides a comprehensive open-source platform for in situ characterization of wettability in porous materials with implications for gas geo-storage, fuel cells and electrolyzers, filtration, and catalysis.

Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach.

Mild cognitive impairment plays a crucial role in predicting the early progression of Alzheimer's disease, and it can be used as an important indicator of the disease progression. Currently, numerous studies have focused on utilizing the functional brain network as a novel biomarker for mild cognitive impairment diagnosis. In this context, we employed a graph convolutional neural network to automatically extract functional brain network features, eliminating the need for manual feature extraction, to improve the mild cognitive impairment diagnosis performance. However, previous graph convolutional neural network approaches have primarily concentrated on single modes of brain connectivity, leading to a failure to leverage the potential complementary information offered by diverse connectivity patterns and limiting their efficacy. To address this limitation, we introduce a novel method called the graph convolutional neural network with multimodel connectivity, which integrates multimode connectivity for the identification of mild cognitive impairment using fMRI data and evaluates the graph convolutional neural network with multimodel connectivity approach through a mild cognitive impairment diagnostic task on the Alzheimer's Disease Neuroimaging Initiative dataset. Overall, our experimental results show the superiority of the proposed graph convolutional neural network with multimodel connectivity approach, achieving an accuracy rate of 92.2% and an area under the Receiver Operating Characteristic (ROC) curve of 0.988.

Multipattern graph convolutional network-based autism spectrum disorder identification.

The early diagnosis of autism spectrum disorder (ASD) has been extensively facilitated through the utilization of resting-state fMRI (rs-fMRI). With rs-fMRI, the functional brain network (FBN) has gained much attention in diagnosing ASD. As a promising strategy, graph convolutional networks (GCN) provide an attractive approach to simultaneously extract FBN features and facilitate ASD identification, thus replacing the manual feature extraction from FBN. Previous GCN studies primarily emphasized the exploration of topological simultaneously connection weights of the estimated FBNs while only focusing on the single connection pattern. However, this approach fails to exploit the potential complementary information offered by different connection patterns of FBNs, thereby inherently limiting the performance. To enhance the diagnostic performance, we propose a multipattern graph convolution network (MPGCN) that integrates multiple connection patterns to improve the accuracy of ASD diagnosis. As an initial endeavor, we endeavored to integrate information from multiple connection patterns by incorporating multiple graph convolution modules. The effectiveness of the MPGCN approach is evaluated by analyzing rs-fMRI scans from a cohort of 92 subjects sourced from the publicly accessible Autism Brain Imaging Data Exchange database. Notably, the experiment demonstrates that our model achieves an accuracy of 91.1% and an area under ROC curve score of 0.9742. The implementation codes are available at https://github.com/immutableJackz/MPGCN.

Encoding scheme design for gradient-free, nonlinear projection imaging using Bloch-Siegert RF spatial encoding in a low-field, open MRI system.

Eliminating conventional pulsed B0-gradient coils for magnetic resonance imaging (MRI) can significantly reduce the cost of and increase access to these devices. Phase shifts induced by the Bloch-Siegert shift effect have been proposed as a means for gradient-free, RF spatial encoding for low-field MR imaging. However, nonlinear phasor patterns like those generated from loop coils have not been systematically studied in the context of 2D spatial encoding. This work presents an optimization algorithm to select an efficient encoding trajectory among the nonlinear patterns achievable with a given hardware setup. Performance of encoding trajectories or projections was evaluated through simulated and experimental image reconstructions. Results show that the encodings schemes designed by this algorithm provide more efficient spatial encoding than comparison encoding sets, and the method produces images with the predicted spatial resolution and minimal artifacts. Overall, the work demonstrates the feasibility of performing 2D gradient-free, low-field imaging using the Bloch-Siegert shift which is an important step towards creating low-cost, point-of-care MR systems.

Titanium dioxide nanoparticles induce apoptosis through ROS-Ca2+-p38/AKT/mTOR pathway in TM4 cells.

Titanium dioxide nanoparticles (TiO2 NPs) can cause apoptosis in TM4 cells; however, the underlying mechanism has not been entirely elucidated. The purpose of this study was to investigate the effects of TiO2 NPs on ROS, Ca2+ level, p38/AKT/mTOR pathway, and apoptosis in TM4 cells and to evaluate the role of Ca2+ in p38/AKT/mTOR pathway and apoptosis. After exposure to different concentrations (0, 50, 100, 150, and 200 µg/mL) of TiO2 NPs for 24 h, cell viability, ROS, Ca2+ level, Ca2+-ATPase activity, p38/AKT/mTOR pathway-related proteins, apoptosis rate, and apoptosis-related proteins (Bax, Bcl-2, Caspase 3, Caspase 9, and p53) were detected. The ROS scavenger NAC was used to determine the effect of ROS on Ca2+ level. The Ca2+ chelator BAPTA-AM was used to evaluate the role of Ca2+ in p38/AKT/mTOR pathway and apoptosis. TiO2 NPs significantly inhibited cell viability, increased ROS level, and elevated Ca2+ level while suppressing Ca2+-ATPase activity. TiO2 NPs regulated the p38/AKT/mTOR pathway via increasing p-p38 level and decreasing p-AKT and p-mTOR levels. TiO2 NPs significantly enhanced the apoptosis. NAC attenuated Ca2+ overload and reduction in Ca2+-ATPase activity caused by TiO2 NPs. BAPTA-AM alleviated TiO2 NPs-induced abnormal expression of p38/AKT/mTOR pathway-related proteins. BAPTA-AM assuaged the apoptosis caused by TiO2 NPs. Altogether, this study revealed that TiO2 NPs elevated intracellular Ca2+ level through ROS accumulation. Subsequently, the heightened intracellular Ca2+ level was observed to exert regulation over the p38/AKT/mTOR pathway, ultimately culminating in apoptosis. These results provides a complementary understanding to the mechanism of TiO2 NPs-induced apoptosis in TM4 cells.

Low-Field, Low-Cost, Point-of-Care Magnetic Resonance Imaging.

Low-field magnetic resonance imaging (MRI) has recently experienced a renaissance that is largely attributable to the numerous technological advancements made in MRI, including optimized pulse sequences, parallel receive and compressed sensing, improved calibrations and reconstruction algorithms, and the adoption of machine learning for image postprocessing. This new attention on low-field MRI originates from a lack of accessibility to traditional MRI and the need for affordable imaging. Low-field MRI provides a viable option due to its lack of reliance on radio-frequency shielding rooms, expensive liquid helium, and cryogen quench pipes. Moreover, its relatively small size and weight allow for easy and affordable installation in most settings. Rather than replacing conventional MRI, low-field MRI will provide new opportunities for imaging both in developing and developed countries. This article discusses the history of low-field MRI, low-field MRI hardware and software, current devices on the market, advantages and disadvantages, and low-field MRI's global potential. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 26 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data.

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

A Four-Channel Broadband MRI Receive Array Coil.

Low-impedance preamplifier decoupling is commonly used in RF coil array construction to minimize coupling between elements through mutual impedance. The trap circuit is an essential component in preamp decoupling techniques, but becomes a limiting factor in constructing multi-tuned, multi-nuclear coil arrays. In principle, it is possible to double-tune or multi-tune the trap circuits, but will add complexity and loss. We present a broadband decoupling approach using high impedance preamplifiers. A dual-tuned prototype four-channel array using this approach which targets 2H and 23 Na at 4.7T, has been previously constructed, evaluated and reported. Without any retuning of the array, the same setup is tested at the 23Na and 31P frequencies for 3T. Initial bench measurements and Chemical Shift Imaging (CSI) results are acquired and presented in this study.Clinical Relevance- This study could reduce the complexity of multi-nuclear array coil design.

A neighborhood-regularization method leveraging multiview data for predicting the frequency of drug-side effects.

MOTIVATION: A critical issue in drug benefit-risk assessment is to determine the frequency of side effects, which is performed by randomized controlled trails. Computationally predicted frequencies of drug side effects can be used to effectively guide the randomized controlled trails. However, it is more challenging to predict drug side effect frequencies, and thus only a few studies cope with this problem. RESULTS: In this work, we propose a neighborhood-regularization method (NRFSE) that leverages multiview data on drugs and side effects to predict the frequency of side effects. First, we adopt a class-weighted non-negative matrix factorization to decompose the drug-side effect frequency matrix, in which Gaussian likelihood is used to model unknown drug-side effect pairs. Second, we design a multiview neighborhood regularization to integrate three drug attributes and two side effect attributes, respectively, which makes most similar drugs and most similar side effects have similar latent signatures. The regularization can adaptively determine the weights of different attributes. We conduct extensive experiments on one benchmark dataset, and NRFSE improves the prediction performance compared with five state-of-the-art approaches. Independent test set of post-marketing side effects further validate the effectiveness of NRFSE. AVAILABILITY AND IMPLEMENTATION: Source code and datasets are available at https://github.com/linwang1982/NRFSE or https://codeocean.com/capsule/4741497/tree/v1.

Wideband receive-coil array design using high-impedance amplifiers for broadband decoupling.

PURPOSE: Multinuclear MRI/S is of increasing interest. Currently, most multinuclear receive array coils are constructed by nesting multiple single-tuned array coils or using switching elements to control the operating frequency, in which case more than one set of conventional isolation preamplifiers and associated decoupling circuits is required. These conventional configurations rapidly become complicated when greater numbers of channels or nuclei are needed. In this work, a novel coil decoupling mechanism is proposed to enable broadband decoupling for array coils with one set of preamplifiers. METHODS: Instead of using conventional isolation preamplifiers, a high-input impedance preamplifier is proposed to create broadband decoupling of the array elements. A matching network consisting of a single inductor-capacitor-capacitor multi-tuned network and a wire-wound transformer was used to interface the surface coil to the high-impedance preamplifier. To validate the concept, the proposed configuration was compared to the conventional preamplifier decoupling configuration on both bench and scanner. RESULTS: 2 The approach can provide more than 15dB decoupling over a range of 25MHz, covering the Larmor frequencies of 23 Na and 2 H at 4.7T. This multi-tuned prototype obtained 61% and 76% of the imaging SNR at 2 H and 23 Na respectively, 76 and 89% in a higher loading test phantom, when compared to the conventional single-tuned preamplifier decoupling configuration. CONCLUSION: With the multinuclear array operation and decoupling achieved using only one layer of array coil and preamplifiers, this work provides a simple approach of building high element-count arrays to enable accelerated imaging or SNR improvement from multiple nuclei.

Robot Versus Fluoroscopy-Assisted Vertebroplasty and Kyphoplasty for Osteoporotic Vertebral Compression Fractures: A Systematic Review and Meta-analysis.

OBJECTIVE: This study aimed to conduct a systematic review and meta-analysis to compare the clinical results and complications of robot-assisted (RA) versus fluoroscopy-assisted (FA) percutaneous vertebral augmentation (PVA) in the treatment of osteoporotic vertebral compression fractures (OVCFs). METHODS: A comprehensive search of online databases including PubMed, Embase, Cochrane Library, web of science, and core journals of China National Knowledge Infrastructure were performed to identify related studies reporting the clinical results and complications of RA versus FA-assisted PVA in the treatment of OVCFs. The rate of bone cement leakage was used to assess the complications. After the surgery, the clinical findings were analyzed using the Visual Analog Scale scores and the Oswestry Disability Index scores. The surgical time, intraoperative fluoroscopy frequency, and x-ray exposure duration were used to evaluate the perioperative results. Forest plots were constructed to investigate the results. RESULTS: RA-PVA had a significantly lower bone cement leakage rate, shorter fluoroscopy frequency, and shorter radiation exposure time of doctors compared with FA-PVA. However, no significant differences were found between RA-PVA and FA-PVA in operative time and radiation exposure time of patients. Furthermore, no statistically differences were found between the 2 groups in Visual Analog Scale and Oswestry Disability Index scores after surgery. CONCLUSIONS: This meta-analysis showed that RA-PVA can reduce bone cement leakage rate, fluoroscopy frequency, and doctors' radiation exposure time. With the advancement of RA technology, we anticipate more high-quality randomized controlled trials of RA versus FA-PVA in the future to validate and update the results of this analysis.

Capillary imbibition and flow of wetting liquid in irregular capillaries: A 100-year review.

Capillary imbibition, such as plant roots taking up water, reservoir rocks absorbing brine and a tissue paper wiping stains, is pervasive occurred in nature, engineering and industrial fields, as well as in our daily life. This phenomenon is earliest modeled through the process that wetting liquid is spontaneously propelled by capillary pressure into regular geometry models. Recent studies have attracted more attention on capillary-driven flow models within more complex geometries of the channel, since a detailed understanding of capillary imbibition dynamics within irregular geometry models necessitates the fundamentals to fluid transport mechanisms in porous media with complex pore topologies. Herein, the fundamentals and concepts of different capillary imbibition models in terms of geometries over the past 100 years are reviewed critically, such as circular and non-circular capillaries, open and closed capillaries with triangular/rectangular cross-sections, and heterogeneous geometries with axial variations. The applications of these models with appropriate conditions are discussed in depth accordingly, with a particular emphasize on the capillary flow pattern as a consequence of capillary geometry. In addition, a universal model is proposed based on the dynamic wetting condition and equivalent cylindrical geometry to describe the capillary imbibition process in terms of various solid topologies. Finally, future research is suggested to focus on analyzing the dynamics during corner flow, the snap-off of wetting fluid, the capillary rise of non-Newtonian fluids and applying accurate physical simulation methods on capillary-driven flow processes. Generally, this review provides a comprehensive understanding of the capillary-driven flow models inside various capillary geometries and affords an overview of potential advanced developments to enhance the current understanding of fluid transport mechanisms in porous media.

A Spinel Tin Ferrite with High Lattice-Oxygen Anchored on Graphene-like Porous Carbon Networks for Lithium-Ion Batteries with Super Cycle Stability and Ultra-fast Rate Performances.

A new type of nano-SnFe2O4 with stable lattice-oxygen and abundant surface defects anchored on ultra-thin graphene-like porous carbon networks (SFO@C) is prepared for the first time by an interesting freezing crystallization salt template method. The functional composite has excellent rate performance and long-term cycle stability for lithium-ion battery (LIB) anodes due to the stable structure, improved conductivity, and shortened migrating distance for lithium-ions, which are derived from the higher lattice-oxygen of SnFe2O4, abundant porous carbon networks and surface defects, and smaller nanoparticles. Under the ultra-high current density of 10, 15, and 20 A g-1 cycling for 1000 times, the SFO@C can provide high reversible capacities of 522.2, 362.5, and 361.1 mAh g-1, respectively. The lithium-ion storage mechanism of the composite was systematically studied for the first time by in situ X-ray diffraction (XRD), ex situ XRD and scanning electron microscopy (SEM), and density functional theory (DFT) calculations. The results indicate that the existence of Li2O and metallic Fe during the lithiation/delithiation process is a key reason for reducing the initial lithium-ion storage reversibility but increasing the rate performance and capacity stability in the subsequent cycles. DFT calculations show that lithium-ions are more easily adsorbed on the (111) crystal plane with a much lower adsorption energy of -7.61 eV than other planes, and the Fe element is the main acceptor of electrons. Moreover, the kinetics investigation indicates that the lithium-ion intercalation and deintercalation in SFO@C are mainly controlled by the pseudocapacitance behavior, which is favorable to enhancing the rate performance. The research provides a new strategy for designing LIB electrode materials with a stable structure and outstanding lithium-ion storage performance.

Assessing the Feasibility of Dynamic 31P Spectroscopy for Metabolic Studies With a 1.0T Extremity Scanner.

OBJECTIVE: The feasibility of conducting in vivo non-localized 31P Magnetic Resonance Spectroscopy (MRS) with a 1.0T extremity scanner and the potential to increase accessibility of this important diagnostic tool for low cost applications is revisited. METHODS: This work presents a custom transmit-only quadrature birdcage, four-element receive coil array, and spectrometer interfaced to a commercial ONI 1.0T magnet for enabling multi-channel, non-1H frequency capabilities. A custom, magnetic resonance compatible plantar flexion-extension exercise device was also developed to enable exercise protocols. The coils were assessed with bench measurements and 31P phantom studies before an in vivo demonstration. RESULTS: In pulse and acquire spectroscopy of a phantom, the array was found to improve the signal-to-noise ratio (SNR) by a factor of 1.31 and reduce the linewidth by 13.9% when compared to a large loop coil of the same overall size. In vivo testing results show that two averages and a four second repetition time for a temporal resolution of eight seconds was sufficient to obtain phosphocreatine recovery values and baseline pH levels aligned with expected literature values. CONCLUSION: Initial in vivo human skeletal muscle 31P MRS allowed successful monitoring of metabolic changes during an 18-minute exercise protocol. SIGNIFICANCE: Adding an array coil and multinuclear capability to a commercial low-cost 1.0T extremity scanner enabled the observation of characteristic 31P metabolic information, such as the phosphocreatinerecovery rate and underlying baseline pH.

Dynamic 13 C MR spectroscopy as an alternative to imaging for assessing cerebral metabolism using hyperpolarized pyruvate in humans.

PURPOSE: This study is to investigate time-resolved 13 C MR spectroscopy (MRS) as an alternative to imaging for assessing pyruvate metabolism using hyperpolarized (HP) [1-13 C]pyruvate in the human brain. METHODS: Time-resolved 13 C spectra were acquired from four axial brain slices of healthy human participants (n = 4) after a bolus injection of HP [1-13 C]pyruvate. 13 C MRS with low flip-angle excitations and a multichannel 13 C/1 H dual-frequency radiofrequency (RF) coil were exploited for reliable and unperturbed assessment of HP pyruvate metabolism. Slice-wise areas under the curve (AUCs) of 13 C-metabolites were measured and kinetic analysis was performed to estimate the production rates of lactate and HCO3- . Linear regression analysis between brain volumes and HP signals was performed. Region-focused pyruvate metabolism was estimated using coil-wise 13 C reconstruction. Reproducibility of HP pyruvate exams was presented by performing two consecutive injections with a 45-minutes interval. RESULTS: [1-13 C]Lactate relative to the total 13 C signal (tC) was 0.21-0.24 in all slices. [13 C] HCO3- /tC was 0.065-0.091. Apparent conversion rate constants from pyruvate to lactate and HCO3- were calculated as 0.014-0.018 s-1 and 0.0043-0.0056 s-1 , respectively. Pyruvate/tC and lactate/tC were in moderate linear relationships with fractional gray matter volume within each slice. White matter presented poor linear regression fit with HP signals, and moderate correlations of the fractional cerebrospinal fluid volume with pyruvate/tC and lactate/tC were measured. Measured HP signals were comparable between two consecutive exams with HP [1-13 C]pyruvate. CONCLUSIONS: Dynamic MRS in combination with multichannel RF coils is an affordable and reliable alternative to imaging methods in investigating cerebral metabolism using HP [1-13 C]pyruvate.

Universal description of wetting on multiscale surfaces using integral geometry.

HYPOTHESIS: Emerging energy-related technologies deal with multiscale hierarchical structures, intricate surface morphology, non-axisymmetric interfaces, and complex contact lines where wetting is difficult to quantify with classical methods. We hypothesise that a universal description of wetting on multiscale surfaces can be developed by using integral geometry coupled to thermodynamic laws. The proposed approach separates the different hierarchy levels of physical description from the thermodynamic description, allowing for a universal description of wetting on multiscale surfaces. THEORY AND SIMULATIONS: The theoretical framework is presented followed by application to limiting cases of wetting on multiscale surfaces. Limiting cases include those considered in the Wenzel, Cassie-Baxter, and wicking state models. Wetting characterisation of multiscale surfaces is explored by conducting simulations of a fluid droplet on a structurally rough surface and a chemically heterogeneous surface. FINDINGS: The underlying origin of the classical wetting models is shown to be rooted within the proposed theoretical framework. Integral geometry provides a topological-based wetting metric that is not contingent on any type of wetting state. The wetting metric is demonstrated to account for multiscale features along the common line in a scale consistent way; providing a universal description of wetting for multiscale surfaces.