Increasing attention has been focused on the study of protein–metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein–protein interactions, protein–metabolite interactions are still not clearly defined. Existing assays for detecting protein–metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein–metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term “interaction” to advance the field of interactomics further. A combination of intermolecular interactions determines the phenotype and functionality of a cell. The total interactome is represented mainly by protein–protein interactions (PPIs) and protein–metabolite interactions (PMIs). The current understanding of the interactions between proteins and small molecules significantly lags behind even the fragmentary knowledge of protein–protein interactions. The current understanding of the interactions between proteins and small molecules significantly lags behind even the fragmentary knowledge of protein–protein interactomes. For example, it is not clear which interactions between proteins to consider relevant . The strCompared with protein–protein interactions, studying protein–metabolite interaction is more complicated since it depends not only on proteomics techniques but also on methods of metabolomics, which are currently less mature compared to the former . PMIs plKnowledge accumulated today is focused mainly on exogenous ligands with promising pharmacological properties . Beyond Recent advances in mass spectrometry presently allow the routine identification and quantification of thousands of proteins and metabolites, but they still need to be improved to enable a complete inventorization of all biomolecules present in biological objects, e.g., cells. In order to explore a large part of the proteomic iceberg, consisting of low-copy proteins and aberrant proteoforms , the proThe complexity of studying the interactome is exacerbated by the fact that existing methods work either in vitro or in vivo but with substantial violation of the natural environment of the cell. The data obtained are contradictory and often do not coincide when analyzed by different methods, which can be caused by both technical reasons and biological ones, for example, in the case of studying different types of cells .The knowledge accumulated today is focused mainly on exogenous ligands that demonstrate potentially valuable properties from a pharmacological point of view. However, the development of high-throughput methods for the exploration of endogenous protein–metabolite interactomes gains speed, offering new opportunities and challenges for the field. The practical demand for information about PMIs contributes to the development of analytical and bioinformatic approaches and advances the omics community in terms of a fundamental understanding of the mechanisms of the interconnection of molecules. In this review, we systematize the current knowledge about PMIs, experimental methods for their detection and annotation, and outline further prospects for protein–metabolite interactomics.Molecular interaction is the cornerstone of all cellular processes, many of which are governed by the branched net of contact between proteins and small molecules. During the last decade, the depth of understanding of the molecular machinery of life has advanced dramatically. This progress is fueled by continuously improving technological (mostly mass spectrometric) solutions for exploring the molecular content of biological samples in “omics” mode. Understanding the interactions between the proteomic and metabolomic layers is the second, much more challenging derivative of the task of proteome and metabolome profiling. This complexity is explained primarily by the lack of clear criteria determining what should be considered an “interaction” and what should not.A pool of covalently bound complexes represents the most investigated area of PMIs, resulting from post-translational modifications or non-enzymatic modifications induced directly by reactive metabolites. However, in interactomics, the focus is generally on non-covalent contacts between proteins and small molecules. These highly dynamic events are particularly exciting to researchers. Such non-covalent binding is characterized by two parameters: specificity and affinity. The degree of specificity distinguishes highly specific  from less specific bindings  . AffinitThe “driving force” of PMI is the synergy of energy exchanges between the protein and the small molecule, surrounded by water and buffer ions. Like any spontaneous interaction, binding between a protein and a metabolite occurs if and only if the change in the Gibbs free energy of the system is negative when the system reaches an equilibrium state. The Gibbs energy change is formed from two fundamental components: the enthalpy change and the entropy change, and these components might compensate for each other. Thus, tight binding resulting from multiple noncovalent contacts between metabolites and proteins decreases enthalpy. Such negative changes are often accompanied by a decrease in entropy resulting from the restriction of the mobility of interacting molecules and subsequent changes in binding free energy . In retuA complete description of a protein–metabolite complex includes data regarding its composition, formation dynamics, stoichiometry of the interacting partners, strength of the interaction, and the environment that allows the interaction to happen. Thus, a comprehensive description of even a single protein–metabolite interaction is nontrivial and usually requires the utilization of several complementary techniques. Furthermore, there is yet no consensus regarding what to consider an interaction between a protein and a metabolite, since in addition to direct covalent or non-covalent interactions, there exist much weaker and less well-defined interactions, e.g., allosteric interactions, which are significantly harder to identify .Even having sorted out all the components of the Gibbs energy and taking into account all the factors that determine the possibility of interaction between a pair of proteins and metabolites in a truly “panoramic” mode, we can only formulate a very flexible, context-dependent definition of protein–metabolic interaction. By PMI, we mean physical contact between protein and metabolite, implying the formation (and subsequent disturbance) of non-covalent bonds as a result of which the specific physicochemical parameters of each interacting partner change. We detected only the change in these parameters, and from this change inferred the interaction event. However, in the absence of any standardized, physically rigorous definition of PMI, this term has come to mean any positive results from methods that capture a physical association between the protein and the metabolite, meaning that the current use of the term heavily reflects the specifics and biases of the methods utilized. Consequently, in order to advance the field of protein–metabolite interactomics, we need to provide an overview of current state-of-the-art methods for the detection of interactions between proteins and metabolites, highlighting their principles of work, as well as their strengths and weaknesses. Even though the interactomics community has yet to develop a standard definition of PMI, we have many more questions than answers. The entire professional community has nothing left but to eat an elephant of PMIs one bite at a time. Next, we consider the main strategies for eating this elephant and outline prospects for further research in this area.The interest in elucidating PMIs has accelerated the development of new methods that rely on the integration of analytical chemistry, synthetic chemistry and systems biology. Traditionally, these methods are divided into three categories: small molecule-to-protein, protein-to-small molecule and panoramic screening. This division separates approaches that aim to identify protein or small molecule targets in panoramic or targeted modes. The major advantage of panoramic approaches over targeted techniques is that discovery methods fuel data-driven research rather than hypothesis-driven research, making them superior for the early stages of biomarker discovery when dealing with complex biological objects. In turn, targeted methods make it possible to focus on the interacting partners of a particular protein or metabolite and to strictly determine the parameters of such an interaction. These and other features of analytical methods and their areas of applicability will be discussed in the following sections.However, the chosen classification system of methods does not take into account such essential characteristics as the need for chemical modification of molecules before their interaction or the possibility of obtaining quantitative information about PMI. In our review, we included methods that deviate from the mainstream classification in the “Modification of small molecules” and “Biophysical approaches” subsections .The variety of PMI methods that start with a particular metabolite is expansive. However, in a broad sense, all of these approaches detect changes in the physicochemical parameters of protein molecules interacting with the metabolite of interest .The methods based on the chemical modification of small molecules  involve several stages of confirming a non-covalent interaction using individual functional groups ,22,22. FThe principal disadvantage of such methods is the presence of additional groups on the metabolite since they can affect the affinity and selectivity of binding to target proteins. Like affinity-based approaches (see below), chemoproteomic target identification is limited by its ability to synthesize suitable derivatives of the compound of interest. In addition, even when using well-functioning photo-reactive modifications to attach the protein to the bait, there is always the possibility of binding to background proteins, which affects the number of false positive findings .Despite the shortcomings, the method of chemoproteomic identification, with proper planning of the experiment and carefully selected functional groups, minimizes false positive results. This method is a reliable option for orthogonally validating results obtained with other methods and new combinations of metabolites and reactive groups, as well as methods for their activation, are being actively developed ,27.The group of the most common approaches to panoramic exploration of PMIs uses limited proteolysis in combination with mass spectrometry (LiP-MS). The main idea behind this method is to compare the results of the proteolysis of free proteins with those of proteins interacting with metabolites. When interacting with a metabolite, a part of the protein becomes sterically inaccessible to proteases, which ultimately affects the final list of detectable peptides. The experiment includes two stages of proteolysis: first, a non-specific protease is used to digest proteins for a short amount of time in conditions close to native ones for the proteins of interest; then, a more specific protease is used for complete digestion in denaturing conditions. Tryptic peptides quantified by MS allow for the comparison of the structural “fingerprints” of all detected proteins, both free and bound to small molecules . To reduE. coli led to the identification of 1678 protein–metabolite interactions (86% of which were detected for the first time), with 2564 proteins identified in total . The mePlasmodium falciparum, which causes malaria in humans [Cellular thermal shift assay (CETSA), combined with mass spectrometry  ), allowsn humans .Thermal shift-based methods are being actively improved. For instance, Shaw et al. used microplates to develop an approach with significantly improved throughput termed HT-CETSA . This meAnother method that leverages thermal profiling is Proteome Integral Solubility Alteration (PISA), which does not use temperature-stability graphs. Similar to CETSA/TPP, aliquots of both experimental (with added metabolite of interest) and control samples are incubated at different temperatures. In the next step, aliquots are pooled back together and the protein abundance change is measured between such combined experimental and control samples, rather than between each aliquot pair . The proAnother MS-based method used for PMI discovery is solvent-induced protein precipitation (SIP) . SIP is The advantages of the SIP method include ease of implementation and the ability to determine the affinity of a metabolite (or a drug) for a protein using dose-dependent analysis. As with thermal shift-based methods, the main disadvantage of SIP is that it is not universal .A. thaliana seedlings treated with a combination of light and heat stress [One of the first methods developed for detecting intermolecular interactions in close-to-native conditions is affinity chromatography (AC), also known as affinity purification (AP). This method features cross-linking of small molecules of interest with the matrix, such as agarose, which are then packed in a chromatography column. Samples, e.g., cell lysates, are then passed through the column, and proteins that interact with the metabolite of interest stay in the column, while the other proteins are eluted. Affinity-based methods have various modifications and are often used together with protein labeling approaches for increased protein target detection rate ,57. In ot stress .The immobilization of small molecules can affect both the affinity and specificity of the interaction, so AC/AP is limited to studying only a handful of stable interactions. Most of the weak noncovalent interactions are destroyed during affinity purification . AnotherIn many cases, the task of identifying a certain PMI originates from the interest in a particular protein being a potential drug target or therapeutic agent. Along with methods for identifying the protein receptors of a preselected small molecule, several approaches have been developed to study small molecules interacting with a target protein.Unfortunately, most of these methods are limited by the availability of purified proteins and the use of incomplete small molecule libraries. Recent examples of protein-to-small molecule strategies include nuclear magnetic resonance (NMR) with ligand detection , differeLigand-detected nuclear magnetic resonance (NMR) spectroscopy is a method utilized to analyze changes in metabolite properties when they interact with the protein of interest. This method was used to study PMIs between several proteins and a couple dozen metabolites, and several new interactions were detected in addition to the ones already known . HoweverE. coli K-12, which demonstrates the analytical capabilities of this method [Another approach to studying protein–metabolite interactions from the protein point of view is the differential radial capillary action of ligand assay (DRaCALA). A mixture of proteins with an isotopically labeled metabolite standard is applied on a nitrocellulose membrane. Nitrocellulose membranes are able to retain proteins due to hydrophobic interactions, while free metabolites can move through nitrocellulose under the action of capillary forces. This method is based on the difference between the mobility of free and protein-bound metabolites . Knowings method . DespiteOne more not so widely used “protein to metabolite” approach is the integration of mass spectrometry with equilibrium dialysis . The fiArabidopsis thaliana, which led to the identification of a new mode of regulation of these enzymes [The tandem affinity purification (TAP) allows the metabolomic identification of interacting small molecules under close to native conditions to be carried out. It is an adaptation of the AP protocol, which is traditionally used to analyze protein–protein interactions. A protein of interest is epitopically labeled and produced in cells. The protein and metabolite complexes are then immunoprecipitated from the native cell lysate with the help of immobilized antibodies designed to recognize the epitope. Finally, partner proteins and metabolites are analyzed using a mass spectrometric platform. The main disadvantage of TAP is its tendency to produce false positive results due to the presence of an epitope tag and non-specific binding to the matrix. Non-specific interactions are usually excluded using several negative controls, such as a vector control with an empty epitope tag, other proteins, several purification steps, and/or subcellular localization filters. Due to the similarity of this method to approaches for studying PPIs, TAP provides an opportunity to study PMI and PPI in parallel under close to physiological conditions, which distinguishes this method from the other approaches described above . In one  enzymes .The above-mentioned approaches use very different concepts, but they all share a common signature: they start from a selected protein or metabolite as bait. These methods allow the exploration of interactions of a target protein or small molecule, but they cannot provide a panoramic picture of the whole interactome.The non-targeted PROMIS (PROtein-Metabolite Interactions using Size separation) strategy allows PMI analysis under native conditions. Protein–metabolite complexes are separated by size exclusion chromatography, followed by quantitative metabolomic and proteomic analyses of the resulting fractions. The definition of putative intermolecular interactions is based on coelution .Saccharomyces cerevisiae cells allowed for the detection of interaction events between 3982 proteins and 74 small molecules, and most of these interactions were detected for the first time [The main advantages of PROMIS are its ability to work at in vivo concentrations and the absence of the need to modify small molecules or proteins. However, by its very nature, coelution is a sign but not a proof of interaction. PROMIS should be considered an exploratory approach for constructing an interactome map, which should be combined with orthogonal methods to refine the composition of complexes. Like gene expression studies, integrating multiple PROMIS datasets will likely be sufficient to double-check putative interaction . Recentlrst time .The ideologically similar TICC (target identification by chromatographic coelution) method is based on identifying a characteristic shift in the chromatographic elution profile of a compound bound to a protein target. TICC was used to test known drug–protein interactions (micromolar to nanomolar Kd) in a native cell lysate or lysate doped with the molecules of interest. The idea is that the recombinant protein is incubated with a mixture of compounds; unbound small molecules are separated via single-stage size exclusion chromatography, and interacting compounds are determined using metabolomics methods. In addition to size exclusion, ion exchange chromatography has also shown the ability to separate free compounds from those bound with proteins.The methods mentioned earlier allow the detection of PMIs in panoramic mode but do not provide information about these interactions’ kinetics, degree of affinity, and specificity. For these purposes, various biophysical methods (such as surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), and nuclear magnetic resonance (NMR) spectroscopy) are used that specialize in the rigorous determination of interaction properties .In the Surface Plasmon Resonance (SPR) method, a protein of interest is immobilized on a sensor chip, and upon binding of a metabolite, the chip detects the change in the refractive index of the complex . SPR reqN-acetyl glucosamine trimer (NAG3) and monomer NAG [Isothermal Titration Calorimetry (ITC) is a solution-based and label-free direct quantitative method measuring the change in heat that is released or absorbed upon interaction between two molecules. This allows us to directly measure such fundamental thermodynamic parameters of binding as the binding affinity or the dissociation constant. As an example of its application, it was utilized in a study of interaction parameters between lysozyme and its inhibitors, omer NAG . This meomer NAG  or the eomer NAG . In someomer NAG . AlthougNuclear Magnetic Resonance (NMR) spectroscopy is a technique in which radio frequency waves are used to induce changes in the energy levels of certain atomic nuclei subjected to a strong magnetic field. In the context of PMI studies, NMR is the method of choice for obtaining structural information about the protein–metabolite complex ,83. The Cryogenic electron microscopy (Cryo-EM) is one of the key methods for resolving the macromolecular structures of proteins, including their complexes with substrates or even intermediate states . SamplesThe microscale thermophoresis (MST) technique utilizes the directed movements of macromolecules in the temperature gradient to detect interactions, including those between proteins and metabolites. In brief, the concentration of macromolecules, identified via fluorescence, decreases in the heated area since macromolecules move to colder zones. Binding of a ligand to a protein can induce changes in the size, charge, hydration shell or conformation of the resulting complex. This can affect the movement of a protein in the temperature gradient, which permits quantification of the interaction constant between the protein and the metabolite . This apEach biophysical method has its limitations and application areas; thus, a comprehensive understanding of thermodynamic, kinetic, structural and dynamic properties of a particular interaction is achieved by a combination of the methods. For instance, FTIR and Raman spectroscopy complement each other quite well: the former is not recommended for water-containing samples, while the latter is the method of choice for such samples. On the other hand, the sensitivity of Raman spectroscopy is drastically reduced in colored or fluorescent samples, but this is not a problem for FTIR spectroscopy. Since the vast majority of biophysical methods are carried out in vitro , the results obtained in such studies cannot be unequivocally transferred to a “cellular” environment. For a correct interpretation of such data, additional experiments should be carried out with methods that work in conditions close to native ones. A brief description of the principles of the methods reviewed in this manuscript, as well as their strengths and weaknesses, are listed in The data itself is often not enough, even in the field of the omics sciences, in which it is common to initiate research projects based on not hypotheses but rather experimental data. New knowledge emerges from the intersection of diverse data in a new way that facilitates data exploration for powerful and systematic analysis. Genomics has passed this path, and now proteomics and metabolomics are passing it, too . Public Although the field of protein–metabolite interactomics is still relatively young, there are already a number of databases that contain quantitative and qualitative information regarding the results of experiments studying protein–metabolite interactions. Experimental confirmation of PMIs is a complicated task, so the amount of knowledge in this area lags behind PPIs. To date, there are 17 databases that aggregate information about PMIs, but fewer resources are regularly updated and curated. Several key databases are listed in From this list of the most influential PMI-related databases, it is apparent that most of the databases are devoted to clinically relevant compounds and some of the databases are protein-centric, rather than metabolite-centric. Thus far, only one of the listed databases, PMI-DB, significantly leverages the aforementioned new high-throughput techniques for capturing PMIs. This reflects the very young state of research into PMIs from a panoramic point of view. However, most of the presented databases are still indispensable for such investigations, as they provide high-quality, ground truth information that is used to annotate PMIs captured during the experiment and separate well-known interactions from new ones. In the near future, we expect that the quantity and quality of PMI-centric databases will grow significantly, reflecting the fast rate of development and application of new technologies for the detection of PMIs.Interactomics, including protein–metabolite interactomics, sits squarely at the intersection of chemistry, biology and bioinformatics, providing an opportunity for systematic studies of the transfer of biological information from one layer to another. A close relationship between the proteome and the metabolome has been demonstrated by the recent development of several models for predicting changes in the metabolome from the proteome ,112. SucAccordingly, in a larger context, knowledge of protein–metabolite interactions is vital for insightful interpretation of multi-omics experiments. In such experiments, several layers of omics data from the same samples are combined to provide the most information possible about the experimental system, for instance, a model of disease . TypicalWhile the importance of detecting and describing protein–metabolite interactions is undoubtedly significant, this area of research is still quite early in its development and has several opportunities for much-needed improvements, especially in the context of high-throughput experiments. The first of these is the need for a systematic and standardized procedure of reporting and integrating experiments. As demonstrated in the present review, there are numerous methods for detecting protein–metabolite interactions, which have widely different requirements and capabilities. Efforts to systematically compare and integrate data from multiple methods, such as the one attempted by the PMI-DB database, are going to be critical for the confident use of data from interactomics experiments, as has been demonstrated for a very similar but more mature field of protein–protein interactomics . AnotherIn spite of all the challenges, the field of protein–metabolite interactomics enters an exciting phase, where freshly developed high-throughput technologies can be applied to many objects of interest, revealing an underappreciated layer of biological complexity. Such information about unannotated interactions between proteins and metabolites can be further annotated by ever-improving targeted structural and functional assays. Overall, considering the importance of the proteome and the metabolome layers in biological systems, as well as the intensive cross-talk between them, the field of protein–metabolite interactomics has the potential to solve many riveting questions in health and disease.


Most research aimed at measuring biomarkers on the skin is only concerned with sensing chemicals in sweat using electrical signals, but these methods are not truly non-invasive nor non-intrusive because they require substantial amounts of sweat to get a reading. This project aims to create a truly non-invasive wearable sensor that continuously detects the gaseous acetone (a biomarker related to metabolic disorders) that ambiently comes out of the skin. Composite films of polyaniline and cellulose acetate, exhibiting chemo-mechanical actuation upon exposure to gaseous acetone, were tested in the headspaces above multiple solutions containing acetone, ethanol, and water to gauge response sensitivity, selectivity, and repeatability. The bending of the films in response to exposures to these environments was tracked by an automatic video processing code, which was found to out-perform an off-the-shelf deep neural network-based tracker. Using principal component analysis, we showed that the film bending is low dimensional with over 90% of the shape changes being captured with just two parameters. We constructed forward models to predict shape changes from the known exposure history and found that a linear model can explain 40% of the observed variance in film tip angle changes. We constructed inverse models, going from third order fits of shape changes to acetone concentrations where about 45% of the acetone variation and about 30% of ethanol variation are captured by linear models, and non-linear models did not perform substantially better. This suggests there is sufficient sensitivity and inherent selectivity of the films. These models, however, provide evidence for substantial hysteretic or long-time-scale responses of the PANI films, seemingly due to the presence of water. Further experiments will allow more accurate discrimination of unknown exposure environments. Nevertheless, the sensor will operate with high selectivity in low sweat body locations, like behind the ear or on the nails. The presence and concentrations of gaseous products of metabolic processes in the breath, often volatile organic compounds (VOCs), are well-established indicators of the state of one’s health, and measuring these has been the subject of much research –12. HoweMost investigations aimed at monitoring biomarkers with wearables, including the few fully functional and available devices, involve analysis of sweat , 25–29. One proposed solution for truly continuous, truly non-invasive monitoring is skin gas sensing: that is, via analysis of the gaseous biomarkers continuously and ambiently emitted directly from the skin. Biomarkers observed in the breath are known to be emitted from the skin, continuously and in quantities proportional to quantities seen in the breath, allowing for easy selection of target analytes –6, 30–33The first step toward novel wearable devices for continuous, non-invasive skin-gas sensing is the establishment of sensing materials that are sensitive and selective to analytes of interest. One such analyte of interest that is found in breath and skin-gas is acetone, the concentration of which in the breath has been studied in relation to blood sugar levels and fat burning rates and could shed light on the progression of diabetic ketosis and weight loss , 28, 34.The chemo-mechanical actuator we propose is a polyaniline-cellulose acetate (PANI-CA) composite film that bends upon exposure to gaseous acetone; we refer to this response as being chemo-mechanical, as the film responds with mechanical deformation when exposed to acetone . This chPolyaniline (PANI), a conjugated polymer derived from the organic solvent aniline, has long been known to exhibit intrinsic electrical conductivity. PANI can exist as fully oxidized pernigraniline, half oxidized-half reduced emeraldine, or fully reduced leucoemeraldine, and each of these oxidation states has a fully protonated salt and a fully deprotonated base version; the emeraldine salt is known to conduct most strongly . The strThis PANI-CA composite has previously been shown to exhibit chemo-mechanical actuation upon exposure to gaseous acetone, bending in response to acetone exposure. Presented here is a study of the repeated exposure of strips cut from PANI-CA composite films to the headspaces above samples of pure acetone, pure ethanol, and mixtures of these with each other and with water . Our stuThe novel, chemo-mechanical actuating films were prepared by first proton-doping leucoemeraldine base PANI (Sigma) with concentrated hydrochloric acid (Fisher) for 24 hours, then washing off the acid with water and drying at room temperature in an open container. Dry, doped PANI was suspended in acetone (Fisher) and combined with 50,000 g/mol number-average molecular weight CA (Sigma) in a 1:5 mass ratio. This mixture was then dried in a loosely covered container at room temperature to leave behind a composite film. Strips measuring approximately 6 mm by 25 mm were cut from full PANI-CA films. We tested 8 strips, obtained by cutting out 2 strips each from 4 larger films. Any small residual curvature in these strips was left uncorrected before testing. Headspace exposure environments were prepared by pouring 60 mL of various pure solvents or mixtures of solvents into 125 mL conical flasks. The headspace exposure environments used here were those above the solvents and mixtures that are identified in Each headspace exposure test consisted of recording a video of the PANI-CA strip suspended in the headspace above a solution, as shown in First, the videos were analyzed by manually determining the timestamp of the maximum deflection in each test video. After this manual analysis, we performed computer vision-based tracking of the bending film, described in sequence below. For this computer-based analysis and for the rest of the manuscript, only 4 out of the 8 strips were analyzed: specimens 1 through 4, collected from films 1 and 2, exhibited high degrees of non-ideal deformation, twisting laterally out of the video image plane. We ignored films exhibiting such out of plane deformation in this study; we will examine them in a future study or engineer forming processes to prevent them in future work.Videos were first cropped and magnified to focus on the filament, cropping out inconsequential visual details such as the flask holding the solution. These cropped videos were then processed by a bespoke MATLAB program, which first splits the video into frames at 30 Hz, and then converts them into binary black and white images . From thWe used two other methods of tracking the filament shape. First, we did manual tracking of the filament shape by the following method: a computer program displayed a frame of the video at 5 seconds apart to a human user; the human user then clicks on 7 points on the filament, starting from root to tip, and then moving to the next frame. The 7 points are asked to be roughly equidistant, but for our analysis, they need not be exactly equidistant. We used cubic spline interpolation to densely fill the rest of the filament between these points. This manual tracking provided a ‘ground truth’ for evaluating the accuracy of the automatic tracking. We performed this manual tracking for two of the filaments.Our automatic tracking algorithm from the previous paragraph relied on relatively classical image processing and machine learning methods: binary thresholding and k-means clustering. We sought to test whether more modern computer vision techniques could track the filament better. To do this, we used the Deep Neural Networks based software called DeepLabCut . This soWhile the filament shapes look relatively simple to the human eye, we sought to see if the set of filament shapes was indeed as low dimensional as it seemed. So, we performed principal components analysis on the full set of points comprising the tracked filament shapes, across all time and across all exposure conditions. This PCA then results in the principal components, which are the directions along which the shape variance is highest (first PC), second highest (second PC) and so on. This method allows us to see what the effective dimensionality of the filament shape is—that is, how many numbers it takes to represent the shape of the filament to a certain degree of accuracy.We constructed two types of models: (1) forward models that attempt to predict the filament bending response based on acetone and ethanol exposures, and (2) inverse models that attempt to predict the acetone and ethanol concentrations based on characteristics of the filament bending response.As an example of a forward model, we computed a linear model from acetone and ethanol fractions in solution to the delta change in the tip angle over a trial as well as the dominant time constants of the bending response. We tested whether inclusion of the initial angle for each trial in the inputs changed the predictive power. A more general forward model would be a time-series model or differential equation model that aims to explain the shape changes of the filament given the changes in the acetone or ethanol fractions.a0, a1, a2, a3) and the exponents  being the unknowns solved by fitting to the data. These parameters describe how the tip angle changes. We then fit a linear model that took these parameters as input during each exposure phase to predict the corresponding acetone and ethanol concentrations.As an example of inverse models that go from the bending response to the acetone and ethanol concentrations, we first fit a triple exponential to the tip angle transient response to every new exposure: R2 for a cubic polynomial model that predicts the automatic results from the manual results.In As seen in We found that the filament shape can be quite accurately represented using 1 to 4 parameters. Specifically, with increasing concentrations of acetone, a faster response time is seen; here the response time reported is the time to maximum deflection.We did not see any systematic correlation between the steady state bending state (tip angle) of the filament and the acetone/ethanol concentrations. However, we do see systematic changes in the speed or vigor of the response with the nature of the solution . A looseR2 . This R2 value indicates some predictive ability for the shape changes, but with some errors. Indeed, only the constant term and the exponents of the exponential matter for this linear model; including the other coefficients increases R2 by only 5%. Conversely, building a forward model to predict the change in tip angle from the acetone and ethanol concentrations via a linear-model results in similar explanatory power (about 40% R2). We posit that a key mechanism for this not-too-high explanatory power is the presence of history dependence (hysteresis), which could be captured by models with additional state variables–for instance, characterizing the internal state of the filament and thus its propensity to bend.these demonstrate that not only is the sensitivity to acetone greater than the sensitivity to ethanol when considered as pure solvents but also that the presence of acetone overpowers the response to ethanol so much that responses to solutions 3 and 4 were largely indistinguishable. This is further supported by the response times to exposures to solution 5, which was composed of equal volume fractions of ethanol and water; such response times mostly resembled the response times to exposure to pure ethanol. This raises the question of the effect of water on the measurements, which in turn guides us to position the sensor strips behind the ears or on the nails, for example.The most striking result of this work is the degree of exposure-history dependence that the samples displayed. The next most significant result reported here is the trend in response time reported in Other important result includes the fact that our original tracking program strongly agreed with the manual tracking and the fact that nearly all of the bending variance can be attributed to 4 principal components. These mean that no new computer vision tracking program will have to be written for future experiments and that the performance consistency of samples made in the future can be measured against this benchmark of 4 principal components to totally describe their motion. Furthermore, it has been made apparent that additional sample modification is required for the use of DNN tracking to monitor sample bending; some method of making points on the samples more visually distinctive would be needed. An ideal marking method would involve making marks that are easy for the computer to see but also do not influence the motion of the sample by mechanical or chemical means.Two other significant questions are raised here that will be the focus of future investigation. The first of these is if the tremendous magnitude of exposure history dependence seen here retains this level of influence when the entire history of the sample includes only exposures to analyte concentrations on the much lower magnitudes representative of VOC concentrations that are emitted from human skin. The second is what extra steps can be taken to produce samples that behave more uniformly therefore efforts are underway to standardize the size, shape and thickness of the sensing strips.


Viviparity and the development of a placenta are two of the major reasons for the success of the mammals in colonizing all habitats, both terrestrial and aquatic. The placenta is an apposition of fetal to maternal tissue which serves two main, but competing functions: to maximize oxygen transfer and the acquisition of nutrients from the mother, but to minimize immunological rejection by the maternal immune system. This has resulted in the evolution of four main types differing in the degree of loss of the maternal uterine epithelial (UE) barrier: epitheliochorial, synepitheliochorial, endotheliochorial, and hemochorial, all providing a successful safe balance between the needs of mother and fetus. Epitheliochorial is the least invasive, a simple apposition and microvillar interdigitation of the apices of uterine epithelium and trophoblast. It is suggested to have evolved as a response to the increase in the size of the animal to provide a sufficiently long gestation to produce a single altricial (run/swim-soon-as-born) neonate as in the Cetartiodactyla. The mother needs to have good control of the fetal demands so the UE barrier is maintained. However, in the synepitheliochorial placenta, characteristic of all ruminants, the fetus has evolved a means of increasing, or at least maintaining, demand without the need for invasion. This has been achieved by the development of the trophoblast binucleate cell which, uniquely, can fuse with a UE cell to form fetomaternal hybrid tissue. This can maintain some maternal barrier function but also deliver fetally synthesized immunomodulatory and metabolic messages to the maternal circulation. This review provides the evidence for this remarkable evolutionary step and also considers an alternative explanation for the formation of the structure of the ruminant placenta. In this review, individual cells will be identified by the number of nuclei in the cell: for example, Uni, Bi, Tri, or Multinucleate. Other criteria such as large or mature may also be used but not a term such as giant, which is insufficiently informative.The mid-term ruminant placenta is formed by a species-specific number of placentomes connected by flat interplacentomal areas. The placentomes consist of interdigitated fetal and maternal villi vastly increasing the surface area available for maternofetal transport. The placental uterine epithelium is formed either by uninucleate cuboidal cells occasionally interrupted by fetomaternal trinucleate cells  or by fetomaternal syncytial plaques , see below for details.The apposed trophoblast is a sheet of columnar uninucleate cells that also contain 15–20% characteristic ruminant binucleate cells (BNCs).These ruminant trophoblast BNCs, when fully differentiated  by a cell division without cytokinesis which produces an immature BNC presumably with two 2N nuclei. This process occurs throughout pregnancy from implantation to term and results in a constant 15–20% of the trophoblast being BNC at various stages of maturation  and 10 . This prSyt-Rum-1 [A second advantage is the ability of the mature BNC to migrate and fuse with a UE cell. To achieve this, the BNC has co-opt an endogenous retroviral gene, yt-Rum-1 . The matyt-Rum-1 .Syt-Rum-1 gene and the content of the BNC is expelled into the UE cell , continues with further BNC migration and fusion. This produces syncytial plaques in place of the UE cells which are either incorporated into the syncytium or eliminated by death and phagocytosis by the trophoblast cells . In the The next requirement is an increase in the surface area of the placental membranes to allow a steady increase in maternofetal food transport as the fetus grows. This is accomplished by a mutual growth of caruncular trophoblast and the uterine epithelium or derivative to form the placentomes which consist of enmeshed fetal and maternal placentomal villi .In the case of the ewe and goat, this growth is based on continual trophoblast division and growth, producing BNCs that migrate and fuse to form the maternofetal syncytial plaques . No one It is not yet clear what advantages the syncytial plaque system provides. A continuous syncytium as found in the endothelial and hemochorial placentas is a more effective fetal barrier to maternal cellular translocation and is also active in synthesizing fetal responses to maternal immunological attack. There is no evidence that the ruminant syncytial plaques synthesize anything specific, but their formation results from continuous BNC migration, fusion, and delivery of granules containing fetally produced mediators of maternal metabolism. They are fetomaternal tissue and may provide a maternal immunological buffer zone. They may contain only one maternal nucleus to the 22–25 fetal nuclei in each plaque if they are formed by sequential BNC fusion into one original uterine trinucleate cell (TNC). Alternatively, if lateral fusion between uterine TNC is possible, then plaques could contain one maternal nucleus to each two BNC nuclei. The exact nature of each plaque awaits future research.The cow and deer use a different strategy for placental growth but still retain the BNC migration and fusion involvement . The synIn 1906, Assheton clearly identified BNC on the light microscope (LM) and suggested they might play a role in forming the uterine epithelium syncytium in the Ewe . Later wIntroduction of the PhosphoTungstic Acid (PTA) stain on non-osmicated, deresinated Araldite EM sections  picked o8, and Subsequent quantitative studies of serial sections and autoradiographic tracing of BNC nuclear movements have confirmed the basic hypothesis diagrammed in Some species do show individual extras in the BNC granules, for example, c-type natriuretic peptide in ewe  and glucWhat controls the BNC production, maturation, migration, and heterologous fusion behavior is not understood. Many fetal interventions such as adrenalectomy, stalk section, hypophysectomy, or injection of mouse epidermal growth factor (EGF) have little or no effect on BNC formation, maturation or migration .Monolayers of Trophoblast cells grown in vitro do produce occasional BNC, but how equivalent these are to the in vivo mature BNC, which are notably uniform in EM and LM structure in all species so far examined, has not yet been established . This sesyncytin genes has provided further support for this. Over many millions of years of evolution, many endogenous retroviral (ERV) genes have been incorporated into the DNA of mammals. A few have retained an important viral function after incorporation into the host genome. ERVenv is one which is expressed in human and mouse placenta and is involved in the placental cell fusion processes forming the syncytial layers which serve as the immunological and functional barrier between mother and fetus [syncytin genes of retroviral origin coopted for a role in placentation present in both ovine and bovine genomes and referred to as Syt-Rum-1. In the bovine only, a second phylogenetically unrelated syncytin gene, BERV K-1 (Fematrin1), has been found [syncytin genes into the ruminant trophoblast could have provided a major driving force for the evolution of the unique BNCs that are the basis for the formation of the synepitheliochorial placenta.This review has been designed to show the importance of BNC fusion for successful ruminant implantation and placental growth. Recent work on the endogenous retroviral origin of the nd fetus . A gene nd fetus . More spnd fetus  have ideen found . Both geen found , 56 suggThe BNC migration and fusion hypothesis has recently been seriously questioned based on the results from an immunofluorescent study of implantation in the ewe by Seo et al.  They sugThe authors dismiss all the EM evidence for the migration and fusion of BNC hypothesis because it was “without the benefit of molecular markers for BNCs and UE cells.” This ignores the LM and EM immunocytochemistry of the BNC granules  and the Seo et al. interpret their elegant immunofluorescent study without allowing for the problem of its inherent lack of resolution. For example, the position of the MVJ between trophoblast and UE cells or derivative has to be guessed (dotted lines in This is critical in the Seo et al. Considering the five bullet points stated in the abstract of the Seo et al. paper:Bullet Point 1. A basic problem with the TGC hypothesis is the lack of any evidence for TGC formation. Occasional individual trinucleate cells have been reported but only in the cow, and they are considered to be formed as a consequence of aberrant mitotic poyploidization processes which normally produce BNC [duce BNC , 17.EM studies have clearly shown that the BNCs differentiate within the trophoblast and below the trophoblast TJ and only migrate and fuse when fully granulated and mature , 8–10 3. The preAre the TGCs formed below the trophoblast TJ? When do they accumulate granules? Do they pass through while maintaining the trophoblast and UE TJs? None of these questions can be answered conclusively with the resolution of LM fluorescent studies.These considerations emphasize the inaccuracy of the claim for the normal presence of TGC in the trophoblast.Bullet point 2. Death of UE cells during syncytial plaque formation has also clearly been shown by EM studies, but resolved by EM evidence of trophoblast phagocytic uptake of the residues [residues , 26, 27 Bullet point 3. The immunofluorescent resolution is insufficient to establish any clear evidence for TGC engulfment of apoptopic UE residues. A few gaps between forming syncytial plaques have been shown to be normal at ovine implantation [antation  However,Bullet point 4. Syncytial plaque fusion is possible, but the plaques do not show the Syt-Rum-1 gene, which as detailed above, is restricted to BNC [d to BNC .Bullet point 5. None of the previous LM and EM studies of ruminant implantation have shown any cells crossing the basal lamina of the UE, nor any accumulation of leucocytes below this layer under normal circumstances. The concept of the TGC delivering UE apoptopic residues to subepithelial leucocytes seems very unlikely especially since the only published example [ example  of such There is no published evidence for the significant synthesis of immunomodulators by the plaques, they appear to act as platforms for delivery of the content of BNC granules to the maternal circulation. In that respect, they are not at all analogous to the human syncytiotrophoblast which is very active in synthesizing such modulators .in vivo. This supports the BNC migration and fusion hypothesis.The wealth of detail of ruminant placental development illustrated diagrammatically in On the other hand, the TGC hypothesis as illustrated in On balance, it seems that the TGC hypothesis must await further specific results before any of its predictions can be seriously considered.There is no potential conflict of interest with any public or commercial organizations.This research did not receive any specific grant from any funding agency in the public, commercial, or not-for-profit sector.


Over the last fifty years extraordinary scientific progress has been attention-deficit/hyperactivity disorder (ADHD) science on a number of levels. First, finding the best way to characterize the condition. Second, understanding of why some are affected by the condition and others in terms of originating environmental and genetic risk, underlying mediating biological and cognitive processes and moderating environments. Third, the awareness of the breadth of ADHD's impact on people's lives   of the ADHD child is the site of the dysfunction that causes ADHD? However, it's become increasingly clear from the start of the 21st century that this is a profound mischaracterisation of ADHD. Furthermore, the question which derives from it is a scientific form of a “red herring” , and brain markers derived from functional structural imaging techniques . In addition to such markers from “within” the individual, they examined variations in environmental exposures to social privation, nicotine, and alcohol use during pregnancy and early maltreatment. While emphasizing that intervention biomarker studies are in their infancy, the way the authors highlight this approach's potential to facilitate better treatment will motivate the field to make a significant investment in biomarkers research.In the first paper in the special issue, Haavik discusses how variations in an individual's genome could, in the future, guide drug therapy. This proposal was built on a review of recent developments in ADHD genetics. While recognizing that the large-scale routine implementation of these approaches remains a considerable way off, he argues for a gradual strategy starting with rare ADHD syndromes with highly penetrant risk genes and well-defined neurometabolic aberrations. For example, he cited the work of Fernandez-Castillo that showed the deficiency of L-2- hydroxyglutarate dehydrogenase (L2HGDH), X-linked creatine transporter deficiency, and HUWE1 mutations presenting ADHD symptoms. Similarly, recently a group of researchers found a single homozygous locus on chromosome 18 affecting N-cadherin function shared by affected individuals in Bedouin pedigree in the south of Israel, diagnosed with ADHD according to the DSM-5 and display severe hyperactive behavior  and associated impairment variations . It has been argued that the patterns of co-occurring difficulties, rather than the core symptoms themselves, create much of the impairment and distress associated with ADHD . The authors describe the range of medications used in a large sample of children with ADHD and/or ASD. Children with ADHD and autism combined had higher rates of anxiety, depression, and ODD than those with just autism or ADHD alone. ADHD or the ADHD + ASD groups took more psychotropic medications than the ASD group. While the ADHD with co-morbid ASD reported the highest rate of non-stimulants, antipsychotics, antidepressants. The second paper in this sub-section, by Yin et al., focused on a feature that occurs so commonly with ADHD that some have called for it to be considered part of the broader ADHD phenotype—sleep disorders. In a convenience sample of 100 unmedicated children the authors looked at the relationship between ADHD and sleep problems as rated by questionnaires. Like previous studies, the authors found a robust link between these sets of difficulties and a strong linear relationship between the severity of ADHD symptoms and sleep problems. In a manner worthy of appreciation, the researchers attached the raw data as a supplementary data. This study reminds us that we should consider individual differences in sleep problems when developing treatment plans.The first paper in the Special Issue to address this issue of the treatment of co-occurring conditions is by Ganjeh et al.) takes a novel approach focusing on developing adolescent mental health problems. It is a large naturalistic longitudinal population-based cohort study looking for the relationship between adolescents' mental health disorders and earlier childhood physical activity. It poses the question of whether physical activity can be protective in relation to later mental health problems. The findings were counterintuitive. Childhood physical activity levels were related to lower levels of ADHD symptoms later on in life during adolescence, extending to a more general pattern of mental health outcomes. The authors argue that giving children the opportunity for physical activity could be valuable clinically as an adjunct to other interventions. We would encourage the authors to explore this hypothesis systematically eventually in RCTs. The final study by Korfmacher et al., compared a form of self-management training against neurofeedback in people with ADHD. They found that both treatment options improved core ADHD symptoms. However, only the self-management treatment resulted in self-concept and quality of life improvements. Thus, they emphasized the added value of self-management training over neurofeedback in the treatment of ADHD.The third study in this sub-section (In this special issue, we bring two under-researched aspects of ADHD translational science together—the need for more precise targeting of specific features and characteristics and, at the same time the need to broaden the focus away from core symptoms to other aspects of functioning. These separate ambitions should jointly inform the next steps of treatment development in ADHD.ES-B and SZ wrote the final version. All authors contributed to the writing of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability of low-sampling-frequency accelerometer (SENS) measurements in patients with knee osteoarthritis. Data were collected simultaneously using SENS and a previously validated sensor (Xsens) during two repetitions of overground walking. The processed acceleration signals were compared with respect to different coordinate axes to determine the test–retest reliability and the agreement between the two systems in the time and frequency domains. In total, 44 participants were included. With respect to different axes, the interclass correlation coefficient for the repeatability of SENS measurements was [0.93–0.96]. The concordance correlation coefficients for the two systems’ agreement were [0.81–0.91] in the time domain and [0.43–0.99] in the frequency domain. The absolute biases estimated by the Bland–Altman method were [0.0005–0.008] in the time domain and [0–0.008] in the frequency domain. Low-sampling-frequency accelerometers can provide relatively valid data for measuring the gait accelerations in patients with knee osteoarthritis and can be used in the future for remote patient monitoring. Knee osteoarthritis (OA) is a prevalent musculoskeletal condition affecting the older population, characterized by articular-cartilage degeneration and joint-space narrowing. Pain and walking problems associated with knee OA affect patients’ quality of life and impose a considerable economic burden on the health care system . A timelIn addition to questionnaires and patient-reported outcome measures (PROMs), wearable motion-tracking sensors, such as Inertial Measurement Units (IMUs), have recently been introduced in telemedicine programs. These wearable sensors provide a practical and cost-effective solution for obtaining objective functional measures regarding the characteristics of movements and physical activities ,7,8. Com®; Copenhagen, Denmark) designed for measuring physical activities in the healthcare sector and for research projects. We investigated whether the accelerations measured with small low-frequency accelerometers in patients with knee OA were valid representations of the true accelerations for kinematic gait assessment. We compared the accelerations measured with SENS sensors at ~12.5 Hz with a standard inertial-sensor-based motion-capture system as the gold standard to determine the criterion validity of accelerometers with low sampling frequencies. We also evaluated the test–retest reliability of the measurements obtained with low-sampling-frequency sensors.The main aim of our study was to validate the measurements performed by a commercially available sensor , a recent history of operations in the lower limbs, neurological movement disorders, and inflammatory arthritis were excluded from the study. The study was approved by Regional Committee on Health Research Ethics .The study participants consisted of patients at different stages of unilateral knee OA referred to Aalborg University Hospital, Denmark, between December 2021 and March 2022 and diagnosed by specialists in orthopedic surgery. We also included asymptomatic individuals of over 50 years of age at stage 0 of knee OA. Participants complaining of pain and discomfort in the spine and other lower-limb joints, and patients with high BMIs  formed the MVN Awinda wireless full-body MoCap system. They consisted of 17 IMUs, which comprised a 3D gyroscope, a 3D accelerometer, and a 3D magnetometer combined with sensor fusion algorithms. The sensors were placed on the head, shoulders, upper arms, forearms, sternum, pelvis, upper and lower legs, and feet A for ful®, Copenhagen, Denmark) only contain a 3D accelerometer. These sensors are medically approved devices designed for the long-term monitoring of patients’ physical activities. In addition, they have an internal memory for storing up to 14 days of data and cloud connectivity via a mobile telephone. Following the manufacturer’s instructions, we attached two SENS sensors on the lateral distal side of each thigh about 10 cm above the lateral femoral epicondyle . Afterward, the processed data were used as inputs for AnyBody Modeling System . AnyBody is musculoskeletal modeling software that enables the simulation and analysis of the kinematics and kinetics of human movements to be performed. Xsens relies on the sensor fusion of a multitude of channels to recover positional information, and the AnyBody Modeling System further processes the signals by taking the kinematic constraints into account before returning the local accelerations. This pipeline, to some extent, compensates for the influence of soft-tissue artifacts on the local accelerations.A musculoskeletal model for each participant was created, and two virtual representations of the accelerometers  were defined in each model on the right and left thighs at the locations of the two SENS sensors. The model accepted the processed data from MVN Analyze as inputs. The outputs consisted of the 3D linear accelerations offset by gravity in the local coordinate systems of the VirtualSENS sensors, which we employed as the criterion to validate the SENS signals. For convenience, we refer to the signals obtained with VirtualSENS as Criterion signals in this article. x, accy, and accz along the three perpendicular local axes, x, y, and z, respectively  and four axes .We inspected the frequency contents of both signals to determine a cutoff frequency and performed a residual analysis of the difference between filtered and unfiltered signals over a wide range of cutoff frequencies . A fourtAnyBody interpolated the high-frequency data from Xsens, resulting in a continuous Criterion signal that can be resampled at any frequency. The Fourier method was used to reconstruct a continuous interpolation of the SENS signals drawing upon their periodic nature to improve the bridging between samples .x-axis) were used as the reference axis for synchronization. The resulting time stamps were employed for the signals along the other axes. The five synchronized cycles were subsequently segmented into five individual cycles using autocorrelation as described by Yang et al. [http://www.python.org (accessed on 15 January 2022)).With the SENS system measuring continuously, there was no simple way to establish a mutual time stamp for the SENS and Xsens signals. Given that the participants were elderly and suffering from knee pain, they could not be required to perform a fast move or a jump to synchronize the two systems. Cross-correlation was, therefore, used to synchronize a sequence of five gait cycles within the two signals in the middle of the trial, and this appeared to be reliable, given the inevitable deviations between the steps in a sequence. The acceleration signals in the vertical direction . For the statistical analyses, we used the R statistical package, version 4.1.0 (RStudio Team (2021); RStudio: Integrated Development for R. RStudio, PBC, Boston, MA, USA; URL http://www.rstudio.com/ (accessed on 8 May 2022)).Descriptive statistics were used to quantify the characteristics of the participants. Anthropometric data and gait cadence were presented as means and standard deviations, age was described with median and range, and knee OA scores were reported as median and interquartile range. To compare the continuous Criterion and SENS signals, we discretized the signals of the average gait cycles by resampling them at the rate of 60 Hz. Kolmogorov–Smirnov tests were applied to check for the assumption of normal distribution (Interclass correlation coefficient (ICC) estimates and 95% confidence intervals were calculated for the measured accelerations in the averaged gait cycles between the first and second trials with respect to different coordinate axes and the vector magnitude. The ICCs for both SENS and Criterion signals were calculated by employing two-way mixed effects, absolute agreement, and the single-rater model.The synchronized SENS and Criterion time series were visually assessed to determine the agreement between the signals in the time domain. The scatterplots for the correlations between the SENS and Criterion measurements were generated. The concordance correlation coefficient (CCC) introduced by Lin  was empls) was calculated for the correlations between the differences and Criterion to demonstrate the stability of the bias across the range of Criterion values.Regarding the absolute agreement, the method described by Bland and Altman  was emplNSI) [d is the distance between the SENS and Criterion signals, measured with the DTW method, and M (Criterion) is calculated by multiplying the number of the samples in the Criterion signals by the range of the acceleration values of Criterion (accMax – accmin). The dynamic time warping (DTW) algorithm  was usedNSI)  was thenTo compare the signals in the frequency domain, we performed the Fast Fourier Transform and evaluated the frequency contents of the signals. After generating the signals’ power spectral densities (PSDs), the frequencies of the peaks and the powers corresponding to those frequencies were compared between the SENS and Criterion signals. Scatterplots and Bland–Altman plots were sketched, and non-parametric LoAs and CCCs were calculated to evaluate the agreement between the SENS and Criterion signals regarding the frequencies and the powers of the PSD peaks.T is the cycle time computed as Subsequently, the Fourier series representation of the signals was generated using the following equations as described by Skejø et al. :y(t)=a0+An overview of the experimental protocol used in this study is depicted in We included 44 participants  at different stages of unilateral knee OA (stages 0–IV). The characteristics of the subjects are demonstrated in The result of the test–retest reliability evaluation showed an excellent repeatability of the acceleration measurements obtained with SENS between the first and the second trials. The calculated ICCs for SENS were comparable with those for Criterion . x-axis and 0 on the y- and z-axes (n), a relative widening of the SENS accelerations was observed for higher Criterion accelerations. The scatterplots of the accelerations measured with SENS and Criterion demonstrated a high correlation between the measurements. However, a slight gradual drift from the line of equality was observed as the accelerations increased or decreased from −1 on the d z-axes . Furtherx-, y-, and z-axes, and the vector magnitude (n), respectively  than with respect to the z-axis , respectively . Both sides’ rs were −0.07, 0.15, 0.23, and 0.06 with respect to the x-, y-, and z-axes, and the accelerations’ magnitude (n), respectively.The scatterplots and Bland–Altman plots in The CCC of the agreement between SENS and Criterion was excellent regarding the value of the frequencies of the PSDs and the Fourier coefficients; however, it was more variable among the different axes with respect to the powers of peaks of the PSDs .The absolute agreement of the frequencies and powers of the PSD peaks, and the Fourier coefficients of SENS and Criterion are provided in This study evaluated the validity and reliability of the linear accelerations measured with low-sampling-frequency (~12.5 Hz) accelerometers in elderly individuals with and without knee OA during simple overground walking. The results demonstrate that in this group of patients, after the same signal processing, the measurements obtained with these accelerometers were highly correlated with previously validated inertial-based motion-capture gait analysis in both the time and frequency domains. In addition, the high repeatability of the measurements of these accelerometers was demonstrated.IMUs with a wide range of characteristics are used for human movement analysis, and one of the most determining characteristics of an IMU is its sampling rate. A higher sampling frequency creates higher-quality data but comes at a price. As the rate of sampling increases, more data must be handled, stored, and transferred, which would challenge the continuous monitoring of patients for more extended periods. Moreover, a higher sampling frequency results in higher energy consumption and shorter battery lifetime and requires more expensive, complex, and bulky sensors. Accordingly, several AI algorithms have recently been proposed to increase the accuracy of the low sampling frequency of IMUs ,33. It hMeasurements are never entirely free from errors. Random errors or noise can affect the precision of the measurements or how reproducible the exact measurement is under similar circumstances, and systematic errors or biases affect the accuracy of measurements or how close the observed value is to the actual value. In this study, we evaluated the accuracy and precision of the measurements performed by a low-sampling-frequency accelerometer (SENS) by comparing the accelerations in the time and frequency domains against an IMU system that has been proved to have high reliability and validity in motion analysis. Our findings show that the accelerometer with a sampling frequency of ~12.5 Hz could adequately capture the gait acceleration signals, even though the cadence of the gait in our study participants was not low (110 ± 11 steps per minute). In this study, we determined the test–retest reliability to evaluate the consistency of the measurements across time and based on the guidelines for reporting ICCs in reliability research ; the tesx- and y-axes . The correlation with respect to the z-axis  was relatively lower. The magnitudes of the SENS and Criterion signals’ vectors demonstrated a relatively high correlation. DTW and correlation capture different aspects of similarity between two time series; nonetheless, the DTW method to calculate the distance between the compared signals was also in agreement with the correlation analysis. The lower correlation with respect to the mediolateral axis might be due to the higher frequency and lower amplitude of the movements on this axis, which might lead to an inferior performance of the sensors. Although the accelerations in the anteroposterior and vertical directions have higher magnitudes, there could also be essential data in the mediolateral direction, especially in pathologic gaits, and it should not be overlooked. In the time-domain comparison, the highest correlations were found with respect to the x, y, and z), which implies that the magnitude of the accelerations might be more accurate and reliable than each of the individual perpendicular axes.The results of the absolute agreement between the measurements based on the Bland–Altman method demonstrated a slight bias in all directions. Several studies have provided information regarding the differences in the accelerations measured by inertial sensors attached to the thighs among patients with knee OA and asymptomatic control groups ,41, and z-axis compared with the other axes suggest a higher noise content and an inferior capability of the sensors of capturing data entirely along the mediolateral axis. The comparison of the Fourier coefficients, however, demonstrated higher correlations, lower biases, smaller ranges of the LoAs, and more stable performances with respect to all coordinate axes, which suggests that it could be a more appropriate and reliable method for further signal analysis of data recorded with these low-sampling-frequency sensors.Comparing the frequencies of the signals also demonstrated near-perfect concordance between the frequencies of the peaks of the signals, which signifies that these sensors could reliably measure the periodicity and harmonics of the gait. However, as expected, due to the limits of SENS, the differences in the measured frequencies increased at higher frequencies. Regarding the powers of the peaks of the PSDs, the broader LoAs and the weak correlation coefficients with respect to the Previous studies reporting the performance of low-sampling-frequency accelerometers have mainly focused on the sensors’ capability of performing physical-activity measurement and classification. To our knowledge, this is the first study evaluating the accuracy and precision of the acceleration measurements obtained with small low-sampling-frequency wearable sensors in the elderly population with and without knee OA. In addition, the assessment of the signals in both the time and frequency domains provides a basis for further studies of the capabilities of these sensors in kinematic gait analysis. The most prominent strength of our study was that by applying musculoskeletal modeling to process the Criterion signals, we could compare the linear accelerations at the exact location of the sensors, which allowed us to favorably compare the acceleration signals in an ordinary outpatient clinic environment, without using sophisticated gait laboratories. The most important limitation of our study was that we could not synchronize the signals during data recording. Since the participants were elderly and suffered from knee OA, we could not ask them to perform a fast move or a jump. Instead, we had to synchronize the signals during postprocessing using the cross-correlation method. The sensors’ sampling frequency of 12.5 Hz is insufficient for a comprehensive gait analysis but may suffice to detect abnormal gait and grade gait deviation. The essential prerequisite in this regard would be the capability of the sensors to measure the accelerations as accurately and reliably as allowed by the limitations of current technology, which was this study’s primary objective. The path is now open to exploring the capability of these sensors in remote gait-quality assessment in telemedicine. A detailed comparison of the signals in the time and frequency domains helps us to identify the most reliable and beneficial features of machine learning algorithms and interpret the data from wearable sensors in remote patient monitoring, similar to myoelectric biomarkers, which have been proposed in post-stroke gait . This stIn elderly patients with and without knee OA performing overground walking, low-sampling-frequency accelerometers can provide measurements with relatively high accuracy and precision, especially with respect to the anteroposterior and vertical axes. Furthermore, the validity and reliability of the measurements, especially with respect to the Fourier coefficients of the signals, indicate their applicability in telemedicine for the remote monitoring of patients. However, further studies are required to demonstrate their practicality for this purpose.


F1 score of 0.85 in identifying clinic CT images of early lung nodules. This provides a promising way of identifying early lung nodules from CT images using deep learning with small-size labelled samples.The small size of labelled samples is one of the challenging problems in identifying early lung nodules from CT images using deep learning methods. Recent literature on the topic shows that deep convolutional generative adversarial network (DCGAN) has been used in medical data synthesis and gained some success, but does not demonstrate satisfactory results in synthesizing CT images. It primarily suffers from the problem of model convergence and is prone to mode collapse. In this paper, we propose a generative adversarial network (GAN) model with prior knowledge to generate CT images of early lung nodules from a small-size of labelled samples, i.e., SLS-PriGAN. Particularly, a knowledge acquisition network and a sharpening network are designed for priori knowledge learning and acquisition, and then, a GAN model is developed to produce CT images of early lung nodules. To validate our method, a general fast R-CNN network is trained using the CT images generated by SLS-PriGAN. The experiment result shows that it achieved a recognizing accuracy of 91%, a recall rate of 81%, and  Neural networks, particularly deep learning models, have shown promising results in the field of cancer-targeted detection. Some studies have shown the ability of trained neural network models can reach the level of human physicians in lesion detection –4. AlmosThe small size of labelled samples has become one of the challenging problems in identifying early lung nodules from CT Images using deep learning technology. When there are a small number of patients screened for early pulmonary nodules, it is difficult to develop large datasets of CT images for the purpose of machine learning methods and model developments. It is formulated in , 7 that A common solution to this problem is to design data synthesis methods. A typical one is to process the data set image by using translation, rotation, flip, and zoom to achieve data augmentation. Such a method has some drawbacks. Due to their inherent characteristics, medical images tend to lose their original interpretation during the process. Moreover, it is difficult to add additional useful information to the dataset for the training of neural network algorithms. Usually, the datasets used by these networks have both input and expected output, and the difference between the generated and the expected output is defined as the loss function –11. It iGenerative adversarial networks (GAN), which have a generator and a discriminator, are used to do data augmentation by learning statistical laws of the input data and generating data samples similar to the input data but not completely repeated . In receAn autoencoder is developed for priori knowledge curation and to retain information on the spatial position, size and shapeA priori knowledge sharpening layer is embedded to make the model converge quicklyIn this work, we developed a method to synthesize quality CT images of early lung nodules using a priori knowledge GAN model from small-size labelled samples. We designed the network to acquire some priori knowledge before executing the generation task. Especially, a knowledge acquisition network and a sharpening network are designed for priori knowledge acquisition, and then, a GAN model is developed to produce CT images of early lung nodules. The contributions of this work are as follows:F1 score of 0.85 in identifying clinic CT images of early lung nodules. This provides an effective way of identifying early lung nodules from CT Images generated by deep learning methods with small size samples. It shows that the data enhancement and generation method based on GAN is feasible in alleviating the dilemma caused by the small number of samples.As a result, the CT images generated by SLS-PriGAN are used to train a fast R-CNN network. It achieves a recognition accuracy of 91%, recall rate of 81%, and It is worth noting that the prior knowledge GAN is a problem and domain-specific deep learning algorithm, which is different from the concept of “transfer learning.” Transfer learning uses a pretrained machine learning model for a new but similar problem.We herewith propose a GAN-based generative model, named, SLS-PriGAN, to generate high-resolution CT images of early lung nodules. As shown in The generator accepts a random vector and integrates the output of priori knowledge module to synthesize the image. In image synthesis, a random control module is introduced to achieve the goal of diversification. If the random control module is removed, the generated image will be more inclined to the original image, and the randomness of the generated image will be destroyed. During data simulation, it is found that it is sufficient to use up to 2 random control modules. The discriminator network module reads both pseudo and actual images to analyze and understand their differences and eventually achieve the syntheses.Incorporating priori knowledge into the generation module is one of the main contributions of SLS-PriGAN, which accelerates the convergence of the model and makes the synthesized image more realistic thus producing more accurate results.During generating process, it uses noise, such as image attributes and categories, to fit feature information. We need to adjust the distribution of high dimensional vectors from low dimensional vectors to synthesize high-resolution CT images, which is a challenge in the case of using small size labelled samples.The design of the generation network is shown in The priori knowledge acquisition network is constructed by a convolutional autoencoder (CAE) . The CAEx be the input image, h be the latent variable, and W1, b1 are encoding weights and offsets, W2, b2 are decoding weights and offsets, and σe, σd are the nonlinear transformations used in the encoding and decoding processes. The convolutional autoencoder uses convolution to linearly transform the input signal and implements a weight sharing strategy. The reconstruction process is a linear combination of basic image blocks that are hidden and coded.Let quations  and 2),,x be theThe encoder encodes the input high-dimensional data to obtain the expression of the low-dimensional hidden layer. The decoder decodes the low-dimensional hidden layer to reconstruct the high-dimensional output with the same size as the input. It is further handed over to the lower network to extract and express knowledge. We used variational autoencoders (VAE) in SLS-PriGAN to extract priori knowledge. As shown in The knowledge acquisition module further refines the acquired knowledge. Image feature learning is one of the important capabilities of convolutional neural networks (CNN). We access the knowledge sharpening module after acquiring a priori information in order to fully exploit the acquired knowledge features. The sharpening network prevents the collapse of the network. The priori knowledge is further extracted, and the global information of the original image is retained. The priori knowledge module is able to overcome the notorious problems that GAN models often encounter, i.e., difficult to convergence and easy to collapse.A fusion control module is added to the generator, which implements the additions to the generator's priori knowledge. As well, a random control module is applied to give the generated image a variety and authenticity of detailed information.W and the fusion of random noise N. As shown in W is summed with the feature map S channel by channel, which realizes the control of the priori knowledge over the body structure of the generated image.The fusion control network is shown in The initial random noise input often makes the generated image closer to the real image. We introduced random noises at different locations in the network to make the composite images more diverse. Generally, the lower layers of the network control the high-dimensional information of the generated image, such as the overall structure of the lungs. The relatively high layer tends to control the detailed information of the generated image, such as the information on lung nodules. We added scaled noises to each channel of the generation network at different resolution stages to make the noise only affect the subtle changes in the picture style and then obtain different visual expressions by changing the resolution level.The loss consists of two parts, which are from priori knowledge acquisition module and the generative adversarial network module. The loss can be calculated by Equation  24, 25], 2524, 2Dloss and Gloss are the loss of discriminator and generator, respectively.x and outputThe purpose of the priori knowledge acquisition network is to minimize the error between input L is taken as the loss function. The value of parameter λ ranges from 0 to 1, which is used to control the weight W. Using λ, we suppress the influence of static noise, improve the generalization ability of the model, and avoid overfitting. As for the second part, we use Wasserstein Gradient Penalty (WGAN-GP) [Cross-entropy function WGAN-GP)  loss to The study was approved by the ethical committee of The Third Hospital of Shandong Province. According to the ethical committee policy, this is a completely anonymous, retrospective study. All sensitive information of the data was removed. The experimental protocols were approved by the hospital, and all methods were carried out per relevant guidelines and regulations.The training and testing dataset of CT images are collected all from The Third Hospital of Shandong Province, China. We curated a total of 673 lung cancer CT images, among which 373 lung cancer CT images are with nodules and selected as the training dataset. The location of the lesion in the CT image is marked by radiologists. The remaining 300 CT images of lung nodules were used as the test set for validation.The structure of the SLS-PriGAN makes model training efficient by introducing a priori knowledge module and a random control module. It does not require redundant techniques hence avoiding possible model collapse. The model has achieved high-quality and high-resolution image generation compared to some work in the current literature, e.g., the method outperformed deep convolutional GAN (DCGAN), see As shown in F1 score of 0.85 in identifying clinic CT images of early lung nodules. The detailed information can be found in It is usually less possible to train an accurate recognition network model using only about 300 images and then recognize the remaining 300 CT images in the test set. With SLS-PriGAN, we generated 4000 CT images to enhance the training set. The generated training set is used to train a general faster R-CNN for lung nodule recognition from CT images. It is shown, in F1 score of 0.85 in identifying early lung nodules in clinic CT images. This provides a viable way of identifying early lung nodules from CT Images by deep learning with small-size labelled samples. Our study focused on CT images of lung nodules; it is believed that the proposed network model should perform equally well for other problems.In this paper, we propose an SLS-PriGAN model with prior knowledge to generate CT images of early lung nodules from small-size labelled samples. Specifically, a knowledge acquisition network and a sharpening network are designed for priori knowledge learning, and then, a GAN model is developed to produce CT images of early lung nodules. As a result, a general FR-CNN network is trained by the CT images generated SLS-PriGAN, and it achieved a recognizing accuracy of 91%, recall rate of 81%, and For future research, it is worth investigating the priori knowledge-driven learning strategies, as well as the other kind of cancers of CT images with a small size of labelled samples –29, feat