Motor imagery is a complex mental task that represents muscular movement without the execution of muscular action, involving cognitive processes of motor planning and sensorimotor proprioception of the body. Since the mental task has similar behavior to that of the motor execution process, it can be used to create rehabilitation routines for patients with some motor skill impairment. However, due to the nature of this mental task, its execution is complicated. Hence, the classification of these signals in scenarios such as brain–computer interface systems tends to have a poor performance. In this work, we study in depth different forms of data representation of motor imagery EEG signals for distinct CNN-based models as well as novel EEG data representations including spectrograms and multidimensional raw data. With the aid of transfer learning, we achieve results up to 93% accuracy, exceeding the current state of the art. However, although these results are strong, they entail the use of high computational resources to generate the samples, since they are based on spectrograms. Thus, we searched further for alternative forms of EEG representations, based on 1D, 2D, and 3D variations of the raw data, leading to promising results for motor imagery classification that still exceed the state of the art. Hence, in this work, we focus on exploring alternative methods to process and improve the classification of motor imagery features with few preprocessing techniques. Motor skill impairment (MSI) is a common symptom in certain conditions and diseases. Health conditions such as strokes, brain injuries, and neuromuscular diseases present a progressive degradation of the use of motor function and fine motor skills . This coThe brain’s communication system is complex, consisting of the synapses and interaction of neurons to excite or inhibit each other through an electrochemical transmission of signals. Cognitive processes make use of large and specialized neural networks to receive, integrate, and transmit information . The actIn the literature, a variety of machine learning classifier methods have been explored for MI-based EEG classification. Common methodologies make use of preprocessing to reduce artifact interference of the signals and signal processing to isolate the frequency range of specific cognitive processes ,12,13. TIn recent years, the application of deep learning (DL) has stretched to analyze 1D signals that describe a variety of physiological behaviors. The literature expands into different approaches to analyze physiological data from different sources for a variety of medical applications . For insNovel EEG signal transformations to account for spatial distributions of electrode placements.Novel DL models that were explicitly designed to address the strengths of each of the different EEG data representations, with results that exceed those presented previously in the state of the art.In this work, we present a comprehensive exploration of a variety of input representations for EEG signals. These different data inputs take into consideration image-based representations of the EEG signals, as well as 1D, 2D, and 3D array representations of the raw EEG signal data. The forms of representation were designed around the motor cortex electrode positioning, as shown in For this work, we use the publicly available Physionet’s Physiobank of Motor Movement/Motor Imagery Database . The datThe recordings of interest for this work are those from Task 2, which would correspond to motor imagery movement of either left or right hand. Therefore, we used three recordings for each subject in the database: Recording 4, Recording 8, and Recording 12. As previously mentioned, three classes were considered for this work. The three classes for this work are left-hand MI, right-hand MI, and rest. Due to incomplete annotations, the recordings from subjects 88, 92, 100, and 104 were excluded, leaving 105 subjects that were used for this work.Alternative forms of representations were adopted for this work with the purpose of analyzing the EEG signal. The spectrogram image representation is a common method that has been employed to analyze EEG as well as other time-varying signals. These images generated from the EEG signal can represent its behavior in the frequency-time domain and differentiate the neural activity in different frequency bands and the difference in energy levels tied to the execution of a mental task. Additionally, the channel down-sampling variations allow limiting the information from the recordings to the area of interest, i.e., those closer to the motor cortex. Channel selection is a common method employed for EEG analysis. The selection is most commonly employed with a symmetrical pairing of electrodes over the region of interest from both hemispheres . This woFor all the distinct data representations, 80% of all the generated samples were randomly selected for training, and 10% were randomly selected for a validation set, with the remaining 10% of the samples belonging to the test set. This partition method is based and that presented in previous research  and is sSpectrogram images generated from an EEG recording allow to analyze the signal in the time-frequency domain using standard computer vision techniques. Many studies have taken advantage of the information that can be obtained from this domain. For this work, the EEG signals containing Task 2 were used to generate the spectrogram images. Two methods to generate spectrogram image representations of the signals were considered. The first method generates single-channel spectrogram images, generating an image for each interval of time representing a class for each of the channels. The second method vertically stacks together the resulting spectrograms for each channel, and then transforms them into an image.To generate the single-channel spectrogram images, the signal was segmented into time intervals of 4 s. The spectrograms were generated using Python’s API Scipy’s  signal fThis stage considers an alternative method to generate the spectrograms to represent the data from the EEG signals. The method consists of generating a single image containing the spectrograms of the 64 channels stacked vertically, as illustrated on In addition, variant forms of vertically stacked spectrograms were generated. These images were constructed by reducing the number of electrodes of subjects’ recordings and limiting the information of the recording to channels that correspond to the motor cortex, as seen in The first variant follows the same procedure for the vertically stacked spectrograms with 64 channels; however, the EEG signal data were limited to the three central channels  and their 18 surrounding electrodes that correspond to the region of the cerebral cortex of interest for MI, for a total of 21 channels. The selected channels used for these variants are shown in The second variant that spins off the 64-channel vertically stacked spectrograms takes a similar approach as its predecessor. However, the number of channels considered was further reduced to obtain a cross-shaped array of the channels, as shown in Due to the low amount of samples that were generated through the two previously described variants, the third and fourth variant methods were designed to increase the number of samples without the need to increase the number of EEG signal recordings. The third and fourth variants, similar to their predecessors, are based on spatial reduction representation. Therefore, for the third variant the twenty-one channels used for the first variant were subdivided into matrices of nine electrodes. Each of the constructed samples makes use of one of the central electrodes , and their eight adjacent electrodes. This allowed us to split the information from the 21-channel vertically stacked spectrogram into three samples containing the information of nine electrodes. The resulting spectrograms share information with each other due to the adjacency positioning of the electrodes. The spectrograms constructed with the C3 electrode as the central channel share the information of electrodes FC1, C1, and CP1 with the spectrograms generated with the Cz electrode as the central channel. Additionally, information from the spectrograms constructed with C4 as the central channel shares the information of electrodes FC2, C2, and CP4 with the spectrograms generated with the Cz electrode as the central channel. This approach allowed to triplicate the number of samples from the first variant method, generating a total of 28,890 sample images, where 14,175 instances belong to class T0 (rest), 7152 instances belong to class T1 (MI left hand), and 7023 instances belong to class T2 (MI right hand).As previously stated, the fourth variant follows the same procedure as the third variant with the cross-channel selection as its basis, as shown in In addition to the preparation of spectrogram images, the raw EEG signals were prepared in order to serve as an alternative form of input for the CNN models. The preparation of the raw signals followed a similar procedure as the one previously described in Similar to the procedure explained in Following the same steps as in Given that a set of stacked arrays does not provide a spatial representation of the EEG channels, the 21-channel samples were re-arranged to mimic the spatial positioning of the EEG electrodes. For that reason, the two-dimensional matrix of 21 × 640 was restructured to a three-dimensional array with dimensions 7 × 3 × 640 as shown in For the 13-channel raw EEG signal data, the process to generate the data samples were similar to the ones described in Similar to the process of To increase the number of samples of raw EEG signals, the 21-channel array was subdivided into three matrices of dimensions 3 × 3 electrodes. The same process was done as in The two-dimensional instances of data generated through the 9-channel arrangements of data were reconstructed to create a new form of data representation that seeks to mimic the EEG electrode arrangement observed in Following the same process as in the one described in This form of data representation is the last variant for multi-channel representation of the data. The structure follows the same process as the one described in The last variant form of input considered for this work was single-channel arrays of the raw EEG signal data. The basis of the preparation for this data takes the same steps as the generation of the spectrogram images described in With the RGB spectrogram images as input, three 2D CNN models are proposed in this work for MI 3-class classification. Each convolutional layer of these models is constructed with a convolution filter, a ReLU activation, and a maxpooling, with a fully connected layer to perform the classification. The implementation of these models was carried out in PyTorch . For eacThe first proposed model consists of a single-layer CNN, as exemplified in The second proposed model, shown in The third proposed model consists of a three-layered model with three convolution layers, with the basic structure shown in For this work, two additional models are proposed by making use of transfer learning. Both models make use of the pre-trained model of ResNet18 . To makeAs described in For the first architecture of the proposed models, a single layer was used. The model makes use of a 128 kernel filter with a size of 5, a stride of 2, and no padding. The layer makes use of batch normalization, ReLU activation, and a 1D maxpool of size 3 with a stride of 2. The output of the convolution layer is passed to a fully connected linear network to perform the 3-class classification of MI. The structure of the single-layer 1D CNN model is presented in For the second 1D CNN proposed model, an architecture with two layers was designed. The basic architecture is presented in The third proposed 1D CNN model makes use of three convolutional layers. For the first layer a convolution with 64 kernels was used, with a size of 5, stride of 1, and padding of 1. The layer makes use of batch normalization and ReLU activation. The second layer is built upon a convolution with 128 filters, a kernel size of 3, and a stride of 1. The layer makes use of batch normalization and dropout and ReLU activation. The last convolution layer of this model makes use of 256 kernel filters, with a size of 3, stride of 1, and padding of 1. The last layer also includes batch normalization and dropout, along with a ReLU activation function. The basic structure of this model is presented in For the fourth model, a 1D CNN with four layers was designed. The basic structure is illustrated in The fifth and last proposed 1D CNN model was designed with 5 convolutional layers. The first layer of the model makes use of a 1D convolution of 32 kernel filters with a size of 5, stride of 2, and padding of 1. The layer makes use of batch normalization, ReLU activation, and a maxpooling with a kernel size of 2 and stride of 1. The second layer convolution makes use of a 64 filter kernel, with a size of 3, stride of 2, and padding of 4. In addition, the layer makes use of batch normalization and dropout, along with ReLU activation, and maxpooling with a kernel size of 2 and stride of 1. The third layer of the model makes use of 128 kernel filters, with a kernel size of 1 and a stride of 1. Additionally, the layer makes use of batch normalization and ReLU activation. For the fourth layer of the model, 256 filters were used, with a kernel size of 3, stride of 1, and a padding of 4. A ReLU activation function along with batch normalization is also included in the fourth layer. The last layer of the proposed model makes use of a reduction of filters, using 128 filters with a kernel size of 5, stride of 1, and padding of 4. The last layer also makes use of batch normalization, ReLU activation, and dropout. Finally, the model makes use of a fully connected linear network to perform 3-class classification. The basic architecture of this model is illustrated in Python, with the libraries of numpy and pandas for data manipulation, scipy for signal transformation and Pytorch for the DL implementation of the proposed CNN models.The hardware employed for this work consisted of a personal computer with an AMD Ryzen 3 3200 g processor with a base clock of 3.6 GHz, 16 GB RAM running at 3200 MHz, and an NVIDIA GEFORCE RTX 2060 SUPER with 8 GB VRAM. The models were implemented in This section evaluates and discusses the classification accuracy results on the Physionet Motor Movement/Imagery Dataset’s Task 2 recordings, for three-category classification. Classification accuracy is measured as the ratio of the correctly classified samples over a total number of samples of the test set. For comparison with the state-of-the-art methods in the literature, the results of two studies are reported, both of them presenting a similar paradigm of three-class MI classification using the Physionet public database , with diThe results and discussion section is divided into four subsections: This subsection presents the results using the proposed CNN models on the image-based representations. The results from these experiments using the test set are summarized in ResNet18 + Single-Layer CNN and ResNet18 + Three-Layer CNN improved on the previous state of the art significantly, attaining accuracies of 90.55% and 93.32%, respectively.From In contrast, when we evaluate our models using different data representations based on stacking spectrograms, we see that there is a considerable drop in performance from all models. We argue this result is due to the considerable data reduction from generating the samples and the intra-class variability within the same EEG recording, as reported previously .This subsection presents the results from the CNN models that use the 2D matrix representations as inputs. The results from these experiments are summarized in From Even though the combination of the two-layer model with the 9-channel selection samples was able to outperform the state of the art and their spectrogram image counterpart, it was unable to compare favorably against the single-channel spectrograms for the transfer learning implementations.This subsection presents the results from the proposed CNN models with the 3D matrix representations for the raw EEG signal. The results from these experiments are summarized in Similar to the results returned through the 2D input presented in The last input modality considered in this work consists of 1D arrays from the raw EEG signal. Each data sample represents an interval of time in which a labeled task was executed. For this input format, five models were proposed. The results from these experiments are summarized and compared against the state of the art in ResNet18 + Three Layer CNN transfer learning implementation, there is a significant reduction in computational costs since the time required to prepare 1D arrays is considerably less than that required to generate the spectrograms.The single-layer 1D CNN model returned a classification accuracy of 64.69%. The second proposed model, consisting of a two-layer 1D CNN model, returned a classification accuracy of 76.29%; with the three-layer 1D CNN model returning 84.87% classification accuracy. The four-layered model outperformed the previous models, with a classification accuracy of 86.12%. Lastly, the five-layered 1D CNN returned an accuracy of 79.58%, leading to better accuracies than those obtained using 2 and 3D data representations. Additionally, we can see a significant improvement in the vertically stacked spectrogram representations of the signal. This implies that the information conveyed by the individual channels helps the feature extraction process that represents the MI task. In contrast to the state of the art, the result from the proposed single-layer model was not able to outperform the reported results from ,26, withExtensive experimentation with different EEG representation modalities using single-channel spectrograms, 3D raw EEG signal, and 1D raw EEG signal analysis.Novel deep learning algorithms are designed specifically for each of the data representation modalities.Analysis of the selection of distinct EEG channels for optimal MI representation.EEG signals present a 1D representation of the physiological activity of mental tasks. The recordings obtained from EEGs show excellent time-domain granularity, sacrificing spatial resolution. To overcome this obstacle, different forms of data representation can be designed to mimic the spatial representation of the actual physical signals. In this work, an extensive experimentation over different forms of EEG data representations was explored. Data representations included the generation of spectrogram images from single channels in a single image, and multiple channels within a single data sample. In addition, the raw signal was rearranged into 2D and 3D matrices of the EEG’s most significant channels. The best accuracy performances were observed with the single-channel spectrogram representation with a transfer learning using a pre-trained ResNet18 in combination with a three-layered CNN model for MI classification, with an accuracy of 93.32%. As can be observed on Finally, we consider it relevant to point out that the 9- and 5-channel EEG representations are highly important. Although channel selection is a common method employed for EEG analysis, the selection is usually performed over a complete region of interest with a symmetrical pairing of electrodes from both hemispheres. The presented 9- and 5-channel selection process consists of the segmentation of the recording into samples that represent different areas of the motor cortex, independently of a symmetrical pairing among hemispheres. Furthermore, through this data preparation, additional samples representing the MI task were able to be generated. Although the results obtained with these forms of the EEG signals’ representation were not the highest, the results were able to provide an accuracy performance higher than that reported previously in the state of the art, and should be explored further in future endeavours.The end-line of this work would seek a real-time application. This work could be implemented for rehabilitation routines for patients with a form of motor skill impairment. Therefore, future work would involve the improvement of the analysis of the 1D array as well as the 2D and 3D matrix representation, since these formats entail the lowest computational resources. Additionally, the implemented models used to analyze the 1D, 2D, and 3D representations provided promising results, and further exploration of these representations of the EEG signal could lead to viable applications for an online experiment. After improving the models, the following step would be to test the models to analyze EEG signals of previously unseen data.Likewise, given their ability to abstract information, our models could be used in other research/engineering problems based on EEG signals. For example, they could be used as pre-trained models used in epilepsy, sleep, and ADHD, just to name a few. This way, it would be possible to use transfer learning for EEG analysis.


Based on the analysis of the microstructure, the influence mechanism of curing methods on the mechanical properties of materials was explored. The results showed that the early compressive strength of MSUHPC under steam curing (SM) is much higher than that of the other three curing methods, but the difference gradually decreases with the increase of age. The compressive strength of MSUHPC under SM is higher than that of river sand UHPC (RSUHPC). The bending strength and compressive strength of MSUHPC under different curing methods are similar, and the bending strength of 28 days steam cured samples is the highest. The uniaxial tensile properties of MSUHPC did not show significant difference under standard curing (SD), sprinkler curing (SP) and saturated Ca(OH)2 curing (CH), and the uniaxial tensile properties of MSUHPC under SM are slightly better than RSUHPC. The ultimate bond strength and fiber pullout energy of steel fiber and MSUHPC increase with the development of age. The bond strength and fiber pullout work of SM is higher than those of the other three curing methods, but there are lower increases in the later stage than that of the other three curing methods.The mechanical properties of ultra-high performance concrete (UHPC) made of manufactured sand (MS) under four curing methods  Since the second half of the 20th century, with the rapid development of engineering structures and economic growth, people also have new requirements for the performance of engineering materials, and have begun to explore and research high performance and even ultra-high performance engineering materials. In 1994, the concept of ultra-high performance concrete (UHPC) was proposed [UHPC is a high density cement-based composite material with high strength and high ductility, which has been applied in maintenance and reinforcement, bridge engineering and other fields ,6,7,8,9.The curing method of concrete has an important influence on the formation of its strength and later development. The commonly used curing method for UHPC is high-temperature steam curing, which is relatively complex and more suitable for preparing prefabricated components in fixed places such as prefabricated yards . For som2 erosion; by using a Ca(OH)2 solution, wetting concrete can improve its density and compressive strength, and this method can solve the problem of CO2 erosion of cement and concrete materials.Gao H et al.  studied 2 curing (CH)) on mechanical and microstructural properties of concrete are investigated. It is greatly significant to promote the application of MS in UHPC preparation.Existing research results have shown that different curing conditions have a great influence on the hydration reaction rate and physical and mechanical properties of UHPC ,34. The 2 content 97%) produced by Hengyuan Material  were used. The steel fiber was made of copper-plated steel fiber produced by Sida Steel Fiber Industry . The shape diagram is shown in This study adopted ordinary Portland cement (P·O52.5) produced by Mengdian Cement Industry . The physical properties of the cement are shown in The workability of concrete is characterized by the slump of the specification GB/T 50080-2016 . The tesThe mechanical properties of the specimen are characterized according to the compressive, tensile and bending strength of specification T/CECS 864-2021 . The comA steel fiber pullout test was used to study 3, 7, 14 and 28 days bond strength between steel fiber and matrix according to specification CECS 13-2009 . The 8-sffb, can be calculated according to Equation (1). The pullout work, Wcon, which can be reflected by the area enclosed by the Load-slip curves and x axes, can be calculated according to Equation (2).The bond strength between steel fibers and the UHPC cement matrix, Fmax is the maximum pullout load, lem represents the embedment length of the target fiber in the concrete matrix.In Equation (1), The samples used for the microscopic test were taken from UHPC specimens with four different curing methods and 28 days age. Scanning electron microscopy  was used to analyze the morphology and the internal microstructure of concrete under different curing conditions. Small pieces of samples (about 3 mm thick at least) were selected, and the surface of the samples was sprayed with Au to make them better conductive. The test condition was in a vacuum and the test voltage was 15 kV. X-ray diffraction  using a diffractometer at 40 kV and 35 mA. UHPC samples were ground to 200 mesh in a mortar before the test. The pore structure of the UHPC samples was analyzed using mercury intrusion porosimetry . Firstly, the specimens were broken into approximately 5 mm pieces, and then to stop hydration the specimens were soaked in ethyl alcohol. The samples were dried at 50 °C in a drying oven for 20 h before experiment. The specimen tests were then carried out under high pressure of 413.70 MPa and low pressure of 0.28 MPa, respectively.The results of the UHPC compressive strength test are shown in With the increase of age, the gap between the compressive strength of MSUHPC with other three curing methods and SM is gradually narrowed. CH can better improve the compressive strength of MSUHPC. Among them, the size rule of 28 days compressive strength of the material is as follows: CH > SD > SP, reaching 82.2%, 81.1% and 79.6% of the compressive strength of SM at the same age, respectively. When the curing age was 56 days, the compressive strength of CH, SD and SP reached 90.2%, 89.6% and 86.3% of that of SM, respectively.3S and C2S in cement clinker to produce calcium carboaluminate hydrate, which makes the structure of UHPC more compact [Under the SM method, the compressive strength of MSUHPC at all ages is higher than that of RSUHPC. On the one hand, because of the high strength of the MS itself, it has the characteristics of rough surface, irregular shape and many corners, and has strong mechanical bite force between the cementitious material, so the compressive strength is higher than that of RSUHPC . On the  compact .The results of the UHPC bending strength test are shown in The tensile stress-strain curves of MSUHPC under different curing methods are shown in The main performance indexes of the material in uniaxial tensile test include initial cracking strength , tensile strength (ultimate tensile stress) and the ratio of tensile strength to initial cracking strength, as shown in 2 required for pozzolanic reaction, so that the hydration of the MSUHPC more sufficient and the matrix is more compact. Replacing RS with MS can slightly enhance the crack resistance of UHPC. Under SM, the initial cracking strength and tensile strength of RSUHPC are 7.44 MPa and 8.54 MPa, respectively, which are 4.0% and 2.1% lower than those of MSUHPC. In addition, the ratio of tensile strength to initial cracking strength of MSUHPC under the four curing methods is greater than 1.1, according to the classification of tensile properties in T/CECS 10107-2020, MSUHPC strain hardening characteristics under various curing methods, which can well explain the strain hardening phenomenon in uniaxial tensile process.Combined with The load–slip curves of 28 days steel fiber and UHPC matrix under different curing methods are shown in The bonding properties and force failure characteristics of steel fibers and UHPC matrix are mainly related to three factors: steel fiber, UHPC matrix and the bonding force between them . The curUnder the four curing methods, the ultimate bond strength and fiber pullout energy of steel fiber and MSUHPC increased with the increase of curing age. Under SM, when the curing age is 3 days, the bond strength and fiber pullout energy of steel fiber and MSUHPC matrix are significantly higher than those of the other three curing methods, and the later strength and energy growth level are relatively flat. This is because SM can promote the hydration reaction at an early stage, thereby increasing the strength of the cementitious matrix. Under SD, SP and CH, with the increase of age, the hydration degree of the MSUHPC matrix is also increasing, and its ultimate bond strength with steel fiber and fiber pullout energy have a high increase. Under SM, the ultimate bond strength and fiber pullout energy of steel fiber and MSUHPC matrix increased slightly, and the 28 days age increased by 8.9% and 7.68% compared with the 3 days age, respectively. However, the ultimate bond strength of the other three curing methods increased by more that 16%, and the fiber pullout energy increased by at least 41%. From 3 days to 28 days, the ultimate bond strength and pullout energy increased the most under CH mode, by 36.2% and 57.8%, respectively.3AH6) produced in the cement hydration process with gypsum in the cement 2, C3S, C2S, AFt and C-S-H. Among them, the MSUHPC uses limestone MS as the fine aggregate, so a strong CaCO3 diffraction peak is detected, while the RSUHPC uses the RS as the fine aggregate, so a strong SiO2 diffraction peak is detected. The Ca(OH)2 diffraction peak under SM is relatively weak, which confirms the promotion effect of SM on secondary hydration reaction of UHPC matrix. C3S and C2S diffraction peaks were found in each group of MSUHPC, because the water–binder ratio of MSUHPC was extremely low, the cement hydration reaction was insufficient, and there was unhydrated cement clinker in the sample. Among them, the diffraction peaks of C3S and C2S under SP are relatively strong, indicating that due to the relatively low hydration degree of cementitious materials under SP, there are more unhydrated cement clinkers.The XRD patterns of MSUHPC under different curing methods are shown in The pore size distribution curves of MSUHPC under different curing methods are shown in It can be seen from The fractal dimension can reflect the pore characteristics of concrete and quantitatively describe the roughness and complexity of concrete pore surface ,48. The (1)Under SM, the compressive strength of MSUHPC increased greatly in the early stage, and the later strength increased less. The 28 days compressive strength increased by only 1.5% compared with the 3 days compressive strength. The 28 days compressive strength of CH, SD and SP increased by 19.1%, 52.3% and 16.3% respectively compared with 3 days compressive strength. In addition, the compressive strength of four curing methods was only negatively increased in SM at 56 days. The development law of bending strength and compressive strength of MSUHPC under different curing methods is similar, but due to the significant influence of steel fiber on the bending properties, the difference in bending strength of the four curing methods is small.(2)Under SD, SP and CH methods, the tensile properties of MSUHPC do not show significant differences. Compared with the other three curing methods, SM method increased the initial crack strength and tensile strength of MSUHPC by more than 19%. The ratio of tensile strength to initial crack strength of MSUHPC is greater than 1.1 under all four curing methods and shows strain hardening characteristics. The tensile properties of MSUHPC are slightly better than RSUHPC under SM method.(3)The ultimate bond strength of steel fiber and MSUHPC matrix under SM has a small increase in the later period, and the 28 days ultimate bond strength only increases by 8.9% compared with the 3 days ultimate bond strength. The 28 days ultimate bond strength of CH, SD and SP increased by 36.2%, 18.3% and 16.5% respectively compared with 3 days ultimate bond strength. Moreover, compared with the other three curing methods, SM shows that the increase of fiber pullout energy of MSUHPC is more different. The SM 28 days fiber pullout energy increases by 7.7% compared with 3 days fiber pullout energy, while the other three curing methods all increase by 40%.(4)2 diffraction peak is relatively weak, indicating that SM promoted the secondary hydration reaction of UHPC matrix. However, SM has an adverse effect on the long-term performance of concrete, so it needs to be kept wet in the later stage to eliminate its adverse effects. Under SD, CH and SP, the porosity and the most probable pore diameter of MSUHPC are relatively close, but larger than the SM method. The pore fractal dimension of MSUHPC is the largest under SM, indicating that the pore distribution in the material is more complex and the proportion of small pores is higher.Under SM, the porosity and the most propable pore diameter of MSUHPC were lower, and the Ca(OH)In this research, UHPC was prepared by fully replacing RS with MS. The effects of different curing methods on the mechanical properties of MSUHPC and the bonding properties between steel fibers and MS were analyzed through mechanical property tests combined with microscopic tests. The following conclusions were drawn:


The aggregation of intracellular proteins may be enhanced under stress. The expression of heat-shock proteins (HSPs) and the accumulation of osmolytes are among the cellular protective mechanisms in these conditions. In addition, one should remember that the cell environment is highly crowded. The antiaggregation activity of HSPB5 and the effect on it of either a crowding agent (polyethylene glycol (PEG)) or an osmolyte (betaine), or their mixture, were tested on the aggregation of two target proteins that differ in the order of aggregation with respect to the protein: thermal aggregation of glutamate dehydrogenase and DTT-induced aggregation of lysozyme. The kinetic analysis of the dynamic light-scattering data indicates that crowding can decrease the chaperone-like activity of HSPB5. Nonetheless, the analytical ultracentrifugation shows the protective effect of HSPB5, which retains protein aggregates in a soluble state. Overall, various additives may either improve or impair the antiaggregation activity of HSPB5 against different protein targets. The mixed crowding arising from the presence of PEG and 1 M betaine demonstrates an extraordinary effect on the oligomeric state of protein aggregates. The shift in the equilibrium of HSPB5 dynamic ensembles allows for the regulation of its antiaggregation activity. Crowding can modulate HSPB5 activity by affecting protein–protein interactions. HSPB5 , a member of the small heat-shock protein (sHSP) superfamily, is an important component in the system of protein quality control in the cell . sHSPs aAll sHSPs consist of an α-crystalline domain (ACD) and flexible C- and N-terminal extensions with an intrinsically disordered structures ,18. It iThe flexibility of the sHSP quaternary structure is currently being studied as one of the ways to regulate their functioning and activity ,20,30,31It is implied that the cell is crowded with macromolecules  so tTypically, the crowded milieu is mimicked in vitro by adding high concentrations of suitable inert polymers or proteins, so-called crowding agents, such as polyethylene glycols (PEG) of different molecular weights, polysaccharides (Ficoll and dextran), polyvinylpyrrolidone (PVP), or proteins  . It is cb (Phb).Crowding, being the factor which markedly influences protein–protein interactions (including aggregation and supramolecular association) adds another degree of complexity to the dynamic structure–function relationships of sHSPs ,40,41,42Osmolytes  are accumulated in the cell under stress conditions . Osmolytes protect protein molecules from denaturation by stabilizing their tertiary structure . Since tb used as the target protein. Crowding significantly reduced the chaperone-like activity of the HSPB5 in that case. Therefore, it is of interest to test how common this crowding effect is with other target proteins. The aim of the present work is to study the effects of a crowding agent (PEG), an osmolyte (1 M Bet), and their mixture on the chaperone-like activity of HSPB5. Bovine liver glutamate dehydrogenase (GDH) and hen egg white lysozyme (Lyz) were used as the target proteins. These proteins drastically differ in size, oligomeric structure, and aggregation kinetics.It should be noted that the antiaggregation functioning of sHSPs is based on the interactions with target proteins that differ widely in size, mass, molecular structure, charge, and other parameters. It is reasonable to assume that the activity of sHSPs depends not only on their oligomeric structure and the environment, but also on the properties of the target protein. The attempt to quantify the activity of HSPB5 towards a target protein under crowding conditions was made in our previous work  with a hGlutamate dehydrogenase (GDH) [EC 1.4.1.2-4] is one of the key metabolic enzymes which catalyze the oxidative deamination of L-glutamate to α-ketoglutarate . The GDHMw ~ 14 kDa, functioning as N-acetylmuramidase 0) are the following  = 1 mg/mL) was incubated for 15 min at 37 °C in the presence of 20 mM DTT, centrifuged 30 min at 20,000× In the case of GDH, SV runs were performed at 25 °C in 0.1 M Na-phosphate buffer, pH 7.6, containing 0.01 M NaCl, with or without the crowding agent, or the osmolyte, or the mixture of both. The rotor speed was 48,000 rpm. All GDH samples were incubated for 10 min at 50 °C before the run.c(s) versus s) were determined using SEDFIT program [Sedimentation profiles were recorded by measuring the absorbance at 280 nm. All cells were scanned simultaneously. The time interval between scans was 2.5 min. The differential sedimentation coefficient distributions  of the buffer, Bet, and PEG solutions, and their mixture, were measured at 25 and 37 °C using an AMVn microviscometer  with a 1.6/1.500 capillary system. The densities (ρ) of these solutions were measured at 37 and 50 °C with the DMA 4500 densitometer . The obtained values of densities and viscosities are given in the Crowding adds another level of complexity not only to the regulation of sHSPs function, but to the protein aggregation as well. The net effect of crowding on protein–protein interactions depends on the shape and other physical parameters of the target proteins, and on the sum of interactions of different types of crowders with these proteins. Some of the compounds that can create the crowding effect also have other ways to influence protein structure and stability. For example, Bet is an osmolyte and may be considered as a crowder (at high concentrations). It should also be remembered that HSPB5 assemblies are highly dynamical structures, and changes in their composition are linked to the regulation of substrate specificity and activity of HSPB5 . The sysIn this work, we approached the analysis of such a system from the side of a protein function, which is a direct result of all the above-mentioned complicated and dynamic interactions. Our point was to characterize and compare this protein function, namely the chaperone-like activity of HSPB5, at the different stages of protein aggregation using target proteins with different sizes, oligomeric structures, and aggregation kinetic regimes. To quantify the chaperone-like activity of HSPB5, an analysis was conducted of the aggregation kinetic curves obtained by DLS. The oligomeric state of the target proteins and their aggregates upon the interaction with HSPB5, in the presence and in the absence of osmolyte and/or crowder, was monitored by AUC.It was shown that, in the case of heat-induced aggregation of GDH at 50 °C (the rate-limiting stage is the stage of protein denaturation), HSPB5 suppresses aggregation at both nucleation and subsequent aggregate growth stages. In the presence of 1 M Bet, 25 mg/mL PEG, and their mixture, the activity of HSPB5 decreased, judging by the kinetic parameters . However, the AUC data showed that, although in some cases crowding and osmolytes caused the formation of larger aggregates, the presence of HSPB5 resulted in the prevention of GDH precipitation. The protein aggregates remained soluble. Thus, even the decreased activity of HSPB5 was sufficient in the long-term protection of the target protein.In contrast, the DTT-induced aggregation of Lyz at 37 °C has the rate-limiting stage of the aggregation of unfolded protein molecules (the denaturation proceeds rapidly). We observed mixed and often controversial effects at different stages of aggregation in the presence of different additives and concentrations of HSPB5. While the previous test system with the rate-limiting stage of denaturation primarily reflected in its aggregation kinetics the changes in the target protein stability, the Lyz-based system revealed all the complexity of possible ways of interaction between denatured protein, HSPB5, osmolyte, and polymeric crowder. We observed the acceleration of aggregation by low concentrations of HSPB5 (0.01–0.025 mg/mL), and the prevention as well as promotion of this effect by Bet and PEG at different aggregation stages. The higher concentrations of HSPB5 suppressed Lyz aggregation and Bet, and PEG caused an increase in chaperone-like activity in some cases, and a decrease in other cases. Nevertheless, the AUC data also showed the overall protective effect of high enough concentrations of HSPB5 (0.05 mg/mL) on the partially denatured target protein at 37 °C. The general effect of the chaperone in the presence of Bet, PEG, and their mixture on the comparative size/mass of protein aggregates was mostly a decrease in the aggregates size and the changes in the conformation of Lyz monomers and dimers.This work provides information for a deeper understanding of the complex relationship between structure and function of sHSPs in more native-like surroundings than the usually studied dilute buffer solutions. It may be useful for further research in the field of molecular chaperones, their functioning, regulation, and involvement in physiological and pathological processes, and may provide possible applications in the treatment of the latter.


Cardiovascular diseases (CVDs) were accountable for 24% of the total deaths in Cambodia, one of the low- and middle-income countries, where primary health care (PHC) settings generally do not perform well in the early detection, diagnosis, and monitoring of leading risk factors for CVDs, that is, type 2 diabetes (T2D) and hypertension (HT). Integrated care for T2D and HT in the Cambodian PHC system remains limited, with more than two-thirds of the population never having had their blood glucose measured and more than half of the population with T2D having not received treatment, with only few of them achieving recommended treatment targets. With regard to care for T2D and HT in the public health care system, 3 care models are being scaled up, including (1) a hospital-based model, (2) a health center–based model, and (3) a community-based model. These 3 care models are implemented in isolation with relatively little interaction between each other. The question arises as to what extent the 3 care models have performed in providing care to patients with T2D or HT or both in Cambodia.This protocol aims to show how to use primary data from a population-based survey to generate data for the cascades of care to assess the continuum of care for T2D and HT across different care models.We adapt the HIV test-treat-retain cascade of care to assess the continuum of care for patients living with T2D and HT. The cascade-of-care approach outlines the sequential steps in long-term care: testing, diagnosis, linkage with care, retention in care, adherence to treatment, and reaching treatment targets. Five operational districts (ODs) in different provinces will be purposefully selected out of 103 ODs across the country. The population-based survey will follow a multistage stratified random cluster sampling, with expected recruitment of 5280 eligible individuals aged 40 and over as the total sample size. Data collection process will follow the STEPS (STEPwise approach to NCD risk factor surveillance) survey approach, with modification of the sequence of the steps to adapt the data collection to the study context. Data collection involves 3 main steps: (1) structured interviews with questionnaires, (2) anthropometric measurements, and (3) biochemical measurements.As of December 2021, the recruitment process was completed, with 5072 eligible individuals participating in the data collection; however, data analysis is pending. Results are expected to be fully available in mid-2022.The cascade of care will allow us to identify leakages in the system as well as the unmet need for care. Identifying gaps in the health system is vital to improve efficiency and effectiveness of its performance. This study protocol and its expected results will help implementers and policy makers to assess scale-up and adapt strategies for T2D and HT care in Cambodia.International Standard Randomised Controlled Trials Number (ISRCTN) registry ISRCTN41932064; https://www.isrctn.com/ISRCTN41932064DERR1-10.2196/36747 Globally, cardiovascular diseases (CVDs) are responsible for the death of 17.9 million people annually, accounting for 31% of all deaths . In regiIn Cambodia, CVDs were estimated to account for 24% of the total deaths in 2018 , and in hospital-based model, (2) a health center–based model, and (3) a community-based model.The response to these T2D and HT epidemics requires concerted effort from both global health governance and Cambodia’s health system. Cambodia has a pluralistic health system, with a public health care system operated by the Ministry of Health, complemented by many private health care services that mainly offer outpatient curative care, operating largely without sufficient steering and coordination from the government . The govThe hospital-based model is a standard care model for T2D and HT that is available at district or provincial referral hospitals as part of outpatient consultation. These referral hospitals provide ambulatory care and support the health centers in treating serious cases. Health centers are allowed to take care of mild or stable cases without complications. The referral hospitals will treat serious cases . In 2018The health center–based model has been given increasing attention by the Ministry of Health with support from the World Health Organization through the adoption of the WHO PEN for PHC . The NatThe community-based model is predominantly run by a Cambodian nongovernmental organization called MoPoTsyo that operates Peer Educator Networks with 4 main key services for patients with T2D or HT or both. These services include (1) self-management training through peer educator visits, (2) laboratory tests, (3) physician consultations, and (4) low-cost medicines delivered through a revolving drug fund program to the members in the network in 8 out of 24 provinces across the country. By 2019, 255 peer educators have been trained to serve over 40,000 patients [These 3 care models are implemented in isolation with relatively little interaction between each other. There have been few empirical studies on their performance. The question arises as to what extent the 3 care models have performed in providing care to people with T2D or HT or both in Cambodia. Care models that are integrated in terms of shared information and resource coordination have shown to be effective and efficient in many contexts ; howeverInspired by noticeable successes in providing the continuum of care to people living with HIV in Cambodia , we adapGiven that Cambodia implements T2D and HT services through 3 different care models, we propose the cascade-of-care approach to assess the performance of these care models along the continuum of care. We will do so using primary data collection. This study protocol aims to serve as a tool to generate the cascades of care for T2D and HT for the 3 care models using the primary data of a population-based survey. The specific aims are as follows:To generate the cascades of care for T2D for (1) hospital-based care, (2) health center–based care (WHO PEN), (3) community-based care (Peer Educator Network), and (4) coexistence of 1, 2, and 3;To generate the cascades of care for HT for (1) hospital-based care, (2) health center–based care (WHO PEN), (3) community-based care (Peer Educator Network), and (4) coexistence of 1, 2, and 3;To compare the cascades of care for T2D between the care models 1-4;To compare the cascades of care for HT between the care models 1-4.This study protocol is part of a larger population-based survey—the SCUBY  project , which iTo meet the aforesaid specific aims, 5 operational districts (ODs) in different provinces will be purposefully selected out of 103 ODs across the country. The selection is based on a mapping exercise conducted in the SCUBY project. Only in the OD Daunkeo the 3 care models coexist. The OD Samrong in a bordering province hosts a typical noncommunicable disease (NCD) clinic at the referral hospital (the WHO PEN and Peer Educator Network are not there yet). The OD Kong Pisey does not have the WHO PEN and the NCD clinic at the referral hospital (only the Peer Educator Network of MoPoTsyo exists—with relatively strong network). The OD Pearaing is one of the ODs piloting the WHO PEN and started implementing the program since 2015. At the time of study, 8 of 9 health centers have implemented the WHO PEN. The OD Sotr Nikum is historically and significantly influenced by the financial aid of various development partners and nongovernmental organizations—contextual factor is a focus. The referral hospital in this OD has a Chronic Disease Clinic, where people with T2D or HT or both and those with HIV seek treatment and care . At the This population-based survey includes 2 questionnaires  and 2. OThis population-based survey follows a multistage stratified random cluster sampling . For theBased on the multistage stratified random cluster sampling, 3 stages of stratification are applied, and in each stage randomization is employed. ODs are the strata, and health centers impacted by the types of care models are selected. For the first stage of stratification, sampling with equal probability  will be used to determine the number of villages under the catchment areas of the impacted health centers in each OD. This equal selection is also made to ensure oversampling for some ODs. If using proportionate allocation of the sample units across the ODs, the sample selected from the ODs representing certain types of care models would be too low to have enough statistical power for the analyses. In this survey, disproportionate allocation is done to randomly select equal-sized samples in the 5 ODs . For theThe sample size of households in all the selected 5 ODs is calculated based on the following formula :h is the parameter to be calculated and the sample size in terms of the number of households to be selected; z is the statistic that defines the level of confidence desired (1.96 for the 95% level of confidence); r is an estimate of a key indicator to be measured by the survey ; f is tvey 2016  and the vey 2016 ); k is avey 2016 ); p is tvey 2016 ); γ is tsus 2019 ); and e While HT prevalence is higher, T2D prevalence of 10%  is used Adults aged 40 and above are the target population. This age group is appropriate for screening for T2D and HT according to the national standard operating procedure for T2D and HT management in primary care . Other rThe starting point of recruitment is a list of all the eligible households in the selected villages . The lisData collection process will follow the STEPS survey approach , with mo1c (HbA1c) and creatinine for known participants with T2D or participants having FBG of 126 mg/dl or more , the On Call Plus (ACON USA), which is compliant with the US Food and Drug Administration regulations , will be Program , will bertension , will bertension .P<.2) will be included in a multiple logistic regression model. In addition to the aforementioned variables, BMI, lifestyle, knowledge of T2D and HT, self-management, and social support will be included in the multiple logistic regression model for the outcome variables—in care, in treatment, and under control bars [P value will be eliminated from the model one by one at a time. The process will be repeated until all the variables in the model are statistically significant with a cut-off point of P value <.05. This knowledge will allow us to identify which characteristics stimulate the probability of not reaching the next step in the cascade of care, thereby identifying patient groups not adequately reached.For the analysis plan, 6 cascade bars, as explained in rol bars ,25. In tBecause of the multistage stratified random cluster sampling, a 3-stage weighting procedure will be applied to account for (1) the fixed number of villages selected in each OD, (2) the fixed number of households selected in each village, and (3) the number of household members aged 40 and above in each selected household ,30. The Variables used to generate the cascade of care for T2D and HT are shown in The population in the catchment areas is not confined to seek care only in the designated public health facility. To address this, we will also collect patient registry data from the public health facilities in the study setting with regard to patients receiving treatment for triangulating the care model selected.This study protocol has been approved by the National Ethics Committee for Health Research in Cambodia (reference number 105 NECHR) and by the Institutional Review Board of Institute of Tropical Medicine . The study is also registered as part of the SCUBY protocol at the International Standard Randomised Controlled Trials Number (ISRCTN) registry, number ISRCTN41932064 .Data collection was carried out from mid-July to mid-October 2020. By June 2021, the data cleansing process was finished and cleaned data were properly managed as data sets. As of December 2021, the recruitment process was completed, with 5072 eligible individuals participating in the data collection; however, data analysis is pending. Results are expected to be fully available in mid-2022.This protocol aims to assess the performance of the 3 dominant care models for T2D and HT through the cascade of care framework, using the population-based survey. This framework will allow us to identify the leakages in the system and the unmet need for care . In addiThe strengths of the study are primary data collection, a large sample size, and multiple types of data. This allows us to assess multiple outcomes and to link them with other indicators such as health care utilization, health seeking behavior, morbidity profile, and sociodemographic characteristics. The limitations relate to its complicated set up. The multilevel stratification and the collection of multiple types of (outcome) data make the research design and practical organization difficult. The clustering on more than 2 levels and the different outcomes make it challenging to calculate an ideal sample size following all regulations. We have addressed this by seeking optimal balance between maximizing precision and minimizing costs for feasibility. The purposive selection of ODs based on the mapping of existing care models limits the generalizability of results. However, through randomization within ODs, we will strive for maximum internal validity.Despite these limitations, this study protocol has a large potential to produce evidence of the performance of different care models for T2D and HT in Cambodia. These insights will help implementers and policy makers to assess scale up and adapt strategies. This is of vital importance owing to the increasing burden of CVDs, T2D, and HT in the country ,12,13. A


Lysozyme is a part of human and animal noncellular immunity. The regulation of its activity by hormones is poorly studied. The aim of this study was to test the Micrococcus luteus in the early exponential growth stage. The activity of lysozyme was assessed based on the rate of change in the OD of the test culture. Molecular docking was performed using SwissDock server (http://www.swissdock.ch/docking), and molecular structures were further analyzed and visualized in the UCSF Chimera 1.15rc software. Hormones were incubated with lysozyme, and the activity of lysozome was further determined using a test culture of According to the results, EPN and NE increased lysozyme activity up to 180% compared to the hormone-free enzyme. Changing the pH of the medium from 6.3 to 5.5, increased the lysozyme activity in the presence of E2 up to 150-200 %. The results also showed that exposure to hormones could modify lysozyme activity, and this effect depends on the temperature and pH value. The molecular docking revealed a decrease in the activation energy of the active site of enzyme during the interaction of catecholamines with the amino acid residues, asp52 and glu35 of the active site. Our findings demonstrate an additional mechanism for the involvement of lysozyme in humoral regulation of nonspecific immunity with respect to human pathogenic microflora and bacterial skin commensals by direct modulation of its activity using human hormones.  The concentration of lysozyme in human blood plasma ranges from 7 to 13 mg/L[2]. Lysozyme  is an ubiquitous enzyme present in all living organisms and induces noncellular immunity in birds and mammals. This enzyme is a component of lacrimal fluid and found in human milk, saliva, and other body fluids. It has also been identified in granulocytes, cells of the mononuclear phagocytic system, various exocrine glands, and articular cartilage[3]. Klenha and Krs[4] have shown the presence of lysozyme on human’s skin surface at the amount of 5 × 10-3 µg/cm2. Papini et al.[5] have demonstrated a consistent decrease in lysozyme activity in healthy skin in the untreated diabetic patients. They also found that the presence of lichen planus, psoriasis, conglobate acne, and chronic tuberculoid cutaneous toxoplasmosis did not affect serum and skin lysozyme levels in either normoglycemic or diabetic patients.There is scant information on the presence, characteristics, and regulation of the activity of lysozyme on skin surface. One of the first mentions of lysozyme activity on the skin refers to the work of Ogawa and coauthors, including O-acetylation. The most common human skin commensals or opportunistic pathogens among Gram-positive bacteria are members of the genera Staphylococcus, Kytococcus, Cutibacterium, and Micrococcus. Inside the cavities of skin glands and hair follicles, they are capable of forming stable biofilms that may provoke a number of diseases, such as vulgar acne, dermatitis, and folliculitis. At present, interest in the bacterial biofilms associating with human tissues has increased due to the influence of human’s hormonal regulation factors on their formation and activity. In particular, the systems of catecholamines (EPN and NE), biogenic peptides (natriuretic peptides), and reproductive hormones (E2 and androgen) in terms of their influence on the formation of bacterial biofilms and bacterial persistence have been widely studied in vitro[8-10]. Lysozyme is effective against most Gram-negative bacteria in which lysis occurs even at low concentrations. At the same time, some Gram-positive bacteria inactivates lysozyme function through different mechanisms[11], there are few data on the influence of human hormones on this enzyme. In this work, we tested the hypothesis of the influence of human hormones  on the activity of lysozyme from chicken egg white.Despite widespread studies in the effect of various regulators of lysozyme activity, including alkyl-hydroxybenzenesMicrococcus luteus as a substrate[12]. We also evaluated the effect of hormones on lysozyme activity by incubating the enzyme solution with the hormones EPN, NE, E2, ANP, and BNP at concentrations  and close to the physiological norm in human blood plasma  was dissolved in 0.07 M of phosphate buffer (pH 6.3). To assess the activity of the enzyme, we used a culture mediumcontaining d plasma . The mixM. luteus culture was prepared by two steps. The first step was performed to obtain an overnight culture of the stationary growth phase. For this purpose, one loop of the 24-h culture grown on agar was used to inoculate into 20 mL of the modified RCM medium . The culture was then incubated in 50-mL conical flasks closed with cotton plugs with constant stirring at 180 rpm at 34°C. In the second step, 1 mL of the overnight culture was seeded into 20 mL of the fresh liquid RCM and incubated at the same above conditions for 3 h to reach an OD of 0.7-0.9. Then 20 μL of the enzyme solution, following incubation with the hormones , was added to 2 mL of the bacterial suspension containing lysozyme . The OD of the suspension was measured at 540 nm at 10-s intervals for 1 min using a spectrophotometer . The enzyme activity was expressed at the lysozyme concentration of 400 μg/mL and initial ODs (0.7-0.9) of M. luteus suspension in pH 6.3. To assess the effect of hormones on the degradation of M. luteus cells by lysozyme, hormone solutions together with M. luteus inoculum, were added to a liquid RCM at appropriate concentrations  between control and experimental averages were considered significant and valid. Molecular docking was performed using SwissDock server (http://www. swissdock.ch/docking), and further analysis and visualization of the molecular structures of lysozyme were conducted in the UCSF Chimera 1.15rc softwareM. luteus cultures were incubated with the hormones showed that long-term incubation did not affect the properties of M. luteus cell wall, therefore, lysozyme activity did not change compared to the control (untreated cells). For this reason, all subsequent experiments were performed according to the procedure described in the "Materials and Methods" section. Since this work was carried out with a bacterium inhabiting human skin (skin surface temperature of 30.5-35.5 °C), incubation of lysozyme solution with hormones at 34 °C and pH of phosphate buffer 6.2-6.3 was considered as the standard conditions. Lysis rate values ranged from 0.14 to 0.3 units (OD/min) under standard conditions without hormone modification of lysozyme.Preliminary experiments in which -10 mg/mL. The value of the correlation coefficient in this condition was 0.64, which was very close to the values found for EPN. The effect of BNP at all concentrations was weak and insignificant. Thus, following concentration increment from physiological norm to more than 10-fold of the physiological concentration, a trigger effect was not observed, which may be related to the saturation of hormone-binding cites with ligand molecules, leading to the lack of further change in the enzyme activity.Two types of effects relating to hormones on lysozyme activity can be distinguished in Lysozyme activity significantly decreased at 37 °C and was less noticeable at 40 °C at the physiological concentrations. The activity of E2, a steroid hormone, increased proportional to temperature, likely due to an alteration in the position of the molecule in the active site of lysozyme. The activity of the enzyme was not correlated with temperature when exposed to catecholamines. At 40 °C, the change in the lysozome activity was not fundamental for EPN/NE/NUP, except for weakening of the heating effect . The impA decrease in pH value to 5.5 affected the effectiveness of hormones on lysozyme activity in different ways. The level of enzyme activation decreased in the presence of ANP, and its inactivation occurred in the presence of EPN. No changes in the enzyme activity were observed in the presence of BNP, and no activity was detected upon NE incubation . The actThe underlying mechanism of hormone binding to lysozyme was evaluated to explain the observed effects. Modeling the interaction of EPN and NE with lysozyme  showed that these two catecholamines took different energetically positions in the lysozyme molecule; minimum binding energy ∆G was -9.1077 for EPN and -7.2916 for NE. In the lysozyme molecule, both catecholamines occupied a location close to the ionized asparagine residue at position 52, which is one of the two critical centers of catalysis, along with glutamic acid at position 35 .nd hydrogen atom of the ligand formed a hydrogen bond with asparagine at the 52nd position, and its third hydrogen atom bound to glutamine at the 35th position. Negative ∆G values confirmed that the reaction between proteins and catecholamines was spontaneous. When a number of combinations of spatial localization of hormone molecules are realized, and if the lysozyme molecule is attacked by several EPN molecules, the active site will be electrostatically altered, potentially affecting the enzymatic activity of the lysozyme molecule. It has been previously shown that the catecholamine precursor L-Dopa can bind to lysozyme and change its biophysical characteristics, such as its hydrodynamic radius and intrinsic fluorescence[14]. This situation is not the same for E2. Its activity against lysozyme is obviously not related to the attack of the active site but to a change in the conformation of the entire enzyme molecule. This observation was confirmed by the data of molecular docking, showing that the most energetically favorable associations of the ligand (E2) occur on the sections of the enzyme molecule remote from its active center  and lysozyme in rainbow trout tissues has been reported upon exposure to hormone stress[23]. In our experiments, the impact of hormones and natriuretic peptides on the lysozyme molecule was direct, which allowed us to evaluate the degree of influence of these compounds on the acetyl muraminidase activity of enzyme.Lysozyme, as a factor of animal innate immunity, is the most studied enzymes due to its importance in bacterial activity. Recently, its regulatory effect on gene transcription of the immune system of monocytes has been discovered[8]. Interestingly, despite the structural similarities of EPN and NE molecules, the latter behaved in exactly the opposite way. At temperatures of the skin, NE did not change lysozyme activity, while at 37 °C, the enzyme activity increased. NE is believed to be able to stimulate phagocytosis by neutrophiles[24], which contain lysozyme granules. This process may explain the reason why neutrophils isolated from 24-hour wounds, unlike those from 120-hour wounds, demonstrated no NE-responsiveness of the phagocytic processes, which is in contrast to the circulating 24-hour and 120-hour neutrophils isolated from the restraint-stressed animals[23]. Nicholls et al.[25] have demonstrated that NE increases the metabolic activity of neutrophiles proportionally to the incremental hormone concentration. In this study, myeloperoxidase concentration increased in the supernatants of the lysed neutrophiles treated with NE. Thus, we can assume that under the low temperature conditions, EPN activates that part of the lysozyme pool, which is localized on the skin. However, NE is more effective against the lysozyme synthesized by the cells of the immune system and in the tissues of internal organs. Although somewhat speculative, this hypothesis needs to be further tested in both in vitro and in vivo experiments, using model organisms. At 37 °C, the peptide factors ANP and BNP were found to stimulate lysozyme activity. Under the impact of hormones, the values of lysozyme activity were widely varied relative to the control values. Nevertheless, the essential activation of lysozyme by EPN at temperatures of the skin surface at concentrations higher than the physiological ones suggests that in the stress situations, the enzyme shows more effective inhibitory properties against Gram-positive bacterial commensals of the skinin vivo. The optimal pH of human skin is known to be 4.7-5.5. In this context, it has been reported that the values between 4.5 and 4.7 are normal, based on the examination of the untreated skin[26]. Our study showed that when pH decreased to 5.5 at 34 °C, lysozyme activity returned to the control level under the influence of EPN and ANP. It has been recognized that the synthesis of both compounds is activated during physical activity accompanied by increased sweating and production of lysozyme. Under these conditions, the return of lysozyme activity to the level of the control values may be a preventive measure for protecting the useful skin commensals against the damaging effects of the enzyme.The results of temperature activation of the mixture of E2 and lysozyme suggest the presence of both temperature- and pH-dependent natural mechanisms of the lysozyme activity regulated by this hormone Metabolically, NE is less active than EPN. At the same time, at physiological concentrations, the lysozyme activity slightly decreased at pH 5.5 compared to the background of the control. The muramidase activity of lysozyme and natriuretic peptide conjugates relatively differed from the control at pH 5.5, which is in agreement with their localization in plasma, where the pH is between 7.35-7.45, and it goes beyond the optimum activity of pure lysozyme (6.0-6.8). [27]. This process itself has a bacteriostatic effect on pathogenic microflora. Thus, increase in the lysozyme activity under the influence of estrogen in an acidic environment is an important factor contributes to the normalization of the composition of the vaginal microflora. Our findings have been confirmed by studies performed on the cervical fluid of women who took estrogen medications for a long time[27]. This study showed a significant increase in the lysozyme activity of intrauterine mucus against Micrococcus lysodeikticus. Ascomycetes, e.g. Candida albicans, play a huge role in the pathogenesis of the vagina, in addition to Gram-positive bacteria. The cell wall of these fungi includes a β-glucan-chitin cortex. Earlier, Krupyanskiiy et al.[11] have reported the lysozyme activity against chitin. Thus, lysozyme is capable of restraining not only Gram-positive bacteria but also yeast fungi. In the present study, the most interesting results obtained when analyzing the activity of a mixture of lysozyme and estrogen. Decreasing the pH to a value of 5.5, significantly increased its bacteriolytic activity in proportion to the concentration of the hormone. It has been reported that in healthy women, vaginal pH is regulated by the concentration of estrogen. In other words, increase in glycogen secretion due to estrogen stimulation, leads to the proliferation of lactobacilli and, consequently, acidification of the environment-6 of catecholamine molecules and 1.6-16 × 10-7 of E2 molecules per enzyme molecule. This result indicates that the effect of hormones is not due to a change in the physicochemical environment, i.e., NaCl and Ca2+[17] level. Indeed, it is mediated by binding to specific sites, and the nature of the reaction is spontaneous, verifying by the negative values of the Gibbs energy. Analysis of the ratio of the molar concentrations of lysozyme to hormones showed that there are 2.57-35.6 × 10[18], lysozyme is activated by positively charged catecholamine molecules. This activation was dependent on temperature and pH. In this regard, the activating role of EPN was pronounced at pH 6.3 and 34 °С and that of NE at 37 °С, but it did not depend on pH values. Recently, lysozyme has been recognized not only as a factor of nonspecific innate immunity but also as an enzyme controlling the bacterial population. It also has a number of immunomodulatory functions, including participation in inflammatory reactions[28]. As reported by KaiserIn view of the fact that lysozyme-like enzymes participate in the processes of normal bacterial activity, its activation can lead to an imbalance in the development of both planktonic and biofilm forms of pathogenic bacteria. Our study demonstrates another possible way for regulating the lysozyme activity against human bacterial microflora through a mechanism of direct action of hormones on the reaction site of this enzyme molecule. The peculiarities of such regulation should be considered in clinical application for the increased doses of catecholamines and E2.We are grateful to Prof. V.K. Plakunov  for all the advice and comments in preparing the material for publication.Not applicable.The raw data supporting the conclusions of this article are available from the authors upon reasonable request.TP: conceptualized the study, obtained the experimental data, discussed the results, and wrote the manuscript; AG discussed the results, and wrote the manuscript; YN conceptualized the study, discussed the results, and wrote the manuscript.None declared.Russian Science Foundation (grant 19-74-10071) and with partial support from the Ministry of Science and Higher Education, Russia.This work was carried out with funding from the


Double-filtration plasmapheresis (DFPP) is a blood cleaning technique that enables the removal of unwanted substances from the blood. In our case study, we performed near-infrared (NIR) spectroscopy measurements on the human hand tissue before and after a specific DFPP treatment (INUSpheresis with a TKM58 filter), along with NIR measurements of the substances extracted via DFPP (eluate). The spectral data were analyzed using the aquaphotomics approach. The analysis showed that the water properties in the tissue change after DFPP treatment, i.e., an increase in small water clusters, free water molecules and a decrease in hydroxylated water as well as superoxide in hydration shells was noted. The opposite effect was observed in the eluates of both DFPP treatments. Our study is the first that documents changes in water spectral properties after DFPP treatments in human tissue. The changes in tissue water demonstrated by our case study suggest that the positive physiological effects of DFPP in general, and of INUSpheresis with the TKM58 filter in particular, may be associated with improvements in water quality in blood and tissues. Introduced in the early 1980s by Agishi et al. [The DFPP technique allows pathophysiological relevant molecules  and toxins  to be removed from the blood of a subject. This blood cleaning procedure has been successfully used therapeutically in many diseases ,3,4, incAlthough DFPP has been widely and routinely used in clinical practice in Asia, especially in Japan, for decades, its use is not yet widespread in the West. For a few years now, however, DFPP has been attracting increasing attention in Europe, largely due to the development of a specific type of DFPP, called INUSpheresis, by developers and scientists from Germany. For example, these researchers have recently shown that this type of DFPP has great therapeutic potential for the treatment of metabolic and non-metabolic peripheral neuropathy , borreliIn the case of the treatment of neuropathy patients , a signiγ-globulins, triglycerides, total cholesterol, HDL-cholesterol, LDL-cholesterol, liporotein(a), ferritin, fibrinogen, IgG, IgM, IgA, total protein, INR, quick, platelets and an increase in erythrocytes, haematocrit and leukocytes. Furthermore, proteomics showed significant changes in the concentration of apolipoprotein-related proteins, parameters of the coagulation system, immunoglobulins, parameters of the complement system and other inflammation-related proteins.The blood composition of people treated with DPFF using the INUSpheresis technology with a specific filter (TKM58) has recently been shown to change significantly . A decreHuman blood consists about 55% of plasma which is composed of 91% of water, 7% of proteins  and 2% of other solutes . The com2O)n which can be neutral, protonated, deprotonated or auto-ionized [Water in the tissue and blood exists in the form of free and bound water. Around dissolved ions or dipoles, the dipoles of the water molecules are oriented and form a hydration shell. The hydration shell of ions contains strongly bound water in the first hydration layer, and less bound water in the further layers. In case of proteins, water plays an essential role for folding, stability, and binding with other molecules . Single -ionized ,75. WateSince the physicochemical properties of water can change when passing through a filter or when the concentration of substances in the water change, we hypothesized that such changes in the water of the blood plasma would also have to occur with blood washing using DFPP. In order to verify this experimentally, we carried out corresponding measurements using near-infrared (NIR) spectroscopy before and after a specific DFPP treatment (INUSpheresis with the TKM58 filter) in one subject as a case study. It has not previously been investigated how DFPP affects the water in the tissue and what water properties the eluate has.DFPP INUSpheresis (with TKM58 filter) treatments were performed on a 39-year-old man  at a private clinic in Switzerland in February 2022 on two different days, one day apart. Each treatment lasted about 2.5 h. No official ethical approval was necessary to conduct the measurements and report the results since it is a case report  and measurements were conducted by and on the first author (FS). The DFPP treatment was performed as part of a routine medical treatment.T = 31.5 °C). Furthermore, the eluate of the first and second DFPP treatment was also measured by placing the NIR spectrometer on the plastic bag with the eluate inside and also measuring the spectrum of the plastic bag as a reference. This measurement was performed at an instrument temperature of T = 35.4, 35.5 and 35.6 °C.A portable and ultra-compact NIR spectrometer  was used to measure the diffuse reflectance spectra of the hand palm of the left hand and of the eluate. Measurements were performed in the spectral range 950–1650 nm with a nominal spectral resolution of 6.25 nm, an integration time of 11.7 ms and an average of 1000 scans for each spectrum to ensure an optimal signal-to-noise ratio. 15 consecutive measurements were performed for each measurement. Measurements were conducted in the morning one day before the first DFPP treatment (day 1), in the morning on the day of the first DFPP treatment (day 2), 1 h after the first DFPP treatment (day 2), in the morning on the day of the second DFPP treatment (day 4), 1 h after the second treatment (day 4) and in the morning one day after the second DFPP treatment (day 5). For each measurement it was ensured that the hand had subjectively the same temperature and the NIR spectrometer had also always the same temperature  algorithm for baseline correction. SNV normalization was performed on both datasets separately.XDiff) were calculated for the first and second DFPP treatment according to1 and A2  refer to the calculation of the difference spectra for the 1st DFPP  based on the data measured on the same day  and 24 h apart . With this approach, the spectroscopic measurement after the DFPP treatments were compared to two baselines .For the analysis of the hand tissue measurements, the difference spectra  were analyzed in particular by calculating the absorbance of the difference spectra in these 12 spectral regions and visualizing the results on aquagrams to determine the specific water absorbance spectral pattern [To analyze the water spectral changes, the aquaphotomics approach was used ,79,80,81 pattern ,77. A liData processing and visualizations were carried out in Matlab  and R (2022.02.0).2O),1,4), hydrated superoxide water clusters (O2-(H2O4)) and the H2O symmetrical stretch vibration (2ν1) [The spectroscopic analysis of the hand tissue before and after the DFPP treatments revealed clear differences in the spectral features in the first overtone spectral region of water, i.e., in the region of the 12 WAMACS . The coron (2ν1) . In gene2O)1,2,4) and C3 (1370–1379 nm), corresponding to symmetrical and asymmetrical stretching vibration of water molecules (ν1  +  ν3) [The spectroscopic analysis of the eluate obtained after the first and second DFPP treatment also revealed clear differences in the spectral features in the first overtone spectral region of water . The cor3 1370–13 nm, corrTo complement the aquaphotomics-based NIR spectral analysis, the eluate obtained from both DFPP treatment was also analyzed for toxic ingredients . The analysis revealed the presence of several toxins . The tenIn this study, we have shown that the water properties in the tissue change after DFPP treatment (INUSpheresis with TKM58 filter). DFPP caused an increase in free water molecules, small water clusters and a decrease in hydroxylated water clusters, superoxides of water solvation shells and weaker H-bonded water. The opposite effect was observed in the eluates of both DFPP treatments.In our opinion, these observations can be caused by two processes involved. First, the treatment filters out many molecules from the blood plasma to which water is bound in the form of hydration shells. This is the fraction of rather weakly H-bound water (which was reduced in the tissue after DFPP and was enriched in the eluates). Second, the process of filtration will cause water passing through the fine filter pores in a hydrophilic material to be structurally altered ,84. The Our study is the first to date to investigate changes in tissue water after a DFPP treatment and also the first to perform a spectroscopic investigation of the eluates obtained by DFPP. In particular, it is also the first analysis of this kind concerning this specific type of DFPP, i.e., INUSpheresis with the TKM58 filter. We know of only one comparable study that investigated the dialysate after dialysis using NIR spectroscopy and the aquaphotomics approach . This stThe changes in tissue water demonstrated by our case study suggest that the positive physiological effects of DFPP in general and of INUSpheresis with the TKM58 filter in particular, may be associated with improvements in water quality in blood and tissues related to the respective water molecular structures. Such an improvement in water quality could, for example, be associated later on with improved blood circulation and optimized metabolic processes. Our small study should serve as a stimulus to explore these possibilities through further, larger and more comprehensive studies.It should be noted that our study is based on the measurements of a single person and two eluates. Generalizations of our results are only possible to a limited extent. As noted, our case study serves to stimulate further research on the interesting results and to show how an aquaphotomics-based analysis of NIRS data can be performed to investigate the effects of a DFPP.