The fast and accurate determination of molecular properties is highly desirable for many facets of chemical research, particularly in drug discovery where pre-clinical assays play an important role in paring down large sets of drug candidates. Here, we present the use of supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten topological classes of drug candidates. We demonstrate that the gas-phase clustering behavior probed in our experiments can be used to predict the candidates’ condensed phase molecular properties, such as cell permeability, solubility, polar surface area, and water/octanol distribution coefficient. All of these measurements are performed in minutes and require mere nanograms of each drug examined. Moreover, by tuning gas temperature within the differential mobility spectrometer, one can fine tune the extent of ion-solvent clustering to separate subtly different molecular geometries and to discriminate molecules of very similar physicochemical properties. The fast and accurate determination of molecular properties is particularly crucial in drug discovery. Here, the authors employ supervised machine learning to treat differential mobility spectrometry – mass spectrometry data for ten classes of drug candidates and predict several condensed-phase properties. However, differential mobility spectrometry (DMS) has recently shown promise in this regard4. Most commonly employed as a narrow-pass filter to separate ions from chemical noise6, DMS takes advantage of an oscillating asymmetric electric field to drive rapid cycles of microsolvation and evaporation in environments that are seeded with low partial pressures of solvent vapor8. In examining how ion microsolvation properties are affected by specific structural attributes, the influence of electronic and resonance effects9, the steric hindrance of charge sites11, and the influence of intramolecular hydrogen bonding have been observed12. Thus, a picture is now emerging which suggests that the dynamic DMS environment facilitates swift and statistical sampling of an analyte’s potential energy surface. Consequently, strong correlations may be observed between an analyte’s clustering behavior within the DMS instrument and molecular properties in the condensed phase, such as cell permeability, which are partially dependent on the interaction potential between the analyte and its condensed phase environment15.Although mass spectrometry-based techniques have long found utility for fast and sensitive compound detection and for gas-phase properties measurements , the efficacy of employing gas-phase measurements to predict condensed phase properties remains a contested subject17. Thus, from the perspective of rational drug design, a fast and sensitive technique that can aid in predicting molecular properties is highly desirable. At present, there are several benchtop in vitro techniques available to measure cellular permeability 18 and other physicochemical properties that serve as preliminary appraisals of in vivo behavior 19. However, it is often challenging to discriminate between closely related structural analogues using the current state-of-the-art techniques21. Moreover, while exceptional progress has been made in refining these methods24, they still require considerable resources and often experience issues with reproducibility26. As a result, efforts to develop complementary techniques for assaying molecular properties are ongoing. A recent example of such a development is the experimental polar surface area (EPSA) technique28, which employs supercritical CO2 fluid chromatography to assess the partitioning of an analyte between a polar stationary phase and a non-polar mobile phase. EPSA has been demonstrated as a highly efficient means for indirect detection of intramolecular hydrogen bonding27. Such intramolecular hydrogen bonds (IMHBs) are common features of organic molecules, playing a critical role in defining the conformations of small molecules29, as well as the secondary structures of peptides and proteins30. With regard to drug design, introducing IMHBs has been put forward as a strategy to mask regions of higher polarity that might otherwise reduce cell permeability28. However, prior to the advent of the EPSA technique, identification of IMHBs, by either computation or experimentation, was challenging27. Thus, recent EPSA determinations of IMHB-containing molecules are quickly becoming a gold standard by which to test the ability of DMS to discern such features32.During the early stages of drug development, it is the goal of medicinal chemists to relate the cellular availability of a drug candidate to its physicochemical properties and chemical structure33, making them good candidates for study via DMS. As added functional groups on drug molecules, CRGs react with specific nucleophilic residues in proteins, serving to silence enzymatic activity until protein re-synthesis can occur37. To date, dozens of CRG-modified drugs have been approved for treatment of hyperlipidemia, infectious diseases, and cancer37. These drugs contain electrophilic moieties such as carbamates, acetates, β-lactones, β-lactams, and acrylamides, and several reviews have described the efforts to develop covalent inhibitor therapeutics37, including attempts to address-negative side-effects of these functional groups40. Given this considerable interest, there is an impetus towards a more detailed understanding of physicochemical and pharmacological properties of molecules of this type.Unlike the majority of the molecules studied here, whose passive and fundamental physicochemical properties were our focus, the CRG species key attribute lies in understanding the reactivity trends of these molecules. These chemical reactivities are strongly influenced by their interaction potentials41. Specifically, the more strongly a molecule interacts with solvent molecules purposefully added inside the DMS cell, the more negative the CV value at a given SV. This CV/SV pairing encodes the molecule’s interaction potential with the solvent system on a microscopic scale. However, variations in dynamic clustering behavior  across different molecule types/chemistries can render such a comparison ineffective. By taking a global view of the complete DMS behavior using a dispersion plot10, one can qualitatively determine whether the molecule of interest exhibits strong-clustering (type A), weak-clustering (type B), or hard sphere collision (type C) interactions with the gaseous environment of the DMS cell10. Note, that in some cases the species of interest does not strictly follow one of these three common behavior types  and optimal compensation voltage (CV) for transmission of the molecule through the DMS cellvide infra)4. We have also undertaken a parallel study of 24 acrylamide-based covalent reactive groups (CRGs) and have likewise included these species in a global analysis for all 89 small molecule drug candidates. We demonstrate that, with the aid of supervised machine learning (ML), the full range of DMS dispersion plot SV/CV data can be used to provide a quantitative means of accurately assessing a variety of molecular properties which are related to an analyte’s interaction potential.To ensure that the DMS-based assessment of molecular physicochemical properties is applicable across different chemistries, it is necessary to assess the properties of several molecular topologies jointly. To this end, we have combined the experimental results of our previous studies on ten 2-methylquinoline derivatives and twenty-two 2-methylquinolin-8-ol derivatives with the results from thirty-three IMHB-containing drug candidates  are shown in Fig. 2210. The IMHB-containing molecules are classified based on the IMHB data mining study of the Cambridge Structural Database published by Kuhn et al.29. Here, four of the most common IMHB topologies were investigated, which we label as topologies A–D10. The numeric portion of the label  methanol vapor at DMS cell temperatures of 150 °C and 300 °C. The search for appropriate DMS conditions to effect isomer separation was a critical aspect of this study as isomer separation is indicative of differences in the isomers’ relative interaction potentials. DMS experiments conducted using a pure N2 environment, or a N2 environment seeded with water, yielded hard sphere (Type C) behavior for all species10, and hence no separation between isomers. A N2 environment that was seeded with isopropyl alcohol vapor, on the other hand, yielded only strong-clustering (type A) behavior for all isomers10. Consequently, methanol—a protic solvent having properties intermediate of the previous two solvents tested—was chosen as the chemical modifier. Even so, at a temperature of 150 °C three sets of isomers (including set A1) could not be unambiguously separated into their isomeric components. However, at elevated temperatures the Gibbs’ energy of binding for the ion-solvent clusters is reduced to the point where one can clearly distinguish between molecules exhibiting IMHBs and those not exhibiting IMHBs with DMS , the site of protonation was comparatively distal to the site of IMHB formation10. In these instances, as one might expect, the DMS behaviors of isomers were relatively similar and separation was more difficult to affect. We hypothesize that, as the site of protonation (and hence solvent interaction) is relatively distant from the site of IMHB formation in these species, it is necessary that the degree of solvent clustering for these molecules be relatively large to ensure that the hydrogen-bonding network of the protic solvent can interact with the site of IMHB formation. Experimentally, there are two ways to test this hypothesis; one can either vary the partial pressure of solvent vapor to influence the size of ion-solvent clusters formed during the low-field portion of the SV duty cycle, or one can vary the temperature of the gas to achieve this same outcome . Figure 2 environment that was modified with 1.5% methanol vapor. At 300 °C, where the size of the ion-solvent clusters is expected to be relatively small, both isomers exhibit nearly identical dispersion plots. However, as the temperature of the collision gas is reduced, and the average size of the solvent clusters increases, we observed improved separation between the two isomeric species. Note also that the isomer which exhibits the IMHB (C3a) displays a more weakly clustering behavior than the non-IMHB isomer (C3b).In all 16 IMHB sets studied, the isomer exhibiting an IMHB displayed DMS behavior indicating weaker ion-solvent interactions than did the non-IMHB-containing analogue(s). This is worth additional consideration as our study probes protonated forms of the targeted molecules, whereas the EPSA technique which identified the IMHB-containing molecules is presumed to probe the neutral compounds33. These molecules can be classified into four different molecular topologies: (I) unsubstituted aromatic derivatives, (II) substituted aromatic derivatives, (III) substituted derivatives which do not contain heteroatoms, and (IV) substituted derivatives which contain heteroatoms. A more detailed description of these four structural motifs and the DFT-optimized geometries of all 24 derivatives are available as Supplementary Data10. In the case of the acrylamide CRGs, a N2 environment  that was seeded with 1.5% isopropyl alcohol vapor provided the widest distribution of analyte behavior . The dispersion plots for these species and all others included in this study are provided as Supplementary Figures 4610.In addition to the 33 IMHB-containing molecules, 24 acrylamide CRGs were studied, which were selected from an earlier examination of the intrinsic reactivities of these species42. Model selection is based on finding a compromise between bias and variance while avoiding over-fitting. Briefly, bias in a ML model can result from preselecting a functional description for your data, while high variance in a ML model will yield very different results depending on how data are partitioned into training and test sets. Over-fitting a model leads to poor applicability outside the range of molecules used for training. Decision Tree43-based models are unbiased learners and in particular Random Forest Regression44 yields results with low variance and shows a low susceptibility to over-fitting. Further details of our ML study are provided in Supplementary Method10.Although it is satisfying that DMS measurements can, for example, discriminate between isomeric species that do or do not contain IMHBs, the ultimate goal of this work is to demonstrate the use of DMS as a tool for quantitatively determining condensed phase physicochemical properties of molecules. To this end, we have introduced supervised machine learning (ML) as a means of inferring the relationships/correlations between molecular properties and DMS behavior. Preliminary efforts involved a survey of a few ML models45. As other types of DMS behavior do not show such a turn-around point, generalizing these findings across all molecular types is not trivial. To remedy this shortcoming, machine learning was used to infer relationships between physicochemical properties and the entire DMS SV/CV datasets. (i.e. data corresponding to the entire dispersion plot are utilized). Specifically, we train our ML model using Random Forest with 500 random decision trees. In general, we were able to observe a relatively strong correlation between the observed DMS behavior of drug candidates and physicochemical properties. This is in line with expectation since many properties are to a large extent dependent on molecular size and on the solvent interactions, i.e., propensity for desolvation and transport across the hydrophobic lipid bilayer15. It therefore stands to reason that including the molecular collision cross section (CCS) as a descriptor in the ML database should improve the accuracy and precision of the ML model. To do this, CCSs were calculated with the updated MobCal code reported by Campuzano et al.46 using DFT-optimized molecular structures48. A comparison of ML fits for cell permeability that include only DMS data with analogous ML fits that include DMS data and calculated CCSs are provided in Supplementary Methods, Supplementary Figures 5010. Although the fit containing only the DMS data exhibit a strong correlation with the experimentally determined RRCK rates of cell permeability (R2 = 0.889), inclusion of the CCSs for the protonated drug molecules results in a significant improvement (R2 = 0.989). On the other hand, if only CCS values are used to predict permeability we observe no significant correlation10.In our previous work on methylquinoline-8-ol derivatives, we were able to identify a weak correlation between the turn-around point in the dispersion plot of specifically type B systems and cell permeabilitiesN-fold cross-validation procedure (where N is the number of molecules in the set of compounds). Figure 50, EPSA28, and cell permeability are provided in Supplementary Methods, Supplementary Figures 5610. The CCS fits 10 involve a smaller subset of the ML database for which these condensed phase properties are available. Regardless of this limitation, correlation with DMS data are relatively strong. Because of this, we are optimistic that DMS behavior can also be used to predict these properties once a more extensive ML training database is constructed.In the case of CCS fits, absolute errors tended to increase with increasing molecular size. However, the percent error in CCS was relatively constant across the dataset. For the 89 species studied, we find an average error of .5% Fig.  for deteviz. CCS), one can correlate dynamic gas-phase clustering behavior within the DMS environment with condensed phase physicochemical properties. This suggests that DMS behavior is affected by the molecular interaction potentials of the analyte and collision gas within the DMS cell. This is demonstrated by, e.g., the fact that DMS can separate isomeric species based on the presence or absence of an IMHB; species that exhibit IMHBs display weaker ion-solvent clustering behavior than do the non-IMHB-containing analogues. The strong correlations observed between the DMS data and molecular physicochemical properties is appealing as it suggests that DMS might find use as a fast and accurate means of simultaneously screening molecular properties such as polar surface area, distribution coefficients, solubility, and cell permeability. This, in conjunction with the fact that DMS measurements can be conducted in minutes using only nanograms of sample, makes properties determination via DMS an attractive possibility for drug discovery. To advance this technology, it is necessary to develop an extensive database of DMS behaviors and properties for a wide range of molecules. Our work is ongoing in this regard.In summary, through application of supervised ML and consideration of molecular size  system was mounted on a 5500 QTRAP® system (SCIEX), between a TurboV TM ESI source and the mass spectrometer’s sampling orifice. Nitrogen was used as the curtain gas (3.5 L/min), throttle gas (0 or 0.7 L/min), and target gas (~3 mTorr) for the MS/MS experiments.Drug candidates were provided by Pfizer Inc. and were used without further purification. HPLC-grade methanol  was also used without further purification. Distilled deionized water (18 MΩ) was produced in-house using a Millipore  Integral 10 water purification system. In each experiment, an analyte solution (10 ng/mL) was infused at a rate of 15 μL/min into DMS-MS instrument via an ESI source operating at 5.5 kV, with a source temperature of 300 °C, nebulizing gas pressure of 20 psi, and auxiliary gas pressure of 20 psi. The DMS cell  of protonated for each molecule by generating all possible tautomers/protomers and optimizing them individually at the B3LYP/LANL2DZ level of theory. For the most stable structures, atomic partial charges were calculated using the ChelpG partition scheme52. The cluster PES was then modeled using the Universal Force Field53. To search the PES, the dihedral angles associated with single bonds were randomly distorted by −5° ≥ θ ≥ + 5° at each iteration of the BH code. In total, ~20,000 geometries were sampled for each molecule. Unique structures were identified based on zero-point corrected energy and geometry. Unique structures were then carried forward for geometry optimization at the PM7 level of theory for all molecules. To verify that calculations at the PM7 level produced appropriate geometries, the following subset of molecules were also geometry optimized at the B3LYP/6-311++G level of theory: A1a, A1b, A4a, A4b, B1a, B1b, C4a, C4b, C4c, D1a, D1b.To determine the most stable molecular geometries, a custom-written basin hopping (BH) search algorithm was used to map the potential energy surface (PES) of each drug candidate. The BH algorithm has been described in detail elsewhereFor all molecules in the above subset, PM7 calculations produced the same global minimum structure and the same relative energy order of tautomers/protomers as did the B3LYP/6-311++G calculations. In addition to geometry optimizations, normal mode analyses were also conducted to ensure that each structure was a local minimum on the PES.54. The labeled molecules were entered into a database which also included DMS data and calculated collision cross-sections (CCSs) as determined using the updated MobCal code reported by Campuzano et al.46. The Random Forest Regression employed 500 random decision trees and determined molecular properties via leave-one-out. This procedure trains the ML model using all molecules in the data except one, and then determines the property for the omitted molecule. This procedure is repeated for every molecule in the set. A series of correlation plots showing how the random forest (RF) model improves with additional data are shown in Supplementary Figures Supervised machine learning was conducted using the Orange3 Python packageTo produce learning curves, the database is split in to training and test sets. A ML model is produced from the training set and the mean absolute errors for the training and test sets are determined by applying the model to both. The plots provided in Fig. Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2


A novel relative humidity sensor that is based on a linear piezoelectric micromachined ultrasonic transducer (pMUT) array was proposed and microfabricated for high sensitivity, fast response, and good stability. The humidity-sensitive graphene oxide (GO) film was deposited on the pMUT array with a facile drop-casting method and characterized by scanning electron microscope (SEM), atomic force microscope (AFM), and Fourier transform infrared spectrum (FTIR). With the humidity level ranging from 10% to 90% RH, the sensor exhibited a high sensitivity of 719 Hz/% RH and an extremely high relative sensitivity of 271.1 ppm/% RH. The humidity-sensing results also showed good short-term repeatability and long-term stability, fast response and recovery, and low hysteresis. Moreover, the temperature coefficient of frequency (TCF) of the present humidity sensor was investigated and it could be easily compensated owing to the pMUT array structure design. This work confirmed that the GO functionalized pMUT is an excellent candidate in humidity detection and it may enable many potential applications, such as ultrasensitive mass detection and simultaneous detection of multiple parameters. Miniaturized humidity sensors enabled through micro-/nano-fabrication technologies have gained extensive attention due to distinctive advantages of small size, low cost, fast response, high sensitivity, and high stability. These micromachined humidity sensors have great potential for portable electronic systems and they play crucial roles in a wide range of applications, such as environmental monitoring, industrial process control, agricultural production, and medical treatments ,2. In geIt is well known that humidity-sensing properties, such as sensitivity, response, and stability are largely affected by the selected sensitive thin film. Until now, various kinds of humidity-sensing materials have been employed, including metal oxides ,15, ceraIn this work, a linear pMUT array based humidity sensor functionalized with GO sheets were proposed and fabricated with the microelectromechanical system (MEMS) technology. The GO thin film was directly deposited on the microfabricated pMUT array with a simple and facile drop-casting method and was characterized by scanning electron microscope (SEM), atomic force microscope (AFM), and Fourier transform infrared spectrum (FTIR). The humidity sensing characteristics of developed sensors, such as response/recovery, hysteresis, stability, and temperature effect, were investigated and discussed. Impedance and phase curves at different RH levels were also used to characterize the GO coated pMUT humidity sensor. Last, the sensing mechanism of the present sensor was analysed. This research demonstrates the potential of the pMUT based humidity sensor for highly sensitive mass detection in a wide range of applications.2/200 nm Pt/1.9 μm MPB-PZT/200 nm Pt.The pMUT based humidity sensor contains a pMUT linear array that was fabricated on a released silicon-on-insulator (SOI) wafer and a GO thin film deposited on the pMUT array. The schematic structures of the pMUT array and pMUT based humidity sensor are illustrated in 2 layer was deposited on the SOI wafer for electrical insulation, and then 200 nm Pt/10 nm Ti thin films were deposited by direct current (DC) magnetron sputtering and patterned as the bottom electrodes by Ar ions. After that, a layer of 1.9 μm MPB-PZT was formed using the sol-gel process and patterned through wet-etching. Next, another 200 nm Pt/10 nm Ti thin films were deposited and patterned as top electrodes. Last, the Si substrate was etched by deep reaction-ion etching (DRIE) to release the membrane. The 100 nm Au was formed as wire bonding pads. The as-fabricated pMUT array was then functionalized by drop-casting of diluted GO dispersions with concentration of 1 mg/mL. The original GO dispersions was prepared by the modified Hummers’ method and supplied from Suzhou Tanfeng Graphene Technology Co., Ltd. . After deposition, the prepared pMUT humidity sensors were placed into the oven and heated at 60 °C for 48 h. Before the experiments, all pMUT humidity sensors under the test were packaged and wire-bonded in the double in-line package (DIP) holders. The polydimethylsiloxane (PDMS) was applied to protect the bonding wires from damage during chip picking up and seal the backside released cavity for eliminating the interferences of moistures and gases that are trapped in the cavity when exposed to humidity conditions.The fabrication process started with an n-type SOI wafer with 10 μm thick device layer. A 1 μm SiOt, L, W, E, ρ, and ν are the thickness, length, width, Young’s modulus, Poisson’s ratio, and density of the rectangular membrane. It is shown that the operating frequency of the pMUT is proportional to the Young’s modulus and inversely proportional to the density. Therefore, coating a selectively sensitive thin film on the pMUT surface could shift the operating frequency upward or downward, which is mainly dependent on the material properties and thickness of the deposited layer. However, the physisorption or chemisorption of the analyte molecules on the sensitive thin film would result in a frequency decrease due to the mass or density change, well-known as the mass-loading effect. The frequency shift Δf of the developed pMUT humidity sensor can be estimated by the following equation [m and Δm are the mass of the effective vibration membrane and mass change after absorption of water molecules, respectively. According to the mechanical resonant frequency Expression (1) and the dimensions, the mass sensitivity per unit area of the designed pMUT is estimated at 16 ag/Hz/μm2.The basic resonant structure of one rectangular pMUT element is a PZT/Si layered rectangular membrane with fully clamped boundaries. The resonant characteristics are mainly determined by the material properties and the dimensions of the vibrating membranes. The fundamental flexural-mode operating frequency for a rectangular membrane is derived by Ref. (1)f0=0.equation :(2)Δf=−1Rq and Ra of 32.4 nm and 27.2 nm, respectively.The surface morphologies of the pMUT humidity sensor before and after coating GO film were characterized by SEM operated at 15 kV, as shown in −1 obtained by a high-resolution FTIR microscope at the transmission mode. Two peaks appeared at 2357 cm−1 and 2333 cm−1 due to the formation of unique chemical species in the presence of CO2 [−1 suggests the presence of carboxyl groups (C=O stretching vibrations). The strong and broad valley at 3579 cm−1 is correlated to the –OH stretching vibrations.e of CO2 . A chara2O5 desiccant and humidifier filled with deionized (DI) water were used together to achieve humidity levels from 10% RH to 90% RH with a 10% RH interval. The relative humidity in the test chambers was manually changed by adjusting the flow rate of the evaporative moistures and it was recorded in real time by a precise relative humidity meter. A precision impedance analyser  was used to measure the resonant frequency of the pMUT humidity sensors and the data were transmitted to a personal computer (PC) for saving and off-line processing. The temperature in the chamber was kept constant at 24 ± 0.5 °C to eliminate the interference from temperature changes. A Xiaomi Mijia bluetooth humidity temperature sensor  was also applied to track both the humidity and temperature values per second. During the experiments, the sensors were placed in one chamber first to obtain the response at one humidity level, and then were rapidly transferred into the other chamber to obtain the response at another humidity level. In this way, the humidity-sensing response and hysteresis characteristics could be explored by changing the relative humidity in two chambers from 10% RH to 90% RH and then back to 10% RH.The experimental setup for humidity-sensing property measurement is illustrated in pp. Subsequently, both the impedance and phase curves of the pMUT humidity sensors were measured before and after coating the GO thin film, as plotted in The finite element method (FEM) simulation that was based on COMSOL Multiphysics v5.2a was first employed to study the resonant characteristics of the pMUT. f with respect to the relative humidity levels can be fitted and described by the following expression:a, b, c, and d are the fitting coefficients and they are estimated correspondingly as 0.0979, 2.551, 0.03379, and −4.5518.The pMUT humidity sensors were tested in the well-sealed plastic chamber with the relative humidity increasing from 10% to 90% RH. The frequency shift to different humidity levels is shown in S and relative humidity sensitivity RS are defined asf0 is the fundamental resonant frequency of the pMUT humidity sensor with the GO thin film at 10% RH. Therefore, the sensitivity and relative sensitivity of the pMUT humidity sensor in the RH range of 10% to 90% RH are calculated as 719 Hz/% RH and 271.17 ppm/% RH, respectively. When compared with other resonant humidity sensors, our present pMUT sensor tends to exhibit an extremely high relative sensitivity, as summarized in The humidity sensitivity The hysteresis loops versus RH levels of the pMUT humidity sensor is shown in To study the dynamic response and recovery behaviours of the pMUT humidity sensor, two test chambers were kept at 10% RH and 85% RH, respectively. The sensor was manually transferred from one to the other chamber and was then transferred back. It took about 2 s to transfer the pMUT sensor. The response and recovery curves are shown in f and the limit of detection (LOD) of the humidity sensor [2 gas. It should be mentioned that during the CO2 sensing experiment, the relative humidity and temperature in the chamber were kept at about 10% RH and 24 ± 1 °C, respectively, to eliminate the interference from humidity and temperature changes. The CO2 concentration was initially kept at 0 ppm by introducing dry N2 gas for 10 min. The measurement results demonstrate that this sensor has no obvious response, even when exposed to CO2 gas with the concentration up to 20,000 ppm.The frequency stability including short-term stability and long-term stability is an important parameter during the measurements. The short-term stability can be expressed as an Allan deviation ) to estimate the noise Δy sensor . The aveThe impedance and phase response spectra were also recorded at various RH levels, as shown in TCF), defined by:T is the change in temperature. The TCFs of the pMUTs with and without GO thin film are −36.3 ppm/°C and −77.9 ppm/°C, respectively. It is noted that such temperature impact on the resonant frequency can be easily compensated due to the perfect linear frequency-temperature relationship and the multi-pMUT array structure, which is much more advantageous than other humidity sensors. Benefiting from the linear pMUT array design, we can use some untreated pMUT elements as the reference sensor to detect the temperature change and then compensate the temperature effect of the functionalized pMUT sensors during the humidity detection. Moreover, the pMUT array also confers a potential advantage in the simultaneous detection of humidity and temperature by adopting two pMUT elements to sense them independently.In practical applications, the environment temperature change would inevitably affect the frequency shift of the sensor and lead to an inaccurate testing result, namely the temperature effect. In this work, the temperature effect of the pMUT before and after coating the GO thin film was measured in the temperature range of 20 °C to 50 °C, as shown in The present humidity-sensing results indicate that the GO film functionalized pMUT is highly sensitive to water molecule adsorption, exhibiting a nonlinear frequency response and a high sensitivity of 719 Hz/% RH, as well as a high relative sensitivity of 217 ppm/% RH. A speculative mechanism explanation for these behaviours is illustrated in In summary, a highly sensitive linear pMUT array based humidity sensor functionalized with a GO thin film was designed, fabricated, and characterized. The impedance and phase analysis was used to analyse the frequency characteristics of the pMUT before and after the deposition of the GO film and investigate the humidity-sensing properties of the pMUT humidity sensor. The developed sensor exhibited excellent humidity sensitivity (719 Hz/% RH) and relative sensitivity (271 ppm/% RH) in a wide sensing range of 10% to 90% RH. Meanwhile, rapid response and recovery, small hysteresis, good stability, and a linear frequency-temperature response were also achieved. A possible humidity-sensing mechanism of the GO film coated pMUT was proposed according to the tested experimental results. This work demonstrates that the GO thin film functionalized pMUT is a good candidate for humidity detection and holds potential in the simultaneous detection of multiple parameters, owing to its highly integrated array structure.


Researchers combine moiré patterns to break the geometric limitations in graphene superlattices, and access new energy scales. When two-dimensional (2D) atomic crystals are brought into close proximity to form a van der Waals heterostructure, neighbouring crystals may influence each other’s properties. Of particular interest is when the two crystals closely match and a moiré pattern forms, resulting in modified electronic and excitonic spectra, crystal reconstruction, and more. Thus, moiré patterns are a viable tool for controlling the properties of 2D materials. However, the difference in periodicity of the two crystals limits the reconstruction and, thus, is a barrier to the low-energy regime. Here, we present a route to spectrum reconstruction at all energies. By using graphene which is aligned to two hexagonal boron nitride layers, one can make electrons scatter in the differential moiré pattern which results in spectral changes at arbitrarily low energies. Further, we demonstrate that the strength of this potential relies crucially on the atomic reconstruction of graphene within the differential moiré super cell. Van der Waals heterostructures allow combining different two-dimensional (2D) materials into functional stacks . Because the lattice constants of graphene and hBN are relatively close to each other, the alignment between the two crystals leads to the formation of a moiré pattern  created by top and bottom hBN in hBN/graphene/hBN heterostructures. Such super-moiré patterns are not related to the difference in the lattice constants between the two crystals and thus can be of any arbitrary wave number, which makes it possible to arrange the spectrum reconstruction at arbitrary low energies.α and θβ). The fabrication and transfer procedures have been previously described in (2. We then use a thin film of polypropylene carbonate (PPC) on Polydimethylsiloxane (PDMS) to lift the hBN from its substrate. This film then facilitates bringing the top hBN into contact with a graphene crystal  as a function of carrier concentration is shown in xyR) measured in nonquantized magnetic field , which should result in two secondary Dirac points for electrons and two for holes. However, if electrons can feel potential from both moiré patterns simultaneously, then second-order processes can be allowed, which would result in the reconstruction of the electronic spectrum at many other wave vectors.The longitudinal resistance  are the moiré reciprocal lattice vector between the α or β hBN layer , δ is the graphene-hBN lattice constant mismatch, θα, β is the misalignment angle for α or β, and a is graphene’s lattice constant. One of the observed periods is larger than that could be expected for graphene aligned with hBN . However, it is clear that when a second aligned hBN layer is added, the width of the 2D peak increases by a factor of ~2  should have periodicities described by To evaluate the degree of strain within our super-moiré samples, we have used Raman spectroscopy. In or of ~2 . We attrTo conclude, graphene’s electronic spectrum is substantially altered by scattering from super-moiré structures described by the preexisting moiré between graphene and its substrate and encapsulating hBN layers. These alterations may be considered in two ways: as double-scattering events from both graphene-hBN moiré patterns or as single-scattering events from a reconstructed graphene layer. Such super-moiré potential can be of an arbitrarily small wave vector , which allows modification of the graphene band structure at arbitrarily low energies.2 wafer.In addition to the sample preparation described previously, the general process is as follows. We used PPC, or sometimes polymethyl methacrylate, spun on a thick PDMS membrane to facilitate moving and orientating the crystals. This membrane was used to pick up the first hBN layer. The crystal was then positioned and aligned to graphene before the two were brought into contact. We removed the membrane quickly which lifted the graphene off from its substrate. At this point, we inverted the membrane and perform various characterization techniques on the half-assembled heterostructure. We then picked up a further thin hBN crystal, before repeating our characterization. These previous steps could have been repeated multiple times to produce increasingly complex heterostructures with a variety of crystals . Last, the stack of crystals was positioned and brought into contact with a final “substrate” hBN. The membrane was removed slowly so that all the crystals were left on a SiOSL-resolution AFM images of the moiré patterns were taken in PeakForce Quantitative Nanomechanical Mapping (PF-QNM) mode on a Bruker Icon AFM. PF-QNM allows the capture of individual tip-sample force curves for each pixel in the image. These force curves were used to extract additional elastic information about the sample as well as topographic information. We used “ScanAsyst Fluid+” probes whose resolution is regularly better than 2 nm.Raman spectroscopy measurements were performed on a HORIBA XploRA PLUS Raman spectrometer. The laser wavelength was 532 nm with a power of 0.5 mW through a ×100 objective.The analysis of moiré patterns in double-aligned heterostructures was performed following an approach described in (−6 eV/Å.Molecular dynamics simulations were performed for the single-aligned hBN/graphene and the double-aligned hBN/graphene/hBN by allowing the relaxations of both hBN and graphene layers. We used the bond-order Brenner potentials for the graphene layer, Tersoff potentials for the B-N interaction in the hBN layers, and the Morse potential developed in (Using the relaxed structures, we calculated the shift of the 2D peak and the entire Raman spectrum of the graphene layer. Calculations were performed using the prescriptions given in (


TF4 (transcription factor–like protein 4) gene. A comparison of attributes between model-generated sequences and chordate introns showed that the MD model with proper parameter settings could generate sequences that have attributes matchable to chordate introns, whereas the MI model with any parameter settings failed in doing so. These data suggest that the surveyed chordate introns have evolved from a long ancestral sequence through gradual reduction in length. The established methodology provides an effective measure to study the evolutionary pattern of intron sequences from organisms of various taxonomic groups. (C++ scripts of MD and MI models are available upon request.)Introns are well known for their high variation not only in length but also in base sequence. The evolution of intron sequences has aroused broad interest in the past decades. However, very little is known about the evolutionary pattern of introns due to the lack of efficient analytical method. In this study, we designed 2 evolutionary models, that is, mutation-and-deletion (MD) and mutation-and-insertion (MI), to simulate intron evolution using randomly generated and mutated bases by referencing to the phylogenetic tree constructed using 14 chordate introns from  The evolution of intron sequences has aroused broad interest in the past decades. However, 2 contrary hypotheses, namely, the “introns-early” versus the “introns-late” theories, put forward to explain the evolutionary mechanisms of introns are still under debate. Introns-late theory proposes that introns are an innovation of eukaryotes and intron gain has been a continuous process during the evolution of eukaryotes.813 However, the proponents of introns-late theory have still not been persuaded. The main reasons, as we understand, come from 2 aspects. One is that no evidence has been obtained to prove the existence of introns in ancestral bacterial protein-coding genes. The other is that introns-early theory cannot explain why intron number and length increase with the complexity of eukaryotic organisms. It must be confessed that addressing these 2 concerns is confronted with great difficulty. First, all ancestral bacteria are not available today. Thus, no ancient bacterial gene samples are available for examination about intron existence. Yet, this problem could be circumvented to some extent by searching and examining horizontally transferred bacterial genes harbored in eukaryotes. Using this approach, we have found an intron-containing bacterial gene harbored in sea anemone, which suggests possible existence of introns in ancestral bacterial genes.15 Second, the lengths and base sequences of introns vary greatly across various organisms. With a group of introns having different lengths, currently there is no measure to find out whether they are evolved from a longer ancestral sequence through gradual reduction in length or from a shorter ancestral sequence through gradual increase in length. Phylogenetic analysis has been widely used to infer evolutionary patterns of gene and protein sequences. However, it is inefficient for studying the evolutionary pattern of introns because the phylogenetic tree formed by intron sequences generally has very poor statistical support, based on which no evolutionary pattern can be inferred to explain how intron sequences have evolved.In recent years, more and more data have been obtained to favor the introns-early theory.1618 The established models are effective in simulating evolution of real sequences,20 in establishing databases of simulated protein alignments,21 and in exploring early events in the ecological differentiation of bacterial genomes.22 However, these methods were mostly developed for simulation of gene or protein sequences which possess high conservatism. They are not suitable for simulation of intron sequences that have undergone high number of nucleotide mutation and large pieces of nucleotide deletion or insertion. Therefore, in this study, we designed and constructed 2 types of evolutionary models, that is, mutation-and-deletion (MD) and mutation-and-insertion (MI), to simulate the evolution of introns from a chordate gene using randomly generated and mutated sequences. Then, we compared attributes of model-generated sequences with real chordate introns. The results show that the MD model with proper parameter settings could generate sequences that have attributes matchable to chordate introns, whereas the MI model with any parameter settings failed in doing so. These results suggest that the surveyed chordate introns should have evolved from a longer ancestral sequence through gradual reduction in length. The established methodology provides an effective measure to study the evolutionary pattern of introns from organisms of specific taxonomic groups.Sequence simulation is an important measure for evolutionary studies. A considerable number of statistical models and methods have been developed and tested for inferring the evolutionary relationship of nucleotide and protein sequences.TF4 (transcription factor–like protein 4) has only 1 phase zero intron in chordates. Exon sequences flanking this intron are highly conserved. This intron was selected as the research subject to ensure that the introns of different chordates come from the common ancestor. This intron is 112 to 1975 bases long in the 14 species chosen to represent various classes of chordates , RK2+I (ratio of transition to transversion under K2+I parameter model25), TSML (topology score of the constructed ML tree), which were found to be 2139 bases, 1.53, 1.084, 0.089, and 28, respectively. Detailed steps for determining these attributes are described in Supplementary File 1.The gene segment coding for bHLH (basic helix-loop-helix) motif of hordates . These 1tic tree  using ma1) through gradual reduction/increase in length accompanied by base mutation  were generated at random and were referred to bases A, G, T, and C, respectively. After a sequence of demanded length was obtained, its first and last 2 bases were replaced by GT and AG to mimic the structure of an intron. Starting from the AS1 sequence, the formation of each chordate intron can be divided into separate stages. For instance, intron Cm376  was evolved from AS2, and AS2 itself was evolved from AS1. The evolutionary models we constructed have 5 adjustable parameters, that is, LAS1 , LAS12 , M1 (mutated bases per 1 branch length), LI/D (length of bases inserted or deleted each time), and MI/D (number of bases mutated each time). Parameters LAS1, LAS12, and M1 define how many bases should be deleted/inserted and mutated at each stage, whereas LI/D and MI/D define how many bases should be deleted/inserted and mutated each time within a stage. Once the values of these 5 parameters are set, an evolutionary event for a specific intron sequence can be simulated. As an example, Supplementary File 2 describes detailed steps of how to generate a 376-base sequence for simulating evolution of intron Cm376 using the MD model. The evolution of all other ancestral sequences and intron sequences can be simulated in the similar way.To simulate intron evolution, we assume that the 14 chordate introns are evolved from 1 common ancestral sequence  should have a valid length to ensure simulation of intron evolution. That is to say, in the MD model, AS2 to AS4, AS6, and AS8 to AS12 must be longer than the correspondent introns evolved from them. As such, the minimum value of LAS12 was set to 2000 bases .As shown in 00 bases  to ensur40 bases  to ensurR) is adjustable in MD and MI models. However, a fixed value is given to it prior to model running in accordance with the attributes of intron sequences for study. This value is set to 1.5 in both MD and MI models because the R value of the 14 chordate introns is 1.53 as revealed by model testing .The transition to transversion ratio . For data from orthogonally designed models, “univariate” analysis was conducted to evaluate the effects of various factors on attributes of model-generated sequences. Significance of variance was analyzed through comparing the main effect of each factor without investigating the interaction between factors. Duncan post hoc test was used to conduct multiple comparisons for observed means, which were then plotted to view the optimal level of each factor. For data from optimized MD and MI models, “independent samples 1) was evolved to form AS2 to AS12, which were then evolved to form the 14 chordate introns. The formation of each ancestral sequence or chordate intron sequence was considered as the result of both base mutation and base deletion/insertion, which are related to evolutionary distance shown above or below each branch of the tree. In other words, each sequence in the tree (except AS1) was evolved from its specific AS after a target number of bases was mutated and a target length of bases was deleted or inserted. Nevertheless, base mutation and base deletion/insertion are a continuous process, meaning that a target number of bases to be mutated and a target length of base deletion/insertion should be completed at consecutive stages. Therefore, we have designed and constructed evolutionary models to simulate base mutation and base deletion/insertion alternately before the target number of bases is mutated and the target length of bases is deleted/inserted. In addition, the evolutionary models were designed to receive parameters in batch so that a set of 14 sequences could be generated at one time. Detailed flow charts for the construction of MD and MI models are shown in Figures S1 and S2, respectively. The constructed MD model assumes that the 14 chordate introns were evolved from a fairly long AS1 sequence through a gradual reduction in length accompanied by base mutation, whereas the constructed MI model assumes that the 14 chordate introns were evolved from a fairly short AS1 sequence through a gradual increase in length accompanied by base mutation. Both programs were compiled with C++ language.The ML tree obtained from previous step  was refe16(4*5) orthogonal table  uses 6000 bases as LAS1, 2000 bases as LAS12, 600 bases as M1, 31 to 50 bases as LI/D, and 21 to 30 bases as MI/D , which are obtained from allowing each of the 14 chordate intron sequences to mutate by only 1 base  for 10 times and thus obtained their attributes in the “M ± SD” form.In  as MI/D . Among ty 1 base . This waM1 and LI/D. So, in test MD18, we used 2500 bases as LAS12 and 800 bases as M1 while keeping other parameters unchanged. But sequences generated by MD18 still have significant difference in P < .1 level. After this, we have tried various values for parameters LAS1, LAS12, and MI/D to run the MD model (tests MD19-MD24). It was found that 1 or 2 of the 5 attributes of sequences generated by tests MD19 to MD22 are still significantly different from those of the 14 chordate introns. However, all 5 attributes of sequences generated in tests MD23 and MD24 have no significant difference with chordate introns  uses 10 bases as LAS1, 120 bases as LAS12, 200 bases as M1, 31 to 50 bases as LI/D, and 31 to 40 bases as MI/D  of the model-generated sequences are significantly different from those of chordate introns. So, in test MI18, we changed MI/D to 11 to 20 bases while keeping other parameters unchanged. The model-generated sequences have no significant difference in LMSA but have significantly higher TSML value. After this, we have tried various values for all 5 parameters to run the MI model (tests MI19-MI24). It was found that 2 or 3 of the 5 attributes of sequences generated by tests MI19-MI24 are always significantly different from those of the 14 chordate introns (By referencing to  as MI/D . However introns . Thus, i27 and microRNAs.28 The unavailability of common sequence features makes it very difficult to study the evolutionary pattern of introns through phylogenetic analysis because intron sequences generally yield a phylogenetic tree with very poor statistical support. This is true even when the introns are from the same location of a gene in closely related organisms. For example, the bootstrap values in ML tree shown in Introns are well known for their high variation not only in length but also in base sequence. So far, no common sequence structures/features have been found in introns except those containing transposable elements1114 While previous studies have made it clear that intron number has been reduced, it remains unclear whether intron length has been increased or decreased during intron evolution. Our present work provides an example of intron length reduction during the evolution of chordate TF4 gene. Theoretically, the established methodology can be used to study the evolution of introns at different numbers and lengths and from different organisms because the intron sequences for study are only used to construct the original ML tree (eg, So far, studies about evolution of intron sequences have mainly focused on the presence or absence of introns in certain genes across various organisms. Through investigating a large number of intron-gain and intron-loss events, it has been revealed that the evolution of eukaryotic genes primarily involves intron loss.tree eg, , which i29 higher intron retention rate in human than in fruit fly and nematode,30 reductive evolution of genomes in complex archaeal ancestor,31 and intron-rich ancestor of eukaryotes.32 This higher efficiency in lower organisms could be due to higher reproduction rates of lower organisms than higher organisms because frequent genome replication provides more chances for genome streamlining.34 Therefore, more and longer introns existing in higher organisms are not the result of continuous intron gain, but are the result of low efficiency in reducing intron number and length. This may be considered new evidence to support the introns-early theory and to persuade the proponents of introns-late theory.Then, if intron number and length have been substantially reduced during evolution, why do intron number and length increase with the complexity of eukaryotic organisms? This is the main supportive fact of introns-late theory that has not been given a rational explanation from introns-early theory. Here, we propose our explanation to this question: higher organisms are not as efficient as lower organisms in reducing number and length of introns. This explanation is consistent with frequent intron loss in yeast,In this study, through designing and constructing MD and MI evolutionary models to simulate the evolution of 14 chordate introns, we found that these chordate introns should have evolved from a longer sequence through gradual reduction in length accompanied by random base mutation. Although successful simulation seems to be highly dependent on parameter settings for the constructed models, it does provide an effective measure to infer the evolutionary pattern of introns, especially in view of intron length variation. The established methodology is expected to facilitate more studies on evolutionary pattern of intron sequences from organisms of various taxonomic groups.Click here for additional data file.Supplemental material, Supplementary_file_1_xyz299583cdc1c74 for Simulation of Chordate Intron Evolution Using Randomly Generated and Mutated Base Sequences by Guang-Dong Wang, Yong Wang, Zhen Zeng, Jun-Ming Mao, Qin-Liu He, Qin Yao and Ke-Ping Chen in Evolutionary BioinformaticsClick here for additional data file.Supplemental material, Supplementary_file_2_xyz299586f3629a1 for Simulation of Chordate Intron Evolution Using Randomly Generated and Mutated Base Sequences by Guang-Dong Wang, Yong Wang, Zhen Zeng, Jun-Ming Mao, Qin-Liu He, Qin Yao and Ke-Ping Chen in Evolutionary BioinformaticsClick here for additional data file.Supplemental material, Table_S1_xyz29958520c37c8 for Simulation of Chordate Intron Evolution Using Randomly Generated and Mutated Base Sequences by Guang-Dong Wang, Yong Wang, Zhen Zeng, Jun-Ming Mao, Qin-Liu He, Qin Yao and Ke-Ping Chen in Evolutionary BioinformaticsClick here for additional data file.Supplemental material, Table_S2_xyz299588fc8496b for Simulation of Chordate Intron Evolution Using Randomly Generated and Mutated Base Sequences by Guang-Dong Wang, Yong Wang, Zhen Zeng, Jun-Ming Mao, Qin-Liu He, Qin Yao and Ke-Ping Chen in Evolutionary Bioinformatics


The relationship between the abrasion loss and the friction coefficient of the composites with different graphene contents was obtained. A thermogravimetric analyzer was used to investigate the crystallization and pyrolysis behavior of the composites; correspondingly, the temperature range that composites work in was determined. The heat conduction mechanism was analyzed through the thermal conductivity model of composite materials. The composite material is expected to play an important role in the development of high-performance thermal equipment.Perfluoroalkoxy (PFA) material exhibits perfect corrosion resistance under both acid or alkaline circumstances; thus, steel heat exchangers are being substituted by those made of PFA in high corrosion atmospheres. However, the low thermal conductivity of PFA degrades its heat transfer efficiency. Based on the extremely high heat conductivity of graphene, a novel grapheme-PFA composite was proposed to simultaneously meet the demands of heat transfer and corrosion resistance. Ultrasonic dispersion technology was used to disperse the aggregated graphene in the composite. Graphene–PFA composites with different graphene contents and using different dispersing solvents were prepared with a hot pressing method, and thermal conductivity, abrasion resistance, crystallization and pyrolysis properties were investigated. The thermal conductivity of PFA composites with graphene content of 20 wt % reached 5.017 W (m·k) Heat transfer materials can transfer the heat efficiently from hot fluid to cold fluid, improving the energy conversion efficiency and the cascade of energy utilization in the fields of electric heating, aerospace, chemical engineering, material science, and energy power. Metallic heat exchangers have been widely utilized in many industrial processes; however, when used in corrosive situations, their short campaign life has been a serious defect. Moreover, a heat dissipation component, characterized by high thermal conductivity, resistance to acid and alkali corrosion and anti-aging properties with a lightweight and flexible structure is widely required. Fluoropolymer is characterized by a high degree of crystallinity and high molecular weight and possesses excellent physical properties, especially for its corrosive resistance, whereas its low heat diffusion capacity hampers its application as a heat exchanging material ,2,3,4. GMeng et al.  preparedIn this study, grapheme-PFA composite was prepared, and the properties of thermal conductivity and abrasion resistance were investigated emphatically. Considering the strong aggregation tendency of graphene caused by strong van der Waals forces and π–π stacking between graphene layers , the ultThe PFA powder, supplied by Tenghai Engineering Plastics , was modified PFA, with a density was 2.16 and an average particle size of 21.816 μm. The infrared spectrum of PFA is shown in 3, respectively. The Raman spectrum is shown in The graphene nanosheet (GT-G03) was supplied by Kaina Graphene . The graphene layers were only 1 to 3 (manufacturer’s test report), and the water content was 1.08%. The average diameter , and bulk density of the nanosheet were 8.96 μm and 0.04 g/cmThe alcohol with 99.97% purity was supplied by the Sinopharm Group . The density was 0.79 g/mL.Graphene sheets are easily aggregated due to the extremely strong van der Waals forces and π–π stacking and formed multi-layered graphene platelets c. It hasUltrasonic treatment technology is often utilized to improve the dispersion effect of micro-particles in dispersions. In this study, the optical density of the dispersion liquid was used to evaluate the dispersion effect. The optical density of the dispersions was detected by spectrophotometer (TAS-990) immediately after the ultrasonic treatment was accomplished. Firstly, the dispersion effects of graphene in alcohol after ultrasonic treatment with different amounts of power and treating times were compared to obtain the optimal operation parameters. After that, the effects of the surface tension of dispersions were investigated. Dispersions with specific surface tensions were prepared through mixing alcohol and water at certain ratios. The surface tension of the dispersions was measured by the pendant drop method; the measurement results are listed in In order to minimize the effect of water on the experimental results, graphene and PFA were dried to remove moisture before the experiments. The preparation of graphene-PFA composites is shown in The fracture surface morphology of the graphene-PFA composite material was observed with a scanning electron microscope (SEM)  to determine the distribution of the graphene in the PFA. The morphology and layers of graphene were observed by transmission electron microscopy (TEM) .The friction and wear properties of the composites were tested through a reciprocating friction and wear testing machine; the schematic diagram is shown in The thermal stability of the graphene–PFA composites was investigated by thermogravimetry and differential scanning calorimetry (DSC) . A sample of approximately 15 mg was loaded into a crucible and then heated from 50 to 800 °C at a rate of 10 °C/min in an argon atmosphere and then immediately cooled from 800 to 50 °C at the same rate.The thermal conductivity of the composites was detected by the Hot Disk method  to ascertain the effects of the different dispersions and different graphene dosages on the thermal conductivity of the composites.Avoiding the agglomeration of graphene in PFA is a critical step for obtaining a good performance composite. The optical density of dispersions was detected to indicate the dispersion effect of graphene—the higher the optical density was, the better the graphene dispersed. 2, far smaller than that of the pure water, which was 73.1 mJ/m2. Therefore, alcohol exhibited a better wettability than water. 2 and the average value of three runs was 0.35. A higher or lower surface tension broke the equilibrium of forces between the micro-particles and liquid molecules, causing the agglomeration of graphene; thus, the optical density decreased.The properties of dispersions, especially the surface tension and wettability to graphene, are important factors for dispersion and for avoiding the re-agglomeration of graphene after complete dispersion. The surface tension of pure alcohol was only 21.6 mJ/m2 weres used in the subsequent experiments , and the graphene dispersion was sonicated for 120 min using a power of 300 W.Consequently, the dispersions with a surface tension of 29.6 mJ/mHm) of the composites tended to increase. In order to study the influence of the graphene content on the crystallinity of the PFA matrix, DSC was used to analyze the melting behaviors of the composites. Tm) of the five samples were similar and ranged from 299 to 301 °C. Clearly, compared to pure PFA, the melting enthalpy decreased slightly when 1% graphene was added, while improving the graphene contents further from 1% to 15% increased the melting enthalpy gradually. The crystallinity (Xc) of the composites was calculated using the formula, Xc = Hm/(WPFA × Hm*), where ΔHm* is the melting enthalpy of 100% crystalline PFA and WPFA is the mass fraction of the PFA [The melting temperatures ( the PFA . The cryThe thermogravimetric (TG) curves of pure PFA, graphene and five composites are shown in Besides improving the mechanical properties, another important role of nanocarbon material addition is to promote the electro-thermal properties. According to the evolution of thermal conductivity of the composites with graphene contents shown in 2. The ultrasonic power and processing time were 300 W and 120 min, respectively. The graphene dispersion in the PFA is the most critical factor affecting the thermal conductivity of a composite. Ultrasonic treatment and proper surface tension of dispersion can promote the effective dispersion of graphene. The optimum dispersions of the graphene were composed of 45% alcohol and 55% water, with a surface tension of 29.6 mJ/mThe melting temperatures of all prepared composites stabilized in the range of 299–300 °C, and there were never decomposition or phase transition occurrences below 300 °C. Thus, a high thermal stability was exhibited. The graphene was able to provide heterogeneous nucleation and crystallization sites for the PFA, but a large amount of fine graphene limited the diffusion and migration of the PFA segments, leading to a decrease in crystallinity. The friction coefficient of the composites increased with the addition of graphene. The transfer films and attached films of the self-lubricating graphene reduced the wear volume of the composites, improving the abrasion resistance. When the graphene content was higher than 10 wt %, the wear volume increased slightly due to the appearance of weak regions and the reduction in the crystallinity degree caused by excessive graphene. The thermal conductivity of the composite improved effectively by increasing the graphene content in PFA. The maximal thermal conductivity of PFA composites reached 5.017 W/(m·K) when the graphene content was 20% by weight, meeting the demands of many industrial applications. The SEM images of the composite materials indicated that the scaly substance increased when the graphene content increased; the graphene distribution in the PFA matrix also improved. The dispersed graphenes formed a scale-like structure and provided a large number of thermal conduction paths; therefore, the thermal conductivity was enhanced. Through hot pressing, the interlayer spacing and porosity decreased, favoring the reduction of the interfacial thermal resistance between graphene and PFA. Ultrasonic treatment improved the connectivity of graphenes, reducing the phonon scattering, resulting in good thermal conductivity of the composites.


N-acetylcysteine (NAC).Idiopathic pulmonary fibrosis (IPF) is a major public health problem worldwide. There is no curative treatment for IPF except lung transplantation. Chinese herbal medicines (CHMs) are widely used in the treatment of IPF in China. However, their effectiveness and safety are still obscure and deserve further investigation. The aim of the study was to assess the efficacy and safety of CHMs in treating IPF compared with This review summarizes and meta-analyzes randomized controlled trials (RCTs) of CHMs for the treatment of IPF. RCTs compare either CHMs alone or in combination with NAC or conventional medicine treatment (CMT) vs NAC alone or in combination with CMT have been included. The following electronic databases have been searched: PubMed, Cochrane Library, Embase, CNKI, CBM, VIP, and WANFANG DATA. The methodologic quality of RCTs has been assessed using the Cochrane risk assessment tool. All trials included are analyzed according to the criteria of the Cochrane Handbook. Review Manager 5.3, R-3.5.1 software, and GRADE pro GDT web solution are used for data synthesis and analysis.2), and safety in patients with IPF.This review evaluates the effects of CHMs on acute exacerbation, mortality, the quality of life, 6-minute walking test distance, lung function , partial pressure of oxygen in blood (PaOThis review provides clear evidence to assess the effectiveness and safety of CHMs for IPF. Progression of IPF is characterized by increased respiratory symptoms such as dyspnea and cough, worsening lung function, and High Resolution CT with increased fibrosis or acute respiratory failure. The incidence of IPF increases with age. Typical symptoms usually appear in the 60 to 70 age group. More men have been reported with IPF than women, and most patients have a history of smoking. One study showed that the cumulative prevalence of IPF increased from 202 to 495 cases per 100,000 people aged 65 years and older from the years 2001 to 2011. Due to its rapid progress, poor prognosis, and high mortality, IPF has been called a refractory lung disease. The median survival time from respiratory symptoms to death is 2 to 3 years.–5 It has a very poor prognosis with a 5-year survival rate of 30% or below. The patient's survival rate is similar to that of a malignant tumor, which seriously threatens the health of human beings. Increasing rates of hospital admissions and deaths due to IPF also suggest an increasing burden of disease.–10Idiopathic pulmonary fibrosis (IPF) is defined as a special type of chronic, progressive, interstitial lung disease of unknown etiology. IPF occurs in the elderly and the lesion is confined to the lungs. Adults with chronic labor dyspnea due to unexplained causes, and the clinical manifestations of cough, lung bases  At present, glucocorticoids and immunosuppressive agents are widely used in clinical treatment, but their curative effect is not exact, what is worse, it is easy to form dependence with many side effects. The evidence-based guidelines of IPF have strongly rejected these therapies, so the prevention and treatment of IPF have become a problem in today's medical research. The 2015 edition of IPF's diagnostic and therapeutic guideline lists N-acetylcysteine (NAC) treatment as a weak and unrecommended level. Although the guideline suggests that IPF should not to be treated with NAC, NAC monotherapy can improve mental health and 6-minute walking distance. Therefore, patients who have started NAC monotherapy are not recommended to discontinue treatment.,13 Although Pirfenidone and Nintedanib administration have been shown to delay the decline in lung function, they are rarely used clinically in China because of their significant adverse events and long-term economic costs. Therefore, NAC is widely used in the treatment of IPF because of its low price, good tolerance, and easy oral administration. However, due to the lack of evidence-based medical research, its safety and effectiveness remain controversial.The pathogenesis of IPF is still unclear, and there is no definitive conclusion at home and abroad. In addition to lung transplantation, there is no specific treatment for IPF in Western medicine, and lung transplantation is also only available for a small number of patients who can tolerate surgery. As a result, there is currently no consensus on the optimal treatment of IPF. Unfortunately, there is no evidence to prove which drug is effective in treating the disease, and only a few studies suggest that certain drugs may be beneficial for IPF. Hundreds of different herbs have been used in numerous TCM formulations to treat IPF. These TCM formulations are derived from the wisdom of Chinese clinicians and the experience accumulated over thousands of years of using these herbs. In recent years, the experimental and clinical research on the treatment of IPF by TCM has gradually increased. Recent studies have shown that TCM treatment of IPF has become feasible and provide new ideas for solving the controversial problem of selecting effective drugs. It is important to highlight that more and more formulas, herbal compounds, and single herb extracts were proved to have an effect in preclinical experimental studies of IPF.–18 TCM is a potential therapy for IPF due to its advantages of syndrome differentiation and treatment, small adverse reactions, slow and long-lasting effects, overall regulation, and no obvious drug dependence. A widespread strategy underlying the TCM treatment of IPF is to effectively relieve clinical symptoms and improve the quality of life (QOL) and reduce mortality of the patients.–21 However, due to the small sample size, large difference of intervention measures, and the comparison of glucocorticoid not recommended in the guidelines, the evidence for the efficacy and safety evaluation of TCM treatment for IPF is insufficient.Today, traditional Chinese Medicine (TCM), as an adjuvant therapy to Western medicine, has its unique superiority and proper effect and is still used extensively for the treatment of IPF in China.The goals of this systematic review are to obtain evidence on the efficacy and safety of CHMs compared with NAC for IPF.2http://www.crd.york.ac.uk/PROSPERO), registration number: CRD 42018105348. The procedure of this protocol is based on PRISMA-P guidance.This study has been registered in PROSPERO  and 4 Chinese medical databases . The databases are extensively searched from their inceptions up to June 4, 2018. The search strategy is based on the guidance of the Cochrane Handbook. Terms searched include:  AND  AND (random∗). To guarantee comprehensive search, all relevant publications are researched, including academic dissertation and conference articles. The language is limited to Chinese and English.2.22.2.1Only randomized controlled trials (RCTs) are included.2.2.2 In addition, we also refer to the consensus of Chinese experts on IPF diagnosis and treatment formulate by the interstitial pulmonary disease group of the Chinese academy of respiratory medicine in 2016.Male or female patients of any age or ethnic origin who have been diagnosed with IPF have been included. Diagnostic criteria refer to the 2011 international evidence-based guidelines for IPF diagnosis and treatment jointly published by ATS/ERS/JRS/ALAT.2.2.3The experimental group is treated with Chinese herbal medicines , the control group is treated with NAC, and both groups are treated orally with a treatment period of not <12 weeks. CHMs include Chinese medicine decoction, Chinese medicine preparation , Chinese medicine single prescription, Chinese medicine compound prescription, and Chinese patent medicine. Both groups can receive the same routine western medicine treatment. Basic medical treatment of Western medicine includes oxygen therapy, infection control, and nutritional support.2.2.4Primary outcomes: The rate of acute exacerbation and mortality after treatment and the health-related QOL have been analyzed as the primary outcome. The health-related QOL has been assessed using St George's respiratory questionnaire scores.2), and adverse events. The time endpoint of the above outcomes is no earlier than 12 weeks after starting the medication.Secondary outcomes: The secondary outcome assessments consist of pulmonary function test—especially forced vital capacity (FVC), total lung capacity (TLC), the diffusing capacity of the lungs for carbon monoxide (DLCO), 6-minute walk test (6MWT) distance, partial pressure of oxygen in blood ; excluding the duplicate literature using NoteExpress3.2 and excluding the unrelated articles by reading the title and abstract; and reading the full text and reserving clinical studies that meet the inclusion criteria. Two researchers (GJ and LWY) extract the data independently using a self-developed data extraction form. The differences encountered in the process have been resolved by discussing with another team member (LB), to determine, by agreement, the final selection of studies.Referring to the Cochrane collaborative network system evaluator handbookData extraction contents include: general information: research ID, author, title, publication status, report sources, and fund support; methodology information: design, number of arms, random sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting, sample size calculation, and baseline comparability; participant information: diagnostic criteria, inclusion criteria, exclusion criteria, setting, population, sample size, age, gender, and course of disease; intervention information: name of intervention and comparation, syndrome differentiation of TCM, types of CHMs, dosage form, comparison, duration of treatment, and patient follow-up; outcomes; and adverse events.http://www.prisma-statement.org/)  Fig. .2.5 Risk of bias has been assessed as follows: adequacy of generation of the allocation sequence, allocation concealment, double blinding, incomplete outcome date, selective outcome reporting, follow-up, and other bias. These domains classify “Yes” if adequate, “No” if not adequate, and “Unclear” if not well described by the authors in such a way that its adequacy is describable.The Cochrane risk assessment tool has been used.The 2 researchers (GJ and LWY) independently assessed the risk of bias for each included study. “L,” “H,” and “U” have been used as a code for the evaluations of the above bias risks. “L” indicates a low risk of bias, “H” indicates a high risk of bias, and “U” indicates the risk of bias is unclear. Disagreements resolved by discussion between all the researchers. When necessary, the study authors have been contacted to inquire some missing information. Trials of high risk of bias will be considered when conducting sensitive analysis.2.6 Quantitative synthesis has been done when clinical heterogeneity is not considered by at least 2 authors in discussion. Continuous variable has been described by mean difference (MD), P–value, and 95% confidence interval (CI). For dichotomous outcomes, the relative risk (RR) has been used, with 95% CI and P-values, to evaluate the efficacy and safety of CHMs. I2 test has been used to judge the heterogeneity of meta-analysis. I2 value >50% or more will be considered as an indication of substantial heterogeneity. If heterogeneity exists in the pooled studies, the data have been analyzed using a random effects model. Otherwise, a fixed effect model has been adopted. If there is significant clinical heterogeneity, the cause of heterogeneity should be explored, and sensitivity analysis or subgroup analysis should be performed when necessary. Sensitivity analysis has been used to ensure the robustness of results by eliminating low-quality trials. Subgroup analysis will be performed according to the characteristics of the study subjects, such as different interventions, treatment duration, and outcome measures. If the data extraction is insufficient, qualitative analysis will be adopted.Review Manager Software  has been used to analyze and synthesize the outcomes. Clinical heterogeneity is the primary source of heterogeneity in the systematic reviews of TCM. Clinical heterogeneity can be derived from the potential factors such as the ingredients and formulations of CHMs.2.7The publication bias has been analyzed using funnel plot when the number of studies included in a meta-analysis is no <10. If the number of included studies is <10, the Egger test will be applied. The analysis software is R 3.5.1 for Windows.2.8This study evaluates the evidence according to GRADE standard, which refers grading of recommendations assessment, development and evaluation. These factors that may reduce the quality of evidence should be considered, such as limitations in study design, inconsistency of results, indirectness of evidence, inaccuracies, and publication bias. In addition, large magnitude effect, possible confounders that can reduce the effect and dose-response gradient that increase the quality of evidence cannot be ignored. GRADE Pro GDT online software will be used to form the summary of findings table (SoF table).3 These problems are caused by the complexity of the TCM treatment and the difficulty to clarify the heterogeneity of the scheme, resulting in the low reliability of evaluation. It is well known that CHMs in China are based on thousands of years of clinical observation and practice by Chinese doctors. In general, many herbs are used in combination to treat diseases. Each herb in Chinese medicine preparations may have several different active ingredients. The content and bioactivity of these ingredients can be affected according to the cultivation region, soil, climate and sunshine, as well as the season of cultivation and harvesting and the method of preparation. Therefore, clinical trials must provide detailed information about the CHMs used. Different from modern clinical research, it is difficult to compare the results of TCM research. Properly speaking, in the same Chinese medicine prescriptions, batch, medicinal processing, dosage, dosage form of the herbs, route of administration, and duration of therapy are all factors that lead to different characteristics of the CHMs, and more importantly, these factors will affect the treatment effect of the disease. Therefore, we believe that the combination of CHMs and modern medical technologies and therapies is the premise and foundation for understanding the potential effectiveness of CHMs in IPF treatment.The CHMs have been used for thousands of years in China, and now it is widely used in the world. Currently, several types of CHM extracts are reported to be effective in animal models of IPF. The purpose of this study is to summarize and analyze the efficacy and safety of clinical trials using CHMs to treat IPF. Although there are many trials on the treatment of diseases by CHMs in China, the overall quality is not high. Comparing with the systematic review of modern medicine, there are few studies on high-quality systematic review of CHMs and lack of reasonable evaluation system leads to insufficient reliable evidence for clinical use of CHMs. However, the pharmacologic mechanism of TCM treatment of IPF and more large-scale studies and randomized, placebo-controlled studies are needed. Furthermore, the morbidity, survival time and mortality of patients with IPF require further attention and research. It is important to highlight that this review wants to determine whether CHMs, comparing with NAC, can improve acute exacerbation, mortality, the QOL, 6MWT, lung function , PaO2 and adverse events in patients with IPF. Previous clinical studies have paid little attention to the acute exacerbation and mortality of patients with IPF. Only 1 relevant systematic review mentions the impact of CHMs on acute exacerbation and mortality in patients with IPF and concludes that CHMs can reduce the acute exacerbation rate but have no effect on mortality. However, due to the problems of small sample size, poor methodologic quality, and large variability of CHMs, the evidence for the result is weak. To avoid these limitations as much as possible, the clinical trials of TCM included this review will be conducted strictly and carefully and recommended to follow the CONSORT—CHM Formulas 2017. Besides, we also achieve important implications from this review for future high-quality clinical trials to get explicit evidence. We hope this review will stimulate proper evaluation of CHMs.The mechanism of TCM treatment of IPF is not completely clear. One possible explanation is that the mechanism of TCM prevention and treatment of IPF mainly regulates cytokines and signal transduction pathways, inhibits extracellular matrix synthesis, and regulates oxidative stress and coagulation-fibrinolysis systems. This hypothesis has been supported by a number of recent clinical trials, which have demonstrated that CHMs for activating blood circulation and removing blood stasis, tonifying qi and nourishing Yin, clearing heat and reducing phlegm and tongluo are effective in improving pulmonary fibrosis.3.1This review does not require ethical approval because the included studies are published data and do not involve the patients’ privacy. The results of this review will be reported in accordance with the PRISMA extension statement and disseminated to a peer-reviewed journal.This study is initiated by Fei Wang and Jing Guo. Fei Wang and Jing Guo were involved in the design of the study and the interventions of the protocol. Jing Guo, Bin Li, and Wenyuan Li will develop the search strategies, conduct data collection, and analyze independently. Yi Pan, Zhichao Wang, and Yuxiao Wu will revise it.All authors have approved the final manuscript.Conceptualization: Fei Wang.Methodology: Jing Guo, Bin Li, Wenyuan Li, Yuxiao Wu.Software: Zhichao Wang.Supervision: Fei Wang.Writing – original draft: Jing Guo, Bin Li.Writing – review & editing: Jing Guo, Bin Li, Wenyuan Li, Yi Pan.


Background: In recent years, great improvement has been made in immunotherapies for non-small cell lung cancer (NSCLC). Current data have suggested that Programmed cell death ligand 1 (PD-L1) expression might not be an ideal marker for patient selection in isolation. Evidence has been increasing that alternative markers, such as neutrophil-to-lymphocyte ratio (NLR), a biomarker of systemic inflammation response (SIR) previously associated with outcomes in a variety of cancers including NSCLC, might be a predictor for patient selection and the response to therapy. No reports have examined the prognostic value of combination of PD-L1 expression and inflammatory markers such as NLR in NSCLC. This retrospective study explores the relationship between NLR and PD-L1 expression in NSCLC as well as the prognostic value of combination of PD-L1 expression and NLR.Method: We evaluated tumor PD-L1 expression in 235 surgically resected NSCLC cases by immunohistochemical analysis. Carcinoma cells showing membranous staining for PD-L1 were considered PD-L1-positive cells . Cases with ≥1% tumor membrane staining were considered PD-L1-positive. The association of clinicopathological characteristics with PD-L1 expression was assessed by univariate and multivariate analyses. Moreover, univariate and multivariate analyses were performed to evaluate the predictive impact of PD-L1 expression and other factors on disease-free survival (DFS) and overall survival (OS).Result: PD-L1 protein expression was elevated in 34.0% of patients at cut-off value of 1%. Univariate analyses showed that PD-L1 expression was significantly higher in men , heavy smokers , and patients with squamous cell carcinoma . No correlations were noted between PD-L1 expression and age, EGFR mutation status or clinical stage. No significant correlations between PD-L1 protein expression and NLR were found. Multivariate logistic regression revealed that smoking index ≥400 was independent predictor of PD-L1 expression . The results of univariate survival analyses showed that clinical stage  was associated with DFS. Smoking index , clinical stage  and adjuvant treatment  were significantly associated with OS. Neither PD-L1 expression nor NLR was found to be associated with DFS or OS. Of interest, when patients were divided in two groups according to combined PD-L1/NLR: patients with PD-L1+/ high NLR as Group 1, other patients as Group 2, Group 1 had significantly shorter DFS as well as OS than Group 2 . In the multivariate analysis, Cox proportional hazards regression models showed that, PD-L1+/ high NLR was associated with a significantly shorter DFS and OS . Stratified analysis showed that the prognostic value of combined PD-L1/NLR can only be observed in cases without epidermal growth factor receptor (EGFR) mutations . In EGFR mutation subgroup, combination of PD-L1 expression and NLR has no relationship with DFS or OS.Conclusion: We found that combination of PD-L1 expression and NLR may be a promising prognostic indicator, and may also be a good marker for tumor recurrence, especially in the patients with wild-type EGFR. Lung cancer is currently the top cause of cancer deaths worldwide Accordingly, the identification of biomarkers that predict the clinical efficacy of immune checkpoint blockade therapy is urgent. Furthermore, effective combined therapies with immunotherapy are required for improved clinical benefit. Current data have indicated that the correlation between PD-L1 expression by immunohistochemistry and the response to immune checkpoint inhibition varies by both tumors histologic features, suggesting that PD-L1 might not be an ideal marker for patient selection in isolation Patients who underwent surgery for primary adenocarcinoma (AD) and squamous cell carcinoma (SCC) between 2012 and 2015 were identified from a retrospective review of a prospectively maintained database at Tianjin Cancer Institute & Hospital, Tianjin Medical University, Tianjin P.R. China. We excluded patients: (1) who were lost to follow up, (2) without R0 resections, (3) with distant metastases, (4) had insufficient laboratory data, (5) had clinical evidence of infection or other inflammatory conditions, (6) had received preoperative chemotherapy or irradiation, (7) whose matched paraffin embedded formalin-fixed (FFPE) tissue specimens were unavailable. Finally, 235 patients entered our study. Information about demographics, data regarding surgical procedures, preoperative blood variables, postoperative course, pathologic findings, and follow-up was collected. The NLR was defined as the absolute neutrophil count divided by the absolute lymphocyte count. DFS and OS were stratified by median of NLR (2.3), which was also consisting with previous studies All Patients were observed until death or July 1, 2017. The median follow-up period was 35 months. Disease-free survival (DFS) was defined as the time from resection to the first disease recurrence and was censored at the last follow-up date if no events had occurred. Overall survival (OS) was calculated from the date of surgery to the date of death or last follow-up. Prior consent from all patients and approval from the Research Ethics Committee of Tianjin University were obtained for the use of clinical and pathological data.2O). Antigen retrieval was performed with EDTA buffer per the manufacture's, then sections were incubated in 3% hydrogen peroxide (H2O2) for 10min in order to inhibit endogenous peroxidase activity. The sections were incubated overnight at 4℃ with the monoclonal antibodies at 4°C overnight. To visualize the antigen, the immune complex was detected with a DAKO EnVision Detection System (Dako). Finally, sections were then counterstained with hematoxylin, and mounted. The primary antibody was an anti-human PD-L1 rabbit monoclonal antibody ). Carcinoma cells showing membranous staining for PD-L1 were considered PD-L1-positive cells. The proportion of PD-L1-positive cells was independently estimated as the percentage of total carcinoma cells in whole sections by two investigators (X.W. and L.C.). Cases with ≥1% tumor membrane staining were considered PD-L1-positive.Tumor PD-L1 expression was evaluated by immunohistochemistry on formalin-fixed and paraffin embedded tumor tissue sections according to the previously described PD-L1 immunohistochemistry protocol 2 test or Fisher's exact test when necessary. The logistic regression model was used to estimate influence of preoperative factors on PD-L1 expression. Survival probability was estimated by the Kaplan-Meier method, and log-rank test was used for the comparison of survival. Multivariate analyses were performed using the Cox proportional hazards regression model to evaluate significant recurrence predictors and prognostic factors. All tests were 2-sided. Statistical analyses were performed in SPSS 17.0 for Windows software (SPSS Inc). P values of <0.05 were considered to indicate statistical significance.Categorical variables are presented as numbers and percentages. Distribution of continuous variables is reported as median and range. Qualitative data were compared by the χThe clinicopathological characteristics of the 235 patients with NSCLC (130 with AD and 105 with SCC) are summarized in Table P < 0.001), was found to be independently associated with PD-L1 expression.Immunohistochemical staining for PD-L1 was detected at the membrane of tumor cells  was associated with DFS. Smoking index , clinical stage  and adjuvant treatment  were significantly associated with OS. Neither PD-L1 expression nor NLR was found to be associated with DFS or OS. Of interest, when patients were divided in two groups according to combined PD-L1/NLR: patients with PD-L1+/ high NLR as Group 1, other patients as Group 2, DFS and OS showed significant difference between groups. Group 1 had significantly shorter DFS as well as OS than Group 2  patients died. Univariate and multivariate analyses were performed to evaluate the predictive impact of PD-L1 expression and other clinicopathological factors on OS and DFS . Moreover, clinical stage, smoking index, adjuvant treatment, and combined PD-L1/NLR remained independent factors of OS  and a trend for worse DFS  in patients without adjuvant chemotherapies. In patients received adjuvant chemotherapies, PD-L1+/ high NLR was identified to be associated with worse DFS  but not OS .Subgroup analyses were performed for EGFR mutation status and adjuvant treatment Table . In EGFRIn the present study, PD-L1 protein expression was elevated in 34.0% of patients with NSCLC who underwent surgery (41.0% of lung SCC samples and 28.5% of lung AD samples). PD-L1-positive expression was more frequently observed in male, heavy smokers and patients with SCC. Multivariate analysis revealed that smoking index ≥400 was independent predictor of PD-L1 expression. No significant correlations between PD-L1 protein expression and NLR or EGFR mutation status had been found. Patients with pretreatment NLR > 2.3 and PD-L1 expression was associated with inferior DFS and OS. Moreover, we demonstrated that such poor prognosis was only observable in cases without EGFR mutations, and the prognostic effect for NLR/PD-L1 might be affected by adjuvant therapy and subsequent treatment.In previous reports, clinical factors, such as smoking history, were reported to be associated with the PD-L1 expression. Wu et al. reported that PD-L1 protein expression is higher in men than women, smokers than never smokers Previous researches have also shown that PD-L1 protein expression is associated with EGFR mutations. Takada et al. pointed out that PD-L1 positivity was significantly associated with wild-type EGFR Many studies have evaluated the prognostic impact of PD-L1 protein expression in NSCLC, including the present study. However, the results of these studies vary: some studies have shown that expression of PD-L1 was correlated with poor clinical outcomes in NSCLCThere are a number of well-established systemic inflammation-based prognostic scores for patients with NSCLC Mechanism of regulation of PD-L1 expression has not yet been sufficiently clarified. Previous reports often focused on oncogene-driven mechanisms The validation of PD-L1 expression and inflammatory markers may be significant because they offer the potential for a feasible test that can be used to help evaluate the patient's prognosis. However, the tumor-host immune and inflammatory response is a complex interaction, with the key processes underlying this response still unclear. Our study has several limitations. First, the current study included patients with only operable NSCLC, and these patients were not treated with PD-1 or PD-L1 axis therapies. Further studies in patients treated with PD-1 axis therapies should be performed to confirm the findings obtained in the current study. Second, the PD-L1 analysis for surgically resected NSCLC was conducted using a specific antibody against PD-L1 (E1L3N) and with one cut-off value. Immunohistochemical staining of PD-L1 had been performed using different PD-L1 antibody clones in the different studies and clinical trialsTaken together, our findings indicate that combination of PD-L1 expression and NLR may be a promising prognostic indicator, and may also be a good marker for tumor recurrence. However, to suggest potential intervention strategies aimed at cancer-associated inflammation so as to improve outcome of immune checkpoint blockade, further study of these mechanisms, including research from a molecular biological approach, and covering more advanced NSCLC cases, is required.