In recent years there has been growing interest in the electronic properties of ‘few layer’ graphene films. Twisted layers, different stacking and register with the substrate result in remarkable unconventional couplings. These distinctive electronic behaviours have been attributed to structural differences, even if only a few structural determinations are available. Here we report the results of a structural study of bilayer graphene on the Si-terminated SiC(0001) surface, investigated using synchrotron radiation-based photoelectron diffraction and complemented by angle-resolved photoemission mapping of the electronic valence bands. Photoelectron diffraction angular distributions of the graphene C 1s component have been measured at different kinetic energies and compared with the results of multiple scattering simulations for model structures. The results confirm that bilayer graphene on SiC(0001) has a layer spacing of 3.48 Å and an AB  stacking, with a distance between the C buffer layer and the first graphene layer of 3.24 Å. Our work generalises the use of a versatile and precise diffraction method capable to shed light on the structure of low-dimensional materials. Graphene comprises a single layer of sp2-bonded carbon atoms that are organised in an open hexagonal network. The electronic band structure of graphene shows linear dispersion around the Fermi energy at the K-point of the surface Brillouin zone, instead of the parabolic dispersion that is observed in graphite. However, its detailed electronic band structure depends on several factors such as the number of layers, the symmetry of the lattice, the stacking ordering and their interlayer distances4. Here we report the results of a determination of the structural parameters, namely the stacking order and interlayer distances, of bilayer graphene grown on the Si-terminated face of SiC(0001). While bulk graphite is known to have the AB stacking structure and an interlayer distance of 3.35 Å, these parameters have been the subject of some debate for bilayer and multilayer graphene.Graphene has attracted considerable interest in recent years in the scientific, technological and industrial communities due to its novel physical and electronic properties, which offer the possibility of fabricating new devices for future carbon-based nanoelectronics5. Prepared in this way the material has the same AB stacking between layers as bulk graphite. Graphene can be also grown by chemical vapour deposition (CVD) onto a range of substrates such as Cu and Ni, the resulting structural properties being strongly dependent on the growth conditions. Epitaxial growth of graphene on SiC is an alternative approach capable of producing large scale, high quality, graphene films that cannot be obtained by mechanical exfoliation. Graphene grown on the two polar faces (Si-face and C-face) of SiC had been thought to have different stacking sequences that would result in different electronic properties. Low energy electron diffraction (LEED) patterns of graphene on the Si-terminated Si-face appear sharp, but LEED patterns obtained from graphene on the C-face are generally smeared out into a continuous diffraction ring , implying contributions from either twisted adjacent layers or adjacent AB-stacked grains of different azimuthal directions. The electronic valence band structure of bilayer graphene on the Si-face shows splitting into two π band branches, but bilayer graphene on the C-face was commonly found to show linear dispersion near the Dirac point, as in monolayer graphene. However, angle-resolved photoelectron spectroscopy (ARPES) from single μm-sized grains has recently shown that multilayer graphene on the C-face has the same stacking and electronic structure as on the Si-face and also shown how different stacking sequences strongly influence the electronic structure8. There have been different theoretical predictions regarding the structural model of stacked graphene namely; AB stacking, AA stacking and twisted graphene layers10. Here we have investigated the alternative AB and AA stacking models and show that only AB stacking of bilayer graphene on the Si-face is consistent with our experimental data. In AB stacking, the two adjacent layers are displaced laterally exactly as in bulk graphite, whereas in AA stacking the carbon atoms of two adjacent layers have exactly the same lateral positionsIn recent years, a number of different methods of graphene production have emerged, each method resulting in different structural and electronic properties. Conceptually, the simplest such method is mechanical exfoliation, pioneered by Novoselov and Geimet al.11 revealed the possibility of tuning the energy gap of bilayer graphene via its interlayer spacing. The calculation showed that a relatively small variation of the interlayer spacing can result in significant changes in the field-induced gap. It is therefore interesting to determine the interlayer distances between graphene layers in order to provide key information required to understand the electronic properties of multilayer graphene as well as of graphene heterostructures with other two-dimensional materials12. There have been a number of theoretical and experimental studies that have sought to establish the interlayer spacings of multilayer graphene. DFT calculations based on the generalized gradient approximation (GGA) indicated that the interlayer distance of AB stacked bilayer graphene is larger than that of bulk graphite, and was predicted to be 3.58 Å13. Another DFT study, which included the effect of van der Waals dispersion forces, obtained an interlayer distance of 3.49 Å for AB-stacked bilayer graphene14, while the value found in the calculations of Guo et al. was 3.34 Å11. The only experimental investigation sharing the same goals involved surface x-ray reflectivity by Hass et al.15 was directed particularly to understanding the structure of the SiC/graphene interface; the results included a determination of the interlayer distance between the carbon buffer layer (that lies between the SiC and the graphene) and the first graphene layer of 2.32 Å, but also the interlayer spacings of the first and second graphene layers (3.5 Å) and between successive graphene layers (3.35 Å). However, the signal from the graphene layers was particularly weak compared to the spots originated by the massive SiC substrate, naturally indissociable in experiment. Ferrah et al.16 reported the results of an X-ray photoelectron diffraction (XPD) of ‘few layer graphene’ (FLG) on SiC(0001) but their study focused on the epitaxial relationship between FLG and the SiC substrate and not on the interlayer spacings. Another photoelectron diffraction study by Lima et al.17 was described by the authors as XPD, but was actually a study of the angular distribution of photoelectrons at kinetic energies of only ~160 eV, comparable to the work we report here; the XPD name is normally reserved for experiments with much higher photoelectron energies when the angular distribution is dominated by zero-order forward ‘focussing’ diffraction, whereas at much lower energies both forward scattering and backscattering can be important. However, this work by Ferrah et al. was focused on the structure of the carbon buffer layer, although they also studied one preparation with monolayer graphene. Their results showed that the buffer layer is formed by two domains rotated by 60◦ with respect to each other, the distance between the buffer layer and the graphene layer being found to be 3.75 Å.Another important structural parameter in graphene is the spacing between the graphene layers. Density functional theory (DFT) calculations by Guo The objective of the investigation reported here was to determine the stacking sequence of bilayer graphene and the associated interlayer spacings in an effort to reconcile the different values found in existing theoretical and experimental literature. To achieve this goal we used angle-scan photoelectron diffraction (PED) to study single-domain graphene bilayers grown on the Si-terminated SiC(0001) surface. In this technique, the intensity of a core level photoemission peak is measured as a function of the emission angle at fixed values of the photoelectron kinetic energy. Coherent interference of the directly emitted photoelectron wavefield with other components elastically scattered by the surrounding atoms leads to an angular distribution measured outside the surface that is characteristic of the local atomic environment of the emitter atoms. This structure can be determined by comparison of the experimentally-measured angular distributions with the results of multiple scattering simulations for different possible trial structures. Trial and error iteration of the model structure is then undertaken to achieve the optimal agreement between theory and experiment.18.Figure The quality and character of the graphene deposition in our experiments, including the number of layers, is provided by an ARPES measurement of the valence band structure. The ARPES ‘image’ of the valence band structure of bilayer graphene measured perpendicular to the Γ-K direction at a photon energy of 100 eV is shown in Fig. D) is located −320 meV relative to the Fermi level (EF) due to charge transfer from the substrate to the graphene layer that causes it to be n-doped. There is a 120 meV gap, as expected in the bilayer graphene on SiC(0001), due to the electric dipole that exists between the graphene and SiC interface; this induces an electrostatic asymmetry between the layers19. These electronic features displayed in Fig. The Dirac point , 460 meV (SiC), and 1.36 eV (BL). Photoelectron diffraction data were extracted from the intensity modulations of the graphene C 1s component (Gr) measured at different azimuthal and polar emission angles.Figure d1 and the distance between the first and second graphene layers is defined as d2. The AB stacking model is one of the two possible stacking sequences considered in the multiple scattering PED calculations, discussed in the succeeding sections.The atomic plane arrangement of bilayer graphene with AB stacking is depicted in Fig. 20. Summarizing, as at high kinetic energy, forward scattering is dominant, the evaluation of the photoelectron diffraction patterns of conveniently selected carbon emitters at the graphene bilayer provide precise structural information of the lattice bilayer symmetry. On the contrary, at low kinetic energy, since the backscattering is significant, the photodiffraction modulations are particularly sensible to rather small variations of the interlayer spacing of the graphene bilayer and the distance to the buffer layer.The PED measurements of the graphene C 1s component presented here were obtained at two photon energies (hν), 600 and 900 eV, which correspond to 326.1 and 638.6 eV photoelectron kinetic energies, respectively. The photoelectron diffraction technique is typically performed in two different ranges of photoelectron kinetic energy that strongly influences the dominant scattering paths that contribute to the measured angular distribution. At high kinetic energies backscattering is weak and the dominant scattering processes are forward scattering zero-order diffraction with scattering angles close to zero; these ‘forward focussing’ peaks in the angular distribution correspond to interatomic directions. At low kinetic energies backscattering is much more significant (though forward scattering is still strong), allowing the technique to be used to locate adsorbate atoms relative to the underlying solid on which they are adsorbed. ‘High’ and ‘low’ typically means greater than, or less than, approximately 500 eV, although there is no sharp change in behaviour at this energy. The data we present here are thus recorded at intermediate energies in this range, but one may expect the higher-energy data to be more strongly influenced by forward scattering21. The lower energy distribution is expected to be more strongly influenced by backscattering that can prove to be sensitive to the detailed structure.The PED angular distributions of the graphene component of the C 1s photoemission recorded at the two different photon energies are shown in Fig. In order to extract more quantitative structural information, azimuthal angle-dependent plots of the lower kinetic energy graphene C 1s intensity modulations at specific polar emission angles  in the hexagonal unit mesh of graphene were used as emitters. Within the graphene bilayer unit mesh there are a total of four C emitters, two each from the first and second graphene layers, that contribute to the graphene C 1s photoemission component. Scattering potentials were calculated within the muffin-tin potential for atoms in a crystal lattice according to the prescription of Mattheis26. Atomic mean square displacements of 0.003 Å2 were used to account for thermal vibrations by means of Debye-Waller factors. The inner potential was set to V0 = −16.5 eV, the value used in the earlier XPD study of this system21; because of the relatively high kinetic energies used in our study, small changes in the inner potential have no significant effect on our structural conclusions (see Supplementary Information).In order to achieve a more reliable structure determination, based on comparisons with simulated azimuthal distributions for different model structures, the data set was enlarged to cover eight polar emission angles of θ = 12.2°, 20.2°, 26.7°, 32.7°, 37.5°, 46°, 52.5° and 56.8°. Multiple scattering simulations of these photoelectron diffraction azimuthal spectra were based on the structural model depicted in Fig. d1 (the distance between buffer layer and the first graphene layer) and d2 (the distance between the first and second graphene layers), as illustrated in Fig. R) that has been used extensively in scanned-energy mode photoelectron diffraction27 defined asThe values of the parameters d1 and d2 in the range 3.00 Å to 3.90 Å, in steps of 0.1 Å, in order to identify the best-fit values. Figure R-factor over this grid, and clearly identifies the optimum values of d1 and d2 as approximately 3.3 Å and 3.5 Å, respectively. Figure R as a function of the value of d1 with the value of d2 fixed at 3.50 Å, while Fig. R as a function of the value of d2 for a fixed value of d1 of 3.30 Å. Based on these plots we conclude that the best-fit values of the d1 and d2 parameters are 3.24 Å and 3.48 Å respectively. In order to estimate the precision of these values we also follow the approach used in scanned-energy mode photoelectron diffraction27 of defining a variance in the minimum value of the R-factor28N is the number of independent pieces of structural information within the data being compared. Notice that N is not simply the number of data points; the azimuthal plots that form the basis of our experiment/theory comparison clearly show that several data points are required to define a modulation, and it is the number of modulations, or more particularly the possible number of modulations (an absence of a modulation is significant) that define N. In scanned-energy mode photoelectron diffraction, the value of N can be estimated, as proposed by Pendry29 in LEED intensity-energy spectra, by dividing the total energy range by a characteristic peak width related to imaginary part of the inner potential that describes the inelastic scattering. In the present case an equivalent procedure would be to divide the total azimuthal angle range of the data (8 × 180°) by the half-angle of the narrowest observed features, i.e. ~5°. This gives a value of N of 288 so as Rmin ≈ 0.26 the variance is ≈0.022 and all values of the structural parameters leading to R value less than 0.302 lie within these nominal precision limits. This leads to final values for the two parameters of d1 = 3.24 ± 0.20 Å and d2 = 3.48 ± 0.10 Å. The much lower precision in the determination of d1 is clearly reflected in Fig. Simulations were performed over a two-dimensional grid scan of d1, is less than the interlayer spacing in bulk graphite (3.35 Å), and that the interlayer spacing of the graphene bilayer, d2, is larger than the value for bulk graphite, is broadly consistent with the results of previous  studies. Specifically, our value of d1 of 3.24 ± 0.20 Å can be compared with the experimental X-ray reflectivity value of Hass et al.15 of 2.32 Å, and the theoretical values of Mattausch and Pankratov24 (3.3 Å) and of Kim et al.30 (3.35 Å), while our value of d2 of 3.48 ± 0.10 Å may be compared with Hass et al.’s experimental value of 3.5 Å and the theoretically derived values of values of 3.49 Å14, 3.58 Å13 and 3.34 Å11.Our conclusion that the spacing of the buffer layer and the first graphene layer, d1 and d2 values for the AB stacking. By contrast, the simulations for AA stacking clearly show major discrepancies between theory and experiment at polar emission angles of 20.2° and 32.7°; these angles correspond approximately to the first and second nearest neighbour interlayer scattering directions, respectively, for AB stacking .Based on previous theoretical and experimental studies, AB stacking, and not AA stacking, is the equilibrium structure for graphene grown on the Si-terminated SiC(0001) surface. To establish whether our experiments are consistent with this view we have also performed multiple scattering calculations on a model having the AA stacking sequence. Figure R-factor values of 0.26 for AB stacking and 0.45 for AA stacking. Figure This clear difference in visual comparison is also found in the associated x, y and z as well as rotation both in polar (θ) and azimuthal (φ) angles. The geometry of the experiments was as follows. The surface normal, the incident beam direction and the electron emission detection direction define a common plane. The angle between the incident beam and the electron energy analyzer is 45°. Measurements of the complete photoelectron diffraction angular distributions were performed by rotating the azimuth of the sample within the 180° range in steps of 1° at specific polar angles. In order to cover a total polar angle range of around 60° this procedure was repeated at polar angles of 19° and 43°. All measurements were recorded with the sample cooled by liquid nitrogen to 120 K. The photoemission and photoelectron diffraction experiments were obtained using an incident photon beam spot size of 140 μm.The photoemission and photoelectron diffraction experiments were carried out at the ANTARES beamline of Synchrotron SOLEIL in Saint-Aubin, France. Photoelectron spectra were obtained using a Scienta R4000 concentric hemispherical analyser equipped with a 2-D detector to allow parallel measurement of a 25° range of collection angles. The sample was placed in the analysis chamber on a sample manipulator with five degrees of freedom, allowing precise orthogonal translation in 31. After an initial degassing at 900 °C and surface flattening at 1050 °C, the graphitisation was achieved by holding the SiC substrate at 1220 °C for six minutes, leading to high quality bilayer graphene on the Si-terminated SiC(0001) surface.The bilayer graphene sample was prepared by the standard graphitisation procedure described in more detail elsewhere32. The computational scheme takes into account the finite energy and angular resolution of the electron energy analyser. In the present calculations single and double scattering events were included. This program is embedded in software that searches for the best agreement between experiment and theory in a multi-dimensional parameter space using an adapted Marquardt algorithm.Multiple scattering simulations of the measured photoelectron diffraction data were performed using a computer program, based on the magnetic quantum-number expansion formalism, developed by FritzscheIn summary, we have exploited the combination of photoelectron diffraction angular distributions and multiple scattering simulations to show that bilayer graphene grown on the Si-terminated SiC(0001) face has an AB stacking and has an interlayer distance between the buffer layer and the first graphene layer of 3.24 ± 0.20 Å while the interlayer spacing of the bilayer graphene is found to be 3.48 ± 0.10 Å. The smaller spacing between the buffer layer and the first graphene layer than in bulk graphite (3.35 Å), and the fact that the interlayer graphene bilayer spacing is larger than that in bulk graphite, are consistent with several previous theoretical studies. Even if the present results are more precise they are still in agreement with the only other experimental investigation, based on X-ray reflectivity.Supplementary Information


The humidity sensor with single active material has a limit in the detecting ranges, where the GO, PEDOT: PSS, and Methyl Red materials have sensing responses of 0 to 78% RH, 30 to 75% RH, and 25 to 100% RH, respectively. However, a humidity sensor using combined three active materials can respond to much wider range of RH with high sensitivity, where the IDTs and the active regions were prepared using ink-jet printing and spin coating, respectively. This proposed sensor can detect a full range of 0% RH to 100% RH. The response and recovery times are 1 sec and 3.5 sec, respectively. Our single sensing device using multiple IDTs connected different active materials in series can overcome the limitations of single transducer based sensor for the high performance sensor applications.Single transducer with humidity sensing materials has limitations in both range and sensitivity, which cannot be used to detect the full range of humidity with consistent sensitivity. To enlarge range and improve sensitivity in the all range relative humidity (RH), we propose a highly sensitive and full range detectable humidity sensor based on multiple inter-digital transducer (IDT) electrodes connected in series with poly doped poly (styrene sulfonate) anions (PEDOT: PSS), C Humidity sensors are designed to detect the amount water content in the surrounding environment by monitoring change in various parameters such as resistance, capacitance, refractive index, inductance, piezoelectric response and so on12. In particular, resistive and capacitive sensors have received high interest because of their low cost, facile fabrication, environment-friendly nature, and seamless integration with electronic circuits enabling a direct electrical readout13. Research on humidity sensors focuses on improving response and recovery times, sensitivity, and detection range. To achieve these target parameters for certain customized applications, different approaches have been proposed by designing different sensor structure15, changing active materials17 and adopting different fabrication technologies3.Relative humidity (RH) sensing has garnered great attention due to versatile aspects of monitoring and controlling humidity for medicines, other sensitive products like food, human health, industry, and even in space environments18, polymers19, organic and inorganic semiconductors20, composites22 and oxides23. Although some humidity sensors were previously demonstrated with some advantages such as low cost, easiness to use, and low operational temperature with long lifetime, low sensitivity and limited detection range are still main concerns. To overcome these challenges, researchers are in the development using different materials and fabrication technologies3, but still further investigations are needed. Particularly, implementation of alternative materials can be a great momentum to improve sensitivity and detection range25. However, research is still lack to achieve the cost effective and full range detectable humidity sensor.Different kinds of materials have been studied for humidity sensing using ceramics15H15N3O2 (methyl red) as the active sensing materials. Sensing ranges significantly depend on active materials. Sensor using GO thin film showed linear sensing response towards low humidity levels from 0% RH to 75% RH. For PEDOT: PSS-based sensor, it linearly responded to mid-range humidity levels from 25% RH to 75% RH. Sensor with methyl red-based active region showed linear sensing response towards higher humidity levels from 25% RH to 100% RH. To maximize sensing range further, these three active regions were connected in series to fabricate a single humidity sensing device enabling full range detection of relative humidity from 0% RH to 100% RH with high sensitivity. The three IDTs were fabricated by ink-jet printing as shown in Fig. In this work, we demonstrated a highly sensitive and full range detectable humidity sensor based on multiple inter-digital transducer (IDT) electrodes connected in series using graphene oxide (GO), poly doped with poly (styrene sulfonate) anions (PEDOT: PSS), and C26. The schematic of electrodes was firstly designed as shown in Fig. 27. Figure Inter-digital typed electrodes were used as the transducing elements in the proposed humidity sensing device. IDT are expected to provide higher sensitivity by efficiently detecting any change in active film characteristics on all electrode fingers. It is similar to a combination of two electrode chemristors connected in parallel with an output equivalent to their net effect. The finger width and spacing of each IDT were 100 µm and 50 µm, respectively, as shown in Fig. 2O with 2 mg/ml, PEDOT: PSS in paste form, methyl red in powder form, and dimethylformamide (DMF) solvent were purchased from Sigma Aldrich, Korea. The 10 weight percent (wt %) methyl red was dissolved in DMF using bath sonication for 2 hours at 30 °C  and it became as gel26. PEDOT: PSS paste was diluted by 10 wt% in de-ionized water, followed by stirring for 1 hour at room temperature. GO solution was used without further processing. Then, these materials were independently deposited over different sets of silver IDTs using spin coating to complete multiple active regions. The PET substrate was treated with UV-ozone plasma for 5 minutes just before deposition of each active layer thin film. UV surface treatment removes impurities and improves the hydrophilicity of the surface, leading to better adhesion of the active layer material to the surface. The treatment also results in production of hydroxyl ions on the surface that helps the materials to temporarily bond to the surface and allow homogenous thin film fabrication. All films were spin-coated at 2200 rpm and were cured for 2 hours at different temperatures for the proper sintering such as 120 °C for graphene oxide, 100 °C for PEDOT: PSS, and 130 °C for methyl red.The active or sensing layer is the most important part of the humidity sensor. The detection of humidity or water vapors in the surrounding environment using the sensing layer can be achieved by monitoring change in resistance or capacitance. All active materials aforementioned exhibit resistance changes in response to variation in the amount of surrounding RH. The GO dispersion in HThe surface morphology of the sensing layers was analyzed using FE-SEM (JEOL JSM-7600F). NV-2000  non-contact surface profiler was employed in phase shifting interferometry (PSI) mode to investigate the proper fabrication of IDTs. The humidity sensing characterization setup is presented as shown in Fig. RH was controlled inside the enclosed environmental chamber where the reference sensor (HTU-21D) and the sensor being evaluated were placed side by side for data recording. EDC-1630 digital LCR meter was used to record the impedance and resistance data of the fabricated sensors. The real time data curves were also plotted on an oscilloscope through a DAQ circuit. The airtight humidity chamber can cover full range of relative humidity from 0% to 100% RH. The RH inside the chamber was maintained using automatic feedback controller. Compressed air dryer along with dry nitrogen were used to decrease the level of RH while a desktop humidifier was used to increase the humidity. The flow rate was controlled through electronic valves and the real time data was displayed on an LCD along with data logging and plotting on a computer. The humidity level was changed by ~5 ± 1% RH per step and was maintained until the real time readings of the device under test (DUT) were stable. The humidity responses of the proposed devices using 4 different active layers PEDOT: PSS, methyl red, GO thin film, and the series combination of all three materials were taken for the range of 0% RH to 100% RH and the temperature was maintained at ~21 °C for the whole experiment. The impedance response of each sensor was measured for a full humidity range from 0% RH to 100% RH and the test frequency was kept constant at 1 kHz. The reading was allowed to become stable before incrementing to the next step and an average of 20 points was taken for every specific value at 5% RH interval.Surface morphologies of the active regions consisting of PEDOT: PSS, methyl red and GO thin film analyzed using FE-SEM are presented in Fig. −1 to 1800 cm−1. The IR bands of 1520 cm−1, 1310 cm−1 and 1201 cm−1 are mainly due to C = C and C – C stretching of quiniod structure of the thiophene rings and sulfonic acid group of PSS. The band at 1143 cm−1 is due to C – O – C bond stretching in the ethylene dioxide units while the bands at 982 cm−1 and 691 cm−1 are attributed to C – S stretching mode28. These bands indicate the pure nature of the PEDOT: PSS without any contamination even after deposition.The chemical composition of PEDOT: PSS, methyl red and GO thin films were investigated by using FTIR spectroscopy as shown in Fig. −1, 1112 cm−1, 1145 cm−1, 1239 cm−1 and 1274 cm−1 are the Ring−H stretching modes while the seven bands at 836 cm−1, 1197 cm−1, 1365 cm−1, 1467 cm−1, 1530 cm−1, 1575 cm−1 and 1596 cm−1 are the Ring−C stretching modes attributed to benzene rings in methyl red. Band at 1395 cm−1 shows a strong characteristic N = N stretching and a weak band at 1419 cm−1 is assigned to the CH3 symmetric deformation. The band at 1085 cm−1 is CH3 rocking and band at 1341 cm−1 is attributed to C − O – H while 1312 cm−1 band is amine N − C29. These bands suggest that methyl red thin film has no impurities and contamination. Figure −1 to 3960 cm−1and the spectrum of GO has a well-built and broad − OH stretching vibration band at 3408 cm−1, carboxyl C = O stretching band at 1720 cm−1, C − OH deformation vibration band at 1402 cm−1 and C − O stretching vibration at 1085 cm−1 because of extensive oxidation30. These bands also conform the pure nature and composition of GO without impurities.The FTIR spectrum of methyl red is shown in Fig. 31 and its resistance drops in response to increase in humidity but this drop is very small at lower RH as compared to higher RH levels. This result can be attributed that the water molecules are not directly participating in the generation of ionic current, but the facilitation of electronic current flow becomes strong by providing wider, shorter, and more connected paths to current13. Adsorption of water molecules in PEDOT: PSS causes swelling and dissociation of PEDOT and PSS bonds leading to increased bond energy between PEDOT chains. Since the amount of physically- adsorbed water molecules is very low at lower humidity levels, there is almost no swelling or dissociation and the change in impedance is very low. In this case, the change in capacitance as result of the physisorbed water molecules dominates the resultant change in impedance of the sensor. At higher humidity levels, hydroxyl groups in water molecules can form a coulombic bond with PEDOT increasing its positive charge density. This can cause PEDOT: PSS to change from a benzoid structure to a quiniod structure that has straighter chains and facilitates current flow resulting in the impedance to drop. Finally, after 80% RH, there is no further visible change in impedance inducing the saturation point, which is defined as a maximum detection limit of the PEDOT: PSS based active layer. The impedance of every individual sensor element can be expressed by the following Eq. , and C is the capacitive factor of the equivalent circuit of the sensor. From Eq.  which are charge carriers and leads to decrease of the impedance further. Another factor resulting in higher sensitivity of GO thin film-based sensor towards lower humidity levels is the physical morphology of the thin film that has a highly rough surface allowing the water molecules to readily get trapped inside. The sensor with GO thin film-based active layer exhibits linear decrease in impedance from 1.65 MΩ to 0.15 MΩ as increasing the humidity levels from 0% RH to 75% RH. Upon further increase in the RH, the physisorbed water acts as layer like liquid and thus no further visible change occurs in impedance of the sensor producing the saturation point. As it can be observed, the responses of individual sensor aforementioned are different in terms of detection range and sensitivity towards different humidity levels. GO thin film-based sensor has high sensitivity in range from 0% RH to 75% RH. PEDOT: PSS active layer-based sensor detection range is from 25% RH to almost 80% RH. The sensor with active region based on methyl red thin film detects humidity with high sensitivity from 25% RH to 100% RH. To detect the full range of relative humidity from 0% RH to 100% RH, the response of the above three sensors was to be ideally merged. Therefore, we connected the three sensors in a series combination producing a single sensor with multiple sensing elements having different active sensing layers and different detection ranges. The simplified equivalent circuit of the resulting sensor is presented in Fig. The schematic illustration of the sensing mechanisms is presented in the Fig. Furthermore, each sensor and the whole device were tested for their stability and robustness by recording the impedance at four different humidity levels during consecutive 20 days, where the devices were stored under ambient conditions to prevent from harsh external environment. Figure Afterward, the device was tested in the open natural environment to check its real time application and stability as shown in Fig. Highly sensitive humidity sensor enabling to detect all range of RH level was successfully demonstrated using IDT electrodes connected in series utilizing PEDOT: PSS, methyl red and GO thin film as active layer materials. The conductive polymer PEDOT: PSS based sensor portion responds linearly to mid-range of humidity levels from 25% RH to 75% RH, which is linked to simple adsorption inducing resistance drop due to increase in electronic current. The methyl red-based active region shows linear sensing response towards higher humidity levels from 25% RH to 100% RH, which is correlated with ionic and dipole based polarization due to the physisorbed water molecules and hydroxyl ions. The GO thin film-based active region exhibits linear sensing response towards lower humidity levels from 0% RH to 75% RH and the sensing mechanism can be interpreted by chemisorption of hydroxyl ions resulting in ionic conduction. However, using the three IDTs fabricated by ink-jet printer on the flexible PET substrate as well as the active regions deposited by spin coating, sensor with combined three active sensing materials in series can achieve full sensing range of 0% RH to 100% RH. The response and recovery times were 1 second and 3.5 seconds, respectively. This approach of multiple IDTs in a single sensing device can be an alternative to single transducer-based sensing devices with limitations in nature and therefore applicable to develop for the performance sensors.


We have witnessed abundant breakthroughs in research on the bio-applications of graphene family materials in current years. Owing to their nanoscale size, large specific surface area, photoluminescence properties, and antibacterial activity, graphene family materials possess huge potential for bone tissue engineering, drug/gene delivery, and biological sensing/imaging applications. In this review, we retrospect recent progress and achievements in graphene research, as well as critically analyze and discuss the bio-safety and feasibility of various biomedical applications of graphene family materials for bone tissue regeneration. Victims of severe maxillofacial infection, trauma, tumor, and congenital deformity suffering jaw bone defects, usually require prolonged convalescence. Unlike many other tissues, the bone has an outstanding capability to regenerate when damaged , 2. Howe2-hybridized carbon atoms, firstly was isolated from graphite by Novoselov and Geim in 2004 . It serves as an abundant origin for calcium and phosphorus, which can be easily absorbed. Beta-tricalcium phosphate (β-TCP) is highly biocompatible and creates a resorbable interlocking network within the defect site to promote healing [Tricalcium phosphate, analog of calcium phosphate, is a tertiary calcium phosphate also known as bone ash [Ca healing . Wu et a healing . It is k healing , 98. In  healing . A novel healing .Fig. 3ScChitosan (CS), a highly versatile biopolymer, derived from the shells of crustaceans , 87, hasw/v) GO) showed better physic-chemical properties than CS/Gn scaffolds. The addition of GO at the concentration of 0.25% to CS/Gn scaffolds exhibited enhanced absorption of proteins, extensive apatite deposition. The 0.25% GO/CS/Gn scaffolds were cyto-friendly to rat osteoprogenitor cells, and they enhanced differentiation of mouse mesenchymal stem cells into osteoblasts in vitro  and the elasticity of scaffolds were manipulated to regulate osteogenesis –110. How2 (Si/SiO2).) and investigated the influence of graphene on BMSCs differentiation. They summarized that the graphene coating was cytocompatible and contributed to enhance the osteogenic differentiation of BMSCs at a rate comparable to differentiation under the influence of BMP-2 in the osteogenic medium [Graphene family materials have been widely applied in diverse forms of medical applications for bone regeneration. As a coating, graphene family materials can be transferred on two dimensional (2D) flat non-metal or metal substrates to induce spontaneous osteogenic differentiation of several types of mesenchymal stem cells (MSCs) . Nayak ec medium . Similarc medium . Simultac medium . The capc medium , 112.Since titanium (Ti) and medical-grade Ti alloy have been extendedly applied in the orthopedic and dental fields –115, satOsseointegration is a complicated biological process determined by the surface properties of implants . The graOverall, the strategy of applying graphene family materials as coating onto a surface is charming. Through currently available techniques or methods, such as CVD , electroBarrier membranes are standardly used in oral surgical procedures, applying in guided tissue regeneration (GTR) and guided bone regeneration (GBR), for the treatment of periodontal bone defects and peri-implant defects, as well as for bone augmentation , 125. GBHowever, non-resorbable membranes need to be removed by a second operation. Thus, a resorbable membrane is recommended owing to avoid a second intervention during operation, which can diminish the risk of infection and the loss of the regenerated bone. But the resorbable membranes made of collagen or chitosan usually has poor mechanical property. The addition of graphene family materials improves the weaknesses of resorbable membrane. For instance, De et al. attempted to prepare absorbable collagen membranes enriched with different concentrations of GO. The presence of GO on the membrane altered the mechanical features of the membrane, by conferring lower deformability, improving stiffness, and increasing roughness . Tian etAlthough pristine graphene is basically incompatible with organic polymer to form homogeneous composite, and even decrease the cell viability in some cases if the amount of graphene is excessive . The incDue to their small size, intrinsic optical properties, large specific surface area, low cost, and useful noncovalent interactions with aromatic drug molecules, graphene family materials exhibit excellent efficacy as delivery vehicles of genes and biomolecules. Moreover, simple physisorption via π-π stacking, hydrogen bonding, and electrostatic interaction is able to assist in high drug loading of hydrophobic drugs without compromising potency or efficiency . The theFor better bone regeneration, we sometimes need the help of osteogenic drug or macromolecular osteogenic protein. It was reported that the adsorbed drugs or loaded growth factors on graphene or its derivatives could enhance the osteogenic differentiation of cells due to the increased local concentration . For exaNot only small molecular osteogenic drug, but also macromolecular proteins can be loaded by graphene family materials for bone regeneration. Bone morphogenetic proteins (BMPs) are the most potent osteoinductive protein for bone regeneration. Thus, BMP-2 was loaded on the surface of Ti/GO through π-π stacking and the interaction between negatively charged carboxylic groups at the edges of GO and positively charged amino acid residues of BMP-2 . Ti/GO/BCurrently, more and more teams get down to designing new drug delivery system to improve the practical applications. The loading of large doses, controlled release, and retention of the bioactivity of the therapeutic proteins are still difficulty in research on drug delivery system.Studies on the graphene family materials on biological applications is emerging rapidly, especially their potential applications for bacteria inhibition and inducing stem cell osteogenic differentiation. Before their biological applications are considered for clinical trial, the biocompatibility of graphene family materials is of vital importance. However, the challenges exist and must be overcome. These challenges include a thorough understanding of the graphene-cell  interaction and cellular uptake mechanism as well as mechanism(s) of potential toxicity. We summarize and analyze several articles and conclude that the cytotoxicity and in vivo biocompatibility of graphene family materials are influenced by numerous factors, including surface functionalization, concentration, size, and shape. At low concentration, graphene family materials are cytocompatible, with little negative influence on cell morphology, viability, and proliferation. Furthermore, it was reported that graphene family materials with flat shapes having better biocompatibility, because the flat shape materials were expected to have minor interaction with the cellular membranes . AlthougOn the one hand, a lot of researches have pointed out that graphene family materials possess the capability of bacteria inhibition, due to their functional chemical groups, sharp edges, and synergistic effect with other drugs. Besides, bone remolding and regenerating successfully in an infective bone defect area is challenging. Peri-implant infection and poor osseointegration are also major challenges we confront. The use of graphene family materials in the design and development of antimicrobial bone regeneration application will capture tremendous attention in the future.On the other hand, lots of teams painstakingly did researches to design and fabricate the new strategies of applying graphene family materials in bone tissue engineering. 3D graphene-based scaffold is a promising biocompatible scaffold, which can enhance pre-osteoblasts or stem cells osteoblastic differentiation. Graphene family materials also can be added as a reinforced material aiming to strengthen the composite scaffold mechanical properties and improve physicochemical characterization. In addition, the strategy of applying graphene or its derivatives as coating onto a surface is charming, which is expected to possess the antibacterial activity and better osseointegration, especially the 3D coating. It has been generally hypothesized that the surface characteristics of graphene family materials including nanostructures, surface roughness, protein absorption ability, electrostatic interactions, and surface hydrophilicity, exert an enormous effect on the molecular pathways which control the fate of stem cells , 115. ThMoreover, graphene family materials show great potential in GBR and DDS as well. Graphene family materials improve poor mechanical property of the resorbable membranes made of collagen or chitosan without compromising their intrinsic property. Osteogenic drug or macromolecular osteogenic protein can be adsorbed on graphene or its derivatives via π-π stacking, hydrogen bonding, and electrostatic interaction with high loading and good efficiency. Taking the varied merits into consideration, graphene family materials hold great potential to bone tissue regeneration.Considering that many supreme properties graphene and its derivatives have, especially in vitro osteogenesis enhancing ability and excellent in vivo bone-forming ability, although they still have drawbacks, graphene family materials still are promising candidates used for bone regeneration applications.


PD-1/PD-L1 blockade has received approval for clinical application due to its encouraging benefit with improving prognosis in selected populations. Unfortunately, the response to immunotherapy for many patients remains unsatisfactory. It remains a great challenge to generate potential combinations that will outperform single agents alone with regard to anti-tumor activity.Using NSCLC cell lines and mouse models, we explored the effects of combined niclosamide and PD-L1 blockade on tumor growth and T cell function. Furthermore, we investigated the relationship between PD-L1 and p-STAT3 expression in tumor samples from patients with NSCLC using IHC, as well as their relationship to patient survival.In vitro, niclosamide, an antihelmintic drug, enhanced the cancer cell lysis mediated by T cells in the presence of PD-L1 blockade. Accordingly, mice treated with niclosamide and PD-L1 antibody showed significant delay in tumor growth and increased survival which were associated with the increase of tumor infiltrating T cells and granzyme B release. Importantly, we found niclosamide could decrease the expression of PD-L1 in both a concentration- and time-dependent manner in NSCLC cells, which was linked to the blockage of p-STAT3 binding to the promoter of PD-L1.An enhancement of PD-L1 antibody by niclosamide was observed in inhibition of NSCLC growth in vitro and in vivo, which was involved in blockage of p-STAT3 binding to promoter of PD-L1 and finally downregulation of PD-L1 expression. These encourage the combination therapy of niclosamide and PD-1/PD-L1 blockade to be further studied in clinic.Supplementary information accompanies this paper at 10.1186/s40425-019-0733-7. Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality and has poor prognosis . In receSome clinical trials about combining anti-PD-1/PD-L1 with anti-CTLA-4 have obtained positive results in various cancers, including melanoma  and NSCLPD-L1 expression is related to poor prognosis for patient survival in several tumor types . High leNiclosamide, approved by FDA for its antihelmintic efficacy, is a cell-permeable salicylanilide and can exert uncoupling effects to cause conformational transitions in mitochondrial catalytic proteins, further to kill the tapeworm , 20. BeyIn the current study, we identify that niclosamide in combination with PD-1/PD-L1 antibody can act a synergistic anti-tumor effect in vitro and in vivo for treatment of NSCLC through decreasing PD-L1 expression and further promoting cytotoxic T cell activity and enhancing antitumor immune responses. Furthermore, we demonstrate that PD-L1 downregulation induced by niclosamide is related to the inhibition of STAT3 phosphorylation and its binding to PD-L1 promoter. Our findings establish an alternative strategy targeting PD-L1 to improve immunotherapeutic efficacy by repurposing existing antitapeworm drug.The following compounds and reagents were used: niclosamide was purchased from Selleck Chemicals, USA, and prepared in dimethyl formamide (DMF) at a concentration of 5 mmol/L and maintained at − 20 °C. Niclosamide was diluted in DMF for working solutions and used at concentrations ranging from 0.25 μmol/L to 2 μmol/L for the treatment of cells. The following antibodies were purchased from Cell Signaling Technology, USA: phospho-STAT3 (p-STAT3), STAT3. Human B7H1/PD-L1 polyclonal antibody was purchased from Santa Cruz Biotechnology, USA, and GAPDH antibody was from Abcam, UK. Polyclonal goat anti-mouse antibody and goat anti-rabbit antibody  were used for Western blotting.2 incubator. Peripheral blood mononuclear cells (PBMCs) were cultured in T cell medium , and IL-2 (100 IU/mL).NSCLC cell lines , Lewis lung cancer (LLC) cell line, and 293 T cell line were obtained from the American Type Culture Collection  and validated by short-tandem-repeat (STR) analysis (except for LLC). Cells were cultured in either RPMI-1640 (for NSCLC cell lines) or DMEM (for LLC cells and 293 T cells) containing 10% fetal bovine serum and maintained at 37 °C in a humidified 5% COCytotoxicity studies were performed using the thiazolyl blue tetrazolium bromide (MTT) assay. MTT assay was performed to examine sensitivity of the cells to niclosamide as described previously . ExperimCells were treated with the indicated concentrations as shown in the figures and washed twice with cold PBS. Whole cell extracts were collected in RIPA lysis buffer , and protein concentration of the lysates was measured using a BCA Protein Assay Kit . The protein samples were electrophoresed through a 10% SDS-PAGE gel and transferred to a polyvinylidene difluoride (PVDF) membrane . After blocking, membranes were probed with primary antibodies (1:1000) followed by washing and incubation with a secondary antibody (1:5000) conjugated to horseradish peroxidase . Protein bands were visualized by applying a chemiluminescent reagent .STAT3 and PD-L1. Data were normalized to GAPDH levels. Experiments were performed in triplicates. The primes are as follows: Stat3 forward: CTTGACACACGGTACCTGGA; reverse: CTTGCAGGAAGCGGCTATAC; PDL1 forward: TATGGTGGTGCCGACTACAA; reverse: TGCTTGTCCAGATGACTTCG; β-actin forward: TCCTGTGGCATCCACGAAACT; reverse: GAAGCATTTGCGGTGGACGAT.Total cellular RNA was isolated using Trizol  according to the manufacturer’s protocol. For first-strand cDNA synthesis, 5 μg of total RNA was reverse-transcribed using the GoScript™ Reverse Transcription System kit  followed by quantitative polymerase chain reaction (qPCR) with GoTaq qPCR Master Mix , according to the manufacturer’s instructions. Real-time PCR analyses were conducted using the Biorad CFX96 system with SYBR green  and the appropriate primers to estimate the mRNA expression levels of STAT3 shRNAs and a shRNA scramble control  (nucleotides − 762 to + 106 base pair (bp) relative to the translation initiation site) was PCR-amplified from H460 cell line genomic DNA and inserted into the promoter-less plasmid pGL3-Basic , designated as p868. A series of 5′-deletions were produced by PCR using p868 as a template with the distinct 5′ primers a common 3′ primer  and the JASPAR database (http://jaspar.genereg.net). The STAT3 cDNA was PCR amplified with the relevant primers  and 0.2 μg/well pRL-TK . The relative luciferase activity was examined by Dual Luciferase Assay Kit  in accordance with the manufacturer’s protocols.An 868-bp PD-L1 promoter fragment . The purity of the isolated cells was > 95%, as determined in flow cytometry (FCM). Briefly, 24-well plates were coated overnight with 5 μg/ml anti-CD3 , then washed twice with PBS. PBMCs were plated in complete TCCM medium (IMDM with human AB serum (5%), penicillin–streptomycin, HEPES, 2-mercaptoethanol, and gentamicin). As target cells, cancer cells were pre-treated with niclosamide (2 μmol/L) for 24 h; control cells were without niclosamide pre-treatment. Then, cells were treated with PD-L1 Ab or not and co-cultured with activated PBMCs at several target-to-effector ratios  . After 4 days of co-incubation, 24-well plates wells were washed with PBS twice to remove PBMCs and then the survived tumor cells were fixed and stained with Giemsa staining solution. The dried plates were scanned and quantified the intensity.6/well in 6-well plates and then co-cultured with tumor cells pre-treated with niclosamide at 4:1 ratio for 24 h. Anti-human PD-L1 antibody, atezolizumab  (50 μg/ml), was added to the appropriate wells. After co-culturing, the PBMCs were isolated and stained with anti-CD3 and anti-CD8 antibodies to estimate the CD8+ cell proportions. For TNF-α and granzyme B analysis, PBMCs were harvested and then treated with brefeldin A  at 37 °C for an additional 3 h to prevent extracellular secretion. Subsequently, PBMCs were fixed and permeabilized with the Intracellular Fixation and Permeabilization Buffer Set Kit  following the manufacturer’s instructions. Then percentages of TNF or Granzyme B positive cells in CD3+ T cells or CD8+ T cells were labeled via intracellular staining and detected by flow cytometry. Antibodies for flow cytometry analysis were purchased from eBiosciences, USA. Matched isotype controls were used for each antibody to determine gates. FlowJo  software was used for the analysis of flow cytometry data. Standardized fluorescence intensities were calculated by dividing the median fluorescence intensities of specific antibodies by the median fluorescence intensities of isotype controls. The results are expressed as mean ± SD of three independent experiments.6-well plates were coated overnight with 5 μg/ml anti-CD3 , then washed twice with PBS. PBMCs were plated at a density of 1 × 10C57BL/6 mice were obtained from Guangdong Medical Laboratory Animal Center, China, and kept in a specific pathogen-free (SPF) barrier facility at the Animal Center of Sun Yat-sen University Cancer Center. The female mice with 8–12 weeks old were used for all animal experiments. Experiments were approved by the institutional committee of Sun Yat-sen University Cancer Center, and conducted in accordance with protocols approved by the Guangdong Provincial Animal Care and Use Committee.5 cells in 200 μL growth medium) were subcutaneously injected into the right flank of immunocompetent C57BL/6 mice. Tumor growth was measured with calipers every 3 days and the tumor volumes were calculated by applying the following formula: 1/2(length x width2). When tumors reached approximately 100 mm3, mice were randomized into control or experimental groups. A terminal event was defined as tumors reaching a size of 2000 mm3, at which point animals were euthanized [LLC cells . Niclosamide (20 mg/kg) or saline was administered intraperitoneally from day 13, every 5 days, after tumor implantation. Anti-PD-L1 antibody therapy (10 mg/kg) was administered intraperitoneally weekly on days 16, 23, 30, 37, and 44. Survival analysis was performed using Kaplan-Meier analysis and log-rank test.Mice were treated with niclosamide or rat anti-PD-L1 antibody  alone, the combination of niclosamide and αPD-L1, or saline and IgG2bκ  by intraperitoneal injection . Clinical data was collected from pathology reports and unprocessed medical files. The study was conducted with the permission of the Ethics Committee of the Sun Yat-sen University Cancer Institutional Board, and all patients involved provided informed, written consent.For IHC staining of the xenografts, tumor tissues were fixed, embedded, and sectioned (3 μm thick). Immunohistochemistry staining for human and mouse tissues was performed in accordance with standard procedures . The folhttp://www.pdb.org/pdb/home/home.do) (PDB ID: 4e68). For molecular docking purpose, the substrate dsDNA M67 and crystal water molecules were removed, and all hydrogen atoms were subsequently added to the unoccupied valence of heavy atoms at the neutral state (pH 7). The small molecule niclosamide downloaded from pubchem database (https://pubchem.ncbi.nlm.nih.gov/) was employed to perform the docking process. Two parameters bloat and threshold, which determine how far a potential ligand should extend outside of the concavity and how deep into the protein the atomic probes, were used to define the protomol. For niclosamide, the protomol was generated using the residue approach, and the bloat and threshold were set to 0.4 and 1, respectively.Since no human STAT3 crystal structure has been elucidated at present, mouse STAT3 (PDB ID: 4e68) was selected as receptor for docking as the identity of key residues and the high sequence identity (76.5%) sharing between human and mouse STAT3. Residues Lys591, Arg609, Ser636, Glu638 and residues Val637, Ile653 constitute the phosphotyrosine binding site and hydrophobic binding site of STAT3, respectively. Thus, we defined residues 591, 609, 636–638, 653 as putative binding sites to investigate the binding of niclosamide to STAT3. The docking simulations were carried out using Surflex module of SYBYL software , which combines Hammerhead’s empirical scoring function with a molecular similarity method  to generate putative poses of ligand . The cryP < 0.05.Statistical analysis was carried out using IBM SPSS Statistics 19 software or GraphPad Prism using Student’s t-test or one-way ANOVA or Dunnett’s test. All experiments were repeated in triplicate. Data are expressed as mean ± standard deviation (SD). Statistical significance was defined as First, in order to exclude any underlying bias caused by variation in growth suppression induced by the niclosamide, we performed growth inhibition curves for different cell lines and established an inhibitory concentration of 30% (IC30) Fig. a. Then, In LLC cell tumor-bearing mice, mice receiving niclosamide plus PD-L1 Ab treatment showed a more significant delay in tumor growth Fig. a-c and pTo further explore the potential mechanism of enhancement of PD-L1 antibody by niclosamide, we evaluated whether niclosamide could have an impact on PD-L1 expression. The maximum niclosamide concentration tested (2 μM) was added to these NSCLC cell lines, which was lower than the IC30. Applying flow cytometry analysis, we observed downregulation of PD-L1 expression after niclosamide treatment for 24 h , named sh1–4. Both protein and mRNA expression levels of PD-L1 were potently reduced in STAT3 knockdown tumor cells  and JASPAR databases (http://jaspar.genereg.net)  Fig. f. The seet) Fig. g. We theet) Fig. h, indicaet) Fig. i. These Due to the observations that niclosamide downregulates PD-L1 expression dependent on the inhibition of STAT3 phosphorylation, accordingly, we asked whether genetic downregulation of STAT3 in NSCLC cells could influence the response of anti-PD-L1 therapy. After co-culturing with PBMCs, there was a significant decrease in colony formation in STAT3 sh cancer cells with the treatment of PD-L1 Ab than other groups Fig. a, suggesSimilarly, to demonstrate whether STAT3 sh promoting the efficacy of PD-L1 Ab is associated with an increase in CD4+ and CD8+ T cells as well as the enhanced T cell cytotoxicity, we detected the proportion of CD3 + CD4+ T cells and CD3 + CD8+ T cells using flow cytometry. There was a significant increase in the two kind of T cell populations among PBMCs co-cultured with STAT3 sh tumor cells in combination with anti-PD-L1 Fig. b-e. ImpoP < 0.001, r = 0.801, Spearman rank correlation coefficient; Fig. Given the above findings, we investigated the correlation between the expression of p-STAT3 and PD-L1 in NSCLC, we used immunostaining to detect the levels of these two proteins in 28 NSCLC patients who received immunotherapy Fig. a and b. P = 0.007)   prediction. The cutoff expression values were 2.925 and 3.8 for PD-L1 and p-STAT3, respectively. Kaplan-Meier curves and log-rank test were performed. We observed that patients with high PD-L1 level had a longer PFS time than those with low PD-L1 level


Turning while walking has a frequent occurrence in daily life. Evaluation of its dynamic stability will facilitate fall prevention and rehabilitation scheme. This knowledge is so limited that we set it as the first aim of this study. Another aim was to investigate spatiotemporal parameters during turning.Fifteen healthy young adults were instructed to perform straight walking, 45° step turn to the left and 45° spin turn to the right at natural speed. Dynamic stability was measured by margin of stability (MoS) in anterior, posterior, left and right direction at each data point where significant differences were detected using 95% bootstrap confidence band. Common spatiotemporal parameters were computed in each condition subdivided into approach, turn and depart phases.Results showed that minimum anterior MoS appeared at middle of swing while minimum lateral MoS at contralateral heel strike in all conditions. Posterior MoS decreased before middle of turn phase in spin whereas after middle of turn phase in step. Lateral MoS and stride width declined in turn phase of spin while in depart of step. Spin had a long step and stride length. Long swing phases were observed in turns.These data help explain that people are most likely to fall forward at middle of swing and to fall toward the back and the support side at heel strike. Our findings demonstrate that instability mainly exist in turn phase of spin and depart phase of step turn.The online version of this article (10.1186/s12938-018-0558-5) contains supplementary material, which is available to authorized users. Turning during ambulation is crucial to daily life. Turning steps constitute a considerable percentage about 20–50%) of steps taken during activities of daily living , 2. Turn0–50% of According to whether there are obvious transition steps or not, turns can be divided into “steady-state” turns, such as circular walking, and “transient” turns, such as 90° turns , 6. The Researches show that there exists turning preference. Dixon et al. , 9 suggeBecause turning increases risk of falling  and injuFurthermore, both turning strategies demand modification of spatiotemporal parameters from those of straight walking, which can somewhat reflect biomechanical adaptations simply and effectively . PreviouThere had been no previous study to compare MoS of healthy young adults during straight line walking, step turn and spin turn and this was the first aim of this study. In addition, spatiotemporal parameters were also comprehensively compared in three conditions. We hypothesized that both MoS and stride width of step turn would be greater while those of spin turn smaller than straight walking , 13. We -2; leg length (measured as the distance between the greater trochanter and the floor), 0.850 (0.064) m; mean (standard deviation)] from Tianjin University were included in this analysis. All participants are right-footed determined by kicking a ball [Fifteen healthy young adults [six males, nine females; age, 23.6 (3.1) years; mass, 55.6 (12.2) kg; height, 1.686 (0.105) m; body mass index, 19.4 (2.3) kg·mg a ball . All parParticipants were instructed to perform practice sessions and then complete three barefoot walking tasks at the self-selected preferred walking speed: straight walking, 45° step turn to the left and 45° spin turn to the right , see Fig. The present study only analyzed the trajectory data of 10 reflective markers placed bilaterally over anatomical landmarks on anterior and posterior superior iliac spine (ASIS and PSIS), lateral malleolus, heel and tip of the toe. Raw marker position data were low-pass filtered using a fourth-order zero-lag Butterworth filter with a cut-off frequency of 4 Hz. All gait data were analyzed in the duration from the first heel strike (HS1) to the third toe off (TO3)  eigenfrequency of the pendulum, g is the earth gravitational acceleration , l is the equivalent length of pendulum defined as the distance between CoM and the lateral malleolus, and BoS is the anterior, posterior, left or right boundary of BoS (during double support phase) or virtual BoS (during swing phase) [A) was computed as the distance from XCoM to the line through left and right toe markers, while posterior MoS (MoSP) the distance from XCoM to the line through bilateral heel markers. Left and right MoS (MoSL and MoSR) were defined as the distance from left and right lateral malleolus to the line between CoM and XCoM. Positive and negative MoS values suggested stable and unstable states (XCoM inside and outside BoS), respectively. We computed MoS at every time point, mean MoS over whole stance phase (including double support period) and MoS at key events (heel strike and mid stance [vCoM) was computed using a first order central difference method. Walking speed was computed as average speed of CoM, viz. the distance travelled divided by the duration of the interval.Based on assumptions of inverted pendulum model, extrapolated center of mass (XCoM) and MoS were computed as follows :1\documeg phase) . Anteriod stance ). Considd stance . InstantTurning gait can be commonly separated into approach, turn, and depart phases , 25, 31.vCoM and the x-axis of the laboratory coordinate system [Walking direction was defined as the angle between the direction of e system  then avee system  and non-e system . Periodse system .A one-way repeated measures analysis of variance was conducted to test for significant differences between matching steps of different walking tasks and between different steps of the same task. Post hoc analyses using the Bonferroni adjustment were carried out to detect the significance of pairwise comparisons. When to determine the difference between two steps of the same task, a paired-samples t-test was used. Variability of spatiotemporal parameters was estimated using 95% confidence interval (CI). These aforementioned statistical analyses were performed using SPSS . Variability of gait variables varying throughout gait cycle was estimated using 95% bootstrap confidence band (CB) , 35 in Mp < 0.0005) and depart (p = 0.045) phases of step turn than matching phases of spin turn  and spin (both p < 0.0005). There was no significant difference among the third steps of straight walking and turns.The walking direction in straight walking appeared a cosine curve with an amplitude of 10° Fig. . The meaA and MoSP had opposite shape: an inverted peak vs a peak in each step  .The curves MoStep Fig. . In mostL, spin turn was smaller than other conditions in the first step whereas step turn was the smallest in the second and third step. MoSR of spin turn was the smallest in all three steps. For step turn, mean MoSL over depart was less than turn phase (p < 0.0005) which in turn was less than approach (p < 0.0005)   which in turn was less than depart (p = 0.003). Minimum MoSL existed at left pre-swing (right loading response). Example trajectories of CoM, XCoM and lateral malleoli are shown in Fig. For MoSse) Fig. . For spip < 0.000 which inp < 0.0005) and much narrower (p < 0.0005) in depart of step turn  and little wider in depart (p = 1.000). Negative values indicated that the stance foot landed laterally to the swing foot stride vector. Step and stride length were longer in spin condition than in step and straight . The stance time, single support time, and cycle time were all significantly longer in turns than straight .Compared to straight gait, stride width was wider in turn phase  at the self-selected preferred walking speed: straight walking, 45° step turn to the left and 45° spin turn to the right (Right lower limb is the turning limb in both turn conditions). Four force plates were used in straight walking while three in turns.Additional file 2. Spatiotemporal parameters, MoSA, MoSP, MoSL and MoSR for healthy young adults (n = 15). Mean . Statistically significant difference (p < 0.05) from the first phase of same condition (a), the second phase of same condition (t), straight walking (*), between turn conditions (†). ‡: Statistically significant difference (p < 0.05) between the absolute mean value of walking direction in spin turn and that in step turn.


Our results further suggest that fluoxetine-induced neuroplasticity does not solely depend on 5-HTT blockade, but might rely, at least in part, on 5-HTT-independent direct activation of TrkB.Selective serotonin reuptake inhibitors are among the most prescribed antidepressants. Fluoxetine is the lead molecule which exerts its therapeutic effects, at least in part, by promoting neuroplasticity through increased brain-derived neurotrophic factor (BDNF)/tropomyosin-related receptor kinase B (TrkB) signalling. It is unclear however, to which extent the neuroplastic effects of fluoxetine are solely mediated by the inhibition of the serotonin transporter (5-HTT). To answer this question, the effects of fluoxetine on neuroplasticity were analysed in both wild type (WT) and 5-Htt knock-out (KO) mice. Using Western blotting and RT-qPCR approaches, we showed that fluoxetine 10 µM activated BDNF/TrkB signalling pathways in both CD1 and C57BL/6J mouse primary cortical neurons. Interestingly, effects on BDNF signalling were observed in primary cortical neurons from both 5-Htt WT and KO mice. In addition, a 3-week  Despite decades of research, the neurobiological mechanisms underlying its pathogenesis are still poorly understood. Several hypotheses have been put forward, implicating dysregulation of monoamines, stress hormones, neuroplasticity and neuroinflammation3. However, none of them has emerged as a lead mechanism and probably all are involved, most likely in an intricate fashion, in triggering or maintaining this disease. Imaging studies have nevertheless revealed that structural modifications, such as hippocampal atrophy5, could occur in recurrent major depression, demonstrating the role of neuroplasticity in this pathology. This was further confirmed in rodent models, reporting impaired neurogenesis (see6 for a review), neuron dendrite shrinkage and glial cell loss8, as well as in postmortem brains, with a decrease in brain-derived neurotrophic factor (BDNF) expression in depressed subjects11.Depression is a complex and heterogeneous neuropsychiatric disorder with a high prevalence and incidence12. One of the most prescribed antidepressants, the selective serotonin reuptake inhibitor (SSRI) fluoxetine, targets the presynaptic serotonin (5-HT) transporter (5-HTT) to increase synaptic 5-HT levels. The beneficial effects of fluoxetine have been shown to be dependent, at least in part, on 5-HT availability since a tryptophan depletion regimen, which decreases brain 5-HT content, leads to a transient depressive relapse in depressed patients during fluoxetine-induced stable remission13.Several types of therapies, including psychotherapy, electroconvulsive therapy, deep brain stimulation and pharmacotherapy have shown some efficacy for treating depression. Treatment with available antidepressant molecules still has serious limitations because of their delay of action and lack of efficacy in about one third of patients16. Fluoxetine is also known to activate several signaling pathways such as MAPK (Mitogen-activated protein kinase)17 and CREB (cAMP response element binding protein)18 and to modulate BDNF levels21. BNDF is a neurotrophin that plays a key role in the regulation and maintenance of the central nervous system. It activates three main signaling pathways, i.e. MAPK, phospholipase C-γ (PLCγ) and phosphoinositide 3-kinase (PI3K) pathways through binding with its high affinity receptor: tropomyosin receptor kinase B (TrkB)22. TrkB activation promotes cellular survival, proliferation and differentiation as well as synaptic plasticity23. Several antidepressants, including fluoxetine, have been shown to phosphorylate TrkB, either in acute or in chronic conditions of treatments24. Interestingly, data indicate that fluoxetine can directly induce the phosphorylation of TrkB even in the absence of 5-HTT, suggesting that this effect could be independent of 5-HT reuptake blockade25 or even of a 5-HT surge, since in 5-Htt null mice, extracellular 5-HT levels measured by microdialysis were unchanged in the hippocampus after fluoxetine administration26. In addition, the neurogenic effects of fluoxetine have also been suggested to be partly independent of 5-HTT blockade and have been proposed to involve other targets such as 5-HT receptors27. However, the way antidepressant-induced neuroplasticity is related to the 5-HTT site is still unclear.In rodents, chronic fluoxetine treatment is known to promote neuroplasticity, more specifically hippocampal neurogenesis, by inducing cell proliferation, enhancing newborn cell survival, and accelerating neuronal maturation. An increase in both dendritic arborization and length has also been observed in the dentate hippocampus after fluoxetinein vitro and in vivo experiments using wild-type (WT) and 5-Htt constitutive KO28 mice to investigate the effects of fluoxetine on markers of neuroplasticity.In this study, we aimed to further dissect the mechanisms of action of fluoxetine on plasticity to better understand to which extent 5-HTT and TrkB/BDNF signaling interact in mediating the neuroplastic effects of this antidepressant. To this aim, we performed both Bdnf expression, we used primary cortical neurons from Swiss CD1 mouse strain. In these cells, 10 µM fluoxetine significantly increased Bdnf expression in a time-dependent manner  = 7.918, P < 0.001; treatment: F = 138.6, P < 0.0001; time: F = 7.840 P < 0.001)  = 19.67; P < 0.001), whereas no time effect  = 1.180, P = 0.3424) nor a treatment x time interaction  = 1.133, P = 0.3595) was observed  Fig. . These d01) Fig. , fluoxetved Fig. .Figure 1Bdnf mRNA expression was not modified by 5-HT (1 µM)  = 2.928, P = 0.1063), time effect F = 2.659, P = 0.0835, treatment × time interaction F = 2.720, P = 0.0790)  or BDNF (1 nM), as a control, for 1 h. As expected, BDNF treatment increased TrkB phosphorylation (t-test: t(11) = 4.078, p = 0.0018), as well as its downstream target, P-Erk (t-test: t12) = 8.516, p < 0.0001). However, BDNF did not increase P-Akt (t-test t(12) = 0.7816, P = 0.4668). Similarly, treatment with fluoxetine enhanced P-TrkB levels (t-test: t(12) = 2.924, P = 0.0127), but upregulated neither P-Erk (t-test: t(13) = 0.9271, P = 0.3708) nor P-Akt (t-test: t(13) = 0.7440, P = 0.4701)  = 2.778, P = 0.0321; Egr1 t(6) = 7.789, P = 0.0002; cFos: t(6) = 4.064, P = 0.0066). When cells were incubated with fluoxetine (10 µM), a Student t-test also revealed an effect of fluoxetine on Egr1 and cFos mRNA expression (Egr1: t(6) = 3.275, P = 0.0169; cFos: t(6) = 3.026, P < 0.0232), while Arc mRNA expression was not increased (t-test: t(6) = 1.299, P = 0.2417)  Fig. .Figure 3Bdnf mRNA expression in cells from both WT and 5-Htt KO mice after a 4 h incubation  = 9.110, P = 0.0166). Neither an effect of genotype, nor an interaction between genotype and treatment was observed  = 0.05434, P = 0.8215; treatment x genotype interaction: F = 0.04610, P = 0.8354), evidencing that fluoxetine could exert its effects on Bdnf mRNA expression even in the absence of 5-HTT  = 6.317, P = 0.0307), with a tendency of the antidepressant to increase that of cFos  = 4.585, P = 0.0579) without affecting that of Arc  = 1.803, P = 0.2090). There was no effect of genotype  = 0.6246, P = 0.4477; Egr1: F = 0.08641, P = 0.7748; cFos: F = 0.4333, P = 0.5252) and no treatment × genotype interaction  = 0.4748, P = 0.5065; Egr1: F = 0.09487, P = 0.7644; cFos: F = 0.2259, P = 0.6448)  effect on 48) Fig. .in vitro data, fluoxetine effects were also investigated in in vivo conditions using WT and 5-Htt KO mice. Mice were injected with saline or fluoxetine (15 mg/kg/d i.p.) for three weeks. RT-qPCR experiments were performed on both the hippocampus and the frontal cortex of the left hemisphere of each mouse.In order to support the Bdnf mRNA expression  = 11.87, P = 0.0015), but no effect of genotype  = 0.255, P = 0.616) nor a treatment × genotype interaction  = 2.42, P = 0.1288). Chronic fluoxetine did not modify Trkb mRNA expression  = 0.3263, P = 0.5734; genotype: F = 0.08824, P = 0.7691; treatment x genotype interaction: F = 0.06707, P = 0.7980), nor that of Creb  = 0.5533, P = 0.4621; effect of genotype: F = 3.662, P = 0.0641; treatment × genotype interaction: F = 0.02366, P = 0.8787)  Fig. .Figure 5Bdnf, TrkB, and Creb mRNA expression in both fluoxetine-treated WT and 5-Htt KO mice  = 138.0, P < 0.0001; Trkb: F = 20.38 p < 0.0001; Creb: F = 32.53, P < 0.0001). Bdnf and TrkB mRNA expression also showed a treatment × genotype interaction  = 8.488, P = 0.0062, TrkB: F = 12.64 P = 0.0016). A Bonferroni post-hoc test revealed a higher mRNA expression of Bdnf and TrkB in the fluoxetine-treated 5-Htt KO mice (Bdnf: control vs fluoxetine: WT mice: t(20) = 6.709, P < 0.001; 5-Htt KO mice: t(15) = 9.795, P < 0.001; TrkB: control vs fluoxetine: WT mice t(13) = 0.7041 P > 0.05; 5-Htt KO mice t(11) = 5.510, P < 0.001). There was no genotype effect  = 0.05875, P = 0.8099; TrkB: F = 0.7872, p < 0.3838). Regarding Creb mRNA expression, in addition to a treatment effect, a genotype effect was also observed with a global decreased expression in 5-Htt KO mice  = 174.3 P < 0.0001), without treatment x genotype interaction  = 2.503 P < 0.1221)  Fig. .To further study TrkB signaling at protein level, we performed Western blots to analyze plasticity-related protein activation. Unexpectedly, a 3-week treatment with fluoxetine up-regulated neither TrkB nor CREB phosphorylation in the hippocampus and the frontal cortex, regardless of the genotype  = 0.1881, P = 0.671; P-CREB F = 2.218 P = 0.1586; frontal cortex: P-TrkB F = 0.2616, P = 0.6139; P-CREB F = 0.3038, P = 0.5868)   nor the antidepressants (amitryptiline and imipramine) were similar to our conditions, hindering any direct comparison between these studies. Besides, in our hands, fluoxetine activated neither MAPK pathway (Erk) nor Akt phosphorylation, in contrast to BDNF. This might be due to the delayed action of fluoxetine on the downstream signaling pathways, only occurring after the initial TrkB phosphorylation. In addition, the high affinity of BDNF for TrkB probably allows the peptide to initiate this pathway with a better efficacy than fluoxetine. Besides, Akt and Erk activation on hippocampal primary cells has been shown to occur only after 4–5 days of fluoxetine incubation, indicating the requirement of a prolonged stimulation with an antidepressant to activate these pathways36.The present data further demonstrate that fluoxetine can promote plasticity in both primary cell cultures and after chronic Bdnf activation took place between 2 and 6 hours after the beginning of fluoxetine exposure, while both early gene expression and TrkB phosphorylation occurred as soon as 1 h after incubation, at a time where BDNF levels were not yet increased. The magnitude of TrkB phosphorylation expression was lower after fluoxetine than after BDNF incubation probably because BDNF directly activated TrkB receptors, while the effect of fluoxetine occurred probably through its transactivation, further suggesting that TrkB can be activated independently from neurotrophins, as proposed by Rantamaki et al.25. This last study indeed showed a rapid action of imipramine , on TrkB phosphorylation both in the presence and absence of BDNF, suggesting that antidepressants do not require BDNF to activate TrkB.However, Bdnf and Egr1 mRNA expression were still observed in primary cortical cell cultures where the main target of SSRIs, the 5-HTT, was inactivated. This effect strongly suggests that the ability of fluoxetine to upregulate Bndf and Egr1 mRNA expression in vitro was, at least in part and in our conditions, 5-HTT-independent. Although not significant, fluoxetine also tended to activate cFos mRNA expression. These data were also supported by those obtained in in vivo conditions. Using adult WT and 5-Htt KO mice, chronic fluoxetine treatment induced neurotrophic effects that were independent of the genotype. Although in the hippocampus fluoxetine effect was limited to Bdnf expression, in the PFC, the 3 week-fluoxetine treatment upregulated Bdnf, TrkB and Creb mRNA in both WT and KO mice, and at least for Bdnf and TrkB, fluoxetine effects appeared to be greater in absence than in presence of 5-HTT. Whether the 5-HTT–independent mechanisms involved in the effects of fluoxetine were up regulated in the absence of the uptake site is a question to be addressed, but however, these data do not endorse those reported in 5-Htt mutated rats, where Bdnf expression was much lower than in WT, and where chronic fluoxetine was not able to significantly up-regulated it37. Nevertheless, our data indicated a more efficient effect of this antidepressant on BDNF/TrkB signaling in the mouse frontal cortex, stressing again the role of this structure in the pathogenesis of depression, as supported by other studies38.Interestingly, fluoxetine effects on 22, even in 5-Htt-deficient mice25. Whether protein phosphorylation might be regulated through post-translational mechanisms is a question to be further addressed.Regarding protein levels, chronic fluoxetine failed to enhance plasticity-related proteins in both the PFC and the hippocampus, whatever the genotype, in contrast to the reported acute effects of this antidepressant on TrkB activation39. In addition, fluoxetine effects on cell proliferation has also been reported to be dependent on the duration of the treatment as well as the dose and the method used to treat the animals42. Conversely, in 5-Htt mutated mice in which only the SSRI binding site was inactivated, fluoxetine has been shown to lose its proliferative properties, suggesting, that, in vivo, potentiated 5-HT signaling was essential to the ability of SSRIs to stimulate subgranular stem cell expansion43, in discrepancy with our data.Finally, proliferation studies also indicated a similar tendency of fluoxetine to increase the number of proliferative cells in 5-Htt KO and WT mice, further supporting the 5-HTT independency of fluoxetine on plasticity. The rather low efficiency of fluoxetine-induced proliferation we found in the present study was not unexpected, since this has also been reported by other studies, especially those performed in well controlled conditions, i.e., in the absence of any aversive environment such as stressin vitro and in vivo conditions, whatever the marker to be studied, fluoxetine induced similar effects in 5-Htt KO mice than in WT paired controls, suggesting that in the absence of 5-HTT, fluoxetine was still able to act on neuroplasticity. Whether these effects are linked to 5-HT, or related to a direct activation of BDNF/TkrB complex, or to other pathways is subject of future investigation.Yet, in a null 5-Htt mutant model, in both 25. TrkB receptors have been demonstrated to be transactivated by e.g. glucocorticoids receptors44 or adenosine receptors45. Our data mostly support and extend this hypothesis by proposing that also in cultured cortical primary cells, fluoxetine enhances Bdnf gene expression. These results highlight the potential direct effect of the BDNF/TrkB signaling pathway in the mechanism of action of antidepressants46 and might further indicate that TrkB is a valuable target to treat depression47.The notion that antidepressants can transactivate brain TrkB receptors independently of monoamine reuptake blockade has been proposed some years agoAltogether, these data showed that fluoxetine might induce neuroplastic effects through 5-HTT-independent activation of BDNF/TrkB complex signaling. Whether these effects are mediated through a direct transactivation of the TrkB receptor, and how BDNF/TrkB signaling, 5-HT, and/or other processes and pathways are interconnected in regulating fluoxetine-induced neuroplasticity awaits further research.All procedures concerning animal care and treatments were carried out in accordance with the protocols approved by the ethical committee # C2EA -05 Charles Darwin for the use of experimental animals and were licensed by the Directorate General for Research and Innovation (French Ministère de l’Enseignement Supérieur et de la Recherche), under protocol authorization # 00966.02.28. Genotyping was performed as previously described in detail48. Experiments were started at 2–3 months of age when body weight in each genotype equally ranged between 20 and 25 g. After weaning, males were housed in groups of 4–6 animals per cage and maintained under standard laboratory conditions .CD1 and C57BL/6J mice were purchased from Janvier Laboratories . Experiments were performed using homozygous 5-Htt−/− (KO), and 5-Htt+/+ (WT) littermates born from heterozygous (+/−) mutants, bred on a C57Bl/6J background28.We used cells from two different mouse strains, Swiss CD1 and C57Bl/6J. The first data were obtained in CD1 mice in which we could collect several embryos. When the experimental conditions were set up, we performed the next experiments on the C57BL/6J mouse strain, on which the 5-Htt constitutive knock down was performed2 for quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) purposes, or on poly-D-lysine-coated 25 cc flasks at a density of 1600 cells/mm2 for Western blotting. Cells were cultured in complete Neurobasal medium supplemented with B27 , containing 0.5 mM L-glutamine, 10 U/ml penicillin G, and 10 mg/ml streptomycin , and used for experiments on the fifth day in vitro (DIV5).Embryos from pregnant Swiss CD1 strain, as well as from 5-Htt WT and KO mice on a C57BL/6J strain were isolated at embryonic day 16. The dissection of the cortices was performed in Neurobasal medium , and, after collection, the tissues were maintained in Hank’s Balanced Salt Solution (HBSS)  until trypsinization. Dissociation of the tissues was first achieved with trypsin , and then terminated with a mechanical dissociation with a glass pipette. Cells were counted and plated on 6-well cultures plates coated with poly-D-lysine (Sigma Aldrich) at a density of 1000 cells/mm50. Mice were sacrificed by cervical dislocation 24 hours after the last injection. The hippocampus and frontal cortex from the left hemisphere were collected and immediately frozen in liquid nitrogen and stored at −80 °C until use. The right hemisphere was frozen in isopentane at −30 °C, and subsequently stored at −80 °C until further use.Ten-week-old WT and KO male mice were injected (i.p.) daily with fluoxetine  or saline solution  for 3 weeks according to published workBdnf, Arc, Egr1 and cFos gene was assessed in vitro and that of Bdnf TrkB and Creb gene awas analyzed in vivo 51 or vehicle (milli-Q water) at doses previously published51. Total mRNA extraction was performed using NucleoSpin RNA II kit . TRIzol RNA Isolation Reagent  was used to isolate the total RNA from the hippocampus and the frontal cortex of WT and KO mice. cDNA synthesis was performed using a High Capacity cDNA Reverse Transcription kit  according to the manufacturer’s protocol. Amplification was performed with KAPA SYBR FAST qPCR kit  using the 7300 Real Time PCR System (Applied Biosystems). The sequences of primers used are indicated in Supplementary Table ΔΔCT (Delta-Delta Comparative Threshold) method was used to normalize the fold change in gene expression. Gene expression was normalized using Actb as a reference gene that was shown to be stable over the groups.mRNA expression of in vitro study, protein expression levels for TrkB, P-TrkB, Akt, P-Akt, Erk, P-Erk and the reference protein GAPDH were assessed by Western blotting (see Supplementary Table 3VO4 (1 mM) and NaF (10 nM); protease inhibitor cocktail ). The lysate was then centrifuged for 15 min at 14,000 g and the supernatant was collected. The protein concentration was determined by the Lowry protein assay . The extract was mixed with sample buffer  containing 188 mM trisHCl, 6% SDS, 30% glycerol, 15% β-mercaptoethanol, and 0.01% bromophenol blue. The mix was heated for 5 minutes at 95 °C. 20 µg of proteins were then separated in a 10% SDS-PAGE gel and transferred on a nitrocellulose membrane for 1 h at 100 mV. The membranes were blocked with 1:1 v/v Odyssey blocking buffer  in PBS for 1 h at room temperature (RT), incubated with the primary antibody overnight at 4 °C and, subsequently, with secondary antibodies  according to a previous described protocol52. Briefly, the proteins were washed and dissolved in a 1:1 (v/v) mixture of 8 M urea and 1% SDS and sonicated. Protease and phosphatase inhibitor tablets were added to the mixture . Protein concentration was determined by the PierceTM BCA Protein Assay Kit (Thermo Fisher Scientific). Extracts were mixed with NuPAGE™ LDS Sample Buffer (Thermo Fisher Scientific). The mix was heated for 10 minutes at 95 °C. 30 μg of protein was then separated using a gradient 4–12% gel  and transferred on a nitrocellulose membrane with the iBlot® Dry Blotting System (Thermo Fisher Scientific) for 7 min. Membranes were blocked  for 1 h at RT, incubated with the primary antibody overnight at 4 °C and with secondary antibodies for 1 h at RT. The dilution and reference of the antibodies are indicated in Supplementary Table In the The membranes were finally scanned using the Odyssey CLx system  and analyzed with ImageJ.2O2 (in TBS) for 30 min at RT, and subsequently incubated with a rabbit anti-Ki67 antibody  at 4 °C overnight. The second day, the sections were rinsed with TBS-T and TBS, and incubated with the secondary antibody for 1 h at RT (1/800), with Vectastain ABC kit  for 45 min at RT and with 3,3′-diaminobenzidine (DAB) for 10 min at RT in the dark. The sections were then rinsed successively in 70%, 90%, 100% ethanol, and Histoclear , after which they were mounted, and visualized using a microscope. Ki67 positive cells were counted using the plugin “cell counter” of ImageJ software.Brains were cut into 20-μm-thick coronal sections. Frozen hippocampal sections (from Bregma −1.34 to Bregma −3.20) were fixated with 4% paraformaldehyde  for 10 min at 4 °C, rinsed with TBS, incubated with 0.3% Hpost hoc testing for group-wise comparisons. In addition, a two-way ANOVA (treatment x genotype) followed by Bonferroni post hoc was used to investigate the effect of the chronic administration of fluoxetine in WT and KO mice. The level of significance was set as P < 0.05. Statistical analyses were performed with GraphPad Prism . Fluoxetine effect on cell proliferation was compared between WT and KO mice, using multiple t-test with a Holm-Šídák correction for multiple comparisons.Data are represented as mean ± SEM. A Student’s t test was used for comparison between two groups. When comparing more than two conditions, a one-way analysis of variance (ANOVA) was applied, followed by Bonferroni Supplementary Dataset 1


We believe this work may serve as guidelines for the formation of different structures of macro-porous, rough, and grooved nanofibers with interior porosity through electrospinning.This work presents a simple and reliable method for directly generating polyvinylidene fluoride (PVDF) nanofibers with secondary surface morphology  by using single/binary solvent systems and relative humidity. We clarified the mechanisms responsible for the formation of these morphologies by systematically exploring the molecular interactions among the polymer, solvent(s), and water vapor. Our results proved that the formation of secondary surface morphology needed the presence of water vapor, a non-solvent of the polymer, at an appropriate level of relative humidity. The formation of secondary surface morphology was dependent on the speed of evaporation of the solvent(s) , as well as the inter-diffusion and penetration of the non-solvent (water) and solvent(s). The results of NThe online version of this article (10.1186/s11671-018-2705-0) contains supplementary material, which is available to authorized users. Electrospinning is a fiber formation method that involves electrostatic forces for ejecting and stretching polymer jets into fibers. The process currently produces fibers with a diameter ranging from few nanometers to several micrometers . VariousElectrospun nanofibers have shown excellent properties such as high specific surface area, flexibility, ease of functionality, variety of morphology and structure, superior directional strength, and high porosity which makes them a preferred material form for different applications such as energy harvesting , sensorsPreviously, we have reported the production of polystyrene fibers with tunable macro-pore structures and distributions by using a microfluidic nozzle containing three channels that allows for liquid mixing from two input channels and synchronized electrospinning of the resulting mixture from the other output channel . FurtherIn this study, we demonstrate the fabrication of polyvinylidene fluoride (PVDF) nanofibers with the macro-porous, rough, and grooved surface structures and interior pores using electrospinning without involving any special collection method or post-spinning treatment. Here, PVDF was selected as the model because it can be dissolved in different solvents.To the best of our knowledge, so far, no studies have been systematically investigated maneuvering the formation of the macro-porous (> 300 nm), rough, and grooved electrospun PVDF nanofibers with internal porosity by controlling the relative humidity. Herein, we reported the electrospinning of PVDF solutions at four levels of relative humidity  using both single and binary solvent systems. The main objective of this work is to investigate the feasibility of fabricating macro-porous, rough, and grooved fibers with solid and porous interior structures using different levels of relative humidity, and to discover their formation mechanisms. By systematically investigating the effect of the relative humidity on the secondary surface morphology of electrospun PVDF fibers, we concluded that the relative humidity plays an important role in determining the surface and the internal morphology of PVDF fibers. This study can provide useful guidelines for the preparation of secondary surface structure of nanofibers through electrospinning.PVDF pellets  were purchased from Sigma-Aldrich, Inc. Acetone (ACE) and N,N-dimethylformamide (DMF) were purchased from Shanghai Chemical Reagents Co., Ltd., Shanghai, China. All materials were used without further purification.w/v) PVDF solution, 35% DMF (w/v) PVDF solution, and 25% (ACE/DMF) (w/v) PVDF solutions at the solvent ratios of  were prepared, respectively, and each solution was loaded into a plastic syringe. In this work, the solvent ratio was the volume ratio, and the solution concentration was weight/volume (w/v) (g/ml). A 21 gauge syringe needle was used as the spinneret, which was fixed on a syringe pump  connected to a high-voltage supplier . A grounded drum collector (40 cm in length and 20 cm in diameter) was placed 18 cm away from the spinneret, and the rotating speed was set at 2 rpm to obtain randomly oriented fibers. All the experiments were carried out at 20 °C under different levels of relative humidity . The temperature was adjusted by the laboratory central air conditioning system and the relative humidity was controlled by the environmental humidity, which could be further set with a narrow window (± 2%) by using a humidifier/dehumidifier. All samples were prepared at feeding rate and applied voltage of 1.5 ml/h and 18 kV, respectively. All previous parameters were adjusted to get fibers with different morphologies and similar diameters.In order to obtain macro-porous, rough, and grooved fibers, 18% ACE  were measured to determine the specific surface area, pore distribution, and total pore volume.The surface morphology and cross-section of the electrospun PVDF nanofibers were checked under field-emission scanning electron microscopy (FE-SEM)  after gold coating. Cross-sections of the fibers were prepared by placing them in liquid nitrogen and breaking them manually. Fiber diameter was measured using image analysis software (Adobe Acrobat X Pro 10.1.2.45) according to the SEM images. NThe capacity of oil absorption was measured at 25 °C using the following method. Then, 15 mL of water–oil mixture with a ratio of 1:1 was prepared and put in a beaker. Further, 0.3 g of the sorbent was added to the beaker to absorb oil for 1 h, and then the wet sorbent was moved to a screen mesh and drained for ~  40 min to ensure that no oil droplets remained on the sorbent. The capacity of oil absorption was calculated according to the following equation:Q is the capacity of oil absorption (g/g), m0 is the total mass of the wet sorbent after oil absorption drained for ~  40 min (g), and m1 is the mass of the sorbent before absorption (g).where w/v) PVDF solution with ACE, 35% (w/v) PVDF solution with DMF, and 25% (w/v) PVDF solutions with different ACE/DMF ratios were electrospun.To explore the effect of the relative humidity on the secondary morphology of electrospun PVDF fibers, 18%  PVDF solution were electrospun at different levels of relative humidity .Herein, 35%  PVDF solutions with different ACE/DMF ratios  were electrospun at different levels of relative humidity. For 25% (w/v) (ACE/DMF at the solvent ratio of 4:1), smooth fibers were formed at the relative humidity of 5%  (ACE/DMF at the solvent ratio of 2:1), smooth fibers were formed at the relative humidity of 5%  (ACE/DMF at the solvent ratio of 1:1) at the relative humidity of 5%, smooth fibers were observed  (ACE/DMF at the solvent ratios of 1:2 and 1:4), when the relative humidity ≤ 25%, smooth fibers were seen  under different levels of relative humidity . We discovered that no phase separation occurred at the relative humidity of 5% by using the previous solvents, resulting in the formation of smooth fibers with a solid interior. We found that the formation of macro-pores on the surface of the fibers with solid interior at the relative humidity of 25% should be attributed to TIPS due to the high vapor pressure of ACE and nucleation mechanism, while the formation of macro-pores on the fibers surface with interior pores at the relative humidity of 45% and 65% should be ascribed to the coexistence of both TIPS and VIPS mechanisms. Additionally, we found that the low vapor pressure of DMF played a core role in the production of the rough fibers with pores in the interiors by VIPS. While wrinkle-based elongation mechanism played a key role in fabricating the grooved fibers with a porous interior structure. The macro-porous fibers (> 300 nm) exhibited the highest performance of oil absorption of 50.58 ± 5.47 g/g, 37.74 ± 4.33 g/g, and 23.96 ± 2.68 g/g for silicon oil, motor oil, and olive oil, respectively. Importantly, our understanding of the mechanisms responsible for the formation of the macro-porous, rough, and grooved PVDF fibers with interior porosity can serve as an important reference for the fabrication of electrospun fibers by regulating the solvent and relative humidity.Additional file 1:Figure S1. SEM pictures (lower magnification) of the porous electrospun PVDF fibers. Figure S2 The diameter of fiber. Figure S3 and S4 SEM pictures (lower magnification) of the rough and grooved electrospun PVDF fibers. (DOCX 2656 kb)