The strongest correlations between noise and engineering defects, where normalized noise amplitude as a function of frequency f is described by a model of AN/fγ, are that γ increases with graphene area and contact area but decreases with device total perimeter, including internal features. We did not find evidence of a correlation between the scalar amplitude, AN, and the device channel geometries. In general, for a given device area, the least noise was observed on the least-etched device. These results will lead to an understanding of what features are needed to obtain the optimal device resistance and how to reduce the 1/f noise which will lead to improved sensor performance.Chemiresistive graphene sensors are promising for chemical sensing applications due to their simple device structure, high sensitivity, potential for miniaturization, low-cost, and fast response. In this work, we investigate the effect of (1) ZnO nanoparticle functionalization and (2) engineered defects onto graphene sensing channel on device resistance and low frequency electrical noise. The engineered defects of interest include 2D patterns of squares, stars, and circles and 1D patterns of slots parallel and transverse to the applied electric potential. The goal of this work is to determine which devices are best suited for chemical sensing applications. We find that, relative to pristine graphene devices, nanoparticle functionalization leads to reduced contact resistance but increased sheet resistance. In addition, functionalization lowers 1/ Grapheristics ,8. This eristics . Even theristics ,11,12,13LF) noise behavior. Understanding the noise behavior of electronic devices is important from an application point of view as well because this LF noise determines the smallest amount of information or signal from a device that can be detected, whether a device is functioning as an amplifier, a transducer, or merely a conveyer of information [f or flicker noise, in which the power spectral density (PSD) of electrical fluctuations is inversely proportional to exponentiated frequency, fγ, with γ ranging from 0.5 to 2 but often close to 1 [fγ noise has been observed in many material systems [Graphene chemical vapor sensors, fabricated as two-terminal devices exposed to ambient, operate on the principle that their electrical resistance changes in response to changes in ambient molecule concentration and, in particular, to adsorption from chemical vapors. Maintaining a low contact resistance in these devices is crucial for maximizing the relative effect of chemical-exposure-induced changes in device resistance. Published values of graphene–metal Ohmic contact resistance vary considerably depending on the fabrication approach ,20,21,22ormation . The LF ose to 1 ,25,26,27 systems ,29. This2 mesa. Such a large device size is relevant to sensor applications where it is important to minimize ultra-low frequency noise. A large area device will maximize the number of charge carriers and so minimize 1/f noise [In this work, we carried out electrical conductivity and LF current noise measurements at room temperature in air ambient conditions on large-area monolayer graphene devices with and without ZnO nanoparticle functionalization. Zinc oxide nanoparticles are one of the most widely investigated structures for chemical sensing due to their high thermal and chemical stability. Zinc oxide is an n-type semiconductor with a wide band gap energy of 3.37 eV. It has been reported that ZnO nanoparticles alone, and as part of a graphene composite system, have a high sensitivity towards methane, nitrogen dioxide, hydrogen, and ammonia, as well as ethanol and acetone ,31,32. M/f noise , as well2) as a chemical linker, which results in increased functionality of the graphene films without degradation of its electrical properties [Epitaxial graphene films were grown on the Si face of semi-insulating, on-axis 6H-SiC substrates by Si sublimation at high temperature in a chemical vapor deposition reactor . As descoperties . Then, zoperties .2 SiC chips bearing 4 die, each die with 13 sensor devices  and devices having interdigitated electrodes, with 10 µm  and 20 µm  channel lengths and corresponding channel widths of 3600 µm and 7600 µm, respectively. They were printed in either of two orthogonal orientations in order to explore directional dependence of conductivity in the epitaxial graphene. Group 2 consisted of patterned devices with slots aligned with charge transport . Group 3 consisted of patterned devices with slots transverse to charge transport . Group 4 consisted of patterned devices with regular 2D hole patterns including 16 point (ME7) and 6 point stars (ME2), 10 × 10 µm2 squares (MS), and 10 µm dia. circles (MC). The TLM structures were fabricated with contact separations from 3 to 30 µm and a uniform width of 20 µm. The Ti/Au contacts were 70 × 100 µm2 rectangles that overlapped 5 µm of the graphene films, thus adding a metal–graphene edge junction of nominally 30 µm at each contact. See Graphene devices were fabricated on 8 × 8 mmA Keithley 236 source measurement unit (SMU) supplying 1 VDC was used to measure device resistance. Two configurations were used for room temperature low frequency noise measurements, from 0.24 Hz to 97.5 Hz and from 0.001 Hz to 1 Hz, as shown in cR), sheet resistance (shR), and contact resistivity (ρc) of the graphene films were determined from the TLM structures described above. Next, we measured the devices’ total resistance. Finally, we calculated the effective width, contact area, contact resistance, and sheet resistance for the device structures.In this section, we report the resistance properties of pristine and ZnO functionalized graphene films and devices. First, the contact resistance  of the material in a plane normal to the direction of current flow. For graphene films, we assume that the films are of uniform thickness, so that the cross-section is determined entirely by the effective device width. For the group 2, 3, and 4 devices  of any material is given by  devices , Table 1graphene ,39,40. TRT) of the TLM structures plotted against the varying graphene channel lengths for both pristine and functionalized graphene films. The slope of the linear fit in shR/W) of the graphene sheet resistance. The contact resistance, cR, is extracted from the extrapolated resistance at zero channel length, 2 × cR. The contact resistivity is determined from the equation ρc = cR × W × TL, where TL is the transfer length, the average distance that an electron travels in the material beneath the contact before it flows up into the contact. The transfer length, TL, is obtained from the extrapolated length at zero resistance (−2LT = x-intercept) [We present in tercept) .cR, shR, LT, and ρc for pristine and functionalized graphene films are shown in shR is consistent with a previous investigation [12 cm−2 and an increase in carrier mobility of functionalized films relative to pristine. Following the approach of Nath, et al. [T is the carrier transmission probability, h is Planck’s constant, n is the sheet carrier concentration, and e is the electron charge underneath the metal [n = 4 × 1012 cm−2, of about 57 Ω-µm. In our case, the functionalized sheet resistance has increased by 20% compared to pristine graphene. Assuming the previously observed drop in sheet carrier concentration after functionalization to have also occurred here, then the nearly two-fold reduction in contact resistance must be due to increased T, which could be explained by changes in the film work function induced by functionalization that lowers the interfacial barrier height.The calculated tigation , which a, et al.  we applyhe metal ,42,43. FT of the pristine and functionalized graphene devices  had the highest RT values, and group 1 (I1–I4) the lowest. Group 1′s unpatterned device U, group 2, and group 4 had similar total RT values 4.5–9 kΩ. The relatively smaller normalized resistance of the Group 4 devices is not surprising, as some current spreading in the regions between the etched features is to be expected.Next, we measured the R devices . In geneCR, and the device sheet resistance shR of pristine and functionalized graphene device structures for the different device geometries. The extrinsic contact resistance Rc (not the intrinsic resistivity) for the actual sensor device is calculated from geometry and values of LT and ρc calculated from the TLM structures, estimating cR for each sensor device from ρc as derived from the TLM data, the overlapping contact width cW, and the calculated transfer length TL from Finally, we calculated the effective contact area, the device contact resistance sh = (RTR − C)2R × ch/LW, where chW is the effective channel width after accounting for etched features and neglecting lower resistance  sections  can be calculated using the relation Weff = (shR × L)/chR, where shR is the sheet resistance calculated from the TLM data  to areas of low concentration  but usually leave a fixed charge behind which may be partially screened. In the devices discussed here, defects are present from both the structures etched into the graphene, as well as when the organic linkers and nanoparticles are added during the functionalization process. The precise nature and impact of each will be addressed in future work.The S2(fn) of the device noise, we note that the discrete Fourier transform of a set of N voltage samples d,kV, k = 0, 1, …, N − 1, collected across the device at time intervals of width Δ, where the device is in series with the resistance R dividing a voltage V:d,nV because the first term in the right-hand expression vanishes due to orthogonality, and the negative sign on the second term vanishes when the term is squared during subsequent processing, leaving the following:The current noise of our devices was measured by sampling the voltage developed across a resistor in series with the sensor device when a 1V DC bias was applied across the pair. A wire-wound resistor was used  to reduce the contribution to the measured noise from that component . The valVS/V2 vs. f generally between 0.015 and 0.2 Hz. The observed noise is low compared to other published work [The voltage data were obtained by sampling at 2.31 Hz over 665 s durations. Work not reported here established a corner frequency of 1–10 Hz, and there was generally no significant power or signal at higher frequencies to be aliased into this frequency range. A Hann window was applied to each frame prior to the transform, and the resulting frequency data between 0.00451 and 1.15 Hz were averaged for at least 5 such scans. After normalizing the FFT of each device response by the average voltage across the device, the results are plotted and shown by device in hed work ,47,48,49hed work .f is the frequency, γ is the frequency scaling exponent, and AN is related to the Hooge parameter αH through NA = HNα, where N = n + p [NA is a scalar measure of the 1/f noise level and generally reflects the quality of a material or a device, depending on the number of charge carriers and extrinsic parameters such as device channel area and structural and chemical condition of the material: a higher value of NA usually corresponds to a lower quality device. By comparing devices of similar area as fabricated from a common material with a common process, we can assume the number of carriers to be roughly comparable across the devices as well.Low frequency noise in graphene under ambient conditions has been attributed to multiple sources, including slow traps, generation/recombination (GR) centers, scattering from impurities, and dynamic changes in the scattering cross-section, presumably due to the chaotic impact on dipole screening of the constrained motion of charge carriers in the 2D film . In orde = n + p . The ampNA and γ values for all devices were calculated by fits to linear portions of the data  shown in 1–I4 indicate a large number of slow processes of duration longer than 1/2πf for frequencies below 1 Hz. The large size of these devices and the extended data acquisition to low frequencies may allow other processes to occur and be observed that have not previously been considered. For comparison of our observed noise to other published work, e.g., Rumyantsev et al. [f fit to 10 Hz, where our data are dominated by Johnson noise, and multiply the predicted value of SV/V2 by device length and effective width from −8 and 1 × 10−7 µm2/Hz. In comparison, we find a generally consistent and systematic variation: Group 1 devices exhibit greatly reduced noise, Group 2 devices exhibit marginally reduced noise, Group 3 devices exhibit increased noise, and Group 4 devices exhibit a wide range of noise levels, overlapping the range of Rumyantsev et al.The f noise on device geometry, functionalization, effective graphene area, metal contact area, and mesa etched graphene perimeter. The areas and perimeters  of graphene mesas measured after etching and metal contact areas were calculated for the geometries shown in 1, H2, and U devices. These three are also the quietest devices, implying that the factors contributing to noise, and passivated by functionalization, are not present in certain devices  but exist in others, specifically V1, V2, MC, MS, ME2, and ME7. Consideration of these device geometries suggests that while a single trap/excitation, generation/recombination, adsorption/desorption, etc., event can simultaneously induce changes in mobility and carrier concentration δnµ, δpµ, δn, and δp equivalently in the H1, H2, and U devices, such changes are not equivalent in the V1, V2, MC, MS, ME2, and ME7 devices, where considerable graphene is spatially remote from the primary transport paths. Thus, the direct effect on mobility through the regions defined as direct paths between electrodes from events outside those paths is minimal, while generated charges can easily diffuse into the areas of current flow where drift mobility  are significant. The ME2 is somewhat exceptional. However, the sparser hole array relative to the MS and MC devices and reduced internal perimeter relative to the low field, high carrier concentration area as compared to the ME7 device may explain the observed results. These results suggest a rather complex relationship between mobility and carrier concentration.To investigate which device feature influences noise the most, we studied the dependence of 1/NA and γ, we present plots of these in NA) and N. If one neglects the four interdigitated devices, where the relatively high currents and negligible perimeter relative to area suggests different relevant physical phenomena, there may be a correlation between perimeter and noise scalar NA. We do see strong correlations between the device area and γ and the contact area and γ and an inverse correlation between the perimeter and γ. This strongly suggests that different mechanisms exist in the generation of noise in the different regions of the film, which can be used to improve sensor design.In an effort to look for correlations between measurable parameters of graphene area, contact area, active device perimeter, and noise parameterization terms Ultimately, the question of which is the optimal sensor design is still unsettled. Although in this work we have addressed the relationship of design to intrinsic device noise, we have not fully examined the relationship of design to extrinsic signal, i.e., chemiresistive response. It may be the case that the noisiest device geometries are also the most sensitive. In any case, a characteristic and reproducible response behavior defined as change in conductance should be achieved rapidly. Furthermore, the design of the sensor should strongly favor detecting perturbations in conductivity induced by adsorption of target species over others. Perturbations due to background or benign chemicals, which are also considered to be noise, should be minimized. Finally, nanoparticles other than 50–80 nm dia. ZnO may have different impacts on noise and sensor response. These factors will be explored in future work.1, H2, and U), implying that the factors contributing to noise, and passivated by functionalization, are not present in some geometries but exist in others. The resistance measurements showed that devices with long etched stripes orthogonal to the direction of the applied electric field have the highest resistance, and short and wide channel interdigitated devices have the lowest resistance for both pristine and ZnO-functionalized graphene. The graphene–metal Ohmic contact resistances (CR) demonstrate that ZnO-functionalized graphene has lower contact resistance, but higher graphene sheet resistance (shR) compared to the pristine graphene. There is no strong evidence for a correlation between the scalar noise power and actual graphene channel area, contact area, and total perimeter . However, there is a strong direct correlation between noise frequency dependence and graphene area and contact area. Furthermore, there is an inverse correlation between noise frequency dependence and perimeter. This work highlights that the electrical and low frequency noise measurements are critical for the selection of appropriate device structure in graphene/ZnO chemical sensors.We have carried out measurements of resistance and low-frequency noise in graphene devices to determine the effects of ZnO nanoparticles functionalization and the engineered defects of the graphene channel. The goal of our work was to understand which device is best suited for chemiresistive sensing applications. For comparison purposes, all of the devices studied had the same graphene mesa area, but with different defects patterns of interdigitation or etched internal holes. These hole patterns included arrays of coarse and fine long slots etched perpendicular and parallel to the applied field, and two-dimensional arrays of squares, circles, and few and many pointed stars. We find that functionalization generally lowers noise, with the exceptions being the three quietest devices (H


The record covers the period 8–130 ky b2k (y before 2000) with a temporal resolution of ≈0.5 and 7 y at the top and the bottom of the core respectively and contains important climate events such as the 8.2 ky event, the last glacial termination and a series of glacial stadials and interstadials. At its bottom part the record contains ice from the Eemian interglacial. Isotope ratios are calibrated on the SMOW/SLAP scale and reported on the GICC05 (Greenland Ice Core Chronology 2005) and AICC2012 (Antarctic Ice Core Chronology 2012) time scales interpolated accordingly. We also provide estimates for measurement precision and accuracy for both δ18O and δD.We report high resolution measurements of the stable isotope ratios of ancient ice ( Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14216441 Typically expressed with the δ notation (isotopic abundances are reported as deviations of a sample’s isotopic ratio relative to that of a reference water (e.g. VSMOW) expressed in per mille (‰) as: δi = 14.The isotopic composition of ice from deep cores drilled on Greenland and Antarctica has traditionally been used as a proxy for past temperatures19. With the advent of fast electronics and room-temperature, turn-key, solid state laser sources, there emerged various optical spectroscopic techniques, typically in the near Infra-Red spectral region that allowed for quasi-simultaneous analysis of the three isotope ratios 2H/1H, 17O/16O and 18O/16O on the same water sample21. Commercial instruments using Cavity Enhanced Spectroscopic techniques allowed for routine simultaneous measurements of δ18O and δD using a greatly reduced amount of sample and with precision and accuracy comparable, if not better than that of IRMS23. The new techniques offered the possibility for in-situ measurements of the isotopic composition of vapour26 while in combination with continuous melter systems, and micro-volume flash vaporisers, they yielded ice core data sets of unprecedented resolution31.Stable water isotope analysis has traditionally been performed with Isotope Ratio Mass Spectrometry (IRMS) utilising an array of different implementations, each one presenting advantages and disadvantages with respect to precision, accuracy, sample consumption and throughput, as well as ease of use and labour intensityδ18O and δD) record32 from the Greenland NEEM ice core, drilled from 2007 to 2012 at 77.45°N–51.06°W. The record covers the segment of the ice core below 1210.5 m - a depth that corresponds to an age of 8000 y b2k (y before 2000 AD)- and extends to a final depth of 2536.5 m. The oldest age of the record is 129,258 y b2k at the depth of 2432.15 m. We report ages on the GICC05 (ages 8–121.446 ky b2k) and the AICC2012 (ages 60–129.258 ky b2k) time scales interpolated accordingly. Beyond that point and for the deepest 104 m, an age scale cannot be reconstructed as the record is too disturbed due to ice folds33.Here, we present a high-resolution (0.05 m) water isotope  from Picarro Inc. . All measurements are reported on the international VSMOW-SLAP  isotope scale after careful calibration using a triplet of local standards calibrated against the primary International Atomic Energy Agency reference materials. We describe the methodology of the measurement including information on instrument calibration procedures and an accuracy and precision assessment.34.The dataset will be useful for future studies of past climate variability on long  and short  time scales. Studies focusing on the spectral properties of the isotopic signal will also benefit from the high resolution apparent in the record. The record includes significant past-climate events such as the 8.2 ky climatic transition, the last Glacial Termination, as well as the series of Glacial Stadial-Interstadials of the last 120 ky 02 (roughly 12% of the cross section of the ice core) was cut using a band saw with a resolution of 0.025 m for the top 601.7 m and 0.05 m for the interval 601.75–2536.5 . From that point, the samples were stored frozen until the time of isotopic analysis using CRDS. Prior to analysis, 200 μl of sample were transferred from the PE-LD bottles to 2 ml short thread glass vials with integrated 300 μl micro-inserts (La-Pha-Pack 11092357) sealed with triple layer Teflon/Silicon/Teflon caps (La-Pha-Pack 09150480).Subsequently, and after arrival to the Stable Isotope Lab at the Niels Bohr Institute, the samples were melted at room temperature in air-tight containers and transferred to 10 ml PE-LD narrow mouth bottles (CRDS) in the near Infra-Red region. For this study, we have used three different models of the Picarro L21XX-i analyser namely the L2120-i, L2130-i and L2140-i. The record contains a total of 26,519 data points acquired with the three instruments. The exact number of data points per instrument is given in Table Isotope analysis is performed with Cavity Ring Down Spectroscopy . The light leaking out of one of the cavity mirrors is measured by a solid state photodiode. When the light intensity reaches a certain threshold, the laser is abruptly switched off and the light intensity signal on the photodiode decays exponentially. The time constant of the exponential decay, commonly referred to as ‘ring-down time’ depends on the concentration of the absorbant molecules present in the optical cavity. For this study we have performed measurements of δ18O and δD, thus the CRDS protocol used, takes into account absorption lines of the 1H216O, 1H218O and 1H2H16O isotopologues.All three models follow the same spectroscopic principle for the measurement of isotope ratios and are very similar with respect to their performance. As we show in section 4, the L2130-i and L2140-i models reach higher levels of precision and accuracy when compared to the older generation L2120-i instrument. CRDS measurements are performed in the vapour phase with the sample continuously flowing through a high finesse optical cavity. The cavity consists of three high reflectivity mirrors in a V-shape configurationδ18O / δD analysis23. A nano-flow of the liquid sample generated by a micro-volume syringe, is vaporised continuously on a hot stainless steel plate at a temperature of 170 °C within a stream of dry air ([H2O] < 30 ppm). The CRDS analyser pumps the water vapour sample into the optical cavity with a flow rate of 30 cc/min (STP) via an open split connection with the inlet of the vaporiser.Vaporisation of the sample is performed using a high throughput, low volume vaporiser (Picarro-A0212 – discontinued model as of 2016). The A0212 vaporiser utilises a continuous vaporisation approach similar to that developed for the Continuous Flow μl of liquid water sample is injected with a liquid flow of 50 nl/s using a 10 μl syringe (SGE 10R-C/F5). The stability of the sample injection is a critical prerequisite for achieving high quality measurements with the continuous vaporisation method. An injection cleaning procedure is performed on a daily basis using an ultrasonic acetic acid bath followed by several manual rinses with deionised water. The thorough daily cleaning is vital for achieving measurements of high quality. The injection is performed using a CTC GC-PAL autosampler (PAL hereafter) with an injection method that results in a pulse duration of ≈80 sec and a pulse amplitude of 20,000 ppm. The amount of time required for one injection, including the filling and rinsing steps of the injection method is ≈135 sec. In Table δD signal over a sequence of four injection pulses.A total of 3.8 In Table 27 and following the IAEA recommended procedures. For the record presented here, we have used 4 water isotopic standards calibrated against the primary IAEA reference waters VSMOW and SLAP . The main quality-control metric of the protocol is the calibrated value of the “check” standard, as well as the standard deviation calculated based on the three “check” standard blocks. The first value gives an estimate of the measurement’s accuracy, while the second is an estimate of the individual run’s noise level. The data analysis scheme selects the appropriate number of injections to be considered as described in Sec. 2 and generates a list of the analysed samples with their unique ID’s and isotopic composition expressed in per mille. Possible outliers in the run can occur in the rare case of faulty sample injections. These outliers are identified by visual inspection of the run results, as well as an inspection of the injection level and stability of the suspected unknowns. These data points are marked and then filled by means of linear interpolation. The record contains a total of 251 interpolated points accounting for 0.9% of the record 38 timescales have all been transferred to NEEM high resolution depths using the approaches of40.The GICC05 (Greenland Ice Core Chronology 2005)39 for matching details between NGRIP1, NGRIP2 and NEEM). Note that GICC05 comes with an estimate of the Maximum Counting Error (MCE) of the time scale9. In a standard deviation context, the maximum counting error should be regarded as 2-σ041.The GICC05 NEEM depths down to 1335.85 m have been transformed to NGRIP1 depths by linear interpolation between ECM match points and tephra horizons, and from these, GICC05 ages were obtained using the NGRIP1 GICC05 depth-age relationship. For the section from 1335.90 m to 1955.90 m, the NEEM depths have been transformed to NGRIP2 depths by linear interpolation between ECM match points and tephras, and from these, GICC05 ages were obtained using the NGRIP2 GICC05 depth-age relationship (see39 for matching details). Below 2203.55 m NEEM depths, the ice in the NEEM core contains time reversals due to foldings. A detailed discussion of the matching and GICC05modelext timescale transfer between NEEM and NGRIP in this folded part of the core can be found in33. The depth-age relationship for this section is interpolated to 5 cm resolution from the lower resolution relationship given in the supplementary data file in33.The GICC05modelext time scale has been transferred to NEEM depths in the following ways: Between 1955.95 m to 2203.55 m, the NEEM depths have been transformed to NGRIP2 depths by linear interpolation between ECM match points and tephras, and from these, GICC05modelext ages were obtained using the NGRIP2 GICC05modelext depth-age relationship . For the section below 2196.95 m the ice in the NEEM core contains time reversals due to foldings. The depth-age relationship for this section is derived from a matching of the NEEM and EDML cores33. The lower resolution NEEM-EDML1 timescale given in the33 supplementary data has been interpolated to 5 cm resolution, and EDML1 ages have subsequently been converted to AICC2012 ages using the relationship between EDML1 and AICC2012 (see42 and40). It should be noted, that transferring AICC2012 to NEEM from NGRIP2 and from EDML does not yield the exact same ages for all NEEM depths due to matching uncertainties. Between 2196.95 m and 2197.00 m the two transfers of AICC2012 do, however, yield intersecting ages. Hence, to ensure continuity of the NEEM AICC2012 time scale, the AICC2012 ages are transferred from NGRIP2 down to 2196.95 m NEEM depth, while AICC2012 ages are transferred from EDML starting from 2197.00 m NEEM depth.The AICC2012 time scale has been transferred to NEEM depths in the following ways: For ages younger than 60.2ka (1955.95 m NEEM depth) AICC12 is constructed to be practically identical to GICC0532 can be found in the PANGAEA data repository (10.1594/PANGAEA.925552). In Table δD and δ18O) whereas in Fig. The datasetδ18O and δD in columns 8 and 9 of the data set. Similarly, the difference between the assigned and the post-calibration value of the “check” standard is used as a measure of the accuracy of the run. This metric is given in columns 10 and 11 for δ18O and δD respectively.The measurement precision of every run, is estimated from the calculation of the standard deviation of the valid injections of the “check” standard blocks . Measurement precision is given for δ18O measurement. This behaviour is explained by the more accurate spectroscopic corrections, as well as the more precise control of the optical cavity’s temperature and pressure of the newer L2130-i and L2140-i models. The statistics of the distributions for the two metrics are outlined in Table σ (95%) interval as defined by the precision of the “check” standard measurement for all the runs of the record.In Fig.


Electrochemical studies revealed that the 3D graphene materials were active in the oxygen reduction reaction (ORR). The outstanding ORR activity of 3D structured graphene materials should not be ascribed to heteroatom catalytic centers since such heteroatoms were successively removed upon increasing the carbonization temperature. XPS data showed that the presence of transition metals and nitrogen  in G-materials was marginal. The results highlight the importance of structural factors of electrodes in the case of graphene-based materials for Zn–air batteries and ORR.The manuscript presents results on the influence of external pressure on graphene exfoliation and subsequent 3D structuring by means of liquid-phase exfoliation. In contrast to known and applied exfoliation methods, the current study exploits the enhancement of splitting forces caused by the application of high pressure. The manufacturing pathway allowed to increase the surface area from 750 m This fact, along with a relatively high price of typical 2D graphene3, turned researchers’ attention to other sources of graphene, i.e. graphite and its exfoliation to less agglomerated structures. Several exfoliation methods were reported, such as liquid-phase, electrochemical, mechanical and microwave12. Liquid-phase exfoliation is regarded as a scalable method yielding graphene flakes of high quality and purity and low agglomeration13. The quality of such exfoliated graphene sheets may be as high as for graphene sheets obtained by chemical vapour deposition or SiC-based synthesis. However, liquid-phase exfoliation does not solve the problem of the lack of a durable pore structure resulting from the exfoliation process. The interflake spaces, even if created, collapse after the removal of the liquid phase. To avoid this effect, which practically excludes exfoliated graphene from electrode manufacturing, some additional measures must be undertaken. Such procedures should fix the split flakes at a certain distance from each other. Thus, in our previous studies, the liquid phase splitting was supplemented by the 3D structuring of exfoliated graphene flakes16. 3D structuring employs a nanopowder, of which particles  penetrate the newly formed spaces between the exfoliated graphene flakes, providing a permanent separation of them and avoiding a secondary stacking due to π–π attracting forces. Polyfurfuryl alcohol was used as “carbon glue” for a durable fixation of the obtained 3D-structured graphene flakes. Although the manufacturing scenario was practised recently, the answer to an important question is still missing: which factors may support the process of spontaneous graphene flake splitting in the liquid phase. Originally, the process relies on the appropriate surface tension of the liquid and the wettability of the graphene surface. Too high surface tension and too low wettability prevent the penetration of the dispersing liquid in-between the stack graphene flakes and hinder their subsequent separation. Thus, a hypothesis was raised that external high pressure set on the dispersing liquid should increase the effectiveness of the whole exfoliation process. The current paper addresses this pressure-exfoliation issue and is intending, as a pioneering study, to prove the positive influence of forced liquid exfoliation of graphene nanoplates to less agglomerated graphene structures, which can be structured to 3D electrode materials.Graphene still attracts researchers’ attention due to some outstanding features, such as high  electric conductivity, heteroatom insertion ability, and high chemical stability. The feature might predestine graphene for an ideal electrode material in electrochemical devices like batteries and supercapacitors. However, two important features of common electrode materials cannot be ascribed to pristine, i.e. strictly monolayer graphene: low manufacturing cost as well as a well-developed surface area and pore structure. Although a surface area of 2640 m17. In summary, 2 g of pristine graphene nanoplatelets  was added to 4 g of CaCO3  and mixed mechanically. Then, 20 ml of 1-methyl-2-pyrrolidinone, NMP  with 0.1 ml of cationic surfactant CTAC was added and mixed well. Next, the flask was placed for 1 h in an ultrasonic bath (room temperature) and then, 15 ml of furfuryl alcohol (POCh) was added, mixed with 3 drops of concentrated phosphoric acid aqueous solutions (75%) in an ARE-250 planetary mixer. Next, everything was poured into a Teflon container and finally placed in a miniclave drive pressure reactor . The temperature (− 0.55 °C) in the reactor was kept constant for 2 h at a pressure of 12 bar. After 2 h, the temperature was increased sharply to 25 °C. Then, it was dried in an electric furnace at 50 °C for 2 h. Next, the material was divided into four samples for carbonization at temperatures in the range of 700–900 °C. The mass was heated under the flow of nitrogen at a rate of 10 °C/min in a tube furnace (Thermolyne F21100). After this process, the prepared samples remained in a tube furnace under the flow of nitrogen in order to reach room temperature. After carbonization, the samples were treated as in our previous works18 with concentrated (34–37%) HCl for 20 min (1 g of carbon was used per 12 ml of HCl) and then washed with distilled water using a Büchner funnel, until the pH of the solution reached 6–7. The action of HCl opened pores in the carbon matrix by etching the template. Then, it was dried in an electric furnace at 100 °C for 24 h18. The term 3D graphene is when graphene flakes get stack randomly in a 3D volume24. Thus, we receive a 3D bulky structure (graphene flakes are building stones) resembling a sponge. “3D Graphene” is only a mental shortcut. The “3D structured graphene flakes” is a better description, however too long to be commonly permanently, and therefore we consequently use shorter forms in the text. The three-dimensional graphene samples obtained in the proposed method were denoted as G-T, where T means the carbonization temperature of 700 °C (G-700), 800 °C (G-800) or 900 °C (G-900). The precursor of 3D materials is called GF-750.Mesoporous graphene was formulated similar to our previously published protocols3 g−1) was obtained as a result of numerical integration as the difference of the surface area under the desorption and adsorption branches. Raman spectra were obtained by a micro-Raman spectrometer . The laser was tightly focused on the sample surface through a 50× microscope objective. To prevent any damage to the sample, excitation power was fixed at 2 mW. The resolution was 4 cm−1, CCD temperature 223 K, laser spot diameter 2.0 μm, and total integration time 100 s (50 × 2 s) were used. X-ray photoelectron spectroscopy  measurements were performed using a monochromatic Al Kα X-ray source. Survey spectra were recorded for all samples in the energy range of 0–1300 eV with a 0.5 eV step, high-resolution spectra were recorded with a 0.1 eV step. The electrochemical studies of the oxygen reduction reaction (ORR) and Zn–air batteries measurements were carried out with a potentiostat  and were performed according to methods published in our other article17.The morphology of the obtained high surface mesoporous graphene was analyzed by scanning electron microscopy with STEM mode  and high-resolution transmission electron microscopy . The graphene samples obtained prior to the HRTEM microscopic analysis were dispersed in ethanol and treated with an Inter Sonic IS-1K bath for 15 min and deposited on holey carbon-coated copper grids. The volumetric elemental composition  of the materials was analyzed by means of a combustion elementalanalyzer . The structural properties and nitrogen sorption isotherms of obtained graphene materials were determined by nitrogen physisorption experiments at − 196 °C using an ASAP 2020 Plus instrument . The surface area was calculated by the Brunauer–Emmett–Teller (BET) model. The pore size distributions were determined from the nonlocalized density functional theory (NLDFT) method in the SAIEUS program. The hysteresis loop area in adsorption units  as reference material. Cyclic voltammetry (CV) results were recorded at a 10 mV s−1 scan rate and in the potential range from 0 to 0.8 V, while linear sweep voltammetry (LSV) data was recorded at a 5 mV s−1 scan rate and a rotating disk electrode (RDE) speed in the range from 800 to 2800 rpm. The number of electrons (n) taking part in the oxygen reduction reaction was calculated on the basis of the LSV plot using the Koutecky–Levich (K–L) formulas presented below:The electrochemical studies of the oxygen reduction reaction (ORR) were carried out with a potentiostat . The tests were performed in a three-electrode system, in which Ag/AgCl was used as the reference electrode, the platinum plate was the counter electrode, and the glassy carbon  with the applied catalyst as the working electrode. The performed potential measurements were converted in relation to the RHE hydrogen electrode (Reversible Hydrogen Electrode). An ink for electrochemical measurements was prepared by dispersing 2.5 mg of carbon catalyst together with distilled water, ethanol and aqueous Nafion (0.5 wt.% aqueous solution) for 60 min. An appropriate amount of the catalyst was applied to the glassy carbon, and then the solvents were allowed to dry for a few minutes. The results of the catalytic activity were carried out in saturated OL is assigned to the limiting current density, and JK is defined as the kinetic current density; ω is the angular velocity of the electrode. The value of n, as mentioned above, is the number of electrons involved in the charge transfer in the oxygen reduction reaction; F is defined as a Faraday constant of 96,485 C mol−1; C0 is the total oxygen concentration in 0.1 M KOH being 1.2 * 10−6 mol L−1; D0 is the oxygen diffusion coefficient in 0.1 M KOH (1.9 * 10−5 cm2 s−1); ν is defined as the kinetic viscosity of the electrolyte used (0.01 cm2 s−1 for 0.1 M KOH). By determining the slope of the K–L plot, it is possible to estimate the number of electrons transferred in the oxygen reduction reaction.The individual parameters of the formula can be defined as J measured current density, J2) and then dried at 80 °C for 30 min. The battery electrolyte was a mixture of 6 M KOH and 0.2 M ZnCl2 solutions. All results of galvanostatic charge/discharge (600 charge/discharge cycles − 5/5 min) were carried out on the Autolab potentiostat . The reference material was an assembled battery with a platinum-based carbon catalyst  as the air electrode.The Zn–air battery was prepared to characterize the applicability of the obtained materials. The obtained carbon catalyst played the role of a cathode, and the anode was a zinc plate. To assemble the Zn–air battery, you need a microporous membrane  and carbon paper with the catalyst applied, as a separator and current collector, respectively. The ink was prepared by dispersing 10 mg of catalyst with 0.9 ml of a mixture of isopropanol and Nafion solutions  for 1 h. An appropriate amount of the catalyst was applied to carbon paper  due to the thermal destruction of oxygen species bonded to graphene planes. Figure 3/g (G-900). The contribution of the mesopore volume Vme to the total pore volume Vt also increased from 87 to 91% for all G-samples. All structural improvements, i.e. surface area, total pore volume and the volume share ascribed to mesopores, are particularly expected assuming that the G-materials are expected to be effective electrodes for ORR. A well-developed surface area positively influences the numbers of catalytic centres, while a well-developed and adequately tailored pore structure positively influences molecular transport to and off the electrode surface (reduction of electrode polarization). In summary, the applied manufacturing protocol led to positive structural changes compared to pristine GF-750 graphene nanoplatelets.Table 29. N atoms are seen as catalytic centers replacing Pt-based catalytic centers as in the commercial reference material denoted as Pt/C in this study. Therefore, the observed high electrochemical ORR activity of the G-materials cannot be attributed to the presence of N atoms and must result from other factors like the improved pore structure and surface area, as proved in the subsequent chapters.The XPS data Table  prove thD/IG ratio did not change considerably, too. This means that the size of crystalline domains and the number of defects were comparable for all electrode material samples and GF-750. The intensity ratio I2D/IG and ID/IG of investigated samples indicate that the obtained samples are consisting the multilayer nature of the graphene. The Raman data allowed us to conclude that the liquid phase splitting of GF-750 dispersed graphene packages existing GF-750 in the liquid phase rather than intensively splitting the packages to SLG. The carbonization temperature of the samples affects both the position of the G and 2D bands. G-900 sample had more vacancies and disorders since it has the highest ID/IG ratio and the highest carbon at.%30. However, the G band position of G-900 was in a higher wavenumber compare to G-700 or G-800 sample. The observed 2D band intensity decreases with increasing numbers of graphene layers and carbonization range31.As mentioned, the manufacturing procedure assumed that graphene planes stacked into bigger blocks were first fragmented to less complex structures (due to the high-pressure liquid phase method), then reorganized, and finally fixed into a 3D porous structure. Thus, single graphene layers (SLG) or few-layered graphene (FLG) were expected to be a building stone of the final structure. Raman spectroscopy studies were performed to investigate this issue. Table 3. These images showed a dense 3D pore structure of materials. Generally, the SEM images do not fully allow to determine the degree of graphene sheets deglomeration in these 3D structures.The exfoliation to FLG, although not fully confirmed by the Raman studies, was proved by HRTEM structural investigations. Figure 2 hybridized atoms (band C 1 s at a binding energy of 284.6 eV), which is characteristic of graphene materials. The C1s spectra of the obtained materials are composed of seven peaks corresponding to C=C bond (sp2) peak at 284.6 eV32, C–C bonds (sp3) peak at 285.0 eV33, C–O–C or C–OH bond peak at 286.3 eV34, C=O or O–C–O bond peak at 287.7 eV32, and peaks with binding energy at 289.6 eV and 292.1 eV are associated with shake-up excitation32. The total amount of oxygen in the obtained samples is in the range of 3.3–3.9 at.%, respectively. In turn, the raw material GF-750 has 6.8 at.% of oxygen.High-resolution XPS studies were performed to determine the chemical structure of the investigated G-materials and pristine GF-750 graphene. Surface elemental composition data are presented in Table 2 saturated electrolyte of 0.1 M KOH. Table The catalytic activity of carbon materials was investigated using the techniques of cyclic voltammetry (CV) and linear sweep voltammetry (LSV). All measurements were made in 0.1 M KOH saturated with oxygen and nitrogen. The voltammogram in Fig. 35 proved that in carbon materials, some intrinsic structures as armchair edges and zigzag edges could strongly interact with neighbouring carbon atoms and change their electronic structure similar to the inserted heteroatoms doping in several different configurations could also significantly alter the electron distribution of dopants and adjacent C atoms via the delocalization of π electrons, thereby affecting the physical and chemical properties. Similarly, constructing a vacancy/defect by removing a C atom may not only destroy the unity of π conjugation but also exert a significant influence on the electronic structure of adjacent C atoms.Szczesniak et al.36 defined a possible ORR mechanism involving two key stages: an associative mechanism involving a HOO* species and a direct O2 dissociation mechanism in an acid or alkaline electrolyte. In an alkaline environment, the following steps may occur:Nørskov et al.2 dissociation of oxygen atoms starts with the following elementary step:The mechanism via direct Oby steps  in alkalWhere * stands for an active site on the surface of electrocatalyst, , while the chemical purity was much better for G-materials. Therefore, the high ORR activity of G-materials should not be ascribed to heteroatom catalytic centers since such heteroatoms were successively removed upon increasing the carbonization temperature. The XPS data showed that the presence of transition metals  in G-materials was marginal. Also, the content of nitrogen atoms potentially being active in ORR was neglectable. The results point out the importance of the structural factors of electrodes in the case of graphene-based materials and ORR.2. The charge/discharge tests for the best catalyst were compared to a metal-air battery made of a platinum-based carbon catalyst (Pt/C 20 wt.%). Three hundred charging/discharging cycles were performed (one cycle consisted of 300 charges and 300 discharges). The stability of the open circuit potential (OCP) confirms the effective operation of Zn–air batteries 1.38 V. Compared to the Pt/C catalyst, whose potential was 1.45 V, the potential for the catalyst obtained with our method was close to this value. It can be assumed that the excellent charge/discharge stability, shown in Fig. 2. They showed stability in the range of 2.27 V for a charged battery and 0.9 V for the discharged process. The potential range for Zn–air batteries with a G-800 catalyst is stable over a long period of time. After 6 h of charge/discharge cycles, the potential of the battery with the commercial catalyst falls, while the battery with the metal-free catalyst keeps its charging stability throughout the charging/discharging process. The obtained mesoporous graphene material can be successfully used as electrode material in devices using the oxygen reduction reaction, and due to the appropriate specific surface area and pore size, the use of this material can be extended for application in supercapacitors.Based on the results of the oxygen reduction reaction, the material with the highest transferred electron number was selected and used as an oxygen catalyst in the construction of the metal–air battery. The purpose of this study was to check the applicability of the obtained graphene materials. Zn–air batteries were self-designed and handmade Fig. d. For thIn summary, we have demonstrated a low-cost method to obtaining high surface area mesoporous graphene materials. We have shown that the porous structure of the material is of key importance in electrochemical applications. The obtained mesoporous graphene material can be successfully used as electrode material in devices using the Oxygen Reduction Reaction, and due to the appropriate specific surface area and pore size, the use of this material can be extended for application in supercapacitors. These aspects of the performed study will be continued in further research.


Idiopathic pulmonary fibrosis (IPF), the most lethal form of interstitial pneumonia of unknown cause, is associated with a specific radiological and histopathological pattern  and has a median survival estimated to be between 3 and 5 years after diagnosis. However, evidence shows that IPF has different clinical phenotypes, which are characterized by a variable disease course over time. At present, the natural history of IPF is unpredictable for individual patients, although some genetic factors and circulating biomarkers have been associated with different prognoses. Since in its early stages, IPF may be asymptomatic, leading to a delayed diagnosis. Two drugs, pirfenidone and nintedanib, have been shown to modify the disease course by slowing down the decline in lung function. It is also known that 5–10% of the IPF patients may be affected by episodes of acute and often fatal decline. The acute worsening of disease is sometimes attributed to identifiable conditions, such as pneumonia or heart failure; but many of these events occur without an identifiable cause. These idiopathic acute worsenings are termed acute exacerbations of IPF. To date, clinical biomarkers, diagnostic, prognostic, and theranostic, are not well characterized. However, they could become useful tools helping facilitate diagnoses, monitoring disease progression and treatment efficacy. The aim of this review is to cover molecular mechanisms underlying IPF and research into new clinical biomarkers, to be utilized in diagnosis and prognosis, even in patients treated with antifibrotic drugs. Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung disease . The epiThe management of IPF is currently based on clinical data, such as symptoms, lung function tests, and radio-histological patterns, due to a lack of reliable molecular markers. However, the identification of clinical biomarkers, diagnostic, prognostic, and theranostic would allow an evaluation based on underlying pathobiological mechanism of disease, leading to adequate phenotyping of patients in terms of diagnosis, prognosis, and response to therapy. This review aims to cover molecular mechanisms underlying IPF and research into new clinical biomarkers, to be utilized in diagnosis and prognosis, even in patients treated with antifibrotic drugs.Biomarkers are defined as “characteristics that are objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic intervention” . At any A good-quality biomarker should be reproducible, very sensitive, specific, and accurate. It should be validated in large multicentric trials and heterogeneous populations. Moreover, to be used on a large scale, it should be easily available and accessible. Biomarkers detectable on peripheral blood, exhaled breath condensate or broncho-alveolar lavage (BAL) offer an increased range of applications compared with a transbronchial or surgical lung biopsy. Finally, the cost-effectiveness ratio should be acceptable .The development of new molecular biomarkers for IPF is based on two different approaches. The hypothesis-driven method selects new candidate biomarkers a priori based on previous evidence about the disease. In contrast, the unbiased approach utilizes methods from systems biology to screen a large number of candidate biomarkers for their association with the disease. Although the former has the advantage of a strong rationale but lacks efficiency, the latter is more efficient but also burdened by the risk of false discovery . Historically, IPF was considered a chronic inflammatory disorder, gradually leading to fibrosis. However, anti-inflammatory, and immunosuppressive therapy have shown to be ineffective and associated with increased mortality . Up to dThe main biomarkers analyzed in this review and their possible applications are resumed in Mechanism or biological pathways linked to disease predisposition are reflected by predisposition biomarkers. They should provide information through inexpensive and non-invasive sampling with high sensitivity, specificity, and predictive value. Using predisposition biomarker, a patient could be address to informative counselling, preventive measures, and early therapy .Surfactant proteins are secreted in surfactant by type II alveolar epithelial cells (AEC). They are encoded by SFTPA, SFTPB, SFTPC, and SFTPD genes . Among sA common single nucleotide polymorphism (SNP) in the putative promoter region of the mucin 5B (MUC5B) gene rs35705950), which encoded a glycosylated macromolecular component of mucus, has been associated with familiar pulmonary fibrosis and sporadic pulmonary fibrosis ,32,33,34705950, wThe telomerase complex is involved in protection of chromosomes from loss of material, catalyzing the addition of repeated DNA sequences in the telomere region . SeveralAlthough the relationship between IPF and immunity is controversial, several components of the immune system have been evaluated as predisposition biomarkers in IPF. Toll-like receptors (TLR), fundamental components of innate immunity, have been implicated in the pathogenesis of IPF. TLR-2 mRNA is overexpressed in IPF patients and has shown pro-fibrotic features in mice. TLR-3 has shown antifibrotic features both in human and mice through downregulation of transforming growth factor beta 1 (TGFB1) and upregulation of prostaglandin E2 (PGE2). The loss-of-function variants L412F) of TLR-3 lead to enhanced fibrotic responses. Some evidence, mainly in animal models, has been shown also for TLR2/4, TLR9, and TLR4 2F of TLR,43,44. APredisposition biomarkers help understanding the pathogenesis of IPF and predicting the predisposition and prognosis of the disease. However, to date, none of these biomarkers is completely specific and sensitive for the diagnosis of sporadic pulmonary fibrosis, nor validated in clinical use. In familiar cases of pulmonary fibrosis, a consult with a geneticist and a screening for the most common biomarkers should be proposed to patients. Diagnostic biomarkers should reflect the mechanism or biological pathways that distinct IPF from the other ILDs. They should be easy to evaluate and reproducible, such as blood, urine, BALF derived or imaging-based biomarkers. Ideally, they should improve diagnosis, reduce the risk of diagnostic tests, reduce the number of unclassifiable cases, helping discriminate IPF from other ILDs accurately . SeveralThe detection of surfactant proteins in serum of patients with pulmonary diseases reflects an injury of the alveolar epithelial barrier. SP-A and D have been studied as diagnostic markers in IPF. Although BALF levels of surfactant proteins are reduced both in IPF and other ILDs compared with healthy controls ,47, seruKrebs von den Lungen-6 (KL6)/mucin 1 (MUC1) is a glycoprotein expressed on the extracellular surface of type II AECs and bronchiolar epithelial cells in the lung largely studied in ILDs due to its overexpression in affected lung and regenerating type II AECs ,54. KL-6Circulating caspase-cleaved cytokeratin-18 (cCK-18) is the cleaved fragment of cytokeratin-18 (CK-18), a cytoskeletal protein found in AECs. Since cCK-18 is produced during apoptosis in response to stress, it has been evaluated as a diagnostic and prognostic marker in one study: cCK18 was significantly elevated in the serum of IPF patients compared with normal controls and patients with other ILDs although it was not associated with prognosis .TLRs have been studied widely for their implication in IPF pathogenesis and predisposition. However, some evidence has highlighted a possible role in diagnosis. Higher levels of TLR in BALF of IPF patients, particularly TRL-7 has been noted. In the same study, TLR also showed different profiles of expression in fibrotic and granulomatous disorders .Metalloproteases (MMP) are another class of proteins widely studied for their role in in the aberrant fibrotic process, but the mechanisms are not completely understood and characterized as it seems they are implied both in deleterious and beneficial effects on the fibrotic process  They areOsteopontin (OPN) is a phosphorylated glycoprotein that work as a mediator of inflammation and wound healing . OPN is Prognostic biomarkers should contribute to quantitative assessment of mechanism or biological pathways relevant to disease progression. They should be repeatable over time without significant risk for patients, such as blood and urine-based biomarkers. Moreover, they should have a low intra-patient, inter-test variability with calibration and discrimination values clearly established. They could be integrated in multiparametric models in order to improve prognostic counselling . PotentiSeveral studies highlighted a relationship between elevated values of KL-6 and mortality or progression in IPF ,83,84,85With regard for immune mediators, the SNP in theTLR3 (L412F) has been associated to increased mortality and accelerated progression in independent cohort ,92. AlphMMPs, in particular MMP-7, have been studied not only as diagnostic biomarkers but they can be useful tools in predicting prognosis and transplant free survival in IPF patients ,66. MMP-The evidence on the prognostic value of OPN is scarce ,95. HoweTherapeutic biomarkers should provide quantitative assessment or indicate the presence or absence of mechanisms or biological pathways targeted by therapy. Since these biomarkers work as surrogate endpoints, they should be measurable over time with low risks for patients, low intra-patient, inter-test variability, and should improve clinical decision making of therapeutic intervention. Finally, when a therapeutic biomarker is evaluated threshold for change should be established reflecting meaningful therapeutic response .Since many biomarkers have been studied before the introduction of antifibrotic therapy, evidence on their usefulness in monitoring response to therapy is more limited.Surfactant proteins serum levels have shown potential efficacy as outcomes in IPF therapy. SP-A levels in IPF patients treated with pirfenidone or nintedanib from baseline to 3 and 6 months were found to predict progression , while SPromising data have been shown in several studies on KL-6 and its use in the monitoring of antifibrotic therapy. Response to pirfenidone therapy correlates with changes in serum KL-6 over time in one study  BergantiMMP-7 has recently been studied in with other biomarkers of transplant free survival in a study on 325 patients, 68 of them treated with antifibrotic therapy, to evaluate the role of such biomarkers in patients on antifibrotic therapy. The study revealed that these biomarkers predict differential transplant free survival in patients on antifibrotic therapy but at higher thresholds than in non-treated patients. Moreover, plasma biomarker level generally increases over time in non-treated patients but remain unchanged in patients on antifibrotics .The need of reliable biomarkers is becoming more and more fundamental. The validation of useful and accurate diagnostic markers could reduce uncertainty and the use of invasive procedure. Prognostic and therapeutic markers could help stratify patients based on severity and disease behavior in order to personalize management. Moreover, reliable markers able to predict AEs could implement prevention measures and modify the prognosis of such events, which, to date, is poor. Several molecules have shown potential value as biomarkers in IPF. However, many of them have been evaluated mainly in Asiatic cohorts of patients, where their use is more common. Their accuracy should be confirmed also in Caucasian cohorts in order to routinely apply them in the management of IPF. The use of biomarker index composed by multiple biomarkers already studied separately, with the aim of improve diagnostic accuracy in distinguish IPF from other ILDs or healthy controls is promising, but, for now, has shown controversial results.Finally, none of these biomarkers have been validated in large clinical trials, which still remain an unmet need. However, as a remark of the importance of biomarkers in IPF, many clinical trials evaluating as primary or secondary outcomes known and new biomarkers, have been conducted  or are s


A 32-year-old Japanese man experienced out-of-hospital cardiac arrest. On arrival, computed tomography (CT) showed ground-glass opacity in the right lung. Emergency coronary angiography revealed triple vessel disease, then he underwent percutaneous coronary intervention. We also diagnosed him with heterozygous familial hypercholesterolemia and administered rosuvastatin and evolocumab. His clinical course was uncomplicated, and he was discharged on the 21st day of admission.Follow-up CT performed two years later revealed multiple areas of consolidation with sarcoid galaxy sign and mediastinal lymphadenopathy. We diagnosed him with pulmonary sarcoidosis by histopathological evaluation of the biopsied specimen via endobronchial ultrasound-guided transbronchial fine-needle aspiration of enlarged subcarinal lymph nodes. After we administered oral prednisolone with a gradual taper, his CT findings improved. Sarcoidosis is a systemic disease characterized by the formation of non-caseous granulomas secondary to unknown pathomechanisms. Most patients with sarcoidosis are asymptomatic or show a self-limited disease course; however, cardiac sarcoidosis can cause sudden death .Familial hypercholesterolemia (FH) is an autosomal dominant disorder secondary to a mutation of the low-density lipoprotein (LDL) cholesterol receptor gene. Patients with FH, particularly homozygous FH, usually show elevated serum LDL cholesterol levels and are predisposed to ischemic heart disease associated with coronary atherosclerosis at an early age ,3. We reA 32-year-old Japanese man with an unremarkable medical history (except for untreated hypercholesterolemia) developed out-of-hospital cardiac arrest while playing baseball. Cardiopulmonary resuscitation performed immediately by witnesses resulted in return of spontaneous circulation after a single defibrillation attempt via an automated external defibrillator, and he was transported to our hospital by the emergency medical service.Upon arrival at our hospital, contrast-enhanced whole-body computed tomography (CT) revealed a right upper lobe ground-glass opacity (GGO), without any other significant finding Figure .Emergency coronary angiography revealed triple-vessel disease and 99% stenosis of the left anterior descending artery for which he underwent percutaneous coronary intervention (PCI). Blood test results showed elevated total serum cholesterol of 330 mg/dL and LDL cholesterol of 261 mg/dL. His Achilles tendon thickness was 16 mm, and he revealed a family history of FH in his biological mother. Therefore, we diagnosed him with heterozygous FH. Elevated serum LDL cholesterol levels persisted despite rosuvastatin treatment; therefore, we added evolocumab to his regimen. His post-PCI clinical course was uncomplicated, and he was discharged on the 21st day of admission.Follow-up CT performed two years later revealed multiple areas of consolidation and mediastinal lymphadenopathy. The GGO in the right upper lobe appeared as a central core of coalescing granulomas, with peripheral nodules reminiscent of a globular cluster or galaxy  every other day with a gradual taper for pulmonary sarcoidosis. After the steroid treatment, the patient’s chest CT findings improved Figures , 2B, andIt is well known that FH induces coronary artery disease, which can be a cause of sudden cardiac death . In the Some clinical studies have reported poor cardiac outcomes in patients with sarcoidosis. Ekström et al. reported severe coronary artery disease in four of 38 (11%) autopsy cases in which cardiac sarcoidosis had led to sudden death . UngprasThe pathomechanisms that explain the association between sarcoidosis and coronary artery disease remain unclear. However, recent research in lipidomics has proposed some hypotheses. For example, chitotriosidase (human chitinase 1) is considered an indicator of macrophage activity and may be an inflammatory pathomechanism shared by sarcoidosis and atherosclerosis .Although further research is warranted to conclusively establish this association, we conclude that sarcoidosis may have accelerated coronary atherosclerosis secondary to FH in the present case. Notably, corticosteroids administered for sarcoidosis may cause adverse effects, including hypertension and dyslipidemia .In the present case, both sarcoidosis and FH can be the causes of cardiac sudden death. Recent studies in the field of lipidomics report that sarcoidosis and atherosclerosis share several inflammatory mechanisms that contribute to their pathogenesis. Clinicians should be aware of the potential risk of sarcoidosis in patients with atherosclerosis, particularly in those with concomitant risk factors such as FH.


Adverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results.We propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity.In the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives.Our proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.The online version contains supplementary material available at (10.1186/s12874-021-01450-3). Agence Nationale de Sécurité du Médicament et des Produits de Santé, ANSM). It contained around 450 000 reports at the end of December 2017. Currently, about 36 000 reports are reported annually.Because the conditions of exposure of an active drug in real life are very different from those of clinical trials, the adverse effects of drugs are often identified once they are introduced on the market. This may be due to a complex interaction with subcategories of population, or to a long latency period after exposure. Post-marketing pharmacovigilance aims to detect as early as possible these adverse effects that have not been identified during the safety assessment stages of drug development. Pharmacovigilance systems rely on large databases of individual case safety reports of adverse events (AEs) suspected to be drug-induced. Many countries currently have a spontaneous reporting system as well as supranational entities such as the European Medicines Agency or the Uppsala Monitoring Centre in charge of pharmacovigilance for the World Health Organization. In France, the national pharmacovigilance database is maintained by the National Agency for the Safety of Drugs and Health Products . We propose two new AWs derived from (i) a lasso logistic regression for which the regularization parameter is chosen using the BIC, and (ii) CISL. These AWs are then incorporated into a lasso logistic regression using the BIC to choose the regularization parameter. We compare both versions of our approach to (i) more classical implementations of the adaptive lasso in high dimension –25, (ii)We first present the lasso-based detection approaches. Then we detail the detection approaches based on the propensity score in high dimension. In a third step, we detail implementations of adaptive lasso proposed in the literature and we present our proposals based on the adaptive lasso.N denote the number of spontaneous reports (i.e. the number of observations) and P the total number of drug covariates. Let X denote the N×P binary matrix of drug exposures and let xi be a 1×P vector of covariates for the ith observation. Let y be the N-vector of binary responses that indicates the presence or absence of the AE of interest. For i∈{1,...,N}, the corresponding multiple logistic model is Let β0 is the intercept and β is a P-vector of regression coefficients associated with drug covariates. Although we are not in the P>>N context, P is typically very large, which can cause some numerical problems with classical regression. The penalized logistic lasso consists in estimating: where l is the log-likelihood of model (λ is the regularization parameter and pen(λ) is defined as where of model , λ is thL1 penalty in  is calculated. This procedure is repeated so that each fold is used only once as a validation set. An average value of the performance metric and a standard deviation are then calculated over the nf obtained values. In the lasso regression context, cross-validation is performed for each tested λ value. The selected λ according to cross-validation is the one with the best result in terms of the prediction performance metric selected. In this work, we used the deviance, and we set nf to 5.We considered three strategies for selecting the penalization parameter: cross-validation, BIC and permutations. One round of cross-validation involves partitioning the dataset into λ, we implemented the BIC as follows: An alternative strategy for selecting the penalization parameter is to rely on model selection criteria such as the BIC , 27. Forlλ is the log-likelihood of the classical multiple logistic regression model, which includes the set of covariates with a non-zero coefficient in λs lead to the same subset of retained covariates, then the non-penalized models resulting from these λs are the same, as is the BIC. Consequently, this approach selects the subset of covariates that leads to the classical model which minimizes the BIC defined in  is defined as the probability of being exposed to a drug of interest given the observed covariates . It is arth percentile or above the (1−r)th percentile of weights, the value of the rth or (1−r)th percentile, respectively [r=2.5%.In Courtois et al. , severalectively . Here weFor a given PS-based approach, each drug was evaluated using one-sided hypothesis testing. To account for multiple testing, we used the procedure proposed by Benjamini and Yekutelli  to contrL1 penalty: As defined by Fan and Li , an optip is defined by λp=λ×wp. The higher the value of the weight wp, the more the variable p is penalized and the less likely the variable is to be included in the model. By assigning a higher penalty to small coefficients and a lower penalty to large ones, the adaptive lasso makes it possible to consistently select the right model and produce unbiased estimates. Thus, Zou showed in his work that under certain conditions for the AWs, the adaptive lasso enjoys the oracle properties.The penalty applied to the covariate β∗, the P-vector of regression coefficients. To this end, he considered p and defined the associated penalty weight γ>0. However, in the high-dimensional context, it is non-trivial to find a consistent estimate for constructing the AWs since computing the maximum likelihood is not feasible.To build the AWs, Zou used an initial consistent estimator of γ=1. In both the lasso and the adaptive lasso, the penalization parameter λ was selected through cross-validation. This two-stage procedure, which involves an initial lasso step with cross-validation, was proposed in the logistic case under the name of iterated lasso [P-vector of lasso regression coefficients determined with lasso-cv, the AWs associated with the drug covariate p in the adaptive lasso stage are defined as: In the linear case, Bühlmann and Van De Geer  proposeded lasso . By denoThus, a covariate that has not been selected with lasso-cv in the first stage is automatically excluded in the adaptive lasso stage. In the following, we refer to this approach as adapt-cv.p, we defined the following AWs associated with the drug covariate p in the adaptive lasso stage: In the linear case, Huang et al.  showed tAs in the work of Huang et al., we chose the penalisation parameter according to cross-validation in the adaptive lasso stage. We refer to this approach as adapt-univ.λ for cross-validation-based adaptive lasso is obtained by deriving adaptive weights for each training set (i) directly for adapt-univ or (ii) using an embedded cross-validation for adapt-cv. This optimal λ is then used on the full data to obtain the final adaptive lasso estimates.Following Ballout et al. , the optAlthough adaptive lasso is an appealing variable selection procedure, to our knowledge it has never been used for signal detection. Since the aim in pharmacovigilance is to select the right subset of drugs associated with an AE, we sought to develop a signal detection approach by enhancing the performance of this method in terms of variable selection. To this end, we first use the BIC as defined above to identify the final subset of covariates in the adaptive lasso stage instead of cross-validation. We also propose two new AWs that aim to under-penalise variables that have been considered relevant by lasso-based variable selection methods, and to increase the penalty applied to, or even exclude, variables considered as less relevant.P-vector of unpenalized regression coefficients estimated in the first stage with lasso-bic, we define the following AWs associated with the drug covariate p in the adaptive lasso stage by: The first one consists in using the BIC in the first stage. By denoting A covariate that has not been selected by the lasso-bic in the first stage is automatically excluded in the adaptive lasso stage.The second proposed AWs are derived from the CISL approach. We first compute CISL by considering a non-zero constraint in calculating of the quantity  instead E first models provided by the lasso regularization path. We define AW for covariate p according to the B-vector This quantity measures the proportion to which a variable has been selected in B subsamples, the smaller is its associated AW.Thus, the more In the following, we refer to these approaches as adapt-bic and adapt-cisl.We performed a simulation study to assess the performances of the proposed adaptive lasso strategies and to compare all the methods described above. We investigated a large number of scenarios in terms of event prevalence, number of true signals, exposure frequency and strength of association. We compared the ability of each method to detect true signals and not detect false signals through sensitivity and FDR.yi∼Bernoulli(αi) with N and P were set at 100 000 and 500 respectively. We investigated 27 scenarios that differed according to: β0, the latter being used to simulate outcomes of varying scarcity β0∈{−2,−4,−6};the value of the intercept nTP∈{0,5,20};the number of drugs associated with the outcome among the 500 drug covariates: nTP true predictors: βTP∈{1,2};the value of the regression coefficients for the the reporting frequency of the true predictors (if any): frequent (at least 100 reports over 100 000) or rare (between 20 and 100 reports over 100 000).We simulated the occurrence of a given AE according to a logistic regression model nTP true predictors were chosen randomly for each of the 500 replications.Note that for each scenario, the In order to measure the relevance of using the BIC with the adaptive lasso, we included a method based on the same AWs as adapt-univ in the comparison. However, instead of cross-validation, we used the BIC to perform variable selection in the adaptive lasso stage. In the following we refer to this approach as adapt-univ-bic. For the sake of clarity, Table We declared as signals all drugs positively associated with the outcome for all the lasso and adaptive lasso-based approaches. For PS-based approaches, we applied a supplementary filter by considering only drugs which had more than three reports in common with the outcome. Drugs discarded by the filter had their associated p-value set to one . For theIn total, we compared 14 signal detection approaches: one disproportionality method, four lasso-based approaches, five adaptive lasso-based approaches and four PS-based approaches. All these approaches (except RFET) are implemented in the R package adapt4pv available on the CRAN. All the analyses were performed with R version 3.6.0. All the logistic regressions were computed with the speedglm R package v0.3-2 designed to handle sparse matrices efficiently. All lasso regressions were implemented using the glmnet R package v3.0-2.Table β0=−2 and β0=−4 (scenarios 4 to 7 and 8 to 11) adapt-cv and adapt-univ showed a high sensitivity at the cost of a high FDR, especially for adapt-univ. This is particularly the case in scenarios 8 and 10. For scenarios 12 to 15, where the outcome is rare (β0=−6), these two approaches showed a lower sensitivity. In these scenarios, adapt-univ showed a low FDR while adapt-cv had an unstable behaviour in terms of FDR, with a high average FDR in scenarios 12 to 14, and very low in scenario 15. By comparing adapt-univ and adapt-univ-bic, we find that using the BIC to perform variable selection in the adaptive lasso step reduced the FDR for β0=−2 and β0=−4. For β0=−6, this is not the case, adapt-univ showing particularly low FDR and low sensitivity in this setting. By comparing approaches which rely on BIC in the adaptive lasso stage, namely adapt-univ-bic, adapt-bic and adapt-cisl, we see that approaches based on our proposed AWs performed better overall, since they showed both a lower FDR and a higher sensitivity than adapt-univ-bic. These differences in performance were particularly noticeable in scenarios 4, 8, 10, and 13 to 15. In scenarios 12 to 15, adapt-bic and adapt-cisl showed a better sensitivity than approaches based on cross-validation with a lower FDR. Nonetheless, adapt-univ-bic had a slightly lower FDR than adapt-bic and adapt-cisl when there were no true predictors (scenarios 1-3), with an FDR around 0.10 for adapt-cisl and adapt-bic, and around 0.05 for adap-univ-bic.We first compared the performances of our proposed approaches versus the other adaptive lasso-based approaches. Figure Among our proposals, adapt-cisl generally performed better than adapt-bic with a lower FDR and a slightly higher sensitivity, in particular in scenarios 12 to 15 when the outcome was rare. In these scenarios, the FDR of adapt-cisl ranged from 0.01 to 0.10 and its sensitivity ranged from 0.06 to 0.72, while the FDR of adapt-bic ranged from 0.03 to 0.12 and its sensitivity ranged from 0.03 to 0.68.Simulation results for scenarios 16 to 27, i.e. for true predictors reported between 20 and 100 times, are shown in Fig. Overall, although the differences in performance are less clear-cut than in Fig. nTP=0 . It decreased with the intercept value for all the approaches. Overall, our proposals showed more stable results in terms of FDR compared to adapt-cv, adapt-univ and adapt-univ-bic with lower or equal standard deviations. In terms of sensitivity, all approaches presented results of comparable standard deviations in the majority of scenarios, except in some scenarios where our approaches showed greater standard deviations. This is particularly the case in scenario 19 for adapt-bic.For all methods, the variability in the FDR estimation over the 500 replications was greater in scenarios where In supplementary materials, Table A shows the average number of signals generated by adapt-cv, adapt-univ, adapt-univ-bic, adapt-bic, adapt-cisl across all the scenario settings. For all these approaches, as the outcome and true predictors became rarer, the number of signals generated decreased. When the outcome is frequent, adapt-cv and adapt-univ generated more signals on average than adapt-univ-bic, adapt-bic and adapt-cisl. The latter generated approximately the same number of signals. For these three approaches, their number of signals was close to the number of true predictors.β0=−6, CISL showed an increase in its FDR. This increase was noticeable in scenarios 3, 12 and especially in scenarios 24 to 26, where CISL showed an FDR between 0.15 and 0.20. Overall, lasso-bic had a slightly higher sensitivity and FDR than CISL. Although lasso-bic had a fairly stable behaviour, it showed a surprising increase in its FDR in scenarios 14 and 20, with an FDR at 0.15 and 0.10, respectively.Since adapt-cisl and adapt-bic showed the best performances among the adaptive lasso-based methods, we retained only these two methods for the remaining comparisons with the other approaches. Figure Compared to lasso-bic, our proposals showed an equivalent or lower sensitivity and a lower FDR in all scenarios. In particular, this difference in FDR was noticeable in scenarios 12 to 15 comparing adapt-cisl to lasso-bic. Like lasso-bic, adapt-bic and adapt-cisl showed an increase in terms of FDR for scenario 20 with an FDR of 0.12 for adapt-cisl and 0.10 for adapt-bic.nTP=0. For all other scenarios, the average number of signals generated was consistent with the observed performance in terms of true and false discoveries. Approaches such as lasso-cv, ps-iptwT, ps-iptw, RFET and ps-adjust generated too many signals compared to the number of true predictors, particularly when they were highly reported (scenarios 4-15). Lasso-bic, lasso-perm, CISL and to a lesser extent ps-mw, behaved like our proposals by generating a number of signals close to the number of true predictors across all the settings.In supplementary materials, Table B and Table C show the average number of signals generated by RFET, the lasso-based and the PS-based approaches across all the scenario settings. The number of generated signals for all approaches considered here decreased with the scarcity of the outcome and true predictors. This was particularly the case for ps-mw which did not generate any signals for scenarios 20 to 27. All the approaches except ps-iptw had a low number of generated signals on average when nTP=0. The stability of the results in terms of FDR decreased with the intercept value β0 as before, except for RFET and ps-mw. All approaches showed comparable stability results in terms of sensitivity, with an increase of the standard deviation when βTP=2.Lasso-based approaches showed larger standard deviation compared to other families of approaches when We applied the aforementioned signal detection approaches to the French pharmacovigilance data extracted from 1 January 2000 to 29 December 2017. We discarded spontaneous reports involving (i) drugs recorded as vaccines, phytotherapy, homeotherapy, dietary supplements, oligotherapy or enzyme inhibitors, (ii) reactions recorded as overdoses or medication errors. Drugs are listed according to their active substance which is coded with the 5th level of the Anatomical Therapeutic Chemical (ATC) hierarchy. AEs are coded according to the Preferred Term (PT) level of the Medical Dictionary for Regulatory Activities (MedDRA). This extraction of the French pharmacovigilance database included 452 914 reports with 6 617 different AEs and 2 378 different drugs.To assess the performances of these approaches, we used a reference signal set pertaining to the adverse event Drug-Induced Liver Injury (DILI) , 29. TheOver the study period, the database consisted of 1 692 different drugs reported more than 10 times. Among these drugs, 1 136 had more than three reports in common with a DILI. As in the simulation work, RFET and all the PS-based signal detection approaches were implemented on these 1 136 drugs and the remaining 556 had their p-value set to one. In the end, the DILI reference signal set contained 203 true negative controls and 133 true positive controls among the 1 692 drugs. Of the 1 136 drugs retained after filtering, the reference signal set contained 123 true negative controls and 119 true positive ones.Table Figure The development of novel signal detection methods is crucial for improving the responsiveness and the efficiency of post-marketing surveillance systems. In this work we propose new approaches for signal detection based on an appropriate methodology for variable selection: the adaptive lasso. In addition to defining new adaptive penalty weights derived from lasso-based approaches, we used the BIC to perform variable selection. To assess the performances of our strategies, we performed an extensive simulation study conducted for multiple scenario configurations and an application to real data, where we compared our approaches to other implementations of the adaptive lasso in high dimension found in the literature, as well as to other detection approaches recently proposed based either on lasso regression or on PSs. Methods for signal detection in pharmacovigilance must both be able to avoid time-wasting false positive signals in the context of further assessment resource constraints, and they must also not miss true positive signals for obvious public health issues. Thus, we chose in this work to evaluate our methods using the two criteria of sensitivity and FDR. We developed an R package available on CRAN that implement all the methods compared in the present work.By comparing all the adaptive lasso-based approaches including our two proposals, adapt-bic and adapt-cisl, we first demonstrate that our defined AWs and the use of the BIC for variable selection are relevant for signal detection. Cross-validation for the adaptive lasso is a computationally intensive procedure since it requires deriving adaptive weights for each training set, and shows an unstable behaviour in terms of detection. The broader comparison that includes state-of-the-art signal detection approaches shows that our proposals are particularly competitive.Compared to lasso regression where BIC is used to perform variable selection, an approach we called lasso-bic here, our proposals tend to show a lower FDR at the cost of a slightly lower sensitivity. For adapt-bic, this result is not surprising since by construction, the covariates selected by this approach are a sub-sample of the covariates selected by the lasso-bic approach.Our work also confirms that CISL is a relevant signal detection approach. The choice of the quantile, which we set at 10%, seems appropriate for a large number of settings, except when the outcome and true predictors are rare. As expected, cross-validation is not appropriate for signal detection. All the approaches based on this criterion: lasso-cv, adapt-cv and adapt-univ show a high sensitivity at the cost of a very high FDR in the vast majority of scenarios. The use of the permutation method with lasso regression (lasso-perm) does not show fully satisfactory results with an FDR generally higher than the one of our proposals and a moderate gain in sensitivity.Among the PS-based approaches for signal detection, results are concordant with our previous work . Our simOverall, our approaches show very satisfactory performances in terms of false discoveries and a good sensitivity. This behavior remains stable over all simulation scenarios, with a slight increase in FDR when there are no true predictors. Among our two proposals, adapt-cisl performs slightly better. Lasso-bic and CISL are also relevant detection approaches, with the few nuances in terms of performance detailed above. RFET and to a lesser extent ps-adjust provide sensitivity that is sometimes superior to our proposals but with a fluctuating FDR which can be high, especially when the number of predictors increases and are strongly associated with the response. Finally, making the concession of a large number of false discoveries , lasso-cv is the approach that provides the best sensitivity in all situations.It is more difficult to assess the differences in performances of the approaches from the results of the application to real data. As the DILI adverse event is highly reported and since the majority of drugs registered in the French pharmacovigilance database are much less reported, the most comparable simulation scenarios to this situation are scenarios 16 to 19. The behaviour of the approaches differs slightly from that observed in simulation, but among all the approaches tested, our adapt-cisl approach showed the best compromise between a very low FDR, an acceptable sensitivity and a reasonable number of generated signals.P<<N situation, implementing the original BIC to perform variable selection seemed reasonable. In the case of the adaptive lasso, Hui et al. [Using the BIC as a criterion to select the penalisation parameter in lasso regression for variable selection has been widely studied –42. In pi et al.  developeTo preserve the specificities of pharmacovigilance datasets, i.e. a large size and sparsity, we based our simulation study on real data. This strategy has already been used in the literature to simulate large health care data . We variA major issue in the development of signal detection methods is the lack of reliable and sufficiently large sets of reference signals to evaluate performance in real-life conditions. Here we considered a set of reference signals pertaining to a common adverse event: DILI. Although this set is very broad, it still has its limitations. Indeed, the performances of the approaches are more difficult to interpret since they can only be evaluated on signals whose status has been assessed. It would be interesting to extend this application to other adverse events.The simulation and the application results suggest that the adaptive lasso is an appealing methodology for pharmacovigilance when the adaptive penalty weights are cleverly chosen, and when an appropriate variable selection criterion is used. Although the BIC does not make it possible to control the FDR, we are confident that it is relevant in view of the results of our simulations. Finally, our approaches do not require much more computation time than lasso-based approaches, and take far less time than that needed for PS-based approaches. An interesting development could consist in integrating external relevant information through adaptive penalty weighting in the pharmacovigilance context. Under the weighted lasso designation, this technique has proved to be very attractive in an area such as genomics for improving penalised regression performances in terms of prediction and variable selection , 46.Additional file 1 Supplementary materials : average number of signals generated across simulation scenarios Average number of signals generated by adaptive lasso-based approaches (Table A), RFET and lasso-based approaches (Table B) and PS-based approaches (Table C) across all simulated scenarios