The attrition of women in academic careers is a major concern, particularly in Science, Technology, Engineering, and Mathematics subjects. One factor that can contribute to the attrition is the lack of visible role models for women in academia. At early career stages, the behaviour of the local community may play a formative role in identifying ingroup role models, shaping women’s impressions of whether or not they can be successful in academia. One common and formative setting to observe role models is the local departmental academic seminar, talk, or presentation. We thus quantified women’s visibility through the question-asking behaviour of academics at seminars using observations and an online survey. From the survey responses of over 600 academics in 20 countries, we found that women reported asking fewer questions after seminars compared to men. This impression was supported by observational data from almost 250 seminars in 10 countries: women audience members asked absolutely and proportionally fewer questions than male audience members. When asked why they did not ask questions when they wanted to, women, more than men, endorsed internal factors . However, our observations suggest that structural factors might also play a role; when a man was the first to ask a question, or there were fewer questions, women asked proportionally fewer questions. Attempts to counteract the latter effect by manipulating the time for questions (in an effort to provoke more questions) in two departments were unsuccessful. We propose alternative recommendations for creating an environment that makes everyone feel more comfortable to ask questions, thus promoting equal visibility for women and members of other less visible groups. AlthIn this study, we examine a form of visibility that is more common and frequent, and apparent earlier in the pipeline : question-asking behaviour at local departmental academic seminars . Social role theory suggests that women should benefit from being exposed to successful ingroup role models at all points along the leaky pipeline. Before attending academic conferences and seeing women present their work, and before gaining a familiarity with the authors of papers in a particular research area, undergraduate and postgraduate students are exposed to the role-modelled behaviours of the women and men who work in their department. Given social role theory explanations for how gendered expectations of certain roles develop based on who is seen occupying those roles, we argue that the behaviour of the local community may play a formative role in identifying ingroup role models at an early career stage. Few studies have investigated such local phenomena, but these reveal a potential bias against women. For example, female undergraduate students are less likely to volunteer to answer an instructor’s questions in class, and somewhat less likely to pose their own questions . Such diOur aims were to determine whether women and men differ in their visibility at academic seminars and which factors might underlie any differences. With regards to the first aim, we tested the hypothesis that women would ask fewer questions at departmental seminars, thus limiting their potential visibility to others. We were interested in individuals’ actual question-asking in seminars, to quantify directly any disparity that might exist. With regard to the second aim, we were interested in perceptions of question-asking in seminars, to understand the motivations and beliefs that underlie any disparity. Thus, our data collection also took two approaches. First, we ran an online survey that collected data on over 600 academic respondents’ self-reported attendance and question-asking in seminars, their perceptions of others’ question-asking behaviour in seminars, and their beliefs about why they themselves and others do and do not ask questions in seminars. Second, we collected observational data at almost 250 seminars in 10 countries to quantify the attendance and question-asking behaviour of women and men in departmental seminars.Using these two data sets, we asked three questions. First, we asked whether there was a gender disparity in the question-asking of audience members in academic seminars (Question 1). Using data collected in the survey, we asked academics whether they perceived a disparity in women’s question-asking in seminars (Q1a). We also used our observational data to describe women’s and men’s actual question-asking behaviour at seminars (Q1b). Second, we aimed to understand why there is a disparity in women’s question-asking in academic seminars (Q2). Using the survey data, we asked both women and men why they did not ask questions when they wanted to, and for those that thought there was a gender disparity in question-asking, we asked why they believed there to be a disparity (Q2a). Next, we used our observational data to identify factors associated with the disparity (Q2b). Finally, we aimed to explore ways of addressing the disparity (Q3). Based on preliminary findings from the first year of our observational data collection, we ran an experiment in two departments to manipulate the time given to questions, in an attempt to promote a gender balance in the audience’s question-asking (Q3a). We also asked the survey respondents what they thought could be done to ameliorate the gender disparity (Q3b).The survey received ethical approval from the Science and Health Faculty Ethics Sub-Committee of the University of Essex. Participants declared their consent prior to participation and could withdraw from the survey at any time or leave any question unanswered. After completion, participants were briefed about the purpose of the study and provided with contact information in case they wanted further details. No identifying information was collected during the survey, and all data were pooled prior to analyses. To ensure data privacy, the survey was administered through Qualtrics .The survey was advertised via social media  and emails to relevant academic groups, and was active between 16th June 2016 and 22nd August 2016.The survey asked for details on the participants , the structure of academic seminars at their institution , and their own attendance and question-asking behaviour at seminars. Finally, we asked for their impression of any gender disparity in question-asking and potential reasons for it (for the full survey design see https://memforms.apa.org/apa/cli/interest/ethics1.cfm). Observers were in the same fields as the authors (biology or psychology), chosen to represent as much geographic distribution as possible; they were based in 10 different countries and 35 different institutions. We solicited observers’ help by explaining the motivation for the study and our preliminary findings , rather than the total number of different people asking questions. This is because after most talks, there is a limited amount of time for questions; multiple questions asked by the same questioner therefore raises the visibility of that particular gender in proportion to the number of questions asked.N = 62, comprising 18, 18, and 26 seminars in each of three departments). These data indicated a correlation between the number of questions asked and the imbalance in questions, with the imbalance approaching 0 as more questions were asked . Based on this preliminary finding, we hypothesised that we could increase the number of questions asked by women by increasing the amount of time devoted to questions after seminars. We thus designed a manipulation at two institutions to test whether decreasing the length of talks  would lead to more equal question-asking from male and female audience members. While these seminar series previously had indicated to speakers that presentations should last for about 45 minutes, during the manipulation we asked speakers in the invitation email to plan for their talk “to last for 40 min with 20 min for questions. This format is designed to encourage a more discursive and inclusive question session in our department.”We (AJC and DL) collected preliminary observations of question-asking from the University of Cambridge during the 2014–2016 academic years , 38% were post-graduates (N = 192), 20% were postdoctoral researchers (N = 102), 5% were research fellows (N = 26), 29% were faculty (N = 150), 5% were “other” (N = 27). The participants who completed the online survey were from 19 different countries (9 participants did not provide information about country) and 28 fields of study (28 participants did not provide information about field of study). The majority of respondents who indicated their field of study  were from the same fields as the authors of this study: biology and psychology.In total, 600 people provided consent and started our online survey, and 518 (90%) recorded a response when asked their gender (the last question in the survey), including 303 (58%) women, 206 (40%) men, 4 transgender/non-binary, and 5 who preferred not to report their gender. We restricted our analyses to the responses of women and men given the small number of respondents who did not consider themselves within these categories, resulting in a sample of 509 responses for our analyses. Survey respondents were from the academic community: 2% were undergraduates .N = 200, 35%) or fortnightly/bi-weekly . On average, there were 34 people in attendance at the seminars that were observed . The majority of seminars  started between 16:00 and 16:59, and attendance increased slightly for seminars starting later in the day compared to earlier in the day . On average, we observed 6 questions  over 12 min of question time .We first aimed to describe the general patterns of academics’ attendance at and question-asking in departmental seminars. Overall, most people reported in the online survey that they attended seminars weekly (2(4) = 1.58, p = 0.82, or observed attendance  = 1.54, 95% CI = 0.50, 0.53, p = 0.13). However, fewer women than men were seminar speakers . Seminars later in the day were attended by proportionally more women than those earlier in the day .There was no difference between men and women in self-reported frequency of attendance, χt’s < 1.10, p’s > 0.25. However, approximately twice as many men (33%) as women (16%) reported being motivated to ask a question because they felt like they spotted a mistake, t(362.2) = 4.61, p < 0.001 .In general, men and women did not differ in their motivations for asking questions; approximately equal proportions of men and women reported being motivated by an interest in the subject , the need for clarification , the desire to act as a model for more junior academics , or to establish a connection with the speaker , N = 219; 42%) or required only contacting the host . However, men and women differed in their perceptions of the availability of a speaker, χ2(3) = 14.50, p = 0.002, with female respondents reporting twice as frequently as men that the speaker only met with relevant faculty .Most respondents reported that meeting a speaker informally was actively encouraged in their department (N = 279 (58%); see N = 182 women (60%) vs. 97 men (47%); χ2(2) = 8.40, p = 0.01).We aimed to quantify whether academics perceive a gender disparity in the proportions of men and women who ask questions in seminars, and whether this perception differs according to gender. Most respondents reported that gender played a role in who asked questions at seminars, reporting that they believed that men were more likely to ask questions (2(4) = 21.71, p < 0.001: women self-reported asking questions less frequently than men (see N = 277 women (91%); 189 men (92%); overall 92%).These perceptions about a gender disparity in question-asking were borne out by the self-report data. Men and women differed in how frequently they reported asking questions, χ men see . DespiteM = -0.19, 95% CI = -0.16, -0.22, t(245) = -12.55, p < 0.001, We next examined whether the observational data substantiated these perceptions and self-reports of a disparity in women’s question-asking after seminars. To test whether the proportion of questions asked by women differed from the proportion of women present in the audience, we ran a two-tailed t-test comparing the difference in these proportions to 0 (no difference). Survey respondents’  general belief that men ask more questions than women was supported by the actual behaviour observed in seminars: proportionally fewer women asked questions after seminars than would be expected given the proportion of women in the audience , than by men  (We next aimed to understand why there is a disparity in women’s question-asking at seminars. The vast majority of our online survey respondents (91% of women and 92% of men) reported sometimes not asking a question when they had one. We asked them what prevented them from asking a question in these cases on a Likert scale from 1  to 5 (extremely important). The results are summarised by gender in  = 1.36) ; Fig 2. Nmale = 9, Nfemale = 17); consideration for colleagues , the speaker  and the audience ; not being picked by the moderator ; someone else asking the question ; not enough time ; and fear of judgment from members of the audience .We coded the 106 open-ended responses to this question. Common responses included: worries and personal characteristics , than by men   .Next, using our observational data, we examined potential predictors of a gender disparity in the questions asked after seminars. We used generalised linear mixed effects models with a binomial response, with questions from female audience members coded as cases, and questions from male audience members coded as noncases. To control for repeated measures, all models included the country, and the department nested within the institution as random effects. We did not include the observer as a random effect because most observers collected data within only one department within an institution.z = 13.03, p <0.001); we thus used the number of questions rather than the duration for questions, but found qualitatively similar results when using the number of questions asked (see below).We aimed to test the following fixed effects: the proportion of women in the audience (centred at 0.50), to estimate whether differences in the number of questions asked by women and men reflect differences in individual contributions rather than just their share of the audience; the gender of the speaker , to understand, for example, whether attendees might feel more comfortable asking a question of a person of the same gender; the gender of the first person to ask a question  to understand whether a social role model effect might occur within sessions (see below); the total number of questions asked (centred at the median of 6 questions) and the duration of the question time (centred at the median of 12 min) to understand whether perceived or real competition over asking one of the questions limited some individuals; the hour of the day that the seminar started (integer ranging from 10 to 18) as childcare needs differ throughout the day; the proportion of the permanent staff (faculty) in the host department who were female (centred at 0.50) to understand whether gender biases among individuals asking questions were associated with seniority; the number of attendees to understand whether the genders differed in their response to the size of the audience for their question; the field of study  to understand whether differences in norms or gender roles in different fields influenced participation; and whether the speaker was internal  or not to understand whether familiarity with the speaker influenced who asked a question. Unsurprisingly, there was covariation between the duration of the question time and the number of questions that were asked  the total number of questions asked and (b) the number of attendees to affect the gender disparity in the questions asked. In addition, because the first person to ask a question may set the “tone” for the subsequent (disparity in) questions asked, we investigated the interaction between the gender of the first person to ask a question and (a) the total number of questions asked and (b) the gender of the speaker. Such social influence biases have been found in online interactions, where, for example, the tone of the first comment posted influences the tone of subsequent comments [We also included a number of interactions that we predicted comments . This rea priori predictors and our modelling approach was exploratory in nature, we used stepwise model simplification to obtain minimal models whose retained components significantly explained the variation in the response . We thus started with models that included a set of predictors  and interaction terms, and then used backwards elimination of non-significant terms until a minimal model remained that explained the variation in the gender disparity in questions. Then, each dropped term was added back to the final model, one at a time, to check that it remained a non-significant predictor of the gender disparity.Because we had a large number of In predicting the proportion of questions asked by women, we could not include the gender of the first person asking a question, since the first person biases the overall gender ratio of questions, in particular when only few questions were asked. We thus ran two sets of analyses using slightly different data and predictors in the starting models to account for this. The first model included the complete dataset and all fixed effects and interactions not including the gender of the first attendee to ask a question. The second model used a reduced dataset, with the first question removed, and included the gender of the first person asking a question as an additional predictor.Using the complete dataset, we found that the probability that a question was asked by a female audience member was predicted by the proportion of the audience that was female, the proportion of female faculty in the department, the number of questions asked, the gender of the speaker, and whether the speaker was internal or not ; Fig 3. N = 3) that had only one question—the first one. To control for the factors that affected the proportion of questions asked by women as found in the “complete” model above, our starting model for this second set included the main effects included in the “complete” model of the first set, as well as the main effect of the gender of the first attendee to ask a question and the two interactions involving that effect.To deal with the problem of the gender of the first questioner influencing the gender ratio of questions asked, the second model predicted the probability of a question asked by women subsequent to the first question using a modified data set with the first question removed. For example, for a talk that had a male-first question, and totals of 3 questions from women and 5 from males, the new dataset had totals of 3 questions from women and 4 from males, reflecting the numbers of questions asked subsequently to the first one. Additionally, we removed any talks  but not in the complete data set .As reported earlier, there was significant covariation between the duration of the question time and the number of questions that were asked. When we included in the model the duration of the question time, instead of the total number of questions asked, it was a significant predictor of the proportion of questions asked by women in the reduced dataset  and having an opportunity to ask the question in person (M = 3.48). The factors they thought least likely to encourage them to ask more questions were having a moderator (M = 2.29), or having a better moderator that engages the audience (M = 2.60). Women were more likely than men to think that all of the factors we listed would encourage them to ask more questions , t(186) = 5.85, p < 0.001 and women , t(275) = 4.19, p < 0.001.Given our finding that having more questions results in a greater proportion of questions from women, it is of particular interest that neither men nor women who responded to the survey thought that it would be helpful to have a longer time to formulate questions; one-sample t-tests were significantly below the midpoint (of 3: “might help a bit”) for both men (t(297) = 2.45, p = 0.02; desiring clarification, β = 0.12, t(297) = 2.13, p = 0.03; and wanting to act as a model for more junior academics, β = 0.26, t(297) = 4.54, p < 0.001. Next, we tested a second model, in which we entered (simultaneously) three factors that are under departments’ control: how much time is provided for questions, how many people usually attend, and how easy it is to meet the invited speakers informally. The only factor that predicted women reporting to ask more questions was fewer people attending the seminar, β = -0.25, t(297) = 4.36, p < 0.001. Finally, we tested the extent to which the proportion of women faculty and women postgraduates (entered simultaneously) predicted women reporting to ask more questions, though this would obviously be difficult and time-consuming to change. Neither the proportion of women faculty, β = 0.02, t(292) = 0.26, p = 0.80, nor the proportion of women postgraduates, β = 0.06, t(292) = 0.77, p = 0.44, predicted women reporting that they would be more likely to ask questions.It is possible, however, that people are not aware of factors that might actually be helpful. In order to uncover factors that could potentially be targeted to increase the number of women asking questions, we ran a series of multiple linear regressions on women survey respondents only, predicting how often they reported asking questions. First, we ran a model in which we entered (simultaneously) the five motivations for asking questions see . Three mdecreased in the other institution from 10 to 7 min. However, when considering the departments separately, these changes were not significant (p > 0.05). Thus, unsurprisingly, our manipulation did not have an effect on the proportion questions asked by women .Building on the findings from our pilot data that showed that the disparity decreased with a longer time dedicated to questions, we performed a manipulation that asked speakers to shorten their talks by 5–10 min in an effort to increase the proportion of questions from women. Over the two departments involved in the experiment, we collected data during 30 seminars, including 17 controls and 13 experimental seminars. Our manipulation was not successful: the duration of the time for questions was no longer in our treatment group than in the control group. In fact, there was a significant interaction between the institution and the treatment; on average, the time dedicated to questions increased in one institution from 14 to 16 min, but post hoc linear model investigating whether shorter seminars led to longer question periods using our full sample of observational data. Surprisingly, across all observed seminars, we found no association between the length of the seminar and the length of the question time . This result suggests that manipulating the talk duration would not result in a change in the time dedicated to questions. Therefore, the manipulation may have been more successful had we aimed to manipulate directly the time dedicated to questions rather than indirectly trying to affect this by manipulating the talk duration.To further explore whether it is possible to manipulate the time dedicated to questions to increase the gender balance in the questions asked, we ran a The visibility of women role models at all career stages is important for redressing problems of the leaky pipeline. Our results add to a growing body of evidence showing that women are less visible than men, both conceptually and literally, in various scientific domains. Other studies have reported a similar bias in visibility, with men participating more already in school classrooms ,27,28, aand a cause of that same problem. As we explained earlier in this paper, research on role modelling suggests that having access to successful ingroup role models  can be a key factor in determining what course of study or occupation a person will pursue [The lack of visible female role models asking questions at departmental seminars is likely to be both a symptom of the leaky pipeline l pursue ,9, and, l pursue ,8. In thl pursue ,18,19, al pursue –13. GiveOne possible alternative interpretation of the low proportion of questions asked by women in our observational data is that more senior audience members are more willing to ask questions after seminars, and the data could accurately reflect the gender discrepancy in the proportions of senior audience members. That is, there could be a confound between seniority and gender, and the effect we observe is an effect of seniority, not of gender. Because we did not expect our observers to be familiar with the seniority of the members of the audience of all of the seminars they attended, we did not collect data on the seniority of the attendees asking questions. However, two lines of evidence suggest that the disparity we observed is not due only to this. First, in our observational data we controlled for the proportion of female faculty members in the host department and, while this proportion significantly predicted the proportion of questions asked by women, variation remained that was explained by other factors in the models. Additionally, this effect was “shallower” than a direct relationship would predict, with a 5% increase in the proportion of women faculty predicting only a 1.5% increase in the proportion of questions from women. This may suggest that senior women asked proportionally fewer questions than their senior male counter-parts, which is supported by our second line of evidence from the survey data. Men self-reported asking questions after seminars at higher frequencies than women at every career stage, suggesting that even amongst senior faculty men ask questions after seminars more frequently than women  will allow more questions to be asked during a given question period, and could be an alternative method to allow a greater proportion of questions from women. Although we cannot be sure of the causative relationship between the gender of the first questioner on the disparity in the subsequent questions asked, we would recommend that, should the opportunity arise, a female-first question be prioritised. This is because (a) a female-first question was a good predictor of low disparity in the questions asked in our observational data and it is possible that gender stereotype activation is responsible for the observed difference and (b) by choosing a female-first question, a female-friendly environment may be fostered over time.Nmale = 9, Nfemale = 10) or is going off-topic, or a speaker who goes over time. In addition, moderators could be trained to see the whole room , and to maintain as much balance as possible with respect to gender and seniority of question-askers. In the open-ended survey questions, respondents complained that moderators call on people they know or more senior people, overlooking the rest . Although it may seem fair to call on people in the order that they raise their hands, doing so may inadvertently result in fewer women and junior academics asking questions, since they often need more time to formulate questions and work up the nerve. In our observational data, we did not record whether a moderator was present, and we did not record the gender of people who attempted to ask a question; our data cannot elucidate whether women asked fewer questions because fewer women raised their hand or because fewer women were chosen to ask a question. It is likely that the discrepancy results from a combination of both, supporting the potential benefits of an active moderator.Generally, we feel that more could be done through active changes in speakers’, attendees’ and particularly moderators’ behaviour. Having an active, trained moderator may avoid those situations where one audience member seems to be “showing off”  were generated with the idea of increasing women’s visibility, they are likely to benefit everyone. It is not only women who are underrepresented in academia; aspiring and early career academics would also benefit from ethnic minorities being more visible.Our results have implications for redressing the leaky pipeline in academia and indicate that without active steps, the various factors that contribute to women choosing other careers over academia are unlikely to change. Our results support a self-perpetuating feedback loop, where the absence of visible role models influences the behaviour of women in a way that is likely to increase their decision to leave, further reducing their visibility. However, our data show that women are not inherently less likely to ask questions when the conditions are favourable—there is no gender discrepancy when a woman asks the first question. Our suggestions should be seen as aims to create favourable conditions that remove the barriers that restrain anyone from speaking up and being visible.S1 FileThe list of questions and the flow of the Qualtrics survey.(PDF)Click here for additional data file.S2 FileThe instructions that were distributed to the observers who agreed to collect data at their local seminars.(PDF)Click here for additional data file.S3 FileThe tables show a more detailed breakdown of the survey data by gender than what is reported in the text. Included are tables that break down the answers to the questions about (1) why respondents sometimes do not ask questions when they have one; (2) if they believe that women ask fewer questions, why they think that women do not ask questions; and (3) factors they think may be important in encouraging women to ask questions after academic seminars.(PDF)Click here for additional data file.S4 FileThe results of linear mixed models are presented that test for relationships between survey respondents’ gender and seniority on their self-reported frequency of asking questions. A figure is also included that reflect these data.(PDF)Click here for additional data file.


However, while administration of BDNF has shown beneficial effects, BDNF itself has a poor pharmacokinetic profile. Here, we compare two TrkB-targeted BDNF-mimetics, the structural-mimetic, tricyclic dimeric peptide-6 (TDP6) and the non-peptide small molecule TrkB agonist LM22A-4 in a cuprizone model of central demyelination in female mice. Both mimetics promoted remyelination, increasing myelin sheath thickness and oligodendrocyte densities after 1-week recovery. Importantly, LM22A-4 exerts these effects in an oligodendroglial TrkB-dependent manner. However, analysis of TrkB signaling by LM22A-4 suggests rather than direct activation of TrkB, LM22A-4 exerts its effects via indirect transactivation of Trk receptors. Overall, these studies support the therapeutic strategy to selectively targeting TrkB activation to promote remyelination in the brain.The neurotrophin, brain-derived neurotrophic factor (BDNF) promotes central nervous system (CNS) myelination during development and after injury. This is achieved  Its actin vitro  mediated mechanism. Collectively these data further verify that targeting TrkB activation is a cogent strategy to promote myelin repair in the brain and that alternate small molecule mimetic strategies are effective towards this end. Further studies aimed at elucidating the precise mechanism of action are warranted to optimize the therapeutic potential of this approach.Here, we compare the effect of intra-cerebroventricular (ICV) administration of these BDNF mimetics following cuprizone demyelination in mice. Both mimetics promoted myelin repair, increasing myelin sheath thickness, after 1-week recovery. Interestingly, LM22A-4 increased the density of oligodendroglia in the corpus callosum more than the structural BDNF mimetic, TDP6. Importantly, the effects of LM22A-4 were dependent on TrkB, as post-cuprizone treatment with LM22A-4 in mice with conditional deletion of TrkB from oligodendrocytes abrogated both remyelination and increased oligodendroglial density. While this indicates that LM22A-4 promotes myelin repair in a TrkB dependent manner, assessment of TrkB phosphorylation and signaling Female C57BL/6 mice aged 8 weeks were fed 0.2% cuprizone in normal chow  for 6 weeks to induce demyelination. Female mice were used due to the increased incidence of MS in females  vehicle r et al. . BrieflyMice were anesthetized and transcardially perfused with 0.1 M sterile mouse isotonic phosphate buffered saline (PBS) followed by 4% paraformaldehyde (PFA). Brains were collected and post-fixed overnight in 4% PFA. The first millimeter of the right hemisphere from the sagittal midline was selected for EM processing as previously described .Antibodies used were: rat anti-MBP , rabbit anti-Olig2 , mouse anti-CC1 , goat anti-platelet derived growth factor receptor-α , goat anti-Iba1 , mouse anti-glial fibrially acidic protein  and rabbit anti-phosphorylated TrkBg-ratio were analyzed manually using FIJI/ImageJ . For g-ratios at least 100 axons from three mice per group were measured. Resin embedding, sectioning and post-staining and EM imaging were performed at the Peter MacCallum Centre for Advanced Histology and Microscopy.Semi-thin (0.5–0.1 μm) sections of caudal corpus callosum in a sagittal plane were collected on glass slides and stained with 1% toluidine blue to select region of analysis. Ultrathin (0.1 μm) sections were subsequently collected on 3 × 3 mm copper grids and specimens examined using a JEOL 1001 transmission electron microscope. Images were captured with MegaView III CCD cooled camera operated with iTEM AnalySIS software (Olympus Soft Imaging Systems GmbH). A minimum of six distinct fields of view were imaged at 5,000 or 10,000× magnification for each animal. The proportion of myelinated axons, axon diameter and Imaging was performed blind to treatment group and restricted to the caudal corpus callosum approximately −1.1 to −3.0 mm from Bregma in the rostral-caudal direction. Tracts contributing to the dorsal hippocampal commissure were excluded from analysis. For each analysis, a minimum of three sections per animal was imaged.To quantify the level of remyelination images of MBP stained sections were collected with an AxioVision Hr camera attached to a Zeiss Axioplan2 epi-fluorescence microscope under a 20× objective. Uniform exposure times were used. Remaining images were acquired with a Zeiss LSM780 or LSM880 confocal microscope with 405 nm, 488 nm, 561 nm and 633 nm laser lines. For each fluorescent stain uniform settings were used.2 for each section. Data were expressed as a percentage area of positive staining in a single ROI.MBP staining was measured on single channel grayscale images using the default automated threshold function in FIJI/ImageJ and limited to a standard region of interest (ROI) of 625,000 μm2 applied over the caudal corpus callosum. Briefly, for oligodendroglia, Olig2 positive cells were counted first, and then CC1 and/or PDGRα channels were overlaid and double-positive cells were counted. Excess cells, that were Olig2+ only were then visually inspected to confirm negative staining for CC1 and PDGFRα. For pTrkB staining, the Hoechst positive nuclei were counted first and then cells positive for pTrkBS478. Data were expressed as the number of cell/mm2 or proportion out of the total number of nuclei.All cell counts were performed blind to sample identity, manually in FIJI/ImageJ using the Cell Counter plugin using a standard ROI size of 625,000 μmThe Flp-In 293 cell system (ThermoScientific) was used to generate isogenic TrkB expressing HEK293 cell lines where TrkB was integrated into the host cell genome. Flp-In cells express a hygromycin resistance gene, which enables the use of hygromycin (50 μg/μL) to exert selective pressure for cells carrying the Flp-In construct.Briefly, to generate the TrkB expressing HEK293 cell line , Ntrk2 (2 in Dulbecco’s modified eagle medium (DMEM) with 10% fetal bovine serum, 1% L-glutamine, 1% penicillin and 1% streptomyocin. TrkB expression by the isogenic TrkB Flp-In HEK293 cells was verified by Western blot , p44/42 MAPK  and phosphorylated ERK1/2 . All blots shown are representative of at least three independent experiments. Optical density value for each band was determined using FIJI/ImageJ and corrected to loading control and normalized against the relevant control condition.Lysates were separated by SDS-PAGE  and transferred to PVDF membrane and probed with antibodies against TrkB  and pTrkBt-test, one-way analysis of variance (ANOVA) or mixed effect models for repeated measures (GraphPad Prism 8), to test the effect of TrkB agonist treatments with post hoc multiple comparisons as appropriate. Statistical significance was set as p < 0.05.Data were analyzed by unpaired We have previously shown treatment with TDP6, a structural mimetic of BDNF, enhances the number of axons remyelinated and increases myelin sheath thickness during recovery after 6-weeks cuprizone challenge in an oligodendroglial-TrkB dependent manner  the percentage area of MBP+ staining compared to treatment with the aCSF vehicle (p = 0.09) increase towards more remyelinated axons compared to those receiving aCSF, whereas for those receiving LM22A-4, no increase was observed  reduction in mean g-ratio indicative of increased myelin thickness (p = 0.0032), but no change in slope (p = 0.35) indicating that g-ratio was reduced across all axonal diameters, whereas for LM22A-4 there was a significant increase in slope (p = 0.006), indicative of reduced g-ratio and thicker myelin on smaller diameter axons. Collectively, these data are consistent with our previous findings that BDNF-TrkB signaling increases myelin sheath thickness during remyelination in vivo , Olig2+CC1+ post-mitotic oligodendrocytes  and an Olig2+PDGFRα−CC1− intermediate oligodendroglial population (+ oligodendroglia compared to treatment with aCSF vehicle (p < 0.0001). Interestingly, LM22A-4 treatment exerted a more profound effect, increasing the density of Olig2+ oligodendroglia above TDP6 (p = 0.0001). Both TDP6 and LM22A-4 treatments increased the density of Olig2+CC1+ post-mitotic oligodendrocytes compared to aCSF vehicle (p = 0.013), consistent with the pro-differentiation effect of TrkB activation on oligodendroglia. However, assessment of Olig2+PDGFRα+ OPCs indicated LM22A-4 also increased the density of OPCs compared to treatment with TDP6 or aCSF vehicle (p = 0.011). Overall, these data suggest that selectively targeting TrkB during remyelination primarily enhances oligodendroglial differentiation.Next, we assessed oligodendroglial populations in the corpus callosum by co-immunostaining Olig2 with platelet-derived growth factor receptor-alpha (PDGFRα) and CC1 to identify Olig2pulation . Counts +PDGFRα+, Olig2+CC1+ and Olig2+ only cells out of the total Olig2+ population were assessed (+ only cells (26% ± 7%) compared to treatment with either TDP6 (6% ± 8%) or aCSF vehicle . These data suggest LM22A-4 treatment may exert a greater effect than TDP6 to increase the proliferation or survival of oligodendroglia during myelin repair.To examine whether these effects of LM22A-4 and TDP6 were due to alterations in lineage progression during differentiation, the proportion of Olig2assessed . The proS478 with PDGFRα and CC1 to identify OPCs and post-mitotic oligodendrocytes, respectively . Assessment of the proportion of pTrkBS478+ cells positive for the OPC marker PDGFRα indicated treatment with TDP6 and LM22A-4 had no effect on TrkB activation on OPCs (p = 0.21). However, the proportion of TrkBS478+CC1+ post-mitotic oligodendrocytes increased with TDP6 treatment compared to treatment with LM22A-4 (p = 0.046). These data suggest that LM22A-4 can signal via TrkB during remyelination in vivo and is consistent with previous findings that TDP6 stimulates TrkB phosphorylation on CC1+ oligodendrocytes.To determine if TDP6 and LM22A-4 infusions stimulated TrkB phosphorylation on oligodendroglia we performed triple immunolabeling for pTrkBectively . This re+/– × TrkBfl/fl mice in which TrkB is genetically deleted from maturing oligodendrocytes. These mice have a 3-fold reduction in TrkB+ oligodendroglia but adult myelination and oligodendrocyte populations are unaffected . Similarly, EM analysis (p = 0.85) or the mean g-ratio . These data are consistent with oligodendroglial TrkB expression being necessary for LM22A-4 to increase myelin sheath thickness during remyelination.To determine whether the effects of LM22A-4 on myelin sheath thickness and oligodendrocyte populations during myelin repair are dependent on oligodendroglial TrkB expression, we repeated the infusion experiment in CNPaseCreanalysis  revealed+ oligodendroglia . Collectively, these data confirm that the action of LM22A-4 in increasing oligodendroglial populations is dependent on oligodendroglial expressed TrkB.To determine if LM22A-4 treatment increased oligodendroglial populations during remyelination in oligodendroglial TrkB knockout mice, triple immunolabeling for Olig2-PDGFRα-CC1 was performed in the contralateral caudal corpus callosum . Counts S478 with oligodendrocyte markers PDGFRα and CC1 was performed (S478+ cells compared to the aCSF vehicle (p = 0.24). This was also reflected with no change in the proportion of pTrkBS478+ cells positive for oligodendroglial markers PDGFRα  cell line using the Flp-In system . TrkB exin vitro studies. The 293-TrkB cells were treated with 4 nM BDNF or 500 nM LM22A-4 for a time course of 5, 15, 30, 60 and 240 min and assessed for TrkB and ERK1/2 phosphorylation by Western blot (p = 0.012), LM22A-4 did not significantly increase levels of phosphorylated TrkB until 240 min of treatment (p = 0.02). The effects of LM22A-4 treatment on ERK1/2 phosphorylation where levels peaked at 5 min of treatment, and significantly declined compared to BDNF from 15 to 240 min (As determined in the original report characterizing LM22A-4 as functional BDNF mimetic (Massa et al., ern blot . Densitoern blot  revealed 240 min . Collectin vitro analysis comparing the effect of LM22A-4 and BDNF upon TrkB and ERK1/2 phosphorylation which demonstrates that LM22A-4 activates BDNF-TrkB signaling pathways with substantially different kinetics and magnitude, compared to BDNF. Importantly, use of the oligodendroglial TrkB knockout mice to test its therapeutic efficacy provides the first in vivo genetic evidence that LM22A-4 action requires the presence of TrkB, although our data suggests that the mode of TrkB activation may differ to what was previously appreciated.There is an unmet clinical need for therapies that promote myelin repair to halt disease progression in MS. Here, we have shown that targeting oligodendroglial TrkB activation during remyelination increases post-mitotic oligodendrocyte density and myelin sheath thickness. By comparing TDP6, a structural peptide mimic of the Loop 2 region of BDNF, and LM22A-4, a small molecule TrkB agonist we have identified that while both promote myelin repair, they exert disparate effects upon OPCs and intermediate oligodendroglial populations. The effects of both TDP6 and LM22A-4 are dependent on the expression of oligodendroglial TrkB, indicating that while these two molecules putatively activate the same receptor, they may result in biased or differential signaling within oligodendroglia, as indicated by the differences in remyelination profile and progression of oligodendrocyte differentiation. This may reflect our For decades the therapeutic promise of BDNF-TrkB signaling as a treatment for neurodegenerative and demyelinating conditions has been recognized (McTigue et al., in vitro myelinating co-culture or neuronal survival assays (O’Leary and Hughes, in vitro (Ebner et al., The selective influence that LM22A-4 exerted upon OPCs remains to be explained. The doses used for TDP6 and LM22A-4 were determined from reported concentrations required for these compounds to mimic the neurotrophic activity of BDNF in via MAPK/ERK in oligodendrocytes to promote myelin sheath growth (Ishii et al., Both TDP6 and LM22A-4 infusion increased myelin sheath thickness in a manner dependent on oligodendroglial TrkB expression, consistent with the demonstrated effects of TrkB signaling in vivo genetic evidence that LM22A-4 requires TrkB for activity. Although LM22A-4 may exert its effect through direct activity on TrkB, our data and findings by Boltaev et al. (Concerningly, a recent report indicates that LM22A-4 does not activate TrkB at all (Boltaev et al., v et al.  raise thv et al.  used a mv et al. . Notablyv et al. . Whilst v et al.  limited v et al.  and is ain vitro is consistent with Trk-receptor transactivation, where it can take up to 6 h to elicit detectable Trk receptor phosphorylation, and results in signal bias towards Akt (Lee and Chao, via GPCRs (via unidentified GPCRs which engage Src-family kinases, most likely Fyn in oligodendrocytes; this results in intracellular phosphorylation of TrkB receptors confined in transport vesicles and not expressed at the cell surface (in vitro with LM22A-4 treated TrkB-293 cells or primary oligodendrocytes co-treated with Src inhibitors. Full understanding of the mode of action for LM22A-4 is warranted in order to optimize its therapeutic potential. This is particularly important for assessing the potential for off-target effects. It also has implications for the use of LM22A-4 as a tool for probing neurotrophin biology as the conclusions drawn from the assumption it elicits a typical BDNF-TrkB signaling cascade may be misleading. Overall, our hypothesized mode of action for LM22A-4 is parsimonious with known roles of Src-family kinases/Fyn in Trk-transactivation (Rajagopal et al., in vitro and TrkB-dependent remyelination outcomes in vivo.The extended 4-h timeframe required to detect TrkB phosphorylation following LM22A-4 treatment ia GPCRs . Multiplia GPCRs . In our  surface . This covia a peptide mimetic or small molecule partial agonist, enhances myelin repair after a central demyelinating insult by increasing the density of post-mitotic oligodendrocytes and increasing myelin sheath thickness. By directly comparing these two strategies we have also provided necessary insight on the recent controversies about the fidelity of small molecule TrkB agonists. Through in vivo genetic deletion of oligodendroglial TrkB, we have shown LM22A-4 is dependent on TrkB receptor expression for its effects on increasing oligodendroglial populations and myelin thickness, while our in vitro studies have shown that LM22A-4 acts in a delayed manner, potentially through GPCR-mediated TrkB transactivation. Overall, our results further verify that targeting TrkB with small molecule mimetics is a viable therapeutic strategy to promote myelin repair in central demyelinating diseases, such as MS.We have demonstrated targeting TrkB activation on oligodendrocytes either The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.The animal study was reviewed and approved by The Florey Animal Ethics Committee, Florey Institute of Neuroscience and Mental Health.JF, JX and SM conceived and designed the study. JF, HN, RW, AP and SM performed the experiments. JF, HN and SF analyzed the data. SF provided reagents and analytic tools. JF wrote the first draft. JF, HN, SF, JX and SM reviewed and revised the manuscript. All authors read, revised and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The purpose of this work is the structural analysis of graphene oxide (GO) and by means of a new structural model to answer the questions arising from the Lerf–Klinowski and the Lee structural models. Surface functional groups of GO layers and the oxidative debris (OD) stacked on them were investigated after OD was extracted. Analysis was performed successfully using Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-Vis), X-ray photoemission spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDX), Raman spectroscopy, solid-state nuclear magnetic resonance spectroscopy (SSNMR), standardized Boehm potentiometric titration analysis, elemental analysis, X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The analysis showed that graphene oxide layers, as well as oxidative debris contain different functional groups such as phenolic –OH, ketone, lactone, carboxyl, quinone and epoxy. Based on these results, a new structural model for GO layers is proposed, which covers all spectroscopic data and explains the presence of the other oxygen functionalities besides carboxyl, phenolic –OH and epoxy groups. Having unique properties, graphene layers are a quite interesting nanomaterial for industrial applications. However, the generation of graphene layers from graphite is quite expensive, but can be achieved at lower cost via controlled oxidation of graphite . GraphenOxidation of graphite leads to the formation of GO whose structure and the mechanism of formation are still unclear and need further investigation. The structural examination of GO is a necessary step for further functionalization, for the reduction and for determining the structure of its origin-graphene. This helps to reduce the manufacturing expenses of graphene production and to synthesize new types of graphene nanoparticles for future applications such as lithography, biosensoring, drug delivery, etc. ,3. RecenUp to now, several structural models ,9,10,11 The oxidation process also activates a side product formation during GO synthesis. Research on carbon nanotubes has revealed that during oxidation of the nanocarbons oxidative debris (OD) is generated and adsorbs on the nanotubes . OD infl13C NMR data shows the existence of both groups [To the best of our knowledge, so far the proposed structural models for GO leave some open questions. The Lerf–Klinowski model—the most cited model for GO—does not give answers to the following problems: (a) in this model, the carboxyl groups are shown at the edges even though the NMR data do not support the existence of this group, and (b) the hydroxyl groups are located close to each other, leading to the suspicion that there is an electrical instability in the structure ,11. The In this paper, we try to uncover the formation of GO and OD, and to analyze the structure of both and to propose a new structural model for GO layers. The model explains the origin of lactone peaks, the presence of defects and quinone groups, which have never been discussed in the literature before. The experiments showed that during synthesis OD is generated, which increases the difficulties in terms of functional group determination of GO. For successful analysis, OD is extracted from GO layers and precipitated by the Hiradate method . This meThe proposed structure will help to modify the Lerf–Klinowski model and open new directions in the field of graphene-like nanomaterials synthesis.2SO4, ACS, 95.0–98.0%), potassium permanganate  and potassium hydrogen phthalate  were purchased from Alfa Aesar . Sodium nitrate , sodium carbonate , sodium hydroxide solution  and hydrogen chloride  were purchased from Merck  and aluminum chloride  were purchased from Merck Millipore . Hydrogen peroxide  was purchased from Carl Roth . Hydrazine monohydrate , sodium bicarbonate , parafilm®, DAX-8 resin and 50WX2-100 Dowex ion exchanger were purchased from Sigma-Aldrich . Graphite , sulfuric acid  was synthesized from graphite (4.0 g) by Hummers method  in conceCGO was dispersed in 1.0 M NaOH, shaken for 3 hours, refluxed for an hour at 80 °C, vacuum-filtered in order to remove ~30 wt.% oxidative debris (OD) from the CGO dispersion and after the filter cake was washed until a neutral pH, it was freeze-dried on a Gamma 1-16 LSC plus machine. Then, the Na-form of the base-washed graphene oxide (bwGO) was re-dispersed in 1.0 M HCl solution and refluxed at 80 °C for an hour to regenerate the acidic groups ,20.2.0 grams of CGO and 2.0 grams of bwGO were separately dispersed in ultrapure water (1.0 L) in two different flasks, sonicated and reacted with hydrazine monohydrate (10 mL) at 100 °C for 24 hours .3), sodium carbonate (Na2CO3), and sodium hydroxide solution . The vials were nitrogen-purged for 2 hours, sealed and placed in a shaking incubator  at 90 l/min speed for 24 hours. Samples were then filtered through qualitative Grade 1 filter paper  and 10 ± 0.02 mL aliquots were taken by pipette from the solutions. The aliquots of the reaction bases NaHCO3 and NaOH were acidified by 20 mL 0.05 M HCl . However, the aliquots of Na2CO3 were acidified by the addition of 30 mL 0.05 M HCl solution. During titration, all aliquots are covered with a seal of parafilm®, which maintained around the electrode and burette, and bubbled continuously with nitrogen flow to ensure sufficient removal of CO2. 0.05 M NaOH solution was used as a titration base for these experiments and the correctness of NaOH was controlled by potassium hydrogen phthalate (0.2 g) solution in 20 mL Milli-Q ultrapure water (>18 MΩ·cm at 25 °C). Blank samples were also titrated for the accuracy of the measurements.The general titration procedure was carried out by a “standardized” Boehm titration method ,23. A knExfoliation experiments were conducted both for OD-containing graphene oxide and OD-stripped graphene oxide samples, base-washed GO (bwGO), in order to detect the effect of oxidative debris on agglomeration of graphene layers. 10 aqueous dispersions of both graphene samples were prepared ranging from 0.002 wt.% to 0.1 wt.% and their UV absorbance was analyzed on an UVmini-1240 spectrophotometer at 600 nm after sonication in a bath. According to the result the correlation curves were plotted . Using c3 solution to precipitate the colored solution resulting in a clear supernatant. Then, the white dispersion was passed through a 50WX2-100 Dowex ion exchanger column to regenerate the acidic groups. The slimy yellowish sediment was separated from the solution via a separator funnel and freeze-dried (OD-1). The column was eluted successively with 1 L of 0.1 M HCl, 1 L of ultrapure water and 500 mL of 0.1 M NaOH sequentially. All fractions were treated by passing through a 50WX2-100 Dowex ion exchanger corresponding to OD-1 and labelled as OD-2, OD-3 and OD-4, respectively.OD was isolated, fractionated and purified by methods shown elsewhere ,20. The −9 Torr.). Before the experiments, the pre-load chamber was degassed and the graphene oxide samples positioned in the UHV analytics chamber. The analysis were conducted in the area of 700 µm × 300 µm with an acceleration depth of app. 5 nm. All the spectra were calibrated to the C1s signal maintained at 284.5 eV. For the evaluation and validation of the data, CASA-XPS version 2.3.18 was used. Before calculation, the background subtraction (linear or Shirley) was applied for the deconvolution of the different regions of the files. Fourier transform infrared (FTIR) spectra were recorded in attenuated total reflectance (ATR) mode on a Bruker ALPHA FT-IR spectrometer . The transmittance measurements were collected at ambient temperature in a spectral range of 400–4000 cm−1 at a resolution of 4 cm−1 and average of 64 scans. UV-Vis spectra were collected on an UVmini-1240 spectrophotometer  in the absorbance mode with a 10 mm cuvette in the wavelength range of 190–1100 nm applying 0.02 wt.% solutions. Solid-state NMR (SSNMR) experiments were performed on a Bruker Avance II 400 spectrometer  equipped with a 4 mm double resonance probe. Direct excitation 13C magic angle spinning (MAS) NMR spectra were obtained with a 45° pulse length of 2.05 μs and recycle delay of 30 s at an operating frequency of 100.66 MHz. 13C{1H} cross polarization (CP) MAS spectra were acquired using ramped polarization transfer with a 1H 90° pulse length of 4.0 µs, contact time of 1 ms, and a repetition delay of 2 s. Two-pulse phase-modulated (TPPM) decoupling was used during the acquisition for both experiments. All the experiments were conducted with a spinning frequency of 13 kHz at room temperatures. Elemental analysis (EA) was carried out with a EuroEA Elementar CHNSO Analyser . The amounts of carbon, hydrogen, nitrogen, and oxygen were quantitatively determined by the dry combustion method. The standardized Boehm titration method was carried out on a fully automatized Titrino Plus 848  equipped with a 20 mL burette using a 0.05 M NaOH solution. Before experiments, the NaOH solution was standardized with potassium hydrogen phthalate. Raman spectra were obtained using a Senterra  Raman spectrometer equipped with a 532 nm excitation laser and 10 fold objective lens. The results were estimated by extracting each single spectrum and the areas corresponding to the D mode  and the G mode  have been evaluated by two Gaussian fits. A D8 discover X-ray diffractometer  with Cu Kα radiation  was applied for the XRD experiments of the graphene oxide samples at a scanning rate of 1° min−1. Air-tight sample holders  were used to prevent any contaminations of the samples. Thermal gravimetric analysis (TGA) was used to investigate the mass loss of functionalized GO samples as a function of temperature. The analysis was carried out on a Netzsch TG209 F1 Iris instrument  under argon flow (50 mL min−1) from 25 °C to 800 °C at 10 K min−1. Scanning electron microscopy experiments were carried out on a Merlin . Before investigating the surface and cross section morphology of the samples, they were coated with approx. 2 nm Pt using a sputter coating device MED 020 . Secondary electron (SE) images and energy-dispersive X-ray (EDX) spectra were taken at accelerating voltages of 2–3 kV and at 10 kV, respectively. Transmission electron microscopy was carried out on a Tecnai F20 G2  at 120 kV in the bright field mode. Graphene oxide was diluted in chloroform (0.001wt.%). A droplet of 3 µL was put on a Lacey grid and dried.The XPS analysis of the graphene oxide samples was carried out on a Kratos AXIS Ultra DLD spectrophotometer  using an Al-Kα X-ray source operated at 225 W under ultra-high vacuum  decreases, the number of layers reduces [The formation of graphene oxide (GO) depends on several factors such as the concentration of acids, the strength of oxidizer and the decomposition of the intermediate compounds. By following Hummers method, we used highly concentrated sulfuric acid (18 M) and sodium nitrate. The first intercalation occurs immediately by sulfuric acid after adding graphite, graphite bisulfate generates  and thistructure , yieldin reduces  and yiel4 is added to the stage-1 GIC, which has a high intercalation potential in sulfuric acid, it starts to oxidize graphene layers thoroughly and gas evolution is observed when the formed graphite foam heats up to 35 °C. The synthesized graphite-intercalated suspension is not stable when it is in contact with water. These processes finish with the formation of different functional groups  at both peripheral edges and defects on the basal planes. Due to incomplete removal of sulfuric and nitric residuals trapped between the graphene layers [When KMnOe layers , XPS ande layers .The results from O1s spectra of GO samples confirm the C1s results according to which the graphite structure is strongly oxidized and contains different oxygen functionalities. Therefore, the signal around ~530 eV corresponds to quinone, ~532 eV (C=O and O–C=O from carboxylic group), the signal around ~533 eV corresponds to phenolic –OH and epoxy groups, and ~534 eV to lactone groups . O1s XPS−1, assigned to physisorbed water [−1 denotes C–OH stretching vibrations. The high-wavenumber shoulder (~3600 cm−1) observed in the GO spectrum can be identified with the –OH stretching (high frequency) vibration of edge hydroxyl groups [−1 indicates the high hydrophilicity of bwGO. Stretching vibrations of carbonyl groups observed between 1718 and 1738 cm−1 indicate the formation of carboxyl, quinone and six-membered lactone moieties after oxidation of graphite. When GO is freed from OD, a strong peak at 1585 cm−1 appeared showing conjugated benzene rings. –OH bending and C–O stretching vibrations from –COOH groups of the samples can be observed at 1410 or 1370 cm−1 and 1220 or 1230 cm−1, respectively. Epoxy group (C–O–C) stretching vibrations are found at 1045 and 1050 cm−1. After reduction with hydrazine hydrate, these peaks disappear.ed water ,30 by hy−1 attributes to C–OH stretching vibrations mainly located at the edges. The peaks at 1524, 1525, 1526 and 1558 cm−1 belong to benzene rings. The carbonyl (C=O) groups show the stretching vibrations at 1597, 1643 and 1673 cm−1, however the peak of lactone is at 1735 cm−1. –OH bending and C–O stretching vibrations of –COOH groups of OD can be observed at 1400–1425 cm−1 and 1230 cm−1, respectively. The FTIR spectra of OD is shown in The FTIR analysis of OD showed that it also contains oxygen functionalities. The broad band between 3300 and 3500 cm2 clusters and C=C chromophore units. The shoulder peak at ~300 nm corresponds to an n-π* plasmon peak of carboxyl, other carbonyl and hydroxyl auxochromes. The rest of the samples do not show any peaks, which is explained by reduction and substitution of oxygen functionalities. From UV-Vis spectroscopic studies b, it is 13C magic angle spinning (MAS) NMR spectra of GO and bwGO exhibit similar features, showing three dominant signals at 60, 70 and ~130 ppm, which can be assigned to the epoxide (C–O–C), hydroxyl group , and sp2 carbon of graphene, respectively. These characteristics are in agreement with previous reports, demonstrating that graphite is highly oxidized. In bwGO, additional weak signals occur at 104, 175 and 190 ppm. The signals at ~100 ppm can be attributed to a six-/five-membered lactone ring containing hydroxyl groups (lactol) along the periphery of the GO layer [2 carbon are observed at 120 and 130 ppm for bwGO, suggesting the presence of sp2 species in different local environments. 13C MAS NMR results of “washed” graphene oxides confirm that both H– and Na–form contain ketone, quinone, carboxyl, lactone with hydroxyl group (lactol), epoxy and C−OH functional groups, which are distributed around the sp2 graphene structure  is responsible for the signal at 130 ppm. Moreover, the signals corresponding to lactone with -OH moieties are clearly visible in the CP spectra. In contrast, reduced GO showed very different MAS NMR spectra. For both reduced GO (reduced graphene oxide (rGO), and base-washed and reduced graphene oxide (rbwGO)), only a very broad sp2 signal is observed at 120 ppm, which is lower than that of GO and the signals corresponding to other oxidative moieties are not visible. Furthermore, tuning and matching of 1H and 13C channels to obtain MAS NMR spectra of reduced GO were extremely difficult and CP MAS NMR spectra could not be acquired. It is likely that the reduction procedure yields highly conductive materials, which are similar to pure graphite. However, it is not clear whether all the oxidative moieties are completely eliminated due to the poor spectral quality.Solid-state NMR experiments were performed to study the molecular structure of the GO samples . Direct GO layer . The sigtructure . To obtameasured . Since t13C{1H} CP MAS NMR experiment for OD-1 shows that ketone, carboxylic and hydroxyl groups are the mainly distributed functionalities , corresponding to the backscattering of a phonon at a hole point and it is much broader than other bands. This peak is a significant evidence that distinguishes GO samples from graphite. No defects are required for the activation of 2D and 2D’ bands [−1) shape can be useful for determination of the number and orientation of graphene layers. A single sharp peak at 2D-band peak is considered to be an existence of single-layer graphene. During experiments bulk form of graphene oxide samples were used and the results show that GO samples contain many single layers , the degree of order of the structure alters and the intensity of D-band increases in comparison with graphite, and the G-band in case of reduced forms decreases . The D p an edge . AnotherD’ bands . Therefoe layers . This reTesting the thermal stability is also one of the methods to investigate the existence of oxygen containing functionalities through the degradation of the investigated samples. In order to evaluate the oxygen containing functional groups of GO samples thermogravimetric analysis was performed under argon atmosphere. Exfoliation experiments were conducted for the analysis of the effect of oxidative debris  on aggloIn Thorough analysis showed that graphene oxide layers contain a large variety of oxygen functionalities. During synthesis by oxidation reaction, the interlayer distance increases, crystallinity decreases, the number of layers reduces, and soluble oxidative debris and gaseous products are obtained. Titration methods revealed that GO layers contain acidic surface oxygen functionalities, but the basic functional group analysis was not performed due to the possibility of experimental errors which could arise from the π electron cloud of benzene rings. Spectroscopic tools showed that on graphene layers there are also lactone, quinone and ketone sites located mainly at peripheral and defect areas. The suggested structural model explains the existence of NMR peaks corresponding to lactones. The lactone groups have been confirmed also by the standardized Boehm titration method. Experiments showed that in some regions the ketone groups are involved in keto-enol tautomerism with aromatic hydroxyl groups. Based on the obtained results, new possible structures for GO layers and for OD are implemented, the latter acting as a natural surfactant which stabilizes GO layers in dispersions. Unlike previous structural models, our model gives answers to the remaining questions from the Lerf–Klinowski and Lee models mentioned in the introduction. Experiments showed that the small amount of sulfur moiety covalently linked onto the graphene oxide surface is organosulfate, which is negligible for the proposed GO model. The proposed model does not abate the importance of the Lerf–Klinowski model; however, our model makes corrections on the weak points of the formerly proposed structure models. The newly proposed structure may be interesting for those researchers who work on graphene synthesis and functionalization and, moreover, for material scientists in order to prepare new types of composite materials.


The new analysis of a simplified plane model of single-layered graphene is presented in this work as a potential material for reinforcement in ultralight and durable composites. However, owing to the clear literature discrepancies regarding the mechanical properties of graphene, it is extremely difficult to conduct any numerical analysis to design parts of machines and devices made of composites. Therefore, it is necessary to first systemize the analytical and finite element method (FEM) calculations, which will synergize mathematical models, used in the analysis of mechanical properties of graphene sheets, with the very nature of the chemical bond. For this reason, the considered model is a hexagonal mesh simulating the bonds between carbon atoms in graphene. The determination of mechanical properties of graphene was solved using the superposition method and finite element method. The calculation of the graphene tension was performed for two main directions of the graphene arrangement: armchair and zigzag. The computed results were verified and referred to articles and papers in the accessible literature. It was stated that in unloaded flake of graphene, the equilibrium of forces exists; however, owing to changes of inter-atom distance, the inner forces occur, which are responsible for the appearance of strains. The observed trend in “thinning” the parts of machines and devices as well as structural elements, especially in the aviation or armament industry , forces the development of new materials with particular emphasis on composites. Currently, in the world, a lot of attention is devoted to the potential use of graphene as a reinforcement matrix of polymer or polymer–carbon composites. It is assumed that the presence of dispersed graphene flakes will change the mechanics of system cracking. In the case of propagation of cracks and the potential impact of the fracture front with the encountered graphene structure, the energy will be dispersed according to the direction of spatial arrangement of the given graphene flake. This phenomenon can significantly improve the fracture toughness of the entire system, especially when operating in cryogenic conditions, for example, an unmanned aerial vehicle. However, the main problem of the design of composite elements reinforced with graphene is the enormous literature chaos associated with the mechanical parameters of the material, resulting mainly from the fact of projecting the graphene structure onto a lattice model, without taking into account the nature of the chemical bond. This fact undoubtedly makes it impossible to carry out a detailed finite element method (FEM) analysis enabling the design of the morphology and geometry of structural elements. For this reason, we first set up the systematization of mechanical parameters of graphene as an isolated material from the matrix of the composite. 2, and water vapor from the atmosphere with a graphene surface, which is not without significance and requires the stabilization of atmospheric composition on a level of ppm [The two-dimensional nature and carbon hybridization give graphene a unique set of properties, which are electrical ,2,3, optl of ppm .For this reason, a number of scientific centers have attempted to theoretically determine the mechanical properties of graphene . These aGr is characterized by different values. Likely, the values of Poisson’s ratio are different and range, according to the literature, from 0.16 to 0.6. The results in some papers can also be incorrect, for example, in the work of [Gr ≅ 0.49 ÷ 0.6. For the above reason, the author decide to developed an analytical model based on the theory of elasticity, assuming the incompressibility conditions that are used in continuity mechanics. The proposed model was validated by numerical calculation (finite element method software).The authors of ,47 also  work of , PoissonGr and Poisson’s ratio ν for the graphene bar. The model is based on the ideal, chemically pristine structure of graphene, being a single layer of carbon atoms without any structural or chemical defects. We decided that any modelling based on a structure consisting of two or more graphene layers is not justified, as the presence of van der Waals interactions between them dramatically changes the properties of the material.In the present work, the analysis of the plane element of graphene consists of one layer of carbon atoms embedded in polymer matrix. The basic model was hexagonal mesh combining detailed atoms of graphene, which simultaneously are structural nodes . SimilarThe analysis of the graphene tension was carried out for two basic directions .2 . The tension stresses of elementary distance with dimensions a, h and 3/4l amount to σx = x acting in the repeatable element of structure is determined by the following:ave = Y, one can calculate the force Young’s modulus determined on the basis of data from the literature ,25,27,32To simplify, substituting continuous elementary segments of graphene by the bar system, it was assumed that the schematic model includes interactions between atoms in the graphene owing to tensions and bending . The shoThe elongations of system owing to tension are as follows:The elongations of system owing to bending can be written as follows:The strain in the x-direction of the segment caused by bending and tension is expressed as follows:By transforming the strain in the x-direction, one obtains the following:s = ¾ l is as follows:The elongation of the segment of the graphene structure at the length of LThe strain of the alternative segment of the graphene structure with dimensions a, h and length L is as follows:s. Young’s modulus was taken from the literature [Gr = Y = 1.15 TPa = 11.5 nN/Å2. Hence, substituting Young’s modulus of graphene is determined as follows:With the assumption of the bar structure of graphene and substitution of the repeatable element of structure by rectangular shape, one can compare both strains terature ,32,38 asIn this same way as above, the strain in the perpendicular direction was obtained:On the basis of Equations (7) and (11), the substitute Poisson’s ratio of hexagonal structure of graphene can be calculated as follows:By transforming Equation (11), the force of repeatable element of structure for given strain can be calculated as follows:The numerical model of graphene was discussed in many works ,30,32,47x = 14.91 Å, Ly = 14.76 Å, and different diameters of bar ; Equation (12), lines 1–7; and Equation (17), lines 8–14), Young’s modulus of the graphene bar EGr and substitute Poisson’s ratio of the hexagonal structure were estimated. These values are included in Gr of elementary segment consisting of the graphene bar amounts to 13.09 TPa. However, the Poisson’s ratio determined according to the above relation (Equation (12) and (17)) is equal to 0.63 and does not correspond to the real value obtained empirically (νexp = 0.19–0.22). Moreover, this value is greater than 0.5 . Therefore, it is interesting if the assumed scheme of the graphene band with omitting forces between atoms responds to the real graphene. It can be assumed that, for the decrease of strain in the perpendicular direction, inner forces in the atom structure are responsible. Poisson’s ratios obtained in an analytical and numerical way for some mechanical properties are comparable , (11), (15), (18)), the Poisson’s ratio dependent upon tension forces can be written as follows:xy can be determined as follows:Introducing the ratio of forces as follows:xy should equal 0.433 (notice the ratio of forces as well). Hence, it can be concluded that it is necessary to consider the interaction between the graphene atoms, introducing additional bars in the mathematical model of graphene, which is the aim of further work of the authors.For example, to obtain Poisson’s ratio equal to 0.215 in the range of proportionality, and taking the data of graphene, kIn the present work, the new simplified model of the plane graphene structure was investigated. In it, numerous analyses and studies included in the literature of basic mechanical properties (Young’s modulus and Poisson’s ratio) of graphene were presented. The basic model that was used was a hexagonal mesh associated with detailed atoms of graphene, which were simultaneously nodes. The issue was solved on the basis of the superposition method. As in the classic method of the strength analysis, the easy tension of the graphene band  was considered to determine the mechanical properties of bars of the mesh. The study of tension was performed for two basic directions (for the “armchair” and “zigzag” direction). It turned out that, for the analytical and numerical calculations, the relations between Young’s moduli of continuous graphene structure and hexagonal structure relate to homogenization principals established in the theory of orthotropic medium. However, the value of Poisson’s ratio of hexagonal mesh does not correspond to values of 0.06, 0.19, and 0.22–0.55, approved in the literature . Also, i


Acute exacerbation of IPF (AE-IPF) is associated with high mortality. We studied changes in pathogen involvement during AE-IPF and explored a possible role of infection in AE-IPF.  Our purpose is to investigate the role of infection in AE-IPF.  Overall, we recruited 170 IPF patients  and 70 controls at Shanghai Pulmonary Hospital. Specific IgM against microbial pathogens and pathogens in sputum were assessed. RNA sequences of pathogens in nasopharyngeal swab of IPF patients were detected by PathChip. A panel of serum parameters reflecting immune function were assessed. γ, MIG, IL-17, and IL-9) vs. stable IPF and controls. Mortality of AE-IPF in one year (39.5%) was higher compared to stable IPF (28.7%).Conclusions. IPF patients had different colonization with pathogens in sputum and nasopharyngeal swabs; they also displayed abnormally activated immune response, which was exacerbated during AE-IPF. Antiviral/bacterial IgM was higher in IPF vs. controls and in AE-IPF vs. stable IPF. Thirty-eight different bacterial strains were detected in IPF patient sputum. Bacteria-positive results were found in 9/48 (18.8%) of AE-IPF and in 26/122 (21.3%) stable IPF. Fifty-seven different viruses were detected in nasopharyngeal swabs of IPF patients. Virus-positive nasopharyngeal swabs were found in 18/30 (60%) of tested AE-IPF and in 13/30 (43.3%) of stable IPF. AE-IPF showed increased inflammatory cytokines (IL-6, IFN- Idiopathic pulmonary fibrosis (IPF) is a fatal disease with unknown etiology, characterized by a radiographic and pathologic pattern of usual interstitial pneumonia (UIP). Patients with IPF have a median survival time of 3 years , followiThe previously used definition of AE-IPF  has provIn this study, we asked whether IPF patients may be particularly sensitive to common infectious triggers, some of them then developing AE-IPF. We aimed to investigate the association of AE-IPF with microbial colonization and/or latent infection.We prospectively enrolled 170 definite IPF patients  who were managed at the Shanghai Pulmonary Hospital between April 2005 and August 2012, either in the outpatient or inpatient department . The diaFasting venous blood specimens were sampled from IPF patients and healthy controls on the day after admission or on the day of medical examination. All IPF patients underwent pulmonary function tests before acute exacerbation. Arterial blood was sampled from IPF patients (at room air) on the day of admission to perform arterial blood gas analysis.The severity of pulmonary fibrosis and lesion involvement was assessed according to the Helbich quantified HRCT scoring system , and groLegionella, Mycoplasma pneumoniae, Q fever Rickettsia, Chlamydia pneumoniae, Adenovirus, respiratory syncytial virus, influenza A virus, influenza B virus, and parainfluenza virus. The cytomegalovirus (CVM) IgM ELISA kit  was used to detect the IgM antibody response to CMV.Peripheral blood was centrifuged in a procoagulant tube at 3000 rpm for 15 min, and serum was separated for determination of pathogen-specific IgM. The immunofluorescence-based Pneumoslide IgM kit  was used to detect specific IgM antibody against Spontaneous sputum samples were collected from all IPF patients in the early morning for 3 consecutive days after hospital admission, and samples were immediately (within 30 min) sent for sputum culturing. Qualified samples needed to meet the following criteria: (1) squamous cells < 10 cells/low magnification; (2) white blood cells (WBC) > 25 cells/low magnification; or (3) the squamous cell: WBC ratio < 1 : 2.5. Consistent results obtained more than twice of each sputum culture from the same patient were considered as a single result. If the results of the same patient were inconsistent, sputum cultures were performed again.Random nasopharyngeal swab samples were selected with the help of computational random number generators. RNA was extracted from nasopharyngeal swab stored in virus sampling tube using QIAamp Viral RNA Mini Kits  according to the manufacturer's instructions. The RNA was stored at -80°C. The PathChip  can detect over 70,000 full-genome RNA sequences of pathogens  clinically relevant to humans . The RNAn = 6), stable IPF (n = 6), and AE-IPF (n = 6) were profiled using the RayBio® L-Series 507 Biotin Label-based Antibody Array system . The next step, we designed a new array  by using 30 different cytokines selected according to our observations and literature reports, and 60 samples  were examined. The array was used by following the manufacturer's instructions to measure the following cytokines: BMP-7, EOT, Flt-3L, G-CSF, GM-CSF, ICAM-1, IFN-γ, IL-10, IL-12 p40, IL-12 p70, IL-13, IL-15, IL-17, IL-1α, IL-1β, IL-22, IL-4, IL-6, IL-7, IL-9, leptin, LIGHT, MCP-1, MCP-2, MIG, MMP-7, NT-3, PDGF-BB, TGF-β1, and TSLP. After developing, slides were scanned, and the images were processed and quantified using Axon GenePix4400A microarray scanners. Intensity was normalized to internal positive controls for comparison.The first step, soluble proteins from the serum samples of healthy controls . In AE-IPF patients, 19 patients  died within one year after the AE episode; of them, 57.9% (11 of 19) died within one month. In stable IPF patients, the 1-year mortality was 28.7% (35/122), which was significantly lower than that of AE-IPF (p = 0.041). All deaths occurred within the follow-up period (Sept. 2014).A total of 170 patients with IPF were enrolled from Shanghai Pulmonary Hospital. Clinical characteristics of the patients are summarized in p = 0.002). AE-IPF patients showed a slightly higher neutrophil percentage than stable IPF, which could indicate latent infections in patients developing AE-IPF. Compared with stable IPF, AE-IPF patients had significantly lower arterial oxygen pressure (p = 0.001) and saturation (p < 0.0001), while pulmonary function tests indicated that AE-IPF patients had worse FVC (% pred) (p = 0.02), worse FEV1 (% pred) (p = 0.007), and increased FEV1/FVC ratio (p = 0.01). The HRCT scores of AE-IPF patients were significantly higher than that of stable IPF patients (p = 0.001).IPF patients had significantly higher total WBC counts and neutrophils than healthy controls  and healthy controls (p = 0.0016), and a nonsignificant difference in the positive rates of IgM was seen when comparing stable IPF and healthy controls.Detection of IgM antibody against 10 common pathogens revealed that mycoplasma was the pathogen with the highest IgM positive rate (12.2%) in the serum of AE-IPF patients, followed by legionella (7.3%), adenovirus (7.3%), and RSV (4.9%). In the serum of stable IPF patients, mycoplasma also showed the highest IgM positive rate (5.6%), followed by legionella (4.6%), RSV (3.7%), and influenza B viruses (2.8%). For healthy controls, only mycoplasma (7.1%), adenovirus (2.9%), and parainfluenza virus (1.4%) were positive . The totKlebsiella pneumonia was most abundant (26.3%), followed by Mycobacterium tuberculosis (21.1%), and Acinetobacter baumannii (10.5%). The total detection rates in AE-IPF and stable IPF patients were 18.8% and 21.3%, respectively (The categories and constituent ratio of pathogens detected in the sputum of 170 IPF cases are listed in ectively . There wp = 0.1965). In the AE-IPF group, HHV (human herpesvirus) and INF A (influenza virus A) accounted for 37.5% (15/40) and 30% (12/40), respectively. In stable IPF patients, HHV and HRV (human rhinovirus) were both accounted for 23.5%.The categories and constituent ratio of viruses detected by PathGEN® PathChip Kit in the throat swab of 30 stable IPF and 30 AE-IPF cases are listed in n = 20), stable IPF (n = 20), and AE-IPF patients (n = 20) using RayBiotech QAH-CUST microarray. When compared with controls and stable IPF patients, AE-IPF patients showed significant increases in the levels of IL-6, IFN-γ, MIG, IL-17, and IL-9 .IPF is a disease of unknown etiology that is usually progressive, either in a linear or stepwise manner . In thisThe earlier definition of AE-IPF warranted exclusion of acute infections of the lower respiratory tract, ideally by tracheobronchial aspirate or bronchoscopy. This is not always and consistently done across centers, not even within the rigid setting of clinical trials in which AE-IPF were used as endpoint . Our cliγ, MIG, IL-17, and IL-9 are important inflammatory cytokines in the anti-infection immune response [In our study, we basically used the AE-IPF definition proposed in the 2016 document , even ifresponse –44 and wOur study has several limitations. The data is observational and even with the significant increase in the anti-infectious cytokines, we have no clear evidence to support that the infections caused AE-IPF through this immune response. However, our group had reported the relationship between virus infection and AE-IPF using animal model  recentlyTaken together, our data suggest that latent infections and/or abnormal colonization in the lungs of IPF patients and “common cold”-associated virus infections together with a significantly elevated anti-infection immune response might be a common triggering factor for AE-IPF.


In this paper, acquisition of the valence Compton profile of few-layer graphene using electron energy-loss spectroscopy at large scattering angle is reported. The experimental Compton profile is compared with the corresponding theoretical profile, calculated using the full-potential linearized augmented plane wave method based on the local-density approximation. Good agreement exists between the theoretical calculation and experiment. The graphene profile indicates a substantially greater delocalization of the ground state charge density compared to that of graphite. The quantity measured in an EELS experiment is the double differential scattering cross-section2R is the Rydberg energy; k0 and k are wavevectors of the fast electron before and after scattering, respectively; df/dE is the generalized oscillator strength (GOS) that is a function of the momentum transfer q and the energy loss E. The physical information of the GOS depends on the q and E regimes5. For small q, the GOS contains information about the unoccupied density of states: On the one hand, when r represents the electron orbital radius, the dipole-allowed transitions dominate the energy-loss spectrum. On the other hand, when 7. The GOS provides information about the ground-state electron momentum density, known as electron Compton scattering from solids (ECOSS)8. The EELS is acquired by tilting the incident beam through a few degrees (~5°) for ECOSS measurement so that only large-angle-scattered electrons are collected10. The ECOSS technique has proven to be an important alternative for experimentally investigating the electron momentum density distribution of nanomaterials12.Electron energy-loss spectroscopy (EELS) is a powerful tool to study the electronic structure properties of solids15. In general, understanding the many fascinating properties of graphene requires a detailed knowledge of its electronic structure. Although sufficient work is available regarding the structural, electronic and optical properties of graphene, the experiment and theoretical electron momentum density of graphene still remains unexplored21.The past few years have seen a prodigious amount of research on nanocarbon materials, and on graphene in particular26. The shape of the Compton profile is sensitive to the state of the valence electrons of the atoms which combine to form the condensed phase of the material7. It offers a straight way to test the band structure calculations for different materials25. Since most of the theoretical studies of the electronic structure of graphene have been carried out using the density functional theory (DFT) and the local density approximation (LDA), it is important to test the applicability of these approaches. In this work, the Compton scattering experiments on graphene are reported using the ECOSS technique. The valence Compton profile of graphene is obtained by subtracting the theoretical core-electron Compton scattering from the experimental profile. In addition, we calculated the valence Compton profile of graphene using a full-potential linearized augmented plane wave (FLAPW) within the framework of the density functional theory27. The experimental results are in excellent agreement with the theoretical Compton profile of graphene. Further, the graphene Compton profile is compared with that of graphite both experimentally and theoretically, where a reasonable agreement between experiment and theory was achieved.Compton scattering is a well-established and unique tool to study the electronic structure of materials via their electron momentum density28. A characterization angle of 15 mrad and the ratio of quasi-elastic to inelastic scattering cross-sections of 0.8 were chosen for the background simulation. The core Compton scattering spectrum as a function of energy loss (eV) was determined using the Hartree-Slater method at a momentum transfer of 9.25 a.u., as shown in Fig. 29.The raw electron Compton scattering spectrum of graphene by recording EELS at the scattering angle of ~73 mrad is shown in Fig. pq). The theoretical profile was convolved with the experimental resolution function. Details of our experimental data processing procedure have been previously described30. The Compton profile of graphene was calculated as a longer lattice constant along the c-axis (20 Å), while graphite was 6.77 Å. In order to compare experimental results with our calculations, the theoretical in-plane profile was obtained by averaging the Compton profiles calculated for 31. The experimental Compton profile of graphite was obtained under the same experimental conditions as that of graphene by ECOSS technique32. Figure pq = 0.75 a.u. may arise from π-bonding anisotropy in graphite33. The observed narrowness of the graphene Compton profile with respect to that of graphite can be interpreted as a larger delocalization of valence electrons in the graphene than in graphite. In graphite, the electrons form π bonds between two layers via the weak van der Walls forces. In graphene, however, these π bonds are incomplete and the electrons forming such bonds behave almost as delocalized electrons, associated with the curving graphene layer by means of dangling bonds.Figure To summarize, the valence Compton profile of few-layer graphene was obtained by measuring the EELS in a TEM and comparing it with the FLAPW calculation based on the local-density approximation band theory framework. The experimental results are in good agreement with the theoretical calculations. The differences between the Compton profiles of graphene and graphite obtained both experimentally and via FLAPW calculations showed good agreement. Both the experimental and theoretical results indicate that the ground-state electronic density is more delocalization in graphene than in graphite.https://graphene-supermarket.com) were used, where the graphene was produced by chemical vapor deposition and consisted of 1–6 layers. The EELS technique was used to measure the electron Compton scattering of the few-layer graphene film suspended over the TEM holey carbon film holes, where the TEM was in the diffraction mode with a parallel incident beam. Though the operating energy of the incident beam was well above the knock-on threshold of graphene, the dose was spread over a sufficiently large area so that the electron beam damage to the samples was not expected to be significant over the short acquisition times (30 s) used herein34. The camera length was 300 mm and the spectrometer entrance was set to 2.5 mm. Further, the collection semi-angle was 2.44 mrad and the corresponding momentum transfer uncertainty was 0.16 a.u. The energy resolution was determined from the full width at half maximum of the zero-loss peak at 3 eV, which when converted to a momentum resolution was found to be 0.012 a.u. The total momentum resolution was estimated to be on the order of 0.1 a.u.10. To reduce the statistical error, more than five spectra were acquired from the sample, whereupon additional measurements were obtained to determine morphological variations on the sample to ensure that serious radiation damage had not occurred.The experiments were performed with a transmission electron microscope  equipped with an imaging filter (Gatan Tridiem), where the TEM was operated at an accelerating voltage of 120 kV. Commercially-available 300-mesh copper TEM grids with amorphous holey carbon film supporting transferred graphene (Graphene Supermarket,


Aberrations in the expression of the transcription factor forkhead box C2 (FOXC2) have been linked to a number of malignancies. Here, we characterized the relationship between FOXC2 and cancer progression by conducting a meta-analysis of studies that reported the frequency of FOXC2 expression in tumors of different stages .Relevant articles were retrieved from the Medline database by searching for the terms “FOXC2” and “cancer”; then, the retrieved articles were reviewed individually, and studies that were of multivariate cohort design, evaluated FOXC2 expression via immunohistochemical staining, and assessed the relationship between FOXC2 expression and cancer T-stage were included in our meta-analysis.p = 0.004).Our search terms identified 139 studies, 9 of which met all inclusion criteria. A total of 1433 tumor samples were evaluated in the 9 studies; 596 samples were from early-stage (T1-T2) tumors, and 838 were from late-stage (T3-T4) tumors. FOXC2 was expressed in 46.0% of all samples, in 32.4% of early-stage tumor samples, and in 55.6% of late-stage tumor samples. When calculated relative to early-stage samples, the pooled risk for FOXC2 expression in late-stage samples was 1.367 (95% CI = 1.103–1.695, The results from our meta-analysis of 9 studies indicate that FOXC2 is 36.7% more likely to be expressed in late-stage tumors than in early-stage tumors. Cancer can arise via the accumulation of genetic mutations, which cause the cancer cells to proliferate without restriction, leading to tumor growth and metastasis ; howeverThe goal of this study was to characterize the relationship between FOXC2 expression and cancer progression by conducting a meta-analysis of studies that reported the frequency of FOXC2 expression in tumors of different stages  [Our meta-analysis was conducted and reported according to guidelines provided in the PRISMA for Network Meta-Analyses (PRISMA-NMA) checklist . To reduThe initial Medline search was conducted on and identified 139 articles ; 122 of Each individual study had selection bias, because the study samples were not randomly selected. Furthermore, the immunohistochemical methods used to detect FOXC2 expression varied across the studies, and this variation could also induce bias.p < 0.001)  of T1-T2 samples and in 80.9% (186/230) T3-T4 samples (n = 270) were evaluated from the tumors of patients with breast cancer; 186 samples were from stage T1-T2 tumors, 84 samples were from stage T3-T4 tumors, and the proportion of FOXC2-positive tumors in each group was 28.5% (53/186) and 20.2% (17/84), respectively. The authors found that FOXC2 expression was associated with a higher tumor grade (p = 0.178), and that patients with high FOXC2 expression had poorer prognoses and survival.Samples (n = 163) were evaluated from the tumors of patients with oral squamous carcinoma; 31.0% (9/29) of stage T1 tumor samples, 22.4% (17/76) of stage T2 tumor samples, 22.9% (8/35) of stage T3 tumor samples, and 17.4% (4/23) of stage T4 tumor samples were positive for FOXC2 expression. The proportion of FOXC2 positive was not significant (p = 0.6915). Data from the T1 and T2 tumors and from the T3 and T4 tumors were combined for our meta-analysis; thus, FOXC2 expression was observed in 24.8% (26/105) of T1-T2 samples and in 20.7% (12/58) of T3-T4 samples.Samples (n = 62) were evaluated from the tumors of patients with renal cell carcinoma; 80% of cancerous renal tissue samples were positive for FOXC2 expression. Among the 62 tumor samples, the proportion of FOXC2-positive cells was significantly (p = 0.0005) for T3-T4 tumors  than for T1-T2 samples .Samples (n = 61) were evaluated from the tumors of patients with oral squamous cell carcinoma; 45 of the samples were from T1-T2 tumors, and 16 samples were from T3-T4 tumors. The frequency of FOXC2 expression was 31.1% (14/45) in T1-T2 tumor samples and 50% (8/16) in T3-T4 tumor samples.Samples (n = 206) were evaluated from the tumors of patients with colorectal cancer. The samples were divided into three groups: stage T1-T2 (n = 38), stage T3 (n = 110), and stage T4 (n = 58). The frequency of FOXC2 expression in the three groups was 42.1% (16/38), 48% (53/110), and 65.5% (38/58), respectively, and differed significantly across the three groups (p = 0.015). For our meta-analysis, data from the T3 and T4 tumors were summed, indicating that FOXC2 expression was observed in 54.2% (91/168) of T3-T4 samples.Samples (n = 77) were evaluated from the tumors of patients with cholangiocarcinoma. The samples were categorized as stage T1-T2 (n = 40) or stage T3-T4 (n = 37), and the frequency of FOXC2 expression in the two groups was 17.5% (7/40) and 29.7% (11/37), respectively.Samples (n = 84) were evaluated from the tumors of patients with hepatocellular carcinoma. Data for each of the four tumor stages were assessed independently; 37.5% (3/8) of stage T1 samples, 20% (5/25) of stage T2 samples, 31.7% (13/41) of stage T3 samples, and 50% (5/10) of stage T4 samples were positive for FOXC2 expression. Thus, when data from the T1 and T2 tumors and from the T3 and T4 tumors were combined for our meta-analysis, FOXC2 expression was observed in 24.2% (8/33) of T1-T2 samples and in 35.3% (18/51) of T3-T4 samples.Samples (n = 185) were evaluated from the tumors of patients with colon cancer. The frequency of FOXC2 expression differed significantly (p = 0.011) across the four tumor stages; 0% (0/7) of T1 samples, 39% (9/23) of T2 samples, 50 % (37/73) of T3 samples, and 59% (49/82) of T4 samples were positive for FOXC2 expression. Thus, when summed for our meta-analysis, FOXC2 expression was observed in 30.0% (9/30) of T1-T2 samples and in 55.5% (86/155) of T3-T4 samples.Samples   Thus, the results from our meta-analysis indicate that FOXC2 is 36.7% more likely to be expressed in stage T3-T4 tumors than in stage T1-T2 tumors  of all samples, in 32.4% (193/596) of T1-T2 samples, and in 55.6% (466/838) of stage T3-T4 samples. Within the individual studies, the relative risk of FOXC2 expression tended to be greater in T3-T4 tumor samples than in T1-T2 tumor samples  tumors than in early stage (T1-T2) tumors and, consequently, that FOXC2 may be a marker for the T-stage of cancer.