Current research indicates deficits in cognitive function together with widespread changes in brain activity following long-term cannabis use. In particular, cannabis use has been associated with excessive spectral power of the alpha rhythm (8–12 Hz), which is also known to be modulated during attentional states. Recent neuroimaging studies have linked heavy cannabis use with structural and metabolic changes in the brain; however, the functional consequences of these changes are still not fully characterized. This study investigated the electrophysiological and behavioral correlates of cannabis dependence by comparing patients with a cannabis use disorder  with cannabis nonuser controls (N = 24), using resting state electroencephalogram (EEG) source-imaging. In addition to evaluating mean differences between groups, we also explored whether particular EEG patterns were associated with individual cognitive-behavioral measures. First, we replicated historical findings of elevated levels of (relative) alpha rhythm in CUD patients compared with controls and located these abnormalities to mainly prefrontal cortical regions. Importantly, we observed a significant negative correlation between alpha spectral power in several cortical regions and individual attentional performance in the Go/NoGo task. Because such relationship was absent in the nonuser control group, our results suggest that reduced prefrontal cortical activation  could be partly responsible for the reported cognitive impairments in CUD. Our findings support the use of electroencephalography as a noninvasive and cost-effective tool for biomarker discovery in substance abuse and have the potential of directly informing future intervention strategies.The online version contains supplementary material available at 10.3758/s13415-022-01016-w. Delta-9-tetrahydrocannabinol (THC), the principal constituent of cannabis, binds to cannabinoid-type 1 receptors (CB1R), which are most abundant in the frontal cortex, hippocampus, and cerebellum  provides a valuable electrophysiological method for noninvasive exploration of high temporal resolution  electrocortical fluctuations . This was achieved by examining the potential EEG abnormalities of CUD patients compared with nonusers and comparing measures of cognitive performance in the Go/NoGo task between CUD patients and nonusers controls.We first hypothesized that patients with CUD exhibit significant changes in EEG spectral profile and attentional deficits compared with cannabis nonuser controls and that these brain-behavioral measures may be cross-correlated. Specifically, based on earlier work  and 24 age- and sex-matched healthy controls without history of cannabis use .A clinical screening interview was conducted with each potential participant to determine eligibility for the study. Participants with past or current psychiatric or neurological disorders, past or current clinically significant medical condition and central nervous system disorder, addictive disorders , or current psychotropic treatment were excluded.Control participants followed the same screening interview as patients and were selected to match cannabis users on key demographics . Demographics parameters differences between CUD patients and nonuser controls were not significant except degree of cannabis use as expected , a short screening questionnaire to evaluate the use of different substances  and consequences associated . The task included 1 practice block, which contains 24 trials with 20 Go and 4 NoGo stimuli, and 2 experimental blocks in which design was repeated 20 times. At the end of the task, the subject’s performance parameters were recorded, as the numbers of Go and No-Go responses for each condition: correct, incorrect on a NoGo, incorrect on a Go, and missed.Results were analysed using the framework of Signal Detection theory  according to the 10-20 international system. The ground electrode was placed on the scalp equidistant between Fpz and Fz. Electrical signals were amplified with the 21-channel Mitsar EEG system , and all electrode impedances was set below 5 kOhm. For online recording, electrodes were referenced to linked earlobes, and then the common average reference was calculated offline before further analysis.EEG data were continuously recorded at a sampling rate of 250 Hz and then filtered with an offline bandpass filter of 0.5-50 Hz.Quantitative EEGs were obtained after subjects passed screening examination. A multichannel EEG cap was used to measure whole-scalp activity in baseline recording, consisted of resting state measurement of 3 minutes under eyes open and closed conditions. The scalp signals were recorded using a 19 Ag/AgCl electrodes cap . We removed stereotypical eye movement artifacts, such as saccades or blinking, using Infomax ICA decomposition . In line with previous approaches using a similar EEG setup in clinical populations  and a Hanning windowing function . Based on the total number of collected and averaged epochs per subject, quantitative estimates of Absolute Power  was computed, and percent Relative Power  was calculated as the ratio of the mean power in a specific EEG band and the broadband power (1-45 Hz).p < 0.05 threshold across the four EEG bands. Individual attentional performance was measured by performance on the Go/NoGo task, using the Hit rate variable. Data from CUD group was subsequently used in a between-subject regression analysis (with a p < 0.05 threshold) using the SPM toolbox.Source-space (voxel-wise) of band-limited surface spectral power were export by using the Brainstorm Toolbox. Statistical comparisons between CUD patients and controls were performed using Statistical Parametric Mapping toolbox (SPM12) via independent two-tailed t-tests with a Between-group comparisons in inhibitory control (commission errors) and attention (hit rate) in the Go/NoGo task were performed by using independent two-tailed t-tests with a probability of type I error (α) = 0.05. One control subject outlier was excluded from the analysis, because his EEG power measure was 2 standard deviations above the sample’s mean.Considering the previous studies reporting alpha band abnormalities in CUD . On the other hand, relative theta and beta power were reduced within the temporal lobe . No significant differences were found in the delta band.CUD was associated with relative alpha power increases within frontocentral regions, with a maximum in the sensorimotor cortex  or hit rate (p > 0.05) between CUD patients and controls, indicating that inhibitory control and sustained attention were similar between the two groups.There were no statistical differences in either commission errors (p < 0.05) with attentional performance indicates that decrease Hit rate accuracy was predicted by increases in alpha power within a homologous set of nodes in somatosensory and motor Brodmann areas. This is consistent with the literature linking attention with frontal activity in healthy subjects  in the Go/NoGo task. As seen in Fig. In other words, was there evidence for a selective disruption of alpha rhythm in cannabis addiction that impacted attentional function? Consequently, we performed a conjunction of tests  to identify the overlap, if any, between the statistically-significant (p < 0.05) group-level relative alpha power differences ).Interestingly, we identified a statistically reliable overlap within alpha band and attentional performance in the The present study focused on the relationship between alpha oscillations and attention in adult cannabis users, using an experimental design with resting EEG and behavioral task conditions. By examining the cortical dynamics of CUD patients using EEG signals, and calculating spectral power profiles, our results indicate that: 1) CUD patients showed increased alpha power over frontal regions compared to controls, and 2) their attentional performance (hit accuracy) inversely correlates with individual levels of frontal alpha power in the sensorimotor cortex (Table 1).It has previously been reported that theta/alpha oscillations reflect modulations of neuronal processing within cortical circuits . This excess of alpha rhythmicity may be associated with cortical inhibition or reduced information processing  and their cognitive-behavioral performance. We confirmed previous work showing that cannabis users display statistically reduced cortical activation, indexed by excessive alpha power. This loss of “alpha desynchronization” suggests stronger inhibition of ongoing neuronal activity that may interrupt attentional processing. Importantly, we found that this abnormal EEG pattern predominates over frontal cortical regions and correlates with detection performance  on an attentional task.Thus, potentially treating cannabis abusers with therapies that decrease alpha rhythm production, such as neurofeedback, may be a promising approach for mitigating cognitive deficits and/or preventing drug relapse, as it is already used for improving attentional performance in ADHD


We argue that regular biomarkers could be used safely as partof an antifungal stewardship strategy to reduce antifungal use, by both screening forinfection in patients not on prophylaxis and ruling out infection in patients treatedempirically.Mould-active antifungal prophylaxis is frequently used to prevent invasive fungalinfection in patients with acute leukaemia being treated with intensive chemotherapy.Invasive fungal infections are difficult to diagnose, and despite the use of prophylaxis ahigh proportion of patients still receive therapeutic antifungals. Antifungal medicationshave important interactions, can cause serious adverse events, and may drive theproliferation of antifungal resistance. The use of two biomarkers, such as galactomannanin combination with the less-specific β- Additionally, such investigations maynot be deliverable in a critically ill, immunocompromised patient. Less invasive diagnosticmethods, such as blood biomarkers, are highly desirable and increasingly used, but acontroversial question is whether biomarker surveillance during periods of risk can replacemould-active antifungal prophylaxis. We believe it can.The diagnosis of an invasive fungal infection (IFI) in patients undergoing intensivechemotherapy for acute leukaemia is undoubtedly difficult.3 The IDSA,4European Conference of Infections in Leukaemia (ECIL),5 German Society of Haematology and Medical Oncology(DGHO)6 and ESCMID7 all recommend using posaconazole inhigh-risk patients predicted to have a prolonged episode of neutropenia related to intensivechemotherapy. Meta-analyses have also shown reductions in IFI and IFI-related mortality withthe use of mould-active prophylaxis versus either fluconazole or placebo, but have not shown areduction in overall mortality,8including a posaconazole-specific study from 2020.9A landmark study in 2007 found that patients with acute myeloid leukaemia or myelodysplasticsyndrome who received antifungal prophylaxis using posaconazole, compared with fluconazole oritraconazole, had significantly lower incidences of invasive aspergillosis (2% versus 8%) and100 day all-cause mortality (14% versus 21%).et al.trial3 with only 5% deemed tohave had probable or proven IFI. In some reports, exposure to therapeutic antifungals isnearly 50% in real-world populations of intensive chemotherapy-treated patients.10 Even in high-risk patients, continuingsuch an approach in the era of emerging antimicrobial resistance is highly undesirable,particularly as challenging to diagnose and treat breakthrough infections still occur despiteprophylaxis.11A fundamental problem with prophylaxis is that despite all patients receiving an antifungalduring high-risk periods, a high proportion of patients still receive additional therapeuticantifungals: 27% in the posaconazole group in the Cornely Candida andAspergillus species.12 MDR Candida auris is an exemplar of a pathogenic fungusthat can become endemic both in community and healthcare environments, results in considerableinfection control challenges, and causes life-threatening infections in vulnerablepopulations. We know that the use of antifungals promotes colonization with resistantfungi,13 and our knowledge ofthe unrelenting march of antibiotic resistance in Gram-negative bacteria andMycobacterium tuberculosis should forewarn us that we need to optimizeantifungal use now. Global and national antimicrobial stewardship programmes are increasinglyacknowledging the importance of including antifungal agents in their strategies.14Resistance to azoles is increasing for both 15Posaconazole specifically requires an acidic environment for gastrointestinal absorption,which can be problematic as proton-pump inhibitors are commonly taken by patients with acuteleukaemia.16 Interactions canalso occur due to inhibition of cytochrome P450 3A4, notably with an increased incidence ofvincristine toxicity in patients taking posaconazole.17 To avoid such interactions alternative antifungalprophylaxis, such as weekly liposomal amphotericin, with less convincing efficacy data, isrequired. In our experience, patients also prefer to take fewer medications.Antifungal drugs also carry a risk of adverse events, such as gastrointestinal disturbancesand hepatic and cardiac toxicity.18 A number of newantifungals are in development, but these will be expensive. A non-prophylaxis approach,however, must undoubtedly ensure that drug-related savings are not lost by an increase ininfections or poorer outcomes.Preventing and treating IFIs is expensive. In our own hospital the use of posaconazole andliposomal amphotericin accounts for two-thirds of the hospital’s total antifungal costs. AGerman study found the incremental cost of treatment of an IFI was €21 063, 36% of which wasthe antifungal.7 and is used in somecentres that have not experienced an increased incidence of IFIs. A surveillance approachcould involve regular, twice-weekly biomarker monitoring with clinical assessment and furtherbiomarker-driven investigation for IFI prior to the onset of clinical symptoms. Given arelatively high negative predictive value, negative surveillance biomarkers could providereassurance to clinicians that empirical antifungal therapy is not required in the presence ofprolonged fever and encourage cessation of empirical therapy when the risk of IFI has beenassessed as low. There is a lack of high-quality evidence to allow robust comparisons ofantifungal prophylaxis versus diagnostic approaches without prophylaxis, but even when usedwith prophylaxis diagnostic strategies in the empirical setting appear to reduce antifungalprescribing safely. A previous prospective observational study used thrice-weeklygalactomannan (GM) testing combined with CT scanning (± BAL) in patients treated withfluconazole prophylaxis. Mould-active antifungal treatment was only advised if there was bothmycological and radiological evidence of IFI; this approach found substantial reductions inantifungal use during episodes of neutropenic fever (from 35% to 7.7%).19 The randomized trial by Morrisseyet al.,20 whichcompared a standard culture and histology diagnostic strategy versus a GM plusAspergillus PCR approach, supports these findings with an associatedreduction in empirical antifungal therapy from 32% to 15%. Importantly, the increase inprobable fungal infection in the GM/PCR group was thought to be due to the diagnosticinferiority of the traditional approach and was not associated with an increase in all causeof IFI-related mortality. A multicentre Spanish study compared surveillance with GM andAspergillus PCR to GM alone in leukaemia patients not receiving anyantimould prophylaxis; rates of IFI in the GM-PCR group were lower than GM alone (4.2% versus13.1%). Whilst not directly comparable, these latter results are similar to IFI rates ofpatients on prophylaxis in other trials.21 In the UK, a relatively large observational study investigated adiagnostic-driven approach where patients, mostly treated with itraconazole prophylaxis, hadregular biomarker monitoring that informed management in the empirical setting. This studyappeared to produce favourable results with a reduction in unnecessary antifungal use withoutexcess mortality.22A biomarker-based surveillance approach is mentioned as an alternative in the ESCMIDguidelineAspergillus species, using for example GM or AspergillusPCR alone or in combination without antifungal prophylaxis, is that other IFIs may not bedetected. Although these occur less commonly than Aspergillus infections whenprophylaxis is administered , this risk is potentially mitigated bythe use of a second non-specific IFI biomarker such as β-d-glucan that can alsodetect Candida, Pneumocystis and some othernon-Aspergillus fungal species.23 There have also been concerns about the performance of IFI biomarkersduring antifungal prophylaxis.24Indeed, the decrease in the rate of diagnosis of IFI without an associated reduction inoverall survival in many of the meta-analyses of prophylaxis could be due to this reducedperformance of biomarkers or other reasons, such as mortality being predominantly driven bythe underlying leukaemia rather than IFI.One of the concerns with any diagnostic approach that specifically targetsAspergillus PCR) or an abnormal CTscan. The diagnostic-driven strategy was cost-saving, although this study was model-based;neither arm received antimould prophylaxis.25 A similar Australian economic comparison, where patients had a mixture ofdifferent antifungal prophylaxis, found that a diagnostic strategy was cost-effective if therewas a survival benefit.26 What we doknow is that reducing the use of antifungals empirically when there is no evidence of IFIappears to reduce costs without adversely affecting mortality.27A biomarker-based diagnostic strategy may also be cost-effective. An economic comparison,based on UK costings, compared a standard strategy of empirical treatment for IFI in patientswith 72–96 h of neutropenic fever to only initiating antifungal therapy if the patientscreened positive on biomarkers (GM or 28 There is evidence that combining assays can improve pooledsensitivity and specificity, but the optimal approach is yet to be identified or adequatelytested.29 A large trialcomparing antimould prophylaxis directly to surveillance biomarkers without prophylaxis, suchas the BioDriveAFS trial,30 which isdue to start in the UK in 2022, is justified and overdue.Biomarkers have been criticized for having poor individual sensitivity and specificity fordiagnosing IFI, and higher than desirable false positive and negative results. Variation inthe performance characteristics reported between studies is likely to be due to multiplefactors including, for example, the assay used, positivity cut-offs adopted and the patientpopulation.31 Another limitation in the appreciation of IFI as a clinical problem is thelack of mandatory reporting of IFI in the UK with rates of infection based on estimates. Thisacts as a barrier to haematology units knowing what their level of risk is and how theybenchmark against other comparable units.32 Identifying patient groups and units with high rates of IFI, and moreimportantly understanding why this is the case, is a research gap that needs to beaddressed.We do not advocate a one-size-fits-all approach and recognize we are on an evolving pathwaytowards personalized IFI prevention, where the highest-risk patients, identified before andduring periods of risk by emerging technologies, are targeted for antifungal prophylaxis whilemost undergo biomarker surveillance. Even without such scientific advances, in selectedhigh-risk patients there may still be a role for antifungal prophylaxis when external factors,such as construction work, are present.33 In the future, a personalized strategywill be the optimal approach, and this is what the IFI research community should be workingtowards.There is mounting evidence to demonstrate that IFI biomarkers can be used safely to guide theprescription of antifungals in certain patient groups in both the surveillance and empiricalsettings. Undoubtedly this area needs to be investigated further within robustly designed anddelivered clinical trials. Based on the evidence to date, a diagnostic-driven approach appearsto reduce antifungal use safely in the management of patients with acute leukaemias.


The intrinsic proportion of fPSA in tPSA was successfully evaluated by direct use of the Pt to Au intensity ratio (Pt/Au ratio), exhibiting better differentiation between healthy and unhealthy, benign prostatic hyperplasia (BPH) and cancer individuals compared with solo fPSA or tPSA. We generated thresholds respectively for prostate disease differentiation, envisioning that this standard-free SMB nanoplatform would establish a standardized methodology with uniform criteria worldwide in cancer diagnosis, staging, and postoperative assessments.Explicit interpretation of heterogeneity between prostate-specific antigen (PSA) subtypes is essential for prostate cancer differentiation during different disease courses, whereas a universal protocol with uniform criteria is still lacking across the globe. In this work, a standard-free single magnetic bead (SMB) nanoplatform utilizing metal nanoparticles with optimal diameters was proposed for prostate disease differentiation in a 134-donor model. The inaccuracy of detection in absolute quantification was diminished  A standard-free stable single magnetic bead nanoplatform was proposed in this work. The use of metal signal ratio was directly applied for intrinsic biological fPSA to tPSA ratio evaluations for prostate disease differentiation. Due to the high heterogeneity of PSA molecular subtypes, elevated levels of serum tPSA are also found in benign prostate diseases.4 An explicit revelation of the PSA subtype proportion during different prostate cancer stages is undoubtedly of great significance.5,6 Clinically, the free to total PSA ratio (% fPSA) in serum is reported to correlate with the severity of prostate disease, but reliable evaluations are challenged by two situations. One is the separate solo measurements of fPSA and tPSA for % fPSA calculation which may provide discordant results and misinterpretations of prostate conditions.4,7 The other is the lack of gold-normalized protocols which result in biased clinical criterion settings among users or labs across the globe.8 Standardized methodologies with uniform outputs are never out of date to bring simultaneous evaluations of fPSA, tPSA, and % fPSA.Precise differentiation and diagnosis of prostate cancer is necessary to unveil the different molecular expressions during varying stages of this second most frequent malignancy among men worldwide.9–14 However, the absolute intensity of biomarkers obtained from the total probe concentration can be affected by physical factors such as poor washouts or uneven bindings, possibly giving rise to inaccurate readouts.15,16 Moreover, specific standards are still required since the stoichiometry of the total probe concentration is hard to precisely determine.17 As a solution, a standard-free strategy where the intrinsic proportion of biomarker subtypes can be revealed directly by the ratio of probe signals, attracts much attention. The construction of a stable data acquisitions and processing platform with unitary criteria is an ever-increasing demand.Absolute quantification of these biomarkers in a single sample is always required for accurate diagnostics.18–24 Based on the theory in a typical sp-ICP-MS analysis, nanoparticles are injected one by one into the detector where only one nanoparticle is measured during each reading period. Benefiting from ample metal content in one nanoparticle, sensitivity is largely improved in heterogeneous bioassays where absolute quantifications can be easily achieved by measuring the total numbers of target-specific nanoparticles.25–27 In addition, sp-ICP-MS allows evaluations of controllable nanoparticle aggregates by target recognition, realizing homogeneous bioassays without washing or physical separations.28–31 In data acquisition and processing, sp-ICP-MS focuses on each measured nanoparticle and provides the intensity calculated by the uniform distribution of transient signals.32 More intriguingly, the calculated intensity of nanoparticles is relatively stable and is not affected by the nanoparticle concentration.20,21,33–35 In other words, the inaccuracy of detection in absolute quantification can be potentially diminished even if the total probe concentration is fluctuating in parallel samples. If this unique advantage can be reasonably adopted with a standard-free strategy and multiplex bioassay, developing a standardized and universal platform with stable outcomes becomes possible.Endowed with high throughput and resolution, single particle ICP-MS (sp-ICP-MS) has gained great attention in multiplex biological practice and single-cell analysis.Herein for the first time, a standard-free nanoplatform named a single magnetic bead (SMB) nanoplatform was proposed for prostate disease differentiation. In the methodology shown in i.e., AuNPs and PtNPs were used as biomarkers probes. As expected, the most abundant isotopic ions 194Pt+ and 197Au+ can be detected with high sensitivity without any interferences as shown in 58Fe+ transient signals. In element mass analysis, Fe+ isotopes are easily affected by Ar- and Ca-based ions, therefore requiring the use of kinetic energy discrimination (KED) mode to reduce the interferences.36 According to the results shown in Fig. S1,58Fe+ (∼0.3%) applied in this work exhibited distinct spike pulses which linearized with the number of magnetic beads, averting the common use of KED mode which could reduce signal sensitivity. In the presence of fPSA and tPSA, the corresponding PtNP and AuNP probes could efficiently bind to the surfaces of the magnetic beads due to the sandwich immunosorbent. The resulting sandwich structure was clearly observed by TEM, elemental mapping  of nanoparticle probes plays a crucial role in immunoassay efficiency. In this work, three different sizes of PtNPs and AuNPs  and intensity can be concluded as Formula a  in On one hand, the relationship between the nanoparticle size and intensity fitted to the above 194Pt+ or 197Au+ were detected in the SMB nanoplatform at elevated fPSA or tPSA concentrations. Also, from the frequency distribution of Pt and Au intensities on a single magnetic bead  were 12 pg mL−1 for fPSA and 21 pg mL−1 for tPSA respectively which were comparable with or better than other cutting-edge strategies (shown in Table S4−1 for tPSA and 1 ng mL−1 for fPSA).8 Combined with the recovery shown in Table S2,−1 fPSA and tPSA  were found in patients with worse prostate conditions (P was 0.67 for solo tPSA and 0.59 for solo fPSA). Receiver operating characteristic (ROC) analysis revealed that the Pt/Au ratio considerably differentiated healthy donors from unhealthy individuals  with an area under the curve (AUC) of 0.79  nanoplatform for sensitive yet stable bioassays and demonstrated its standard-free analytical performance towards simultaneous evaluations of two key prostate cancer-related biomarkers (fPSA and tPSA) and the discovery of biological proportion within the PSA subtypes for prostate disease differentiation. The stable SMB platform has been realized by judiciously addressing key challenges associated with the size of metal nanoparticle probes in sphere magnetic bead-centered immunoassay. The inaccuracy from fluctuating total probe concentrations was also successfully diminished https://doi.org/10.1039/d2sc00928e.The datasets supporting this article have been uploaded as part of the ESI.Zili Huang and Rui Liu conceptualized the project methodology. Zili Huang designed and implemented the experiments, collected and analyzed the experimental data, and wrote the original manuscript. Bei Xu provided all the serum samples from Mianyang Central Hospital. Zili Huang, Jianyu Hu, Xiaobo Xie, and Rui Liu revised the manuscript. Rui Liu and Yi Lv supervised the investigation.There are no conflicts to declare.SC-013-D2SC00928E-s001


The aim of this study was to investigate the neural bases of facial emotion processing before the onset of clinical psychotic symptoms in youth belonging to well-defined developmental trajectories of psychotic-like experiences (PLEs). A unique sample of 86 youths was recruited from a population-based sample of over 3800 adolescents who had been followed from 13 to 17 years of age. Three groups were identified based on validated developmental trajectories: a control trajectory with low and decreasing PLEs, and two atypical trajectories with moderate to elevated baseline PLEs that subsequently decreased or increased. All had functional magnetic resonance imaging data collected during a facial emotion processing task. Functional activation and connectivity data were analyzed for different contrasts. The increasing PLE trajectory displayed more positive psychotic symptoms while the decreasing trajectory exhibited more negative symptoms relative to the control group. During face processing, both atypical trajectories displayed decreased activations of the right inferior frontal gyrus (IFG), while the increasing trajectory displayed a negative signal in the precentral gyrus. The increasing PLE trajectory also displayed impaired connectivity between the amygdala, ventromedial prefrontal cortex, and cerebellum, and between the IFG, precuneus, and temporal regions, while the decreasing trajectory exhibited reduced connectivity between the amygdala and visual regions during emotion processing. Both atypical PLE trajectories displayed alterations in brain regions involved in attention salience. While the increasing trajectory with more positive symptoms exhibited dysconnectivity in areas that influence emotion salience and face perception, the decreasing trajectory with more negative symptoms had impairments in visual information integration areas. These group-specific features might account for the differential symptom expression. The first clinical signs are commonly observed during late adolescence, a critical developmental period characterized by cognitive and emotional maturation, and marked neuroanatomical and neurofunctional reorganization3. While diverse neurodevelopmental abnormalities  have been associated with risk for psychotic disorders, few have been directly and specifically linked to psychosis. The study of Psychotic-Like Experiences (PLEs) offers a unique opportunity to investigate more specific processes.Psychotic disorders affect 0.31% of the US population4 with, at one end, schizophrenia, a mental disorder characterized by delusions, hallucinations, disorganized speech, and behavior; an intermediate treatment-seeking Clinical High Risk syndrome characterized by “attenuated” or “brief” symptoms that do not necessarily meet full diagnosis criteria; and, at the other end, PLEs. PLEs are defined as subclinical psychotic symptoms that can be observed in the general population, including 17% of children aged 9–12 and over 7% of adolescents5.Psychosis is thought to exist on a continuum8. Recent cohort studies provide evidence of heterogeneous PLE developmental trajectories during adolescence. For example, in 2566 youths from the general population and followed between the ages of 13–16, three trajectories were identified: 83.9% of adolescents exhibited a typical trajectory of low PLE levels that decreased further over time, 7.9% exhibited high PLE levels that subsequently decreased, and 8.3% reported moderate PLE levels that increased9. These trajectories have been consistently replicated in separate samples12, which supports their relevance in adolescent development. While the increasing trajectory is thought to be associated with increased risk of psychosis13, outcomes in the decreasing trajectory have not been investigated. Furthermore, the neural correlates of these well-documented trajectories are unknown. Studying PLEs and their developmental trajectories provides a unique opportunity to identify clinical and neural correlates of psychosis vulnerability without the confounding effects of antipsychotic medication and comorbidities.While most PLEs are transient in nature and do not extend into adulthood, persistent PLEs confer a four- to ten-fold increased risk for psychosis14. The neural correlates underlying these deficits have been well-documented15 and include impaired limbic activations16 and decreased amygdala–prefrontal connectivity17 during facial emotion processing.Socio-emotional dysfunction is considered a hallmark of psychotic disorders. It is manifested by deficits in emotion perception, responsivity, and expression, and is strongly associated with impaired social functioning in schizophrenia patients18 or to threatening faces19, while others have observed decreased responses to emotional faces in the prefrontal, parahippocampal, and striatal regions20. In addition to their association with local activity changes, PLEs were associated with decreased amygdala–prefrontal connectivity during negative emotion processing21. These findings suggest that functional alteration of frontolimbic regions during the processing of emotional content may constitute a marker of a broad vulnerability to psychosis. However, no study has integrated knowledge on the heterogeneity of PLE trajectories to examine potential neural differences in emotion processing.Some studies in youth reporting PLEs have found increased limbic responses to ambivalent facesThe current study aimed to investigate whether youth belonging to distinct developmental PLE trajectories can be differentiated on clinical and socio-emotional information processing indices. To our knowledge, this is the first study to investigate the neural correlates of emotion processing in youth belonging to well-defined PLE developmental trajectories. Participants were a unique general population sample selected from over 3900 adolescents characterized on population-based data-driven latent developmental trajectories and being followed for 10 years. We hypothesized that those following an increasing trajectory of PLEs would show altered activity and connectivity patterns in limbic and prefrontal regions involved in emotion processing and salience, respectively. We also hypothesized that the decreasing trajectory would present distinct clinical and neural correlates compared to the control and increasing trajectory.Demographic and clinical variables for each of the three trajectories are reported in Table PLE scores at age 17 were significantly higher in the PLE-2 group compared to PLE-0 and PLE-1 . The bilateral amygdala were also used as seed regionsFunctional connectivity differed between groups Table . During p value. The decreasing trajectory also displayed hypoconnectivity between the right amygdala and the right parietal angular cortex relative to the two other groups. In comparison, PLE-2 exhibited increased connectivity between the right amygdala and the left cerebellum Crus I lobule relative to the two other groups.During negative emotion processing , PLE-1 showed hypoconnectivity between the right amygdala and the left visual association cortex compared to PLE-0; however, this effect was not significant at the Bonferroni-corrected r = 0.326; p = 0.004). This finding was near significance using a Bonferroni-corrected p value of 0.001. Other CAPE scales did not significantly correlate with brain activations.Across groups, right amygdala to left cerebellum connectivity positively correlated with CAPE positive bizarre thinking score during negative emotion processing  26. In the specific context of emotion processing, the region plays a critical role in emotion regulation27. However, since IFG hypoactivation was observed during facial cue processing (but not during negative processing), this result is more likely indicative of abnormal filtering of facial/social information, rather than emotional valence. Second, during negative emotion processing, both atypical trajectories showed a hyperactivation of the right dACC and left precuneus—brain regions that are involved in salience attribution28 and self-attribution29, respectively. Therefore, their increased activation could reflect increased recruitment of attentional resources and self-attribution of negative emotional stimuli—potentially reflecting an over attribution of negative valence to the face cue.These findings of general and trajectory-specific neural activation patterns during emotion processing might help to explain heterogeneity of findings in the literature. First, during facial cue processing, both atypical trajectories exhibited hypoactivation of the right IFG. This is in line with right IFG hypoactivation during facial emotion processing in schizophrenia30 and is important for the motor preparation processes during the response to another’s intention. The fact that we captured the response to dynamic emotional expressions (as opposed to static images) in the fMRI task strengthens this interpretation. Consistent with findings in schizophrenia16 this suggests that the neural mechanisms involved in neutral facial processing are impaired in those at a higher risk of psychosis in the increasing trajectory. Similarly, activation alterations during neutral facial processing at age 14 has predicted psychotic symptoms at 2-year follow-up18, indicating that the processing of neutral faces, which is often overlooked in neuroimaging studies, may differentiate youths at risk of psychosis.Altered activations in right IFG, right dACC, and left precuneus were found in both decreasing and increasing trajectories, meaning that these alterations are not trajectory-specific. In comparison, during neutral facial processing, a negative signal in the left precentral gyrus was specifically observed in the increasing trajectory, whereby this region was hyperactivated during the opposite contrast . The precentral gyrus encompasses the primary motor and premotor cortex and is thought to contain “mirror neurons” that are activated when viewing and understanding actions performed by others31 which has also been observed in clinically confirmed psychotic disorders32. Interestingly, altered amygdala–prefrontal connectivity has been reported in adolescents at Clinical High Risk33, and in youths on the psychosis spectrum34. The aberrant amygdala–prefrontal connectivity over time was shown to correlate with an increased severity of positive symptoms, suggesting that abnormal neurodevelopment of the amygdala could be involved in the development of psychotic symptoms34. While both decreasing and increasing trajectories displayed altered activity of right IFG and left precuneus, only the increasing trajectory exhibited a hypoconnectivity between the IFG and precuneus. Additionally, this trajectory displayed abnormal connectivity between the right IFG and the lateral temporal pole and the middle temporal gyrus, which are known to be involved in the processing of visual social cues35 and decoding facial features36, respectively. Therefore, the increasing trajectory appears to exhibit alterations in coupling between frontal and temporal regions playing a key role in face processing. Finally, the increasing trajectory also exhibited amygdala–cerebellum hyperconnectivity during negative emotion processing, specifically within the nonmotor Crus I cerebellar lobule, which correlated with levels of bizarre thinking. This result is consistent with evidence showing that schizophrenia is associated with functional and structural changes within the Crus I lobule37, and propositions that complex interactions between cognition and emotion are involved in delusion formation38. The decreasing trajectory was also shown to be uniquely associated with hypoconnectivity between the amygdala and regions of the extended visual system . This feature might reflect a lack of integration of basic visual information during the early processing of emotional stimuli, leading to the aberrant response to emotional stimuli via bottom-up mechanisms39 potentially explaining the trajectory’s susceptibility to negative symptoms in young adulthood.Connectivity analyses highlighted significant differences between PLE trajectories. In the increasing trajectory, during facial cue processing, we noted amygdala-vmPFC hyperconnectivity. The vmPFC is involved in fear-associated learning; therefore, this result may indicate increased salience attribution or aversive conditioning to facial cuesDespite the novel and robust research design of the Pro-Venture cohort, this study is not without limitations. The sample sizes of the atypical PLE trajectories were limited due to the low prevalence of decreasing and increasing trajectories in the general population and the difficulty in recruiting these participants into a neuroimaging study during young adulthood. The modest sample size could also account for the absence of differences on behavioral measures of emotion recognition and limbic system activation, and why dACC and precuneus activation effects were only evident using a more liberal threshold. Additionally, effective connectivity was not explicitly measured in this study, and therefore the directionality of altered connections cannot be determined. Nevertheless, this study successfully recruited a unique sample of psychosis-prone youths from a large population sample that was followed over 5 years, and it highlighted neural alterations that are specific to different PLE trajectories.40, and frontotemporal regions involved in face perception. The decreasing trajectory exhibited amygdala–visual cortex dysconnectivity potentially involved in the early processing of visual emotional stimuli. Some of the key findings emerged from the facial cue processing contrast as opposed to the emotion processing contrast, highlighting the importance of investigating both types of deficits. These findings also suggest that the increasing and decreasing PLE trajectories should be considered when studying risk profiles for psychosis and when attempting to understand the neurodevelopmental markers of risk.The current study showed that youth in atypical PLE trajectories exhibit differential clinical outcomes and altered neural processing of emotion stimuli. While both atypical trajectories displayed impaired activations of brain regions involved in information filtering during face processing, the current results distinguished between increasing and decreasing PLE developmental trajectories. The increasing trajectory displayed alterations in frontolimbic regions involved in aversive conditioning, which is consistent with the aberrant emotion salience hypothesis of psychosis41. A total of 3966 Grade 7 students from 31 high schools in the greater Montreal area were part of the Co-Venture cohort as they completed annual clinical assessments during class time from 13 to 17 years of age. Using latent class modeling with the 4-year data from ages 13 to 16, as described in the Supplementary Materials, three developmental trajectories of PLEs were identified among the total sample: a low-decreasing trajectory , a high-decreasing trajectory , and a moderate-increasing trajectory 42.The ongoing Pro-Venture study  is a 5-year longitudinal neuroimaging study that follows adolescents who showed one of three different PLE trajectories over a previous 5-year period during early adolescence. It is a neuroimaging add-on to the larger Co-Venture study, a cluster-randomized controlled trial evaluating the effectiveness of personality-targeted interventions on substance useThe Pro-Venture subcohort consists of 86 adolescents aged 16–20 at entry . Participants from the three PLE trajectories who had consented to being contacted for future research were identified and participants were then recruited randomly. The presented results are based on data collected at the first neuroimaging assessment. Ethical approval was obtained from the CHU Sainte-Justine Research Ethics Committee in Montreal. All participants actively assented or consented to the study procedures, and parent consent was obtained for minors.Exclusion criteria for the Pro-Venture study included DSM-5 psychiatric disorders, family history of schizophrenia, taking antipsychotic medication, neurological disorders or IQ < 70 and a contraindication to undergo MRI examination. Prior to neuroimaging acquisition, participants were administered a urinary test for the recent consumption of substances and participants who had used alcohol, cannabis, MDMA, cocaine, or opioids did not undergo neuroimaging. Since cannabis metabolites can be detected up to 7 days following its sporadic use, individuals who tested positive but reported not having consumed cannabis within 24 h (and did not display signs of intoxication) prior to neuroimaging were invited.Adolescent Psychosis Screening Scale (APSS) using nine items that have previously been described9. Three questions from the questionnaire were shown to have significant positive predictive power for interview-verified PLEs that ranged between 80 and 100%43. Psychotic symptoms were measured using the CAPE44. Substance use during the past 12 months was measured using the DEP-ADO questionnaire45. Participants rated their consumption of alcohol, tobacco, cannabis, and other substances on a scale of 6 levels, ranging from “never” to “everyday”46. More detailed accounts of quantity and frequency of substance use were also collected using the timeline follow-back method47 for the 4 weeks prior to assessment. The Development and Well-Being Assessment (DAWBA) package was used to generate ICD-10 and DSM-IV psychiatric diagnoses48. Externalizing and internalizing behaviors were evaluated with the Strength and Difficulties Questionnaire (SDQ)49. Finally, the Penn Emotion Recognition task ER-40 (ref. 50) was used to assess facial emotion recognition ability.PLEs  in the last 12 months were assessed with the 51 which was used by the IMAGEN consortium in youths with PLEs18. The task consisted of short sequences (2–5 s) of black and white videoclips of dynamic facial expressions. Compared to static emotional faces, dynamic facial expressions are more representative of social interactions and have been shown to elicit larger activations in brain areas involved in social and emotional processing53. Three categories of videoclips were presented: (1) actors portraying neutral facial expressions turning into angry expressions, (2) actors portraying neutral facial expressions turning into different neutral expressions, and (3) a control condition consisting of a dynamic video of expanding/contracting circles. The control condition was designed to roughly match the physical properties of the dynamic facial expressions in terms of color, light, contrast, and motion characteristics.We employed a task-based fMRI face recognition paradigm derived from Grosbras and Paus3; 42 slices). Functional slices were oriented in transverse plane and were angled to be parallel to the AC-PC line. During EPI image acquisition, an inline retrospective motion correction algorithm was employed. During the same session, high-resolution T1-weighted anatomical images were acquired .Blood oxygenated level dependent (BOLD) signal was acquired at the Unité de Neuroimagerie Fonctionnelle de l’Institut de Gériatrie de l’Université de Montréal on a Siemens Prisma Fit 3T scanner. Functional imaging data were acquired with a T2-weighed multiband echo planar imaging sequence 54. Functional images were realigned, corrected for motion artifacts with the Artifact Detection Tools (ART), high pass filtered (>0.008 Hz) and co-registered to the corresponding anatomical image. Outliers were detected by scanning the global BOLD signal and removing volumes associated to signal changes above 4 standard deviations, or framewise displacements above 0.5 mm. The anatomical images were segmented into gray matter, white matter, and cerebrospinal fluid. Functional images were then normalized to Montreal Neurological Institute stereotaxic space using the deformation field from the corresponding anatomical images and were smoothed with a 6-mm 3D isotropic Gaussian kernel. The anatomical component-based noise correction method (CompCor)55 was used as it removes confounding effects from the BOLD timeseries, such as the physiological noise originating from the white matter and cerebrospinal fluid56.Preprocessing was conducted using the CONN-fMRI functional connectivity toolbox (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/)57 in Matlab version 2020a. The experimental conditions of Angry , Neutral , and Control (expanding circles) were convolved to a canonical hemodynamic response function. The “FAST” model was implemented to account for temporal correlations58. Our primary contrast of interest was defined as [ − Control] and isolates facial cue processing. We also implemented two other contrasts: the , which isolates negative emotion processing, and the  contrast, which isolates neutral facial processing. Between-group differences were evaluated using a full analysis of variance (ANOVA) F-test comparing the three groups for each of the three contrasts of interest, using subject as a random effect. Estimated parameters of the regressors in the GLM model (beta weights) were extracted using the Marsbar toolbox59. The statistical threshold for significance was determined using a Monte Carlo simulation60. Assuming a per probability of p < 0.001, after 10,000 simulations, a cluster size of 21 resampled voxels was indicated to correct for multiple comparisons at p < 0.05. Considering the amygdala’s key role in emotion processing, we further used a small volume correction approach to perform a region of interest analysis61.A general linear model (GLM) was mapped for each participant using SPM12 , a physiological regressor (the seed’s BOLD timeseries), and an interaction term consisting of the product of the psychological and physiological terms. The psychophysiological interaction term was then modeled against the time course for all voxels in the brain to test for significant connectivity effects. Seed regions were defined based on the regions that significantly differed between groups during the activation analyses. Given its role in threat-perception and negative emotion processing, the bilateral amygdalae, as defined in the Harvard-Oxford atlas, were also used as seeds of interest. Between-group differences were evaluated using a full ANOVA for each of the selected contrasts. The peak threshold was set at p < 0.001 with a cluster threshold of p < 0.05 corrected for the false discovery rate. Beta weights were extracted for all significant findings. Post hoc analyses using beta weights were performed with and without Bonferroni correction for the number of seeds being tested.Voxel-wise seed-based functional connectivity analyses were performed in CONN. To examine differences in task-related connectivity between groups, generalized psycho-physiological interaction (gPPI) analyses were performed. The gPPI model generated a regressor of each condition , representing the level of task-modulated functional connectivity between the seed and every voxel in the brain. This approach has been shown to generate a more accurate model of the interaction between the conditions and the neural activityχ2 tests and pairwise comparisons were performed. Significance was set at p < 0.05. Analysis of covariance assessed effects of clinical variables on the between-group differences in brain activity and connectivity. Bivariate Pearson correlation analyses were performed to determine the relationship between clinical variables  and activation and connectivity strength estimates across groups .Between-group differences in continuous data were examined with ANOVAs. Post hoc analyses were performed using Tukey’s Honestly significant difference test. For dichotomous data, Supplemental material


Different animal models have been used for hair research and regeneration studies based on the similarities between animal and human skins. Primary knowledge on hair follicle (HF) biology has arisen from research using mouse models baring spontaneous or genetically engineered mutations. These studies have been crucial for the discovery of genes underlying human hair cycle control and hair loss disorders. Yet, researchers have become increasingly aware that there are distinct architectural and cellular features between the mouse and human HFs, which might limit the translation of findings in the mouse models. Thus, it is enticing to reason that the spotlight on mouse models and the unwillingness to adapt to the human archetype have been hampering the emergence of the long-awaited human hair loss cure. Here, we provide an overview of the major limitations of the mainstream mouse models for human hair loss research, and we underpin a future course of action using human cell bioengineered models and the emergent artificial intelligence.  This review aims to comprehensively compile the differences between mouse and human hair biology, and their implications in translational studies for hair loss therapy. We start by appraising several mouse models used in hair research studies and by underlining major distinctive features between human and mouse hair. We then discuss the limitations of the mainstream mouse models and how human cell-based approaches start being prioritized for high-throughput drug screenings and bioengineering solutions toward the development of effective therapies against alopecia.1Human hair follicle (HF) function and regulation are not yet completely understood. The main reasons behind this are: the inability to manipulate human HFs in vivo and the HFs’ scarcity for in vitro and ex vivo studies. The major advancements in the HF research field have primarily arisen from the use of in vitro and in vivo animal models, including rats, hamsters, rabbits, sheep, monkeys, and mainly mice. Natural or genetically engineered loss-of-function and gain-of-function mice have been extensively used to understand the molecular mechanisms controlling HF morphogenesis and cyclic growth, which provided an invaluable contribution to the understanding of hair loss disorders. Moreover, mice models have been crucial to test the efficiency and safety of pharmacological and bioengineering treatments. Yet, one major caveat has been the differences between animal and human hair, which may explain why human hair loss disorders are still seeking for effective therapeutic strategies. Whereas stem cell-based therapy has proven relatively effective in mouse models, it remains intangible to humans. The probable reason behind this is the distinctive biological identity of the human HF that makes it an incredibly unique organ. Future research should extensively characterize the human HF cell populations and their microenvironment to come up with regenerative therapies able to tackle hair loss disorders.2), rats,3 goats,4 and monkeys.5 Despite being more divergent from humans than other models, mice have been predominantly used in hair research due to their easier handling, well-established protocols and ethical approvals. Several mouse strains are currently used for hair growth studies,6 the most popular being C57BL/6 and C3H, whose pelage (but not ear and tail) skin pigmentation is merely dependent on their follicular melanocytes.7 This way, anagen cycles of hair growth are easily detected by the darkening of shaved skin.8 Up to date, foremost knowledge on HF biology and cycling has arisen from studies using spontaneous and/or genetically engineered mouse mutants with hair loss outcome.9 Some relevant examples of spontaneous mutants exhibiting HF defects are listed in 2 Accordingly, a continuously growing number of genetically engineered mutants unveiled the mechanisms controlling hair morphogenesis, cycle, and pigmentation . Although these models provided valuable knowledge to conceive hair loss therapeutic interventions, inconsistent outcomes have often arisen when conveyed into a human background. Attention should be given to species-specific differences.Different animal models have been used for hair loss and regrowth studies in vivo, including mice  anagen growth phase takes 2 weeks in mice compared to 3-5 years in human19; (ii) hair shaft shedding (exogen) is a well-controlled process in mice, in which old hair shafts are kept for several HF cycles20; (iii) contrarily to rodents, human hair cycle occurs asynchronously in the scalp.21 In addition, the hair types are also different between humans and mice. Adult humans have two major hair types visible on the scalp, terminal hairs (pigmented) and vellus hairs .22 Melanin is transferred to the hair fiber cortex in humans, whereas primarily to the hair fiber medulla in mice.7 Mice have many different hair types, namely truncal (pelage) hairs, vibrissae, muzzle hairs, tail, cilia, and perianal hair.23 These hair types should be deeply investigated in hair studies since distinct molecular mechanisms orchestrate different HF populations.24 Besides, the mouse hair does not transit from a vellus to terminal hair. Regarding the hair structure during cycling, mouse and human do not differ substantially, with human scalp follicles showing larger and longer hair shafts than mouse pelage follicles. However, in vibrissae follicles the dermal papilla (DP) never reaches the telogenic bulge, making it hard to explain hair follicle stem cell (HFSC) migration from the bulge to the follicular base during anagen onset.25 HFSCs are a pool of quiescent multipotent stem cells in the outermost layer of the HF which are activated by the DP to proliferate and promote hair growth.Major differences exist between mouse and human hair, namely in the HF morphogenesis, cycling, structure, and microenvironment  . First, 26 Also, human scalp sensitivity to androgens is distinctive in comparison to other animals, which lack androgens-driven phenotypes .26 In fact, mice do not suffer from AGA and key mechanisms controlling androgen-dependent HF miniaturization in the human scalp are not recapitulated in mice.18 Human and murine HF respond differently to distinct androgen and hormone stimulation. To circumvent this issue, an androgen-dependent mouse model expressing a human androgen receptor (AR) transgene, K5-hAR, was generated. Still, and noteworthy, in this model the human AR is expressed in basal epidermis and outer root sheath (ORS) mouse cells, and not in the DP,28 perhaps explaining why these mice do not exhibit a phenotype resembling human AGA.29 Therefore, caution is needed when resorting to the K5-hAR mice to validate drug therapies. In human AGA, two main factors determine terminal hair miniaturization to vellus: anagen growth shortening and decreased size of the DP and hair matrix.31 Studies using human DP spheroid cultures suggested that spheroid size is essential for HF inductivity, although not directly translating into thicker regenerated hair.32 In mice, selective ablation of DP cells in vivo showed that DP cell number determines the thickness and type of hair produced.34 The follicular papilla cell number and total papilla size are maximal by anagen VI, then decreasing by fibroblast migration out of the late anagen/early catagen papilla into the proximal connective tissue sheath.34 Thus, hair cycle-associated plasticity of the HF mesenchyme is likely clinically relevant.Moreover, androgenic effects in mice HF proved quite distinct from that observed in human scalp HF. The most common form of human hair loss is androgenetic alopecia (AGA), which is mainly caused by an androgen action on the DP. Androgens alter the production of regulatory factors by the DP cells, causing anagen shortening and follicle miniaturization. Strong evidence has pointed out the human scalp HF sensitivity to androgens as the main reason for hair miniaturization, as opposed to androgens’ effect on hair from other body regions .35 Moreover, different biomarkers were found in mouse and human HF. For instance, CD34 and K15 markers, identified as murine bulge stem cell-specific markers and used for HFSC isolation,37 are not expressed in the human bulge region .38 On the other hand, CD200, follistatin, and DIO2 appear to be human-specific markers of the bulge stem cells.41 Regarding the mesenchymal DP cells, integrin alpha 942 and CD13343 have established cell surface markers for the mouse but not human DP. Importantly, the development of stem cell-based therapies for hair loss has been limited by the lack of robust human DP cell surface markers, which precludes their isolation by cell sorting.44 Therefore, another mesenchymal multipotent pool in the skin, the skin-derived progenitors (SKPs), has gained alternative attention in the hair research field. Mouse SKPs express neural crest biomarkers  and their role in DP renewal during the hair cycling has been demonstrated,45-47 as well as their dermal stem cell properties explored in bioengineering approaches.45 However, the study of human SKPs (hSKPs) has remained technically challenging and barely reported. The nestin, fibronectin, and vimentin markers expressed by hSKPs are also present in other skin mesenchymal populations,49 thus constraining the isolation, expansion, and validation of hSKPs’ therapeutical potential.Furthermore, not only hair cycle regulation differs between humans and mice, but also the stem cell niches  within the HF. Different HF stem and progenitor cells have been intensively investigated for their ability to generate tissue engineering applications to treat hair loss. However, the identification of specific biomarkers is still missing for their effective isolation and expansion. Even though mouse studies have significantly elucidated stem cell activation mechanisms during the hair cycle, major constraints still remain when translating these findings into tissue engineering strategies. Whereas the mouse anagenic bulge compartment (a reservoir of multipotent stem cells in the adult HF ORS) can be easily identified, within the human ORS there is a keratinocyte pool without apparent distinctive morphologies.50 Therefore, their percutaneous absorption capacity is likely distinct, which should be considered in translational drug studies. Furthermore, immunological specificities during wound healing might dictate paradoxical outcomes in mice and humans. For example, mouse epidermal dendritic T cells secrete FGF-9 during wound healing, further triggering FGF-9 secretion by dermal fibroblasts.51 This mechanism explains the presence of HFs in mice but not in human scars.52 Such immunological specificities of the mouse skin are also disadvantageous to study alopecia areata (AA), an autoimmune disease that is the second most common cause of hair loss.53 Still, mouse models have provided invaluable means for studying the factors underlying immune regulation of this autoimmune skin disease.54 For example, the therapeutic effect of JAK inhibitors (Ruxolitinib and Tofacitinib) on AA was validated in the C3H/HeJ mouse model.55 However, C3H/HeJ mice develop spontaneous AA at a very low frequency, with histologic features that do not resemble those in human AA.56 Human AA is characterized by intra- and pre-follicular infiltration of CD4+ and CD8+ T lymphocytes. In murine AA, inflammatory cells infiltrate to the distal follicle and can reach the bulge. Key differences between the C3H/HeJ model and human AA pathobiology, genetics, and immunobiology have been reviewed,57 questioning the suitability of the mouse model to validate the efficacy of therapeutic compounds for human AA. For this reason, a humanized mouse model has been generated for preclinical studies.58Finally, despite their similar structural layering and dramatic changes in dimension during the hair growth cycle, human and mouse skins differ in overall thickness. In mice, the epidermis is loose comprising only three cell layers (<25 µm), whereas the human epidermis is firm comprising many layers (>50 µm). Also, the human dermis is substantially thicker than the mouse dermis and contains fewer HFs.1 Mohammadi et al,59 Nilforoushzadeh et al60). To cope with any constraining differences between mice and humans, human scalp skin xenografts in severe combined immunodeficiency (SCID) mice have been used. Noteworthy, this is currently the single preclinical assay that allows the establishment of a human hair cycle in vivo.18 Nude and SCID mutant mice have been routinely used in hair bioengineering approaches because: (i) nude mice constitute a unique model to study de novo hair formation, as they lack visible hair fibers, making xenografts easy to follow and study; (ii) SCID mice are deficient in T and B cells, which prevents the rejection of human scalp skin or human bioengineered transplants.61Animal models have been extensively used to functionally validate the ability of stem cell populations expanded in vitro to induce HF formation. Specifically, most hair inductive studies monitor the formation of “hair-like” structures upon implementation of bioengineered instructive mini-germs in mouse skin. HF bioengineering approaches have resorted primarily to human HFSCs or DPCs co-transplanted with mouse neonatal cells. Yet, successful generation of functional mature HFs exclusively from human adult HFSCs and dermal papilla cells (DPCs) has not been achieved  ex vivo have become increasingly popular in preclinical studies for hair growth/anti-AGA drugs.62 One common pitfall of preclinical drug validation in both mice and HFOCs is the fact that only gain/loss of anagen HFs can be monitored, and not the reversion of HF catagen and/or miniaturization.63 In fact, only maximally growing anagen VI HFs are used in HFOCs, which are thus unsuitable to study hair shaft elongation. Moreover, only occipital  HFs are used in HFOCs, which are more resistant to DHT-induced miniaturization .Apart from xenografts, animal models have been also used to explore the efficacy of topical treatments to induce hair growth in vivo.In sum, although several morphogenic and regenerative signaling pathways are evolutionarily conserved, species-specific differences may dictate if one given therapy will succeed in the human background. Studies of hair loss mouse mutants have been useful to (i) identify crucial genes on HF function, (ii) uncover the molecular mechanisms underlying hair morphogenesis, cycle, and pigmentation, and (iii) investigate the functional role of genes of interest through mutant phenotypes. However, mouse models might be also hindering the discovery of next-generation treatments for human hair loss as (i) they do not mimic the causal mechanisms behind human AGA and AA, and (ii) there are species-specific differences in HF growth and regulation as highlighted above.Recent studies have disclosed innovative in vitro human models, based on bioengineered 3D co-culturing systems and organoids. These models aim to deliver a cellular array akin to the human HF, allowing to mimic the physiological response to drugs in high-throughput screenings. Moreover, the human cellular models answer to the 3Rs  guidelines on animal experimentation while providing an advanced alternative to safety testing. Nevertheless, it is unclear which specific factors are indeed crucial to maintain HF inductivity and cycling in vitro. Therefore, different combinations of factors and cellular architectures have been developed and tested .66 Although DPCs are a well-established pool of cells for evaluating hair growth, they do not resemble the physiology of the whole HF. Therefore, 3D culture systems have further evolved toward the development of 3D-like structures that better recapitulate HF cell organization and communication.683D culture systems have long been applied in hair loss studies. 3D spheroid cultures using DPCs were shown to partially restore the transcriptional signature and hair inductive potential of human DPCs.68 More recently, a 3D co-culture system of hDP cells and hORS cells in an ultra-low attachment 96-well plate was established in which the two cell types constituted a polar elongated structure, that gradually increased while maintaining functional integrity as determined by the upregulation of hair growth-associated genes upon treatment with hair growth-promoting molecules.67 Furthermore, the combination of 3D printing technology with biomaterials has driven the large-scale production of architecturally relevant hair germs.69 3D bioprinted multilayer scaffolds based on a gelatin/alginate hydrogel and different cell structures have also been used to mimic the DP microenvironment in the human scalp and thus reproduce a more physiological condition.70 Likewise, hair-like structures featuring human keratinocytes and spheroid-shaped human DP cells can be obtained by coaxial vertical bioprinting and culturing.71A method for efficient production of folliculoid keratinocytes-DP microtissues on a poly (EVAL) surface has been developed, which in comparison to hanging drop methodology, generated hybrid spheroids with compartmented core (DP)-shell (keratinocytes) structure and able to grow hairs in vivo.72Moreover, a biomimetic developmental approach of HF bioengineering was disclosed in which human HFs were generated within human skin constructs printed to incorporate keratinocytes and DP spheroids overexpressing the Lef-1 inductive factor. In addition, vascularization of hair-bearing human skin constructs before engraftment was shown to allow for efficient human hair growth in immunodeficient mice.73-76 In a recent approach, skin organoids containing pigmented HFs were obtained by a stepwise modulation of the TGF-β, BMP, and FGF signaling pathways in human-induced pluripotent stem cells (iPSCs) to co-induce differentiation into cranial epithelial cells and neural crest cells.75More complex bioengineered human HF organoid models have emerged in recent years where pigmentation and innervation have been additionally included to physiologically resemble the human hair even deeper.78 An EU COST Action for skin engineering and modeling (#21108) has just been approved to drive the development of cell-based and computational skin models, including the development of artificial intelligence (AI) models, for dermatological research. This holds strong potential to increase clinical outcomes and decrease animal experimentation in hair research.Finally, organ-on-a-chip microfluidic technology applies cultured cells under fluid flow to recapitulate the physiology and pathophysiology of an organ. Skin and hair on-a-chip can thus be used to create patient-specific preclinical models.80 Noteworthy, AI algorithm has been accelerating computer-assisted scalp diagnosis and automated hair loss count.81 Automated machine learning models could be also applied to other areas of hair research, including drug development and screening. In foreseeable future, AI might evolve to assist in patient-tailored treatments for hair loss, as well as to reprogram non-regenerating HFs to healthy cycling ones in combination with future whole-genome synthesis technology.The end goal of human hair-like structures’ bioengineering is the development of a disruptive hair loss replacement therapy. However, the complexity of the human HF still waits for an aesthetically relevant solution that brings added value over existing treatments such as the Follicular Unit Excision (FUE) transplant. Attention should now be given to the application of human bioengineered models to accelerate drug discovery and testing, as they more accurately forecast the physiology and drug response of human patients.Mouse models have certainly contributed to the understanding of HF biology and the preclinical validation (efficacy and side effects) of hair loss treatments. The brief hair cycle experienced by the mouse HF is extremely advantageous in hair research, as it enables a hair cycle analysis across several regenerative loops and even during the animal’s life span. However, the structural and functional differences between mice and human HFs have forfeited the robustness of mouse models to meet the human hair loss therapeutic needs. Future research should therefore unequivocally address and establish the distinctive features between mouse and human hair in order to better extrapolate the results and predict clinical translation. Mouse and human HF regenerative capacity differs significantly, and human cell-based bioengineering in animal models has not completely succeeded, questioning the adequacy of mouse models to attain human HF regenerative therapies. In fact, the FDA-approved drugs for hair loss treatment available so far, Finasteride and Minoxidil, were identified based on their side effects on human hair growth during clinical trials, and not from studies in mice. It is thus possible that many hair treatment solutions developed over the past years and/or still under development may fail due to the inadequacy of animal models.82 The inclusion of DP cells in those bioengineered models allows for the screening of androgen-blocking agents. Hence, future efforts should concentrate on the development of in vitro human biomimetic platforms to be substantially explored on a preclinical basis.Although the clinical use of bioengineered HFs to treat hair loss disorders remains elusive, human cell-based bioengineered models should nevertheless be largely exploited in drug screening and testing. Indeed, high-throughput bioengineering models have been reported as easy and reliable to validate the hair growth-promoting effect of library molecules.In the near future, we anticipate that the technological developments will allow testing strategies in human cell models as the gold standard in the hair research field. Omics approaches to characterize the molecular signatures of HF cell populations, in both bald and non-bald scalp regions, should be given priority in order to accelerate bioengineering strategies. This tactic shift will help to demystify the holy grail of human HF regeneration, as well as will prevent the recurrent drawbacks in the later stages of clinical testing or the overlooking of potentially effective treatments that fail in mouse model testing.


Lymphoma has traditionally earned the nickname “the great mimicker”. Its presentation as a primary spinal tumor is rare, and therefore seldom included in the differential diagnosis. However, its mimicking nature and diverse presentation make it very difficult to exclude entirely. Here, we present an elderly patient with histology-confirmed spinal lymphoma presenting as both an epidural mass with transforaminal extension into the retropleural space as well as vertebral body compression fracture, together leading to severe spinal stenosis and compressive myelopathy. Additional non-malignant compression fractures found in our patient allow for an interesting discussion on disease presentation and imaging-based diagnosis. We discuss our approach to diagnosis, surgical treatment, and post-operative medical care. Primary spinal cord lymphomas are rare, accounting for only 1.7% of all primary spinal cord tumors in adults ,3 presenIn addition to spinal cord compression from epidural mass effect, spinal lymphomas can invade the vertebral column leading to pathologic fractures . FurtherLymphoma is colloquially referred to as the “great imitator/mimicker” of central nervous system disease ,13, giveClinical presentationA 92-year-old female with no known significant past medical history presented to the hospital with progressive mid-back pain and weakness that started six months prior and has progressed in severity. At the time of the presentation, she was unable to ambulate independently. She did not report any bladder or bowel incontinence, or saddle anesthesia. Neurologic exam revealed decreased strength in bilateral lower extremities . Workup at the time included an abdominal CT that demonstrated a chronic compression fracture of L4.NeuroradiologyMRI of the thoracic spine revealed a severe compression fracture of the T8 vertebral body with abnormal hypointense signal on T1-weighted images and abnormal enhancement following administration of gadolinium-based contrast agent (GBCA) Figure . The comSurgical approach and tumor pathologyTo alleviate spinal cord compression a decompressive laminectomy and instrumented fusion were performed at T7 - T9. The epidural mass was visualized and resected off the dura Figure . HistoloPost-operative course2) was started with eight infusions at two-week intervals, followed by monthly maintenance infusions. An MRI obtained seven weeks after initiating rituximab therapy demonstrated positive disease response with resolution of the epidural mass and a decrease in size of the retropleural extension  and further illustrate the importance of a wide differential diagnosis given the mimicking nature of spinal lymphomas specifically and lymphomas in general.Vertebral body compression fractures are not uncommon in the elderly population . Most ofFurther complicating the diagnosis, in this case, was the observed transforaminal extension of the tumor into the paraspinal space. This phenotype mimics that of peripheral nerve sheath tumors, schwannomas, ependymomas, or more rarely meningiomas, which can invade through neural foramina and present with symptoms of spinal nerve radiculopathy . SchwannThe differential diagnosis of epidural masses is broad, including infectious processes, epidural hematoma, and primary malignancies. Aside from imaging studies, pathologic evaluations and clinical findings are crucial in differentiating these etiologies. Epidural masses may go unnoticed until they cause symptomatic cord compression. The cord compression seen in this patient was likely multifactorial and due to the large compressive epidural tumor (lymphoma), thoracic spine instability, and exaggerated thoracic kyphosis secondary to the T8 compression fracture. Clinical judgment to operate was guided by the established Spinal Instability Neoplastic Score (SINS) ,19, charSpinal lymphomas remain a diagnostic challenge to the clinician, in part due to their rarity and propensity to mimic other pathologies. MRI, especially pre-contrast T1-weighted and post-contrast fat-saturated sequences, can help differentiate malignancy-associated pathological compression fractures from osteoporotic compression fractures and is especially important in elderly patients where these are frequent findings. The decision to operate on elderly patients with spinal lymphomas should be a multidisciplinary one. The surgeon must weigh the need for rapid spinal cord decompression versus the morbidity of surgery. These tumors can respond well to rituximab, which should be considered as monotherapy in patients unable to tolerate conventional chemotherapy.


Bone tissue engineering (BTE) strategies are increasingly investigated to overcome the limitations of currently used bone substitutes and to improve the bone regeneration process. Among the natural polymers used for tissue engineering, dextran and pullulan appear as natural hydrophilic polysaccharides that became promising biomaterials for BTE. This systematic review aimed to present the different published applications of pullulan and dextran-based biomaterials for BTE. An electronic search in Pubmed, Scopus, and Web of Science databases was conducted. Selection of articles was performed following PRISMA guidelines. This systematic review led to the inclusion of 28 articles on the use of pullulan and/or dextran-based biomaterials to promote bone regeneration in preclinical models. Sixteen studies focused on dextran-based materials for bone regeneration, six on pullulan substitutes and six on the combination of pullulan and dextran. Several strategies have been developed to provide bone regeneration capacity, mainly through their fabrication processes , or the addition of bioactive elements. We have summarized here the strategies employed to use the polysaccharide scaffolds , and we highlighted their relevance and limitations for BTE applications. Improving bone regeneration after traumatic injuries, pathologies, or tumors resection has been and still remains a surgical challenge since decades. To date, autologous bone grafting  is the gBone tissue engineering (BTE) strategies have thus emerged to develop alternatives to conventional autologous bone graft reconstruction. BTE approach is usually based on a combination of scaffolds with cells and/or bioactive molecules to provide the conditions required for bone formation. Design of scaffolds that mimic the mechanical and structural features of bone extracellular matrix  that is Recently, a number of natural polymers, such as chitosan, collagen, gelatin, hyaluronic acid, or alginate has gained an increasing interest . They ofThe use of these two polysaccharides for BTE has thus been investigated to design biological scaffolds for bone regeneration. In the last decades, several studies focused on the production of pullulan and/or dextran-based scaffolds combined with CPC or growth factors, and investigated their potential for BTE. However, there is no report summarizing the development of pullulan and/or dextran-based bone materials, functionalization methods and their applications to promote bone regeneration. This review aims to identify the different strategies of pullulan and/or dextran-based substitutes for bone tissue engineering applications in preclinical studies since no clinical application were reported in the online search.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021220920.This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . A protoThis systematic review was performed to address the following focused question: “What are the best strategies of using pullulan and/or dextran in bone regeneration in preclinical models?”An electronic search of the MedLine (PubMed), Embase (Scopus), and Web of science databases was carried out. Medical subject headlings (MeSH) terms were combined with keywords and Boolean operators to search databases. All searches were performed from October 2020 to February 2022 by focusing on studies written in English or French and published between January 2000 and February 2022. The searching query used for the research was: (“pullulan” OR “dextran”) AND [“bone (MeSH)” OR “bone regeneration (MeSH)”]. Additional articles were also searched by manually screening the list of references of all publications selected by the search.In vitro studies, clinical trials and reviews were excluded.Preclinical controlled trials using pullulan and/or dextran substitutes to induce bone regeneration were considered. All animal studies  were eligible if they assessed new bone formation in a bone defect or subcutaneously. Two independent reviewers (NA and MF) screened the titles and abstracts. For eligible studies that matched with the inclusion criteria, full texts were then assessed. Any disagreement between the reviewers over the eligibility of particular studies was resolved through discussion with a third reviewer (J-CF).In order to extract relevant data from included studies, structurable tables were made with the following data: authors, animal models, type of defect or implantation site, conditions tested (with the number of defects created or scaffolds implanted), composition of the scaffolds, cross-linking reagents, adjuvants , material design, observation period, and experimental analysis with results. For any missing data, authors were contacted by e-mail to complete tables.Methodological quality of individual studies was assessed using the collaborative Evidence-Based Complementary and Alternative Medicine approach to meta-analysis and review of animal data in experimental infarction (CAMARADES) 10-item quality checklist  and SYRCDatabase screening yielded to 713 publications after duplicates removal. Among these publications, 35 articles were selected for potential inclusion after title and abstract reading. Full texts of these 35 articles were reviewed. Seven of the 35 studies were excluded for not meeting the inclusion criteria. No additional study was added for selection after manually screening the list of references of selected publications. Finally, 28 studies were included for this systematic review: 16 used dextran-derived biomaterials  for boneMost studies (25 out of 28 studies) were carried out on small animals , and only few studies investigated bone formation in larger animal models . Experimental procedures were performed ectopically (17%), or orthotopically (83%) . EctopicAmong the 28 studies included, 24 used either dextran or pullulan as the main component for biomaterial design  and six Physical cross-linking and chemical cross-linking were used for hydrogels . PhysicaWe have identified three main routes of administration of these biomaterials in implantation sites. They could be implanted as an injectable hydrogel or microspheres/microbeads , as an aqueous solution adsorbed on a collagen or a polyvinyl alcohol sponge  or as aTo induce or enhance bone formation, various elements were directly added in these scaffolds to generate composite scaffolds . BioceraAnother reported strategy to enhance bone formation was the incorporation of bioactive molecules to these biomaterials to create a delivery system. Growth factors were employed in 13 studies, namely, bone morphogenetic proteins  e.g., B (bFGF), Incorporation of cells into the polysaccharide scaffolds was the last identified procedure to promote bone regeneration (three studies). Three types of cells have been incorporated to scaffolds to promote bone regeneration: rat mesenchymal stromal cells  (MSCs), For all these different strategies, the performance for regenerating bone tissue is examined in the following sections.Twelve studies investigated orthotopic bone formation by using dextran-based biomaterials and four studies investigated ectopic bone formation in rodents , 3.To determine bone regeneration ability of dextran-derived biomaterials, several studies combined this polysaccharide with natural or synthetic polymers. For example, polyvinyl formal (PVF) sponges have been coated with dextran to enhance the poor osteogenic ability of this synthetic polymer due to its fibrous construction . InvestiThree studies assessed the osteogenic capability improvement of dextran-based hydrogels loaded with mineral particles. Dextran-based hydrogel combined with a synthetic polymer (polyacrylamide) loaded or not with hydroxyapatite (HA) particles was implanted into a rabbit femoral defect . Bone reFinally, an injectable hydrogel composed of dextran and poly (L-glutamic acid) inspired from mussel adhesion to design an adhesive, self-healing biomaterial with osteogenic properties through HA and bisphosphonate incorporation inside the scaffold was designed by Regenerating Agents (RGTA®) are polysaccharides designed to replace altered heparan sulfate in injured tissues . They ar.  cross-link the polymeric scaffold to enable the encapsulation of growth factors or 2) to directly interact with them.Four studies investigated the osteoinductive potential of dextran-based materials as a delivery growth factor system in ectopic bone formation models  Table 2Table 2.In another study, the number of carboxymethyl and benzylamide groups  on dextran backbone was modified to investigate subcutaneously BMP-2 delivery . A dose-The impact of sulfated groups on different polymers  and its ability to release rhBMP-2 (10 µg) was determined in a mouse muscle implantation model . Dextran. confirmed these findings by loading unmodified dextran with rhBMP-2. It appeared that dextran scaffold generated few trabecular structures after 8 weeks.The last study in ectopic model by Five studies reported the use of dextran-based materials as a delivery growth factor in orthotopic bone models. Dextran-based microbeads modified with an amino group, diethylaminoethyl (DEAE) was investigated in a rat arthrodesis model . DextranAnother team assessed the potential of dextran/gelatin scaffolds to act as a delivery growth factors system [e.g., BMP-2  or IGF-1Another approach concerns the dextran functionalization with glycidyl methacrylate at different degrees of substitution  combined with IGF-1 . DextranFinally, dextran scaffold was successfully loaded with stromal-derived growth factor (SDF-1) in a mice calvarial defect. Bone regeneration was significantly improved compared with the empty defect , but witSix studies investigated orthotopic bone formation by using pullulan-based biomaterials and one study assessed ectopic bone formation in rodents , 4.Numerous studies described the performance of composite materials with pullulan. Combination of pullulan to alginate and bioactive glasses with various percentage of copper oxide to regenerate bone was investigated in a rat femoral defect and was compared with a commercial β-TCP/HA substitute embedded in an alginate-pullulan composite scaffold . A progrPhosphorylated-pullulan mixed with β-TCP had been tested in three different models, a rabbit ulnar defect, a pig vertebral defect, and a mouse femoral injection . It showThree studies mentioned the substitution of pullulan main chains by cholesteryl and acryloyl groups to proceed cross-linking between pullulan macromolecules and then to establish a growth factor delivery system . ModifieA nanogel membrane made with cholesteryl and acryloyl-binding pullulan was evaluated for guiding bone regeneration . This naSix studies investigated orthotopic bone formation by using pullulan/dextran-based biomaterials in which one study also assessed ectopic bone formation in rodents and goats , 5.One team focused on a composite scaffold combining a pullulan/dextran-based material with HA particles to promote osteogenesis . FirstlyPullulan/dextran polysaccharide-based scaffold supplemented with MSCs was compared with a commercially available CPC bone substitute   for theiThe purpose of this work was to review the design of pullulan and/or dextran-derived biomaterials used for bone regeneration. Polysaccharide scaffolds were systematically analyzed. These scaffolds were mostly used as a bone substitute (13 studies) or as a growth factor delivery system (13 studies). Three studies investigated the ability of these polysaccharide-based scaffolds to act as cell carriers for BTE applications and only one study suggested the use of a pullulan-based nanogel as a membrane for guided bone regeneration . The preA wide range of functionalization methods were used for dextran-based materials, making it impossible to select a specific one for these polysaccharide-based materials. Interestingly, no chemical modification of pullulan/dextran-based matrix was also reported .This review also highlights the significant use of cross-linking reagents for these polysaccharide-based biomaterials synthesis. Cross-linkers have attracted much attention to enhance the biological functionality and mechanical properties of biopolymers . Cross-lin situ forming hydrogels were directly injected into the defect area, molding the defect site (Biomaterial design also plays a key role in promoting bone healing. In the present review, we outlined that these polysaccharide-derived biomaterials are mainly used as hydrogels or microbeads. A growing interest for polymer hydrogels in BTE is arising  as they ect site , therebyect site . Microbeect site .in situ resorption latencies (Hydrogels also are good candidates to incorporate cells or growth factors and act as a delivery system. Pullulan and/or dextran-based materials display unique properties in the field of bone regeneration compared with commonly used bone substitutes. They exhibit resorption ability , therebyatencies . Anotheratencies  meaning Several studies investigated the osteogenic properties of these polysaccharides-derived biomaterials through the adjunction of mineral contents. Most of these studies show a significant improvement of bone regeneration by incorporation of mineral contents. HA supplementation was used in eight of these 10 studies. This stimulates bone tissue formation and gives osteoconductive properties to the biomaterial. In addition, dextran and/or pullulan have been reinforced with mineral materials to overcome their weak physical properties  and thenAnother reported strategy to enhance bone regeneration of dextran and/or pullulan-based materials was the encapsulation of growth factors. Growth factors are widely used in BTE to provide more regulating cues to target cell proliferation and differentiation and effective bone repair . A totalInterestingly, no study reported the use of a pullulan/dextran-based material to deliver growth factor. This could be explained by the ability of these matrices to retain local growth factors . The comFinally, three studies investigated dextran or pullulan/dextran-based hydrogels capability to incorporate progenitor cells to promote bone healing . Two appin vivo, thereby preventing their replacement by newly formed bone. However, further studies comparing these polysaccharides to commercially used bone substitutes should be conducted to draw formal conclusions.Only three studies  comparedFinally, there are limitations related to the present systematic review that must be mentioned. One limitation of this study is the heterogeneity in animal models and the wide types of bone defects performed in the included studies, thereby making it difficult to compare studies. Calvarial and femoral defects were the most commonly used model to assess bone regeneration in these studies. Femoral defect may be more appropriate to consider load-bearing capacity of polysaccharide scaffolds when necessary . AnotherThe present study emphasized that pullulan and/or dextran-based materials display unique properties for BTE applications. Moreover, this review is expected to provide clear information on the chemical functionalization of the pullulan-based and dextran-based materials and the cross-linking strategies for BTE applications. Interestingly, these polysaccharides are used as injectable hydrogels or microbeads that easily fit the bone defect. Incorporation of mineral contents such as hydroxyapatite to the pullulan and/or dextran-based biomaterials significantly enhance bone regeneration. We also outlined that the association of pullulan and dextran is a solution to obtain a biomaterial, deprived of growth factors or living cells to promote bone formation. Studies comparing these biomaterials with commercialized and clinically used products are too limited and further studies are required to draw more conclusions.