The 3- and 6-turn MI biosensors exhibited similar characteristics where the MI ratio peaked at a similar value. Our results suggest that the MI ratio can be increased by increasing the sensing area size and film thickness rather than the number of turns. We showed that an optimal film thickness to sensing area size ratio is required to obtain a high MI ratio. Our findings will be useful for designing highly sensitive MI biosensors capable of detecting low biomagnetic signals.Thin-film magneto-impedance (MI) biosensors have attracted significant attention due to their high sensitivity and easy miniaturization. However, further improvement is required to detect weak biomagnetic signals. Here, we report a meander thin-film biosensor preparation to investigate the fabrication parameters influencing the MI effect. Specifically, we hypothesized that an optimal film thickness and sensing area size ratio could be achieved to obtain a maximum MI ratio. A meander multilayer MI biosensor based on a NiFe/Cu/NiFe thin-film was designed and fabricated into 3-, 6-, and 9-turn models with film thicknesses of 3 µm and 6 µm. The 9-turn biosensor resembled the largest sensing area, while the 3- and 6-turn biosensors were designed with identical sensing areas. The results indicated that the NiFe film thickness of 6 µm with a sensing area size of 14.4 mm Magnetic sensors have received significant attention in electrical and biomedical research over the past few decades ,2,3,4. T20Ni80) permollay (Py) soft magnetic material has shown a significant contribution to the construction of MI biosensors. This material demonstrates large permeability, low magnetostriction, and no crystalline anisotropy [N film [Biosensor film thickness significantly affects MI performance. The thickness must be optimized to attain the maximum MI response with regard to magnetic material selection. Recently, the iron–nickle  process, including-beam evaporation and photoetching electroplating and other technical steps. The MI response of these sandwich meander-shaped MI biosensors was studied in different structural conditions. The results indicated that both the sensing area size and thickness had a significant influence on the MI response. We further analyzed and discussed the relationship between the number of turns, thickness, and the sensing area size of the meander lines.f defines the AC frequency, and σ and μ indicate the NiFe conductivity and permeability, respectively. In practice, the δ defines the biosensor cross-section. When the biosensor is subjected to the external magnetic field and the AC flows through it, the employed field modifies the permeability of the biosensor material, resulting in an MI effect [MI effect depends considerably on the permeability that is controlled by adjusting the biosensor geometry. Geometry includes shapes such as meander line shape, thickness, and the number of turns. Here, we optimized these structural parameters to achieve a highly sensitive MI biosensor. The MI effect of a magnetic material is usually expressed as the relative change of impedance given by Equation (2) and is expressed in Zmin [The MI effect describes the electrical impedance change in magnetic materials when subjected to an external magnetic field. In this study, nickel–iron (NiFe) was used as magnetic material, and copper (Cu) served as a conductive material for biosensor construction. MI effect variation mainly depends on the skin effect, which is the penetration of the electromagnetic field within the Cu conductive material or Cu surface when an alternating current (AC) flows through it . The penI effect . Hence,  in Zmin . It is tZmin is the minimum impedance during biosensor measurements when the magnetic field strength magnetically saturates the material. In this work, the impedance is measured when the magnetic field is off, i.e., Zmin = Z (eH = 0). When the value of Z is at its maximum, the MI is at its maximum. This occurs when the magnetic field intensity “eH” saturates the NiFe material, resulting in maximum permeability and minimum skin depth. Hence, we used a permanent magnet to generate a constant and strong magnetic field to maintain high permeability.MI response and its relationship to the magnetic field and sensor geometry. The maximum MI response occurs when Z(H) exhibits a maximum change as anisotropy affects it. The maximum MI response can be achieved by longitudinal anisotropy, which occurs when the MI biosensor is placed parallel to the applied external magnetic field and the current flow. Transverse anisotropy arises when the MI biosensor is perpendicularly positioned to the applied external magnetic field and the current flow. For the longitudinal anisotropy configuration, at eH = 0, the transverse permeability is maximum. For transverse anisotropy, the transverse permeability is at its maximum at Hk, which is given by Equation (3) [K indicates the anisotropy constant, and sM defines saturation magnetization. The maximum MI response is achieved when Hk is at its minimum.A critical factor in biosensor design is the tion (3) :(3)Hk=2KA magneto-impedance biosensor was designed and constructed using NiFe soft magnetic material. It was designed and patterned into a meander structure with a length of 5 mm and with various line widths and a gap of 60 µm between each line . Three c2O3) as an insulation layer, and 300 nm chromium/gold (Cr/Au) to be used for potential biosensor functionalization. The NiFe magnetic layer was processed to the length of 5000 μm in all configurations, a width of 160 μm in the 6- and 9-turns configurations, a width of 320 µm in the 3-turn configuration, and a thickness of 3 and 6 μm in all of the MI biosensors. The Cu conductive layer had a similar length of 5000 µm, a width of 120 µm, and a thickness of 4 µm.A meander thin-film MI biosensor was constructed with double magnetic layers based on two different thicknesses to investigate the effect of MI when it is based on thickness variation and to determine an optimal thickness to sensing area size ratio to achieve maximum MI response. 2O3/Cr/Au films, as shown in 2O3 film served as an insulation layer and was inserted between the sensing element and the immuno-platform.The overall MI biosensor design consisted of Cr/Cu/NiFe/Cu/NiFe/Al2O3 that was 300 nm thick was then deposited by means of electron-beam evaporation followed by an etching process to bare the Cu electrode  [A critical parameter in MI biosensor fabrication and measurement is the external magnetic field used to define the anisotropy during fabrication and to magnetize the magnetic sensing element during testing. A constant magnetic field Htion (3) . The layHe, as shown in ® ZVL vector network analyzer with two contact terminals. The AC flowed through the MI biosensors with a sweep frequency range of 1–40 MHz and a constant current amplitude of 10 mA, as shown in Z(H) curves and was defined by Equation (4) [The magnetic domain is defined along the meander length and width and is perpendicular to the meander plane. It resembles the longitudinal, transverse, and perpendicular MI response, as shown in tion (4) :(4)MI%=1Z(H) is the magneto-impedance when the longitudinal magnetic field eH is ON, and Z(H0) is the magneto-impedance when the magnetic field is OFF. The Z(H) is obtained by the equation for impedance, Equation (5) [tion (5) :(5)Z=R+jZ describes the impedance, R denotes the resistance, the real part of the impedance, and L represents the inductance, the imaginary part of the impedance.Z). The relative change in Z indicates the magnitude of the MI response, which is denoted as the MI ratio, and it is calculated from the Z(H) curves when the external magnetic field is applied. GraphPad Prism 7.0 software and Microsoft Excel were used to perform statistical analysis and to create plots. Optimization of the sensing area size and thickness was performed using the MI response values obtained from the biosensor. This represents the electrical impedance changes of the sensing element measured when the magnetic field was ON and OFF. The 3-, 6- and 9-turn MI biosensors were used for testing, and five measurements for each biosensor type were conducted. Average MI ratio values were derived and compared between the three biosensor types using a one-way ANOVA test. Relative standard deviation (RSD) was calculated using Microsoft Excel. The resultant sensing area size from the biosensor turns, and the different film thicknesses were compared. The optimal sensing area size and thickness was chosen based on the largest MI response and the biosensor with the least amount of variation. A bar chart was generated, and p-values ≤ 0.05 were considered statistically significant.We used Microsoft Excel to convert the real and imaginary phase data into impedance  denotes the MI response when the magnetic field eH was ON; the permanent magnet was placed at a 4 cm distance away from the MI biosensor, and Z(H0) was OFF when the permanent magnet was removed. The results indicated that the impedance change peaked at 14.2 MHz and maximized when subjected to the applied external magnetic field at 7.1 Oe. The maximum MI effect was found at 7.1 Oe when the permanent magnet was placed 4 cm away from the MI biosensor. The MI ratio was calculated based on Equation (4), where Here, the increase in the MI ratio can be partially attributed to the number of turns made by the meander line. The number of turns (9 turns) acts as a magnetic flux, where the magnetic flux of the adjacent sandwich NiFe film interacts with each other by the mutual inductance of the magnetic chain. Due to this mutual-inductance effect, the more turns that are made, the greater the change in the inductance ratio in the meander structure is. Hence, a more extensive change ratio can be obtained by Equation (4), leading to a greater MI response. Moreover, the skin effect, which is the tendency of the alternating current to become distributed within the Cu conductive layer such that the current density is larger on the surface of the Cu layer and decreases exponentially with greater depths in the Cu material (skin depth), has been attributed to the MI response increase. Here, the skin depth matches the NiFe/Cu/NiFe material thickness, whereas the NiFe is 6 µm thick, achieving a 70% increase. Furthermore, the sandwich structure geometry has contributed to MI enhancement in the areas where the large permeability magneto-inductive effect dominates the skin effect, allowing it to overcome the skin depth, resulting in large impedance changes. To achieve a higher MI response, we optimized structural parameters such as film thickness, the number of turns, and sandwich layout.In this work, the MI response peaked at 238% and 168% at 14.2 MHz under 7.1 Oe in the 9-turn sample with 6 µm and 3 µm film thicknesses. This peak value is much larger than the maximum MI ratio reported by Yang et al.,where more turns (10-turns) were employed, and the peak field of 7.1 Oe is much lower than their reported peak, 17 Oe . UsuallyeH”, of 0–20 Oe. The maximum impedance change was at 14.2 MHz under 7.1 Oe, as the permanent magnet was placed 4 cm apart from the MI biosensor. The 6-turn MI biosensor is identical to the 9-turn MI biosensor, with the exception of the number of turns used to investigate the turn impact on the MI response based on 3 µm and 6 µm film thicknesses. The same measurement setup and conditions were used: 1–40 MHz and an external magnetic field, “It is worth noting that the number of turns taken by the film is essential to the design of an MI biosensor when trying to achieve a higher MI response. We found that the MI ratio in the 6- and 9-turn models increased as the number of turns increased at the proper thickness. For the 6-µm film thickness, the MI ratio of 238% (9-turns) was higher than the MI ratio of 157% (6-turns) by 81%. It should be noted that an increase of three turns increased the MI ratio by 81%. Similarly, for the 3 µm film thickness, the MI ratio increased by 60%, from 108% to 168%, in the 6- and 9-turn configurations. Hence, the 9-turn meander line with a 6-µm film thickness could be the ideal parameters for MI biosensor design to achieve a higher MI response. As discussed above, the increase in the MI ratio can mainly be attributed to the large magnetic flux produced by the 9-turn configuration compared to the 6-turn configuration, as the magnetic flux is proportional to the number of turns. This may differ if the size of the meander line is altered; hence, further investigation is required. Moreover, the 6-turn MI biosensor displayed an MI response that was higher than the MI response reported by Wang et al. using the same biosensor conditions .The 3-turn MI biosensor exhibited similar characteristics to the 6- and 9-turn MI biosensors, with the exception of the number of turns and the width of the meander line. The width of the meander line was 160 µm wider than the 60- and 9-turn configurations. The measurements were conducted using the same setup and conditions as above.The 3- and 6-turn MI biosensors were expected to provide different MI ratios because when there are more turns, the MI ratio is higher, especially when compared to the configuration results of the 6-turn and 9-turn models. This discrepancy could be attributed to the meander line geometry of the 3-turn MI biosensor possess. Hence, further investigation is required to understand whether the MI ratio increase was mainly dependent on the number of turns taken by the meander line or the width of the turns (more details in the following sections). Still, the maximum MI ratio, 155%, in the 3-turn MI biosensor was comparable to reported MI ratios ,47,49.2 in the 9-turn configuration and 9.6 mm2 in the 6- and 3-turn configurations. The 9-turn MI biosensor contained the largest sensing area size, whereas the 6- and 3-turn MI biosensors exhibited a similar sensing area size despite the differences in the number of line turns because the line width in the 3-turn biosensor is doubled. Sensing area size variation was employed to investigate its influence on the MI effect and to determine the ratio of the optimal sensing area size to the number of turns required to enhance the MI response. Moreover, the intention was also to investigate whether the MI ratio is greatly affected by the sensing area size or the number of turns, as partially shown in the previous sections.An optimal ratio of the film sensing area size and the number of turns is critical to the design of highly sensitive MI biosensors. Here, three sensing area sizes were designed: 5 mm × 2.88 mm, 5 mm × 1.92 mm, and 5 mm × 1.92 mm. The sensing area size was determined by the number of turns taken by the meander line  and the line width, which was 160 µm wide in the 9- and 6-turn configurations and was 320 µm wide in the 3-turn MI biosensors. The resultant sensing area sizes were 14.4 mm2 sensing area yielded the maximum MI ratio, as the biosensor comprises nine turns. The 6- and 3-turn MI biosensors exhibited almost similar MI ratios, as they possess a similar sensing area size of 9.6 mm2. The maximum MI ratios based on the sensing area sizes, 14.4 mm2, 9.6 mm2 and 9.6 mm2, were 238%, 157%, and 155%, representing the 9-, 6- and 3-turn MI biosensors, respectively. Thus, the relationship between the number of turns and the MI ratio is not proportional. We noted a linear relationship between the MI ratio and the film sensing area size; the MI ratio increased as the film sensing area size increased.2) improved compared to the permeability of the 9.6 mm2 film sensing area. This could be attributed to the reduction in the eddy current loss controlled by the shape and the size of the film sensing area rather than the number of turns. It is worth noting that the eddy current loss in the 14.4 mm2 sensing area shape is much less than it is in the 9.6 mm2 sensing area biosensors. This explains why the permeability retains its value at 14.2 MHz, achieving a high MI response. Based on these results, a 14.4 mm2 sensing area size comprising a 160 µm and 5 mm meander line width and length, 9-turns, and 6 µm film thickness are the preferred parameters for designing highly sensitive MI biosensors. These findings are congruent with the MI ratio increase reported by Arribas et al., where they linked the increase in the MI ratio to the permeability changes based on different film sensing areas sizes despite using a single magnetic layer sensor [The MI response variation could be attributed to the permeability of the magnetic films affected by the meander line size. At 14.2 MHz, the permeability of the film sensing area , 9.6 mm2 (6-turns), and 9.6 mm2 (3-turns) with 3-µm and 6-µm film thicknesses . Using one-way ANOVA compassion, the %MI was not significantly different between the 3- and 6-turns MI biosensor types (p = 0.06075). Nonetheless, the 9-turn MI biosensor (6 µm thick) was significantly different and displayed the largest MI ratio compared to the 6- and 3-turn MI biosensors. Moreover, the 9-turn MI biosensor (3 µm thick) also showed a larger MI ratio than the 3- and 6-turn MI biosensors. The 3- and 6-turn MI biosensors showed similar trend variation in the MI ratio, which may be related to the similar sensing area size variations. Based on the calculated RSD values, the 9-turn MI biosensor (14.4 mm2 sensing area size) in 6 µm film thickness preserved its magnetic properties and showed excellent reproducibility with an RSD of 2.12%, indicating good precision compared to the RSD of 3.22% of the same sensor with a 3 µm film thickness. In the 6- and 3-turn MI biosensors (9.6 mm2 sensing area), the RSD increased to 3.14% and 3.10% for the 6 µm film thickness and to 4.12% and 4.04% for the 3 µm film thickness. Both the 6- and 3-turn MI biosensors displayed almost similar RSD, as they exhibited similar MI ratios. Therefore, excellent reproducibility is retained in the 9-turn biosensors.2) excellent stability was attributed to the appropriate ratio of the film thickness (6 µm) and the sensing area size (14.4 mm2), resulting in the stability and excellent superparamagnetism of the NiFe film. Here, the NiFe film thickness directly impacts the skin effect, where the permeability increases as the penetration depth decreases. The penetration depth matched the material thickness at 14.2 MHz, producing significant impedance variations. The excellent reported precision of the 9-turn MI biosensor supports the reliability of measurement results, making it a suitable biosensor for medical applications compared to other reported low MI biosensor responses. For example, MI biosensors have been reported to detect the cancer biomarker alpha-fetoprotein (AFP) with an MI ratio of 55% to 70% [MI biosensor geometry plays a vital role in magnetic signal detection. This work optimized structural parameters to achieve highly sensitive biosensors based on biosensor sensing area size, thickness, and the number of turn variations. Based on these findings, the 9-turn MI biosensor could be suitable for biomagnetic detection applications. The 9-turn MI biosensor’s (14.4 mm% to 70% ,50, the % to 70% , the pro% to 70% , and pro% to 70% .2, which resembled a 9-turn MI biosensor, is the optimum ratio yielding the maximum MI ratio. We found that the 3- and 6-turn MI biosensors shared a common characteristic: the MI ratio peaked at an almost similar value at the 3-µm and 6 µm film thicknesses. Thus, the relationship between the number of turns and the MI ratio is not proportional. We noted a linear relationship between the MI ratio and the film sensing area size; the MI ratio increased as the film sensing area size increased. It is worth noting that the magnetic peak field increased with the increase of the sensing area size and thickness, and the MI ratio in the 3-, 6- and 9-turn biosensors reached the maximum at the same magnetic field intensity indicating good stability and reproducibility. Excellent stability and reproductivity were retained in the 9-turn MI biosensors, as indicated by the low RSD of 2.12%, making its structural parameters ideal for biosensor design. These findings provide valuable information for the design of highly sensitive MI biosensors to detect weak biomagnetic signals in medical applications. However, other parameters, such as Cu conductive layer thickness, its line dimension, and the gap between each meander line, may also impact the MI response and should be investigated in future studies.We have investigated structural parameters of biosensors that are able to influence the MI effect. We fabricated an MI biosensors-based sandwich NiFe/Cu/NiFe film with a meander configuration by means MEMS technology into 3-, 6-, and 9-turn configurations with different film thicknesses prepared using electroplating technology. The structural parameters, including film thickness, sensing area size, and the number of turns, and how they influence the MI effect, have been thoroughly investigated. We speculated an ideal NiFe film thickness and sensing area size ratio to obtain the maximum MI ratio. We predicted that the MI ratio significantly depends on the sensing area size rather than the number of turns by designing 3- and 6-turn models with equal sensing area sizes to investigate this hypothesis. The MI ratio results showed a variation trend from increasing to decreasing with the increase of the film sensing area size and the thickness rather than the number of turns. The maximum MI ratio obtained by the 9-turn layout was 80% larger than the 3- and 6-turn structures at 14.2 MHz and 7.1 Oe. The NiFe film thickness of 6 µm with a sensing area size of 14.4 mm


Behavioral variant frontotemporal dementia (bvFTD) is a common form of younger-onset dementia with a proportion of cases overlapping pathologically and genetically with amyotrophic lateral sclerosis (ALS). Previous studies have identified that the human endogenous retrovirus K (HERV-K) is elevated in ALS serum and is associated with ALS TDP-43 pathology. In contrast, little is known about HERV-K changes in bvFTD. Here, we investigated the possible role of HERV-K in bvFTD.env gene in sporadic bvFTD (N = 63), sporadic ALS (N = 89), and control (N = 21) serum by ddPCR. We also analyzed HERV-K env, by qPCR, and the HERV-K reverse transcriptase protein, by confocal immunofluorescence microscopy, in the disease-affected superior frontal cortex of bvFTD with TDP-43 pathology.We measured the HERV-K env levels are significantly elevated (P = 3.5 × 10−6) in bvFTD compared to control serum, differentiating cases with an AUC value of 0.867. HERV-K env levels are also specifically elevated in the superior frontal cortex of bvFTD with TDP-43 pathology, with the HERV-K reverse transcriptase protein and TDP-43 deposit localized to the neuronal cytoplasm. Furthermore, in a neuronal cell line overexpression of TDP-43 induces HERV-K env transcription.Here, we show that HERV-K These results suggest that manifestation of HERV-K is associated with bvFTD TDP-43 pathology. Analysis of HERV-K in bvFTD may provide insight into an unrecognized but targetable perturbed pathology. Dementia is a brain disorder characterized by the progressive loss of cognitive abilities. Frontotemporal dementia usually manifests in younger ages compared to other forms of dementia, and sometimes the disease characteristics, such as accumulation of deposits of a protein called TDP-43, resemble those of amyotrophic lateral sclerosis (ALS), a progressive nervous system disease characterized by loss of motor control. Human Endogenous Retrovirus K (HERV-K), a virus that has integrated the human genetic material, has been found to be elevated in patients with ALS with TDP-43 deposits in the nervous system. Here, we measured the level of HERV-K in the blood and brain tissue of patients with frontotemporal dementia and found that it was elevated in both, and this was associated with presence of TDP-43 deposits in the brain. These results improve our understanding of the disease characteristics of frontotemporal dementia. Phan et al. assess the presence of human endogenous retrovirus K (HERV-K) in the serum and brain samples of individuals with behavioral variant frontotemporal dementia (bvFTD) with TDP-43 pathology, as seen in amyotrophic lateral sclerosis. The authors show HERV-K is associated with manifestations of bvFTD with TDP-43 pathology. Amyotrophic lateral sclerosis  is a related neurodegenerative disease characterized by loss of motor neurons leading to physical motor impairment7. bvFTD and ALS exist on a disease spectrum with the majority of cases having the same causal pathogenic protein, trans-activation response (TAR) DNA-binding protein 43 (TDP-43), encoded by TARDBP10. TDP-43 is a DNA/RNA-binding protein that regulates a number of transcriptional and translational processes11. It is present in diverse brain cells including neurons. Abnormal deposits of TDP-43 are present in >50% of bvFTD patients and most of ALS patients12. Mutations in TARDBP are associated with sporadic and familial ALS13 and in rare cases, bvFTD14.Frontotemporal dementia (FTD) is a common form of younger-onset dementia, of which the most common clinical syndrome is the behavioral variant (bvFTD). bvFTD is characterized by disinhibited behavior, loss of emotional processing, and impaired executive function16, and a small subset of cases present with both behavioral and physical symptoms17. Currently there are no definitive fluid-based biomarkers for the TDP-43 pathology in these two diseases, although a number of proteins are currently being assessed, including TDP-4319. Recently, the human endogenous retrovirus K (HERV-K) has been considered as a candidate biomarker for ALS22. HERV-K is a retrovirus that belongs to the Beta-retrovirus genus within the Retroviridae family24. As a retrovirus, it converts its RNA genome into DNA using its reverse transcriptase pol gene; the viral DNA then integrates into the human genome at multiple sites. Once integrated, the viral genes are largely inactive or dormant, but can be activated under certain conditions26. Activation or anomalous expression of HERV-K has long been associated with ALS pathology27. HERV-K DNA, proteins and reverse transcriptase activity have all been shown to be elevated in either or both serum and CSF of ALS patients compared to healthy controls29. There is also evidence that the human immunodeficiency virus (HIV), which is also a retrovirus belonging to the same Retroviridae family, is associated with the activation of HERV-K30 and that antiretroviral drug therapy reduce ALS symptoms in HIV patients with ALS32.Up to 15% of bvFTD cases go on to develop ALS symptoms and vice versaHowever, despite the fact that bvFTD pathogenesis overlaps with that of ALS, little is known about HERV-K in bvFTD. We therefore hypothesized that HERV-K is implicated in bvFTD TDP-43 pathology. We measured HERV-K in bvFTD, ALS and control serum side-by-side, and generated ROC curves for biomarker development. We analyzed HERV-K expression in multiple regions of bvFTD brain with TDP-43 pathology and determined its cellular localization. We also investigated the role of TDP-43 in HERV-K transcription in a neuronal cell line. We found that HERV-K levels are elevated in both serum and brain tissue in bvFTD compared to controls, and that TDP-43 induces HERV-K expression in a neuronal cell line.N = 63), sporadic ALS (N = 89) and healthy controls (N = 21) were recruited from FRONTIER, the FTD clinical research group at the University of Sydney Brain and Mind Centre, from the ForeFront FTD and motor neuron disease clinic at the University of Sydney Brain and Mind Centre, and from a panel of healthy study volunteers33 with no neurological or psychiatric disorders, notably no evidence of cognitive impairment. Blood samples were taken at two time points (i.e. visit 1 and visit 2) for each individuals. The mean of number of months between the two visits were FTD 28, ALS 9 and controls 22 months. The study was approved by the University of New South Wales  and the University of Sydney  human research ethics committees. All methods were carried out in accordance with the relevant guidelines and regulations. Blood samples were obtained following written informed consent from the participant and/or primary carer as legal representative. All patients underwent a neurological examination, a comprehensive cognitive assessment and structural brain MRI, and met current consensus diagnostic criteria for bvFTD34, ALS35 or no neurological disease. Blood samples (9 mL) were collected in tubes (BD Vacutainer SST II Advance Tube #367958), and serum prepared by centrifugation at 3500 rpm for 10 min at 4 °C, which was then aliquoted and stored at −80 °C until use.Individuals diagnosed with sporadic bvFTD (37 or no significant neuropathology (controls)39. Brain tissue samples were obtained following written informed consent from the participant and/or primary carer as legal representative. Consent to publish demographic information in Table Fresh-frozen post-mortem brain tissue samples were obtained with consent from the Sydney Brain Bank and NSW Brain Tissue Resource Centre. All brain donors underwent standardized assessments in life and standardized neuropathological examination, and met current consensus diagnostic criteria for either bvFTD with TDP-43 pathology32. 300 µL serum were centrifuged for 10 min to remove cellular debris and total nucleic acids extracted using the QIAmp Viral RNA Mini Kit  following the manufacturer’s instructions. Nucleic acids were not treated with DNAse as HERV-K viral particles contain both RNA and DNA42. 2.5 µL extracted nucleic acids were added to a reaction mix that consisted of 12.5 µL ddPCR Supermix (no dUTP) , 1.25 µL HERV env primer (900 nm) and probe (250 nm) mix, 1.25 µL ddPCR RPP30 copy number assay and 7.5 µL nuclease-free water. Droplets were generated on a QX100 Droplet Generator  and PCR performed on a BioRad C1000 Thermal Cycler  according to the following program: 10 min at 95 °C, 40 cycles of 30 s at 95 °C and 1 min at 60 °C, and 10 min at 95 °C. Droplets were assessed in a QX200 Droplet Digital PCR System . HERV-K and HERV-W levels were recorded as the ratio of HERV DNA copies to RPP30 DNA copies. Primer and probe sequences were as follows: HERV-K Fwd: 5′ ATTTGGTGCCAGGAACTGAG 3′; HERV-K Rev: 5′ GCTGTCTCTTCGGAGCTGTT 3′ and HERV-K Probe: 5′ 6-FAM-AGGAGTTGCTGATGGCCTCG-Iowa Black FQ 3′ , HERV-W Fwd: 5′ GTATGTCTGATGGGGGTGGAG 3′, HERV-W Rev: 5′ CTAGTCCTTTGTAGGGGCTAGAG 3′, HERV-W Probe: 5′ HEX/ZEN-TCCCAACTGACCCAGGTACATAGC-Iowa Black FQ 3′ . RPP30 copy number assay Hex labeled  was used in the HERV-K assay. RPP30 copy number assay FAM labeled  was used in the HERV-W assay.Serum HERV-K and HERV-W cell-free and genomic DNA levels were assessed using ddPCR protocol established by Avindra Nath  and as previously describedenv levels were measured by quantitative polymerase chain reaction (qPCR) using the BioRad CFX Connect  and the fluorescent dye SYBR Green (Bio-Rad), following the manufacturer’s protocol. Briefly, each reaction (20 μl) contained 1× mastermix, 5 pmoles of primers and 1 μl of cDNA template. Amplification was carried out with 40 cycles of 94 °C for 15 s and 60 °C for 1 min. Gene expression was normalized to the geometric mean of three housekeeper genes, GAPDH, β-actin, and PPIA. A no-template control was included for each PCR amplification assay. The primer sequences were –RNA was isolated from brain tissues (20 mg) using TRIzol reagent (Invitrogen) following the manufacturer’s protocol. All procedures were carried out using RNase-free reagents and consumables. Four microgram of RNA was reverse transcribed into cDNA using Moloney-murine leukemia virus (M-MLV) reverse transcriptase and random primers  in 20 μl reaction volume. HERV-K and HERV-W HERV-K: ATTTGGTGCCAGGAACTGAG and GCTGTCTCTTCGGAGCTGTT;HERV-W: GTATGTCTGATGGGGGTGGAG and CTAGTCCTTTGTAGGGGCTAGAG;GAPDH: AATGAAGGGGTCATTGATGG and AAGGTGAAGGTCGGAGTCAA;β-actin: GAATTCTGGCCACGGCTGCTTCCAGCT and AAGCTTTTTCGTGGATGCCACAGGACT; andPPIA: AGGGTTCCTGCTTTCACAGA and GTCTTGGCAGTGCAGATGAA.Formalin-fixed, paraffin-embedded sections (10 µm) from superior frontal cortex (SFC) were deparaffinized in xylene and rehydrated through graded ethanol, followed by antigen retrieval with citrate buffer (pH 6.0) using a pressure cooker  at a peak temperature of ~121 °C and gradually cooling to room temperature. Endogenous peroxidase was blocked with 1% hydrogen peroxide in 50% ethanol. Sections were blocked with 5% normal horse serum, then incubated with HERV-K RT antibody  at 4 °C for two overnights, followed by the secondary antibody ImmPRESS™-AP Anti-Mouse IgG Polymer Detection Kit  as per manufacturer’s instructions. They were then counterstained with hematoxylin and cover-slipped. Negative controls (without primary antibody or secondary antibody) were performed for each immunohistochemistry run, and no signals were detected.4 in 50 mM ammonium acetate buffer (pH 5.0) for 1 h to quench auto-fluorescence signals prior to cover-slipping with anti-fade fluorescence mounting medium  and then sealed with nail polish. Negative controls (without primary antibodies or secondary antibodies) were performed for each immunohistochemistry run, and no signals were detected in each case.Formalin-fixed, paraffin-embedded sections (10 µm) from SFC were deparaffinized in xylene and rehydrated through graded ethanol, followed by antigen retrieval with citrate buffer (pH 6.0) using a pressure cooker  at a peak temperature of ~121 °C and gradually cooling to room temperature. Endogenous peroxidase was blocked with 1% hydrogen peroxide in 50% ethanol. Sections were first labeled with HERV-K RT antibody , followed by the secondary antibody ImmPRESS™-HRP Horse Anti-Mouse IgG (HRP) Polymer  at room temperature for 30 min prior to applying FluorTM 488 Tyramide Reagent . Sections were then stripped using citrate buffer (pH 6.0) and pressure cooker prior to blocking with 2.5% donkey serum and 1% BSA. The primary incubation was conducted with a cocktail containing TDP-43 antibody  and NeuN antibody  at 4 °C for two overnights. Sections were then washed with PBS and incubated with the corresponding secondary antibodies  and 4′,6-diamidino-2-phenylindole DAPI  at room temperature for 2 h. Next, the slides were treated with 70% Sudan Black for 30 min and 10 mM CuSOFor immunohistochemistry, stained sections were scanned using an Olympus VS120 Slide Scanner with the same focus and exposure settings. For immunofluorescence, multiple sections were examined and representative images were captured with a Nikon C2 confocal microscope and associated Nikon NIS Elements software (version 4.60). Images were adjusted for contrast and converted to TIFF format on Fiji software (ImageJ version 2.0.0-rc-69/1.52p).TARDBP gene and the vector-only control43 were cultured in 12-well plates in Dulbecco’s modified Eagle’s medium (DMEM) containing 10% fetal calf serum, 1% Glutamax, 0.5% glucose, 100 IU/ml penicillin and 100 μg/ml streptomycin at 37 °C in humidified air containing 5% CO2. After 48 h the cells were harvested and total RNA prepared for gene expression studies.SHSY-5Y neuronal cells overexpressing the human wild type P < 0.05. Receiver operating characteristic (ROC) analysis was used to determine the best cut-off for HERV-K and curves generated in SPSS 26. All other graphs were generated using GraphPad Prism 7.Statistical analyses were performed using SPSS Statistics software 26 . For comparisons between bvFTD, ALS, and control groups, univariate analysis , with age and gender as covariates, was used and significance set at 29. We measured HERV-K levels in serum collected from ALS patients (N = 9) that were treated with the antiretroviral drug Triumeq in a Phase I clinical trial32. We also measured HERV-W, which is phylogenetically different to HERV-K24 , sporadic ALS (N = 89), and control (N = 21) serum side-by-side  in pathologically affected neurons. We prepared formalin-fixed paraffin-embedded tissue sections from the superior frontal cortex (SFC) and stained with HERV-K RT antibody. HERV-K RT was detected in neurons in the FTD cases Fig.  with a lWe were also interested in whether HERV-K levels change with disease progression. Blood samples were taken at two time points (i.e. visit 1 and visit 2) for each individual. The average number of months between the two visits were 28 for bvFTD, 9 for ALS and 22 for controls (note that ALS is a rapid-progressing disease). We found that HERV-K levels were not altered between the two time points for all three groups Fig. . Likewisenv DNA in bvFTD, ALS and control serum side-by-side. First, we confirmed that HERV-K levels were elevated in ALS serum compared to controls. Importantly, we revealed that HERV-K levels were also elevated in bvFTD serum compared to controls, and that the levels were even higher than those of ALS serum. To understand the link between serum and brain HERV-K levels, we measured HERV-K env transcript expression in the SFC (a FTD affected region) and cerebellum (a FTD unaffected region) in bvFTD with TDP-43 pathology and control brain. We found that HERV-K env transcript expression was elevated in bvFTD with TDP-43 pathology compared to controls only in the SFC. Furthermore, we demonstrated that TDP-43 pathology promoted HERV-K env transcription and that HERV-K RT, along with TDP-43, was localized to the neuronal cytoplasm. These results suggest that HERV-K is associated with bvFTD with TDP-43 pathology, and can be measured in serum, as is the case with ALS.The present series of studies investigated the possible role of HERV-K in bvFTD TDP-43 pathology and measured HERV-K levels in bvFTD serum and brain tissues. We used the validated ultrasensitive ddPCR to detect and quantitate HERV-K 45. Although the integrated retroviral genes are largely inactive, they are thought to still retain some of their functions47, and/or are activated under certain conditions. In ALS, these conditions are, for example, inflammation48, binding of TDP-43 to RNA49, relaxation of heterochromatin by C9orf72 peptides50 and infection of another virus52. Consistent with this, HIV, which belongs to the same Retroviridae family as HERV-K, appears to exacerbate ALS pathology. Clinical studies have shown that antiretroviral HIV drugs lower HERV-K levels and ALS symptoms53. HERV-K levels were also shown to be closely associated with the development of motor neuron symptoms in HIV patients30.The significance or ramification of HERV presence in humans is underscored by the fact that retroviral genes have permanently integrated into the human genome and constitute up to 8% of the human genome27. In vivo and in vitro studies have revealed that increases in HERV-K expression causes cell toxicity, degeneration of the motor neurons and progressive motor dysfunction, indicating that HERV-K load is associated with neurodegeneration severity22. In mouse models of ALS, the expression of HERV-K env resulted in decreases in motor cortex volume and synaptic activity in pyramidal neurons, and muscle atrophy and motor dysfunction22. Interestingly, the HERV-K RT protein co-localizes with TDP-43 deposits in ALS brain27. The abnormal TDP-43 deposits are present in the brain of most of ALS patients and ~50% of bvFTD patients12. In a Drosophila model of bvFTD expressing human TDP-43, Gypsy, a Drosophila endogenous retrovirus, was shown to facilitate TDP-43-mediated propagation of neurodegeneration54. Consistent with these observations, our results showed that the HERV-K env transcript expression is elevated in the SFC of bvFTD brain with TDP-43 pathology, and that TDP-43 localizes with the RT protein in the cytoplasm of neurons in bvFTD brain. Furthermore, we demonstrated, using a neuronal cell model, that TDP-43 expression induces HERV-K env transcription. As stated before, TDP-43 is a transcriptional factor that binds to both DNA and RNA and regulates numerous genes at both transcriptional and translational levels. In neurons, TDP-43 binds to thousands of RNAs55. In the case of HERV-K, TDP-43 binds to a promoter sequence in the long terminal repeat upstream of HERV-K22. Interestingly, TDP-43 also binds to the TAR element of HIV; the TAR element is essential for viral promoter activation and subsequent virus replication56. However, a previous study found that although TDP-43 bound to the HERV-K promoter during inflammation or proteasomal deficiencies, HERV-K transcription was not altered. Instead, HERV-K viral protein accumulated in neurons with overexpression of pathogenic forms of TDP-4357. Another study using murine models showed that both overexpression and depletion of TDP-43 upregulated HERV-K expression among other transposable elements49. Further adding to the complexity of the relationship between TDP-43 and HERV-K expression, another study found that knockdown of TDP-43 reduced HERV-K expression22. Clearly, further research is required to fully understand the exact relationship between TDP-43 and HERV-K expression.Of relevance to the present studies, HERV-K transcripts have been detected in cortical and spinal neurons of ALSRetroviridae negative internal control) levels were consistently unaltered in bvFTD, as well as in ALS. Our results are consistent with previous findings that showed that HERV-W env levels are unaltered in ALS serum, and that there is no association between HERV-W levels and ALS59. HERV-W is phylogenetically distant from HERV-K and it could be under different activation pathways or unresponsive to the same mechanisms that regulate HERV-K . However, in one study, the HERV‐W element encoding syncytin was shown to be elevated in muscle biopsies of ALS patients. However, the authors concluded that this elevation is likely to be due to downstream macrophage response to inflammation rather than a primary pathogenic event in ALS59.In contrast to HERV-K, HERV-W ). Research on the possible link between HERV-K and other neurodegenerative diseases is emerging. For example, HERV-K RNA was shown to be detectable more frequently in Alzheimer’s disease (AD) CSF compared to controls, and that there was a distinct correlation of upregulated HERV-K and Toll-like receptor 8 RNA expression in AD brain61. However, another study found that there was no difference in serum or CSF HERV-K protein levels in AD compared to controls21. Interestingly, other HERVs, such as HERV-Fc1, have been linked to AD through Tau pathology62.In this study, we also examined the potential of HERV-K as a diagnostic tool for bvFTD. We showed that HERV-K is a good discriminator of bvFTD from controls. However, although HERV-K levels were significantly higher in bvFTD compared to ALS serum, the AUC value was not high enough to be a good discriminator of bvFTD from ALS. This is not surprising knowing that there are clinical and pathological overlap in these two diseases. When considering HERV-K as a diagnostic tool for bvFTD, it is also important to recognize that HERV-K levels can also be affected by other conditions or factors. For example, HERV-K levels are elevated with certain cancersIn conclusion, we demonstrated that HERV-K is biochemically related to bvFTD with TDP-43 pathology and that HERV-K could potentially serve as a blood biomarker for bvFTD. Though further multicenter studies are required to confirm the association between serum HERV-K levels and to clarify the link between HERV-K and TDP-43 pathology, our study has provided new insights into an unrecognized perturbed pathology in bvFTD and opened a new area of research for understanding the pathogenesis of bvFTD with TDP-43 pathology.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1


Graphene-based drugcarriers provide a promising addition to currentcancer drug delivery options. Increased accessibility of high-qualitygraphene made by plasma-enhanced chemical vapor deposition (PE-CVD)makes it an attractive material to revisit in comparison to the widelystudied graphene oxide (GO) in drug delivery. Here, we show the potentialof repurposing the metabolic drug phenformin for cancer treatmentin terms of stability, binding, and pH-responsive release. Using covalentattachment of poly(ethylene glycol) (PEG) onto pristine (PE-CVD) graphene,we show that PEG stabilized graphene nanosheets (PGNS) are stablein aqueous solutions and exhibit higher binding affinity toward phenforminthan GO. Moreover, we experimentally demonstrate an improved drugrelease from PGNS than GO at pH levels lower than physiological conditions,yet comparable to that found in tumor microenvironments. This minimizes unwanted side effects and increases exposureof drug at the tissue of interest, which makes the therapy more efficientcompared to conventional drug-based therapy.30 While the production of grapheneusually renders polydisperse sheet sizes, multiple protocols to obtaina narrow size separation of graphene down to a nanometer range havebeen described previously,36 making graphene an ideal candidate to fully exploit the EPR potential.Furthermore, the lower pH found in tumor microenvironments as a resultof rapid cancer cell growth37 can be usedfor targeted drug release. GO has previously shown amphiphilic propertiesstemming from hydrophobic basal regions among the hydrophilic functionalgroups,39 allowing for targeted release of loadeddrugs at lower pH.41 However, in contrast to GO, this ability remains to be investigatedfor CVD graphene, with its lower defect levels and fewer functionalgroups, as the pH-dependent release from GO has been attributed tothe lower binding with these groups42 affected bythe protonation of the loaded drug in lower pH.44Besides size limitations dueto intravenous administration, deliveryof nanosized carriers requires that graphene sheets are small enoughto pass the leaky vessel endothelium in tumors to accumulate utilizingthe enhanced permeability and retention (EPR) effect.46 that has previously been tested in cancer models using micellesas a drug carrier.45 The analogue metformin,commonly used to treat diabetes type 2, has recently been studiedextensively for its potential as a cancer drug, inhibiting key metabolicpathways needed for cell growth.47 Phenforminacts similar to metformin in cancer cell lines, but has higher potency.48 Phenformin was discontinued in diabetes treatmentin the early 1980s due to undesired side effects,48 but its newly discovered beneficial effects in cancer treatmentmay outweigh the previously experienced risks.53 Importantly, these side effects could be further mitigated by theuse of a dedicated drug carrier, such as graphene nanosheets. On astructural level, phenformin contains a guanidine group, which couldform hydrogen bonds between the amine groups in guanidine and thecarboxyl groups on graphene sheets.55 In addition,phenformin contains a phenol residue that could bind to graphene throughpi–pi interactions due to delocalized electrons on the graphenesurface.55 Drug delivery of phenformin’s analogue,metformin using carbon nanotubes in cancer cells has been reported,56 as well as controlled metformin drug releaseusing GO hydrogels in mice.58 Moreover, GO has recentlybeen used to selectively deliver metformin to triple-negative breastcancer,59 demonstrating increasing interestand relevance for improving biguanide drug delivery using graphene-baseddrug carries.To test graphene’s potentialas a drug carrier, we usedphenformin, a biguanide antidiabetic drug,Here, we report on the stability and binding propertiesof twographene-based drug carriers, PEGylated graphene nanosheets (PGNS)and GO, in relation to the biguanide drug phenformin. This work ishighly relevant for expanding the cancer drug repertoire and holdspromise for overcoming challenges related to using metabolic drugsin cancer treatment.To increase the solubilityof PE-CVD graphene, we covalently attached poly(ethylene glycol) (PEGylation)onto PE-CVD graphene sheets. The PEGylation approach was used to retainthe defect-free graphene properties of PE-CVD, while obtaining anirreversible increase in water solubility, but without introducingoxidations of the basal plane as in GO. This should preserve the pielectrons that are necessary for drug adsorption and yielding loweroxygenation levels in the PEGylated graphene nanosheets (PGNS). Throughoutthis work, we compare the PGNS to a commercially available GO fromACS materials.The atomic ratio of oxygen to carbon, determinedby X-ray fluorescence (XRF), was around 5 times lower in PGNS comparedto GO 1. ThissDτ) and ζ-potential (ZP) of GO and PGNS. The ZP and Dτ measurements showed no significant variation atpH 7.5 and 6.5 between PGNS and GO . However, at pH 5, the Dτ wasdecreased for GO and was 3-fold lower for PGNS compared to pH 7.5.This substantial change in Dτ indicateschanges in either the shape or the hydrodynamic radius of the particle.The ZPs of PGNS and GO at physiological pH were −21.5 and −37.5mV, respectively. The more negative ZP of GO is probably linked toits higher oxygen content compared to PGNS. Furthermore, acidic pHhad a minimal effect on the ZP of GO compared to PGNS, the latterbeing almost neutral at pH 5 . When dispersed in fetal bovine serum over 5 days, GO showed moresigns of protein adsorption than PGNS. Thereafter, both GO and PGNSmaintained their Dτ and ZP levelscompared to day 1 (Table S1).Dynamic light scattering(DLS) was used to compare the effect ofpH on the diffusion coefficient (S2). The AFM scan also revealedpossible solvent residues on top of the PEGylated graphene sheets,which could be caused by the solvent being trapped by the PEG chainsduring evaporation. The remaining solvent complicates the estimationof the layer numbers. GO was found to consist of primarily monolayersheets of vastly varying size compared to PGNS (S2). Therewas a high dispersity between sheet sizes, ranging from few nanometersto multiple micrometers in the largest sheet diameter.Atomicforce microscopy (AFM) was used for the shape characterizationof both materials. The PGNS sample was found to consist of sheetsthat are relatively similar in size (mean diameter = 262 ± 75nm) but, on average, showed smaller diameters than GO (mean diameter= 448 ± 226 nm) 1a and S2 to PGNS 1b and S2Figure S3).The PE-CVD graphene used in this study was vertically grown onthe substrate and therefore rendered few (∼less than 10) layersthick sheets that were initially hydrophobic and not dispersible inwater. However, after PEGylation, the resulting graphene sheets werestable and dispersed in deionized water for more than 1 week and couldeasily be redispersed by gentle shaking . Such agglomeration was not observedin PGNS at the same phenformin concentrations. A titration experimentwas conducted to measure the extent of GO and PGNS stability uponincreasing phenformin concentrations. Here, a centrifugation stepwas introduced to isolate the dispersed supernatant of GO and PGNSin a solution mixed with increasing concentrations of phenformin (15.6–1000μM) after 24 h of interaction time. The graphene concentrationsin the collected supernatants were calculated by integrating the areaunder the absorbance spectrum in the visible range. A decrease inmeasured supernatant graphene concentration would signify agglomerationdue to the faster sedimentation of larger formed particles under equalcentrifugal forces. We found that PGNS had lower levels of agglomerationcompared with GO at concentrations of up to 1 mM phenformin  to both dispersions. Visually detected destabilizationof GO sheets and formation of agglomerates were seen at concentrationsof 250–1000 μM phenformin 2a and co60 For example, the higher stability in GO comparedwith reduced GO is due to decreased strength of the electrical doublelayer in reduced GO.60 Similarly, the loweroxygen content on PE-CVD graphene would also result in a decreasedelectric double layer, making it unstable in water without the conjugationof PEG. However, contrary to GO, the addition of phenformin showednegligible effect on PGNS stability, most likely due to the PEGylation.Depending on the pH of the solution, phenformin exists in the solutionas either a divalent or monovalent ion. Therefore, addition of phenforminwill increase the ionic strength of the solvent, in this case, water,and is expected to reduce the repulsive colloidal stability of grapheneby reducing the strength of the electrical double layer surroundingthe sheets. This double layer is also expected to be larger in GOdue to higher oxygen content leading to increased electron density,which in turn would be more effective in overcoming the attractiveforces that would otherwise bring the graphene sheets together.+ ionconcentrations to a critical coagulation concentration of 44–60mM.61 Furthermore, reports of divalent ions Ca2+ and Mg2+ interaction with GO61 show critical coagulation concentrations comparable tothe 1 mM phenformin concentrations used in our study, which supportsour findings that phenformin compromises GO stability in solution.Moreover, it has been shown previously that steric stabilization usingPEG prevents ion-mediated agglomeration,62 supporting our observations that phenformin induced less agglomerationin PGNS than GO. The significance of this effect in the bloodstreamis critical for the choice of nanoparticles in drug delivery. Theionic destabilization, here represented by phenformin, gives an indicationof the stability of graphene if exposed to the naturally occurringelectrolytes in the bloodstream. Blood electrolytes, such as sodiumand calcium cations, maintained at concentrations over 100 and 2 mmol/Lrespectively,63 may pose a concern towardthe stability of repulsively stabilized nanoparticles such as GO.However, from our data , time-correlatedfluorescence imaging, and time-resolved absorption measurements wereused to understand the kinetics and affinity of the interaction ofphenformin with graphene dispersions.kd values of 3.625 ±0.551 and 16.25 ±2.138 μM, respectively (MST traces were collectedafter 24 h of phenformin interaction in a titration series (0–1mM) with PGNS and GO to calculate the binding at each concentration.As MST relies on the temperature-induced changes in fluorescence tocalculate binding, PGNS was covalently labeled with an Atto-488 dye,while the autofluorescence of GO was sufficient to calculate the binding.Hill’s slope-fitted fractional binding curves showed 4.5 timeshigher binding affinity of phenformin toward PGNS compared to GO with ectively 3.To monitor the binding kinetics at an equilibrium state,the adsorptionof 50 μM phenformin was monitored over time on 0.5 and 1 mg/mLof GO and PGNS, respectively. There was rapid drug adsorption withinthe first 4–8 h 3, where 65 The initial binding of equalfluorescein concentrations onto PGNS and GO was 32.15 and 17.66%,respectively. Since the lifetime of fluorescein is affected by pH, we tracked the change of fluoresceinbinding onto PGNS and GO after the addition of phenformin. The bindingwas reduced to 20.3 and 6.37% in PGNS and GO, respectively. A spatialresolution of the binding efficiency showed that the displacementof fluorescein by phenformin appears to be more considerable in thenonagglomerated regions in GO, suggesting a higher phenformin bindingin these regions  pair.ed by pHS5, we tr10 Thus, the probabilityof carboxylic groups, and their interaction with phenformin’samines, should increase. This interaction is detectable by DLS inthe absence of graphene . Onthe other hand, the removal of oxygen correlates with an increasein the sp2 hybrid orbital fraction on the graphene surface.66 Therefore, a less defected basal plane as inPGNS would allow for more pi–pi and cation–pi interactionswith phenformin. However, the lower number of carboxylic groups onPE-CVD graphene limits their interaction with external amines comparedto GO. In addition, a proportion of these groups are converted intoamides during the covalent attachment of PEG in PGNS, decreasing theirconcentration even further. In contrast, the addition of the oxygen-richPEG arms in PGNS adds a new mechanism and binding opportunity forphenformin. This may happen as we found that the ZP of PEG alone isattenuated after phenformin addition .In addition to agglomeration, the difference in oxygen contentand the extent of surface defects could play a critical role in thediscrepancy of phenformin interactions with GO versus PGNS. The measured5-fold higher oxygen content in GO 1 means mThe effect of pH on the release profile of phenforminfrom GO and PGNS was studied after 24 h of interaction. The amountof unbound phenformin was measured at pH values that simulate shiftsbetween normal tissues and tumor microenvironment of pH 5, 6.8, and7.4 with increasing graphene concentrations.A change in phenforminadsorption capacity onto graphene was affected by pH in both PGNSand GO 5a.ThereS1). Theshift toward positive ZP corresponding to more basic pH levels wassteeper in PGNS compared to GO, spanning over a 3.8-fold increasedcharge range in the tested pH values. An increased positive electrostaticpotential at the shear planes of PNGS could cause an enhanced releaseof the positively charged phenformin at acidic pH values, comparedto GO. The decrease in negative charges appeared to correlate withthe pH-dependent release of the adsorbed phenformin in PGNS and GO(S1). Thus, as the pH drops lower than physiologicallevel, similar to that observed in the microenvironment of tumor orlysosomes,21 more phenformin is released.These results correspond to thechange in ZP of PGNS and GO atpH values ranging from 3 to 11.5 5b and S1S and GO5b and S167 However, this type ofinteraction is less likely under acidic conditions. Carboxyl groupsare increasingly protonated, leading to lower binding and the consequentrelease of phenformin. Previous studies demonstrating the pH-dependentdrug release of the cancer drug doxorubicin from GO have ascribedthis effect to weakened hydrogen bonding under acidic conditions.69 While it could be argued that PGNS is alsoaffected by the same mechanism, the lower density of carboxylic groupson its surface should limit this contribution compared to GO. Therefore,this might not explain the enhanced pH-dependent release observedin our experiment using PGNS. However, the higher capacity to participatein pi interactions due to the low-defected surface, such as in PGNS,makes it more relevant for the adsorption of phenformin than in GO.The interaction with the basal plane of graphene could occur througheither pi–pi interactions with the phenol ring or cation–piinteractions with the amine group of phenformin. Cation–piinteractions, in particular, are known to increase in strength withincreasing pH.70 Thus, the increased probabilityof establishing pi–interaction can provide a reason for thehigher pH-responsiveness in PGNS compared to GO.The difference in the modular affinity between phenformin and GOor PGNS at acidic versus basic pH values could be due to a combinationof different interaction mechanisms between phenformin and graphene.In GO, the degree of protonation of carboxylic groups on GO and guanidiniumin phenformin might be a key factor. At neutral pH levels, carboxylicgroups are more likely to interact with the protonated biguanide coreof phenformin.Figure S7), this ability can be limitedby possible PEG adsorption onto the graphene surface. Poly(ethyleneglycols) have been shown to adsorb onto activated carbons in a pH-dependentmanner, where the adsorption was at its lowest at pH 5.71Additionally,the PEG arms in PGNS could be involved in the pH-responsivebinding of phenformin. While PEG could interact with phenformin itself and monofunctional 2K mPEG-Amine  wereadded to the washed PE-CVD graphene and sonicated with the graphenefor 5 min. The cross-linking was then mediated by adding 1.5 mg of1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)  to the mixture under sonication for 30 min. Thedispersion was left under constant agitation overnight. The excessEDC and mPEG were then removed by dialysis for 48 h using 30 mL ofSlide-A-Lyzer Dialysis Cassettes in DiH2O that is replaced after 2, 6, and 24 h. The PEG–graphenesuspension was sequentially sonicated in a bath sonicator and thenwashed with DiH2O using 100 kDa Vivaspin  centrifugal concentrators three times.Thereafter, the UV–vis spectrum of the filtrate was controlledfor the absence of absorbance peaks differentiating from a blank DiH2O control (WL: 200–900 nm).PE-CVD graphene flakes  were dispersed inDiH2O that is then cross-filtratedusing Vivaflow 50  to neutralizethe pH and eliminate any existing contaminants.Commercially availablegraphene oxide prepared by modified Hummer’s method  was used in this study. A 1 mg/mL waterdilution was made in DiHPGNS and GO suspension (50μL) was placed dropwise on a silver membrane filter . The samples were dried at 50°C for >30 min and measured on an S4 PIONEER X-ray spectrometer the following day. To calculatethe relative oxygen content, the intensity values from carbon andoxygen of blank filters were subtracted from the samples. To compensatefor variations in the amount of PGNS and GO placed on the filters,the oxygen content of all filters was normalized based on the measuredcarbon content. The relative oxygen content was then calculated bydividing the resulting relative oxygen content of GO by PGNS.For analysis, 20 μLof the suspensions were placed on a mica substrate and evaporatedat 50 °C for >30 min and allowed to cool before analysis.Thesample was analyzed in repulsive mode on an MFP-3D-BIO . Noise filtration was performed usingtwo-dimensional fast Fourier transform (2D-FFT) filtering in Gwyddion2.57. Analysis of sheet diameter distribution is based on the radiusfrom the center of mass calculated from a minimum of 150 sheets segmentedby height.DLS-basedsurface chargeand intensity-weighted sizes of PGNS and GO were measured using ZetasizerNano ZSP  with an inlineMPT-2 degassed titrator. The titration compartment containing 10 mLof GO or PGNS was kept under constant agitation and real-time pH measurementto adjust the pH at each titration step using HCL and NaOH.Phenformin was added in increasing concentrations (15μM to 1 mM) to 1 mg/mL of PGNS and GO suspensions. After 24h, 3000 RCF centrifugation for 10 min was performed to sediment largeragglomerates. The supernatants were collected, and their concentrationwas measured using UV–vis spectroscopy by integrating the areaunder the spectrum in the visible range between 400 and 900 nm.T = 0.Size measurement of PE-CVD graphene and grapheneoxide at 100 μg/mL concentration was carried out continuouslyover time using DLS  for 150 min withand without 1 mM phenformin. The calculated sizes were normalizedto the initial measured size at In plastic UV cuvettes,1 mL of 50 μM phenformin (the concentration based on a pilotexperiment showing equilibrium reached within 24 h under same parameterswithout graphene) was separated from 400 mL of water suspensions ofPGNS and GO at concentrations of 500 and 1000 μg/mL using a20K MWCO RC membrane. Continuous absorbance measurements at 233 nmwere taken  using a Shimadzu UV-1800 UV–visiblespectrophotometer. The decrease in absorbance correlated with thedecrease of phenformin in the lower compartment and its binding tographene. The adsorption of phenformin is then calculated after backgroundsubtraction  and the ratiosof interpolated measured values by the maximum phenformin concentrationof 10.26 μg/mL.Tables S2 and S3).Lifetimes of(15 μg/mL) fluorescein  in exposureto 100 μg/mL of graphene with and without the addition 0.5 and1 mM of phenformin was recorded using a Leica TCS SP8 falcon platform. The lifetime decays werecollected and fitted to a two-exponential tail fit decay to calculatethe intensity-based mean lifetimes and the FLIM-FRET changes correspondingto phenformin doses . Series concentrations of phenformin(0–1 mM) in DiH2O were mixed with GO or Atto-488-PGNSto a final concentration of 50 μg/mL. Afterward, each of themixtures was pulled into a capillary and set into the Monolith NT.115capillary compartment. MST power (40%) was used in combination withthe blue LED power to determine the MST traces. The change in thethermophoresis of the fluorescence correlates with higher phenforminbinding at each concentration used. MST traces derived fraction boundvalues were plotted against the phenformin molar concentration inMO. Affinity analysis software was used and kd was calculated from the dose–response curve.First, fluorescentPGNS were produced similar to the production of PGNS described inthe 2O and acetonitrile (30:70%) as the mobile phase.The bindingof phenformin at different pH levels is determined via HPLC. Phenformin(50 μM) was mixed with a series dilution of PGNS and GO (15–500μg/mL) in triplicate at 3 pH levels of 5, 6.8, and 7.4. After24 h, 300 μL of the mixture was moved to a 10 kDa MWCO filtrationplate and centrifuged over a collection plate at 500 rpm until completefiltration. Each filtrate (100 μL) was then measured for unboundphenformin at 233 nm using HPLC  over Phenyl-Hexyl 2.7 μm column using a gradient of DiH


The Earth has been beset by many crises during its history, and yet comparing the ecological impacts of these mass extinctions has been difficult. Key questions concern the kinds of species that go extinct and survive, how communities rebuild in the post-extinction recovery phase, and especially how the scaling of events affects these processes. Here, we explore ecological impacts of terrestrial and freshwater ecosystems in three mass extinctions through the mid-Phanerozoic, a span of 121 million years (295–174 Ma). This critical duration encompasses the largest mass extinction of all time, the Permian–Triassic (P–Tr) and is flanked by two smaller crises, the Guadalupian–Lopingian (G–L) and Triassic–Jurassic (T–J) mass extinctions. Palaeocommunity dynamics modelling of 14 terrestrial and freshwater communities through a long sedimentary succession from the lower Permian to the lower Jurassic in northern Xinjiang, northwest China, shows that the P–Tr mass extinction differed from the other two in two ways: (i) ecological recovery from this extinction was prolonged and the three post-extinction communities in the Early Triassic showed low stability and highly variable and unpredictable responses to perturbation primarily following the huge losses of species, guilds and trophic space; and (ii) the G–L and T–J extinctions were each preceded by low-stability communities, but post-extinction recovery was rapid. Our results confirm the uniqueness of the P–Tr mass extinction and shed light on the trophic structure and ecological dynamics of terrestrial and freshwater ecosystems across the three mid-Phanerozoic extinctions, and how complex communities respond to environmental stress and how communities recovered after the crisis. Comparisons with the coeval communities from the Karoo Basin, South Africa show that geographically and compositionally different communities of terrestrial ecosystems were affected in much the same way by the P–Tr extinction. However, mass extinctions provide a series of natural experiments of varying levels of severity from which biologists can seek common features of the killing mechanisms and the subsequent recovery. A series of crises from the early Permian to Early Jurassic, some 295–174 million years ago (Ma), may have shared a common physical driver that relates to the current environmental crisis, namely extinction following the massive release of carbon dioxide and other greenhouse gases into the atmosphere .Three of the mid-Phanerozoic mass extinctions, the Permian Guadalupian–Lopingian (G–L), Permian–Triassic (P–Tr) and Triassic–Jurassic (T–J), are of differing magnitude, with 64–80% , approxiMost studies of mass extinction ecology and dynamics have focused on marine environments where fossil records are assumed to be more uniformly sampled and hence more comparable than terrestrial fossil records. However, the terrestrial environment cannot be ignored: today there are many more species on land than in the oceans, although that was likely not the case in the Permian to Jurassic, but the effects of catastrophic environmental change might be expected to be different, perhaps even more severe on land than in the oceans, which can show some buffering against atmospheric changes ,8. FurthSuccessive analyses of trophic structure over deep time can be employed to assess important ecological questions. Recently developed food web models are a powerful tool for exploring and quantifying community dynamics, simulating how the structures of modern –12 and a. 2(a)a). According to palaeogeographic reconstructions, this basin was located at a latitude of about 45° N from the Permian and during the entire Mesozoic [d) that mainly correspond to alluvial and lacustrine settings [During the Permian to Jurassic, the Junggar basin of modern Xinjiang, China was located on the Junggar block, a part of the Pangaea supercontinent iS = 45.267r + 46.997, where Si is the predicted richness of guild i and r is the link ratio) between insect species richness, and the ratio of insectivore richness to the number of insectivore prey guilds, recognizing that most of the insectivores belong to insectivore-carnivore guilds [Daptocephalus Assemblage Zone and Middle Triassic Cynognathus Asssemblage Zone of the Karoo Basin, South Africa [The complete data used in this study are stored in the Dryad Digital Repository. In summary, we conducted an in-depth literature review to maximize the completeness and robustness of our early Permian to Early Jurassic dataset for nonmarine species in northern Xinjiang, China. Based on updated stratigraphic information and taxonomic revisions, a database of 436 species among 10 major clades  was constructed at the formation/member level . The insect body fossil record of north Xinjiang is limited in certain formations; here we used the approach of  to estime guilds . The estc), namely the Permian Lucaogou , Hongyanchi , Quanzijie , Wutonggou , Lower Guodikeng  formations, the Triassic Upper Guodikeng , Jiucaiyuan , Shaofanggou , Lower Kelamayi , Upper Kelamayi , Huangshanjie  and Haojiagou  formations, and the Lower Jurassic Badaowan  and Sangonghe  formations .Here, a total of 14 palaeocommunities spanning approximately 121 million years from the early Permian to Early Jurassic was assembled based on the fossil records from multiple sections in north Xinjiang Here, we define a guild as a set of species whose members share ecologically relevant characteristics, namely habitat, body size ranges and potentially the same predator and prey when present in the same community  ,26. TherThere is a finite ensemble of species-level food webs consistent with the assigned links between guilds. The species-level food webs vary in the distribution of interspecific trophic links, which we stochastically generated by applying mixed exponential–power-law link distributions uniformly to all 14 communities, consistent with the hyperbolic distributions typical of modern food webs ,26 and o(d)Dissimilarity among the palaeocommunities based on their functional structures was analysed in a two-step procedure. First, palaeocommunities were ordinated according to the presence or absence of guilds using nonmetric multidimensional scaling (NMDS) based on intercommunity Jaccard distances. The Jaccard distance accounts for overlap in guild composition only, ignoring taxon richness within guilds. Second, the NMDS analysis was repeated, but this time using Bray–Curtis dissimilarity indices, with both the presence of guilds and their taxon richness accounted for . Here, w(e)Here we assess stability and palaeocommunity dynamics using the cascading extinction on graphs (CEG) model, designed specifically to take into account uncertainty in palaeontological community data . Even a Differences between pre- and post-extinction stability and resistance would also suggest that trophic restructuring occurred, altering palaeocommunity stability. The CEG model was applied to examine the resistance or vulnerability of the palaeocommunities to secondary extinction when one or more components of the communities were perturbed. In this case, primary production was the quantity perturbed, and vulnerability was measured as the fraction of species that became secondarily extinct as a consequence. In general, this type of CEG perturbation yields a response where a community's resistance is high, and relatively uniform over a broad range of perturbations, as the magnitude of perturbation is increased incrementally . CommuniIn almost all cases examined, however, a point of perturbation is reached where secondary extinction increases dramatically. This is defined as a collapse threshold, and reductions of primary production beyond this threshold are expected to change the community significantly, reducing richness and altering the composition. We therefore quantified community resistance, or compositional stability, based on variations of collapse thresholds , with higher collapse thresholds indicating higher resistance or compositional stability. Principal components analysis was also used to capture the CEG dynamics at relatively low perturbation level . For each community, we calculated the thresholds on all 100 simulations using the R package ‘changepoint.np', in which statistical changes in the data sequence were detected by implementing the pruned exact linear time, or PELT, algorithm .2), directed connectance). Network structure properties were calculated using custom code written by P.D.R. in Julia [This combined approach  allows us to describe how the structures of the communities differ and then determine how those differences translate into community performance in the face of perturbations. In addition, the structure of species-level food webs was compared between communities using network properties A total of 25 guilds were recognized from these 14 palaeocommunities, although not all guilds are present in each palaeocommunity , definina) shows that the three Early Triassic communities  are distinguished from a main cluster that includes all the other communities, indicating compositional differences among the two groups of communities.Pairwise palaeocommunity Jaccard distances and Bray–Curtis dissimilarities were applied in NMDS analyses to ordinate and compare palaeocommunities in terms of their guild compositions and richnesses among the Permian–Jurassic communities . The Jacfigure 2Trophic space analyses show that early Permian to Early Jurassic communities from Xinjiang fall into two main clusters, the upper being the aquatic invertebrates cluster and the lower the insects cluster , and tro(b)2) increases in the Capitanian QZJ, Early Triassic UGDK, JCY and SFG, and latest Triassic HJG community, and decreases in the subsequent recovery periods . Mean L/S values maintain low volatility except for a striking spike in the Late Triassic HSJ community (p < 0.05), but not with L/S .Connectance . Threshold values declined stepwise during the middle Permian, and were significantly lower in the Capitanian QZJ community compared to the preceding HYC community, prior to the G–L mass extinction (b). Thresholds then rebounded rapidly in late Permian. The most significant transition is highlighted by a dramatic and significant decline in threshold value across the P–Tr boundary, with average collapse thresholds decreasing from 0.633 in the LGDK to 0.569 in the UGDK communities (b). The other two Early Triassic communities  also exhibit similar CEG dynamic patterns to the UGDK and have very low average collapse thresholds and highly variable threshold ranges (b). Thus, community stability and resistance in the earliest Triassic remained low, but highly variable and unpredictable.Pronounced anomalies in community resistance reflected by the CEG results occurred across the G–L, P–Tr and T–J mass extinctions . Moreover, the Early Triassic JCY and Late Triassic HJG communities have relatively high secondary extinction even at relatively low levels of perturbation (0.1–0.4) . Collapse thresholds varied throughout the series, and there were several transitions where thresholds differed significantly between successive communities .Collapse thresholds did not recover to pre-extinction levels until the Middle Triassic, increasing significantly between the SFG and LKMY communities, and reaching an acme in the Late Triassic HSJ community. The Middle–Late Triassic communities  possessed high, stable thresholds. A sharp decrease in thresholds occurred again in the latest Triassic between the HSJ and HJG communities, then resurged significantly and rapidly in the earliest Jurassic BDW community and remained at relatively high levels in the Early Jurassic b.. 4(a)Despite uncertainties about dating the PTB in continental successions, we can be confident we are comparing pre- and post-extinction communities. This is because for studies of this kind, we simply have to be sure that we are looking at communities below and above the line, even though we cannot perhaps always mark the exact P–Tr mass extinction and PTB horizons in the rock successions. Here, the mass extinction is calibrated at the middle of the Guodikeng Formation based on biotic changeover and organic carbon isotopic correlations with the marine Meishan GSSP, South China –37. Our Our study does not conform with some recent analyses of marine ecosystems where massive biodiversity loss was detected through the P–Tr mass extinction, as expected, but functional richness remained unaltered . Here, wPlunging community diversity caused connectance values in the earliest Triassic food webs to increase sharply, from a pre-extinction value of 0.04 (LGDK) to a post-extinction value of 0.08 (UGDK), with continued increases in the Early Triassic . It is ia), which all show much reduced community stability in CEG dynamics across the P–Tr boundary, coinciding with biodiversity extinction. Furthermore, the very weak resilience to ecological perturbations and unusual functional structures of Early Triassic communities in the Karoo Basin delayed biotic recovery following the P–Tr mass extinction [The widespread decline in primary production across the terrestrial P–Tr boundary has already been documented empirically –43. Flortinction .Connectance decreased in the Middle Triassic but did not recover to late Permian level, L/S values remained steady throughout the Triassic except for a spike in the HSJ community, which is because of the evolution of highly connected species (e.g. insects and notostracans). However, connectance of the HSJ community did not change much, because increased species richness offset the influence of increased L/S. Threshold values achieved pre-extinction levels in the Middle Triassic LKLMY community, representing the full recovery of community stability after the P–Tr extinction Community composition showed no significant variations during the G–L and T–J transitions , althoug3Decreased threshold values also mark major community destabilizations in both the G–L and T–J transitions . HoweverA sharp drop in thresholds is also observed in the terminal Triassic (Rhaetian) HJG rather than in the earliest Jurassic BDW community . This weComparable extinction patterns of communities are also observed in the terrestrial Late Cretaceous terrestrial communities in North America , where pAll three of the mass extinctions investigated here may have been driven primarily by large igneous province volcanism and consequent impacts on the environment . Therefore, the contrast between the huge P–Tr and the smaller G–L and T–J crises may reflect their relative extinction magnitudes , but the. 5Our results indicate three significant decreases in palaeocommunity stability in Xinjiang, coinciding with the G–L, P–Tr and T–J extinctions. The G–L and T–J transitions were each preceded by low-stability communities, but the subsequent recoveries were rapid. However, ecological recovery from the P–Tr mass extinction was prolonged, and the Early Triassic terrestrial and freshwater communities showed low stability and highly variable and unpredictable responses to perturbation primarily due to the huge losses of species, guilds. We confirm that the unusually low community stability in the Early Triassic, which was first observed in the Karoo Basin, is a global phenomenon. Increased connectance, reduced trophic space and decreased collapse thresholds are observed in all three extinction events, suggesting that generalities not only exist in ancient food web structure but also in their responses to perturbation during extinction events. Global correlations show that the same extinction and recovery patterns of terrestrial ecosystems exist not only across a wide geographic range, but also among compositionally different communitiesClick here for additional data file.


The quantum Hall effect is the seminal example of topological protection, as charge carriers are transmitted through one-dimensional edge channels where backscattering is prohibited. Graphene has made its marks as an exceptional platform to reveal new facets of this remarkable property. However, in conventional Hall bar geometries, topological protection of graphene edge channels is found regrettably less robust than in high mobility semi-conductors. Here, we explore graphene quantum Hall regime at the local scale, using a scanning gate microscope. We reveal the detrimental influence of antidots along the graphene edges, mediating backscattering towards upstream edge channels, hence triggering topological breakdown. Combined with simulations, our experimental results provide further insights into graphene quantum Hall channels vulnerability. In turn, this may ease future developments towards precise manipulation of topologically protected edge channels hosted in various types of two-dimensional crystals. It was suggested that the breakdown of the quantum Hall effect in graphene originates from the coupling between counter propagating edge modes. Here, by using scanning gate microscopy, the authors propose a microscopic mechanism of this coupling due to antidots present at graphene edges. Substantial advances in the manipulation of QHECs in semiconductor-based 2DESs lead to envision new approaches in quantum computing7 and open the way toward electron quantum optics8. These breakthroughs require a robust topological protection of QHECs.Quantum Hall edge channels (QHECs), formed as Landau levels (LLs) cross the Fermi energy near the borders of two-dimensional electronic systems (2DESs), are almost ideal one-dimensional systems, where quasiparticle scattering is topologically prohibited9. In that framework, different strategies relying on QHEC propagation along p-n junctions have already been implemented in this material14. However, the confinement of charge carriers at graphene borders appears much more difficult to control than in semiconductor-based 2DESs, seriously impairing the topological protection of its QHECs. The explanation lies in different fundamental reasons, including the complex electrostatic screening of the back gate potential related to the presence of fringing fields in most device layouts investigated up to now16, and the difficulty to control defects at the borders of etched graphene20. The best proof of these detrimental influences is that some fractional quantum Hall signatures visible in extremely clean geometries were only observed in the case of edgeless device layouts such as the Corbino geometry22.Graphene, characterized by the massless nature of its charge carriers, offers even more promising perspectives in terms of QHECs manipulation, thanks to its rich spectrum of relativistic quantum Hall phenomena24, combined with theory15, led to a revision of the QHECs picture at graphene device edges. Instead of a single type of QHECs propagating along the border in clockwise or anticlockwise fashion as in semiconductor-based 2DES, the new proposed picture involves coexisting downstream and upstream QHECs separated by few 100-nm-wide incompressible (i.e. insulating) strips. Topological breakdown of graphene QHECs would therefore originate from the coupling between up- and downstream QHECs. This coupling has been revealed by Marguerite et al. through scanning probe measurements24: on the one side, inelastic scattering was identified as a source of thermal dissipation along up- and downstream QHECs, with no incidence on transport, and on the other side, elastic tunneling was found to cause the coupling between these channels. However, the exact tunneling mechanism, and in particular a clear connection between scanning probe images and macroscopic transport properties, are still lacking.Recently, local probe measurementsRxx as a function of back gate voltage Vbg, showing fingerprints of the QH regime in graphene: Rxx vanishes , while it is maximal around ν = ± 4n  to build a full microscopic picture of QHECs topological protection breakdown in graphene. For this purpose, we studied two devices G1 and G2), consisting in 250-nm-wide encapsulated graphene constrictions as presented in Fig.  and G2, Rxx, while close to zero, exhibits fluctuations  and by Coulomb charging energy on the other hand (electrostatic contribution). A more in-depth discussion on the different contributions is given in Supplementary Note Rxx rings in Fig. Rxx between the rings corresponds to Coulomb blockade34  remote charged impurities in the dielectric hBN layer37. Both sources lead to local variations of Dirac point energies , probably ubiquitous in all hBN/graphene/hBN heterostructures. It is noteworthy that a potential fluctuation giving rise to an antidot on the p-doped side would yield a dot on the n-doped side. While our experiment does not allow to discriminate between strain- or impurity-induced potential fluctuations, it provides data on antidots distance from device borders, as well as on their spatial distribution along the borders of graphene devices : the typical distance between neighboring antidots is in the range 100–500 nm, from data in Fig. 38. Since the tip-induced potential perturbation extends beyond 500 nm, Coulomb rings originating from remote antidots can superimpose, as shown on Fig. A fundamental question emerging at this point concerns the origin of the observed antidots. Atomic defects at the edges of graphene have often been invoked as source of perturbation for charge transport. However, if they were involved in the present case, it would remain to explain how they could yield potential landscapes similar to the one presented in Fig. Vbg. For this purpose, we scan the tip across one of the antidots as indicated in Fig. Vbg in the vicinity of ν = −6 for a constant Vtip , signatures of this resonance vanish. This Vbg threshold is independent of Vtip as demonstrated in Supplementary Fig. Vbg has a strong influence on the position of the upstream QHEC , while a finite Rxx region  in the different backscattering regimes. Using the KWANT packagentip∣ : at low ∣Δntip∣, finite Rxx peaks are separated by Rxx ~ 0 states and at larger ∣Δntip∣, Rxx remains finite. The ∣Δntip∣ scale (distance between the peaks) depends mainly on the size of the considered antidot as well as on Coulomb interactions, not captured in our simulations. Since all the parameters vary among the antidots, the comparison between experimental and simulated typical ∣Δntip∣ scales will remain qualitative. The sequence of JDOS maps shown in Fig. Rxx  and antidots at graphene edges in QH breakdown. Both ingredients are likely ubiquitous in most graphene-based heterostructures studied up to now, but with variations in the importance of the different contributions. Indeed, fringing fields become much weaker when the gate is placed closer to graphene, for example when a graphite back gate is used below hBN. Furthermore, charged impurities at hBN etched edges depend on the etching recipe, and Dirac point inhomogeneites may be more or less pronounced depending on strain accumulated in the layers or on the quality of hBN.14. Noteworthy, the main outcome of this work, that full control over topological edge states in graphene will only be provided through meticulous engineering of electrostatic landscape at device borders, can also be transposed to other types of 2D crystal-based devices hosting topologically protected edge states.SGM data obtained at high magnetic field allow to get precise information on active antidot locations , putting constraints on their possible origin. The fine control over antidot size and coupling to QHECs provided by the tip and back gate voltages was shown here to be the key to disentangle the complexity of the QH effect phenomenology in graphene. It allows to image and tune antidot-mediated QH effect breakdown, which constitutes a prerequisite toward advanced control and manipulation of QHECs in more complex devices such as QH interferometers. These findings are indeed relevant, for example, in the case of p-n junction-based interferometers where semi-reflecting mirrors are defined at the edges2 layer. A 250-nm-wide constriction shape has been lithographically defined, similarly to18. The four contacts allow to measure the longitudinal resistance Rxx.Sample G1, depicted in Fig. Rxy in addition to Rxx.Sample G2, depicted in Supplementary Fig. B up to 14 T has been applied perpendicularly to the graphene plane. Electrical signals have been recorded using a classical lock-in technique at a frequency of 77 Hz. The longitudinal resistance is obtained from a four probe measurement to avoid the contribution from contacts resistance. Charge carriers type and density can be tuned by changing the back gate voltage Vbg.The sample has been anchored to the mixing chamber of a dilution refrigerator whose base temperature is 100 mK and a magnetic field f ~ 32 kHz. The tip is electrically contacted so that a voltage Vtip can be applied on it. The tip can be moved in x, y, z directions thanks to piezo scanners. After scanning the surface in topography mode, the distance dtip between the tip and the graphene plane can be fixed. Applying the bias Vtip introduces an electrostatic perturbation for conduction electrons. The conductance can then be recorded for each tip position, yielding a SGM map.The local gate used for SGM characterization consists in a commercial metal-coated AFM tip glued on a tuning fork whose resonance frequency is 41. We modeled one edge of the device, neglecting the bulk region contribution . More details on simulations are available in Supplementary Note To decrease computation time, a scaling factor of four, without incidence on the output results, was applied to the real lattice parameter of graphene (the interatomic distance is Supplementary InformationPeer Review File


Alzheimer’s disease (AD) is traditionally defined by the presence of two types of protein aggregates in the brain: amyloid plaques comprised of the protein amyloid-β (Aβ) and neurofibrillary tangles containing the protein tau. However, a large proportion (up to 57%) of AD patients also have TDP-43 aggregates present as an additional comorbid pathology. The presence of TDP-43 aggregates in AD correlates with hippocampal sclerosis, worse brain atrophy, more severe cognitive impairment, and more rapid cognitive decline. In patients with mixed Aβ, tau, and TDP-43 pathology, TDP-43 may interact with neurodegenerative processes in AD, worsening outcomes. While considerable progress has been made to characterize TDP-43 pathology in AD and late-onset dementia, there remains a critical need for mechanistic studies to understand underlying disease biology and develop therapeutic interventions. This perspectives article reviews the current understanding of these processes from autopsy cohort studies and model organism-based research, and proposes targeting neurotoxic synergies between tau and TDP-43 as a new therapeutic strategy for AD with comorbid TDP-43 pathology. Alzheimer’s disease (AD), a devastating, fatal neurodegenerative disease of aging, features the presence of extracellular amyloid-β (Aβ) plaques and intracellular tangles of hyperphosphorylated tau in the brain . For decWhile historically AD has been thought of as a single disease entity driven by dysfunction or accumulation of Aβ and tau, it may more accurately resemble a spectrum of diseases with variable but overlapping clinical and neuropathological features . AmyloidOne outstanding question is whether these pathologies have a contributory effect on AD neuropathology or the clinical expression of the disease –15. In pTARDBP, cause ~ 4% of familial inherited ALS, directly linking TDP-43 dysfunction with disease [TDP-43, an essential protein, has a variety of critical cellular functions that include regulating pre-mRNA splicing, mRNA transport, and forming and stabilizing stress granules . TDP-43  disease , 20. Pat disease –23. In t disease –27. TDP- disease , 29, and disease , 31. Ind disease , 32. At  disease –35. Rece disease , 36, 37.In a small but well-characterized group of resilient individuals with ADNC from the Adult Changes in Thought (ACT) autopsy cohort, the absence of TDP-43 distinguished those without cognitive impairment (resilient) from those with cognitive decline (AD dementia). When compared to individuals with dementia and matched levels of ADNC, resilient individuals did not have any differences in Lewy bodies or vascular brain injury . The key difference between the resilient and AD dementia cohorts was the presence of TDP-43 pathology, indicating aberrant or aggregated TDP-43 may be a significant contributing factor to developing dementia . Other cIn addition to studying the correlations between TDP-43 and cognitive decline, cohort studies have also evaluated the relationship between TDP-43 and the neuropathologic hallmarks of AD, amyloid and tau pathology. Data from the Religious Orders Study and the Memory and Aging Project (ROS/MAP) inform models that implicate amyloid pathology, particularly neuritic plaques, in TDP-43 pathology . The relIn other AD cohorts, there is evidence for a relationship between TDP-43 and tau pathology, which appear to develop in a similar pattern throughout the brain, beginning in limbic structures and later advancing to neocortex . JosephsC. elegans co-expression model of tau and TDP-43 found that tau and TDP-43 synergize leading to increased pathological protein accumulation, neuronal dysfunction, and neurodegeneration [The models used to study Alzheimer’s disease focus on amyloid and tau pathology, either alone or in combination . TDP-43 neration . In cellneration . Recent neration . Finallyneration . Althougneration .While the observations from both human cohorts and limited studies using model systems suggest an interaction between tau and TDP-43, the mechanisms that underlie that interaction are unknown. However, there is a wealth of information known about the physiological roles of both tau and TDP-43, as well as their respective roles in other neurodegenerative disease, that serve as a guide and potential place to start the investigation. As a component of tubulin assembly, tau binds and stabilizes microtubules. Tau localizes throughout the cytoplasm, and is enriched in axons, where it assists with cargo trafficking along the microtubule network . PhosphoA majority of patients with sporadic late-onset AD exhibit comorbid TDP-43 pathology, which strongly correlates with cognitive outcomes. The presence of pathologic TDP-43 may enhance tau pathology and concomitant neurotoxicity, either independent of or in concert with amyloid pathology. If true, this may help explain the limited efficacy of amyloid focused therapeutics and argue for the need to develop a multitargeted approach to preventing tau progression and subsequent neurodegeneration. However, while there are clearly intriguing associations observed between tau and TDP-43 in human autopsy and model organism studies, additional work is needed to broaden understanding of these processes. Some suggested areas for future study are described below.Additional community-based cohort studies that include cognitive assessments in life paired with complete neuropathological examination after death are need to supplement the published literature, which currently relies on relatively few cohorts. There are several detailed examinations of TDP-43 pathology described in the literature , 45, 66.Further development of model organisms that express multiple pathologic proteins is necessary, including those that combine amyloid, tau, and TDP-43 pathologies. Early work in model systems suggests that the presence of TDP-43 exacerbates tau neurotoxicity, worsening disease phenotypes. Therefore, TDP-43 may be a highly relevant therapeutic target. However, these models are only beginning to emerge. It will be necessary to integrate data from human studies into the development of more refined model systems, allowing dissection of interactions between tau and TDP-43. These models should include simple cell culture or invertebrate systems allowing large-scale screening and rapid progress, as well as vertebrate models to investigate disease relevant phenotypes in a mammalian brain. Studies should incorporate consideration of aging as a factor influencing disease. Additionally, conditional expression models will be necessary to study the temporal relationships between tau and TDP-43 and better understand whether perturbation of one is upstream and capable of inducing pathology of the other. Finally, models co-expressing all three proteins  are also necessary to better understand how TDP-43 fits into the A,T(N) framework. As understanding of the pathological role of TDP-43 in AD progresses, essential future work will be to develop therapeutic strategies that prevent or remove disease-promoting TDP-43 and restore healthy brain function.Overall, determining the contributory roles TDP-43 plays in the progression of AD will require combining autopsy studies of the human pathology with large-scale human genetics and model organism-based experiments. The rewards are great, as determining the nature of TDP-43 contributions to AD may open up a novel path in the pursuit of AD therapeutics.