Pitx3−/− mice, a genetic model of PD with early onset dopaminergic depletion in the dorsolateral striatum (DLS). This dysregulated BDNF/TrkB signaling might contribute to the pathophysiology of direct and indirect pathway striatal projection neurons in PD. Motor dysfunction is among the main symptoms of Parkinson’s disease (PD). This is closely linked to the loss of the neurotransmitter dopamine (DA) in the midbrain and in the striatum. Additionally, the ability of striatal neurons to undergo adaptive cellular alterations and synaptic plasticity is impaired. Dopamine receptor D1 (DRD1) stimulation is needed for the establishment of long-term potentiation (LTP) at synapses of striatal spiny projection neurons (SPNs), which leads to enhanced neurotransmission. In contrast, dopamine receptor D2 (DRD2) stimulation is needed for the formation of long-term depression (LTD) in SPNs, which leads to the opposite effect. The tropomyosin receptor kinase B (TrkB) and its ligand brain-derived neurotrophic factor (BDNF) are centrally involved in plasticity regulation at the corticostriatal neurons synapses. There are two populations of striatal SPNs, with different projection targets in the brain. DRD1 is expressed in direct pathway spiny projection neurons (dSPNs) and its activation enhances TrkB sensitivity for BDNF by increasing the levels of TrkB at the cell surface. In this study, we showed that the activation of DRD2 in cultured striatal indirect pathway spiny projection neurons (iSPNs) and cholinergic interneurons causes the retraction of TrkB from the plasma membrane. This provides an explanation for the opposing synaptic plasticity changes observed upon DRD1 or DRD2 stimulation. In addition, TrkB was found within intracellular structures in dSPNs and iSPNs in Pitx3−/− mice, a genetic model of PD with early onset dopaminergic depletion in the dorsolateral striatum (DLS). This dysregulated BDNF/TrkB signaling might contribute to the pathophysiology of direct and indirect pathway striatal projection neurons in PD.Motor dysfunction in Parkinson’s disease (PD) is closely linked to the dopaminergic depletion of striatal neurons and altered synaptic plasticity at corticostriatal synapses. Dopamine receptor D1 (DRD1) stimulation is a crucial step in the formation of long-term potentiation (LTP), whereas dopamine receptor D2 (DRD2) stimulation is needed for the formation of long-term depression (LTD) in striatal spiny projection neurons (SPNs). Tropomyosin receptor kinase B (TrkB) and its ligand brain-derived neurotrophic factor (BDNF) are centrally involved in plasticity regulation at the corticostriatal synapses. DRD1 activation enhances TrkB’s sensitivity for BDNF in direct pathway spiny projection neurons (dSPNs). In this study, we showed that the activation of DRD2 in cultured striatal indirect pathway spiny projection neurons (iSPNs) and cholinergic interneurons causes the retraction of TrkB from the plasma membrane. This provides an explanation for the opposing synaptic plasticity changes observed upon DRD1 or DRD2 stimulation. In addition, TrkB was found within intracellular structures in dSPNs and iSPNs from  Parkinson’s disease (PD) is the second most common neurodegenerative disorder and is characterized by a progressive loss of dopaminergic neurons in the substantia nigra (SN) ,2,3,4. TDefective dopaminergic signaling from the SN to the basal ganglia plays a central role for these altered motor functions. Dopamine (DA) released from the nigrostriatal terminals onto spiny projection neurons (SPNs) in the dorsolateral striatum (DLS) modulates circuit activity and plasticity, including long-term potentiation (LTP) and long-term depression (LTD) at the glutamatergic synapses between the corticostriatal afferences and striatal SPNs ,14,15,16s/olf subunit and, upon DA binding, activates adenylyl cyclase 5 (AC5), which facilitates cyclic AMP (cAMP) production ; RRID: MMRRC_000230-UNC). DRD2-eGFP mice were bred on a FVB/NJ genetic background.All experiments were approved by a license for animal testing  and performed in accordance with supervision through the local veterinary authority (Veterinaeramt der Stadt Wuerzburg) and Committee on the Ethics of Animal Experiments, i.e., Regierung von Unterfranken, Wuerzburg, Germany.Pitx3−/− mice (RRID: IMSR_JAX:000942), and brain tissue was dissected from adult (P56-P84) male and female mice. DRD1-td-Tomato x Pitx3−/− mice were bred on a C57Bl6/J genetic background.For IHC analyses, BAC-transgenic DRD1-td-Tomato mice (Tg(Drd1a-tdTomato)6Calak; RRID: IMSR_JAX:016204) were crossed with wild-type or with Pitx3−/−mice were provided by Dr. Rosario Moratalla, Instituto Cajal, Consejo Superior de Investigaciones Cientificas (CSIC), 28002 Madrid, Spain. The BAC transgenic mice were used to identify the striatal direct and indirect pathway neurons. All experimental procedures were approved by the Cajal Institute Bioethics Committee and by the CSIC Ethics Committee and fulfilled the requirements of Spanish (RD 53/2013) and European Union (63/2010/EU) legislation.DRD1-td-Tomato x wildtype/All mice were genotyped via PCR analysis. Mice were housed in groups of 4–6 per cage, with a constant room temperature (21–22 °C) and 12 h light/dark cycle (lights on at 7:00 h) and were given free access to food and water. The studies with rats were performed as previously reported . In brieFor this procedure, 6-OHDA injections were performed as previously described . BrieflyStriatal primary cultures were prepared from P1–3 newborn pups, as described previously . BrieflyEnrichment of primary DRD2-eGFP neurons was accomplished via flow cytometry-based cell sorting using the MACSQuant Tyto Cell Sorter . First, the sample was prepared through dissection and dissociation of the striatum, as described above. Prior to sorting, around 2–2.5 mL of cell suspension was loaded into the cartridge. For this procedure, the cartridge  was prepared by wetting all the channels interconnecting the chambers with NB media supplemented with serum. Additionally, 170 µL of medium was added to the positive collection chamber to provide a suitable and nurturing environment to the sorted DRD2-SPNs. Before adding the cell suspension into the input chamber, the cells were filtered using a 20 µm pre-separation filter  in order to remove cell aggregates and large particles that could clog the channels and hinder the sorting. Gating was set up to detect the GFP-positive neuronal population. The sorting procedure was performed at 25 °C, using a mixing speed of 100 rpm and the automatic pressure adjustment. Thereafter, the purified neurons were resuspended in an appropriate volume of media supplemented with horse serum and immediately plated on polyornithine/laminin-111-coated cell culture dishes. The cell culture medium was completely replaced with a serum-deprived cell culture medium after 24 h to prevent any serum-related effect on the dopamine receptors or TrkB stimulation. Cell cultures were maintained for 3–14 days in vitro (DIV) before preparation for ICC and Western blotting (including surface biotinylation). Enriched DRD2-eGFP neurons were pharmacologically treated with 500 nM sumanirole maleate  and/or stimulated with 10 ng/mL BDNF (homemade). After stimulation, the cells were washed with 1 × PBS before further processing. Primary striatal neurons (DIV5-DIV10) were washed once with 1× PBS and fixed with 4% paraformaldehyde  in 1 × PBS  for 15 min at RT. Subsequently, they were washed twice with 1 × PBS for 5 min. Permeabilization and blocking was performed with 0.1% Triton X-100  and 10% donkey serum  in 1 × PBS for 1 h. For immunostaining, the following antibodies were applied in a blocking solution overnight at 4 °C: chicken anti-GFP , goat-anti-mTrkB , rabbit anti-DARPP-32 , mouse anti-ß-III-tubulin , goat anti-ChAT , rat anti-Lamp-1 , and sheep anti-mouse SORCS-2 . After washing three times with 1XPBS for 10 min, primary antibodies were detected with the following secondary antibodies (incubation: 60–90 min at RT): donkey anti-chicken Alexa Fluor 488 , donkey anti-mouse Alexa Fluor 647 , donkey anti-rabbit Cy3 , donkey anti-rabbit DyLight550 , donkey anti-goat Alexa Fluor 647 , donkey anti-rat Alexa 405 , and donkey anti-sheep Alexa Fluor 647 . Nuclei were stained with 0.4 mg/mL 4′,6-diamidino-2-phenylindole (DAPI). Coverslips were again washed three times for 10 min with 1× PBS and the cells were mounted on Superfrost_Plus glass slides  using MERCK-FluorSave reagent . For cell surface staining, after stimulation, the media was discarded and cold media with the goat anti-mTrkB  antibody was added to the wells. The cells were incubated for 30 min at 4 °C. Afterwards, the cells were washed twice with ice-cold PBS and blocked for 1 h at RT. Then, incubation with the following other primary antibodies was performed: chicken anti-GFP , rabbit anti-DARPP-32 , mouse anti-GFAP , and rabbit anti-GFAP .The mice were anesthetized with pentobarbital  and then transcardially perfused with 4% paraformaldehyde dissolved in PB , dissected, and the brains were immersed overnight in the same fixative solution. The brains were washed and kept in PBS until they were used. The brains were then embedded in 6% agarose  and 40 mm free-floating, coronal brain sections were obtained using a LEICA Vibratome VT1000S (RRID:SCR_016495). Sections were stored in “Cryoprotection Anti-Freeze Buffer” , and ethylene glycol  at −20 °C. 2O, and mounted on SuperfrostPlus glass slides  with FluorSave mounting reagent .Immunohistochemistry was performed using a free-floating staining protocol previously described . In brieCell surface proteins were biotinylated using the Pierce Cell Surface Protein Isolation kit , according to the manufacturer’s instructions. Between 150,000 and 300,000 cultured neurons were required per condition. The neurons were plated on 12 mm glass coverslips and biotinylated on either DIV7 or DIV8. The cells were washed with 1× cold PBS and incubated with EZLINK Sulfo-NHS-SS-biotin at 4 °C for 30 min, followed by the addition of a quenching solution. The cells were then lyzed with lysis buffer containing a protease inhibitor cocktail , sonicated one time, and centrifuged. The cell lysate was applied to the NeutrAvidin agarose gel. The flow through (intracellular protein fraction) was saved for Western blotting. The biotinylated cell surface proteins in the NeutrAvidin agarose gel were pulled down with sodium dodecyl sulfate (SDS) sample buffer (pH 6.8) with 50 mM dithiothreitol (DTT). Each sample was mixed with Laemmli buffer, heated to 95–99 °C for 5 min, and centrifuged shortly before loading onto 5–11% polyacrylamide gel. Polyacrylamide gel electrophoresis was performed in an electrophoresis cell chamber  at 70 V for the stacking gel; then, the voltage was changed to 120 V once the samples reached the separating gel. The proteins were then transferred via wet blotting onto polyvinyl difluoride (PVDF) membranes  in a blotting cell  for 1 h at 4 °C with 135 V and 35 mA in blotting buffer . The membranes were blocked for 1–2 h with 5% milk powder in 1× TBS-T at RT and then incubated with either primary goat polyclonal antibodies , rabbit polyclonal anti-phosphoTrkB (Tyr816) , and rabbit anti-phospho TrkB ), chicken polyclonal anti-Calreticulin , or goat-anti DARPP-32  overnight at 4 °C on a rocker. Following this step, incubation with horseradish peroxidase (HRP)-conjugated secondary antibodies was performed ). For the relative quantification of the effects of sumanirole treatment on the surface distribution of TrkB, the signal densities in the intracellular fractions, the membrane protein fractions, and the intracellular protein fractions were normalized to those of the marker proteins for their respective fractions.Image acquisition was performed with an Olympus FluoView 1000 confocal laser microscope . The different color channels were visualized using lasers with the wavelengths of 405 nm, 488 nm, 559 nm, and 633 nm. The microscope was equipped with the following objectives: HC PL APO CS2 40× (oil NA: 1.30) and HC PL APO CS2 60× . Acquisition was performed using the Olympus FV10-ASW 3.0 (RRID: SCR_014215) software ; z-stacks of different focal planes were obtained; images were saved in the Olympus.oib format and analyzed with ImageJ image processing software version 1.53t  (RRID: SCR_003070). For visualization, the z-stacks were projected as a maximum intensity projection. The line scan colocalization analysis was performed using ImageJ. Brightness and contrast modifications were performed according to the rules of good scientific practice and the changes were documented for every image that was processed.For the quantification of TrkB clusters, the number of TrkB-positive puncta were manually counted by the experimenter. TrkB cluster numbers were then normalized to the investigated area. Allocation to the D1- or D2-SPNs was conducted via colocalization of the TrkB clusters with either td-Tomato or Met-Enkephalin, respectively. t-test was performed, and for more than two groups a one-way ANOVA with Tukey’ multiple comparison test was applied. For data where a control group has been set to 100%, a one sample t-test was performed. For all tests, a confidence level of p ≤ 0.05 was set. For the statistical analysis, GraphPad Prism software version 6.01  (RRID: SCR_002798) was used. The type of each statistical test used has been indicated in the respective figure legends.For all data used for quantification purposes, a normal distribution was assumed based on experience with similar kinds of experiments. For two groups, a two-sample To investigate DRD2-mediated effects on the translocation of TrkB in indirect pathway iSPNs, we developed and tested a protocol for the culture of flow cytometry-enriched DRD2 SPNs . Our sorDue to their important functional role in the control and modulation of SPNs in the striatum and the presence of DRD2 on their cell surface , we werei-coupled receptors would induce the inhibition of adenylyl cyclase activity and a progressive downregulation in cAMP levels. This decrease in cAMP could prevent TrkB from being translocated to the cell surface. In order to study such effects, we performed surface biotinylation and cell surface immunostainings of TrkB in the presence or absence of sumanirole. Biotinylation of cell surface proteins of DRD2 neurons treated under different time courses showed that TrkB surface levels decreased after 10 min of treatment with sumanirole and decreased further over time for up to 24 h (t-test was performed. Control: mean 100% (n = 7); 10 min. Sum: mean 77.6%, p = 0.05 (n = 7);; 60 min. Sum: mean 75.8%, p = 0.19 (n = 4); 6 h. Sum: mean 31.5%, p = 0.08 (n = 2); Sum 24 h: mean 28.0%, p = 0.06 (n = 2)). Confocal imaging also showed that DRD2’s activation with sumanirole led to a marked decrease in TrkB on the cell surface in comparison to their DA-depleted controls (t-test was performed. Control: mean 100% (n = 7); Sum + BDNF 10 min: mean 46.64, p = 0.006 (n = 7); Sum + BDNF 60 min: mean 95.37%, p = 0.807 (n = 3); Sum + BDNF 24 h: mean 114.2%, p = 0.728 (n = 3)). We also assessed the ratio of pTrkB to cell surface TrkB, as seen with the surface biotinylation experiments at 10 min, 60 min, and 24 h treatment of sumanirole. A gradual increase in pTrkB/surface TrkB was observed . Bafilomycin treatment for 10 min  or 4 h  did not enhance the colocalization of TrkB with SORCS2, indicating that a reduced fusion of endosomes with lysosomes has no positive effect on the recycling of TrkB to the cell surface.TrkB is usually present on the dendritic spines of iSPNs that receive inputs from BDNF-positive corticostriatal afferences. We therefore investigated how the activation of DRD2 with sumanirole affects dendritic TrkB in cultures of enriched DRD2-expressing striatal neurons. Activation of D2 receptors by sumanirole rapidly reduced cell surface TrkB expression in the soma and dendrites of iSPNs A. Blockip = 0.0011) and 4 h (mean: 61.2% (n = 5); one-way ANOVA with Tukey’s multiple comparisons test, CTR vs. 4 h Baf: p < 0.0001) of bafilomycin treatment (p = 0.0186), but the simultaneous treatment with bafilomycin and sumanirole could not further increase the bafilomycin-induced TrkB–lysosome association (mean: 63.8% (n = 2); one-way ANOVA with Tukey’s multiple comparisons test, CTR vs. 4 h Baf, 10 min. Sum: p = 0.0015). This indicates that TrkB is degraded by lysosomes in the cell soma. Taken together, these data demonstrate that TrkB is rapidly retracted from the cell surface both in the dendrites and cell soma after the activation of DRD2 with sumanirole, and that dendritic TrkB is retrogradely transported to the soma, where these TrkB-positive vesicles then fuse with lysosomes. When we investigated the effect of sumanirole treatment on TrkB distribution in the cell body of DRD2-expressing neurons in enriched cultures of striatal iSPNs, we found a rapid uptake of TrkB A and thereatment B,C. FurtPitx3−/− mouse model, we investigated TrkB distribution in striatal tissue slices from these mice at an age range from 6 months to 11 months. We found that TrkB clusters arose in the striatum of Pitx3−/− animals : 14.4; mean Pitx3−/− (n = 6): 28.9; unpaired t-test: p = 0.0376) higher number of these clusters than sections from wild-type control animals . Also, there was no significant change in the number of TrkB clusters after prolonged DA depletion .We previously showed that the subcellular distribution of TrkB in SPNs is altered upon dopamine depletion in a rat model in which 6-OHDA is unilaterally injected into the medial forebrain bundle . In this animals A and tha animals B. We fur/− model C,D. Thise lesion E,F, and p = 0.0003; 2 wk p.l. intact vs. 4 m p.l. intact: p = 0.0007; 2 wk p.l. lesioned vs. 1 m p.l. lesioned: p = 0.0574; 2 wk p.l. lesioned vs. 4 m p.l. lesioned: p = 0.0047; 2 wk p.l. intact vs. 2 wk p.l. lesioned: p = 0.9374; 1 m p.l. intact vs. 1 m p.l. lesioned: p = 0.1132; 4 m p.l. intact vs. 4 m p.l. lesioned: p = 0.9921) as well as in layer V  of the motor cortex, both of which are areas that send projections to the striatum. Interestingly, there was a trend towards a reduced number of BDNF-positive cells in the motor cortex of the lesioned hemisphere when compared to the intact hemisphere from 1 month after the lesion onwards  release from nigrostriatal terminals onto SPNs in the DLS modulates circuit activity and long-term plastic changes, including long-term potentiation (LTP) and long-term depression (LTD) at the glutamatergic synapses between corticostriatal afferences and striatal SPNs ,13,14,15in iSPNs . This prHere, we also showed that TrkB’s localization in DRD2 neurons can be further modulated. Chronic activation of D2 receptors in vitro was achieved by stimulating enriched DRD2 neurons in culture over a period of 24 h or longer. This led to a further reduction in TrkB receptors at the cell surface of DRD2 striatal neurons. In parallel, intracellular levels of TrkB increased. This suggests that not only was TrkB’s translocation to the cell surface inhibited, but that the existent surface receptors might have been internalized, consequently reducing sensitivity to BDNF after acute stimulation of D2 receptors. Altered TrkB phosphorylation was already observed at 5 min after treatment with sumanirole. Interestingly, at 60 min and 24 h of treatment with this DRD2 agonist, no further reduction in pTrkB was observed. At that time point, pTrkB levels increased again when related to the levels of cell surface-exposed TrkB. This could reflect a cell autonomous mechanism that is due to a sensitization of the TrkB receptors left at the cell surface after undergoing a long period of stimulation. Long-term D2 receptor stimulation with sumanirole could lead to a compensatory or adaptive mechanism specific for DRD2 striatal neurons against chronic D2 agonist exposure. This effect of enhanced TrkB phosphorylation, despite low levels of TrkB at the cell surface after long-term stimulation with a DRD2 agonist, might as well be explained by an additional non-cell-autonomous effect due to the presence of DRD2-expressing astrocytes in the sorted D2 receptor-expressing cells . AstrocyWe have also shown that 7.9% of the DRD2-expressing sorted striatal neurons are cholinergic interneurons. These interneurons regulate the synaptic plasticity and excitability of striatal SPNs and are thought to induce inhibition in these cells. Additionally, cholinergic interneurons are characterized by a multiphasic change in activity, showing a pause in firing which is triggered by aversive and salient stimuli. These pauses have been associated with changes in DA neuron activity. Furthermore, a blockage of D2 receptors in the cholinergic interneurons inhibited the generation of the pause, and the upregulation of D2 receptors prolonged the pause, which is associated with delayed inhibitory learning . The chrPitx3−/− mice, acute depletion of dopaminergic afferents in 6-OHDA-treated rodents, as well as chronic levodopa therapy in PD models that induce dyskinesia have shown to induce changes in the morphology and synaptic activity of SPNs from both direct and indirect pathways [Overall, many changes take place in SPNs during the course of PD pathophysiology. Long-term dopamine depletion from early developmental stages in pathways ,73,92,93pathways . On the pathways . These sPitx3−/− mice. Pitx3−/− or aphakia mice represent a genetic model of PD, in which most of the dopaminergic neurons from the substantia nigra do not develop properly. Consequently, dopaminergic innervation of the DLS is massively reduced from early developmental stages onwards. When we examined TrkB expression in striatal brain slices of adult Pitx3−/− mice, we found that they showed an increase in perinuclear TrkB cluster formation compared to their wild-type littermates. These clusters were apparent in both dSPNs and iSPNs in the DLS. Interestingly, the aggregation of TrkB was not restricted to the dSPNs, as opposed to what we have previously shown for 6-OHDA-treated mice and rats [Furthermore, we studied the effect of DA depletion in DRD2 striatal neurons and the subsequent alterations of TrkB expression under pathological conditions of chronic DA depletion in and rats . We systPitx3−/− mouse model, the animals already lack striatal dopaminergic inputs during early development. This intrinsic lack of DA in the DLS could lead to developmental compensatory mechanisms counteracting potentially harmful changes in the motor network. This is also reflected by the absence of an apparent motor phenotype in these animals [Pitx3−/− mice.A possible explanation for this finding is the differences between the two models. In the 6-OHDA model, dopaminergic denervation occurs rapidly within a few days after the 6-OHDA injection, while in the  animals . Thus, T2A receptor signaling. This would render iSPNs less prone to TrkB cluster formation, as intracellular TrkB levels are decreased compared to dSPNs in a DA-depleted state. In the Pitx3−/− mice, there might be a compensatory downregulation of A2A receptors to protect the iSPNs from excitotoxic signaling, thus reducing this protective effect on TrkB cluster formation. It is apparent that TrkB clusters form upon DA depletion in both models, but iSPNs are spared from TrkB cluster accumulation in the 6-OHDA model. Potentially, dSPNs are more vulnerable to DA depletion than iSPNs, which could explain that TrkB cluster formation appears exclusively in the dSPNs in this model. This could be due to the distinct signal transduction processes of the different dopamine receptors. Under physiological conditions, D1 receptor activation leads to TrkB surface expression by shuttling TrkB from the intracellular stores to the cell surface. When this action is missing, TrkB can no longer be transported to the cell surface and is retained in the cell soma, favoring TrkB cluster formation. Conversely, D2 receptor activation normally leads to decreased TrkB surface expression. When this mechanism is impaired through DA depletion, TrkB surface expression is favored, which is likely facilitated via still intact APitx3−/− mice. This might be due to the low abundance of these DRD1/DRD2-coexpressing cells. Also, we cannot exclude the possibility that these cells might exhibit mixed responses to DA depletion, leading to a more complex effect on TrkB translocation. We did not investigate this in the 6-OHDA rat model, as D1-SPNs could not be directly identified by suitable markers, making it impossible to identify DRD1-expressing neurons that also express DRD2 or Met-Enkephalin.Furthermore, in the striatum, there is also a SPN population which expresses both DRD1 and DRD2 simultaneously ,95,96. HInterestingly, BDNF expression in the cortex increased gradually after 6-OHDA injection both on the ipsilateral lesioned side and the contralateral non-lesioned side. As BDNF itself is crucial for motor learning and execution , and sin−/− model are not directly comparable due to their different natures and degrees of DA depletion within the striatum. The conclusion from one model cannot be directly transferred to the other. However, we see these two models rather as complementary to each other to investigate how DA depletion affects striatal pathology either in an acute or in a chronic manner. Furthermore, the number of animals used for the quantification of the 6-OHDA experiments was low (n = 3). This was accounted for using non-parametric statistical testing when the data were assumed to be not normally distributed. Western blot analyses showed strong tendencies; however, due to low n numbers, no statistical significance could be reported in some cases.There are several limitations in our study. We identified the presence of DRD2-expressing cholinergic interneurons in our cell culture model, but how the activation of DRD2 with sumanirole specifically in these cells contributes to TrkB internalization in this and other striatal cell populations has not been studied. For this, further investigations with striatal organoid or organotypic slice cultures are needed for being able to investigate the responses of these interneurons in a synaptic context with D1 and D2 dopamine receptor-expressing SPNs. Also, the 6-OHDA rat model and the Pitx3Pitx3−/− model of early onset dopaminergic depletion in the dorsolateral striatum, we found TrkB clusters within intracellular structures in the iSPNs and dSPNs. Thus, dendritic and spine atrophy correlate with altered TrkB cell surface expression, indicating that dysregulated BDNF/TrkB signaling contributes to the pathophysiology of direct and indirect pathway striatal projection neurons in PD, and that long-term stimulation of DRD2 might re-establish TrkB phosphorylation to basal levels, rescuing spine morphology. This regulatory mechanism might be crucial for positive or negative modulation of corticostriatal synaptic plasticity. Altogether, these findings provide new insights about the disease pathomechanisms and possible therapeutic targets.In summary, we found that the activation of D2 receptors in iSPNs reduces TrkB cell surface levels. Upon retraction from the cell surface, TrkB accumulates in intracellular clusters and associates with Lamp-1-positive lysosomal structures. Biotinylation experiments indicated that this reduction in TrkB increases over time, suggesting a role for iSPNs in the complications induced by chronic treatment with levodopa. Interestingly, TrkB cell surface reduction did not affect TrkB phosphorylation levels after long-term treatment with a DRD2 agonist, which might explain why previous studies have reported that high doses of levodopa that induced dyskinesia rescue spine morphology in iSPNs. Additionally, using a


Graphene presents practical applications in energy storage devices, especially supercapacitors. However, mainstream synthesis of graphene includes toxic chemical usage, which threatens the environment. With the recent attention shift to synthesizing nanomaterials from agro-waste due to their easy availability, cost-effectiveness, and, most importantly, their environmental friendliness, we present, in this work for the first time, a novel and green synthesis of few-layered graphene sheets using pomegranate peels as a precursor at a low temperature of 80 °C. The surface morphology and microstructural properties are determined by Transmission Electron Microscopy (TEM), Energy Dispersive X-Ray spectroscopy (EDX), X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), UV–visible spectroscopy (UV–vis), and the electrical properties determined by Hall Effect Measurement. The application as a supercapacitor is also examined using Cyclic Voltammetry (CV), Charge–Discharge Cycling (GCD), and Electrochemical Impedance Spectroscopy (EIS). The resulting supercapacitor delivers an areal capacitance of  Graphene is a unique nanomaterial with zero band gap4 and the only carbon allotrope where carbon atoms are tightly bonded to their neighbors by a unique electronic cloud deviating slightly from quantum mechanical principles5. These electrons move freely between the 2pz orbitals1, explaining why graphene exhibits unparalleled electrical conductivity5.Graphene is a two-dimensional planar structure of s6. Graphene’s unique large surface area finds practical applications in energy storage, specifically batteries and supercapacitors7. Despite all these excellent properties and uses, the synthesis of high-quality, large-area graphene in a cost-effective way remains a significant challenge. Graphene was first isolated through mechanical exfoliation8, which has remained the typical conventional method of graphene synthesis8. Also, synthesis has been carried out via routes such as chemical exfoliation, chemical synthesis, thermal chemical vapor deposition, and unzipping carbon nanotubes9.Graphene exhibits excellent electronic, mechanical, and thermal properties10. The process’s cost of highly pure graphite remains another challenge11. Consequently, there is an attention shift to the synthesis of nanomaterials and graphene from natural sources12, most especially agro-waste14. Apart from easy availability and access to agro-waste, synthesis from agro-waste is straightforward, safe, and typically produces more stable materials15. They also offer an effective way of waste management16. Consequently, there has been the synthesis of nanomaterials and graphene from waste in general and agro-waste as well17,18 and used in electrochemical storage devices, wastewater treatment, and metal ion sensing, among others10. For example, graphene nanosheets were obtained by oxidizing graphite into graphene oxide using the Hummer method and highly concentrated acids, then using pomegranate juice as a reducing and capping agent to reduce graphene oxide19. Also, rice husk was combusted at 1123 K and chemically treated to obtain graphene sheets20. Furthermore, green tea-synthesized graphene sheets using a high-temperature pyrolysis technique have also been reported21. In all these works, graphene was obtained at elevated temperatures, and the synthesis process includes several steps, including chemical treatment.A significant drawback to the conventional graphene synthesis method involves using chemicals that are usually highly corrosive, explosive, and toxic22 and are most readily available as waste, globally estimated to be 1.62 million tons22. The pomegranate peel contains a variety of bioactive substances, including hydrolyzable tannins, flavonoids, complex polysaccharides, and minerals, with gallic acid and punicalagin being the major phenolic compounds which makes them useful in many fields23. Although the peels have been utilized in synthesizing carbon dots for several applications28, the low-temperature synthesis of graphene from the pomegranate peel is yet to be reported.Pomegranate peels constitute about 43% w/w of the entire fruitHerein, we report on a one-pot synthesis of few-layered graphene sheets from a green and abundant precursor of pomegranate peel which was used solely to produce graphene, where no industrial chemicals were used in the synthesis process. The latter makes our method entirely green and energy efficient as the synthesis temperature does not exceed 80 °C. The morphology, microstructure, and electrical properties are determined, and the potential application as a supercapacitor is also examined.Pomegranate fruit was sourced from a local market. The peel was removed and washed thoroughly with distilled water to remove the dust and impurities. The clean peels were subsequently dried in an oven at 40 °C overnight and blended into powder form using a blender.The few-layered graphene sheets were synthesized via the hydrothermal method as follows:A mass of 0.3 g of fine pomegranate peel powder was dissolved in 45 ml of distilled water and sonicated for 60 min. The solution was then transferred into a 750 ml Teflon-Line stainless steel autoclave and heated at 80 °C for 10 h. The solution was allowed to cool naturally, and filtration using a syringe and a 0.22 μm filter membrane was done for two turns. The solution was further filtered using a dialysis membrane against deionized water for 48 h. The resultant supernatant was stored at 4 °C for further use.Quartz and FTO substrates were used in the studies for electrical measurements. In cleaning the substrates, firstly, the substrates were dipped in a solution of 2 ml of Hellmanex III and 200 ml of hot distilled water and sonicated for 10 min. The substrates were then rinsed thoroughly with distilled water. The substrates were further cleaned using an ozone cleaner for 10 min at a temperature of 24 °C. Thin film deposition of the few-layered graphene sheets was achieved by drop casting on the substrate and allowing it to air dry.29. One significant benefit of LIG is high conductivity30, a required condition for substrate selection in making electrodes for electrochemical measurements. Therefore, as part of this work, we also aim to examine the practicality of LIG as conducting substrates.Electrochemical measurements were performed on specially designed electrodes fabricated by the Laser-Induced Graphene (LIG) technique. The LIG is a graphene synthesis technique that involves exposing a polymeric precursor to laser light to cause photochemical and thermal conversion into grapheneLIG electrode patterning was achieved in one step on a Kapton paper by using a high-powered 200 μl of the synthesized graphene was then drop-casted on LIG, which functioned as the conducting substrate for our synthesized graphene. After drying at room temperature for 48 h, a gel electrolyte layer was deposited, and the sample was dried for 6 h. Gel electrolyte was made by dissolving 5 g of PVA powder in 5 ml of The microstructure of the samples was studied using a high-resolution transmission electron microscope (TEM), JEM-2100F from JEOL Company, fortified with a 200 kV field emission gun. The elemental analysis was achieved by EDX, using an Oxford instrument 80 Experimental research and field studies on plants (either cultivated or wild), including the collection of plant material, must comply with relevant institutional, national, and international guidelines and legislation.Figure The elemental composition estimated by Energy dispersive X-Ray spectroscopy (EDX) is tabulated below. From Table 31. For the 2D band, three peaks are observed at ~ 2834.42, 3087.70 21. Also, the observed 2D band at 2834.42 cm–1 is blue-shifted due to the strain and vibrational phonons related to impurities32. On the other hand, the combinations of deconvoluted peaks at 3087.7 cm-1 and 3199.02 cm-1 can be ascribed to the D + G band33. The G to the 2D band ratio (i.e. IG/I2D) was also calculated to be 0.98, indicating layer numbers n < 534, which is consistent with that obtained from the HRTEM images.Figure 35. The broad spectrum observed is due to the amorphous nature of the synthesized few-layered graphene sheets36, specifically, the disorder of graphite layers caused by empty rooms between graphite layers37. The broad peak is also observed due to the broadening of the d spacing21.The XRD pattern of the synthesized few-layered graphene sheets is presented in Fig. 38, and the peak located at 364 nm is attributed to the n-π* transitions of the fewer C=O bonds present.UV–vis spectra of the synthesized few-layered graphene were studied using the U-3900 spectrophotometer in the 200–800 nm wavelength range. As shown in Fig. –OH groups and the absorbed water molecules. The peaks observed at 2925.10 COOH group stretching vibration and C=C skeletal vibrations from graphitic domains in the ketone or quinone groups. The weak peak observed at 1380.91 C–O, and lastly, the observed peaks between 721.996 and 570.56 C-H41.Figure Oxygen functional groups on the surface of the synthesized few-layered graphene sheets make it hydrophilic, explaining its solubility in water.To examine the electrical properties of the synthesized few-layered graphene sheets, Hall Effect measurements were conducted. Figure The pomegranate peels consist mainly of hydrolyzable punicalagin, a bioactive compound with a chemical structure including gallagic acid and ellagic acid connected through a glucose molecule. This compound has a common feature, the presence of labile oxygen linkages, responsible for the cyclization and metamorphosis of the punicalagin molecules. Most likely, the heating temperature at 80 °C and the pressure inside the autoclave are capable of hydrolyzing punicalagin and breaking these labile oxygen linkages resulting in the release of ellagic acid. Therefore the reaction proceeds by the breaking of the labile bonds, followed by the release of 42. Rectangular-shaped CV curve also indicates low contact resistance and ideal charge propagation of the electrodes43.Cyclic voltammetry was performed for the LIG Fig. 44. A plot of the Capacitance versus the Scan rates is shown in Fig. 43. Also, at low scan rates, a more homogeneous potential with minimal variations is created along the material45. The specific areal capacitance at 20 m 46.Comparatively, LIG + PG has a larger CV area, indicating higher capacitance and better rate capability. Also, from Fig. The GCD behavior of the samples was studied at various current densities within the range of 10–50 μA 45. Voltage drop for LIG was found to be 0.11 V, and that of LIG + PG was found to be 0.082 V, with the difference equating to 0.028 V. This indicates that our synthesized few-layered graphene sheets contributed a relatively small amount, almost negligible, to the voltage drop.A sharp voltage drop is observed at the beginning of discharge for both samples, which is due to the diffusion-limited mobility of the electrolyte ions in the electrode pores and could result from the interface resistance between the current collector and the active layer or the conductivity of highly porous activated carbon47 and thus indicating good capacitor behavior of our synthesized graphene. A summary of the electrochemical performance of the sample can be found in Table A plot of the areal specific capacitance versus current density is shown in Fig. entclass1pt{minima42, corroborating the results obtained in the CV measurements.EIS was further used to characterize the performance of the graphene samples. The experiment was performed in the frequency range of 100 MHz to 0.1 Hz. Figure 46. The charge transfer resistance (46. Our synthesized graphene's relatively lower resistant values make it an excellent choice for energy storage since lower resistant values are necessary for minimizing supercapacitor losses and increasing overall efficiency.EIS plot fitting was done using the EIS Spectrum Analyzer to get the equivalent circuit model of the EIS plot, shown in Fig. In summary, we have successfully developed for the first time a green, facile, and energy-efficient method of synthesizing few-layered graphene sheet from pomegranate peels via the hydrothermal method at a low temperature of 80 °C without the aid of any passivating agent. The microstructure, morphology, and electrical properties studies show that our synthesized graphene is of high quality. Excellent electrochemical performance has also been achieved with the areal capacitance of 3.39 mF


In recent years, there has been an increased demand for highly sensitive and selective biosensors for neurotransmitters, owing to advancements in science and technology. Real-time sensing is crucial for effective prevention of neurological and cardiovascular diseases. In this review, we summarise the latest progress in aptamer-based biosensor technology, which offers the aforementioned advantages. Our focus is on various biomaterials utilised to ensure the optimal performance and high selectivity of aptamer-based biosensors. Overall, this review aims to further aptamer-based biosensor technology. Neurotransmitters are molecules responsible for transmitting signals between nerve cells and play crucial roles in the central nervous system and the cardiovascular system ,2. SinceAptamers are peptides or oligo-nucleic acids with high sensitivity and selectivity for the detection of various types of analytes ranging from nucleotides, peptides, proteins, and small molecules to cells ,27,28. AAptamer sensors based on various new biomaterials have been developed. For example, an aptamer sensor based on a gold material can be freely immobilised using various chemical conjugations ,41,42,43In this review, aptamer-based sensors are classified into five categories depending on the material used to detect the neurotransmitter: (1) gold-based, (2) gold/metal-hybrid-based, (3) graphene-based, (4) graphene/gold-hybrid-based, and (5) polymer/metal-hybrid-based . AccordiGold nanoparticles (GNPs) can be stably synthesised via simple methods. Additionally, they are biocompatible and have excellent electrical conductivity, high surface-to-volume ratios, and unique optical properties, making them suitable for use in biosensors ,74. FurtRecently, a GNP-based aptamer has successfully been used to detect serotonin (5-HT) by virtue of the optical characteristics of the GNPs . Gold ioIn studies, GNPs have been synthesised in a variety of forms, which have then benefitted from testing by a variety of analytical methods . For exaIn addition to 3D nanostructures, aptamer sensors in the form of gold-film electrodes have been studied. In a study by Zhao et al., DA was successfully detected using a dual-nanopore aptamer sensor with a gold-film deposition . In suchPlasmodium falciparum (pf) glutamate dehydrogenase in serum samples using an aptamer sensor in which FETs were structurally interlocked with gold electrodes [The use of physically interdigitated gold-based electrodes and field-effect transistors (FETs) has been shown to be effective in the sensitive detection of analytes. In 2019, a group reported the detection of ectrodes . The senGold nanostructures have unlimited potential depending on their application. They can be grown, attached by chemical bonding, or deposited onto the sensor surface. Similarly, the sensitivity of the sensor to detect an analyte can be fine-tuned. Modification of gold in the aptamer sensor improves the fundamental properties of the metal and aptamer binding performance, which is an important advantage of this biomaterial in that it does not damage the sensor.Recently, various metals, such as Au, Pt, Ag, and Cu, have been used for biosensor development ,78,79,80. Three-dimensional bimetallic Au/Pt nanoflowers were synthesised by anchoring a Au spike with improved conductivity, adding a catalytic layer of Pt on top, and finally adding the aptamer. The LOD and linear range were estimated at 2.5 μM and 2.5–447 μM, respectively , with large internal surface areas and various configurations and adjustable cavity sizes, were used as materials for catalysis [4. The detection results were measured using SERS. The linear range and LOD were 0.01–0.25 nM and 0.008 nM, respectively. High selectivity was demonstrated by detecting DA in human serum samples, and excellent reproducibility was also confirmed. These results suggest that MOF-type metal-hybrid aptamer sensors are suitable for sensitive detection of DA.In 2022, a successful report was published on the detection of DA using an aptamer sensor based on a Ni/Pt hybrid . An aptaectively B. These ectivity C. Only natalysis ,83. Shi atalysis . After tHybrid forms with two or more metals exhibit high sensitivity to analytes due to the synergistic effect of the metals. Moreover, hybridism circumvents the drawbacks of sensors that are based on a single metal and provides high selectivity. Combinations of metals in multiple phases may be particularly effective candidates for sensing a range of analytes, given that their sensing properties can vary depending on the metal types and proportions.Graphene is a carbon allotrope with a hexagonal honeycomb-shaped two-dimensional planar structure and has attracted much attention due to its excellent electrical conductivity, thermal conductivity, chemical and physical stability, and biocompatibility ,85.In 2021, Hwang et al. successfully detected DA at the fM level using crumpled graphene . Using gThere has also been an attempt to detect multiple neurotransmitters using graphene. Gao et al. successfully detected DA and 5-HT by arranging graphene with different aptamers on a single substrate in such a way that the circuits did not overlap . After fIn 2022, a group succeeded in quantitating DA using a graphene multi-transistor array (gMTA) functionalised with a selective DNA aptamer . Micron-The above studies suggest that graphene-based aptamer sensors have various advantages over other neurotransmitter sensors. Graphene has proven to be an easily implementable substrate for aptamer immobilisation that does not induce damage to biological samples. Moreover, the facile handling and superior analyte adsorption properties of graphene are expected to advance the development of aptamer sensors based on this biomaterial.It has been reported that metals, such as Al, Cu, Au, and Mo, used in sensors have high biocompatibility and excellent electron transfer characteristics, enabling them to be used as electrochemical sensors ,87,88. IMahmoud et al. developed an aptamer sensor decorated with GNPs on carboxylated carbon nanotubes (CNT) and succeeded in sensing histamine (HA) sensitively and selectively . To cons2 and rGO was developed to monitor DA [Reduced graphene oxide (rGO), Al, and Cu are widely used as electrochemical sensors because of their high chemical stability and ability to synergistically interact with various materials . In one nitor DA . rGO wasnitor DA ,96. In anitor DA ,100,101.nitor DA . The higMetal/graphene hybrids can improve the signal between aptamers and analytes. Additionally, the properties of graphene can be exploited to use biosensors with immunoassays for simpler adsorption of metals or aptamers. This type of aptamer sensor, combining the advantages of metals and graphene, has efficient sensing performance. Moreover, it has better sensing performance than sensors that are based solely on graphene or metal. Nevertheless, additional research on the hybrid technology and the factors affecting detection performance is needed to achieve optimum performance.Since polymers are composed of repeatedly connected units, they can be fabricated so as to have flexible forms ,104,105.2O3 and Au/Ti electrodes through spin coating and oxygen plasma etching on a Si substrate  are crosslinked polymers, which are synthesised based on the template molecule, and in which removal of the template generates the target-specific binding site . Like otIt has been proven that the combination of polymer and metal not only improves sensing functionalisation through flexible adsorption and penetration in vivo but also maximises the sensing performance of the aptamer sensor alone. A polymer/metal-based-hybrid aptamer sensor can be an excellent candidate to effectively detect various targets by taking advantage of the fact that results vary depending on how the material and composition are utilised.This review summarised the recent research on aptamer-based biosensors for detecting and monitoring neurotransmitters in various materials. All the biomaterials reported above have demonstrated excellent efficacy for neurotransmitter detection. Metal-based aptamer sensors have excellent electron-transfer and signal-amplification properties, which vary according to the structural arrangement of the aptamer, and thus such sensors can be freely applied to electrochemical or optical systems. Graphene-based aptamer sensors feature excellent stability and high adsorption of analytes and chemicals. Likewise, the sensing performance of the metal/graphene hybrid is amplified by the synergistic effect of the strengths of each material. In addition, aptamer sensors based on polymer/metal hybrids can be used in various environments, and their high biocompatibility allows stable sensing in biological samples.Since an increasing number of human nervous system diseases are being reported every year, researchers need to develop sensors that can easily detect various types of neurotransmitters. A particular problem is that in many situations the analyte of interest is present at low concentrations in a complex medium. Therefore, further research on the materials and technologies that can improve the sensitivity, accuracy, specificity, and reliability of biosensors is needed. In addition, although the sensing performance of biosensors with rigid or complex structures is usually stable and efficient, they are generally not suitable for real-time or sub-cellular monitoring. Therefore, in addition to increasing the sensing performance, efforts should be made to improve the biocompatibility and reproducibility of biosensors, and to prevent potential problems that may be experienced during on-site performance.


Anxious individuals selectively attend to threatening information, but it remains unclear whether attentional bias can be generalized to traumatic events, such as the COVID-19 pandemic. Previous studies suggested that specific threats related to personal experiences can elicit stronger attentional bias than general threats. The current study aimed to investigate the relationship between content-specific attentional bias and trait anxiety during the COVID-19 pandemic.Attentional bias was assessed using the dot-probe task with COVID-19-related, general threat-related, and neutral words at two exposure times, 200 and 500 ms.We found participants with high trait anxiety exhibited attentional bias toward COVID-19- related stimuli and attentional bias away from general threat-related stimuli, while participants with low trait anxiety showed attentional bias away from both types of stimuli.Results suggest that individuals with high trait anxiety show a content-specific attentional bias to COVID-19-related information during the COVID-19 pandemic. Apart from the innate attentional bias toward biological threats, individuals with high trait anxiety may also learn from trauma and develop trauma-specific attentional bias. The COVID-19 pandemic has had a profound impact on multiple aspects of life, causing widespread traumatic stress for many people , but intPrevious studies have suggested that there was a positive association between attentional bias toward COVID-19-related stimuli and anxiety symptoms. For example, Cannito et al.  found leA prior meta-analysis indicates that specific threatening stimuli which is related to individuals’ anxiety type  can elicit a stronger attentional bias than general threat-related stimuli . This smIndividuals with high levels of trait anxiety have been shown to process and react differently to threat-related resources compared to those with low levels of trait anxiety . Trait aCOVID-19 stimuli are distinct from ordinary disease stimuli as they have a comprehensive impact on multiple domains of life. As proposed by Taylor , the psyRegarding the temporal mechanisms underlying attentional bias, there has been consistent debate on whether anxiety symptoms are linked with elevated orienting toward, or impaired disengagement from, threatening stimuli. The vigilance-avoidance model and the attention maintenance model emerged as two dominant views. In the vigilance-avoidance model, anxious individuals demonstrate an initial vigilance toward threatening stimuli and a subsequent avoidance away from the stimuli . In contA range of experimental paradigms have been developed to measure attentional bias, including the widely-used dot-probe task. In this task, a neutral stimulus and a threat-related stimulus are presented simultaneously on a computer screen . SubsequA previous meta-analysis suggested that stimuli types (words or pictures) may also be an important factor in the dot-probe task . SpecifiDue to the inconsistent results in previous COVID-19-related attentional bias studies and the lack of evidence regarding how trait anxiety is associated with COVID-19 attentional bias, further research is needed. According to evolutionary models, all humans possess an innate ability to rapidly detect environmental threats of survival significance, regardless of their susceptibility to anxiety . HoweverA priori power analysis using G*power 3.1.9.7 . The participants were all right-handed, without physical disease, had normal or corrected-to-normal vision and had no color blindness or color weakness. Participants were also evaluated using the Chinese version of the Mini International Neuropsychiatric Interview by phone calls , Is there a limit on your outdoor activities  and How long have you been quarantined . A general score for the extent of exposure to the COVID-19 pandemic was computed by summing the three item scores.M = 20.82, SD = 1.10) to rate the valence and arousal of these word stimuli using a 9-point Likert scale . There was a significant difference between the valence of COVID-19-related words  and neutral words , t (28) = 15.48, p < 0.001, and between general threat-related words  and neutral words, t (28) = 17.35, p < 0.001, but there was no significant difference between the valence of COVID-19-related words and general threat-related words, t (18) = 0.43, p = 0.68. There was a significant difference between the arousal of COVID-19-related words  and neutral words , t (28) = 20.25, p < 0.001, and between general threat-related words  and neutral words, t (28) = 19.49, p < 0.001, but there was no significant difference between the arousal of COVID-19-related words and general threat-related words, t (18) = 1.05, p = 0.31.The current study used word stimuli including COVID-19-related words, general threat-related words, and neutral words. COVID-19-related words were selected from the COVID-19 vocabulary approved by China International Publishing Group. General threat-related words and neutral words were taken from word lists used in previous studies examining attentional biases toward general threats  and the www.naodao.com. During the experiment, participants needed to share their computer screens with the researcher via an online meeting software to avoid distraction. The experiment adopted the dot-probe task. In each trial, a cross fixation “ + ” appeared in the center of the screen for 500 ms. After the fixation disappeared, one emotional word  and one neutral word were simultaneously presented on the left and the right parts of the screen. Emotional words and neutral words were presented at random positions on the left and right parts of the screen. The two words were colored white, spaced 12 cm apart, and were presented for 200 or 500 ms. After the words disappeared, a target dot (“※”) randomly appeared at one of the previous positions occupied by the two words. Participants needed to press the key “F” or “J” to judge whether “※” was on the left or the right of the screen. After participants pressed the key, the trial terminated, and the next trial commenced after a 1,000 ms blank screen  data were analyzed after removing incorrect responses (3%). Median RTs were used to reduce the effect of outliers in the dot-probe task , 39. ForM = 45.03, SD = 8.40). In the current study, participants who had an STAI-T score below 43.5 were assigned to the low anxiety group  and participants who had an STAI-T score above 43.5 were assigned to the high anxiety group . The independent samples t-test showed there was a significant difference in the scores on STAI-T between the low anxiety group  and the high anxiety group , t (60) = 10.97, p < 0.001.The median STAI-T score was 43.5 . We adopted an exclusion criterion on error rates above 20% used by Fani et al.  = 1.68, p = 0.20, ηp2 = 0.03. Exposure Time had no main effect on the attentional bias index, F  = 0.02, p = 0.90, ηp2 = 0.00. Stimuli Type had no main effect on attentional bias index, F  = 0.54, p = 0.47, ηp2 = 0.01.We conducted a 2 × 2 × 2 mixed design analysis of variance (ANOVA) on attentional bias index with Anxiety Group (low and high) as a between-participant factor and Exposure Time (200 ms and 500 ms) and Stimuli Type  as within-participant factors. Anxiety Group had no main effect on the attentional bias index, F  = 4.40, p = 0.040, ηp2 = 0.07. Further simple effect analysis of the interaction revealed that participants in the high anxiety group paid more attention to COVID-19-related words  than general threat-related words , p = 0.050. For participants in the low anxiety group, there was no significant difference between COVID-19-related words  and general threat-related words , p = 0.349.There was a significant Anxiety Group × Stimuli Type interaction on the attentional bias index see , F  = 3.09, p = 0.08, ηp2 = 0.05. There was no significant Exposure Time × Stimuli Type interaction, F  = 1.42, p = 0.24, ηp2 = 0.02. There was no significant three-way Anxiety Group × Exposure Time × Stimuli Type interaction, F  = 0.25, p = 0.62, ηp2 = 0.00.There was no significant Anxiety Group × Exposure Time interaction, The present study is the first to examine the differences between attentional bias toward COVID-19-related and general threat-related stimuli in trait anxiety. Participants with high trait anxiety exhibited an attentional bias toward COVID-19-related stimuli and an attentional bias away from general threat-related stimuli, while participants with low trait anxiety showed an attentional bias away from both types of stimuli. The results indicate that individuals with high trait anxiety are more susceptible to chronic stress exposure and show specific attentional bias toward threatening information corresponding to their current worries.We found the attentional bias toward COVID-19-related stimuli among individuals with high trait anxiety. This result aligned with previous studies which found a positive association between attentional bias toward COVID-19 stimuli and health anxiety  and COVINotably, we did not find an attentional bias toward general threat-related stimuli among individuals with high trait anxiety. This result was inconsistent with a previous meta-analysis which indicated a stable pattern of anxiety-linked attentional bias toward threats . The incWe did not find an attentional bias toward either COVID-19-related or general threat-related stimuli among individuals with low trait anxiety. This result is consistent with previous studies . AlthougAbout the components of attentional bias, we did not find a significant effect of exposure time on the attentional bias index. Participants with high trait anxiety showed attentional bias toward COVID-19-related stimuli and then maintained their attention, within the time course from 200 ms to 500 ms. In contrast, they at first biased away from general threat-related stimuli and then directed their attention to general threat-related stimuli. Participants with low trait anxiety showed an opposite pattern. These results align with a meta-analysis that found attentional bias in a wide range of exposure times among anxious individuals . HoweverThe current study has implications for increasing intervention efficacy, particularly for ABMT and post-disaster interventions. Prior meta-analyses examining ABMT have demonstrated small-to-medium effect sizes for reducing anxiety symptoms , 55. To Despite the valuable contributions of the present study, certain limitations should be acknowledged. First, the sample size of the current study is only 62, which may be the reason why there is no significant correlation between trait anxiety and attentional bias. Previous studies which found a significant correlation between trait anxiety scores and attentional bias indexes had a larger sample size. For example, Salemink et al.’s study (2007) recruited 133 participants and Rudaizky et al.’s study (2014) recruited 72 participants. A larger sample size is needed to explore the linear relationship between trait anxiety and attentional bias. Second, the cross-sectional design precluded the ability to establish causal relationships between trait anxiety and attentional bias. To address this limitation, future investigations may consider implementing a longitudinal design to explore whether a bidirectional and mutually facilitating causality exists between anxiety and attentional bias , whereasThe present study contributes to the field by providing evidence on content-specific attentional bias toward COVID-19-related stimuli in trait anxiety. Participants with high trait anxiety exhibited an attentional bias toward COVID-19-related stimuli and attentional bias away from general threat-related stimuli, while participants with low trait anxiety showed attentional bias away from both types of stimuli. Our findings suggest several theoretical implications. We contribute to the field of content specificity by investigating personalized specificity in a group of individuals who shared similar chronic stress exposure. The current study also has clinical implications for ABMT and post-disaster intervention. The use of content-specific stimuli which are related to participants’ personal experiences could improve intervention efficacy.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving humans were approved by the University Committee on Human Research Protection of East China Normal University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.YZ: Data curation, Writing−original draft. XJ: Investigation, Methodology, Writing−review and editing. SP: Software, Visualization, Writing−review and editing. HJ: Writing−review and editing. YW: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing−review and editing.


Plant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant–microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decades, machine learning-based methods have been developed, accelerating the discovery of SSPs to some extent. However, existing methods highly depend on handcrafted feature engineering, which easily ignores the latent feature representations and impacts the predictive performance.in silicomutagenesis experiment, ExamPle can discover sequential characteristics and identify the contribution of each amino acid for the predictions. The key novel principle learned by our model is that the head region of the peptide and some specific sequential patterns are strongly associated with the SSPs’ functions. Thus, ExamPle is expected to be a useful tool for predicting plant SSPs and designing effective plant SSPs.Here, we propose ExamPle, a novel deep learning model using Siamese network and multi-view representation for the explainable prediction of the plant SSPs. Benchmarking comparison results show that our ExamPle performs significantly better than existing methods in the prediction of plant SSPs. Also, our model shows excellent feature extraction ability. Importantly, by utilizing https://github.com/Johnsunnn/ExamPle.Our codes and datasets are available at  Plant Small Secreted Peptides (SSPs) are crucial intercellular messenger molecules that regulate a multitude of processes . SSPs arArabidopsis thaliana plant  by liquid chromatography–mass spectrometry analysis  to discover SSPs. For example, a novel 15-aa SSP named CEP1 encoded by AT1G47485 is identified in Arabidopsis thaliana  was proposed by The Siamese network can be used to quantify the similarity of two sequences. For example, aiming to finding matching and non-matching pairs of images, After gaining the hidden state output Aiming to decrease the distance between representation vectors of peptide sequences with the same label and increase the distance between representation vectors of peptide sequences with the different label, we use contrastive loss  L1 as thIn this way, we can use gradient descent algorithm to optimize the parameters of the network and generate unified representation for each inputs pair. Also, we need a discriminator To be mentioned, if the FNN and the discriminator are connected directly, the parameters will be updated jointly, which will result in the catastrophic forgetting . In ordeRefer to previous works, we use following classification metrics to evaluate our model: accuracy (ACC), specificity (SP), sensitivity (SE), and Matthew correlation coefficient (MCC). The formulas of these metrics are as follows:In the formulas, TP is the number of true-positive samples, FP is the number of false-positive samples, TN is the number of true-negative samples, and FN is the number of false-negative samples. Accuracy is the ratio of the correctly classified samples to the total samples, which is the most intuitive performance measurement. Specificity measures the proportion of the negative samples which are classified correctly, and Sensitivity measures the proportion of the positive samples which are classified correctly. MCC takes into account positives and negatives, so that it is generally regarded as a balanced metric to measure the overall performance of a model.To evaluate the performance of ExamPle, we adopted several classic comparative models including Naive Bayes , LogistiIn order to verify the effectiveness of the feature representation learned by ExamPle, we compared the performance of different handcrafted feature methods with our model. With the help of iLearnPlus , 2021, wAs shown in It is rarely clear how the deep learning model works during the training process. For a more transparent understanding, we reduced our model’s latent space to a 2D space using principal component analysis (PCA)  and t-diGiven the excellent performance of our complex neural network architecture, we are targeting further exploration of the sequential characteristics of plant SSPs from a computation perspective. To this end, we performed in silico mutagenesis (ISM) interpretation method for every peptide in our dataset and mutated each reference amino acid of a peptide into other nineteen alternative amino acids to evaluate the predicted probability change. Moreover, we conducted amino acid statistical probability for each position on SSPs dataset and the non-SSPs dataset respectively in Glutamine), and P (Proline) have a significant inhibitory effect on the prediction score. To be specific, in the position between 10 and 16, when the amino acid is mutated with Q or P, the prediction score is significantly reduced. The possible reason for this is that Q and P are frequently present in the non-SSPs and are present less frequently in the SSPs, as illustrated in the L-Cysteine) is frequently present in the tail of the non-SSPs. And observations from l-isoleucine), L (Leucine), and F  have a stimulative effect on the prediction score in most positions of the sequence. The possible reason for this is that, as shown in Here, we select one 59-bp-length sequence as the sample to generate the heat map in Glycine) in the SSPs is smaller than that in the non-SSPs, but our model considers that G contributes more to the prediction score in the SSPs, showing our model can mine features that cannot be counted by traditional statistical methods. To sum up, these experiments indicate that our model can mine the sequential characteristics of SSPs.Then, to make the results more statistically meaningful, we computed the contribution of each amino acid on the whole dataset. For each peptide of SSPs and non-SSPs, we calculated amino acid scores by accumulating each position score from the ISM results. In detail, we can sum the score of one row in Methionine) has a more functional impact on the central region, while amino acid C (Cysteine) has a more functional impact in the tail region, which are both meaningful points to be noticed in the peptide design. Next, to further intuitively illustrate which region is more important, we divided each peptide in the dataset into 25 parts and accumulated the amino acid scores to get the comprehensive score of the position. In We next seek to understand the connection between the functional impact and the sequence region. To this end, regardless of the positive or negative effect of one position, we utilized the absolute value subtracted from the ISM experiment as the importance scores to evaluate how the contextual region affects the plant SSPs’ function. To unify all the sequences into one fixed length, we split the sequence into 50 parts. Here, each part is referred as a position ratio. In To better verify the phenomenon in P-value. The lower P-value indicates a higher degree of motif consistency. After we utilize importance scores to intuitively find which region is important for the peptide function, it is very natural to explore the motif of this region from a computation perspective. In In this study, we present a novel explainable contrastive hybrid-view dual Siamese framework ExamPle for predicting the plant SSPs and discovering the sequential pattern of the SSPs. The experiment results demonstrate that the Siamese framework with secondary structure information can learn discriminative feature information and effectively enhance the performance of our model for the plant SSPs prediction. Besides, by adopting dimension reduction methods, the visualization of latent space clearly shows the great learning ability of our model and outperforms traditional handcrafted feature engineering. Utilizing ISM interpretation methods, ExamPle can discover sequence characteristics and identify the contribution function of each amino acid, which are mostly consistent with the statistical results. In addition, by accumulating functional differences between the variant and original sequences, the key novel principle learned by our model is that the head region in peptides and some specific sequential patterns are strongly associated with the SSP function. Thus, our well-trained model ExamPle can not only provide the prediction for plant SSPs but also pave the way for effective and stable plant SSPs design.btad108_Supplementary_DataClick here for additional data file.


Streptococcus mutans BSI (33% [9/27]) followed by S. sanguinis (31% [20/64]), S. gordonii (23% [5/22]), S. gallolyticus (16% [12/77]), and S. oralis (12% [14/115]). In multivariate analysis, previous IE, high-grade BSI, native valve disease, prosthetic valve, congenital heart disease, and community-onset BSI were independent risk factors for IE. After adjusting for these factors, S. sanguinis , S. mutans , and S. gallolyticus  were significantly associated with higher risk of IE, whereas S. pneumoniae  and S. constellatus  were associated with lower risk of IE. Age, hospital-acquired BSI, ischemic heart disease, and chronic kidney disease were independent risk factors for mortality in streptococcal IE. Our study points to significant differences in the prevalence of IE in streptococcal BSI according to species.This study aimed to identify which streptococcal species are closely associated with infective endocarditis (IE) and to evaluate risk factors for mortality in patients with streptococcal IE. We performed a retrospective cohort study of all patients with streptococcal bloodstream infection (BSI) from January 2010 to June 2020 in a tertiary hospital in South Korea. We compared clinical and microbiological characteristics of streptococcal BSIs according to the diagnosis of IE. We performed multivariate analysis to evaluate the risk of IE according to streptococcal species and risk factors for mortality in streptococcal IE. A total of 2,737 patients were identified during the study period, and 174 (6.4%) were diagnosed with IE. The highest IE prevalence was in patients with IMPORTANCE Our study of risk of infective endocarditis in patients with streptococcal bloodstream infection demonstrated that Streptococcus sanguinis, S. mutans, and S. gallolyticus were significantly associated with higher risk of infective endocarditis. However, when we evaluated the performance of echocardiography in patients with streptococcal bloodstream infection, patients with S. mutans and S. gordonii bloodstream infection had a tendency of low performance in echocardiography. There are significant differences in the prevalence of infective endocarditis in streptococcal bloodstream infection according to species. Therefore, performing echocardiography in streptococcal bloodstream infection with a high prevalence of, and significant association with, infective endocarditis is desirable. Streptococcus species is one of the most common causes of infective endocarditis (IE) after Staphylococcus aureus . Of the 2,737 patients, 1,177 (43%) underwent echocardiography within 1 month of streptococcal BSI, and 174  were diagnosed with IE. As shown in Table S1, older patients (P = 0.008) and those with high-grade BSI (P < 0.001), a history of IE (P < 0.001), and underlying heart disease were more likely to undergo echocardiography; those with community-onset BSI (P < 0.001) and polymicrobial BSI (P < 0.001) were less likely to undergo echocardiography. In terms of streptococcal species, patients with BSIs caused by S. mitis (P < 0.001), S. oralis (P < 0.001), S. gallolyticus (P = 0.011), and S. sanguinis (P = 0.015) were significantly more likely than other patients to undergo echocardiography, while those with BSIs with S. anginosus (P = 0.009), S. pneumoniae (P < 0.001), and S. constellatus (P = 0.047) were significantly less likely.A total of 2,985 patients with streptococcal BSI were identified during the study period. Of these, 249 patients with streptococcal bacteremia due to unidentified species were excluded from the analysis, leaving 2,737 patients with streptococcal BSI included in the analysis. 10-year trends of streptococcal BSI according to species are presented in Fig. S1 in the supplemental material. The annual number of streptococcal BSI did not change significantly (P < 0.001), underlying heart disease (P < 0.001), high-grade BSI (P < 0.001), and history of IE (P < 0.001). The highest IE prevalence was found in patients with Streptococcus mutans BSI (33% [9/27]) followed by S. sanguinis (31% [20/64]), S. gordonii (23% [5/22]), S. gallolyticus (16% [12/77]), and S. oralis (12% [14/115])  . Mitis g14/115]) . When beS. sanguinis , S. mutans , and S. gallolyticus  remained risk factors for IE, whereas S. pneumoniae  and S. constellatus  were associated with lower risk of IE , have hospital-acquired BSI (P = 0.021), a prosthetic valve (P = 0.030), heart failure (P = 0.003), ischemic heart disease (P = 0.011), diabetes mellitus (P = 0.006), a solid cancer (P < 0.001), and chronic kidney disease (P = 0.001). They were less likely to have congenital heart disease (P = 0.041), undergo valve surgery within 3 months of BSI (P < 0.001), or have a BSI with S. mitis (P = 0.032) or mitis group bacteria (P = 0.026).One-year mortality due to streptococcal IE was 23% (40/174). Comparisons of the clinical and microbiological characteristics of deceased and surviving streptococcal IE patients are shown in P = 0.001), hospital-acquired BSI , ischemic heart disease , and chronic kidney disease  were independent risk factors for mortality in patients with streptococcal IE.Because of the strong correlation between heart failure and ischemic heart diseases, we retained only ischemic heart disease in the multivariate analysis for mortality . In addiMortality rates in patients with streptococcal IE were significantly lower than in those with non-IE streptococcal BSI , the prevalence of S. parasanguinis in the present study was only 2.5%. In the present study, S. mutans, S. sanguinis, and S. gallolyticus were independent risk factors for IE in streptococcal BSI. However, when we compared patients with streptococcus BSI according to the use of echocardiography, patients with S. mutans or S. gordonii BSI were not more likely to undergo echocardiography than other patients. Based on these results, it seems desirable to perform routine echocardiography in streptococcal BSI caused by species with a high prevalence of, and significant association with, IE .In the present study, infections with d et al.  who obsestreptococcus in the anginosus group were less likely to undergo echocardiography. Although the prevalence of IE among infections with streptococci in the anginosus group was low in our study, the absolute number of anginosus group patients with IE was the second largest due to the large proportion of the streptococcal BSI cases due to members of the anginosus group. Furthermore, the absolute number of pyogenic group patients with IE was the third largest. This was mainly attributed to S. agalactiae IE accounting for the third largest IE cases following S. mitis and S. anginosus. In a recent U.S. study of 296 patients with streptococcal IE, the pyogenic group accounted for 17% of streptococcal IE, and S. agalactiae was the most common pathogen (78%) in this pyogenic group . Informed consent was waived by the ethics committee of Asan Medical Center because no intervention was involved, and no patient-identifying information was included. To protect personal privacy, identifying information in the electronic database was encrypted.Streptococcus from one or more blood cultures. Community-onset BSI was classified as blood culture obtained within 48 h of hospitalization  were diagnosed based on clinical data and echocardiographic results according to the modified Duke criteria, and cases with Duke-possible IE who had no evidence of vegetation on echocardiography were not classified as IE . Species and antimicrobial susceptibilities were determined with a Vitek  or Microscan  in accordance with the standard criteria of the Clinical and Laboratory Standards Institute . MALDI-Tt test or the Mann-Whitney U test was used to compare continuous variables, and the Pearson chi-squared test or Fisher’s exact test was used to compare the respective categorical ones, as appropriate. We performed multivariate analysis to identify risk factors for IE in patients with streptococcal BSI using logistic regression. To evaluate the risk factors for mortality in streptococcal IE, we performed a Cox regression analysis. A two-tailed P value of <0.05 was considered statistically significant. All statistical analyses were performed with R software, version 3.3.3 .Trends in frequency of streptococcal BSI according to streptococcal species were evaluated with the Mann-Kendall trend test. Prevalences of IE at the streptococcal species level were calculated as number of IE cases due to each species divided by total number of BSI caused by that streptococcal species during the study period . Student