Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces. Multichannel recordings of the surface electromyographic signals (sEMG) allow inferring on the activity of different groups of muscles involved in certain movements ,2,3,4,5.Recent advances in hardware and software for sEMG recording and real-time data analysis fostered the use of sEMG human–machine interfaces for controlling a variety of devices such as, e.g., personal computers ,10, prosMost methods of extraction of the representative features from sEMG signals are based either on amplitude characteristics and autoregressive models or on the time-frequency analysis and spatiotemporal features ,16,17. TThe main difficulty in achieving high performance with different persons resides in a wide set of individual characteristics of different subjects, which requires a tedious fine-tuning of interfaces. Moreover, even for the same user, some characteristics may change in time. For instance, the interface performance can degrade significantly due to displacement or shift of recording electrodes, perspiration of skin, fatigue, muscles “crosstalk”, muscle fitness, etc. ,31. ThusTo approach this problem here, we employ two complementary experimental strategies. We investigate the interface performance in synthetic tests  and in a gaming environment. In the former case, we achieve controllable and repeatable conditions, while in the latter, we examine the user experience in “real-life” scenarios. We then systematically study the latent factors influencing the interface performance. In particular, we quantify (i) the degree of muscle cooperation, i.e., the coordinated contribution of synergist and antagonist muscles in a hand movement and (ii) the user’s constitution, i.e., the content of the body fatty tissue. We show that these factors can significantly limit the performance of an sEMG-interface in synthetic tests and provide insight on the social groups of subjects influenced by each factor. We also study how short-term and long-term training can affect the use of interfaces. Surprisingly, the short-term training plays no role in synthetic tests, but significantly increases the gaming performance. We then provide a method for quantification of the gesture recognition fidelity in a dynamic environment. Note that in this case, most of the indexes commonly used in the literature are not applicable. We then discuss the differences between the effects of short-term and long-term training, and how this information can be used for optimizing the training strategy and adapting the signal processing algorithms to the needs of individual users.For experimental purpose, we recruited 37 healthy volunteers of either sex (24 women and 13 men) from 18 to 41 years old, and of different fitness and training (12 trained and 25 not trained subjects). In the context of this work by “trained”, we mean persons who regularly practice sport or other activities related to manual small motility . The study complied with the Helsinki declaration, adopted in June 1964  and revised in October 2000 . The Ethics Committee of the Lobachevsky State University of Nizhny Novgorod approved the experimental procedure (protocol No. 6 from 06.07.2017). All participants gave their written consent. All subjects had no previous experience in dealing with sEMG-interfaces. Fourteen out of 37 subjects (8 women and 6 men) also participated in a ten-day training sessions that consisted in practicing individual synthetic hand gestures and playing a testing arcade game.For experimental assessment of an sEMG-interface, we have developed a hardware–software complex called MyoCursor. The system consists of a MYO Thalmic bracelet worn on a user’s forearm, and a PC with a Bluetooth receiver running specially designed software A.The bracelet is equipped with eight sensors equally spaced around the forearm that simultaneously acquire myographic signals. The signals are sent through a Bluetooth interface to a PC. We used the MYO software development kit to access raw eight-channel data, while the built-in software of the bracelet was disabled. Acquired signals are processed by MyoCursor software v1.12 in real-time. The software recognizes hand gestures and estimates the muscle effort that finally are used for controlling a game module.The game module replicates the well-known arcade game “pacman” A, inset.0, hand at rest . The collected data were processed offline. Note that synthetic data also were used for a supervised training of ANN.To process in real-time sEMG signals, we employed two different approaches: (1) ANN-based and (2) linear discriminant analysis (LDA).The data flow valuated B 32]:(1:(1x(t)∈ℝ neurons B.The network neurons apply weighted sum over their inputs lgorithm .1–G4) corresponds to a single target class. Thus, each output neuron (6) during learning, we used the target value x-axis (y-axis) we used the value proportional to the difference of the output neurons  we employed an approach similar to that described in References ,36. We ean power B.Δ along the x- and y-axes on the screen is given byDue to some intrinsic jitter in the muscle tone, we usually observed a slow involuntary drift of pacman on the screen. To eliminate this artifact, the trend defined by the relaxed hand state A, Rst waF-measure [TP is the number of true positives, i.e., correctly recognized gestures; FP is the number of false positives, i.e., a classifier recognizes other gesture as its own; and FN is the number of false negatives, i.e., a classifier does not recognize its own gesture. Then, the F-measure is given byFor estimating the performance of the sEMG-interface in synthetic tests, we employed two measures. The first was the so-called -measure , which i-measure :(6)P=TPTThis measure is convenient for quantifying the interface performance in synthetic tests, since it can be calculated both for each gesture separately, and for all of them together. Note that in the latter case, a classifier is tested on “known” gestures, hence F-measure for comparing the performance of the ANN and LDA classifiers in the task of classification of four main gestures G1–G4. However, the compound gestures (G5–G8) require different approach. Indeed, in this case, the output of the ANN is not binary, and thus the assignment of TP, FP, and FN is not straightforward. We then used the mean squared error of the difference between the network output M neurons in the last layer (in our case M = 7):We used the The mean squared error (8) was calculated for the network training and testing sets. It served as a criterion to stop the learning procedure, as soon as the error started increasing on test samples. On average, the learning process required about 5000 training epochs and took less than 1 min on a standard Intel Core i5 PC.Our experimental data show that The distribution of To test the correlation between the classification error and anatomic features of the users, for each subject we estimated the amount of fatty tissue in the body by the fat monitor OMRON BF306. Personal anthropometric characteristics, i.e., weight, height, age, and sex were introduced into the analyzer. The device measures the impedance from hand to hand and calculates the body fat (BF) in percentage based on the collected data. In our study, the subjects had the BF index in the range %.Each body movement involves contraction and extension of various muscles. These concurrent processes must be perfectly synchronized and tuned to perform movements optimal in terms of the energy consumption and precision. To verify the influence of the “muscle functional efficiency” of a subject on the sEMG-interface performance, we developed a novel measure: the coefficient of activation of synergist-antagonist muscles or SAC. It will be thoroughly discussed in To study the performance of the sEMG-interface in real-life scenarios, we conducted experiments with the pacman game. While playing, the subjects freely move their hands, thus we get no clear reference on the gesture performed in each time instant. Therefore, the above-described indexes are not applicable directly. We then developed a measure based on an analysis of trajectory of pacman controlled by users while persecuting cherries A, inset.At each game trial, the trajectories of pacman and of cherries . Then, we calculate the unit vector:Given the best direction (12) (or (13)) and the real direction of pacman movement, we can evaluate the angular deviation To evaluate the fidelity of gestures identified by the ANN, we use the ANN output vector B:(14)δ. In the same test to drive pacman, the subjects also used more common interfaces: a joystick, and a computer mouse. To get insight on the effect of short-term learning, testing sessions were repeated during ten days. As was expected, we obtained quite diverse game scores for different types of interfaces. Note that the hand movements, and hence sEMG patterns, are quite similar for sEMG-interface and joystick, and differ significantly from the mouse control. Computer mouse was the handiest for playing the game. However, to our surprise, results shown with joystick  were much closer to the sEMG-interface than to mouse . In such a case, we can evaluate the performance index Re, which quantifies the error at the neural network output. Then, we study the correlation between two types of experimental approaches. Each individual subject first performed synthetic gestures, and we evaluated Re and then the same subject played the pacman game, and we recorded the obtained score. Besides testing the sEMG-interface in the gaming environment, we also evaluated its performance in synthetic tests while subjects were performing separate individual gestures , which has been previously shown to be effective for the analysis of multielectrode recordings of local field potentials (LFP) [The MYO bracelet records sEMG signals from a forearm, which has a number of muscles that participate in performing different gestures A. We thels (LFP) ,42,43. Tflexor carpi radialis (FR), (2) flexor carpi ulnaris (FU), (3) extensor carpi radialis longus (ER), (4) extensor digitorum (ED), and (5) extensor carpi ulnaris (EU). Other muscles  may also contribute to gestures G1–G4 and, consequently, to sEMGs, but their signals are weak enough and can be neglected while dealing with the SAC.Our studies have shown that sEMG signals are mainly contributed by five sources, i.e., m=5 B. Moreov muscles A,B: (1) 1–G4. The activity exhibits clear patterns for each of the four gestures.Thus, given that the MYO bracelet has been placed correctly on a subject’s forearm, we can accept that electrodes 2, 4, 5, 6, and 8 capture exclusively the activity of the corresponding main muscles. 1–G4:For the sake of simplicity and taking into account the symmetry of activations, we selected four muscles out of five: FR, ER, EU, FU A. Depend1 (“left”): G2 (“right”): G3 (“up”): G4 (“down”): GWithin our approach, each gesture has two synergist muscles and two antagonist ones. We thus introduce the synergist or V¯∈ℝ8 C:G1  serves for normalization of the coefficient. Then SAC = 0 means that synergist and antagonist muscles are equally activated by the gesture, whereas SAC > 0 (SAC < 0) indicates prevalence of synergetic (antagonistic) muscles’ contraction. We thus expect that higher SAC values correspond to better coordination of muscles, while performing different gestures and hence should result in a lower error rate of the sEMG-interface.1–G4. We remind that by “trained”, we mean persons who regularly practice sport or other activities related to manual small motility. For three gestures out of four (G2–G4) the mean value of the SAC is higher for trained people, as we expected. For G1 (wrist flexion), the means are practically the same. This is because G1 is the most natural gesture that does not require strong muscle activation. On the available data, the statistically significant difference appears in the case of G2 only . Note that this is the gesture (wrist extension) with the maximal SAC, which requires strong muscle activation and coordination. Non-significant differences observed in other cases may be due to not sufficient statistics.Above, we mentioned that different types of classifiers usually provide similar performance when applied to sEMG signals. Let us now confirm this observation in an example of the ANN and LDA classifiers.F-measure of the gesture recognition fidelity  for the ANN and LDA classifiers applied over the same data set (subjects performing synthetic gestures G1–G4). For both classifiers, the mean values lie in the rather narrow interval . However, the dispersion of the measure  over different subjects is quite high, in the range . Note that the lower bound F = 0.8 corresponds to a strongly uncomfortable situation for a user. The LDA method performs slightly better than the ANN on gestures G1 and G4, equally well on G2, and worse on G3. Nevertheless, the statistical analysis shows no significant difference between the classifiers. Thus, the equal means and high dispersion of the performance suggest that, to a great extent, the limiting factors for sEMG-interfaces can be related to the individual properties of different subjects, and to a lesser extent, to the type of classifier. Then, a perfect classifier should take into account the individual user’s properties.It is reasonable to assume that long-term training of hand muscles in daily life can lead to a more efficient motor control. Eventually, it will be reflected in more coordinated sEMG-patterns and, consequently, will lead to a better performance of the sEMG-interface. Let us now crosscheck this hypothesis.t-test, 2 exhibits statistically significant difference between trained and not trained subjects  at several points of pacman’s trajectories, i.e., the directions of pacman movement that would lead to the fastest target interception B. As we We then averaged the results shown in The discussed statistics for a single user confirms that short-term training can improve the user experience with the sEMG-interface. However, it does not shed light on the question of the way it happens. To get additional information on the user’s decisions, we separated gestures identified by the ANN controlling the movement of pacman into “optimal” and “incorrect”, according to the angle between the user’s selected and the best directions. Then, we identified the rates of “optimal” and “incorrect” right, left, up, and down gestures. Note that in a gaming environment, the definition of pure gestures cannot be introduced. Instead, we applied a threshold criterion to the ANN output .The articulation of gestures improves after the short-term training. The most notable result was obtained in the problematic gesture “up”. Its incorrect rate decreased while the optimal one increased significantly. Besides, the user improved the rates of optimal gestures “right” and “left”. We note that the experiment was “blind”, i.e., the user was not alerted after the first day about the problem he had with gesture “up”. Nevertheless, in a commercial use of an sEMG-interface, such a knowledge could be useful for a user, and may allow reaching better training results. We also note that in our experiments we observed that different users had problems with different gestures. Thus, the training process should be individual. It also means that efforts to improving the sEMG-interface can be directed to the interface customization for individual users.In this work, we have systematically studied the latent factors determining the performance of sEMG-interfaces. To this end, we have employed two complementary experimental strategies. On the one hand, we tested the interface performance in a gaming environment, which allowed us to examine the user experience in scenarios close to real ones. The developed “pacman” game also permitted keeping the motivation of subjects during short-term training lasting ten days. On the other hand, to discard the influence of factors extrinsic to the interface features  and to work in controllable and repeatable conditions, we also performed synthetic tests. In this case, the subjects were asked to repeat a set of individual gestures .The subjects recruited for experiments were wont to use computer mouse in their daily life, and much less accustomed to joystick, and had never used the sEMG-interface. The gaming tests showed that short-term training with the sEMG-interface practically doubled the game score achieved by the users. At the same time, the results obtained with joystick also showed a significant increase, while playing with computer mouse revealed no changes. Moreover, the mean game score achieved with joystick was much closer to the sEMG-interface than to mouse. Thus, the game design was appropriate, since it minimized the influence of gaming skills, i.e., a high-level reasoning, and unveiled features exclusively related to the interfaces. The success in the short-term training obtained with the sEMG-interface allows us foreseeing its high potential, given that appropriate training conditions will be met.Re, which quantifies the error at the neural network output; (b) the F-measure, which estimates the rate of correctly and incorrectly identified gestures; and (c) the synergist–antagonist coefficient (SAC), which reflects the muscle functional efficiency. Our gross results have confirmed the earlier reported data [1–Q3 interval of the F-measure lies in the range . The lower limit (F = 0.8) makes the use of an interface strongly uncomfortable for a user. These figures have been obtained with two different classifiers based on linear discriminant analysis and artificial neural networks. At different gestures, the LDA method performed either slightly better or worse than ANN, but we observed no statistically significant difference between the classifiers. This suggests that a qualitative leap in the sEMG-interface performance may require novel approaches to the user training or ANN post-training procedures. A promising approach in this direction can be based on the novel concept of “high-dimensional brain” [Then, we have analyzed the latent factors determining the sEMG-interface performance in synthetic tests. We thus introduced (a) the performance index ted data ,30,34,45l brain” .Re and the body fat index.Analyzing different user groups in synthetic tests, we found statistically significant differences between men and women and between physically trained and not-trained subjects. The higher interface performance found for men can be linked to the content of fat tissue in the body. Earlier, this factor has been discussed in several studies . Fat tiNext, we focused on investigating the impact of unspecific  long-term training of users. We selected a group of “trained” subjects regularly practicing sports or other activities involving manual small motility . This group consisted of men and women, and had a decreased body fat index. However, the decrease was not significant. Thus, we hypothesized that the difference observed between physically trained and not-trained people cannot simply be reduced to the body fat index, but is also explained by the degree of functional muscle cooperation.flexor carpi radialis, flexor carpi ulnaris, extensor carpi radialis longus, extensor digitorum, and extensor carpi ulnaris. For each individual gesture, we defined synergist and antagonist muscles and evaluated their activation ratio, SAC. We thus expected that higher SAC values should correspond to better coordination of muscles, and hence, should result in a lower error rate of the sEMG-interface. We have checked that the SAC is not sensitive to body fat, and hence can be used to contrast our hypothesis.Using independent component analysis, we have shown that sEMG signals are mainly contributed by five sources that coincide in space with anatomical location of five muscles: 1) were the same, because it is the most natural gesture that requires no strong muscle activation. Thus, the difference between physically trained and not-trained subjects besides the body fat index can be explained by long-term training of hand muscles, and related brain circuits involving motoneurons, which lead to a more efficient motor control.We have shown that the success in handling the sEMG-interface indeed depends on the SAC of a subject. For three out of four gestures, the mean value of the SAC was higher for trained people, as expected. The means for the fourth gesture “go left”  and the rates of all optimal gestures increased . We note that such a result was obtained in “blind” experiments, i.e., the subjects were not alerted after the first day about the problems they might have. Nevertheless, in a commercial use of sEMG-interfaces, such knowledge could be useful for optimizing the training process by paying strong attention to problematic gestures. Thus, the training process and efforts to improving sEMG-interfaces should be directed to their individual tuning.Overall, the obtained data suggest that short-term training can improve the interface performance by some plastic changes occurring at the upper cognitive level. To achieve progress at the low level of muscles and motoneurons, long-term training is required. However, such training demands strong motivation from a user. In our experiments, we observed a significant drop in motivation already after ten days. In this respect, it seems promising to study sEMG-interfaces with amputees who do not have the opportunity to use standard interfaces. In this case, the long-term training may provide social rehabilitation and improvement of the life quality through access to online services. Then, the mechanisms of transferring of skills acquired in short-term training to long-term neuromotor synchronization can be revealed.Finally, to support our conclusions, we recall EEG studies of motor performance. In particular, it was shown that EEG of athletes exhibits changes depending on the kinematic characteristics of the performed sport and sex of subjects. These factors also influence the success in using of a neurophysiological feedback while training . In line


UA) modulates outcomes of neurological diseases, although little is known about cerebrospinal fluid (CSF) UA levels in neuromyelitis optica spectrum disorders (NMOSDs).Previous studies have shown that serum uric acid  and controls with noninflammatory and non‐neurodegenerative diseases . Correlation analysis was performed between CSF UA and clinical characteristics, serum UA, and blood–brain barrier integrity in NMOSDs.Cerebrospinal fluid and serum UA levels in NMOSDs were significantly higher than in CTLs (p = .002), while serum UA differences between NMOSDs and CTLs were not statistically significant. In NMOSDs, CSF UA levels were significantly higher in patients with an impaired blood–brain barrier than in patients with an intact one (p < .001), and significantly higher in longer disease duration than in shorter disease duration patients (p = .002). CSF UA levels were also significantly higher in active patients upon MRI than in inactive patients (p < .001), and significantly higher in patients with brain lesions than without brain lesions (p = .024). CSF UA was significantly associated with the serum UA levels , disease duration , and blood–brain barrier index , but did not correlate with age, gender, annualized relapse rate, duration, or severity of NMOSD. Multiple regression analysis demonstrated that CSF UA was independent of the blood–brain barrier index  and serum UA levels  in NMOSDs.Cerebrospinal fluid UA levels were elevated in NMOSD patients during relapse, and were likely modified by serum UA levels and blood–brain barrier integrity.Cerebrospinal fluid  Neuromyelitis optica spectrum disorders (NMOSDs) comprise NMO, Asian optic‐spinal MS, optic neuritis or longitudinally extensive myelitis associated with systemic autoimmune disease, optic neuritis or myelitis associated with brain lesions typical of NMO , and limited forms of NMO   and the procedures followed were in accordance with the Helsinki Declaration of 1975, as revised in 2008. All participants involved in this study provided written informed consent.2.2n = 30). All patients had been hospitalized during clinical acute relapse one or more time from 2007 to 2012. The NMOSDs had been diagnosed according to the criteria described previously  were enrolled as controls (CTLs) during the same period. All samples were collected prior to treatment. None of the patients had been treated with acetylsalicylic acid, thiazide diuretics, steroids, or other drugs that could affect UA levels within the previous 12 weeks, and none of the patients were diagnosed with diabetes mellitus, renal disorders, hepatic disorders, or malignancies.Our study recruited 68 patients, including 38 NMOSD patients  and controls  according to the manufacturer's instructions.Cerebrospinal fluid and serum albumin were quantified to calculate the CSF Albumin Index (CSF AI) as a validated marker for BBB integrity  scanning to detect NMOSDs was performed using a GE 1.5T MR scanner . The conventional MRI protocols were described in our previous paper , gender, disease duration , annualized relapse rate (ARR), disease activity, brain or spinal cord lesions upon MRI, and EDSS score. Correlations between CSF UA levels and age, serum UA levels, and CSF AI were analyzed using the Pearson test, and correlations between CSF UA levels and disease duration, EDSS score, CSF routine, and disease activity on MRI were analyzed using the Spearman's rank test. Multiple regression analysis using a stepwise method was performed to identify factors affecting CSF UA levels. A p < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS 16.0 .Data are presented as mean3Patient demographic characteristics are presented in Table p = .002)   Fig. A. Howeve77) Fig. B.p = .002). Patients with an impaired BBB (CSF AI ≥ 9) had significantly higher CSF UA levels than those with an intact BBB (CSF AI < 9) (p < .001). Patients with MRI activity had significantly higher CSF UA levels than those with MRI inactivity (p < .001), and patients with brain lesions upon MRI had significantly higher CSF UA levels than those without brain lesions (p = .024). Additionally, CSF UA levels in male patients were higher than in female patients, CSF UA levels in severe disability patients (EDSS > 3.5) were higher than in those with mild disability (EDSS ≤ 3.5), CSF UA levels in patients with higher ARR (ARR > 1) were higher than in those with lower ARR (ARR ≤ 1), CSF UA levels in patients with longer spinal cord lesions  were higher than in those with shorter spinal cord lesions , although these differences were not statistically significant  or CSF AI , and a positive correlation between CSF UA levels and disease duration . Additionally, results showed a positive correlation between CSF to plasma UA ratio and CSF AI . However, there were no significant correlations between CSF UA levels and age, ARR, EDSS, CSF WBC, CSF TP, CSF GLU, CSF CL, or disease activity upon MRI , and CSF parameters in NMOSD patients Table , Fig. 2.R2) was the percentage of dependent variables explained by independent variables added to the model. Results of the multiple regression analysis are shown in Table p = .019), and between CSF UA levels and CSF AI . Serum UA levels and CSF AI together explained 69.9% (R2 = .699) of the CSF UA variance.Multiple linear regression analysis was performed to investigate the influence of independent variables on CSF UA levels in NMOSDs patients. The dependent variable in this study was CSF UA levels in NMOSDs. The variables were compiled from clinical data . The test collinearity diagnostics indicated that all independent variables were sufficient for the regression model. The unstandardized partial regression coefficients (β) were coefficients from the estimated regression model. The coefficient of determination (4r = .454). Additionally, because serum UA levels in NMOSD are similar to levels found in controls, the mechanisms involved in increased CSF UA levels in NMOSD patients are not immediately clear. This small difference in serum UA might be partially responsible for the increased CSF UA, which is potentially “helped” by the imbalance of cerebral cell energy metabolism causing increased UA production.Results from the present study showed that UA levels in the CSF increased in NMOSD patients during clinical relapse. UA, which is the end product of adenine nucleotide catabolism, together with other oxypurines (xanthine and hypoxanthine), reflect the rate of ATP catabolism  in NMOSDs patients. Recently, BBB destruction has been considered to play an important role in NMO progress (Shimizu et al., Furthermore, CSF UA levels were dependent on CSF AI (BBB integrity, In conclusion, CSF UA levels significantly increased in NMOSDs patients, and were likely modified by serum UA and BBB integrity. Results also suggested that CSF UA levels reflected oxidative stress and excitotoxicity in NMOSDs. Further studies are needed to assess the role of UA in NMOSDs.The authors declare no potential conflict of interests.


In the present study, cerebrospinal fluid (CSF) concentrations of GABA were analyzed in 41 first-episode psychosis (FEP) patients and 21 age- and sex-matched healthy volunteers by high-performance liquid chromatography. We found lower CSF GABA concentration in FEP patients compared with that in the healthy volunteers, a condition that was unrelated to antipsychotic and/or anxiolytic medication. Moreover, lower CSF GABA levels were associated with total and general score of Positive and Negative Syndrome Scale, illness severity and probably with a poor performance in a test of attention. This study offers clinical  However, although many symptoms can be linked to dopaminergic dysregulation, it has been suggested that causative abnormalities may lie elsewhere.3 In this regard, focus has been directed to glutamatergic dysregulation and, in particular, to an N-methyl-D-aspartate receptor hypofunction.6 Thus, patients with schizophrenia show elevated levels of post-mortem brain and cerebrospinal fluid (CSF) kynurenic acid, an endogenous N-methyl-D-aspartate receptor antagonist.10 Several lines of research have also implicated the inhibitory neurotransmitter γ-aminobutyric acid (GABA) in the pathophysiology of schizophrenia and a number of studies have identified deficits in parvalbumin containing GABA neurons in schizophrenia.11 One of the most consistent post-mortem findings in schizophrenia is a decreased expression of the 67 kDa isoform of glutamic acid decarboxylase, a key enzyme in the biosynthesis of GABA.15 In line with this, several studies have shown an association between GAD1, the gene for the enzyme 67 kDa isoform of glutamic acid decarboxylase, and schizophrenia.18 Furthermore, congruent with a reduced expression of 67 kDa isoform of glutamic acid decarboxylase, post-mortem studies reveal lower GABA levels in multiple brain regions including the nucleus accumbens, thalamus, amygdala and hippocampus in patients with schizophrenia.23Schizophrenia is characterized by positive and negative symptoms, as well as by cognitive deficits, in particular in domains related to attention and verbal working memory.in vivo studies of GABA in schizophrenia have been inconclusive. Using proton magnetic resonance spectroscopy (1H-), some studies found a decrease,26 some an increase,28 and yet others found no changes in GABA levels31 in patients with schizophrenia. Differences in GABA levels between patients and unaffected controls appear dependent on the brain area investigated, the duration of illness, as well as on the medication.31 Recently, a study using positron emission tomography utilizing [11C]flumazenil suggested an impaired GABA neurotransmission in patients with schizophrenia, a finding that was also associated with positive symptoms.32 Further, several studies analyzing CSF GABA in patients with schizophrenia, most of them performed during the 1980s, have yielded mostly negative and partly inconsistent results.41In contrast to genetic and post-mortem studies, Taken together, there is an increasing body of evidence from genetic and post-mortem studies implicating an altered GABA transmission as a significant component of schizophrenia pathophysiology. However, robust evidence from CSF studies of an involvement of GABA is still lacking. We here analyze CSF GABA and four other amino acids, that is, glutamate, glycine, taurine and tyrosine, with a sensitive analytical assay, in well-characterized groups of healthy controls and patients with first-episode psychosis (FEP), most of them drug naive to antipsychotic medication. We hypothesize that CSF GABA is reduced in FEP patients, and that low levels of GABA associate to worse symptoms and cognitive deficits.The study was approved by the Regional Ethics Committee in Stockholm and conformed to the tenets of the Declaration of Helsinki. All subjects were included from March 2011 through January 2014, after providing written informed consent. This study formed part of the Karolinska Schizophrenia Project, a multidisciplinary research consortium that investigates the pathophysiology of schizophrenia.n=12), schizophreniform disorder (n=14), severe depression with psychotic features (n=1), delusional disorder (n=3), brief psychotic disorder (n=1), psychotic disorder not otherwise specified (n=9) or schizoaffective syndrome (n=1) were recruited from psychiatric emergency wards and 3 psychiatric outpatient clinics in Stockholm. Diagnosis was established based on a structured clinical interview of the DSM-IV or a consensus diagnostic procedure. All patients were re-assessed after approximately 1.5 years and were then found to meet the criteria for the following DSM-IV diagnoses: schizophrenia (n=25), psychotic disorder not otherwise specified (n=5), delusional disorder (n=4), brief psychotic disorder (n=1), schizoaffective syndrome (n=3) and no diagnosis (n=3). Exclusion criteria were neurologic or severe somatic illness, substance abuse and autism spectrum disorder. Absence of major brain abnormalities was confirmed using magnetic resonance imaging. All patients underwent an extensive clinical characterization, including the Global Assessment of Functioning , the Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression (CGI), Alcohol Use Disorders Identification Tests and Drug Use Disorders Identification Tests. All patients included in this study were somatically healthy and free from any substance abuse disorder. Tobacco use was permitted and 11 of the 41 patients (27%) used tobacco (smoking or snuff). Occasional medication with sedatives and anxiolytics were allowed during the course of the study. At the time of CSF sampling, 12 out of 41 patients (29%) were treated with benzodiazepines (BZDs). Eighteen out of 41 patients (44%) were under antipsychotic treatment at the time of CSF sampling (mean time (±s.e.m.) 7.2±1.82 days). Patients with more than 1 month of treatment with antipsychotics were not included in the study, with the exception of the inclusion of one patient that had been treated for 57 days. Twelve out of 41 patients were naive to all medications. Antipsychotics used were olanzapine, aripiprazole, risperidone, quetiapine or haloperidol (see n=34), GAF, PANSS, cognitive testing and lumbar puncture were all performed within a 10-day period (mean time (±s.e.m.): 5.5±0.4 days), whereas seven of the patients underwent these investigations during a period from 14 to 40 days (mean time (±s.e.m.): 19.4±3.6 days).Forty-one FEP patients  who met the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria for schizophrenia  were recruited by advertisement. Medical examination was made by routine laboratory blood and urine tests, physical examination, as well as a brain magnetic resonance imaging examination. The Mini International Neuropsychiatric Interview was used to exclude previous or current psychiatric illness. Further exclusion criteria were previous or current use of illegal drugs and first-degree relatives with psychotic illness. All participants were free from medication and any form of substance abuse evaluated with Alcohol Use Disorders Identification Tests/Drug Use Disorders Identification Tests at the time of the study. None of the subjects had any first-degree relative with a psychiatric diagnose. In all but one case, no structural brain abnormality was detected using magnetic resonance imaging, as evaluated by an experienced neuroradiologist at the MR Centre, Karolinska University Hospital, Solna. This individual exhibited signs of demyelinating disease on magnetic resonance imaging, but did not fulfill criteria for a clinically isolated syndrome or multiple sclerosis,as the clinical neurological exam was normal and there was no history of relevant neurological symptoms. CSF examination revealed oligoclonal bands, but no other abnormalities. Test results were similar to other controls and therefore this subject was not excluded from the analysis. For all healthy controls, cognitive test session and lumbar puncture were all performed within mean time (±s.e.m.): 14.5±3.01 days.42 These efforts include the use of a disposable atraumatic needle  that was inserted at the L 4-5 level with all individuals in the right decubitus position. Further, the same volume of CSF (18 ml) was allowed to drip into a plastic test tube, protected from light. CSF supernatant from all subjects was divided into 10 aliquots that were frozen at −80 °C within 1 h of sampling following centrifugation  for 10 min) to separate cells and supernatant, respectively. The majority of subjects  underwent the lumbar puncture between 0745 and 2200 h after a night’s sleep. Owing to clinical routines, morning sampling was not possible in the remaining FEP patients (n=18). To control for this confounding factor, seven controls also underwent lumbar puncture during the same time interval . All subjects were instructed to avoid physical activity during the preceding 8 h; however, it was not feasible to monitor rest or posture in this regard. Importantly, no correlation between CSF GABA levels and the point of time for lumbar puncture was observed . This is in analogy with BenMenachem et al.,43 showing no differences in CSF GABA in healthy controls between lumbar puncture in the afternoon and next morning sampling.Efforts were made to reduce confounding factors of the lumbar puncture procedure that could influence analysis of CSF amino acids.A fresh sample was analyzed for cell numbers, albumin, immunoglobulin G content and the presence of immunoglobulin G and immunoglobulin M antibodies to Borrelia, as well as with immune electrophoresis.O-phthaldialdehyde/2-mercaptoethanol reagent. The reagent was prepared by dissolving 27 mg O-phthaldialdehyde in 0.5 ml ethanol (99.5%), 4.5 ml borate buffer (0.4 M boric acid adjusted to pH 10.4 with sodium hydroxide) and 20 μl 2-mercaptoethanol was added.Samples were subsequently analyzed for GABA  with a gradient elution reversed-phase high pressure liquid chromatography system, including a gradient pump , a degasser (Spectra System SCM 400), a Luna 100 C18(2) column  and a fluorescence detector  operating at excitation and emission wavelengths of 344 and 495 nm, respectively. The chromatographic separation was performed at room temperature (22 °C). CSF from FEP patients and healthy controls were derivatized for 60 s at room temperature with M sodium acetate buffer (pH 6.95) containing 2.5% (v/v) of methanol, 2.5% (v/v) of tetrahydrofuran and mobile phase B consists of methanol. The flow rate of the mobile phase was 0.7 ml min−1 throughout the analysis and all gradient changes were linear. The gradient conditions were as follows: initial conditions are 100% mobile phase A; from time 0 to 11 min the gradient changes to 70% mobile phase A and 30% mobile phase B; from 11 to 13 min the gradient changes to 10% mobile phase A and 90% mobile phase B; from 14 min the gradient changes to 100% mobile phase A and remains in this condition until the next injection. Samples of 20 μl were manually injected into the system. The signals from the fluorescence detector were transferred to a computer and analyzed by Datalys Azur Software . Approximate retention time of GABA was 9.8 min .Detection of amino acid gradients was performed with two degassed mixture mobile phases. Mobile phase A consists of 0.04 44 was used to evaluate cognitive function. This battery captures key cognitive domains relevant to schizophrenia. The Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery includes 10 tests that measure 7 cognitive domains: Speed of processing ; Attention/vigilance ; Working memory ; Verbal learning ; Visual learning ; Reasoning and problem solving  and Social cognition . One psychologist (HFB) administered all the tests.The Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive BatteryU-test) were performed in the comparison between CSF GABA levels in FEP patients and healthy controls, and in the correlation analyses as a directional change, that is, decreased GABA levels in FEP patients, could be hypothesized at this stage. Two-tailed tests were performed to determine the possible effects of various medications on CSF GABA (Table 2), as the direction of any change in CSF GABA levels could not be anticipated. To assess the relative importance of potential confounders, we used binary logistic regression or χ2-test, as well as the R package ‘relaimpo’.45 Here individual regressor’s contribution to a multiple linear regression model is quantified using six different methods. Although the different methods produced similar results, we here report an approach based on sequential R2s that takes care of the dependence on orderings by averaging over orderings using simple unweighted averages (lmg). Comparisons of cognition between FEP patients and controls were analyzed using the unpaired t-test with equal s.d. Reported correlation coefficients are Pearson’s r. Bonferroni correction was used in the comparison of different cognitive tests between healthy controls and FEP patients, giving an α-threshold of 0.005 (0.05/10). Only those cognitive tests that remained significant after the Bonferroni correction was used for the correlation with CSF GABA in FEP patients giving an α-threshold of 0.0083 (0.05/6). With regard to the correlation studies between CSF GABA and cognitive tests in healthy controls, the α-threshold was set to 0.005 (0.05/10). Symptom ratings were highly correlated . No significant associations were found between CSF GABA levels and age, gender, body mass index or tobacco use (see P=0.85). Neither did CSF GABA levels differ between treated and untreated FEP patients with regard to BZDs (P=0.30), zopiclone (P=0.47), antidepressants (P=0.37), phenothiazine derivatives (P=0.62) or all drugs combined (P=0.15)  followed by age (25%), whereas the remaining predictors accounted for 15% or below (P=0.047).The CSF GABA concentration was significantly lower in FEP patients compared with healthy controls (median 2.88 μ use see . CSF GAB.15) see . To furtor below . Given tP=0.16).We also analyzed additional amino acids, that is, glutamate, taurine, glycine and tyrosine, in our samples. The CSF concentration of these was not significantly different between patients and healthy controls . Furtherr=0.37, P=0.01; which remained borderline significant following the Bonferroni correction (significance threshold of P<0.0083)  and social cognition . Only the correlation with social cognition remained after Bonferroni correction (threshold of P<0.005).Compared with healthy controls, FEP patients showed a reduction in performance in all cognitive domains tested, the most salient related to attention, speed of processing and visual/verbal learning. Six of the cognitive domains remained significant after Bonferroni correction . Moreove<0.0083) . No assor=−0.30, P=0.03) in FEP patients . Correlation analyses using the subscale measuring positive symptoms reached a nearly significant association , whereas the subscale negative symptoms did not . Moreover, CSF GABA was found to correlate positively to scores of the symptom dimension of GAF . With regard to scores of the functioning dimension of GAF a trend towards statistical significance was observed . A negative correlation between CSF GABA and CGI scores  was detected.Symptoms among patients were profiled using PANSS, CGI and GAF. We observed negative correlations between CSF GABA and total score on PANSS . However, evidence for this is mainly indirect and analyses of GABA in CSF of patients with schizophrenia have failed to give a conclusive result in this regard. Although Van Kammen et al.41 found significantly lower CSF GABA levels in young women with schizophrenia, most previous studies analyzing GABA in the CSF from patients with schizophrenia have failed to observe any difference compared with controls.40 These studies, all performed in the 70s or 80s, have been limited by the lack of a control group of age-matched healthy volunteers or by the sensitivity of the GABA assay . Thus, discrepancies between present data, utilizing a well-characterized cohort, healthy volunteers as controls and top-of-the-art high-performance liquid chromatography analysis of GABA and previous literature may be explained by differences in study design and methods of GABA analysis.Mounting clinical and experimental data suggest a role of GABA in the pathophysiology of schizophrenia (cf. Introduction). Further, in excellent agreement with present findings, a recent positron emission tomography study, utilizing the GABA-A receptor ligand [11C]flumazenil, indicate an impaired GABA transmission in the orbital frontal cortex in patients with schizophrenia.32 Our findings also reveal negative correlations between CSF GABA and total and general PANSS, such that low CSF GABA levels predicted high general severity of illness. In addition, low CSF GABA concentrations were associated with reduced scores of the symptom and functioning dimensions of GAF, as well as with high CGI scores, indicating that symptoms and illness severity associate with lower levels of CSF GABA. In line with a large body of studies, investigating cognitive functions in schizophrenia, FEP patients showed a significant reduction in performance compared with healthy controls in all parts of our cognitive test battery (P<0.008), a cognitive test that measures attention, a cognitive domain impaired in patients with schizophrenia.47 A relationship between CSF GABA and cognitive performance is in line with a recent magnetic resonance spectroscopy study showing that GABA predicts working memory.24 Notably, in contrast to patients, healthy controls showed a negative correlation between CSF GABA and performance in the social cognition test. This finding is in line with several clinical reports showing that increased GABA transmission, induced by BZDs or vigabatrin  is associated with cognitive impairments.50The present finding of lower CSF GABA levels in FEP patients likely reflects a reduced overall GABAergic neurotransmission in the brain. The observed direction of changes is in line with previous post-mortem studies showing a reduced GABA synthesis in schizophrenia ( battery . Moreove31 Furthermore, increased CSF GABA levels were observed following 30 days of treatment with sulpiride as well as following treatment with different neuroleptics for many years in long-stay hospitalized patients with schizophrenia.36 In addition, Gattaz et al.51observed no change in free CSF GABA levels in patients with schizophrenia after three months of haloperidol treatment. Altogether, it seems implausible that the lower levels of CSF GABA seen in FEP patients are a consequence of antipsychotic treatment.A strength of the present study is that the majority of the patients were drug naive with respect to antipsychotics at the time of the lumbar puncture. No differences in CSF GABA levels were observed between patients on treatment and those without antipsychotic treatment , indicat52 and, in consonance, administration of diazepam to rats and mice results in increased brain GABA concentrations.54 Taken together, given previous results on the effects of BZDs or antipsychotic drugs on brain GABA concentrations it appears unlikely to be that the presently observed reduction in CSF GABA levels in FEP is the result of such medications. This is strengthened by the fact that CSF GABA levels did not differ between the drug-naïve patients and those patients that were on BZD treatment at the time of lumbar puncture (A limitation of the present study is the fact that 29% of the FEP patients were on BZDs, drugs well known to primarily affect GABAergic neurotransmission. However, previous studies have shown that intravenous injection of diazepam to neurological patients is associated with an increase in CSF GABA levelspuncture  and by t55 recent meta-analysis give no evidence for antipsychotic efficacy of additional benzodiazepine medication.56 Further, administration of GABA-A receptor antagonists is not typically associated with psychotomimetic symptoms. Similarly, clinical trials with GABA-B receptor agonists, such as baclofen or γ-hydroxybutyric acid, have given no unanimous picture for antipsychotic effects of these drugs.58 Indeed, the most common adverse effects observed of vigabatrin is behavioral disturbances, ranging from irritability and confusion to psychotic reactions.59 Thus, the many experimental and post-mortem studies, suggesting a role of GABA in the pathophysiology of schizophrenia is not supported by the clinical experience of medication with GABA receptor agonists. Although CSF GABA correlated to PANSS scores, illness severity and attention, it remains to be evaluated whether the reduction in CSF GABA is primarily involved in the generation of positive/negative symptoms.A question of importance is if the lower CSF GABA in patients with schizophrenia contributes to the pathophysiology and specific symptoms of the disease. BZDs, known to increase GABAergic transmission via a modulatory action at the GABA-A receptor, are frequently used as adjunctive medication in first-episode patients. It is generally accepted that these drugs are primarily used for anxiolysis and do not affect positive or negative symptoms of the disease. Although a few studies report some favorable effects of BZDs on psychotic symptoms,For decades, studies have implicated a reduced GABAergic transmission as part of the pathophysiology of schizophrenia. To the best of our knowledge, the present study is the first to show that FEP patients display low CSF GABA levels and that this condition is associated with the severity of illness, psychotic symptoms and probably attentional deficits.


Transfer of charges delocalised over multiple molecules can be described using the properties of the component molecules. Although Marcus theory is widely used to describe charge transfer in molecular systems, in its usual form it is restricted to transfer from one molecule to another. If a charge is delocalised across multiple donor molecules, this approach requires us to treat the entire donor aggregate as a unified supermolecule, leading to potentially expensive quantum-chemical calculations and making it more difficult to understand how the aggregate components contribute to the overall transfer. Here, we show that it is possible to describe charge transfer between groups of molecules in terms of the properties of the constituent molecules and couplings between them, obviating the need for expensive supermolecular calculations. We use the resulting theory to show that charge delocalisation between molecules in either the donor or acceptor aggregates can enhance the rate of charge transfer through a process we call supertransfer (or suppress it through subtransfer). The rate can also be enhanced above what is possible with a single donor and a single acceptor by judiciously tuning energy levels and reorganisation energies. We also describe bridge-mediated charge transfer between delocalised molecular aggregates. The equations of generalised Marcus theory are in closed form, providing qualitative insight into the impact of delocalisation on charge dynamics in molecular systems. In particular, donor–acceptor delocalisation is critical to understanding adiabatic electron transfer, as first emphasised by Hush,16i.e., between any donor molecule and any acceptor molecule) is small compared to the strength of their coupling to the environment. Furthermore, to ensure that charges within either aggregate (or both) are delocalised among the constituent molecules, we assume that the couplings between the molecules are stronger than their coupling to the environment.Here, we study the problem of charge transfer from one delocalised molecular aggregate to another. In order to be able to speak of two distinct aggregates, we assume that the coupling between the aggregates , which describes the exciton transfer rate between two chromophores and is, like MT, derived from second-order perturbation theory in the donor–acceptor coupling. Sumi developed generalised FRET (gFRET) to describe the transfer of excitons between delocalised aggregates in photosynthetic antenna complexes,MT also allows a description of bridge-mediated charge transfer, where the donor and acceptor are not directly coupled, but a coupling between them is mediated by intervening ‘bridge’ molecules, whose states are sufficiently high in energy to prevent actual charge transfer from the donor to the bridge.22.1ND donor molecules and an aggregate of NA acceptor molecules, with each molecule coupled to an independent environment of thermalised harmonic oscillators. Three approximations make it possible to define two distinct aggregates  and vkk′ (in the acceptor).The full Hamiltonian is H0D and H0A as the aggregate basis, being, respectively,  and , with energies Eα and Eβ. Similar to site indices j and k, index α is consistently used to denote only donor eigenstates, and β acceptor eigenstates.We refer to the eigenstates of Vjk is the coupling between the jth donor and kth acceptor molecules.Inter-aggregate coupling is described by the Hamiltonianωξ is the frequency of the ξth environment mode, with creation operator b†ξ. We can also write HE = HDE + HAE, with the environment modes partitioned between those that couple to donor and acceptor molecules.The environment is described by a set of harmonic oscillators:, where gjξ is the dimensionless coupling of the ξth environment mode to the charged jth donor molecule, relative to the uncharged state. Qk is defined analogously. The assumption of a local environment means that, for a fixed ξ, only one of gjξ can be non-zero.The donor-environment and acceptor-environment interaction Hamiltonians are, respectively,ρ(t) is the density matrix of the system, and TrE is the trace over the environmental modes. As detailed in the Appendix, kD→A can be calculated using second-order perturbation theory in HC and, because we assumed separable environments, generates a time-dependent transfer rateThe charge-transfer rate is the rate of change of the charge population on the acceptor,Vjk be weaker than all other couplings means that the donor aggregate will relax to a thermal state faster than the charge transfer. In other words, we assume that the initial density operator of the system ρ(0) will, before charge transfer takes place, relax to a state ρth in which both the donor and acceptor aggregates are in equilibrium with their own environments (see Appendix for details). This gives a time-independent transfer rate,ρth is split into donor and acceptor components, ρth = ρD ⊗ ρA. Because the donor-environment coupling is weak, the thermal state of the donor will approximately factorise to , where the electronic population distribution is  and the thermal environment is ρDE = exp(–HDE/kBT)/TrDE(exp(–HDE/kBT)). The thermal state of the acceptor is ρA = ρAE = exp(–HAE/kBT)/TrAE(exp(–HAE/kBT)). Finally, we have also written HD = H0D + HDE + HDE, and similarly for HA.To proceed, we consider the rate in the aggregate basis. The requirement that  Eqn (10)–(13) are analogous to the MC-FRET treatment of delocalised exciton transfer.Gβ(t) analogously defined. For a thermally populated environment, the occupation of environmental modes is given by the Bose–Einstein distribution n(ν) = (exp(ħν/kBT) – 1)–1.Evaluating eqn (12) and (13) for independent harmonic environments gives (see Appendix for details) goes rapidly to zero beyond a cut-off frequency ωc. Then, we first assume the high-temperature limit kBT ≫ ħωc, so that n(ν) ≈ kBT/ħν ≫ 1, givingt ≪ 1/ωc ⪅ 1/ωξ. With ωξt ≪ 1, we expand the trigonometric functions in eqn (17) to leading order:The preceding equations are appropriate at a wide range of temperatures and environmental spectral densities. Although we could stop here, to obtain a clear comparison with MT, we now make two additional approximations that are also made in deriving ordinary Marcus theory. To do so, we assume that the spectral density , and similarly for the acceptor sites, λk. The change of basis  gives the reorganisation energy of aggregate eigenstatesξ couples to two different sites (gjξgj′ξ = gjξ2δjj′), givingλβ.We now define the reorganisation energy for the donor sites as Eαβ = Eβ – Eα and λαβ = λα + λβ, demonstrating that gMT takes the same form as MT, with all parameters defined analogously to—and expressible in terms of—their site-basis counterparts. These results are also summarised in Substituting eqn (18) and (20) into eqn (14) and (15) we find2.2N molecules, each modelled as a single site, where the donor molecules only couple to the first bridge state, B1, the acceptor molecules only couple to the last bridge state, BN, and each bridge molecule only couples to its two nearest neighbours in the chain, as shown in ElB are considered energetically distinct from the donor and acceptor aggregates, i.e.,VlBl+1B are the intra-bridge couplings and Eα/β is the characteristic energy of donor and acceptor eigenstates .Like MT, gMT can be expanded to include the case where the coupling between the donor and the acceptor aggregates is not direct, but is instead mediated by a bridge consisting of higher-lying states of intervening molecules. We consider a bridge of HDBA = HB + H0D + HDB + H0A + HAB using H0D and H0A as above and adding the bridge Hamiltonian HB and the coupling of the bridge to the donor, HDB, and acceptor, HAB,l is the state of a charge being located on bridge site Bl.We define the donor–bridge–acceptor Hamiltonian 1 and BN as coupling to donor and acceptor sites, we can also consider them as coupling to the aggregate eigenstates. In the aggregate basis, HDBA becomes and .Instead of thinking of Bα to each acceptor eigenstate |Aβ independently, using the mathematics already established for single-site bridge-mediated transfer.HDBA as dDBA = , with eigenvalue EDBA.We calculate the rate of charge transfer from each donor eigenstate |DHDBA – IEDBA)dDBA = 0, we find thatd1B and dNB can be found by considering the bridge subspace, (HB – IEDBA)dB = – where dB consists of the bridge elements of dDBA in the same order. The solution of this equation is dB = GB, using Green's function GB = (IEDBA – HB)–1.Since (d1B and dNB into eqn (30) and (31), we findÊ are the perturbed energies of aggregate eigenstates due to coupling with the bridge,V is the effective coupling between the donor and acceptor eigenstates, mediated by the bridge,By substituting this solution for GB in terms of a Dyson series. Because |VlBl+1B| is small (see eqn (24)), we keep only the lowest-order term,27EDBA is an eigenvalue of the entire donor–bridge–acceptor system, we are only interested in the donor/acceptor subspace. Because ElB – Eα/β is large relative to inter-site couplings and energy differences (eqn (24)), we can approximate EDBA – ElB ≈ Eα/β – EB, for average bridge energy EB. This allows us to simplify eqn (36) using the geometric mean of the bridge couplings VBB,VBB < Eα/β – EB (eqn (24)). Substituting eqn (34)–(37) into eqn (23), we have the rate of bridge-assisted gMT:To find the Green's function, we expand 3The summary of results in However, the presence of delocalisation in aggregates leads to significant differences between MT and gMT. We can analyse the influence of delocalisation on charge transfer by separating its impact on the electronic and nuclear components of the MT rate. includes a coherent sum involving electronic amplitudes in each of the donor and acceptor aggregates, allowing both constructive and destructive interference to affect the transfer rate. If the interferences is constructive, leading to enhanced transfer rates, we call the effect supertransfer, and if it is destructive, subtransfer, borrowing terminology from the similar problem of MC-FRET.28The gMT electronic coupling factor 1 and D2, with a charge delocalised between them in the  state. The donors are coupled to a single acceptor A with strengths V1AD and V2AD respectively. If we were to apply Marcus theory between each donor and the acceptor independently, we would expect a transfer rate proportional to the square of each coupling, , with the factors of 1/2 indicating the population on each donor. However, this naive approach fails to include coherent effects of the superposition. These are treated correctly by gMT, which predicts a transfer rate of . The presence of rate-enhancement due to supertransfer is apparent if V1AD = V2AD, which implies kgMT = 2kMT. In contrast, if the two transfer pathways interfere destructively, V1AD = –V2AD, gMT predicts subtransfer with kgMT = 0. We refer to states that enhance the charge-transfer rate through supertransfer as bright, while those that retard it as dark, in analogy to the terms used in the literature on superradiance.For illustration, we consider an aggregate of two identical coupled donors, DSupertransfer is also sensitive to the system's geometry. Changing the distance and orientations between donors and acceptors will affect the electronic wavefunction overlaps due to the exponential decay of electronic wavefunctions with distance, consequently modifying the electronic couplings. To explore the consequences of this geometric sensitivity, we consider a model consisting of two donor molecules transferring a unit of charge to an acceptor molecule, shown in We can compare these results with gFRET, the analogous theory of excitation-energy transfer between molecular aggregates.kBTλαβ)–1/2 exp(–(ΔEαβ + λαβ)2/4kBTλαβ), has several features in common with ordinary MT. For example, for a fixed λαβ, the nuclear factor is maximised when –ΔEαβ = λαβ, and the inverted regime is possible when –ΔEαβ > λαβ. However, the nuclear term also possesses features not predicted by ordinary MT, allowing for both enhancement or retardation of the transfer rate.The nuclear factor in gMT ,  to a value of typical site energies. On the other hand, λαβ is reduced by delocalisation. Since , for a state purely localised on j, λα = λj. However, in a fully delocalised state  of ND identical donors (λj = λ), the reorganisation energy is decreased ND-fold:. A reduction in λ leads to an exponential narrowing of both 𝒟ααD(ω) and 𝒜ββA(ω). Therefore, because the charge-transfer rate depends on the overlap of the two spectra (eqn (10)), the reduction in λ will reduce the transfer rate between most pairs of eigenstates, the exception being ones where ΔEαβ = –λαβ.The nuclear factor depends on two energies, ΔEαβ and λαβ, because of the rate's exponential sensitivity to (ΔEαβ + λαβ)2. The different effects are illustrated with another example, shown in 1, whose site energy and reorganisation energy are such that the transfer from D1 to A is very slow (–ΔE1AD ≫ λ1AD). Another donor D2 is then introduced, but is weakly coupled to A due to its distance. A naive application of classical MT might suggest that, because D2 hardly interacts with A, it would serve to only steal charge density from D1, reducing the already slow transfer rate. Generalised MT, however, shows that it is possible to choose the energy and reorganisation energy of D2, as well as its coupling to D1, so that a coherent superposition between D1 and D2 will enhance the total transfer rate above what is possible with either D1 or D2 alone. This is true even if supertransfer is neglected, as shown in Eαβ and λαβ to minimise (ΔEαβ + λαβ)2. This result shows that even if an unfavourable donor must be used in a donor–acceptor system (for whatever reason), another donor can be added to tune the nuclear term's contribution to the charge transfer rate.The presence of different processes affecting the nuclear factor means that delocalisation can have a complicated effect on the charge-transfer rate, even apart from supertransfer. Critical to the rate is the relative size of ΔOur results also extend gMT to treat bridge-mediated charge transfer, showing that the usual equations still apply when considering delocalised aggregates. Indeed, including the effects of bridge-mediated charge transfer on gMT does not qualitatively change the effects of supertransfer and energetic tuning, except that the coherent effects depend on the geometry of the donor aggregate with respect to the first bridge molecule, and the acceptor aggregate with respect to the last. In particular, the results shown in 4The theory presented in this work is the first description of charge transfer between delocalised molecular aggregates. Therefore, we anticipate that it will have broad applications in fields where charge transfer and electronic coherence intersect, including organic photovoltaics, photosynthesis, and inorganic complexes.The major prediction of gMT is that delocalisation within an aggregate can significantly affect charge transfer rates through two mechanisms: supertransfer and nuclear tuning. The first is a consequence of the constructive interferences of charge-transfer pathways, while the latter is the ability of a charge-transfer rate to be modified by adjusting effective energy levels and reorganisation energies by delocalising electronic states over different molecules.Both of these predictions are suited to being tested experimentally. The simplest approach would be to construct covalently linked donors and acceptors in geometries that approximate those in e.g., high temperature, slow environmental modes) can be omitted and more general intermediate results used directly –(13)). Although it is not clear whether a simple, closed-form expression could be derived, a number of improvements to gMT can be envisaged, including adiabatic charge transfer, quantum-mechanical vibrational corrections,In this work, we restricted ourselves to deriving the delocalised generalisation of the simplest Marcus-theory formula. We are confident that many of the subsequent advances that have occurred in charge-transfer theory can also be incorporated as extensions to gMT. Indeed, our derivation is more general than the final result, and some of the approximations needed to derive an MT-like equation (


Rbp4 in the ovary remain unclear.Ovarian retinoid homeostasis plays an important role in the physiological function of the ovary. Retinol-binding protein 4 (RBP4) acts as the mediator for the systemic and intercellular transport of retinol and is heavily involved in cellular retinol influx, efflux, and exchange. However, the expression patterns and regulatory mechanisms of Rbp4 was examined in immature mice during different developmental stages and in adult mice during different stages of the estrous cycle. The potential regulation and mechanisms of ovarian Rbp4 expression by estrogen and related gonadotropins in mouse ovaries were also investigated.The expression pattern of ovarian Rbp4 remained constant before puberty and increased significantly in the peripubertal period. In adult female mice, the expression of Rbp4 increased at proestrus and peaked at estrus at both the mRNA and protein levels. The protein distribution of RBP4 was mainly localized in the granulosa cell and theca cell layer in follicles. In addition, the expression of Rbp4 was significantly induced by follicle-stimulating hormone (FSH) or FSH + luteinizing hormone (LH) in combination in immature mouse (3 weeks old) ovaries in vivo and in granulosa cells cultured in vitro, both at the mRNA and protein levels. In contrast, treatment with LH or 17β-estradiol did not exhibit any observable effects on ovarian Rbp4 expression. Transcription factors high-mobility group AT-hook 1 (HMGA1), steroidogenic factor 1 (SF-1), and liver receptor homolog 1 (LRH-1) (which have been previously shown to be involved in activation of Rbp4 transcription), also responded to FSH stimulation. In addition, H-89, an inhibitor of protein kinase A (PKA), and the depletion of HMGA1, SF-1, and LRH-1 by small interfering RNAs (siRNAs), resulted in a dramatic loss of the induction of Rbp4 expression by FSH at both the mRNA and protein levels.The present study demonstrated that the ovarian expression of Rbp4 is mainly regulated by FSH through the cAMP-PKA pathway, involving transcriptional factors HMGA1, SF-1, and LRH-1, in the mouse ovary during different stages of development and the estrous cycle.These data indicate that the dynamic expression of  Rbp4 expression remains constant before puberty, increases significantly around puberty in immature mice, and peaks at estrus in adult mice, which is mainly regulated by FSH through the cAMP-PKA pathway partly and involves transcriptional factors HMGA1, SF-1, and LRH-1.Ovarian Retinol (vitamin A) and its derivatives, collectively known as retinoids, play crucial roles in ovarian development and normal physiological function . RetinolRbp4 expression during follicular development remains an interesting and important point of study and would provide an explanation for the possible mechanisms involved in changing ovarian retinoid levels during follicular development.The data from our laboratory suggest that FSH enhances retinol uptake, accumulation, and metabolism in the mouse ovary (unpublished data), but the mechanisms remain unclear. Retinol-binding protein 4 (RBP4), which acts as the mediator for the systemic and intercellular transport of retinol, plays an important role in cellular retinol influx, efflux, and exchange ; and see2) production and the functions of granulosa and theca cells. FSH and LH exert their actions by activating their membrane receptors  thereby resulting in an increase in intracellular cyclic AMP (cAMP), a second messenger involved in the transduction of hormonal or growth signals that regulate cell functions such as proliferation, differentiation, and metabolism [The regulatory mechanisms of follicular development and ovarian function are primarily realized through neuroendocrine activities in the hypothalamus–pituitary–ovary (HPO) axial, although early stage occurs independently of the HPO axis. Follicle-stimulating hormone (FSH) or FSH+ luteinizing hormone (LH), which are released by the pituitary gland, principally control follicular development and ovulation by regulating estradiol , ovarian activities  alternate periodically during the estrous cycle  [F2α) released from the uterus [In the adult female mouse (after 6–8 weeks old), under the influence of reproductive hormones  , 16. Dur48–72 h) . During 48–72 h) –19. Afte48–72 h) ; and dur48–72 h) . Metestre uterus , 21.cis-regulatory sequences of the mouse Rbp4 gene contain several copies of an AT-rich motif, known to be the binding site for HMGA1 proteins. It has also been reported that HMGA1 can bind the Rbp4 promoter and recruit the transcription factors steroidogenic factor 1 (SF-1) and liver receptor homolog 1 (LRH-1) to form an activation complex and induce Rbp4 transcription in Hepa1 mouse hepatoma cells [The structural protein high mobility group AT-hook 1 (HMGA1) is important in promoter regulation by uncovering chromatin and facilitating the recruitment of multiple transcription factors . The cisma cells .Rbp4 during different stages of development and the estrous cycle. We also explored the potential regulatory mechanisms governing ovarian Rbp4 expression by estrogen and related gonadotropins in mouse ovaries and examined the involvement of HMGA1, LRH-1, and SF-1. The results of this study are expected to provide mechanistic evidence for the fluctuations in ovarian retinoid levels during follicular development and estrous cycle.The present study investigated the expression patterns of 2, we choose immature female mice (3 weeks old) to investigate hormonal effects on ovarian Rbp4 expression. They were injected intraperitoneally with a single dose of FSH , LH , or 17β-estradiol  [Immature  and adult  BALB/c mice were obtained from the Medical Department of Jilin University . The mice were raised in an environment with controlled temperature (22–24 °C) and humidity (60–70%) in a 12 h light/dark cycle and provided with food and water ad libitum. Vaginal smear tests were performed on adult female mice (8 weeks old), and ovaries were collected from the mice at various stages of the estrous cycle. Some of the adult female mice (8 weeks old) were mated with male mice, and the ovaries of newborn female mice were collected at different developmental stages . To avoid the effect of endogenous gonadotrophins and Ema, USA) . Mice weTo retrieve ovarian samples from adult mice (8 weeks old) during each of the four stages of the estrous cycle, we performed the vaginal smear tests to identify the specific estrous cycle stages. Briefly, we placed the tip of a plastic pipette filled with 40 μl of phosphate buffered saline after  into the mouse vagina, and gently flushed the vagina 3–5 times. Vaginal contents  from each mouse were collected into a PCR tube and then smeared onto a glass slide. The evaluation of the vaginal smear images was performed under a microscope IX71 system . Proestrus was characterized by the mostly nucleated and some cornified epithelial cells in the smear; estrus by cornified squamous epithelial cells without visible nuclei; metestrus by the predominance of leucocytes and a few nucleated epithelial and/or cornified squamous epithelial cells; and diestrus was characterized by a predominance of leukocytes , 25. Thev/v) BSA in PBS . Sections were incubated overnight at 4 °C with mouse monoclonal antibody specific to RBP4 at a 1:200 dilution in PBS . In order to validate the specificity of RBP4 antibody, 5% nonimmune goat serum [v/v) Triton X-100 in PBS, sections were incubated with goat-anti-mouse second antibody  for 1 h at room temperature, stained using ABC kits, and counterstained with hematoxylin.Intact ovaries collected from immature mice (3 weeks old) that were that were untreated controls or treated with FSH for 48 h were fixed in 4% paraformaldehyde  for 12 h at 4 °C. After washed with PBS and dehydrated, these tissues then were embedded in paraffin wax ; and paraffin-embedded tissue sections (5 μm) were deparaffinized. After washing with PBS, the sections were treated with 10 mM citric acid buffer (pH 6.0)  for antigen activation for 15 min in boiling water. The sections were washed with PBS and blocked by 5% , LH (100 IU/L), 8-Br-cAMP , or H-89 .Primary granulosa cells were isolated from immature female mouse (3 weeks old) ovaries, as described previously , 32. TheHmga1, Sf-1, and Lrh-1 were designed and synthesized by GenePharma . The sequences that provided successful knockdown were Hmga1 siRNA, 5′-GAGTCAGAAAGAGCCCAGT-3; Sf-1 siRNA, 5′- GCCTCGATGTGAAATTCCT-3′; and Lrh-1 siRNA, 5′-GCAGAAGAAAGCCCTCATT-3′. The negative control (NC) siRNA was 5′-TTCTCCGAACGTGTCACGT-3′. Cells were transfected at 50–70% confluency with 50 nM of siRNA duplexes using the FuGENE HD transfection reagent  in accordance with the manufacturer’s instructions. FSH was added to the culture medium 24 h after siRNA transfection and cells were treated for an additional 24 h.siRNA oligonucleotides specific for mouse Rbp4 (NM_011255.3), 5′-AGTCAAGGAGAACTTCGACAAGG-3′, 5′-CAGAAAACTCAGCGATGATGTTG-3′; Hmga1 (NM_016660.2), 5′-GCAGGAAAAGGATGGGACTG-3′, 5′-AGCAGGGCTTCCAGTCCCAG-3′; Sf-1 (NM_139051.3), 5′-CCAGACCTTTATCTCCATTGTCG-3′, 5′-AGTGTCATCTGGTCAGCCACCT-3′); Lrh-1 (NM_030676.3), 5′-TCATGCTGCCCAAAGTGGAGA-3′, 5′-TGGTTTTGGACAGTTCGCTT-3′. The expression level of mouse β-actin (NM_007393.3), 5′-TCTGGCACCACACCTTCTA-3′, 5′-AGGCATACAGGGACAGCAC-3′, was used as an internal reference. The relative gene expression levels were calculated using the 2−ΔΔCt method. All primers were obtained from Sangon Biotech , all experiments were repeated at least three times.Total RNA from ovarian tissue and granulosa cells cultured in vitro were extracted using an RNAprep pure Micro Kit  and reverse transcribed into cDNA using a PrimeScript RT reagent kit  according to the manufacturer’s instructions. Real-time PCR was performed on a sequence detection system  using a SYBR Premix Ex TaqII kit . The gene-specific primers  that we used for real-time quantitative PCR amplification were as follows: Protein samples were obtained by homogenizing whole ovaries and by lysing granulosa cells in lysate buffer  with a 10 μg/ml protease and phosphatase inhibitor cocktail . Samples were then centrifuged at 13,000 g at 4 °C. Tissue and cell extracts were normalized to the sample protein concentration, as determined by a protein assay kit . Proteins (40 μg) were separated via sodium dodecyl sulfate polyacrylamide gel (12%) electrophoresis and transferred to polyvinylidene difluoride membranes. The membranes were then blocked in Tris-buffered saline with Tween 20 (TBST) containing 5% non-fat instant milk and incubated overnight at 4 °C with mouse monoclonal antibodies specific to RBP4 or GAPDH ; and rabbit monoclonal antibodies specific to HMGA1, SF-1, or LRH-1  each at 1:5000 dilution. After washing with TBST, the membranes were incubated with horseradish peroxidase-conjugated secondary antibody  for 1 h. Enhanced chemiluminescence (ECL) detection was performed using an ECL system according to the specifications of the manufacturer . Protein levels were normalized to GAPDH and quantified via densitometry using a Tanon gel imaging system .P < 0.05. All the statistical analyses were performed using SPSS 22.0 for Windows .The statistical analyses of the data were conducted via one-way ANOVA followed by Tukey’s multiple-range test. Differences were considered significant at Rbp4 in whole mouse ovaries during different developmental stages. Rbp4 mRNA expression in mouse ovaries remained constant at 1 to 3 weeks postnatally and notably increased at 4 weeks , and dropped at 5 weeks  having normal cycles increased at estrus  were treated with FSH, LH, or FSH + LH. The results showed that the expression levels of Rbp4 in whole ovarian samples increased significantly after treatment with FSH  or 10 μM H-89 (a PKA inhibitor). If the cAMP-PKA pathway was involved in FSH induction, 8-Br-cAMP would show similar induction effects and H-89 would prevent FSH induction. As expected, the treatment of mouse granulosa cells with 500 μM 8-Br-cAMP for 24 h resulted in a dramatic increase in Rbp4 expression  and the LH receptor (LHR), both of which belong to the G-protein coupled receptors , 14. Stiion Fig. , and the-89 Fig. . Thus, tRbp4 and recruit SF-1 and LRH-1 to regulate the transcriptional activity of Rbp4 [Rbp4, we sought to knock down the corresponding genes using specific siRNAs. As expected, the expressions of these genes at the mRNA and protein levels were diminished to approximately 50% in cells transfected with 50 nM siRNA compared with controls , and dropped at 5 weeks. Numerous studies have supported the concept that the gonadotropin-releasing hormone (GnRH) neuronal network generates pulse and surge modes of gonadotropin secretion that are critical for puberty [Rbp4 expression may be related to the pituitary gonadotropin wave at the onset of puberty. After puberty, some follicles in ovary start to grow and ovulate irregularly under the influence of serum gonadotropins, and at 5 weeks of age, some mice may be in the follicular-growth phase and some in the post ovulatory period. The different levels of gonadotrophins in these mice may constitute a possible explanation for the drop in Rbp4 expression at 5 weeks. Furthermore, the expression of Rbp4 in the ovaries of adult mice (8 weeks old) having normal cycles was found to increase at proestrus and peaked at estrus, the phase with higher serum levels of FSH and LH. In a previous study, higher RBP4 protein concentrations were observed in the follicular fluids of dominant follicles relative to small follicles [Rbp4 in mouse ovaries during different stages of development and the estrous cycle motivated us to pose an alternative hypothesis: that the expression of ovarian RBP4 is affected by gonadotropins and gonadal steroids and is related to follicular growth.Firstly, the present study presented the dynamic expression patterns for  puberty , and stiollicles . The dynRbp4 in the mouse ovary. The expression of Rbp4 was significantly stimulated by administration of FSH and combined FSH + LH, but unaffected by administration of LH or 17β-estradiol. In addition, intense immunostaining was observed in granulosa cell layers of large antral follicles in the ovaries of mice treated with FSH. To determine whether the induction of Rbp4 by FSH and FSH + LH occurred due to increased expression of Rbp4 in granulosa cells, we examined primary granulosa cells cultured in vitro. Our results showed that the expression of Rbp4 was induced by FSH and combined FSH + LH treatments, and that the induction by combined FSH + LH treatment was stronger than that by FSH alone. The administration of LH alone did not exert an effect. Because the primary granulosa cells were isolated from the immature female mouse (3 weeks old) ovaries without FSH or equine chorionic gonadotropin (eCG) pretreatment and with no or low LHR expression, the cells were mainly immature granulosa cells without LHR expression [Rbp4 expression by FSH + LH than by FSH alone, and the higher levels of RBP4 in the follicular fluids of the large antral follicles [We then investigated the potential regulatory effects of gonadotropins and estrogen on the dynamic expression of pression . This mapression , and durpression , 36, 37,ollicles .Rbp4 by FSH was mediated by the cAMP-PKA pathway, we examined the effects of the cAMP analog 8-Br-cAMP and the PKA inhibitor H-89 on the induction of Rbp4 in granulosa cells. The treatment of cells with 500 μM 8-Br-cAMP showed a dramatic increase in Rbp4 expression, whereas 10 μM H-89 significantly prevented the induction of Rbp4 by FSH. Thus, the cAMP-PKA pathway likely participates in the induction of Rbp4 by FSH.To determine whether the induction of Rbp4 gene contain a bipartite promoter, i.e., a proximal region necessary for basal expression and a distal segment that contains several binding sites for the structural HMGA1 proteins that are structural components of chromatin [Rbp4, and HMGA1 proteins can bind upstream sequences of the Rbp4 promoter. In the present study, Hmga1 was sensitive to FSH and this response was prevented by the PKA inhibitor H-89; whereas the knockdown of Hmga1 with siRNA resulted in a dramatic loss of FSH-induced Rbp4 expression. In addition, HMGA1 can interact with SF-1 and LRH-1 and recruit them to the complex [Rbp4 expression. SF-1 was shown to be expressed at higher levels in theca cells, and also found in granulosa cells; whereas LRH-1 expression was found to be abundant and highly restricted to the granulosa cells of developing follicles [Rbp4 was likely regulated by FSH via the cAMP-PKA pathway, involving HMGA1, SF-1, and LRH-1.The cis-regulatory sequences of the mouse hromatin , 38. HMGhromatin . There a complex . In the ollicles , 41. Conollicles , 41, as ollicles  and can ollicles , 43 or Lollicles . Thus, tRbp4 in the mouse ovary during different stages of development and the estrous cycle, which was predominantly regulated by FSH. Furthermore, the cAMP-PKA pathway and transcription factors HMGA1, SF-1, and LRH-1 were likely involved in the induction of Rbp4 by FSH (Fig. Rbp4 expression patterns in the ovary during different stages of development and the estrous cycle. Although further research needs to be conducted in this area, our data are important for the understanding of the molecular regulation of retinol transport and accumulation in the follicular fluids during ovarian follicular growth.In conclusion, the present study showed a dynamic expression pattern of FSH Fig. . This wo


Arterial conduits are necessary in nearly 5% of all liver transplants and are usually constructed utilizing segments of donor iliac artery. However, available segments of donor iliac artery may not be lengthy enough or may not possess enough quality to enable its inclusion in the conduit. Although there are few reports of arterial conduits constructed solely utilizing prosthetic material, no previous reports of conduits composed of a segment of donor iliac artery and prosthetic material  were found in the medial literature to date. Two cases reporting successful outcomes after creation of mixed biologic and prosthetic arterial conduits are outlined in this report. Reason for creation of conduits was complete intimal dissection of the recipient's hepatic artery in both cases. In both cases, available segments of donor iliac artery were not lengthy enough to bridge infrarenal aorta to porta hepatis. Both patients have patent conduits and normally functioning liver allografts, respectively, at 4 and 31 months after transplant. Mixed biologic and synthetic arterial conduits constitute a viable technical option and may offer potential advantages over fully prosthetic arterial conduits. Arterial conduits are necessary in nearly 5% of all liver transplants , 2. A seAlthough there are few reports of arterial conduits constructed solely utilizing prosthetic material , 5, no pReason for creation of conduits was complete intimal dissection of the recipient's hepatic artery in both cases reported herein. Arterial conduits solely utilizing segments of donor iliac artery to bridge infrarenal aorta to HAA were planned. However, available segments of donor iliac artery were not lengthy enough to reach porta hepatis.A 51-year-old female underwent deceased donor liver transplant for HCV-related cirrhosis and hepatocellular carcinoma within Milan criteria. Available segment of donor iliac artery was sewn to recipient's infrarenal aorta utilizing running 5-0 polypropylene sutures .The conduit was placed prepancreatic and retrogastric, and the segment of donor iliac artery was sewn to a 6 mm polytetrafluoroethylene (PTFE) graft .Distal end of PFTE graft was sewn to Hepatic Artery of the Allograft (HAA) utilizing running 7 mm polypropylene sutures .Patient was maintained on intravenous heparin for 72 hours and transitioned to oral acid acetylsalicylic 100 mg/day thereafter. Complications included urinary sepsis and transient neurologic toxicity to tacrolimus. She was discharged home on posttransplant day 25. MAC is patent and allograft has normal function 31 months after transplant.A 61-year-old female also underwent deceased donor liver transplant for HCV-related cirrhosis and hepatocellular carcinoma within Milan criteria. A MAC was created through sewing a number 8 PTFE graft to recipient's infrarenal aorta utilizing 4-0 running polypropylene sutures. Distal end of PTFE graft was sewn to the available segment of donor iliac artery utilizing running 5-0 polypropylene sutures. Distal end of donor iliac artery was sewn to HAA utilizing running 7-0 sutures. Patient was maintained on intravenous heparin. Posttransplant complications included transient acute renal failure and reoperations for intraperitoneal hemorrhage on posttransplant day 8 and for fascial dehiscence on day 27. Following reoperation for intraperitoneal bleeding, heparin was stopped, and patient was switched to oral acid acetylsalicylic 100 mg/day. She was discharged home with patent arterial conduit and normal allograft function on posttransplant day 57. She currently has a patent MAC and normal liver allograft function at 4 months after transplant.Nikitin et al. reported similar long-term survival for LT utilizing arterial conduits as compared to LT employing standard arterial reconstructions . AlthougArterial conduits were utilized in 6 out of our last 150 LTs. In the two cases reported herein, segments of donor iliac artery were not lengthy enough to reach HAA. Instead of utilizing a full synthetic graft, MACs utilizing the available segment of donor iliac artery and a shorter segment of a PTFE graft were created. Although utilization of MACs could have been performed elsewhere before, the authors did not find any previous report on such technique in the medical literature. Thus, to our knowledge, this is the first report of LT utilizing MAC. In both cases, prevention of arterial thrombosis was performed through initial intravenous heparin drip, both patients being transitioned to oral salicylic acid during the first ten posttransplant days.As utilization of prosthetic material may confer additional risks to those of conduits solely utilizing vessels from a deceased donor, there is reluctance across transplant surgeons to use prosthetic conduits in abdominal transplant recipients. Potential risks of prosthetic arterial conduits include increased potential for abdominal sepsis secondary to infection of the prosthetic graft. Thus, the literature reports of arterial conduits utilizing prosthetic material are scarce . Such reAlthough conduits may be related to worse outcomes after LT , 5, outc