N = 82) tested whether closely matching the faces of the probes and irrelevants, as required for a fair lineup, undermines RT-CIT usefulness. Preregistered Experiments 2a and 2b (Ns = 48), tested the role of eyewitness cooperativeness for RT-CIT validity. All participants watched a mock crime video and then completed an RT-CIT. As expected, the usefulness of the RT-CIT was moderated by picture similarity, with better detection for non-matched faces. Unexpectedly, eyewitness cooperation , did not affect the validity of the RT-CIT. A large CIT effect observed in Experiment 2b further suggested that—even with matched faces—the RT-CIT might be of use when encoding conditions during the crime were favorable. Cases in which witnesses are unwilling or afraid to make an explicit identification might concern another possible application.The reaction time-based Concealed Information Test (RT-CIT) has high validity in assessing recognition of critical information. Findings on its usefulness for diagnosing face recognition in eyewitnesses are inconsistent. Experiment 1 ( They can help resolve a case  and assessing their reactions to each option using physiological and behavioral measures . The reaction time-based CIT  required in a fair lineup may have lowered the diagnosticity of the RT-CIT . Twelve participants were excluded because they knew one or more of the individuals presented in the RT-CIT personally. No participants were excluded based on high error rates  for any response category  or a large number of non-completed trials . Participants’ native languages were German (52.4%), Dutch (23.2%), English (8.5%), French (3.7%), Greek (2.4%), or other (9.8%). The majority were students (95.1%) while the rest were working professionals. Participants could choose between either study credit (1/2) or a €5 gift voucher for their participation. The study was approved by the Ethics Committee of the faculty.Based on Sauerland et al. (n = 42) or non-matched faces (n = 40) conditions. A mixed 2 (stimulus similarity: matched vs. non-matched) × 2 (stimulus type: probes vs. irrelevants) factorial design was used to assess the effect of face matching on reaction times for different stimulus types.Participants were randomly assigned to the matched faces  were presented with a short description of the actor and then presented with a set of six pictures that either included the probe or did not include the probe. Tredoux’s E  ranged from 3.8 to 4.8 (of a possible 6), thereby marking them as a fair picture selection , two randomly selected targets , and ten irrelevants . During the practice block, each picture was presented twice, resulting in 28 trials. During the main task, each picture was displayed 21 times, resulting in a total of 294 trials per condition.To test participants’ explicit recognition of the two actors, participants were presented with a photo display that depicted the facial images of the actors, targets and irrelevants. It consisted of 14 photos arranged in four rows of four or three photos. Each image was numbered .Participants completed the informed consent form and provided demographics. They were then requested to remove any possible distractions they had on them . Before participants started watching one of the two stimulus film versions, they were instructed to observe carefully and pay special attention to the faces of the individuals involved. Subsequently, the RT-CIT started. The experimenter remained with the participants for the practice block to answer any questions, and left the lab when the main task started. Next, participants were asked whether they recognized any of the people presented in the pictures from outside of the experiment . Participants were then handed the photo display depicting the faces of all individuals included in the CIT and were asked to indicate the two actors from the film. If participants were unsure, they were prompted to give their best guess . Finally, participants were debriefed, reimbursed and thanked for participating. The whole experiment lasted about 15–20 min.https://osf.io/syr3u/).All data are available on the open science framework  with a reaction time between 150 and 1500 ms were taken into account.F = 44.44, p < .001, η2 = 0.36, but not matching, F = 2.47, p = .120, η2 = 0.03, emerged. The expected stimulus type × matching interaction was statistically significant, F = 7.14, p = .009, η2 = 0.08. Post-hoc comparisons showed that the difference between probes and irrelevants was statistically significant for both matched pictures, t(41) = 3.22, p = .003, d = 0.50, 95% CI , and non-matched pictures, t(39) = 5.90, p < .001, d = 0.93, 95% CI , with the effect being stronger in the non-matched condition. Figure Table F, 80 = 44M = 0.85) and the victim (M = 0.90) above chance level, p < .001.3A binomial test against 1/7 odds (chance level of 0.15) showed that participants identified the thief  was tested as a possible moderator.The preregistration of the sampling plan, variables, methods, experimental script, and analysis plan of Experiment 2a and 2b are available on the open science framework . The following sections apply to both studies. The experiments were approved by the Ethics Committee of the faculty.d = 0.97) of concealment on the RT-CIT  and nonsphericity correction was set to 1. This analysis resulted in a minuscule sample size of only 12 participants . Following rule #2 of Simmons, Nelson, and Simonsohn’s  were recruited through advertising on the laboratory web page of the university. None had to be excluded based on our predetermined inclusion and exclusion criteria , the majority were students (85.4%) while the rest were working professionals. The majority of native languages were Dutch (40%), English (10%), German (8%), Spanish (8%) or other (34%).For Experiment 2b, 50 participants were recruited, 2 of which were excluded based on revised inclusion and exclusion criteria that were applied in this study. More specifically, we excluded participants who could not explicitly identify the probes from a photo display following the RT-CIT, in addition to the rest of the criteria mentioned above (see Experiment 1). Of the 48 included participants  or financial reimbursement (€7.50) for their participation and had the chance to receive an additional bonus (1/2 study credit or €5) based on their performance.n = 24) and non-cooperative (n = 24) conditions. A mixed 2 (cooperativeness: cooperative vs. non-cooperative) × 2 (stimulus type: probes vs. irrelevants) factorial design was used to assess the effect of participant cooperation on reaction times for different stimulus types.Participants were randomly assigned to the cooperative  and the facial stimuli measured 208 × 260 pixels. Each picture was shown sequentially with the caption ‘Is this the target?’ , and the locations of the YES and NO answers were not counterbalanced, but constant across all trials . After the practice block was completed, the target pictures were shown again for 30 s (not 15 s), and response feedback was given throughout the practice block and the main task. In addition, during the main task, the stimuli were presented with a randomized interstimulus interval of either 250 ms, 500 ms, or 750 ms. Each picture was displayed 30 times, resulting in a total of 420 trials per condition. An additional probe-learning phase was added in Experiment 2b. Here, participants viewed the CIT probe pictures separately (rather than simultaneously) for 30 s with instructions to memorize these faces because these individuals were involved in the crime they were about to witness in the stimulus film. Following the presentation of the stimulus film, the probe pictures were shown again, separately for 30 s each.Across both experiments, the matched pictures of Experiment 1 were used.The photo display depicted the facial images of the actors, targets and irrelevants. It consisted of 14 photos arranged in two rows of seven photos with a caption under each photo stating “Suspect 1”, “Suspect 2”, and so on.A manipulation and motivation check was included  as well as questions regarding the film to ensure participants paid attention to the stimulus event and had seen the probes, for example: “Where did the thief and victim bump into each other?”Experiments 2a and 2b followed roughly the same procedure as Experiment 1, with a few additions. The only procedural difference between experiments 2a and 2b concerns an additional exclusion criteria and probe-learning phase in Experiment 2b. After watching the stimulus film, participants were informed that the thief from the film was either their friend (who they needed to help avoid being identified by police) or their enemy (who they wanted to help the police identify), by randomly receiving one of the two conditional instructions for (non-)cooperation. In the cooperative condition, participants were told the following:You have just witnessed a crime and the police are going to interview you as a witness. You realize that the perpetrator in the crime is a long standing enemy of yours and you want to help the police catch them. You decide to reveal everything you know about the crime (the identity of the perpetrator and victim) to the police so that they catch the perpetrator.In the non-cooperative condition, participants are told the following:You have just witnessed a crime and the police are going to interview you as a witness. You realize that the perpetrator in the crime is a long standing friend of yours and you want to make sure the police doesn’t catch them. You decide to conceal everything you know about the crime (the identity of the perpetrator and victim) from the police so that they won’t catch the perpetrator.Participants were then informed that they would receive additional money or study credit if they successfully managed to reveal or conceal their knowledge based on their given instructions. Upon completion of the CIT, participants called the researcher back in who subsequently explained that they “must ignore their previous instructions to cooperate or not and answer honestly during the following part of the experiment.” This would not affect their bonus. This was to ensure that participants in the non-cooperative condition did not believe that they had to continue hiding their recognition during the survey and photo display. The participants received the photo display after the completion of the CIT, as well as a memory and manipulation check. Finally, participants were debriefed and reimbursed. The whole experiment lasted approximately 45 min.https://osf.io/syr3u/).All data are available on the open science framework  were more motivated to conceal than participants in the reveal condition ; t(46) = 6.24, p < .001; d = 1.79, CI . Vice versa, participants in the reveal condition  were more motivated to reveal than participants in the conceal condition ; t(46) = 5.10, p < .001; d = 1.48, CI .Manipulation checks on participants’ self-reported motivation to conceal or reveal their information deemed the experimental manipulation successful. More specifically, participants in the conceal condition  = 19.31, p < .001, F = 0.13, p = .72, F < 0.01, p = .973, M = 521.04, SD = 79.21) were longer than RTs for irrelevants , d = 0.64, 95% CI , but this effect was not moderated by participant cooperation  and the victim (M = 0.52) above chance level, p < .001.A binomial test against 1/7 odds (0.15 chance level) showed participants identified the thief M = 0.5 and the n = 18). This was done to verify the existence of an effect of condition while controlling for lack of memory. Results again showed a significant main effect of stimulus type, F = 6.51, p = .021, F = 0.43, p = .523, F = 5.66, p = .030, M = 507.49, SD = 84.06; M = 480.14, SD = 67.43), t(7) = 2.37, p = .050, d = 0.68, 95% CI . No such effect was found for cooperative witnesses, t(9) = 0.25, p = 0.808, d = 0.02, 95% CI . This provides preliminary  support of the role of cooperation in the RT-CIT.Because response inhibition may only be at play when explicit recognition is given (there needs to be recognition to suppress recognition), we conducted a 2 × 2 ANOVA with stimulus type and participant cooperation as factors including only data from participants who successfully identified both the thief and victim during the photo display  = 68.56, p < .001, F = 0.01, p = .913, F = 0.21, p = .649, M = 545.42, SD = 72.34) were slower than for irrelevants , d = 1.21, 95% CI  but this effect was not moderated by participant cooperation  among several plausible, yet maximally different alternatives . Contrasting the stolen amount with highly similar items  runs the risk that the knowledgeable examinee may no longer differentiate between them. Indeed, the greater the resemblance between the critical and the control items, the smaller the CIT effect -to-strong  RT-CIT effect for faces across three studies, they all used the same stimulus materials (with various procedures). Although the current results suggest that the RT-CIT could be an option in real live cases, good encoding conditions provided, this conclusion is based on a single set of materials, and therefore requires replication. Furthermore, the large effect sizes observed in Experiment 2b concern a sample with 100% accurate explicit recognition. This was facilitated by an additional probe-learning phase, in which the CIT probe picture was presented. Strictly speaking, participants’ task during the CIT protocol thus concerned a picture recognition rather than a face recognition task , we also identified very specific situations under which it could form a promising alternative to current explicit identification procedures. These include crimes with relatively long exposure time (60 to 90 s), allowing good viewing conditions, close resemblance of the perpetrator at the time of the crime and the identification procedure, and cases in which witnesses do not want to make an explicit identification, for example because they are threatened.


Although previous research in alcohol dependent populations identified alterations within local structures of the addiction ‘reward’ circuitry, there is limited research into global features of this network, especially in early recovery. Transcranial magnetic stimulation (TMS) is capable of non‐invasively perturbing the brain network while electroencephalography (EEG) measures the network response. The current study is the first to apply a TMS inhibitory paradigm while utilising network science (graph theory) to quantify network anomalies associated with alcohol dependence. Eleven individuals with alcohol‐dependence (ALD) in early recovery and 16 healthy controls (HC) were administered 75 single pulses and 75 paired‐pulses (inhibitory paradigm) to both the left and right prefrontal cortex (PFC). For each participant, Pearson cross‐correlation was applied to the EEG data and correlation matrices constructed. Global network measures  were extracted for comparison between groups. Following administration of the inhibitory paired‐pulse TMS to the left PFC, the ALD group exhibited altered mean degree, clustering coefficient, local efficiency and global efficiency compared to HC. Decreases in local efficiency increased the prediction of being in the ALD group, while all network metrics (following paired‐pulse left TMS) were able to adequately discriminate between the groups. In the ALD group, reduced mean degree and global clustering was associated with increased severity of past alcohol use. Our study provides preliminary evidence of altered network topology in patients with alcohol dependence in early recovery. Network anomalies were predictive of high alcohol use and correlated with clinical features of alcohol dependence. Further research using this novel brain mapping technique may identify useful network biomarkers of alcohol dependence and recovery. The current study provides preliminary evidence of altered network connectivity in patients with alcohol dependence in early recovery. These network anomalies were predictive of high alcohol use and associated with clinical features of past alcohol use severity. Therefore, by combining brain stimulation techniques with network science, it may be possible to identify useful network biomarkers of alcohol dependence and recovery. Electroencephalography (EEG), an economic and convenient neuroimaging tool with high temporal resolution, is capable of characterising brain network connectivity within a millisecond timescale.global effects of the TMS‐perturbation on the distributed network activity of patients with ALD in early recovery.Combined transcranial magnetic stimulation and electroencephalography (TMS‐EEG) is a novel technique which allows researchers to non‐invasively perturb the brain network (elicit a transient change in the network state) while EEG measures the network response within a millisecond timescale.Therefore, the current study had three major objectives: (i) Characterise global properties of network connectivity following a perturbation (TMS pulse) in patients with ALD in early recovery when compared to healthy controls, (ii) assess whether altered network features can predict the likelihood of membership in the ALD group and (iii) examine whether these global properties are related to clinical features of alcohol dependence.2This collaborative research effort between Monash Alfred Psychiatry Research Centre, Turning Point and the Weizmann Institute of Science was approved by the Alfred Human Subjects Research and Ethics Committee. Participants were required to sign a detailed informed consent form prior to study enrolment and were informed that all participation was voluntary and they could withdraw at any time without prejudice.2.12.1.1Wechsler Test of Adult Reading standardised score higher than 100, to indicate no significant intellectual disability; (2) no drug or alcohol use (according to self‐report) since completion of the detoxification programme; and (3) no current comorbid mental health disorder (such as co‐morbid depression or psychosis). To confirm abstinence, the Timeline Follow Back (TFB) was administered, a 4‐week calendar where participants retrospectively reported their alcohol consumption over the previous month. Anyone reporting recent alcohol consumption via the TFB was excluded. Exclusion criteria also included acute medical or physical illness, engaging in pharmacotherapy treatment (including anti‐craving or anti‐depressant treatment), history of major depression or other drug dependence, reporting psychotic symptoms or suicidal ideation, head injury, epilepsy or history of seizures or metal implants.Eleven participants meeting criteria for DSM IV‐TR alcohol dependence  within 2 years of successful completion of a detoxification programme  were recruited for the study. For initial screening, a phone call interview was conducted to assess psychiatric and medical history. Recruitment occurred through treatment agencies by self or clinician referral. Inclusion criteria were: (1) a 2.1.2Sixteen healthy control subjects, without any previous or current history of alcohol/drug use disorder, psychiatric illness, head injury, epilepsy or seizures, were recruited through posters and local advertisements.All participants received $40 reimbursement for their participation. Participants completed a general demographics questionnaire; the obsessive–compulsive drinking scale 2.2For the current study, network analysis techniques were applied to TMS‐EEG data previously collected and published.2.2.1Biphasic TMS pulses were applied to the cortex via a figure‐of‐eight cooled coil connected to the MagPro R30 stimulator with a MagOption unit . The TMS coil was held over the scalp, with the handle of the coil pointed backwards, angled approximately 45° from the midsagittal line and held perpendicular to the presumed direction of the central sulcus. In a single session, active TMS was applied to the left motor cortex (to ascertain the appropriate stimulation parameters) and this was followed by stimulation of the right dorsolateral prefrontal cortex (PFC) and the left dorsolateral PFC. The order of the stimulation site  was counterbalanced across participants.2.2.2For motor cortical stimulations, TMS was applied to the left motor cortex while electromyography (EMG) recorded the motor‐evoked potentials (MEP) response via disposable disc electrodes placed over the contralateral first dorsal interosseous (FDI) muscle. EMG was recorded using Signal software , amplified and filtered  by a powerlab/4sp system  and processed offline. Single pulse stimulation was applied to the left motor cortex to determine the Resting Motor Threshold (RMT), Active Motor Threshold (AMT) and 1 mV peak‐to‐peak (measured over cap). RMT is determined by the minimum stimulus intensity required to elicit peak‐to‐peak MEP > 50 μV as measured by the contralateral FDI muscle in at least 5/10 trials . AMT is determined as the minimum stimulation intensity, during active FDI muscle contraction (uniformly pressing index finger against a spring), needed to induce peak‐to‐peak MEPs of >100 μV in 3/5 consecutive trials. For the 1‐mV peak‐to‐peak measure, stimulator intensity is adjusted until it elicits peak‐to‐peak MEP of approximately 1 mV over 10 consecutive trials; this was conducted over the EEG cap (1 mV measure over cap) and measured by the contralateral FDI muscle contraction to determine the appropriate stimulation parameters for the application of frontal TMS‐EEG.2.2.3TMS‐evoked cortical activity within the frontal regions was measured through a custom‐made 24‐channel EEG cap and recorded by the Synamps2 EEG system . All electrodes (sintered Ag/AgCl) were fixed in plastic electrode clips according to the standard 10–20 positions  and were referenced to an electrode placed posterior to the Cz electrode . All EEG signals were recorded DC at a sampling rate of 20 kHz and filtered through a low pass filter of 3500 Hz. Following TMS discharge, recharging capacitors were set to a 1‐s delay, to minimise TMS‐related artefacts in the EEG data. To reduce any potential TMS‐induced auditory effect, earphones with white noise (95 dB) were worn by all participants .2.2.4conditioning pulse, then the inter‐stimulus interval (ISI) of 100 ms, which is followed by a suprathreshold test pulse. This results in the conditioning pulse suppressing the cortical response produced by the test stimulus. This process is illustrated in Figure Long interval cortical inhibition (LICI) is an inhibitory paired‐pulse TMS paradigm which delivers two suprathreshold pulses with the stimulus intensity based on the 1‐mV peak‐to‐peak measure (established by the motor stimulations). The process involves applying the suprathreshold test stimuli) and 75 paired‐pulses (LICI: conditioned stimuli followed by test stimuli). Between these stimuli, there was an ISI of 3 s, and to minimise any order effects, stimuli application was randomly counterbalanced between subjects.For this protocol, both single and paired‐pulse frontal stimulation was directed between AF3 and F3 (left PFC), and between AF4 and F4 (right PFC). There were 150 stimuli delivered to each site. The stimuli consisted of 75 single pulses  from averaged event‐related potentials (ERP) were quantified. The process is detailed more extensively in our previous publication.2.3Vi[t]  with t between 0 and a given duration T . The constructed graph  consists of a set of vertices (or nodes) V and a set of edge weights (or connections) E between any two vertices in the graph. The nodes represent signals over the defined time span which are denoted by Vi. The edge eij between two nodes Vi and node Vj, is computed by the normalised pairwise Pearson cross‐correlation coefficient over the given time span for a given time delay τ,Vi¯ is the time average of Vi. To account for cross‐cortical conduction times and neurophysiological processes,τ ranged between 0 and 150 ms and was chosen as the delay time maximising the cross correlation. Consideration of this maximal delay τ (up to 150 ms) led to the construction of a directed network. Notably, although quite rare, when the pairwise delay approached 150 ms , it was reasonable to extract meaningful connectivity data from 150 ms (when examining the 300‐ms epoch). The global mean degree was examined across a maximal delay range of 150, 100 and 50 ms when single TMS was applied to the left PFC, see Figure Functional connectivity graphs were generated from the EEG data traces and provide a topographical representation of brain network activity over a fixed time span. The time span consisting of discrete time points, can be defined as all sample points of the original referenced EEG signals eij is then defined as the maximum of c(τ) over all τ. If eij > eji, only eij is kept and eji is set to zero. Defining eij in this manner represents statistical dependence of the signals between the nodes over the fixed time span. This results in a graph which is both directed  and weighted (retaining the edges' correlation co‐efficient index). Individual networks were constructed for each of the participants. Please refer to Figure An edge For each network, a global threshold of 60% was set to remove weak or spurious correlations  and thus allow identification of topological properties of the network. A fixed threshold allows an examination of the average number of active connections between nodes and allows us to identify how this differs between the groups. Before settling on the 60% absolute threshold, the global mean degree was examined across a range of thresholds for both stimulation sites (left and right PFC) and both pulse‐type (single and paired‐pulse), see Figure 2.3.1k of a node denotes the number of edges, or links connecting inward (in‐degree) and outward (out‐degree) from the node. Nodes of high degree demonstrate increased connectivity with the other vertices and may represent hubs in the network.In a directed network, the degree 2.3.2Ci quantifies the occurrence of two neighbors of the same node Vi being connected, which produces a triangle in the graph. Ci presents information regarding local connectivity and structure within a network.ti is the number of triangles that node Vi participates in, then Ci = 2ti/ki (ki − 1), where ki is the degree of node Vi. Global clustering coefficient computes the average clustering coefficient of all nodes.The clustering coefficient 2.3.3Lij between two nodes refers to the minimum number of edges which is required to pass from node Vi to node Vj  and is also described as the shortest path length. We set Lij=∞ for any disconnected node pairs Vi and Vj. The global efficiency GE is defined as the average inverse shortest path length distance in the network GE=1VV−1∑i,j,j≠iLij−1. The local efficiency LE is a bit more delicate, defined as the average inverse shortest path length of the sub‐graph that includes all the neighbors of a node, but with the node itself removed . Local efficiency is thus related to the clustering coefficient, for example, if all of a node's neighbors are linked then both its clustering coefficient and its local efficiency are maximal, and equal 1. These efficiency measures reflect the transfer of information and capacity for integrated processing of the network, both globally (for the GE measurement) and locally (for the LE measurement).The characteristic path length 2.4χ2‐tests for categorical variables (Table https://statistics.laerd.com) was conducted to identify the presence of significant interactions between group (ALC vs. HC), pulse (single pulse vs. paired‐pulse) and side (left vs. right PFC stimulation).Comparability of the basic demographics of the ALD group and healthy controls was assessed using independent t‐tests for continuous variables and es Table . All datAcross these network metrics, Binomial Logistic Regression and Receiver Operating Characteristic (ROC) Curve analysis were applied to ascertain the likelihood for these network metrics (predictors) to successfully discriminate between participants with ALD or HC. Finally, Pearson's correlation coefficients were examined to assess whether global network metrics correlated with clinical features of alcohol dependence (in the ALD population) according to Total Score on the severity of alcohol dependence questionnaire (SADQ) and measures of cortical inhibition . Additionally, to address any potential variability due to the range in duration of abstinence, Pearson's correlation was applied to examine whether there were any significant associations between duration of abstinence and any of the network metrics. No significant correlations were observed, and therefore, it was not necessary to include duration of abstinence in the statistical analysis below as a covariate.33.13.1.1F  = 1.749, p = 0.201, partial eta squared = 0.080. There was a statistically significant two‐way interaction observed between Pulse and Group, F  = 7.971, p = 0.01, partial eta squared = 0.285. A nearly statistically significant two‐way interaction was observed between Side and Group, F  = 4.146, p = 0.055, partial eta squared = 0.172. All other two‐way interactions were not statistically significant (p > 0.05). No significant main effects were observed.There was no statistically significant three‐way interaction observed between Pulse, Side and Group, As the current study consists of a small sample size, to place context of the averaged data, we also visually inspected the individual participant mean degree network response following the TMS perturbation across the Pulse Types (single and paired‐pulse) for each Stimulation Site (left and right PFC) in Figure 3.1.2F  = 1.978, p = 0.175, partial eta squared = 0.090. A statistically significant two‐way interaction was observed between Pulse and Group, F  = 8.486, p = 0.009, partial eta squared = 0.298. All other two‐way interactions were not statistically significant (p > 0.05). Statistical significance of a simple main effect was assessed. There was a significant simple main effect of Group following paired‐pulse stimulation, F  = 4.649, p = 0.043, partial eta squared = 0.189, but not following single pulse stimulation, F  = 1.261, p = 0.275, partial eta squared = 0.059. Pairwise comparisons were performed for these statistically significant simple main effects. Sidak corrections were made with comparisons within each simple main effect. Adjusted p‐values, estimated mean (EM) and standard error (SE) are presented. In response to paired pulse stimulation, the HC presented with an increased index of clustering coefficient  compared to ALC , with a mean difference of 0.04, 95% CI , p = 0.043. Within the ALC group, the clustering coefficient index in response to the single‐pulse  was increased compared to the paired‐pulse , a mean difference of 0.043, 95% CI , p = 0.003, while no significant mean difference was identified in the healthy controls.There was no statistically significant three‐way interaction observed between Pulse, Side and Group, 3.1.3F  = 5.564, . When compared across groups, there was a statistically significant simple two‐way interaction between Pulse and Side for the alcohol group, F  = 5.200, , but not for healthy controls, F  = 1.036, . When we compared across stimulation sites, there was a statistically significant simple two‐way interaction between Pulse and Group following stimulation to the left PFC, F  = 5.847, , but this was not evident following stimulation to the right PFC, F  = 0.052, . To further explore this relationship, a two‐way ANOVA was applied and paired‐pulse administered to the left PFC was statistically significantly different between groups, F  = 4.533, , but was not significantly different following single‐paired pulse, F  = 0.062, p = 0.805, partial eta squared = 0.002. To examine the differences between groups, a simple pairwise comparison was run between groups following paired‐pulse administration to the left PFC. A Sidak adjustment was applied. Local efficiency following paired‐pulse administration to the left PFC for ALC was 0.59 (SD = 0.11) and healthy controls was 0.66 (SD = 0.061). There was a statistically significant mean difference between local efficiency of 0.071, 95% CI , p = 0.043.With respect to local efficiency, there was a statistically significant three‐way interaction between Pulse, Side and Group, F  = 2.943, p = 0.102, partial eta squared = 0.128. A statistically significant two‐way interaction was observed between Side and Group, F  = 6.359, p = 0.02, partial eta squared = 0.241. All other two‐way interactions were not statistically significant (p > 0.05). The statistical significance of simple main effects was assessed. There was a significant simple main effect of Group at the left side of stimulation, F  = 6.181, p = 0.022, partial eta squared = 0.236, but not at the right side of stimulation, F  = 0.704, p = 0.411, partial eta squared = 0.034. Pairwise comparisons were performed for these statistically significant simple main effects. Sidak corrections were made with comparisons within each simple main effect. Adjusted p‐values, estimated mean (EM) and standard error (SE) are presented. In response to left stimulation to the PFC, the HC presented with increased global efficiency  compared to ALC , a mean difference of 0.04, 95% CI , p = 0.022.With regards to Global Efficiency, there was no statistically significant three‐way interaction observed between Pulse, Side and Group, 3.23.2.1χ2(2) = 6.047, p < .192. The model explained 32% (Nagelkerke R2) of the variance in the ALC group and correctly classified 68% of cases. Sensitivity was 60%, specificity was 75%, positive predictive value was 69% and the negative predictive value was 67%. Local efficiency following TMS applied to the left PFC was statistically significant as an independent predictor  and pulse type (paired‐pulse and single pulse) across each of the network measures  could predict the likelihood of the participants being in the ALD group. For the binomial logistic regression, the network data were standardised . For Local Efficiency, the logistic regression model was not statistically significant, 3.2.2When paired‐pulse stimulation was applied to the left PFC, the area under the ROC curve for 1. Local Efficiency was 0.742 , 2. Global Efficiency was 0.758 , 3. Mean Degree was 0.767  and 4. Clustering Coefficient was 0.754 , which is an acceptable discrimination according to Hosmer et al (2013). For single‐pulse stimulation to either stimulation site and paired‐pulse stimulation to the right PFC, the area under the ROC curve for all network measures ranged between 0.39 to 0.59 which is considered poor discrimination.3.3Site 1. Left PFC. In the single pulse condition, the total score on the Severity of Alcohol Dependence Questionnaire (SADQ) was negatively related to both MD  and CC . Similarly, in the paired‐pulse condition, total score on the Severity of Alcohol Dependence Questionnaire (SADQ) was negatively related to both MD  and CC . No significant correlations between any network metrics and cortical inhibition were identified.Site 2. Right PFC. In the paired‐pulse condition only, the total score on the Severity of Alcohol Dependence Questionnaire (SADQ) was negatively related to both MD  and CC . No significant correlations between any network metrics and cortical inhibition were observed.4This is the first study to utilise an inhibitory paired‐pulse paradigm (LICI) combined with EEG to identify anomalies in network connectivity in a sample of participants with alcohol dependence during early recovery. We find it significant that following single‐pulse stimulation, no differences in network topology were observed between the groups, while application of LICI to the left PFC elicited an altered network response within the ALD group (relative to controls) that was not seen with right sided stimulation. Based on these findings, we suggest that LICI stimuli transiently elicited an ‘inhibitory’ response within the addiction circuitry, while EEG characterised the altered global network response.Similar to previous fMRI and DTI studies, the current study observed alterations in both global and local integration within locally specialised connectivity (mean clustering coefficient) and in the hub‐like structure (mean degree). Following paired‐pulse stimulation to the left PFC, a decrease in local integration  was found to significantly increase the odds of membership in the ALD group, while network metric classifiers , were capable of adequately discriminating between the groups. Furthermore, in the ALD group, reductions in both locally specialised connectivity (mean clustering coefficient) and hub‐like structure (mean degree) were associated with increased severity of alcohol dependence. When combined, our results support the notion that directly targeting the addiction circuitry (via the LICI perturbation) while utilising EEG technology can provide further insight into clinically relevant altered network topology associated with long‐term alcohol use during early recovery.4.1The first network feature explored in the current study was Degree Centrality (hub‐like structure) which represents the integration and processing of network information and communication within the network.Another key topological feature of the brain network that has been examined by addiction studies is locally specialised connectivity of the network. The clustering coefficient, which indexes the number of connections existing between a node's nearest neighbours, provides a measure of locally specialised connectivity.With regard to the preliminary EEG studies, data modelling was applied to EEG data to ascertain the predictive value of detecting an ALD group compared to healthy group based on network features. Reduced clustering coefficient was found to reliably predict the ALD group under various stimulus conditions.A prominent focus of addiction network studies is the efficiency (a measure of information transfer) of the brain network. Measure of Efficiency, which is represented by a network with short characteristic path length, provides a measure of parallel information transfer between nodes and integrated processing.While many fMRI and DTI studies predominantly identify reduced global efficiency, only a small number of EEG studies have characterised these network anomalies and present mixed findings. In terms of short‐term alcohol consumption, a preliminary resting‐state EEG study examined network connectivity in social drinkers following alcohol administration as compared to placebo in healthy social drinkers.Therefore, the current study identified a shift in the balance of network integration and segregation following a paired‐pulse TMS perturbation to the left PFC. Human brain networks have been found to exhibit small world networks which rely on an efficient balance between information segregation and integration .4.2Recent studies have provided preliminary evidence for the utility of combined neuroimaging‐brain stimulation techniques to map and explore network change.Recent studies have examined whether the neurophysiology of short‐term alcohol exposure can be reflected by alterations in global connectivity. One preclinical study identified alterations in network organisation following acute ethanol exposure.The efficacy of brain stimulation techniques  to alter network topology4.3Although these findings provide preliminary EEG evidence of the presence of altered network connectivity among individuals with alcohol dependence in early recovery, various methodological issues should be addressed. First, in applying graph theory to neuroimaging data there are numerous approaches to network construction, reference choice and thresholding considerations. The current study utilised a widely applied network construction technique (Pearson correlation) in which cross‐correlation data are extrapolated from EEG traces4.4The current study is the first to directly target the addiction circuitry (via an inhibitory TMS paradigm) whilst simultaneously utilising EEG to quantify anomalies in network topology associated with persisting features of alcohol dependence. Identified network anomalies included reduced global integration, locally specialised connectivity, and hub‐like structures. These network alterations were related to clinical features of alcohol dependence, whereby reduced global integration was predictive of high alcohol use, while decreased locally specialised connectivity and hub‐like structures were associated with increased alcohol use severity. Therefore, whilst preliminary, the current study provides compelling evidence of the potential efficacy of TMS‐EEG combined with network science to identify network biomarkers associated with alcohol dependence and early recovery. Further studies are required to confirm and extend these preliminary findings. A better understanding of the global features of the network may contribute to current neurobiological models of alcohol dependence. Additionally, it is anticipated that these network biomarkers may be used to identify risk factors associated with the development of alcohol dependence and as potential markers of treatment efficacy.Jodie Naim‐Feil: Conceptualization, Methodology, Formal Analysis, Writing–Original Draft. Paul B. Fitzgerald: Conceptualization, Methodology, Writing–Review and Editing, Supervision, Project administration. Mica Rubinson: Formal Analysis, Writing–Review and Editing. Dan I. Lubman: Project administration, Writing–Review and Editing. Dianne M. Sheppard: Conceptualization, Methodology, Writing–Review and Editing, Supervision. John L. Bradshaw: Conceptualization, Methodology, Writing–Review and Editing, Supervision. Nava Levit‐Binnun: Formal Analysis. Elisha Moses: Conceptualization, Methodology, Writing–Review and Editing, Formal Analysis, Supervision.Dr. Jodie Naim‐Feil was a recipient of the Senior Post‐doctoral Fellowship at the Weizmann Institute, the Curwen‐Lowy Post‐doctoral Fellowship and the Clore Post‐doctoral Fellowship that supported the further analysis conducted for the study. Prof. Paul B. Fitzgerald has received equipment for research from MagVenture A/S, Nexstim, Neuronetics and Brainsway Ltd and funding for research from Neuronetics. He is a founder of TMS Clinics Australia. The study was supported by Dr. Nava Levit‐Binnun's Israel Science Foundation grant (No. 1169/11) and by the National Institute of Psychobiology in Israel. Prof. Elisha Moses is supported by the Minerva Foundation (Germany) and by the Israel Science Foundation. There is no conflict of interest to declare. All of the sponsors who donated to the authors involved in the study had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.


Thyroid disorders are common and often require lifelong hormone replacement. Treating thyroid disorders involves a fascinating and troublesome delay, in which it takes many weeks for serum thyroid‐stimulating hormone (TSH) concentration to normalize after thyroid hormones return to normal. This delay challenges attempts to stabilize thyroid hormones in millions of patients. Despite its importance, the physiological mechanism for the delay is unclear. Here, we present data on hormone delays from Israeli medical records spanning 46 million life‐years and develop a mathematical model for dynamic compensation in the thyroid axis, which explains the delays. The delays are due to a feedback mechanism in which peripheral thyroid hormones and TSH control the growth of the thyroid and pituitary glands; enlarged or atrophied glands take many weeks to recover upon treatment due to the slow turnover of the tissues. The model explains why thyroid disorders such as Hashimoto's thyroiditis and Graves' disease have both subclinical and clinical states and explains the complex inverse relation between TSH and thyroid hormones. The present model may guide approaches to dynamically adjust the treatment of thyroid disorders. A new mathematical model for the thyroid axis shows that slow changes in thyrotroph mass are responsible for hormone delays in the treatment of thyroid disorders. Thyroid disorders affect about 5% of the population and can cause severe symptoms  T4 and T3 regulate metabolism, affecting almost every cell in the body , to the test results after diagnosis and during treatment . Median FT4 was evaluated in bins of TSH. The relation between TSH and FT4 shows hysteresis, where for a given FT4 level, TSH is higher after diagnosis than before diagnosis  Fig .n = 26,327, n = 105,812, respectively), which causes increased FT4 and decreased TSH. TSH levels are lower for a given FT4 level after diagnosis with Graves' disease than before diagnosis . We find an inverse relationship between TSH and T4 as previously reported. The relationship can be divided into three regimes. At TSH above its normal range (TSH >5 mIU/L), corresponding to individuals with Hashimoto's before diagnosis and treatment, TSH is a declining function of FT4, with a log‐linear slope of −0.228 ± 0.007. When TSH is below its normal range (TSH <0.5 mIU/L), corresponding to individuals before diagnosis and treatment of Graves' disease, the log‐linear slope is −0.23+/−0.04. Serum TSH level usually drops to undetectable levels when FT4 begins to exceed its upper normal range (FT4 > 20 pmol/L) Fig .In the next sections, we first present a mathematical model for the HPT axis. Second, we show how the model explains (i) delays and hysteresis, (ii) the ability of the HPT axis to compensate for physiological changes by means of gland‐mass growth and shrinkage, (iii) the transition between subclinical and clinical states of Hashimoto's and Graves' diseases, and (iv) the three‐regime TSH/T4 relationship.et al, et al, et al, et al, et al, et al, et al, et al, To explore the mechanism for these phenomena, we developed a mathematical model of the HPT axis. The innovation in the model is the consideration of the slow timescale regulation of the functional mass of the thyroid and pituitary thyrotroph cells Fig . This reP , to give half‐lives of 7d for TH, 1 h for TSH, and 6 min for TRH  of thyrotrophs is kP.The pituitary thyrotroph mass, et al, et al, et al, et al, The growth of thyrotroph cells can occur by additional means . Slower turnover times show qualitatively similar effects.The timescales of the mass Equations  and 5)  5) are mP≪1/kP,T≪1/kT), the gland masses can grow or shrink to provide compensation for physiological changes. For example, low TH levels lead to two complementary compensation mechanisms. The classic fast mechanism is that more TSH is secreted from the pituitary, increasing TH secretion from the thyroid gland to restore homeostasis. This rise of TSH in the model is due primarily to the release of inhibition of thyroid hormones on TSH production in the pituitary. A secondary effect, which is not essential for our conclusions, is due to the release of TRH inhibition. We note that the role of TRH in this context is debated in the literature ..P≪1/kP,TbPaP Fig .bTH. Such a reduction is expected for example on low iodine. Low iodine activates rapid compensation mechanisms such as increased iodine uptake, and if these mechanisms saturate, one can expect the TH secretion rate per unit thyroid mass bTH to drop. As a result, the model shows that the thyroid enlarges over months leading to increased TH secretion, restoring homeostasis . The drug causes a decline in T4 levels, resulting in increased TSH secretion per thyrotroph in the pituitary; however, for TSH to fully recover in the model, the thyrotroph mass needs to be renewed. This process is much slower, due to the slow turnover of thyrotrophs, explaining the TSH recovery delay after T4 is normalized.Hysteresis in the model occurs also in the case of hyperthyroidism due to Graves' disease Fig . In Gravquations  and 4)..Ab to TSquations  . These two formulae, together with a formula by Dietrich on thyroid functional mass . When compensation breaks, the result is first subclinical and then clinical hypothyroidism  curve. The same goes for change in the hypothalamic signal u, for example, following change in temperature, nutritional state, or health condition.However, conditions which change the pituitary parameters, such as thyrotroph adenoma which affects P in the simple model without carrying capacities  and clinical hypothyroidism (Graves' disease and toxic nodules). The middle regime corresponds to compensated thyroid hormones, composed of a distribution of thyroid set points within a healthy population. The mean slope of the middle region can be explained in the model by two types of effects (i) fast timescale effects such as individual differences in the production and removal rates of hormones, and (ii) slow timescale effects such as differences in the growth and removal rates of the endocrine cells, as may occur during aging. Both sources of variation provide similar predictions for the mid‐regime slope and may both occur physiologically.bTH in the model, the maximal thyroid hormone secretion rate per thyrocyte, drops below a critical value.The model also explains compensation and its breakdown in the case of goiter due to iodine deficiency. Fast timescale compensation occurs by increased avidity to iodine and other physiological processes. On the slow timescale, once these fast compensation processes have saturated, the model indicates that the thyroid and thyrotroph functional masses both grow, as clinically observed . Therefore, changes in hormone production and degradation parameters, such as changes in iodine supply, are expected to be compensated. On the long timescale of weeks‐months, TH and TSH are guaranteed to reach a set point that is defined only by the proliferation and death rate of the thyrocytes and thyrotrophs, respectively.The dynamic compensation mechanism by changes in mass in equations  and 5)  5) is anet al, et al, et al, et al, P and T volumes changes  concentration. Dietrich and colleagues observed increased FT3 concentration and increased calculated deiodinase capacity (SPINA‐GD) in beginning hypothyroidism, but this was dependent on the amount of thyroid tissue  was reduced by half.For simulations, we used the following parameter set based on the experimental literature. Hormone removal rates are 1/6 min for TRH, 1/1 h for TSH, and 1/7 days for T4, thyrotroph mass turnover = 1/30 days, and thyrocyte mass turnover = 1/30 days,  = TSH), the nullclines take the simple form of:In the simple case of For details, see Pst=1, Tst=1. Thyroid and pituitary gland carrying capacities were estimated as 5.5, 5.3 times their normal volume following data from Liu et al  st. st. point was defined as . In Fig aT was varied as follows: aT= where aTbasal=130days. In Fig Ab was varied as follows: Ab=0,0.5TSHst,1.2TSHst,2TSHst where TSHst−st is the healthy TSH level. In Fig bTH values were taken to be bTH,0.1bTH,0.04bTH,0.02bTH, where bTH is the thyroid hormone production rate per thyrocyte under normal iodine conditions. All simulations were performed using Wolfram Mathematica 12.2. Code is provided online at GitHub https://github.com/yaelkorem-weizmann/HPT_glandmass_model.We used the following parameters: Removal rates for the hormones and glands as indicated above. Hormone production rates were chosen to give steady‐state hormone values of FT4 = 15 pmol/l, TSH = 1.5 mIU/l and steady‐state glands masses of iu et al  and Khawiu et al , respectP  basal TSH levels TSHbasal, (ii) TSH after TRH stimulation  TSHstimulated, and (iii) basal T4 levels T4basal. Given that the TRH test uses a saturating amount of TRH, TRH≫kTRH, we approximate the temporal derivative using the difference divided by Δt:ΔTSH, the rise in TSH following the TRH test when taking into account TSH turnover rate aTSH, as:P=1bTSHΔtΔTSH1+T4kT4To estimate bTSH, the maximal TSH secretion rate per thyrotroph per unit TRH and unit T4, is a characteristic of the system that we assume is constant, and Δt is a test parameter, the time for the TSH measurement after TRH stimulation, which can be calibrated to improve the accuracy of this formula for practical use.Note that P∼ΔTSH1+T4kT4 is an estimate for the thyrotroph functional mass  that can be used to estimate the change in thyrotroph mass of an individual during the treatment of thyroid diseases.Therefore, the formula TSH1 and TSH2, respectively  before diagnosis and (2) after diagnosis and during treatment. We define TSH levels at a given T4 level on these trajectories, vely Fig . In thisFrom equation , for a cFrom equation , at steaTH0:Therefore, for a constant thyroid hormone level TSH1TSH2=P1P2Hence: Uri Alon: Conceptualization; supervision; funding acquisition; methodology; writing – original draft; writing – review and editing. Yael Korem Kohanim: Conceptualization; software; investigation; visualization; methodology; writing – original draft; writing – review and editing. Tomer Milo: Conceptualisation; writing – review and editing. Moryia Raz: Conceptualisation; writing – review and editing. Omer Karin: Conceptualisation; writing – review and editing. Alon Bar: Conceptualisation; writing – review and editing. Avi Mayo: Conceptualisation; software; investigation; writing – review and editing. Netta Mendelson Cohen: Data curation; conceptualisation; writing – review and editing. Yoel Toledano: Conceptualisation; writing – review and editing.CRediT author contributions listed above, the contributions in detail are:In addition to the Conceptualization: YKK, UA, TM, MR, OK, AB, AM, NMC, YT; Methodology: YKK, UA; Investigation: YKK, AM; Software: YKK, NMC, AM; Writing—original Draft: YKK, UA; Writing—review & editing: YKK, TM, MR, OK, AB, AM, NMC, YT, UA; Visualization: YKK; Data curation: NMC; Funding Acquisition: UA; Supervision: UA.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Expanded View Figures PDFClick here for additional data file.


Simulation Group. Correction of this error consequently reduced its sample size from 242 to 169 individuals.Due to an administrative error, 73 individuals were wrongfully assigned to the This did not affect the development of the ADHD Credibility Index (ACI). However, it resulted in an underestimation of the index’ classification accuracy in its initial validation.Simulation Group  and Control Groups . The gender distribution in this group also differed from the Credible (χ2 (1) = 42.518, p < 0.01) and Overreporting Control Groups (χ2 (1) = 20.289, p < 0.01) as well as the ADHD Groups (credible: χ2 (1) = 16.176, p < 0.01; non-credible: χ2 (1) = 13.327, p = 0.01). In terms of education, instructed simulators differed from credible participants in the Control Group  and the ADHD Group , but not from overreporting controls  or non-credible patients with ADHD .The corrected demographic data of the Simulation Group further required the revision of Tables higher sensitivity rates and larger effect sizes than previously reported. Rather than the small effect described in the original publication, the ACI yielded a large effect for the comparison of instructed simulators and credible adults with ADHD . As was previously the case, the largest effect could be observed on the Supposed Symptoms subscale, followed by Exaggerated Symptoms, Selectivity, and lastly Symptom Combinations (see amended Appendix Correcting the


Medicago sativa L.) is extensively planted to reduce chemical fertilizer input to the soil and remedy damaged fields. The soil mechanism of these effects is potentially related to the variations in alfalfa-mediated interactions of the soil microbial community. To understand the impact of planting alfalfa on the soil microbial community in degraded black soil cultivated land, a 4-year experiment was conducted in degraded black soil cultivated land. We assessed soil parameters and characterized the functional and compositional diversity of the microbial community by amplicon sequencing that targeted the 16S rDNA gene of bacteria and ITS of fungi in four systems under corn cultivation at the Harbin corn demonstration base : multiyear corn planting ; 2 years of alfalfa-corn rotation (OC); 3 years of alfalfa planting (TA); and 4 years of alfalfa planting (FA). It was found out that alfalfa led to changes in the alpha diversity of soil bacteria rather than in fungi in the degraded arable land. The abundance of the bacterial groups Gemmatimonadetes, Actinobacteria, Planctomycetes, and Chloroflexi was increased in OC, while Proteobacteria and Acidobacteria and the fungal group Glomeromycota were increased in TA and FA. OC, TA, and FA significantly increased the pH level but reduced soil electrical conductivity, but they had no impact on soil available nitrogen and soil available potassium at the 0–15 cm soil depth. However, with the years of alfalfa planting, soil available nitrogen and soil available potassium were reduced at the 15–30 cm soil depth. OC, TA, and FA significantly reduced the soil available phosphorus and soil total phosphorus at the 15–30 cm soil depth. There was no significant impact made on soil total nitrogen. FA significantly reduced the soil organic matter at the 15–30 cm soil depth. Planting alfalfa in degraded black soil cultivated land can reduce the salt content of the soil, and the nutrient content of soil planted with alfalfa without fertilization was equivalent to that of degraded corn cultivated land with annual fertilization. Besides, alfalfa recruited and increased contained taxa with the capacity to improve soil nutrient utilization and inhibit the harmful influences of pathogens for subsequent crops. Meanwhile, the planting of alfalfa can modify soil conditions by promoting the proliferation of specific beneficial microbiota groups.Legume alfalfa ( Black soil generally refers to Mollisols, which are the fertile soils distributed in four major regions around the world: North America, southeastern Europe, Central Asia, including northeast China, and the Pampas of South America . HoweverPrevious studies have shown that planting high-quality forage is an important measure to improve the quality and fertility of degraded arable soils . MeanwhiAlfalfa has long been the preferred forage grass for rotation due to its high perennial persistence, protein content, and biomass production. Alfalfa is a potential “microbial hotspot crop” , as studLegume alfalfa also has the ability to improve the content of soil organic matter and nutrients . HoweverHerein, our aim is to investigate the impact of planting alfalfa on the soil microbial community in degraded black soil cultivated land. Thus, we drew comparison between multiyear corn planting (more than 30 years), 2 years of alfalfa-corn rotation, 3 years of alfalfa planting, and 4 years of alfalfa planting in all parts of cropping system experiment. It was predicted that (i) alfalfa recruits and increases the microbial community, including taxa with the capacity to improve soil nutrient utilization and promote luxuriant growth for subsequent crops, and (ii) long-term alfalfa planting can modify soil conditions by promoting the proliferation of specific beneficial microbiota groups.−2  was applied annually to the corn field. The alfalfa was sown in May 2016 and 2017, respectively. The alfalfa fields were fertilized in the year of sowing with 54 kg·hm−2 N and 138 kg·hm−2 P. Only a foliar spray was applied 10 days before each crop was mown during the growth of alfalfa.Conducted at the Harbin corn demonstration base, the research is a long-term agricultural experiment carried out at Wujia Town  in Heilongjiang Province. The annual average temperature and precipitation are 3.5°C–4.5°C and 400–600 mm, respectively. The soil type of the experimental site is black soil. The cultivars of corn and alfalfa used were “Tiannong 9” and “Dongnong 1,” respectively. A potassium sulfate compound fertilizer substrate of 600 kg·hmThe four treatments included: multiyear corn planting ; 2 years of alfalfa corn rotation (OC), where corn was cultivated following two-year alfalfa in 2019; 3 years of alfalfa planting (TA); and 4 years of alfalfa planting (FA). Each treatment was replicated 4 times. The size of each plot was 20 m × 20 m, with 5 m between plots. The topography of the plots was flat, and the soil quality was uniform.Soil samples were collected in mid-September 2019 (after crop harvesting). Within each plot, 10 sample points were randomly selected with a 2.5 cm diameter soil auger; then, the soils were mixed into one sample. The root system of alfalfa was a typical taproot system purposed to study the effect of alfalfa on different soil layers for each treatment, with samples collected from two soil depths: 0–15 cm and 15–30 cm. Within each plot, 10 sample points were randomly selected with a 2.5 cm diameter soil auger. Then, the soils were mixed into one sample. Then, the samples were sieved and divided into two parts. One part was air-dried for soil parameters determination, while the other was reserved at −80°C for microbial analysis.3 extraction-molybdenum antimony anti-colorimetric method. Soil available potassium (AK) was extracted with neutral ammonium acetate and tested with a flame photometer. Soil organic matter (SOM) was tested by means of oxidation with potassium dichromate-sulfuric acid solution  was measured by using the ring knife method. The soil moisture content (MC) was tested by the weight loss of the samples after drying at 105°C. Soil pH and electrical conductivity (EC) were measured using a pH meter and a conductivity meter, respectively , KOD Polymerase (1 μl), 10 μΜ Primer F (1.5 μl), 10 μM Index Primer (1 μl), 10 μM Primer R (1.5 μl), or 10 μM Universal PCR Primer (1 μl). The reaction conditions for the PCR are as follows: 94°C (2 min), 98°C (10 s), 65°C (30 s), and a final extension at 68°C (10 min). Then, all raw tags were filtered into sequences to obtain high-quality tags. In order to study the compositional diversity information on the species of the samples, all valid tags of all samples were clustered using UPARSE software. Then, the sequences were clustered into OTUs  with 97% agreement by default. The results were calculated and the absolute abundance and relative information of tags in each sample were calculated for each OTU. UPARSE selects representative sequences (the tag sequence with the highest abundance in OTUs) in the process of constructing OTUs. The Naive Bayesian assignment algorithm of RDP Classifier was applied to annotate these representative sequences with the Greengenes database. These representative sequences were then annotated with the Greengenes database using the Naive Bayesian assignment algorithm of RDP Classifier (with a confidence threshold of 0.8~1), so as to obtain the species annotation information of each OTU.The diversity and composition of the soil bacterial and fungal communities were determined by Dene Denovo from Guangzhou. We amplified the V3–V4 region of bacterial 16S rDNA and the ITS2 region of fungal ITS rDNA by using specific primer pairs with barcodes 341F: 5′CCTACGGGNGGCWGCAG3′/806R: 5′ GGACTACHVGGGTATCTAAT3′ and ITS3_KYO2: 5′ GATGAAGAACGYAGYRAA3′/ITS4: 5′ TCCTCCGCTTATTGATATGC3′. Each sample was subjected to PCR in a 50 μl mixture. The mixture included 10× Buffer KOD (5 μl), 2 mM dNTPs (5 μl), 25 mM MgSOp < 0.05).Soil parameters, Chao1, Shannon, and Sob were determined by means of one-way ANOVA, PCA, and PERMANOVA tests. The community composition at the OTU level of soil microorganisms was analyzed through PCoA (using Bray–Curtis distances) and PERMANOVA tests. Pearson’s correlation analysis was conducted to identify soil physicochemical factors and determine the relative abundance of bacterial and fungal phyla. All data analyses and graphing were performed with the assistance of SPSS 26 and Origin 2019b, respectively. The comparisons between the means were performed through Tukey’s test . At the 15–30 cm soil depth, soil bacterial communities differed significantly between corn and alfalfa plantings, while the closer distance between OC and MC1 along axis 1 indicated the slight differences (p = 0.181). The distance between TA and FA was small, while the two bacterial communities were similar and insignificantly different (p = 0.543). The fungal communities were clustered at different sites. The differences in fungal communities between maize and alfalfa plots were observed along axis 1 for years of cultivation, with closer distances between samples from three-year alfalfa and four-year alfalfa plots, indicating that short-term alfalfa cultivation exerted no significant effect on fungal community composition (p = 0.005) between the plantings.The changes in planting practices affected the bacterial and fungal communities. At the 0–15 cm soil depth, the bacterial and fungal communities were generally clustered by sample point ,B. Besidferences . Also, Pposition . PerMANOWe detected a total of 3,265,694  effective tags of bacteria from 32 soil samples, with each ranging from 302 to 478 in length. A total of 80,163 OTUs were detected through clustering. Moreover, we detected 3,310,770  fungal active labels from 32 soil samples, with each ranging from 206 to 478 in length. These tags were then clustered into 24,894 OTUs.p < 0.05; p < 0.05; OC has no significant impact on Chao 1, Shannon, and sobs –L. TA anp < 0.05; p < 0.05). TA and FA increased the abundance of Acidobacteria by 56.83% and 81.06% (p < 0.05), respectively, with the abundance of Verrucomicrobia improved by 204.45% and 416.06% (p < 0.05), respectively; however, they reduced the abundance of Gemmatimonadetes by 10.17% and 38.70% (p < 0.05), respectively, with the abundance of Actinobacteria increased by 31.93% and 35.69% (p < 0.05), respectively, with the abundance of Chloroflexi improved by 24.11% and 36.31% at the 0–15 cm soil depth , respectively, with the abundance of Acidobacteria improved by 149.02% and 168.44% (p < 0.05), respectively, with the abundance of Planctomycetes increased by 20.12% and 2.51% (p > 0.05), respectively; by contrast, the abundance of Actinobacteria was reduced by 52.17% and 52.45% (p < 0.05), respectively, with the abundance of Gemmatimonadetes improved by 63.92% and 64.73% (p < 0.05), respectively, with the abundance of Chloroflexi reduced by 40.47% and 42.93% at the 15–30 cm soil depth , respectively, with the abundance of Mucoromycota increased by 130.03%, 107.81%, and 414.69%  caused a significant difference in soil physicochemical properties between them. Also, the difference between TA and FA is insignificant .At the 15–30 cm soil depth, PC1 explained 48.2% of the variation, and PC2 explained 13.8% of the variation, according to the principal component analysis. A total of 62% of the variation was explained by the principal component analysis. As can be seen from the figure, the physicochemical properties of the corn field along the first sorting axis were significantly different from those of the alfalfa field. Additionally, the planting method was positively correlated with soil available potassium and soil organic matter. There was a significant separation between MC1 and OC along the second ordination axis, indicating that the soil physicochemical properties differed significantly due to the change in cropping practice. However, the difference between the physicochemical properties of TA and FA was less significant .At the 0–15 cm soil depth, Proteobacteria and Acidobacteria were negatively correlated with soil electrical conductivity and soil available nitrogen to a significant extent under different planting methods. Actinobacteria and Chloroflexi were positively correlated with soil electrical conductivity and soil available nitrogen to a significant extent. In addition, Acidobacteria and Rokubacteria showed a significant negative correlation with soil total phosphorus. Meanwhile, Acidobacteria (r = −0.735) and Verrucaria (r = −0.6) were negatively correlated with soil moisture content to a significant extent, and Actinobacteria (r = 0.716) showed a significant positive correlation with soil moisture content. Gemmatimonadetes (r = −0.50) and Actinobacteria (r = −0.765) were negatively correlated with soil bulk density to a significant extent, while Acidobacteria (r = 0.779) and Verrucomicrobia (r = 0.65) were positively correlated with soil bulk density to a significant extent. Rokubacteria was negatively correlated with soil total phosphorus and soil available phosphorus to a significant extent. The Planctomycetes was positively correlated only with soil organic matter to a significant extent .Nitrospira  were negatively correlated with soil available nitrogen and soil available potassium to a significant extent. Gemmatimonadetes (r = −0.694), Actinobacteria (r = −0.802), and Chloroflexi (r = −0.77) were negatively correlated with soil bulk density to a significant extent. Proteobacteria (r = 0.726) and Acidobacteria (r = 0.779) were positively correlated with soil bulk density to a significant extent. Unlike the 0–15 cm soil depth, soil available phosphorus was associated with more bacterial communities. Gemmatimonadetes (r = 0.697) was positively correlated with soil available phosphorus to a significant extent, while Verrucomicrobia (r = −0.660), and Rokubacteria (r = −0.705) were negatively correlated with soil available phosphorus to a significant extent , Actinobacteria , and Chloroflexi  were positively correlated with soil available nitrogen and soil available potassium to a significant extent. While, Proteobacteria , Acidobacteria , and t extent .The correlations between fungal community composition and soil factors under different cultivation methods were examined by conducting Pearson’s correlation analysis. According to the results, soil physicochemical properties had significant effects on the relative abundance of fungal phylum, and the effects varied by soil depth. At the 0–15 cm soil depth, Mucoromycota was negatively correlated with soil moisture content to a significant extent (r = −0.610). Basidiomycota was negatively correlated with soil available potassium to a significant extent (r = −0.536). Ascomycota was positively correlated with soil total nitrogen (r = 0.513) and soil organic matter (r = 0.521) to a significant extent . It indiAlfalfa led to changes in the alpha diversity of soil bacteria rather than in fungi in the degraded arable land. Adding alfalfa to the rotation recruited and increased the microbial community, including taxa with the capacity to improve soil nutrient utilization and promote luxuriant growth for subsequent crops, such as Gemmatimonadetes, Actinobacteria, Planctomycetes, and Chloroflexi. Long-term alfalfa planting can modify soil conditions by promoting the proliferation of specific beneficial microbiota groups like Proteobacteria and Acidobacteria. Planting alfalfa in degraded black soil cultivated land can help reduce the salt content of the soil, and the nutrient content of soil planted with alfalfa without fertilization was equivalent to that of degraded corn cultivated land with annual fertilization.Corn and alfalfa rotation makes no significant impact on the alpha diversity of soil bacteria and fungi. However, planting alfalfa for 3 or 4 years led to variations in the alpha diversity of soil bacteria rather than in fungi in the degraded arable land, which is consistent with the outcomes of a previous research . Corn isFusarium  increased fungal diversity , which iFusarium . Herein,In the natural soil, the fungal community in the rhizosphere of maize is similar to that in the original soil, with most of the fungi in the rhizosphere stemming from the soil. There were similar major bacterial phyla after adding alfalfa to the rotation, but the relative abundance of dominant taxa changed significantly. OC increased the abundance of Gemmatimonadetes, Actinobacteria, Planctomycetes, and Chloroflexi, which are contained in nutrient cycling, organic matter turnover, and plant growth-promoting activities, at the 0–15 cm soil depth. Plant microbial interactions and specific groups outside of larger soil communities are recruited by plants . The micMeanwhile, the abundance of Proteobacteria and Acidobacteria was increased by TA and FA at the soil depths of 0–15 cm and 15–30 cm. Proteobacteria prefer environments with high effective carbon contents  and playThe relative abundance of Ascomycota also showed no significance but a slightly elevated difference. Fungi are not sensitive to aboveground vegetation changes and farming patterns, and fewer soil physicochemical factors are significantly associated with the relative abundance of the fungal phylum, which is consistent with previous research results . The abuNitrospira was increased, which is an aerobic chemolithoautotrophic nitrite-oxidizing bacteria  on soil microbial community structure and soil nutrients of degraded black soil cultivated land, thus providing a basis for the in-depth understanding of soil microbial groups and contributing to the restoration of degraded black soil cultivated land. In the future, it is necessary to advance the potential of soil microorganisms and produce potential biological inoculants by conducting an additional research on microbial community functional character through meta-proteome analysis or meta-transcriptome.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.LM and NZ performed the data analyses and wrote the manuscript. QW and YC contributed significantly to the analysis and manuscript preparation. DL performed the experiment. GC contributed to the conception of the study. All authors contributed to the article and approved the submitted version.This work was financially supported by Forestry Science and Technology Extension Demonstration Project of central Finance (TG 02), the China Postdoctoral Science Foundation (2021M690575), and Young Talents Project of Northeast Agricultural University (20QC17).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Senescence is a complex cellular process that is implicated in various physiological and pathological processes. It is characterized by a stable state of cell growth arrest and by a secretome of diverse pro‐inflammatory factors, chemokines and growth factors. In this review, we summarize the context‐dependent role of cellular senescence in ageing and in age‐related diseases, such as cancer. We discuss current approaches to targeting senescence to develop therapeutic strategies to combat cancer and to promote healthy ageing, and we outline our vision for future research directions for senescence‐based interventions in these fields. Targeting senescence to treat cancer and ageing raises increasing enthusiasm in both academia and industry. Current approaches to target senescence with a discussion of their benefits and drawbacks have been proposed. This review also explores future research directions for senescence‐based interventions to combat cancer and ageing. While in the senescent state, cells secrete a variety of pro‐inflammatory factors, chemokines and growth factors, which together are known as the senescence‐associated secretory phenotype (SASP) , 2. SeneSenescence can be highly variable and heterogenous. There are four main types of senescence that cells can undergo, depending on the inducer: replicative senescence (RS); oncogene‐induced senescence (OIS); stress‐induced premature senescence (SIPS) and therapy‐induced senescence (TIS) , 5, 6, 72Senescence plays a context‐dependent role in cancer and in other diseases, such as lung fibrosis , 15. In 2.1Pten‐deficient tumours by triggering immunosurveillance when combined with Janus Kinase 2 (JAK2) inhibitor NVP‐BSK805 [OIS is an intrinsic tumour suppression mechanism. It prevents premalignant cells that harbour an initial oncogenic hit from becoming fully transformed cancer cells . HoweverP‐BSK805 . HoweverP‐BSK805 , 21, 22.P‐BSK805 .Notably, the elimination of senescent cells delays tumorigenesis . The cle2.2Caloric restriction in wild‐type mice is known to prolong a healthy lifespan by delaying the accumulation of senescent cells , 27. ThiThe first generation of senolytics was discovered based on the intrinsic resistance of senescent cells to apoptosis . SenesceXpdTTD/TTD and naturally aged mice [Another study has shown that flavonoid polyphenol fisetin can serve as a potent senolytic drug. This drug has been shown to reduce the senescent cell burden in wild‐type mice and in human adipose tissue explants . More imged mice .In a recent study, urokinase‐type plasminogen activator receptor (uPAR) was identified as a prevalent cell‐surface and secreted senescence biomarker . AlthougHigh senescent cells are structurally and functionally important in ageing organisms [High senescent liver sinusoidal endothelial cells (LSECs) could lead to disruption of blood‐tissue barriers and subsequent fibrosis [As discussed above, several studies have demonstrated the beneficial effects of senolytics in the context of ageing and age‐associated disorders in animal models. However, whether they are equally effective in human clinical trials is yet to be determined given the paradoxical and context‐dependent role of senescence. Particularly, senolytics typically only eliminate a fraction of senescent cells and are known to induce side effects such as pleural effusion and dose‐limiting thrombocytopenia . When trrganisms . Eliminafibrosis . Thus, cGiven that the potential side effects caused by senolytics and that the SASP plays an important role in promoting ageing‐related diseases, targeting the SASP by senomorphics becomes an alternative option.3The SASP plays a context‐dependent role in cancer and ageing , 46, and3.1The SASP‐associated factors secreted from OIS cells can trigger immune surveillance to remove premalignant cells that harbour initial oncogenic hits . This im3.2Given that eliminating senescent cells by senolytics can cause unwanted side effects and that the detrimental effects of senescent cells are largely mediated by the SASP, an alternative approach to targeting senescent cells is to develop senomorphics. Senomorphic drugs, such as BET inhibitor I‐BET‐762 and histone lysine demethylase subfamily 4 (KDM4) inhibitor ML324, aim to suppress the SASP , 56. Not3.2.1OIS cells have long been known to induce the formation of visible senescence‐associated heterochromatic foci (SAHF) . These fTarget SASP‐promoting transcription factors is challenging due to the fact that they lack catalytic active sites for drugs to bind. Therefore, chromatin modifiers that have drug binding pockets become ideal targets for suppressing the SASP. For example, mixed lineage leukemia protein‐1 (MLL1), which mediates histone H3 lysine 4 (H3K4) methylation, contributes to SASP upregulation in both OIS and TIS models . Consist6‐methyladenosine (m6A) modification [In addition, a recent study has shown that the disruption of the chromatin loop that drives the expression of SASP‐associated genes might also be used to reduce the SASP . The metfication . Howeverfication . Specifification . In wildfication . Notablyfication . Thus, t3.2.2The innate immune cGAS‐STING pathway is a critical regulator of the SASP Fig. . This paIn OIS or RS cells, LINE‐1 acts as a driver of type I interferon (IFN‐I) signalling via cGAS‐STING activation to induce chronic inflammation . In contStrategies that dampen the upstream triggers of cGAS‐STING signalling might provide an equally effective therapeutic approach. For example, Lamivudine, which is a nucleotide reverse transcriptase inhibitor, has been shown to suppress LINE1 formation . This coOverall, senomorphics aim at suppressing the SASP without interfering with the cell growth arrest of senescent cells. Despite that, senomorphics show promising efficacy in suppressing the SASP expression in multiple types of senescent cell models and animal models, strategies that target on other signalling pathways may provide additional avenues for therapeutic interventions.4Given that senescence is accompanied by metabolic alteration, DNA damage, and stem‐like signatures, strategies targeting these alterations may remodel the senescence‐associated microenvironment for treating ageing‐related diseases . Other s4.1+/NADH ratio, which activates the energy sensor, AMPK signalling, to elicit the SASP [+ can also serve as a cofactor for sirtuin family proteins (SIRTs) and for poly‐ADP‐ribose polymerase (PARP), which both protect cells from senescence in a manner that depends on their ability to consume NAD+ [+ protects against senescence‐associated growth arrest, one study has shown that once senescence is fully established, NAD+ promotes inflammatory SASP during OIS and TIS [+/NADH ratio suppresses the AMPK signalling pathway to enhance NFκB transcriptional activity, which upregulates the SASP [Metabolic dysfunction is known to induce senescence responses . The altthe SASP . NAD+ caume NAD+ , 89, 90.ume NAD+ , 92, 93.ume NAD+ . Althoug and TIS . Mechani and TIS . An incrthe SASP . Consistthe SASP . In addithe SASP , 97, 98.the SASP .Reactive oxygen species (ROS) also accumulate in mitochondria during senescence . Increas4.2cip1 tumour suppressor pathway plays a vital role in regulating senescence [cip1, which plays a critical role in mediating cell cycle exit during senescence [cip1 expression [cip1 knockout in TIS cells triggers apoptosis via caspase activation [The p53/p21nescence . p53 cannescence , 104. p5nescence . p53 tranescence , 107. Fupression . This cotivation .Tp53−/−/Atm−/− mice that lack both p53 and ATM, cells fail to senesce and the mice develop tumours earlier than their single knockout counterparts do [In the absence of p16, p53 inactivation prevents human senescent fibroblasts from undergoing senescence . In addiparts do . These fparts do , 116. Thparts do .4.3INK4a, p21cip1, and p53, are also critical regulators of cancer stem‐cell‐like phenotypes [in vitro [It is well‐known that crucial signaling components of senescence pathways, such as p16enotypes . A gain enotypes . For exain vitro . Moreovein vitro . Overall5Senescence is a complicated cellular process that is characterized by a variety of phenotypic changes and pleiotropic functional consequences. These phenotypic changes are spatially and temporally dynamic and depend on stress inducers and the genetic context . While tTechnical advancements and innovations bring new avenues for the development of novel senescence‐based interventions. The advances of whole‐genome next‐generation sequencing (NGS) are greatly accelerating biomarker identification and targeted therapy in cancer , 122. ThThe authors declare no conflict of interest.CW, XH and RZ conceived and wrote the manuscript.


Although her shortness of breath and chest tightness worsened as the tumor compressed her heart and left main bronchus, and her performance status (PS) decreased to 3, third‐line treatment with pazopanib (800 mg once daily) was commenced. The treatment led to suppression of tumor growth and resulted in 4‐month progression‐free survival. Therefore, in cases of adult RMS, considering pazopanib treatment as an option may be beneficial, even with previous ineffective treatments or poor PS.Rhabdomyosarcoma (RMS) is a common soft tissue sarcoma usually observed in children. However, RMS rarely occurs in adults. The prognosis of adult RMS is poor and a standard chemotherapy regimen has not yet been established. Herein, we report the case of a 60‐year‐old Japanese woman with primary anterior mediastinal alveolar RMS . The tumor increased aggressively despite first‐line treatment with doxorubicin (60 mg/m Rhabdomyosarcoma (RMS) is rarely observed in adults and the prognosis is very poor. Despite previous ineffective therapy and poor performance status (PS), treatment with pazopanib suppressed aggressive tumor growth in the case reported here. Pazopanib treatment should be considered as a viable treatment option in cases of adult RMS with poor PS. Rhabdomyosarcoma (RMS) is a type of soft tissue sarcoma (STS), observed mostly in children and rarely in adults. STS accounts for 1% of all adult cancers.First‐line treatment comprises surgery in patients with early‐stage RMS3) without calcification or obvious fat components  as a first‐line regimen. On treatment initiation, her performance status (PS) was 1. Furthermore, the patient had dyspnea on exertion and chest tightness due to aggressive tumor progression over 2 months after her initial hospital visit . After two cycles of eribulin treatment, the patient experienced an adverse event (AE) of grade 3 neutropenia. Her dyspnea remarkably worsened. Moreover, the primary tumor grew further, compressing the heart, left main bronchus, and pulmonary artery . However, the tumor had invaded the aorta and was unresectable. She was administered standard chemotherapy, doxorubicin . After pazopanib initiation, her shortness of breath slightly improved. Enhanced CT showed a slight decrease in tumor size and contrast effect inside the tumor Figure . FurtherThe objective response rate (ORR) and progression‐free survival (PFS) are poor in patients with RMS undergoing primary treatment of surgery for early‐stage cases or chemotherapy for unresectable cases. The five‐year overall survival (OS) in adult RMS, including resectable cases, is 21%, which is far poorer than that in pediatric RMS.Although a combination of vincristine, actinomycin D, and cyclophosphamide is well established for RMS in children, doxorubicin is a widely accepted first‐line therapy for STS,In a randomized phase III trial study comparing eribulin and dacarbazine for previously treated liposarcoma or leiomyosarcoma, eribulin significantly prolonged the OS. However, no significant differences in PFS (2.6 months) and ORR (4%) were observed.p < 0.0001), with few AEs above grade 3.In a phase III trial study of pazopanib in patients with metastatic STS and PS of 0–1, pazopanib significantly prolonged the median PFS compared with that with placebo (4.6 vs. 1.6 months, In conclusion, pazopanib treatment should be considered as a viable treatment option in patients with RMS and poor PS. Furthermore, we consider this case report to be helpful in formulating a treatment strategy for extremely rare cases of adult RMS with poor PS.The authors declare that they have no conflict of interest.


Although arthroplasty provides satisfactory pain relief, functional improvement, and reduced flexion contracture in patients with hemophilia arthropathy, bleeding remains the primary problem associated with hemophilic arthropathy joint replacement. We aimed to explore the differences in perioperative blood loss (PBL) associated with joint replacement surgery in patients with and without hemophilia.This study retrospectively analyzed 61 cases of PBL in patients undergoing joint replacement surgery, including 37 patients with hemophilia and 24 patients without hemophilia. All patients demonstrated severe joint flexion contractures that seriously affected their daily lives and required surgical intervention. PBL was compared between the two groups.p < 0.001) and hidden (p < 0.001) blood losses were significantly higher for patients with hemophilia than those for patients without hemophilia. However, after adjustment for multiple factors, there was no significant difference in overall blood loss between the two groups (p = 0.731). In addition, sex, age group, and surgical site did not affect blood loss in patients with hemophilia.In univariate analysis, the overall (Overt bleeding did not increase significantly in patients with hemophilia, compared with that in patients without hemophilia. In terms of blood loss, joint replacement surgery for patients with hemophilia is relatively safe. The results of this study must be verified by a prospective follow-up study with larger sample size.Trial registration Retrospectively registered. Hemophilia A and B are rare, congenital, X-linked coagulation disorders caused by factor VIII (FVIII) and factor IX (FIX) deficiencies, respectively . GloballTreatment of mild to moderate hemophilic arthritis may include support/splinting, joint movement, progressive strengthening, stretching, and pain management styles, as well as patient education on pain management, activities to avoid, and other joint disease management recommendations . For patA previous study attempted to search for biomarkers that could be used to monitor bleeding and assess the progression of hemophilic arthropathy in clinical practice and evaluate the effectiveness of treatments. Nevertheless, the clinical use of biomarkers was still limited in clinical practice because of a lack of standardization , 9. ThusAlthough previous studies reported that arthroplasty provides satisfactory pain relief, functional improvement, and reduced flexion contracture in patients with hemophilia arthropathy , 11, bleWe retrospectively analyzed 61 patients with hemophilia A, including 46 men and 15 women. Among the patients, 34 had hip joint disease, and 17 had knee joint disease. Preoperative imaging showed that the damaged joints were all severely deformed; bone degeneration was evident. According to Arnold and Hilgartner's staging [Preoperatively, the patients with hemophilia underwent routine coagulation factor pharmacokinetic assessments and received human recombinant coagulation factor VIII. The blood concentration of the coagulation factor was measured immediately and 30 min, 9 h, 24 h, and 48 h after its infusion. These data were used to determine the half-life of the coagulation factor in the body while also testing for the presence of coagulation factor inhibitors. Regular infusion of coagulation factor VIII during the postoperative period ensured that the coagulation factor concentration remained at 100% that on the day of surgery and 3 days after surgery. No patients used heparin prophylactically, and tranexamic acid was only for topical use on wounds. The coagulation function of patients without hemophilia was normal.Clinical information (presenting signs and symptoms) and demographic information for the patients were obtained from the hospital’s computerized medical record information system. The main symptoms, recorded by the doctor-in-charge, were based on each patient’s performance and self-reporting.2) [The demographic information included each patient’s sex and age. The participants were grouped into three age categories: < 40 years, 40–64 years, and >  64 years. Each patient’s body mass index (BMI) was calculated as the individual’s weight (kg) divided by the square of the individual’s height (m2) .The preoperative blood volume was calculated using the Nadler equation . Data we3) + K2 × weight (kg) + K3. For males, variables are defined as follows: K1 = 0.3669, K2 = 0.03219, and K3 = 0.6041. For females, variables are defined as follows: K1 = 0.3561, K2 = 0.03308, and K3 = 0.1833.The following equation was used: theoretical TBL = preoperative blood volume (BV) × (preoperative HCT – postoperative HCT)/mean HCT, where BV (L) = K1 × height  was calculated as the intraoperative blood loss plus PBL, and hidden blood loss (HBL) was calculated as TBL minus DBL.t-tests. Categorical variables  were presented as numbers and frequencies; between-group comparisons were performed using χ2 tests. All analyses were conducted using SPSS for Windows ; p < 0.05 was considered statistically significant.Continuous variables  are presented as means and standard deviations (SDs). Comparisons of the continuous variables were made using Student’s This study included 46 men (75.4%) and 15 women (24.6%). The mean age of the patients was 51.9 years, and the 40–65 age group accounted for the largest proportion (54.1%). Total hip replacements accounted for 54.1% of the surgeries. The mean preoperative and intraoperative HCTs were 40% and 30%, respectively (Table p = 0.002). Overall blood loss (p < 0.001) and HBL (p < 0.001) of patients with hemophilia were significantly higher than those of patients without hemophilia. However, a significant between-group difference in the apparent intraoperative blood loss was not observed (p = 0.731). In this study, the rate of red blood cell transfusion in patients without hemophilia was higher than that in patients with hemophilia , but there was no significant difference (p = 0.094) (Table The mean preoperative HCT of the patients with hemophilia 0.42%) was significantly higher than that of the patients without hemophilia .Table There was no significant difference in the amounts of apparent bleeding between patients with and without hemophilia, but the HBL and overall blood loss volumes were significantly higher in patients with hemophilia than those in patients without hemophilia.Joint replacement in patients with hemophilia includes obstacles, particularly intraoperative and postoperative bleeding caused by abnormal coagulation function. Previous studies have shown that effective clotting factor replacement can reduce the risk of perioperative bleeding for patients undergoing total knee arthroplasty. Macgillivray et al.  showed tHowever, the factors affecting blood loss remain controversial . In patiWith careful perioperative preparation and the help of FVIII and other blood products, hemophilia is no longer an absolute contraindication to surgery. Symptomatic and supportive treatment, anti-infective treatment, and joint replacement in patients with hemophilia are feasible and worthwhile. This paper provides a certain reference for clinical practice, but because the number of included cases is small, more clinical data are still needed to verify the results.This study has some limitations. First, the patients in this study were all treated at a single center, and the represented population was limited. Second, this was a retrospective study that involved a small number of cases. Thus, a prospective, controlled study involving a larger number of patients is necessary to confirm the present results. Finally, this study did not collect information regarding patient medication use and medical history. A prospective follow-up study will need to collect additional relevant information and involve a multivariate analysis.The amount of bleeding in patients with hemophilia did not increase significantly during joint replacement surgery, compared with that in patients without hemophilia. This study shows that joint replacement surgery is generally safe for patients with hemophilia, provided appropriate monitoring and follow-up are included in their care.