As eGFR decreased, the lean tissue index and HGS significantly decreased. However, the eGFR did not affect the fat tissue index. The risk of sarcopenia increased approximately 1.6 times in patients with eGFR < 45 mL/min/1.73 m2. However, proteinuria was not associated with sarcopenia. With a decrease in eGFR, the lean muscle mass and muscle strength decreased, and the prevalence of sarcopenia increased. In patients with late stage 3 CKD, further assessment of body composition and screening for sarcopenia may be needed.Most epidemiologic studies assessing the relationship between chronic kidney disease (CKD) and sarcopenia have been performed in dialysis patients. This study aimed to evaluate the relationship between estimated glomerular filtration rate (eGFR), proteinuria, and sarcopenia in patients with non-dialysis-dependent CKD. A total of 892 outpatients who did not show any rapid changes in renal function were enrolled in this observational cohort study. We measured the muscle mass using bioimpedance analysis and handgrip strength (HGS), and sarcopenia was defined as low HGS and low muscle mass. Sarcopenia was found in 28.1% of the patients and its prevalence decreased as the body mass index (BMI) increased; however, in patients with BMI ≥ 23 kg/m However, regular assessment of body composition and screening for sarcopenia is not yet routinely recommended in CKD patients4. Moreover, recent reports have demonstrated limitations of body mass index (BMI) in patients with CKD, and obesity is not protective in this population.Malnutrition, sedentary lifestyle, and inflammation are common in patients with chronic kidney disease (CKD). These are further associated with metabolically unfavorable changes in body composition leading to increased body fat, and loss of muscle mass and strength. Epidemiologic studies demonstrated that muscle wasting, obesity, and volume excess are prevalent in patients with CKD, and these changes in body composition may be associated with long-term outcomes in this population11; however, few studies have attempted to evaluate sarcopenia and its risk factors in patients with NDD-CKD13. A low-protein diet in patients with NDD-CKD may increase the risk of muscle loss, and sarcopenia could be associated with physical performance, renal progression, and mortality; nevertheless, risk stratification for sarcopenia in NDD-CKD has been poorly evaluated.Sarcopenia is characterized by the progressive loss of muscle mass and strength leading to frailty, a decreased quality of life, and death. Sarcopenia is highly prevalent (14% to 44%) and associated with poor physical performance and mortality in patients undergoing dialysis15. Protein-energy wasting (PEW) is more common in the advanced stages of CKD, and the risk of PEW significantly increased with a decreasing estimated glomerular filtration rate (eGFR)16. Physical activity is associated with eGFR and renal progression18.In previous studies, we reported the risk factors of sarcopenia and their impact on long-term outcomes in hemodialysis patients and demonstrated that longitudinal muscle loss and fat gain significantly increased mortality in patients undergoing peritoneal dialysisWe postulated that CKD stages and proteinuria can be associated with the prevalence of sarcopenia and evaluating risk factors for sarcopenia in patients with NDD-CKD could provide further information for planning nutritional interventions in this population. In this study, we measured muscle mass with bioimpedance analysis and handgrip strength (HGS) to assess muscle strength in patients with NDD-CKD. We evaluated the relationship between eGFR, proteinuria and sarcopenia, and risk factors associated with sarcopenia in the population with NDD-CKD.This observational cohort study was conducted on adult patients visiting the outpatient clinic at the Department of Nephrology at Hallym University Sacred Heart Hospital between March 2015 and July 2020. Among patients who met the CKD criteria by visiting the outpatient department of nephrology at least twice at 3-month intervals and measuring the renal function through blood and urine tests, those with no rapid change in renal function were enrolled, and dialysis patients were excluded. Among the patients who underwent body composition monitoring (BCM) at least once, those with information on renal function measured on the same day were included in the final analysis  and Hallym University Sacred Heart Hospital Clinical Trial Review Committee waived informed consent in consideration of the retrospective nature of this study. This study was also performed in accordance with the guidelines of the 2013 Declaration of Helsinki.Cr), sodium, potassium, calcium, phosphorus, and total CO2 levels were recorded. The urine protein/creatinine ratios (uPCr) were calculated using random urine samples.The results of blood and urine laboratory analyses performed on the day of BCM were collected from electronic medical records. Hemoglobin, total cholesterol, total protein, serum albumin, uric acid, blood urea nitrogen, serum creatinine (S2) = 141 × min α × max −1.209 × 0.993Age × 1.018 [for females]; κ = 0.7  or 0.9 ; α = − 0.329  or − 0.411 ; “min” indicates the minimum of SCr/κ or 1; and “max” indicates the maximum of SCr/κ or 1. Then, it was divided into five stages as follows: eGFR ≥ 60, 45 ≤ eGFR < 60, 30 ≤ eGFR < 45, 15 ≤ eGFR < 30, and eGFR < 15.The eGFR was calculated using the CKD-Epidemiology Collaboration formula: eGFR (mL/min/1.73 m2) was calculated using the height and weight at the time of BCM measurement and was divided into five groups based on the criteria for the Asian population: BMI < 18.5, 18.5 ≤ BMI < 23.0, 23.0 ≤ BMI < 25.0, 25.0 ≤ BMI < 30.0, and BMI ≥ 30.0.Diabetes was defined as fasting blood glucose level ≥ 126 mg/dL, glycated hemoglobin ≥ 6.5%, or the use of an antidiabetic agent. Moreover, hypertension was described as systolic or diastolic blood pressure of ≥ 140 mm Hg and ≥ 90 mm Hg, respectively, or the use of antihypertensive agents. BMI (kg/m2). Low muscle mass was defined using the LTI as two standard deviations or more below the sex-specific mean of the young person. HGS was measured using a Jamar handheld dynamometer  and muscle strength was accordingly calculated. Low muscle strength was defined as an HGS of ≤ 28 kg in males and ≤ 18 kg in females, according to the 2019 Asian Working Group for Sarcopenia consensus19. Sarcopenia was defined when both low muscle mass and low muscle strength were satisfied11.Body composition was measured using a portable, whole-body bioimpedance spectroscopic device . All subjects stood in front of the device and underwent assessment with both arms extended to the sides. This instrument presents the objective indicators of muscle mass (lean tissue mass), fat mass, and hydration status. The lean tissue index (LTI) and fat tissue index (FTI) were obtained by normalizing the lean tissue mass and fat mass to the height-squared , it was reasonable to view the results according to sex. In addition, a multiple logistic regression model was used to confirm the effect of various factors on sarcopenia. This model included traditional risk factors associated with sarcopenia. The association between biochemical markers and sarcopenia was analyzed by dividing the patients according to the eGFR 45 mL/min/1.73 m2. Final multiple logistic regression analysis was conducted in a backward stepwise manner. A P-value < 0.05 was considered statistically significant. Statistical analyses were performed using IBM SPSS Statistics software for Windows, version 20.0 .Categorical variables, described as frequencies and proportions, were compared using chi-square test or Fisher’s exact test. To examine the normality assumption of continuous variables, the Kolmogorov–Smirnov test was performed. After a test for normality, non-normally distributed variables were expressed as medians with interquartile ranges and were compared using the Mann–Whitney 2. Nearly 54.8% patients had a BMI > 25.0 kg/m2. The median eGFR measured at the time of enrollment was 44.4 mL/min/1.73 m2, and the distribution for each CKD stage is described in Supplementary Information Table A total of 892 patients were included. The median age of the subjects was 66 years, and 523 (58.6%) were males. Approximately 40% were aged > 71 years. Diabetes and hypertension were identified in 39.7% and 62.9% of the patients, respectively, and the median BMI was 25.4 kg/m2 were significantly lower, whereas serum levels of blood urea nitrogen and SCr were significantly higher in patients with sarcopenia than in those without. Additionally, patients with sarcopenia had significantly higher levels of pulse pressure and lower eGFR than those without. In contrast, proteinuria did not differ between the two groups. Among body compositional parameters, FTI and overhydration index were significantly higher in sarcopenic patients than those without sarcopenia. These results suggest that BMI could not be an indicator of sarcopenia.Patients with sarcopenia were older and had a BMI lower than those without sarcopenia Table . The preβ = 0.866, P < 0.001), but not correlated with HGS  or LTI  in females , LTI , and FTI  were positively correlated with BMI in males  and HGS  decreased significantly with age, and FTI  and age showed a U-shaped correlation ; however, LTI  and HGS  tended to decrease as renal function decreased. Furthermore, HGS showed a significant decrease toward higher stages of CKD  or HGS .LTI ; however, a BMI of 23 kg/m2 and above did not affect the prevalence of sarcopenia. Sarcopenia was significantly associated with the eGFR  and CKD stages . When divided by an eGFR of 45 mL/min/1.73 m2, the prevalence of sarcopenia in patients with eGFR less than this value increased rapidly. However, in patients with eGFR < 45 mL/min/1.73 m2, there was no significant difference in the prevalence of sarcopenia according to CKD stages.Sarcopenia was diagnosed in 28.1% of the patients, and a univariate analysis showed that age, BMI, diabetes, hypertension, blood pressure, eGFR, hemoglobin, serum levels of total cholesterol, protein, albumin, total CO2 had a prevalence of sarcopenia approximately 2.4 times (P < 0.001) higher than that of those with eGFR ≥ 45 mL/min/1.73 m2. Despite adjustments for age and BMI, which are both highly related to sarcopenia, its prevalence in the former remained 1.5 times higher than that in the latter group of patients. Statistical significance remained after further adjusting for diabetes and hypertension , and for serum albumin and hemoglobin  . Even when sex, age, BMI, pulse pressure, diabetes, hypertension, serum albumin, and hemoglobin were adjusted for, the risk of sarcopenia increased approximately 1.5 times in patients with eGFR < 45 mL/min/1.73 m2. The prevalence of sarcopenia increased even when other risk factors were considered, especially in patients with low serum albumin levels.In this study of CKD patients before dialysis, as the CKD stage progressed, the lean muscle mass and muscle strength decreased, and the prevalence of sarcopenia increased. In particular, the prevalence of sarcopenia increased approximately twice from that when the eGFR was 45 mL/min/1.73 m20. In the present study, sarcopenia was confirmed in approximately 28% of the patients, similar to the findings of a previous study21. Several studies have reported that sarcopenia is common in CKD patients, particularly at more advanced stages; however, the number of patients in these studies was extremely small23. Most recently, using data from the Korea National Health and Nutrition Examination Survey in 2014–2017, it was reported that the prevalence of low HGS was higher in CKD patients, which showed a significant correlation with higher stages of CKD, particularly from stage 3A. However, this study is limited in terms of generalizability because the number of advanced CKD patients studied was relatively small24. The present study evaluated a large number of patients at all stages of CKD; in particular, many advanced patients with pre-dialysis CKD were included, which suggests that it has a significant strength as a cohort study.Recently, the prevalence of sarcopenia in CKD patients has been reported in various ways8. In addition, similar to recent studies that reported a significant association between the progression of CKD stage and decrease in HGS24, we showed that the decrease in HGS was more pronounced than that in the LTI with advancing age and advanced stages of CKD. This suggests that the evaluation of the reduction in muscle strength is more important than the evaluation of muscle mass reduction alone, and physical status evaluation through HGS is crucial.Regarding the occurrence of sarcopenia, the finding that a decreased HGS had a greater effect than a change in the LTI is consistent with the results of studies which showed that muscle strength is more important than merely muscle mass, and it is also closely related to the clinical prognosis2, anemia and total CO2 were important related factors, whereas in patients with eGFR < 45 mL/min/1.73 m2, their effect was reduced. In patients with moderately reduced renal function with CKD stage 3B or higher, reductions in hemoglobin and total CO2 are common, and its importance is judged to be less than that of renal function itself, i.e., the effect of a decline in renal function on sarcopenia may be greater than that of anemia and total CO2 levels. Instead, the association between serum albumin levels and sarcopenia remains significant. In other words, since biochemical markers known to be related to sarcopenia have contradictory effects on sarcopenia based on the eGFR of 45 mL/min/1.73 m2; in those with eGFR < 45 mL/min/1.73 m2, sarcopenia should not only be evaluated using markers. Particularly, in patients with advanced CKD above stage 3B, serum albumin levels need to be closely monitored, and if these levels are low, it is necessary to exercise more caution and actively evaluate sarcopenia.It is noteworthy that CKD stage 3B is an important risk factor for the prevalence of sarcopenia, even after adjusting for important related factors such as age, BMI, pulse pressure, diabetes, and hypertension. In patients with eGFR ≥ 45 mL/min/1.73 m25, proteinuria did not show any correlation with body composition or sarcopenia, which may be due to relatively mild proteinuria, which is about 1/4 of our cohort in patients with proteinuria of 1 g/day or more. No remarkable results were found in patients in whom diabetes caused CKD or in patients with eGFR < 45 mL/min/1.73 m2, for whom proteinuria can be severe.However, unlike the previous meta-analysis results28. Moreover, the median BMI in this study was 25.4 kg/m2, and about half of the patients with sarcopenia had a BMI of ≥ 25 kg/m2. Considering these findings, patient surveillance should be performed using markers other than BMI. Muscle mass and muscle strength in females cannot be assessed using BMI regardless of renal function  advanced CKD above stage 3B should be monitored for sarcopenia. In patients with CKD, measurements of muscle mass, fat mass, and muscle strength using BCM could be used as nutritional markers. With the increasing number of patients with CKD, nephrologists should take note of the diagnosis, identification, and management of sarcopenia.Supplementary Information.


Losses disguised as wins (LDWs) are slot machine outcomes where players gain fewer credits than they wager. Despite being losses, slot machines celebrate LDWs with positive sounds and animations, leading gamblers to respond to them as wins. It is unknown how manipulating the sound following LDWs may influence gamblers’ behaviour. In Experiment 1, participants played two conditions on a realistic slot machine simulator: a (standard) positive sound condition , and a negative sound condition (LDWs and losses paired with negative sound). We measured participants’ behavioural responses [post-reinforcement pauses (PRPs)], win estimates, and subjective experience. In the negative sound condition, participants behaviourally responded to LDWs in a more loss-like and less win-like fashion, as measured by PRPs. Win estimates were reduced, and subjective experience was significantly impacted, but only when the negative sound condition was played second. In Experiment 2, we employed a much more subtle manipulation, pairing only LDWs with negative sound, and observed similar effects. Through these two experiments, we show that pairing LDWs with negative sound is an effective way to modify players’ responses to LDWs, causing them to respond to them more like the losses they are, rather than the wins they seem. Gambling has long been a part of the Canadian landscape. For many, gambling is a harmless, enjoyable activity. However, for a small subset of the population, it may morph into a serious problem impacting diverse aspects of living Wong . In CanaNot only are slot machines associated with higher rates of problem gambling than traditional methods of gambling  following wins, losses, and LDWs, the reinforcing stimuli that accompanied wins was associated with increased physiological arousal, as evidenced by higher SCRs . All participants were currently enrolled in psychology courses and were recruited through an online participant pool. Members of the pool are pre-screened for a number of variables. In order to be eligible, members of the pool needed to be at least 19 years old (the minimum age to play slot machine games in Ontario), have played a slot machine at least once in the past year, and not currently be in, or seeking treatment for, problem gambling. At the time of testing, 85% of participants reported playing a slot machine between 1 and 5 times in the past year, 7% reported playing between 6 and 11 times in the past year, and 1% reported playing once a month within the past year. 7% (5) participants reported that they had not played a slot machine at all in the past 12 months, even though all participants had indicated slot machine participation on the pre-screen. Since all participants indicated that they had played a slot machine at least once in the last 12 months when filling out the information letter, they were deemed eligible for participation.We recruited a sample of 73 undergraduate students  from the University of Waterloo between the ages of 19 and 34 , and a multiline slot machine simulation was used for game play. The slot machine simulator used in the experiment is highly realistic and presented on a computer monitor mounted inside an existing slot machine cabinet that has been used in previous studies , or the machine that presented the negative sound condition (where LDWs and losses are followed by a negative sound). Sound length was titrated to the amount of credits gained in each spin so that the length of each sound type  was identical across conditions, and the sole manipulation between conditions was the valence of the sound paired with the LDW and full loss outcomes. Outcomes were presented in one of two fixed sequences. These sequences were determined by two randomizations of the 200 spins. The order of the spins was counterbalanced such that they were reversed in the participant’s second condition . This ensured variability in outcome order between conditions but allowed the overall number of credits gained, and the number of LDWs presented across sound conditions to be identical. The reinforcement schedules were based on the programming documents of an actual slot machine. Accordingly, full losses were the most frequently occurring outcome, followed by LDWs, small wins, and finally, large wins , as well as how many credits and lines they would be betting on each spin. Participants were also informed that they could win up to $10 at the end of the study depending on their final credit balance; since all spin sequences were predetermined and equated, each participant left with $10 CDN. They were then given the opportunity to ask the experimenter questions for clarification. Throughout each block, force and PRP data was recorded by the PowerLab and LabChart 7.2 software. After the first 200 spins, participants were presented with a message stating that they had reached the end of the block. Participants then filled out the shortened version of the GEQ .After survey completion, participants were directed to the second slot machine to complete the second session of spins. The experimenter reiterated the basic slot machine information, and once again gave participants the opportunity to ask questions. Following this, the participant proceeded to play the next slot machine game with the opposite sound valence and a reversal of spin order sequence. As with the first session, the participants completed the shortened GEQ at the end of the session. Then, participants’ win estimates for conditions 1 and 2 were gathered. Participants were asked to estimate (out of 200 spins) on how many spins they won more than they wagered. Separate estimates were gathered for each condition. At the conclusion of the study, participants were thanked for their time, given their course credit, $10 in slot machine winnings, a feedback letter, brochures on responsible gambling, and a wallet card outlining provincial mental health helplines.PRPs were calculated in seconds and defined as the time between when the last animated reel stopped spinning , and when the participant initiated their next spin by pressing the spin button.The different outcomes were binned into four categories: losses, LDWs, small wins (credit gains between 21 and 30), and large wins (credit gains over 30). Participants’ PRP data corresponding to each of the four bins, for each condition (positive sound/negative sound), were averaged. At the end of individual participant analyses, each participant had a total of 8 averaged data points—four outcomes , for each of two conditions  to be used in statistical analyses. Prior to calculating these averages, outliers were detected and removed using a sliding criteria for rejection based on the number of observations in the specific condition  for each condition  resulting in six data points per participant. Win estimates were calculated as the participant’s win estimates for each condition .Due to the nature of the sound manipulation, we expected order effects. For example, we expected that those playing the negative sound condition first would recognize that LDWs are really losses, and this realization would influence their behavioural responses and win estimates. Such influences could manifest in that condition, but if learning takes place, could also impact estimates in the second condition. Thus, our analytical strategy was to include order as a factor in our overall analyses, and upon seeing a main effect of order or interaction involving order, we first conducted between-subjects analyses in which those who played the negative sound game first would be directly compared to those who played the positive sound game first—the only comparison uncontaminated by order effects. We then compared those who played the negative game second to those who played the positive game second. If those effects did not differ (account for the order effect or interaction), we then looked at the within subjects’ comparisons . In the event of violations of sphericity, a Greenhouse–Geisser correction was applied to the degrees of freedom.Using PGSI categories established by Currie et al., , 70 partF = 5.363, p < .05, ηp2 = .07, order F = 4.22, p < .05, ηp2 = .056, and outcome F = 81.54, p < .001, ηp2 = .535, along with a 3-way interaction between game, order, and outcome, F = 25.43, p < .001, ηp2 = .264.To analyse PRP responses, a 2  × 2  × 4  mixed model ANOVA was conducted with order as the between-subjects variable. This analysis revealed significant main effects of game F = 6.52, p = .013, ηp2 = .084, outcome, F = 105.34, p < .001, ηp2 = .597, and a significant outcome by game interaction, F = 5.89, p = .001, ηp2 = .077. Independent t-tests were subsequently conducted to uncover the source of the interaction, and it was found that the PRPs for losses, t(55.58) = − 5.21, p < .001, small wins, t(71) = − 2.37, p < .05, and large wins, t(71) = − 2.52, p < .05, but not LDWs, varied as a function of game  = 3.62, p = .001. We then conducted a similar analysis, but this time comparing the differences between LDWs and small wins for those playing the positive and negative sound game. Consistent with the idea that positive sounds make LDWs more similar to wins, the differences between LDWs and small wins in the positive sound game were significantly smaller than in negative sound game, t(66.19) = − 3.69, p < .001.We then assessed the hypotheses that positive sounds following LDWs should make them win-like, and negative sounds following LDWs should lead participants to react to them more like the losses that they are. If positive sounds make the LDWs more win-like, then there should be relatively large differences between full losses and LDWs, whereas if negative sounds make LDWs more loss-like, there should be smaller differences between full losses and LDWs. An independent F = 47.71, p < .001, ηp2 = .402. All other effects were non-significant .A comparison of the participants who played the positive game second to those who played the negative game second indicated only a significant main effect of outcome, t-tests for game 1 and game 2, comparing those that played the positive sounds with those that played the negative sounds for each game. In the case of violations of equality of variances, the degrees of freedom were corrected and reported below.Three components of the GEQ were assessed: flow, positive affect, and negative affect. In addition, we also assessed participants’ win estimates. First, to assess the impact of order on participants’ ratings, we conducted a 2 (game) × 2 (order) mixed model ANOVA with order as the between-subjects variable for each subscale, and for the win estimates. Then, if a main effect or interaction involving order was found, we followed up with independent samples Table F = 8.27, p < .01 ηp2 = .104. The independent samples t-tests contrasting those playing the positive sound game versus the negative sound in game 1 revealed no significant differences between groups, t(65.13) = -.19, p > .05. The game 2 comparison also did not reveal any significant differences between groups, t(71) = .34, p > .05. The interaction of game by order was caused by those playing the positive sound game first (order 1 in Table t(35) = 2.46, p = .019. For those playing the negative game first, (order 2 in Table t(36) = − 1,71, p = .10.The mixed model ANOVA revealed no significant main effects, but a significant game by order interaction, F = 8.34, p < .01 ηp2 = .105. The independent samples t-test for game 1 revealed no significant difference between groups, t(71) = − 1.08, p > .05. However, when a similar analysis was conducted for game 2, it was revealed that PA significantly decreased in the negative sound condition, t(60.98) = 4.45, p < .001. For the dependent comparisons, there were significant differences between the positive game and the negative game for order 1 in Table t(35) = 4.29, p < .001, but not for order 2 (those playing the negative game first), t(36) = .309, p = .759.The mixed model ANOVA revealed a main effect of game, order, and a significant game by order interaction, F = 13.32, p < .001 ηp2 = .158. The independent samples t-test for game 1 revealed no significant difference between groups, t(71) = .81, p > .05. However, when a similar analysis was conducted for game 2, it was revealed that negative affect significantly increased in the negative sound condition, t(71) = − 2.63, p = .01. For the dependent comparisons, there were significant differences between the positive game and the negative game for order 1 in Table t(35) = − 3.93, p < .001, but not for order 2 (those playing the negative game first), t(36) = 1.14, p = .262.The mixed model ANOVA revealed main effects of game, order, and a significant game by order interaction, F = 5.86, p < .05 ηp2 = .076. No significant differences occurred for the between subjects’ comparisons (positive vs. negative game 1 or positive vs. negative game 2). The within subjects’ comparisons revealed that for those who played the positive game first, their win estimates were significantly higher for the positive game than the negative game which they played second, t(35) = 5.16, p < .001. For those playing the negative game first, their win estimates for this negative game were no different from their win estimates for the positive game which they played second, t(36) = .287, p > . 05  positive sound condition. These between-subject differences can be seen by comparing the smaller magnitude of the white bars (those playing the game with positive sounds first), compared to the black bars (those playing the negative sounds first) for three of the four outcomes. The only outcome where the PRPs were equated was the LDWs. We interpret this effect as attributable to the positive sounds following LDWs elevating PRPs (players react to them as though they are wins). This elevation was not present in the group playing the negative sound game first.The results of Experiment 1 indicate that pairing both losses and LDWs with negative sounds had a significant effect on players’ behaviour and subjective experience. In terms of PRPs, when participants who played the game with positive sounds first were compared to those who played the negative game first (the only comparison uncontaminated by order) the group playing the game with positive sounds had smaller PRPs than the group playing the game with negative sounds for every outcome Further evidence for the positive sounds elevating PRPs comes from the difference score analyses. In the positive sound game where positive sounds followed LDWs, the PRPs became less loss-like (larger differences between regular losses and LDWs) than in the negative sound game where the negative sounds made LDWs more loss-like . The difference score analyses also demonstrate that positive sounds following LDWs make players respond to them in a more win-like manner: there were smaller differences between LDWs and small wins for those playing the positive sound game, compared to the negative sound game. The negative sounds made LDWs seem less win-like, leading to larger differences between PRPs for LDWs and true wins.We note that these difference score analyses are not subject to the complications involved in having between group differences due to sampling error. Regardless of the particular group’s overall propensity to have large or small PRPs, the positive sounds following LDWs should make them less loss-like, and more win-like. Conversely, the negative sounds following LDWs should make PRPs more loss-like and less win-like. In assessing the more subtle effects that we expect in Experiment 2, where in the negative sound condition only the LDWs will be paired with negative sounds, we will use these difference scores as a priori contrasts.For flow, positive affect, and negative affect, there were order effects—the effects on these variables depended on which game was played first. Nevertheless, there were clear effects of the negative sounds. In terms of flow, those playing the positive sound game first endorsed deeper flow for this game than the negative game which they played after. For those playing the negative game first, there was no difference in flow ratings between the positive and negative games. A similar pattern of results emerged for both positive and negative affect. The ratings of players were only significantly different for those who played the game with positive sounds first followed by the game with negative sounds. We suggest that these relatively low frequency gamblers are taken in by the disguise of LDWs in the positive sound game when they play it first. When immediately afterward, they are exposed to a game where they can use a single dimension rule to tell whether they won or lost, these negative sounds expose LDWs for the losses that they are. This idea is supported by players’ win estimates, which are significantly lowered  in the game with negative sounds. Taken together, the data converge to indicate that pairing negative sounds with both full losses and LDWs served to unmask the disguise in LDWs. It is important to note that it remains unclear why there were no effects for those who played the negative game first. Prior to attempting to interpret this finding, we sought to assess the reliability of this finding by replicating it in Experiment 2.In terms of PRPs, having the negative sounds accompany LDWs made players react to them in a more loss-like fashion. The negative sound game was also associated with lowering flow compared to the positive sound game, for those who played the positive game first. In terms of harm reduction, our negative sound manipulation may provide players a more veridical representation of how often they won during play. The reduction in flow during the negative sound game could also mean that players would be less likely to spend more time and money than intended. Both of these aspects may make the inclusion of negative sounds following losses and LDWs a strategy to help counteract the harms associated with LDWs.Unfortunately, regulators, operators, and game designers may be unlikely to adopt these measures as the negative sounds also made game play a less enjoyable experience. In this experiment, pairing both full losses and LDWs with negative sounds afforded players the ability to use a single dimension rule to tell whether they won or lost . Since full losses are by far the most frequent outcome, this meant that players heard negative sounds on 86.5% of trials. Such a high percentage of “negative” spins might have led to the lowered positive affect and heightened negative affect scores observed, and would pose a relatively unpalatable harm reduction strategy for those who seek to promote the enjoyment of slots play for recreational gamblers. Therefore, in Experiment 2, we sought to further investigate the impact that negative sounds have on participant responses to LDWs, and to assess whether we could still show that negative sounds could alter the manner in which players code LDWs (reacting to them as being more loss-like in terms of PRPs and lowering win overestimations), but with a more subtle manipulation of only including negative sounds following LDWs.M = 20.8, SD = 1.48). Participants were screened to ensure they met inclusion criteria (described in Experiment 1). Of the 70 participants recruited, 75.7% reported playing a slot machine between 1 and 5 times in the past year, 15.7% reported playing between 6 and 11 times in the past year, and 2.8% reported playing either once a month or 2–3 times a month within the past year. 5.7% (4) of participants reported having not bet or spent money on slot machines in the past 12 months. Since all participants indicated slot machine experience at the level of the pre-screen and all participants actively endorsed this eligibility criteria during the informed consent process at the time of testing, all participants were retained for data analysis.We recruited a sample of 70 undergraduate students  from the University of Waterloo between the ages of 19 and 25 .All procedures were identical to Experiment 1, with the sole exception being that Using established cut-off criteria  = 72.817, p < .001 ηp2 = .544, as well as a game by outcome by order interaction, F = 21.12, p < .001 ηp2 = .257. As in Experiment 1, the file was split to directly compare those who played the positive game first versus those who played the negative game first, and those who played the positive game second versus those who played the negative game second , order  and outcome , as factors, to examine higher order interactions. This analysis revealed a main effect of outcome, F1.0, 63.66 =F = 103.83 p < .001, ηp2 = .611. There was no outcome by sound interaction (p = .216). Due to their theoretical significance, we conducted the same difference score analysis as in Experiment 1 to assess whether the positive sounds following LDWs made them more win-like compared to negative sounds, which should make LDWs more loss-like. There were no differences between the positive and negative sound games when comparing the difference scores between PRPs for losses and LDWs, t(66) = .089, p = .930. There were significant differences between the positive and negative sound games when comparing the PRP differences between small wins and LDWs, t(66) = 2.50, p = .015. As in Experiment 1, the positive sounds following LDWs made players react to them in a more win-like fashion, whereas the negative sounds created more of a distinction between these outcomes for players.For the first game comparison, there was a main effect of outcome, F = 38.51, p < .001, ηp2 = .368, but no outcome by sound interaction (p = .218). There were however differences between the positive and negative sound games in terms of the difference scores between the PRPs for losses and LDWs, t(66) = 5.44, p < .001. As in Experiment 1, the positive sounds following LDWs made them less loss-like (greater differences between full losses and LDWs), whereas the negative sounds made them more loss-like . There were significant differences between the positive and negative sound games when comparing the differences between LDWs and small wins, t(66) = 3.30, p = .002. Here, the positive sounds following LDWs made players react to them in equivalent manner as wins, whereas the negative sounds led to larger differences between LDWs and small wins (rendering them less win-like).For the second game comparison, there was a main effect outcome, For all three subjective variables, there were interactions with order. The means for flow, positive and negative affect are presented in Table F = 5.29, p = .025, ηp2 = .072, and a significant game by order interaction, F = 4.29, p = .042, ηp2 = .059. This interaction was caused by a significant reduction in flow among those who played the positive game first followed by the negative game second, t(34) = 2.86, p = .007. For the group that played the negative game first there was virtually no change in flow across games, t(34) = .178, p = .86.For flow, there was a main effect of game, F = 11.85, p = .001, ηp2 = .148, and a game by order interaction, F = 13.77, p < .001, ηp2 = .168. Those playing the positive game first experienced a significant drop in positive affect when they then played the negative game, t(34) = 5.51, p < .001. For those playing the negative game first, there was no change in positive affect when they then played the positive game, t(34) = .176, p = .86.For positive affect, there was a significant main effect of game, F = 4.95, p = .029, ηp2 = .068. For those who played the positive game first, there was a significant increase in negative affect while playing the negative sound game second, t(34) = 3.03, p = .005. For those who played the negative sound game first there was no change in negative affect from this game to the positive game they played second, t(34) = .200, p = .843.For negative affect, there was a game by order interaction F = 4.41, p = .039, ηp2 = .061, with win estimates being 7 wins lower in the negative sound condition. There was no effect of order or game by order interaction . For comparison with Experiment 1, we compared the contrasts between those playing the positive game first versus negative game first. This comparison revealed no differences in win estimations. We also conducted the within-subject comparisons to see if we would replicate the findings of Experiment 1: win estimation effects only for those who played the positive game first. As in Experiment 1, there were significant differences between the positive game and negative game win estimates for those who played the positive game first, t(34) = 2.42, p = .021, but again no difference between win estimates for those playing the negative game first . PRP difference scores between small wins and LDWs were significantly larger in the negative sound game than in the positive sound game .Subjective results and win estimates in Experiment 2 replicated those of Experiment 1. In terms of participants’ subjective experience of game play, for participants who played the positive sounds game first, we found decreased ratings of flow and positive affect, and increased ratings of negative affect, in the negative sound condition when this condition was experienced second. For those that played the negative sounds game first, we found that ratings of flow, positive affect, and negative affect did not change significantly between conditions. A similar pattern was observed for win estimates. Those that played the positive sound game first had significantly decreased win estimates in the negative sound condition. Those that played the negative sound game first had win estimates that did not differ between conditions.The overarching goals of Experiment 1 and 2 were to investigate whether adding a negative sound following LDW outcomes would influence how participants behaviourally respond to these outcomes (as measured by PRPs) and to replicate past findings by Dixon et al.  that negcelebrating an outcome where they are losing money. While past studies have demonstrated the similarity in PRPs following LDWs and small wins, no studies have investigated whether this measure of behavioural reactivity changes when LDWs are paired with a negative sound. In Experiment 1, where LDWs and losses were paired with a negative sound, we found that while those that played the positive sound game first had shorter PRPs overall, by pairing LDWs with positive sound, these participants elevated their PRPs for LDWs to that of the participants that had the negative sound first, who had longer PRPs overall. This demonstrates that sound influences the way in which participants behaviourally respond to LDW outcomes.The clearest evidence for negative sound changing the way LDWs are encoded by participants comes from changes in behavioural responses to these outcomes, as measured by PRPs. Previous research has demonstrated that as win size increases, PRP length increases concomitantly  because participants were using the animations instead of sound valence as a cue to determine whether they won or lost.Additionally, while this study provides evidence that negative sound impacts players’ positive and negative affect in comparison to a positive sound game, it remains unknown whether this change in affect differs from baseline. Future studies should prioritize incorporating baseline affect measurements to assess how various features of slot machine games, such as positive or negative sounds, may influence slot machine gamblers. Additionally, future studies should address whether a negative sound manipulation might impact how long gamblers will voluntarily play a machine  with the negative sound. These studies demonstrate that negative sound is a viable way of unmasking the disguise of LDWs, resulting in participants responding to them as the losses they are rather than the wins they seem.


The temporal dynamics by which linguistic information becomes available is one of the key properties to understand how language is organized in the brain. An unresolved debate between different brain language models is whether words, the building blocks of language, are activated in a sequential or parallel manner. In this study, we approached this issue from a novel perspective by directly comparing the time course of word component activation in speech production versus perception. In an overt object naming task and a passive listening task, we analyzed with mixed linear models at the single-trial level the event-related brain potentials elicited by the same lexico-semantic and phonological word knowledge in the two language modalities. Results revealed that both word components manifested simultaneously as early as 75 ms after stimulus onset in production and perception; differences between the language modalities only became apparent after 300 ms of processing. The data provide evidence for ultra-rapid parallel dynamics of language processing and are interpreted within a neural assembly framework where words recruit the same integrated cell assemblies across production and perception. These word assemblies ignite early on in parallel and only later on reverberate in a behavior-specific manner. Language behavior concerns a complex, multi-component process where different linguistic representations  need to be retrieved and merged together in order to produce and perceive communicative signals. Insights into “when” and “how” different linguistic knowledge becomes activated is therefore key to understanding the cortical mechanics that can sustain this unique human behavior . DespiteIn sequential models of word production  and percThe above temporal evidence paints a mirror image between language behaviors where cortical activation of different word components seems to progress in functional delays of some 100 ms and with reversed order between speech production and perception. However, this temporal segregation between the retrieval of meaning and sounds for words has received criticisms both in language perception  and prodAccording to the above “Integration Models” (IMs), the neurophysiological data demonstrating sequential dynamics do not concern the first-pass (ignition) activation of language representations. Rather it is assumed to reflect second-pass (reverberatory) activation associated with linguistic operations upon the parallel retrieved words such as verbal working memory, semantic integration, grammatical inflection, and articulatory control . In otheHowever, proponents of sequential brain language models remain skeptical whether such early parallel activation of lexical and phonological word representations is plausible. One frequently uttered reservation concerns the possibility that these early effects highlight sensorial and/or attentional differences between stimuli-sets rather than linguistic brain activity . Indeed,To address these criticisms, in the current study we explored the time course of a lexico-semantic and a phonological variable both in speech production and perception. That is, we directly contrast the same brains, stimuli and, importantly, psycholinguistic manipulations across the two language modalities. Including “language modality”  as variable to assess the cortical activation time course of different linguistic components has a marked advantage compared to prior spatiotemporal research in word production and perception done in isolation: It allows us to assess time course from a different perspective, which removes both the problem of physical variance and the conceptual problem of whether “fast” equals “parallel.” This is because the relevant dimension for testing the hypotheses now becomes the “relative” time course of the sequence of events between the language modalities, rather than the exact, absolute value of when a linguistic component becomes active within each behavior. This feature of our design is crucial in order to reliably compare two different language modalities, which rely on two different inputs : 1) by contrasting “effects” between modalities , rather than modalities per se, we reduce physical variance between the modalities , code, analyses pipelines and materials are available in the project OSF repository at Comité Protection des Personnes”—“Committee for the Protection of Individuals”: CPP 2017-A03614–49.26 participants took part in the experiment . All were recruited through Aix-Marseille University and paid €20 for participation. All participants were right handed and reported no hearing, speech or neurological disorders, and had normal or corrected-to-normal vision. Two participants were rejected due to equipment failure, and five participants were rejected due to exclusion of a large number of error trials (defined more below), leaving a sample of 19 participants for analysis. The experiment was granted ethical approval by Aix-Marseille Université “P’s < 0.001 on the crucial word and phonotactic variables: for word frequency (LPF), t(54) = −13.85; for word frequency (HPF), t(54) = −15.5; for phonotactic frequency (LWF), biphone: t(54) = −15.23, phoneme: t(54) = −10.24; and for phonotactic frequency (HWF), biphone: t(54) = −14.6, phoneme: t(54) = −9.98. .There were two within-participant manipulations of interest, tapping into the lexico-semantic and phonological/phonetic levels of linguistic processing see . The lexLaboratoire Parole et Langage of Aix-Marseille University using an RME fireface UC audio interface and a headset Cardioid Condenser Microphone (AKG C520) at a sampling rate of 44 100 Hz. The tokens were segmented and then normalized in intensity to a level of 60 dB using Praat software  see . Coloredsoftware . For theThe stimuli were presented in fully randomized lists per participant and per task. Task order was counterbalanced across participants. The experiment was presented using E-Prime (version 2.0) on a standard computer monitor. Naming responses were collected with a microphone placed in front of the participants for the picture naming task and participants heard the auditory stimuli through non-conductive headphones, and their button press responses on go trials were recorded by a response buttonbox.Participants first were informed about the study and gave informed consent. Electrodes were fitted to participants (see below) before participants began the experiment proper. Participants were familiarized with the stimuli before the experiment began. For production this entailed seeing all the pictures with their correct name written below, and to be matched in exposure, for perception, this entailed hearing all the stimuli names. Participants began with either the production or the perception task . During each task participants were able to take a short break (around 10s) every 60 trials, and a longer break (a few minutes) between tasks. After the experiment participants were fully debriefed.Experiment instructions were written presented in white on a black background and centered in the middle of the screen. Production task trials began with a fixation cross (500 ms), followed by a blank screen (500 ms), picture presentation (630 ms), and ended with an inter-trial interval blank screen (2000 ms). Perception task trials followed the same structure and began with a fixation cross (500 ms), followed by a blank screen (500 ms) before auditory stimulus presentation , and ended with a blank screen (2000 ms) see . The whoEEG was recorded with 64 Biosemi active electrodes with standard 10/20 positioning. Four additional Ag/Ag-Cl electrodes were placed for eye movements , and two electrodes were placed on the left and right mastoids for referencing. Electrode impedances were kept below 5 microVolts. The EEG signal was continuously recorded at 512 Hz with an online bandpass filter of 0.03 Hz—40 Hz using BrainVision software.Speech latencies were automatically recorded in E-Prime using a voicekey. Each trial was manually checked (online) to determine if an error was made; errors included production of an incorrect target name, hesitations, and other disfluencies (such as sneezes and coughs). All errors were rejected (14%). In the perception task, correct go-trials (filler items) corresponded to more than 90% performance in all participants.Behavioral data preprocessing), along with filler trials. Data from each channel were then visually inspected, and channels, which were bad were removed and interpolated (using the “weighted” method). On average, 1.96 channels  were interpolated for production, and 1.88  for perception. Trials with signal above 150 mV were also discarded. We then ran an ICA decomposition (with 40 components) to remove components relating to eye movements using the “fastica” method, per participant. Single channels for the horizontal and vertical eye movement channels were created, and each ICA component was correlated with these two channels. All components were visually inspected by plotting their topographies and time courses, and in conjunction with the correlation results artefactual ICA components were rejected before unmixing. Trials were then visually inspected to remove any trials containing excessive noise, which had not been captured. If more than 60% trials per condition for each participant were removed during the preprocessing procedure then that participant was rejected from further analysis (five participants were rejected). From the remaining participants, 74% data was retained .EEG data, for the production and perception task separately, were preprocessed using FieldTrip  of analysis in an objective manner , the global field power (GFP) of the baselined EEG signal was calculated (baseline of −100 to 0 ms). GFP is a measurement of the strength of the signal measured across all electrodes, conditions, and participants. GFP was calculated per participant per task using FieldTrip in Matlab. GFP was then averaged across participant and task to test for peaks in the GFP signal. GFP peaks were tested for using iPeak  in MatlaFull-width windows around these peaks were then manually identified. To do this, using the iPeak output per peak, we manually stepped backwards and forwards until reaching the first time point in the backwards or forwards trough. Carrying out the procedure in this way enables asymmetrical widths on either side of the peak, rather than taking a general average of the whole peak window where the peak value may not be centered. This procedure identified the three TWs of interest as TW1: 74—145 ms, TW2: 186—287 ms, and TW3: 316—369 ms see . The cleThe data from each TW were tested in R using linear regression and linear mixed effects models (lme4). Channels were recoded by their lateral placement into left , mid , and right , and recoded by their frontal coronal plane placement into anterior , central , and posterior .For the mixed effects models (and linear regression models), all categorical predictors were sum to zero coded, and all continuous predictors were centered and scaled. One model per TW was first run with the main effect predictors word frequency (HWF vs. LWF), phonotactic frequency (high vs. low), language modality (production vs. perception), channel laterality (left vs. mid vs. right), and channel anteriority t greater than |2| was taken as a marker of significance and confidence intervals were generated using the confint function with the profile method in the lme4 package. P values were generated using the ANOVA function with type III tests selected in the Car package  > 100, P < 0.001), with lower microvolt recordings in the production task than the perception task. There were no main effects of word frequency nor phonotactic frequency, but phonotactic frequency significantly interacted with modality  > 100, P < 0.001). Phonotactic frequency interacted with the channel variable laterality  = 12.915, P = 0.002). The three-way interaction between modality, word frequency, and both channel variables was significant  = 11.724, P = 0.003; 2X = 10.746, P = 0.005), suggesting that the word frequency effect differed in scalp location between modalities. The interaction between modality, phonotactic frequency, and laterality was also significant  = 9.812, P = 0.007), suggesting that the phonotactic frequency effect was distributed differently across the scalp in each modality.In TW 1, there was a main effect of modality .In sum, we observed that modality interacted with the word frequency and phonotactic variables; in order to understand the nature of these interactions, separate analyses per modality were performed  > 100, P < 0.001), and no main effects of word or phonotactic frequency. The word frequency by modality interaction was significant  = 18.549, P < 0.001), and the phonotactic frequency by modality interaction was also significant  > 100, P < 0.001). The word frequency by anteriority interaction was significant  = 8.014, P = 0.020), and the phonotactic frequency by anteriority interaction was significant  = 24.756, P < 0.001). The three-way interaction between word frequency, modality, and anteriority was significant  = 30.465, P < 0.001), and the three-way interactions between phonotactic frequency, modality, and both channel variables were also significant  = 28.579, P < 0.001; 2X = 32.350, P < 0.001). As for TW1, these interactions were further assessed with separate analyses per modality .For TW 2, very similar results were found to TW 1. Again, there was a main effect of modality  > 100, P < 0.001) and no main effects of word and phonotactic frequency. There was a word frequency by modality interaction  = 13.442, P < 0.001), and word frequency by anteriority interaction  = 21.817, P < 0.001). The three-way interaction between word frequency, modality, and anteriority was significant  = 30.021, P < 0.001). Phonotactic frequency interacted with modality  = 16;712, P < 0.001) and was involved in a three-way interaction with modality and anteriority  = 76.734, P = 0.022). Separate analyses per modality were performed in order to assess the nature of these interactions .In TW3, the same main effects patterns were found: main effect of modality  = 11.90, P = 0.003) , diff = 0.16 mV), which was absent for high-frequency items , diff = 0.10 mV). There was also a significant interaction between phonotactic frequency and laterality  = 6.974, P = 0.031) , Mlow = −1.98 (SE = 0.04)), and a significant increase from right to left electrodes for high-frequency items , diff = 0.13 mV), which was absent for low-frequency items , diff = 0.08 mV).Main effects of word frequency and phonotactic frequency were not significant. Word frequency, however, interacted with laterality (003) see  and 4. P031) see  and 4. P2X = 11.419, P = 0.003) ; low frequency, P = 0.045, M = 1.03 (SE = 0.03), diff = 0.21 mV) and central sites ; low frequency, P = 0.001, M = 1.1 (SE = 0.03), diff = 0.08 mV) compared to posterior sites (Mhigh = 1 (SE = 0.03), Mlow = 1.03 (SE = 0.03), diff = 0.03 mV). For phonotactic frequency we found a significant interaction with laterality  = 16.487, P < 0.001), with a trend towards significance between low- and high-frequency items at mid electrodes , Mhigh = 0.93 (SE = 0.04)), a significant voltage increase from left towards mid sites for low-frequency items , diff = 0.33 mV), which was absent for high-frequency items , diff = 0.06 mV), and a significant increase from mid to right sites for high-frequency items  to 1.19 (SE = 0.03), diff = 0.26 mV), which was absent for low-frequency items , diff = 0.03 mV)  see  and 4. P mV) see  and 4.2X = 7.752, P = 0.021)  to 0.93 (SE = 0.03), diff = 1.6 mV) compared to high-frequency items  to 0.76 (SE = 0.03), diff = 1.43 mV). Phonotactic frequency interacted with anteriority  = 40.678, P < 0.001), with a marginally significant difference between high- and low-frequency items at anterior sites , Mlow = −0.52 (SE = 0.04), diff = 0.3 mV), and a stronger increase in positivity from central to anterior sites for low-frequency items  to −0.52 (SE = 0.04), diff = 0.29 mV) compared to high-frequency items  to −0.82 (SE = 0.04), diff = 0.07 mV)  = 6.293, P < 0.043), with stronger positivity in left compared to right sites for low-frequency items , diff = 0.19 mV), which was absent for high-frequency items , diff = 0.05 mV).There were no main effects of word nor phonotactic frequency. Word frequency did interact with anteriority (021) see  and 4. P mV) see  and 4. P2X = 33.636, P < 0.001), where post hoc inspection suggests a stronger increase in negativity from anterior to posterior electrodes for low-frequency items  to 0.06 (SE = 0.03), diff = 3.72 mV) compared to high-frequency items  to 0.11 (SE = 0.03), diff = 3.44 mV)  = 15.256, P < 0.001), with low-frequency items being more negative than high-frequency items at central electrodes , Mhigh = 2.98 (SE = 0.03)), and a stronger increase in positivity from posterior to central sites for high frequency , diff = 2.82 mV) than low-frequency items , diff = 2.61 mV). The phonotactic frequency by laterality interaction was also significant  = 33.699, P < 0.001), with trends for low-frequency items being more negative than high-frequency items at left , Mhigh = 1.99 (SE = 0.03)) and mid sites , Mhigh = 2.9 (SE = 0.04)), and a stronger increase in negativity from right to left electrodes for low-frequency items , diff = 0.33 mV) compared to high-frequency items , diff = 0.08 mV)  see  and 4. A mV) see  and 4.2X = 51.868, P < 0.001) , Mhigh = 2.67 (SE = 0.04)), and a stronger increase from frontal towards posterior electrodes for low frequency  to 3.24 (SE = 0.04), diff = 3.87 mV) than high-frequency items  to 2.67 (SE = 0.04), diff = 2 mV). Phonotactic frequency interacted with anteriority  = 8.049, P = 0.018) to 3.17 (SE = 0.04), diff = 2.53 mV) compared to high-frequency items  to 2.73 (SE = 0.04), diff = 3.4 mV).No main effects of either frequency variable were found. Word frequency interacted with anteriority (001) see  and 4. P.018)see  and 4. PNo main effects of word frequency or phonotactic frequency were found, nor any interactions with the channel variables see  and 4.https://osf.io/hp2me/—but only descriptively report the main findings. In general, the results were quite similar to the mixed models analysis in that they confirmed the presence of word and phonotactic frequency effects for both modalities in all TWs . The biggest, and quite noteworthy, difference with the mixed models analyses is that the linear regression analyses displayed main effects of both frequency variables in several of the TWs analyzed (see the osf repository). In other words, the word and phonotactic frequency effects were more marked and broader in the traditional regression analyses compared to the mixed effect models analyses.We also carried out a linear regression analysis on the three TWs, akin to a more typical ANOVA style analysis of the data, given that this still concerns the most performed analyses on neurophysiological data and is therefore interesting as a comparison. For brevity and clarity, we will not discuss the statistical details of these analyses—they can be consulted in full in the online repository at A deeper investigation into these discrepancies suggested that there is a substantial amount of variance attributable to the items used. This variance is accounted for as random variation in the mixed effects models, i.e., the variance is treated as “noise” and not as part of the variables of interest. In linear regression, this variance is instead  grouped with variation in the variables of interest, leading to a higher chance of a significant effect. In this manner, we believe that the mixed effect models analysis is a better approach to single out the “pure” frequency effects, since item variance between conditions is better controlled for compared to traditional ANOVAs and linear regression analyses. This difference between the types of analyses might have important consequences for all neurophysiological research, which relies on conditions containing physically different stimuli. For present purposes, we will not go any further into this interesting methodological issue but see , and insWe investigated the temporal dynamics of word activation by comparing the event-related brain potential elicited by lexico-semantic and phonological knowledge in production versus perception, and analyzing the data at the single-trial level with mixed linear effects models. The results showed significant word frequency and phonotactic frequency effects  in early TWs (74–145 ms and 186–287 ms) for both word production and perception see . At a laThe main objective of this study was to shed novel light on a central and longstanding question in the psycho- and neurolinguistic literature: Are words processed in a hierarchical sequential manner or in a parallel integrated manner? The data were clear-cut and demonstrated that lexico-semantic and phonological-phonetic word knowledge are rapidly activated in parallel, both when uttering and comprehending wordsWith regard to physical variance between stimuli , in the present study, the input is different between production  and perception (auditory presented words), but nonetheless the same temporal effects between the modalities were present for the word frequency and phonotactic frequency effects. Therefore, explaining the data as due to sensorial differences between the low- and high-frequency conditions is highly unlikely (if not impossible) since it would mean that by chance the same sensorial differences are present for both the visual and auditory input, for both manipulations and in both early TWs. Instead, parsimony clearly favors a linguistic explanation since across the two modalities, identical word processing components are targeted. Adding further support to this conclusion is the observation that we replicated the typical ERP morphology found for word frequency and phonotactic frequency in prior production and perception studies in isolation. In production, ERPs elicited by low-frequency items (both word and phonotactic) display more positive going waveforms than high-frequency items , while iprior to word frequency in perception and that same phonotactic frequency manifesting after word frequency in production. No such interaction was present, and instead the ERP data revealed effects of lexico-semantic and phonological word processing, which were indistinguishable between language production and perception in our early time windows .The current cross-modal timing cannot be reconciled with those brain language models assuming functional delays between word components in the range of 100 s ms . MoreoveThe fact that we can contrast the activation time course of a lexico-semantic and phonologic variable across two modalities offers an important advantage over the traditional within-modality studies, namely that the points of comparison are doubled. For example, taking the first TW where we observed significant effects for both variables (74–145 ms), this means that in order to claim fast sequentiality, the maximal possible onset delay that remains between lexico-semantic and phonological access in production and (vice versa respectively) in perception has to be less than 35 ms. At this point one may question how such small  delays would still support the notion of sequentiality. For one, these short delays perfectly fit with axonal conduction velocities between different brain regions , hereby While in language perception few of such very early effects linked to word processing have been reported before , to our In sum, the uncovered parallel cortical dynamics of distinct word components in both production and perception cannot be accounted for by the dominant sequential hierarchical brain language models in the literature . In contAnother intriguing observation in our data was that for the late TW of analysis (316—369 ms) language production and perception displayed different patterns. Both the word and phonotactic frequency effects remained significant for the language production task, but were no longer significant for the language perception taskhe) runs,” during ignition the “base” representation “(to) run” would quickly become activated, and subsequently during reverberation this “base” representation is morphologically inflected to “runs” in function of the syntactic context and constraints (s/he). So how could such neurophysiological dynamics of ignition and reverberation account for the fact that in our data word and phonotactic frequency effects remained significant for the language production task, but not for the perception task? A possible answer lies in the nature of the task and behavior that participants had to produce: In production, the goal-directed behavior of the participants is to utter the specific word associated with the input. Hence, the input-specific word representation, with its associated lexico-semantic and phonetic knowledge, has to be kept online in order to successfully execute the task and articulate the unique word response. In contrast, in the perception task, the goal-directed behavior of the participants is geared towards the assessment whether the word associated with the input fits in the general category knowledge of “food items.” In other words, in the production task, the focus is on the single word representation that needs to be uttered while inhibiting other potential word candidates that could interfere with the goal-directed behavior. Of course, this does not mean that no related lexical or conceptual representations are active in the production task, but that in order to correctly utter a single word, at some point in the process the target representation has to be boosted while related information needs to be inhibited. In contrast, in the perception task, the activated word representation needs to be integrated with other word and category knowledge, thus promoting the additional activation of related words and concepts. This concurrent activation of related words and concepts may therefore cause less pronounced word-specific effects in the perception task compared the production task. Alternatively, the difference may stem from the need to perform a response in production versus the absence of a response for the no-go trials in the perception task. Regardless of the exact source, both explanations highlight that a difference in goal-directed behavior between the two language behaviors could account for the modality-specific differences later on in processing as predicted by neural assembly models of language.A similar idea of distinct functional states linked to the different neuronal firing properties of ignition and reverberation can be adopted to language processing , and itsFrom this point of view, the full electrophysiological data pattern we observed in our study can be accounted for by the neural assembly model of words in the brain in the following manner see : After eTo conclude, our data are also relevant to constrain integrated models of production–perception interactions and conversation. While traditionally both language modalities were investigated separately, more recently, there is an increased interest about the nature of language processing under more ecologically valid conditions such as the dyad . ImportaThe current data may provide some hints towards this objective, given the overlapping activation time course between production and perception at the level of words. This temporal dynamic fits the notion that words, and their respective linguistic components, share the same cortical representations in production and perception. And though it remains plausible that the time course of processing may be similar between perception and production, but their spatial and functional arrangement is not—an important question to be explored in future research—the similar ERP pattern across the language modalities as obsered here neatly follows the predictions of IMs . Key to For example, a key element in current conversational models is the assumption that successful alignment  relies on predictive processing . In shorOur ERP data revealed surprisingly rapid and parallel activation of lexico-semantic and phonology-phonetic word knowledge, which was indistinguishable between the production and perception of language. These data contrast with the traditional sequential hierarchical models of word production and perception, and instead offer strong support for parallel integration models based on Hebbian-like cell assembly theory where words reflect the Gestalts of language in the brain . Additio


Premeditated induction chemotherapy followed by surgical resection is accepted as safe and effective. Studies on salvage surgery in patients with incompletely cured lung cancer are lacking. This study aimed to demonstrate the safety and efficacy of salvage surgery.We conducted a retrospective multi‐institutional cohort study on patients who underwent salvage surgery for advanced (stage III and IV) non‐small cell lung cancer (NSCLC) between January 2005 and December 2016 at the 14 hospitals of the Chubu Lung Cancer Surgery Study Group. A total of 37 patients were assigned to the salvage surgery group; a lobectomy with mediastinal lymph node dissection was performed. The survival benefit was assessed using the Kaplan–Meier method and the Cox proportional hazard model.p < 0.05). S’‐stage was the most significant factor (p < 0.01) associated with long‐term survival.Although postoperative complications were observed in 11 patients (29.7%), surgery‐related death occurred in only one patient  resulting from respiratory failure caused by interstitial pneumonia exacerbation. Postoperative recurrence was observed in 22 patients (61.1%), the incidence of brain metastasis being high (nine patients: 40.9%). The five‐year survival rate from the first day of treatment was 60%. The survival of the postoperative pathological stage (s'‐stage) I group was significantly better  than that of the other groups  should be the goal because surgical procedures were tolerated despite intense treatment. The complication rate after salvage surgery was 29.7%. Salvage surgery for pulmonary lung cancer is safe and may result in long‐term survival, particularly in those with p‐stage I. Recently, much progress has been made in the field of medicine for primary lung cancer. In particular, the advances in efficacy and safety have been astounding. However, a complete cure remains beyond expectations for many patients. Advancements have also been made in supporting therapy for patients undergoing high‐risk surgery, including anesthesia. Based on these circumstances, premeditated induction chemotherapy followed by surgical resection has become an acceptable procedure, being both safe and effective. Similarly, patients with incompletely cured lung cancer after intense medical treatment can be treated with surgical intervention. Generally, salvage surgery is recognized as a surgical treatment performed for patients with residual tumors or recurring tumors even after definitive chemotherapy and/or radiation therapy. Recently, salvage surgery trials have been attempted in many research institutes, but conclusive findings are still very few. Therefore, the benefits of the procedure are not clarified, and precise guidelines have not been established. This study aimed to demonstrate the safety and efficacy of salvage surgery for patients with advanced lung cancer.A retrospective cohort study on salvage surgery was conducted in 14 participating institutes from the Chubu Lung Cancer Surgery Study Group. This study was approved by the Institutional Review Board of Mie Chuo Medical Center  and affiliated institutes. The requirement for informed consent was waived owing to the retrospective nature of this study. A total of 10 033 patients underwent surgical treatment for primary lung cancer between January 2005 and December 2016. Of these surgeries, 37 (0.37%) were recognized to be salvage surgeries. Herein, a salvage surgery was defined as the surgical resection of remnant or recurrent lung tumors after definitive medical treatment (which included chemotherapy and chemoradiotherapy) that intended radical cure for advanced  non‐small cell lung cancer (NSCLC). Cases of surgery for second lung cancer, metastatic recurrence, and surgery after scheduled induction therapy (neoadjuvant surgery) were excluded. The initial staging was performed using computed tomography (CT) with or without fluorodeoxyglucose positron emission tomography (FDG‐PET). Pathological confirmation of metastasis was not compulsory because this was a retrospective cohort study based on everyday medical practice.The median age was 60 years ; six patients (16.2%) were female. Histological subtypes included adenocarcinoma, squamous cell carcinoma, and large cell carcinoma in 30, four, and two patients, respectively. One patient had an undefined morphological pattern of NSCLC. The c‐stage was IIIA, IIIB, and IV in 16, nine, and 12 patients, respectively. The medical treatment included chemotherapy and chemoradiotherapy in 23 and 14 patients, respectively. Of the 37 patients, 33 were treated with platinum‐based chemotherapy (platinum doublet or triplet). Other modalities without platinum included pemetrexed single‐agent therapy, vinorelbine single‐agent therapy, and epidermal growth factor receptor‐targeted tyrosine kinase inhibitors (EGFR‐TKI) single‐agent therapy. In 14 patients treated with chemoradiotherapy, the radiation dose was >60 Gy in 10 patients and >40 Gy in three patients. In one patient using EGFR‐TKI, radiation was restricted at 30 Gy.Indications for salvage surgery have also been evaluated in the clinical setting without the use of tissue specimens. Cancer viability was assessed using CT alone in patients with recurrent tumors. For patients with remnant tumors, assistance with FDG‐PET was necessary, which revealed increased uptake of FDG in the tumor. In this evaluation, cases of N2 or N3 disease , cases with T3 or T4 disease, and cases with metastasis were rejected before surgery. The case of complete response regarding mediastinal lymph nodes and/or metastatic lesions was included. Cases with severe complications that were supposed to be dangerous for surgical procedures  were excluded. Therefore, pure resectable patients became candidates for salvage surgery. Among them, many patients refused surgery, of course, for various reasons. Finally, 37 patients underwent surgery. The total number of patients could not be defined because this study was designed for patients who underwent salvage surgery and to evaluate the safety and benefit of the surgical intervention.The average time between initial therapy and salvage surgery was 23.1 ± 27.1 months (mean ± SD). The intended surgical procedure was lobectomy with mediastinal lymph node dissection according to the guidelines of the Japanese Lung Cancer Society.Statistical analysis2‐test and Fisher's exact method. Statistical significance was defined as p < 0.05. Multivariate Cox proportional hazard model analysis was used to assess factors associated with long‐term survival: patient age, surgical curability, and s'‐stage. Regarding the curability, R0 was defined as “1”, and non‐R0 (R1 and R2) was defined as “0”. Regarding the overall therapeutic process, c‐stage factors on the first day of medical treatment were also introduced. All statistical analyses were performed using the Excel statistical software package (Excel Statistics 2010).TNM staging was based on the seventh edition. Overall survival (OS) was calculated in months from the first day of medical treatment or the day of salvage surgery. The significance of the differences between the two groups was evaluated using the χComplete resection (R0) was achieved in 35 patients. Two patients were defined as R1 resection by postoperative pathological evaluation because of surgical margin problems. The average surgical time was 296.7 ± 108.8 min (mean ± SD), and average blood loss was 498.3 ± 704.6 ml (mean ± SD). The average length of hospital stay was 18.9 ± 12.0 days (mean ± SD). Perioperative complications were observed in 11 patients (29.7%). Prolonged air leakage  and arrhythmia  were the most frequently observed complications. Other complications included pulmonary infarction, chylothorax, and respiratory failure in one patient. No patient required any additional surgical procedure. Surgery‐related death was observed in one patient  who died from respiratory failure caused by exacerbation of interstitial pneumonia on the 31st postoperative day.Among the 36 patients who tolerated surgery, s'‐stage was I in 18 , II in six, and III in 12. Postoperatively, 12 patients received chemotherapy, but this number is not suitable for statistical analysis because of few number. With regard to prognosis, 14 patients (38.9%) died of cancer, and eight (22.2%) were alive bearing cancer, while 14 (38.9%) were cancer‐free. In other words, postoperative recurrence was observed in 22 patients (61.1%). Locomediastinal recurrence was observed in nine patients (25%), including malignant pleuritis and lymphangitis (in one patient). Regarding distant metastasis, the incidence of brain metastasis was extremely high , whereas other distant metastases, i.e., lung and bone metastases, were observed in two patients, respectively.p < 0.05). OS curves from the first day of treatment are shown in Figure p < 0.01). Downstaging to s'‐stage I was the most important factor for good survival.The OS curve from the first day of treatment is shown in Figure p < 0.01). Regarding the factors associated with good survival of the overall therapeutic process, the results of the Cox analysis are shown in Table p < 0.005).Regarding the factors associated with good postoperative survival, the results of the Cox analysis are shown in Table Regarding the safety of salvage surgery, in a study by Sonobe et al.,In this study, the accurate assessment of the efficacy of salvage surgery is impossible because this is a retrospective cohort study conducted by surgeons belonging to the Department of General Thoracic Surgery. The appropriate control group could not be set up in this study. However, almost all patients were incompletely cured, apart from four whose Ef3 was confirmed pathologically after surgery. Accordingly, the expected five‐year survival rate was as low as 10% based on the estimation that 33 patients (four confirmed to be cancer‐free) would not live for more than five years in this study period because the efficacy of second‐ and third‐line chemotherapy had been evaluated based on progression‐free but not on overall survival. A five‐year survival rate of 60% can be considered a benefit of salvage surgery. Sonobe et al.p < 0.0001) prognostic factor, as well as lymph node ratio (p = 0.001). Although our study included no data about lymph node ratio, there is no reason to dismiss this finding.The survival curve was significantly better in the s'‐stage I group. Cox analysis also revealed s'‐stage as the most important prognostic factor, implying that downstaging to as low as s'‐stage I during the initial treatment is the most important factor. Further, Sonobe et al.In an ordinary surgical treatment, curative resection (R0) is an important prognostic factor. It is also true in surgeries for N2 NSCLC.In conclusion, salvage surgery is a safe and feasible modality for advanced lung cancer therapy. Postoperative complications were observed in 11 patients (29.7%), and surgery‐related death occurred in only one patient . Moreover, postoperative recurrence was observed in 22 patients (61.1%), and there was a high incidence of brain metastasis (nine patients: 25%). Downstaging to s'‐stage I with previous medical treatment was the most important factor for good survival. Complete resection (R0) should be performed because ordinary surgical procedures are tolerable even after intensive treatment. Salvage surgery will provide patients with advanced lung cancer a chance to achieve long‐term survival.This research did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors. The authors report no proprietary or commercial interest in any product mentioned or concept discussed in this article.


Serological methods may not be reliable for RBC antigen typing, especially in multi‐transfused patients. The blood group systems provoking the most severe transfusion reactions are mainly Rh, Kell, Kidd, and Duffy. We intended to determine the genotype of these blood group system antigens among Iranian alloimmunized thalassemia patients using molecular methods and compare the results with serological phenotyping.Two hundred patients participated in this study. Blood group phenotype and genotype were determined using the serological method and PCR‐SSP, respectively. The genotypes of patients with incompatibility between phenotype and genotype were re‐evaluated by RFLP‐PCR and confirmed by DNA sequencing.Discrepancies between phenotype and genotype results were found in 132 alleles and 83 (41.5%) patients; however, there was complete accordance between the three genotyping methods. Most discrepancies were detected in Rh and Duffy systems with 47 and 45 cases, respectively, and the main discrepancy was in the FY*B/FY*B allele when serologically showed Fy(a+b+). All 39 undetermined phenotypes, due to mixed‐field reactions, were resolved by molecular genotyping.Molecular genotyping is more reliable compared with the serological method, especially in multi‐transfused patients. Therefore, the addition of blood group genotyping to serological assays can lead to an antigen‐matched transfusion in these patients. Molecular genotyping is more reliable compared with the serological method, especially in multi‐transfused patients. Therefore, the addition of blood group genotyping to serological assays can lead to a better‐matched transfusion in these patients. Informed consent was taken from all patients and the demographic data were collected in case record form.2.2Two hundred alloimmunized thalassemia patients  receiving treatment and follow‐up at the Tehran Adult Thalassemia Clinic, a referral center for complicated thalassemia patients, agreed to participate in this study. Peripheral blood was collected in two separate K2EDTA tubes of 5 ml. One tube was used for serological RBC phenotype and the other tube was used for DNA extraction.2.3The Rh, Kell, Kidd, and Duffy blood group phenotypes were determined using the tube method and commercial blood group typing reagents (Immundiagnostika) according to the manufacturer's instructions. Cells with either the presence or absence of the antigens to be tested were selected from an in‐house screening cell panel that was validated with commercial panel cells . These cells were used as positive and negative controls to ensure the expected reactivity of antisera to be used in the testing. To exclude sample mix‐up all procedures for the handling and labeling of samples were revised according to the Iranian Blood Transfusion Organization (IBTO) standard operating procedure template and serological test were done for every samples twice and in discrepant cases serological tests were repeated.2.4Genomic DNA was extracted from blood samples using a column‐based DNA extraction kit  and The DNA concentration of each sample was determined by the measurement of optical density at 260 and 280 nm. The extracted DNA was stored at −20°C.2.52.5.1PCR‐SSP method is based on the fact that primer extension, and consequently a successful PCR relies on a good match at the 3′ end of both primers. Therefore, only when the primers are entirely matched, the amplification of the target sequence is obtained. In this study, SSP‐PCR was performed for all 200 samples. The sequence of primers was chosen according to the previous studies and is shown in Table 2.5.2In order to verify the results of SSP‐PCR, RFLP‐PCR was performed for patients with incompatibility between phenotype and genotype results. PCR products amplified using primers, amplification conditions as described in the previous studies, and primer sequences are presented in Table After amplification, the PCR products were digested overnight at 37°C with the appropriate restriction enzymes (Thermo Scientific), in a final volume of 32 µl using 10 µl of amplified product and enzyme in 1x buffer according to the manufacturer's instructions. The enzymes, including Pst I, Ssp I, Bsm I, Nla III, and Ban I, were employed to determine RH*D/RH*C/RH*c (insertion in RHC), RH*C/RH*c (−292G>A), KEL*01/KEL*02 (698C>T), KEL*3/KEL*4 (481C>T), and FY*A/FY*B (125G>A), respectively. Moreover, Mnl I restriction enzyme was also used to determine RH*E/RH*e (676C>G) in the RHCE gene and JK*A/JK*B (838G>A) in SLC14A1. RFLP bands were analyzed after electrophoresis in 2% agarose gel for RH*D/RH*C/RH*c, KEL*01/KEL*02, KEL*3/KEL*4, and FY*A/FY*B genotyping and in 8% polyacrylamide gel for RH*E/RH*e and JK*A/JK*B genotyping.2.5.3http://www.ensembl.org/index.html) and two sets of primers were designed using the Primer 3 program (http://frodo.wi.mit.edu/). Hairpin and autodimer formations were evaluated using Autodimerv1removal (http://www.cstl.nist.gov/biotech/strbase/AutoDimerHomepage/AutoDimerProgramHomepage.htm) .To check the accuracy of protocols, 10 DNA samples, previously genotyped by PCR‐SSP and PCR‐RFLP, and all samples with undetermined phenotype and discrepancies between phenotype and genotype results were sequenced for related exons in all studied blood group systems. For KEL, Kidd, and FY sequencing, the PCR amplification was performed using primers and amplification conditions as described in the previous studies  Table . For new33.1n = 108), beta‐thalassemia intermedia (n = 90), and sickle beta‐thalassemia (n = 2).A total of 200 alloimmunized thalassemia patients, including 81 males and 119 females with a median age of 30 ± 10.93 years (range: 4–65 years) were genotyped for Rh, Kell, Kidd, and Duffy blood group systems. Patients were diagnosed with beta‐thalassemia major  out of 200 patients showed discrepancies between genotyping and phenotyping results Table . Four paIn 101 cases, the discrepancies had clinical significance meaning that these patients had the potential of alloimmunization and in four cases, the discrepancies resulted in antibody production. Three patients phenotyped as E+ e+ and genotyped as RHe/RHe had anti‐E, and one patient serologically phenotyped as RhD‐positive and genotyped as RHD‐negative, had anti‐D in his serum.4This study indicated the relevance of performing molecular analysis to determine the most clinically important blood group systems, including Rh , Kell , Kidd (Jka and Jkb), and Duffy (Fya and Fyb) in Iranian alloimmunized transfusion‐dependent thalassemia patients. We observed discrepancies between phenotyping and genotyping results in a total of 132 alleles in 83 (41.5%) patients mainly in Rh and Duffy systems with 47 and 45 cases, respectively; however, there was a complete agreement between the results of SSP‐PCR, RFLP‐PCR, and DNA sequencing. In 101 of these discrepancies, the patients were at risk of making antibodies and in four cases, the discrepancies resulted in antibody production. All undetermined serologic phenotypes because of the mixed field reactions were resolved by genotyping.RBC alloantibody formation is a major complication associated with repeated transfusions in multi‐transfused thalassemia patients and limits the availability as well as the safety of subsequent transfusions , Kell , Kidd (Jka and Jkb), and Duffy (Fya and Fyb) in Iranian alloimmunized transfusion‐dependent thalassemia patients.In the present study, we observed discrepancies between phenotyping and genotyping results in a total of 132 alleles in 83 (41.5%) patients; however, there was a complete agreement between the results of SSP‐PCR, RFLP‐PCR, and DNA sequencing. Previous studies have shown that, in multi‐transfused patients, the rate of discrepancies between phenotyping and genotyping results were variable and reported at 42.1%–90%  and the discrepancies usually were observed in the transfusion‐dependent patients owing to the recent or chronic transfusion, as well as the presence of a mixed population of the erythrocytes  were related to the state of the phenotype E− e+ and genotype RHE/RHE; it seems that the phenotype reactivity was due to donor RBCs in the circulation of the patients because of the phenotype‐matched transfusion. However, the segments of the transfused units were not available to confirm it. Nonetheless, using molecular typing and accurate determination of E (RHE) antigen, we can remove the previous obligation to search for E‐negative RBC units in these patients. This discrepancy has been seen previously in the study conducted by Bakanay et al.,  in two pTen cases were found to have discrepancies in the Kell blood group system that most of them (six cases) were related to the state of the K− k+ phenotype and KEL*01/KEL*02 genotype  antigens in any patients. Kpa is a low‐incidence RBC antigen that affects all Kell antigens encoded by the same haplotype and can markedly weaken their expression  were related to the state of the phenotype Jk (a+ b+) when genotyped as JK*A/JK*A. Twelve discrepant cases were also phenotyped as Jk (a+ b+) and genotyped as JK*B/JK*B. Kidd antigens have been frequently implicated in delayed hemolytic transfusion reactions (DHTR) , of which most of them (27 cases) were related to the state of Fy (a+ b+) phenotype and FY*B/FY*B genotype. The same discrepancy was also seen in the study conducted by Guelsin et al., . The higIn patients who had been transfused with genotype mismatched RBC units, because of the observed genotype‐phenotype discrepancies, 101 patients had the potential to induce alloantibodies. Surprisingly, just in four patients (anti‐E in 3 patients and anti‐D in 1 patient), it results in antibody production because many factors affect the risk of alloimmunization, including antigen immunogenicity, the duration of transfusion therapy, and genetic and environmental factors  in phenotype and the FY*B/FY*B in genotype with normal GATA‐1‐binding motif; thus, in these patients, further assessments are needed for the accurate determination of antigen expression status considering the potential silent variants.5According to the obtained results, using DNA‐based antigen testing is effective to improve the effectiveness and reliability of blood group antigen typing and resolve undetermined blood group phenotypes in transfusion‐dependent cases. Although genotyping is associated with several advantages, it cannot be regarded as an alternative; nonetheless, it is applicable as a complementary tool to overcome the limitations of serological assays. Also, using blood group genotyping combined with serological assays in transfusion‐dependent patients can result in antigen‐matched RBC transfusions and can improve patients care.The authors report no conflicts of interest in this work.SR: methodology, validation, writing—review & editing. OA: conceptualization, project administration. DTR, JSF, MF, GS: methodology, validation. AA: methodology. NA: conceptualization, supervision.