We examined how prior experience encountering targets affected attention allocation and event-based prospective memory. Participants performed four color match task blocks with a difficult, but specified prospective memory task (Experiment 1) or an easier, but unspecified prospective memory task (Experiment 2). Participants were instructed to search for targets on each block. Participants in the prior experience condition saw targets on each block, participants in the no prior experience condition only saw targets on the fourth block, and, in Experiment 2, participants in the mixed prior experience condition encountered some of the targets on the first three blocks, and saw all the targets on the fourth block. In Experiment 1, participants in the no prior experience condition were less accurate at recognizing targets and quicker to respond on ongoing task trials than participants in the prior experience condition. In Experiment 2, we replicated the effect of prior experience on target accuracy, but there was no effect on ongoing trial response time. The mixed experience condition did not vary from the other conditions on either dependent variable, but their target accuracy varied in accordance with their experience. These findings demonstrate that prospective memory performance is influenced by experience with related tasks, thus extending our understanding of the dynamic nature of search efforts across related prospective memory tasks. This research has implications for understanding prospective memory in applied settings where targets do not reliably occur such as baggage screenings and missing person searches. The research was motivated by the legal problem of missing targets in search tasks such as not noticing a missing person in one’s midst or illicit behavior or objects while performing another task. These targets rarely occur which may impact engagement and performance during future versions of the task. A prospective memory task involves a person forming an intention to complete a task  and then engaging in other activities  until the target appears, signaling the opportunity to complete the prospective memory task. The present research investigated how prior experience encountering targets affects attention to and performance on future tasks. Participants either encountered targets on four prospective memory blocks (prior experience), encountered some targets on the first three blocks and all targets on the final, critical block , or only encountered targets on the final, critical block (no prior experience). Prior experience encountering targets resulted in higher accuracy at recognizing targets than prior experience not encountering targets. In Experiment 1, which contained a difficult, but clear prospective memory task, participants in the prior experience condition devoted more attentional resources to searching for targets than participants in the no prior experience condition. This finding did not occur in Experiment 2, when the prospective memory task was easier, but less clear. Understanding how prior experience affects prospective memory performance can help us to work to improve performance at legally relevant prospective memory tasks.Event-based prospective memory involves remembering to complete an intended action when the proper conditions occur . The participant is instructed to engage in the intended action, usually pressing a button on the keyboard, when they encounter the target word within the lexical decision task.monitoring the environment for the target to perform the intended action. Performing prospective memory tasks alongside ongoing tasks results in “costs,” or slowing in participants’ response time on the ongoing task  and higher task interference  (H2) compared to no prior experience.β) of 0.9, we conducted an a priori power analysis based on the effect size of a similar study  f = 0.227, α = 0.05, and two between-subjects groups. The total sample size estimated for this power level with an effect of that size was 206 participants. Two hundred and five general psychology students participated for course credit. The sample was 70.2% (n = 144) female, and the average age was 19.85 years . The sample was Caucasian (74.6%), African American (5.4%), Hispanic (5.4%), Asian (5.4%), bi-racial (3.9%), mixed race (1.0%), American Indian (0.5%), or did not specify their race (1%). The majority of the sample was right-hand dominant  but a small minority of participants were left-hand dominant (11.2%) or ambidextrous (0.9%). The majority of the sample’s native language was English (88.3%). Participants who indicated that they did not speak English fluently (n = 1) or that they did not have normal color vision (n = 3) were excluded from analyses. Software errors led us to exclude eight participants. We excluded participants who scored less than 25% accurate on an ongoing task. The exclusion criteria were set before analyses were conducted. Therefore, a total of 183 participants’ data were submitted to our analyses, which still allowed us to detect a main effect of prior experience with a power of 0.86.To determine the sample size needed for statistical power  were randomly chosen  approximately 83 × 60 pixels (or 1.5in by 1.3in) in size, on a black background. The colors presented were randomly assigned. Each word was displayed in one of the five colors. Some words were displayed in the color of one of the four color screens that preceded it , and some words were displayed in the one color that did not match any of the four color screens that preceded it . Each word was randomly assigned to be a color match or no match. This led to four different trial types: match prospective memory targets, no match prospective memory targets, match words, and no match words.Each color screen appeared for 500 ms followed by a black screen for 250 ms. Words appeared on the screen in 18 point font and remained on the screen until a response was made. A black screen appeared after the word screen for 1000 ms.Experimenters obtained consent and demographic information from participants, and then participants began the experiment in SuperLab Version 4. Participants were instructed to press the ‘y’ key on the keyboard if the color of the word matched one of the four preceding color blocks and the ‘n’ key if the color of the word did not match one of the four preceding color blocks. Participants completed two color match tasks that consisted of six trials and 28 trials, respectively.After the color match tasks, participants received instructions about the prospective memory tasks. Participants were asked to look for prospective memory target words while completing the color match task. All participants received the same instructions that established the expectation that one set of prospective memory targets would appear on each block. Participants were instructed to press the ‘~’ key if they spotted a prospective memory target word. Participants memorized the corresponding prospective memory target words to criterion and recalled them in order three times, to control for any effect of prior experience on retrospective memory, before beginning each block.Before beginning the first block, participants had to correctly indicate the correct key to press if the word was (a) a match, (b) a non-match, or (c) a prospective memory target word. Participants were instructed to ask the experimenter if they did not know the answer. This ensured that all participants knew the correct key to press for each type of response on the tasks.After completing each block, participants were asked to indicate the key they were asked to press if they saw a prospective memory target and to recall the prospective memory targets. Participants in the no prior experience condition only encountered prospective memory targets on the last block. Participants in the prior experience condition encountered prospective memory targets on all four blocks. After completing all four blocks, participants were asked if they felt suspicious while completing the study, if they encountered the prospective memory targets they were told to look for, and which task they thought was most important.In this experiment, we examined how prior experience affected prospective memory performance and attention allocation on future prospective memory tasks. We hypothesized that prior experience would result in increased accuracy at identifying prospective memory targets (H1) and slower response time on the ongoing task (H2) in contrast to no prior experience.The results are organized into four sections: prior experience and prospective memory performance (H1), prior experience and ongoing task performance (H2), prospective memory performance across tasks, and self-report responses. Prospective memory accuracy was calculated as the average accuracy for all target items in a block. Ongoing task accuracy was calculated as the average accuracy for all non-target items in a block. Response time was calculated as the average response time of accurate target (prospective memory) or non-target (ongoing task) responses in the block. We trimmed ongoing task response times that were three standard deviations beyond the average response time for the participant.M = 0.24, SE = 0.03) than participants in the prior experience condition , F = 24.18, p < 0.001, ɳ2p = 0.118. The main effect of match, F = 3.06, p = 0.082, ɳ2p = 0.017, and the interaction between match and experience conditions, F = 2.18, p = 0.141, ɳ2p = 0.012, were not significant.We assessed the impact of prior experience of encountering targets on related blocks on future prospective memory performance by examining how participants performed on the final block, wherein all participants encountered target words. We conducted a 2 (target match vs. target no-match) × 2 (prior experience: experience or no prior experience) repeated measures ANOVA with prospective memory target accuracy on the final block as the dependent variable. As predicted (H1), participants in the no prior experience condition were substantially less accurate at identifying prospective memory target words  than on match items , F = 98.34, p < 0.001, ɳ2p = 0.35. We found a significant effect of block, F = 23.07, p < 0.001, ɳ2p = 0.113 (Greenhouse–Geisser).Next, we examined whether prior experience affected ongoing task performance across all blocks. Mean accuracy and response time rates by condition and block are displayed in Tables F = 3.81, p = 0.014, ɳ2p = 0.021 (Greenhouse–Geisser). Follow-up comparisons revealed that there was no effect of condition on the first block, F = 0.08, p = 0.784, ɳ2p < 0.001, or the second block, F = 0.73, p = 0.396, ɳ2p = 0.001. Participants in the no prior experience condition  were less accurate on the third block than participants in the prior experience condition , F = 4.19, p = 0.042, ɳ2p = 0.023. Participants in the no prior experience condition  performed similarly to participants in the prior experience condition  on ongoing task accuracy on the final block, F = 3.84, p = 0.052, ɳ2p = 0.021. There was no effect of experience condition, F = 2.18, p = 0.141, ɳ2p = 0.012, no interaction between the match and experience condition, F = 0.002, p = 0.967, ɳ2p < 0.001, match and block, F = 2.46, p = 0.066, ɳ2p = 0.013, and no three way interaction between match, block, and experience condition, F = 2.18, p = 0.093, ɳ2p = 0.012.Critically, we were interested in whether prior experience interacted with block to affect ongoing task accuracy on the final block, wherein all participants were shown prospective memory targets. We found a significant interaction between block and experience condition, F = 43.76, p < 0.001, ɳ2p = 0.20 (Greenhouse–Geisser). There was also an effect of experience condition. Participants in the no prior experience condition  responded more quickly on the ongoing task than participants in the prior experience condition , F = 15.75, p < 0.001, ɳ2p = 0.080. Critically, we found a significant interaction between block and experience condition, F = 10.07, p < 0.001, ɳ2p = 0.053  = 4.27, p = 0.04, ɳ2p = 0.023, the second block, F = 17.51, p < 0.001, ɳ2p = 0.088, the third block, F = 28.32, p < 0.001, ɳ2p = 0.136, and the fourth block, F = 11.55, p = 0.001, ɳ2p = 0.06. Participants in the prior experience condition responded more slowly to ongoing task trials than participants in the no prior experience condition and the size of this effect increased from the first block to the third block, but decreased in the fourth block. This finding is in line with hypothesis 2 that no prior experience encountering targets would result in less attention being allocated to the ongoing task than prior experience encountering targets.A repeated measures ANOVA on ongoing task response time found an effect of block, F = 0.03, p = 0.866, ɳ2p < 0.001.Given that experience had an impact on response time even in block 1, we tested the impact of experience on response time before the first target was ever presented in block 1 to determine if the groups differed in performance before the introduction of the experience manipulation. We conducted a one-way ANOVA with experience as an independent variable on accurate ongoing task response time on the nine trials that appeared before the first target in block 1. We found no effect of experience on ongoing task response time before the first target in block 1, p = 0.008. Whereas participants in the no prior experience condition progressively sped up on each block , except from the third block  to the fourth block , p = 0.155. This pattern of results indicates that participants in the no prior experience condition may have started monitoring during the final block, and thus did not continue the pattern of speeding up on the final block. There was no effect of match, F = 0.75, p = 0.388, ɳ2p = 0.749, no interaction between match and experience condition, F = 3.23, p = 0.074, ɳ2p = 0.018, no interaction between block and match, F = 0.18, p = 0.909, ɳ2p = 0.001, and no three way interaction between match, block, and experience condition, F = 1.44, p = 0.231, ɳ2p = 0.008.To further understand the interaction between block and prior experience, we examined the effect of block order by condition. In both conditions there was an effect of block. Participants in the prior experience condition showed a pattern of progressively speeding up, although only significantly so from blocks 3 to 4, F = 7.61, p < 0.001, ɳ2p = 0.041, and condition, F = 11.12, p = 0.001, ɳ2p = 0.059, but no interaction, F = 1.41, p = 0.217, ɳ2p = 0.008. The two conditions followed a similar pattern of response time. Participants responded more slowly on the second bin of trials  which occurred after encountering the first target as compared to the first bin of trials before they encountered any targets , p = 0.002. This finding held when we tested the effect of target bin in the no prior experience condition alone (p = 0.015) and indicates that the no prior experience condition responded similarly to the first target as the prior experience condition, though the no prior experience was still monitoring less overall. In addition, participants responded more quickly on the first target bin (before encountering any targets) than in the second, third, or fourth target bins, ps = / < . 01. Participants sped up on the final target bin as compared to the second, third, or fourth target bins, ps < 0.01. Finally, and perhaps most interestingly, there was no effect of whether the participants in the no prior experience condition detected the first target on their response time to the second bin of trials, which occurred just after the first target was displayed, F = 0.16, p = 0.666, ɳ2p = 0.002. Perhaps some participants in the no prior experience condition realized they had encountered the first target even if they missed it, and began engaging in monitoring for the remaining targets.To follow up on this pattern of findings, we analyzed the ongoing task response time data in bins by which target they preceded in the fourth block. This allowed us to examine the pattern of task interference across the fourth block. We conducted a 2  × 6  mixed-effects ANOVA with trial bin as a within subjects variable. We found an effect of trial bin, F = 5.19, p = 0.002, ɳ2p = 0.056. Pairwise comparisons with Bonferroni corrections revealed that participants were more accurate on the first block  than the third block , p = 0.011, and the fourth block , p = 0.024. These findings indicate that participants in the prior experience condition may have experienced a fatigue effect or may have experienced proactive interference across tasks. Participants were more accurate at responding to prospective memory target no match items  than prospective memory target match items , F = 11.44, p = 0.001, ɳ2p = 0.116. There was no interaction between match and block, F = 0.799, p = 0.492, ɳ2p = 0.009. Then, we examined response time to prospective memory targets across tasks in the prior experience condition  < 0.001, p = 0.986, ɳ2p < 0.001, no effect of block, F = 1.47, p = 0.239, ɳ2p = 0.037 (Greenhouse–Geisser), and no interaction between match and block, F = 0.957, p = 0.388, ɳ2p = 0.025 (Greenhouse–Geisser).Next, we examined whether participants in the prior experience condition improved at the prospective memory task across blocks  due to a software error. All but one these participants were in the prior experience condition. A chi-square revealed that participants in the prior experience condition were more likely to indicate that the target words were presented than participants in the no prior experience condition, χ2  = 6.27, p = 0.012, Ф = 0.212. A chi-square on task importance revealed no effect, χ2  = 6.96, p = 0.073, Ф = 0.22; 55% of participants in the no prior experience condition and 76% of participants in the prior experience condition prioritized the prospective memory task of searching for the target words.At the end of the study session, we asked participants whether they thought the target words they were asked to look for were presented to them during the tasks (yes or no) and which task they thought was most important . We inadvertently failed to ask these questions to 23% of participants  on ongoing task response time and prospective memory target accuracy on the final, critical block. We used G*Power  were randomly chosen  in the no experience and mixed prior experience conditions. On the first three blocks, all six target words were replaced with non-target filler words in the no prior experience condition. On the first three blocks, there was a version that replaced all of the animal target words with non-target filler words and another version that replaced all of the clothing target words with non-target filler words in the mixed experience conditions.Four blocks of color match tasks  and six target  or non-target filler trials. The prospective memory targets were presented in trials 16, 27, 38, 49, 60, and 71 in each block to maximize the distance between each prospective memory target word.The procedure for Experiment 2 was the same as the procedure for Experiment 1 with the following exceptions. Participants were asked to look for the same two categories of prospective memory target words on each of the four color match blocks. All participants received the same instructions that established the expectation targets would appear on each block.Participants were given example questions for each category  with feedback and validation. Then, participants completed a recall test for each category with feedback and validation. Once participants reported each category of words they were asked to look for they completed a block of trials. The recall test was given before the beginning of each block.In this experiment, we examined how prior experience affected prospective memory performance and attention allocation on future prospective memory tasks. We hypothesized that prior experience would increase accuracy at identifying prospective memory targets (H1) and slow response time on the ongoing task (H2) relative to no prior experience. In addition, we hypothesized that the mixed prior experience condition would either perform similarly to the prior experience condition, or that the mixed prior experience condition would be less accurate at identifying targets and faster on the ongoing task than participants in the prior experience condition. We hypothesized that the mixed prior experience condition would be more accurate at identifying targets and slower on the ongoing task than participants in the no prior experience condition.The results are organized into four sections: prior experience and prospective memory performance (H1), prior experience and ongoing task performance (H2), prospective memory performance across tasks, and self-report. The dependent variables were calculated using the same procedure as in Experiment 1.F = 4.41, p = 0.013, ɳ2p = 0.032. We followed up the significant main effect with Tukey’s post hoc tests. Replicating the finding from Experiment 1, participants in the no prior experience condition were less accurate at identifying prospective memory target words  than participants in the prior experience condition , p = 0.009. Descriptively, the mixed prior experience condition performed between the prior experience and no prior experience conditions, but there were no significant differences between mixed prior experience condition and the other experience conditions. The main effect of match, F = 2.04, p = 0.154, ɳ2p = 0.007, and the interaction between match and experience conditions, F = 0.38, p = 0.686, ɳ2p = 0.003, were not significant.We assessed the impact of prior experience of encountering targets on related blocks on prospective memory performance by examining how participants performed on the final block, wherein all participants encountered target words. We conducted a 2 (target match vs. target no-match) × 3  repeated measures ANOVA with prospective memory target accuracy on the final block as the dependent variable. The main effect of prior experience was significant, M = 0.49, SE = 0.04) than the no prior experience targets , F = 6.79, p = 0.011, ɳ2p = 0.071. In fact, the prior experience target accuracy  in the mixed experience condition was similar to the target accuracy of participants in the prior experience condition , and the no prior experience target accuracy  in the mixed experience condition was similar to the target accuracy of participants in the no prior experience condition   on the mixed prior experience condition. We found that participants in the mixed prior experience condition were more accurate at detecting the prior experience targets  = 37.89, p < 0.001, ɳ2p = 0.123 (Greenhouse–Geisser). Pairwise comparisons revealed a significant pattern of declining accuracy across blocks, all ps < 0.05, though the overall decline in performance was small from 87.4% accuracy on block 1 to 81.4% accuracy on block four. We found a significant effect of match. Participants were more accurate on no match items  than on match items , F = 127.13, p < 0.001, ɳ2p = 0.008.Next, we examined whether prior experience affected ongoing task performance across blocks. Mean accuracy and response time rates by condition and block are displayed in Tables F = 1.64, p = 0.148, ɳ2p = 0.012 (Greenhouse–Geisser). There was no effect of experience condition, F = 0.56, p = 0.58, ɳ2p = 0.004, no interaction between the match and experience condition, F = 1.03, p = 0.358, ɳ2p = 0.008 (Greenhouse–Geisser), match and block, F = 3.78, p = 0.012, ɳ2p = 0.014 (Greenhouse–Geisser), and no three-way interaction between match, block, and experience condition, F = 1.35, p = 0.238, ɳ2p = 0.01 (Greenhouse–Geisser).Critically, we were interested in whether prior experience interacted with block to affect ongoing task accuracy on the final block, wherein all participants were shown prospective memory targets. The interaction between block and experience was not significant, F = 1.21, p = 0.285, ɳ2p = 0.004 (Greenhouse–Geisser). There was no effect of experience condition, F = 0.28, p = 0.76, ɳ2p = 0.002. Descriptively, as in Experiment 1, the no prior experience condition slowed down from block 3  to block 4 . There was no interaction between block and experience condition, F = 1.41, p = 0.242, ɳ2p = 0.01. (Greenhouse–Geisser).A repeated measures ANOVA on ongoing task response time revealed no effect of block, F = 0.373, p = 0.542, ɳ2p = 0.001 (Greenhouse–Geisser), no interaction between match and experience, F = 0.13, p = 0.88, ɳ2p = 0.001. (Greenhouse–Geisser), no interaction between block and match, F = 1.74, p = 0.183, ɳ2p = 0.006 (Greenhouse–Geisser), and no three-way interaction between match, block, and experience, F = 0.40, p = 0.775, ɳ2p = 0.003 (Greenhouse–Geisser).There was no effect of match, F = 0.2.7, p = 0.052, ɳ2p = 0.029. (Greenhouse–Geisser), no effect of match, F = 0.36, p = 0.552, ɳ2p = 0.004, and no interaction between match and block, F = 0.52, p = 0.67, ɳ2p = 0.006. (Greenhouse–Geisser).Next, we examined whether participants in the prior experience condition improved at the prospective memory task across blocks  = 1.08, p = 0.306, ɳ2p = 0.032, no effect of block, F = 1.1, p = 0.302, ɳ2p = 0.032 (Greenhouse–Geisser), and no interaction between match and block, F = 1.12, p = 0.299, ɳ2p = 0.033 (Greenhouse–Geisser).Then, we examined response time to prospective memory targets across tasks in the prior experience condition  = 40.49, p < 0.001, Ф = 0.344; 81.39% of participants in the prior experience condition, 66.28% of participants in the mixed prior experience condition, and 41% of participants in the no prior experience condition reported that they thought all categories of targets were presented and did not note any exceptions. We conducted follow-up chi-square analyses including two prior experience conditions in each analysis, with Bonferroni corrections on our alpha level which led us to set our alpha level at 0.017. A chi-square revealed that participants in the prior experience condition were more likely to indicate that the target words were presented than participants in the no prior experience condition, χ2  = 29.94, p < 0.001, Ф = 0.41. A chi-square revealed that participants in the mixed prior experience condition were more likely to indicate that the target words were presented than participants in the no prior experience condition, χ2  = 11.14, p < 0.001, Ф = 0.25. A chi-square revealed that participants in the prior experience condition were no more likely to indicate that the target words were presented than participants in the mixed prior experience condition, χ2  = 5.09, p = 0.024, Ф = 0.172. A chi-square on task importance revealed no effect χ2  = 3.11, p = 0.795, Ф = 0.107; 51.11% of participants in the prior experience condition, 53.33% of participants in the mixed prior experience condition, and 49% of participants in the no prior experience condition thought searching for the target words was more important than the ongoing task.At the end of the study, we asked participants whether they thought the target words they were asked to look for were presented to them during the blocks of tasks  and which task they thought was most important . A chi-square revealed that there were differences in reporting of the encountering of targets by experience condition, The purpose of this experiment was to replicate the findings of Experiment 1 and to examine how mixed prior experience with encountering prospective memory targets on separate but related tasks affected ongoing task costs and prospective memory performance on a future task. As in Experiment 1, participants completed four blocks of prospective memory tasks. We replicated and extended the design of Experiment 1 by adding a mixed prior experience condition, in which participants had a chance to encounter one type of prospective memory target, but not the other on the first three blocks followed by a chance to encounter both types of targets on the last block. As in Experiment 1, we examined the effect of prior experience on prospective memory and ongoing task performance.We partially replicated the results of Experiment 1. Participants in the prior experience condition were more accurate at detecting targets on the final, critical block than participants in the prior experience condition. The mixed prior experience condition did not differ in their accuracy from either of the other two experience conditions overall, but they did perform differently at recognizing the targets based on their experience with them and their performance on each category mapped onto the between subjects conditions by experience. There was no effect of experience on ongoing task response time. Perhaps the effect on response time was eliminated due to participants searching for categories of targets rather than multiple individual targets as in Experiment 1.The purpose of this research was to examine how prior experience with encountering prospective memory targets on separate but related tasks affected attention allocation and prospective memory performance on a future task. Participants completed four blocks of prospective memory tasks. In Experiment 1, participants searched for specific target words; in Experiment 2, participants searched for words belonging to two target categories. Participants were asked to search for targets on four blocks of tasks. However, participants in the no prior experience condition only had a chance to encounter prospective memory targets on the last block (Experiments 1 & 2). Participants in the mixed prior experience condition encountered one category of targets on every block, but only had a chance to encounter the other category of targets on the last block (Experiment 2). Comparing the conditions to a prior experience condition allowed us to test the effects of repeated failure to encounter targets on related tasks. The effect of prior failure to encounter targets informs us about the effects of related experience on future prospective memory performance. It is analogous to the experience of repeatedly encountering missing and wanted persons alerts but not encountering the individuals themselves. Mixed prior experience encountering targets belonging to categories is analogous to the experience of searching for classes of targets with different prevalence rates, and is important when missing low prevalence classes of targets has high consequences, such as detecting suspicious behavior or problematic items in an airport scanner, while conducting an ongoing task.In both experiments, we found support for the hypothesis that participants in the no prior experience condition were less accurate at noticing prospective memory targets on the final block than participants in the prior experience condition. In Experiment 1, participants in the no prior experience condition responded more quickly to the ongoing task than participants in the prior experience condition, indicating that they devoted fewer attentional resources to completing the prospective memory task. In fact, the effect was so strong that there were differences between the conditions on ongoing task response time starting in the first block, after the first target was displayed. The results from the ongoing task indicate that prior experience encountering targets on highly related prospective memory tasks affected the effort participants put into a subsequent prospective memory task. These findings suggest that prior experience with a prospective memory task may affect future prospective memory performance when attention benefits prospective memory. The latter finding was not replicated in Experiment 2 wherein participants searched for targets by category rather than individual instantiations of targets. Further, participants in the mixed experience condition did not perform differently from participants in the prior experience or no experience conditions in regard to overall prospective memory accuracy or ongoing task response time in Experiment 2. However, the mixed prior experience condition did perform similarly on target accuracy to the prior experience condition on targets they had prior experience encountering and to the no prior experience condition on targets they did not have prior experience encountering on the previous blocks. The prospective memory task was more difficult, but also more specified in Experiment 1 than Experiment 2, and we interpret the mixed results across experiments in light of those differences.Capacity sharing theories like the preparatory attention model (PAM; Smith & Bayen, The dynamic multi-process model (Scullin et al., Recently, an alternative account of task interference has been posited. Delay theory accounts posit that the cause of task interference is the time it takes to decide whether a stimulus is a target or not (Heathcote et al., Surprisingly there was no effect of prior experience on attention allocation in Experiment 2. We propose several reasons why there was no effect of prior experience on task interference in Experiment 2 while accounting for the effect of prior experience on task interference in Experiment 1. Consider task differences between the experiments. In Experiment 1, participants searched for six unrelated targets on each block, and were told the exact targets they needed to search for before the block began. In Experiment 2, participants searched for two categories of targets to allow us to incorporate the mixed prior experience condition in the design. First, the categorical nature of the targets in Experiment 2 may explain the lack of effect. Marsh et al.  found grDespite the lack of effect of experience on monitoring in Experiment 2, prior experience impacted accuracy at detecting targets on the final block indicating that prior experience impacted some process or behavior important for prospective memory performance. In addition, when we examined performance at target accuracy in the mixed prior experience condition by experience, we found a difference in performance by experience. Specifically, we found that accuracy at the no prior experience targets was similar to the no prior experience condition’s target accuracy and that accuracy at the prior experience targets was similar to the prior experience condition’s target accuracy. This finding converges with Wolfe et al.’s  finding Expectations and motivation are mechanisms that may have driven the effects of prior experience on prospective memory performance. There is a rich body of literature demonstrating that expectations influence attention allocation and performance on prospective memory tasks. These include studies of context expectations (Cook et al., In addition to adding to theoretical knowledge of the impact of experience-based metacognition on prospective memory, we were also interested in the effects of prior failure to encounter targets because this is akin to legally applied search tasks. Specifically, Experiment 1’s design was analogous to the experience of citizens who encounter missing or wanted persons alerts. People repeatedly encounter alerts and yet recoveries via sightings occur infrequently. Although infrequent, recoveries via sightings occur, and field research has demonstrated that the majority of searchers will not notice a missing person in their midst even under very good conditions (Moore & Lampinen, The design of Experiment 2 was analogous to the experience of security and law enforcement officers being on the lookout for suspicious behavior or classes of unspecified objects as in monitoring crowds, live or recorded CCTV footage, and scanning baggage in screeners at the airport while conducting an ongoing task. Importantly, we know that encountering targets in these settings can occur at low prevalence rates and that this affects performance at detecting targets in visual and hybrid search tasks (Wolfe et al., In the current research, we did not employ a traditional filler task before having participants begin the task. Many consider this to be a hallmark feature of the prospective memory paradigm. In the current research, a filler task presented multiple problems. The filler task could have had a greater impact on the first block than proceeding blocks. In addition, while filler tasks are designed to engage the mind to eliminate other content from working memory, it is difficult to ensure participants are truly engaged and participants in the prior experience block may have engaged with the filler task less than participants in the no prior experience block. When taken in the context of the larger psychology literature, the tasks we used still most closely resembles a prospective memory task and our results converge with related prospective memory studies. Additionally, people sometimes form intentions and then immediately begin a task wherein they have the chance to fulfill those intentions. Lastly, participants in our study performed comparably on all measures to other prospective memory studies. Most notably, participants in our study performed similarly, and in fact, somewhat worse, at allocating attention to the prospective memory task. This indicates that participants were not performing the tasks purely as a vigilance task.Overall, we found that a repeated lack of prior experience in encountering prospective memory targets when they were expected to appear reduced prospective memory accuracy, even within-subjects, on a final task wherein prospective memory targets appeared. In addition, when the prospective memory task was well defined participants who had prior experience encountering targets devoted more attention toward the prospective memory task than participants who had prior experiences wherein they did not encounter targets they were told to search for. This research has implications for understanding the impact of past experience on effort on related prospective memory tasks. This effect needs to be investigated further in the relevant applied contexts such as missing and wanted persons search and baggage screening to understand what impact failing to encounter targets has on future search performance and how to design interventions to reduce this problem.


Bile acids (BAs) are closely related to nutrient supply and modified by gut microbiota. Gut microbiota perturbations shape BA composition, which further affects host metabolism.We investigated BA profiles in plasma, feces, and liver of mice fed ad libitum, fasted for 24 h, fasted for 24 h and then refed for 24 h using ultraperformance liquid chromatography coupled to tandem mass spectrometry. Gut microbiota was measured by 16S rRNA gene sequencing. Expressions of BA biosynthesis‐related genes in the liver and BA reabsorption‐related genes in the ileum were analyzed.Akkermansia, Parabacteroides, Muribaculum, Eubacterium_coprostanoligenes and Muribaculaceae were increased in the fasting mice whereas Lactobacillus and Bifidobacterium were decreased. All these changes in BAs and gut microbiota were recovered under refeeding. Akkermansia was negatively correlated with plasma levels of unconjugated PBAs, unconjugated SBAs and glucose, whereas it was positively correlated with plasma conjugated SBAs, fecal unconjugated PBAs, and fecal unconjugated SBAs.Compared with the controls, unconjugated primary BAs (PBAs) and unconjugated secondary BAs (SBAs) in plasma were decreased whereas conjugated SBAs in plasma, unconjugated PBAs, unconjugated SBAs and conjugated SBAs in feces, and unconjugated SBAs in liver were increased in the fasting mice. The expression of BA biosynthesis‐related genes in the liver and BA reabsorption‐related genes in the ileum were decreased in the fasting mice compared with the controls. Compared with the controls, We characterized the BA profiles, gut microbiota, and gene expression responsible for BA biosynthesis and intestinal reabsorption to explore their rapid changes in response to food availability. Our study highlighted the rapid effect of nutrient supply on BAs and gut microbiota. HighlightsFor the first time, our study contributed a systematic evaluation of bile acid (BA) profiles in plasma, feces and liver tissue and gut microbiota changes triggered by fasting‐refeeding cycle in mice.Our work clearly demonstrated that overnight fasting leads to significant, but reversible changes in BA profiles and gut microbiota composition.Our results demonstrated that gene expression responsible for BA biosynthesis and intestinal reabsorption were suppressed under fasting status, whereas refeeding ameliorated this effect.Our study emphasized the important and rapid effects of nutrient supply on BA profiles and gut microbiota. Alterations in gut microbiota composition are associated with various metabolic functions, including energy absorption, glucose homeostasis, and intestinal barrier homeostasis.Akkermansia was increased in the fasting state and negatively correlated with plasma unconjugated PBAs, plasma unconjugated SBAs, and glucose levels, whereas it was positively correlated with plasma conjugated SBAs, fecal unconjugated PBAs, and fecal unconjugated SBAs. Hence, our research characterized the BA profiles, gut microbiota, gene expression related to BA biosynthesis, and intestinal reabsorption in fasting and refeeding mice, identifying the rapid changes in response to fasting‐feeding cycle. Our results thus highlighted the important and rapid effects of nutrient supply on BA profiles and gut microbiota.In this study, we contributed a systematic evaluation of changes of BA profiles , gene expression related to BA biosynthesis and intestinal BA reabsorption, as well as gut microbiota in mice fed ad libitum, fasted for 24 h, fasted for 24 h and then fed ad libitum for 24 h. We identified specific and rapid changes in BA components, microbial taxa, BA biosynthesis, and intestinal reabsorption‐related gene expression in response to nutrient availability. Of note, 22.1n = 36) purchased from Shanghai Laboratory Animal Center were housed in groups of four mice per cage in a stable environment with free access to water. Mice were reared without any intervention for 1 week to acclimate to the new environment and then randomly divided into three groups: (a) mice in normal chow diet (NCD) fed ad libitum, (b) mice in NCD starved for 24 h, and (c) mice in NCD starved for 24 h and then fed ad libitum for 24 h  in combination with multiple reactions monitoring methods on the ACQUITY UPLC system (Waters). All calibration standard solutions were mixed at appropriate concentrations and analyzed every 10 samples for quality control.BAs were divided into four groups according to the degree of conjugation,2.32O . All samples were amplified in triplicate. PCR amplification conditions were as follows: initial denaturation at 95°C for 3 min, amplification for 27 cycles , single extension at 72°C for 10 min, and end at 4°C. PCR products were extracted from 2% agarose gel, then purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences) and quantified using Quantus™ Fluorometer (Promega). 16S rRNA gene sequencing data were processed using the Illumina MiSeq PE300/NovaSeq PE250 platform, details are provided in the Supplementary Materials.Using the E.Z.N.A.® Soil DNA Kit (Omega Bio‐tek), total microbial genomic DNA was extracted from feces. DNA quality and concentration were determined by 1.0% agarose gel electrophoresis and NanoDrop ND‐2000 spectrophotometer (Thermo Scientific Inc.). DNA was stored at −80°C for subsequent analysis. The V3‐V4 hypervariable region of the bacterial 16S rRNA gene was amplified by ABI GeneAmp® 9700 polymerase chain reaction (PCR) thermocycler (ABI) with primer pairs 338F (5'‐ACTCCTACGGGAGGCAGCAG‐3') and 806R (5’‐GGACTACHVGGGTWTCTAAT‐3').2.4Total RNA was extracted from liver and ileum tissues using Eastep Super Total RNA Extraction Kit (Promega) and 1 μg of RNA was transcribed following the manufacturer's instruction by using the Reverse Transcription System Kit (Promega), which produced high‐quality complementary DNA. A real‐time PCR was performed on LightCyclerH 480 (Roche) using SYBR Green II Master (Takara). All mRNA quantification data were normalized to the housekeeping 36B4 gene. All samples were run in duplicate in a single 384‐well reaction plate, and data of the reaction were collected and analyzed according to the ∆∆CT method. Primers used in this study are listed in the Supplementary Materials.2.5t test. All of these data were tested for the normality before using Student's t test. For nonnormal distributions, nonparametric statistical analysis such as Mann–Whitney U‐test was used. GraphPad prism version 5.01 (GraphPad software) and SPSS Statistics version 23.0 (IBM SPSS) were used for statistical analyses. Using Spearman's correlation analysis, correlations between BA levels, glucose level, and abundance of gut microbiota were performed, and the post hoc correction was performed using the false discovery rate method. Differences between experimental groups were considered significant at p < .05. The Majorbio Cloud platform (https://cloud.majorbio.com) was used to carry out bioinformatic analysis of gut microbiota. See the Supplementary Materials for detailed analytical methods.Results are expressed as mean ± SEM. BA data were analyzed using one‐way analysis of variance (ANOVA) test followed by Bonferroni's post hoc test. Significant differences between the two groups were assessed by unpaired two‐tailed Student's 33.1We first measured BA levels in plasma, feces, and liver tissues from the libitum fed mice, fasting mice, and refeeding mice Figure  using UPNext, we found that fecal unconjugated PBAs , unconjugated SBAs , and conjugated SBAs  were significantly elevated in the fasting mice compared with the libitum fed mice. Although the level of conjugated PBAs subgroup did not change in fasting mice, T‐β‐MCA, TUDCA, and GCA were significantly increased. Similarly, these changes were overall recovered in mice after refeeding, back to the levels of those in the libitum fed mice Figure . Of noteIn liver, compared with the libitum fed mice, the levels of unconjugated PBAs subgroup and conjugated PBAs subgroup did not change in the fasting mice, whereas changes in individual BAs were observed. In the fasting mice, level of UDCA in unconjugated PBAs were significantly increased, whereas TUDCA, T‐α‐MCA, and T‐β‐MCA in conjugated PBAs were significantly decreased. The alterations of these BAs were reversed in the refeeding mice Figure . UnconjuTo determine which fecal BAs associated the most with plasma BAs under fasting‐refeeding condition, we further examined the correlation between BAs in plasma and feces Figure . Levels 3.25‐C27‐steroid dehydrogenase (HSD3B7), CYP8B1, aldo‐keto reductase family 1 member D1 (AKR1D1) and CYP27A1, and “alternative pathway” involves CYP27A1 and CYP7B1. A species‐specific sterol 6β‐hydroxylase (CYP2C70) is required for the conversion of MCAs from CDCA in mouse liver. Unconjugated PBAs are subsequently conjugated to glycine or taurine by BAAT and BACS  , whreeas in refeeding mice, bacterial diversity was again decreased   Figure . Further) Figure . Venn di) Figure . A list ) Figure . OverallBifidobacterium), RF39 , Erysipelotrichales , Clostridia_UCG‐014 , Christensenellales , and Eubacteriales  , Verrucomicrobiota , Vampirivibrionia , Proteobacteria , Peptococcales , Clostridia (from order to genus), Defluviitaleaceae , and Oscillospirales   were significantly enriched  tool to identify specific communities within different groups Figure .38 In ) Figure . In the ) Figure . In the d Figure .Lactobacillus. Fasting for 24 h increased the relative abundance of norank_f_Muribaculaceae and Akkermansia and decreased the relative abundance of Lactobacillus and Bifidobacterium, compared with the libitum fed mice. These alterations were reversed in mice after refeeding for 24 h  and Bifidobacterium  were significantly decreased, whereas norank_f_Muribaculaceae , Akkermansia , Muribaculum , Parabacteroides , and norank_f_Eubacterium_coprostanoligenes_group  were significantly increased in fasting mice, compared with libitum fed mice. These alterations were also reversed in mice after refeeding for 24 h  and unconjugated SBA levels , whereas norank_f_Muribaculaceae and Akkermansia were negatively correlated with unconjugated PBAs and unconjugated SBA levels. Akkermansia showed different correlation patterns with conjugated SBA levels. It was positively correlated with the levels of T‐ꙍ‐MCA, TDCA, and TLCA and negatively correlated with THCA levels , GCA (p = .043), and glucose (p = .001) were significantly associated with the gut microbiota structure , DCA (p = .002), and LCA (p = .001) were significantly associated with the gut microbiota structure , TCDCA (p = .018), TDCA (p = .002), and TLCA (p = .001) were significantly associated with the gut microbiota structure , gut microbiota and BA biosynthesis and intestinal BA reabsorption‐related genes expression changes triggered by fasting‐refeeding cycle in mice. We found that in the fasting mice, plasma unconjugated PBAs and plasma unconjugated SBAs decreased whereas plasma conjugated SBAs, fecal unconjugated PBAs, fecal unconjugated SBAs, fecal conjugated SBAs, and liver unconjugated SBAs were increased, compared with the libitum fed mice. All these changes in BA composition were completely recovered after refeeding. BA biosynthesis in liver and intestinal reabsorption were suppressed under fasting status, whreeas refeeding ameliorated the decrease in biosynthesis and reabsorption of BAs due to fasting. Correspondingly, comparing with the gut microbiota in the libitum fed mice, 35 such as Lactobacillus and Bifidobacterium as uncovered in our study).In our results, compared with the ad libitum fed mice, the levels of plasma unconjugated PBAs and unconjugated SBAs were decreased in the fasting mice, whereas fecal unconjugated PBAs, unconjugated SBAs, and conjugated SBAs were increased in the fasting mice. These BAs showed an opposite changing pattern in feces and plasma in response to the fasting‐refeeding cycle. This might be because of the alterations in hepatic biosynthesis and intestinal reabsorption in the fasting mice, which play a critical role in the regulation of enterohepatic circulation of BAs, BA pool size, and BA composition.CR intervention induces a long‐term reduction in nutrition supply. A clinical trial including 80 postmenopausal women who were overweight or obese showed 8‐week VLCD intervention increased HCA level and decreased GCDCA, GDCA, DCA TCA, and TDCA levels in feces, compared with a 4‐week weight maintenance.Lactobacillus and Bifidobacterium decreased whereas Akkermansia, Parabacteroides, Muribaculum, Eubacterium, and Muribaculaceae increased in mice fasted for 24 h, and these alterations were also found to be reversed in mice refed for 24 h. Lactobacillus and Bifidobacterium were involved in BA metabolism including BA deconjugation via BSH and 7α‐dehydroxylation via 7α‐dehydroxylase.Lactobacillus and Bifidobacterium in fasting mice presented in our study. Thus, the bacterial contribution to the observed BA phenotype in response to rapid fasting‐refeeding cycle is likely connected with the regulation of the conversion of conjugated BAs. More importantly, our study showed overnight fasting also induced a marked, but reversible change in the relative abundances of Muribaculum, Parabacteroides, Eubacterium, and Muribaculaceae, which have not been reported before. Bacteria in the family Muribaculaceae and genus Muribaculum produce propionate as the final product of fermentationParabacteroides was found to alleviates obesity by producing succinate and SBAs in mice.Eubacterium, a representative family of butyrate‐producing species, can alleviate intestinal inflammation, T2DM and obesity symptoms by producing short‐chain fatty acids.Consistent with the effects of sustained CRLactobacillus and Bifidobacterium decreased whereas Akkermansia increased in mice fasted for 24 h, and these alterations were reversed in refeeding mice, reflecting the rapid changes of these taxa to nutrient supply. Previous studies showed the abundance of Akkermansia was increased whereas the abundance of Bifidobacterium was decreased in wild type C57BL/6 mice fed NCD after IF and CR intervention,Lactobacillus, previous studies have shown inconsistent results, with some studies suggesting that CR and IF interventions led to an increase in the abundance of LactobacillusLactobacillus.Lactobacillus and Akkermansia compared with the control mice.Bifidobacterium and remarkably increased the relative abundances of Lactobacillus and Akkermansia compared with the mice fed ad libitum.Lactobacillus‐predominated microbial community was attained in mice after 2‐week 30% CR.Lactobacillus, whereas 5:2 IF (2‐day fasting followed by a 5‐day ad libitum period) regimen led to a significant reduction in the Lactobacillus.Akkermansia, and these effects disappeared after the cessation of fasting.Of note, we found Lactobacillus, Bifidobacterium, and Akkermansia were closely related to the changes of BAs and glucose during the fasting‐refeeding cycle. Lactobacillus and Bifidobacterium were proved to be related to BA metabolism in previous studies.Akkermansia has been proven to improve obesity, T2DM, hepatic steatosis, intestinal inflammation, and different cancers in mice.Akkermansia was negatively correlated with plasma unconjugated PBAs, plasma unconjugated SBAs, and glucose levels, whereas it was positively correlated with plasma conjugated SBAs, feces unconjugated PBAs, and feces unconjugated SBAs. We highlighted the potential impact of Akkermansia on regulating BA metabolism during the fasting‐refeeding cycle. Several small sample size clinical studies have also demonstrated the change of BAs is closely related to the change of gut microbiota under CR diet intervention. Schwartzenberg et al. found VLCD‐induced Clostridium difficile enrichment was associated with decreased BAs levels.Bifidobacterium, has great impact on the SBAs metabolism pathway.Akkermansia and allolithocholic acid (an isomer of LCA).We noticed that changes of microbiota taxa especially Our correlation analysis demonstrated that gut microbiota showed closer associations with plasma BAs than fecal BAs, suggesting changes in plasma BA profile were most closely related to the changes in gut microbiota during rapid fasting‐refeeding cycle. As BAs circulate, the majority (~ 95%) are actively absorbed across the distal small intestine wall.Furthermore, we found that the BA changes in plasma, feces, and liver during the fasting‐refeeding cycle may also contribute differently to microbial community. Plasam glucose contributed the most to the changes of gut microbiota, followed by plasma GCA and TDCA. LCA in feces and TDCA in liver contributed the most to gut microbiota remodeling. Thus, our study demonstrated that during rapid fasting‐refeeding cycle, there existed an interaction between BAs and gut microbiota, which could conceivably have a significant impact in the dietary intervention‐resulted metabolic outcome. A comprehensive assessment of BA profile‐gut microbiota crosstalk during the fasting‐refeeding cycle could provide insights into possible pathways that define the pathogenesis of metabolic diseases.In summary, our work clearly demonstrated that overnight fasting led to significant, but reversible changes in BA profile in plasma, feces, and liver. Overnight fasting also induced reversible alterations in the gut microbiome structure. BA biosynthesis in liver and intestinal BA reabsorption were suppressed under fasting status, whereas refeeding ameliorated these changes. More broadly, this study emphasized the important and rapid impact of nutrient supply on BA profile and gut microbiota, allowing the mechanistic dissection of complex relationships between the host, microbiome, and BAs metabolism.The authors declare that the study was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.Data S1. Supporting InformationClick here for additional data file.Figure S1.Baseline data of bile acids profile and gut microbiota. Baseline data of fecal bile acids profile (A) among the three groups. Baseline α‐diversity (Shannon index) of the three groups at the operational taxonomic unit (OTU) level (B). Principal coordinate analysis (PCoA) based on Bray–Curtis dissimilarity at the OTU level (C). Statistical significance between the experimental groups was evaluated using analysis of variance with Bonferroni correction for multiple testing; n = 4; *p < .05. **p < .01. Data are presented as mean ± SEM.Click here for additional data file.Figure S2. Correlations between bile acids in plasma and feces. Spearman's correlations of the bile acids concentration in plasma and feces. The gradient colors represent the correlation coefficients, with red color being positive and blue color indicating negative. *p < .05, **p < .01 .Click here for additional data file.Figure S3. Venn diagram for the comparison of bacterial genera to identify common and uniquely enriched operational taxonomic units (OTUs) among the three groups.Click here for additional data file.


Its composition and structural properties constantly evolve to accommodate follicle development and expansion. Tissue preparation for culture of primordial follicles within the native ECM involves mechanical loosening; this induces undefined modifications in the ECM network and alters cell–cell contact, leading to spontaneous follicle activation.Fresh ovarian cortical biopsies were obtained from six women aged 28–38 years (mean ± SD: 32.7 ± 4.1 years) at elective caesarean section. Biopsies were cut into fragments of ∼4 × 1 × 1 mm and cultured for 0, 2, 4, or 6 days (D).Primordial follicle activation, stromal cell density, and ECM-related protein  positive area in the entire cortex were quantified at each time point using histological and immunohistological analysis. Collagen and elastin content, collagen fibre characteristics, and follicle distribution within the tissue were further quantified within each layer of the human ovarian cortex, namely the outer cortex, the mid-cortex, and the cortex–medulla junction regions.6 at day 0 (D0) to 2.8 ± 0.1 × 106 cells/mm3 at D2 (P = 0.033) and a dynamic remodelling of the ECM. Notably, collagen content gradually fell from 55.5 ± 1.7% positive area at D0 to 42.3 ± 1.1% at D6 (P = 0.001), while elastin increased from 1.1 ± 0.2% at D0 to 1.9 ± 0.1% at D6 (P = 0.001). Fibronectin and laminin content remained stable. Moreover, collagen and elastin distribution were uneven throughout the cortex and during culture. Analysis at the sub-region level showed that collagen deposition was maximal in the outer cortex and the lowest in the mid-cortex , and cortical collagen staining overall decreased from D0 to D2  then stabilized. Elastin showed the converse distribution, being most concentrated at the cortex–medulla junction , and cortical elastin peaked at D6 compared to D0 . This was corroborated by a specific signature of the collagen fibre type across the cortex, indicating a distinct phenotype of the ovarian cortical ECM depending on region and culture period that might be responsible for the spatio-temporal and developmental pattern of follicular distribution observed within the cortex.Primordial follicle activation occurred concomitantly with a loosening of the ovarian cortex during culture, characterized by an early decrease in stromal cell density from 3.6 ± 0.2 × 10N/A.Ovarian cortical biopsies were obtained from women undergoing caesarean sections. As such, the data obtained may not accurately reflect the ECM distribution and structure of non-pregnant women.in vitro activation and in vitro growth techniques.Clarifying the composition and architecture signature of the human ovarian cortical ECM provides a foundation for further exploration of ovarian microenvironments. It is also critical for understanding the ECM–follicle interactions regulating follicle quiescence and awakening, leading to improvements in both Medical Research Council grant MR/R003246/1 and Wellcome Trust Collaborative Award in Science: 215625/Z/19/Z. The authors have no conflicts to declare.N/A. Primordial follicles are the most abundant population of ovarian follicles and represent the functional unit of the mammalian ovary. The majority of them are quiescent at any time, and in women this can be for decades, ensuring a prolonged reproductive lifespan. From primordial follicle formation until menopause, resting follicles are recruited into the growing pool, supplying the ovary with its population of growing follicles. This process of folliculogenesis makes the ovary one of the most dynamic organs of the human body during the reproductive life span of women.The repetitive processes of follicular development and degeneration (atresia), ovulation, corpus luteum formation, and regression require extensive structural remodelling during each reproductive cycle, provided by its extracellular matrix (ECM) . Among tin vitro studies of isolated murine and primate follicles grown in alginate hydrogels of varying concentrations further confirm that a stiff environment is necessary to maintain primordial follicle quiescence and survival, but negatively affects secondary follicle growth, steroid production, and meiotic potential .Fresh ovarian cortical biopsies were obtained from six women aged 28–38 years (mean ± SD: 32.7 ± 4.1 years) undergoing elective caesarean section. Ovarian tissue was transported to the laboratory in dissection medium . At the laboratory, excess stromal and haemorrhagic tissue as well as follicles measuring >80 μm were removed and the ovarian cortex was mechanically loosened, as previously described . Briefly, the tissue was anchored to the base of the petri dish with a needle, and using the blunt edge of a scalpel blade, gentle pressure was applied along the tissue surface, stretching the tissue away from the anchor point such that the size of the cortical sample is increased by at least 10%. The cortex was then cut into fragments of ∼4 × 1 mm and ∼1 mm thick.2 with half the media being removed and replaced every second day. After 0, 2, 4, or 6 days of culture, the ovarian fragments were fixed in 10% normal buffered formalin for histological and immunohistological evaluation.One to three fragments were selected from each biopsy as Day 0 (D0) controls. The remaining fragments (two to three per day of culture) were incubated individually in 24-well cell culture plates  as previously described (Fixed ovarian fragments were dehydrated in increasing concentrations of ethanol (70–100%), embedded in paraffin and serially sectioned at 5 μm thickness. Every fifth section was stained with haematoxylin and eosin, and both follicle counts and stromal cell density were assessed. Follicles were classified according to their developmental stage as primordial follicles (oocyte surrounded by a few flattened granulosa cells), transitory follicles , or growing follicles (oocyte surrounded by one or more complete layer(s) of cuboidal granulosa cells). Only follicles that contained an oocyte nucleus were counted to prevent double counting. Follicle spatial distribution within each sub-region of the ovarian cortex, namely the outer cortex, the mid-cortex, and the cortex–medulla junction, was assessed by dividing the cortex into three equal layers of 300 µm each from the epithelium surface to the medulla side. The outer cortex and cortex–medulla junction regions were defined in the sections based on the identification of the surface epithelium and tunica albuginea layers on one side, and small cortical arterioles on the opposite side, respectively.For PicroSirius Red (PSR) staining, sections were deparaffinized and rehydrated in a series of ethanol baths of decreasing concentrations. The slides were immersed in a PSR staining solution  for 1 h at room temperature, then washed twice with 0.5% glacial acetic acid and three times with 100% ethanol. The slides were cleared in xylene and mounted with DPX. All slides from the same patient were processed at the same time to minimize staining variation.Immunohistochemistry and immunofluorescence were performed for the evaluation of various ECM components. Briefly, after deparaffinization and rehydration, slides to be stained for laminin, fibronectin, and elastin were immersed in Tris-EDTA buffer (pH 8.5) for 40 min at 95°C, then immersed in 3% (v/v) hydrogen peroxide to quench endogenous peroxidase activity. Non-specific binding sites were blocked using 5% normal goat serum for 1 h at room temperature. Sections were subsequently incubated overnight at 4°C with Anti-LAMB2 , Anti-FN1 , or Anti-elastin . The next day, slides targeting laminin and fibronectin were incubated for 1 h at room temperature with a biotinylated secondary antibody , rinsed twice then incubated with Streptavidin Horseradish Peroxidase for 30 min at room temperature. After washing, the signal was visualized using 3,3′-diaminobenzidine, then counterstained with haematoxylin, dehydrated and mounted with DPX. Slides targeting elastin were incubated with an Alexa Fluor 488-conjugated goat anti-rabbit secondary antibody  for 1 h at room temperature, washed then mounted in Vectashield with DAPI (Vector Labs). Negative controls were obtained by replacing the primary antibody with blocking solution containing non-immune serum, while human testis was used for positive controls of all tested antibodies . All was3) per day of culture and per patient were captured using an inverted microscope (Leica DMIRB) equipped with Retiga 2000R camera and QCapturePro software . Stromal cell nuclei were counted using FIJI (Image J) and the mean stromal cell density was calculated by dividing the stromal cell number by the volume of tissue analysed.Brightfield images of 15 random fields  and fluorescent (elastin) images of entire cortical sections or each layer of the human ovarian cortex, namely the outer cortex, the mid-cortex, and the cortex–medulla junction regions, were captured using a light microscope (Leica) at ×2.5  and ×5 (elastin) or ×40 magnifications . Quantification of the average area of positive staining per condition and per patient was performed using FIJI.2 regions of interest across the ovarian cortex. CT-FIRE was applied to overlay each collagen fibre and then extract information of the individual fibre characteristics including their length, width, straightness, and angle, while CurveAlign was used to overlay each collagen fibre, which was then converted into a direction heat map, allowing for quantification of the coefficient of alignment.PSR samples were further analysed under polarized light for bulk  and individual fibre assessment of collagen features. Birefringent images were acquired using an Axiovert 200 microscope  at ×40 magnification. Collagen fibre colour, which reflects fibre diameter and packing density, was quantified using a custom FIJI macro. Briefly, the hue (colour) of each pixel was determined and a colour threshold was used to isolate the three main colours seen in PSR-stained samples under polarized light: red (thick fibres), yellow (mid-sized fibres), and green (thin fibres). In order to streamline the data obtained and juxtapose them with previously described collagen measurements in the ovary, the thresholds on the hue histogram were set as follows, based on 2; the straightness coefficient represents how close the shape of the fibre is to straight line and ranges from 0 to 1, where 1 indicates a perfectly straight fibre and lower values indicate curvier fibres; fibre angle defines the angle between the horizontal axis and a straight line between the fibre’s curve endpoints, ranging between 0° and 180°; and the alignment coefficient indicates the dispersion of the fibre orientations on a scale from 0 to 1, with 1 indicating perfectly aligned fibres, and smaller values representing more randomly distributed fibres. Images were processed as ≈1000 fibres per image; three images per patient per group; six patients per group; and >17 000 fibres analysed per group.Fibre density was calculated as the total number of fibres per 100 µmpost hoc least significant difference multiple comparisons was applied for the analysis of follicle activation, stromal cell density, and the percentage of ECM components positive area in the overall tissue. The study was based on a limited number of patients, and it is acknowledged that natural biological variation exists in human samples. Thus, for multivariable analyses, we first calculated the random effects variance in primordial follicle density using the intraclass correlation coefficients (ICCs) . Normality was verified by the Levene test then one-way ANOVA followed by Tukey’s s (ICCs) . ICC wasP < 0.0001), and this drop was balanced by a significant increase in the percentage of both transitory follicles from 33.6 ± 3.1% to 51.6 ± 1.6% and growing follicles from 10.0 ± 1.7% to 25.0 ± 1.5% . These results confirmed the culture-induced remodelling at the follicular level, so we deepened our exploration of the ovarian cortical stromal tissue and its ECM.A total of 5703 follicles from six patients were examined under the light microscope and classified according to their developmental stage . Primord6 cells/mm3 at D0 to 2.8 ± 0.1 × 106 cells/mm3 at D2 (P = 0.033) and then remained stable throughout the remainder of the culture period  (P = 0.001)   . In cont= 0.001) . Elastin= 0.001) . Fibrone= 0.001)  and leve = 0.88) . Taken t culture .P < 0.0001), and decreased from D0 to D2  then stabilized. The decline in collagen over time was localized to the outer cortex, dropping from 77.0 ± 1.9% positive staining at D0 to 66.3 ± 1.5% at D2 (P = 0.002) then stabilizing, while collagen remained stable in both the mid-cortical and the cortex–medulla junction collagen throughout culture  (P < 0.0001), and peaked at D6 compared to D0 . The elastin increase was particularly visible at the medullary border, rising from 2.2 ± 0.2% positive staining at D0 to 5.6 ± 0.5% at D6 (P < 0.0001), and more moderately at the mid-cortex, from 1.1 ± 0.2% to 2.7 ± 0.3% (P = 0.004), while its level remained stable at the outer cortex   . Convers = 0.64) . These dP = 0.002; mid-sized fibres—P = 0.003; thin fibres—P < 0.0001) but not remodelled over time   . NotablyP < 0.0001) and longer , which is consistent with an increased straightness  and reduced density . These results mirror the increased amount of thick collagen fibres observed under polarized light, suggesting a tightly packed matrix of larger collagen fibres. In contrast, collagen fibres at the cortex–medulla junction are smaller and curvier, and more randomly organized, with less alignment compared to the mid-cortical fibres . The mid-cortex is composed of a mesh-like network of sparse and thin collagen fibres, in accordance with the higher proportion of green fibres under the polarized light, yet they are long and aligned in a more orderly manner than at the cortico-medullary junction. Importantly, these aspects of the collagen micro-architecture did not seem to be reshaped during the culture period, with no significant variations in the collagen morphology in each of the cortical sub-regions over time.We further quantified collagen fibre metrics, including density, width, length, straightness, angle, and alignment, in each sub-region of the ovarian cortex and throughout the culture period, using CT-FIRE and CurveAlign software . A totalP = 0.949), but tended to move towards the centre of the ovary at the end of the culture period when compared to the ovarian surface, although not significant .In order to link follicle dynamics with ovarian cortex remodelling during culture, a total of 1058 and 1747 follicles from six patients were examined at D0 and D6, respectively, and classified according to both their developmental stage and spatial position within the tissue. Follicles were not homogenously distributed throughout the ovarian cortical parenchyma, being predominantly located in the mid-cortical sub-region, representing 67.9 ± 2.9% and 63.5 ± 4.5% of the total follicle number at D0 and D6, respectively . The proP = 0.706 and P = 0.798, respectively) and D6 . In contrast, growing follicles, including primary and secondary follicles, had an initial uniform distribution between both regions  but shifted towards the medulla border after 6 days of culture . Moreover, no secondary follicles were ever seen in the outer cortical region.The spatial distribution of follicles within the cortical sub-regions was also correlated with their stage of development. Although all follicular stages were predominantly located in the mid-cortex, primordial and transitory follicles were evenly distributed between the outer cortex and cortico-medullary sub-regions both at D0 . It is likely that the regional differences within the ovarian stroma, whether in terms of ECM composition, architecture and mechanical stress, might influence ISR action and indirectly regulate primordial follicle activation.The correspondence between primordial follicle activation and ovarian cortical ECM remodelling during culture may be related to the easing in the local mechanical tension. It has been shown that mouse oocytes in primordial follicles are compressed by surrounding granulosa cells secreting ECM proteins, leading to a state of high mechanical stress, and that incubation of mouse ovaries with a collagenase‐containing solution triggers activation  concentration with advancing gestational age (in vitro culture systems and refined bioengineered matrices for artificial ovaries, as well as contribute to improving the in vitro activation technique. Understanding ovarian ECM dynamics during homeostasis and pathological conditions has extensive clinical implications and becomes crucial to overcoming female infertility. Structural changes involving an aberrant ECM and a densely collagenized, thickened and stiffer cortex have been described in ovaries of patients with PCOS and ageing women (in vitro activation treatment is more likely to restore ovarian function in patients with POI whose ovarian cortex is stiffer (Taken together, our data confirm that changes in the overall abundance, structure, and organization of individual ECM components directly affect the 3D spatial architecture, and potentially the biomechanical properties, of the matrix surrounding cells. It is likely that cells, including those within ovarian follicles, can sense their local environment and modulate their shape and behaviour accordingly. Future investigations should aim at clarifying the role of the ovarian ECM during folliculogenesis, and more specifically, determining which biochemical and biomechanical properties of the matrisome are necessary to regulate follicle activation. Overall, characterization of the native ovarian cortical ECM and its dynamic changes during culture will facilitate the development of more effective dead008_Supplementary_Figure_S1Click here for additional data file.dead008_Supplementary_Figure_S2Click here for additional data file.dead008_Supplementary_Figure_S3Click here for additional data file.


To investigate whether the Fibrosis‐4 index can help stratify the risk of diabetes mellitus in patients with fatty liver disease.Based on fatty liver disease and Fibrosis‐4 index , we retrospectively divided 9,449 individuals, who underwent at least two annual health checkups, into four groups stratified by sex: normal; high Fibrosis‐4 index without fatty liver disease; low Fibrosis‐4 index with fatty liver disease; and high Fibrosis‐4 index with fatty liver disease.Onset rates for diabetes mellitus in the normal, high Fibrosis‐4 index without fatty liver disease, low Fibrosis‐4 index with fatty liver disease and high Fibrosis‐4 index with fatty liver disease groups were 1.6%, 4.3%, 6.8% and 10.2%, respectively, in men, and 0.6%, 0.9%, 5.3% and 7.0%, respectively, in women. Compared with the normal group, the high Fibrosis‐4 index without fatty liver disease, low Fibrosis‐4 index with fatty liver disease and high Fibrosis‐4 index with fatty liver disease groups were at a significant risk for diabetes mellitus onset in both male and female participants. Furthermore, in both sexes, high Fibrosis‐4 index with fatty liver disease remained a significant risk factor on multivariate analysis .Individuals with fatty liver disease and high Fibrosis‐4 index had a higher risk of diabetes mellitus onset. Therefore, Fibrosis‐4 index can help stratify the risk of diabetes mellitus in patients with fatty liver disease and identify patients requiring intervention. Fatty liver disease is a well‐known risk factor for the development of type 2 diabetes mellitus. However, it has a high morbidity and affects a large number of patients; therefore, it is difficult to provide adequate attention and intervention to all patients. The Fibrosis‐4 index can be used to stratify the risk of developing type 2 diabetes mellitus in patients with fatty liver disease and efficiently identify patients who require intervention.  Type 2 diabetes mellitus is considered a risk factor for various diseases, such as diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, atherosclerosis and cancerFatty liver disease (FLD) is a well‐known risk factor for the development of type 2 diabetes mellitusIn the present retrospective, longitudinal study, we investigated whether the FIB‐4 index could be used to stratify the risk of developing diabetes mellitus in individuals with FLD.A total of 9,817 Japanese individuals  aged 21–78 years were enrolled in this community‐based, longitudinal cohort study. Participants who underwent at least two annual health checks between April 2003 and March 2017 at the Ehime General Health Care Association were eligible for enrollment. During the health check, data pertaining to medical history, medication usage, lifestyle habits, physical measurements and results of routine blood tests were collected. The participants wore a light gown and no footwear when their bodyweight and height measurements were taken. Blood pressure was measured using an automatic sphygmomanometer in the sitting position. Blood samples were collected after fasting for >10 h, and platelet and liver enzymes – such as AST, ALT and gamma‐glutamyl transpeptidase – were analyzed. Lipid profiles were created by measuring triglycerides and high‐density lipoprotein cholesterol. The risk of diabetes mellitus was determined based on the fasting plasma glucose and hemoglobin A1c (HbA1c) levels. The creatinine level was measured to assess renal function.9/L] × ALT [IU/L]1/2)The FIB‐4 index was calculated using the formula (age × AST [IU/L])/, registered in the Japanese clinical trial registry  and carried out in accordance with the tenets of the Declaration of Helsinki. The study protocol fulfilled the local ethical and legal requirements, and the Guidelines for Good Clinical Practice. All participant information and data were managed anonymously, and stored in a secure database during the study period. Because this was a retrospective study and all participant data were de‐identified, the ethics committee waived the need to obtain informed consent from the participants.The FIB‐4 index data were analyzed based on a cut‐off value of 1.3; a lower value indicated a lower risk of having fibrosis; recommendations were made at the first screening for NAFLDn = 120), fasting glucose levels ≥6.99 mmol/L (n = 267), HbA1c ≥6.5% (n = 310),and missing data , systolic blood pressure (mmHg), HbA1c (%), creatinine (μmol/L), total cholesterol (mmol/L), triglycerides (mmol/L), exercise habits, snacking habits, drinking habits, smoking status and family history of diabetes mellitus, with or without components of the FIB‐4 index and FLD, were carried out to calculate the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for developing diabetes mellitus. All data are expressed as the median (interquartile range) or number (percentage); a P‐value <0.05 was considered to show statistical significance.JMP software version 14.2.0  was used for all statistical analyses. Normal assumptions were assessed using the Lilliefors test; however, as no continuous variables were normally distributed, the Steel–Dwass test was used for the analysis; the χn = 96), HbA1c (n = 116) and the start of antidiabetic drug treatment (n = 27), in the normal, high FIB‐4 without FLD, low FIB‐4 with FLD and high FIB‐4 with FLD groups were 1.6%, 4.3%, 6.8% and 10.2% in men, and 0.6%, 0.9%, 5.3% and 7% in women, respectively  in men  or diabetes mellitus, and examined the effects of FLD, insulin resistance (homeostatic model assessment [HOMA]‐IR ≥2.0), central obesity  and liver fibrosis (FIB‐4 index) on the development of diabetes mellitus. They observed that the combination of FLD, insulin resistance and the highest quartile of FIB‐4 index significantly increased the risk of diabetes mellitus onset in men, but not in womenThe FIB‐4 index is widely used as a simple predictive marker of fibrosis in the liver, and is associated with liver‐related diseases and pathophysiology of diabetes mellitus. In a longitudinal study, Ampuero et al.The mechanism by which the FIB‐4 index enhances the risk of diabetes mellitus onset in individuals with FLD might be associated with its effect on insulin secretion. Fujita In a previous study, our colleagues examined 20 patients with and 21 patients without cirrhosis; they compared them with delta C‐peptide immunoreactivity using the glucagon challenge test and pancreatic perfusion parameters through dynamic contrast‐enhanced ultrasoundn = 1], platelet values [n = 5], data regarding exercise habits [n = 2] and data regarding snacking habits [n = 2]). However, the present study had several limitations. First, although abdominal ultrasonography can diagnose FLD with a high sensitivity and specificityThe present study had several strengths; first, it was a community‐based study. Second, health workers asked all participants to complete a questionnaire before physical examination; thus, although this was a large study, there were limited missing data , and it conforms to the provisions of the Declaration of Helsinki.Informed consent: N/A. As this study protocol was retrospective in manner and all participants’ data were de‐identified, it was not necessary to obtain informed consent from participants in this study.Registry and the registration no. of the study/trial: 3 October 2013. University Hospital Medical Information Network ID: UMIN000011953.Animal studies: N/A.


B: by 6.74 log10 IU/ml; p > .05) at Week 240. Patients who achieved undetectable levels of serum HBV DNA (<20 IU/ml) at Week 240 were similar between groups . Both groups achieved similar HBeAg seroconversion rates at week 240 . Both groups achieved similar normalization of ALT  at Week 240 (p > .05). For the HBeAg‐negative CHB patients, the mean HBV DNA level had similarly decreased from baseline in both groups  at Week 240. Patients who achieved undetectable levels of serum HBV DNA at Week 240 were similar between groups (A:100% vs. B:100%). Both groups achieved similar normalization rates  of ALT at Week 240 (p > .05). In conclusion, long‐term ETV maleate treatment was safe and efficient in Chinese CHB predominantly of genotype B or C.This study aimed to evaluate the efficacy and safety of entecavir(ETV) versus ETV maleate in Chinese patients with chronic hepatitis B(CHB). This was a randomized, double‐blind, double‐dummy, controlled, multicentre study. Patients were randomly assigned to receive 48 weeks of treatment with 0.5 mg/day ETV (group A) or 0.5 mg/day ETV maleate (group B), then, all patients received treatment with 0.5 mg/day ETV maleate from week 49 onwards. Patients were regularly followed up. Serum hepatitis B virus (HBV) markers were detected. Adverse events (AE) were recorded. The primary endpoint was the decline in HBV DNA in each group at the end of treatment. Secondary endpoints included the rate of HBV DNA below the lower limit of detection (LLOD) (20 I U/ml) at the end of treatment, the rate of hepatitis B e antigen (HBeAg) loss, the rate of HBeAg seroconversion and serum alanine aminotransferase (ALT) normalization. One hundred and thirty‐seven (71 in group A) patients with HBeAg‐positive CHB and 46 (21 in group A) patients with HBeAg‐negative CHB completed the 240‐week treatment and follow‐up. Baseline characteristics were well balanced between the two groups. For the HBeAg‐positive CHB patients, the mean HBV DNA level had similarly decreased from baseline in both groups (A: by 6.67 log AEadverse eventALTalanine transaminaseanti‐HBehepatitis B e antibodyARadverse reactionCHBchronic hepatitis BETVentecavirFASfull analysis setHBeAghepatitis B e antigenHBVhepatitis B virusHCChepatocellular carcinomaLLODlower limit of detectionLOCFlast observation carry‐forwardMmedianNAnucleos(t)ide analoguesPCRpolymerase chain reactionQinterquartile rangeSAEserious adverse eventSDstandard deviationTAFtenofovir alafenamideTDFtenofovirULNupper limit of normal1Hepatitis B virus (HBV) infection can lead to chronic liver diseases such as cirrhosis and liver cancer. Chronic HBV infection is still a substantial health burden in China, with 70 million people chronically infected by HBV.Although the efficacy and safety of long‐term ETV therapy have been reported previously, the limitations of those reports make it necessary to investigate the long‐term use of ETV at the dose of 0.5 mg daily in genotypes B and C chronic HBV infection.22.14 IU/ml for those with positive hepatitis B e antigen (HBeAg) or HBV DNA ≥2 × 103 IU/ml for those with negative HBeAg, alanine transaminase (ALT) ≥1.3 and <10 × upper limit of normal (ULN) and serum total bilirubin ≤2.5 × ULN. Patients with evidence of liver decompensation, hepatocellular carcinoma, co‐infection or super‐infection with other viruses, such as hepatitis A virus, hepatitis C virus, hepatitis delta virus, hepatitis E virus, Epstein–Barr virus or cytomegalovirus, were excluded.The characteristics of the study population have been reported previously.2.2The design of this two‐stage study design has been reported previously.ClinicalTrials.gov (NCT01926288).The study was designed by the sponsor  in collaboration with primary investigators. The study was conducted in accordance with the guidelines of the Declaration of Helsinki and the principles of Good Clinical Practice and was approved by the Ethics Committee of Peking University First Hospital. All patients signed written informed consent. This trial was registered with 2.3The primary endpoint was the decline in HBV DNA from baseline in each group at the end of treatment. Secondary endpoints included the rate of HBV DNA below the lower detection limit (20 I U/ml), the rate of HBeAg loss, the rate of HBeAg seroconversion and serum alanine aminotransferase (ALT) normalization at the end of treatment.2.4During the study, we detected the blood routines, urine routines, blood urea nitrogen and serum creatinine regularly at every visit. Abdominal sonography and alpha‐fetoprotein were detected every 48 weeks. Adverse events (AEs) were recorded at every visit.2.5The Viral Laboratory of Peking University First Hospital was put in charge of the Virological and serological tests. HBV serological markers  were detected by commercially available chemiluminescence immunoassays (Abbott Laboratories). Serum HBV DNA was tested by Roche Cobas Ampliprep/Cobas Taqman™ polymerase chain reaction (PCR) assay that reached a lower limit of detection (LLOD) of 20 IU/mL. Genotyping and resistance analysis were completed by direct sequencing PCR.2.6t‐tests or Wilcoxon rank sum test where appropriate. Chi‐square test or Fisher's exact test was used for comparison of categorial data. We used SPSS  ver. 17.0 (SPSS) to perform statistical analysis. All statistical tests used a bilateral test. Two‐tailed p value of < .05 was considered statistically significant.Hepatitis B virus DNA levels were logarithmically transformed for analysis. Continuous variables were expressed as mean with standard deviation (SD) or median (M) with interquartile range (Q) and were compared by 33.1In total, 279 patients were randomized and received at least one dose of study drug.3.2One hundred and thirty‐seven (71 in group A) patients with HBeAg‐ positive CHB and 46 (21 in group A) patients with HBeAg‐negative CHB completed the 240‐week treatment and follow‐up  and 240  were similar in group A and B (Table p > .05) in group B at Week 240   . However, HBeAg seroconversion rates were not statistically different between the two groups of HBeAg‐positive patients  at Week 240 (p > .05) (Table p > .05) at Week 240 as well.The rates of ALT normalization through Week 240 are described in Table 3.510 IU/ml. At Week 192, this subject possessed a mutation pattern of lamivudine resistance  and a HBV DNA level of 3.26 log10 IU/ml.None of the HBeAg ‐ negative CHB patients developed ETV resistance through Week 240. For HBeAg‐positive CHB patients, seven patients developed resistance at Week 144. Only 2 of 7 were NA‐naive and received ETV maleate monotherapy, which resulted in an ETV resistance rate of 1.16% in NA‐naïve HBeAg‐positive CHB at Week 144.3.6p > .05). Overall, 2 and 4 cases of adverse reaction (AR), all of which were mild, occurred in group A and group B (p > .05), respectively. AR included a mild increase in ALT, increase in total bilirubin, abdominal pain, viral relapse due to discontinuation of study drug.In total, 279 patients were included in the safety analysis set. Safety reports for the first 144 weeks of the study have been published.p > .05), respectively. Subjects 31 and 41 in group A received an induced abortion to terminate an unwanted pregnancy. The spouses of subject 44 in group A, subjects 36 and 228 in group B got pregnant. None of the other SAEs was judged to be related to the study drug. No discontinuation due to AE or AR occurred. No liver cancer or death occurred.Three and 2 cases of SAE occurred in group A and group B  had higher HBeAg loss rate than Group B . The difference may be due to the reappearance of HBeAg in 14 patients who had experienced HBeAg loss at Week 144. Given the fact that HBeAg seroconversion rate, the more important landmark in the control of HBV infection, was comparable between groups, the difference between HBeAg loss rate was not so significant. For clinical setting, this difference reminded us of the instability of HBeAg status in the early stage of HBeAg loss.The present study had some positive features. First, it was the largest prospective multi‐centre study of ETV maleate treatment in China. Second, it adopted highly sensitive tests, the Cobas Taqman assay (LLOD 20 IU/ml), to measure serum HBV DNA levels. With 87.88% of HBeAg‐positive and 100% of HBeAg‐negative patients achieving undetectable HBV DNA levels at Week 240, it further verified that ETV maleate is a NA with extremely high antiviral potency. Third, approximately 70% patients completed the 240‐week follow‐up, which laid the foundation for the quality of the clinical trial.However, there are some limitations. First, the sampling bias was inevitable. The subjects enrolled in this study were not able to represent the population of HBV patients. Therefore, we focused on the patients predominantly infected by genotype B and C, which is the characteristic of the subjects in this study. Second, histological outcome at Week 240 was not available because it's not in the follow‐up plan. Third, at the beginning of the study, we did not use FibroScan to measure liver fibrosis because the technology was new at that time. Future research may pay more attention to these issues. Finally, the number of HBeAg‐negative patients was relatively small. Only 57 patients were included in FAS in this study. However, the number had met the requirement of sample size for statistics.In conclusion, a 240‐week ETV maleate monotherapy was effective and safe in Chinese CHB patients. It showed potent HBV suppression, with 87.88% of HBeAg‐positive and 100% of HBeAg‐negative patients achieving undetectable HBV DNA. ETV resistance was rare in NA‐naive CHB patients. The safety profile of ETV maleate was promising.Yan‐Yan Yu, Chong‐Wen Si, Jun Dai, Xi‐Quan Zhang and Zhong‐Nan Xu designed the study. Jing‐Hang Xu, Zheng Zeng, Jun Li, Qing Mao, Da‐Zhi Zhang, Hong Tang, Ji‐Fang Sheng, Xin‐Yue Chen, Qin Ning, Guang‐Feng Shi, Qing Xie and Qing Xie collected data. Jing‐Hang Xu and Ya‐Nan Fan analysed and interpreted the data and wrote the manuscript. Yan‐Yan Yu approved the final manuscript. Chong‐Wen Si supervised the study. All authors had full access to the final version of the report and agreed to the submission.Qing Xie has acted as a consultant for Novartis, Bristol‐Myers Squibb and Roche. Guang‐Feng Shi has been a member of advisory committees or review panels, received consulting fees from Novartis, GlaxoSmithKline and Bristol‐Myers Squibb. Guang‐Feng Shi has acted as a consultant for Novartis, Bristol‐Myers Squibb, GlaxoSmithKline and Roche. Other authors have nothing to declare.The authors have read the CONSORT 2010 statement, and the manuscript was prepared and revised according to the CONSORT 2010 statement.


Thoracic SMARCA4‐deficient undifferentiated tumors are a new type of neoplasm that commonly occur in the mediastinum, progress rapidly, and show a poorer prognosis. We report a case of thoracic SMARCA4‐deficient undifferentiated tumor in the right thoracic cavity in a patient with a history of heavy smoking and presenting with respiratory distress and hemoptysis. Imaging showed pleural effusion and thickening. A diagnostic right pleural biopsy yielded multiple white nodules and pale bloody pleural effusion accumulated in the right thoracic cavity. Histopathologically, the tumor cells were large, some exhibited rhabdoid cytology, and they were surrounded by an infiltration of inflammatory cells. These tumor cells were negative for SMARCA4, p40, NUT, and claudin‐4, leading to establishing a diagnosis of thoracic SMARCA4‐deficient undifferentiated malignancy. We treated the patient with atezolizumab, carboplatin, and nab‐paclitaxel. The patient achieved stable disease at 7 months during this study. Although there is no standard treatment of this disease, our reported treatment may contribute to improved prognosis, requiring further research. Thoracic SMARCA4‐deficient undifferentiated tumors are a new type of neoplasm that commonly occur in the mediastinum and are associated with poor prognosis. To date, there is no standard treatment of thoracic SMARCA4‐deficient undifferentiated tumors. We report a case of thoracic SMARCA4‐deficient undifferentiated tumor with hemoptysis. We treated the patient with atezolizumab, carboplatin, and nab‐paclitaxel, which resulted in achieving stable disease for 7 months. Our reported treatment may contribute to improved prognosis, which requires further evaluation. Thoracic SMARCA4‐deficient undifferentiated tumors have recently been recognized. SMARCA4 deficiency is also found in non‐small‐cell lung cancer. However, it is classified as an independent disease due to differences in tissue morphology, ribonucleic acid expression, and prognosis. SMARCA4 deficiency was first defined in the 5th edition of the World Health Organization classification of tumors.Herein, we report the case of a patient with a right thoracic SMARCA4‐deficient undifferentiated tumor who underwent chemotherapy and achieved stable disease for 7 months.A 72‐year‐old man with an 80‐pack year smoking history presented with respiratory distress and bloody sputum. Computed tomography revealed significant emphysema, right pleural thickening, and pleural effusion Figure . Tumor mHistopathologically, large tumor cells with inflammatory cell infiltrates were found, some of which showed rhabdoid cytology. We initially suspected malignant pleural mesothelioma. However, since the cells were poorly differentiated and showed rhabdoid cytology, we performed immunohistochemical staining for makers for epithelial tumors such as lung cancer. Immunohistochemical staining showed that the tumor cells were negative for p40, NUT, claudin‐4, and SMARCA4 Figure . We subsChemotherapy with atezolizumab, carboplatin, and nab‐paclitaxel did not improve the symptoms, including respiratory distress and bloody sputum. Imaging showed bone infiltration, but pleural thickening disappeared and pleural effusion decreased Figure . We therSMARCA4 is a gene involved in the switch/ sucrose‐nonfermenting (SWI/SNF) chromatin‐remodeling complex. Transcription is regulated by the polycomb repressor complex (PRC). Moreover, the SWI/SNF complex acts as a tumor suppressor. When SMARCA4 is deficient, the PRC of the SWI/SNF complex is not suppressed, leading to tumor progression.4 and have a simultaneous deficiency of SMARCA4 and SMARCA2, whereas SMARCA4‐deficient lung cancer has a less simultaneous deficiency. Negative p40 and NUT results are useful for establishing auxiliary diagnoses.Immunostaining can help distinguish thoracic SMARCA4‐deficient undifferentiated tumors from SMARCA4‐deficient lung cancer or proximal‐type epithelioid sarcoma.There is no standard treatment of thoracic SMARCA4‐deficient undifferentiated tumors. However, enhancers of zeste homolog 2 inhibitors that suppress PRC and tumor progression, which act as a catalytic subunit of PRC, are in clinical trials.Regarding advanced non‐small‐cell lung cancer, the combination of anti‐PD‐1/PD‐L1 antibody and a cytotoxic drug has been shown to be effective regardless of PD‐L1 expression in tumor cells. Furthermore, the synergistic effect of anti‐PD‐1/PD‐L1 antibody and anti‐angiogenic agents has also been demonstrated.More patients with thoracic SMARCA4‐deficient undifferentiated tumors might be seen in clinical practice, therefore we should consider thoracic SMARCA4‐deficient undifferentiated tumors when identifying pathological features such as large tumor cells showing rhabdoid cytology with infiltration of inflammatory cells assessed by hematoxylin and eosin staining. There have been few reports on the effectiveness of immune checkpoint inhibitor combination therapy in thoracic SMARCA4‐deficient undifferentiated tumors, which may be a new treatment option.A patient with thoracic SMARCA4‐deficient undifferentiated tumor who was treated with chemotherapy and immune checkpoint inhibitors displayed 7 months of stable disease. Further research is necessary to establish a standardized treatment strategy. There have been a few reports of treatment with immune checkpoint inhibitors, and more cases need to be accumulated to evaluate whether this treatment is effective.T.U.: conceptualization, data curation, investigation, and writing of the original daft. Y.T. and T.M.: supervision. N.Y.: pathological diagnosis. M.F. and K.K.: treatment.This research did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors.None declared.