In visual search tasks, distractors similar to the target can attract our attention and affect the speed of attentional disengagement. The attentional disengagement refers to shifting attention away from stimuli that are not relevant to the task. Previous studies mainly focused on the attentional disengagement of one feature dimension. However, the mechanisms of different feature dimensions on attentional disengagement in single and conjunction visual search remain unclear. In the current study, we adopted the oculomotor disengagement paradigm and used saccade latency as an indicator to explore the effects of different feature dimensions of center stimuli on attentional disengagement. In both single and conjunction feature search tasks, participants began each search by fixating on a center stimulus that appeared simultaneously with search display but would not be the target. Participants were instructed to ensure the first saccade to the target location. In Experiments 1A (single feature search) and 1B (conjunction feature search), we found that the attentional disengagement was significantly delayed or accelerated when center stimuli shared color features with the target or salient distractor, but not in shape feature. Moreover, we found that the difference between the two feature dimensions might be caused by their different search difficulty (Experiment 1C). Therefore, in Experiment 2, we matched the difficulty of searching for color and shape tasks before exploring whether there were differences in the effects of different feature dimensions on attentional disengagement. However, the results in Experiment 2 were similar to those in Experiment 1A, indicating that the different effects of feature dimensions on attentional disengagement were caused by feature asymmetry. Therefore, in Experiment 3, we improved the salient discernibility of shape dimension and matched color search to it. The results showed that although the attentional disengagement was delayed in shape dimension, it was still smaller than that in color dimension. Our results supported that goal-oriented attention sets were the main cause of delayed attentional disengagement. By series of experiments, we found that the utilization of different feature dimensions was associated with task difficulty and the features asymmetry in both single and conjunction visual search. In our daily life, we are often surrounded by a wealth of information, therefore attentional selection is important for us. Due to the limitation of cognitive resources, the attentional selection mechanism can help us to filter and exclude irrelevant or unimportant information. However, some irrelevant information can still capture our attention. In visual search tasks, attention could be captured by the task-irrelevant stimulus that was similar to the target even though the target would not be present at that location or time . This tyAlthough there are a lot of research on attentional capture, the mechanism of attentional capture is still controversial. The attentional capture paradigm of previous behavioral studies mainly included the spatial cueing paradigm, the additional singleton paradigm, and their variants . These pFor attentional disengagement, previous studies mainly focused on one feature dimension and tested it in single-feature search . HoweverIn the current study, the target was defined by both color and shape in single and conjunction feature search tasks. However, the degree of feature utilization was different in the two search tasks. Participants can find the target by using one feature in single-feature search, while must utilize two features to identify the target in conjunction feature search. Therefore, we adopted the oculomotor disengagement paradigm and used saccade latency as an indicator to explore the effects of different feature dimensions of center stimuli on attentional disengagement in both single and conjunction feature search tasks. In Experiments 1A and 1B, we found that the color feature and shape feature of the center stimulus have different effects on attentional disengagement in single and conjunction feature searches. Therefore, we further investigated whether there was a difference in difficulty between color and shape feature dimensions in Experiment 1C. In Experiments 2 to 3, we matched the task difficulty of searching for color and shape before exploring the attentional disengagement effects of different feature dimensions.Thirty-six healthy young adults : 20.92 ± 1.57 years; age range: 18-25 years) participated in Experiment 1A. All participants had normal or corrected-to-normal color vision. All participants signed an informed consent form and were naive to the purpose of the experiment. Each participant was paid for the experiment. The research was approved by the ethics committee.a luminance level of 75 cd/m2. One stimulus was located at the center of the display. Six stimuli were equidistantly spaced around an imaginary circle with a diameter of 7.8°. Inside peripheral stimuli, each contained a small black dot  that was randomly distributed on the left or right. The peripheral stimuli consisted of a target (circle) and four non-salient distractors (squares) and a salient distractor (diamond). The experiments matched the area of different shapes. The target color was red  or green  and varied between subjects, the color of the non-salient distractor was yellow , and the color of the salient distractor was opposite to the target color. For example, if the target color was green then the salient distractor color was red, and vice versa. The center stimulus varied between seven conditions  background with 2 or its velocity reached 30 deg/s. The participants were positioned 63 cm to view the screen in a dimly lit room. We used a chin rest to fix the participants’ head position.All stimuli were displayed on a 37.5 × 30 cm monitor with a spatial resolution of 1,280 × 1,024 pixels and refresh a rate of 75 Hz. The stimuli were presented using Psychtoolbox3.0  in MATLAThe sequence of displays in a typical trial of search tasks was shown in Trials were excluded from the analysis if (a) the participant blinked or made a saccade at the time when the search display appeared, or the saccadic latency was less than 80 ms (mean ± SD: 7.01 ± 5.47%); (b) the location of the first saccade did not within 2° of target location (mean ± SD:14.26 ± 9.20%). Behavioral and eye movement data used the same exclusion criteria. Therefore, two participants were excluded from the analysis because the accuracy of the keypress response was less than 50%. The final sample size was 34. The accuracy of the keypress response was 98.32 ± 3.00%, and the mean RT of correct trials was 0.68 ± 0.11 s. Only correct trials were included in the analysis of eye movement data. We used the same trial exclusion criteria and data analysis methods in all experiments. The saccadic reaction time (SRT) (ms), reaction time (s), accuracy, and saccadic accuracy for all conditions of participants are shown in F = 64.08, p < 0.001, ηp2 = 0.66), shared features  = 13.67, p < 0.001, ηp2 = 0.29), and their interaction  = 29.30, p < 0.001, ηp2 = 0.47). Further simple-effects analysis (Bonferroni-corrected comparisons) revealed that the SRT difference scores of the T_C + S + and the T_C + S– condition were significantly longer than that of the T_C-S + condition (T_C + S + : t(33) = 6.38, p < 0.001, Cohen’s d = 1.09, T_C + S–: t(33) = 7.88, p < 0.001, Cohen’s d = 1.35). No significant difference was found between the T_C + S + and the T_C + S– condition (t(33) = 2.15, p = 0.12, Cohen’s d = 0.37). Significant differences were found between the T_C + S + and the D_C + S + condition (t(33) = 8.34, p < 0.001, Cohen’s d = 1.43), between the T_C + S– and the D_C + S– condition (t(33) = 6.68, p < 0.001, Cohen’s d = 1.15). No significant difference was found between the T_C-S + and the D_C-S + condition (t(33) = 0.61, p = 0.55, Cohen’s d = 0.10). No significant differences were found between the D_C + S + and the D_C + S– condition (t(33) = 0.22, p = 1.00, Cohen’s d = 0.04), between the D_C + S + and the D_C-S + condition (t(33) = −2.32, p = 0.08, Cohen’s d = −0.40), and between the D_C + S– and the D_C-S + condition (t(33) = −2.27, p = 0.09, Cohen’s d = −0.39).We used the SRT as an indicator to analyze the saccadic disengagement. We subtracted the SRTs of the baseline condition from the SRTs of every other condition. The SRT difference scores were shown in t-tests comparing the SRT difference scores to zero. The results revealed that significant delay attentional disengagement in the T_C + S + (t(33) = 6.49, p < 0.001, Cohen’s d = 1.11) and the T_C + S– condition (t(33) = 8.10, p < 0.001, Cohen’s d = 1.39). The attentional disengagement was significantly accelerated in the D_C + S + (t(33) = −2.81, p < 0.01, Cohen’s d = −0.48) and the D_C + S– condition (t(33) = −2.44, p < 0.05, Cohen’s d = −0.42). No significant difference was found in the T_C-S + (t(33) = 1.41, p = 0.17, Cohen’s d = 0.24) and the D_C-S + condition (t(33) = 0.48, p = 0.63, Cohen’s d = 0.08).We further examined whether the attentional disengagement was delayed or accelerated by one-sample In the single-feature search task (Experiment 1A), we found that there was a significant delay in attentional disengagement when the center stimulus was the same color as the target, regardless of shape. And when the center stimulus shared color features with the salient distractor, attentional disengagement was accelerated. However, no significant effect was found on attentional disengagement for shape features. In the single-feature search (Experiment 1A), we found that the utilization of different feature dimensions was different, such as color and shape. Participants can find the target by using one feature in single-feature search, while in conjunction feature, search participants must utilize two features to identify the target. The effects of two feature dimensions on attentional disengagement in conjunction feature search were still unclear. Therefore, we further explored this question in Experiment 1B.Thirty-six healthy young adults : 20.9 ± 1.57 years; age range: 18-25 years) participated in Experiment 1B. All participants had normal or corrected-to-normal color vision. All participants signed an informed consent form and were naive to the purpose of the experiment. Each participant was paid for the experiment. The research was approved by the ethics committee. The participants who took part in Experiment 1A except one who took part in Experiment 1B. An additional participant was recruited in Experiment 1B.In Experiment 1B, we adopted the conjunction feature search task. The target color was red  or green  varying between subjects. The periphery of the search display consisted of three circles (one of them is the target) and three squares. Two of the three squares were the same color as the target. Another square and the two circles except the target were the opposite color to the target. Therefore, the colors of half periphery stimuli were red and the others were green , reaction time (s), accuracy, and saccadic accuracy for all conditions of participants are shown in F = 40.82, p < 0.001, ηp2 = 0.55). Post hoc comparisons with Bonferroni correction revealed that the SRT differences scores of the T_C + S + condition were significantly longer than the T_C + S– and the T_C–S + condition (T_C + S–: t(33) = 2.90, p < 0.05, Cohen’s d = 0.50, T_C–S + : t(33) = 8.19, p < 0.001, Cohen’s d = 1.41). Significant difference was found between the T_C + S– and the T_C–S + condition (t(33) = 6.77, p < 0.001, Cohen’s d = 1.16). We further examined whether the attentional disengagement was delayed or accelerated by one-sample t-tests comparing the SRT difference scores to zero. The results revealed significant delay in attentional disengagement in the T_C + S + (t(33) = 5.92, p < 0.001, Cohen’s d = 1.02) and the T_C + S– condition (t(33) = 5.37, p < 0.001, Cohen’s d = 0.92). No significant difference was found in the T_C–S + condition (t(33) = −0.60 p = 0.55, Cohen’s d = −0.10).We subtracted the SRT of the baseline condition from the SRT of each condition. The SRT difference scores were shown in The result of the conjunction feature search task (Experiment 1B) showed that there was a significant delay in attentional disengagement when the center stimulus was the same color as the target, regardless of shape. The delayed disengagement was the longest when the center stimulus was identical to the target. And when the center stimulus shared color features with the salient distractor, attentional disengagement was accelerated. In both single and conjunction feature search tasks (Experiment 1A and 1B), we found that attentional disengagement was significantly delayed or accelerated when center stimuli shared color features with the target or salient distractor. However, no significant effect was found on attentional disengagement for shape features. It was still unclear whether the difference between the two feature dimensions was caused by their different search difficulty. Therefore, we tested whether there was a significant difference in the search difficulty between color and shape feature dimensions in Experiment 1C.Another 34 healthy adults : 20.50 ± 1.75 years; age range: 18-25 years) participated in Experiment 1C. All participants had normal or corrected-to-normal color vision. All participants signed an informed consent form and were naive to the purpose of the experiment. Each participant was paid for the experiment.In Experiment 1C, we adopted the additional singleton paradigm. The participants were required to complete two tasks: a color search task and a shape search task. No center stimulus was presented in search tasks. In the color search task, the peripheral stimuli consisted of six circles, and the colors were the same as those in Experiment 1A. The target color was red or green and varied between subjects. In the shape search task, the shapes of peripheral stimuli were the same as those in Experiment 1A and the color was consistent with the target color of the color search task  the participant blinked or made a saccade at the time when the search display appeared, or the saccadic latency was less than 80 ms  and (b) the location of the first saccade did not within 2° of target location . The accuracy of the keypress response for the color search task and shape search task were 99.56 ± 0.91% and 99.45 ± 1.34%, respectively. The mean RT of correct trials for color and shape search tasks were 0.69 ± 0.16 s and 0.74 ± 0.14 s, respectively.t-test of the SRT for the color and shape search tasks revealed significant difference between searching for specific color (mean ± SD: 249.65 ± 59.59 ms) and shape (mean ± SD: 272.98 ± 52.53 ms), t(33) = –3.96, p < 0.001, Cohen’s d = –0.68 (see t(21) = 4.18, p < 0.001, Cohen’s d = 0.72). The results showed that there was a significant difference in the search difficulty between color and shape feature dimensions, and the saccade latency of searching for color was shorter than that of searching for shape. It was unclear whether the difference in the effects of two feature dimensions on attentional disengagement (the results of Experiment1A and 1B) was due to their search difficulty or due to the asymmetry of the features. Therefore, in Experiment 2, we matched the search difficulty of color and shape feature dimensions before exploring whether different feature dimensions had different effects on attentional disengagement. Moreover, a previous study has found feature asymmetry between the color and the shape feature dimensions : 20.37 ± 2.09 years; age range: 18-25 years) participated in Experiment 2. All participants had normal or corrected-to-normal color vision. Each participant signed an informed consent form.To ensure that the search difficulty of color and shape feature dimensions was identical in the search task phase, we carried out a matching phase before the search task phase. The search display of the matching phase was similar to the search display of Experiment 1C see , except The average SRT of shape search tasks was calculated for each participant (mean ± SD: 252.32 ± 27.09 ms). We calculated the SRTs of color search task under different color levels and gained the curve according to Sigmoid curve fitting. In this formula, the color level was served as x, and SRTs were served as y. After Sigmoid curve fitting, we used the curve and served average SRT of the shape search task which was y to calculate the color level for each participant (mean ± SD: 12.66 ± 5.93). This color level was used for stimuli in the search task phase.The apparatus was the same as those in Experiment 1. The sequence of displays in a typical trial of the matching phase was the same as that in Experiment 1C. In the matching phase, participants completed one practice block of 72 trials in color search and shape search tasks, respectively. The color search task consisted of 10 blocks  for a total of 360 trials. The shape search task consisted of four blocks  for a total of 144 trials. In the search task phase, participants completed one practice block of 42 trials. The main experiment consisted of 12 blocks  for a total of 504 trials. Participants were given the option to rest at the end of each block.About 4.36 ± 5.02% trials were excluded from the analysis because the participant blinked or made a saccade at the time when the search display appeared, or the saccadic latency was less than 80 ms. About 14.20 ± 9.05% trials were excluded from the analysis because the location of the first saccade did not within 2° of the target location. The accuracy of the keypress response was 95.65 ± 16.48%, and the mean RT of correct trials was 0.62 ± 0.07 s. The SRT (ms), reaction time (s), accuracy, and saccadic accuracy for all conditions of participants are shown in F  = 91.13, p < 0.001, ηp2 = 0.73), shared features  = 6.64, p < 0.01, ηp2 = 0.17), and their interaction  = 26.60, p < 0.001, ηp2 = 0.45). Further simple-effects analysis (Bonferroni-corrected comparisons) revealed that the SRT differences scores of the T_C + S + condition were significantly longer than that of the T_C + S– and the T_C–S + condition (T_C + S–: t(33) = 3.85, p < 0.01, Cohen’s d = 0.66, T_C–S + : t(33) = 5.38, p < 0.001, Cohen’s d = 0.92). The SRT difference scores of the T_C + S– condition were significantly longer than that of the T_C–S + condition (t(33) = 2.97, p < 0.05, Cohen’s d = 0.51). Significant differences were found between the T_C + S + and the D_C + S + condition (t(33) = 8.30, p < 0.001, Cohen’s d = 1.42), between the T_C + S– and the D_C + S– condition (t(33) = 7.43, p < 0.001, Cohen’s d = 1.28). No difference was found between the T_C–S + and the D_C–S + condition (t(33) = 1.83, p = 0.08, Cohen’s d = 0.31). When the center stimulus shared feature with distractor, no significant difference was found between the D_C + S + and the D_C + S– condition (t(33) = 0.85, p = 1.00, Cohen’s d = 0.15). Significant differences were found between the D_C + S + and the D_C–S + condition (t(33) = –4.45, p < 0.001, Cohen’s d = –0.76), and between the D_C + S– and the D_C–S + condition (t(33) = −6.38, p < 0.001, Cohen’s d = −1.09).We subtracted the SRTs of the baseline condition from the SRT of every other condition. The SRT difference scores were shown in t-tests comparing the SRT difference scores to zero. The results revealed that significant delay in attentional disengagement in the T_C + S + (t(33) = 6.17, p < 0.001, Cohen’s d = 1.06) and the T_C + S– condition (t(33) = 4.02, p < 0.001, Cohen’s d = 0.69). The attentional disengagement was significantly accelerated in the D_C + S + (t(33) = −5.11, p < 0.001, Cohen’s d = −0.88) and the D_C + S– condition (t(33) = −7.27, p < 0.001, Cohen’s d = −1.25). No significant difference was found in the T_C–S + (t(33) = 0.98, p = 0.33, Cohen’s d = 0.17) and the D_C–S + condition (t(33) = −0.91, p = 0.37, Cohen’s d = −0.16).We further examined whether the attentional disengagement was delayed or accelerated by one-sample After matching the search difficulty between color and shape feature dimensions, we found that there was a significant delay in attentional disengagement when the center stimulus was the same color as the target, regardless of shape. When the center stimulus shared color features with the salient distractor, attention disengagement was accelerated. Moreover, no significant effect on attentional disengagement was found when the center stimulus shared shape features with either the target or the salient distractor. In Experiment 2, we found the differences in the effects of color and shape feature dimensions on attentional disengagement after matching the search difficulty. These results were similar to that in Experiment 1A and suggested that the different effects were caused by the feature asymmetry. Thirty-two healthy adults : 20.44 ± 1.34 years; age range: 18-31 years) participated in Experiment 3A. All participants had normal or corrected-to-normal color vision. Each participant signed an informed consent form.y. After Sigmoid curve fitting, we used the curve and served average SRT of the shape search task as y to calculate the color level for each participant (mean ± SD: 13.61 ± 5.05). This color level was used for stimuli in the search task phase and we added the same black outline as the matching phase . We calculated the SRTs of the color search task under different color levels and gained the curve according to Sigmoid curve fitting /rate))). In this formula, the color level was served as x, and SRTs were served as The apparatus parameters were the same as those in Experiment 1. The experimental procedure was the same as that of Experiment 2.About 6.33 ± 7.48% trials were excluded from the analysis because the participant blinked or made a saccade at the time when the search display appeared, or the saccadic latency was less than 80 ms. About 17.40 ± 9.00% trials were excluded from the analysis because the location of the first saccade did not within 2° of the target location. The accuracy of the keypress response was 98.54 ± 1.59%, and the mean RT of correct trials was 0.63 ± 0.05 s. The SRT (ms), reaction time (s), accuracy, and saccadic accuracy for all conditions of participants are shown in F = 65.87, p < 0.001, ηp2 = 0.68), shared features  = 8.09, p < 0.001, ηp2 = 0.21), and their interaction  = 26.96, p < 0.001, ηp2 = 0.47). Further simple-effects analysis (Bonferroni-corrected comparisons) revealed that the SRT difference scores of the T_C + S + condition were significantly longer than that of the T_C + S– and the T_C–S + condition (T_C + S–: t(31) = 4.04, p < 0.001, Cohen’s d = 0.71, T_C–S + : t(31) = 5.02, p < 0.001, Cohen’s d = 0.89). The SRT difference scores of the T_C + S– condition were significantly longer than that of the T_C–S + condition (t(31) = 2.70, p < 0.05, Cohen’s d = 0.48). Significant differences were found between the T_C + S + and the D_C + S + condition (t(31) = 7.36, p < 0.001, Cohen’s d = 1.30), between the T_C + S– and the D_C + S– condition (t(31) = 6.54, p < 0.001, Cohen’s d = 1.16), and between the T_C–S + and the D_C–S + condition (t(31) = 3.64, p < 0.001, Cohen’s d = 0.64). When the center stimulus shared a feature with a salient distractor, the result was the same as that of Experiment 2. No significant difference was found between the D_C + S + and the D_C + S– condition (t(31) = –0.38, p = 1.00, Cohen’s d = –0.07). Significant differences were found between the D_C + S + and the D_C–S + condition (t(31) = –5.31, p < 0.001, Cohen’s d = -0.94), and between the D_C + S– and the D_C–S + condition (t(31) = –3.00, p < 0.05, Cohen’s d = –0.53).We subtracted the SRTs of the baseline condition from the SRT of every other condition. The SRT difference scores were shown in t-tests comparing the SRT difference scores to zero. The results revealed significant delay in attentional disengagement in the T_C + S + (t(31) = 5.47, p < 0.001, Cohen’s d = 0.97), the T_C + S– (t(31) = 4.21, p < 0.001, Cohen’s d = 0.74) and the T_C–S + condition (t(31) = 3.48, p < 0.01, Cohen’s d = 0.61). The attentional disengagement was significantly accelerated in the D_C + S + (t(31) = −4.44, p < 0.001, Cohen’s d = −0.79) and the D_C + S– condition (t(31) = −3.70, p < 0.001, Cohen’s d = −0.65). No significant difference was found in the D_C–S + condition (t(31) = −0.43, p = 0.67, Cohen’s d = −0.08). These results showed that attentional disengagement was delayed when the center stimulus shared features with the target. And the speed of attentional disengagement would accelerate when the center stimulus was the same color as the salient distractor, regardless of shape. The different results between Experiments 3A and 2 were that the influence of shape features on attentional disengagement was found in Experiment 3. This effect occurred only when the center stimulus shared shape features with the target, but it was still smaller than that in color dimension. And these results suggested that although there was a delay in attentional disengagement in the shape dimension, the feature asymmetry still existed.We further examine whether the attentional disengagement was delayed or accelerated by one-sample Thirty-four healthy adults : 20.85 ± 2.45 years; age range: 18-27 years) participated in Experiment 3B. All participants had normal or corrected-to-normal color vision. Each participant signed an informed consent form.x”, discriminability between different colors increases. In the color search task of the matching phase, the color of each stimulus was randomly selected from the six levels with equal proportions. In the shape search task, the peripheral stimuli consisted of one circle (target) and five squares (distractors) that were presented as black outline without color filling. In the color and shape search task, participants were instructed to ensure the first saccade to the target location as quickly as possible and respond to whether the small black dot  within target was on the left or right side by pressing the left and right arrow keys. We matched the level of color according to the difficulty of the shape. The average SRT of shape search tasks was calculated for each participant (mean ± SD: 249.47 ± 27.46 ms). We adopted the same method and formula as in Experiment 3A to obtain the color level of each participant (mean ± SD: 10.09 ± 4.05). This color level was used for stimuli in the search task phase and we added the same black outline as the matching phase , reaction time (s), accuracy, and saccadic accuracy for all conditions of participants are shown in F = 21.28, p < 0.001, ηp2 = 0.39). Post hoc comparisons with Bonferroni correction revealed that the SRT differences scores of the T_C + S + condition were significantly longer than the T_C + S– and the T_C–S + condition (T_C + S–: t(33) = 3.55, p < 0.01, Cohen’s d = 0.61, T_C–S + : t(33) = 5.76, p < 0.001, Cohen’s d = 0.99). Significant difference was found between the T_C + S– and the T_C–S + condition (t(33) = 3.51, p < 0.01, Cohen’s d = 0.60). We further examined whether the attentional disengagement was delayed or accelerated by one-sample t-tests comparing the SRT difference scores to zero. The results revealed significant delay in attentional disengagement in the T_C + S + condition (t(33) = 7.32, p < 0.001, Cohen’s d = 1.25), the T_C + S– condition (t(33) = 7.15, p < 0.001, Cohen’s d = 1.23) and the T_C–S + condition (t(33) = 2.59, p < 0.05, Cohen’s d = 0.44).We subtracted the SRTs of the baseline condition from the SRTs of each condition. These SRT difference scores were shown in In Experiment 3B, we improved the salient discernibility of shape dimension and matched the color search to it. The results showed that there was a significant delay in attentional disengagement when the center stimulus shared features with the target, regardless of shape or color. Furthermore, the delay in attentional disengagement was greatest when the center stimulus was identical to the target.In the current study, we adopted the oculomotor disengagement paradigm by manipulating the center stimulus and used saccade latency as an indicator to investigate the effects of different feature dimensions on attentional disengagement. In both single and conjunction feature search tasks (1A and 1B), we found that attentional disengagement was significantly delayed when the center stimulus was the same color as the target, regardless of shape. When the center stimulus shared color features with the salient distractor, attentional disengagement was significantly accelerated. Furthermore, the delay attentional disengagement was greatest when the center stimulus was identical to the target. The result was consistent with the previous research that attentional disengagement was delayed when the fixated stimulus shared color features with the target ,b. HowevTherefore, in the following experiments, we matched the search difficulty of color and shape feature dimensions before exploring the attentional disengagement effect. However, no significant attentional disengagement effect was found in shape feature dimensions (Experiment 2). These results suggested that the different effects of color and shape feature dimensions were caused by feature asymmetry. Several studies showed that when the color feature was used in conjunction with other features, the visual system can use it more efficiently than other features . When prThe results suggested that the features of the center fixation stimulus could delay or accelerate disengagement of attention. Previous literature had many discussions on the causes of attentional delay disengagement. Some research suggested that attentional delay was because the simultaneous presentation of irrelevant stimuli influences the subsequent deployment of attention . HoweverWe found that the similarity between the center stimulus and the target might influence the speed of attentional disengagement. Specifically, when the center stimulus was more similar to the target, the disengagement of attention from the center stimulus location was slower (after matching difficulty). Previous studies provided evidence for the delayed disengagement effect ,b. HowevIn conclusion, we found the attentional disengagement effect in both single and conjunction feature search tasks. The attentional disengagement was delayed when the task-irrelevant stimulus shared features with the target. And the speed of attentional disengagement would accelerate when the center stimulus was the same color as the salient distractor, regardless of shape. The effects of different feature dimensions on attentional disengagement were influenced by feature asymmetry.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics Committee of School of Educational Science, Minnan Normal University, China. The patients/participants provided their written informed consent to participate in this study.YH and QZ conceived, designed the experiment, performed the experiments, and wrote the manuscript. YH, QZ, and MS analyzed the data. ZW revised the manuscript critically for important intellectual content. QZ provided technical guidance and site support. All authors contributed to the article and approved the submitted version.


Furthermore, gene set enrichment analysis revealed that among a total of 63 unique pathways , 40 (63%) were consistently suppressed, 16 (25%) were conditionally expressed, and only 7 (11%) were consistently activated. Overall, they were strictly regulated by both time and initial structures. Therefore, we proposed that microorganism responses and the induced gene expression changes play important roles in the process of community succession.Taxonomic convergence is common in bacterial communities but its underlying molecular mechanism remains largely unknown. We thus conducted a time-series transcriptional analysis of a convergent two-species synthetic community that grew in a closed broth-culture system. By analyzing the gene expression and monitoring the community structure, we found that gene expression mainly changed in the early stage, whereas community structure significantly changed in the late stage. The significant change of gene expression occurred even at the very beginning, which was designated as “0 h effect”, suggesting the effect of species interaction on gene expression was inevitable. Besides, the effect of interaction on gene expression has a “population effect”, which means that majority species have greater impact on gene expressions of minority species than  Convergence is a common feature of evolution and has great effect on the succession of microbial communities. For natural microbial communities such as the microbiome of gut , soil 22, sedimeEscherichia coli K-12 (EC) and Pseudomonas putida KT2440 (PP), and reproduced a convergent community assembly in closed broth-culture system. In monocultures, the growth curves of both E. coli and P. putida fitted well with the bacterial growth model, and fell into a logarithmic phase at the first 4 h of bacterium culture and a stationary phase at subsequent 20 h (after the first 4 h)  Fig. . When sa h) Fig. . By cont h) Fig. . Besidesure Fig. , suggest5:108 (1:1000), 108:108 (1:1), and 108:105 (1000:1), respectively, and they converged to approximately 109:1010 after 24 h cultivation  were mainly identified in early stage  showed that the ors Fig. . The E. ure Fig. . Likewisime Fig. , and theure Fig. . OverallE. coli had more DEGs than P. putida in the “1:1000” coculture  and 46%  of all the investigated metabolic pathways, respectively. Overall, about half of the metabolic pathways in both species are involved in the regulation of community convergence.GSEA was employed to reveal small but evidenced expression changes, and the results showed that a total of 50 and 38 pathways were significantly changed in ely Fig. . These pE. coli fell into five groups  of 1:1000, 1:1, 1000:1, respectively [A two-species coculture system was established by previously described procedures using two bacteria at the initial ratio of ectively . All strP. putida and E.coli reference genome using hisat2 [E. coli and the other one for P. putida, and these two parts were used as DESeq2 input, separately [Standard strand-specific RNA-seq library construction protocol was followed and sequencing was carried out on the HiSeq X-Ten platform. After quality control, 2.6–3.9 M paired reads, and 3.9–5.9 G base pairs were obtained from each sample. High-quality reads were aligned against the g hisat2 . All theg hisat2 . The reaparately . When wehttps://CRAN.R-project.org/package = ggVennDiagram).Functional enrichment analysis was performed using ClusterProfiler with the KEGG pathway annotations . Gene SeSupplementary information


Coprinopsis cinerea thriving in oxic habitats enable the germination, growth, and dispersal of the obligate anaerobic soil bacterium Clostridium acetobutylicum. Time-resolved optical oxygen mapping, microscopy, and metabolite analysis revealed the formation and persistence of anoxic circum hyphal niches, allowing for spore germination, growth, and fermentative activity of the obligate anaerobe in an otherwise inhabitable environment. Hypoxic liquid films containing 80% ± 10% of atmospheric oxygen saturation around single air-exposed hyphae thereby allowed for efficient clostridial dispersal amid spatially separated (>0.5 cm) anoxic sites. Hyphae hence may serve as good networks for the activity and spatial organization of obligate anaerobic bacteria in oxygenated heterogeneous environments such as soil.Anoxic microsites arising in fungal biofilms may foster the presence of obligate anaerobes. Here, we analyzed whether and to which degree hyphae of  Our results demonstrate that the occurrence and activity of aerobic mycelia can create anoxic microniches and facilitate activity and spatial distribution of obligate anaerobes in an otherwise oxic environment. This enlarges our understanding of the ecological role of the mycosphere for microbial dynamics and functional stability at oxic-anoxic interfaces in heterogeneous ecosystems.Anoxic and hypoxic microsites foster the presence and activity of even obligate anaerobes in oxic environments –4 such a–9–19–ass,dw−1 . Unlike ass,dw−1  and to bass,dw−1 . Forming topsoil , mycelia topsoil , enablin topsoil –29, horitopsoil –, or predtopsoil –. Expressn genome . It furtn genome  and, hennditions . Its oxynditions . SpatialC. cinerea (t) of 0.3 h to 18 ± 0.0 mm at t of 120 h. At the mycelial borders, we observed steep oxygen gradients from 100% to 0% air saturation over a distance of ~2 mm , accounting for a >1,300-fold increase in cells numbers from 3.5 × 105 ± 3.3 × 103 (t = 0 days) to 4.8 × 108 ± 3.5 × 107 cells g−1agar at t of 7 days  as signs of endospore formation. Comparison of total and viable cell counts . In the absence of mycelia, no growth of the obligate anaerobes was observed  Micrograph of C. acetobutylicum cells after 7 days in the presence (a) and absence (b) of C. cinerea. (c) C. acetobutylicum cell number density on the day of inoculation (t = 1 day) and on the day of harvest (t = 7 days) in the presence and absence of the fungus C. cinerea. Arrows highlight suspected polar attachment of C. acetobutylicum to C. cinerea hyphae. Data represent the average and standard deviation (n = 3) of total and viable C. acetobutylicum cell counts. ND, not detected. Download FIG S1, TIF file, 2.3 MB.Copyright © 2022 Xiong et al.2022Xiong et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mbio.00769-22.3FIG S2C. acetobutylicum and C. cinerea growing under ambient oxic conditions. Data represent mean and standard deviation of n = 2 for agar pads inoculated with C. acetobutylicum and C. cinerea and n = 3 for the treatments with C. acetobutylicum only, C. cinerea only, or agar only. BDL, below the detection limit. Download FIG S2, TIF file, 0.3 MB.Concentration of butyrate and 1-butanol in agarose pads with different inocula. Butyrate and 1-butanol were solely detected in agarose pads inoculated with Copyright © 2022 Xiong et al.2022Xiong et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. acetobutylicum germination near active growing C. cinerea hyphae further evidenced the presence of anoxic niches in the mycosphere , C. acetobutylicum (ii), and C. cinerea (iii). Low DNA amounts (<1 μg g−1) in controls with C. cinerea only pointing at a low DNA extraction efficiency for C. cinerea by the method applied. DNA amounts presented in the figure are hence likely to predominantly reflect extracted DNA from C. acetobutylicum. Data represent the average and standard deviation of n = 3 DNA extractions. Download FIG S5, TIF file, 0.2 MB.Extracted DNA after 7 days in agar pads A and B in presence of Copyright © 2022 Xiong et al.2022Xiong et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mbio.00769-22.7TABLE S1C. acetobutylicum cells in liquid SM824 medium at different oxygen levels. The fraction refers to the percentage of n = 249 to 281 cells detected in the focal plane during microscopic observation of triplicate samples. Download Table S1, PDF file, 0.4 MB.Fraction of actively swimming Copyright © 2022 Xiong et al.2022Xiong et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mbio.00769-22.10VIDEO S3C. acetobutylicum swimming along C. cinerea air-exposed hyphae. Download Movie S3, AVI file, 11.4 MB.Copyright © 2022 Xiong et al.2022Xiong et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. cinerea form anoxic niches  of 5 to 25 μm s−1  was cultivated for 3 days at 37°C under strictly anoxic conditions with an N2 headspace in 200-mL serum bottles containing 50 mL of SM824 medium (600) of 0.81 was then removed and centrifuged at 4,000 × g at 10°C for 5 min, the supernatant was discarded, and the cells were resuspended in 2 mL air-saturated SM824 medium and then used as inoculum (see below). The remaining culture was further cultivated until endospore formation was observed  . The basidiomycete C. cinerea strain AmutBmut , 1 mm) . The second cover slide was removed immediately. Five sterile agarose pads  cut from a 1-cm-thick SM824 agar plate were evenly placed around the inoculated pad to keep the microcosm moisturized.All microcosms were handled and used under laboratory atmosphere conditions. Microcosms for mycelial profile mapping consisted of a fungus-inoculated agarose pad   that was), 1 mm)  as descr), 1 mm) . Brieflyt = 0 h), oxygen profiles around the fungal inoculum  were monitored for 15 min at intervals of 30 s, using a commercial detector unit . Combining microscopic observation , and optical sensing techniques, the mycelial development and oxygen concentrations in the whole pad were then mapped at t of 0.3, 48, 72, and 120 h. As the commercial oxygen sensing unit did not support centimeter-scale sensing, the microcosm was fixed to a connection arm  that was mounted on a software-controlled microscope stage . To map the agarose pad with an area of ca. 250 mm2, >100 spots of 1.8 by 2.5 mm were analyzed and stitched using open-source software ImageJ (https://imagej.nih.gov/ij/). An interval of 3,000 ms was allowed between shifting two spots. In this period, the oxygen profile at a given spot was measured with the detector unit. After oxygen mapping , bright-field microscopic images of the mycelium were taken at each time point using the large image acquisition function of the microscope (×15 magnification). The microcosms  was inoculated to the center of the agar pad that had been preincubated with C. cinerea for 48 h. Then, the pad was flipped over and attached to the glass bottom of a Petri dish . To moisturize the air in the petri dish, five sterile agarose pads  were evenly placed around the inoculated pad. The microcosms were incubated as described above and examined daily with a microscope. At t of 7 days, the microcosms were harvested for (i) DNA extraction and Sanger sequencing of the 16S rRNA gene, (ii) total and viable bacterial cell counting, and (iii) analysis of water-soluble metabolites  following the manufacturer’s protocol and quantified with a NanoDrop 1000 spectrophotometer . The 16S rRNA gene was partially amplified and sequenced on an ABI Prism 3130xl genetic analyzer  using the primers of 27f and 519r as described elsewhere  was inoculated to the agarose pad  was placed onto the fungal inoculum in one agarose pad. Immediately thereafter, the fungus-bacteria-inoculated pad (agar pad A) and exclusively fungus-inoculated pad (agar pad B) were both flipped over and placed at a distance of 5 mm on a microscopy slide . The microscopy slide was then transferred to a plastic petri dish , and five circular agarose pads  were evenly placed around the slide to keep the microcosm moisturized during incubation for 7 days at 30°C and ambient air. All microcosms were prepared in triplicate and monitored daily by microscopy to examine the formation of hyphae between two agar pads and the presence of C. acetobutylicum cells moving along them, respectively. To exclude potential bacterial contamination and to provide quantitative molecular evidence for the presence of C. acetobutylicum, DNA was extracted, and DNA concentration was measured photometrically using a NanoDrop ND-1000 UV-visible (UV-Vis) spectral photometer (Thermo Fisher Scientific Inc.) from both agar pads at t of 7 days. DNA was sequenced as described above.Two 1-mm-thick agarose pads were inoculated with C. cinerea hyphae was also measured using custom-made, lifetime-based, oxygen-sensitive beads (Ø = 8 μm). C. cinerea was inoculated to the 1-mm-thick agarose pad and incubated at 30°C for 48 h. The oxygen-sensitive beads were dispersed in sterile deionized water, and three drops (1 μL each drop) of suspension were placed at ~2 mm distance from growing hyphal tips to the agar surface. The agarose pad was then incubated overnight at 30°C to let the hyphae overgrow the beads. Position- and lifetime-based luminescence quenching of the ruthenium-phenanthroline-based phosphorescence dye in individual oxygen beads was measured using an Opal system  connected to an automated inverted Zeiss microscope . The 532 nm LED of the Opal and a filter cube with 531/40 nm (excitation) and 607/70 nm (emission) were inserted in the optical light path during measurements. Oxygen content in the liquid film surrounding C. acetobutylicum in liquid SM824 medium with different oxygen levels was examined microscopically as described in Swimming activity of


There is a positive correlation between serum b2MG and 24 h urinary protein, BUN, serum creatinine, disease activity, and damage score (p < 0.001), while it was negatively correlated with GFR, C3, and C4 (p < 0.001). Serum b2MG has proven to be a predictor of LN in SLE patients , also being a predictor of the activity of the disease as well as damage index  , respectively. Serum b2MG level can be used as a valuable predictor for LN, clinical disease activity, and damage score.A strong correlation between lupus nephritis (LN), disease activity, and serum beta 2-microglobulin (b2MG) was observed. The current study examines the correlation between serum b2MG and renal involvement, damage score, and disease activity in systemic lupus erythematosus (SLE) patients. One hundred SLE patients from Ain Shams University Hospital were enrolled and categorized into two groups. Group I had 40 patients with negative b2MG, while Group II had 60 patients with positive b2MG levels. Medical history, clinical examination, and assessing disease activity based on SLE disease activity index (SLEDAI-2 K), and damage score were recorded for all patients. Laboratory examinations, such as serum b2MG, complete blood count, blood urea nitrogen (BUN), serum creatinine, glomerular filtration rate (GFR), urine analysis, 24 h urinary protein excretion, Antinuclear antibodies (ANA), anti-dsDNA antibody, and serum complement . BUN, 24 h urinary protein, serum creatinine, active urinary sediment, SLEDAI score, and damage score were all elevated in group II compared to group I ( Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, which primarily affects young women. It is characterized by the formation of autoantibodies against cytoplasmic and nuclear antigens. Though its etiology is unknown, several genetic, epigenetic, and environmental risk factors have been identified .Around 60–80% of SLE cases present variable degrees of renal involvement in the form of Lupus nephritis (LN). Common symptoms include renal insufficiency, proteinuria, hematuria, and irreversible renal failure in about 10–20% of the cases . IndividBeta-2 microglobulin (b2MG), an 11 kDa molecular weight protein, is linked to the light chain of major histocompatibility complex class I (MHC-I) antigens. It is mainly secreted by the immune-associated cells, such as activated T-cells, macrophages, and B-lymphocytes . B2MG plOur study examines the association of serum b2MG with renal involvement, disease activity, and damage score in SLE patients and evaluates its validity as a predictor of LN.This cross-sectional study recruited 100 SLE patients, age > 18 years, based on the Systemic Lupus International Collaborating Clinics classification criteria , who attPatients with a history of a bacterial or viral infection 3 months before enrollment; patients with hypertension, other cardiovascular diseases, malignant tumors, rheumatoid arthritis, central system diseases, diabetes, liver diseases; patients under immunosuppressive medicines or any drugs that impact serum creatinine or b2MG levels; patients suffering from renal damage due to other factors were excluded from the study.This study was performed according to the criteria described in the World Medical Association's Helsinki Declaration after due approval from the local ethics committee of Ain Shams University. The reference number of the study was FWA00001786 on 25 May 2021. Written informed consents obtained from all subjects.The sex, age, duration of the disease, and clinical and rheumatological history of the patients were recorded. Laboratory investigations were conducted, including C-reactive protein (CRP) by Cobas C6000 autoanalyzer , kidney function tests  by AU680 Beckman Coulter autoanalyzer , complete blood count (CBC) by Sysmex XT-1800i autoanalyzer , erythrocyte sedimentation rate (ESR) by Westergren method, and routine urine analysis besides assessing active urinary sediments . In addition, 24-h urine protein excretion and eGFR were estimated using the original Modification of Diet in Renal Disease (MDRD) equation :\documenRenal biopsy was performed by the assessment of Activity ibex (AI) and chronicity index (CI) of the LN patients. Histopathological classification of LN was conducted based on the revised classification of the International Society of Nephrology and the Renal Pathology Society . STA Comg for 15 min. Separated sera was kept at – 80 ℃ until further analysis. A commercially available human ELISA kit was used for b2MG determination . The detection range of the ELISA assay was 0–3 µg/ml, with a sensitivity of 0.1 µg/ml. Patients who had serum b2MG ≥ 3 µg/ml were reported positive for the marker [A sterile venipuncture was used to collect the peripheral venous blood sample from each participant. It was then placed in a simple vacutainer tube with a clot activator for serum separation. Clotting of blood was allowed for 30 min prior to centrifugation at 3000e marker .SLEDAI = 20 indicates very high activity,SLEDAI = 11–19 indicates high activity,SLEDAI = 6–10 indicates moderate activity,SLEDAI = 1–5 indicates mild activity,SLEDAI = 0 indicates no activity.Assessment of SLE disease activity was done based on SLEDAI-2 K . SLEDAI The SLICC/ACR Damage Index (SDI) was incorporated for measuring irreversible cumulative damage induced by the disease activity in SLE. SDI includes parameters that signify persistent, irreversible impairment in the lupus patients. Apart from stroke and myocardial infarction, most indications have to last at least 6 months. Damage is considered for the following twelve systems: malignancies (0–2), gonadal (0–1), endocrine (diabetes) (0–1), skin (0–3), musculoskeletal (0–7), gastrointestinal (0–6), peripheral vascular (0–5), cardiovascular (0–6), pulmonary (0–5), renal (0–3), neuropsychiatric (0–6), and ocular (range 0–2). Damage may be either steady or incremental with time  .t test was used to compare the quantitative data of the two groups, chi-square test was used to examine the relationship between their qualitative variables. Mann–Whitney Test (U test) was used for nonparametric variables. Fisher's exact test was incorporated to determine the association between the qualitative variables in case the expected count was lower than five in more than 20% of cells. The correlations between b2MG level and other parameters were examined using the Spearman rank correlation coefficient.SPSS software, v. 25 was used to perform all statistical analyses of the collected data. Parameters were analyzed according to the data type. Normality tests were conducted to check the data distribution . Qualitative results were represented as frequencies and percentages and quantitative data were represented as mean ± standard deviation, range, and median values. While unpaired Student’s P values as follows:P > 0.050: non-significant (NS).P < 0.050: significant (S).P ≤ 0.001: highly significant (HS).A Receiver Operating Characteristic (ROC) curve analysis was conducted to examine the diagnostic value of b2MG. The area under the ROC curve (AUC) for the diagnostic/predictive value was interpreted as follows: excellent: 0.9–1.0; good: 0.8–0.89; fair: 0.7–0.79; poor: 0.6–0.69; and fail: < 0.6). Specificity was expressed as a percentage. The level of significance was determined from the The demographic data of the groups were compared. The difference in age and duration of disease was insignificant between the groups. However, the composition of sex varied between the groups significantly, as shown in Table A ROC curve was plotted, and its AUC was determined to evaluate the diagnostic value of serum b2MG in LN and identify its role as a marker of damage index and disease activity. Cutoff values and performance characteristics are presented in Table Serum b2MG value of > 2.6 µg/ml was predictive of LN with 74.47 specificity, 92.45 sensitivity, 80.3 positive predictive value (PPV), 89.7 negative predictive value (NPV), and 0.874 AUC. Meanwhile, serum b2MG, a value of > 2.2 µg/ml was predictive of disease activity with a specificity of 85, sensitivity of 96.67, PPV of 90.6, NPV of 94.4, and an AUC of 0.961. As a predictor of damage, serum b2MG performed as follows: the cutoff value was > 2.6 µg/ml with 65.45 specificity, 93.33 sensitivity, 68.9 PPV, 92.3 NPV, and an AUC of 0.836.Systemic lupus erythematosus (SLE) is an illness of diffuse connective tissue concerning many organs, which manifests due to deficiency in immunological tolerance. Its primary pathogenic characteristics include autoantibody secretion, deposition, and aggregation of immune complexes . ComplicIt is evident that b2MG contributes to SLE pathogenesis, suggesting that the protein is involved in the increased secretion of autoantibodies leading to hypergammaglobulinemia . Severalp < 0.05). Nevertheless, there was no difference between both groups concerning age and disease duration (p > 0.05). Choe et al. demonstrated that age and duration of disease were not markedly correlated with serum b2MG (p > 0.05) [p < 0.05). These results align with the study by Kim et al., who showed that SLE cases with serositis, nephritis, and oral ulcer had markedly elevated levels of b2MG compared to those without them. However, arthritis was the only exception [Both study groups substantially differed in sex, with group II being substantially higher ( > 0.05) . Other pxception . Contrarxception .p < 0.05). These results are in accordance with those of Choe et al., who revealed a positive relationship between serum b2MG and protein/creatinine ratio, ESR, and BUN, but not with an anti-dsDNA titre, complements, or CRP [. assessed the link between CRP, ESR, and serum b2MG, and found a positive association between both CRP and ESR with b2MG [Furthermore, the current study revealed a significant difference in 24 h urinary protein excretion, serum creatinine, BUN, eGFR, active urinary sediment, albuminuria, consumed C3, C4 ( which are parameters of lupus nephritis), ESR, and CRP between the two groups with significantly elevated levels in group II , with a significantly higher level in-group II. Kim et al., in their study on the Korean lupus population, revealed a positive association between SLEDAI and b2MG, which is concurrent to our findings [. showed elevated b2MG associated with the disease activity [. also revealed a significant relationship between b2MG and SLEDAI scores [SLE disease activity was calculated by the SLEADI score, which showed a positive relationship between SLEADI score and serum b2MG . This is in agreement with the findings of Skare et al., which indicated a negative correlation between serum b2MG with both HGB and C3 , respectively [Our study revealed negative association with HGB levels  but is negatively linked to eGFR (p < 0.001). This is in accordance with the finding of Liu et al., which suggested a negative correlation between serum b2MG and GFR  [Serum b2MG enhances LN progression besides inducing additional kidney damage, as detected in this study. It shows that b2MG is substantially linked to creatinine, urea levels, and proteinuria (= 0.000) . Kim et = 0.000) .r = 0.603, p < 0.001) and was more prominent in-group II (p < 0.001). These results concluded the role of serum b2MG not only as a disease activity index but also in determining damage score. Wang et al. demonstrated the role of serum b2MG as an effectively specific and sensitive biomarker for early monitoring of change in renal function due to secondary kidney damage [In this study, renal, cardiac, and neurological damages were substantially higher in-group II. In addition, SLE cumulative damage measured by SLICC/ACR was positively linked to serum b2MG (y damage .), with normal values of 604–2786 ng/ml) may be considered as another reason for these differences.In contrast to our results, Skare et al. have not revealed any link between b2MG and SLE cumulative damage assessed by SLICC/ACR , which cROC curve analysis comprehensively and accurately assessed the diagnostic performance of serum b2MG as a predictor of LN, disease activity, and cumulative damage in cases with SLE. Our study revealed that the best cutoff value of serum b2MG to predict LN is > 2.6 µg/ml, specificity of 74.47, PPV of 80.3, the sensitivity of 92.45, NPV of 89.7, with AUC of 0.874, while for prediction of disease activity, the best cutoff value was > 2.2 µg/ml with a specificity of 85, sensitivity of 96.67, PPV of 90.6, NPV of 94.4, and an AUC of 0.961. Meanwhile, for cumulative damage prediction cutoff value was > 2.6 with specificity, sensitivity, PPV, and NPV were 65.45, 93.33, 68.9, and 92.3, respectively, with an AUC of 0.836. The current findings align with Liu et al., demonstrated that kidney damage can be effectively predicted by serum b2MG levels using ROC curve analysis. According to them, AUC was 0.881, denoting an elevated predictive value. The cutoff value of b2MG was 7.48 mg/L. Meanwhile, AUC was 0.863 for predicting the disease activity of SLE, with an elevated predictive value, while the cutoff value of b2MG levels was 6.69 mg/L [Huang et al. studied the role of both Anti-α-enolase antibodies along with b2MG for the prediction of LN in SLE patients and found that b2MG can be used as a predictor of LN with an AUC of 0.845. They also concluded that AUC became higher (0.927), and the prediction of LN increased when Anti-α-enolase antibody and serum b2MG are combined together .Serum b2MG levels may be utilized for anticipating LN progression as well as identifying disease severity and cumulative damage for proper management with significant sensitivity and specificity.Our findings need to be interpreted carefully considering the following limitations. First, our study is cross-sectional in a group of patients at a certain time and requires data from patients follow. Second, a few numbers of articles studied the relation between serum b2MG and damage score. Finally, yet importantly, the results of our study are for a certain geographical area, so these results may not be completely generalized.Serum b2MG is significantly associated with lupus nephritis, disease activity, and cumulative disease damage. In addition, it can be utilized as a useful noninvasive biomarker in SLE with elevated specificity as well as sensitivity to determine the development of LN, degree of disease activity, cumulative damage, and disease progression for proper management.


Enterobacter cloacae is a troublesome pathogen causing refractory infections of the lower respiratory tract, urethra and abdominal cavity, endocarditis, osteomyelitis, and neonatal septicemia. It is prone to developing resistance to ordinary antibiotics and has brought a serious problem to clinical treatment. An artful synergistic combination of an antibacterial natural product allicin and a newly isolated bacteriophage, named BD523, was constructed herein. This combination significantly lowered effective dosage of allicin and effectually overcame bacterial drug-resistance. We experimentally evidenced that allicin interacts with bacterial DNA in the groove region by inserting itself into the DNA double helix and, subsequently, disrupts the bacterial DNA by cleaving phosphate diester bonds of deoxynucleotides. Further, BD523 destroys the cell wall and membrane of bacteria by synthesizing lyase proteins, including holin and endolysins. Thus, the synergistic effect of the combination benefits from complementary targeting mechanisms of allicin and BD523. They cooperatively act on bacterial DNA, cell wall, and membrane to improve antibacterial efficiency and avoid drug-resistance.IMPORTANCE Bacterial drug-resistance is a serious problem afflicting pharmacologists all over the world. Many strategies have been developed and practiced to overcome it, but almost no one is satisfactory due to the continual change of bacteria. Combinations of antibiotics and bacteriophages are promising because of the cooperation of 2 bacterial killers with distinct mechanisms. The combination of allicin and an Enterobacter cloacae bacteriophage reported herein can significantly improve the effect of allicin against E. cloacae. Its synergistic effect was even superior to the combination of bacteriophage and neomycin, of which the MIC was significantly lower than allicin. It was ascribed to the complementary antibacterial and the possible resistance-proof mechanism of bacteriophage and allicin. This study provided a pragmatic way to conquer the cunning bacterium, and may offer reference for research and development of new bacterial killers. Enterobacter cloacae is a troublesome pathogen causing refractory infections of the lower respiratory tract, urethra and abdominal cavity, endocarditis, osteomyelitis, and neonatal septicemia  group resistant to more than 90 antibiotics such as penicillin, cefoxitin, amphotericin, and tetracycline. The bacterium resists antibiotics through regulating expression of proteins, including β-lactamase, membrane transporter, and efflux pump . Agarose gel electrophoresis showed that allicin reacted with DNA and resulted in DNA lag and degradation (a) and dissociation constant (Kd) values were 106 and 107, respectively, which reflect a strong binding of allicin to DNA. A positive peak at 276 nm and a negative peak at 245 nm in circular dichroism spectrum (CD) decreased in an allicin concentration dependent manner  were detected, respectively . In addition, fragmented product ions (m/z 136 and 152) for dA and dG were also observed in the secondary mass spectrum. Similarly, in a simulation test using allicin and deoxyribonucleotide triphosphates (dNTPs), which are basic construction units of DNA, all 4 deoxynucleosides were identified by detecting the respective parent ions and product ions, namely, at m/z 228.0974  and 112 for deoxycytidine (dC), m/z 268.1036  and 152 for dG, m/z 252.1087  and 136 for dA, and m/z 243.0964  and 127 for thymidine (T) . In contrast, none of such deoxynucleosides were detected in the control group without allicin added .In interaction of allicin with bacterial DNA monitored by liquid chromatography tandem mass spectrometry (LC-MS/MS), deoxyadenosine (dA), and deoxyguanosine (dG) were found since their parent ions at Table S2 illustrates comparative antibacterial activities of allicin and its analogues  evaluated by MIC in a potency descending order as ethylicin or methyl methanethiolsulfonate, allicin, diallyl disulfide, and 1,7-octadiene, providing a basis for structure-activity assessment.E. cloacae ATCC 13047 obtained from Shanghai Beinuo Biotechnology Co., Ltd. was used to screen the bacteriophage. The strain was shown to tolerate a lot of antibiotics, such as β-lactams, macrolides, aminoglycosides, tetracyclines, and quinolones. After an extensive screening, a bacteriophage against this strain was obtained and named BD523. It produced a plaque about 1 mm in diameter in solid medium (Escherichia phage IMM-002 (NC 048071.1) , the percentages of identity and coverage were 88.43% and 89%, respectively. BD523 was determined as the Caudoviricetes, Caudovirales, Autographiviridae, Studiervirinae, Kayfunavirus, Escherichia phage.d medium . A singld medium . Conside9 CFU/mL, and optimal multiplicity of infection (OMOI) was 0.1 (Table S3). According to a one-step growth curve , the latent period of BD523 was 0 to 30 min, and the rise period was 30 to 50 min. The amount of burst size was 1.58 × 103 PFU/cell, and the optimum temperature range was between 35 and 40°C. Over 40°C, activity of BD523 attenuated inversely related to temperature . Optimal pH for BD523 was 7.0, and its titer decreased substantially beyond this value . After treatment with 10% chloroform, neither its titer nor its lysis ability obviously decreased .Based on plaque counting, bacteriophage titer was calculated as 1.2 × 10In vitro antibacterial experiments of BD523 showed that OD600 values of experimental groups increased slowly at first, followed by a rapid decrease. Compared with the control group, the OD600 in different multiplicity of infection (MOI) groups reduced significantly within 80 min .The genome of the bacteriophage consists of a double-stranded linear DNA of 39,686 bp with GC content of 52.7%. A genome annotation revealed that BD523 contained a total of 49 open reading frames (ORFs), including 7 for structural proteins, 19 for nonstructural proteins, and the rest for hypothetical or unknown proteins. Its genome  was composed of an invasion system (orange), replication system (green), assembly system (red), structural proteins (light blue), lysis system (purple), and hypothetical proteins (yellow). The lyase proteins, including holin and endolysins, were successfully expressed with recombinant plasmids constructed using 3 amplified lyase gene sequences , and the plasmid pET22b vector . MICs of proteins E, GP17.5, and GP18.5 were determined to be 138 ± 11.4, 148 ± 9.5, and 107 ± 14.1 μg/mL, respectively.P < 0.01) and neomycin + BD523 group (P < 0.05). Nevertheless, no significant difference was observed between the neomycin + BD523 group and the neomycin alone group . Hydrophobicity of the bacterial wall and membrane was decreased time-dependently by BD523, allicin, and allicin + BD523. Within 1 to 5 h, the adsorption rate in allicin + BD group decreased most significantly, and was negatively correlated with time (P < 0.05) . Membrane injury rate detected by flow cytometry was 27.5, 29.78, and 25.33% in BD523, allicin, and neomycin groups, respectively (P < 0.05). Moreover, this rate in allicin + BD523 group was as high as 38.71% . In the BD523 group, turbidifaction began to appear after 4 h, and the intragroup difference reached a maximum at 8 h . Antibacterial activity of allicin + BD523, ciprofloxacin + BD523, and polymyxin + BD523, especially the first 2 combinations, were stronger than that of other combinations including penicillin + BD523, neomycin + BD523, sulfadiazine + BD523, and metronidazole + BD523 . In addition, allicin + BD523 had a stronger and longer antibacterial effect than BD523 alone . Leakage of proteins and nucleic acids in all groups was positively correlated with time, and that in the allicin + BD523 group was higher than that in the allicin alone group  in the treatment groups, especially in the allicin + BD523 group as it was up to 80% , thiosulfoxide , disulfide , and ethylene  decreases in order. Among them, analogues with disulfide moiety are more potent than those with ethylene moiety. Thirdly, analogues containing thiosulfone moiety or thiosulfoxide moiety seem more potent than those containing disulfide moiety. Thus, the oxygen atom of moieties of thiosulfone and thiosulfoxide is unusually important for antibacterial activity. Both thiosulfone and thiosulfoxide are stronger nucleophilic groups due to unshared pair electrons of the oxygen atom. Disulfide moiety is in favor of increasing electron density of the oxygen atom to intensify nucleophilic capacity of thiosulfone and thiosulfoxide. Therefore, we deem thiosulfoxide moiety to be the major antibacterial functional group of allicin.Based on MICs of allicin and its analogues, we have established a structure-activity relationship and uncovered the antibacterial pharmacophoric group of allicin. Firstly, the terminal double bond of allicin is almost unrelated to antibacterial activity because 2 analogues with this moiety e.g., 1,7-octadiene and diallyl disulfide, are far less potent than allicin while another 2 analogues without this moiety, e.g., ethylicin and methyl methanethiolsulfonate, are more potent than allicin. Secondly, antibacterial potency of compounds with moieties of thiosulfone , since E. cloacae is inclined to mutate into a tolerant strain in response to stress of BD523. Further, we isolated the resistant strain from the turbid lysate, which was not lysed by BD523. Similarly, the stress of bacteriophages also facilitates development of antibiotic-resistant bacteria. It was reported that the bacteriophages that preferentially target antibiotic-sensitive bacteria may promote antibiotic-resistant subpopulations through a phenomenon similar to “competitive release” observed in antibiotic combinations   .It is generally believed that antibiotics or natural products and bacteriophages double-target specific bacterial targets, reducing the probability of drug-resistance and bacterial variation. The combination also reduces dosage of antibiotics and severity of disease , 24. By To prove this conjecture, the degradation of DNA inside the bacteria after treatment with allicin or BD523 alone and their combination was studied. The results  showed tInterestingly, a positive drug, neomycin, did not exhibit such a potent synergistic effect with BD523 , although it showed a stronger activity (MIC 31.2 μg/mL) than allicin (MIC 125 μg/mL) when used alone (Table S1). The reason may be that neomycin, as one of the aminoglycoside antibiotics, interferes with protein synthesis through binding to bacterial ribosomes. This process may coincidentally hamper production of holin and endolysins, or hinder proliferation of the bacteriophage . Thus, tIn summary, the combination of allicin and BD523 has a synergistic effect due to their complementary targeting mechanisms. They cooperatively act on bacterial DNA, cell wall, and membrane so as to improve antibacterial efficiency and avoid drug-resistance.6 CFU/mL) to give final concentrations of these antibiotics as 15.6, 31.2, 62.5, 125, and 250 μg/mL, respectively. Minimum concentration that completely suppressed bacterium growth after 8 h was regarded as MIC. The Kirby Bauer method was employed to evaluate the antibacterial effect of antibiotics. Sterilized 5 mm circular paper slips were dripped with the above serial solutions and then placed on a plate coated with bacterial suspension. After they were cultured in an incubator for 16 h, the diameter of the inhibition zone was measured , and serially added into bacterial solution  was used for data fitting analysis, and ITC Run software (version 3.4.1.0) was used for obtaining thermodynamic parameters and fitting images. Binding mode of allicin to DNA was explored using circular dichroism spectrometer. Allicin solutions of 0.5 and 1 MIC were incubated with 10 mM DNA at 37°C for 30 min, respectively. Pure water was used as control. The measurement wavelength was set at 230 to 300 nm with a bandwidth of 1 nm. The sampling interval of a single data point was 0.5 s, and data were exported by Chirascan software (version 4.0) .(i) Sample preparation. About 10 μL of bacterial DNA (50 μg/mL) and 10 μL of 10 mM dNTPs were separately mixed with equal volume of 1 MIC allicin, and incubated overnight at 37°C. Three times the volume of methanol was added and a 0.22 μm microporous filter membrane was used to collect filtrate. Products were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) to determine cleaving position. DNA and dNTPs solution in pure water was used as control.(ii) LC-MS/MS analysis. Each 3 μL of sample was injected into an Acquity UPLC BEH C18 column  and analyzed at oven temperature of 35°C on a Thermo Q-Exactive high resolution mass spectrometer (Thermo Scientific) equipped with an electrospray ionization (ESI) interface source. Gradient elution was performed using 0.1% formic acid solution in pure water as mobile phase A and methanol as mobile phase B, at a flow rate of 0.3 mL/min with an elution program consisting of 10 to 90% B at 0 to 18 min, 90 to 10% B at 18 to 20 min, and constant 10% B at 20 to 25 min. Injection volume for each run was 3 μL. The ionization source was set to positive ion mode to obtain a total ion flow diagram of ESI-MS. The scanning range was 100 to 1000 m/z, and electrospray voltage and collision energy were set at 5500 and 20 V, respectively. Pyrolysis process was inferred through literature review, database retrieval, and analysis of pyrolysis rule by mass spectrometry. Relevant parameters of 4 deoxynucleosides are shown in table S4.The antibacterial functional group of allicin was identified by comparing the MICs of allicin and its structural analogues, including ethylicin, 1,7-octadiene, methyl methanethiolsulfonate, and diallyl disulfide determined with the Kirby Bauer method. All these compounds have similar linear skeletons but differ in functional groups. Thus, the active functional group of allicin was identified through inter-comparing the MICs of these compounds.Two grams of fresh rat feces were selected and placed in 10 mL of lysogeny broth (LB) medium. They were extruded and mixed, incubated at 37°C for 2 h, and centrifuged at 8000 r/min for 10 min. Then, the supernatant was filtered through a 0.22 μm filter membrane to obtain total bacteriophage solution. The solution was dropped into semi-solid medium containing host bacterium for inverted culture at 37°C for 12 h using double agar plates. In the case that potent bacteriophage of the host bacterium was present, plaque would be observed in solid culture medium. The plaque with good morphology was further purified and cultured three times. Purification was completed as long as the plaque with uniform morphology and consistent size was obtained. The plaque was picked and added to a sterile medium containing 7% dimethyl sulfoxide (DMSO). Then, it was mixed and frozen at −80°C , 31–33.31–9 PFU/mL) was placed on a Formvar/carbon film coated grid and stayed for 10 min on a filter paper. A drop of 1% (wt/vol) uranyl acetate (pH 4.5)  was dropped onto the grid surface for 90 s. After dying, the grid was absorbed by the filter paper, and then clamped on the filter paper to dry for 3 h. The grid was reloaded at 120 kV acceleration voltage onto a JEM-1400 Plus transmission electron microscope (JEOL USA Inc.) with image capture software (iTEM and KeenView Soft Imaging System).The bacteriophage supernatant was collected in the same way as that described in the bacteriophage isolation section. An ultracentrifuge  was used for enrichment of BD523 at 30000 r/min for 2 h. The precipitate was mixed with 20 μL of ultrapure water. Then, 10 μL of bacteriophage suspension (10https://rast.nmpdr.org/). All ORFs were verified using NCBI protein alignment tool BLASTp (www.ncbi.nlm.nih.gov/BLAST/). Whole genome circle mapping was completed using SnapGene (www.snapgene.com)  , 35. To ene.com) , 35.A drop of bacteriophage solution was suspended on a copper grid. After being naturally precipitated for 10 min, it was fixed with 1% osmium acid and stained with 2% phosphotungstic acid. Then, it was naturally dried for 30 min in the dark. Finally, morphological characteristics such as head, tail, tail sheath, and base plate of the bacteriophage were observed by TEM.(i) Bacteriophage titer assay. Bacteriophage titer refers to the number of bacteriophages per milliliter of fluid. Plaque was picked with sterile spear and blown into medium to obtain stock solution of bacteriophage. Then, the stock solution was 10-fold serially diluted to 8 wells of a 96-well plate, and 10 μL of host bacterium solution was added to each well and fully mixed. This process was repeated 3 times. The mixture was cultured upside down using double agar plates at 37°C for 8 h, and then bacteriophage titer was obtained by counting plaque in the plate.(ii) OMOI determination. OMOI means multiplicity of infection with the highest titer. MOI refers to ratio of bacteriophage to bacterium. Bacteriophage and host bacterium at a concentration of about 6 × 108 CFU/mL were mixed in accordance with MOI of 1, 0.1, 0.01, 0.001, 0.0001, and 0.00001, and cultured for 1 h. Then, bacteriophage titer was determined.(iii) One-step growth curve plotting. Host bacterium was mixed with BD523 at OMOI, and 100 μL was removed at 0, 10, 20, 30, 40, 50, 60, 70, 80, and 100 min . Bacteriophage titer of the supernatant was measured after centrifugation. The experiments were repeated three times in each group, and the average value was taken. Then, a one-step growth curve was drawn according to measured results. Based on the determined bacteriophage titer, the amount of bacteriophage burst was calculated using the following formula: amount of bacteriophage burst = bacteriophage titer at the end of burst/concentration of bacterium at the beginning of infection (nfection .(iv) Optimum temperature determination. About 100 μL of bacteriophage solution was incubated in a water bath at 30, 40, 50, 60, 70, and 80°C, and sampled at 30 and 60 min, respectively. Then, bacteriophage titer was determined. The process was repeated three times and the average was taken.(v) Optimum pH determination. About 100 μL of bacteriophage solution was mixed with LB medium at pH 3 to 12, respectively, and incubated in a water bath at 37°C for 1 h. Then, bacteriophage titer was determined. The process was repeated three times and the average was taken.(vi) Tolerance to chloroform test. About 1 mL of bacteriophage solution was added into 9 mL of chloroform and the mixture was fully mixed. The solution was placed at room temperature for 30 min; then, upper solution was removed for titer determination. The process was repeated three times and the average was taken.8 CFU/mL was added. Then, OD600 value was measured at different time points within 80 min.Bacteriophage solution was diluted into LB medium at different MOI, and 100 μL of host bacterium solution of 6 × 10(i) Lyase recombinant plasmid construction. Primers (Table S5) were designed and 3 segments of lyase gene sequences  were amplified by PCR. The PCR protocol was as follows: denaturation at 94°C for 5 min followed by 25 cycles of 10 s at 94°C, 10 s at 55°C, 30 s at 72°C, and 5 min at 72°C. The amplified sequence and plasmid pET22b vector were subjected to double digestion (sites NdeI and XhoI), and were then connected to construct recombinant plasmids containing histidine label.(ii) Target protein expression. Recombinant plasmids pET22b-E, pET22b-GP17.5, and pET22b-GP18.5 were transferred into competent E. coli BL21(DE3) by electroporation, respectively, and then expressed in BL21(DE3) cells to obtain recombinant proteins. At logarithmic growth phase, 1 mM isopropyl-β-d-thiogalactoside (IPTG) was added to induce expression overnight at 25°C, and then the bacterium was collected by centrifugation. The collected bacterium was resuspended and broken in an ice bath with a cell fragmentation device. Total supernatant proteins were collected by centrifugation, and target proteins were purified on the basis of specificity of NI-IDA magnetic beads with histidine label. Then, 20 μL of each purified target protein was used in bicinchoninic acid (BCA) protein assay kit  for concentration determination, and calibration curve finally established was Y = 0.1575X + 0.0193, r = 0.9955 . Purified lyases were successively diluted 5-fold with bacterial solution at logarithmic growth stage (106 CFU/mL). Minimum concentration that completely inhibited bacterial growth after 8 h was considered to be MIC.8 CFU/mL) was divided into 6 aliquots of 1 mL. The aliquots were centrifuged, suspended, and diluted to 106 CFU/mL. Then, they were added with sterile water, 10 μL of BD523 with a titer of 109 PFU/mL, 1 MIC neomycin, 1 MIC allicin, 1 MIC neomycin + BD523, and 1 MIC allicin + BD523, respectively, to allow final reaction system to be 0.5 mL. Next, the aliquots were cultured under shaking at 37°C, and sampled at 2 and 4 h, respectively. After that, absorbance of supernatant was measured at 260/280 nm by ultra-micro Uv-Vis spectrophotometer , and contents of nucleic acids and proteins leaked were calculated.Bacterial solution at logarithmic growth stage .Bacterial suspension of 106 CFU/mL with PBS; then, it was added with sterile water, BD523, 1 MIC neomycin, 1 MIC allicin, 1 MIC neomycin + BD523, and 1 MIC allicin + BD523, respectively. After mixing, the bacterium was incubated at 37°C for 2 h. Then, it was stained with 50 μg/mL propidium iodide (PI). The impaired bacterium stained with PI was analyzed by flow cytometer . A drop of bacterial solution of allicin + BD523 group was added to a sterile cover glass for 30 min so that the bacterium could adsorb onto the glass as much as possible. Electron microscope fixative was added to fix the bacterium for 10 min, and was then sucked dry. After ethanol gradient dehydration and vacuum drying, platinum staining was sprayed. Finally, the prepared specimen was observed by scanning electron microscope FEI INSPECT S50 with image capturing software ThruSight and MAPS.Logarithmic bacterial precipitate was suspended and diluted to 10In vitro.(i)  To investigate synergistic antibacterial effect of combination, 100 μL of bacterial solution at logarithmic stage was dropped into 10 mL of LB medium and mixed. The resulting solution was divided into 6 aliquots and used for preparing samples of model, bacteriophage, 0.5 MIC allicin, 0.5 MIC neomycin, 0.5 MIC allicin + bacteriophage, and 0.5 MIC neomycin + bacteriophage. A blank control using PBS was also set. Each group contained 3 duplicate samples. The samples were cultured under shaking at 37°C for 24 h, and then the OD600 value was measured at 0, 4, 8, 16, and 24 h.To investigate influence of bacteriophage on antibacterial effect of allicin, a PBS blank group, model group, bacteriophage group, 1 MIC allicin group, 1 MIC allicin + bacteriophage group, 0.1 MIC allicin + bacteriophage group, 0.01 MIC allicin + bacteriophage group, and 0.001 MIC allicin + bacteriophage group were set. Each group contained 3 duplicate samples. The samples were cultured under shaking at 37°C for 12 h, and then the OD600 value was measured at each time point. In order to confirm the synergistic effect and mechanism of allicin and bacteriophage on DNA degradation, the above groups were incubated at 37°C for 24 h, and then bacterial DNA was extracted from each group for agarose gel electrophoresis.In vivo.(ii) G. mellonellas fed in a dark environment at 18°C was used for synergistic antibacterial test in vivo. The larvae, which were more than 1 cm long and weighing 250 to 300 mg with uniform color and good vitality and without black or gray spots on the body surface, were selected as qualified experimental subjects. Control groups were divided into a blank control and PBS control. All selected larvae, except those in the control groups, were infected with 10 μL of bacterial solution of E. cloacae at a concentration of 106 CFU/mL and divided into 6 groups, including model, BD523, 1 MIC vancomycin, 1 MIC allicin, 1 MIC vancomycin + BD523, and 1 MIC allicin + BD523. Each group contained 10 larvae. The bacterium was injected into the larvae through the left, lower hind foot using a microsampler. After injection, the larvae were placed in a 26°C incubator. In the case it did not respond to touch stimulation, it would be considered dead . Survival curves were plotted using the Kaplan-Meier method and comparison between groups was performed using the log-rank test. OD600 values were expressed as mean ± SD and statistical significance was assessed by One-way ANOVA. A https://www.ncbi.nlm.nih.gov/bioproject/PRJNA881462 under accession number PRJNA881462.The data that support the findings of this study are available on request from the corresponding author. The raw data and the assembled bacteriophage genome of BD523 have been uploaded to the BioProject database and can be downloaded at


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In our previous studies, 25a, a promising diuretic candidate inhibiting UT, was discovered and showed potent diuretic activities in rodents. Here, a sensitive liquid chromatography–tandem mass spectrometry method for the quantitation of 25a in rat plasma, urine, feces, bile, and tissue homogenates was developed and validated to support the preclinical pharmacokinetic studies. The tissue distribution, excretion, and plasma protein binding were investigated in rats. After a single oral dose of 25a at 25, 50, and 100 mg/kg, the drug exposure increased linearly with the dose. The drug accumulation was observed after multiple oral doses compared to a single dose. In the distribution study, 25a exhibited a wide distribution to tissues with high blood perfusion, such as kidney, heart, lung, and spleen, and the lowest distribution in the brain and testis. The accumulative excretion rate of 25a was 0.14%, 3.16%, and 0.018% in urine, feces, and bile, respectively. The plasma protein binding of 25a was approximately 60% in rats and 40% in humans. This is the first study on the preclinical pharmacokinetic profiles of 25a.Urea transporter (UT) inhibitors are a class of promising novel diuretics that do not cause the imbalance of Na Diuretics are the first-line drugs for hypertension, heart failure, and edema ,2,3. Cla25a with a potent inhibitory effect on UT-B at the sub-micromolar level (0.14 μM in rat and 0.48 μM in mice) was synthesized and investigated by our research group + were chosen as parent ions. The parent ions for 25a and IS were 287.1 and 271.1, respectively. For product ion selection, the most abundant ion was 245.0 for 25a and 74.0 for IS (25a was conducted in multiple reaction monitoring (MRM) mode.First, for mass spectrometry (MS) method development, an electrospray ionization (ESI) source was tested in both positive-ion and negative-ion modes for 0 for IS . The quaSecond, for liquid chromatography (LC) analysis, a C18 column was used for reliability, compatibility, and reproductivity. Different mobile phase compositions were tested in different proportions to achieve better peak shape and retention. Methanol (MeOH), isopropanol (IPA), and acetonitrile (ACN) were selected as potential organic mobile phases for the assessment. Although IPA exhibited the strongest elution among the candidates, it led to increased baseline noise for the analyte, which confined the limits of quantitation (LOQ). Additionally, the high viscosity of IPA made the column pressure too high.v/v) formic acid (FA) and 5 mM ammonium formate were added to improve chromatographic performance and achieve a proper retention time. A 5 min gradient elution process was employed to obtain high productivity for LC analysis  and IS (2.73 min).Representative chromatograms of blank samples, blank matrices  spiked with 25a in four biological matrices was set as 1–1000 ng/mL. The linear regression was presented as y = ax + b with a 1/x weight factor. The correlation coefficients for all standard curves showed r > 0.99 . The response of the LLOQ was over five times greater than that of the blank sample. The accuracy of LLOQ samples in all matrices was within ±20% (n = 6), and precision was within the acceptance criteria (<15%).The concentration range for the calibration curve of r > 0.99 . The lown = 6) on three successive days of analysis  and coefficient of variation (CV) of quality control (QC) samples at 3, 100, and 800 ng/mL  interfering with the ionization of 25a in the ion source 25a and IS were dissolved in DMSO to generate the stock solutions. The working calibration of 25a was gradient-diluted with MeOH/H2O . The stop solution was 50 ng/mL of IS in ACN. Five microliters of working solutions were spiked with 45 μL of the blank biological matrices  to obtain the calibration standards. The final concentrations of the standard samples were 1, 2, 10, 50, 250, 500, and 1000 ng/mL. The quality controls at three concentrations  were generated similarly.The analyte g for 10 min at 4 °C, and 2 μL of supernatant was injected for analysis.The protein precipitation method was employed for sample preparation. Briefly, 750 μL of stop solution was added to 50 μL of calibration standards and rat biological samples and vortexed for 1 min for protein precipitation. The mixture was centrifuged at 18,000× The method was adequately validated in terms of the Guidance for Industry on Bioanalytical Method Validation (US FDA).25a. Biological matrices from six individual rats were detected to distinguish the interference from the endogenous components in matrices and the LC–MS/MS systems.The selectivity was assessed in blank biological matrices, blank matrices spiked with working solutions, and biological samples of rats after p.o. administration of 25a/IS peak area ratios (y) versus the nominal concentrations (x) with a simple regression model combined with a weight factor of 1/x. The lower limit of quantitation (LLOQ) was the lowest concentration of 25a in biological samples that could be determined accurately and that met S/N > 10 with a signal response at least five times higher than the blank samples. The RE and CV for LLOQ should be within ±20%. The peak areas of extracted QC samples  were compared with post-extraction samples spiked with the analyte for recovery assessment. The matrix effect was assessed by comparing the peak area of samples containing matrix to that without matrix (n = 6).A seven-point calibration curve  was fitted by plotting the 25a in the biological matrices  was assessed at 3, 100, and 800 ng/mL (n = 6) under different storage conditions, which included storage at room temperature for 4 h, storage at 4 °C for 24 h, three freeze–thaw cycles, and storage at −80 °C for 30 days.The accuracy and precision were evaluated by calculating the intraday and interday RE and CV of the QC samples. The RE should be within ±15% of the theoretical concentrations . The CV values should be no more than 15%. For the LLOQ, the cutoff values of RE and CV were 100% ± 20% and <20%. The stability of 25a and IS were RE ≤20% and 5%, respectively. The dilution integrity of the method was assessed by diluting 10,000 ng/mL 25a to 500 ng/mL with the matrix. The RE should be within ±15% and the CV should be ≤15% (n = 6).The carry-over effect was assessed by comparing the peak areas of a DB sample following ULOQ with that of LLOQ at the retention time of analytes. The acceptable criteria for The animal experiments complied with the stipulations of the Ethics Committee of Peking University . Male adult Sprague-Dawley rats  were purchased from Vital River  and fed freely at 22 ± 2 °C with 55% ± 5% humidity and 12 h/12 h light/dark cycles.25a in 0.5% (w/v) CMC-Na saline solution was administered at 25, 50, and 100 mg/kg body weight. Blood samples were collected via orbital venous plexus into tubes with K2–EDTA on the ice at 0.083, 0.167, 0.25, 0.5, 1, 2, 4, 6, 8, 12, and 24 h. For the i.v. group, 25a dissolved in a mixed solvent  was intravenously injected at 1 mg/kg body weight via the tail vein. Blood was collected at 0.033, 0.083, 0.25, 0.5, 1, 2, 3, 4, 6, 12, and 24 h. Plasma was obtained by centrifugation at 3000 rpm for 10 min at 4 °C. For the PK–PD relationship study, the total urine of rats within 24 h was collected and weighed. The urine output was approximately calculated by assuming the density of urine was 1 g/mL. The diuresis effect was the urine output increment over the baseline urine output of each rat. The plasma and urine samples were stored at −80 °C until analysis. The PK parameters were calculated by DAS 3.2.8 software  using the noncompartmental analysis (NCA) model. The oral bioavailability (F) was calculated by F = (AUCp.o. × Dosei.v.)/(AUCi.v. × Dosep.o.).For oral administration groups, 25a at 100 mg/kg. Blood and tissues samples  were collected at 0.25 h, 0.5 h, 2 h, 6 h, 12 h, and 24 h after dosing. The tissue samples were rinsed with cold saline and dried on filter papers, weighed, and homogenized with fivefold volumes of ultrapure water using a homogenizer  on ice. The right shin bone of the rat was crushed and soaked in methanol for 12 h at 4 °C. Tissue homogenate or bone soak solution was prepared and analyzed using the above method. Samples were stored at −80 °C until analysis.Twenty-four SD rats were randomly divided into six groups and received a single oral dose of 25a at 100 mg/kg orally. Feces and urine were collected and weighed at 0–6 h, 6–12 h, 12–24 g, 24–36 h, 36–48 h, 48–60 h, and 60–72 h after administration. Parallelly, four SD rats received bile duct cannulation and were given a single dose of 25a at 100 mg/kg orally. Bile was collected and weighed at 0–2 h, 0–4 h, 0–6 h, 0–8 h, 0–10 h, 0–12 h, 0–24 h, 0–36 h, and 0–48 h after administration. All samples were stored at −80 °C until analysis.Four SD rats were adapted in metabolic cages for 3 days in advance and then administrated with 25a was assessed via equilibrium dialysis in SD rats and human plasma. Four hundred microliters of plasma containing 50, 500, or 2500 ng/mL 25a were loaded into the dialysis bags (plasma side), and the bags were placed into tubes containing 4 mL of the dialysis buffer (buffer side). The tubes were then rotated at approximately 100 rpm in a humidified incubator with 5% CO2 at 37 ± 1 °C for 5 h.The plasma protein binding (PPB) ratio of v/v). All samples were further processed by adding 600 μL of stop solution (containing 10 ng/mL IS) to precipitate protein for LC–MS/MS analysis. The PPB ratio (%) was calculated by PPB% = 100% × (1 − Conc.buffer side/Conc.plasma side).After the 5 h dialysis, aliquots of 50 μL samples were taken from both the buffer and the plasma sides. An equal volume of the opposite blank matrix (buffer or plasma) was added to each sample to reach a final volume of 100 μL with a volume ratio of plasma to dialysis buffer at 1:1 . An unpaired Student’s 25a, a promising novel diuretic agent targeting UTs. Firstly, we developed and validated a sensitive and reproducible LC–MS/MS method for the quantitation of 25a in various rat biological matrices. Pharmacokinetic properties, including the absorption, distribution, metabolism, and excretion of 25a in SD rats, were investigated systematically. After oral administration, 25a exhibited a rapid absorption with a dose-dependent linear relationship between exposure (AUC or Cmax) and dose. The oral bioavailability was up to 20%, approximately four times higher than that of lead compound 1H (4.4%), sharing a similar diarylamide scaffold. After multiple doses, drug accumulation was observed in rats. In addition, 25a was distributed extensively to various tissues, such as the kidney, heart, lung, fat, and spleen, especially the target tissue, the kidney. After oral administration, the cumulative excretion fractions of 25a were less than 5% in urine, feces, and bile, suggesting that 25a was mainly eliminated via metabolism. The PPB of 25a in rat and human plasma protein was relatively low. These preclinical PK studies can contribute to the further development of these novel diuretics targeting urea transporters.This study is the first report on the preclinical pharmacokinetic profiles of