People more accurately remember faces of their own racial group compared to faces of other racial groups; this phenomenon is called the other-race effect. To date, numerous researchers have devoted themselves to exploring the reasons for this other-race effect, and they have posited several theoretical explanations. One integrated explanation is the categorization-individuation model, which addresses two primary ways (categorization and individuation) of racial face processing and emphasizes the emergence of these two ways during the encoding stage. Learning-recognition and racial categorization tasks are two classical tasks used to explore racial face processing. Event-related potentials can facilitate investigation of the encoding differences of own- and other-race faces under these two typical task demands. Unfortunately, to date, results have been mixed. In the current study, we investigated whether categorization and individuation differ for own- and other-race faces during the encoding stage by using racial categorization and learning-recognition tasks. We found that task demands not only influence the encoding of racial faces, but also have a more profound effect in the encoding stage of recognition tasks for other-race faces. More specifically, own-race faces demonstrate deeper structural encoding than other-race faces, with less attentional involvement. Moreover, recognitions tasks might ask for more individual-level encoding, requiring more attentional resources in the early stage that may be maintained until relatively late stages. Our results provide some evidence concerning task selection for future racial face studies and establish a groundwork for a unified interpretation of racial face encoding. While individuals tend to better remember own-race faces, less time is needed to categorize faces of other races, an effect referred to as other-race category advantage5. Several theoretical explanations have been proposed to account for these phenomena, including perceptual experience theory6, the race-feature hypothesis8, the multidimensional space hypothesis9, and social cognitive theory10. Through integration of previous theories, Hugenberg and Young11 posited the categorization-individuation model (CIM) that there were two different ways of processing faces during the encoding stage: categorization and individuation. Categorization refers to the classification of the face into a certain group based on facial characteristics . Individuation consists of distinguishing and identifying a face as belonging to a specific individual. Although categorization and individuation play an important role in shaping racial face encoding, the two ways do not have equal influences. Categorization, the first stage of facial encoding, occurs faster for other-race faces than for own-race faces, which causes observers to have less motivation to individualize other-race faces, whereas does not influence the automatic individuation of own-races faces. Consequently, other-race faces are classified by race faster, whereas own-race faces are more accurately recognized. Thus far, numerous studies have used various tasks to investigate differences in categorization and individuation of faces.Humans are experts in facial recognition but can more accurately remember faces of their own race relative to faces of individuals of other races. This phenomenon has been called the other-race effect or own-race bias14, the oddball paradigm16, face orientation judgment17, face/race categorization task18, visual search task8, remember–know–guess judgment19, and the repetition lag paradigm20. For example, Levin8, using a visual search task, showed that faces of other races were more easily detected than faces of the individual’s own race. Moreover, using the learning-recognition paradigm, Wiese, Kaufmann, and Schweinberger13 asked participants to complete an additional racial categorization task in the learning phase and found that own-race faces were categorized more slowly, but were better recognized than other-race faces. Among the wide variety of research paradigms mentioned above, the racial categorization task and learning-recognition paradigm are highly favored in addressing racial face processing. By using these two tasks, researchers can directly compare the categorical and individual processing of racial faces.A variety of classical tasks have been used to investigate racial face processing, including the learning-recognition paradigm11 pointed out that differences between categorization and individuation processing occur almost exclusively during facial encoding . Event-related potentials (ERPs) are an appropriate method to test individuation and categorization encoding of racial faces. Of the possible time points to measure ERPs, N170 has been shown to be the most important component in face related studies. N170 is a negative deflection that peaks at approximately 170 ms over occipito-temporal sites, and is thought to reflect the structural encoding of faces21 or configural processing of facial features22. Interestingly, results from previous studies at N170 under these two tasks are inconsistent. For racial categorization tasks, the majority of previous studies have found a non-significant difference in N170 of own- and other races29; while other studies have reported either a larger N170 for own- relative to other-race faces30, or a reversed trend, in which other-race faces exhibit a larger N170 when compared to own-race faces5. Previous results examining N170 during a learning-recognition task are even more complicated, as many studies embedded other tasks in the learning phase, such as a racial categorization task, or facial attractiveness evaluation task31. Although the result of N170 was reported in many of these studies, it was derived from different stages of the learning-recognition task, including the learning14 and test phases27. Ito and Senholzi4 suggested that these inconsistent results raised the question as to whether categorical and individuation processing of racial faces might be brought about by task demands, rather than racial face processing per se. Similarly, Wiese27 posited that task demands play an important role in the emergence of the N170 racial effect.Although, these studies provide some evidence that supports the categorization-individuation model (CIM), Hugenberg and YoungIn this current study, we aimed to answer the simple and important question of whether categorization and individuation differ for processing of own- and other-race faces during the encoding stage. To address this aim, we used racial categorization and learning-recognition tasks, to provide information for future studies related to racial face encoding.32, but is also modulated by stimulus category33 and influenced by selective attention34. Recent studies have found P1 might also be modulated by task demands. Colombatto and McCarthy35, using a categorization task demand, found own-race faces had a larger P1 than other-race faces. As for demand of an individualization task, Wang and colleagues36 found P1 was influenced by race of faces and facial configural processing when face was attended to.In addition to the N170 component, several other ERP components were of interest in this study. The early component, P1, which is a positive deflection over occipital areas, peaks at approximately 100 ms after the presentation of a visual stimulus. P1 is sensitive to basic stimulus properties, such as spatial frequency, contrast, and luminance37, and is considered an index of other racial-processing38. Research has shown larger P2 amplitudes for other-race faces, revealing greater attention orientation to other-race faces44, with a majority of these studies have using a racial categorization task demand. This larger amplitude of P2 for other-race faces appeared robust, even being observed when the categorization task was focused on gender45. Volpert-Esmond, et al.46 compared a race-irrelevant task  and racial categorization task, and observed a larger P2 for Black than White faces from White participants for both tasks. However, Ito, et al.47 asked participants to passively look at racial faces, and think about whether they like or dislike the particular face being viewed, and did not observe a P2 difference in Black and White faces from White participants. At minimum, the results of these two studies suggest a potential influence of task demands on the amplitude of P2 for racial faces. Furthermore, when Asian faces are taken into consideration, results are also mixed41, Willadsen-Jensen and Ito42 found that both Asian and White participants have larger P2 for other-race faces using a racial categorization task; whereas, He, et al.48 did not observe a difference in P2 between White and Asian faces for White participants using a racial irrelevant task. Nevertheless, the CIM framework describing the two methods of encoding own-face and other-race faces was applied in the current study to directly compare how individualization and categorization task demands affect P2 when encoding racial faces, so that we could explore whether attentional involvement of P2 results from differences in processing quantity of facial categories and whether task demands also play a role in this processing for Asian participants.The midline P2 component is a positive deflection peaking approximately between 100–250 ms, which has been associated with selective attention39, task complexity49, degree of relevance, and motivational significance of stimuli50. Specifically, P300 amplitude varies between different task demands, reflecting focused attention on facial processing51 and discrimination between differences in identity52. While some studies related to racial faces have observed larger P300 amplitude for other-race faces when compared to own-race faces in racial categorization tasks29, another study, utilizing an identity matching task did not observe racial differences in P30036. However, despite the White and Asian faces used in these three studies, the racial results of P300 still varied between task demands. In the current study, we hoped to provide evidence of this important component related to racial face processing under recognition and racial categorization tasks at the encoding stage.P300 is the third ERP component to be investigated in our study. It is the most positive peak between 300 and 600 ms and is particularly sensitive to social category informationThe present study aimed to investigate the effect of task demand on neural processing of own- and other-race faces during the encoding stage. By comparing the categorization task with the learning phase of the recognition task, based on the CIM, we hypothesized that task demands would not automatically affect the individual processing of own-race faces. Thus, own-race faces should demonstrate similar N170 for both categorization and recognition tasks, whereas N170 would be larger for recognition tasks for other-race faces, when compared to categorization tasks. Since we were uncertain how the task demands influenced racial face encoding related to P1, P2, and P300, we had no strong predictions regarding such conditions.Twenty-seven Chinese participants  were initially selected. Ethical approval for the study was granted by Northwest Normal University in accordance with the Declaration of Helsinki. All participants signed informed consent before the experiment.53. All faces were converted to grayscale, and placed on a black background (240 × 240 pixels), informed consent to publish identifying images was obtained.Stimuli consisted of 100 White  and 100 Asian  faces with neutral expressions that were selected from the CAL/PAL DatabaseThroughout the experiment, participants sat in a stationary chair in a dimly lit room, holding a distance of approximately 60 cm. A 23-inch monitor with a resolution of 1920 × 1080 pixels was used for the experiments. Before the formal experiment, participants completed 8 practice trials to familiarize themselves with the experimental procedure and response button. Each participant completed eight task demand blocks (four blocks of categorization tasks and four blocks of recognition tasks) in the formal experiment, which were counterbalanced across participants . There were 128 faces randomly assigned to four recognition task blocks, with 32 faces  in each block. Another different 64 faces were used in four categorization task blocks with 16 faces of each .Each recognition task block included a learning and a test phase. During the learning phase, 16 faces  were presented randomly once, and then learned again in the same order. Participants were asked to remember the presented faces. Each trial started with a fixation (500 ms), followed by presentation of the face (2000 ms) that participants were asked to passively observe and remember. The test phase started when participants indicated they were ready. In the test phase, 32 faces were presented randomly (16 previously presented faces and 16 new faces). In the test phase, after the fixation (500 ms), the test face was presented for a maximum duration of 5000 ms or when the participant made a response. Participants were asked to press "F" for previously-viewed faces or "J" for new faces; then, the next trial was started , then a racial face was displayed for a maximum of 5000 ms, or until the participant pressed a response key: "F" for White faces and "J" for Asian faces  , which was referenced on-line to the vertical central parietal (Cpz). All electrode impedances were kept below 5 KΩ. EEG data processing was performed off-line using EEGLab, recalculated to average reference, and band-pass filtered (0.1–30 Hz). Contributions of eye blink artifacts were corrected. The EEG was segmented from − 200 to 800 ms relative to stimulus onset, with the first − 200 ms as baseline. To eliminate artifacts, we used an independent component analysis (ICA) from the EEGLAB toolbox54 with ICLabel55 to distinguish and exclude non-brain components. The remaining artifacts exceeding ± 100 mV amplitude were rejected. After preprocessing, the mean number of remaining trials were 110.0 and 108.9  for White and Asian faces of the learning phase of the recognition task, respectively, and 47.6 and 47.8  in the categorization task, respectively. Electrode selection was based on previous studies31. The peak latency of each component was selected based on the grand average waveform across all participants and conditions (as recommended by Luck and Kappenman56). Then, a ± 20 ms-window around the grand average peak latency for P1 and N170, ± 30 ms-window for P2, and a ± 200 ms-window for P300, were used to calculate the mean amplitude of each respective measure56, with P1 (120–160 ms O1/O224), N170 (180–220 ms P7/P8/PO7/PO857), P2 (180–240 ms Fz/ Cz/ Pz41), and P300 . Since the main purpose was to compare the perceptual differences of racial faces under the two different task demands, we only recorded the learning phase of the recognition task to enable direct comparison with the categorization task to be made. Greenhouse–Geisser corrections were used to address violations of sphericity. A Bonferroni correction was applied for each pairwise comparison of all experiments in this study to control for type I error.EEG was recorded using 64 Ag/AgCl electrodes mounted on an elastic cap  and continuously sampled at 500 Hz by an AsaLab Amplifier  = 90.536, p < 0.001, ηp2 = 0.777, with a faster response speed in the categorization task than the recognition task. The main effect of race was also significant, F = 6.989, p = 0.014, ηp2 = 0.212, with White faces exhibiting faster response speed when compared to Asian faces. However, the interaction between task demands and face race was not significant .The mean corrected reaction times (RT) of the two face types under the recognition task demand  the and categorization task demand  were analyzed by a repeated-measures ANOVA, with race (White vs. Asian) and task demands (recognition vs. categorization) as within-subject factors. The main effect of task demands was significant, F = 117.005, p < 0.001, ηp2 = 0.818, reflecting more accuracy in the categorization task relative to the recognition task. Both the main effect of race and the interaction between task demands and race were not significant .We analyzed accuracy of race under the recognition  and categorization  task demands using the same analysis method of RT. The main effect of task demand was significant, F = 9.926, p = 0.004, η2p = 0.276, with the categorization task (1.56 ± 0.48 µV) exhibiting a larger amplitude than the recognition task. The interaction between task demand and hemisphere was also significant F = 10.381, p = 0.003, η2p = 0.285, with the categorization task demonstrating a larger amplitude than the recognition task in both the left  and right  hemispheres, but the variations between the two task-evoked P1 amplitudes were different, with a larger difference noted in the left hemisphere. Other main effects and the interaction did not reach significant levels .For P1, a 2  × 2  × 2  repeated-measure ANOVA was conducted  = 14.786, p = 0.001, η2p = 0.363, with recognition (− 3.95 ± 0.64 µV) demonstrating a larger N170 amplitude than categorization (− 2.65 ± 0.56 µV) task demand. The main effect of race and the interaction effect of race and task demand were not significant . The interaction between the hemisphere and task demand was significant F = 8.856, p = 0.006, η2p = 0.254; further analysis found that the right hemisphere demonstrated larger N170 amplitudes than the left hemisphere for both categorization  and recognition tasks , but with differing magnitudes. The interaction between hemisphere and race was significant F = 8.225, p = 0.008, η2p = 0.240, with White faces (− 4.02 ± 0.67 µV) evoking larger N170 amplitude than Asian faces (− 3.79 ± 0.69 µV) in the right hemisphere , and the N170 amplitude of White faces (− 2.71 ± 0.52 µV) and Asian faces (− 2.76 ± 0.51 µV) was not significantly different at the left hemisphere . The interaction between site, task demand, and race was significant F = 8.016, p = 0.009, η2p = 0.236. Further separate analysis found that the interaction between site and race was significant for the recognition task F = 4.400, p = 0.046, η2p = 0.145, with a marginally significantly larger N170 amplitude for White (− 4.81 ± 0.66 µV) than Asian (− 4.55 ± 0.68 µV) faces at P7/8 (p = 0.090), whereas a non-significant difference of N170 amplitude was found for both White (− 3.16 ± 0.64 µV) and Asian (− 3.28 ± 0.63 µV) faces at PO7/8 (p = 0.458). The interaction between the site and race was not significant in the categorization task F = 3.067, p = 0.092, η2p = 0.106. Other post hoc separation analyses of site, task and race did not exhibit significant two-way interaction effects . The interaction between task demand, race, and hemisphere was not significant F = 2.510, p = 0.125, η2p = 0.088, for the left hemisphere (P/PO7), the interaction between task demand and race was not significant F = 0.900, p = 0.352, η2p = 0.033. Regarding the right hemisphere (P/PO8), the interaction between task demand and race did not reach a significant level F = 0.153, p = 0.69, η2p = 0.006. The main effect of site was significant F = 39.848, p < 0.001, η2p = 0.605, with P7/8 (− 4.01 ± 0.60 µV) demonstrating a larger N170 amplitude than PO7/8 (− 2.58 ± 0.57 µV). The main effect of the hemisphere was significant F = 10.036, p = 0.004, η2p = 0.279, so larger N170 amplitudes were observed for the right (− 3.86 ± 0.68 µV) versus (− 2.74 ± 0.51 µV) the left hemisphere. The other main and interaction effects were not significant .For N170, a 2  × 2  × 2 (hemisphere) × 2  repeated-measure ANOVA was performed  = 31.402, p < 0.001, η2p = 0.547, with the recognition (1.62 ± 0.27 µV) task exhibiting a larger P2 amplitude than the categorization (0.77 ± 0.24 µV) task. The main effect of race was significant, F = 4.377, p = 0.046, η2p = 0.144, with White faces (1.26 ± 0.25 µV) resulting in a larger P2 than Asian(1.13 ± 0.24 µV) faces. The main effect of site was also significant, F = 15.132, p < 0.001, η2p = 0.368, reflecting a larger amplitude of P2 at Fz than Pz , and Cz than Pz . No other main or interaction effects reached significance .Analysis of P2 was conducted with a 2  × 2  × 3  repeated-measured ANOVA  = 16.538, p < 0.001, η2p = 0.389, revealing that the recognition task (0.81 ± 0.15 µV) demonstrated a larger P300 than the categorization task (0.39 ± 0.20 µV). The main effect of site was significant F = 4.071, p = 0.027, η2p = 0.135, reflecting a larger amplitude of P300 at Pz (1.41 ± 0.32 µV) than Fz  and Cz . The interaction between the site and race was significant, F = 6.610, p = 0.003, η2p = 0.203. Post-hoc analysis found that for Asian faces, a smaller amplitude of P300 was found at Fz (− 0.47 ± 0.34 µV) than Cz  and Pz ; for White faces, a smaller amplitude of P300 was found at Fz (− 0.18 ± 0.33 µV) than Cz . The other main and interaction effects were not significant .For P300, a 2  × 2  × 4  repeated-measured ANOVA was performed  with 80% power (α = 0.05), revealing that an observed effect size larger than η2p = 0.050 would have 80% power to detect the effect.To confirm that the given number of participants in the study were allowed to detect of sufficient power, we conducted a sensitivity power analysis using G*Power softwareUsing a within-subject design, the present study investigated the effect of task demand on neural processing of own- and other-race faces during the encoding stage. The behavioral results demonstrate that other-race faces had a faster categorization speed than own-race faces. With regard to ERP results, we observed a larger P1 for the categorization task than the learning phase of the recognition task. Conversely, N170 and P2 demonstrated a larger amplitude for the learning phase of the recognition task than the categorization task. Moreover, both N170 and P2 revealed larger amplitudes for other relative to own-race faces, but this result of N170 amplitude was only observed at the right hemisphere. Specifically, for N170, a marginally enhanced N170 for other-race faces was observed during learning of the recognition task at anterior locations (P7/8). Finally, P300 was larger for recognition relative to racial categorization task. These ERP results are discussed in the following paragraphs.61 and contrast32, the main effect of enhanced P1 for the categorization task compared to the learning phase of the recognition task is unlikely due to the variation of these low-level differences, since the racial faces were randomly assigned across two tasks, and the presentation of the two tasks was counterbalanced across participants. Stahl, et al.12, using a recognition task, and with additional instruction in the learning phase that either asked participants to complete a attractiveness rating task or a racial categorization task, found that the categorization task demonstrated a larger P1 than the attractiveness rating task. Similarly, such differences between task demands of P1 in our experiment also reflected differences of early attentional arousal in early visual processing of different task demands.The P1 analysis revealed that P1 was larger for the categorization task than the learning phase of the recognition task. Although previous studies have suggested that the P1 component is sensitive to visual properties, such as luminance13. However, if we narrow down the facial race stimuli to White and Asian and the race of participants to Asian and White, the majority of studies did not observe that the racial effect was on N17029, and only Wiese et al.13 found a larger N170 for other-race faces in the learning phase. Note that in this study, the learning phases of the recognition task embedded an additional racial categorization task. Indeed, participants were invited to complete two different racial tasks, similar to our study. It seems that when participants were asked to process racial faces that simultaneously included racial categorization and individualization demands, a larger N170 for other than own-race faces would be observed. The possible explanation is that the simultaneous completion of categorization and individuation demands requires the processing of both racial category and face identity information, leading to deeper encoding than the single-task demand, thus causing differences between the racial faces to emerge. Considering the decreased amplitude of N170 reflects deeper structural encoding of faces21, such as upright compared to inverted faces62. Therefore, the increased N170 amplitudes of other-race faces observed in the current study reflect that the structural processing of own-race faces is stronger than other-race faces for Chinese participants at the right hemisphere.We found that other-race faces elicited larger N170 amplitudes than own-race faces at the right hemisphere. The racial effect was consistent with that of previous studies12 of racial faces, resulting in deeper individual face encoding in the recognition task compared to race categorization task. Therefore, our result indicates that the task demands alone could induce the differences observed in N170, which may enhance the individual identity processing of racial faces.Additionally, we found larger N170 amplitude in the learning phase of the recognition task compared to categorization task. The task demand of recognition typically demonstrates deeper overall individual encoding when compared to the racial categorization. This might be interpreted as the memorization instruction in the recognition task inducing individual processing13. This trend suggested that when the task demand asked the participant to perform individual-level encoding of racial faces, own-race faces probably elicited stronger structural encoding than other-race faces, which is not in line with the current CIM-based hypothesis. In fact, this result does not appear to be explained by other theories, such as perceptual experience theory6 or social cognitive theory10. Thus, the theory that accounts for these differences remains to be elucidated.Most importantly, we observed an interaction between site, facial race, and task demand. Marginal differences between own- and other-race faces at P7/8 were observed for the recognition task. Although this racial effect only reached marginal significance in our study, the trend of observing the racial effect of N170 at anterior electrodes for the recognition task is consistent with that of Wiese, et al.44. Considering that smaller amplitude of P2 reflects less attentional orientation to racial faces40, and Willadsen-Jensen and Ito42 found the consistent result that both Asian and White participants had a larger P2 for other-race faces, we can infer that the overall encoding of own-race faces employs less attention than other-race faces for Chinese participants. Additionally, we observed that larger P2 amplitudes for recognition task compared to the racial categorization task. P2 has been shown to be sensitive to racial categorization information, with the enhancement of P2 reflecting increased attentional involvement45. This result indicates that the recognition task may require enhanced attention allocation compared to the categorization task. Furthermore, in previous studies41,Willadsen-Jensen and Ito42 observed a larger P2 for other-race faces using a racial categorization task for both Asian and White participants. However, He, et al.48 did not observe a difference in P2 between White and Asian faces for White participants. This inconsistency might be accounted by the task demand, as they asked participants to complete a racially irrelevant task (gender categorization). By using two racial related tasks, racial categorization and recognition task demands, we still observed that other-race faces elicited a larger P2 than own-race faces. Thus, we can infer that this race-related P2 component has inter-task stability, which can help us understand the attention resource allocation of racial face encoding in further studies.The current results show that other-race faces elicited a larger P2 than own-race faces, which is consistent with the results of the majority of previous studies45, which may be the so-called vertex positive potential (VPP)63. Meanwhile, the analyzed electrodes of P2 in our study overlapped with the electrodes’ sites of VPP . Just as Volpert-Esmond and Bartholow45 posited that the studies related to P2 may be integrated with VPP. In our study, the midline P2 component may alternatively be a measurement of VPP. So far, the facial-related P2 and VPP have not been integrated yet, but our results provide some evidence that can be used in future studies.In addition, when comparing the results of N170 and P2 together, we found some interesting patterns. Overall, other-race faces elicited larger amplitudes than own-race faces of N170 (at the right hemisphere) and P2 (at both hemispheres). Furthermore, both N170 and P2 observed larger amplitudes for the recognition task relative to categorization task. These similar trends of results of N170 and P2 raise an alternative explanation of P2; that is, P2 may share similar cognitive facial processes as N17049, and discrimination between differences in identity52, social category information39, which require less cognitive resources, resulted in a lower amplitude of P30064. Therefore, in our experiment, the larger P300 of the recognition task may demonstrate an increase in the cognitive resources needed when compared to the racial categorization task. Furthermore, a meta-analysis by Meissner and Brigham65 found that recognition tasks have similar processing mechanisms to perceptual individualization tasks, with the instruction to memorize faces given in the recognition paradigm possibly constituting individualization12. Hence, one possible inference is that the individualization processing underlying the recognition task requires more cognitive resources than categorization of racial face. However, considering that the significance of P300 might be a consequence of the difference found for P2, we should be cautious about this conclusion.As for P300, the recognition task elicited an enhanced amplitude of P300 when compared to the categorization task. In previous studies, P300 was influenced by the task complexity66. A study with overall low power would reduce the chance of detecting the true effect and the reproducibility of significant results66. Further studies should adopt larger sample size to increase the possibility of detecting a true effect66, to ensure that the effect size would not be inflated67, and to avoid false-positive results68. Besides, O'Toole et al.69 suggested that race typicality is related to racial face memorization. In future studies, using more other-race faces and adopting a cross-over design of participants may help reveal clearer evidence relating to how memory goals and task demand play a role in racial face processing.As only Chinese participants were involved in our study, we could not estimate how the experience affected our results. To thoroughly investigate the question that we posited, an ideal study design should be a complete cross-over design . In addition, although the sensitivity power analysis provided evidence that supported the robustness of the results in the current study, there still existed the possibility that the overall power is underpowered because of the small sample sizeThe present results provide evidence that task demands not only influence the encoding of racial faces, but also has a more profound effect on the encoding stage of a recognition task for other-race faces when compared to own-race faces. More specifically, own-race faces elicit deeper structural encoding than other-race faces (N170), with less attentional involvement (P2). Moreover, a recognition task might promote individual-level encoding (N170) requiring more attentional resources in the early (P2) stage that are potentially maintained until relatively late (P300) stages. These results may assist researchers in making clearer inferences in future racial face studies.


The COVID-19 pandemic has created tremendous challenges for organizations’ corporate social responsibility (CSR), communication, and relationship management with internal stakeholders such as employees. This study conducted an online survey of 466 employees working for large Chinese corporations during the pandemic. A structural equation model based on insights from expectancy violation theory was used to examine how negative violation valence increases employees’ turnover intention as mediated by uncertainty, CSR cynicism, and distrust. The survey results showed that employees’ negative violation valence positively influenced their uncertainty about their organizations’ CSR activities, which fostered their cynicism about CSR and distrust of their organization. Employees’ CSR cynicism increased their distrust toward their organizations, which increased their turnover intention. The theoretical and practical implications of the study are discussed as well. Recent research has shown that organizations and their management decisions can significantly affect stakeholders’ quality of life and physical and psychological well-being . StakehoAlthough many studies ,8 have dScholars have found significant differences in corporations’ CSR activities in Western and Eastern contexts due to their different technological, cultural, and economic systems. For instance, Tang et al.  found thTo fill the above research gap and enrich the crisis management literature, this paper draws on expectancy violation theory  and focuExpectancy violation theory (EVT) is a communication theory that explains how individuals deal with unexpected violations of social norms and expectations . It was According to Burgoon , expectaEVT has previously been applied to the field of crisis communication. Scholars have used EVT to analyze business interactions between companies or between companies and consumers. Sohn and Lariscy  used EVTAccording to Guerrero and Burgoon , if some“Uncertainty” refers to a state of limited knowledge about existing states, future outcomes, or multiple possible outcomes . ScholarDavis  defined Trust is defined as “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor”  (p. 712)Contrary to actual turnover, turnover intention refers to employees’ subjective attitudes about leaving the company or their perceived likelihood to change jobs in the near future . IntentiThe relationship between violation behavior and uncertainty has been explored by communication and management scholars. Smith  suggesteHypothesis (H1).Negative violation valence is positively related to uncertainty about CSR activities.Scholars have examined the antecedents of employee cynicism. For example, Andersson  found thHypothesis (H2).Employees’ uncertainty about CSR activities is positively related to their CSR cynicism.Researchers have explored the relationship between CSR and trust/distrust. Lee  argued tHypothesis (H3).Employees’ uncertainty about their organization’s CSR activities is positively related to their distrust in the organization.In addition to uncertainty, CSR cynicism may influence employee trust/distrust. Employees may have negative responses to CSR . For exaHypothesis (H4).Employees’ CSR cynicism is positively related to their distrust in their organization.Employees’ CSR perceptions may influence their turnover intention . PositivHypothesis (H5).Employees’ CSR cynicism is positively related to their turnover intention.In addition to CSR cynicism, employees’ trust in their organization may be connected to turnover intention. Zeffane and Melhem  examinedHypothesis (H6).Employees’ distrust in their organization is positively related to their turnover intention.To test our hypotheses see , we condn = 240) versus 48.5% male (n = 226). In terms of age, 81.5% (n = 380) of the respondents fell into the range from 25 to 39, 9.4% (n = 44) from 28 to 24, and 9% (n = 42) from 40 to 74. Among the 466 participants, 40.8% (n = 190) were in lower level management positions, followed by 26.6% (n = 124) in non-management positions, 29.6% (n = 138) in middle management positions, and 3% (n = 14) in top management positions. The average tenure of the respondents at their current company was approximately 4.3 years (SD = 3.5). Over one fifth of the participants  worked in manufacturing corporations, followed by information technology  and education companies .In the sample, the gender ratio was 51.5% female  to “5” (strongly agree).This construct was gauged by adopting Kim’s  measuresTo measure employees’ uncertainty about their organizations’ CSR activities, this paper adopted Kim’s  scale. TFollowing the literature about CSR cynicism , we adopTo measure this construct, we applied the scale from Cheng and Chen  and utilFollowing Lapointe et al. , we adopThe literature has shown that the working experience of employees and perceived CSR fit influence participants’ CSR cynicism, distrust, and turnover intention ,72,73. CTo test the six hypotheses, we performed correlation analysis and structural equation modeling (SEM).M = 2.60, SD = 1.26) and uncertainty about their organizations’ CSR activities . In addition, employees reported a low level of agreement with distrust toward organizations , CSR cynicism , and turnover intention . As shown in To demonstrate the mean and standard deviation of each construct, we adopted the Likert scale, using “low” (1.00–1.99)”, “moderately low” (2.00–2.99), “neutral” (3), “moderately high” (3.01–3.99), and “high” (4.00–5.00) as the standard. The data indicated that respondents reported moderately low levels of negative violation valence . The factor loadings of all items ranged from 0.64 to 0.91. According to Fornell and Larcker’s [Before testing the hypotheses, we conducted a confirmatory factor analysis (CFA). The most conservative joint model testing criteria, according to Hu and Bentler , are as arcker’s  criteriaarcker’s  approachχ2 = 192.678, df = 123, p < 0.001, χ2/df = 1.566, CFI = 0.986, TLI = 0.980, IFI = 0.986, SRMR = 0.043, RMSEA = 0.035 (90% CI = 0.025 − 0.044). All of the path coefficients of the structural model are shown in To test the hypotheses, we employed a structural equation model for data analysis. The results indicated an excellent model fit: p < 0.001), supporting H1. H2 further predicted that uncertainty exerts a positive impact on CSR cynicism, which was supported by the results . Uncertainty significantly positively influenced employees’ perceived distrust toward their organization , which means that a higher level of uncertainty about the organization’s CSR activity during the pandemic would lead to increased distrust from internal employees. Thus, H3 was supported. Hypothesis 4 posited that CSR cynicism is positively related to distrust. The data demonstrated that a significant and positive impact flow existed between CSR cynicism and distrust , supporting H4. In addition, the results showed that CSR cynicism significantly positively affects turnover intention . Thus, H5 was supported. Finally, employees’ distrust led to their intention to quit their job , supporting H6.Specifically, H1 predicted a positive relationship between negative violation valence and uncertainty. The results showed that negative violation valence had a significant and positive impact on uncertainty about CSR  via Amos 20. Negative violation valence had a significant and positive indirect effect on CSR cynicism through the mediation of uncertainty . Uncertainty significantly mediated the relationship between negative violation valence and distrust . Negative violation valence had a positive indirect effect on turnover intention through the serial mediation of uncertainty, CSR cynicism, and distrust . Moreover, uncertainty had a positive indirect effect on turnover intention through the channel of distrust and CSR cynicism .Indirect effects in the current model were estimated through a bootstrapping procedure  might influence the relationship between employees’ CSR perceptions and their distrust and turnover intention. Second, as the magnitude of violation valence might influence perceived uncertainty, future scholarship could continue to explore how employees’ reactions to a violator, such as the organization during a crisis, might influence their trust in organizations and work performance. Third, employees’ individual differences might influence their perceptions of an organization during a crisis. Future work could investigate how CSR involvement or value orientations  predict This paper presented a timely study on CSR perceptions, distrust, and turnover intention of employees in crises of contemporary Chinese society. It contributed to the body of knowledge on CSR in building trust and shed light on the application of ethical CSR in crisis communication of China. This study also built on the most up-to-date literature and addressed an important issue on CSR practice in China from an internal employee’s perspective.


Especially for high performance devices, the crystal structure and morphology of this perovskite layer should be optimized. In this experiment, by employing solvent–antisolvent vapor techniques during a modified sequential deposition of PbI2–CH3NH3I layers, the morphology engineering was carried out as a function of antisolvent species such as: chloroform, chlorobenzene, dichlorobenzene, toluene, and diethyl ether. Then, the optical, morphological, structural, and surface properties were characterized. When dimethyl sulfoxide  and diethyl ether (antisolvent) vapors were employed, the CH3NH3PbI3 layer exhibited relatively desirable crystal structures and morphologies, resulting in an optical bandgap (Eg) of 1.61 eV, crystallite size (t) of 89.5 nm, and high photoluminescence (PL) intensity. Finally, the stability of perovskite films toward water was found to be dependent on the morphologies with defects such as grain boundaries, which was evaluated through contact angle measurement.Organometal halide perovskite (CH 3NH3PbI3) semiconductors have been promising candidates as a photoactive layer for photovoltaics.Organometal halide perovskite (CH The perovskite material is a light-harvesting component of the PSCs, and is able to offer many desirable characteristics such as low-temperature solution processability,6,7 high absorption coefficient,8 long carrier diffusion length,9 high charge carrier mobility,10 and adjustable direct bandgap with suitable alternative metals, halogens, and organic cations.11–14 These characteristics can be further modified by using additives,15–17 compositional adjustments,18,19 and solvent–antisolvent extraction approaches.20 Hence, the PSCs have been a promising candidate for commercialization in the current PV industries.Organometal halide perovskite solar cells (PSCs) have received tremendous interest for a next-generation photovoltaic (PV) technology.21–32 For example, if there is a trap site  in a perovskite layer, it acts as carrier recombination sites,33 resulting in a reduced performance of devices. Thus, the morphology and crystallinity of the perovskite thin film should be very important for fabricating high-efficiency PV devices.34In general, the PV performance of PSCs relies on the morphologies of the perovskite thin film because the structural characteristics of a photoactive layer decide PV performances of devices.35,36 For example, thermal annealing of a perovskite film at 85–120 °C has been employed.37 Furthermore, low-temperature antisolvent assisted fabrication of devices are one of the useful techniques for obtaining a film with desired morphologies.18,19 Importantly, it is notable that the additive and antisolvent strategies are both significantly promising in improving the performance of PSCs.38–49 Moreover, the dipping time,50 precursor's type and concentration,51 spin-speed,52 solvent types,53,54 and temperature are important processing factors for optimizing a perovskite layer. In the sequential deposition of the PbI2 and CH3NH3I (MAI) layers, the MAI's intercalation into the PbI2 layer is critically important to obtain a high quality perovskite without any unreacted precursor material. If there is an incomplete conversion of PbI2–MAI into a perovskite, it may be a problem for device performances.55 However, for improving the stability of PSCs, there are researchers who used a PbI2 interfacial nanolayer in their device configuration.56–60To date, numerous approaches have been developed to obtain a high quality and defect-minimized perovskite thin film.2O) were tested, which may act as an extractor of a solvent, dimethyl sulfoxide (DMSO). Then the properties of CH3NH3PbI3 thin films were investigated as a function of antisolvent species, which may include UV-vis light absorption, micro-/nano-structural morphologies, crystal structures, photoluminescence (PL) emission, and surface analysis through the water contact-angle measurements. In this study, it was observed that when a perovskite layer is well crystallized, the surface polarity of perovskite films remains a longer time, i.e., an enhanced stability toward water or its vapor.In this work, we employed a modified sequential deposition method for fabricating organometal halide perovskite thin films. For this purpose, the solvent–antisolvent vapor techniques were adopted as a method of morphology engineering. The five anti-solvents such as chloroform (CF), chlorobenzene (CB), 1,2-dichlorobenzene (DCB), toluene (Tol), and diethyl ether  and synthesized using a hydrothermal method.27 CH3NH3I (MAI) was synthesized by reacting methylamine  and hydroiodic acid  in an ice bath for 2 h with stirring. Then the solvent was evaporated using a rotary evaporator and the precipitate was collected and washed using Et2O three times and dried at 60 °C for 24 h in a vacuum oven. The resulting product, MAI, was used without further purification. To obtain a CH3NH3PbI3 precursor, the synthesized PbI2 and MAI were deposited on the top of poly:polystyrene sulfonate (PEDOT:PSS)-coated substrate using a modified sequential deposition technique: (a) PbI2/DMSO deposition, (b) MAI/IPA deposition at a 1 : 1 mole ratio, and (c) solvent–antisolvent exposure, in which the solvent is DMSO, and the antisolvents are CF, CB, DCB, Tol, and Et2O.In all synthesis methods, analytical grade high purity reagents were used. All solvents and antisolvents were purchased from Fine Chemicals Ltd. Indium tin oxide/fluorine-doped tin oxide (ITO/FTO) coated glass substrates were purchased from TECHINSTRO Chemicals Ltd. PbI2.2.2 (461.78 mg ml−1 of DMSO) solution and annealed at 80 °C for 12 hours. ITO coated glass substrates were used to deposit the samples and sequentially washed with detergent, DI water, and ethanol in an ultrasonic bath. A hole transporting material PEDOT:PSS was deposited on the top of the ITO glass substrate. Then, the PbI2 precursor solution was filtered by a 2 μm sterile polytetrafluoroethylene (PTFE) membrane filter. PbI2/DMSO was spin-coated on the top of the PEDOT:PSS layer and heated at a temperature of 100 °C. Then, the as-synthesized MAI in isopropyl alcohol (IPA) solution was spin-coated on the top of the PbI2 layer. After deposition of the MAI, the thin film was exposed to a DMSO vapor at 80 °C for 10 minutes, and then the crystallizable perovskite layer was exposed to different antisolvent vapor at 70 °C for 10 minutes. The optical, structural, morphological, and surface properties of organometal halide perovskite thin films were then investigated, accordingly.To study the effect of a solvent, DMSO was used to prepare 1 M of PbI2.3λ = 1.54060 Å operating at an applied voltage of 45 kV with a current intensity of 40 mA. The equatorial scans in the continuous mode were taken from 2θ = 4° to 80° at a step of 0.017° with a scan step time of 24.4 seconds. To probe the electron transition behavior of samples photoluminescence (PL) spectroscopy was utilized by F-7000 fluorescence spectrophotometer, Hitachi, Japan, using a xenon lamp at a wavelength of 532 nm with an emission wavelength starting from 600 nm to 850 nm at a scan speed of 1200 nm min−1. The morphologies of perovskite films were investigated by field-emission scanning electron microscopy  at an accelerating voltage of 5.0 kV. Energy-dispersive X-ray spectroscopy (EDX) investigation has been performed to probe the elemental distribution variations present in the thin film. Mapping analysis has been done to obtain elemental maps in the range of nanoscale. Contact Angle Goniometer  was used to record and analyze the effect of antisolvents on the surface energies of perovskite thin films. To determine contact angle, the edge detection of the water droplet was fitted by a polynomial fitting approach. Measurements were taken in time intervals of 40 ms over a period of 30 seconds.UV-visible spectra measurements were taken on thin films using Shimadzu UV-2600-Series, diffused reflectance spectrophotometer double-beam source at a range of 350–800 nm fitted with deuterium and halogen lamps as sources. Transmission and reflection modes were recorded simultaneously. ITO glasses have been used as a background reference for thin films. The X-ray diffraction (XRD) patterns of the as-prepared perovskite samples have been characterized by Philips X'pert PRO-240 mm diffractometer provided with an integrated germanium detector Cu-Kα, radiation source at 3.2 is deposited on the top of PEDOT:PSS/ITO substrate, then, the MAI is spin coated on the top of PbI2/PEDOT:PSS/ITO. Finally, the solvent (DMSO) and antisolvent  vapors are sequentially exposed to the perovskite layer. The properties of solvent and antisolvents were summarized in δ) with the dimension of 1/2 is in the order of 14.5 (DMSO) > 10 (DCB) > 9.5 (CB) > 9.2 (CF) > 8.9 (Tol) > 7.4 (Et2O), indicating that DMSO is the most polar, whereas Et2O is the most nonpolar. Furthermore, the boiling point (bp) is in the order of 189 °C (DMSO) > 180 °C (DCB) > 131 °C (CB) > 111 °C (Tol) > 61 °C (CF) > 35 °C (Et2O), displaying Et2O is the most volatile.61,62 Furthermore, it is notable that although perovskite is hygroscopic and hydrophilic, the measured water-contact angle was reported to be very high . This paradox was solved by recognizing that the hydrophobic PbI2 is formed at the interface of water and CH3NH3PbI3.63 In other words, the measured water contact angle is not for CH3NH3PbI3, but for PbI2 . Here, of course, the contact angle data may include the effect of the morphologies of a film including grain boundaries.For solvent–antisolvent vapor engineering, the solvent/antisolvent should be miscible, whereas the perovskite/antisolvent immiscible. During the film-formation process, if the number of nucleation cites is reduced, the crystal and grain size of perovskite may increase, resulting in a high quality film with small grain boundaries. For this purpose, the solvent DMSO molecules should be quickly extracted from the wet DMSO/perovskite film by help of antisolvent.δ) is equal to a square root of cohesive energy density (CED), i.e., , where Ûvap is the molar heat of vaporization, and V̂ is the molar volume.64 Furthermore, two small organic molecules  are expected to be miscible because of a large entropic gain, although there is an enthalpic cost from the apparent dissimilarity in solubility parameters. Hence, ΔGmix = ΔHmix − TΔSmix < 0, in which ΔGmix, ΔHmix, andΔSmix denote the Gibbs free energy, enthalpy, and entropy of mixing, respectively, and T is temperature. On the other hand, for the intermolecular interactions between antisolvent and perovskite, the relation should be ΔGmix > 0, facilitating a wet perovskite film to undergo a drying process.i.e., the optical bandgap (Eg), is a little bit different due to a non-identical ordering state of a film. Here, the absorption data was replot using the Tauc model,65α is the absorption coefficient, β is a constant (disorder parameter), h is Plank's constant and ν is the frequency of light. The value ‘n’ is 1/2 for a direct bandgap semiconductor and 2 for an indirect bandgap.66 Hence, n = 1/2 can be used because CH3NH3PbI3 is included in the former.αhν)2vs. hν, results in the optical bandgap of ∼1.61–1.63 eV. As an example, the absorption edge is 770.19 nm (Eg = 1.61 eV) for Et2O vapor condition, whereas it is 760.74 nm (Eg = 1.63 eV) for ‘None’ condition, i.e., the perovskite sample was not exposed to any solvent/antisolvent vapor. Here, the small bandgap indicates that the perovskite semiconductor has a well-organized structure, as observed in other stereoregular polymer semiconductors through red-shift in the absorption spectra.67–69 Note that, if the perovskite becomes a single crystalline wafer, the bandgap was reported to be much smaller like 1.36 eV, corresponding to the light absorption onset at 910 nm.70 This trend indicates that the allowed energy states of an electron increase with reducing defect densities in the crystalline lattice forming a periodic potential. In other words, the energy band increases and the bandgap decreases if the quality of perovskite films is improved. Furthermore, if there are any defects in perovskite, the typical trap energies are known to be shallow because of its defect-tolerance property.71–73 Hence, based on the optical data, the ordering of perovskite materials is in the order of: Et2O > Tol > DCB > CB > CF > ‘None’. Interestingly, if there is no solvent–antisolvent vapor treatment, the perovskite sample exhibits the smallest optical absorption, indicating that the vapor treatment is a useful technique for organizing the perovskite films.As shown in 2O. Interestingly, as expected from the UV-vis absorption data, (f) Et2O-vapor and (e) Tol-vapor conditions display the most organized crystal structures with three-dimensional cubic or cuboid shapes. However, (a) ‘None’ and (b) CF vapor conditions show two-dimensional flake-like structures, whereas (c) CB and (d) DCB antisolvent conditions exhibit non-uniform morphologies. Finally, if the two conditions, (e) Tol and (f) Et2O are compared each other, the latter has a better morphology with uniformity and fewer pinholes than the former as shown in 2O's properties such as δ = 7.4 1/2 and bp = 34.6 °C should be helpful to extract DMSO from the wet DMSO/perovskite film.2O. Here, the mapping data follows the morphologies of a sample according to the SEM images 1/2 and bp = 35 °C] and Tol [δ = 8.9 1/2 and bp = 111 °C] are relatively nonpolar and volatile, allowing the wet DMSO/perovskite film to be dried fast . This rapid crystallization results in a complete reaction between PbI2 and MAI. Furthermore, based on the most intense peak at (110) crystallographic planes in 2O). d-spacing is 0.623 nm. Here the crystallite size (t) was calculated based on Scherrer's equation as follows,76,77λ (= 0.154 nm) is the wavelength of X-ray, and B is a full width at half maximum (FWHM) at diffraction angle, θ. Here, d-spacing was calculated based on the Bragg's law (λ = 2d sin θ).2O), indicating the PL peak positions have no direct relationship with the optical bandgap (Eg) shown in Eg in the UV-vis absorption data. For example, when Eg is 1.61 eV (the most red-shift sample), the PL intensity is highest, indicating that, when crystallite size is large in a well-organized morphology, the radiative recombination process is carried out abundantly, resulting in the highest intensity of PL. In other words, when morphologies have a lot of defects like in ‘None’ or ‘CF’ conditions, the probability of non-radiative recombination is increased, resulting in a weak intensity of PL as proved in θc: here, subscript ‘c’ stands for contact angle) was measured  were 130° and 6.3 mJ m−2, respectively. However, in this work, the perovskite film  shows the water contact angle of about 120° and the average surface energy of ∼11.5 mJ m−2 (see Step 1 in 2), morphologies (including grain boundaries), and others.The raw contact-angle data at step number 17 is displayed in 2O and Tol compared to the others, indicating that, when the perovskite materials were well crystallized  should be significantly improved in humid conditions. The next observation is that at steps 17 and 18, the contact angle was saturated as shown in 78 this stability-enhanced perovskite film should be important, providing a general insight for the necessity of a single crystal70 without any grain boundary as an ideal condition if there is a practical processibility.The change of water contact angle with time was smaller for the cases of Etd recall , the sta4.3NH3PbI3 thin films were studied by varying solvent–antisolvent vapor treatment conditions, for which the solvent was dimethyl sulfoxide (DMSO), and the antisolvents were chloroform (CF), chlorobenzene (CB), dichlorobenzene (DCB), toluene (Tol), and diethyl ether (Et2O). Major findings are as follows. First, according to UV-vis absorption data, the optical bandgap of perovskite films ranged from 1.61 eV (Et2O) to 1.63 eV (‘None’: without any solvent–antisolvent vapor treatment). Second, according to SEM images, when antisolvent was Et2O or Tol, the morphologies and crystal structures of perovskite films were improved. Third, when Et2O or Tol was used as an antisolvent, the precursor materials (PbI2 and CH3NH3I) were completely reacted  according to the XRD patterns. Forth, according to PL emission data, when the crystallite size (t = 89.5 nm for both ‘Et2O’ and ‘Tol’ conditions) was large, the PL intensity was higher than those of the other conditions . Fifth, by measuring the water contact angle as a function of antisolvent species, the surface energy (γsv) of each perovskite film was estimated. Initially, the average γsv for all samples was 11.53 ± 0.64 mJ m−2. However, when the contact angle data were saturated at step number 17, the γsv values were different depending on the antisolvent condition: γsv = 25 mJ m−2 (Et2O), and γsv = 54.3 mJ m−2 (None), indicating that the high-quality films (exposed by Et2O) have more stability toward water compared to the others. Hence, the solvent–antisolvent vapor technique should be useful for the enhanced stability of perovskite layers if it is well utilized. Finally, our future works may include the device performances by extending the current study, leading to the processing-structure–property-performance relationship of perovskite solar cells.The morphologies and surface properties of CHThe authors declare no competing financial interest.


Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis.Methods: CRC’s data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients’ overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan–Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes’ expression was examined in the GSE41258 dataset.Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan–Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset.Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC. Colorectal cancer (CRC) is widespread as one digestive system malignancy, which is the second and third most common, respectively, in females and males . AccordiNecroptosis is one programmed cell necrosis that differs from apoptosis and is independently regulated by aspartic acid. Cells with necroptosis have typical necrotic features such as swelling and cell membrane rupture, and release numerous damage-related molecules, inflammatory cytokines, and chemokines . Necropthttps://xenabrowser.net), and a total of 69 necroptosis-related genes (NRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.kegg.jp/kegg/) and a previously published article. Then 27 differentially expressed necroptosis-related genes (DENRGs) were acquired by intersecting the differentially expressed genes (DEGs) in CRC with NRGs. Based on the 27 DENRGs, a prognostic signature was constructed and then determined through a series of statistical analyses. Six significant biomarkers were acquired and then verified in the GSE41258 dataset. Correlations between risk scores and different clinical characteristics were also analyzed. Gene Ontology (GO) and KEGG enrichment analyses were implemented on the two different risk groups using gene set enrichment analysis (GSEA) software, and the immune infiltration analysis was performed using the single sample gene set enrichment analysis (ssGSEA) algorithm.In our study, the sequencing datasets of CRC with the survival information and clinical data were acquired from The Cancer Genome Atlas (TCGA) database  score, PD-L1 treatment score, T-cell rejection score, and T-cell dysfunction score were also analyzed in both groups. Drug sensitivity analysis was also implemented using the pRRophetic algorithm. Through the aforementioned analysis, we depicted the role of necroptosis in CRC from different aspects. Our study may provide a much more comprehensive theory for further studies.Data regarding colon adenocarcinoma (COAD), as well as rectum adenocarcinoma (READ), were obtained from the TCGA database. The TCGA-COAD and READ datasets included 616 tumor samples and 51 normal samples, of which 584 tumor samples contained clinical information and were regarded as a training set for the construction of prognostic signatures. Furthermore, the GSE39582 and GSE41258 datasets related to CRC were obtained from the Gene Expression Omnibus (GEO) database. The GSE39582 dataset, containing 562 tumor samples with survival information, was defined as an external validation set for confirming the prognostic signature. The GSE41258 dataset, containing 186 tumor and 54 normal samples, was used to further examine the expression of signature genes. Furthermore, we downloaded NRGs from the KEGG database; after merging them with NRGs collected from a previous study  and remop-value < 0.05 and |log2fold change (FC)| > 0.5 were set as the cutoff values. Next, the DENRGs were screened via overlapping the DEGs and NRGs by Venn tool.The DEGs were identified between 661 tumor samples and 51 normal ones from the TCGA database using the limma package of R software. Adjusted p-value < 0.2 in the training set. Afterward, the results obtained earlier were incorporated into the least absolute shrinkage and selectionator operator (LASSO) algorithm  between the two groups were compared by the Kaplan–Meier method and log-rank test. In addition, receiver operating characteristic (ROC) curves were plotted to assess the efficiency of the model using the survivalROC package . SimilarThe wilcox.test function was applied to explore the correlations between risk scores and different clinical characteristics in the training set. The variables included in the wilcox.test analysis included age, gender, pathologic T, pathologic N, pathologic M, cancer status, and tumor stage. Next, univariate and multivariate Cox regression analyses were used to select the independent prognostic factors from the risk score and different clinical characteristics. Finally, a nomogram was drawn using the R package RMS on the basis of multivariate Cox regression analyses. A calibration curve and decision curve (DCA) of the nomogram were used to assess the performance of the nomogram on the basis of the prognostic signature.p-value < 0.05 was regarded as significant enrichment.The GO biological process and KEGG pathways concerning the prognostic signature were explored based on all genes in the two different risk groups in the training set by GSEA v 4.0.3 . At the The ssGSEA algorithm was used to calculate the 28 immune cell infiltration abundances of CRC patients in high- and low-risk groups from the training set. Moreover, the expressions of immune checkpoints between the two subgroups were extracted. TIDE was used to evaluate the TIDE score, PD-L1 score, T-cell exclusion score, and T-cell dysfunction score of two subgroups.According to the TCGA gene expression profile and the cell line expression profile in the Genomics of Drug Sensitivity in Cancer (GDSC) database, drug IC50 was predicted using the pRRophetic algorithm .The expressions of the genes mentioned earlier were extracted from expression validation set GSE41258. Moreover, R package ggplot2  was used2-humidified incubator at 37°C. Before the treatment of total RNA isolation, the cells were grown on coverslips in 6-well plates overnight.The cell lines used in our study include NCM460, HCT116, and SW480, which were obtained from the China Center for Type Culture Collection (CCTCC). The cells were cultured in Dulbecco’s modified Eagle’s medium  supplemented with 10% fetal bovine serum  and 1% penicillin-streptomycin  in a 5% COThe Total RNA was extracted using a RNAeasy™ Animal RNA Isolation Kit with Spin Column , and cDNA was synthesized using a HiScript III RT SuperMix for qPCR (+gDNA wiper) . The quantitative real-time PCR (qPCR) was performed on a StepOne Plus system  with ChamQ SYBR qPCR Master Mix . The primers used to amplify the specific genes are listed as follows:ACTB (human): forward 5′- CATGTACGTTGCTATCCAGGC -3′, reverse 5′-CTCCTTAATGTCACGCACGAT -3’; CHMP2B (human) forward 5′- GGCTATAATCAGAGATCGAGCAG -3′, reverse 5′- CTCGTCTTCTGTTTCCGTAGATG -3’; CHMP6 (human): forward 5′- GACAAGCTGAGGCAGTACCAGA -3′, reverse 5′- CTGCTCCTGGTATCGCTTCTTC -3’; RIPK3 (human): forward 5′- GCTACGATGTGGCGGTCAAGAT -3′, reverse 5′- TTGGTCCCAGTTCACCTTCTCG -3’; CXCL1 (human): forward 5′- AGCTTGCCTCAATCCTGCATCC -3′, reverse 5′- TCCTTCAGGAACAGCCACCAGT -3’; GPX4 (human): forward 5′- GAGGCAAGACCGAAGTAAACTAC -3′, reverse 5′- CCGAACTGGTTACACGGGAA -3’; TRAF2 (human): forward 5′- TCCCTGGAGTTGCTACAGC -3′, reverse 5′- AGGCGGAGCACAGGTACTT -3’.t test was used in real-time fluorescence quantitative PCR analysis, and the mRNA relative expression was presented as the mean ± SEM. Unless otherwise indicated, a p-value < 0.05 was regarded as significant statistically.The statistical analysis was implemented with R 4.0.5, and the wilcox.test method was applied to determine whether the differences were significant or not. Student’s According to the criteria for DEG, 5316 DEGs (2880 up-regulated and 2436 down-regulated) were selected between 616 tumor samples and 51 normal ones of TCGA . Next, ap = 0.065, 95% CI = 0.977–2.135) and GPX4  were more than 1, while the HR of the CHMP2B , RIPK3 , IL18 , CXCL1 , TLR3 , and CHMP6  were less than 1  of TRAF2  and low-risk groups (n = 292) (p = 0.00071). The ROC curves indicated that the area under the curve (AUC) reached 0.71 at 1 year, 0.653 at 3 years, and 0.699 at 5 years (According to the median risk score (−1.578880721), the patients in the training set were divided into two groups: high-risk (n = 292) . As show 5 years .n = 281) and low-risk (n = 281) groups by the median of risk scores (−9.306512262)  . Likewis6512262) .p-value < 0.05; p-value < 0.05; To understand the correlation between the risk score and different clinicopathological features, the differences in the risk scores among the different clinical features  were analyzed in the training set. As depicted in In total, 584 patients in the training set were involved in constructing a prognostic nomogram, predicting 1-, 3-, and 5-year OS with 3 independent prognostic factors . As shown in p < 0.05), including activated B cells, activated CD4 T cells, and activated CD8 T cells , and mediated by receptor-interacting protein kinase 3 (RIPK3) and mixed lineage kinase domain-like pseudokinase (MLKL) . It is iColorectal cancer is one of the most common malignant tumors with high incidence and mortality, which is still a major threat to global health. The pathogenesis and progression of tumors are closely concerned with necroptosis, and the vulnerability to necrosis is also considered to be a potential weakness of malignant tumors, which may be targeted for antitumor therapy . HoweverOur study built a prognostic signature on the basis of DNRGs in CRC and obtained six biomarkers, including CHMP2B, TRAF2, RIPK3, CXCL1, GPX4, and CHMP6. Previous research has demonstrated that TRAF2 acts as both a negative regulator of death receptor-induced apoptosis and an inhibitor of TRAIL- and CD95L-induced necroptosis . RIPK3 ivia evolutionary-conserved motifs for recognizing the pathogen-associated molecular patterns (PAMPs) in CRC  have been proven to play an important role ) in CRC . When th) in CRC . Further) in CRC . P53 is ) in CRC . The p35) in CRC , and was) in CRC . In addi) in CRC . BTNL2 i) in CRC . The hig) in CRC .According to drug sensitivity analysis based on GDSC, patients in the high-risk group were found to be more sensitive to Lapatinib. Lapatinib is an inhibitor of human epidermal growth factor receptor type 2 (HER2) and epidermal growth factor receptor (EGFR), which is applied in combination with capecitabine in the treatment of HER2-positive metastatic breast cancer . Lapatinvia scavenging mitochondrial free radicals (via the ß-catenin/Tcf-4 signaling pathway, thereby inducing necroptosis, and is considered a potential compound from diet for the treatment and prevention of CRC (in vitro validation experiments. As a result, although we used ROC curves, line plots, and gene expression experiments for validation, further and deeper validation experiments are still needed. In addition, gene RIPK3 was not validated at the level of cell lines, which may be ascribed to the limitations of cell lines. Despite the lack of further experiments to confirm our findings, it is undeniable that our study lays the foundation in the field of necroptosis, and we will continuously pay attention to the DENRGs.Our study further completes the field of the role of necroptosis in CRC to some degree. At present, the research works on necroptosis in CRC mainly concentrate on certain substances or specific signaling pathways, while there are few in-depth discussions on NRGs in colorectal cancer. For example, the resistance of hypoxic colorectal cancer cells to necroptosis is produced by the glycolytic metabolism, partly radicals . In addiradicals . Some suradicals . ABIN-1 radicals . The cytradicals . Carnosin of CRC . The abon of CRC . It invoOur study established a six-gene signature comprising CHMP2B, TRAF2, RIPK3, CXCL1, GPX4, and CHMP6, and a prognostic nomogram, which can reliably predict the overall survival of CRC. In addition, we provide a range of clinical risk factors for patients with CRC available for reference.


However, its poor water solubility and metabolism in the digestive tract reduce its efficacy. The Octa-GA-Malt-PPI microcapsule was prepared as follows: gum Arabic (GA):maltose :pea protein isolate (PPI) = 2:1:2; core:shell = 1:7.5; emulsification temperature 70 °C; pH 9.0. An in vitro simulated gastrointestinal tract was used to analyze the digestion behavior. C57BL/6 mice were selected to establish an obesity model induced by a high-fat diet (HFD) to evaluate the effect of Octa monomer and the microcapsule. The diffusivity in water and storage stability of Octa improved after encapsulation. The microcapsule was ascribed to electrostatic interactions, hydrogen bonding, and hydrophobic interactions. The sustained release of Octa from the microcapsule was observed in a simulated gastrointestinal tract. Compared with Octa monomer, the microcapsule was more effective in alleviating the symptoms of weight gain, hypertension, and hyperlipidemia induced by HFD in mice. In conclusion, the construction of microcapsule structure can improve the dispersibility and stability of Octa in water, achieve sustained release of Octa in the gastrointestinal tract, and improve its efficiency in alleviating the effects of HFD on the body. AEncapsulation, which is an approach to protect the natural active substance and create delivery systems, has been shown to improve the activity of the functional substances in the gastrointestinal tract and achieve sustained release . Pea proDue to high surface hydrophobicity and aggregated state, pea protein isolates (PPI) can be an appropriate choice as a nanocarrier for liposoluble bioactives than other proteins because it is a non−allergen protein . Most ofIn this study, we used PPI, gum Arabic (GA), and maltose  to fabricate a new complex for Octa encapsulation. Initially, the water solubility and the preliminary molecular mechanism of Octa encapsulation by GA−Malt−PPI complexes were investigated. Then, the digestion behavior of Octa monomer and its microcapsule in the simulated gastrointestinal tract was determined and discussed. Finally, we constructed an HFD−induced obesity model in C57BL/6 mice, and compared the effects of Octa on blood lipid levels in the obese mice before and after microcapsule construction.Octa (with 90% purity) extracted from rice bran wax was obtained from Shengtao Biotechnology LLC . PPI, GA, and Malt were purchased from Shanghai Macklin Commercial Co., Ltd. . Pepsin, lipase, and cholic acid were purchased from Aladdin Biochemical Technology Co. Ltd. . All other reagents and chemicals were of analytical pure grade.An Octa emulsion was prepared according to Li’s method with a slight modification . BrieflyPrevious studies have confirmed that PPI contains several hydrophobic groups exposed to alkaline conditions . Thus, pThe temperature condition of the emulsion were adjusted to the following values: 50, 60, 70, 80 and 90 °C. GA−Malt−PPI ratios were set to 2:1:2. The pH was maintained at 9.0 during the emulsion process. After 30 s of emulsification reaction, adjust the pH to 7.0.The particle size in the samples were determined by dynamic light scattering using a Zetasizer Nano−S instrument  at 25 °C.Octa content in the supernatant was extracted using trichloromethane , and sub1 represents the content of free Octa (mg), and M represents the total content of Octa (mg).EE was calculated using the following formula:1 represents the amount of encapsulated Octa (mg), and A represents total complex content (mg).LA was calculated by the following formula:2) carrier gas at 45.0 mL/min; hydrogen (H2) carrier gas at 40.0 mL/min; fuel gas (air) carrier gas at 450.0 mL/min; detector, FID. For the quantification of Octa, a stock solution (10 mg/mL) was prepared by dissolving Octa in chloroform. To obtain the calibration curve, standard solutions at concentrations ranging from 0 to 2.5 mg/mL were prepared. The peak time of Octa and the calibration curve were shown in The content of Octa was analyzed by gas chromatography (GC) (Shimadzu Corporation), according to the Feng et al. method  with minAccording to Liu’s ,23 and MThe Octa−GA−Malt−PPI stock solution and Octa stock solution were prepared by dissolving Octa−GA−Malt−PPI microparticles or Octa, respectively, in water at a concentration of 10 mg/mL.v/v). Subsequently, CaCl2 was added to it and pH was adjusted to 2.0 using 2 M HCl at 37 °C under continuous shaking for 10 min. Further, pepsin (2000 U/mL) was added and samples (8 mL) were analyzed at different time intervals . Before gas chromatography−mass spectrometry (GC−MS) analysis, the samples were heated at 70 °C for 3 min to inactivate pepsin.A simulated gastric juice mixture was prepared by mixing the Octa−GA−Malt−PPI stock solution or Octa stock solution and SGF at a ratio of 1:3 (v/v) with SIF. The simulated intestinal juice mixture was preheated at 37 °C for 10 min. Subsequently, cholic acid (0.02 mol/L) and lipase (260 U/mL) were added. During the entire reaction period, the pH was maintained at 7.4, and the pH of the reaction system was adjusted with an automatic pH constant meter . Samples (8 mL) were collected at different time intervals  and were heated at 70 °C for 3 min to inactivate digestive enzymes.After 2 h of incubation in SGF, the pH of the solution was adjusted to 7.4 using 2 M NaOH and mixed 1:3 (1 represents the amount of free Octa in the microcapsule after simulated digestion (mg), and X represents the total amount of Octa in the microcapsule without simulated digestion (mg).The sustained RR of Octa from the microparticles in the SGF or SIF was calculated by the following formula:To evaluate the digestion behavior of Octa in the gastrointestinal tract, the Octa monomer was first digested in the SGF for 120 min  and subsequently rapidly transferred into the SIF for 120 min. Digestive products were analyzed using a GC−MS system . GC conditions were as follows: DB−WAX column ; injection volume, 2.0 µL; injector temperature, 300 °C; non−split injection; constant current mode; flow rate, 2 mL/min. Column temperature, 50 °C, held for 1 min, subsequently raised at 20 °C/min to 200 °C, further raised at 3 °C/min to 240 °C, held for 20 min. MS conditions were set as follows: an ion source, electron ionization; ion source temperature, 230 °C; quadrupole temperature, 150 °C; mass range, 33−500 amu. To study the interaction between components of the microparticles, the samples were analyzed using a fluorescence spectrophotometer . Emission fluorescence was monitored at 300−450 nm, whereas excitation fluorescence was monitored at 280 nm, with a slit width of 10 nm.−1 using an FTIR spectrometer .The mass ratio of the sample to KBr was controlled as follows: sample:KBr = 1:100, using a tableting machine to press the ground mixed powder into a uniform transparent sheet. The samples were scanned 16 times at 25 °C in the wavelength range of 400−4000 cmThis experiment was performed according to Animal Care and Use Committee of Animal Nutrition Institute of Zhejiang University of Technology, Chinese Guidelines for Animal Welfare and Experimental Protocol, and the 1964 Declaration of Helsinki and its amendments. Specific−pathogen−free (SPF) male C57BL/6 mice  were purchased from Slac Laboratory Animal Co. Ltd.  and housed in an environmentally controlled facility  in Experimental Animal Center of Zhejiang University of Technology . All mice in this experiment had free access to pure drinkable water during the 15−week experiment period.n = 10), which received a normal diet (20% of fat energy ratio), and an experiment group (n = 40), which received HFD (60% of fat energy ratio) to establish the obesity model. After 6 weeks of HFD treatment, 40 successfully modeled mice in the experiment group were divided into 4 groups randomly (n = 10): HFD group, which received HFD; HFD + Octa group (HFD+O), which received HFD and the daily gavage administration of Octa (20 mg/kg BW); HFD + shell material group (HFD+S), which received HFD and the daily gavage administration of the shell material (150 mg/kg BW); and HFD + microcapsule group (HFD+M), which received HFD and the daily gavage administration of Octa−GA−Malt−PPI microcapsule (170 mg/kg BW). Mice in CON group (n = 10) continued to be fed with the normal diet. The mice were weighed weekly.After a 7−day acclimation, the mice were divided into 2 groups randomly: a control group (CON)  and the supernatant was taken as the plasma sample. Mice were sacrificed by cervical dislocation. The livers were dissected according to the morphological features and rinsed with normal saline. Approximately 0.1 g of undamaged liver sample was fixed in 10% formaldehyde phosphate buffer. Approximately 0.1 g of undamaged liver sample was immersed in optimum cutting temperature (OCT) compound (Sakura Finetech) and snap frozen with the rest of the liver samples in liquid nitrogen for further analysis.After 15−week period, the mice were fasted overnight for 12 h and then anesthetized by 5 min isoflurane exposure. The blood sample was collected from the orbital sinus of mice into anticoagulant tubes. The whole blood in the anticoagulant tube was centrifuged .The levels of total cholesterol (kit A111−1−1), triglyceride (kit A110−1−1), high−density lipoprotein cholesterol , and low−density lipoprotein cholesterol  in the plasma were quantitatively determined using corresponding kits .For histopathological analysis, the liver sample that fixed in 10% formaldehyde phosphate buffer was embedded in paraffin. Hematoxylin and eosin (H−E) staining was performed on 5 μm serial sections to analyze the histopathological changes in the liver.For liver fat accumulation analysis, the liver sample that was embedded in OCT compound was sectioned using a cryomicrotome . Tissue sections (5 μm) were stained with Oil Red O for lipid deposition using corresponding kit . Images were captured using an optical microscope  and analyzed by Image−Pro Plus 5.0 software .−ΔΔCT method was used to determine the gene expressions. The sequence of the primers is shown in Total RNA of liver was extracted with Trizol reagent . The total RNA concentration in each sample was quantified by NanoDrop Spectrophotometer . A qRT−PCR kit based on SYBR green was employed and the reaction was performed in 7900HT instrument . The product specificity was assessed by melting curve analysis. The 2p < 0.05.Each experiment was performed in triplicates. Data were analyzed using SPSS  expressed as the mean ± standard deviation. Comparisons between different groups were performed using one−way analysis of variance with Duncan’s test for post hoc analysis, and the significance level was set at As shown in The characterization of the complexes prepared at different emulsifying temperatures is presented in Hydrophobic amino acid residues, such as tryptophan (Try) and tyrosine (Tyr), are the main fluorescent components of proteins. Fluorescence spectroscopy is usually used to determine the conformational change in proteins that react with hydrophobic bioactive substances, which can lead to changes in the fluorescence properties of the system ,28. Try −1 was caused by the vibration of the O−H bond of the hydroxyl groups or the N−H bond [−1. The peak at approximately 2921.49 cm−1 was obtained by the saturated C−H stretching vibration [−1 and 1537.29 cm−1, respectively [−1 (amide III) were due to the N−H bending [−1 arising from the −COO− symmetric stretching vibration [−1 and 2937.12 cm−1 corresponded to the O−H and C−H stretching vibrations, respectively. The characteristic peaks of Octa mainly included the O−H characteristic peak at approximately 3377.42 cm−1, sharp −CH2− asymmetric stretching vibration absorption peaks at approximately 2916.63 cm−1, and sharp −CH3 symmetric stretching vibration absorption peaks at approximately 2848.37 cm−1. Moreover, the −CH3 bending vibration and −CH2− bending vibration in Octa produced characteristic absorptions at 1462.91 cm−1 and 1378.58 cm−1, respectively [FTIR analysis was used to elucidate the possible mechanism underlying the formation of micro−complexes among Octa, GA, Malt, and PPI . The banN−H bond . The chaibration . The C=Oectively . The pea bending . For GA,ibration . Furtherectively .2, −OH, and −SH, which can readily participate in the crosslinking reactions [2 in PPI and −COO− in GA [−1, which also indicated that the PPI molecular interaction was weakened. The shift in the absorption band of C=O from 1652.76 cm−1 to a lower wavenumber (1633.28 cm−1) indicated that the electron cloud density decreased, which further implied that a strong hydrogen bond was formed between the amino acids in PPI and GA [−1, which was because of the Maillard reaction between the carboxyl group at the reductive end of GA and the amino group of PPI molecule that resulted in covalent crosslinking and the formation of a new N−H bond. After the formation of the GA−Malt−PPI complex, the vibration stretching of N−H or O−H moved to a high wavenumber of 3394.80 cm−1, which indicated that the addition of Malt was conducive to the further covalent crosslinking between the GA and PPI [PPI contains many reactive side groups, such as −NHeactions ,38. AfteO− in GA . After tI and GA . Compare and PPI . Further2− and the symmetric stretching vibration of −CH3 at approximately 2917.30 cm−1 and 2849.03 cm−1 was observed, which indicated that the stretching vibration interfered with the C−H in Octa and PPI [2− had a greater blue shift in the micro−complex of Octa−GA−Malt−PPI than that of the GA−Malt−PPI complex, which implied the presence of electrostatic interactions in the system. Therefore, the formation of the Octa−GA−Malt−PPI microcapsule was due to hydrogen bonding, hydrophobic interactions, and electrostatic interactions among Octa, GA, Malt, and PPI.In contrast to PPI and GA, the −OH peaks of the GA−PPI, GA−Malt−PPI, and Octa−GA−Malt−PPI microcapsules were significantly shifted, which indicated the presence of hydrogen−bonding interactions among Octa, GA, Malt, and PPI. The characteristic peaks of Octa and GA−Malt−PPI were observed in the Octa−GA−Malt−PPI complex, which further confirmed that Octa did not affect the structure of the GA−Malt−PPI complex, and we could speculate that Octa might mainly bind to the hydrophobic surface area of PPI. A coupling effect of the symmetric stretching of −CH and PPI . Additio−1) was mainly studied in the present study because the changes in the amide I on the protein secondary structure were most obvious. The secondary structure includes the intermolecular β−sheet (1610−1624 cm−1), intramolecular β−sheet (1624−1640 cm−1), random coil structure (1640−1650 cm−1), α−helix (1650−1663 cm−1), and β−turn (1666−1692 cm−1) [The changes in the secondary structure of the PPI molecule were determined by the FTIR spectrum combined with deconvolution and second derivative analysis by OMNIC software (Nicolet) and OriginPro software (Originlab). The amide I (1700−1600 cm92 cm−1) . The α−h92 cm−1) . As showFor the commercial applications of the Octa−GA−Malt−PPI microcapsule, the shelf life of food is very important. Therefore, they should be stable throughout their shelf life. By detecting the change in the ζ−potential, particle size, and polymer dispersity index value (PDI) during the three months of storage, we determined whether the complex had a good storage stability. As presented in The physiological conditions of the human gastrointestinal tract are extremely complex, containing various digestive enzymes and inorganic salts. The content and activity of the digestive enzymes and pH change with the intake of food ,44. To dAs shown in In addition, both the microcapsule and Octa monomer significantly reduced the abnormal increase in systolic blood pressure and diastolic blood pressure induced by HFD in mice. The microcapsule showed a better blood−pressure−lowering effect D,E. As sAdditionally, H−E staining A and OilFurthermore, the mRNA expression of genes related to lipid metabolism, including glycerol kinase (GK), Srebp1c, Fas, and Acc1, was significant altered in HFD−treated mice C−F. The Octa is a natural bioactive substance. Due to its poor water solubility, its application in food product research and development is limited. In addition, we found that Octa was digested into short−chain alkanes or alkenes after gastrointestinal digestion, which also limited its biological function efficiency. In order to solve the above problems, GA−Malt−PPI complexes were constructed to encapsulate Octa. The formation mechanism of the Octa−GA−Malt−PPI microcapsule was due to the hydrogen bonding, hydrophobic interactions, and electrostatic interactions among Octa, GA, Malt, and PPI. After encapsulation, the dispersion of Octa in water improved greatly. The microcapsules had a better storage stability and achieved an ideal sustained release in the simulated gastrointestinal tract. The moderating effect of Octa on the effects of HFD was significantly increased after it was encapsulated by GA−Malt−PPI complexes. The present study showed that these microcapsules can be used in different functional food and beverages to increase the dispersion and storage stability in the food matrix and achieve a sustained release and efficient fat reduction in Octa.