Visual attention is often inadvertently captured by salient stimuli. It was suggested that it is possible to prevent attentional capture in some search tasks by suppressing salient stimuli below baseline. Evidence for attentional suppression comes from a probe task that was interleaved with the main search task. In the probe task of Gaspelin et al. (Psychol Sci 26(11):1740–1750, 2015. 10.1177/0956797615597913), letters were shown on the stimuli of the search display and participants had to identify as many letters as possible. Performance was found to be worse for letters shown on the distractor compared to non-salient non-target stimuli, suggesting that distractor processing was suppressed below baseline. However, it is unclear whether suppression occurred at the level of perception or decision-making because participants may have reported letters on the distractor less frequently than letters on nontargets. This decision-level bias may have degraded performance for letters on distractor compared to nontarget stimuli without changing perception. After replicating the original findings, we conducted two experiments where we avoided report bias by cueing only a single letter for report. We found that the difference between distractor and nontarget stimuli was strongly reduced, suggesting that decision-level processes contribute to attentional suppression. In contrast, the difference between target and non-target stimuli was unchanged, suggesting that it reflected perceptual-level enhancement of the target stimuli.The online version contains supplementary material available at 10.1007/s00426-022-01734-3. From the many stimuli in the environment, only a few can be selected for in-depth processing. A number of theories have been advanced to explain how selection is achieved  = 1.68  and a power of 0.99. There were 40 undergraduate psychology students in Experiment 1 . Students participated for class credit or were paid 20 Swiss Francs. They reported normal or corrected-to-normal vision.The study was modeled after Experiment 3 in Gaspelin et al. , which hn et al.  reportedTo display the stimuli, we used a 22.5-inch VIEWPixx Light  at a temporal resolution of 100 Hz and a spatial resolution of 1920 × 1200 pixels. Color coordinates and luminance were measured by a ColorCAL MKII colorimeter . Participant’s head was restrained by a chin/forehead rest at a distance of 66 cm. The experiment was run by the Psychtoolbox  was shown unless otherwise noted. One geometric shape was shown left, right, above and below the fixation cross at a distance of 2° (center-to-center). The circle had a diameter of 1.4°. The square and diamond had a side length of 1.2°. The hexagon had horizontal and vertical diameters of 1.3° and 1.5°, respectively. The dots were squares with a side length of 0.1° and were shown 0.5° to the left or right of the center of the geometric shapes. As probe stimuli, letters in Arial (height of 0.7°) were shown. To avoid that any of the letters would stand out in the probe display because of its size, we applied the following selection criteria. The letters were selected to have a width between 0.5° and 0.7° and a total number of lit pixels between 1280 and 1680. In addition, the letter Q was removed because of its similarity to the letter O. The 21 letters fulfilling these criteria were A, B, C, D, E, F, G, H, K, L, N, O, P, R, S, T, U, V, X, Y, Z. The hash sign was used as mask. The response display showed a 5 × 5 matrix with the 21 possible letters in alphabetical order and four fillers  at the end to have a square matrix. The spacing between letters in the response display was 1.3° (center-to-center). Feedback in the probe task was provided by surrounding the correct letters with a circle (diameter of 1.3°) for 1000 ms. Feedback about wrong responses in the search task was provided by a short flicker of the fixation mark (50 ms on/off in three cycles).2 or L* = 61, saturation of 59, rotation of 0° and 180°). The xyY coordinates were red  and green  with Y in cd/m2. The stimuli were presented against a medium gray background . The dots and letters were shown in the same gray as the background. The letters in the response display were shown in light gray . Selected letters and mouse-overs in the response display were indicated in bright red .The geometric shapes were shown in one of two colors selected in CIELAB-space on an isoluminant color wheel . The distractor was present on 60% of the trials and absent on 40%. On distractor-absent trials, all shapes were shown in the same color. On distractor-present trials, a randomly determined nontarget shape was shown in a different color. The locations of target and non-target shapes were random. Similarly, the location of the dot inside each shape was random with the constraint that there be two dots on the left and two on the right. The letters in the probe task were selected randomly without replacement from the 21 available letters. The target shape (circle or square) and the majority color (red or green) were fixed for each participant and counterbalanced across participants. After 100 trials, feedback about the percentage of correct responses in each task was displayed for at least 2 s in a self-terminated break. Participants worked through three blocks of 200 trials. Thus, there were 108 probe + 252 search trials = 360 trials with distractor and 72 probe + 168 search trials = 240 trials without distractor.A trial started with the presentation of the fixation cross for randomly 700–1050 ms. On search trials, the search display appeared and remained on the screen until a response was collected. Participants were instructed to locate the target shape and indicate the dot location by mouse click. Left and right dot locations were mapped onto left and right mouse buttons, respectively. Participants were told to respond as rapidly and accurately as possible while making less than 10% errors. They were informed that the target shape would never be shown in the singleton color. On probe trials, the search display was shown for 200 ms before the probe letters were superposed for 100 ms and then masked for 200 ms. Participants were instructed to identify the probe letters in the following response display. To select and unselect letters in the response display, they pressed the left mouse button. To indicate that they were done, they pressed the right mouse button. Participants were forced to choose at least one letter and were encouraged to guess if they were unsure. They were also told that speed did not matter in the probe task.Before the experiment started, participants performed three training blocks. In the first and second training blocks, the search and probe tasks were performed separately. In the third training block, the tasks were mixed as in the experiment. Participants practiced the task until they felt comfortable with it, but at least for 50 trials in training blocks 1 and 3, and for 25 trials in training block 2.t(39) = 4.68, p < 0.01, Cohen’s dz = 0.74.For the search task, we removed trials with RTs longer than 2000 ms and trials with choice errors. This resulted in the loss of 0.3% and 2% of trials, respectively. Mean RTs and probe performance are shown in Fig. In the probe task, participants selected 2.5 letters on average and 61% of these letters were correct. In comparison, 1.7 letters were selected with 94% correct in Experiment 3 of Gaspelin et al. , 2.1 lett(39) = 4.86, p < 0.01, Cohen’s dz = 0.77, yielding a mean suppression score of 6.5%. Our suppression score appears smaller than the suppression score of 12% reported by Gaspelin et al. , n et al. , but comn et al.  and the n et al. F = 8.20 p < 0.01, ηp2 = 0.174, confirming that suppression of the distractor freed resources for the processing of target and nontarget stimuli as suggested by Gaspelin et al.  = 190.95, p < 0.01, ηp2 = 0.882, suggesting that the probe task reflected the expected allocation of attention to the target shape. We refer to the difference between target and nontarget shapes as target advantage score. The interaction was not significant, F = 2.22, p = 0.145, ηp2 = 0.054. Next, we evaluated differences between probe letter identification at target and nontarget locations as a function of distractor presence. We subjected individual percentage of correct responses to a 2  × 2  ANOVA. The percentage of correct identifications was 1.4% larger on distractor-present than -absent trials (49.7% vs. 48.3%), n et al. . In addiThe present experiment replicated the main results from Gaspelin et al.  with ourIn Experiment 2, we changed the probe letter task to reduce report bias and memory load see Fig. . A respoM = 21.5 years, SD = 3.4). The methods were the same as in Experiment 1 except that the probe task was different. On probe trials, the probe display was followed by the response cue, which remained on the screen until a response was registered. As a response cue, the fixation cross extended one of its branches by 0.2° toward one of the stimuli and participants had to report the letter at the indicated location. Participants selected a single letter in the response display and confirmed their choice by a left mouse click. In the response display, the response cue was superposed on the letter “P”, but remained visible because it was black whereas the letters were gray. The same design as in Experiment 1 was used except that participants performed 1000 instead of 600 trials. More trials were necessary because target, distractor and nontarget stimuli were probed on different trials, and not simultaneously as with free recall. There were 300 probe trials which were distributed as follows. On distractor-present trials, there were 45 trials for the target shape, 45 trials for the distractor shape and 90 trials for the two nontarget shapes. On distractor-absent trials, there were 30 trials for the target and 90 trials for the nontarget shapes.Forty-four undergraduate psychology students participated, but only 42 participants were retained in the final sample  = 1.93, p = 0.06, Cohen’s dz = 0.30. The group means are shown in Fig. F = 19.00, p < 0.01, ηp2 = 0.192. Further, RTs were shorter in Experiment 1 than 2 (631 vs. 700 ms), F = 12.76, p < 0.01, ηp2 = 0.138. Possibly, participants delayed their responses in the search task to make sure that the response cue for the probe task was not shown. Importantly, the effect of distractor presence was not modulated by experiment, F = 1.77, p = 0.19, ηp2 = 0.022, showing that the delay induced by the response cue in Experiment 2 did not change distractor processing.Two participants did not follow the instructions to keep the error rate in the search task below 10%. Therefore, their datasets were discarded. For the remaining data from the search task, we removed trials with RTs longer than 2,000 ms and trials with choice errors. This resulted in the loss of 1.0% and 1.9% of trials, respectively. Consistent with distractor suppression, RTs in the search task were shorter by 7 ms on distractor-present than -absent trials (696 vs. 703 ms), t(41) = 2.44, p = 0.02, Cohen’s dz = 0.38. However, suppression scores were smaller than with free recall in Experiment 1 (2.7% vs. 6.5%), t(80) = 2.23, p = 0.03, Cohen’s ds = 0.49. Because overall performance on the probe task was lower in Experiment 2 than 1, it may be that the reduced suppression scores resulted from floor effects. Therefore, we excluded four participants with mean performance below 5% in the critical distractor and nontarget conditions. However, the results for the remaining participants were unchanged, which argues against floor effects.To assess distractor suppression in the probe task, we compared percentages of correct identifications for letters shown on distractor and non-targets shapes. Only distractor-present trials were considered. The difference of 2.7% between distractor and non-targets was significant (15.2% vs. 12.5%), F = 0.25, p = 0.62, ηp2 = 0.006, which is at odds with the results from Experiment 1 and Gaspelin et al.  = 219.61, p < 0.01, ηp2 = 0.843, showing that the probe task was sensitive to the expected deployment of visual attention. The interaction was not significant, F = 1.86, p = 0.18, ηp2 = 0.043.Next, we evaluated differences between probe letter identification at target and non-target locations as a function of distractor presence. We subjected individual percentage of correct responses to a 2  × 2  ANOVA. The percentage of correct identifications was similar on distractor-present and -absent trials (36.3% vs. 35.8%), n et al. . Furthert(80) = 0.79, p = 0.79, Cohen’s ds = 0.06. The overall percentage of correct letters was higher in Experiment 1 than 2 (37.6% vs. 25.4%), t(80) = 5.36, p < 0.01, Cohen’s ds = 1.18.Further, we compared the target-advantage scores from Experiments 1 and 2 by independent samples t-test. Target-advantage scores were similar in Experiments 1 and 2 (42.2% vs. 41.2%), p = 0.06) in Experiment 2, it would be justified to perform a one-sided test given the strong expectations derived from Experiment 1 and the literature. In this case, statistical significance would be reached. Second, performance was better for probes on the target compared to probes on nontarget stimuli, showing that the probe task reflected the expected attentional selection of the search target. These findings confirm that the modified probe task did not change basic characteristics of the tasks. However, the modified probe task deviated in an important aspect from the original probe letter task. Notably, the difference between distractor and nontarget stimuli was strongly reduced. Because the improved performance at the target location was unchanged, whereas the worse performance at the distractor location was reduced, the two effects may have different origins. The improved performance at the target location is likely to reflect perceptual enhancement . The methods were the same as in Experiment 2 except that the probe task was different. On probe trials, the duration of the probe display was reduced from 100 to 70 ms to avoid ceiling effects. The probe display was followed by the response cue, which remained on the screen until a response was registered. The letters in the probe display were “o” and “ + ” with diameters of 0.7°, drawn in a pen width of 0.07°. Two letters of each kind were randomly assigned to the stimuli. Responses were collected on a RESPONSEPixx Handheld 5-button response box . Participants pressed the left button for “o” and right button for “ + ”. The same buttons were also used for the search task where participants pressed the left button for a dot on the left and the right button for a dot on the right.Forty students participated and 36 datasets were retained  = 2.19, p = 0.02, Cohen’s dz = 0.37. The group means are shown in Fig. F = 19.98, p < 0.01, ηp2 = 0.213. Further, RTs were shorter in Experiment 1 than 3 (631 vs. 705 ms), F = 13.72, p < 0.01, ηp2 = 0.156. As in Experiment 2, participants may have waited for the response cue to confirm its absence before responding to the search target. Importantly, the effect of distractor presence was not modulated by experiment, F = 0.52, p = 0.47, ηp2 = 0.007, showing that the deployment of attention was not affected by the response cue.For data from the search task, we removed trials with RTs longer than 2,000 ms and trials with choice errors. This resulted in the loss of 0.7% and 1.8% of trials, respectively. Consistent with distractor suppression, RTs in the search task were shorter by 10 ms on distractor-present than -absent trials (700 vs. 710 ms), t5 = 2.19,t(35) = 1.7, p = 0.10, Cohen’s dz = 0.28. Because visual inspection of Fig. t(74) = 2.37, p = 0.02, Cohen’s ds = 0.55. Note that the variance of the suppression scores was similar according to Levene’s test, F < 0.1, p = 0.85.To assess distractor suppression in the probe task, we compared percentages of correct identifications for letters shown on distractor and non-targets shapes. Only distractor-present trials were considered. The difference of 2.1% between distractor and non-targets was not significant (78.1% vs. 80.0%), t5 = 1.7, F < 0.01, p = 0.94, ηp2 < 0.001, which is at odds with the results from Experiment 1 and Gaspelin et al.  = 58.88, p < 0.01, ηp2 = 0.627, showing that the probe task was sensitive to the expected deployment of visual attention. The interaction was not significant, F = 0.12, p = 0.736, ηp2 = 0.003.Next, we evaluated differences between probe letter identification at target and nontarget locations as a function of distractor presence. We subjected individual percentage of correct responses to a 2  × 2  ANOVA. The percentage of correct identifications was similar on distractor-present and -absent trials (85.3% vs. 85.3%), n et al. . Furthert(58.2) = 11.60, p < 0.01, Cohen’s ds = 2.86. Levene’s test showed that the variance of these scores was larger in Experiment 1 than 3 (15.6 vs. 7.7), F = 18.79, p < 0.01, which can be appreciated in the scatter plots of Fig. Further, we compared the target-advantage scores from Experiments 1 and 3 by independent samples t-test. Target-advantage scores were larger in Experiment 1 than 3 (42.2% vs. 9.9%), Finally, we performed additional analyses to evaluate whether the difference between distractor and nontarget shapes was modulated by the correspondence of the probe letters, task switching, or repetition of the distractor. These results are reported in the Supplementary Material and do not affect the conclusion drawn from the results presented in the main text.dz = 0.77 in Experiment 1 to Cohen’s dz = 0.28 in Experiment 3. These results suggest that decision-level suppression  contributed to the worse performance for distractor compared to nontarget stimuli in the original probe letter task. In contrast, effects of perceptual-level suppression are not expected to change when report bias is avoided. Possibly, salient distractors had only little effect on early perceptual-level processing, consistent with the ability to ignore distractors occurring at irrelevant locations  = 0.33, p = 0.75. The question is whether perceptual- or decision-level suppression accounts for the remaining difference. While we can be sure that response cues reduced report bias, it is unlikely that response cues excluded all decision-related processes. For instance, attention was distributed across the peripheral locations at the start of a probe trial, but the response cue appeared in the center, which required a refocusing of attention on central fixation. It may be safe to assume that the refocusing of attention and the interpretation of the response cue took time. After this delay, it was necessary to retrieve the information about the cued stimulus from short-term memory. Given that the distractor never contained useful information in the search task, transfer of information from the distractor location to short-term memory or the retrieval of this information may have been suppressed. Possibly, these biases may be avoided by presenting the response cue at the same time as the probe Below is the link to the electronic supplementary material.


Picea pungens Engelm.). There were significant differences in ET induction between two provenances, i.e. 78.8 ± 12.5% and 62.50 ± 12.8% respectively. Effects of 2,4-Dichlorophenoxy acetic acid , 6-Benzyl amino-purine (6-BA) and/or sucrose on ET proliferation and somatic embryo (SE) maturation were further investigated with four cell lines. The highest ET proliferation rate reached 1473.7 ± 556.0% biweekly. Concentrations of 2,4-D or 6-BA applied at tissue proliferation stage impacted SE maturation among the cell lines, whereas sucrose showed less effects. The highest rate, 408 ± 230 mature SEs/g FW, was achieved in SE maturation cultures. This research demonstrated that the culture conditions, i.e. the specific concentrations of 2,4-D and BA, at ET proliferation stage affected not only ET growth, but also the quality of ET for SE maturation. This study revealed the necessity and benefit in developing both the general and the genotype-specific protocols for efficient production of mature SEs, or somatic plants in blue spruce.Embryogenic tissue (ET) is important for genetic modification and plant re-generation. The proliferation ability and vigor of ET are crucial for plant propagation via somatic embryogenesis. In this study, ET was induced from mature zygotic embryos in blue spruce ( Picea pungens Engelm.) are silver-blue in color throughout the year, which is of high ornamental value for landscaping1. At present, blue spruce is generally regenerated via seed germination in China3. Due to the high price of seeds and seedlings, the number of introduced elite varieties is limited4. The needles of the progeny could be different in color, shape, structure and growth speed, and the quality of seedlings is thus limited. Technology of somatic embryogenesis can be used to produce a large number of somatic plants with consistent genetic traits in a relatively-short time period5, which is thus powerful in large-scale propagation of elite genotypes in blue spruce.The needles of blue spruce (6 used immature zygotic embryos of Norway spruce (Picea abies) to induce embryogenic tissue (ET) and then obtained mature somatic embryos (SEs). Several articles have been published since 2000 on optimization of somatic embryogenesis in coniferous species, including: Pinus radiata7, Picea abies8, Picea glauca9, Larix hybrids10, Korean pine14, Abies nebrodensis15, Abies nordmanniana16, Abies alba17 and blue spruce1. The proliferation of ET together with SE maturation are key steps for large-scale propagation and longterm preservation of coniferous germplasm15. Nielsen et al.16 reported strong clonal effects in most of the traits during the propagation process via SEs in Abies nordmanniana16. The serious cell line dependence between ET proliferation and SE maturation was also reported in Abies alba17. Such kind of research was not reported previously in blue spruce.At present, many tree species could be propagated via somatic embryogenesis. The first success of somatic embryogenesis in coniferous species can be dated back to 1985, when Hakman et al.18 used mature blue spruce embryos, as explants, for ET induction. The highest ET induction rate was 16%, and the number of somatic embryos (SEs) produced was only 9.7 SEs/g FW. Sun et al.4 further optimized the ET induction system. They achieved the induction rate up to 45.5%. However, in the following stages, there were many abnormal embryos resulting in poor SE germination ability. Hazubska-Przyby et al.19 induced ET from zygotic embryo with the highest induction rate of 23.75%, whereas no mature SEs was obtained. Tao et al.1, at the first time, developed a full technical system of somatic embryogenesis in blue spruce, including the cultures of ET induction and proliferation, SE maturation, SE germination and conversion, as well as the hardness of somatic plants. However, the technical system of somatic embryogenesis in blue spruce needs further optimization to solve the problems, such as the low rates of ET induction and proliferation, as well as the poor SE maturation ability. All of these factors limited the large-scale propagation process via somatic embryogenesis in blue spruce. The essence of ET proliferation is the cell division of proembryogenic mass. Factors such as 2,4-Dichlorophenoxy acetic acid , 6-Benzyl amino-purine (6-BA) and sucrose showed great influences on ET proliferation20, and so do the culture conditions at ET proliferation stage. However, there were few studies on their effects on SE maturation thereafter. There are various factors influencing SE maturation22. It is essential to study the effect of culture conditions at ET proliferation stage on SE maturation in order to optimize the entire culture system for mass propagation. In this study, mature zygotic embryos of blue spruce from two provenances were used as explants to induce ET, and the induced ETs were used as experimental materials to explore the effects of 2,4-D, 6-BA and/or sucrose on ET proliferation rates and the ET quality on SE maturation in order to optimize the SE system for efficient production of elite somatic plants in blue spruce.There have been few research reports on the initiation of somatic embryogenesis in blue spruce. Afele et al.24 supplemented with 4.0 mg/L 2,4-D, 1.0 mg/L 6-BA, 10 g/L sucrose, 0.8 g/L acid -hydrolyzed casein , 0.5 g/L l-glutamine, and 4 g/L Gedrite.Mature blue spruce seeds from two provenances were labeled as F1 and F2, and stored in a -40 ℃ freezer before use. The seeds were purchased from Carson Forest (provenance F1), NM, USA in 2010, or San Isabel (provenance F2), CO, USA in 2008. The seeds were of different genotypes, which were produced by various mother trees via open-pollination. Seeds for the test were rinsed with running tap water for 18 h, and then placed on an ultra-clean workbench, sterilized with 75% alcohol for 30 s. The seeds were then rinsed with sterile distilled water for 3–5 times, and sterilized with NaClO solution (4% chlorine) for 15 min, before being rinsed 3–5 times with sterile water. The seed coat and megagametophytes were removed using a scalpel. The embryos were taken out, being used as explants. The dissected embryos were placed on the surface of culture medium horizontally. The ET induction medium was the modified LV (mLV) mediumTwo moths after being cultured with the proliferation medium, the induced ET was used for the experiments of ET proliferation and SE maturation. A new medium was replaced every two weeks, and four selected cell lines were used, including cell lines 2#04, 2#13, 1#16 and 2#02. Among them, cell line 1#16 was induced from F1 source, while cell lines 2#04, 2#13 and 2# 02 were induced from F2 source. The ET of transparent filamentous morphology was selected for subcultures in order to obtain as much ET as possible for subsequent tests. The mLV medium was used for ET proliferation, which was supplemented with 1.0 mg/L 6-BA and 2.0 mg/L 2,4-D. Other culture conditions were the same as the described in Section “For the experiment of 2,4-D test, culture medium of mLV was used as the basal medium. It was supplemented with 0, 1.0, 2.0, 3.0, 4.0, or 6.0 mg/L 2,4-D, 1.0 mg/L 6-BA. The other culture conditions were the same as the described in 2.1. Five Petri dishes, as the minimum, were used for data collection for each treatment and each cell line. The experiment was repeated when it was necessary. The initial ET for inoculation was 0.2 g per Petri dish. After 14 days, the fresh weight (g) of ETs in each dish was weighed in order to calculate ET proliferation rate on the following formula.The same four cell lines were used for this experiment. In the basal mLV medium, 0, 0.5, 1.0, 2.0, or 3.0 mg/L 6-BA was added respectively with 2.0 mg/L 2,4-D. Other culture conditions were the same as the described in Section “The same cell lines were used for the experiment. In the basal medium, 5, 10, 20, or 30 g/L sucrose was added respectively with 2.0 mg/L 2,4-D and 2.0 mg/L 6-BA. Other culture conditions were the same as the Section “l-glutamine, and 2 g/L activated charcoal (AC). Five Petri dishes were used for each treatment with each cell line. The culture was kept in the dark at 23 ± 2 °C, and the number of SEs was counted after a 60-d SE maturation culture. Somatic embryos with well-developed cotyledons were counted as mature SEs.In proliferation cultures, ET of the same four cell lines was treated based on the described in the Sections “Plant tissues were collected at various culture stages including the tissue from ET proliferation culture for 7–10 days (early immature SEs); tissues in SE maturation culture for 5 days (stage I), 10 days (Stage II), 30 days (stage III), 45 days (stage IV), and 60 days (stage V). The plant materials (0.1 g ET) at each stage were stained with 0.1% safranin solution for 10 min and placed on a glass slide, dripped with a drop of water, and covered with a cover slip. The ET was spread evenly by tapping lightly with the flat end of a pencil, and observed immediately and photographed with a microscope .Calculations were performed using Excel 2003 . One-way analysis of variance (ANOVA) and Duncan’s multiple comparisons tests were performed using SPSS 19 . Graphs were constructed with Sigma Plot 12.0 .The authors declare that the study on plants in this research, including the collection of plant materials, complies with relevant institutional, national, and international guidelines and legislation.After being placed on ET induction medium for 7 days, zygotic embryos, as explants, expanded and the cotyledons enlarged Fig. a. Non-emVarious stages of SE development and maturation are shown in Fig. When 2,4-D was not supplemented in the culture medium, few early immature SEs was observed in ET and the structure of SEs was poor Fig. a, since Significant difference existed between the cultures of the control  and those containing 2,4-D of some specific concentrations  and the treatments of some specific 6-BA concentrations  , ET proliferation (Pinus sibirica and Larix sibirica) and SE development/maturation (Medicago sativa)30. Therefore, it is crucial to gain insight into the potential effects of exogenously-applied PGRs on somatic embryogenesis. 2,4-D, functions as auxin, is considered to be the most critical regulator during ET induction in conifers32. In this study, when 2,4-D was absent from the culture medium, ET proliferation rates of the four cell lines were all low, and SE maturation ability, at the following stage, was also poor. Thus, it was confirmed that the supplement of 2,4-D is essential for ET proliferation. Significant differences were also found in SE maturation in certain cell lines. When 2,4-D concentration reached at 6.0 mg/L, the number of early immature SEs decreased in ET. At SE maturation stage, except for cell line 2#02, the number of mature SEs/ g FW dropped remarkably. Cell line 1#16 even lost its embryogenicity. These demonstrated the fact that if 2,4-D concentration is too high, either ET proliferation or SE maturation will be impacted negatively.Plant growth regulators (PGRs) play an important role in different species during the entire process of somatic embryogenesisAcacia arabica33. In this experiment, when 6-BA was absent from the medium, ET proliferation rate and SE maturation ability were lower than that of the 6-BA-added, in the four cell lines. With the increase of 6-BA concentrations, ET proliferation rates increased gradually. Thus, 6-BA is necessary for ET proliferation although the SE maturation ability showed no significant difference. In comparison, 2,4-D concentrations demonstrated a greater effect on SE proliferation rate than 6-BA. When 6-BA exceeded 2.0 mg/L, ET proliferation rate decreased in cell line 2#13. In cell line 1#16, when 6-BA exceeds 3.0 mg/L, both the proliferation rate and the SE maturation ability declined. All of the facts reveal that the optimum concentration of 6-BA plays an important role. Meanwhile, this study also found that, it is well given 2,4 D as well as BA affect the proliferation rate, but it is strongly cell line dependent.Many evidences have proved that the simultaneous use of cytokinin and auxin during ET proliferation is more effective than the single use and these two PGRs are synergistic to a certain extent, such as in 34. For some tree species, glucose and maltose are preferable carbon sources, for example, 2% maltose was better for ET induction than 2% sucrose in Pinus nigra35. High concentrations of sucrose could cause cell plasmolysis, thereby causing dehydration and death of poorly resistant cells36. In this study, the responses to sucrose of various concentrations were different in the four cell lines. When sucrose was 10 g/L, ET proliferation rates were higher than other concentrations in all the four cell lines. When sucrose was in the range of 5–30 g/L, the SE maturation ability was not significantly different in either cell lines 1#16 or 2#02. When sucrose concentration increased up to 30 g/L, SE maturation ability of cell lines 2#04 and 2#13 declined. However, a certain number of early immature SEs could be observed under all the treatments of sucrose concentrations, indicating that the concentration of sucrose was not the major factor promoting early SEs. The number of early SEs in ET decreased when sucrose reached at 30 g/L, which may attribute to the high osmotic potential of the culture, resulting in poor ET proliferation.For conifer embryos, sucrose concentrations in the range of 1%-3% resulted in better development1, the much higher induction rates in this research may result from the different explant sources and/or the better operation in details.On this study, there were significant differences in ET induction between the two provenances used in this research and the highest induction rate was 78.8% ± 12.5% (F2), the lowest was 62.50 ± 12.8% (F1). Since the protocol of ET induction was not modified significantly compared with the previously onel-glutamine, and 4 g/L Gelrite, whereas on Sun and Jia4, 1/2LM medium was used as basal medium supplemented with 1.0 mg/L 2,4-D, 0.5 mg/L 6-B A, 0.5 mg/L KT and the highest embryonic callus proliferation rate was 102.27%.On Tao et al.1, it was 1/2LM supplemented with 6.25 µM (1.38 mg/L) 2,4-D, 3.75 µM (0.84 mg/L) BA for liquid cultures and 7.5 µM (1.69 mg/L) BA, 12.5 µM (2.76 mg/L) 2,4-D for solid cultures, and the highest proliferation rate of embryonic callus was 179.1%. With the optimized protocol in this study, the ET proliferation rate could increase up to 1473.7 ± 556.0%. In addition, in previous studies, the culture conditions of ET proliferation, and SE maturation were often considered separately. Few studies were reported on the influence of the cultures, i.e. the previous culture stage on the following one. This study found that the culture components in the ET proliferation stage not only affected ET proliferation, and also impacted SE maturation at the following stage by the specific concentrations of 2,4-D and 6-BA, which has not been reported in previous studies in blue spruce.It could be mentioned, the proliferation of ET is not always in accordance with SE maturation. When the given parameters were evaluated with the four individual cell lines, the differences among them are apparent. It is a general knowledge that the desired protocol of somatic embryogenesis should fit all the cell lines. However, no such protocol could be developed in practice. In fact, a protocol that fits most cell lines will be selected in order to reduce the cost. This research revealed the remarked difference existed among the cultures that fit individual lines respectively. Thus, for elite cell lines, for example lines of high genetic gain, protocol development on the basis of genotype responses is strongly recommended if the general culture protocol does not function properly for specific cell lines that are essential in practice. On the results of this research, the general culture medium for ET proliferation for all the cell lines was mLV as the basal medium supplemented with 2.0 mg/L 2,4-D, 2 mg/L 6-BA, 10 g/L sucrose, 0.8 g/L casein hydrolysate, 0.5 g/L


Specifically, balancing on the dominant (kicking) leg shows more irregular postural acceleration fluctuations (high SampEn), reflecting a higher postural control efficiency or automaticity than balancing on the non-dominant leg. However, the interaction effects suggest that unipedal balancing training on unstable surfaces is recommended to reduce interlimb differences in neuromuscular control for injury prevention and rehabilitation.The preferential use of one leg over another in performing lower-limb motor tasks  is considered to be one of the internal risk factors for sports-related lower-limb injuries. The current study aimed to investigate the effects of leg dominance on postural control during unipedal balancing on three different support surfaces with increasing levels of instability: a firm surface, a foam pad, and a multiaxial balance board. In addition, the interaction effect between leg dominance and surface stability was also tested. To this end, a tri-axial accelerometer-based smartphone sensor was placed over the lumbar spine (L5) of 22 young adults (21.5 ± 0.6 years) to record postural accelerations. Sample entropy (SampEn) was applied to acceleration data as a measure of postural sway regularity . The results show that leg dominance ( The preference for performing unimanual motor tasks with one leg  is a well-established trait of human motor control resulting from the inherent bilateral asymmetry between the motor control circuitry of the two hemispheres . FurtherEvery human movement has been accepted to result from acceleration  ,12. For Postural control is believed to result from nonlinear interactions among multiple neuromuscular elements as well as internal and external factors ,16. UsinAlthough many gold-standard instruments have been proposed for taking quantitative balance measurements, the cost and complexity of the devices for assessing quantitative data may make it difficult to evaluate and interpret results and limit academic research or clinical settings . As a reIn summary, the current study aimed to investigate the effect of leg dominance on postural control in young adults performing non-exercise physical activity when unipedally balancing on three different support surfaces, each with increasing instability, by assessing postural acceleration using a smartphone sensor. In addition, the effects of surface stability and the interaction between leg dominance and surface stability were also tested. Based on the previous findings that leg dominance effects disappeared when performing unipedal balancing on an unstable surface , it was p2) for comparisons between the dominant and non-dominant legs of 0.37. Based on this computation, with a significant level of Twenty-two young adults , who generally had daily physical activity but no specific sports or exercise participation, participated in the study . This grTri-axial accelerometry was measured using a smartphone . The Physics Toolbox Sensor Suite application (version 2022.09.11), found on the Google Android platform, was used to collect and export the acceleration data at a sampling rate of 200 Hz generated by the smartphone accelerometer . The cenThree different support surfaces were used in the current study : a firm All participants initially started with a 15 s familiarization trial on the foam pad and the balance board without instruction or feedback. Then, on each foot, volunteers performed three 60 s balancing trials barefooted on three different support surfaces: a firm surface, a foam pad, and an MFT Challenge Disc. The order of the tests (right foot–left foot/left foot–right foot) and the order of the surface conditions were randomized. Three trials of balancing were conducted for each foot and each surface. In order to standardize the starting position, each participant was asked to place their hands on the iliac crests, to unipedally barefoot stand by keeping the base of the second metatarsal bone over a reticle crossline marked on each surface , and to TM . A Fourier analysis was performed on the raw acceleration signals, revealing that the highest power resided at frequencies around 5–10 Hz, but that visible power was still observed in the frequency range between 15 and 20 Hz. Therefore, these signals were filtered with a 4th-order zero-phase 20 Hz low-pass Butterworth filter, as similarly reported in a previous study [All data processing was performed using MATLABus study . Sample entropy (SampEn) is a technique used to quantify the regularity of the signal  approprie-series . SampEn -matches . The Sam-matches .The current study applied the SampEn to individual acceleration signals to measure the regularity of acceleration displacements, reflecting postural sway regularity, i.e., postural control complexity ,36,37. SMoreover, two more acceleration-based variables were computed from individual acceleration signals: the root mean square (RMS) as a measure of the magnitude of an acceleration signal, i.e., the variability index of postural movements , and thep2) and observed power (1 − β) were also reported. The Holm–Bonferroni correction [The SPSS software version 26.0  was used for all statistical analyses, with the alpha level set at rrection  was appl = 84.98, p < 0.001, ηp2 = 0.802, 1 − β = 1), SampEn_ML  = 63.23, p < 0.001, ηp2 = 0.751, 1 − β = 1), and SampEn_AP  = 181.81, p < 0.001, ηp2 = 0.896, 1 − β = 1) values than unipedal balancing by the non-dominant leg (ND).All participants completed all the tests without losing their balance. The main results show that leg dominance effects are observed only in the SampEn variables . Specifi = 14.32, p < 0.001, ηp2 = 0.406, 1 − β = 0.998), SampEn_ML  = 25.01, p < 0.001, ηp2 = 0.544, 1 − β = 1), and SampEn_AP  = 107.11, p < 0.001, ηp2 = 0.836, 1 − β = 1). For the RMS variables, surface stability effects are observed in RMS_VT  = 36.25, p < 0.001, ηp2 = 0.633, 1 − β = 1), RMS_ML  = 22.48, p < 0.001, ηp2 = 0.517, 1 − β = 0.999), and RMS_AP  = 21.35, p < 0.001, ηp2 = 0.504, 1 − β = 0.999). Surface stability also has an effect on the 95% ellipse area variable  = 181.81, p = 0.004, ηp2 = 0.303, 1 − β = 0.860). The main results show surface stability effects observed in all acceleration variables. For the SampEn variables, surface stability effects appear in SampEn_VT (FThe post hoc tests represented in p ≤ 0.001), but smaller RMS_VT (p ≤ 0.001), RMS_ML (p ≤ 0.001), RMS_AP (p ≤ 0.001), and 95% ellipse area (p = 0.001) values than balancing on a foam surface. As shown in the first pair (Firm vs. Foam), balancing on a firm surface has greater SampEn_AP (p ≤ 0.001), SampEn_ML (p ≤ 0.001), and SampEn_AT (p ≤ 0.001) values than balancing on an MFT board. Regarding the second pair (Firm vs. MFT), the significant differences between these two surface conditions are observed only in the SampEn, of which balancing on a firm surface has greater SampEn_VT (p = 0.001), SampEn_ML (p = 0.001), SampEn_AT (p = 0.001), RMS_VT (p = 0.001), RMS_ML (p = 0.002), and RMS_AP (p ≤ 0.001) values than balancing on an MFT board.In addition, the third pair (Foam vs. MFT) shows that the significant differences between these two surface conditions are observed only in the SampEn and RMS, a phenomenon which is seen in all acceleration directions. Specifically, balancing on a foam surface has greater SampEn_VT  = 145.82, p < 0.001, ηp2 = 0.874, 1 − β = 1), SampEn_ML  = 137.27, p < 0.001, ηp2 = 0.867, 1 − β = 1), and SampEn_AP  = 246.94, p < 0.001, ηp2 = 0.922, 1 − β = 1). As shown in The main results show that the interaction effects between leg dominance and surface stability are only observed in the SampEn variables: SampEn_VT  which appear during balancing on the dominant (kicking) leg and which may reflect higher postural control efficiency or automaticity than balancing on the non-dominant leg. These findings support the hypothesis that the SampEn variable, a relevant index measuring a given acceleration time series’ variability , could reflect an inherent difference in neuromuscular control between legs. In addition, the current findings agree with the previous notion that measuring the postural accelerations could detect an inherent interlimb difference in postural control ,9,10, reSecondly, the surface stability effects observed in all acceleration-based variables indicate that increasing surface instability could increase the difficulty in controlling postural stability, which is typically seen in higher electromyographic (EMG) activities of the leg muscles when balancing on unstable surfaces than when balancing on a level, rigid surface ,40,41. AThirdly, the interaction effects between leg dominance and surface stability in the SampEn reveal the more irregular postural sway fluctuations (higher SampEn) of the dominant (kicking) leg observed when unipedal balancing on the firm surface, which disappear when balancing on unstable surfaces. In this regard, the changes in SampEn that occurred when going from stable to unstable surfaces might reflect that infrequently balancing on unusual surfaces , faced daily, might alter the neuromuscular control needed to maintain balance. Interestingly, the current findings align with a previous study, which reported that interlimb differences in neuromuscular control disappeared when balancing on an unstable surface . Based oIn summary, the main empirical findings suggest that leg dominance effects could be effectively revealed by the nonlinear method  when applied to postural acceleration and that increasing surface instability facilitates intended neuromuscular control. In addition, leg dominance and surface stability interactions, observed when measuring the regularity of postural acceleration signals, suggest that the exploitation of surface instability benefits should be considered to eliminate the effects of interlimb differences in postural control. Since leg dominance has been listed as one of the possible risk factors for sports-related lower-limb injuries ,3,4, theThe effects of leg dominance on postural control during unipedal balancing on three support surfaces with increasing instability were investigated using a nonlinear technique, namely sample entropy (SampEn), to measure the regularity of postural acceleration fluctuation. The effects of leg dominance and interactions between leg dominance and surface stability were observed in all acceleration directions. Balancing on the dominant leg showed more irregular postural acceleration variations (high SampEn), indicating greater postural control efficiency or automaticity than balancing on the non-dominant limb. In addition, the interaction effects supported the notion that unipedal balance on unstable surfaces may reduce interlimb differences in neuromuscular control.


Pelletizing and crushing the extrudates improved κ, by exploiting and eliminating the effect of extrusion-induced hBN orientations. Gr-filled composites showed better κ than hBN composites due to the higher intrinsic conductivity and bigger particle sizes. A maximum κ of 5.1 and 11.8 W m−1 K−1 was achieved in composites with oriented hBN and Gr through a thin rectangular die and stacking the sheets to fabricate composites with highly oriented fillers.Emergent heat transfer and thermal management applications require polymer composites with enhanced thermal conductivity (κ). Composites filled with non-spherical fillers, such as hexagonal boron nitride (hBN) and Graphite (Gr), suffer from processing-induced filler orientations, resulting in anisotropic κ, commonly low in the through-plane direction. Here, the effects of extrusion and compression molding-induced orientations on κ of hBN- and Gr-filled polyethylene composites were investigated. The effect of extrusion on the hBN orientation was studied using dies of various shapes. The shaped extrudates exhibited hBN orientations parallel to the extrusion flow direction, which prompted additional hBN orientation during compression molding. κ of the composites produced with shaped extrudates varied from 0.95 to 1.67 W m Meanwhile, using injection molding, Zhang et al. [−1 K−1 at 60 wt.% loading [Non-spherical fillers, including hBN, CF, CNT, and Gr, are usually desirably, or undesirably, orientated during processing, such as during extrusion , injecti m−1 K−1 ,21. Cons m−1 K−1  reported m−1 K−1 . Ghose e m−1 K−1  used a lg et al.  aligned  loading . Unlike  loading ,17.Several techniques have been devised to align hBN preferentially and Gr in the direction of heat flow, including magnetic field ,24, elecThis study investigated the effect of extrusion and compression molding processes on the hBN and Gr orientations and, after that, on the thermal conductivity of polyethylene composites. The skin layer and core layer structures were examined at different processing parameters. Composites with random, parallel, and perpendicular hBN orientations were prepared and examined for comparison purposes. To correlate the composite properties with their morphologies and crystal structure, these composites were analyzed using SEM and XRD. In addition, several models were used to predict the thermal conductivities of these composites.3 and melting point of ~122 °C (Lotrene Q2018) was kindly supplied by Qatar Petrochemical Company (QAPCO), Doha, Qatar. HDPE pellets with density of 0.938 g/cm3 and melting point of ~134 °C (Lotrene Q3802) were kindly supplied by Qatar Chemical Company Ltd. (Q-Chem), Doha, Qatar.Hexagonal boron nitride (hBN) powder with 35 µm average particle sizes and 99.0% purity was acquired from Asbury Carbon, Asbury, NJ, USA. Natural graphite flakes (grade 3061) with 99% carbon concentration and + 50 mesh particle size (>80%) were obtained from Sigma Aldrich, St. Louis, MO, USA. Linear low-density polyethylene (LLDPE) powder with density of 0.916 g/cmThe composites were prepared by Xplore Twin Screw 15 mL Micro-Compounder . In each batch, 9 g of the composites , were premixed and fed to the extruder operating at 210 °C, 100 rpm rotation rate, and 10 min residence time. The extrudates were shaped using several extruder dies to form circular (C), triangular (T), square (Sq), and rectangular (R) solid tubes or strands, as shown in The impact of compression molding was studied by pressing the C-shaped extrudates into sheets of 0.5- and 2-mm thicknesses. In addition, the effect of compression pressure and composite sheet thickness was further investigated by preparing composites using a custom-designed mold press (CDP). To study the impact of extrusion on the filler orientations, the composites made from different extrudates were compared to each other, and the composite made from the sample collected directly from the extruder barrel (AE sample).The R-shaped extrudates were pressed perpendicular (P) and random (r) relative to their longest side. For instance, R1P and R1r mean R1 is pressed perpendicular and random to the 5 mm side, respectively. Please note that the random pressing of the R-shaped strands was not controlled because the mold thickness (2 mm) was smaller than the width of the strand. To further investigate the effect of extrusion, the C, T, and Sq-shaped strands were pelletized into pellets (Pe) of 0.5–1 mm thicknesses to ensure that when pressed, the pellets lie down facing the strand’s cross-section. Finally, composites with highly oriented fillers were synthesized through a simple method, in which the R3 strands were stacked to form bars of 1 × 1 × 4 cm dimensions, as shown in The structure and morphology of hBN, Gr, and their LLDPE and HDPE composites were analyzed using VERSA 3D (FEI) SEM. XRD analysis was performed in the 2θ range of 5–65° at a scan rate of 0.05°/min and a step size of 0.01° using Ultima IV X-RAY Diffractometer (Rigaku). TGA was conducted using Discovery TGA  under a nitrogen atmosphere at a heating rate of 10 °C/min and a temperature range of 25–700 °C. The thermal conductivity (κ) was determined by measuring the thermal diffusivity obtained using LFA 500 laser flash  at room temperature using a ½-inch disk with a thickness of 0.5–1.5 mm.Due to their plate-like geometry, hBN particles tend to align during processing, i.e., extrusion and compression molding. The SEM images of the hBN and hBN/HPDE composites are shown in −1 K−1 at 0.5 mm to ~2.5 W m−1 K−1 at 3 mm. To correlate κ of these composites with morphology, these composites were analyzed using SEM, as shown in R, skinR, and coreR are the overall, skin layer, and core layer thermal resistances, respectively, and the resistance is defined by Equation (2), in which ‘t’ (mm) is the layer thickness. As illustrated in skint, skinκ, and coreκ were constants, as shown in −1 K−1, meanwhile, the core layer showed a high κ of 3.59 W m−1 K−1. Therefore, the core layer thickness increases with the composite sheet thickness, leading to enhancement of κ.Conversely, the compression force on the core layer was relatively weak, leading to parallel or random hBN orientations. The skin layer thickness hardly varied with the sheet thickness and was found to fall in the range of 0.08–0.15 mm, supporting the above argument. Meanwhile, the core layer thickness increased with the composite thickness. Thus, the increase of only the core layer thickness improved κ with the sheet thickness. To theoretically evaluate the effect of thickness on κ, the composites κ at each thickness was calculated using the following model :(1)R=Rsk−1 K−1, respectively. As discussed earlier, the hBN particles of the C strand were oriented parallel to its outer surface .−1K−1 in the R3P composite to 1.74 W m−1 K−1 in the AE composite. κ of the AE composite was higher than the shaped composites, indicating that some unfavorable hBN orientations were taking place during the shaping step, i.e., shaping the extrudates. Among the shaped composites, κ of the R1P, R2P, and R3P composites were the lowest, and R2r and Sq composites were the highest. On the other hand, C (C-2 mm), T, and R1r composites manifested similar and intermediate κ. The SEM images of the C and R3 strands displayed parallel hBN orientation at the skin layer; this hBN orientation was also parallel to the extrusion flow direction, as shown in 2, compared to 7, 9, 10, 20, and 10 mm2 of the C, T, R1, R2, and R3 strands, respectively, giving the hBN particles more orientational freedom. Thus, their composite was comparatively resistant to extrusion and compression molding-induced hBN orientations, justifying their relatively higher κ. Despite having well-arranged hBN particles, the symmetry of the C strands led to composites with slightly unfavorable hBN orientations. For example, it has been shown earlier in −1 K−1 and 0.97 to 1.61 W m−1 K−1, respectively, when their strands were pressed randomly instead of perpendicular. The R2 strands accredited to their long side and more arranged hBN particles were more prone to extrusion and compression molding induced hBN orientations, leading to the most significant change in κ when the pressing direction was altered.Conversely, the R strands were asymmetrical; hence, their resultant composites displayed (with considerable dependence on the compression molding direction). Remarkably, the R strands exhibited highly arranged hBN particles parallel to their long side, and when pressed opposite to this side , their composites displayed low κ. However, when pressed randomly (R1r and R2r), their composites increased in κ. κ of R1 and R2 composites was increased from 1.24 to 1.46 W m−1 K−1, respectively. Meanwhile, R3⏊ κ were ~0.87 and 1.44 W m−1 K−1, respectively. The morphology of the R3 strand and the R3 composites, shown in Motivated by the improvement in κ of the R1r and R2r composites, composites with high κ in the through-plane direction were fabricated by stacking the R3 strands to form rectangular bars of 1 × 1 cm sides, as shown in The composites were analyzed by XRD to correlate these findings, as shown in −1 K−1 in the C-0.5 mm to 3.9 W m−1 K−1 in the stacked, owing to their perpendicular and parallel hBN orientations, respectively.To further investigate this point the R3 composites were compared with composites prepared by modifying Yang’s method of sheet stacking and rotation . Figure −1 K−1 was slightly higher than that of the C-2 mm (0.80 W m−1 K−1), and the 2.93 W m−1 K−1 for R3|| κ was lower than that of the stacked (3.9 W m−1 K−1), suggesting that the hBN orientations were not completely perpendicular or parallel in the R3⏊ and R3|| directions, respectively. Owing to the better crystallinity and higher intrinsic κ of the HDPE polymer, R3|| κ of HDPE-22.5% hBN composite was 4.62 W m−1 K−1, much higher than that of the hBN/LLDPE composite (2.93 W m−1 K−1). The R3|| κ of the HDPE- hBN composite was even higher than the stacked hBN/LLDPE composite, suggesting that the stacked hBN/HDPE composite would have higher κ. Nonetheless, R3 strands provide a more facile and practical approach than Yang’s. For example, for maximum κ in Yang’s method required pressing the composites into extremely thin sheets (<< 0.5 mm), followed by stacking the sheets. This was strenuous, time-consuming, and not possible at high filler loading, especially in HDPE-based composites.Moreover, the R3⏊ κ of 0.96 W m−1 K−1 in the R3–0.5 mm composite, with almost perpendicular hBN orientation, to 5.14 W m−1 K−1 in the R3|| composite, with almost parallel hBN orientation. As expected, composites with random hBN orientation, such as the shaped strands, pelletized, and powdered composites, exhibited intermediate κ. As seen in the hBN/LLDPE composite, the R3⏊ κ was higher than that of the C-0.5 mm composite and approximately equal to that of the C composite. This was ascribed to the imperfect hBN orientation in the stacked R3 composites.As was realized previously, the shaping of the extrudates resulted in hBN orientation that, when pressed, was detrimental to κ. Therefore, the shaped extrudates were pelletized into thin pellets to ensure they were pressed perpendicular to their original strands. −1 K−1 in the R3|| Gr/HDPE composite.To further investigate the effect of compression molding on the filler orientation and after that κ, the C and Sq pellets were pressed into ~1- and 3-mm thick sheets using a custom-designed mold press (CDP) of a little pressure, i.e., its weight. The composites prepared by the CDP showed higher κ than those fabricated by the conventional compression mold (Craver), −1 K−1 was achieved when the extrusion products were preferentially aligned in a thin rectangular strand and subsequently pressed in a direction with the best hBN orientations. This κ was 3 and 2 times κ of the C-2 mm and powdered composites, respectively. A maximum κ of 11.8 W m−1 K−1 was observed in Gr/HDPE composite with highly arranged Gr particles. Moreover, incorporating of hBN and Gr in the matrix of XLDPE can enhance κ as well as the mechanical properties. Therefore, this investigating the impact of hBN and Gr on the properties of XLDPE will be the subject of a future study.This study investigated the effect of processes, such as extrusion and compression molding, induced filler orientations in hBN and Gr-based polyethylene (HDPE and LLDPE) composites. hBN/HDPE composite strands of different shapes were prepared through extrusion equipped with various dies. The hBN particles orient differently in each strand shape; therefore, pressing these strands prompted additional hBN alignment, leading to composites with slightly lower κ. For example, κ of the C composites were substantially higher when pressed into 2 mm thick sheets, compared to hot-pressing them into 0.5 mm thick sheets—powdering these composites to eliminate the extrusion-induced hBN orientations led to composites with slightly increased κ. κ of 5.14 W m


Pathways in cancer’ was predicted to be modulated by most umber of proteins. Further, PRKCA, CASP8, and CTNNB1 were predicted to be the top 3 hub genes. In addition, molecular docking studies revealed CYP3A4, CYP1A1, and SIRT1 to be the lead proteins identified from AutoDock 4.2, AutoDock Vina, and Schrodinger suite Glide respectively. Molecular dynamic simulation and MMPBSA were performed for the complex of sinapic acid with above mentioned proteins which revealed a stable complex throughout simulation. The predictions revealed that the mechanism of sinapic acid in breast cancer may be due to regulation of multiple proteins like CTNNB1, PRKCA, CASP8, SIRT1, and cytochrome enzymes (CYP1A1 & CYP3A4); the majorly regulated pathway was predicted to be ‘Pathways in cancer’. This indicates the rationale for sinapic acid to be used in the treatment of breast cancer. However, these are predictions and need to be validated and looked upon in-depth to confirm the exact mechanism of sinapic acid in the treatment of breast cancer; this is future scope as well as a drawback of the current study.Sinapic acid is a hydroxycinnamic acid widespread in the plant kingdom, known to be a potent anti-oxidant used for the treatment of cancer, infections, oxidative stress, and inflammation. However, the mode of action for its chemotherapeutic properties has yet not been unleashed. Hence, we aimed to identify potential targets to propose a possible molecular mechanism for sinapic acid against breast cancer. We utilized multiple system biology tools and databases like DisGeNET, DIGEP-Pred, Cytoscape, STRING, AutoDock 4.2, AutoDock vina, Schrodinger, and gromacs to predict a probable molecular mechanism for sinapic acid against breast cancer. Targets for the disease breast cancer, were identified via DisGeNET database which were further matched with proteins predicted to be modulated by sinapic acid. In addition, KEGG pathway analysis was used to identify pathways; a protein-pathway network was constructed via Cytoscape. Molecular docking was performed using three different algorithms followed by molecular dynamic simulations and MMPBSA analysis. Moreover, cluster analysis and gene ontology (GO) analysis were performed. A total of 6776 targets were identified for breast cancer; 95.38% of genes predicted to be modulated by sinapic acid were common with genes of breast cancer. The ‘ Breast cancer (BC) is the most common cause of death among women, and it is divided into categories based on the organs involved. Blood leakage from the nipple, changes in the shape or texture of the nipple or breast, and a lump in the breast are indications for breast cancer2. Female breast cancer (11.7%) has surpassed lung cancer (11.4%) as the most commonly diagnosed cancer, with an estimated 2.3 million new cases3. The current scenario of breast cancer in the world briefs approx. 46 incidences of breast cancer for every 100,000 females with a mortality of around 13 women in 100,0004. Breast cancer etiology is majorly (> 90%) associated with environmental and lifestyle factors; the major risk factors include obesity, oral contraception, hormone replacement therapy (HRT), late menopause, early menarche, high calorie/fat diet, and alcohol consumption6.Cancer is one of the dreaded diseases portrayed by uncontrolled proliferation of abnormal cells that occurs due to impairment of cell division and apoptotic process8. In the current scenario, the use of plants as a source of medicine has diminished; although prominent in the Asian subcontinent where traditional medicine systems like Traditional Chinese Medicine (TCM) and Ayurveda are still practiced10. A Widely used taxane-diterpene plant metabolite, Taxol (Paclitaxol) obtained from Taxus brevifolia bark is an integral bioactive for the current chemotherapy11. Similarly, Vincristine and Vinblastine are Catharanthus roseus isolates; used to treat clinical leukemia and Hodgkin’s disease12.The utilization of medicinal plants for the treatment of various diseases has a long history; contains multiple diverse metabolites which are pharmacologically active13. It has been proposed to be a potent anti-oxidant by researchers with its effectiveness to be greater than ferulic acid, a hydroxycinnamic acid already used as a natural antioxidant in foods, beverages, and cosmetics14. Balaji et al. reported sinapic acid to possess chemo-preventive potential against 1,2-dimethylhydrazine (DMH)-induced rat colon carcinogenesis15. Also, a study conducted by Eroğlu et al. reported the anti-cancer potential of sinapic acid against PC-3 and LNCaP human prostate cancer cell lines via overexpression of BAX, CASP3, CASP8, CYCS, FAS, TIMP-1, CDH1 and downregulation of MMP-9 in PC-3 cell lines16. In addition, multiple studies have been conducted to assess the cytotoxic potential of sinapic acid on various cell lines like human laryngeal carcinoma cell line (HEp-2)17, chinese hamster lung fibroblasts (V79), human cervical carcinoma (HeLa)18, and breast cancer cell lines MCF7, MDA-MB-23119, and T47D20 which displayed promising potential of sinapic acid as an anti-cancer agent. Hence, making sinapic acid a convincible bioactive against cancer; this additionally, kindled us to assess the potential targets sinapic acid modulates against breast cancer.Sinapic acid is 1 of the 4 most common hydroxycinnamic acids which is widespread in the plant kingdom and is present in various fruits, vegetables, cereal grains, oil seed crops, some spices, and medicinal plants22. Sinapic acid has been tested and reported against various pathological conditions such as infections, oxidative stress, inflammation, cancer, diabetes, neurodegeneration, and anxiety23. It has also been reported to possess protective properties against various chemotherapeutic drugs like doxorubicin, cisplatin, and methotrexate26. In this context, we have utilized system biology tools to predict the possible molecular mechanism for sinapic acid as an anti-cancer agent for the treatment of breast cancer via utilizing tools like molecular docking, gene set enrichment analysis, and gene ontology analysis.System biology tools own great importance in the current scenario of drug discovery as it provides precedence to identify a potent lead hit from multiple test agentshttps://molsoft.com/mprop/) and parameters like number of hydrogen bond donors (NHBD) & acceptors (NHBA), octanol/water partition coefficient (MolLogP), and drug-likeness score were predicted. The reported targets of breast cancer were retrieved from DisGeNET database (https://www.disgenet.org/) using the keyword “Breast Carcinoma” with disease id “C0678222”; 6776 genes were identified. The targets regulated by test agent “Sinapic acid” were retrieved from DIGEP-Pred27 (http://www.way2drug.com/ge/) database via previously retrieved SMILES from PubChem (https://pubchem.ncbi.nlm.nih.gov/compound/637775); targets possessing pharmacological activity greater than pharmacological inactivity were included.The physicochemical properties of sinapic acid were retrieved from the Molsoft LLC database (The gene ontology (GO) of targets modulated by sinapic acid were retrieved from the STRING database; cellular components (CC), molecular function (MF), and biological process (BP) were retrieved for individual analysis. A chord diagram was constructed for the top 5 CC, MF, and BP using the software OriginPro 2021b. Further, GO analysis was performed where a correlation matrix was implemented with two-tailed test for confidence of 95% to compute the Pearson correlation coefficient.DisGeNET database code: C0678222) and a network of protein–protein interaction was constructed via STRING28ver. 11.5 (https://string-db.org/). Further, KEGG pathway analysis was utilized to identify sinapic acid-regulated pathways which were further integrated into pathway-protein interaction via Cytoscape29ver. 3.9.0. The network was treated as directed and analyzed based on edge count by mapping node size and color. Additionally, network analysis was done based on “Node degree distribution” and “Betweenness by degree” where parameters like eccentricity, neighborhood connectivity, in-degree distribution, and outdegree distribution were analyzed.The bioactive regulated targets identified from DIGEP-Pred were matched with the genes involved in the pathogenesis of breast cancer (30 add-on tool (v.2.5.8) in Cytoscape 3.9.0; CC, MF, BP, and KEGG enriched proteins were analyzed by applying functional analysis (two-sided hypergeometric test) with ‘Network specificity’ to be ‘medium’, pV value to be less than 0.05, the ‘GO tree interval’ was kept in the range of ‘3–8 pathway’, and ‘GO term selection of cluster’ was set to three genes minimum with a percentage of 4. Kappa score threshold was kept at 0.4 using the Bonferroni step-down correction method. The cluster was constructed on ClueGo layout with the above parameters.Cluster analysis was performed via ClueGoIn the present study, sinapic acid was docked with 62 proteins using 3 different algorithms; AutoDock Vina, AutoDock 4.2, and Schrodinger suite Glide. Initially, the ligand was prepared by minimizing its energy, followed by target preparation and molecular docking. In addition, docking was performed on the top 5 targets with their respective standard ligands using the three algorithms.VIA Discovery Studio Visualizer; https://discover.3ds.com/discovery-studio-visualizer-download) 2019. The energy of the ligand was minimized using MMFF97 forcefield31 and was converted into .pdbqt format for docking.The 3D conformation of sinapic acid was retrieved in .sdf format from the PubChem database and was converted into .pdb format using Discovery studio visualizer (BOhttps://www.uniprot.org/) database to identify available structures in Protein Data Bank . The targets not available were further modeled using the known FASTA sequence deposited in UniProt database using SWISS-MODEL32 (https://swissmodel.expasy.org)  ver. 0.8 platform to obtain binding affinity of sinapic acid with multiple targets involved in the pathogenesis of breast cancer. Further, the targets with which sinapic acid possessed the least binding energy were visualized in Discovery Studio Visualizer and the poses of ligand pertaining to the least binding energy with maximum intermolecular interactions was selected to be foreseen in Discovery studio.The ligand sinapic acid was docked against the identified proteins using AutoDock Vina at PyRx . Polar hydrogens were added along with Kollman charges to build the protein into .pdbqt. The grid box was set at the center of the macromolecule and docking was performed using a genetic algorithm with 10 genetic algorithm runs with two-point crossover mode. The pose possessing the least binding energy was visualized for protein–ligand interaction.https://www.schrodinger.com/) 2019 was used to prepare the ligand molecule, a low-energy conformation of the ligand was made prior to docking. Further, the protein preparation wizard module was utilized to prepare the protein by adding missing amino acids and the OPLS-3 forcefield was used to minimize the energy. Water molecules were removed and conceivable ionization states for the hetero atoms in the protein were produced; one with the highest stability was chosen.The LigPrep module of Schrodinger suite (https://nodepit.com/node/com.schrodinger.knime.node.gridgen.GridGenNodeFactory) at the center of the identified binding pocket. The Glide module of Schrodinger suite 2019 was used to perform protein–ligand docking using extra precision (XP) mode and OPLS-3 force field was implemented to dock in flexible docking mode; creating confirmations for the input ligand. Further, the Docking score was generated automatically after the completion and results were analyzed via the Glide modules XP visualizer.The site map module was used to identify the binding pocket of the proteins, and a grid box was set with dimensions  using the Glide grid generation wizard (PRKCA (PDB ID: 3PFQ), CASP8 (PDB ID: 1QTN), and CTNNB1 (PDB ID: 2Z6H)) and the complexes possessing the least binding energy with respective algorithms i.e. AutoDock 4.2 (CYP3A4 (PDB ID: 5A1R)), AutoDock Vina (CYP1A1 (PDB ID: 4I8V)), and Schrodinger (SIRT1 (PDB ID: 4ZZI)).The complexes subjected to MD simulation were chosen based on the hub genes (based on edge count) identified from gene enrichment analysis (https://www.gromacs.org/) was used to perform the MD Simulations. The topology of the protein was generated by applying charmm36 forcefield33 using the pdb2gmx module of gromacs. The proteins were solvated using three-point water model in a dodecahedron box with dimensions of 1 nm in all directions. The model system was neutralized by adding sodium (Na+) and Chloride (Cl-) ions as counter ions. Energy minimization was performed using steepest descent integrator with verlet cutoff scheme for a maximum of 50,000 steps to achieve the least energy confirmation. The system was equilibrated using Canonical (NVT) and Isobaric (NPT) for 100 picoseconds. V-rescale, a modified Berendsen thermostat was applied to maintain constant volume and temperature at 300 K. Similarly, a C-rescale pressure coupling algorithm was applied to maintain constant pressure at 1 bar. Particle Mesh Ewald was applied for computing long-range electrostatics, coulomb, and vander waals with a cut-off of 1.2 nm. The LINCS algorithm was used to constrain bond length. Each complex was subjected to MD run for 200 ns; the coordinates and energies were saved at every 20 picoseconds. The trajectories generated were analyzed using in-built gromacs utilities34.Gromacs version 2022.1   Fig. . STRING SS) Fig. . A netwocid Fig. . Additio0005615) scored the lowest false discovery rate of 1.34E-08 via the modulation of 34 observed genes i.e. HMOX1, TIMP1, MMP2, PLAT, CCL2, CD38, IFNG, GAPDH, CAT, KRT1, MMP7, TIMP2, FLT1, CBR1, MMP3, HSPA4, CD14, MMP14, KLK3, TAC1, KRT7, CD86, PRDX6, CTNNB1, TFRC, KRT19, PLAU, NPPB, PRDX4, KRT18, CHEK1, CD36, PRKCA, and KRT8 against 3195 background genes at a strength of 0.53. Similarly, 32 MF were identified in which protein binding (GO:0005515) scored the lowest false discovery rate of 5.26E-06 via the modulation of 46 observed genes i.e. NGFR, SIRT1, HMOX1, TIMP1, PLAT, CCL2, CD38, IFNG, GAPDH, TP53I3, CAT, KRT1, MDM2, CCND2, TIMP2, EPAS1, FLT1, MMP14, NQO1, TAC1, NOS2, CYP3A4, PRDX6, CTNNB1, ATG7, CASP8, TFRC, GYPA, EGLN1, AR, ID1, NPPB, TP73, CYP1A1, SMN2, KRT18, NFE2L2, PPARA, CHEK1, STRAP, CD36, GPX1, PRKCA, RXRA, VDR, and KRT8 against 7026 background genes at a strength of 0.32. Moreover, 550 BP were identified where, response to chemical (GO:0042221) scored the lowest false discovery rate of 7.45E-16 via the modulation of 48 observed genes i.e. NGFR, SIRT1, HMOX1, TIMP1, MMP2, CCL2, CD38, IFNG, GAPDH, CAT, MDM2, TIMP2, EPAS1, FLT1, MMP3, HSPA4, CD14, MMP14, NQO1, TAC1, NOS2, CD86, CYP3A4, PRDX6, CTNNB1, ATG7, CASP8, TFRC, KRT19, EGLN1, ATG5, PLAU, AR, ID1, TP73, CD83, PRDX4, CYP1A1, KRT18, NFE2L2, PPARA, CD36, GPX1, PRKCA, RXRA, VDR, KRT8, and CYP3A7 against 4333 background gene count with a strength of 0.54 (Supp. file The data for GO terms i.e. cellular components (CC), molecular function (MF), and biological process (BP) were retrieved from the STRING database. GO analysis identified 23 CC in which extracellular space GO:000615 scorevs GC & FDR to be − 0.9427 to − 0.4793 & − 0.5649 to 0.5367 with p values 0.0007 & 0.948 respectively, and FDR vs GC & strength to be − 0.6063 to 0.4903 & − 0.5649 to 0.5367 with p values 0.7871 & 0.9476 respectively with a sample size of 13. Moreover, for BP the confidence interval of r for GC vs Strength & FDR was predicted to be − 0.8401 to − 0.7228 & − 0.4338 to − 0.1554 with p values 3.08719507014992e-036 & 0.00009 respectively, Strength vs GC & FDR to be − 0.8401 to − 0.7228 & − 0.06039 to 0.2426 with p values 3.08719507014992e-036 & 0.2334 respectively, and FDR vs GC & strength to be − 0.4338 to − 0.1554 & − 0.06039 to 0.2426 with p values 0.00008 & 0.2334 respectively with a sample size of 165  vs Strength & false discovery rate (FDR) to be − 0.9932 to − 0.7050 & − 0.6301 to 0.8392 with p values 0.0009 & 0.614 respectively, Strength vs GC & FDR to be − 0.9932 to − 0.7050 & − 0.7589 to 0.7471 with p values 0.0009 & 0.977 respectively, and FDR vs GC & strength to be − 0.6301 to 0.8392 & − 0.7589 to 0.7471 with p values 0.6137 & 0.9768 respectively with a sample size of 7. Similarly, for MF the confidence interval of r for GC vs Strength & FDR was predicted to be − 0.9427 to − 0.4793 & − 0.6063 to 0.4903 with p values 0.0007 & 0.787 respectively, Strength ver. 3.9.0 with CC, MF, BP, and KEGG enriched genes. The analysis displayed 1 cluster with 38 groups and 60 (96.77%) identified genes were involved in the cluster . Likewise, VDR possessed 1 hydrogen bond between SER237 and hydroxyl group at position 9  displayed uniformity throughout the simulation with minor fluctuations in the range of ~ 1 Å to ~ 3 Å. The number of hydrogen bonds being formed throughout the simulation was analyzed; a maximum of 4 hydrogen bonds were formed. However, there was no constant bond formation between the ligand and protein. The solvent-accessible surface area helps to identify a free surface for the ligand to bind with protein; was found to be in the range of 35 to 45 nm2 to 35 nm2 was observed  value ranged from ~ 11 Å to ~ 16 Å throughout the simulation and became stable after 170 ns for the rest of the MD run. The highest number of hydrogen bonds formed between the ligand and protein was 5. However, there were no constant hydrogen bonds seen throughout the MD run. Moreover, the solvent-accessible surface area displayed a varied range of values till 100 ns simulation run; thereafter, a slight decrease in the SASA value from 40 nmved Fig. .2 to 70 nm2 and there were observed fluctuations in SASA ranging from 130 to 200 ns  was observed between atoms ~ 1000 to ~ 1300. The radius of gyration ranged between 14Å and 17Å after the stabilization of the complex at ~ 75 ns. A highest of 4 hydrogen bonds were observed during the MD run which were not constant throughout the simulation. The solvent assessable surface area ranged between ~ 70 nmPRKCA complex i.e. 50.57 ± 0.61 and − 29.35 ± 0.11 kcal/mol respectively; whereas, the highest was found to be by CYP3A4 and β-catenin i.e. 54.47 ± 0.36 and − 25.74 ± 0.41 kcal/mol respectively. The total binding energy was found to be least for cytochrome 1A1  indicating it to be the most stable complex  whereas, cytochrome 1A1 and sirtuin 1 complexes possessed the highest i.e. 0.58 ± 0.14 kcal/mol and − 35.23 ± 0.19 kcal/mol respectively. Moreover, total gas phase and total salvation energy were found to be lowest by β-catenin-sinapic acid and 37. Also, it has been reported to possess an ameliorating effect on various organ toxicities if used in combination with chemotherapeutic drugs like cisplatin, doxorubicin, and methotrexate26. Sinapic acid being a plant metabolite is reported to possess ameliorating effect on various organ toxicities by chemotherapeutic agents via the regulation of Nrf2/HO-1 involved in the NF-κB signalling pathway25.The present study aimed to propose the possible molecular mechanism of sinapic acid in the treatment of breast cancer via integrating multiple system biology tools like gene set enrichment analysis, gene ontology, and molecular dynamic simulation. Sinapic acid has been reported to possess time and dose-dependent suppressive action on breast and colon cancer cell lines representing its potential to be a chemotherapeutic drug to be used for the treatment of breast cancerver. 3.9.0 from which three proteins were identified to be lead hits . GO analysis was performed based on three GO terms CC, MF, and BP. In addition, Cluster analysis was also performed to identify various groups of proteins and how they belong to a category biologically38. Molecular docking was performed with three different docking tools i.e. AutoDock 4.2, AutoDock Vina, and Schrodinger suite glide to attain better visibility of ligand–protein docking with matched 62 targets.Initially, we acquired targets involved in breast cancer through DisGeNET database where we identified 6776 genes to be involved in the pathogenesis of breast cancer, which were further matched with proteins predicted to be modulated by sinapic acid; identified from DIGEP-Pred database. These matched proteins (62 proteins) were used to construct a protein–protein interaction network via STRING. KEGG pathway analysis was used to identify various pathways being regulated by sinapic acid; 50 pathways were identified to be involved. Further, a protein-pathway network was constructed using Cytoscape extracellular space”, “protein binding”, and “response to chemical” to possess the least false discovery rate in GO analysis; CC, MF, and BP respectively. Also, we predicted that 79% of genes involved in GO and KEGG mediated proteins were in common which represents a good interaction between the GO terms and KEGG mediated proteins. Further, cluster analysis revealed the presence of “cellular response to oxidative stress” to be majorly modulated by the most number of genes (36 genes).Gene ontology analysis revealed “CYP1A1, CYP3A4, and CYP3A7), CAT, SIRT1, VDR, and NOS2 possessed the lowest binding energy with the ligand sinapic acid. The molecular dynamic simulation revealed that all complexes became stable after the stabilization period (~ 50 ns to ~ 100 ns); a maximum of ~ 2.5 Å to ~ 3.0 Å difference was observed in the RMSD value of the backbone and complex. The complexes of sirtuin 1 & β-catenin with sinapic acid possessed a difference in RMSD values between backbone and complex ~ 2.5 Å which falls in an acceptable range. However, there was a fluctuation in the number of hydrogen bonds although the difference in RMSD values were within the range of ~ 3 Å, this may be due to other hydrophobic and Vander Waal interactions between the ligand and proteins39. Similarly, we observed in the MMPBSA that the complexes possessed low Vander Waal molecular mechanics energy indicating Vander Waal forces to be acting and making the complex stable.We performed docking on 62 targets with sinapic acid using different software’s; it was predicted that cytochrome enzymes (PRKCA) to be involved in majority of pathways identified in breast cancer. Protein kinase C belongs to the class of serine/threonine protein kinase family of enzymes and plays a vital role in the progression of several diseases like cancer, diabetes, autoimmune diseases, heart failure, and Parkinsonism40. It has been reported that the expression of PRKCA is associated with endocrine resistance and poor prognosis in ER-positive (ER +) breast tumors. In addition, expression of PRKCA is elevated in triple negative breast cancer (TNBC) patients and shown to be responsible for chemotherapy resistance and metastasis41. PKCα acts as an upstream regulator of FOXC2, which in turn represses the expression of p120-catenin, an important component of adherens junction that acts as the anchor for E-cadherin42. Hence, PRKCA could be a potent target to be suppressed for a better treatment strategy to control breast cancer. However, there is still scope to understand the role of PRKCA in breast cancer as much is still to be uncovered. Figure Network pharmacology analysis revealed classical protein kinase C α-type ; may be due to the tendency of CYP1A1 to metabolize carcinogens. Reports suggest that upregulation of CYP3A4 is seen in 80% of breast tumors and can be used to identify tumor response in different treatments49.Cytochrome enzymes play a major role in the activation of drugs to activated carcinogens from which CAT) is an antioxidant enzyme that catalyzes mainly the transformation of hydrogen peroxide into water and oxygen50. Although catalase is frequently down-regulated in tumors the underlying mechanism remains unclear. Further, silent information regulation factor 1 , as a kind of NAD + dependent class III histone deacetylation enzyme, involved in tumor proliferation, invasion, and metastasis51. The roles of SIRT1 in breast cancer is multifaceted depending on its substrate from upstream or downstream signaling pathway. Results have displayed that SIRT1 is significantly up-regulated in breast cancer tissues and cells, which is correlated with histological grade, tumor size, and lymph node metastasis52. Studies report that vitamin D has been suggested to prevent and improve the prognosis of several cancers, including breast cancer53; high expression of VDR in invasive breast tumors is associated with favorable prognostic factors and a low risk of breast cancer death54. Hence, a high VDR expression is a positive prognostic factor which may be used to identify response of tumors in different treatments. Expression of inducible nitric oxide synthase (NOS2) has been associated with poor outcome in breast cancer55; the upregulation of NOS leads to tumor angiogenesis by upregulating VEGF56.Moreover, catalase ; the majorly regulated pathway was predicted to be “Pathways in cancer”. This indicates that sinapic acid can be used in the treatment of breast cancer. However, these are predictions based on previous reports, which need to be validated and looked upon in-depth to confirm the exact molecular mechanism of sinapic acid in the treatment of breast cancer; this is future scope as well as a drawback of the current study.The present study aimed to identify a possible molecular mechanism of sinapic acid (SA) in the treatment of breast cancer via the utilization of multiple system biology tools like network pharmacology, Gene ontology enrichment, and molecular dynamic simulation. The predictions revealed that the mechanism of sinapic acid in breast cancer may be due to multiple pathways and proteins like β-catenin, Supplementary Information.


Bacterial infections can significantly impede wound healing and pose a serious threat to the patient’s life. The excessive use of antibiotics to combat bacterial infections has led to the emergence of multi-drug-resistant bacteria. Therefore, there is a pressing need for alternative approaches, such as photothermal therapy (PTT), to address this issue. In this study, for the first time, CuS NPs with photothermal properties were synthesized using sericin as a biological template, named CuS@Ser NPs. This method is simple, green, and does not produce toxic and harmful by-products. These nanoparticles were incorporated into a mixture (XK) of xanthan gum and konjac glucomannan (KGM) to obtain XK/CuS NPs composite hydrogel, which could overcome the limitations of current wound dressings. The composite hydrogel exhibited excellent mechanical flexibility, photothermal response, and biocompatibility. It also demonstrated potent antibacterial properties against both Gram-positive and negative bacteria via antibacterial experiments and accelerated wound healing in animal models. Additionally, it is proved that the hydrogel promoted tissue regeneration by stimulating collagen deposition, angiogenesis, and reducing inflammation. In summary, the XK/CuS NPs composite hydrogel presents a promising alternative for the clinical management of infected wounds, offering a new approach to promote infected wound healing.The online version contains supplementary material available at 10.1186/s12951-023-02035-6. The skin is the body's first line of defense against microbial invasion. When the integrity of the skin is compromised, it can lead to serious infections . BacteriPhotothermal therapy (PTT) has emerged as a potential therapy in recent years, utilizing photothermal agents (PTA) to convert light energy into heat energy and eliminate bacterial infections in wounds without inducing bacterial drug resistance , 7. PTA Sericin (Ser) is a natural macromolecular viscous protein wrapped in the surface layer of silk fibroin and is one of the two main proteins that make up the cocoon of silkworms. Sericin has cell adhesion properties, good biocompatibility, and low immunogenicity, which has been used in combination with other polymers to form wound dressings in different forms , 19. It 2+ to form a PVA-DOPA-Cu (PDPC) hydrogel, which has antioxidative and antibacterial properties 2+ solution. Afterward, it was added to 1% sericin solution (10 mL) with stirring, then 4 M Na2S solution was added dropwise into the mixture until the color of the solution did not change. The volume was added to 40 mL with deionized water to obtain a 2 mM CuS@Ser NPs solution named CuS2. In addition, CuS1 and CuS0.5 were obtained in the same way. All reactions were conducted at indoor temperature.To prevent excessive local CuThe morphology and size of CuS@Ser NPs were analyzed using a transmission electron microscope . The components and distribution of the elements were measured by an X-ray energy-dispersive spectroscopy (EDS) detector. Ultraviolet–visible-near-infrared spectra (UV–vis-NIR) of samples were obtained by UV–vis spectrophotometer . Fourier transform infrared (FTIR) spectra of CuS, sericin, and CuS@Ser NPs were measured by FTIR spectrometer . The chemical state was determined by X-ray photoelectron spectroscopy .2) under 808 nm NIR laser. Briefly, the CuS@Ser NPs solution was set in a 1.5 mL centrifuge tube and exposed to an 808 nm laser. The temperature changes and corresponding thermograms were recorded in real-time with an infrared thermographic camera . Afterwards, irradiating CuS@Ser NPs solution with 808 nm laser (1.5 W/cm2) for 5 min, switching off and cooling down to room temperature, as such cycle three times to test the photothermal stability. The temperature changes were also recorded with the infrared thermographic camera.To test the photothermal property of CuS@Ser NPs solution, irradiating different concentrations of CuS@Ser NPs solution with various light intensities , heated to 80 °C, and stirred uniformly with a high-speed homogenizer; then, xanthan gum was added and stirred uniformly. Adjust the pH of the solution to 7.0, and the mixture was transferred to clean molds and cooled to an indoor temperature to obtain XK hydrogel. Of which the mass fraction of KGM and xanthan gum was 1%, and the mass ratio was 6:4. The deionized water was replaced with CuS0.5, CuS1, and CuS2 solutions to obtain XK/CuS NPs hydrogels, named XK/CuS0.5, XK/CuS1, and XK/CuS2, respectively.The hydrogel samples were lyophilized in a freeze-dryer, and then the microstructures of the samples were analyzed by field emission scanning electron microscopy , and the distribution of various elements was obtained by EDS images. FTIR spectra of XK and XK/CuS hydrogels were measured by FTIR spectrometer . Compression tests and cyclic compression experiments were performed on XK, XK/CuS0.5, XK/CuS1, and XK/CuS2 hydrogels using a universal mechanical tester . Briefly, samples with a height of 15 mm were compressed to rupture at a rate of 5 mm/min, and stress–strain curves were recorded. Next, each sample was compressed to 60% strain, then restored to the free state, and cycled 30 times to record the stress–strain curves. To test the stability of the hydrogels, the same mass of XK, XK/CuS0.5, XK/CuS1, and XK/CuS2 hydrogels were soaked in PBS, photographed daily to confirm the status, and weighed after drying.To test the biocompatibility of the hydrogel, CCK-8 assay, Live/Dead staining, and cytoskeleton staining were used. 10 g of hydrogels  were soaked in 40 mL of DMEM or RPMI 1640 medium containing 10% FBS and 1% penicillin–streptomycin for 24 h to acquire the leachates, respectively. HUVECs and L929 cells were cultured for 24 h in DMEM or RPMI 1640 medium containing 10% FBS and 1% penicillin–streptomycin, respectively. And then, replaced the complete medium with the leaching solutions, and the culture was continued for the 1st, 3rd, and 5th days and performed following assays. CCK-8 assay kit was used to measure cell viability. Briefly, add 10 µL of CCK-8 reagent to the medium (100 µL), and the cells were co-cultured for 1.5 h at 37 °C. The OD value was recorded at 450 nm by a microplate reader . Further, HUVECs and L929 cells were cultured with leachates for the 1st, 3rd, and 5th days, respectively. Then, incubate with live/dead staining solution for 0.5 h and observe under a fluorescent microscope . In addition, cells cultured with leachates for 3 days were fixed with 4% paraformaldehyde for 10 min and treated with 0.5% Triton X-100 for 5 min. FITC Phalloidin solution was used to stain the cytoskeletal actin for 0.5 h. After washing with PBS 3 times, the nuclei were stained with DAPI for 30 s, observed by fluorescence microscope.2) under 808 nm laser, respectively. Briefly, the hydrogel was set in a dish and irradiated with an 808 nm laser. The temperature changes and corresponding thermograms were measured in real-time with the infrared thermographic camera. Afterward, irradiate XK/CuS1 hydrogel with the laser (1.5 W/cm2) for 5 min, switch it off, then cool down to room temperature; cycle three times to test the photothermal stability. The temperature changes were also recorded with the infrared thermographic camera.To test the photothermal property of XK/CuS NPs hydrogel, irradiating different hydrogels  with different light intensities  were added to the surface of the hydrogels. And then, the hydrogels were irradiated with an 808 nm NIR laser for 5 min. PBS solution (2 mL) was added to each well and shaken for 10 min. Collecting the bacterial suspension (100 μL) and coated on LB solid medium and incubated at 37 °C for 12 h, then counted colonies. Moreover, bacterial live/dead staining was performed. Bacterial precipitates were obtained by centrifugation of the bacterial suspension at 12,000 rpm for 10 min, then incubated with a Live/Dead bacterial imaging kit for 15 min at room temperature in the dark and observed under the fluorescent microscope.PBS solution (blank control), XK hydrogel, and XK/CuS1 hydrogel were placed in a 6-well plate. 100 μL of S. aureus suspension (1 × 107 CFU/mL) was injected into each wound 2+ and brown after the addition of Na2S solution without precipitation, which was attributed to the abundant hydroxyl groups in the sericin molecules complexed with Cu2+. After adding S2−, the nano-scale molecular gap caused by the irregular curl structure of sericin prevented CuS from over-aggregating, thus forming CuS NPs with uniform particle size and wrapped by the sericin molecules. The UV–vis spectra of the brown solution (CuS@Ser) showed obvious absorption in the section of 800–1100 nm, suggesting that it can absorb NIR light, consistent with previous study reports [−1 and 1107 cm−1, attributed to the stretching vibrations of Cu–S and S–S [−1, which correspond to the characteristic peak of amide I, II, and III, respectively [CuS NPs were synthesized based on sericin as a biological template and characterized by UV–vis spectrophotometer, FTIR, TEM, EDS, and XPS. As shown in Fig.  reports . The FTI and S–S , 35. In ectively . The spe1/2 and Cu 2p3/2, respectively . The temperatures of XK/CuS0.5, 1, and 2 could reach up to 43 °C, 56 °C, and 70 °C, respectively, which were positively correlated with the concentration of CuS NPs. Compared with XK/CuS2 and XK/CuS0.5, the platform temperature of XK/CuS1 did not cause severe burns to surrounding tissues and was sufficient to kill bacteria, so XK/CuS1 hydrogel was chosen as the antibacterial wound dressing for the subsequent experiments [2, 1.5 W/cm2, and 2 W/cm2 after irradiation for 300 s were 51 °C, 55.8 °C, and 70.7 °C, respectively. After comparison, the power setting of 1.5 W/cm2 was more suitable. XK/CuS1 hydrogel could be warmed up to the previous temperature each time in the three on–off cycles under an 808 nm laser, showing that it also had photothermal stability , the temperature change was monitored in real-time using the infrared thermographic camera. As shown in Fig. The previous experiments showed that XK/CuS1 NPs hydrogel has good mechanical properties, biocompatibility, and photothermal antibacterial ability. Next, a full-skin wound infection model in SD rats was set up to further demonstrate the therapeutic effect of XK/CuS1 hydrogel on infected wounds at the animal level. XK/CuS1 hydrogel and XK hydrogel were placed on infected wounds, and the control group dealt with PBS on the infected wounds. All of them were divided into two groups with or without 808 nm laser irradiation, respectively. Wound healing of rats was observed on days 0, 3, 6, 9, and 12, and the experimental procedure is shown in Fig. The effect of different groups on wound repair was assessed by measuring the change in wound area at 0, 3, 6, 9, and 12 days. As shown in Fig. The effect of XK/CuS NPs hydrogel on wound healing was further evaluated by histological analysis. H&E staining is shown in Fig. On the 12th day, inflammation cells were still present in the tissues of the control and XK hydrogel groups (red arrows), indicating that there was still an inflammatory response, while the XK/CuS1 + NIR group was almost devoid of inflammatory cells. It illustrates that XK/CuS NPs hydrogel can remove bacteria under 808 nm laser radiation and reduce the duration of the inflammatory response. In addition, the epithelial space of the XK/CuS1 + NIR group was narrower than others (black arrows), indicating that the XK/CuS1 + NIR group had the best wound recovery.As a major component of the extracellular matrix, collagen not only plays a role in physical support but also plays a very important part in cell activities, including cell multiplication, migration, and differentiation, and is an important indicator of wound healing . The colDuring wound healing, the persistent inflammatory response leads to local tissue cell necrosis and delays the wound healing process . To asseAngiogenesis is important for wound healing; neovascularization provides oxygen, growth factors, and immune support to the healing tissue . CD31 isIn general, for the first time, we ingeniously synthesized CuS NPs in situ using natural sericin as bio-templates. The CuS NPs obtained by this method are evenly sized and have favorable photothermal properties. Subsequently, CuS@Ser NPs were loaded into xanthan/konjac glucomannan hydrogel as a photothermal agent, and the obtained XK/CuS NPs hydrogel has outstanding mechanical properties, which compression stress higher than 900 kPa at 80% compression strain, as well as excellent compressive stability and mechanical flexibility. Most importantly, benefiting from the good photothermal properties of CuS@Ser NPs, the XK/CuS NPs hydrogel has a stable photothermal antibacterial capacity to eradicate infected wound bacteria. The mechanism of the antibacterial effect is attributed to the irreversible denaturation of bacterial proteins induced by high temperature. The full-skin wound infection model in SD rats further confirmed that XK/CuS hydrogels could accelerate wound healing under NIR irradiation by providing anti-infection capability, reducing inflammatory reactions, and promoting angiogenesis. All these results indicate that XK/CuS NPs hydrogel has excellent mechanical and antimicrobial properties that could accelerate the healing of infected wounds. This work also provides an innovative strategy for designing photothermal antimicrobial wound dressings.Additional file 1: Figure S1. Photograph of XK, XK/CuS0.5, XK/CuS1, and XK/CuS2 hydrogel after soaking in PBS for 0, 1, 3, 5, and 7 days, respectively. Figure S2. Weight change of XK, XK/CuS0.5, XK/CuS1, and XK/CuS2 hydrogel after soaking in PBS for 0, 1, 3, 5, and 7 days, respectively.